Ké Li

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Achieving balance between convergence and diversity is a key issue in evolutionary multiobjective optimization. Most existing methodologies, which have demonstrated their niche on various practical problems involving two and three objectives, face significant challenges in many-objective optimization. This paper suggests a unified paradigm, which combines dominance- and decomposition-based approaches, for many-objective optimization. Our major purpose is to exploit the merits of both dominance- and decomposition-based approaches to balance the convergence and diversity of the evolutionary process. The performance of our proposed method is validated and compared with four state-of-the-art algorithms on a number of unconstrained benchmark problems with up to 15 objectives. Empirical results fully demonstrate the superiority of our proposed method on all considered test instances. In addition, we extend this method to solve constrained problems having a large number of objectives. Compared to two other recently proposed constrained optimizers, our proposed method shows highly competitive performance on all the constrained optimization problems.

Substantial efforts have been devoted more recently to presenting various methods for object detection in optical remote sensing images. However, the current survey of datasets and deep learning based methods for object detection in optical remote sensing images is not adequate. Moreover, most of the existing datasets have some shortcomings, for example, the numbers of images and object categories are small scale, and the image diversity and variations are insufficient. These limitations greatly affect the development of deep learning based object detection methods. In the paper, we provide a comprehensive review of the recent deep learning based object detection progress in both the computer vision and earth observation communities. Then, we propose a large-scale, publicly available benchmark for object DetectIon in Optical Remote sensing images, which we name as DIOR. The dataset contains 23463 images and 192472 instances, covering 20 object classes. The proposed DIOR dataset 1) is large-scale on the object categories, on the object instance number, and on the total image number; 2) has a large range of object size variations, not only in terms of spatial resolutions, but also in the aspect of inter- and intra-class size variability across objects; 3) holds big variations as the images are obtained with different imaging conditions, weathers, seasons, and image quality; and 4) has high inter-class similarity and intra-class diversity. The proposed benchmark can help the researchers to develop and validate their data-driven methods. Finally, we evaluate several state-of-the-art approaches on our DIOR dataset to establish a baseline for future research.

The development of highly active, universal, and stable inexpensive electrocatalysts/cocatalysts for hydrogen evolution reaction (HER) by morphology and structure modulations remains a great challenge. Herein, a simple self-template strategy was developed to synthesize hollow Co-based bimetallic sulfide (MxCo3–xS4, M = Zn, Ni, and Cu) polyhedra with superior HER activity and stability. Homogenous bimetallic metal–organic frameworks are transformed to hollow bimetallic sulfides by solvothermal sulfidation and thermal annealing. Electrochemical measurements and density functional theory computations show that the combination of hollow structure and homoincorporation of a second metal significantly enhances the HER activity of Co3S4. Specifically, the homogeneous doping in Co3S4 lattice optimizes the Gibbs free energy for H* adsorption and improves the electrical conductivity. Impressively, hollow Zn0.30Co2.70S4 exhibits electrocatalytic HER activity better than most of the reported nobel-metal-free electrocatalysts over a wide pH range, with overpotentials of 80, 90, and 85 mV at 10 mA cm–2 and 129, 144, and 136 mV at 100 mA cm–2 in 0.5 M H2SO4, 0.1 M phosphate buffer, and 1 M KOH, respectively. It also exhibits photocatalytic HER activity comparable to that of Pt cocatalyst when working with organic photosensitizer (Eosin Y) or semiconductors (TiO2 and C3N4). Furthermore, this catalyst shows excellent stability in the electrochemical and photocatalytic reactions. The strategy developed here, i.e., homogeneous doping and self-templated hollow structure, provides a way to synthesize transition metal sulfides for catalysis and energy conversion.

This review comprehensively summarizes the progress on the structural and electronic modulation of transition metal nitrides for electrochemical energy applications.

In order to reduce the potential radiation risk, low-dose CT has attracted an increasing attention. However, simply lowering the radiation dose will significantly degrade the image quality. In this paper, we propose a new noise reduction method for low-dose CT via deep learning without accessing original projection data. A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion. Qualitative results demonstrate a great potential of the proposed method on artifact reduction and structure preservation. In terms of the quantitative metrics, the proposed method has showed a substantial improvement on PSNR, RMSE and SSIM than the competing state-of-art methods. Furthermore, the speed of our method is one order of magnitude faster than the iterative reconstruction and patch-based image denoising methods.

There are few methodologies for the use of climate change projections in decision making or risk assessment processes. In this paper we present an approach for climate risk assessment that links bottom‐up vulnerability assessment with multiple sources of climate information. The three step process begins with modeling of the decision and identification of thresholds. Through stochastic analysis and the creation of a climate response function, climate states associated with risk are specified. Climate information such as available from multi‐GCM, multirun ensembles, is tailored to estimate probabilities associated with these climate states. The process is designed to maximize the utility of climate information in the decision process and to allow the use of many climate projections to produce best estimates of future climate risks. It couples the benefits of stochastic assessment of risks with the potential insight from climate projections. The method is an attempt to make the best use of uncertain but potentially useful climate information. An example application to an urban water supply system is presented to illustrate the process.

Selective catalytic hydrogenation of nitroarenes is of great importance for dyestuff and pharmaceutical industry. A critical step toward the rational design of targeted catalysts is to determine their electronic structures and related reaction mechanism. In this review, we summarize the breakthroughs on the development of multiple catalytic technologies in the past decade, including direct hydrogenation using high-pressure hydrogen; transfer hydrogenation using reductive compounds; photocatalytic hydrogenation using hole scavenger; electrocatalytic hydrogenation accompanied with water oxidation. We focus on how to understand the two key element steps including hydrogen dissociation and the activation of nitro group in the process of hydrogenation, and design and fabricate nanostructured catalysts with desired activity and selectivity. For direct catalytic hydrogenation, representative catalysts include metal, metal oxide/sulfide/carbides/nitrides/boride, and functional carbon material, and the crucial factors to tune their activity and selectivity are discussed such as metal-support interaction, size effect, alloy effect, defect engineering, and so on. Catalytic transfer hydrogenation, photocatalytic and electrocatalytic hydrogenation, in which these catalysts abstracts hydrogen species from the hydrogen donor and stabilizes it on the catalyst surface, restricting active H* recombination, and then the active hydrogen species can be promptly transferred to nitroarenes for the hydrogenation. It is worth mentioning that the light harvesting and charge separation of photocatalyst and the conductivity of electrocatalyst should also be considered together for the overall performance. All these experiences lay the foundation for large scale production of anilines and guide the rational design of catalysts for other organic transformation reactions.

Pseudocapacitive energy storage in supercapacitor electrodes differs significantly from the electrical double-layer mechanism of porous carbon materials, which requires a change from conventional thinking when choosing appropriate electrolytes. Here we show how simply changing the solvent of an electrolyte system can drastically influence the pseudocapacitive charge storage of the two-dimensional titanium carbide, Ti3C2 (a representative member of the MXene family). Measurements of the charge stored by Ti3C2 in lithium-containing electrolytes with nitrile-, carbonate- and sulfoxide-based solvents show that the use of a carbonate solvent doubles the charge stored by Ti3C2 when compared with the other solvent systems. We find that the chemical nature of the electrolyte solvent has a profound effect on the arrangement of molecules/ions in Ti3C2, which correlates directly to the total charge being stored. Having nearly completely desolvated lithium ions in Ti3C2 for the carbonate-based electrolyte leads to high volumetric capacitance at high charge–discharge rates, demonstrating the importance of considering all aspects of an electrochemical system during development. Effects from electrolytes on supercapacitor electrodes, especially pseudocapacitive materials, are important but often overlooked. Gogotsi and colleagues demonstrate strong influences from electrolyte solvents on charge-storage processes in a titanium carbide and identify a best-performing electrode/electrolyte couple for supercapacitors.

Z-scheme composite represents an ideal system for photocatalytic hydrogen evolution, but the charge transfer mechanism is still ambiguous, and how to design and construct such system is a big challenge. Herein, we demonstrate that C3N4-W18O49, the type-II composite, can be switched to direct Z-scheme via modulating the interfacial band bending. Experiment and DFT computation results reveal that the adsorption of triethanolamine (TEOA) on C3N4 surface significantly uplifts its Femi level, inverses the continuous interfacial band bending to interrupted one, and thus switches the composite from type-II to Z-scheme, without the assistance of any electron shuttles. Importantly, this Z-scheme C3N4/W18O49 composites exhibit much better photocatalytic H2 activity compared with pure C3N4, and obtain H2 evolution rate of 8597 μmol h−1 g−1 (AQY of 39.1% at 420 nm) with Pt as cocatalyst and TEOA as hole scavenger. Also, using this hypothesis we successfully explain why C3N4/WO3 is inherent Z-scheme composite but the performance is not as good as C3N4/W18O49 and why TEOA is the best hole scavenger for C3N4. This work is expected to give deep insights into understanding the charge transfer in semiconductor composites and rationally designing and constructing Z-scheme photocatalyst for hydrogen evolution.

Tumor hypoxia is one of the major challenges for the treatment of tumors, as it may negatively affect the efficacy of various anticancer modalities. In this study, a tumor-targeted redox-responsive composite biocatalyst is designed and fabricated, which may combine tumor starvation therapy and low-temperature photothermal therapy for the treatment of oxygen-deprived tumors. The nanosystem was prepared by loading porous hollow Prussian Blue nanoparticles (PHPBNs) with glucose oxidase (GOx) and then coating their surface with hyaluronic acid (HA) via redox-cleavable linkage, therefore allowing the nanocarrier to bind specifically with CD44-overexpressing tumor cells while also exerting control over the cargo release profile. The nanocarriers are designed to enhance the efficacy of the hypoxia-suppressed GOx-mediated starvation therapy by catalyzing the decomposition of intratumoral hydroperoxide into oxygen with PHPBNs, and the enhanced glucose depletion by the two complementary biocatalysts may consequently suppress the expression of heat shock proteins (HSPs) after photothermal treatment to reduce their resistance to the PHPBN-mediated low-temperature photothermal therapies.

DNA methylation and demethylation have been proposed to play an important role in somatic cell reprogramming. Here, we demonstrate that the DNA hydroxylase Tet1 facilitates pluripotent stem cell induction by promoting Oct4 demethylation and reactivation. Moreover, Tet1 (T) can replace Oct4 and initiate somatic cell reprogramming in conjunction with Sox2 (S), Klf4 (K), and c-Myc (M). We established an efficient TSKM secondary reprogramming system and used it to characterize the dynamic profiles of 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), and gene expression during reprogramming. Our analysis revealed that both 5mC and 5hmC modifications increased at an intermediate stage of the process, correlating with a transition in the transcriptional profile. We also found that 5hmC enrichment is involved in the demethylation and reactivation of genes and regulatory regions that are important for pluripotency. Our data indicate that changes in DNA methylation and hydroxymethylation play important roles in genome-wide epigenetic remodeling during reprogramming.

Adaptive operator selection (AOS) is used to determine the application rates of different operators in an online manner based on their recent performances within an optimization process. This paper proposes a bandit-based AOS method, fitness-rate-rank-based multiarmed bandit (FRRMAB). In order to track the dynamics of the search process, it uses a sliding window to record the recent fitness improvement rates achieved by the operators, while employing a decaying mechanism to increase the selection probability of the best operator. Not much work has been done on AOS in multiobjective evolutionary computation since it is very difficult to measure the fitness improvements quantitatively in most Pareto-dominance-based multiobjective evolutionary algorithms. Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously. Thus, it is natural and feasible to use AOS in MOEA/D. We investigate several important issues in using FRRMAB in MOEA/D. Our experimental results demonstrate that FRRMAB is robust and its operator selection is reasonable. Comparison experiments also indicate that FRRMAB can significantly improve the performance of MOEA/D.

Abstract 2D transition metal carbides and/or nitrides (MXenes), by virtue of high electrical conductivity, abundant surface functional groups and excellent dispersion in various solvents, are attracting increasing attention and showing competitive performance in energy storage and conversion applications. However, like other 2D materials, MXene nanosheets incline to stack together via van der Waals interactions, which lead to limited number of active sites, sluggish ionic kinetics, and finally ordinary performance of MXene materials/devices. Constructing 2D MXene nanosheets into 3D architectures has been proven to be an effective strategy to reduce restacking, thus providing larger specific surface area, higher porosity, and shorter ion and mass transport distance over normal 1D and 2D structures. In this review, the commonly used strategies for manufacturing 3D MXene architectures (3D MXenes and 3D MXene‐based composites) are summarized, such as template, assembly, 3D printing, and other methods. Special attention is also given to the structure–property relationships of 3D MXene architectures and their applications in electrochemical energy storage and conversion, including supercapacitors, rechargeable batteries, and electrocatalysis. Finally, the authors propose a brief perspective on future opportunities and challenges for 3D MXene architectures/devices.

When solving constrained multiobjective optimization problems, an important issue is how to balance convergence, diversity, and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, a two-archive evolutionary algorithm, for constrained multiobjective optimization. It maintains two collaborative archives simultaneously: one, denoted as the convergence-oriented archive (CA), is the driving force to push the population toward the Pareto front; the other one, denoted as the diversity-oriented archive (DA), mainly tends to maintain the population diversity. In particular, to complement the behavior of the CA and provide as much diversified information as possible, the DA aims at exploring areas under-exploited by the CA including the infeasible regions. To leverage the complementary effects of both archives, we develop a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status. Comprehensive experiments on a series of benchmark problems and a real-world case study fully demonstrate the competitiveness of our proposed algorithm, in comparison to five state-of-the-art constrained evolutionary multiobjective optimizers.

