Middle East respiratory syndrome (MERS) is a viral respiratory disease caused by a de novo coronavirus—Middle East respiratory syndrome coronavirus (MERS-CoV)—that is associated with high mortality. However, the mechanism by which MERS-CoV infects humans remains unclear. To date, there is no effective vaccine or antibody for human immunity and treatment, other than the safety and tolerability of the fully human polyclonal Immunoglobulin G (IgG) antibody (SAB-301) as a putative therapeutic agent specific for MERS. Although rapid diagnostic and public health measures are currently being implemented, new cases of MERS-CoV infection are still being reported. Therefore, various effective measures should be taken to prevent the serious impact of similar epidemics in the future. Further investigation of the epidemiology and pathogenesis of the virus, as well as the development of effective therapeutic and prophylactic anti-MERS-CoV infections, is necessary. For this purpose, detailed information on MERS-CoV proteins is needed. In this review, we describe the major structural and nonstructural proteins of MERS-CoV and summarize different potential strategies for limiting the outbreak of MERS-CoV. The combination
of computational biology and virology can accelerate the advanced design and development of effective peptide therapeutics against MERS-CoV. In summary, this review provides important information about the progress of the elimination of MERS, from prevention to treatment.
In this commentary, I explain my perspective on the relationship between artificial intelligence (AI)/data science and biomedicine from a long-range retrospective view. The development of modern biomedicine has always been accelerated by the repeated emergence of new technologies. Since all life systems are basically governed by the information in their own DNA, information science has special importance for the study of biomedicine. Unlike in physics, no (or very few) leading laws have been found in biology. Thus, in biology, the "data-to-knowledge" approach is important. AI has historically been applied to biomedicine, and the recent news that an AI-based approach achieved the best performance in an international competition of protein structure prediction may be regarded as another landmark in the field. Similar approaches could contribute to solving problems in genome sequence interpretation, such as identifying cancer-driving mutations in the genome of patients. Recently, the explosive development of next-generation sequencing (NGS) has been producing massive data, and this trend will accelerate. NGS is not only used for "reading" DNA sequences, but also for obtaining various types of information at the single-cell level. These data can be regarded as grid data points in climate simulation. Both data science and AI will become essential for the integrative interpretation/simulation of these data, and will take a leading role in future precision medicine.
Smart manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process-safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging information technologies such as artificial intelligence (AI) are quite promising as a means of overcoming these difficulties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety; ② knowledge-based reasoning for process safety; ③ accurate fusion of heterogeneous data from various sources; and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.
Safe, efficient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitations in practice. The burgeoning era of big data is influencing the process industries tremendously, providing unprecedented opportunities to achieve smart manufacturing. This kind of manufacturing requires machines to not only be capable of relieving humans from intensive physical work, but also be effective in taking on intellectual labor and even producing innovations on their own. To attain this goal, data analytics and machine learning are indispensable. In this paper, we review recent advances in data analytics and machine learning applied to the monitoring, control, and optimization of industrial processes, paying particular attention to the interpretability and functionality of machine learning models. By analyzing the gap between practical requirements and the current research status, promising future research directions are identified.