Weinan Si

Methylglyoxal (MGO) is a highly reactive metabolite generated by glycolysis. Although abnormal accumulation of MGO has been reported in several autoimmune diseases such as multiple sclerosis and rheumatoid arthritis, the role of MGO in autoimmune diseases has not yet been fully investigated. In this study, we found that the intracellular MGO levels increased in activated immune cells, such as microglia and lymphocytes. Treatment with MGO inhibited inflammatory cell accumulation in the spinal cord and ameliorated the clinical symptoms in EAE mice. Further analysis indicated that MGO suppressed M1-polarization of microglia cells and diminished their inflammatory cytokine production. MGO also inhibited the ability of microglial cells to recruit and activate lymphocytes by decreasing chemokine secretion and expression of co-stimulatory molecules. Furthermore, MGO negatively regulated glycolysis by suppressing glucose transporter 1 expression. Mechanically, we found that MGO could activate nuclear factor erythroid 2-related factor 2 (NRF2) pathway and NRF2 could bind to the promoter of IκBζ gene and suppressed its transcription and subsequently pro-inflammatory cytokine production. In conclusion, our results showed that MGO acts as an immunosuppressive metabolite by activating the NRF2-IκBζ.

<h2>Abstract</h2><h3>Background</h3> Myasthenia gravis (MG) is an autoimmune disease characterized by generalized skeletal muscle contraction weakness due to autoantibodies targeting neural-muscular junctions. Here, we investigated the relationship between key cytokines and MG type, disease course, antibodies, and comorbidities. <h3>Method</h3> Cytokine levels in serum samples collected from MG (n = 45) and healthy control (HC, n = 38) patients from January 2020 to June 2022 were quantified via flow cytometry. <h3>Results</h3> Levels of IL-6 were higher in the MG group versus healthy individuals (p = 0.026) and in patients with generalized versus ocular MG (p = 0.019). IL-6 levels were positively correlated with QMG score. In patients with MG with both AChR and Titin antibodies, serum levels of sFas and granulysin were higher than in those with AChR alone (p = 0.036, and p = 0.028, respectively). LOMG had a reduction in serum levels of IL-2 compared to EOMG (p = 0.036). LOMG patients with diabetes had lower serum levels of IL-2, IL-4, and IFN-γ (p = 0.044, p = 0.038, and p = 0.047, respectively) versus those without diabetes. sFas in the MG with Abnormal thymus were reduced compared to those in MG with Normal thymus (p = 0.008). <h3>Conclusions</h3> This study revealed a positive correlation between IL-6 level and MG status. Serum cytokine levels of the AChR + Titin MG group differed from those of the AChR group. LOMG had a lower IL-2 level. Comorbidities affect some cytokines in peripheral blood in MG serum.

Five stems of rapeseed with abundant black microsclerotia were collected from Huang Yuan County of Qinghai Province, China, and ten fungal isolates were obtained from the stems. They were identified based on morphology, molecular features and specific PCR detection. The results showed that the 10 fungal isolates belonged to Verticillium longisporum lineage A1/D3. One of the 10 isolates (HW7-1) was tested for virulence on three species of rapeseed, including B. napus Zhongshuang 9, B. rapa Qingyou 9 and B. juncea Tayou 2 by inoculation of conidia of HW7-1 on roots of young seedlings. Control seedlings were inoculated with V. dahliae conidia or water alone. The seedlings of these treatments were transplanted in culture mix and incubated in a growth chamber (20 °C). Results showed that while the control seedlings of three cultivars appeared quite healthy, the seedlings inoculated with HW7-1 showed leaf yellowing, seedling stunting and even death at 22 days post-inoculation. V. longisporum was re-isolated from yellow leaves, thus fulfilling Koch's postulates. Moreover, compared to the control treatments, inoculation with HW7-1 caused flowering delay and seed yield reduction on Tayou 2 with production of microsclerotia on the stems. To our knowledge, this is the first report of V. longisporum lineage A1/D3 on rapeseed in northwestern China.