Bright and stable blue emitters with narrow full-width at half-maxima are particularly desirable for applications in television displays and related technologies. Here, this study shows that doping aluminum (Al3+) ion into CsPbBr3 nanocrystals (NCs) using AlBr3 can afford lead-halide perovskites NCs with stable blue photoluminescence. First, theoretical and experimental analyses reveal that the extended band gap and quantum confinement effect of elongated shape give rise to the desirable blueshifted emission. Second, the aluminum ion incorporation path is rationalized qualitatively by invoking fundamental considerations about binding relations in AlBr3 and its dimer. Finally, the absence of anion-exchange effect is corroborated when green CsPbBr3 and blue Al:CsPbBr3 NCs are mixed. Combinations of the above two NCs with red-emitting CdSe@ZnS NCs result in UV-pumped white light-emitting diodes (LED) with an National Television System Committee (NTSC) value of 116% and ITU-R Recommendation B.T. 2020 (Rec. 2020) of 87%. The color coordinates of the white LED are optimized at (0.32, 0.34) in CIE 1931. The results suggest that low-cost, earth-abundant, solution-processable Al-doped perovskite NCs can be promising candidate materials for blue down-conversion layer in backlit displays.

This study introduces an improved Malmquist–Luenberger productivity index to measure the green productivity growth of China’s manufacturing sector during the 11th Five-Year Period (2006–2010). A three-stage data envelopment analysis model is adopted to measure the effects of government measures on green productivity growth. The main results are: (i) the average value of the Malmquist productivity index is 1.045 and the average value of the Malmquist–Luenberger productivity index accounting for CO2 emissions is 1.027. This indicates imply that the relatively higher values of the former are at the expense of substantial energy usage and CO2 emissions; (ii) China’s energy-saving policies and measures, such as mass promotion and adoption of energy-saving technology, closure and elimination of obsolete production capacity, and reduction of over-capacity are important for green development; (iii) after eliminating the effects of environmental influences and statistical noise on output slacks, the adjusted green productivity changes are smaller while the adjusted technical changes are larger than the corresponding initial levels; (iv) the energy conservation policies implemented in China’s manufacturing sector are far from the optimal level, and more stringent enforcement would be conducive for green productivity growth in the manufacturing sector.

BACKGROUND Sensitive and specific biomarkers for the early detection of esophageal squamous cell carcinoma (ESCC) are urgently needed to reduce the high morbidity and mortality of the disease. The discovery of serum microRNAs (miRNAs) and their unique concentration profiles in patients with various diseases makes them attractive, novel noninvasive biomarkers for tumor diagnosis. In this study, we investigated the serum miRNA profile in ESCC patients to develop a novel diagnostic ESCC biomarker. METHODS Serum samples were taken from 290 ESCC patients and 140 age- and sex-matched controls. Solexa sequencing technology was used for an initial screen of miRNAs in serum samples from 141 patients and 40 controls. A hydrolysis probe–based stem–loop quantitative reverse-transcription PCR (RT-qPCR) assay was conducted in the training and verification phases to confirm the concentrations of selected miRNAs in serum samples from 149 patients and 100 controls. RESULTS The Solexa sequencing results demonstrated marked upregulation of 25 serum miRNAs in ESCC patients compared with controls. RT-qPCR analysis identified a profile of 7 serum miRNAs (miR-10a, miR-22, miR-100, miR-148b, miR-223, miR-133a, and miR-127-3p) as ESCC biomarkers. The area under the ROC curve for the selected miRNAs ranged from 0.817 to 0.949, significantly higher than for carcinoembryonic antigen (0.549; P < 0.0005). More importantly, this panel of 7 miRNAs clearly distinguished stage I/II ESCC patients from controls. CONCLUSIONS This panel of 7 serum miRNAs holds promise as a novel blood-based biomarker for the diagnosis of ESCC.

The effects of high-intensity ultrasound on the emulsifying properties of chicken myofibrillar protein (MP) and the rheological properties and stability of the emulsion stabilized by ultrasound-treated MP were investigated. MP suspensions (0.6 M NaCl) were subjected to ultrasound treatments (frequency 20 kHz, power 450 W, intensity 30 W/cm2) for 0, 3 and 6 min. Ultrasound treatment significantly increased (p < 0.05) the emulsion activity index and emulsion stability index of MP and yielded a more stable emulsion. The frequency sweep and temperature sweep of emulsions indicated that ultrasound treatment of MP enhanced the elasticity and viscosity of MP-stabilized emulsion. Ultrasound significantly reduced the particle size of MP (p < 0.05) and promoted the formation of smaller and more uniform emulsion droplets. Ultrasound treatment increased the absorbed protein concentration (p < 0.05), while SDS-PAGE of adsorbed proteins recovered from the emulsion layer showed that the band intensity of myosin heavy chains and actin were increased. Ultrasound significantly increased the unfolding of MP, showing an increase in the reactive sulfhydryl content, surface hydrophobicity and intrinsic fluorescence intensity. Ultrasound significantly reduced (p < 0.05) the α-helical content and increased the content of β-sheet, β-turn and random coil contents. High-intensity ultrasound induced structural changes in MP and increased interfacial proteins around oil droplets, contributing to an improvement in the emulsifying properties of MP and enhancement in the rheological properties and storage ability of the O/W emulsion. High-intensity ultrasound has important potential for directly enhancing emulsifying characteristics of lean meat proteins on emulsion-type products.

China’s nationwide ozone monitoring network initiated in 2013 has observed severe surface ozone pollution. This network, combined with the recent Tropospheric Ozone Assessment Report (TOAR) data set, offers a more comprehensive view on global surface ozone distribution and trends. Here, we report quantitative estimates of the warm-season (April–September) surface ozone trends and resulting health impacts at Chinese cities in 2013–2019. Both the parametric and nonparametric linear trends for 12 ozone metrics relevant to human health and vegetation exposure are derived. We find that all ozone metrics averaged from Chinese urban sites have increased significantly since 2013. The warm-season daily maximum 8-h average (MDA8) ozone levels increased by 2.4 ppb (5.0%) year–1, with over 90% of the sites showing positive trends and 30% with trends larger than 3.0 ppb year–1. These rates are among the fastest trends, even faster in some Chinese cities, compared with the urban ozone trends in any other region worldwide reported in TOAR. Ozone metrics reflecting the cumulative exposure effect on human health and vegetation such as SOMO35 and AOT40 have increased at even faster rates (>10% year–1). We estimate that the total premature respiratory mortalities attributable to ambient MDA8 ozone exposure in 69 Chinese cities are 64,370 in 2019, which has increased by 60% compared to 2013 levels and requires urgent attention.

KRAS is mutated in ∼20% of human cancers and is one of the most sought-after targets for pharmacological modulation, despite having historically been considered "undruggable." The discovery of potent covalent inhibitors of the KRASG12C mutant in recent years has sparked a new wave of interest in small molecules targeting KRAS. While these inhibitors have shown promise in the clinic, we wanted to explore PROTAC-mediated degradation as a complementary strategy to modulate mutant KRAS. Herein, we report the development of LC-2, the first PROTAC capable of degrading endogenous KRASG12C. LC-2 covalently binds KRASG12C with a MRTX849 warhead and recruits the E3 ligase VHL, inducing rapid and sustained KRASG12C degradation leading to suppression of MAPK signaling in both homozygous and heterozygous KRASG12C cell lines. LC-2 demonstrates that PROTAC-mediated degradation is a viable option for attenuating oncogenic KRAS levels and downstream signaling in cancer cells.

Vitamin D insufficiency/deficiency is commonly observed in black youth.The aim was to determine 25-hydroxyvitamin D [25(OH)D] in response to 2000 IU vitamin D supplementation over time; to evaluate the relation between 25(OH)D concentrations and total body fat mass by dual-energy x-ray absorptiometry; and to determine whether vitamin D supplementation improves arterial stiffness measured by pulse wave velocity (PWV).We conducted a randomized, blinded, controlled clinical trial.Forty-nine normotensive black boys and girls, aged 16.3 ± 1.4 yr, were randomly assigned to either the control group (400 IU/d; n = 24) or the experimental group (2000 IU/d; n = 25).Plasma 25(OH)D values at baseline and at 4, 8, and 16 wk were 34.0 ± 10.6, 44.9 ± 9.4, 51.2 ± 11.1, and 59.8 ± 18.2 nmol/liter, respectively, for the control group; and 33.1 ± 8.7, 55.0 ± 11.8, 70.9 ± 22.0, and 85.7 ± 30.1 nmol/liter, respectively, for the experimental group. The experimental group vs. the control group reached significantly higher 25(OH)D concentrations at 8 and 16 wk, respectively. Partial correlation analyses indicated that total body fat mass at baseline was significantly and inversely associated with 25(OH)D concentrations in response to the 2000-IU supplement across time. Furthermore, carotid-femoral PWV increased from baseline (5.38 ± 0.53 m/sec) to posttest (5.71 ± 0.75 m/sec) in the control group (P = 0.016), whereas in the experimental group carotid-femoral PWV decreased from baseline (5.41 ± 0.73 m/sec) to posttest (5.33 ± 0.79 m/sec) (P = 0.031).Daily 2000 IU vitamin D supplementation may be effective in optimizing vitamin D status and counteracting the progression of aortic stiffness in black youth. Plasma 25(OH)D concentrations in response to the 2000 IU/d supplementation are negatively modulated by adiposity.

The uncertainty principle, which bounds the uncertainties involved in obtaining precise outcomes for two complementary variables defining a quantum particle, is a crucial aspect in quantum mechanics. Recently, the uncertainty principle in terms of entropy has been extended to the case involving quantum entanglement. With previously obtained quantum information for the particle of interest, the outcomes of both non-commuting observables can be predicted precisely, which greatly generalises the uncertainty relation. Here, we experimentally investigated the entanglement-assisted entropic uncertainty principle for an entirely optical setup. The uncertainty is shown to be near zero in the presence of quasi-maximal entanglement. The new uncertainty relation is further used to witness entanglement. The verified entropic uncertainty relation provides an intriguing perspective in that it implies the uncertainty principle is not only observable-dependent but is also observer-dependent.

This study investigates the impacts of investment-driven economic growth model, as well as rationalization and upgrading of the industrial structure on green productivity in 30 Chinese provinces over the period 1997–2010. Two total factor productivities (TFP), namely energy adjusted TFP and energy and carbon dioxide emissions adjusted TFP (denoted as TFEE and TFCE respectively), are estimated using super-efficiency DEA models, and used as indices to reflect green productivity performance in China. The main results of the empirical study are as follow: (1) China’s economic growth model does not improve both TFEE and TFCE; (2) the flow of laborers from the primary, secondary, and tertiary industries helps to improve TFEE and TFCE, while capital transformation does not produce the same effect; (3) the structural changes in the manufacturing industry produce negative and positive effects on TFEE and TFCE respectively.

Bacterial biofilms can be programmed to produce living materials with self-healing and evolvable functionalities. However, the wider use of artificial biofilms has been hindered by limitations on processability and functional protein secretion capacity. We describe a highly flexible and tunable living functional materials platform based on the TasA amyloid machinery of the bacterium Bacillus subtilis. We demonstrate that genetically programmable TasA fusion proteins harboring diverse functional proteins or domains can be secreted and can assemble into diverse extracellular nano-architectures with tunable physicochemical properties. Our engineered biofilms have the viscoelastic behaviors of hydrogels and can be precisely fabricated into microstructures having a diversity of three-dimensional (3D) shapes using 3D printing and microencapsulation techniques. Notably, these long-lasting and environmentally responsive fabricated living materials remain alive, self-regenerative, and functional. This new tunable platform offers previously unattainable properties for a variety of living functional materials having potential applications in biomaterials, biotechnology, and biomedicine.

Renewable energy technology innovation can benefit the environment by promoting green productivity, as proposed by existing theoretical studies. However, recent uneven developments of both environmental performance and renewable energy technology among regions in China remind us to revisit the above theoretical link. In this paper, we relax the hypothesized homogeneity and linearity in traditional empirical models to investigate the effects of renewable energy technology innovation on China's green productivity. The results of the partially linear functional-coefficient models show that the effect of renewable energy technological innovation on green productivity is significant only when the relative income level of a province passes a critical turning point. Beyond the turning point, such an effect increases with the growth of relative income levels. Finally, we provide provincial specific policy implications based on the estimated nonparametric relationship between renewable energy technology innovation and green productivity.

