Yingying Chen

Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in this important venue.

Datasets drive vision progress and autonomous driving is a critical vision application, yet existing driving datasets are impoverished in terms of visual content. Driving imagery is becoming plentiful, but annotation is slow and expensive, as annotation tools have not kept pace with the flood of data. Our first contribution is the design and implementation of a scalable annotation system that can provide a comprehensive set of image labels for large-scale driving datasets. Our second contribution is a new driving dataset, facilitated by our tooling, which is an order of magnitude larger than previous efforts, and is comprised of over 100K videos with diverse kinds of annotations including image level tagging, object bounding boxes, drivable areas, lane markings, and full-frame instance segmentation. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models so that they are less likely to be surprised by new conditions. The dataset can be requested at this http URL

Journal of Geophysical Research: AtmospheresVolume 116, Issue D20 Climate and DynamicsFree Access Improving land surface temperature modeling for dry land of China Yingying Chen, Yingying Chen [email protected] Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, ChinaSearch for more papers by this authorKun Yang, Kun Yang Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorJie He, Jie He State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorJun Qin, Jun Qin Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorJiancheng Shi, Jiancheng Shi State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, ChinaSearch for more papers by this authorJinyang Du, Jinyang Du State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, ChinaSearch for more papers by this authorQing He, Qing He Institute of Desert Meteorology, China Meteorological Administration, Urumqi, ChinaSearch for more papers by this author Yingying Chen, Yingying Chen [email protected] Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, ChinaSearch for more papers by this authorKun Yang, Kun Yang Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorJie He, Jie He State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorJun Qin, Jun Qin Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorJiancheng Shi, Jiancheng Shi State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, ChinaSearch for more papers by this authorJinyang Du, Jinyang Du State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, ChinaSearch for more papers by this authorQing He, Qing He Institute of Desert Meteorology, China Meteorological Administration, Urumqi, ChinaSearch for more papers by this author First published: 19 October 2011 https://doi.org/10.1029/2011JD015921Citations: 373AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract [1] The parameterization of thermal roughness length z0h plays a key role in land surface modeling. Previous studies have found that the daytime land surface temperature (LST) on dry land (arid and semiarid regions) is commonly underestimated by land surface models (LSMs). This paper presents two improvements of Noah land surface modeling for China's dry-land areas. The first improvement is the replacement of the model's z0h scheme with a new one. A previous study has validated the revised Noah model at several dry-land stations, and this study tests the revised model's performance on a regional scale. Both the original Noah and the revised one are driven by the Global Land Data Assimilation System (GLDAS) forcing data. The comparison between the simulations and the daytime Moderate Resolution Imaging Spectroradiometer- (MODIS-) Aqua LST products indicates that the original LSM produces a mean bias in the early afternoon (around 1330, local solar time) of about −6 K, and this revision reduces the mean bias by 3 K. Second, the mean bias in early afternoon is further reduced by more than 2 K when a newly developed forcing data set for China (Institute of Tibetan Plateau Research, Chinese Academy of Sciences (ITPCAS) forcing data) is used to drive the revised model. A similar reduction is also found when the original Noah model is driven by the new data set. Finally, the original Noah model, when driven by the new forcing data, performs satisfactorily in reproducing the LST for forest, shrubland and cropland. It may be sensible to select the z0h scheme according to the vegetation type present on the land surface for practical applications of the Noah LSM. Key Points Improved modeling of land surface temperature in dry land of China Use of newly developed forcing data Improved modeling of land surface energy budget 1. Introduction [2] Arid and semiarid regions in China are mainly located in the north and the west and occupy more than half of the country. These regions are characterized by bare soil and grasslands. Arid and semiarid regions in China have experienced or are experiencing significant environmental changes, such as the enhanced drying trend in northern China [e.g., Wang and Zhai, 2003; Ma and Fu, 2006], enhanced warming signal [e.g., Chase et al., 2000; Ren et al., 2005], grassland degradation and desert extension [e.g., Fu and Wen, 2002], discharge decrease in the Yellow River [Yang et al., 2004; Tang et al., 2008], and drought-area expansion in principal farming areas [Wang and Zhai, 2003]. Understanding the interactions between land and atmosphere and their response to climate change will help people to confront the severe environment problems there. For instance, numerical modeling by Xue [1996] suggested that the desertification in the Mongolian and the Inner Mongolian grasslands would reduce areal rainfall through modification of the surface energy balance. The physical representation of land-atmosphere interactions plays a key role in improving the predictability of weather and climate [Pielke et al., 1999; Chen et al., 2001; Koster et al., 2004; Los et al., 2006]. However, issues concerning the exchange efficiencies of energy and water vapor between the land surface and the atmosphere still remain poorly understood [Chen and Zhang, 2009]. [3] In arid and semiarid regions, surface upward longwave radiation plays an important role in the surface radiation budget; sensible heat flux (H) and ground heat flux (G0) dominate the surface energy budget because of the lack of water replenishments. It is crucial to simulate the land surface temperature (LST), because it modulates both the radiation budget by determining upward longwave radiation (L↑) and the surface energy budget (SEB) by changing ground-air and soil temperature gradients. However, daytime LSTs in arid and semiarid regions are typically not well simulated in current land surface models (LSMs): Hogue et al. [2005] found that the Noah model tends to underestimate the LST and overestimate H during the daytime at semiarid sites; LeMone et al. [2008] also found this phenomenon and pointed out that modifying the thermal roughness length (z0h) scheme is helpful to reproduce the observed LST and H; Chen and Zhang [2009] further examined the observed surface exchange coefficient for heat (Ch) over various surface vegetation types and suggested the coefficient in the z0h scheme should depend on vegetation type; Yang et al. [2007], using data archived in the Coordinated Enhanced Observing Period (CEOP) program [Koike, 2004], pointed out that several operational global circulation models (GCMs) systematically underestimate the diurnal range of surface-air temperature differences, particularly in arid and semiarid regions, since the heat transfer resistances are underpredicted. Yang et al. [2009] further evaluated three off-line LSMs (i.e., Common Land Model, Simple Biosphere Model, version 2, and Noah) and confirmed that the three models significantly underestimate the daytime LST because of the underestimation of heat transfer resistances under dry conditions. Trigo and Viterbo [2003] argued that an underestimation of the diurnal cycle of modeled brightness temperature in clear-sky conditions during daytime can be attributed to the problems in the model surface-to-boundary-layer coupling. These results further argue that a proper representation of the z0h or Ch for dry land is crucial in modeling the LST as well as H. Much of the literature has focused on the parameterization of z0h [e.g., Sheppard, 1958; Brutsaert, 1982; Betts and Beljaars, 1993; Zilitinkevich, 1995; Malhi, 1996; Zeng and Dickinson, 1998; Kanda et al., 2007]. For the evaluation of some of these schemes against observations, the readers are referred to the work by Yang et al. [2008]. [4] On the basis of the work of Chen et al. [2010], the goal of the present study is to extend our modeling work over dry-land surface from the local point scale to a regional scale. More specifically, we intend to improve the performance of the Noah model for the arid and semiarid regions in China. [5] In addition to a realistic representation of land processes in a model, the LST modeling in China encounters three additional challenges. The first is the shortage of the forcing data to drive the model. The China Meteorological Administration (CMA) has not released its own reanalysis data set that is suitable to drive LSMs at the regional scale, whereas available global data sets (e.g., National Centers for Environmental Prediction (NCEP) reanalysis) have evident biases in China [e.g., Ma et al., 2008, 2009]. Wang and Zeng [2011] pointed out that using a reanalysis-based data set developed by Sheffield et al. [2006] tends to produce too-wet soil in northeastern China and too-dry soil in northwestern China, but these deficiencies can be significantly reduced after the in situ measured precipitation data are introduced into the forcing data. In this study, we use two forcing data sets to drive the LSM. They are the Global Land Data Assimilation System (GLDAS) forcing data set and a newly developed forcing data set (hereinafter ITPCAS (for Institute of Tibetan Plateau Research, Chinese Academy of Sciences) forcing data), whose descriptions will be presented in subsection 3.1.2. [6] The second challenge is how to validate the gridbox-based simulation results on a regional scale. Currently, reliable observed LST and turbulent flux data are available at quite a few individual sites. In order to overcome this problem, the Moderate Resolution Imaging Spectroradiometer (MODIS) level 3 0.05° LST products are used to evaluate the model performance. [7] The third challenge is the evaluation method. In the point-scale cases, we can directly measure many variables, such as soil moisture and soil temperature and set the model parameter values to the observed ones, so that the evaluation can be conducted by strictly comparing the simulated variables against their respective observations. However, it is impossible to do this on a regional scale, and hence we evaluate the model performance by computing the error metrics over all grid cells in the entire study region rather than doing this pixel by pixel. [8] The rest of the paper is as follows: Section 2 briefly introduces the Noah model and the new z0h scheme; section 3 introduces the forcing data, validation data, model parameter data sets, and the numerical experiments' design. The simulations are evaluated against the daytime MODIS/Aqua LST products in section 4; section 5 discusses the evaluation against MODIS/Terra LST products, the choice of z0h scheme in practical applications, and the comparison between GDLAS and ITPCAS radiation fluxes. The conclusions are given in section 6. 2. Noah Land Surface Model and Its Improvement Model Description [9] The Noah model is adopted by operational numerical weather prediction models at the U.S.NCEP and the U.S. Air Force Weather Agency (AFWA). The Noah model has been extensively evaluated in both the off-line mode [e.g., Chen et al., 1996; Mitchell et al., 2004] and the coupled mode [e.g., Chen et al., 1997; Ek et al., 2003]. It is also widely used to investigate the feedback between soil moisture and precipitation on both regional and global scales [e.g., Chen et al., 2001; Koster et al., 2004; Zhang et al., 2008]. [10] Noah was developed based on the Oregon State University (OSU) LSM, which includes a two-layer soil model with thermal conduction equations for soil temperature and a diffusive form of Richardson's equation for soil moisture. The Noah model and its predecessors continue to benefit from a series of improvements, especially after being adopted by NCEP and AFWA [e.g., Chen et al., 1996; Koren et al., 1999; Ek et al., 2003]. A more detailed overview of the physics lineage is presented by Ek et al. [2003]. The present study employs the Noah version 2.7.1, and it has four soil layers (with depths of 10, 30, 60, and 100 cm from top to bottom), a single-canopy layer, and a single snow layer; please refer to detailed model physics by Chen and Dudhia [2001]. [11] Concerning the energy exchanges between land surface and atmosphere in arid and semiarid regions, the SEB equation can be written as In the radiation budget equation (1a), Rnet is the net radiation, S↓ and L↓ are the downward shortwave and longwave radiation, respectively, Tsfc is the land surface temperature (i.e., LST), σ is the Stefan-Boltzmann constant, and α and ɛ are the surface albedo and the ground surface emissivity, respectively. In the energy budget balance, equation (1b), H is the turbulent sensible heat flux, LE is the turbulent latent heat flux, and G0 is the surface soil heat flux. In dry conditions, LE accounts for a minor proportion, and H and G0 are thus the dominant terms on the right-hand side of equation (1b). [12] H is calculated through the bulk heat transfer equation: where ρ is the air density, cp is the specific heat capacity of air at constant pressure, u is the wind speed, θair is the air temperature adjusted adiabatically for the height above the surface, and θsfc is the counterpart at the surface. [13] The surface soil heat flux is written as where kT is the soil thermal conductivity that is a function of soil water content (Θ) and soil properties, T1 is the soil temperature in the uppermost layer, and h1 equals half of the first layer depth. In dry conditions, Θ is very small with negligible temporal variations for most of the time. Therefore, the value of kT can be assumed as a constant value at a specific site. As a result, G0 depends mainly on the modeled Tsfc. Improvement of z0h Scheme [14] Ch plays an important role in calculating both LST and H in arid and semiarid regions, which can be obtained through the Monin-Obukhov similarity theory and depends on z0h and the aerodynamic roughness length (i.e., z0m). The z0m often varies quite little with time, and it is typically prescribed according to the vegetation height in large-scale modeling. Then, z0h becomes the determining factor in the calculations of Ch and thus for the LST and H, so that it needs a proper parameterization in LSMs. It is worth noting that the parameterization of z0h plays a more important role in the daytime LST simulation than in the nighttime one. In the daytime, the excess resistance, because of the difference between z0m and z0h, is comparable to the aerodynamic resistance, but the excess resistance in the nighttime is much less than the total resistance because of the stability increase [Yang et al., 2008]. Furthermore, the soil heat flux in the nighttime is a major controller of the SEB, and thus the simulation accuracy of the surface temperature highly depends on the accuracy of soil thermal inertia in addition to the heat transfer resistance. Therefore, we put emphasis on the simulation of LST and H in the daytime. [15] Chen et al. [2010] conducted a sensitivity study using several widely used z0h schemes in the framework of the Noah model. Their results showed that the simulated LST and SEB is very sensitive to these schemes. For instance, the simulated maximum LST and H using currently available z0h schemes can differ by more than 20 K and 200 W m−2, respectively. [16] In the original Noah model, the z0h scheme developed by Zilitinkevich [1995] takes the following form: where k is the von Kármán constant and normally equals 0.4, Re* is the roughness Reynolds number (defined as Re* = z0mu*/ν, where u* is the friction velocity and ν is the fluid kinematical viscosity), and Czil is an empirical coefficient specified as 0.1 by Chen et al. [1997]. After comparing the Noah-simulated Ch against the observed values based on a total of 12 flux data sets, Chen and Zhang [2009] pointed out that Ch tends to be overestimated for short vegetation and underestimated for tall vegetation by using Czil = 0.1 in Noah. Moreover, they suggested that the value of Czil should be vegetation dependent, and they related this value to the canopy height through an empirical regression method. However, such a relationship needs further assessment over different vegetation types. In both Noah version 2.7.1 and the GLDAS, Czil = 0.075 is prescribed, and this default value is used in this study. A discussion about the choice of the Czil value for dry-land surfaces is given in subsection 5.2. [17] In the revised Noah model, another scheme was applied to replace the z0h scheme by Chen et al. [2010]. This new scheme was developed and validated by Yang et al. [2002, 2008]. They used a turbulence-related height (hT) to parameterize z0h. This height separates the fully turbulent layer and the transitional layer and can be determined by the critical Reynolds number (Recrit): where Recrit is equal to 70. [18] The ratio of hT to z0h is parameterized with both the frictional velocity and a temperature scale (θ*, ≡ H/(ρcpu*)) and the final form of z0h is where β = 7.2 m−1/2 s1/2 K−1/2, given by Yang et al. [2008]. [19] Yang et al. [2008] evaluated several widely used schemes against turbulent flux data collected at seven dry-land stations. They indicated that a common feature of z0h over dry lands is its diurnal variation, which has been reported in several earlier studies over bare soil and grassland [Verhoef et al., 1997; Sun, 1999; Ma et al., 2002; Yang et al., 2003]; such a diurnal variation cannot be reproduced by commonly used parameterization schemes, but the scheme of Yang et al. manages to reproduce it, largely because of its ability to capture the diurnal course of θ*. This scheme also performs well for the flux parameterization over a glacier surface [Guo et al., 2011]. After this scheme was implemented into the SiB2 model, the biases in the simulated daytime LST were much reduced at two alpine desert sites [Yang et al., 2009]. Furthermore, Chen et al. [2010] validated this scheme in the framework of the Noah model at several dry-land sites, confirming its advantages over several other schemes. In this paper, we examine the performance of the revised Noah LSM with this scheme over the arid and semiarid areas of China. 3. Data and Methods Forcing Data 3.1.1. GLDAS Forcing Data [20] The 3 hourly, 0.25° × 0.25° forcing data from GLDAS [Rodell et al., 2004] version 1, which is a combination of several data sets, is used in this paper. The near-surface wind speed, air temperature, specific humidity, and surface pressure are provided by the National Oceanic and Atmospheric Administration (NOAA) Global Data Assimilation System (GDAS) atmospheric reanalysis data, which is the operational global atmospheric data assimilation system of NCEP. The precipitation data are derived through the spatial and temporal disaggregation of NOAA Climate Prediction Center Merged Analysis of Precipitation (CMAP) fields, which merged the satellite (infrared and microwave) and gauge observations. The AFWA Cloud Depiction and Forecast System II (CDFSII) data are used to calculate downward radiation fluxes at the ground surface. 3.1.2. ITPCAS Forcing Data [21] For comparison, a new forcing data set for China is also used to drive the Noah model. The new data set was developed by the hydrometeorological research group at the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (hereafter ITPCAS) [He, 2010]. The temporal and spatial resolutions of the ITPCAS forcing data are 3 hourly and 0.25° × 0.25°, respectively. The observations collected at 740 operational stations of the CMA are merged into the corresponding Princeton meteorological forcing data [Sheffield et al., 2006] to produce near-surface air temperature, pressure, wind speed, and specific humidity. The height of air temperature and specific humidity is 1.5 m above ground; the height of wind speed is 10 m above ground. Three precipitation data sets are combined to produce the precipitation field, including the Tropical Rainfall Measuring Mission (TRMM) 3B42 precipitation products [Huffman et al., 2007], precipitation observations from 740 operational stations, and the Asian Precipitation – Highly Resolution Observational Data Integration Toward Evaluation of the Water Resources (APHRODITE) precipitation data [Yatagai et al., 2009]. The downward shortwave radiation was derived by correcting the Global Energy and Water Cycle Experiment – Surface Radiation Budget (GEWEX-SRB) [Pinker and Laszlo, 1992] shortwave radiation data set with reference to radiation estimates from CMA station data using a hybrid radiation model [Yang et al., 2006]. The downward longwave radiation is calculated by the model of Crawford and Duchon [1999] based on the produced near-surface air temperature, pressure, specific humidity, and downward shortwave radiation. [22] The ITPCAS forcing data are still being updated and evaluated. Several independent data sets, including a quality-controlled downward shortwave radiation data set at 95 CMA radiation stations, the high-resolution data collected through GEWEX Asia Monsoon Experiment-Tibet (GAME-Tibet) and the data collected through the CEOP Asia-Australia Monsoon Project-Tibet (CAMP-Tibet) have been used to evaluate the forcing data [He, 2010]. In subsection 5.3 of this paper, both the GLDAS and ITPCAS radiation fluxes are directly compared against field radiation measurements. Validation Data [23] In this paper, the retrieved MODIS LST products of version 5 in 2003 are used as the ground "truth" to examine the performance of the revised Noah. There are many types of MODIS LST products with different spatial and temporal resolutions. Here, the level 3 0.05° gridded products (MOD11C1 and MYD11C1) recommended by the algorithm development team are taken, since the algorithm used to generate these two data sets is the physically based day-night method and has an advantage over the split-window method for the 1 km LST products [Wan et al., 2002]. [24] The MOD11C1 product is produced for the Terra satellite, whose overpass times are around 10:30 A.M. (local solar time) in descending mode and 10:30 P.M. (local solar time) in ascending mode. The MYD11C1 LST product is produced for the Aqua satellite, whose overpass times are around 1:30 P.M. (local solar time) in ascending mode and around 1:30 A.M. (local solar time) in descending mode. Because the MYD11C1 and MOD11C1 LST products are derived from sensors aboard different satellites, these two data sets are used separately to evaluate the performance of the revised Noah and the original one. In this paper, the daytime MODIS/Aqua MYD11C1 LST product is adopted as the primary validation data, since its time (around 1:30 P.M., local solar time) corresponds better to the time when relatively high LST values occur in a diurnal course. Details concerning the usage of MODIS/Terra MOD11C1 LST product will be discussed in subsection 5.1. [25] In order to make a reliable evaluation of the simulated areal LST, MODIS LST values are used only if their quality-control (QC) flags are labeled as 00, indicating that the mean error of these LST values is less than 1 K and the data should have a good quality (refer to MODIS LST User's Guide for details). Moreover, a two-step procedure is used to make a matched comparison between the MODIS LST and the simulated one. First, the fine-resolution (0.05°) MODIS LST values are aggregated to the coarse-resolution (0.25°) ones corresponding to the grid cells on which the simulation is conducted. It is obvious that each coarse grid cell contains 25 fine-resolution MODIS LST pixels. The aggregation step is performed only when the number of fine-resolution LSTs with high-quality values in each coarse grid exceeds 21. Second, given the fact that the simulation time never precisely matches the MODIS overpass time, the simulated LST values cannot be directly applicable; in this paper, we derive the LST value at the desired overpass time simply from the two most adjacent simulated values (based on a linear interpolation). Soil, Vegetation, and Other Input Data Sets [26] The vegetation and soil as well as other parameter data sets used in this study are exactly the same as for the settings in producing the GLDAS 0.25° product of version 1 (i.e., GLDAS_NOAH025SUBP_3H). In other words, the vegetation type map is resampled from a static, 1 km resolution, land cover class that was produced by the University of Maryland (UMD) based on observations from the Advanced Very High Resolution Radiometer (AVHRR) aboard the NOAA-15 satellite. The monthly greenness vegetation fraction climatology data set is derived based on the 5 year (1985–1990) AVHRR Normalized Difference Vegetation Index (NDVI). The percentages of sand, silt, and clay components were horizontally resampled to the 0.25° grid based on the 5 s resolution global soils data set developed by Reynolds et al. [2000]. The NCEP quarterly albedo climatology and maximum snow albedo are both produced based on AVHRR data. The other input parameter data set is the land-sea mask produced by the UMD. These model parameter data sets are available from the NASA Land Information System website and GLDAS website. Design of Numerical Experiment [27] The total land-cover types, on which the simulation experiments are made, add up to 14 according to the UMD vegetation map. To facilitate our evaluation, these vegetation types are further categorized into five broader classes corresponding to primary climatic regions in China (Figure 1), including bare soil, crop, grass, shrub, and forest. This paper is concerned with the performance of the Noah model in arid and semiarid regions. As shown in Figure 1, the arid regions, whose land covers mainly include desert in the northwest and alpine desert in the west Tibetan Plateau, are labeled as bare soil and the semiarid regions are labeled as grassland, including grassland, alpine steppe, alpine meadow, and sparse shrubland in various subclimatic regions. Figure 1Open in figure viewerPowerPoint The gray-scaled sections are domain implemented for land surface simulations. The 14 vegetation types according to the UMD vegetation map are further categorized into five broader classes in China, where the arid regions are labeled as bare soil and the semiarid regions are labeled as grassland. [28] We conducted the land surface modeling experiments from January 2000 to December 2003. The temporal and spatial resolutions are 0.5 hourly and 0.25°, respectively. Four simulation experiments with different configurations of the models (the original LSM and the revised one) and the forcing data sets (the GLDAS and the ITPCAS) are designed to test the model performance. The aims of these experiments are twofold. The first is to check whether improvements are achieved by the revised Noah (subsection 4.1). The second is to investigate whether the ITPCAS forcing data can improve the model performance (subsection 4.2). 4. Results Modeling Improvement by z0h Scheme Replacement [29] In order to determine the effect of using the different z0h schemes as introduced in section 2.2, the widely used GLDAS forcing data are first applied to drive both the original Noah and the revised one. After QC and aggregation, a total of 5171 coarse-resolution daytime (around 1:30, local solar time) Aqua LST values are obtained in 2003 over the bare soil land-cover type in China. Figures 2a and 2c present the scatterplots between the simulated LST values and the corresponding aggregated LST values, and Figures 2b and 2d show the histograms for their differences. Figure 2b shows that the histogram for the original Noah evidently deviates from the zero point and shifts to the negative direction of the axis, indicating negative mean biases. Similarly, Figure 2d shows that the revised model also underestimates the LST, but t