The synchronisation between rotating turbulent flows in periodic boxes is investigated numerically. The flows are coupled via a master–slave coupling, taking the Fourier modes with wavenumber below a given value $k_m$ as the master modes. It is found that synchronisation happens when $k_m$ exceeds a threshold value $k_c$ , and $k_c$ depends strongly on the forcing scheme. In rotating Kolmogorov flows, $k_c\eta$ does not change with rotation in the range of rotation rates considered, $\eta$ being the Kolmogorov length scale. Even though the energy spectrum has a steeper slope, the value of $k_c\eta$ is the same as that found in isotropic turbulence. In flows driven by a forcing term maintaining constant energy injection rate, synchronisation becomes easier when rotation is stronger. Here, $k_c\eta$ decreases with rotation, and it is reduced significantly for strong rotations when the slope of the energy spectrum approaches $-3$ . It is shown that the conditional Lyapunov exponent for a given $k_m$ is reduced by rotation in the flows driven by the second type of forcing, but it increases mildly with rotation for the Kolmogorov flows. The local conditional Lyapunov exponents fluctuate more strongly as rotation is increased, although synchronisation occurs as long as the average conditional Lyapunov exponents are negative. We also look for the relationship between $k_c$ and the energy spectra of the Lyapunov vectors. We find that the spectra always seem to peak at approximately $k_c$ , and synchronisation fails when the energy spectra of the conditional Lyapunov vectors have a local maximum in the slaved modes.

In the research on medical students, the research team obtained the idea of using English teaching to cultivate interdisciplinary talents, and established a computer teaching model to predict the incidence rate of cancer through the use of computer engineering based in-depth learning and oncology. Through the situation of susceptible gene mutation and computer imaging after chromosome staining, an algorithm teaching model using convolutional neural network and linear function is proposed. This model solves the problem that computer engineering students lack knowledge of medicine. This model can enhance medical students' understanding of applied mathematics and neural networks, provide new ideas for the medical community to comprehensively promote in-depth learning, and design social experiments, carry out data analysis and data visualization. The research team analyzed and reported this situation, summarized and communicated.

Cysteinyl aspartate-specific proteinase-1 (caspase-1) is a multifunctional inflammatory mediator in many inflammation-related diseases. Previous studies show that caspase-1 inhibitors produce effective therapeutic outcomes in a rat model of myasthenia gravis. However, tissue toxicity and unwanted off-target effects are the major disadvantages limiting their clinical application as therapeutic agents. This study shows that dendritic cell-derived extracellular vesicles (EVs) loaded with a caspase-1 inhibitor (EVs-VX-765) are phagocytized mainly by macrophages, and caspase-1 is precisely expressed in macrophages. Furthermore, EVs-VX-765 demonstrates excellent therapeutic effects through a macrophage-dependent mechanism, and it notably inhibits the level of interleukin-1β and subsequently inhibits Th17 response and germinal center (GC) reactions. In addition, EVs-VX-765 demonstrates better therapeutic effects than routine doses of VX-765, although drug loading is much lower than routine doses, consequently reducing tissue toxicity. In conclusion, this study's findings suggest that EV-mediated delivery of caspase-1 inhibitors is effective for treating myasthenia gravis and is promising for clinical applications.

The central Monte-Carlo production of the CMS experiment utilizes the WLCG infrastructure and manages daily thousands of tasks, each up to thousands of jobs. The distributed computing system is bound to sustain a certain rate of failures of various types, which are currently handled by computing operators a posteriori. Within the context of computing operations, and operation intelligence, we propose a Machine Learning technique to learn from the operators with a view to reduce the operational workload and delays. This work is in continuation of CMS work on operation intelligence to try and reach accurate predictions with Machine Learning. We present an approach to consider the log files of the workflows as regular text to leverage modern techniques from Natural Language Processing (NLP). In general, log files contain a substantial amount of text that is not human language. Therefore, different log parsing approaches are studied in order to map the log files’ words to high dimensional vectors. These vectors are then exploited as feature space to train a model that predicts the action that the operator has to take. This approach has the advantage that the information of the log files is extracted automatically and the format of the logs can be arbitrary. In this work the performance of the log file analysis with NLP is presented and compared to previous approaches.