Ozone (O3) is an important trace and greenhouse gas in the atmosphere, posing a threat to the ecological environment and human health at the ground level. Large-scale and long-term studies of O3 pollution in China are few due to highly limited direct ground and satellite measurements. This study offers a new perspective to estimate ground-level O3 from solar radiation intensity and surface temperature by employing an extended ensemble learning of the space-time extremely randomized trees (STET) model, together with ground-based observations, remote sensing products, atmospheric reanalysis, and an emission inventory. A full-coverage (100%), high-resolution (10 km) and high-quality daily maximum 8-h average (MDA8) ground-level O3 dataset covering China (called ChinaHighO3) from 2013 to 2020 was generated. Our MDA8 O3 estimates (predictions) are reliable, with an average out-of-sample (out-of-station) coefficient of determination of 0.87 (0.80) and root-mean-square error of 17.10 (21.10) μg/m3 in China. The unique advantage of the full coverage of our dataset allowed us to accurately capture a short-term severe O3 pollution exposure event that took place from 23 April to 8 May in 2020. Also, a rapid increase and recovery of O3 concentrations associated with variations in anthropogenic emissions were seen during and after the COVID-19 lockdown, respectively. Trends in O3 concentration showed an average growth rate of 2.49 μg/m3/yr (p < 0.001) from 2013 to 2020, along with the continuous expansion of polluted areas exceeding the daily O3 standard (i.e., MDA8 O3 = 160 μg/m3). Summertime O3 concentrations and the probability of occurrence of daily O3 pollution have significantly increased since 2015, especially in the North China Plain and the main air pollution transmission belt (i.e., the “2 + 26” cities). However, a decline in both was seen in 2020, mainly due to the coordinated control of air pollution and ongoing COVID-19 effects. This carefully vetted and smoothed dataset is valuable for studies on air pollution and environmental health in China.

Renal ischemia reperfusion injury triggers complement activation, but whether and how the small proinflammatory fragments C3a and C5a contribute to the pathogenesis of this injury remains to be elucidated. Using C3aR-, C5aR-, or C3aR/C5aR-deficient mice and models of renal ischemia-reperfusion injury, we found that deficiency of either or both of these receptors protected mice from injury, but the C3aR/C5aR- and C5aR-deficient mice were most protected. Protection from injury was associated with less cellular infiltration and lower mRNA levels of kidney injury molecule-1, proinflammatory mediators, and adhesion molecules in postischemic kidneys. Furthermore, chimera studies showed that the absence of C3aR and C5aR on renal tubular epithelial cells or circulating leukocytes attenuated renal ischemia-reperfusion injury. In vitro, C3a and C5a stimulation induced inflammatory mediators from both renal tubular epithelial cells and macrophages after hypoxia/reoxygenation. In conclusion, although both C3a and C5a contribute to renal ischemia-reperfusion injury, the pathogenic role of C5a in this injury predominates. These data also suggest that expression of C3aR and C5aR on both renal and circulating leukocytes contributes to the pathogenesis of renal ischemia-reperfusion injury.

Abstract Cytokine release syndrome (CRS) is a major cause of the multi-organ injury and fatal outcome induced by SARS-CoV-2 infection in severe COVID-19 patients. Metabolism can modulate the immune responses against infectious diseases, yet our understanding remains limited on how host metabolism correlates with inflammatory responses and affects cytokine release in COVID-19 patients. Here we perform both metabolomics and cytokine/chemokine profiling on serum samples from healthy controls, mild and severe COVID-19 patients, and delineate their global metabolic and immune response landscape. Correlation analyses show tight associations between metabolites and proinflammatory cytokines/chemokines, such as IL-6, M-CSF, IL-1α, IL-1β, and imply a potential regulatory crosstalk between arginine, tryptophan, purine metabolism and hyperinflammation. Importantly, we also demonstrate that targeting metabolism markedly modulates the proinflammatory cytokines release by peripheral blood mononuclear cells isolated from SARS-CoV-2-infected rhesus macaques ex vivo, hinting that exploiting metabolic alterations may be a potential strategy for treating fatal CRS in COVID-19.

We present AI2, an analyst-in-the-loop security system where Analyst Intuition (AI) is put together with state-of-the-art machine learning to build a complete end-to-end Artificially Intelligent solution (AI). The system presents four key features: a big data behavioral analytics platform, an outlier detection system, a mechanism to obtain feedback from security analysts, and a supervised learning module. We validate our system with a real-world data set consisting of 3.6 billion log lines and 70.2 million entities. The results show that the system is capable of learning to defend against unseen attacks. With respect to unsupervised outlier analysis, our system improves the detection rate in 2.92× and reduces false positives by more than 5×.

Current enzyme-responsive, fluorogenic probes fail to provide in situ information because the released fluorophores tend to diffuse away from the reaction sites. The problem of diffusive signal dilution can be addressed by designing a probe that upon enzyme conversion releases a fluorophore that precipitates. An excited-state intramolecular proton transfer (ESIPT)-based solid-state fluorophore HTPQ was developed that is strictly insoluble in water and emits intense fluorescence in the solid state, with λex/em =410/550 nm, thus making it far better suited to use with a commercial confocal microscope. HTPQ was further utilized in the design of an enzyme-responsive, fluorogenic probe (HTPQA), targeting alkaline phosphatase (ALP) as a model enzyme. HTPQA makes possible diffusion-resistant in situ detection of endogenous ALP in live cells. It was also employed in the visualizing of different levels of ALP in osteosarcoma cells and tissue, thus demonstrating its interest for the diagnosis of this type of cancer.

Insight of the thermal characteristics and potential flame spread over lithium-ion battery (LIB) modules is important for designing battery thermal management system and fire protection measures. Such thermal characteristics and potential flame spread are also dependent on the different anode and cathode materials as well as the electrolyte. In the present study, thermal behavior and flame propagation over seven 50 A h Li(Ni1/3Mn1/3Co1/3)O2/Li4Ti5O12 large format LIBs arranged in rhombus and parallel layouts were investigated by directly heating one of the battery units. Such batteries have already been used commercially for energy storage while relatively little is known about its safety features in connection with potential runaway caused fire and explosion hazards. It was found in the present heating tests that fire-impingement resulted in elevated temperatures in the immediate vicinity of the LIBs that were in the range of between 200 °C and 900 °C. Such temperature aggravated thermal runaway (TR) propagation, resulting in rapid temperature rise within the battery module and even explosions after 20 min of “smoldering period”. The thermal runaway and subsequent fire and explosion observed in the heating test was attributed to the violent reduction of the cathode material which coexisted with the electrolyte when the temperature exceeded 260 °C. Separate laboratory tests, which measured the heat and gases generation from samples of the anode and cathode materials using C80 calorimeter, provided insight of the physical-chemistry processes inside the battery when the temperature reaches between 30 °C and 300 °C. The self-accelerating decomposition temperature of the cell, regarded as the critical temperature to trigger TR propagation, was calculated as 126.1 and 139.2 °C using the classical Semenov and Frank-Kamenetskii models and the measurements of the calorimeter with the samples. These are consistent with the measured values in the heating tests in which TR propagated. The events leading to the explosions in the test for the rhombus layout was further analyzed and two possible explanations were postulated and analyzed based on either internal catalytic reactions or Boiling Liquid Expansion Vapor Explosion (BLEVE).

The synthesis of crystalline two-dimensional polymers (2DPs) with proper bandgaps and well-defined repeating units presents a great challenge to synthetic chemists. Here we report the first solution synthesis of a single-layer/few-layer triazine-based 2DP via trimerization of carbonitrile at the interface of dichloromethane and trifluoromethanesulfonic acid. The processable triazine-based 2DP can be assembled into mechanically strong layered free-standing films with a high specific surface area via filtration. Moreover, the highly crystalline triazine-based 2DP can function as the active semiconducting layer in a field-effect transistor via drop coating and exhibits slightly bipolar behavior with a high on/off ratio of 103 and a remarkable mobility of 0.15 cm2 V–1 s–1.

China is facing intense coastal eutrophication. Large-scale seaweed aquaculture in China is popular, now accounting for over 2/3's of global production. Here, we estimate the nutrient removal capability of large-scale Chinese seaweed farms to determine its significance in mitigating eutrophication. We combined estimates of yield and nutrient concentration of Chinese seaweed aquaculture to quantify that one hectare of seaweed aquaculture removes the equivalent nutrient inputs entering 17.8 ha for nitrogen and 126.7 ha for phosphorus of Chinese coastal waters, respectively. Chinese seaweed aquaculture annually removes approximately 75,000 t nitrogen and 9,500 t phosphorus. Whereas removal of the total N inputs to Chinese coastal waters requires a seaweed farming area 17 times larger than the extant area, one and a half times more of the seaweed area would be able to remove close to 100% of the P inputs. With the current growth rate of seaweed aquaculture, we project this industry will remove 100% of the current phosphorus inputs to Chinese coastal waters by 2026. Hence, seaweed aquaculture already plays a hitherto unrealized role in mitigating coastal eutrophication, a role that may be greatly expanded with future growth of seaweed aquaculture.

Abstract Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as many of its signs and symptoms are similar to other less critical conditions. We develop an artificial intelligence algorithm, SERA algorithm, which uses both structured data and unstructured clinical notes to predict and diagnose sepsis. We test this algorithm with independent, clinical notes and achieve high predictive accuracy 12 hours before the onset of sepsis (AUC 0.94, sensitivity 0.87 and specificity 0.87). We compare the SERA algorithm against physician predictions and show the algorithm’s potential to increase the early detection of sepsis by up to 32% and reduce false positives by up to 17%. Mining unstructured clinical notes is shown to improve the algorithm’s accuracy compared to using only clinical measures for early warning 12 to 48 hours before the onset of sepsis.

The ATLANTIC trial reported that higher PD-L1 expression in tumors was involved in a higher objective response in patients with EGFR+/ALK+ non-small cell lung cancer (NSCLC), indicating the possibility of anti-PD-1/PD-L1 therapy as a third-line (or later) treatment for advanced NSCLC. Therefore, the determination of status and regulatory mechanisms of PD-L1 in EGFR mutant NSCLC before and after acquired EGFR-TKIs resistance are meaningful.The correlation among PD-L1, c-MET, and HGF was analyzed based on TCGA datasheets and paired NSCLC specimens before and after acquired EGFR-TKI resistance. EGFR-TKI resistant NSCLC cells with three well-known mechanisms, c-MET amplification, hepatocyte growth factor (HGF), and EGFR-T790M, were investigated to determinate PD-L1 expression status and immune escape ability. PD-L1-deleted EGFR-TKIs sensitive and resistant cells were used to evaluate the immune escape ability of tumors in mice xenograft models.Positive correlations were found among PD-L1, c-MET, and HGF, based on TCGA datasheets and paired NSCLC specimens. Moreover, the above three resistant mechanisms increased PD-L1 expression and attenuated activation and cytotoxicity of lymphocytes in vitro and in vivo, and downregulation of PD-L1 partially restored the cytotoxicity of lymphocytes. Both MAPK and PI3K pathways were involved in the three types of resistance mechanism-induced PD-L1 overexpression, whereas the NF-kappa B pathway was only involved in T790M-induced PD-L1 expression.HGF, MET-amplification, and EGFR-T790M upregulate PD-L1 expression in NSCLC and promote the immune escape of tumor cells through different mechanisms.

Abstract The checkpoint blockade‐based immunotherapy has recently emerged as a promising approach for tumor treatment, but its clinical implementation has been impeded by poor tumor penetration of the nanocarriers and activation of antitumor immune response. To overcome the obstacles, a tumor acidity‐responsive micellar nanocomplex co‐loaded with programmed death‐ligand 1 (PD‐L1)‐blockade siRNA and mitochondrion‐targeting photosensitizer for the synergistic integration of photodynamic therapy and immunotherapy is reported in the present study. The nanosystem is coated with long‐circulating polyethylene glycol (PEG) shells, which can be shed in response to the weakly acidic tumor microenvironment and lead to significant size reduction and increasing positive charge. These transitions facilitate penetration and uptake of nanocarriers against tumors. Subsequently, under the mild acidic endo/lysosome condition, the micellar nanocomplexes are rapidly protonated and disintegrated to release the PD‐L1‐blockade siRNA and photosensitizer through sponge effect. Results from in vitro and in vivo experiments collectively reveal that the nanosystem efficiently activates a photodynamic therapy‐induced immune response and silences immune resistance mediated by the checkpoint gene PD‐L1. In consequence, melanoma growth is inhibited and the recurrence rate is reduced via triggering systemic antitumor immune responses. This study offers an alternative strategy for the development of efficient antitumor immune therapy.

Cellular reprogramming using chemically defined conditions, without genetic manipulation, is a promising approach for generating clinically relevant cell types for regenerative medicine and drug discovery. However, small-molecule approaches for inducing lineage-specific stem cells from somatic cells across lineage boundaries have been challenging. Here, we report highly efficient reprogramming of mouse fibroblasts into induced neural stem cell-like cells (ciNSLCs) using a cocktail of nine components (M9). The resulting ciNSLCs closely resemble primary neural stem cells molecularly and functionally. Transcriptome analysis revealed that M9 induces a gradual and specific conversion of fibroblasts toward a neural fate. During reprogramming specific transcription factors such as Elk1 and Gli2 that are downstream of M9-induced signaling pathways bind and activate endogenous master neural genes to specify neural identity. Our study provides an effective chemical approach for generating neural stem cells from mouse fibroblasts and reveals mechanistic insights into underlying reprogramming processes.

Nontoxic prodrugs, especially activated by tumor microenvironment, are urgently required for reducing the side effects of cancer therapy.