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The jujube (Ziziphus jujuba Mill.), a member of family Rhamnaceae, is a major dry fruit and a traditional herbal medicine for more than one billion people. Here we present a high-quality sequence for the complex jujube genome, the first genome sequence of Rhamnaceae, using an integrated strategy. The final assembly spans 437.65 Mb (98.6% of the estimated) with 321.45 Mb anchored to the 12 pseudo-chromosomes and contains 32,808 genes. The jujube genome has undergone frequent inter-chromosome fusions and segmental duplications, but no recent whole-genome duplication. Further analyses of the jujube-specific genes and transcriptome data from 15 tissues reveal the molecular mechanisms underlying some specific properties of the jujube. Its high vitamin C content can be attributed to a unique high level expression of genes involved in both biosynthesis and regeneration. Our study provides insights into jujube-specific biology and valuable genomic resources for the improvement of Rhamnaceae plants and other fruit trees.

Thermal nanofluids, the engineered fluids with dispersed functional nanoparticles, have exhibited extraordinary thermophysical properties and added functionalities, and thus have enabled a broad range of important applications. The poor dispersion stability of thermal nanofluids, however, has been considered as a long-existing issue that limits their further development and practical application. This review overviews the recent efforts and progresses in improving the dispersion stability of thermal nanofluids such as mechanistic understanding of dispersion behavior of nanofluids, examples of both water-based and oil-based nanofluids, strategies to stabilize nanofluids, and characterization techniques for dispersion behavior of nanofluids. Finally, on-going research needs, and possible solutions to research challenges and future research directions in exploring stably dispersed thermal nanofluids are discussed.

This paper proposes a coordinated operational dispatch scheme for a wind farm with a battery energy storage system (BESS). The main advantages of the proposed dispatch scheme are that it can reduce the impacts of wind power forecast errors while prolonging the lifetime of BESS. The scheme starts from the planning stage, where a BESS capacity determination method is proposed to compute the optimal power capacity and energy capacity of BESS based on historical wind power data; and then, at the operation stage, a flexible short-term BESS-wind farm dispatch scheme is proposed based on the forecasted wind power generation scenarios. Three case studies are provided to validate the performance of the proposed method. The results show that the proposed scheme can largely improve the wind farm dispatchability.

With the purpose to achieve panchromatic absorption for constructing efficient dye-sensitized solar cells (DSSCs), the cosensitization approach of using two dyes with complementary absorption has been developed with great success. However, this approach usually requires time-consuming optimization of a number of parameters for controlling the ratio and distribution of the two coadsorbed dyes on TiO2 film, which limits the potentials of this strategy. We herein report an alternative approach for developing efficient DSSCs by designing a class of "concerted companion dyes" with two complementary dye components linked covalently. Thus, a newly synthesized organic dye Z2 was linked to a recently reported doubly strapped porphyrin dye XW51 through flexible chains with various lengths to afford XW60-XW63. These dyes exhibit excellent absorption and efficiencies in the range of 8.8%-11.7%. Notably, upon coadsorption with chenodeoxycholic acid, XW61 affords an impressive efficiency of 12.4%, a record for iodine electrolyte-based DSSCs, to the best of our knowledge. In addition, these dyes also exhibit the advantages of easy cell fabrication, simple optimization, as well as excellent photostability.

With the advancement of wireless technologies and sensing methodologies, many studies have shown the success of re-using wireless signals (e.g., WiFi) to sense human activities and thereby realize a set of emerging applications, ranging from intrusion detection, daily activity recognition, gesture recognition to vital signs monitoring and user identification involving even finer-grained motion sensing. These applications arguably can brace various domains for smart home and office environments, including safety protection, well-being monitoring/management, smart healthcare and smart-appliance interaction. The movements of the human body impact the wireless signal propagation (e.g., reflection, diffraction and scattering), which provide great opportunities to capture human motions by analyzing the received wireless signals. Researchers take the advantage of the existing wireless links among mobile/smart devices (e.g., laptops, smartphones, smart thermostats, smart refrigerators and virtual assistance systems) by either extracting the ready-to-use signal measurements or adopting frequency modulated signals to detect the frequency shift. Due to the low-cost and non-intrusive sensing nature, wireless-based human activity sensing has drawn considerable attention and become a prominent research field over the past decade. In this paper, we survey the existing wireless sensing systems in terms of their basic principles, techniques and system structures. Particularly, we describe how the wireless signals could be utilized to facilitate an array of applications including intrusion detection, room occupancy monitoring, daily activity recognition, gesture recognition, vital signs monitoring, user identification and indoor localization. The future research directions and limitations of using wireless signals for human activity sensing are also discussed.

The present study examines the effects of financial instability on consumption-based carbon emission in the presence of international trade, technological innovation, and economic growth in Emerging Seven (E-7) countries from 1995-2018. This study employed second and third-generation panel cointegration methodologies. The result of the cross-sectional dependency and slope heterogeneity test confirms that the panels are correlated, and there exists slope heterogeneity. The results for the short- and long-run confirm the relationship between consumption-based carbon emission, financial instability, imports, exports, technological innovation, and economic growth. In both the short- and long-run, imports and economic growth enhances carbon emission, whereas financial instability, technological innovation, and exports significantly reduce consumption-based carbon emission. The robustness check findings obtained using the Augmented Mean Group (AMG) and Common correlated effect mean group (CCEMG) further validate the results. Policymakers should make financial reforms that actively encourage and give incentives to firms that adopt efficient and environmentally friendly technologies, reducing energy consumption, and carbon emission.

Recent studies have demonstrated that bone marrow mesenchymal stem cells (BMSCs) protect the injured neurons of spinal cord injury (SCI) from apoptosis while the underlying mechanism of the protective effect of BMSCs remains unclear. In this study, we found the transfer of mitochondria from BMSCs to injured motor neurons and detected the functional improvement after transplanting. Methods: Primary rat BMSCs were co-cultured with oxygen-glucose deprivation (OGD) injured VSC4.1 motor neurons or primary cortical neurons. FACS analysis was used to detect the transfer of mitochondria from BMSCs to neurons. The bioenergetics profiling of neurons was detected by Extracellular Flux Analysis. Cell viability and apoptosis were also measured. BMSCs and isolated mitochondria were transplanted into SCI rats. TdT-mediated dUTP nick end labelling staining was used to detect apoptotic neurons in the ventral horn. Immunohistochemistry and Western blotting were used to measure protein expression. Re-myelination was examined by transmission electron microscope. BBB scores were used to assess locomotor function. Results: MitoTracker-Red labelled mitochondria of BMSCs could be transferred to the OGD injured neurons. The gap junction intercellular communication (GJIC) potentiator retinoid acid increased the quantity of mitochondria transfer from BMSCs to neurons, while GJIC inhibitor 18β glycyrrhetinic acid decreased mitochondria transfer. Internalization of mitochondria improved the bioenergetics profile, decreased apoptosis and promoted cell survival in post-OGD motor neurons. Furthermore, both transplantation of mitochondria and BMSCs to the injured spinal cord improved locomotor functional recovery in SCI rats. Conclusions: To our knowledge, this is the first evidence that BMSCs protect against SCI through GJIC to transfer mitochondrial to the injured neurons. Our findings suggested a new therapy strategy of mitochondria transfer for the patients with SCI.

Cobalt-mediated peroxymonosulfate (PMS) activation is an effective way to generate sulfate radicals for the degradation of pollutants. Herein, a heterogeneous Co-based catalyst was prepared by integrating the cobalt species to the cavities of g-C3N4 via the CoN bonds through the methods of impregnation and calcination, which was applied to activate PMS for the degradation of organic contaminates under visible light. The cobalt species were atomically dispersed on g-C3N4, which could provide more reactive sites for PMS activation. The catalytic efficiency of cobalt-doped g-C3N4 was greater than that of pure g-C3N4 or even slightly higher than that of the homogeneous cobalt ions during the photo-assisted PMS activation catalytic processes. The optimum Co loading is 1.0% in weight. In 25 min, the dye of rhodamine B was almost completely degraded with a 70.5% of total organic carbon (TOC) removal under its natural pH value in the presence of such catalytic system. The enhanced catalytic activity of Co-doped g-C3N4 was due to the effective separation of the photo-generated charges as well as the sufficient accessible reactive sites for PMS activation. And the synergistic interaction between the photocatalytic and PMS activation processes facilitated the generation of active species for the removal of pollutants.

Tracking human sleeping postures and vital signs of breathing and heart rates during sleep is important as it can help to assess the general physical health of a person and provide useful clues for diagnosing possible diseases. Traditional approaches (e.g., polysomnography) are limited to clinic usage. Recent radio frequency-based approaches require specialized devices or dedicated wireless sensors and are only able to track breathing rate. In this paper, we propose to track the vital signs of both breathing rate and heart rate during sleep by using off-the-shelf WiFi without any wearable or dedicated devices. Our system reuses existing WiFi network and exploits the fine-grained channel information to capture the minute movements caused by breathing and heart beats. Our system thus has the potential to be widely deployed and perform continuous long-term monitoring. The developed algorithm makes use of the channel information in both time and frequency domain to estimate breathing and heart rates, and it works well when either individual or two persons are in bed. Our extensive experiments demonstrate that our system can accurately capture vital signs during sleep under realistic settings, and achieve comparable or even better performance comparing to traditional and existing approaches, which is a strong indication of providing noninvasive, continuous fine-grained vital signs monitoring without any additional cost.

