Environmental Resilience and Transformation in times of COVID-19: Climate Change Effects on Environmental Functionality

Environmental Resilience and Transformation in Times of COVID-19: Climate Change Effects on Environmental Functionality is a timely reference to better understand environmental changes amid the COVID-19 pandemic and the associated lockdowns. The book is organized into five themes: (1) environmental modifications, degradation, and human health risks;(2) water resources-planning, management, and governance;(3) air quality-monitoring, fate, transport, and drivers of socioenvironmental change;(4) marine and lacustrine environment;and (5) sustainable development goals and environmental justice. These themes provide an insight into the impact of COVID-19 on the environment and vice versa, which will help improve environmental management and planning, as well as influence future policies. Featuring many case studies from around the globe, this book offers a crucial examination of the intersectionality between climate, sustainability, the environment, and public health for researchers, practitioners, and policymakers in environmental science. © 2021 Elsevier Inc.

Impact of COVID-19 lockdown on real-time DO-BOD variation of river Ganga

A comparative assessment of dissolved oxygen (DO) and biochemical oxygen demand (BOD) of river Ganga during prelockdown and lockdown periods was made through analysis of data generated from real-time water quality motoring systems. The concentration data for DO and BOD are examined for (i) prelockdown period (March 15-21, 2020) and (ii) lockdown period (March 22-April 15, 2020). The analysis results show 3%-20% decrease in DO concentration. The slight decrease in DO observed at all locations during the first week after lockdown which may be due to the increased levels of suspended solids and turbidity in the river water because of heavy rains. DO during fourth week of lockdown has shown a decreased value as compared to the prelockdown period at most of the locations. However, in West Bengal the DO has increased in lockdown. BOD value ranged between 1.13 mg/L and 5.56 mg/L during lockdown period, more or less similar to prelockdown range of 1.37-5.58 mg/L. This chapter further discusses the cause of water quality changes during the period of lockdown as compare to prelockdown period. © 2021 Elsevier Inc.

Thermal Decomposition Kinetics of Medical Non-woven Fabrics Wastes: Model-Fitting Method

This research is possibly attempting the current issue of COVID-19 medical waste safe disposal and energy recovery from pathogenic waste. Non-woven fabric waste generation increased in pandemic situations, which need to dispose of safely;meanwhile, energy recovery is also important. Pyrolysis is an economical and harmless way to handle and efficiently convert infectious waste into fuel and chemicals. The chemical kinetic model for the infectious medical waste pyrolysis process was developed using thermogravimetric analysis (TGA) data. The ASTM D3172 proximate analysis determines a volatile matter, moisture, ash, and fixed carbon percentages. Bomb calorimeter is used to determine the exact calorific value of solid infectious medical waste. The proximate and calorimetric investigations show non-woven fabric waste has 35.8 MJ/kg heating values and more than 98.5 weight percentage of volatile matters. The chemical kinetic study focuses on the identification of the reaction model for the non-woven fabric pyrolysis process. It can conclude that this infectious medical waste can become a useful source of energy, chemicals, and fuels. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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.

Spectre of SARS-CoV-2 RNA in the ambient urban waters of Ahmedabad and Guwahati: A tale of two Indian cities.

COVID-19 positive patients can egest live SARS-CoV-2 virus and viral genome fragments through faecal matter and urine, raising concerns about viral transmission through the faecal-oral route and/or contaminated aerosolized water. These concerns are amplified in many low- and middle-income countries, where raw sewage is often discharged into surface waterways and open defecation is common. Nonetheless, there has been no evidence of COVID-19 transmission via ambient urban water, and the virus viability in such aquatic matrices is believed to be minimal and not a matter of concern. In this manuscript, we attempt to discern the presence of SARS-CoV-2 genetic material (ORF-1ab, N and S genes) in the urban water (lakes, rivers, and drains) of the two Indian cities viz., Ahmedabad (AMD), in western India with 9 wastewater treatment plants (WWTPs) and Guwahati (GHY), in the north-east of the country with no such treatment facilities. The present study was carried out to establish the applicability of environmental water surveillance (E-wat-Surveillance) of COVID-19 as a potential tool for public health monitoring at the community level. 25.8% and 20% of the urban water samples had detectable SARS-CoV-2 RNA load in AMD and GHY, respectively. N-gene > S-gene > ORF-1ab-gene were readily detected in the urban surface water of AMD, whereas no such observable trend was noticed in the case of GHY. The high concentrations of SARS-CoV-2 genes (e.g., ORF-1ab; 800 copies/L for Sabarmati River, AMD and S-gene; 565 copies/L for Bharalu urban river, GHY) found in urban waters suggest that WWTPs do not always completely remove the virus genetic material and that E-wat-Surveillance of COVID-19 in cities/rural areas with poor sanitation is possible.

The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021)

The 4th epiDAMIK@SIGKDD workshop is a forum to discuss new insights into how data mining can play a bigger role in epidemiology and public health research. While the integration of data science methods into epidemiology has significant potential, it remains under studied. We aim to raise the profile of this emerging research area of data-driven and computational epidemiology, and create a venue for presenting state-of-the-art and in-progress results-in particular, results that would otherwise be difficult to present at a major data mining conference, including lessons learnt in the 'trenches'. The current COVID-19 pandemic has only showcased the urgency and importance of this area. Our target audience consists of data mining and machine learning researchers from both academia and industry who are interested in epidemiological and public-health applications of their work, and practitioners from the areas of mathematical epidemiology and public health. © 2021 Owner/Author.

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.

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19.

We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.