Multiobjective evolutionary algorithm based on decomposition (MOEA/D), which bridges the traditional optimization techniques and population-based methods, has become an increasingly popular framework for evolutionary multiobjective optimization. It decomposes a multiobjective optimization problem (MOP) into a number of optimization subproblems. Each subproblem is handled by an agent in a collaborative manner. The selection of MOEA/D is a process of choosing solutions by agents. In particular, each agent has two requirements on its selected solution: one is the convergence toward the efficient front, the other is the distinction with the other agents' choices. This paper suggests addressing these two requirements by defining mutual-preferences between subproblems and solutions. Afterwards, a simple yet effective method is proposed to build an interrelationship between subproblems and solutions, based on their mutual-preferences. At each generation, this interrelationship is used as a guideline to select the elite solutions to survive as the next parents. By considering the mutual-preferences between subproblems and solutions (i.e., the two requirements of each agent), the selection operator is able to balance the convergence and diversity of the search process. Comprehensive experiments are conducted on several MOP test instances with complicated Pareto sets. Empirical results demonstrate the effectiveness and competitiveness of our proposed algorithm.

The development of antibacterial and rapid hemostatic wound dressing with good biocompatibility is urgently required for promoting non-healing wounds. In this work, a multifunctional near infrared (NIR) laser-induced hydrogel for infected wound healing is presented. The hydrogel was composed of dibenzaldehyde-grafted poly (ethylene glycol) (PEGDA), lauric acid-terminated chitosan (Chi-LA), and curcumin (Cur)-loaded mesoporous polydopamine nanoparticles ([email protected]) via Schiff base and/or Michael addition reaction. The Cur could rapidly release from [email protected] hydrogel under NIR laser irradiation with on-demand release property. Meanwhile, NIR irradiation could activate the photothermal PDA NPs in [email protected] hydrogel and generate local hyperthermia for killing Escherichia coli and Staphylococcus aureus. It was realized by inducing the “out-diffusion” of K+, inactivating the respiratory chain dehydrogenase and β-galactosidase activity, causing cellular components leakage (protein, DNA and RNA, etc.), reducing the ATP level, and destroying bacterial membrane. Moreover, the [email protected] hydrogel exhibited good biocompatibility. Furthermore, in vivo treatment in a S. aureus-infected full-thickness skin defect model revealed that the [email protected] hydrogel presented good hemostatic function, prominent antibacterial ability, strong anti-inflammatory effect, and good wound healing capacity.

A zinc coordination polymer is a sensor for detection of TNP, Fe³⁺, Cr₂O₇²⁻ and CrO₄²⁻.

Type 2 diabetes mellitus (T2DM) is closely correlated with chronic low-grade inflammation and gut dysbiosis.

Manganese-cobalt spinel oxides are considered as a class of promising and low-cost electrocatalysts for oxygen reduction reaction (ORR), whose performances largely depend on their electronic structures which can be effectively optimized by defect engineering. Herein, metal defects (manganese vacancies and cobalt vacancies, i.e. VMn and VCo) were in-situ introduced into spinel MnxCo3-xO4 via a simple solvothermal treatment followed by thermal calcination. Mn-Co glycerolate precursors not only enable controllable synthesis of spinel oxides with variable metallic ratios, but also play a key role in constructing metal defected crystals for their lamellated structure. As a result of the formation rate difference between manganese and cobalt glycerolate, a unique Mn-enriched surface is formed, leading to the increase of highly active sites for ORR. Importantly, the presence of metal defects, confirmed by XRD (X-ray diffraction), element analysis and XAFS (X-ray absorption fine structure spectroscopy), leads to greatly increased electrical conductivity and O2 adsorption ability, thus bringing about enhanced ORR activity. Especially, metal-defected Mn1.5Co1.5O4, with the optimal Mn/Co ratio, exhibits comparable activity and superior durability to those of the benchmark Pt/C in ORR and displays excellent discharge performance in Zn-air batteries for practical application. This work provides a new way to optimize the electrocatalytic performance of mixed metal spinel oxides via rational defect engineering.

In this work, the antibacterial effects and underlying mechanisms of plasma-activated water (PAW) against Pseudomonas deceptionensis CM2 isolated from spoiling chicken meat were investigated. The population of P. deceptionensis CM2 was significantly reduced approximately 5 log units within 10 min of exposure to PAW. The results of the scanning electron microscope clearly showed distinguishable morphostructural changes in P. deceptionensis CM2 as a consequence of PAW treatment. The permeability change in the outer and cytoplasmic membranes was evaluated by using 1-N-phenylnaphthylamine (NPN) and propidium iodide (PI), respectively. The fluorescence intensities of NPN and PI in P. deceptionensis CM2 cells were significantly increased after PAW treatment (p < 0.05), indicating the disruption of the membrane permeability barrier. Following activation with plasma, PAW acquires an acidic pH and a higher oxidation reduction potential and contains aqueous reactive species, such as H2O2, nitrate, and nitrite anions, which play a crucial role in the inactivation process of P. deceptionensis CM2 cells. These findings indicate that PAW is a promising alternative to traditional sanitizers used in food and food-processing environments.

Abstract The existence of breast cancer stem cells (BCSCs) is a major reason underlying cancer metastasis and recurrence after chemotherapy and radiotherapy. Targeting BCSCs may ameliorate breast cancer relapse and therapy resistance. Here we report that expression of the pseudokinase Tribble 3 (TRIB3) positively associates with breast cancer stemness and progression. Elevated TRIB3 expression supports BCSCs by interacting with AKT to interfere with the FOXO1-AKT interaction and suppress FOXO1 phosphorylation, ubiquitination, and degradation by E3 ligases SKP2 and NEDD4L. The accumulated FOXO1 promotes transcriptional expression of SOX2, a transcriptional factor for cancer stemness, which in turn, activates FOXO1 transcription and forms a positive regulatory loop. Disturbing the TRIB3-AKT interaction suppresses BCSCs by accelerating FOXO1 degradation and reducing SOX2 expression in mouse models of breast cancer. Our study provides insights into breast cancer development and confers a potential therapeutic strategy against TRIB3-overexpressed breast cancer.

Abstract The redox reactions occurring in the Li-S battery positive electrode conceal various and critical electrocatalytic processes, which strongly influence the performances of this electrochemical energy storage system. Here, we report the development of a single-dispersed molecular cluster catalyst composite comprising of a polyoxometalate framework ([Co 4 (PW 9 O 34 ) 2 ] 10− ) and multilayer reduced graphene oxide. Due to the interfacial charge transfer and exposure of unsaturated cobalt sites, the composite demonstrates efficient polysulfides adsorption and reduced activation energy for polysulfides conversion, thus serving as a bifunctional electrocatalyst. When tested in full Li-S coin cell configuration, the composite allows for a long-term Li-S battery cycling with a capacity fading of 0.015% per cycle after 1000 cycles at 2 C (i.e., 3.36 A g −1 ). An areal capacity of 4.55 mAh cm −2 is also achieved with a sulfur loading of 5.6 mg cm − 2 and E/S ratio of 4.5 μL mg −1 . Moreover, Li-S single-electrode pouch cells tested with the bifunctional electrocatalyst demonstrate a specific capacity of about 800 mAh g −1 at a sulfur loading of 3.6 mg cm −2 for 100 cycles at 0.2 C (i.e., 336 mA g −1 ) with E/S ratio of 5 μL mg −1 .

Abstract Exogenous contrast‐agent‐assisted NIR‐II optical‐resolution photoacoustic microscopy imaging (ORPAMI) holds promise to decipher wide‐field 3D biological structures with deep penetration, large signal‐to‐background ratio (SBR), and high maximum imaging depth to depth resolution ratio. Herein, NIR‐II conjugated polymer nanoparticle (CP NP) assisted ORPAMI is reported for pinpointing cerebral and tumor vasculatures. The CP NPs exhibit a large extinction coefficient of 48.1 L g −1 at the absorption maximum of 1161 nm, with an ultrahigh PA sensitivity up to 2 µg mL −1 . 3D ORPAMI of wide‐field mice ear allows clear visualization of regular vasculatures with a resolution of 19.2 µm and an SBR of 29.3 dB at the maximal imaging depth of 539 µm. The margin of ear tumor composed of torsional dense vessels among surrounding normal regular vessels can be clearly delineated via 3D angiography. In addition, 3D whole‐cortex cerebral vasculatures with large imaging area (48 mm 2 ), good resolution (25.4 µm), and high SBR (22.3 dB) at a depth up to 1001 µm are clearly resolved through the intact skull. These results are superior to the recently reported 3D NIR‐II fluorescence confocal vascular imaging, which opens up new opportunities for NIR‐II CP‐NP‐assisted ORPAMI in various biomedical applications.

Abstract Hearing loss is the most common sensory disorder. While gene therapy has emerged as a promising treatment of inherited diseases like hearing loss, it is dependent on the identification of gene delivery vectors. Adeno-associated virus (AAV) vector-mediated gene therapy has been approved in the US for treating a rare inherited eye disease but no safe and efficient vectors have been identified that can target the diverse types of inner ear cells. Here, we identify an AAV variant, AAV-inner ear (AAV-ie), for gene delivery in mouse inner ear. Our results show that AAV-ie transduces the cochlear supporting cells (SCs) with high efficiency, representing a vast improvement over conventional AAV serotypes. Furthermore, after AAV-ie-mediated transfer of the Atoh1 gene, we find that many SCs trans-differentiated into new HCs. Our results suggest that AAV-ie is a useful tool for the cochlear gene therapy and for investigating the mechanism of HC regeneration.

Existing studies on dynamic multiobjective optimization (DMO) focus on problems with time-dependent objective functions, while the ones with a changing number of objectives have rarely been considered in the literature. Instead of changing the shape or position of the Pareto-optimal front/set (PF/PS) when having time-dependent objective functions, increasing or decreasing the number of objectives usually leads to the expansion or contraction of the dimension of the PF/PS manifold. Unfortunately, most existing dynamic handling techniques can hardly be adapted to this type of dynamics. In this paper, we report our attempt toward tackling the DMO problems with a changing number of objectives. We implement a dynamic two-archive evolutionary algorithm which maintains two co-evolving populations simultaneously. In particular, these two populations are complementary to each other: one concerns more about the convergence while the other concerns more about the diversity. The compositions of these two populations are adaptively reconstructed once the environment changes. In addition, these two populations interact with each other via a mating selection mechanism. Comprehensive experiments are conducted on various benchmark problems with a time-dependent number of objectives. Empirical results fully demonstrate the effectiveness of our proposed algorithm.

Aqueous rechargeable zinc metal batteries display high theoretical capacity along with economical effectiveness, environmental benignity and high safety. However, dendritic growth and chemical corrosion at the Zn anodes limit their widespread applications. Here, we construct a Zn/Bi electrode via in-situ growth of a Bi-based energizer upon Zn metal surface using a replacement reaction. Experimental and theoretical calculations reveal that the Bi-based energizer composed of metallic Bi and ZnBi alloy contributes to Zn plating/stripping due to strong adsorption energy and fast ion transport rates. The resultant Zn/Bi electrode not only circumvents Zn dendrite growth but also improves Zn anode anti-corrosion performance. Specifically, the corrosion current of the Zn/Bi electrode is reduced by 90% compared to bare Zn. Impressively, an ultra-low overpotential of 12 ​mV and stable cycling for 4000 ​h are achieved in a Zn/Bi symmetric cell. A Zn–Cu/Bi asymmetric cell displays a cycle life of 1000 cycles, with an average Coulombic efficiency as high as 99.6%. In addition, an assembled Zn/Bi-activated carbon hybrid capacitor exhibits a stable life of more than 50,000 cycles, an energy density of 64 ​Wh kg−1, and a power density of 7 ​kW ​kg−1. The capacity retention rate of a Zn/Bi–MnO2 full cell is improved by over 150% compared to a Zn–MnO2 cell without the Bi-based energizer. Our findings open a new arena for the industrialization of Zn metal batteries for large-scale energy storage applications.

Person re-identification (re-ID), is a challenging task due to the high variance within identity samples and imaging conditions. Although recent advances in deep learning have achieved remarkable accuracy in settled scenes, i.e., source domain, few works can generalize well on the unseen target domain. One popular solution is assigning unlabeled target images with pseudo labels by clustering, and then retraining the model. However, clustering methods tend to introduce noisy labels and discard low confidence samples as outliers, which may hinder the retraining process and thus limit the generalization ability. In this study, we argue that by explicitly adding a sample filtering procedure after the clustering, the mined examples can be much more efficiently used. To this end, we design an asymmetric co-teaching framework, which resists noisy labels by cooperating two models to select data with possibly clean labels for each other. Meanwhile, one of the models receives samples as pure as possible, while the other takes in samples as diverse as possible. This procedure encourages that the selected training samples can be both clean and miscellaneous, and that the two models can promote each other iteratively. Extensive experiments show that the proposed framework can consistently benefit most clustering based methods, and boost the state-of-the-art adaptation accuracy. Our code is available at https://github.com/FlyingRoastDuck/ACT_AAAI20.