Eucalyptus is a highly diverse genus of the Myrtaceae family and widely planted in the world for timber and pulp production. Tissue culture induced callus has become a common tool for Eucalyptus breeding, however, our knowledge about the genes related to the callus maturation and shoot regeneration is still poor.We set up an experiment to monitor the callus induction and callus development of two Eucalyptus species - E. camaldulensis (high embryogenic potential) and E. grandis x urophylla (low embryogenic potential). Then, we performed transcriptome sequencing for primary callus, mature callus, shoot regeneration stage callus and senescence callus. We identified 707 upregulated and 694 downregulated genes during the maturation process of the two Eucalyptus species and most of them were involved in the signaling pathways like plant hormone and MAPK. Next, we identified 135 and 142 genes that might play important roles during the callus development of E. camaldulensis and E. grandis x urophylla, respectively. Further, we found 15 DEGs shared by these two Eucalyptus species during the callus development, including Eucgr.D00640 (stem-specific protein TSJT1), Eucgr.B00171 (BTB/POZ and TAZ domain-containing protein 1), Eucgr.C00948 (zinc finger CCCH domain-containing protein 20), Eucgr.K01667 (stomatal closure-related actinbinding protein 3), Eucgr.C00663 (glutaredoxin-C10) and Eucgr.C00419 (UPF0481 protein At3g47200). Interestingly, the expression patterns of these genes displayed "N" shape in the samples. Further, we found 51 genes that were dysregulated during the callus development of E. camaldulensis but without changes in E. grandis x urophylla, such as Eucgr.B02127 (GRF1-interacting factor 1), Eucgr.C00947 (transcription factor MYB36), Eucgr.B02752 (laccase-7), Eucgr.B03985 (transcription factor MYB108), Eucgr.D00536 (GDSL esterase/lipase At5g45920) and Eucgr.B02347 (scarecrow-like protein 34). These 51 genes might be associated with the high propagation ability of Eucalyptus and 22 might be induced after the dedifferentiation. Last, we performed WGCNA to identify the co-expressed genes during the callus development of Eucalyptus and qRT-PCR experiment to validate the gene expression patterns.This is the first time to globally study the gene profiles during the callus development of Eucalyptus. The results will improve our understanding of gene regulation and molecular mechanisms in the callus maturation and shoot regeneration.

The present study highlights the importance of environmental taxes and R&D in achieving the goal of carbon neutrality. Post Paris conference (COP21), countries set domestic targets to achieve zero carbon or carbon neutrality. Several studies have been conducted to explore the factors affecting environmental quality. However, the literature on the importance of environmental taxes and environmental R&D in affecting environmental quality is scant, and thus, this study investigates the impact of environmental taxes and R&D on consumption-based carbon emissions for G-7 countries over a period of 1990–2019. The cointegration test results show a stable long-run association between environmental taxes, environmental R&D, imports, exports, GDP, and consumption-based CO2 emissions. The results show that in the short- and long-run, environmental taxes, environmental R&D, and exports significantly reduce carbon emissions, whereas GDP and imports significantly enhance carbon emissions. The Dumitrescu and Hurlin Granger causality test results show that any policy that targets environmental taxes, environmental R&D, exports, imports, and GDP significantly changes CO2 emissions. This study recommends that policymakers in G-7 countries should focus on environmental R&D and taxes to achieve the goal of carbon neutrality.

The recent outbreaks of infectious diseases caused by multidrug-resistant pathogens have sounded a piercing alarm for the need of new effective antimicrobial agents to guard public health. Among different types of candidates, antimicrobial peptides (AMPs) and the synthetic mimics of AMPs (SMAMPs) have attracted significant enthusiasm in the past thirty years, due to their unique membrane-active antimicrobial mechanism and broad-spectrum antimicrobial activity. The extensive research has brought many drug candidates into clinical and pre-clinical development. Despite tremendous progresses have been made, several major challenges inherent to current design strategies have slowed down the clinical translational development of AMPs and SMAMPs. However, these challenges also triggered many efforts to redesign and repurpose AMPs. In this review, we will first give an overview on AMPs and their synthetic mimics, and then discuss the current status of their clinical translation. Finally, the recent advances in redesign and repurposing AMPs and SMAMPs are highlighted.

To evaluate the mental health status of hospitalized patients with coronavirus disease 2019 (COVID-19) and to explore the related factors. This was a cross-sectional survey among COVID-19 inpatients in two isolation wards of a designated hospital in Wuhan, China, from March 7, 2020, to March 24, 2020. Participants' demographic data, clinical data and levels of circulating inflammatory markers were collated. Mental health symptoms were evaluated with questionnaires, which included the Insomnia Severity Index (ISI) scale, the 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder (GAD-7) scale, and questions about patients' self-perceived illness severity. Multivariate linear regression analysis was performed to explore factors that associated with mental symptoms, and a structural equation model (SEM) was used to assess the possible relationships between those factors and the patients' mental health. Among the 85 participants, 45.9% had symptoms of depression (PHQ-9 ≥ 5), 38.8% had anxiety (GAD-7 ≥ 5), and 54.1% had insomnia (ISI ≥ 8). According to multivariate regression analysis, female sex, a higher level of interleukin (IL)-1β and greater self-perceived illness severity were all significantly associated with a higher PHQ-9 score, higher GAD-7 score and higher ISI score. In addition, the disease duration and the neutrophil to lymphocyte ratio (NLR) were positively related to patients' self-perceived illness severity. The results of the SEM analyses suggested that sex (β = 0.313, P < 0.001), self-perceived illness severity (β = 0.411, P < 0.001) and levels of inflammatory markers (β = 0.358, P = 0.002) had direct effects on patients' mental health. The disease duration (β = 0.163, P = 0.003) and levels of inflammatory markers (β = 0.101, P = 0.016) also indirectly affected patients' mental health, with self-perceived illness severity acting as a mediator. A majority of COVID-19 infected inpatients reported experiencing mental health disturbances. Female sex, disease duration, levels of inflammatory markers and self-perceived illness severity are factors that could be used to predict the severity of patients' mental symptoms.

The performance of gridded precipitation products is especially important over mountainous regions because of the lack of observations. This study provides a detailed evaluation of the spatial precipitation patterns presented by the ERA-Interim, ERA5, ERA5-Land, and refined HAR datasets with spatial resolutions from coarse to fine (0.7°, 0.25°, 0.1°, and 0.1°, respectively) over the southern slope of central Himalaya, Nepal. The major findings are as follows: (1) The high-resolution ERA5-Land and ERA5 datasets well present the observed spatial pattern of precipitation but with a generally overestimated amount; the refined HAR dataset also presents many fine-scale details, while the coarse resolution ERA-Interim dataset tends to exhibit a too gentle spatial characteristic in mountainous Nepal. (2) The four datasets well reproduce the seasonal precipitation variation (R > 0.90) over the country; however, their performance varies spatially across the western, central, and eastern regions. The spatial resolution is also important in representing the terrain blocking of water vapor from the Bay of Bengal, which is transported from east to west in Nepal. (3) The high-resolution datasets can reproduce the elevation dependency as revealed by the observed dataset, while the coarse-resolution cannot capture the elevation dependency in the high-elevation regions. Moreover, all four datasets show very similar performance in low–elevation plane areas, however, higher resolution simulations (ERA5, ERA5–Land, and HAR) better reflect blocking effect of orographic precipitation and outperform ERA-Interim in high–elevation areas where the terrain is very complex and the slope is very steep.

Some bioactive ingredients in foods are unstable and easily degraded during processing, storage, transportation and digestion. To enhance the stability and bioavailability, some food hydrogels have been developed to encapsulate these unstable compounds. In this paper, the preparation methods, formation mechanisms, physicochemical and functional properties of some protein hydrogels, polysaccharide hydrogels and protein-polysaccharide composite hydrogels were comprehensively summarized. Since the hydrogels have the ability to control the release and enhance the bioavailability of bioactive ingredients, the encapsulation and release mechanisms of polyphenols, flavonoids, carotenoids, vitamins and probiotics by hydrogels were further discussed. This review will provide a comprehensive reference for the deep application of polysaccharide/protein hydrogels in food industry.

Transition metal phosphides (TMP) show great potential to alternative noble metal based electrocatalysts for oxygen evolution reaction (OER) electrolysis but the still unsatisfactory catalytic activity hinders its practical application. Therefore, it is of much significance to rationally design TMP electrocatalysts for achieving high-efficiency OER. Here, we design Mn-doped cobalt phosphide (Mn-CoP) porous nanosheets for highly active OER through the in-situ metal acetate hydroxide transformation and subsequent phosphidation treatment. Benefiting from synergistic effects of the porous structure, high density of active sites and improved charge-transfer capability, the optimized Mn-CoP serving as a pre-catalyst shows an impressive alkaline OER performance with a low overpotential of 288 mV at the current density of 10 mA cm−2 and high stability. Post-electrolysis characterizations show the conversion from Mn-CoP to vertical Mn-CoOOH hexagonal nanosheets during OER, in which the transformed Mn-CoOOH not only provide extra active sites, but serve as the highly active species. Density functional theory (DFT) calculations reveal that Mn doping can increase the gap states near the Fermi level of active O sites, endowing facilitated deprotonation of OH* to O* and reducing the energy barrier of rate-determining step. This protocol provides a novel insight for the construction of highly efficient TMP water oxidation electrocatalysts.

Multiple myeloma (MM) is the second most common hematological malignancy. It is characterized by abnormal transformation and uncontrolled clonal proliferation of malignant plasma cells in the bone marrow (BM), which can destroy bone structure and inhibit hematopoiesis. Although there are new therapeutic methods, they are not curative, mainly because it is difficult to deliver an effective amount of drug to BM, leading to a failure to eradicate MM cells inside the BM. BM homing is an important and unique characteristic of MM cells and it is mainly affected by surface molecules on the tumor cell membrane. Inspired by this mechanism, an MM-mimicking nanocarrier is developed by coating bortezomib (BTZ)-loaded poly(ε-caprolactone)-poly(ethylene glycol)-poly(ε-caprolactone) (PCEC) nanoparticles with the MM cell membrane. The MM-mimicking nanoparticles can enter the BM based on BM homing as a "Trojan horse" and target the tumor cells through homologous targeting. In this way, drug availability at the myeloma site is enhanced so as to inhibit MM growth. In addition, these MM-mimicking nanoparticles can escape phagocytosis by the MPS and have a long circulation effect. The in vivo therapeutic results demonstrate an excellent treatment efficacy for MM. Accordingly, this strategy may be a promising platform for the treatment of MM.

The Internet of Things (IoT) has grown over decades to encompass many forms of sensing modalities, and continues to improve in terms of sophistication and lower costs. The trend of hardware miniaturization and emphasis on user convenience has inspired numerous studies to integrate more varied devices within the IoT into modernizing healthcare systems, facilitating applications, such as activity recognition, fitness assistance, vital signs monitoring, daily dietary tracking, and sleep monitoring. These applications are vital for prevention, detection, and treatment of ailments and can be realized using both dedicated health sensors as well as general-purpose sensors not originally designed for health monitoring. This article surveys such studies, detailing smart health monitoring systems, and the types of sensor components utilized within the IoT. We categorize and analyze these works based on their leverage of device-based techniques (i.e., use of sensors worn or carried by the person) and device-free techniques (i.e., wireless sensing without need to carry hardware), as well as signal processing and classification techniques utilized. In particular, we discuss how different combinations of these techniques can be creatively applied to support professional and commercial health-monitoring IoT networks. We also identify limitations and potential directions that future research may explore.

The state-of-the-art structure design of solar-thermal evaporators demonstrating convenient manufacture, high evaporation rate, high energy efficiency and multiple environment evaporation is urgently needed in solar-driven low-energy water purification/harvesting. Herein, a novel Janus-type graphene oxide (GO) evaporator is proposed by chemical crosslinking to form integrated GO hydrogel and sunlight triggered partial reduction of top GO layer. The partially reduced GO (PRGO) nanosheets present an extremely high solar absorbance of 92.97%, leading to an efficient localized solar-thermal conversion. The porous and hydrophilic GO hydrogel facilitates the fast vertical water transport to the top PRGO photothermal layer. Taking advantages of the synergistic effect in the water-energy nexus, the Janus-type solar-thermal evaporator evaporates water with an ultra-high rate of 3.24 kg·m−2·h−1 via 99.98% energy efficiency from 1 sun irradiation with a low GO loading of 0.146 wt%. The Janus-type GO evaporator presents robust desalination and purification performances in simulated seawater, sewage and acidic/alkaline water. Furthermore, a portable solar-thermal water purification device equipped with the Janus-type GO evaporator desalts a real seawater far above the drinking water standard with a 99.99% salt rejection rate and removes 90.90% of total carbon in a real sewage, highlighting its potential for high-performance clean water harvesting.

Abstract. Reliable precipitation data are highly necessary for geoscience research in the Third Pole (TP) region but still lacking, due to the complex terrain and high spatial variability of precipitation here. Accordingly, this study produces a long-term (1979–2020) high-resolution (1/30∘, daily) precipitation dataset (TPHiPr) for the TP by merging the atmospheric simulation-based ERA5_CNN with gauge observations from more than 9000 rain gauges, using the climatologically aided interpolation and random forest methods. Validation shows that TPHiPr is generally unbiased and has a root mean square error of 5.0 mm d−1, a correlation of 0.76 and a critical success index of 0.61 with respect to 197 independent rain gauges in the TP, demonstrating that this dataset is remarkably better than the widely used datasets, including the latest generation of reanalysis (ERA5-Land), the state-of-the-art satellite-based dataset (IMERG) and the multi-source merging datasets (MSWEP v2 and AERA5-Asia). Moreover, TPHiPr can better detect precipitation extremes compared with these widely used datasets. Overall, this study provides a new precipitation dataset with high accuracy for the TP, which may have broad applications in meteorological, hydrological and ecological studies. The produced dataset can be accessed via https://doi.org/10.11888/Atmos.tpdc.272763 (Yang and Jiang, 2022).

Sustainable development is increasingly important in today's world as it combines social and economic progress with environmental protection, and few studies have examined renewable energy R&D and natural resources rent as important components of achieving a sustainable future. Therefore, this study addresses the gap in the literature by exploring the influence of renewable energy R&D, and natural resources rent on consumption-based carbon emissions for OECD economies from 1990 to 2020. The results obtained using the method of moment quantile regression indicate that renewable energy R&D plays a crucial role in mitigating carbon emissions, with its impact being more pronounced at higher quantiles. The results for natural resources rent show an adverse effect on environmental pollution at the 25th and 50th quantiles, whereas, at the 75th and 90th quantiles, the impact is positive but insignificant. The results for imports and economic growth have a significant positive influence on environmental pollution, while exports have a negative relationship with carbon emissions. Policymakers can decrease carbon emissions to improve environmental quality and enhance economic development in OECD economies by investing in renewable energy R&D and sustainable management of natural resource rent.

Alcohol consumption is one of the leading causes of death worldwide. Adolescence is a critical period of structural and functional maturation of the brain. Adolescent alcohol use can alter epigenetic modifications. However, little is known on the long-term effects of alcohol consumption during adolescence on RNA epigenetic modifications in brain. Herein, we systematically explored the long-term effects of alcohol exposure during adolescence on small RNA modifications in adult rat brain tissues by comprehensive liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) analysis. We totally detected 26 modifications in small RNA of brain tissues. Notably, we observed most of these modifications were decreased in brain tissues. These results suggest that alcohol exposure during adolescence may impose a long-lasting impact on RNA modifications in brain tissues. This is the first report that alcohol use during adolescence can alter RNA modifications in adult brain. Collectively, this study suggests a long-term adverse effects of alcohol consumption on brain from RNA epigenetics angle by comprehensive mass spectrometry analysis.

Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. In this paper, we propose to use spatial information, a physical property associated with each node, hard to falsify, and not reliant on cryptography, as the basis for 1) detecting spoofing attacks; 2) determining the number of attackers when multiple adversaries masquerading as the same node identity; and 3) localizing multiple adversaries. We propose to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. We then formulate the problem of determining the number of attackers as a multiclass detection problem. Cluster-based mechanisms are developed to determine the number of attackers. When the training data are available, we explore using the Support Vector Machines (SVM) method to further improve the accuracy of determining the number of attackers. In addition, we developed an integrated detection and localization system that can localize the positions of multiple attackers. We evaluated our techniques through two testbeds using both an 802.11 (WiFi) network and an 802.15.4 (ZigBee) network in two real office buildings. Our experimental results show that our proposed methods can achieve over 90 percent Hit Rate and Precision when determining the number of attackers. Our localization results using a representative set of algorithms provide strong evidence of high accuracy of localizing multiple adversaries.

Abstract Myeloid-derived suppressor cells (MDSCs) were one of the major components of the immune suppressive network. STAT3 has an important role in regulating the suppressive potential of MDSCs. In this study, we found that the expression of STAT3 could be modulated by both miR-17-5p and miR-20a. The transfection of miR-17-5p or miR-20a remarkably reduces the expression of reactive oxygen species and the production of H2O2, which are regulated by STAT3. MDSCs transfected with miR-17-5p or miR-20a are less able to suppress Ag-specific CD4 and CD8 T cells. Importantly, both miR-17-5p and miR-20a alleviate the suppressive function of MDSCs in vivo. The expression of miR-17-5p and miR-20a in tumor-associated MDSCs was found to be lower than in Gr1+CD11b+ cells isolated from the spleens of disease-free mice. Tumor-associated factor downregulates the expression of both miR-17-5p and miR-20a. The modulation of miR-17-5p and miR-20a expression may be important for the process by which patients with a tumor can overcome the immune tolerance mediated by MDSCs. Our results suggest that miR-17-5p and miR-20a could potentially be used for immunotherapy against diseases, especially cancer, by blocking STAT3 expression.

