Conventional and frugal methods of estimating COVID-19-related excess deaths and undercount factors.

Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India. Excess deaths are defined as the number of additional deaths relative to those expected from pre-COVID-19-pandemic trends. We integrated data from: (a) epidemiological modeling using pre-pandemic all-cause mortality data, (b) discrepancies between media-reported death compensation claims and official reported mortality, and (c) the "wisdom of crowds" public surveying. Our results point to an estimated 14,770 excess deaths [95% CI 9820-22,790] in Pune from March 2020 to December 2021, of which 9093 were officially counted as COVID-19 deaths. We further calculated the undercount factor-the ratio of excess deaths to officially reported COVID-19 deaths. Our results point to an estimated undercount factor of 1.6 [95% CI 1.1-2.5]. Besides providing similar conclusions about excess deaths estimates across different methods, our study demonstrates the utility of frugal methods such as the analysis of death compensation claims and the wisdom of crowds in estimating excess mortality.

Biomedical Text Link Prediction for Drug Discovery: A Case Study with COVID-19.

Link prediction in artificial intelligence is used to identify missing links or derive future relationships that can occur in complex networks. A link prediction model was developed using the complex heterogeneous biomedical knowledge graph, SemNet, to predict missing links in biomedical literature for drug discovery. A web application visualized knowledge graph embeddings and link prediction results using TransE, CompleX, and RotatE based methods. The link prediction model achieved up to 0.44 hits@10 on the entity prediction tasks. The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19, served as a case study to demonstrate the efficacy of link prediction modeling for drug discovery. The link prediction algorithm guided identification and ranking of repurposed drug candidates for SARS-CoV-2 primarily by text mining biomedical literature from previous coronaviruses, including SARS and middle east respiratory syndrome (MERS). Repurposed drugs included potential primary SARS-CoV-2 treatment, adjunctive therapies, or therapeutics to treat side effects. The link prediction accuracy for nodes ranked highly for SARS coronavirus was 0.875 as calculated by human in the loop validation on existing COVID-19 specific data sets. Drug classes predicted as highly ranked include anti-inflammatory, nucleoside analogs, protease inhibitors, antimalarials, envelope proteins, and glycoproteins. Examples of highly ranked predicted links to SARS-CoV-2: human leukocyte interferon, recombinant interferon-gamma, cyclosporine, antiviral therapy, zidovudine, chloroquine, vaccination, methotrexate, artemisinin, alkaloids, glycyrrhizic acid, quinine, flavonoids, amprenavir, suramin, complement system proteins, fluoroquinolones, bone marrow transplantation, albuterol, ciprofloxacin, quinolone antibacterial agents, and hydroxymethylglutaryl-CoA reductase inhibitors. Approximately 40% of identified drugs were not previously connected to SARS, such as edetic acid or biotin. In summary, link prediction can effectively suggest repurposed drugs for emergent diseases.

Ultrastable optical components using adjustable commercial mirror mounts anchored in a ULE spacer.

This paper describes a novel, to the best of our knowledge, approach to build ultrastable interferometers using commercial mirror mounts anchored in an ultralow expansion (ULE) base. These components will play a critical role in any light particle search (ALPS) and will also be included in ground testing equipment for the upcoming laser interferometer space antenna (LISA) mission. Contrary to the standard ultrastable designs where mirrors are bonded to the spacers, ruling out any later modifications and alignments, our design remains flexible and allows the alignment of optical components at all stages to be optimized and changed. Here we present the dimensional stability and angular stability of two commercial mirror mounts characterized in a cavity setup. The long-term length change in the cavity did not exceed 30 nm and the relative angular stability was within 2 µrad, which meet the requirements for ALPS. We were also able to demonstrate 1pm/Hz length noise stability, which is a critical requirement for various subsystems in LISA. These results have led us to design similar opto-mechanical structures, which will be used in ground verification to test the LISA telescope.

Dataset for evaluating fitness index using Adaptive Neuro-Fuzzy Inference System.

With the current global downturn, the organizations need to develop new strategies and innovative approaches to ensure that every aspect of sustainability is achieved. For this purpose, the organizations need an indicator that measures the fitness if an organization. The purpose of this project is to analyze the 'Fitness' of an organization using the dataset related to leanness, agility and sustainability in ANFIS (Adaptive Neuro-Fuzzy Inference System) in order to determine whether the company is fit enough to sustain in global markets or not. The project does so by integrating both neural networks and fuzzy logic principles with lean, agility and sustainability principles. FIT manufacturing is the integration of Lean, Agile and sustainability manufacturing in one system as a whole which would help in attaining maximum output and sustain effectively in global markets. FIT Manufacturing adopts an integrated approach towards the use of Lean, Agility and Sustainability to achieve a level of fitness that is unique to each company. The database in the paper contains lean, agile and sustainable indices reviewed by experts. FIT does not prescribe that every aspect of Lean, Agile and Sustainability methodologies must be applied to every company, but a selective mix of components will provide the optimum conditions for a company to prosper.