The targeted therapy of metastatic melanoma is an important yet challenging goal that has received only limited attention to date. Herein, green tea polyphenols, (-)-epigallocatechin-3-gallate (EGCG), and lanthanide metal ions (Sm3+) are used as building blocks to engineer self-assembled SmIII-EGCG nanocomplexes with synergistically enhanced tumor inhibitory properties. These nanocomplexes have negligible systemic toxic effects on healthy cells but cause a significant reduction in the viability of melanoma cells by efficiently regulating their metabolic pathways. Moreover, the wound-induced migration of melanoma cells can be efficiently inhibited by SmIII-EGCG, which is a key criterion for metastatic melanoma therapy. In a mouse melanoma tumor model, SmIII-EGCG is directly compared with a clinical anticancer drug, 5-fluorouracil and shows remarkable tumor inhibition. Moreover, the targeted therapy of SmIII-EGCG is shown to prevent metastatic lung melanoma from spreading to main organs with no adverse side effects on the body weight or organs. These in vivo results demonstrate significant advantages of SmIII-EGCG over its clinical counterpart. The results suggest that these green tea-based, self-assembled nanocomplexes possess all of the key traits of a clinically promising candidate to address the challenges associated with the treatment of advanced stage metastatic melanoma.

As the largest CO2 emitter in the world, China plays a crucial role in global CO2 emission reduction. Meanwhile, as the largest developing country, China gives top priority to economic development, especially balanced regional development. In order to narrow regional development inequality, China has formulated some economic zone plannings to weaken market segmentation. However, existing studies pay little attention to the impacts of intra-national trade barrier on CO2 emissions within China. This is the first study to investigate the impact of market segmentation on urban CO2 emissions in China, taking the Yangtze River Delta region as a sample that is the most developed region with a high economic integration in China. Using a city-level panel data set during the period of 1995–2014 and the fixed effect model, we verify the nonlinear relationship between market segmentation and urban CO2 emissions measured by three indicators, i.e., total CO2 emissions, per capita CO2 emissions and CO2 emissions per unit of GDP. We also use the generalized method of moments (GMM) to control the endogeneity problem, and further adopt the threshold regression model to check the robustness of the baseline results. The results show that there is a U-shaped curve relationship between market segmentation and urban CO2 emissions. A low level of market segmentation restrains CO2 emissions, while a high level of market segmentation promotes CO2 emissions. This finding is helpful to understand CO2 emission trends and narrow regional economic inequality accompanied with the implementation of China's economic zone plannings.

Although immunotherapy through programmed death 1/programmed death ligand 1 (PD-1/PD-L1) checkpoint blockade has shown impressive clinical outcomes, not all patients respond to it. Recent studies have demonstrated that the expression level of PD-L1 in tumors is one of the factors that correlate with PD-1/PD-L1 checkpoint blockade therapy. Herein, a ⁶⁸Ga-labeled single-domain antibody tracer, ⁶⁸Ga-NOTA-Nb109, was designed and developed for specific and noninvasive imaging of PD-L1 expression in a melanoma-bearing mouse model. <b>Methods:</b> The single-domain antibody Nb109 was labeled with the radionuclide ⁶⁸Ga through a NOTA chelator. An in vitro binding assay was performed to assess the affinity and binding epitope of Nb109 to PD-L1. The clinical application value of ⁶⁸Ga-NOTA-Nb109 was evaluated by a stability assay; by biodistribution and pharmacokinetics studies; and by PET imaging, autoradiography, and immunohistochemical staining studies on tumor-bearing models with differences in PD-L1 expression. <b>Results:</b>⁶⁸Ga-NOTA-Nb109 was obtained with a radiochemical yield of more than 95% and radiochemical purity of more than 98% in 10 min. It showed a highly specific affinity for PD-L1, with an equilibrium dissociation constant of 2.9 × 10⁻⁹ M. A competitive binding assay indicated Nb109 to have a binding epitope different from that of PD-1 and PD-L1 antibody. All biodistribution, PET imaging, autoradiography, and immunohistochemical staining studies revealed that ⁶⁸Ga-NOTA-Nb109 specifically accumulated in A375-hPD-L1 tumor, with a maximum uptake of 5.0% ± 0.35% injected dose/g at 1 h. <b>Conclusion:</b>⁶⁸Ga-NOTA-Nb109 holds great potential for noninvasive PET imaging of the PD-L1 status in tumors and for timely evaluation of the effect of immune checkpoint targeting treatment.

Abstract Background Type 2 diabetes mellitus (T2DM) is closely associated with hyperglycemia, abnormal lipid profiles, chronic low-grade inflammation and gut dysbiosis. Dietary intervention plays a crucial role in the control of diabetes. Flaxseed oil (FO), a plant-derived omega-3 (ω-3) polyunsaturated fatty acids (PUFAs), is rich in α-linolenic acid (ALA) which has been proved to benefit for chronic metabolic disease. However, the exact effects of dietary FO on T2DM remains largely unclear. Methods In the present study, SD rats were randomly allocated into four groups: pair-fed (PF) with corn oil (CO) group (PF/CO); DM with CO group (DM/CO); PF with FO group (PF/FO); DM with FO group (DM/FO). A diabetic rat model was generated by a single intraperitoneal injection of streptozotocin-nicotinamide (STZ-NA). After 5 weeks of intervention, rats were euthanized and associated indications were investigated. Results Dietary FO significantly reduced fasting blood glucose (FBG), glycated hemoglobin (GHb), blood lipid, plasma lipopolysaccharide (LPS), interleukin (IL)-1β, tumor necrosis factor (TNF)-α, IL-6, IL-17A and malondialdehyde (MDA), compared to control group, respectively. Moreover, body mass (BM) and superoxide dismutase (SOD) in DM/FO group were dramatically increased respectively, compared with those in DM/CO group. But insulin (INS) and homeostasis model assessment of insulin resistance (HOMA-IR) remained no significant difference between DM/CO group and DM/FO group. Sequencing analysis of gut microbiota showed a reduction in the relative abundance of Firmicutes and Blautia , as well as a reduction in the ratio of Bacteroidetes-Firmicutes in DM/FO group compared to DM/CO group. An elevation in the relative abundance of Bacteroidetes and Alistipes were detected in DM/FO group. Acetic acid, propionic acid and butyric acid belonging to short chain fatty acids (SCFAs) as gut microbiota metabolites, were dramatically increased after FO intervention. Correlation analysis revealed that the relative abundance of Firmicutes and Blautia were positively correlated with IL-1β, TNF-α, IL-6, IL-17A or LPS, respectively. Additionally, Bacteroidetes and Alistipes were negatively correlated with LPS. Conclusions Taken together, dietary FO ameliorated T2DM via suppressing inflammation and modulating gut microbiota, which may potentially contribute to dietary control of diabetes.

In this paper, we present a regression-based pose recognition method using cascade Transformers. One way to categorize the existing approaches in this domain is to separate them into 1). heatmap-based and 2). regression-based. In general, heatmap-based methods achieve higher accuracy but are subject to various heuristic designs (not end-to-end mostly), whereas regression-based approaches attain relatively lower accuracy but they have less intermediate non-differentiable steps. Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches. We demonstrate the keypoint hypothesis (query) refinement process across different self-attention layers to reveal the recursive self-attention mechanism in Transformers. In the experiments, we report competitive results for pose recognition when compared with the competing regression-based methods.

Computed tomography (CT) is widely used in medical diagnosis and non-destructive detection. Image reconstruction in CT aims to accurately recover pixel values from measured line integrals, i.e., the summed pixel values along straight lines. Provided that the acquired data satisfy the data sufficiency condition as well as other conditions regarding the view angle sampling interval and the severity of transverse data truncation, researchers have discovered many solutions to accurately reconstruct the image. However, if these conditions are violated, accurate image reconstruction from line integrals remains an intellectual challenge. In this paper, a deep learning method with a common network architecture, termed iCT-Net, was developed and trained to accurately reconstruct images for previously solved and unsolved CT reconstruction problems with high quantitative accuracy. Particularly, accurate reconstructions were achieved for the case when the sparse view reconstruction problem (i.e., compressed sensing problem) is entangled with the classical interior tomographic problems.

Various obstacles impede the chemotherapy efficiency of glioma in clinic, such as blood brain barrier (BBB) and blood brain tumor barrier (BBTB). Ligand-mediated polymeric micelles have shown great potential for improving the efficiency of glioma treatment. Herein, we developed a disulfide bond-conjugated prodrug polymer consisted of camptothecin (CPT) and polyethylene glycol (PEG) with further modification of iRGD peptide. The polymer of CPT-S-S-PEG-COOH could self-assemble into nanosized polymeric micelles with diameter around 100 nm, and loaded with photosensitizer IR780 for combination therapy. The micelles displayed good stability with controlled drug release under physiological environment. Importantly, the iRGD modified polymeric micelles demonstrated favorable ability to cross the BBB and target glioma cells via αv β integrin and neuropilin-1-mediated ligand transportation in vitro and in vivo. The whole synthesis process is simple and the drug loading content of CPT in the [email protected] micelles was higher than 10%. Moreover, [email protected] micelles combined chemotherapy with photodynamic therapy (PDT) displayed more excellent tumor-killing capability than the other groups. Thus, both in vitro and in vivo studies suggested that the targeting prodrug system could not only effectively cross various barriers to reach at glioma site, but also significantly enhance the antitumor effect with laser irradiation. Our findings consequently suggested that [email protected] micelles with laser irradiation are a promising drug delivery system for glioma therapy.

Postoperative cognitive dysfunction (POCD) is a common postoperative complication that is associated with increased morbidity and mortality. However, the neuropathogenesis of this complication remains largely unknown. Neuroinflammation, in particular hippocampal inflammation, contributes to POCD. Recently, increasing evidence has supported the involvement of microRNAs (miRNAs) in the regulation of neuroinflammation in human neurological disorders. In the present study, we investigated the role of miR-146a, a key regulator of the innate immune response, in surgery-induced hippocampal inflammation and cognitive impairment. The expression of miR-146a was measured in BV-2 microglial cells stimulated with lipopolysaccharide (LPS) and hippocampal tissues of mice with POCD. Loss of function and overexpression studies were performed via transfection with miR-146a mimic/inhibitor in cultured BV-2 cell lines and intrahippocampal injection of miR-146a agomir/antagomir before surgery/anesthesia to identify the role of miR-146a in neuroinflammation and cognitive impairment. QPCR, Western blot and ELISA were used to determine the expression levels of downstream adaptor proteins and proinflammatory cytokines. Immunofluorescence staining was applied to evaluate the activation of microglia. Increased expression of miR-146a was observed in BV-2 microglial cells stimulated with LPS and hippocampal tissues of mice with POCD. Modulation of miR-146a expression via transfection of microglia with miR-146a mimic or inhibitor regulated the mRNA and protein expression levels of downstream targets of miR-146a (IRAK1 and TRAF6) as well as the release of proinflammatory cytokines (TNF-α, IL-1β and IL-6). In addition, overexpression of miR-146a attenuated hippocampus-dependent learning and memory impairment in mice with POCD, which was accompanied by decreased expression of the IRAK1/TRAF6/nuclear factor (NF)-κB pathway and downregulation of microglial activation in the hippocampus. Conversely, knockdown of miR-146a expression may exacerbate hippocampus-dependent learning and memory deficiency and hippocampal inflammation in mice with POCD. Collectively, our findings demonstrate the important role of miR-146a in the neuropathogenesis of POCD and suggest that miR-146a may be a potential therapeutic target for POCD.

With the disruptive carbon cycle being blamed for global warming, the plausible electrocatalytic CO₂ reduction reaction (CO₂RR) to form valuable C₂₊ hydrocarbons and feedstock is becoming a hot topic. Cu-based electrocatalysts have been proven to be excellent CO₂RR alternatives for high energy value-added products in this regard. However, the selectivity of CO₂RR to form C₂₊ products via Cu-based catalysts suffers from a high overpotential, slow reaction kinetics, and low selectivity. This review attempts to discuss various cutting-edge strategies for understanding catalytic design such as Cu-based catalyst surface engineering, tuning Cu bandgap via alloying, nanocatalysis, and the effect of the electrolyte and pH on catalyst morphology. The most recent advances in <i>in situ</i> spectroscopy and computational techniques are summarized to fully comprehend reaction mechanisms, structural transformation/degradation mechanisms, and crystal facet loss with subsequent effects on catalyst activity. Furthermore, approaches for tuning Cu interactions are discussed from four key perspectives: single-atom catalysts, interfacial engineering, metal-organic frameworks, and polymer-incorporated materials, which provide new insights into the selectivity of C₂₊ products. Finally, major challenges are outlined, and potential prospects for the rational design of catalysts for robust CO₂RR are proposed. The integration of catalytic design with mechanistic understanding is a step forward in the promising advancement of CO₂RR technology for industrial applications.

Development of new porous materials as hosts to suppress the dissolution and shuttle of lithium polysulfides is beneficial for constructing highly efficient lithium-sulfur batteries (LSBs). Although 2D covalent organic frameworks (COFs) as host materials exhibit promising potential for LSBs, their performance is still not satisfactory. Herein, we develop polyimide COFs (PI-COF) with a well-defined lamellar structure, which can be exfoliated into ultrathin (∼1.2 nm) 2D polyimide nanosheets (PI-CONs) with a large size (∼6 μm) and large quantity (40 mg/batch). Explored as new sulfur host materials for LSBs, PI-COF and PI-CONs deliver high capacities (1330 and 1205 mA h g-1 at 0.1 C, respectively), excellent rate capabilities (620 and 503 mA h g-1 at 4 C, respectively), and superior cycling stability (96% capacity retention at 0.2 C for PI-CONs) by virtue of the synergy of robust conjugated porous frameworks and strong oxygen-lithium interactions, surpassing the vast majority of organic/polymeric lithium-sulfur battery cathodes ever reported. Our finding demonstrates that ultrathin 2D COF nanosheets with carbonyl groups could be promising host materials for LSBs with excellent electrochemical performance.