This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where the sensing data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute with abnormal data, which makes the existing cooperative sensing schemes ineffective. To tackle these challenges, we first propose a generalized modeling approach for sensing data with an arbitrary abnormal component. Under this model, we then analyze the impact of general abnormal data on the performance of the cooperative sensing, by deriving closed-form expressions of the probabilities of global false alarm and global detection. To improve sensing data quality and enhance cooperative sensing performance, we further formulate an optimization problem as stable principal component pursuit, and develop a data cleansing-based robust spectrum sensing algorithm to solve it, where the under-utilization of licensed spectrum bands and the sparsity of nonzero abnormal data are jointly exploited to robustly cleanse out the potential nonzero abnormal data component from the original corrupted sensing data. Extensive simulation results demonstrate that the proposed robust sensing scheme performs well under various abnormal data parameter configurations.

Alpine meadow soil is an important ecosystem component of the Qinghai-Tibetan Plateau. However, the alpine meadow soil is undergoing serious degradation mainly due to global climate change, overgrazing, human activities and rodents. In this paper, spatial sequencing was chosen over time succession sequencing to study the changes of soil hydraulic properties under different degrees of alpine meadow degradation. Soil saturated hydraulic conductivity (Ks) and Gardner α both at the surface and at 40–50 cm depth were investigated in the field using tension infiltrometers. Soil physical and chemical properties, together with the root index at 0–10 cm and 40–50 cm soil layer depths were also analyzed. Pearson correlations were adopted to study the relationships among the investigated factors and principal component analysis was performed to identify the dominant factor. Results show that with increasing degree of degradation, soil sand content increased while soil Ks and Gardner α as well as soil clay content, soil porosity decreased in the 0–10 cm soil layers, and organic matter and root gravimetric density decreased in both the 0–10 cm and 40–50 cm soil layers. However, soil moisture showed no significant changes with increasing degradation. With decreasing pressure head, soil unsaturated hydraulic conductivity reduced more slowly under degraded conditions than non-degraded conditions. Soil Ks and Gardner α were significantly correlated (P = 0.01) with bulk density, soil porosity, soil organic matter and root gravimetric density. Among these, soil porosity is the dominant factor explaining about 90% of the variability in total infiltration flow. Under non-degraded conditions, the infiltration flow principally depended on the presence of macropores. With increasing degree of degradation, soil macropores quickly changed to mesopores or micropores. The proportion of total infiltration flow through macropores and mesopores significantly decreased with the most substantial decrease observed for the macropores in the 0–10 cm soil layer. The substantial decrease of macropores caused a cut in soil moisture and hydraulic conductivity. This study improves the understanding and prediction of alpine meadow soil and ecosystem changes and provides guidelines for improving water flow modeling under the background of global climate change over the Qinghai-Tibetan Plateau and similar regions.

Renewable energy is considered as a solution for mitigating energy crisis and environmental pollution. However, there are two main issues in techno-economic assessment of renewable energy resource: difficult to quantify assessment indicators and lack of a general indicator for multi-criteria evaluation. This study aims to develop a calculation methodology to quantify the techno-economic indicators including power generation, economic costs, incomes, and carbon emissions, through simulating the long-term implementation of solar, wind and biomass energy based renewable power systems. Moreover, by using normalization, weighting methods and Radar plot, all the indicators mentioned above are combined into a general evaluation indicator, which is able to provide a numerical result of multi-criteria quality evaluation of renewable energy resources. Hunan Province located in subtropical China where abundant of solar, wind and biomass energy resources, is studied as a case to present the evaluation methodology and its application. Through collecting the resource data of solar, wind and biomass energy and their facility costs data as the inputs, the long-term implementation of renewable power generation systems located in fourteen cities of Hunan Province are simulated; and then the numerical values of the general indicator are compared among different types of renewable energy resource. The general indicator evaluation results show that southern region including Yongzhou city have higher feasibility for wind power, while the eastern and northern areas including Loudi city have better feasibility of solar energy; for biomass energy resource assessment, the Huaihua system has the best feasibility while the Xiangtan system has the lowest among all the cities in Hunan province. The developed evaluation model and general indicator are able to provide a reference for investment decision making and subsidy policy optimizing.

Summary Objective To investigate the effects of long‐term effective control of serum uric acid on renal function in patients with type 2 diabetes and asymptomatic hyperuricemia. Methods Application of randomized open parallel‐controlled methods, a total of 176 patients with type 2 diabetes and asymptomatic hyperuricemia were selected, and was randomly divided into two groups for allopurinol or conventional treatments, respectively. Changes in urinary albumin excretion rate (UAER), the levels of serum creatinine and glomerular filtration rate (GFR) and the incidence of new‐onset diabetic nephropathy (DN) and hypertension in patients before and after 3 years of treatment were measured and compared between groups. Results There were no statistically significant difference in the baseline clinical characteristics of study participants between two treatment groups ( P &gt; 0·05 for all). After 3 years of treatment, compared to the conventional treatment, the allopurinol treatment was more effective in reducing serum uric acid, UAER, serum creatinine ( P &lt; 0·01 for all) and increasing GFR ( P &lt; 0·01). The intention‐to‐treat (ITT) analysis indicated that the incidence of new‐onset DN and hypertension in the allopurinol group showed a declining trend compared to that in the conventional treatment group, despite a lack of significant difference ( P &gt; 0·05). Conclusion Long‐term effective control of serum uric acid can decrease UAER and serum creatinine, increase GFR and may exert kidney protection effects in patients with type 2 diabetes and asymptomatic hyperuricemia.

Porous (Co, Mn)(Co, Mn)₂O₄-based microspheres (CM-11-Ms) and core–shell microspheres (CM-11-CSMs) were firstly synthesized <italic>via</italic> controlled pyrolysis of CoMn-precursor microspheres at different temperatures under nitrogen, exhibiting advanced lithium storage capacities.

To investigate the evolutionary and epidemiological dynamics of the current COVID-19 outbreak, a total of 112 genomes of SARS-CoV-2 strains sampled from China and 12 other countries with sampling dates between 24 December 2019 and 9 February 2020 were analyzed. We performed phylogenetic, split network, likelihood-mapping, model comparison, and phylodynamic analyses of the genomes. Based on Bayesian time-scaled phylogenetic analysis with the best-fitting combination models, we estimated the time to the most recent common ancestor (TMRCA) and evolutionary rate of SARS-CoV-2 to be 12 November 2019 (95 % BCI: 11 October 2019 and 09 December 2019) and 9.90 × 10−4 substitutions per site per year (95 % BCI: 6.29 × 10−4–1.35 × 10−3), respectively. Notably, the very low Re estimates of SARS-CoV-2 during the recent sampling period may be the result of the successful control of the pandemic in China due to extreme societal lockdown efforts. Our results emphasize the importance of using phylodynamic analyses to provide insights into the roles of various interventions to limit the spread of SARS-CoV-2 in China and beyond.

pH-sensitive drug carriers offer promise for tumor targeted drug delivery. An amphiphilic triblock copolymer, poly(ε-caprolactone)-block-poly(diethylaminoethyl methacrylate)-block-poly(sulfobetaine methacrylate) (PCL–PDEA–PSBMA), was synthesized through click reaction of alkyne end-functionalized poly(sulfobetaine methacrylate) (polySBMA–alkyne) onto azide end-functionalized PCL–PDEA (PCL–PDEA–N3) and was used as a pH-sensitive drug carrier in the form of micelles. In particular, the micelles exhibited pH dependency as a result of the protonation of the PDEA block. A hydrophobic drug, curcumin, was chosen as a model drug to investigate the potential application of this triblock copolymer in drug-controlled release. The results indicated that the release rate of curcumin-loaded micelles at pH 5.0 was faster than that at pH 7.4. Furthermore, the results of the pharmacokinetics of the curcumin-loaded micelles in vivo showed that the retention time of the curcumin-loaded micelles in blood could extend and the clearance of curcumin in the micelles was delayed, compared with the curcumin solution. This new pH-sensitive triblock copolymer PCL–PDEA–PSBMA has great potential as a hydrophobic anticancer drug carrier.

The new melokhanines A-J (1-10) and 22 known (11-32) alkaloids were isolated from the twigs and leaves of Melodinus khasianus. The new compounds and their absolute configurations were elucidated by extensive analysis of spectroscopic, X-ray diffraction, and computational data. Melokhanine A (1), composed of a hydroxyindolinone linked to an octahydrofuro[2,3-b]pyridine moiety, is an unprecedented monoterpenoid indole alkaloid. Melokhanines B-H (2-8) possess a new 6/5/5/6/6 pentacyclic indole alkaloid skeleton. Alkaloids 1-16, 25-27, 31, and 32 showed the best antibacterial activity against Pseudomonas aeruginosa (MIC range 2-22 μM). Among the seven dermatophytes tested, compound 1 showed significant inhibitory activity against Microsporum canis, M. ferrugineum, and Trichophyton ajelloi (MIC range 38-150 μM), i.e., half the efficacy of the positive control, griseofulvin.

This paper presents a new steam sterilization approach that uses a solar-driven evaporation system at the water/air interface. Compared to the conventional solar autoclave, this new steam sterilization approach via interfacial evaporation requires no complex system design to bear high steam pressure. In such a system, a reduced graphene oxide/polytetrafluoroethylene composite membrane floating at the water/air interface serves as a light-to-heat conversion medium to harvest and convert incident solar light into localized heat. Such localized heat raises the temperature of the membrane substantially and helps generate steam with a temperature higher than 120 °C. A sterilization device that takes advantage of the interfacial solar-driven evaporation system was built and its successful sterilization capability was demonstrated through both chemical and biological sterilization tests. The interfacial evaporation-based solar driven sterilization approach offers a potential low cost solution to meet the need for sterilization in undeveloped areas that lack electrical power but have ample solar radiation.

The proliferation of wearable devices, e.g., smartwatches and activity trackers, with embedded sensors has already shown its great potential on monitoring and inferring human daily activities. This paper reveals a serious security breach of wearable devices in the context of divulging secret information (i.e., key entries) while people accessing key-based security systems. Existing methods of obtaining such secret information relies on installations of dedicated hardware (e.g., video camera or fake keypad), or training with labeled data from body sensors, which restrict use cases in practical adversary scenarios. In this work, we show that a wearable device can be exploited to discriminate mm-level distances and directions of the user's fine-grained hand movements, which enable attackers to reproduce the trajectories of the user's hand and further to recover the secret key entries. In particular, our system confirms the possibility of using embedded sensors in wearable devices, i.e., accelerometers, gyroscopes, and magnetometers, to derive the moving distance of the user's hand between consecutive key entries regardless of the pose of the hand. Our Backward PIN-Sequence Inference algorithm exploits the inherent physical constraints between key entries to infer the complete user key entry sequence. Extensive experiments are conducted with over 5000 key entry traces collected from 20 adults for key-based security systems (i.e. ATM keypads and regular keyboards) through testing on different kinds of wearables. Results demonstrate that such a technique can achieve 80% accuracy with only one try and more than 90% accuracy with three tries, which to our knowledge, is the first technique that reveals personal PINs leveraging wearable devices without the need for labeled training data and contextual information.

Lignocellulosic biomass is a promising source of renewable biofuels. However, pretreatment of lignocellulosic biomass generates fermentation inhibitors that adversely affect the growth of industrial microorganisms such as Saccharomyces cerevisiae and prevent economic production of lignocellulosic biofuels. A critical challenge on developing S. cerevisiae with improved inhibitor resistance lies in incomplete understanding of molecular basis for inhibitor stress response and limited information on effective genetic targets for increasing yeast resistance to mixed fermentation inhibitors. In this study, we applied comparative transcriptomic analysis to determine the molecular basis for acetic acid and/or furfural resistance in S. cerevisiae.We recently developed a yeast strain YC1 with superior resistance to acetic acid, furfural, and their mixture through inverse metabolic engineering. In this study, we first determined transcriptional changes through RNA sequencing in YC1 versus the wild-type strain S-C1 under three different inhibitor conditions, including acetic acid alone, furfural alone, and mixture of acetic acid and furfural. The genes associated with stress responses of S. cerevisiae to single and mixed inhibitors were revealed. Specifically, we identified 184 consensus genes that were differentially regulated in response to the distinct inhibitor resistance between YC1 and S-C1. Bioinformatic analysis next revealed key transcription factors (TFs) that regulate these consensus genes. The top TFs identified, Sfp1p and Ace2p, were experimentally tested as overexpression targets for strain optimization. Overexpression of the SFP1 gene improved specific ethanol productivity by nearly four times, while overexpression of the ACE2 gene enhanced the rate by three times in the presence of acetic acid and furfural. Overexpression of SFP1 gene in the resistant strain YC1 further resulted in 42 % increase in ethanol productivity in the presence of acetic acid and furfural, suggesting the effect of Sfp1p in optimizing the yeast strain for improved tolerance to mixed fermentation inhibitor.Transcriptional regulation underlying yeast resistance to acetic acid and furfural was determined. Two transcription factors, Sfp1p and Ace2p, were uncovered for the first time for their functions in improving yeast resistance to mixed fermentation inhibitors. The study demonstrated an omics-guided metabolic engineering framework, which could be developed as a promising strategy to improve complex microbial phenotypes.

Moving object detection is an essential, well-studied but still open problem in computer vision and plays a fundamental role in many applications. Traditional approaches usually reconstruct background images with hand-crafted visual features, such as color, texture, and edge. Due to lack of prior knowledge or semantic information, it is difficult to deal with complicated and rapid changing scenes. To exploit the temporal structure of the pixel-level semantic information, in this paper, we propose an end-to-end deep sequence learning architecture for moving object detection. First, the video sequences are input into a deep convolutional encoder-decoder network for extracting pixel-wise semantic features. Then, to exploit the temporal context, we propose a novel attention long short-term memory (Attention ConvLSTM) to model pixelwise changes over time. A spatial transformer network and a conditional random field layer are finally appended to reduce the sensitivity to camera motion and smooth the foreground boundaries. A multi-task loss is proposed to jointly optimization for frame-based classification and temporal prediction in an end-to-end network. Experimental results on CDnet 2014 and LASIESTA show 12.15% and 16.71% improvement to the state of the art, respectively.

Data sharing in the cloud, fueled by favorable trends in cloud technology, is emerging as a promising technique for allowing users to conveniently access data. However, the growing number of enterprises and customers who stores their data in cloud servers is increasingly challenging users' privacy and the security of data. This paper focuses on providing a dependable and secure cloud data sharing service that allows users dynamic access to their data. In order to achieve this, we propose an effective, scalable and flexible privacy-preserving data policy with semantic security, by utilizing ciphertext policy attribute-based encryption (CP-ABE) combined with identity-based encryption (IBE) techniques. In addition to ensuring robust data sharing security, our policy succeeds in preserving the privacy of cloud users and supports efficient and secure dynamic operations including, but not limited to, file creation, user revocation and modification of user attributes. Security analysis indicates that the proposed policy is secure under the generic bilinear group model in the random oracle model and enforces fine-grained access control, full collusion resistance and backward secrecy. Furthermore, performance analysis and experimental results show that the overheads are as light as possible.

<h2>Summary</h2> Following injury, differentiated epithelial cells can serve as a stem cell-independent source for tissue regeneration by undergoing reprogramming into other cell types. The intrinsic molecular basis underlying plasticity of differentiated cells remains largely unaddressed. Here we show that Arid1a, a key component of the SWI/SNF chromatin remodeling complex, controls liver regeneration and gene expression associated with emergence of injury-induced liver-progenitor-like cells (LPLCs). Hepatocyte-specific Arid1a ablation reduces LPLC gene expression in several models of periportal liver injury and impairs liver regeneration, leading to organ dysfunction. Arid1a establishes a permissive chromatin state at LPLC-enriched genes during homeostasis, suggesting it endows hepatocytes with competence to respond to injury-induced signals. Consistently, Arid1a facilitates binding of YAP, a critical regeneration signaling pathway, to LPLC-enriched genes, and Arid1a deletion prevents their YAP-associated induction following injury. Together, these findings provide a framework for studying the contributions of injury-induced LPLCs to periportal liver regeneration.

Fifth generation wireless systems (5G) must achieve high user Quality of Experience (QoE) in order to compete for market share. Each candidate 5G wireless radio frequency (RF) band offers advantages such as longer range or higher data rate than 2G, 3G, and 4G, but no single band or air interface standard by itself fully achieves ubiquitous levels of QoE for the complete range of wireless access devices. Spectrum clearing cannot keep pace with user demand, so public-private spectrum sharing is emerging as an affordable, near-term method of increasing radio access network (RAN) capacities for content delivery. This paper presents a fresh look at QoE; spectrum scarcity; public uses that underutilize candidate 5G radio frequency (RF) spectrum; and emerging public-private radio interference management frameworks that enable near-term spectrum sharing, with positive consequences for 5G price, performance, and total user QoE.

