The lived experiences of elementary school teachers' implementation of social-emotional learning: transitions from in-person and remote settings

This qualitative study examined elementary school teachers' transitions from in-person to remote social-emotional learning during the COVID-19 pandemic in a northeastern US public school district. This study addressed the following central research question using Bandura's (1977) self-efficacy theory and CASEL's Framework (2021) for social and emotional learning: What were teachers' lived experiences while teaching social-emotional learning (SEL) during both remote and in-person instruction in elementary school throughout the Covid-19 pandemic? Eight teachers from one suburban elementary school shared their experiences meeting students' social-emotional needs during the pandemic. This study examined teacher perspectives on social-emotional learning in in-person and remote settings during the COVID-19 pandemic. Interview questions provided narrative inquiry study answers. According to interviews, teachers implemented social and emotional learning with uncertainty, anxiety, and fear. Teachers believed they could teach social and emotional learning remotely and in person despite the pandemic because of their perseverance, awareness, and social interactions. They did this by relying on their colleagues for support and encouragement, realizing the importance of their work with students, and allowing students to express their emotions and feelings while learning remotely and in person. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

SPATIAL GRAPH ATTENTION AND CURIOSITY-DRIVEN POLICY FOR ANTIVIRAL DRUG DISCOVERY

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure - this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

Adapting Disability Research Methods and Practices During the Covid-19 Pandemic: Experiences from the Field*†

People with disabilities are often excluded from research, which may be exacerbated during the ongoing Covid-19 pandemic. This article provides an overview of key challenges, opportunities, and strategies for conducting disability-inclusive research during the pandemic, drawing on the experience of research teams working across ten countries on disability-focused studies. It covers adaptations that are relevant across the project lifecycle, including maintaining ethical standards and safeguarding;enabling active participation of people with disabilities;adapting remote research data collection tools and methods to meet accessibility, feasibility, and acceptability requirements;and promoting inclusive and effective analysis and dissemination. While this article is focused on adaptations during the pandemic, it is highly likely that the issues and strategies highlighted here will be relevant going forward, either in similar crises or as the world continues to move towards greater digital communication and connectedness. © 2022 The Authors, IDS Bulletin © Institute of Development Studies and Crown Copyright 2022.

IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe an Integrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver 100 × to 1000 × improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ∼1011 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the US DOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine. © 2021 ACM.

Stream-AI-MD: Streaming AI-driven adaptive molecular simulations for heterogeneous computing platforms

Emerging hardware tailored for artificial intelligence (AI) and machine learning (ML) methods provide novel means to couple them with traditional high performance computing (HPC) workflows involving molecular dynamics (MD) simulations. We propose Stream-AI-MD, a novel instance of applying deep learning methods to drive adaptive MD simulation campaigns in a streaming manner. We leverage the ability to run ensemble MD simulations on GPU clusters, while the data from atomistic MD simulations are streamed continuously to AI/ML approaches to guide the conformational search in a biophysically meaningful manner on a wafer-scale AI accelerator. We demonstrate the efficacy of Stream-AI-MD simulations for two scientific use-cases: (1) folding a small prototypical protein, namely ββα-fold (BBA) FSD-EY and (2) understanding protein-protein interaction (PPI) within the SARS-CoV-2 proteome between two proteins, nsp16 and nsp10. We show that Stream-AI-MD simulations can improve time-to-solution by ~50X for BBA protein folding. Further, we also discuss performance trade-offs involved in implementing AI-coupled HPC workflows on heterogeneous computing architectures. © 2021 ACM.

Scalable HPC & AI infrastructure for COVID-19 therapeutics

COVID-19 has claimed more than 2.7 × 106 lives and resulted in over 124 × 106 infections. There is an urgent need to identify drugs that can inhibit SARS-CoV-2. We discuss innovations in computational infrastructure and methods that are accelerating and advancing drug design. Specifically, we describe several methods that integrate artificial intelligence and simulation-based approaches, and the design of computational infrastructure to support these methods at scale. We discuss their implementation, characterize their performance, and highlight science advances that these capabilities have enabled. © 2021 ACM.

