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In this study, we describe the epidemiological investigation of the first African swine fever (ASF) outbreak in a local domestic pig farm in the New Territories of Hong Kong in 2021. In the outbreak farm, several affected pigs presented clinical and pathological signs consistent with ASF, while the remaining pigs showed nonspecific clinical signs or did not exhibit any clinical signs. The relative low morbidity and mortality of ASF on this farm resulted in delayed detection and implementation of the control response. Despite this delay, no further spread of the disease from this farm to other farms or wild boars was observed. The clinical presentation of ASF in terms of morbidity and mortality on this farm indicated that it is essential for effective surveillance aimed at early detection for farmers, veterinarians, and pathologists to be educated about the different ways ASF can express itself in domestic pig populations. Epidemiological investigations consisted of field inspection, interviews with farm personnel to assess the management and biosecurity practices within the farm, and laboratory testing of animal and environmental samples. In addition, the complete genome of ASFV was obtained directly from the tissues of an infected pig to facilitate the epidemiological investigation. The genetic relationship at the whole genome level indicated that the isolate shared the highest level of similarity with genotype II ASFVs, including a 2019 isolate from Guangdong province, China (GD2019). Overall, the information presented here from the on-farm investigation with that from diagnostic testing and molecular analyses provides a basis for informed actions to prevent future incidents in farms with similar characteristics. Furthermore, this study highlighted the need to increase current knowledge about the molecular diversity amongst circulating viruses and potentially trace the source of infection.

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Throughout the COVID-19 pandemic, quarantines, mask mandates, product shortages, and business closures have caused serious impact in virtually all regions of the world. The COVID-19 has devastated the modern society and economy, which is particularly detrimental to the food supply chain industry. The crisis is expected to be long-lasting, which demands the food supply chain industry to responsively adapt and evolve through transformation and optimization of its management model. This study examines the impact of the COVID-19 pandemic on the food supply chain in China regarding its disruptions as well as challenges and explores the potential optimization solution to recalibrate its management model. Specifically, it looks at the phenomenon of delays and stoppages in food factories, shortage issues in an imported food market, rising costs and alarming food safety concerns that food supplies encountered, vexing food transportation issues, and the domino effect within the entire industry. To better cope with these problems, the study also aims at developing practical solutions to optimize the supply chain across sectors, including building and maintaining a cooperative relationship with suppliers, initiating contingency plans, and employing effective tools to maximize food traceability. Furthermore, study limitations and future research directions are discussed.

Most machine-learning algorithms assume that instances are independent of each other. This does not hold for networked data. Node representation learning (NRL) aims to learn low-dimensional vectors to represent nodes in a network, such that all actionable patterns in topological structures and side information can be preserved. The widespread availability of networked data, e.g., social media, biological networks, and traffic networks, along with plentiful applications, facilitate the development of NRL. However, it has become challenging for researchers and practitioners to track the state-of-the-art NRL algorithms, given that they were evaluated using different experimental settings and datasets. To this end, in this paper, we focus on unsupervised NRL and propose a fair and comprehensive evaluation framework to systematically evaluate state-of-the-art unsupervised NRL algorithms. We comprehensively evaluate each algorithm by applying it to three evaluation tasks, i.e., classification fine tuned via a validation set, link prediction fine-tuned in the first run, and classification fine tuned via link prediction. In each task and each dataset, all NRL algorithms were fine-tuned using a random search within a fixed amount of time. Based on the results for three tasks and eight datasets, we evaluate and rank thirteen unsupervised NRL algorithms.