Sleep monitoring has drawn increasingly attention as the quality and quantity of the sleep are important to maintain a person's health and well-being. For example, inadequate and irregular sleep are usually associated with serious health problems such as fatigue, depression and cardiovascular disease. Traditional sleep monitoring systems, such as PSG, involve wearable sensors with professional installations, and thus are limited to clinical usage. Recent work in using smartphone sensors for sleep monitoring can detect several events related to sleep, such as body movement, cough and snore. Such coarse-grained sleep monitoring however is unable to detect the breathing rate which is an important vital sign and health indicator. This work presents a fine-grained sleep monitoring system which is capable of detecting the breathing rate by leveraging smartphones. Our system exploits the readily available smartphone earphone placed close to the user to reliably capture the human breathing sound. Given the captured acoustic sound, our system performs noise reduction to remove environmental noise and then identifies the breathing rate based on the signal envelope detection. Our system can further detect detailed sleep events including snore, cough, turn over and get up based on the acoustic features extracted from the acoustic sound. Our experimental evaluation of six subjects over six months time period demonstrates that the breathing rate monitoring and sleep events detection are highly accurate and robust under various environments. By combining breathing rate and sleep events, our system can provide continuous and noninvasive fine-grained sleep monitoring for healthcare related applications, such as sleep apnea monitoring as evidenced by our experimental study.

Magnesium sulfate was proposed to be leaching agent to deal with the ion-adsorption type rare earths ore to reduce or even eliminate ammonia–nitrogen emissions. The effects of temperature, particle size and stirring speed on rare earth leaching process and the leaching behaviors of the single rare earth element were investigated in order to reveal the rare earth leaching characteristics. Besides, the comparison of leaching effects between magnesium sulfate and ammonium sulfate was also studied. The results showed that the rare earth leaching process could be well described with inner diffusion control model and the apparent activation energy was 9.48 kJ/mol. The leaching behaviors of the single rare earth element were brought into correspondence with rare earths. Moreover, when the concentration of leaching agent was 0.20 mol/L, the rare earth leaching efficiency could all reach above 95% and the leaching efficiency of aluminum impurities could be restrained by 10% using magnesium sulfate compared with ammonium sulfate.

In this work, CoMoO₄/Co₃O₄ hollow porous octahedrons are synthesized by thermal conversion of a cyanide-metal framework (CMF) compound of Co₂[Mo(CN)₈]·<italic>x</italic>H₂O.

Abstract Nanobactericides represent one of the most efficient and promising strategies for eliminating bacterial infection considering the increasing resistance threats of conventional antibiotics. Black phosphorus (BP) is the most exciting postgraphene layered 2D nanomaterial with convincing physiochemical properties, yet the study of BP‐based antibiotics is still in its infancy. Here, a compact silver nanoparticle (AgNP)–doped black phosphorus nanosheet (BPN) is constructed to synergistically enhance solar disinfection through the promoted reactive oxygen species (ROS) photogeneration, which is attributed to the improved electron–hole separation and recombination of BPNs as revealed from the systematic experimental studies. An in‐depth density functional theory (DFT) calculation confirms that the integrated AgNPs provide a preferred site for facilitating the adsorption and activation of O 2 , thus promoting the more efficient and robust ROS generation on BPN–AgNP nanohybrids. Besides the enhanced photoinduced ROS, the anchored AgNPs simultaneously lead to a dramatically increased affinity toward bacteria, which facilitates a synergetic pathogen inactivation. Significantly, the convincing antimicrobial BPN–AgNP contributes to the prominent wound healing and antimicrobial ability in vivo with minimized biological burden. This sophisticated design of new 2D nanohybrids opens a new avenue for further exploiting BP‐based nanohybrids in portable bandage and broad‐spectrum disinfection applications.

Fungal laccases have high activity in degrading various persistent organic pollutants. However, using enzymes in solution for water treatment has limitations of nonreusability, short enzyme lifetimes, and high cost of single use. In this study, we developed a new type of biocatalyst by immobilizing fungal laccase on the surface of yeast cells using synthetic biology techniques. The biocatalyst, referred to as surface display laccase (SDL), had an enzyme activity of 104 ± 3 mU/g dry cell (with 2,2-azinobis-3-ethylbenzothiazoline-6-sulfonate (ABTS)). The SDL retained over 90% of the initial enzyme activity after 25 days storage at room temperature, while, in contrast, activity of free laccase declined to 60% of its initial activity. The SDL could be reused with high stability as it retained 74% of initial activity after eight repeated batch reactions. Proof-of-concept evaluations of the effectiveness of SDL in treating contaminants of emerging concern were performed with bisphenol A and sulfamethoxazole. Results from contaminant degradation kinetics and the effects of redox mediator amendment provided insights into the factors affecting the efficacy of the SDL system. This study reports, for the first time, the development of a surface display enzyme biocatalyst as an effective and renewable alternative for treating recalcitrant organic micropollutants.

This paper gives a comprehensive discussion on applying the cloud computing technology as the new information infrastructure for the next-generation power system. First, this paper analyzes the main requirements of the future power grid on the information infrastructure and the limitations of the current information infrastructure. Based on this, a layered cloud-based information infrastructure model for next-generation power grid is proposed. Thus, this paper discussed how different categories of the power applications can benefit from the cloud-based information infrastructure. For the demonstration purpose, this paper develops three specific cloud-enabled power applications. The first two applications demonstrate how to develop practical compute-intensive and data-intensive power applications by utilizing different layered services provided by the state-of-the-art public cloud computing platforms. In the third application, we propose a cloud-based collaborative direct load control framework in a smart grid and show the merits of the cloud-based information infrastructure on it. Some cybersecurity considerations and the challenges and limitations of the cloud-based information infrastructure are also discussed.

This paper proposes a novel direct load control model for scheduling the air conditioner loads (ACLs) in the Southern China region. The model has two objectives: 1) to minimize the total deviations between the total scheduled and instructed shed ACLs over the entire dispatch horizon; and 2) to minimize the disturbance of the residents' thermal comfort. The primary contribution of this paper is to employ an International Organization of Standard thermal comfort model to precisely estimate the residents' thermal comfort degree. The secondary contribution of this paper is to propose a new optimization algorithm called fuzzy adaptive imperialist competitive algorithm to effectively solve the model. A case study including three major cities in Southern China is conducted to verify the efficiency of the proposed method.

The development of battery energy storage system (BESS) facilitates the integration of renewable energy sources in the distribution system. Both distribution generation and mobile BESS (MBESS) can enhance the reliability of the distribution system. MBESS can facilitate the island operation of microgrids. This study proposes a set of methodologies to assess the reliability of power distribution network with the penetrations of MBESS and intermittent distribution sources. First, an analytic approach based on Markov models is applied for assessing the reliability analysis of the MBESS in distribution system. Then the method is verified by Monte Carlo simulation method and extended to a more complex distribution system. The model of MBESS is developed based on the real operation data. Case study on the IEEE test system has successfully verified the effectiveness of the proposed approach.

Given the increasing amount of time people spent on driving, the physical and mental health of drivers is essential to road safety. Breathing patterns are critical indicators of the well-being of drivers on the road. Existing studies on breathing monitoring require active user participation of wearing special sensors or relatively quiet environments during sleep, which are hardly applicable to noisy driving environments. In this work, we propose a fine-grained breathing monitoring system, BreathListener, which leverages audio devices on smartphones to estimate the fine-grained breathing waveform in driving environments. By investigating the data collected from real driving environments, we find that Energy Spectrum Density (ESD) of acoustic signals can be utilized to capture breathing procedures in driving environments. To extract breathing pattern in ESD signals, BreathListener eliminates interference from driving environments in ESD signals utilizing background subtraction and Ensemble Empirical Mode Decomposition (EEMD). After that, the extracted breathing pattern is transformed into Hilbert spectrum, and we further design a deep learning architecture based on Generative Adversarial Network (GAN) to generate fine-grained breathing waveform from the Hilbert spectrum of extracted breathing patterns in ESD signals. Experiments with 10 drivers in real driving environments show that BreathListener can accurately capture breathing patterns of drivers in driving environments.

Abstract Snow and frozen soil are important factors that influence terrestrial water and energy balances through snowpack accumulation and melt and soil freeze‐thaw. In this study, a new land surface model (LSM) with coupled snow and frozen soil physics was developed based on a hydrologically improved LSM (HydroSiB2). First, an energy‐balance‐based three‐layer snow model was incorporated into HydroSiB2 (hereafter HydroSiB2‐S) to provide an improved description of the internal processes of the snow pack. Second, a universal and simplified soil model was coupled with HydroSiB2‐S to depict soil water freezing and thawing (hereafter HydroSiB2‐SF). In order to avoid the instability caused by the uncertainty in estimating water phase changes, enthalpy was adopted as a prognostic variable instead of snow/soil temperature in the energy balance equation of the snow/frozen soil module. The newly developed models were then carefully evaluated at two typical sites of the Tibetan Plateau (TP) (one snow covered and the other snow free, both with underlying frozen soil). At the snow‐covered site in northeastern TP (DY), HydroSiB2‐SF demonstrated significant improvements over HydroSiB2‐F (same as HydroSiB2‐SF but using the original single‐layer snow module of HydroSiB2), showing the importance of snow internal processes in three‐layer snow parameterization. At the snow‐free site in southwestern TP (Ngari), HydroSiB2‐SF reasonably simulated soil water phase changes while HydroSiB2‐S did not, indicating the crucial role of frozen soil parameterization in depicting the soil thermal and water dynamics. Finally, HydroSiB2‐SF proved to be capable of simulating upward moisture fluxes toward the freezing front from the underlying soil layers in winter.

Charge recombination was effectively suppressed by judicious optimization of the substituents of the phenothiazine donor, and the power conversion efficiency was successfully improved from 9.3% to 11.1%.

Interlayer excitons have been extensively studied in monolayer transition metal dichalcogenide (TMD) heterobilayers mainly due to the long lifetime, which is beneficial for a wide range of optoelectronic applications. To date, the majority of investigations of interlayer excitons in TMD heterobilayers have been focusing on the geometric arrangement of structures, spin-valley lifetime, and interlayer valley excitons with interlayer hopping rules. Nevertheless, interlayer excitons in TMD heterobilayers strongly depend on the local atomic registry and coupling strength, which increase the complexity of the device fabrication. Here, we report pronounced interlayer exciton emission in two-dimensional (2D) perovskite/monolayer TMD heterostructures without the need of thermal annealing or specific geometric arrangements, and the interlayer exciton emission is rather general among 2D perovskites and monolayer TMDs. Such interlayer exciton emission completely dominates the emission spectrum at 78 K regardless of the stacking sequence, suggesting the robust interlayer coupling in 2D perovskite/monolayer TMD heterostructures. Furthermore, the interlayer exciton emission shows a large blue-shift with increasing laser intensity due to the repulsive dipole-dipole interaction and can persist above 220 K. Importantly, the interlayer exciton emission also possesses robust circular polarization in chiral 2D perovskite/monolayer WSe2 heterostructures, which can be applied to manipulate the valley degree of freedom for valleytronic devices. Our findings would provide a favorable platform to explore interlayer coupling and related physical processes in 2D perovskites and TMDs and further provoke more investigations into the understanding and controlling of excitonic effects and associated optoelectronic applications in van der Waals heterostructures over a broad-range spectral response.

Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in this important venue.

Mobile devices have brought a great convenience to us these years, which allow the users to enjoy the anytime and anywhere various applications such as the online shopping, Internet banking, navigation and mobile media. While the users enjoy the convenience and flexibility of the ”Go Mobile” trend, their sensitive private information (e.g., name and credit card number) on the mobile devices could be disclosed. An adversary could access the sensitive private information stored on the mobile device by unlocking the mobile devices. Moreover, the user’s mobile services and applications are all exposed to security threats. For example, the adversary could utilize the user’s mobile device to conduct non-permitted actions (e.g., making online transactions and installing malwares). The authentication on mobile devices plays a significant role to protect the user’s sensitive information on mobile devices and prevent any non-permitted access to the mobile devices. This paper surveys the existing authentication methods on mobile devices. In particular, based on the basic authentication metrics (i.e., knowledge, ownership and biometrics) used in existing mobile authentication methods, we categorize them into four categories, including the knowledge-based authentication (e.g., passwords and lock patterns), physiological biometric-based authentication (e.g., fingerprint and iris), behavioral biometrics-based authentication (e.g., gait and hand gesture), and two/multi-factor authentication. We compare the usability and security level of the existing authentication approaches among these categories. Moreover, we review the existing attacks to these authentication approaches to reveal their vulnerabilities. The paper points out that the trend of the authentication on mobile devices would be the multi-factor authentication, which determines the user’s identity using the integration (not the simple combination) of more than one authentication metrics. For example, the user’s behavior biometrics (e.g., keystroke dynamics) could be extracted simultaneously when he/she inputs the knowledge-based secrets (e.g., PIN), which can provide the enhanced authentication as well as sparing the user’s trouble to conduct multiple inputs for different authentication metrics.

PurposeTo evaluate the intraocular pressure (IOP)-lowering efficacy and safety of 10- and 15-μg bimatoprost implant in subjects with open-angle glaucoma (OAG) and ocular hypertension (OHT) after initial and repeated administrations.DesignRandomized, 20-month, multicenter, subject- and efficacy evaluator-masked, parallel-group, phase 3 clinical study.ParticipantsAdults with OAG or OHT in each eye, open iridocorneal angle inferiorly in the study eye, and study eye baseline IOP (hour 0; 8 am) of 22–32 mmHg after washout.MethodsStudy eyes received bimatoprost implant 10 μg (n = 198) or 15 μg (n = 198) on day 1 with readministration at weeks 16 and 32, or twice-daily topical timolol maleate 0.5% (n = 198). Intraocular pressure was measured at hours 0 and 2 at each visit.Main Outcome MeasuresPrimary end points were IOP and change from baseline IOP through week 12. Safety measures included treatment-emergent adverse events (TEAEs) and corneal endothelial cell density (CECD).ResultsBoth dose strengths of bimatoprost implant were noninferior to timolol in IOP lowering after each administration. Mean diurnal IOP was 24.0, 24.2, and 23.9 mmHg at baseline and from 16.5–17.2, 16.5–17.0, and 17.1–17.5 mmHg through week 12 in the 10-μg implant, 15-μg implant, and timolol groups, respectively. The incidence of corneal and inflammatory TEAEs of interest (e.g., corneal endothelial cell loss, iritis) was higher with bimatoprost implant than timolol and highest with the 15-μg dose strength. Incidence of corneal TEAEs increased after repeated treatment; with 3 administrations at fixed 16-week intervals, incidence of ≥20% CECD loss was 10.2% (10-μg implant) and 21.8% (15-μg implant). Mean best-corrected visual acuity (BCVA) was stable; 3 implant-treated subjects with corneal TEAEs had >2-line BCVA loss at their last visit.ConclusionsBoth dose strengths of bimatoprost implant met the primary end point of noninferiority to timolol through week 12. One year after 3 administrations, IOP was controlled in most subjects without additional treatment. The risk-benefit assessment favored the 10-μg implant over the 15-μg implant. Ongoing studies are evaluating other administration regimens to reduce the potential for CECD loss. The bimatoprost implant has potential to improve adherence and reduce treatment burden in glaucoma. To evaluate the intraocular pressure (IOP)-lowering efficacy and safety of 10- and 15-μg bimatoprost implant in subjects with open-angle glaucoma (OAG) and ocular hypertension (OHT) after initial and repeated administrations. Randomized, 20-month, multicenter, subject- and efficacy evaluator-masked, parallel-group, phase 3 clinical study. Adults with OAG or OHT in each eye, open iridocorneal angle inferiorly in the study eye, and study eye baseline IOP (hour 0; 8 am) of 22–32 mmHg after washout. Study eyes received bimatoprost implant 10 μg (n = 198) or 15 μg (n = 198) on day 1 with readministration at weeks 16 and 32, or twice-daily topical timolol maleate 0.5% (n = 198). Intraocular pressure was measured at hours 0 and 2 at each visit. Primary end points were IOP and change from baseline IOP through week 12. Safety measures included treatment-emergent adverse events (TEAEs) and corneal endothelial cell density (CECD). Both dose strengths of bimatoprost implant were noninferior to timolol in IOP lowering after each administration. Mean diurnal IOP was 24.0, 24.2, and 23.9 mmHg at baseline and from 16.5–17.2, 16.5–17.0, and 17.1–17.5 mmHg through week 12 in the 10-μg implant, 15-μg implant, and timolol groups, respectively. The incidence of corneal and inflammatory TEAEs of interest (e.g., corneal endothelial cell loss, iritis) was higher with bimatoprost implant than timolol and highest with the 15-μg dose strength. Incidence of corneal TEAEs increased after repeated treatment; with 3 administrations at fixed 16-week intervals, incidence of ≥20% CECD loss was 10.2% (10-μg implant) and 21.8% (15-μg implant). Mean best-corrected visual acuity (BCVA) was stable; 3 implant-treated subjects with corneal TEAEs had >2-line BCVA loss at their last visit. Both dose strengths of bimatoprost implant met the primary end point of noninferiority to timolol through week 12. One year after 3 administrations, IOP was controlled in most subjects without additional treatment. The risk-benefit assessment favored the 10-μg implant over the 15-μg implant. Ongoing studies are evaluating other administration regimens to reduce the potential for CECD loss. The bimatoprost implant has potential to improve adherence and reduce treatment burden in glaucoma.