Fragment binding to the Nsp3 macrodomain of SARS-CoV-2 identified through crystallographic screening and computational docking.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) macrodomain within the nonstructural protein 3 counteracts host-mediated antiviral adenosine diphosphate-ribosylation signaling. This enzyme is a promising antiviral target because catalytic mutations render viruses nonpathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of 2533 diverse fragments resulted in 214 unique macrodomain-binders. An additional 60 molecules were selected from docking more than 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several fragment hits were confirmed by solution binding using three biophysical techniques (differential scanning fluorimetry, homogeneous time-resolved fluorescence, and isothermal titration calorimetry). The 234 fragment structures explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors.

Fragment binding to the Nsp3 macrodomain of SARS-CoV-2 identified through crystallographic screening and computational docking (preprint)

The SARS-CoV-2 macrodomain (Mac1) within the non-structural protein 3 (Nsp3) counteracts host-mediated antiviral ADP-ribosylation signalling. This enzyme is a promising antiviral target because catalytic mutations render viruses non-pathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of diverse fragment libraries resulted in 214 unique macrodomain-binding fragments, out of 2,683 screened. An additional 60 molecules were selected from docking over 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several crystallographic and docking fragment hits were validated for solution binding using three biophysical techniques (DSF, HTRF, ITC). Overall, the 234 fragment structures presented explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors.

Ipomoeassin-F inhibits the in vitro biogenesis of the SARS-CoV-2 spike protein and its host cell membrane receptor (preprint)

In order to produce proteins essential for their propagation, many pathogenic human viruses, including SARS-CoV-2 the causative agent of COVID-19 respiratory disease, commandeer host biosynthetic machineries and mechanisms. Three major structural proteins, the spike, envelope and membrane proteins, are amongst several SARS-CoV-2 components synthesised at the endoplasmic reticulum (ER) of infected human cells prior to the assembly of new viral particles. Hence, the inhibition of membrane protein synthesis at the ER is an attractive strategy for reducing the pathogenicity of SARS-CoV-2 and other obligate viral pathogens. Using an in vitro system, we demonstrate that the small molecule inhibitor ipomoeassin F (Ipom-F) potently blocks the Sec61-mediated ER membrane translocation/insertion of three therapeutic protein targets for SARS-CoV-2 infection; the viral spike and ORF8 proteins together with angiotensin-converting enzyme 2, the host cell plasma membrane receptor. Our findings highlight the potential for using ER protein translocation inhibitors such as Ipom-F as host-targeting, broad-spectrum, antiviral agents.

Job Tension and Emotional Sensitivity to COVID-19 Public Messaging and Risk Perception.

During the COVID-19 pandemic, government social marketing messages support strategies of suppression (often stay-at-home orders or lockdowns) and/or mitigation (through testing, isolation, and tracing). Success at lowering the virus reproduction rate (R0) depends on social marketing messaging that rapidly changes behaviors. This study explores a potential side effect of a successful antivirus public health messaging campaign, when employees are back at work but the virus threat has not disappeared, that leads to on-the-job stress. The authors surveyed office employees in Shanghai, the People's Republic of China, where a nearly 2-month COVID-19 quarantine ended in late March 2020 and work locations reopened with strong public health messaging to encourage cooperation with continued virus spread suppression strategies-an approach likely to be followed in numerous countries. This study examines the relationship of pandemic public messaging sensitivity with tension and negative emotions on the job. Canonical correlation analysis is used with a sample of 1154 respondents, 4 predictor variables (reference group, self-regulation, media, and risk), and 2 criterion variables (negative emotions and job tension). Results show employees are differentially affected by the pandemic background noise. Those more sensitive to social-level virus risks and more open to reference group influence report increased levels of negative emotions and work tension.