Respiratory tract viral infection caused by viruses or bacteria isone of the most common diseases in human worldwide, while those caused by emerging viruses, such as the novel coronavirus, 2019-nCoV that caused the pneumonia outbreak in Wuhan, China most recently, have posed great threats to global public health. Identification of the causative viral pathogens of respiratory tract viral infections is important to select an appropriate treatment, save people's lives, stop the epidemics, and avoid unnecessary use of antibiotics. Conventional diagnostic tests, such as the assays for rapid detection of antiviral antibodies or viral antigens, are widely used in many clinical laboratories. With the development of modern technologies, new diagnostic strategies, including multiplex nucleic acid amplification and microarray-based assays, are emerging. This review summarizes currently available and novel emerging diagnostic methods for the detection of common respiratory viruses, such as influenza virus, human respiratory syncytial virus, coronavirus, human adenovirus, and human rhinovirus. Multiplex assays for simultaneous detection of multiple respiratory viruses are also described. It is anticipated that such data will assist researchers and clinicians to develop appropriate diagnostic strategies for timely and effective detection of respiratory virus infections. K E Y W O R D S adenovirus, coronavirus, diagnostic methods, influenza virus, respiratory syncytial virus, respiratory viral infection, rhinovirus

The COVID-2019 disease caused by the SARS-CoV-2 virus (aka 2019-nCoV) has raised significant health concerns in China and worldwide. While novel drug discovery and vaccine studies are long, repurposing old drugs against the COVID-2019 epidemic can help identify treatments, with known preclinical, pharmacokinetic, pharmacodynamic, and toxicity profiles, which can rapidly enter Phase 3 or 4 or can be used directly in clinical settings. In this study, we presented a novel network based drug repurposing platform to identify potential drugs for the treatment of COVID-2019. We first analysed the genome sequence of SARS-CoV-2 and identified SARS as the closest disease, based on genome similarity between both causal viruses, followed by MERS and other human coronavirus diseases. Using our AutoSeed pipeline (text mining and database searches), we obtained 34 COVID-2019-related genes. Taking those genes as seeds, we automatically built a molecular network for which our module detection and drug prioritization algorithms identified 24 disease-related human pathways, five modules and finally suggested 78 drugs to repurpose. Following manual filtering based on clinical knowledge, we re-prioritized 30 potential repurposable drugs against COVID-2019 (including pseudoephedrine, andrographolide, chloroquine, abacavir, and thalidomide) . We hope that this data can provide critical insights into SARS-CoV-2 biology and help design rapid clinical trials of treatments against COVID-2019.

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The COVID-19 disease caused by the SARS-CoV-2 virus is a health crisis worldwide. While developing novel drugs and vaccines is long, repurposing existing drugs against COVID-19 can yield treatments with known preclinical, pharmacokinetic, pharmacodynamic, and toxicity profiles, which can rapidly enter clinical trials. In this study, we present a novel network-based drug repurposing platform to identify candidates for the treatment of COVID-19. At the time of the initial outbreak, knowledge about SARS-CoV-2 was lacking, but based on its similarity with other viruses, we sought to identify repurposing candidates to be tested rapidly at the clinical or preclinical levels. We first analyzed the genome sequence of SARS-CoV-2 and confirmed SARS as the closest virus by genome similarity, followed by MERS and other human coronaviruses. Using text mining and database searches, we obtained 34 COVID-19-related genes to seed the construction of a molecular network where our module detection and drug prioritization algorithms identified 24 disease-related human pathways, five modules, and 78 drugs to repurpose. Based on clinical knowledge, we re-prioritized 30 potentially repurposable drugs against COVID-19 (including pseudoephedrine, andrographolide, chloroquine, abacavir, and thalidomide). Our work shows how in silico repurposing analyses can yield testable candidates to accelerate the response to novel disease outbreaks.

Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author’s local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue–venue interaction. To solve this problem, we propose an author topic model–enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues’ capacity of exerting topic influence on other venues. The top-susceptibility captures venues’ propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-the-art methods.

The effects of an ideal/resistive conducting wall, the drift kinetic resonances, as well as the toroidal plasma flow, on the stability of the ideal external kink mode are numerically investigated for a reactor-relevant tokamak plasma with strongly negative triangularity (NTR) shaping. Comparison is made for a similar plasma equilibrium, but with positive triangularity (PTR). It is found that the ideal wall stabilization is less efficient for the kink stabilization in the NTR plasma due to a less 'external' eigenmode structure compared to the PTR plasma. The associated plasma displacement in the NTR plasma does not 'balloon' near the outboard mid-plane, as is normally the case for the pressure-driven kink-ballooning instability in PTR plasmas, but being more pronounced near the X-points. The toroidal flow plays a similar role for the kink stability for both NTR and PTR plasmas. The drift kinetic damping is less efficient for the ideal external kink mode in the NTR plasma, despite a somewhat larger fraction of the particle trapping near the plasma edge compared to the PTR equilibrium. However, the drift kinetic damping of the resistive wall mode (RWM) in the NTR plasma is generally as efficient as that of the PTR plasma, although the RWM window, in terms of the normalized pressure, is narrower for the NTR plasma.

Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this paper, we propose a novel embedding algorithm that incorporates network motifs to capture higher-order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms (by 20%) and the stateof-the-art embedding-based algorithms (by 19%).

Volatile organic compounds (VOCs) in urine are valuable biomarkers for noninvasive disease diagnosis. Herein, a facile coordination-driven modular assembly strategy is used for developing a library of gas-sensing materials based on porous MXene frameworks (MFs). Taking advantage of modules with diverse composition and tunable structure, our MFs-based library can provide more choices to satisfy gas-sensing demands. Meanwhile, the laserinduced graphene interdigital electrodes array and microchamber are laser-engraved for the assembly of a microchamber-hosted MF (MHMF) e-nose. Our MHMF e-nose possesses high-discriminative pattern recognition for simultaneous sensing and distinguishing of complex VOCs. Furthermore, with the MHMF e-nose being a plugand-play module, a point-of-care testing (POCT) platform is modularly assembled for wireless and real-time monitoring of urinary volatiles from clinical samples. By virtue of machine learning, our POCT platform achieves noninvasive diagnosis of multiple diseases with a high accuracy of 91.7%, providing a favorable opportunity for early disease diagnosis, disease course monitoring, and relevant research.