The Global Precipitation Measurement (GPM) mission provides high-resolution precipitation estimates globally. However, their accuracy needs to be accessed for algorithm enhancement and hydro-meteorological applications. This study applies data from 388 gauges in Nepal to evaluate the spatial-temporal patterns presented in recently-developed GPM-Era satellite-based precipitation (SBP) products, i.e., the Integrated Multi-satellite Retrievals for GPM (IMERG), satellite-only (IMERG-UC), the gauge-calibrated IMERG (IMERG-C), the Global Satellite Mapping of Precipitation (GSMaP), satellite-only (GSMaP-MVK), and the gauge-calibrated GSMaP (GSMaP-Gauge). The main results are as follows: (1) GSMaP-Gauge datasets is more reasonable to represent the observed spatial distribution of precipitation, followed by IMERG-UC, GSMaP-MVK, and IMERG-C. (2) The gauge-calibrated datasets are more consistent (in terms of relative root mean square error (RRMSE) and correlation coefficient (R)) than the satellite-only datasets in representing the seasonal dynamic range of precipitation. However, all four datasets can reproduce the seasonal cycle of precipitation, which is predominately governed by the monsoon system. (3) Although all four SBP products underestimate the monsoonal precipitation, the gauge-calibrated IMERG-C yields smaller mean bias than GSMaP-Gauge, while GSMaP-Gauge shows the smaller RRMSE and higher R-value; indicating IMERG-C is more reliable to estimate precipitation amount than GSMaP-Gauge, whereas GSMaP-Gauge presents more reasonable spatial distribution than IMERG-C. Only IMERG-C moderately reproduces the evident elevation-dependent pattern of precipitation revealed by gauge observations, i.e., gradually increasing with elevation up to 2000 m and then decreasing; while GSMaP-Gauge performs much better in representing the gauge observed spatial pattern than others. (4) The GSMaP-Gauge calibrated based on the daily gauge analysis is more consistent with detecting gauge observed precipitation events among the four datasets. The high-intensity related precipitation extremes (95th percentile) are more intense in regions with an elevation below 2500 m; all four SBP datasets have low accuracy (&lt;30%) and mostly underestimated (by &gt;40%) the frequency of extreme events at most of the stations across the country. This work represents the quantification of the new-generation SBP products on the southern slopes of the central Himalayas in Nepal.

The state-of-the-art solar-thermal evaporators demonstrating high energy utilization efficiency, a high evaporation rate, and salt rejection are highly desirable in solar-driven low-energy water purification/harvesting. Herein, a novel Janus solar evaporator is constructed by loading polypyrrole (PPy) nanobelts on the polyvinyl alcohol (PVA) hydrogel. The PPy nanobelts present a high solar absorption of 98.3%, leading to a localized solar-thermal efficiency of 82.5% when insulated from bulk water by the PVA hydrogel. The porous PVA hydrogel and the hydrophilic PPy nanobelts enable the efficient three-dimensional water transport. Taking advantages of the synergistic effect in the water-energy nexus, the Janus PPy nanobelt@PVA hydrogel evaporator evaporates water with a high rate of 2.26 kg m–2 h–1 via 80.1% solar energy from 1 sun irradiance with a low PPy loading of ∼3 mg cm–2 even at a rate of 2.64 kg m–2 h–1 via 96.3% solar energy for a biomimetic conical evaporator. The Janus evaporator presents superior salt-resistant desalination and contaminant purification performance in seawater and sewage. Furthermore, a portable solar-thermal purifier equipped with the Janus evaporator desalts real seawater far above the drinking water standard with over a 99.9% salt rejection rate and eliminates 95.8% of chemical oxygen demand in real sewage, highlighting its potential for advanced clean water harvesting.

As the popularity of voice user interface (VUI) exploded in recent years, speaker recognition system has emerged as an important medium of identifying a speaker in many security-required applications and services. In this paper, we propose the first real-time, universal, and robust adversarial attack against the state-of-the-art deep neural network (DNN) based speaker recognition system. Through adding an audio-agnostic universal perturbation on arbitrary enrolled speaker's voice input, the DNN-based speaker recognition system would identify the speaker as any target (i.e., adversary-desired) speaker label. In addition, we improve the robustness of our attack by modeling the sound distortions caused by the physical over-the-air propagation through estimating room impulse response (RIR). Experiment using a public dataset of 109 English speakers demonstrates the effectiveness and robustness of our proposed attack with a high attack success rate of over 90%. The attack launching time also achieves a 100× speedup over contemporary non-universal attacks.

Existing efforts in audio adversarial attacks only focus on the scenarios where an adversary has prior knowledge of the entire speech input so as to generate an adversarial example by aligning and mixing the audio input with corresponding adversarial perturbation. In this work we consider a more practical and challenging attack scenario where the intelligent audio system takes streaming audio inputs (e.g., live human speech) and the adversary can deceive the system by playing adversarial perturbations simultaneously. This change in attack behavior brings great challenges, preventing existing adversarial perturbation generation methods from being applied directly. In practice, (1) the adversary cannot anticipate what the victim will say: the adversary cannot rely on their prior knowledge of the speech signal to guide how to generate adversarial perturbations; and (2) the adversary cannot control when the victim will speak: the synchronization between the adversarial perturbation and the speech cannot be guaranteed. To address these challenges, in this paper we propose AdvPulse, a systematic approach to generate subsecond audio adversarial perturbations, that achieves the capability to alter the recognition results of streaming audio inputs in a targeted and synchronization-free manner. To circumvent the constraints on speech content and time, we exploit penalty-based universal adversarial perturbation generation algorithm and incorporate the varying time delay into the optimization process. We further tailor the adversarial perturbation according to environmental sounds to make it inconspicuous to humans. Additionally, by considering the sources of distortions occurred during the physical playback, we are able to generate more robust audio adversarial perturbations that can remain effective even under over-the-air propagation. Extensive experiments on two representative types of intelligent audio systems (i.e., speaker recognition and speech command recognition) are conducted in various realistic environments. The results show that our attack can achieve an average attack success rate of over 89.6% in indoor environments and 76.0% in inside-vehicle scenarios even with loud engine and road noises.

The emergence of antibiotic-resistant bacterial strains renders the conventional antibiotic therapy less efficient. The integration of two distinct bactericides into one compact platform provides a promising strategy to realize a combinational antimicrobial therapy. In this work, an efficient chemo-Ag nanohybrid antibacterial platform was facilely developed based on the integration of vancomycin-carrying polydopamine with silver nanoparticles (PDA@Van-Ag). The as-synthesized antibacterial nanohybrid inherited the intrinsic properties of both bactericides to achieve a synergistic antibacterial performance against both Staphylococcus aureus and Escherichia coli strains by attacking bacteria from two distinct fronts. Through this combinational therapy, the efficiency of antibiotic against S. aureus was significantly improved by reducing drug dosage with less opportunity for imparting drug resistance. In addition, this antibacterial nanohybrid, with innate photothermal properties, could achieve auxiliary hyperthermia-assisted bacterial inactivation in the meantime. Furthermore, the outstanding in vivo bacteria-killing activity and wound-healing acceleration were demonstrated in a S. aureus-infected mouse skin defect model. Taken together, this bactericidal nanohybrid could achieve sustained antibiotic release and wipe out bacteria more effectively in a synergistic way, thus reducing the emergence of antibiotic resistance. This work holds great potential to advance the development of novel antibacterial agents and combinational strategies as a promising supplement of antibiotics in the near future.

After observing that the features used in most online discriminatively trained trackers are not optimal, in this paper, we propose a novel and effective architecture to learn optimal feature embeddings for online discriminative tracking. Our method, called DCFST, integrates the solver of a discriminant model that is differentiable and has a closed-form solution into convolutional neural networks. Then, the resulting network can be trained in an end-to-end way, obtaining optimal feature embeddings for the discriminant model-based tracker. As an instance, we apply the popular ridge regression model in this work to demonstrate the power of DCFST. Extensive experiments on six public benchmarks, OTB2015, NFS, GOT10k, TrackingNet, VOT2018, and VOT2019, show that our approach is efficient and generalizes well to class-agnostic target objects in online tracking, thus achieves state-of-the-art accuracy, while running beyond the real-time speed. Code will be made available.

Despite considerable similarities between multiple object tracking (MOT) and single object tracking (SOT) tasks, modern MOT methods have not benefited from the development of SOT ones to achieve satisfactory performance. The major reason for this situation is that it is inappropriate and inefficient to apply multiple SOT models directly to the MOT task, although advanced SOT methods are of the strong discriminative power and can run at fast speeds.In this paper, we propose a novel and end-to-end trainable MOT architecture that extends CenterNet by adding an SOT branch for tracking objects in parallel with the existing branch for object detection, allowing the MOT task to benefit from the strong discriminative power of SOT methods in an effective and efficient way. Unlike most existing SOT methods which learn to distinguish the target object from its local backgrounds, the added SOT branch trains a separate SOT model per target online to distinguish the target from its surrounding targets, assigning SOT models the novel discrimination. Moreover, similar to the detection branch, the SOT branch treats objects as points, making its online learning efficient even if multiple targets are processed simultaneously. Without tricks, the proposed tracker achieves MOTAs of 0.710 and 0.686, IDF1s of 0.719 and 0.714, on MOT17 and MOT20 benchmarks, respectively, while running at 16 FPS on MOT17.

Current gridded precipitation datasets are hard to meet the requirements of hydrological and meteorological applications in complex-terrain areas due to their coarse spatial resolution and large uncertainties. High-resolution atmospheric simulations are capable of describing the influence of topography on precipitation but are difficult to be used to obtain long-term precipitation datasets because they are computationally expensive, while reanalysis data has a long-term coverage and can provide reasonable large-scale spatial and temporal variability of precipitation. This study presents an approach to obtain long-term high-resolution precipitation datasets over complex-terrain areas by combining the ERA5 reanalysis with short-term high-resolution atmospheric simulation. The approach consists of two main steps: first, the ERA5 precipitation is corrected by the high-resolution simulation at the coarse spatial resolution; second, the corrected data is downscaled using a convolution neural network (CNN) based model at daily scale. The proposed approach is applied to the Tibetan Plateau (TP). The downscaled results from ERA5 have a finer spatial structure than ERA5 and can reproduce the spatial patterns of precipitation revealed by the high-resolution simulation. An evaluation based on rain gauge data shows that the downscaled ERA5 has remarkably lower biases than the original ERA5 which overestimates precipitation a lot, and even higher accuracy than the high-resolution simulation data over the TP. The downscaled ERA5 preserves the temporal characteristics of ERA5 which are more consistent with the rain gauge data than that of high-resolution simulation. Since this approach is much less computing resources consuming than the high-resolution simulation, it is an effective method to obtain long-term high-resolution precipitation datasets in complex-terrain areas and is expected to have extensive applications.

Fungi produce millions of clonal asexual conidia (spores) that remain dormant until favourable conditions occur. Conidia contain abundant stable messenger RNAs but the mechanisms underlying the production of these transcripts and their composition and functions are unknown. Here, we report that the conidia of three filamentous fungal species (Aspergillus nidulans, Aspergillus fumigatus, Talaromyces marneffei) are transcriptionally active and can synthesize mRNAs. We find that transcription in fully developed conidia is modulated in response to changes in the environment until conidia leave the developmental structure. Environment-specific transcriptional responses can alter conidial content (mRNAs, proteins and secondary metabolites) and change gene expression when dormancy is broken. Conidial transcription affects the fitness and capabilities of fungal cells after germination, including stress and antifungal drug (azole) resistance, mycotoxin and secondary metabolite production and virulence. The transcriptional variation that we characterize in fungal conidia explains how genetically identical conidia mature into phenotypically variable conidia. We find that fungal conidia prepare for the future by synthesizing and storing transcripts according to environmental conditions present before dormancy.

The electrocatalytic water splitting into oxygen (O2) and hydrogen (H2) is one of promising technique for addressing the problem of energy shortage. Owing to the sluggish reaction kinetics for oxygen evolution reaction (OER), it is urgent to explore efficient and inexpensive OER electrocatalytic materials. Herein, a highly efficient three-dimensional (3D) hybrid of anions (Cl− and CO32−) doped cobalt hydroxide catalyst coupled with nitrogen-doped carbon dots (Co(OH)2@NCDs) is reported via a simple in situ hydrothermal method. Interestingly, the additional mass ratio of NCDs in Co(OH)2 hybrid has strong influence on the morphology of as-prepared composites. The well-defined urchin-like Co(OH)2@NCDs composite exhibits superior OER performance, achieving the low overpotential (η) of 296 mV at 10 mA cm−2 and high durability in 1 M KOH solution. The urchin-like Co(OH)2@NCDs catalyst could achieve 29-fold electrochemical active surface area than that of the pure Co(OH)2. Additionally, after coupling with NCDs, analysis result shows new Co-N charge transfer channel is generated in hybrid and an optimized electronic environment is originated from the strong electronic interaction among Co atom and NCDs, which can provide efficient charge channels for the rapid electron transfer and further improve the intrinsic activity and promote the OER kinetics. The work may endow a new strategy for the construction of excellent transition metal-based hydroxide water oxidation electrocatalysts.

Filamentous fungi of the genus Aspergillus are of particular interest for biotechnological applications due to their natural capacity to secrete carbohydrate-active enzymes (CAZy) that target plant biomass. The presence of easily metabolizable sugars such as glucose, whose concentrations increase during plant biomass hydrolysis, results in the repression of CAZy-encoding genes in a process known as carbon catabolite repression (CCR), which is undesired for the purpose of large-scale enzyme production. To date, the C2H2 transcription factor CreA has been described as the major CC repressor in Aspergillus spp., although little is known about the role of posttranslational modifications in this process. In this work, phosphorylation sites were identified by mass spectrometry on Aspergillus nidulans CreA, and subsequently, the previously identified but uncharacterized site S262, the characterized site S319, and the newly identified sites S268 and T308 were chosen to be mutated to nonphosphorylatable residues before their effect on CCR was investigated. Sites S262, S268, and T308 are important for CreA protein accumulation and cellular localization, DNA binding, and repression of enzyme activities. In agreement with a previous study, site S319 was not important for several here-tested phenotypes but is key for CreA degradation and induction of enzyme activities. All sites were shown to be important for glycogen and trehalose metabolism. This study highlights the importance of CreA phosphorylation sites for the regulation of CCR. These sites are interesting targets for biotechnological strain engineering without the need to delete essential genes, which could result in undesired side effects.IMPORTANCE In filamentous fungi, the transcription factor CreA controls carbohydrate metabolism through the regulation of genes encoding enzymes required for the use of alternative carbon sources. In this work, phosphorylation sites were identified on Aspergillus nidulans CreA, and subsequently, the two newly identified sites S268 and T308, the previously identified but uncharacterized site S262, and the previously characterized site S319 were chosen to be mutated to nonphosphorylatable residues before their effect on CCR was characterized. Sites S262, S268, and T308 are important for CreA protein accumulation and cellular localization, DNA binding, and repression of enzyme activities. In agreement with a previous study, site S319 is not important for several here-tested phenotypes but is key for CreA degradation and induction of enzyme activities. This work characterized novel CreA phosphorylation sites under carbon catabolite-repressing conditions and showed that they are crucial for CreA protein turnover, control of carbohydrate utilization, and biotechnologically relevant enzyme production.

