Prediction of forward osmosis membrane engineering factors using artificial intelligence approach.

Currently, forward osmosis (FO) is widely studied for wastewater treatment and reuse. However, there are still challenges which need to be addressed for the application of the FO on a commercial scale. In the meantime, with a strong capability to solve the complicated nonlinear relationships and to examine of the relations between multiple variables, artificial intelligence (AI) technique could be a viable tool to improve FO system performance to make it more applicable. This study aims to develop an AI-based model for supporting early control and making decision in the FO membrane system. The results show that the artificial neural networks model is extremely suitable for prediction of water flux, membrane fouling, and removal efficiencies. The most appropriate input dataset for the model was proposed, in which organic matters, sodium ion, and calcium ion concentrations played a vital role in all predictions. The best model architecture was suggested with an optimal hidden layers (2-4 layers), and neurons (10-15 neurons). The developed models for membrane fouling show strong correlation between experimental and predicted data (with R2 values for prediction of membrane fouling porosity, thickness, roughness, and density were 0.85, 0.97, 0.97, and 0.98, respectively). The prediction of water flux presented a high R2 and low root mean square error (RMSE) of 0.92 and 0.9 L m-2.h-1, respectively. Prediction of the contaminant removal exhibits a relatively high correlation between the observed and predicted data with R2 values of 0.87 and RMSE values of below 2.7%. The developed models are expected to create a breakthrough in the control and enhancement in a novel FO membrane process used for wastewater treatment by providing us with actionable insights to produce fit-for-future systems in the context of sustainable development.

Probing glycation potential of dietary sugars in human blood by an integrated in vitro approach.

Glycation is referred to as the interaction of protein amino and guanidino groups with reducing sugars and carbonyl products of their degradation. Resulting advanced glycation end-products (AGEs) contribute to pathogenesis of diabetes mellitus and neurodegenerative disorders. Upon their intestinal absorption, dietary sugars and α-dicarbonyl compounds interact with blood proteins yielding AGEs. Although the differences in glycation potential of monosaccharides are well characterized, the underlying mechanisms are poorly understood. To address this question, d-glucose, d-fructose and l-ascorbic acid were incubated with human serum albumin (HSA). The sugars and α-dicarbonyl intermediates of their degradation were analyzed in parallel to protein glycation patterns (exemplified with hydroimidazolone modifications of arginine residues and products of their hydrolysis) by bottom-up proteomics and computational chemistry. Glycation of HSA with sugars revealed 9 glyoxal- and 14 methylglyoxal-derived modification sites. Their dynamics was sugar-specific and depended on concentrations of α-dicarbonyls, their formation kinetics, and presence of stabilizing residues in close proximity to the glycation sites.

DNA like-charge attraction and overcharging by divalent counterions in the presence of divalent co-ions.

Strongly correlated electrostatics of DNA systems has drawn the interest of many groups, especially the condensation and overcharging of DNA by multivalent counterions. By adding counterions of different valencies and shapes, one can enhance or reduce DNA overcharging. In this paper, we focus on the effect of multivalent co-ions, specifically divalent co-ions such as SO[Formula: see text]. A computational experiment of DNA condensation using Monte Carlo simulation in grand canonical ensemble is carried out where the DNA system is in equilibrium with a bulk solution containing a mixture of salt of different valency of co-ions. Compared to systems with purely monovalent co-ions, the influence of divalent co-ions shows up in multiple aspects. Divalent co-ions lead to an increase of monovalent salt in the DNA condensate. Because monovalent salts mostly participate in linear screening of electrostatic interactions in the system, more monovalent salt molecules enter the condensate leads to screening out of short-range DNA-DNA like charge attraction and weaker DNA condensation free energy. The overcharging of DNA by multivalent counterions is also reduced in the presence of divalent co-ions. Strong repulsions between DNA and divalent co-ions and among divalent co-ions themselves lead to a depletion of negative ions near the DNA surface as compared to the case without divalent co-ions. At large distances, the DNA-DNA repulsive interaction is stronger in the presence of divalent co-ions, suggesting that divalent co-ions' role is not only that of simple stronger linear screening.

Sensitive and site-specific identification of carboxymethylated and carboxyethylated peptides in tryptic digests of proteins and human plasma.

Glycation refers to a nonenzymatic post-translational modification formed by the reaction of amino groups and reducing sugars. Consecutive oxidation and degradation can produce advanced glycation end products (AGEs), such as N(ε)-(carboxyethyl)lysine (CEL) and N(ε)-(carboxymethyl)lysine (CML). Although CEL and CML are considered to be markers of arteriosclerosis, diabetes mellitus, and aging, the modified proteins and the exact modification sites are mostly unknown due to their low frequency and a lack of enrichment strategies. Here, we report characteristic fragmentation patterns of CML- and CEL-containing peptides and two modification-specific reporter ions for each modification (CML, m/z 142.1 and 187.1; CEL, m/z 156.1 and 201.1). The protocol allowed sensitive and selective precursor ion scans to detect the modified peptides in complex sample mixtures. The corresponding m/z values identified eight CEL/CML-modification sites in glycated human serum albumin (HSA) by targeted nano-RPC-MS/MS. The same strategy revealed 21 CML sites in 17 different proteins, including modified lysine residues 88 and 396 of human serum albumin, in a pooled plasma sample that was obtained from patients with type 2 diabetes mellitus.