Live poultry trading drives China's H7N9 viral evolution and geographical network propagation

The on-going reassortment, human-adapted mutations, and spillover events of novel A(H7N9) avian influenza viruses pose a significant challenge to public health in China and globally. However, our understanding of the factors that disseminate the viruses and drive their geographic distributions is limited. We applied phylogenic analysis to examine the inter-subtype interactions between H7N9 viruses and the closest H9N2 lineages in China during 2010–2014. We reconstructed and compared the inter-provincial live poultry trading and viral propagation network via phylogeographic approach and network similarity technique. The substitution rates of the isolated viruses in live poultry markets and the characteristics of localized viral evolution were also evaluated. We discovered that viral propagation was geographically-structured and followed the live poultry trading network in China, with distinct north-to-east paths of spread and circular transmission between eastern and southern regions. The epicenter of H7N9 has moved from the Shanghai–Zhejiang region to Guangdong Province was also identified. Besides, higher substitution rate was observed among isolates sampled from live poultry markets, especially for those H7N9 viruses. Live poultry trading in China may have driven the network-structured expansion of the novel H7N9 viruses. From this perspective, long-distance geographic expansion of H7N9 were dominated by live poultry movements, while at local scales, diffusion was facilitated by live poultry markets with highly-evolved viruses

DIVA: A Dirichlet Process Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder

Generative model-based deep clustering frameworks excel in classifying complex data, but are limited in handling dynamic and complex features because they require prior knowledge of the number of clusters. In this paper, we propose a nonparametric deep clustering framework that employs an infinite mixture of Gaussians as a prior. Our framework utilizes a memoized online variational inference method that enables the "birth" and "merge" moves of clusters, allowing our framework to cluster data in a "dynamic-adaptive" manner, without requiring prior knowledge of the number of features. We name the framework as DIVA, a Dirichlet Process-based Incremental deep clustering framework via Variational Auto-Encoder. Our framework, which outperforms state-of-the-art baselines, exhibits superior performance in classifying complex data with dynamically changing features, particularly in the case of incremental features. We released our source code implementation at: https://github.com/Ghiara/divaComment: update supplementary material

Code Reviewer Recommendation Based on a Hypergraph with Multiplex Relationships

Code review is an essential component of software development, playing a vital role in ensuring a comprehensive check of code changes. However, the continuous influx of pull requests and the limited pool of available reviewer candidates pose a significant challenge to the review process, making the task of assigning suitable reviewers to each review request increasingly difficult. To tackle this issue, we present MIRRec, a novel code reviewer recommendation method that leverages a hypergraph with multiplex relationships. MIRRec encodes high-order correlations that go beyond traditional pairwise connections using degree-free hyperedges among pull requests and developers. This way, it can capture high-order implicit connectivity and identify potential reviewers. To validate the effectiveness of MIRRec, we conducted experiments using a dataset comprising 48,374 pull requests from ten popular open-source software projects hosted on GitHub. The experiment results demonstrate that MIRRec, especially without PR-Review Commenters relationship, outperforms existing stateof-the-art code reviewer recommendation methods in terms of ACC and MRR, highlighting its significance in improving the code review process.Comment: The 31st IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER

Seizing the window of opportunity to mitigate the impact of climate change on the health of Chinese residents

The health threats posed by climate change in China are increasing rapidly. Each province faces different health risks. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate and even prevent the achievement of the Healthy and Beautiful China initiatives. The 2021 China Report of the Lancet Countdown on Health and Climate Change is the first annual update of China’s Report of the Lancet Countdown. It comprehensively assesses the impact of climate change on the health of Chinese households and the measures China has taken. Invited by the Lancet committee, Tsinghua University led the writing of the report and cooperated with 25 relevant institutions in and outside of China. The report includes 25 indicators within five major areas (climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement) and a policy brief. This 2021 China policy brief contains the most urgent and relevant indicators focusing on provincial data: The increasing health risks of climate change in China; mixed progress in responding to climate change. In 2020, the heatwave exposures per person in China increased by 4.51 d compared with the 1986–2005 average, resulting in an estimated 92% increase in heatwave-related deaths. The resulting economic cost of the estimated 14500 heatwave-related deaths in 2020 is US$176 million. Increased temperatures also caused a potential 31.5 billion h in lost work time in 2020, which is equivalent to 1.3% of the work hours of the total national workforce, with resulting economic losses estimated at 1.4% of China’s annual gross domestic product. For adaptation efforts, there has been steady progress in local adaptation planning and assessment in 2020, urban green space growth in 2020, and health emergency management in 2019. 12 of 30 provinces reported that they have completed, or were developing, provincial health adaptation plans. Urban green space, which is an important heat adaptation measure, has increased in 18 of 31 provinces in the past decade, and the capacity of China’s health emergency management increased in almost all provinces from 2018 to 2019. As a result of China’s persistent efforts to clean its energy structure and control air pollution, the premature deaths due to exposure to ambient particulate matter of 2.5 μm or less (PM2.5) and the resulting costs continue to decline. However, 98% of China’s cities still have annual average PM2.5 concentrations that are more than the WHO guideline standard of 10 μg/m3. It provides policymakers and the public with up-to-date information on China’s response to climate change and improvements in health outcomes and makes the following policy recommendations. (1) Promote systematic thinking in the related departments and strengthen multi-departmental cooperation. Sectors related to climate and development in China should incorporate health perspectives into their policymaking and actions, demonstrating WHO’s and President Xi Jinping’s so-called health-in-all-policies principle. (2) Include clear goals and timelines for climate-related health impact assessments and health adaptation plans at both the national and the regional levels in the National Climate Change Adaptation Strategy for 2035. (3) Strengthen China’s climate mitigation actions and ensure that health is included in China’s pathway to carbon neutrality. By promoting investments in zero-carbon technologies and reducing fossil fuel subsidies, the current rebounding trend in carbon emissions will be reversed and lead to a healthy, low-carbon future. (4) Increase awareness of the linkages between climate change and health at all levels. Health professionals, the academic community, and traditional and new media should raise the awareness of the public and policymakers on the important linkages between climate change and health.</p

