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Background: The COVID-19 outbreak is a serious concern and has caused a great loss to the global economy. Therefore, COVID-19 has become an urgent public health problem. Although new vaccines and small molecule drugs are now available, these prevention and treatment methods cannot completely control the epidemic due to the constant mutation of SARS-CoV-2. Targeting 3CLpro/RdRp is expected to develop drugs that are not susceptible to the mutation of SARS-COV-2, and it will also have a certain effect on the coronavirus that may appear in the future. Objective: This study aimed to find small molecules against SARS-CoV-2 with research potential and provide relevant data for the rational development of anti-SARS-COV-2 drugs. Methods: Targeting 3CLpro/RdRp, using Shards database (120,000 natural small molecule compounds) in the ZINC database, adopting a step-by-step screening strategy, and taking Lopinavir, Indinavir, and Molnupiravir as screening criteria was done. Moreover, the top scoring compounds were screened using rigid docking, and molecular dynamics simulation and ADME prediction were performed. Finally, the molecules with better scores were screened out. Results: After molecular docking with 3CLpro as the target, 3207 compounds meeting the screening criteria were obtained. After applying Lipinski's rule of five for drug property screening, 1825 compounds that met the criteria were obtained. After molecular docking with RdRp as the target, ZINC04259665 has a good docking score. According to molecular dynamics simulation results, ZINC04259665 is stable in combination with 3CLpro/RdRp. ADME prediction shows that ZINC04259665 has good druggability. Conclusion: Using 3CLpro/RdRp targets and then using a step-by-step strategy to screen the compound with the highest score through molecular dynamics simulation and ADME prediction, it was found that ZINC04259665 has good development potential and can be used as a follow-up hit compound for research. In addition, the data obtained provide relevant information for the rational development of anti- SARS-COV-2 drugs.

Topology impacts important network performance metrics, including link utilization, throughput and latency, and is of central importance to network operators. However, due to the combinatorial nature of network topology, it is extremely difficult to obtain an optimal solution, especially since topology planning in networks also often comes with management-specific constraints. As a result, local optimization with hand-tuned heuristic methods from human experts are often adopted in practice. Yet, heuristic methods cannot cover the global topology design space while taking into account constraints, and cannot guarantee to find good solutions. In this paper, we propose a novel deep reinforcement learning (DRL) algorithm, called Advantage Actor Critic-Graph Searching (A2C-GS), for network topology optimization. A2C-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating, and a DRL actor layer to conduct a topology search. A2C-GS can efficiently search over large topology space and output topology with satisfying performance. We conduct a case study based on a real network scenario, and our experimental results demonstrate the superior performance of A2C-GS in terms of both efficiency and performance.

The 21st century is the era of big data. The wide application of big data in various fields of society has triggered changes in all walks of life, and it has also brought new opportunities and challenges to the cultivation of talents in colleges and universities. The entrepreneurial environment has a strong influence on entrepreneurial activities, and the choice of entrepreneurial environment plays an important role in college students’ entrepreneurship. College students’ entrepreneurship is carried out in a certain environment, and the quality of the entrepreneurial environment will directly affect the effectiveness of college students’ entrepreneurial activities. Under the background of the development of the Internet economy, college students have more ways to start their own businesses. With the support of big data, the types, methods, and performance evaluation of college students’ entrepreneurship can be evaluated based on the effective application of big data. Big data is an important means of constructing an environment for innovation and entrepreneurship. In order to solve the problems of “information island” and low data utilization in the existing innovation and entrepreneurship information platform in colleges and universities, we use big data technology to break the data island and integrate multichannel, multilevel, and multitype data, so as to build a perfect university. The entrepreneurial environment of graduates is of great significance.

People's willingness to forgive corrupt government officials has intrigued many researchers. According to a prominent explanation, citizens tolerate corrupt officials in exchange for their ability to deliver public benefits, such as promoting economic development. We contextualize this corruption–competence tradeoff thesis by assessing individuals' evaluations of local officials in China. We conduct a nationwide vignette experiment with 5527 citizens, and find that the corruption–competence tradeoff exists and is hierarchical. Respondents prefer competent but corrupt low-level officials over those who are honest but incompetent, but this relative preference vanishes when they evaluate high-level local officials. Our interviews reveal that proximity to citizens and position in the power hierarchy primarily drive citizens' sophisticated assessments of officials at different levels.

Hydrothermal technology (HT) has received much attention in recent years as a process to convert wet organic waste into hydrochar. The aqueous phase (HTAP) produced by this process is still a burden and has become a bottleneck issue for HT process development. In this study, we provide the first investigation of the HTAP characteristics, phytotoxicity, and their correlation with persulfate (PS) (PS, 2.0 mmol/g TS)-assisted municipal sludge HT. The results showed that PS accelerated the hydrolysis of protein substances and increased the concentration of NH