In order to improve the functional properties of chickpea protein isolate (CPI), CPI was treated with high intensity ultrasound (HIU) at a frequency of 20 kHz under 300 W for different time. Then the functional properties were evaluated, and the structure was analyzed to illustrate the mechanism. It was found that the solubility of CPI significantly (p = 0.000138, < 0.05) increased from 7.5 mg/mL to 9.5 mg/mL, and the foaming capacity of CPI increased to a maximum value of 136.7%, which was 2.2 times that of the untreated group. The emulsifying index of CPI slightly (p = 0.000017, < 0.05) increased from 22.3 m2/g to 24.17 m2/g. Moreover, the water holding capacity and breaking force of the heat induced CPI gel significantly increased from 58.4% to 80.9% (p = 0.000004, < 0.05), and 78.1 g–201.4 g (p = 0.000024, < 0.05), respectively, which were probably due to the gradually increased free sulfhydryl content, surface hydrophobicity, surface potential and decreased particle size of CPI with extension of ultrasonic time. These results not only demonstrate the relationship between the structure and functional properties of CPI, but also provide a promising way to tailor the functional properties of CPI, which will promote its application in the food industry.

Azvudine (FNC) is a nucleoside analog that inhibits HIV-1 RNA-dependent RNA polymerase (RdRp). Recently, we discovered FNC an agent against SARS-CoV-2, and have taken it into Phase III trial for COVID-19 patients. FNC monophosphate analog inhibited SARS-CoV-2 and HCoV-OC43 coronavirus with an EC50 between 1.2 and 4.3 μM, depending on viruses or cells, and selective index (SI) in 15-83 range. Oral administration of FNC in rats revealed a substantial thymus-homing feature, with FNC triphosphate (the active form) concentrated in the thymus and peripheral blood mononuclear cells (PBMC). Treating SARS-CoV-2 infected rhesus macaques with FNC (0.07 mg/kg, qd, orally) reduced viral load, recuperated the thymus, improved lymphocyte profiles, alleviated inflammation and organ damage, and lessened ground-glass opacities in chest X-ray. Single-cell sequencing suggested the promotion of thymus function by FNC. A randomized, single-arm clinical trial of FNC on compassionate use (n = 31) showed that oral FNC (5 mg, qd) cured all COVID-19 patients, with 100% viral ribonucleic acid negative conversion in 3.29 ± 2.22 days (range: 1-9 days) and 100% hospital discharge rate in 9.00 ± 4.93 days (range: 2-25 days). The side-effect of FNC is minor and transient dizziness and nausea in 16.12% (5/31) patients. Thus, FNC might cure COVID-19 through its anti-SARS-CoV-2 activity concentrated in the thymus, followed by promoted immunity.

Intensive agricultural activities in the North China Plain (NCP) lead to substantial emissions of nitrogen oxides (NOx) from soil, while the role of this source on local severe ozone pollution is unknown. Here we use a mechanistic parameterization of soil NOx emissions combined with two atmospheric chemistry models to investigate the issue. We find that the presence of soil NOx emissions in the NCP significantly reduces the sensitivity of ozone to anthropogenic emissions. The maximum ozone air quality improvements in July 2017, as can be achieved by controlling all domestic anthropogenic emissions of air pollutants, decrease by 30% due to the presence of soil NOx. This effect causes an emission control penalty such that large additional emission reductions are required to achieve ozone regulation targets. As NOx emissions from fuel combustion are being controlled, the soil emission penalty would become increasingly prominent and shall be considered in emission control strategies.

With the advent of Neural Radiance Fields (NeRF), neural networks can now render novel views of a 3D scene with quality that fools the human eye. Yet, generating these images is very computationally intensive, limiting their applicability in practical scenarios. In this paper, we propose a technique based on spatial decomposition capable of mitigating this issue. Our key observation is that there are diminishing returns in employing larger (deeper and/or wider) networks. Hence, we propose to spatially decompose a scene and dedicate smaller networks for each decomposed part. When working together, these networks can render the whole scene. This allows us near-constant inference time regardless of the number of decomposed parts. Moreover, we show that a Voronoi spatial decomposition is preferable for this purpose, as it is provably compatible with the Painter’s Algorithm for efficient and GPU-friendly rendering. Our experiments show that for real-world scenes, our method provides up to 3× more efficient inference than NeRF (with the same rendering quality), or an improvement of up to 1.0 dB in PSNR (for the same inference cost).

This review article aims to provide a comprehensive understanding of radiative cooling technology and their applications, especially on the integration of radiative coolers with devices. Over the past decades, radiative coolers and their applications have been intensively investigated because of their outstanding features for energy saving. The fundamental mechanism and characteristics of radiative cooling, in particular, atmospheric influences, and photothermal manipulation through structural and materials engineering, play essential roles in most of the practical applications. In general, these main factors concomitantly influence the cooling performance of a radiative cooler. However, comprehensive review investigating these main parameters simultaneously remains elusive. In this article, the fundamental features of radiative coolers are discussed, especially the influences of atmospheric conditions at different locations on the radiative coolers, and the photothermal manipulation capability and cooling performance of different types of radiative coolers. The applications, challenges faced in this field and the future trends are also discussed. This article will provide guidance towards integration of radiative coolers with functional devices for both academic researchers and engineers in the fields of energy harvesting, fluidic cooling, energy efficient clothing, and architecture.

Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users&#x2019; historical check-in data. Therefore, it is crucial to model both users&#x2019; general taste and recent sequential behaviors. Moreover, different users show different dependencies on the two parts. However, most existing methods learn the same dependencies for different users. Besides, the locations and categories of POIs contain different information about users&#x2019; preference. However, current researchers always treat them as the same factors or believe that categories determine where to go. To this end, we propose a novel method named Personalized Long- and Short-term Preference Learning (PLSPL) to learn the specific preference for each user. Specially, we combine the long- and short-term preference via user-based linear combination unit to learn the personalized weights on different parts for different users. Besides, the context information such as the category and check-in time is also essential to capture users&#x2019; preference. Therefore, in long-term module, we consider the contextual features of POIs in users&#x2019; history records and leverage attention mechanism to capture users&#x2019; preference. In the short-term module, to better learn the different influences of locations and categories of POIs, we train two LSTM models for location- and category-based sequence, respectively. Then we evaluate the proposed model on two real-world datasets. The experiment results demonstrate that our method outperforms the state-of-art approaches for next POI recommendation.

As a multicellular organism, rice flourishes relying on gene expression diversity among cells of various functions. However, cellular-resolution transcriptome features are yet to be fully recognized, let alone cell-specific transcriptional responses to environmental stimuli. In this study, we apply single-cell RNA sequencing to both shoot and root of rice seedlings growing in Kimura B nutrient solution or exposed to various abiotic stresses and characterize transcriptomes for a total of 237,431 individual cells. We identify 15 and 9 cell types in the leaf and root, respectively, and observe that common transcriptome features are often shared between leaves and roots in the same tissue layer, except for endodermis or epidermis. Abiotic stress stimuli alter gene expression largely in a cell type-specific manner, but for a given cell type, different stresses often trigger transcriptional regulation of roughly the same set of genes. Besides, we detect proportional changes in cell populations in response to abiotic stress and investigate the underlying molecular mechanisms through single-cell reconstruction of the developmental trajectory. Collectively, our study represents a benchmark-setting data resource of single-cell transcriptome atlas for rice seedlings and an illustration of exploiting such resources to drive discoveries in plant biology.

Fluorescent organic dyes have been extensively used as raw materials for the development of versatile imaging tools in the field of biomedicine. Particularly, the development of solid-state organic fluorophores (SSOFs) in the past 20 years has exhibited an upward trend. In recent years, studies on SSOFs have focused on the development of advanced tools, such as optical contrast agents and phototherapy agents, for biomedical applications. However, the practical application of these tools has been hindered owing to several limitations. Thus, in this Perspective, we have provided insights that could aid researchers to further develop these tools and overcome the limitations such as limited aqueous dispersibility, low biocompatibility, and uncontrolled emission. First, we described the inherent photophysical properties and fluorescence mechanisms of conventional, aggregation-induced emissive, and precipitating SSOFs with respect to their biomedical applications. Subsequently, we highlighted the recent development of functionalized SSOFs for bioimaging, biosensing, and theranostics. Finally, we elucidated the potential prospects and limitations of current SSOF-based tools associated with biomedical applications.

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE SelfMatch: Robust semisupervised time-series classification with self-distillation Huanlai Xing, Huanlai Xing School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, ChinaSearch for more papers by this authorZhiwen Xiao, Corresponding Author Zhiwen Xiao xiao1994zw@163.com orcid.org/0000-0001-9651-111X School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China Correspondence Zhiwen Xiao, School of Computing and Artificial Intelligence, Southwest Jiaotong University, 611756 Chengdu, China. Email: xiao1994zw@163.comSearch for more papers by this authorDawei Zhan, Dawei Zhan School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, ChinaSearch for more papers by this authorShouxi Luo, Shouxi Luo School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, ChinaSearch for more papers by this authorPenglin Dai, Penglin Dai School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, ChinaSearch for more papers by this authorKe Li, Ke Li School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, ChinaSearch for more papers by this author Huanlai Xing, Huanlai Xing School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, ChinaSearch for more papers by this authorZhiwen Xiao, Corresponding Author Zhiwen Xiao xiao1994zw@163.com orcid.org/0000-0001-9651-111X School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China Correspondence Zhiwen Xiao, School of Computing and Artificial Intelligence, Southwest Jiaotong University, 611756 Chengdu, China. Email: xiao1994zw@163.comSearch for more papers by this authorDawei Zhan, Dawei Zhan School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, ChinaSearch for more papers by this authorShouxi Luo, Shouxi Luo School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, ChinaSearch for more papers by this authorPenglin Dai, Penglin Dai School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, ChinaSearch for more papers by this authorKe Li, Ke Li School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, ChinaSearch for more papers by this author First published: 13 July 2022 https://doi.org/10.1002/int.22957Read the full textAboutPDF 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 Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Over the years, a number of semisupervised deep-learning algorithms have been proposed for time-series classification (TSC). In semisupervised deep learning, from the point of view of representation hierarchy, semantic information extracted from lower levels is the basis of that extracted from higher levels. The authors wonder if high-level semantic information extracted is also helpful for capturing low-level semantic information. This paper studies this problem and proposes a robust semisupervised model with self-distillation (SD) that simplifies existing semisupervised learning (SSL) techniques for TSC, called SelfMatch. SelfMatch hybridizes supervised learning, unsupervised learning, and SD. In unsupervised learning, SelfMatch applies pseudolabeling to feature extraction on labeled data. A weakly augmented sequence is used as a target to guide the prediction of a Timecut-augmented version of the same sequence. SD promotes the knowledge flow from higher to lower levels, guiding the extraction of low-level semantic information. This paper designs a feature extractor for TSC, called ResNet–LSTMaN, responsible for feature and relation extraction. The experimental results show that SelfMatch achieves excellent SSL performance on 35 widely adopted UCR2018 data sets, compared with a number of state-of-the-art semisupervised and supervised algorithms. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation

In this study, the foaming properties of chickpea protein isolate (CPI) subjected to different pH shifting treatment were investigated. It was found that the foaming ability (FA) of CPI, CPI subjected to treatment with pH shifting at pH 2 and pH 12 (CPI2 and CPI12), CPI subjected to combined treatment of ultrasound with pH shifting at pH 2 and pH 12 (CPI2–U and CPI12–U) were 81.1%, 180%, 225.5%, 212.1%, and 263.3%, respectively, while the foaming stability (FS) of these CPI did not change significantly. These results indicated that pH shifting under extreme acid or alkaline pH condition significantly enhanced the foaming properties of CPI, while the use of ultrasound further intensify this change. The best foaming properties of CPI12–U was ascribed to its smallest particle size, zeta potential, interfacial tension, the largest solubility and surface hydrophobicity. Besides, the free sulfhydryl content of CPI subjected to these treatments increased significantly, facilitating their adsorption to the air-liquid interface. Moreover, the increased content of α-helixes for CPI subjected to these treatments contributed to the formation of more stable interfaces. So this study provided a convenient means to significantly improve the foaming properties of CPI.

The goal of this study is to construct a mortality prediction model using the XGBoot (eXtreme Gradient Boosting) decision tree model for AKI (acute kidney injury) patients in the ICU (intensive care unit), and to compare its performance with that of three other machine learning models.We used the eICU Collaborative Research Database (eICU-CRD) for model development and performance comparison. The prediction performance of the XGBoot model was compared with the other three machine learning models. These models included LR (logistic regression), SVM (support vector machines), and RF (random forest). In the model comparison, the AUROC (area under receiver operating curve), accuracy, precision, recall, and F1 score were used to evaluate the predictive performance of each model.A total of 7548 AKI patients were analyzed in this study. The overall in-hospital mortality of AKI patients was 16.35%. The best performing algorithm in this study was XGBoost with the highest AUROC (0.796, p < 0.01), F1(0.922, p < 0.01) and accuracy (0.860). The precision (0.860) and recall (0.994) of the XGBoost model rank second among the four models.XGBoot model had obvious advantages of performance compared to the other machine learning models. This will be helpful for risk identification and early intervention for AKI patients at risk of death.

