ABSTRACT: Application of commercial antiscalants to mitigate membrane scaling during the zero-liquid discharge treatment of synthetic brackish water and its impact on biofouling development in membrane distillation system
Project description:The biofouling process of membrane distillation for zero liquid discharge seawater desalination: an especial focus on the concentrating-effect
| PRJNA555654 | ENA
Project description:Fouling process of membrane distillation for zero liquid discharge seawater desalination: an especial focus on the interaction between concentrating process and biofouling
Project description:Biofouling and Microbial Communities in Membrane Distillation and Reverse Osmosis
| PRJEB6611 | ENA
Project description:Linking biofilm structure to biofouling of membrane distillation associated with varying feed water quality
| PRJNA532737 | ENA
Project description:membrane biofouling under two different operating modes during the purification process of surface water using membrane distillation system
| PRJNA532978 | ENA
Project description:microbial diversity on membrane distillation
Project description:The transcriptome of P. aeruginosa PA01 biofilm cells was compared to the associated suspended culture, upon growth on reverse osmosis membrane coupon under limited nutrient conditions. Experiment Overall Design: Cells were collected from the RO unit and from the membrane coupon after 20 hours of growth and biofouling of the membrane. A defined media was used, resembeling secondary wastewaters.
Project description:We introduce Affinity Distillation (AD), a method for extracting thermodynamic affinities de-novo from in-vivo immunoprecipitation experiments using deep learning. We show that neural networks modeling base-resolution in-vivo binding profiles of yeast and mammalian TFs can accurately predict energetic impacts of varying underlying DNA sequence on TF binding. Systematic comparisons between Affinity Distillation predictions and other predictive algorithms consistently show that Affinity Distillation more accurately predicts affinities across a wide range of TF structural classes and DNA sequences. Affinity Distillation relies on in-silico marginalization against many sequence backgrounds, resulting in a higher dynamic range and more accurate predictions than motif discovery algorithms. Moreover, we show that Affinity Distillation can learn differential paralog-specific affinities, thereby making it possible to more accurately reconstruct regulatory networks in cells.