Introduction: The third person pronoun and demonstrative resolution are influenced by multiple factors in Mandarin Chinese. The factors influencing reference choice and the relations between discourse prominence and pronoun resolution are discussed. It suggests discourse prominence is an essential organizational principle in reference resolution. The factors that influence pronoun resolution in Mandarin Chinese might be studied and explained from the perspective of discourse prominence. Methodology: A formal operational scheme with conditional inference recursive partitioning tree and random forest analysis analyzing discourse data collected from a picture-sequence-based narrative elicitation method is provided. Results: The results confirmed the idea that the discourse prominence-lending cues, including thematic role, animacy, grammatic role, topic, referential distance, and mentioned number, influence pronoun resolution in Mandarin Chinese. Animacy and reference distance might be involved in the important variances, and reference distance relates to topic maintenance, discourse dynamicity, and structural attraction. Conclusion: The findings demonstrated that the discourse prominence-lending cues influence pronoun resolution in Mandarin Chinese. The third person pronouns often signal topic maintenance and high discourse prominence, while demonstratives often signal topic shift and low discourse prominence. Moreover, demonstratives often signal focus reinforcement. Therefore, topic and focus are also considered as the two crucial elements affecting pronoun resolution in Mandarin Chinese.

With the deepening of the research on knowledge graph embedding, temporal knowledge graphs (TKGs), which are dynamic changes over time, have gradually gained the attention of researchers. Although some TKG-embedding models have been proposed, they did not perform well for certain relationships with insufficient samples, since they all require tremendous training samples. Thus, few-shot link prediction tasks, namely predicting new relation-specific quadruples by observing only a few samples, are still very challenging. In this paper, a method named meta-reasoning for TKGs (MetaRT) is proposed to solve this universal but challenging problem. MetaRT works by extracting the meta-information of a specific relation and updating it quickly, so that the model can learn the most critical information in TKG swiftly and independently. In the meantime, temporal information can be managed well by a TKG learner. Finally, through a large number of experiments, it shows that MetaRT outperforms other existing TKG-embedding models on the problem of few-shot learning.

The COVID-19 pandemic brings the topic of citizen data management (CDAMA) into the public eye. This study is one of the first attempts to analyze the national approaches for CDAMA applied by governments of different countries and continents in public sectors. The study first conducts a systematic overview of the representative contact tracing apps in 21 countries of four continents, collecting information of the four aspects of the CDAMA system. It then summarizes and analyzes the various governments’ approaches to the CDAMA system applied by different countries and continents based on the app overview. We found that governments’ priority between national safety (i.e., public health in this study) and citizen privacy is different in terms of their national approaches for CDAMA. For example, governments of Asian countries are more intrusive and hold a stricter attitude in their national CDAMA approach than countries elsewhere. Our study has contributions both theoretically and practically. Theoretically, it fills the literature gap about data management by discussing the data management in governments; practically, the study provides the background information as well as implications for future debates and discussions on governments’ data management system and citizen data use.

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Jun-Jie Zhang,1,* Yu Zhang,2,* Qian Chen,1 Qi-Ning Chen,1 Xin Yang,1 Xiao-Lin Zhu,1 Chun-Yan Hao,3 Hu-Bin Duan1 1Department of Neurosurgery, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, 030001, People’s Republic of China; 2Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China; 3Department of Geriatrics, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, 030001, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hu-Bin Duan; Chun-Yan Hao, Tel +8613994201664 ; +8613803494928, Email hubinduan68@163.com; haochunyan68@126.comBackground: Immune microenvironment serves a vital role in glioma progression, and a large number of studies have found that tumor progression can be reduced to some extent by modulating the immune process in tumors.Materials and Methods: ImmuneScore of each sample in CGGA datasets were calculated with Estimate R package, and samples were grouped by median ImmuneScore values for differential analysis to obtain immune microenvironment differential genes. We further conducted survival analysis, ROC curve analysis, independent prognostic analysis, and clinical correlation analysis on glioma sample genes in CGGA to obtain glioma prognostic genes, and then identified their intersection with immune microenvironment DEGs by Venn tool. The GEPIA and UALCAN databases were used to verify the differential expression of intersecting genes in the glioma and normal brain and to identify our target gene. After validation of their prognostic value, we constructed a nomogram to calculate the risk score and to estimate the accuracy of prognostic model. We mined co-expression genes, enriched functions and pathways, and correlations to immune cell infiltration of unigene with an online database. Finally, we verified the differential expression of FCGBP in glioma by immunohistochemical staining.Results: We finally selected Fc fragment of IgG-binding protein (FCGBP) as our study gene. The prognostic values of FCGBP were validated by a series of analyses. Immunohistochemical staining showed that FCGBP expression increased in gliomas and was up-regulated with the progression of glioma grade.Conclusion: As a key unigene in glioma progression, FCGBP contributes to the regulation of immune microenvironment and has the potential to be a prognostic biomarker and immune targets.Keywords: glioma, immune microenvironment, FCGBP, tumor progression, therapeutic target