Human heart failure, a leading cause of death worldwide, is a prominent example of a chronic disease that may result from poor cell renewal. The Hippo signaling pathway is an inhibitory kinase cascade that represses adult heart muscle cell (cardiomyocyte) proliferation and renewal after myocardial infarction in genetically modified mice. Here, we investigated an adeno-associated virus 9 (AAV9)-based gene therapy to locally knock down the Hippo pathway gene Salvador (Sav) in border zone cardiomyocytes in a pig model of ischemia/reperfusion-induced myocardial infarction. Two weeks after myocardial infarction, when pigs had left ventricular systolic dysfunction, we administered AAV9-Sav-short hairpin RNA (shRNA) or a control AAV9 viral vector carrying green fluorescent protein (GFP) directly into border zone cardiomyocytes via catheter-mediated subendocardial injection. Three months after injection, pig hearts treated with a high dose of AAV9-Sav-shRNA exhibited a 14.3% improvement in ejection fraction (a measure of left ventricular systolic function), evidence of cardiomyocyte division, and reduced scar sizes compared to pigs receiving AAV9-GFP. AAV9-Sav-shRNA-treated pig hearts also displayed increased capillary density and reduced cardiomyocyte ploidy. AAV9-Sav-shRNA gene therapy was well tolerated and did not induce mortality. In addition, liver and lung pathology revealed no tumor formation. Local delivery of AAV9-Sav-shRNA gene therapy to border zone cardiomyocytes in pig hearts after myocardial infarction resulted in tissue renewal and improved function and may have utility in treating heart failure.

Traditional Light Detection and Rangings (LiDARs) can quickly collect high-accuracy of three-dimensional (3D) point cloud data at a designated wavelength (i.e., cannot obtain hyperspectral data), while the passive hyperspectral imager can collect rich spectral data of ground objects, but are lack of 3D spatial data. This paper presents one innovative study on the design of airborne-oriented supercontinuum laser hyperspectral (SCLaHS) LiDAR with 50 bands covering 400 nm to 900 nm at a spectral resolution of 10 nm and ground sampling distance (GSD) of 0.5 m. The major innovations include (1) development of the high-power narrow-pulse supercontinuum laser source covering 400 nm to 900 nm with 50 bands using multi-core microstructure fibre, all-polarization maintaining fibre and ultra-long cavity structure, (2) a miniaturized aberration correction holographic concave grating spectroscopic and streak tube technique are developed for 50 bands laser echoes detection at high spectral-spatial-temporal resolution and dynamic airborne platform, and (3) the algorithm theoretic basis for SCLaHS LiDAR point cloud data 3D geodetic coordination calculation, including in-flight airborne calibration algorithm. The initial experimental results demonstrated that the designed SCLaHS LiDAR is doable, and a prototype of the (SCLaHS) LiDAR intends to be implemented.

In the past few years, object detection in remote sensing images has achieved remarkable progress. However, the detection of oriented and densely packed objects are still unsatisfactory due to the following spatial and feature misalignments. 1) Most two-stage oriented detectors only introduce an orientation regression branch in the detection head, while still leverage horizontal proposals for classification and regression. This inevitably results in the spatial misalignment problem between horizontal proposals and oriented objects. 2) The features used for classification are in fact extracted from the region proposals which have shifted to the final predictions via the regression branch. This leads to the feature misalignment problem between the classification and the localization tasks. In this article, we present a two-stage oriented object detection method, termed dual-aligned oriented detector (DODet), toward evading the aforementioned problems of spatial and feature misalignments. In DODet, the first stage is an oriented proposal network (OPN), which generates high-quality oriented proposals via a novel representation scheme of oriented objects. The second stage is a localization-guided detection head (LDH) that aims at alleviating the feature misalignment between classification and localization. Comprehensive and extensive evaluations on three benchmarks, including DIOR-R, DOTA, and HRSC2016, indicate that our method could obtain consistent and substantial gains compared with the baseline method. The source code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yanqingyao1994/DODet</uri> .

The deep oxidation of toluene is recognized as a major challenge for photocatalytic oxidation of toluene. Herein, we introduced oxygen vacancies into CeO 2 nanosheets through novel anion-removal of Ce-LDH, with the calcining temperatures of 750, 850, 950 °C. The photocatalytic toluene performance was ordered by CeMO-850 > CeMO-750 > CeMO-950, and CeMO-850 had better activity than P25, common CeO 2 , and CeO 2 -H 2 . Different reaction pathways were founded on CeMO photocatalysts, i.e., on CeMO-950 and CeMO-750, the cresol and hydroquinone intermediates were observed, which hindered toluene adsorption/activation and were hard to deep-mineralization. Whereas, more benzoic acid, open-loop oxygen-containing intermediates were observed on CeMO-850, which were resulted from its oxygen vacancies (Ov), i.e., surface Ov and Ce 3+ were beneficial for toluene adsorption, B acid sites and active radicals’ generation, respectively, and bulk Ov were helpful for oxygen mobility and efficient deep-mineralization. The mechanism of Ov generation and toluene degradation were proposed. • An anion-induced method is used to engineer CeO 2 nanosheet with different Ov. • CeMO-850 with rich Ov shows excellent photocatalytic toluene mineralization. • Ov promotes toluene adsorption, B acid sites, active radicals, and deep-oxidation. • Sulfate ions on CeMO-750 suppress toluene adsorption/activation and poorer activity. • Different reaction pathways are observed on CeMO-850, CeMO-750 and CeMO-950.

Lanthanide-doped near-infrared (NIR) photocatalyst still obstructed by the less impressive photocatalytic efficiency and stability. In this work, we report a novel strategy by introducing the lanthanide-doped ferroelectric perovskites of SiTiO3 and Sr2Bi4Ti4O15 into the glass-ceramic (GC), then an efficient and stable NIR photocatalyst was fabricated through the method of facile in-situ HCl etching GC. The results show that Sr2Bi4Ti4O15, SrTiO3, and BiOCl were exposed to the superficial coating of the core-shell structured photocatalyst and constructed Z-scheme heterojunction, the heterojunction with built-in electric field could significantly facilitate the charge carriers separation and harvest NIR light for photocatalytic reaction simultaneously. The evident increase of Lewis basic sites over defect-rich photocatalyst is found, the •O2- and •OH radicals are generated. During the degradation of norfloxacin (NOR) under NIR light irradiation for 90 min, the NOR degradation rate is 86% (TOC removal rate is 30.7%), the high apparent quantum yield of 2.3% is achieved.

Ultra-short-term power load forecasting refers to the use of load and weather information from the prior few hours to forecast the load for the next hour, which is very important for power dispatch and the power spot market establishment. Based on time series decomposition-reconstruction modeling and neural network forecasting, this study constructed a CEEMDAN-1SE-LSTM model and used it to forecast the ultra-short-term electricity load in Changsha, China, considering meteorological and holiday factors. The article first decomposed the power load data from May 13, 2014, to May 13, 2017, at 24 time points per day for three years to obtain six component series, and then reconstructed them into a two-component series based on the sample entropy analysis to reflect the fluctuation and trend characteristics of the power load. Then, the LSTM neural network model was used to predict and superimpose the reconstructed component series to obtain the final prediction results. It was found that the RMSE, MAE, and MAPE of the CEEMDAN-SE-LSTM model were 62.102, 47.490, and 1.649 %, respectively, which were significantly better than those of the ARMA, LSTM single-prediction, EEMD-LSTM, and CEEMDAN-LSTM models. This study greatly improves the accuracy of ultra-short-term power-load forecasting, provides support for ultra-short-term power dispatching in Changsha, and provides a reference for other cities to develop short-term and ultra-short-term power load forecasting models.

Abstract. Developing a big data analytics framework for generating the Long-term Gap-free High-resolution Air Pollutant concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and Earth system science analysis. By synergistically integrating multimodal aerosol data acquired from diverse sources via a tensor-flow-based data fusion method, a gap-free aerosol optical depth (AOD) dataset with a daily 1 km resolution covering the period of 2000–2020 in China was generated. Specifically, data gaps in daily AOD imageries from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra were reconstructed based on a set of AOD data tensors acquired from diverse satellites, numerical analysis, and in situ air quality measurements via integrative efforts of spatial pattern recognition for high-dimensional gridded image analysis and knowledge transfer in statistical data mining. To our knowledge, this is the first long-term gap-free high-resolution AOD dataset in China, from which spatially contiguous PM2.5 and PM10 concentrations were then estimated using an ensemble learning approach. Ground validation results indicate that the LGHAP AOD data are in good agreement with in situ AOD observations from the Aerosol Robotic Network (AERONET), with an R of 0.91 and RMSE equaling 0.21. Meanwhile, PM2.5 and PM10 estimations also agreed well with ground measurements, with R values of 0.95 and 0.94 and RMSEs of 12.03 and 19.56 µg m−3, respectively. The LGHAP provides a suite of long-term gap-free gridded maps with a high resolution to better examine aerosol changes in China over the past 2 decades, from which three major variation periods of haze pollution in China were revealed. Additionally, the proportion of the population exposed to unhealthy PM2.5 increased from 50.60 % in 2000 to 63.81 % in 2014 across China, which was then reduced drastically to 34.03 % in 2020. Overall, the generated LGHAP dataset has great potential to trigger multidisciplinary applications in Earth observations, climate change, public health, ecosystem assessment, and environmental management. The daily resolution AOD, PM2.5, and PM10 datasets are publicly available at https://doi.org/10.5281/zenodo.5652257 (Bai et al., 2021a), https://doi.org/10.5281/zenodo.5652265 (Bai et al., 2021b), and https://doi.org/10.5281/zenodo.5652263 (Bai et al., 2021c), respectively. Monthly and annual datasets can be acquired from https://doi.org/10.5281/zenodo.5655797 (Bai et al., 2021d) and https://doi.org/10.5281/zenodo.5655807 (Bai et al., 2021e), respectively. Python, MATLAB, R, and IDL codes are also provided to help users read and visualize these data.

T cells form the major component of anti-tumor immunity. A deeper understanding of T cell exhaustion (TEX) heterogeneity within the tumor microenvironment (TME) is key to overcoming TEX and improving checkpoint blockade immunotherapies in the clinical setting.We conducted a comprehensive pan-cancer analysis of TEX subsets from 9564 tumor samples across 30 bulk solid cancer types. Pan-cancer TEX subtypes were identified using literature-derived hierarchical TEX-specific developmental pathway signatures. The potential multi-omics and clinical features involved in TEX heterogeneity were determined.Our study yielded a dynamic, progressive roadmap and a hierarchical dysfunction landscape regarding TEX within the TME. In total, we identified five pan-cancer TEX subtypes, revealing tissue/cancer type-specific TEX patterns in low immunogenic tumors. By contrast, highly immunogenic tumors tend to harbor high frequencies of progenitor TEX subsets. In addition, the TEX profile also revealed distinct prognoses, intrinsic molecular subtype distribution, immune microenvironment and multi-omics features among the cancers. Network analysis identified four previously unknown TEX-associated cancer genes (tolloid-like 1, myosin heavy chain 111, P2Y receptor family member 8 and protein kinase D2), the possible association with anti-PD-1 immunotherapy response was validated using a single-cell dataset. Finally, a machine learning-based gene signature was developed to model the hierarchical TEX stages, verified in single-cell and immunotherapy patient cohorts.Our study provided a TEX-derived system that can be applied for the immune subtyping of cancers and may have implications for the further optimization of personalized cancer immunotherapy.This study was supported by the National Natural Science Foundation of China (Grant No. 62072341 and 61973240). The funders had no roles in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Electrochemical reduction of CO2 to produce fuels and chemicals is one of the most valuable approaches to achieve a carbon-neutral cycle. Recently, a diversity of catalysts have been developed to improve their intrinsic activity and efficiency. However, the dynamic evolution process and the in situ construction behavior of electrocatalysts under the working conditions are typically ignored. Here, we fully reveal the dynamic reduction process and phase transformation of a copper tin sulfide catalyst reconstructed by in situ reduction of the precatalyst Cu2SnS3 and CuS during electrochemical CO2 reduction. Furthermore, the reconstructed catalyst reaches an outstanding electrochemical CO2-to-formate conversion with a high Faradaic efficiency of 96.4% at an impressive production rate of 124889.9 μmol mg–1 h–1 under a partial current density of −241 mA cm–2 (−669.4 A g–1) in a flow-cell reactor. Theoretical calculations further demonstrate the strong charge interaction between the adsorbate and substrate to accelerate the charge transfer and decrease the formation energies of OCHO* and HCOOH* intermediates in the pathway of CO2 to HCOOH, resulting in high selectivity for formate on the surface of the copper tin sulfide catalyst. This work paves the way for revealing the in situ dynamic process of the reconstructed catalyst and designing optimal catalysts with high catalytic activity and selectivity.

