Application of immersive virtual reality mirror therapy for upper limb rehabilitation after stroke: a scoping review.

Mirror therapy is a commonly used rehabilitation intervention in post stroke upper limb rehabilitation. Despite many potential technological developments, mirror therapy is routinely delivered through the use of a static mirror or mirror box. This review aims to synthesise evidence on the application of immersive virtual reality mirror therapy (IVRMT) in poststroke upper limb rehabilitation. A scoping review was performed on relevant English studies published between 2013 to 2023. Literature search was undertaken on APA PsycInfo, CINAHL, Cochrane Library, MEDLINE, PubMed and Web of Science between August 5 and 17, 2023. Additional studies were included from Google Scholar and reference lists of identified articles. A total of 224 records were identified, of which 8 full-text articles were selected for review. All included studies were published between 2019 and 2023, and from high- and upper-middle-income nations. All the studies were experimental (n = 8). The total sample size in the studies was 259, most of whom were stroke patients with upper limb weakness (n = 184). This review identified three major themes and two sub-themes based on the contents of the studies conducted on the application of IVRMT: IVRMT's technical application, feasibility and impact on clinical outcomes (motor recovery and adverse events). IVRMT was concluded to be a safe and feasible approach to post-stroke upper limb rehabilitation, offering enhanced engagement and motor recovery. However, more methodologically robust studies should be conducted to advance this area of practice, and to include a uniform IVRMT intervention protocol, dose, and use of outcome measure.

A community effort in SARS-CoV-2 drug discovery.

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.

DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features.

Computational methods for virtual screening can dramatically accelerate early-stage drug discovery by identifying potential hits for a specified target. Docking algorithms traditionally use physics-based simulations to address this challenge by estimating the binding orientation of a query protein-ligand pair and a corresponding binding affinity score. Over the recent years, classical and modern machine learning architectures have shown potential for outperforming traditional docking algorithms. Nevertheless, most learning-based algorithms still rely on the availability of the protein-ligand complex binding pose, typically estimated via docking simulations, which leads to a severe slowdown of the overall virtual screening process. A family of algorithms processing target information at the amino acid sequence level avoid this requirement, however, at the cost of processing protein data at a higher representation level. We introduce deep neural virtual screening (DENVIS), an end-to-end pipeline for virtual screening using graph neural networks (GNNs). By performing experiments on two benchmark databases, we show that our method performs competitively to several docking-based, machine learning-based, and hybrid docking/machine learning-based algorithms. By avoiding the intermediate docking step, DENVIS exhibits several orders of magnitude faster screening times (i.e., higher throughput) than both docking-based and hybrid models. When compared to an amino acid sequence-based machine learning model with comparable screening times, DENVIS achieves dramatically better performance. Some key elements of our approach include protein pocket modeling using a combination of atomic and surface features, the use of model ensembles, and data augmentation via artificial negative sampling during model training. In summary, DENVIS achieves competitive to state-of-the-art virtual screening performance, while offering the potential to scale to billions of molecules using minimal computational resources.

Energy performance assessment of virtualization technologies using small environmental monitoring sensors.

The increasing trends of electrical consumption within data centres are a growing concern for business owners as they are quickly becoming a large fraction of the total cost of ownership. Ultra small sensors could be deployed within a data centre to monitor environmental factors to lower the electrical costs and improve the energy efficiency. Since servers and air conditioners represent the top users of electrical power in the data centre, this research sets out to explore methods from each subsystem of the data centre as part of an overall energy efficient solution. In this paper, we investigate the current trends of Green IT awareness and how the deployment of small environmental sensors and Site Infrastructure equipment optimization techniques which can offer a solution to a global issue by reducing carbon emissions.