Rapid 3D imaging of whole organs at cellular resolution is vital to pathological research, but this task remains challenging in light sheet microscopy due to the compromise between axial resolution and field of view. In the meanwhile, needed magnification varies when doing different analysis. Here, we report on a high-throughput multi-scale light sheet microscopy which combines adjustable beam shaping system providing continuous imaging magnification from 1.26X~12.6X, sample holding device for fast sample switching, modulation mask to generate thin optical sectioning and large illumination field, capable of imaging a lung lobe with cellular resolution (~2.8μm), centimeter-scale field of view (1.1cm×0.8cm) within 1 minute. We demonstrate the imaging of hundreds of lung lobes of mice and other large-scale tissue. Using our ultra-high throughput light sheet microscopy with multiple magnification, we are able to quickly get the 3D view of the whole tissue and the statistical data of the volume of tumors, the number tumor cells and so on for further biological analysis.

The detection of animal species as a kind of target detection, the application of artificial intelligence techniques to the field of image processing helps us to study animals more intelligently. In this paper, we use CenterNet-based algorithm to achieve target detection of animals. firstly, we perform data enhancement on the used dataset to achieve multi-scene coverage. secondly, we use CenterNet algorithm to train the model on the dataset to improve the accuracy of recognition. finally, we deploy it to the camera for animal recognition in the actual target scene. The experimental results are analyzed and the trained animal detection system recognizes an average accuracy of 90%-97%. This experiment demonstrates the utility of this research in animal detection in the field of image processing.

The recent dramatic progress of transformer-based methods over convolutional neural networks (CNNs) for single image deraining is attributed to the transformer's powerful ability to model non-local messages. Indeed, rich local-global information characterization is equally important to better satisfy the derivation requirements. In this paper, we present an efficient image deraining method that integrates CNN models into the Transformer backbone to accelerate network convergence, termed Asymmetric network based Dilated convolution Transformer (ADT), which leverages Transformer's ability to learn non-local features and seamlessly integrates local detail efficiency and global structural representations. Our framework is an asymmetric architecture because the encoder focuses on shallow features contains rain shadow features, the decoder focuses on deep features, and embedding Dilconv FeedForward Networks (DFNs) within its encoder, and Dilconv self attention (DSAs) and DFNs within the decoder preserves higher similarity, resulting in high-quality image deraining. Extensive evaluation results show that our model performs superiorly and significantly improves the quality of image deraining.

The synchronisation between rotating turbulent flows in periodic boxes is investigated numerically. The flows are coupled via a master-slave coupling, taking the Fourier modes with wavenumber below a given value $k_m$ as the master modes. It is found that synchronisation happens when $k_m$ exceeds a threshold value $k_c$, and $k_c$ depends strongly on the forcing scheme. In rotating Kolmogorov flows, $k_c\eta$ does not change with rotation in the range of rotation rates considered, $\eta$ being the Kolmogorov length scale. Even though the energy spectrum has a steeper slope, the value of $k_c\eta$ is the same as that found in isotropic turbulence. In flows driven by a forcing term maintaining constant energy injection rate, synchronisation becomes easier when rotation is stronger. $k_c\eta$ decreases with rotation, and it is reduced significantly for strong rotations when the slope of the energy spectrum approaches $-3$. It is shown that the conditional Lyapunov exponent for a given $k_m$ is reduced by rotation in the flows driven by the second type of forcing, but it increases mildly with rotation for the Kolmogorov flows. The local conditional Lyapunov exponents fluctuate more strongly as rotation is increased, although synchronisation occurs as long as the average conditional Lyapunov exponents are negative. We also look for the relationship between $k_c$ and the energy spectra of the Lyapunov vectors. We find that the spectra always seem to peak around $k_c$, and synchronisation fails when the energy spectra of the conditional Lyapunov vectors have a local maximum in the slaved modes.