Abstract Electrolytic MnO 2 –Zn batteries with economic advantages and high energy density are viable candidates for large‐scale energy storage. However, the spontaneous reactions between acidic electrolytes and Zn metal anode cause severe proton‐induced hydrogen evolution which is difficult to avoid. Herein, a proton‐barrier separator (PBS) based on poly(vinyl alcohol) (PVA) is fabricated via the Hofmeister effect for preventing hydrogen evolution. Experiments and theoretical calculations demonstrate that the concentrated sulfate enables PVA chains to form a discontinuous hydrogen bond network as well as isolated hydrophilic cavities. This unique feature can effectively obstruct proton migration to impede proton‐induced hydrogen evolution, but allow for fast Zn 2+ transfer with excellent stability. Electrolytic MnO 2 –Zn batteries with PBS deliver high energy retention (91.2% after 200 cycles) and largely enhanced rate performance (20 C) in a high areal capacity of 6.67 mAh cm −2 with a very low cost ($1 m −2 ) as compared to commercial anion exchange membranes (8 C). This work sheds light on new avenues for the development of stable electrolytic MnO 2 –Zn batteries by deploying PBS for preventing hydrogen evolution through a cost‐effective fabrication method, which is a universal approach that can be applied to design other stable aqueous metal‐ion batteries.

In practical mechanical fault detection and diagnosis, it is difficult and expensive to collect enough large-scale supervised data to train deep networks. Transfer learning can reuse the knowledge obtained from the source task to improve the performance of the target task, which performs well on small data and reduces the demand for high computation power. However, the detection performance is significantly reduced by the direct transfer due to the domain difference. Domain adaptation (DA) can transfer the distribution information from the source domain to the target domain and solve a series of problems caused by the distribution difference of data. In this survey, we review various current DA strategies combined with deep learning (DL) and analyze the principles, advantages, and disadvantages of each method. We also summarize the application of DA combined with DL in the field of fault diagnosis. This paper provides a summary of the research results and proposes future work based on analysis of the key technologies.

Sensors provide insights into the industrial processes, while misleading sensor outputs may result in inappropriate decisions or even catastrophic accidents. In this article, the Bayesian estimation algorithms are developed to estimate unforeseen signals in sensor outputs without tuning. The optimal Bayesian estimation method is first derived by incorporating a Gaussian distribution specifying potential unmodeled dynamics into the measurement equation. Since its performance depends on tuning parameters, an iterative Bayesian estimation algorithm is developed using the variational inference technique. Specifically, an inverse Wishart distribution is introduced to describe the predicted covariance of abnormal signals. We then estimate it together with the other independent Gaussian distributions to conditionally approximate the joint posterior distribution, by which the effects of tuning parameters can be replaced adaptively. Testing the proposed algorithms through a simulated electromechanical brake model and a real experimental system shows that the proposed algorithm can satisfactorily estimate additive sensor faults online and services as a sensor monitor that simultaneously provides the locations and magnitudes of faulty signals without tuning.

PdCu/CBC exhibited a remarkable R urea of 763.8 ± 42.8 μg h −1 mg cat. −1 at −0.50 V ( vs. RHE) and an exceptional FE of 69.1 ± 3.8% at −0.40 V ( vs. RHE). Taking advantage of operando spectroscopy characterization, the C–N coupling mechanism was verified.

In China, economic growth targets significantly affect the economic behavior of local governments. This paper analyzes a dataset of the economic growth targets from 230 city government work reports from 2004 to 2019 to reveal how economic growth targets affect local environmental pollution. The empirical results show that the economic growth targets are "overweight" from the higher-level governments to the lower-level governments, which significantly aggravates regional environmental pollution because of environmental regulation relaxation, blocked industrial structure upgrades and technological innovation inhibition. Furthermore, to exceed the economic growth targets, local governments usually adopt the "riding a seesaw" strategy for the treatment of different pollutants. If the economic growth target statements are ambiguous or attainable, then environmental pollution is reduced. Interestingly, the "preemptive" city government, which sets economic growth goals before its province, is more inclined to maintain growth at the expense of the environment. This study provides important evidence for the literature on the impacts of government behavior on environmental pollution.

The electrochemical conversion of nitrate pollutants into value-added ammonia is a feasible way to achieve artificial nitrogen cycle. However, the development of electrocatalytic nitrate-to-ammonia reduction reaction (NO3- RR) has been hampered by high overpotential and low Faradaic efficiency. Here we develop an iron single-atom catalyst coordinated with nitrogen and phosphorus on hollow carbon polyhedron (denoted as Fe-N/P-C) as a NO3- RR electrocatalyst. Owing to the tuning effect of phosphorus atoms on breaking local charge symmetry of the single-Fe-atom catalyst, it facilitates the adsorption of nitrate ions and enrichment of some key reaction intermediates during the NO3- RR process. The Fe-N/P-C catalyst exhibits 90.3 % ammonia Faradaic efficiency with a yield rate of 17980 μg h-1 mgcat-1 , greatly outperforming the reported Fe-based catalysts. Furthermore, operando SR-FTIR spectroscopy measurements reveal the reaction pathway based on key intermediates observed under different applied potentials and reaction durations. Density functional theory calculations demonstrate that the optimized free energy of NO3- RR intermediates is ascribed to the asymmetric atomic interface configuration, which achieves the optimal electron density distribution. This work demonstrates the critical role of atomic-level precision modulation by heteroatom doping for the NO3- RR, providing an effective strategy for improving the catalytic performance of single atom catalysts in different electrochemical reactions.

The hypoxia microenvironment of solid tumors poses a technological bottleneck for ferroptosis and immunotherapy in clinical oncology. Nanoreactors based on special physiological signals in tumor cells are able to avoid various tumor tolerance mechanisms by alleviating the intracellular hypoxia environment. Herein we reported a nanoreactor Cu2–xSe that enabled the conversion of Cu elements between Cu+ and Cu2+ for the generation of O2 and the consumption of intracellular GSH content. Furthermore, to enhance the catalytic and ferroptosis-inducing activities of the nanoreactors, the ferroptosis agonist Erastin was loaded on the ZIF-8 coating on the surface of Cu2–xSe to up-regulate the expression of NOX4 protein, increase the intracellular H2O2 content, catalyze the Cu+ to produce O2 and activate ferroptosis. In addition, the nanoreactors were simultaneously surface functionalized with PEG polymer and folic acid molecules, which ensured the in vivo blood circulation and tumor-specific uptake. In vitro and in vivo experiments demonstrated that the functionalized self-supplying nanoreactors can amplify the ability to generate O2 and consume intracellular GSH via the interconversion of Cu elements Cu+ and Cu2+, and impair the GPX4/GSH pathway and HIF-1α protein expression. At the same time, by alleviating the intracellular hypoxia environment, the expression of miR301, a gene in the secreted exosomes was decreased, which ultimately affected the phenotype polarization of TAMs and increased the content of IFN γ secreted by CD8+ T cells, which further promoted the ferroptosis induced by Erastin-loaded nanoreactors. This combined therapeutic strategy of activating the tumor immune response and ferroptosis via self-supplying nanoreactors provides a potential strategy for clinical application.

As the conflict between energy and the environment intensifies, integrated energy system (IES) is an effective way to reduce tensions with difficulties about multi-energy coupling and multi-agent. This study proposed a multi-agent game operation strategy consisting of energy retailers, suppliers, and users with integrated demand response (IDR). This problem is formulated as a distributed bi-level optimization model with one leader and multi-followers, which is a Stackelberg game among them vertically, and a non-cooperative game between energy suppliers horizontally. The equilibrium of proposed game model is proved the existence and uniqueness, and is solved by a distributed algorithm through genetic algorithm nested quadratic programming. Finally, the effectiveness of the proposed method is verified by the case study in three scenarios. The results shows that the supply-side revenue are effectively improved by 8.57%, the demand-side costs are reduced 1.42% by game model and IDR, and improve the operation and stability of energy supply and utilization.

Precisely regulating the coordination microenvironment of single-atom catalysts (SACs) to achieve enhanced reactivity is significant and desired but still in its infancy. Herein, a coordination-tuned and pyrolysis-free strategy is reported for the fabrication of a Fenton-like SAC containing the axial five-coordinated configuration (Fe–N5). The N species on the N-doped graphene act as anchoring points for iron phthalocyanine (a typical Fe–N4 complex) to obtain isolated Fe–N5 sites, which significantly modulates the electronic state of Fe atoms and lowers the H2O2 activation barrier for •OH production. Moreover, the enriched pyridinic N serve as contaminant adsorption sites shortening •OH diffusion distance, establishing a dual-site reaction mechanism with Fe–N5 sites. As such, the Fe–N5 catalyst exhibits exceptional Fenton activity towards catalytic oxidation of phenol (k = 0.180 min−1). Our work unravels the dependence of Fenton activity on the single-atom coordination environment and provides a platform for precise engineering of SACs.

As the most advanced energy storage devices, lithium ion batteries (LIBs) have captured a great deal of attention and have been developed swiftly during the past decades. However, the improved fast-charging performance is more urgent than ever for time-saving and convenience, which is generally limited by the graphite anode. Recent studies have revealed that the fast-charging performance of graphite anodes is highly dictated by the properties of the electrolyte. Therefore, the investigations on fast-charging graphite based on designs of electrolytes are summarized from two aspects: solid electrolyte interphase (SEI) structures and solvated lithium ion structures. Finally, challenges and prospects for further research toward fast-charging graphite anodes are proposed.

Abstract J-aggregation, an effective strategy to extend wavelength, has been considered as a promising method for constructing NIR-II fluorophores. However, due to weak intermolecular interactions, conventional J-aggregates are easily decomposed into monomers in the biological environment. Although adding external carriers could help conventional J-aggregates stabilize, such methods still suffer from high-concentration dependence and are unsuitable for activatable probes design. Besides, these carriers-assisted nanoparticles are risky of disassembly in lipophilic environment. Herein, by fusing the precipitated dye (HPQ) which has orderly self-assembly structure, onto simple hemi-cyanine conjugated system, we construct a series of activatable, high-stability NIR-II-J-aggregates which overcome conventional J-aggregates carrier’s dependence and could in situ self-assembly in vivo. Further, we employ the NIR-II-J-aggregates probe HPQ-Zzh-B to achieve the long-term in situ imaging of tumor and precise tumor resection by NIR-II imaging navigation for reducing lung metastasis. We believe this strategy will advance the development of controllable NIR-II-J-aggregates and precise bioimaging in vivo.

Chiral shape-persistent molecular nanocarbons are promising chiroptical materials; their synthesis, however, remains a big challenge. Herein, we report the facile synthesis and chiral resolution of a double-stranded figure-eight carbon nanobelt 1 in which two [5]helicene units are fused together. Two synthetic routes were developed, and, in particular, a strategy involving Suzuki coupling-mediated macrocyclization followed by Bi(OTf)3 -catalyzed cyclization of vinyl ether turned out to be the most efficient. The structure of 1 was confirmed by X-ray crystallographic analysis. The isolated (P,P)- and (M,M)- enantiomers show persistent chiroptical properties with relatively large dissymmetric factors (|gabs |=5.4×10-3 and |glum |=1.0×10-2 ), which can be explained by the effective electron delocalization along the fully conjugated belt and the unique D2 symmetry. 1 exhibits local aromatic character with a dominant structure containing eight Clar's aromatic sextet rings.

The aim of the present work is to derive the closed-form solution for the three-dimensional aerodynamic admittance (3D AAF) of the lift on a 5:1 rectangular cylinder in turbulent flow at an angle of attack (AoA), which can be used to study the unsteady effects of AoA. The separated one- and two-wavenumber aerodynamic admittances are introduced to assess the contributions of u and w components of turbulence to the lift force at an AoA. These admittances are divided into two parts: the two-dimensional aerodynamic admittance (2D AAF) for fully coherent gusts and the gust-related spanwise correction factors. The closed-form expressions for the one- and two-wavenumber spanwise correction factors of the 3D AAF are derived provided that the floating parameters in the coherence models of the lift force and turbulence are determined experimentally. Although the proposed 3D AAF model is a semi-analytical solution, it still provides explicit insight into the unsteady effects of u and w components of turbulence and the distortion of the free-stream turbulence. Based on theoretical and experimental investigations, it is shown that the unsteady effects of AoA on the coherence and spectra of the lift become more prominent with the increase of AoA, and reach their maximum as the AoA reaches 10°. This phenomenon can be attributed to the contribution of the u component, which plays a dominant role in determining the aerodynamic behaviour of the lift as the AoA approaches 10°. Notably, the spanwise correction factors of the 3D AAF will amplify the results based on the strip theory at low frequencies as the AoA is within 7°, and vice versa. In addition, it should be noted that the proposed approach allows us to quantitatively study the unsteady behaviour of the lift force on bluff bodies with complicated cross-sectional shapes, such as a streamlined bridge deck.

Lactylation is a lactate-induced post-translational modification best known for its roles in epigenetic regulation. Herein, we demonstrate that MRE11, a crucial homologous recombination (HR) protein, is lactylated at K673 by the CBP acetyltransferase in response to DNA damage and dependent on ATM phosphorylation of the latter. MRE11 lactylation promotes its binding to DNA, facilitating DNA end resection and HR. Inhibition of CBP or LDH downregulated MRE11 lactylation, impaired HR, and enhanced chemosensitivity of tumor cells in patient-derived xenograft and organoid models. A cell-penetrating peptide that specifically blocks MRE11 lactylation inhibited HR and sensitized cancer cells to cisplatin and PARPi. These findings unveil lactylation as a key regulator of HR, providing fresh insights into the ways in which cellular metabolism is linked to DSB repair. They also imply that the Warburg effect can confer chemoresistance through enhancing HR and suggest a potential therapeutic strategy of targeting MRE11 lactylation to mitigate the effects.