Energy and life cycle impact assessment for xylitol production from corncob - Dasgupta, Diptarka; Sidana, Arushdeep; Ghosh, Prasenjit; Sharma, Tripti; Singh, Jasvinder; Prabhune, Asmita; More, Snehal; Bhaskar, Thallada; Ghosh, Debashish (Journal of Cleaner Production, 2021)
The conversion of biobased feedstocks into energy and value-added chemicals is one main approach to address the current global challenge for waste mitigation through the biorefinery mode. Corncob biomass which is one of the most abundant agricultural residue in India with a high hemicellulose content, can serve as a potential low-cost raw material for food-grade xylitol production. The present study demonstrates a process for xylitol production from corncob biomass with detailed energy and life cycle analysis for viability assessment. The major highlight of the process is the use of microbial fermentation, where biomass-derived D-xylose is converted to xylitol with high selectivity. Overall, 0.502 kg of xylitol crystals could be produced from 3.5 kg of corncob biomass. Simulation analysis revealed that the evaporators are the primary consumers of energy, and the process of heat integration can significantly reduce the energy requirements of the overall process. Environmental impacts of the system evaluated showed emission results of 8.68 kg CO2 equivalent and revealed that marine aquatic and freshwater eco-toxicity are the only possible contributors to the environment. The results suggest that the process would have favourable energy balances, which can be used in pilot plant and heat exchanger network design and operation for xylitol production scale up to 500 L. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords: biomass valorization; Life cycle impact assessment; simulation; xylitol

Insights from a pan India Sero-epidemiological survey (Phenome-India Cohort) for SARS-CoV2 - Naushin, Salwa; Sardana, Viren; Ujjainiya, Rajat; Bhatheja, Nitin; Kutum, Rintu; Bhaskar, Akash Kumar; Pradhan, Shalini; Prakash, Satyartha; Khan, Raju; Rawat, Birendra Singh; Tallapaka, Karthik Bharadwaj; Anumalla, Mahesh; et al. (eLife, 2021)
To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS-CoV2 anti-nucleocapsid (anti-NC) antibodies, 95% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95% confidence interval, p value: 2.23, 1.92-2.59, <0.0001), use of public transport (1.79, 1.43-2.24, <0.0001), not smoking (1.52, 1.16-1.99, 0.0257), non-vegetarian diet (1.67, 1.41-1.99, <0.0001), and B blood group (1.36, 1.15-1.61, 0.001).

Machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys - Singh, Prateek; Ujjainiya, Rajat; Prakash, Satyartha; Naushin, Salwa; Sardana, Viren; Bhatheja, Nitin; Singh, Ajay Pratap; Barman, Joydeb; Kumar, Kartik; Gayali, Saurabh; Khan, Raju; Rawat, Birendra Singh; et al. (Computers in Biology and Medicine, 2022)
Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.
Keywords: BBV152; Covaxin; COVID-19; Ensemble methods; Infection; machine learning; SARS-CoV-2

Process development for crystalline xylitol production from corncob biomass by Pichia caribbica - Dasgupta, Diptarka; Sidana, Arushdeep; Sarkar, Bipul; More, Snehal; Ghosh, Debashish; Bhaskar, Thallada; Ray, Anjan (Food and Bioproducts Processing, 2022)
Xylitol, a commodity chemical, is widely used in nutraceutical and pharmaceutical formulations. Microbial xylitol production is a promising alternative to mitigate current industrial practice issues and offers an environment-friendly sustainable conversion route. This study demonstrates a bioprocess for xylitol production from corncob using a mesophilic yeast Pichia caribbica. Corncob was hydrolyzed by dilute acid and steam explosion to recover fermentable xylose and used as the feed for xylitol production. Activated carbon treatment (3% w/v) completely removed the biomass-derived inhibitors furfural and hydroxymethyl furfural from the liquid hydrolysate. The fermenting yeast Pichia caribbica produced 124.1 +/- 0.45 g/L xylitol from the detoxified and concentrated corncob hydrolysate with a high yield of 0.80 +/- 0.02 g/g. The crystallized xylitol with 96.5% purity demonstrated no harmful effects on the cell line used as a control for the in-vitro toxicity studies. This proof of concept can be applied to help scale up for bio-refinery-based large-scale production of xylitol from corncob biomass.(c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.
Keywords: Biomass; Corncob; Detoxification; Fermentation; Toxicity analysis; xylitol