Treatments for coronavirus disease 2019 (COVID-19) are limited by suboptimal efficacy. From January 30, 2020 to March 23, 2020, we conducted a non-randomised controlled trial, in which all adult patients with laboratory-confirmed COVID-19 were assigned to three groups non-randomly and given supportive treatments: Group A, Lopinavir-Ritonavir; Group B, Huashi Baidu Formula (a Chinese medicineformula made by the China Academy of Chinese Medical Sciences to treat COVID-19, which is now in the clinical trial period) and Lopinavir-Ritonavir; and Group C, Huashi Baidu Formula. The use of antibiotics, antiviruses, and corticosteroids was permitted in Group A and B. Traditional Chinese medicine injections were permitted in Group C. The primary outcomes were clinical remission time (interval from admission to the first time the patient tested negatively for novel coronavirus or an obvious improvement was observed from chest CT) and clinical remission rate (number of patients whose clinical time was within 16 days/total number of patients). A total of 60 adult patients with COVID-19 were enrolled at sites in Wuhan, China, and the sample size of each group was 20. In Groups A, B and C, the clinical remission rates were 95.0%%(19/20), 100.0%%(20/20) and 100.0%%(20/20), respectively. Compared with Groups A and B, the clinical remission time of Group C was significantly shorter (5.9 days vs. 10.8 days, p < 0.05; 5.9 days vs. 9.7 days, p < 0.05). There was no significant difference among Groups A, B, and C in terms of the time taken to be released from quarantine. The clinical biochemical indicators and safety indexes showed no significant differences among the three groups. Our findings suggest that Lopinavir-Ritonavir has some efficacy in the treatment of COVID-19, and the Huashi Baidu Formula might enhance this effect to an extent. In addition, superiority was displayed in the treatment of COVID-19 through a combination of the Huashi Baidu Formula and traditional Chinese medicine injection. In future, well-designed prospective double-blinded randomised control trials are required to confirm our findings.

Research shows that entrepreneurial activities significantly promote economic development, which enhances the importance of the innovative entrepreneurial potential of college students. This study analyzes the effect of entrepreneurship education on entrepreneurial intention from the perspective of planned behavior theory. By examining the significant role of entrepreneurship education at colleges and universities on economic and social development, we established a conceptual model. To understand the relationship between entrepreneurship education and entrepreneurial intention, the hypotheses propose the intermediary role of entrepreneurial ability, and the study provides evidence from China the relationship between entrepreneurship education and entrepreneurial intention. Improving entrepreneurial intention and encouraging college students to establish businesses through entrepreneurship education in universities is crucial. This study proposes a hypothetical model of the relationship between entrepreneurial competence and entrepreneurial intention in entrepreneurship education at universities. Using a questionnaire survey of college students with practical experience in the Yangtze River Delta of China, the bootstrap method in the SPSS macro program process software verifies the hypotheses. The results show that entrepreneurial teaching, business plan competition, and entrepreneurial practice support positively affect entrepreneurial competence. In addition, entrepreneurial competence plays an intermediary role in the relationship between entrepreneurial teaching, business plan competition, entrepreneurship practice support, and entrepreneurial intention. Entrepreneurship education improves the ability to establish a business in the present and in entrepreneurial activities in the future. Entrepreneurial competence obtained through entrepreneurship education continuously affects entrepreneurial intention.

To evaluate the efficacy and safety of Hua Shi Bai Du Granule (Q-14) plus standard care compared with standard care alone in adults with coronavirus disease (COVID-19). A single-center, open-label, randomized controlled trial. Wuhan Jinyintan Hospital, Wuhan, China, February 27 to March 27, 2020. A total of 204 patients with laboratory-confirmed COVID-19 were randomized into the treatment group and control group, consisting of 102 patients in each group. In the treatment group, Q-14 was administered at 10 g (granules) twice daily for 14 days, plus standard care. In the control group, patients were provided standard care alone for 14 days. The primary outcome was the conversion time for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral assay. Adverse events were analyzed in the safety population. Among the 204 patients, 195 were analyzed according to the intention-to-treat principle. A total of 149 patients (71 vs. 78 in the treatment and control groups, respectively) tested negative via the SARS-CoV-2 viral assay. There was no statistical significance in the conversion time between the treatment group and control group (Full analysis set: Median [interquartile range]: 10.00 [9.00-11.00] vs. 10.00 [9.00-11.00]; Mean rank: 67.92 vs. 81.44; P = 0.051). The recovery time for fever was shorter in the treatment group than in the control group. The disappearance rate of symptoms like cough, fatigue, and chest discomfort was significantly higher in the treatment group. In chest computed tomography (CT) examinations, the overall evaluation of chest CT examination after treatment compared with baseline showed that more patients improved in the treatment group. There were no significant differences in the other outcomes. The combination of Q-14 and standard care for COVID-19 was useful for the improvement of symptoms (such as fever, cough, fatigue, and chest discomfort), but did not result in a significantly higher probability of negative conversion in the SARS-CoV-2 viral assay. No serious adverse events were observed. ChiCTR2000030288

Summary Objectives Tuberculosis (TB) remains one of the public health problems worldwide. Rapid, sensitive and cost-effective diagnosis of Mycobacterium tuberculosis (M.tb) is critical for TB control. Methods We developed a novel M.tb DNA detection platform (nominated as TB-QUICK) which combined loop-mediated isothermal amplification (LAMP) and CRISPR-Cas12b detection. TB-QUICK was performed on pulmonary or plasma samples collected from 138 pulmonary TB (PTB) patients, 21 non-TB patients and 61 close contacts to TB patients. Acid-fast bacillus (AFB) smear, M.tb culture and GeneXpert MTB/RIF (Xpert) assays were routinely conducted in parallel. Results By targeting M.tb IS6110, TB-QUICK platform could detect as low as 1.3 copy/μL M.tb DNA within 2 h. In pulmonary TB samples, TB-QUICK exhibited improved overall sensitivity of 86.8% over M.tb culture (66.7%) and Xpert (70.4%), with the specificity of 95.2%. More significantly, TB-QUICK exhibited a superior sensitivity in AFB-negative samples (80.5%) compared to Xpert (57.1%) and M.tb culture (46.2%). In the detection of plasma M.tb DNA by TB-QUICK, 41.2% sensitivity for AFB-positive and 31.7% for AFB-negative patients were achieved. Conclusion In conclusion, TB-QUICK exhibits rapidity and sensitivity for M.tb DNA detection with the superiority in smear-negative paucibacillary TB patients. The clinical application of TB-QUICK in TB diagnosis needs to be further validated in larger cohort.

Catalytic DNA circuits represent a versatile toolbox for tracking intracellular biomarkers yet are constrained with low anti-interference capacity originating from their severe off-site activation. Herein, by introducing an unprecedented endogenous DNA repairing enzyme-powered pre-selection strategy, we develop a sequential and specific on-site activated catalytic DNA circuit for achieving the cancer cell-selective imaging of microRNA with high anti-interference capacity. Initially, the circuitry reactant is firmly caged by an elongated stabilizing duplex segment with a recognition/cleavage site of a cell-specific DNA repairing enzyme, which can prevent undesired signal leakage prior to its exposure to target cells. Then, the intrinsic DNA repairing enzyme of target cells can liberate the DNA probe for efficient intracellular microRNA imaging via the multiply guaranteed molecular recognition/activation procedures. This bioorthogonal regulated DNA circuit presents a modular and programmable amplification strategy for highly reliable assays of intracellular biomarkers, and provides a pivotal molecular toolbox for living systems.

Glacier collapse is a fairly new type of glacier-related disasters on the Asian Water Towers (AWTs) in the warming climate. On 16 October and 29 October 2018, two glacier collapses occurred in the Sedongpu Basin, 7 km downstream from Gyala Village, Paizhen Town, Miling County, on the Yarlung Tsangpo River (YTR). The ice and entrained debris flows caused by the glacier collapses blocked the YTR, resulting in a potential threat to residents and transport lines upstream and downstream. Through post-event field investigations with a helicopter and an unmanned aerial vehicle (UAV), remote sensing interpretation, and seismic, hydrological, and meteorological observations, the process and potential mechanisms of the glacier collapse-induced river blocking (GCRB) disasters were investigated. We confirmed that the first glacier collapse event occurred at 22:48 (Beijing time) on 16 October 2018 and the second began at 08:03 on 29 October 2018. Approximately 130 × 106 m3 of ice and debris detached from the glacier during the glacier collapse, and we calculated that the river blocking fans caused by the first and second glacier collapse event covered ~1.36 km2 and ~ 1.29 km2 on the main watercourse of the YTR, respectively. We determined that the GCRB incidents represent a disaster chain of glacier collapse → glacial debris flow → river blockage → dammed lake → outburst flood. These incidents arise due to a combination of factors, including glacier activity, climate warming, heavy precipitation, pre-seismic activity, and high topographic relief. In the context of climate warming on the Tibetan Plateau, such glacier collapse induced disaster chains will continue or even intensify in the future. To protect against glacier collapse disasters in the Grand Canyon on the YTR, we established a monitoring and early warning system (EWS), which has already successfully sounded alerts for GCRB incidents. As a major element of an integrated risk management strategy, the EWS represents a viable and promising tool for mitigating climate change-related risks.

The wide extracellular-intracellular distribution of microRNA requires the on-site, robust and efficient activation of catalytic DNA circuits inside live cells. Herein, we develop an efficient non-enzymatic circuitry activation strategy to realize the orthogonally controlled catalytic DNA (CCD) circuit for achieving high-fidelity in vivo microRNA imaging through multiply guaranteed molecular recognition and progressively accelerated signal amplification. For predictable on-site activation and useful catalytic efficiency, the dominating circuitry fuel strand was initially split into inactive fuel subunits that were grafted into an auxiliary catalytic circuit. There, the in-cell-specific mRNA triggered the orthogonal amplification of the active fuel strands for sensitive target detection through the chief entropy-driven catalytic DNA circuit. We believe that the on-site orthogonal circuitry activation method can contribute to clinical diagnosis and prognosis.

Abstract It is of great challenge to design transition multimetallic carbonate hydroxides with delicate hollow features and defects for efficient electrolytic oxygen evolution reaction (OER). Here, a sequential self‐templating method to synthesize CoNiFe trimetallic carbonate hydroxide hierarchical hollow microflowers (CN‐xFe HMs) with oxygen vacancies (V O ) is reported. The synergistic merits of hollow structure, Fe substitution, and V O endow the CN‐xFe HMs with high active‐site exposure density and increased electrical conductivity. Specially, the optimized CN‐xFe HMs validate the excellent OER performance with an overpotential of 258 mV to drive 10 mA cm −2 and a Tafel slope of 48.7 mV dec −1 . Theoretical calculations reveal that Fe substitution and V O can synergistically regulate the electronic states to achieve near‐ideal adsorption/desorption capacity for oxygenated intermediates. Moreover, the successful synthesis of other six metals substituted CoNiM (M = Cu, Zn, Cr, Mo, Er and La) carbonate hydroxides provides a universal protocol to construct transition multimetallic electrocatalysts with hollow structures for gaining highly efficient energy conversion reactions.

Emerging economies are showing promising growth and economic success, but the growth process has significantly increased carbon emissions in these countries and deteriorated environmental quality. Environmental degradation is an issue of serious concern as it is directly linked to human lives and health. Since the creation of the Sustainable Development Goals (SDGs), the Emerging Seven (E-7) countries have struggled to meet the SDG targets, as it's been a challenge for them to lower carbon emissions and improve the quality of the environment. Thus, the present study explores the key factors that significantly affect environmental quality. This study examines the effect of institutional quality, energy productivity, and eco-innovation on consumption-based carbon dioxide (CCO2) emissions for E-7 economies. The cointegration analysis results show a long-run relationship between institutional quality, energy productivity, GDP, eco-innovation exports, imports, and CCO2 emissions. The results obtained using the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model show that institutional quality, energy productivity, eco-innovation, and exports adversely affect CCO2 emissions and improve environmental quality in the short and long run. In contrast, imports and GDP are positively linked with CCO2 emissions and contribute to environmental degradation. Policies that target institutional quality, eco-innovation, and energy productivity significantly affect CCO2 emissions and help improve environmental quality.

Abnormal DNA methylation contributes to the annoying tumorigenesis and the elevated expression of methylation-related methyltransferase (MTase) is associated with many diseases. Hence DNA MTase could serve as a promising biomarker for cancer-specific diagnosis as well as a potential therapeutic target. Herein, we developed an isothermal autocatalytic hybridization reaction (AHR) circuit for the sensitive detection of MTase and its inhibitors by integrating the catalytic hairpin assembly (CHA) converter with the hybridization chain reaction (HCR) amplifier. The initiator-mediated HCR amplifier could generate amplified fluorescent readout, as well as numerous newly activated triggers for motivating the CHA converter. The CHA converter is designed to expose the identical sequence of HCR initiators that reversely powered the HCR amplifier. Thus, the trace amount of target could produce exponentially amplified fluorescent readout by the autocatalytic feedback cycle between HCR and CHA systems. Then an auxiliary hairpin was introduced to mediate the assay of Dam MTase via the well-established AHR circuit. The Dam MTase-catalyzed methylation of auxiliary hairpin leads to its subsequent efficient cleavage by DpnI endonuclease, thus resulting in the release of HCR initiators to initiate the AHR circuit. The programmable nature of the auxiliary hairpin allows its easy adaption into other MTase assay by simply changing the recognition site. This proposed AHR circuit permits a sensitive, robust, and versatile analysis of MTase with the limit of detection (LOD) of 0.011 U/mL. Lastly, the AHR circuit could be utilized for MTase analysis in real complex samples and for evaluating the cell-cycle-dependent expression of MTase. This developed MTase-sensing strategy holds promising potential for biomedical analysis and clinical diagnosis.

Catalytic DNA circuit, a versatile synthetic molecular nanodevice, shows great potential for in vivo bioimaging application, yet is distorted by its off-site signal leakage. The endogenously activated DNA circuit could guarantee the specific and sensitive in vivo imaging utility yet was still unexplored. In this work, we engineered an endogenously and sequentially activated DNA circuit by using the specific enzymatic regulation strategy. This smart DNA circuit is consisting of two successive reaction modules, the initial site-specific circuitry exposure module and the subsequent circuitry activation module for amplified in vivo biosensing. Initially, the catalytic circuitry reactant was caged with a long elongated duplex, encoding with high energy barriers of circuitry crosstalk, to prevent the undesirable off-target signal leakage prior to its arrival in targeting cells. Subsequently, the as-integrated functional duplex could be specifically cleaved and removed by the endogenously overexpressed DNA excision repairing enzyme of cancer cells, thus liberating the DNA circuitry reactant for participating the catalyzed and amplified imaging of intracellular analyte, e.g., microRNA. Meanwhile, the catalytic DNA circuit stayed inert in normal cells for lacking the indispensable cell-specific exposure of circuitry reactant. Through the sequential circuitry exposure (by endogenous enzyme of specific cells) and activation (by target of interest) procedure, our multiply guaranteed DNA circuit realized the robust in vivo imaging of tumor cells with high precision and reliability, thus supplementing a powerful toolbox for cancer diagnosis and treatment.

Carbon dots (CDs) due to their excellent photostability, good biocompatibility and multicolor fluorescence, have been preferentially selected as luminescent fluorophores and co-assembled with various chiral host templates in recent years. The as-prepared composites exhibit marvellous circularly polarized luminescence (CPL) activity and show promising potentials in the fields of stereopsis and optoelectronic display, which have drawn increasing attention. However, the fussy assembly procedure and over-reliance on template chirality extremely limit the design and application of CDs-composited materials in the field of CPL. In this work, we directly prepared the CDs-composited G-quartet hydrogels with CPL activity using a one-step microwave-assisted method, and their helical chiral structure and CPL orientation can be arbitrarily switched back and forth between left-handed and right-handed through K+ and formation kinetics. Finally, the CDs-composited G-quartet hydrogels can be applied in anti-counterfeiting printing for encrypted information transfer, and their outstanding thermal cycling stability and CPL dissymmetry factor (glum) of up to 10−1 demonstrate a wider application potential in CPL active materials based on CDs.

Satellite precipitation products can provide alternative data in remote areas with sparse surface observations, but the performance of these products must be evaluated before hydrometeorological applications. The western Tibetan Plateau (WTP) covers an area of nearly 1,500,000 km2, where China Meteorological Administration stations are very sparse due to the harsh natural environment. Therefore, previous evaluations of satellite precipitation products have resulted in a gap on the WTP. In this study, hourly data collected from 29 newly established rain gauges on the WTP were applied to evaluate the performance of four Global Precipitation Measurement (GPM)-Era precipitation products without calibration (IMERG-UC and GSMaP-MVK) and with calibration (IMERG-C and GSMaP-Gauge). The evaluation results for the WTP were compared with those for the eastern Tibetan Plateau (ETP). (1) The effect of the calibration of satellite products highly depends on the temporal resolution of precipitation data used for calibration. The GSMaP-Gauge, which are calibrated with daily data, outperforms the IMERG-C, which are calibrated with monthly data, on both monthly scale and daily scale. (2) The calibration effect of the satellite products is not positive in reproducing the precipitation amount on the WTP due to few observations available for calibration but was notable on the ETP. (3) None of the four satellite precipitation products can reproduce the hourly precipitation frequency-intensity structure well. Both IMERG and GSMaP produce too much light precipitation events on the WTP. In addition, the four products produce too early timing of diurnal precipitation peak on the WTP. The calibration does not improve the timing phase of precipitation amount but affects the intensity of diurnal precipitation peaks on the WTP. These new findings provide important information on the accuracy of the four widely used satellite precipitation products on the WTP and thus can be a solid reference for hydrometeorological applications on the WTP.

