Web of scholars : a scholar knowledge graph

In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science. Relying on the knowledge graph, it provides services for fast, accurate, and intelligent semantic querying as well as powerful recommendations. In addition, in order to realize information sharing, it provides open API to be served as the underlying architecture for advanced functions. Web of Scholars takes advantage of knowledge graph, which means that it will be able to access more knowledge if more search exist. It can be served as a useful and interoperable tool for scholars to conduct in-depth analysis within Science of Science. © 2020 ACM

Alpha and lambda interferon together mediate suppression of CD4 T cells induced by respiratory syncytial virus

The mechanism by which respiratory syncytial virus (RSV) suppresses T-cell proliferation to itself and other antigens is poorly understood. We used monocyte-derived dendritic cells (MDDC) and CD4 T cells and measured [(3)H]thymidine incorporation to determine the factors responsible for RSV-induced T-cell suppression. These two cell types were sufficient for RSV-induced suppression of T-cell proliferation in response to cytomegalovirus or Staphylococcus enterotoxin B. Suppressive activity was transferable with supernatants from RSV-infected MDDC and was not due to transfer of live virus or RSV F (fusion) protein. Supernatants from RSV-infected MDDC, but not MDDC exposed to UV-killed RSV or mock conditions, contained alpha interferon (IFN-alpha; median, 43 pg/ml) and IFN-lambda (approximately 1 to 20 ng/ml). Neutralization of IFN-alpha with monoclonal antibody (MAb) against one of its receptor chains, IFNAR2, or of IFN-lambda with MAb against either of its receptor chains, IFN-lambdaR1 (interleukin 28R [IL-28R]) or IL-10R2, had a modest effect. In contrast, blocking the two receptors together markedly reduced or completely blocked the RSV-induced suppression of CD4 T-cell proliferation. Defining the mechanism of RSV-induced suppression may guide vaccine design and provide insight into previously uncharacterized human T-cell responses and activities of interferons

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The incomparability of cause of death statistics under “one country, two systems”: Shanghai versus Hong Kong

Abstract Background Valid and comparable cause of death (COD) statistics are crucial for health policy analyses. Variations in COD assignment across geographical areas are well-documented while socio-institutional factors may affect the process of COD and underlying cause of death (UCD) determination. This study examines the comparability of UCD statistics in Hong Kong and Shanghai, having two political systems within one country, and assesses how socio-institutional factors influence UCD comparability. Methods A mixed method was used. Quantitative analyses involved anonymized official mortality records. Mortality rates were analyzed by location of death. To analyze the odds ratio of being assigned to a particular UCD, logistic regressions were performed. Qualitative analyses involved literature reviews and semi-structural interviews with key stakeholders in death registration practices. Thematic analysis was used. Results Age-standardized death rates from certain immediate conditions (e.g., septicemia, pneumonia, and renal failure) were higher in Hong Kong. Variations in UCD determination may be attributed to preference of location of death, procedures of registering deaths outside hospital, perceptions on the causal chain of COD, implications of the selected UCD for doctors’ professional performance, and governance and processes of data quality review. Conclusions Variations in socio-institutional factors were related to the process of certifying and registering COD in Hong Kong and Shanghai. To improve regional data comparability, health authorities should develop standard procedures for registering deaths outside hospital, provide guidelines and regular training for doctors, develop a unified automated coding system, consolidate a standard procedure for data review and validity checks, and disseminate information concerning both UCD and multiple causes of death

The Application of Artificial Intelligence Could Improve Primary Health Care Provision in China

Recent advancement in artificial intelligence (AI) technology and increased availability of digital clinical data provide an opportunity to apply AI to primary health care provision in China. The lack of effective information exchange and patients' two-way referral mechanism, unfair user fee schedules for primary care and specialist care, low rating on care quality from primary care physicians, and ailing doctor-patient relationship are major challenges faced by primary health care in China. All of these drive patients to attend specialist care directly in secondary or tertiary hospitals. The application of AI would assist primary care physicians to provide better care in terms of examination, diagnoses and prescriptions and care planning. This would improve patients'confidence in primary care services and attract patients to choose primary care service. The application would contain health costs, increase the efficiency and transparency of medical system and narrow geographical gaps in medical resources. It would also offset the lack of quality and quantity of primary care physicians, improve doctor-patient relationship, and better primary care in China

MIRROR: Mining Implicit Relationships via Structure-Enhanced Graph Convolutional Networks

Data explosion in the information society drives people to develop more effective ways to extract meaningful information. Extracting semantic information and relational information has emerged as a key mining primitive in a wide variety of practical applications. Existing research on relation mining has primarily focused on explicit connections and ignored underlying information, e.g., the latent entity relations. Exploring such information (defined as implicit relationships in this article) provides an opportunity to reveal connotative knowledge and potential rules. In this article, we propose a novel research topic, i.e., how to identify implicit relationships across heterogeneous networks. Specially, we first give a clear and generic definition of implicit relationships. Then, we formalize the problem and propose an efficient solution, namely MIRROR, a graph convolutional network (GCN) model to infer implicit ties under explicit connections. MIRROR captures rich information in learning node-level representations by incorporating attributes from heterogeneous neighbors. Furthermore, MIRROR is tolerant of missing node attribute information because it is able to utilize network structure. We empirically evaluate MIRROR on four different genres of networks, achieving state-of-the-art performance for target relations mining. The underlying information revealed by MIRROR contributes to enriching existing knowledge and leading to novel domain insights. © 2023 Association for Computing Machinery

Modeling the Spread of Epidemics Based on Cellular Automata

Mathematical modeling is a powerful tool to study the process of the spread of infectious diseases. Among various mathematical methods for describing the spread of infectious diseases, the cellular automaton makes it possible to explicitly simulate both the spatial and temporal evolution of epidemics with intuitive local rules. In this paper, a model is proposed and realized on a cellular automata platform, which is applied to simulate the spread of coronavirus disease 2019 (COVID-19) for different administrative districts. A simplified social community is considered with varying parameters, e.g., sex ratio, age structure, population movement, incubation and treatment period, immunity, etc. COVID-19 confirmation data from New York City and Iowa are adopted for model validation purpose. It can be observed that the disease exhibits different spread patterns in different cities, which could be well accommodated by this model. Then, scenarios under different control strategies in the next 100 days in Iowa are simulated, which could provide a valuable reference for decision makers in identifying the critical factors for future infection control in Iowa