Healthy cities initiative in China: Progress, challenges, and the way forward

Article discusses how China implemented the first phase of its National Healthy Cities pilot program from 2016-20. Authors recommend aligning the Healthy Cities initiative in China with strategic national and global level agendas such as Healthy China 2030 and the Sustainable Development Goals (SDGs) by providing an integrative governance framework to facilitate a coherent intersectoral program to systemically improve population health

The Lancet Countdown: tracking progress on health and climate change

The Lancet Countdown: tracking progress on health and climate change is an international, multidisciplinary research collaboration between academic institutions and practitioners across the world. It follows on from the work of the 2015 Lancet Commission, which concluded that the response to climate change could be “the greatest global health opportunity of the 21st century”. The Lancet Countdown aims to track the health impacts of climate hazards; health resilience and adaptation; health co-benefits of climate change mitigation; economics and finance; and political and broader engagement. These focus areas form the five thematic working groups of the Lancet Countdown and represent different aspects of the complex association between health and climate change. These thematic groups will provide indicators for a global overview of health and climate change; national case studies highlighting countries leading the way or going against the trend; and engagement with a range of stakeholders. The Lancet Countdown ultimately aims to report annually on a series of indicators across these five working groups. This paper outlines the potential indicators and indicator domains to be tracked by the collaboration, with suggestions on the methodologies and datasets available to achieve this end. The proposed indicator domains require further refinement, and mark the beginning of an ongoing consultation process—from November, 2016 to early 2017—to develop these domains, identify key areas not currently covered, and change indicators where necessary. This collaboration will actively seek to engage with existing monitoring processes, such as the UN Sustainable Development Goals and WHO's climate and health country profiles. The indicators will also evolve over time through ongoing collaboration with experts and a range of stakeholders, and be dependent on the emergence of new evidence and knowledge. During the course of its work, the Lancet Countdown will adopt a collaborative and iterative process, which aims to complement existing initiatives, welcome engagement with new partners, and be open to developing new research projects on health and climate change

Overexpression of Banana ATG8f Modulates Drought Stress Resistance in Arabidopsis

Autophagy is essential for plant growth, development, and stress resistance. However, the involvement of banana autophagy-related genes in drought stress response and the underlying mechanism remain elusive. In this study, we found that the transcripts of 10 banana ATG8s responded to drought stress in different ways, and MaATG8f with the highest transcript in response to drought stress among them was chosen for functional analysis. Overexpression of MaATG8f improved drought stress resistance in Arabidopsis, with lower malonaldehyde level and higher level of assimilation rate. On the one hand, overexpression of MaATG8f activated the activities of superoxide dismutase, catalase, and peroxidase under drought stress conditions, so as to regulate reactive oxygen species accumulation. On the other hand, MaATG8f-overexpressing lines exhibited higher endogenous abscisic acid (ABA) level and more sensitivity to abscisic acid. Notably, the autophagosomes as visualized by CaMV35S::GFP&ndash;MaATG8f was activated after ABA treatment. Taken together, overexpression of MaATG8f positively regulated plant drought stress resistance through modulating reactive oxygen species metabolism, abscisic acid biosynthesis, and autophagic activity

Prognostic Value of CAV1 and CAV2 in Head and Neck Squamous Cell Carcinoma

Background: The CAV family, especially CAV1 and CAV2, is significantly associated with tumor development. In this study, we aimed to explore the pathogenic and prognostic roles of CAV1 and CAV2 in head and neck squamous cell carcinoma (HNSCC) through bioinformatic analysis and verified in human tissue. Methods: We analyzed expression profiles of CAV1 and CAV2 in HNSCC and in normal tissues via data from The Cancer Genome Altas. Prognostic significance was examined by Kaplan–Meier survival curve obtained from the Xena browser together with Cox regression analysis. Co-expressed genes were uploaded to GeneMANIA and applied to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, showing interaction networks. Signaling pathways of CAV1 and CAV2 in HNSCC were analyzed by Gene Set Enrichment Analysis to elucidate potential regulatory mechanisms. Gene–drug interaction network was explored via Comparative Toxicogenomics Database. Immunohistochemistry was performed to verify theoretical results. Results: Compared with normal tissues, expression levels of CAV1 and CAV2 were remarkably higher in HNSCC (p p = 0.027; CAV2: HR: 1.408, p = 0.002). Co-expressed genes (PXN, ITGA3, TES, and MET) were identified and analyzed by FunRich with CAV1 and CAV2, revealing a significant correlation with focal adhesion (p p p p < 0.05). Conclusions: The results revealed that CAV1 and CAV2 are typically upregulated in HNSCC and might predict poor prognosis