Heterointerface engineering has been proved to be an effective strategy to improve the electrochemical performances of electrode materials by overcoming inherent drawbacks of single phase electrode. However, the rational construction of heterogeneous composite with abundant heterogeneous interfaces for lithium-ion batteries (LIBs) remains a great challenge. Herein, graphene aerogel encapsulated FeSe2-Fe2O3 heterojunction nanotubes (FeSe2-Fe2O3@GA) with inner-outer bi-interfacial structures were fabricated by freeze-drying method and partial selenization treatment. The built-in electric fields induced on the FeSe2-Fe2O3 could greatly lower the activation energy for rapid charge transfer kinetics. And GA as outer surface not only could maintain the integrity of active material structure, but also enhance its electronic conductivity. Benefiting from these advantages, the FeSe2-Fe2O3@GA anode exhibits an improved electrochemical performance in term of lithium storage capacity (1515.6 mAh g−1 at 0.2 A g−1), cycle stability and high rate capability (492.7 mAh g−1 after 600 cycles at 1 A g−1). The kinetics analysis and theoretical calculation also interpret the significant role of heterointerface engineering construction in improving the reaction kinetics of lithium storage.

Neuronal signals have emerged as pivotal regulators of group 2 innate lymphoid cells (ILC2s) that regulate tissue homeostasis and allergic inflammation. The molecular pathways underlying the neuronal regulation of ILC2 responses in lungs remain to be fully elucidated. Here, we found that the abundance of neurotransmitter dopamine was negatively correlated with circulating ILC2 numbers and positively associated with pulmonary function in humans. Dopamine potently suppressed lung ILC2 responses in a DRD1-receptor-dependent manner. Genetic deletion of Drd1 or local ablation of dopaminergic neurons augmented ILC2 responses and allergic lung inflammation. Transcriptome and metabolic analyses revealed that dopamine impaired the mitochondrial oxidative phosphorylation (OXPHOS) pathway in ILC2s. Augmentation of OXPHOS activity with oltipraz antagonized the inhibitory effect of dopamine. Local administration of dopamine alleviated allergen-induced ILC2 responses and airway inflammation. These findings demonstrate that dopamine represents an inhibitory regulator of ILC2 responses in allergic airway inflammation.

Chiral benzylic amines are privileged motifs in pharmacologically active molecules. Intramolecular enantioselective radical C(sp3 )-H functionalization by hydrogen-atom transfer has emerged as a straightforward, powerful tool for the synthesis of chiral amines, but methods for intermolecular enantioselective C(sp3 )-H amination remain elusive. Herein, we report a cationic copper catalytic system for intermolecular enantioselective benzylic C(sp3 )-H amination with peroxide as an oxidant. This mild, straightforward method can be used to transform an array of feedstock alkylarenes and amides into chiral amines with high enantioselectivities, and it has good functional group tolerance and broad substrate scope. More importantly, it can be used to synthesize bioactive molecules, including chiral drugs. Preliminary mechanistic studies indicate that the amination reaction involves benzylic radicals generated by hydrogen-atom transfer.

Porous carbon is one of the most promising electrode materials for energy conversion and storage devices due to its high specific surface area, low cost, sustainability, appropriate charging/discharging voltage platform and high interlayer spacing. However, the disadvantage of insufficient energy storage capacity frustrates its wide applications. Herein, a novel N, P co-doped porous carbon derived from sugarcane bagasse (SBNPk) was prepared by hydrothermal method combined with KOH activation and carbonization, and was applied to supercapacitors (SCs) and sodium-ion batteries (SIBs). The SBNPK possesses a special hierarchical porous structure, improved surface area, larger interlayer spacing, and moderate N, P doping level. Specifically, the SBNPk carbonization at 600 °C (SBNPk-600) exhibits the highest specific capacitance (356.4 F g−1 at 1 A g−1), good rate capability and excellent cycle stability (5% loss over 10,000 cycles) in a three-electrode system. Further assembled in a symmetrical two-electrode system, the SBNPK-600//SBNPK-600 can still provide a higher energy density of 6.5 Wh kg−1 at 251.9 W kg−1 and superior cycle performance (96.5% of capacitance retention at 2 A g−1 after 20,000 cycles). Surprisingly, the SBNPK-600 as an anode for SIBs also delivers a high reversible capacity of 304.1 mAh g−1 at 25 mA g−1 and excellent cycle performance (225.7 mAh g−1 after 1000 cycles at 500 mA g−1), indicating its superior sodium storage capability. This work provides a simple and new way to enhance the electrochemical performance of biomass waste-derived carbon by heteroatom doping, which is helpful to further boost its application in the field of energy storage.

Hard carbon is one of the most promising anodic materials for sodium ion batteries (SIBs) because of its low charge/discharge voltage platform, high specific surface area and higher layer spacing. However, the disadvantages of its unsatisfied sodium storage capacity and high cost owing to low carbonization yield of precursors frustrate its practical use. In this paper, we have developed a hierarchical porous hard carbon derived from rice husks (HCRH) synthesized via NaOH solution impregnation method combined with reflux and carbonization at different temperature. This kind of hard carbon kept the natural porous structure of rice husk, and the rich porous structure increased the active site of Na+, shortened the diffusion distance of Na+, and promoted the transport of electrolyte. As an anode for SIBs, the HCRH carbonization at 1200 °C (HCRH-1200) with a suitable graphite layer spacing (0.382 nm)delivered the highest capacity of 328.4 mAh g−1 after 100 cycles at 25 mA g−1. This study could provide a simple method to prepare high performance anode materials for sodium ion batteries using biomass rice husk as carbon source, and this design strategy could be extended to other biomass-based carbon materials.

Abstract Chiral carbon dots (CDs) are a novel luminescent zero‐dimensional carbon‐based nanomaterial with chirality. They not only have the advantages of good biocompatibility, multi‐color‐emission, easy functionalization, but also exhibits highly symmetrical chiral optical characteristics, which broadens their applicability to enantioselectivity of some chiral amino acids like cysteine and lysine, asymmetric catalysis as well as biomedicine in gene expression and antibiosis. In addition, the exploration of the excited state chirality of CDs has developed its excellent circularly polarized luminescence (CPL) properties, opening up a new application scenario like recognition of chiral light sources and anti‐counterfeit printing with information encryption. This review mainly focuses on the mature synthesis approaches of chiral CDs, including chiral ligand method and supramolecular self‐assembly method, then we consider emerging applications of chiral CDs in CPL, biosensing and biological effect. Finally, we concluded with a perspective on the potential challenges and future opportunities of such fascinating chiral CDs.

Over 70 % of the earth's surface is water, though people are facing a shortage of clean water. Desalination and purification plants have been utilizing inexhaustible saline and dirty water for decades, but here we present a portable solar-powered device that works during the journey. The independent steam generator is based on a novel wooden material coated with carbonized carbon dots (CCD). A facile hydrothermal treatment is applied to the balsa wood to produce a superhydrophilic, salt-resisting, and self-insulated wooden sponge. The CCD microspheres show broad absorption over the solar spectrum while the wooden sponge provides a rapid water supply and heat localization. The floating [email protected] sponge evaporator generates steam at the rate of 2.24 kg m−2 h−1 and has an energy efficiency of 85.0 % under 1 sun irradiation. With a unique design of portable water purifier, a traveler could easily obtain clean water after collecting and purifying undrinkable water such as seawater and wastewater on the move as long as the sun shines.

Monitoring α-glucosidase (α-Glu) activity is of great significance for the early diagnosis of type II diabetes. Here the blue fluorescent carbon dots (CDs) were integrated with two different recognizing molecules, β-cyclodextrin and phenylboronic acid, for assembling a multifunctional CDs (mCDs) nanoplatform for sensitively analyzing α-Glu and its inhibitors. The hydrolyzed product of 4-nitrophenyl-α-d-glucopyranoside (α-Glu substrate), p-nitrophenol, could efficiently quench the fluorescence of mCDs due to its cooperative molecular recognition with β-cyclodextrin and phenylboronic acid. The mCDs could be utilized for the detection of α-Glu activity with the limit of detection of 0.030 U/L. Moreover, the present α-Glu detection platform revealed a high selectivity, and other natural enzymes showed scarcely any effect on the present mCDs system. The proposed method could be facilely used to screen α-Glu inhibitors with satisfying performance. The rational mCDs is expected to supplement more comprehensive biosensing platforms for highly sensitive and specific recognition of disease-relevant biomarkers with clinical importance.

Accurate segmentation of nasopharyngeal carcinoma (NPC) lesion areas from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) facilitates subsequent diagnostic and prognostic workups. However, in previous studies, little consideration has been given to incorporating the pharmacokinetic feature Ktrans as auxiliary information for segmenting NPC in DCE-MRI. Therefore, this paper proposes the use of a pharmacokinetic extended Tofts and Kermode (ETK) model to obtain the Ktrans feature from DCE-MRI and combine it with MRI images for nasopharyngeal tumor segmentation. Additionally, this paper proposes a multi-input branch residual U-Net (MBRU-Net) model that effectively fuses DCE-MRI features and Ktrans features. The effectiveness of the multibranch network is validated by comparing MBRU-Net with ResU-Net with DCE-MRI + Ktrans data. Additionally, different models are trained with DCE-MRI and DCE-MRI + Ktrans data separately and compared to validate the effectiveness of multimodal data using the Dice coefficient. Our proposed MBRU-Net achieves the best Dice in this study (67.39 ± 15.79%), higher than ResU-Net's Dice (65.57 ± 17.52) based on DCE-MRI and Ktrans data. U-Net, SegNet, R2U-Net, and ResU-Net achieve better results in terms of segmenting tumor regions with DCE-MRI + Ktrans data than those of the corresponding models with DCE-MRI data alone, where U-Net has the best performance (DCE-MRI + Ktrans: DCE-MRI = 66.31 ± 17.80%: 61.10 ± 24.14%). The results show that it is beneficial to add a pharmacokinetic parametric (Ktrans) map as prior information to the conventional anatomical MRI-based segmentation task, and multibranch network structures perform better than single-branch network structures in terms of NPC segmentation.

According to the theory of traditional Chinese medicine, the main factors related to alcoholic liver disease (ALD) are qi stagnation and blood stasis of the five viscera. Previously, we showed that the bioactive components of Alhagi honey have various pharmacological effects in treating liver diseases, but the influence of Alhagi honey on ALD (and its mechanism of action) is not known. To determine the efficacy of the main active component of Alhagi honey, the polysaccharide AHPN80, in ALD and to explore the potential mechanism of action. AHPN80 was isolated from dried Alhagi honey and identified by transmission electron microscopy, Fourier-transform infrared spectroscopy, and gas chromatography. Venous blood, liver tissue, and colon tissue were collected in a mouse model of alcohol-induced acute liver injury. Histology, staining (Oil Red O, Alcian Blue–Periodic Acid Schiff) and measurement of reactive oxygen species (ROS) levels were used to detect histopathologic and lipid-accumulation changes in the liver and colon. Lipopolysaccharide (LPS) levels and the content of proinflammatory cytokines in serum were measured by enzyme-linked immunosorbent assays. Commercial kits were employed to detect biochemistry parameters in serum and the liver. A terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining kit was used to identify hepatocyte apoptosis. Expression of tight junction-associated proteins in colon tissues and nuclear factor erythroid 2-related factor 2/heme oxygenase-1/toll-like receptor-4/mitogen-activated protein kinase (Nrf2/HO-1/TLR4/MAPK) pathway-related proteins in liver tissues and HepG2 cells were analyzed by immunofluorescence or western blotting. In a mouse model of alcohol-induced acute liver injury, AHPN80 therapy: significantly improved liver parameters (cytochrome P450 2E1, alcohol dehydrogenase, aldehyde dehydrogenase, superoxide dismutase, malondialdehyde, glutathione peroxidase, catalase, total cholesterol, triglycerides, alanine transaminase, aspartate transaminase); reduced serum levels of LPS, interleukin (IL)-1β, IL-6, and tumor necrosis faction-α; increased levels of IL-10 and interferon-gamma. AHPN80 reduced ALD-induced lipid accumulation and ROS production, improved alcohol-induced inflammatory damage to hepatocytes, and inhibited hepatocyte apoptosis. Immunofluorescence staining and western blotting suggested that AHPN80 might eliminate hepatic oxidative stress by activating the Nrf2/HO-1 signaling pathway, repair the intestinal barrier, inhibit the LPS/TLR4/MAPK signaling pathway, and reduce liver inflammation. AHPN80 may activate the Nrf2/HO-1 pathway to eliminate oxidative stress, protect the intestinal barrier, and regulate the TLR4/MAPK pathway to treat ALD in mice. AHPN80 could be a functional food and natural medicine to prevent ALD and its complications.

Transition metal selenides are promising anode candidates for sodium ion batteries (SIBs) because of their higher theoretical capacity and conductivity than metal oxides. However, the disadvantages of severe capacity degradation and poor magnification performance greatly limit their commercial applications. Herein, we have developed a new hollow bimetallic selenides (CoSe2-ZnSe)@reduced graphene oxide (rGO) composite with abundant heterointerfaces. The rGO could not only alleviate the volume variations of hollow CoSe2-ZnSe microspheres during cycling, but also improve the conductivity of composite. The presence of the heterointerfaces could help to accelerate ionic diffusion kinetics and improve electron transfer, resulting in the improved sodium storage performance. As an advanced anode for SIBs, the CoSe2-ZnSe@rGO exhibits an enhanced initial coulombic efficiency of 75.1% (65.2% of CoSe2@rGO), extraordinary rate capability, and outstanding cycling stability (540.3 mAh/g at 0.2 A/g after 150 cycles, and 395.2 mAh/g at 1 A/g after 600 cycles). The electrochemical mechanism was also studied by kinetic analysis, showing that the charging/discharging process of CoSe2-ZnSe@rGO is mostly related to a capacitive-controlled behavior.

Securing communication in mobile wireless networks is challenging because the traditional cryptographic-based methods are not always applicable in dynamic mobile wireless environments. Using physical layer information of radio channel to generate keys secretly among wireless devices has been proposed as an alternative in wireless mobile networks. And the Received Signal Strength (RSS) based secret key extraction gains much attention due to the RSS readings are readily available in wireless infrastructure. However, the problem of using RSS to generate keys among multiple devices to ensure secure group communication remains open. In this work, we propose a framework for collaborative key generation among a group of wireless devices leveraging RSS. The proposed framework consists of a secret key extraction scheme exploiting the trend exhibited in RSS resulted from shadow fading, which is robust to outsider adversary performing stalking attacks. To deal with mobile devices not within each other's communication range, we employ relay nodes to achieve reliable key extraction. To enable secure group communication, two protocols, namely star-based and chain-based, are developed in our framework by exploiting RSS from multiple devices to perform group key generation collaboratively. Our experiments in both outdoor and indoor environments confirm the feasibility of using RSS for group key generation among multiple wireless devices under various mobile scenarios. The results also demonstrate that our collaborative key extraction scheme can achieve a lower bit mismatch rate compared to existing works when maintaining the comparable bit generation rate.

Alkalinized graphitic carbon nitride (CNK-OH) has been synthesized by one-step thermal poly-condensation method, and Cu-modified alkalinized g-C3N4 (Cu-CNK-OH) has been prepared by impregnation approach over CNK-OH. These copper species in Cu-CNK-OH are embedded in the frame of CNK-OH mostly via the Cu-N bonds. Cu-CNK-OH has been employed as a heterogeneous Fenton-like catalyst to degrade rhodamine B (RhB). Both the production efficiency of hydroxyl radicals and the transformation rate of Cu(II)/Cu(I) redox pair increase under visible-light irradiation. As a result, Cu-CNK-OH exhibits improved Fenton-like catalytic activity on the degradation of RhB. The synergetic interaction between Fenton-like process and photocatalytic process also contributes such improvement. The hydroxyl radicals and holes are the major reactive species in the photocatalytically assisted Fenton-like process. This study provides a valuable strategy for metal modification of alkalinized g-C3N4 with enhanced Fenton-like catalytic performance for the degradation of organic contaminants.

Motivated by safety challenges resulting from distracted pedestrians, this paper presents a sensing technology for fine-grained location classification in an urban environment. It seeks to detect the transitions from sidewalk locations to in-street locations, to enable applications such as alerting texting pedestrians when they step into the street. In this work, we use shoe-mounted inertial sensors for location classification based on surface gradient profile and step patterns. This approach is different from existing shoe sensing solutions that focus on dead reckoning and inertial navigation. The shoe sensors relay inertial sensor measurements to a smartphone, which extracts the step pattern and the inclination of the ground a pedestrian is walking on. This allows detecting transitions such as stepping over a curb or walking down sidewalk ramps that lead into the street. We carried out walking trials in metropolitan environments in United States (Manhattan) and Europe (Turin). The results from these experiments show that we can accurately determine transitions between sidewalk and street locations to identify pedestrian risk.