This study aims to investigate the effect of morinda officinalis polysaccharides (MOP) in inflammatory microenvironment on the expression of silent information regulator sirtuin 1 (SIRT1) and NOD-like receptor thermal protein domain associated protein 3 (NLRP3) in periodontal ligament cells.Thirty rats were randomly divided into control group (n=6) and model group (n=24). The model group used orthodontic wire ligation to establish periodontitis, and six rats from each group were killed after 3 weeks. The successful modeling was confirmed by Micro-CT. The remaining rats in the model group were randomly divided into natural recovery group, normal saline (NS) group, and MOP group. In the MOP group, MOP [200 mg/(kg·3d), 50 µL for 4 weeks] was injected into the palatal side of the left maxillary first molar of the rats, while the NS group was injected with equal volume of NS. The natural recovery group did not undergo any treatment. The left maxilla tissues of the rats were collected, and pathological changes in perio-dontal ligament cells were observed by hematoxylin-eosin (HE) staining. The expression of SIRT1 and NLRP3 was detected by immunohistochemistry. Cultivate periodontal ligament fibroblasts in vitro and detect the effect of MOP on cell activity using CCK-8. The 4th generation cells were divided into control group, inflammation group (10 µg/mL lipopolysaccharide), and experimental group (5 µmol/L MOP, 5 µmol/L MOP+10 µg/mL lipopolysaccharide). The expression of SIRT1 and NLRP3 was detected by quantitative realtime polymerase chain reaction (qRT-PCR) and Western blot analyses. The acetylation of NLRP3 and the contents of interleukin (IL)-1β and IL-18 were detected by immunoprecipitation and enzyme-linked immunosorbent assay, respectively. Statistical analysis of data was conducted using Prism 9.0 software.In the vivo experiments, the expression of NLRP3 and SIRT1 in the MOP group decreased significantly compared with that in the natural recovery group and NS group, while the expression of SIRT1 increased (P<0.05) and inflammatory cell infiltration decreased. In the in vitro experiments, the expression of NLRP3 mRNA and protein in the inflammation group increased (P<0.05), while the expression of SIRT1 significantly decreased (P<0.01); MOP upregulated the expression of SIRT1 in inflammatory cells (P<0.05), reduced the expression of NLRP3 and its acetylation level significantly (P<0.05), suppressed the content of IL-1β and IL-18 in the supernatant (P<0.01).The SIRT1 expression decreased, and that of NLRP3 expression increased in inflammatory periodontal ligament cells. MOP intervention promoted SIRT1 expression, resulting in the inhibition of NLRP3. Meanwhile, the acetylation level of NLRP3 reduced through deacetylation, leading to the decreased activity of NLRP3. Thus, MOP acted as inflammatory suppressor.目的: 探讨炎性微环境下巴戟天多糖（MOP）对牙周膜细胞沉默信息调节因子1（SIRT1）及NOD样受体热蛋白结构域相关蛋白3（NLRP3）表达的影响。方法: 将30只大鼠随机分为对照组（n=6）和模型组（n=24），模型组采用正畸丝结扎法建立牙周炎模型，3周后每组各处死6只大鼠，Micro-CT检测确认建模成功。剩余模型组大鼠随机分为牙周炎自然恢复组、生理盐水（NS）组和MOP组。MOP组于大鼠左上颌第一磨牙腭侧注射MOP［200 mg/（kg·3d），50 µL，持续4周］，NS组注射等体积NS，牙周炎自然恢复组不做任何处理。取大鼠左上颌骨组织，采用苏木精-伊红（HE）染色观察牙周膜细胞病理改变，免疫组织化学检测SIRT1、NLRP3表达量。体外培养人牙周膜成纤维细胞（hPDLFs），CCK-8检测MOP对细胞活性的影响。将第4代细胞分为对照组、炎症组（10 µg/mL脂多糖）及实验组（5 µmol/L MOP，5 µmol/L MOP+10 µg/mL脂多糖）。利用实时定量聚合酶链反应（qRT-PCR）及Western blot检测SIRT1和NLRP3表达变化，免疫沉淀及酶联免疫法（ELISA）分别检测NLRP3乙酰化及白细胞介素（IL）-1β、IL-18含量。采用Prism 9.0软件对数据进行统计学分析。结果: 在体内实验中，MOP组较牙周炎自然恢复组和NS组NLRP3表达下降，SIRT1表达升高（P<0.05），炎细胞浸润减少。在体外实验中，与对照组相比，炎症组NLRP3 mRNA和蛋白表达量均增高（P<0.05），SIRT1表达降低（P<0.01）；MOP能上调炎症细胞SIRT1表达（P<0.05），降低NLRP3表达及其乙酰化水平（P<0.05），减少上清液中IL-1β、IL-18含量（P<0.01）。结论: 炎性牙周膜细胞SIRT1表达降低，NLRP3表达升高；MOP干预能促进SIRT1表达，导致NLRP3表达受抑制，同时通过去乙酰化作用使NLRP3乙酰化水平下降，从而NLRP3活性降低，由此发挥抑制炎症作用。.

