sNucConv: A bulk RNA-seq deconvolution method trained on single-nucleus RNA-seq data to estimate cell-type composition of human adipose tissues.

Deconvolution algorithms mostly rely on single-cell RNA-sequencing (scRNA-seq) data applied onto bulk RNA-sequencing (bulk RNA-seq) to estimate tissues' cell-type composition, with performance accuracy validated on deposited databases. Adipose tissues' cellular composition is highly variable, and adipocytes can only be captured by single-nucleus RNA-sequencing (snRNA-seq). Here we report the development of sNucConv, a Scaden deep-learning-based deconvolution tool, trained using 5 hSAT and 7 hVAT snRNA-seq-based data corrected by (i) snRNA-seq/bulk RNA-seq highly correlated genes and (ii) individual cell-type regression models.

Multispecies biofilms on reverse osmosis membrane dictate the function and characteristics of the bacterial communities rather than their structure.

The main reason for the deterioration of membrane operation during water purification processes is biofouling, which has therefore been extensively studied. Biofouling was shown to reduce membrane performance reflected by permeate flux decline, reduced selectivity, membrane biodegradation, and consequently, an increase in energy consumption. Studies of biofouling focused on the identification of the assembled microbial communities, the excretion of extracellular polymeric substances (EPS), and their combined role in reduced membrane performance and lifetime.