Hydrogel-based photobioreactor for Solid-State cultivation of Chlorella vulgaris.

Solid-state cultivation is a promising technology for algal biomass production, achieving high productivities without the need for dewatering. However, such systems have suffered from high evaporation, and capital costs. Here is described a hydrogel photobioreactor (hPBR) with the aim of reducing water demand in solid-state cultivations. Two designs are described with "Design A" offering better humidity control overgrowth conditions. A biomass productivity of 2.41gm-2d-1, and 2.87gm-2d-1 when using physically crosslinked poly(vinyl alcohol) (pPVA) and chemically crosslinked PVA (cPVA) respectively were achieved with Chlorella vulgaris with a water demand around 0.44 kg g-1 of biomass. Over the 23 days of growth, the lipid content increased from 18.9 % to 56.6 % and 13.8 % to 43.2 % for pPVA and cPVA respectively, and the chlorophyll content decreased by more than 81 %. However, cell viability stayed high at over 98 % and surface coverage analysis showed good coverage of the gel surface.

A systematic review on cough sound analysis for Covid-19 diagnosis and screening: is my cough sound COVID-19?

For COVID-19, the need for robust, inexpensive, and accessible screening becomes critical. Even though symptoms present differently, cough is still taken as one of the primary symptoms in severe and non-severe infections alike. For mass screening in resource-constrained regions, artificial intelligence (AI)-guided tools have progressively contributed to detect/screen COVID-19 infections using cough sounds. Therefore, in this article, we review state-of-the-art works in both years 2020 and 2021 by considering AI-guided tools to analyze cough sound for COVID-19 screening primarily based on machine learning algorithms. In our study, we used PubMed central repository and Web of Science with key words: (Cough OR Cough Sounds OR Speech) AND (Machine learning OR Deep learning OR Artificial intelligence) AND (COVID-19 OR Coronavirus). For better meta-analysis, we screened for appropriate dataset (size and source), algorithmic factors (both shallow learning and deep learning models) and corresponding performance scores. Further, in order not to miss up-to-date experimental research-based articles, we also included articles outside of PubMed and Web of Science, but pre-print articles were strictly avoided as they are not peer-reviewed.