The Phase-1 upgrade of the CMS pixel detector is built out of four barrel layers (BPix) and three forward disks in each endcap (FPix). It comprises a total of 124M pixel channels in 1,856 modules, and it is designed to withstand instantaneous luminosities of up to $2 \times 10^{34}\,$cm$^{-2}$s$^{-1}$. Different parts of the detector were assembled over the last year and later brought to CERN for installation inside the CMS tracker. At various stages during the assembly tests have been performed to ensure that the readout and power electronics and the cooling system meet the design specifications. After tests of the individual components, system tests were performed before the installation inside CMS. In addition to reviewing these tests, we also present results from the final commissioning of the detector in-situ using the central CMS DAQ system. Finally we review results from the initial operation of the detector first with cosmic rays and then with pp collisions.

Journal Article Euclid-NexusLIMS: A Customizable Data Management Software for Microscopists with Cloud Computing Outlook Get access Ao Liu, Ao Liu Euclid Techlabs, LLC, Bolingbrook, IL, United States Corresponding author: a.liu@euclidtechlabs.com Search for other works by this author on: Oxford Academic Google Scholar Weinan Si, Weinan Si Euclid Techlabs, LLC, Bolingbrook, IL, United States Search for other works by this author on: Oxford Academic Google Scholar June Lau, June Lau National Institute of Standards and Technology, District of Columbia, United States Search for other works by this author on: Oxford Academic Google Scholar Joshua Taillon, Joshua Taillon National Institute of Standards and Technology, District of Columbia, United States Search for other works by this author on: Oxford Academic Google Scholar Roberto Reis, Roberto Reis Northwestern University, NUANCE, Evanston, IL, United States Search for other works by this author on: Oxford Academic Google Scholar Laura Bartolo Laura Bartolo Center for Hierarchical Materials Design, Evanston, IL, United States Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 3044–3045, https://doi.org/10.1017/S1431927622011369 Published: 01 August 2022

Author(s): Si, Weinan | Advisor(s): Hanson, Gail G | Abstract: This thesis presents an on-going search for self-interacting dark matter from events with two displacedlepton-jets in the CMS detector with pp collision data taken at $\sqrt{s}=13\TeV$ correspondingto 59.74 $\text{fb}^{-1}$ of integrated luminosity during Run 2 of the LHC. Lepton-jet is a groupof collimated leptons in a narrow cone, which can be the signature of dark photon $Z_d$ --a theorized gauge boson which is charged under U(1)$_d$ gauge symmetry, bridging the dark sectorand the Standard Model. As the first-round analysis, this search focuses more on displaced dark photons decaying to muon pairor electron pair. The expected results are presented as 95\% confidence level upper limits on theself-interacting dark matter bound state production cross section, assuming the branch fractionof the bound state to dark photon pair is 100\%.

The automatization of failing workflow handling is discussed. Implementation of a pipeline for DAQ and ML of error logs using big data analysis tools is available at github. Development of a prototype NLP model in Keras is explored.