PurposePromoting clean heating in rural areas is crucial for achieving a low-carbon transition of energy consumption and China's dual-carbon target. The study aims to consider the energy stacking behavior in heating energy use, reveals the determinants that affect household cleaner heating choices under the winter clean heating plan (WCHP), and proposes policy recommendations for the sustainable promotion of clean heating.Design/methodology/approachWith unique rural household survey data covering the clean heating pilot regions in northern China in 2020, this study estimates the relationship between driving factors and heating energy choices through binary and multivariate probit models.FindingsThe regression estimates show that the main drivers of heating energy choices include household income per capita, education level of household head, knowledge of the WCHP, access to heating subsidies and perception of indoor air pollution. There is energy stacking behavior in rural household heating energy use. Household decisions to adopt electricity or clean coal heating are correlated with firewood or soft coal use.Originality/valueThis study is one of the few to investigate the heating energy use of rural households by allowing for the adoption of multiple energy types. Combined with a unique microsurvey dataset, it could provide rich information for formulating proper energy transition planning. The findings also shed light on the importance of heating subsidies, households' knowledge of WCHP and awareness of environmental health in choosing clean heating energy, which has not been fully valued in related research.

MAXPEEM, a dedicated photoemission electron microscopy beamline at MAX IV Laboratory, houses a state-of-the-art aberration-corrected spectroscopic photoemission and low-energy electron microscope (AC-SPELEEM). This powerful instrument offers a wide range of complementary techniques providing structural, chemical and magnetic sensitivities with a single-digit nanometre spatial resolution. The beamline can deliver a high photon flux of ≥1015 photons s−1 (0.1% bandwidth)−1 in the range 30–1200 eV with full control of the polarization from an elliptically polarized undulator. The microscope has several features which make it unique from similar instruments. The X-rays from the synchrotron pass through the first beam separator and impinge the surface at normal incidence. The microscope is equipped with an energy analyzer and an aberration corrector which improves both the resolution and the transmission compared with standard microscopes. A new fiber-coupled CMOS camera features an improved modulation transfer function, dynamic range and signal-to-noise ratio compared with the traditional MCP-CCD detection system.

Yang Deng,1,* Si-Yu Wang,2,* Qing-Guang Wang,3,* Zhao-Han Xu,2 Qiang Peng,2 Shuai-Yu Chen,2 Lin Zhu,2 Ying-Dong Zhang,1,2 Rui Duan1,2 1Department of Neurology, Nanjing First Hospital, China Pharmaceutical University, Nanjing, People’s Republic of China; 2Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, People’s Republic of China; 3Department of Neurology, Jiangyin Hospital Affiliated to Nantong University, Jiangyin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ying-Dong Zhang; Rui Duan, Department of Neurology, Nanjing First Hospital, China Pharmaceutical University, No.68, Changle Road, Nanjing, Jiangsu, People’s Republic of China, Email zhangyingdong@njmu.edu.cn; duanruicpu@163.comPurpose: Our previous study has shown that AVE 0991, a nonpeptide analogue of Ang-(1-7), ameliorates cognitive decline and inhibits NLRP3 inflammasome of astrocytes in Alzheimer’s disease model mice. Additionally, several studies have suggested that activation of autophagy appears to effectively inhibit the progression of neuroinflammation. However, it is unclear whether AVE 0991 can modulate astrocyte autophagy to suppress neuroinflammation in Alzheimer’s disease.Materials and Methods: APP/PS1 mice and Aβ-treated primary astrocytes were used as the research objects in vivo and in vitro, respectively. Water maze test was used to evaluate cognitive function of mice, Nissl staining and immunofluorescence staining was used to assess neuronal damage. ELISA kits were used to detect the levels of Ang-(1-7) and Aβ in the cortex, and qRT-PCR was used to detect the expression of cortical inflammation-related mediators. The expression of autophagy-related proteins in cortex were detected by Western blot. The upstream molecular responses involved in inflammation inhibition by AVE 0991 were validated by means of using the Mas1 antagonist and autophagy inhibitor.Results: We found that 30 days of intraperitoneal administration of AVE 0991 improved. Aβ deposition, neuronal death, and cognitive deficits in APP/PS1 Alzheimer’s disease model mice. Moreover, AVE 0991 treatment greatly suppressed astrocyte-mediated inflammation and up-regulated the expression of autophagy. Furthermore, the inhibitory effect of AVE 0991 on the expression of inflammatory factors was reversed by 3-MA, an autophagy inhibitor.Conclusion: These findings suggest that regulation of autophagy is critical for inhibiting astrocyte neuroinflammatory responses and demonstrate a potential neuroprotective mechanism by which AVE 0991 could suppress neuroinflammatory responses by enhancing autophagy.Keywords: AVE 0991, neuroinflammation, Alzheimer, autophagy, astrocytes

Bronchial asthma is a common clinical disease in pediatrics. It affects brain function and causes cognitive impairment due to long-term hypoxia in the brain. Studies have shown attention deficits in asthma children, but lack the basis for brain studies. Functional magnetic resonance imaging (fMRI) based on functional connectivity analysis was used in this study, and 31 asthma children and typically developing children (TDC) were compared in degree centricity (DC), voxel-mirrored homotopic connectivity (VMHC) and continuous performance test (CPT). The study found that the correct number of CPT in the asthma group was significantly lower than the TDC group. And the reaction time in the asthma group was significantly longer than the TDC group. Compared with TDC group, asthma children exhibited lower DC value in the right superior frontal gyrus (after FDR correction, P