Structural biology of flavivirus NS1 protein and its antibody complexes.

The genus of flavivirus includes many mosquito-borne human pathogens, such as Zika (ZIKV) and the four serotypes of dengue (DENV1-4) viruses, that affect billions of people as evidenced by epidemics and endemicity in many countries and regions in the world. Among the 10 viral proteins encoded by the viral genome, the nonstructural protein 1 (NS1) is the only secreted protein and has been used as a diagnostic biomarker. NS1 has also been an attractive target for its biotherapeutic potential as a vaccine antigen.

Deep learning modeling of public's sentiments towards temporal evolution of COVID-19 transmission.

Public sentiments towards global pandemics are important for public health assessment and disease control. This study develops a modularized deep learning framework to quantify public sentiments towards COVID-19, followed by leveraging the predicted sentiments to model and forecast the daily growth rate of confirmed COVID-19 cases globally, via a proposed G parameter. In the proposed framework, public sentiments are first modeled via a valence dimensional indicator, instead of discrete schemas, and are classified into 4 primary emotional categories: (a) neutral; (b) negative; (c) positive; (d) ambivalent, by using multiple word embedding models and classifiers for text sentiments analyses and classification.

Improving hand hygiene in a medical ward: a multifaceted approach.

Background: Hand hygiene is a fundamental action which is simple, inexpensive and an effective tool in reducing hospital-acquired infections, yet compliance remains low in healthcare settings. In 2014, Changi General Hospital embarked on a pilot project to improve hand hygiene compliance in a pilot ward with the intention to eventually spread a multifaceted set of interventions hospital wide.

Methods: A before and after interventional study of a pilot project.

Data-driven multiscale modelling and analysis of COVID-19 spatiotemporal evolution using explainable AI.

To quantificationally identify the optimal control measures for regulators to best minimize COVID-19's growth (G-rate) and death (D-rate) rates in today's context, this paper develops a top-down multiscale engineering approach which encompasses a series of systematic analyses, namely: (global scale) predictive modelling of G-rate and D-rate due to COVID-19 globally, followed by determining the most effective control factors which can best minimize both parameters over time via explainable Artificial Intelligence (AI) with SHAP (SHapley Additive exPlanations) method; (continental scale) same predictive forecasting of G-rate and D-rate in all continents, followed by performing explainable SHAP analysis to determine the most effective control factors for the respective continents; and (country scale) clustering the different countries (> 150 in total) into 3 main clusters to identify the universal set of effective control measures. By using the historical period between 2 May 2020 and 1 Oct 2021, the average MAPE scores for forecasting G-rate and D-rate are within 10%, or less on average, at the global and continental scales. Systematically, we have quantificationally demonstrated that the top 3 most effective control measures for regulators to best minimize G-rate universally are COVID-CONTACT-TRACING, PUBLIC-GATHERING-RULES, and COVID-STRINGENCY-INDEX, while the control factors relating to D-rate depend on the modelling scenario.

Hybrid deep learning of social media big data for predicting the evolution of COVID-19 transmission.

In this study, a hybrid deep-learning model termed as ODANN, built upon neural networks (NN) coupled with data assimilation and natural language processing (NLP) features extraction methods, has been constructed to concurrently process daily COVID-19 time-series records and large volumes of COVID-19 related Twitter data, as representative of the global community's aggregated emotional responses towards the current pandemic, to model the growth rate in the number of confirmed COVID-19 cases globally via a proposed G parameter. Overall, there were 3 key components to ODANN's development phase, namely: (i) data hydration and pre-processing were performed on COVID-19 related Twitter data ranging between 23 January 2020 and 10 May 2020, which amounted to over 100 million Tweets written in English language; (ii) multiple NLP features extraction methods were subsequently leveraged to encode the hydrated Twitter data into useful semantic word vectors for training ODANN under an optimal set of hyperparameters; and (iii) historical time-series data of defined characteristics were also assimilated into ODANN's selected hidden layer(s) to model the G parameter daily with a lead-time of 1 day. By far, our experimental results demonstrated that by adopting a rolling time-window size of 5 days, with respect to the number of historical time-series records for assimilating different data features, enabled ODANN to outperform other traditional time-series models and recent studies, in terms of the computed RMSE and MAE scores attained from the model's testing step.

Correlating dynamic climate conditions and socioeconomic-governmental factors to spatiotemporal spread of COVID-19 via semantic segmentation deep learning analysis.

In this study, we develop a deep learning model to forecast the transmission rate of COVID-19 globally, via a proposed G parameter, as a function of fused data features which encompass selected climate conditions, socioeconomic and restrictive governmental factors. A 2-step optimization process is adopted for the model's data fusion component which systematically performs the following: (Step I) determining the optimal climate feature which can achieve good precision score (> 70%) when predicting the spatial classes distribution of the G parameter on a global scale consisting of 251 countries, followed by (Step II) fusing the optimal climate feature with 11 selected socioeconomic-governmental factors to further improve the model's predictive capability. By far, the obtained results from the model's testing step indicate that land surface temperature day (LSTD) has the strongest correlation with the global G parameter over time by achieving an average precision score of 72%.

Quantifying the RNA cap epitranscriptome reveals novel caps in cellular and viral RNA.

Chemical modification of transcripts with 5' caps occurs in all organisms. Here, we report a systems-level mass spectrometry-based technique, CapQuant, for quantitative analysis of an organism's cap epitranscriptome. The method was piloted with 21 canonical caps-m7GpppN, m7GpppNm, GpppN, GpppNm, and m2,2,7GpppG-and 5 'metabolite' caps-NAD, FAD, UDP-Glc, UDP-GlcNAc, and dpCoA.

Structures of Zika virus NS2B-NS3 protease in complex with peptidomimetic inhibitors.

Zika virus NS2B-NS3 protease plays an essential role in viral replication by processing the viral polyprotein into individual proteins. The viral protease is therefore considered as an ideal antiviral drug target. To facilitate the development of protease inhibitors, we report three high-resolution co-crystal structures of bZiPro with peptidomimetic inhibitors composed of a P1-P4 segment and different P1' residues.