In-depth analysis of natural organic matter fractions in drinking water treatment performance: Fate and role of humic substances in trihalomethanes formation potential.

In this study we investigate the compositional changes in dissolved organic matter (DOM) fractions across diverse water sources and treatment processes in three Drinking Water Treatment Plants (DWTPs). High-Performance Size Exclusion Chromatography coupled with Diode Array Detection and Organic Carbon Detection (HPSEC-DAD-OCD) was employed to characterize DOM fractions, offering insights into treatment optimization. We examine bulk water parameters, DOM distributions, and the efficiency of treatment trains in reducing DOM fractions. Results reveal distinct DOM composition profiles in river-sourced versus reservoir-sourced waters, with implications for treatment processes. Coagulation, Granular Activated Carbon (GAC) adsorption, Electrodialysis Reversal (EDR), and Ion Exchange (IEX) were evaluated for their efficacy in removing DOM fractions. The analysis highlights the effectiveness of coagulation in reducing high molecular weight (MW) fractions, while GAC filtration targets lower MW fractions. EDR shows significant removal of anions and aromatics, while IEX demonstrates high removal efficiencies for removing humic substances (HS) fractions. Spectroscopic analysis further elucidates changes HS sub-fractions and their role in disinfection by-products (DBP) formation. To quantitatively assess the relationship between HS sub-fractions and trihalomethane formation potentials (THMFP), Pearson correlation analysis were conducted, unveiling robust associations between HS sub-fractions and THM-FP that can be predicted by surrogate parameters such as A254.

GUANIN: an all-in-one GUi-driven analyzer for NanoString interactive normalization.

SUMMARY: Most tools for normalizing NanoString gene expression data, apart from the default NanoString nCounter software, are R packages that focus on technical normalization and lack configurable parameters. However, content normalization is the most sensitive, experiment-specific, and relevant step to preprocess NanoString data. Currently this step requires the use of multiple tools and a deep understanding of data management by the researcher. We present GUANIN, a comprehensive normalization tool that integrates both new and well-established methods, offering a wide variety of options to introduce, filter, choose, and evaluate reference genes for content normalization. GUANIN allows the introduction of genes from an endogenous subset as reference genes, addressing housekeeping-related selection problems. It performs a specific and straightforward normalization approach for each experiment, using a wide variety of parameters with suggested default values. GUANIN provides a large number of informative output files that enable the iterative refinement of the normalization process. In terms of normalization, GUANIN matches or outperforms other available methods. Importantly, it allows researchers to interact comprehensively with the data preprocessing step without programming knowledge, thanks to its easy-to-use Graphical User Interface (GUI). AVAILABILITY AND IMPLEMENTATION: GUANIN can be installed with pip install GUANIN and it is available at https://pypi.org/project/guanin/. Source code, documentation, and case studies are available at https://github.com/julimontoto/guanin under the GPLv3 license.

Sustainable agricultural practices influence s-metolachlor, foramsulfuron and thiencarbazone-methyl degradation and their metabolites formation.

The effect of sustainable agricultural practices, such as mulching or the application of straw residues as an organic amendment, on the degradation, dissipation and persistence in the soil of S-metolachlor (SMOC), foramsulfuron (FORAM) and thiencarbazone-methyl (TCM) is still unclear. The objective here was to conduct a laboratory experiment to evaluate the impact of milled wheat straw (WS) simulating its individual use as mulch or applied as an organic amendment to two agricultural soils: unamended and WS-amended soils on the degradation kinetics of the herbicides SMOC, FORAM and TCM, and on the formation of their major metabolites at two incubation temperatures (14 °C and 24 °C). The degradation rate of SMOC on WS was 6.9-16.7 times faster than that observed for FORAM and TCM at both temperatures. The half-life (DT50) values were 1.1-10.6 times lower for FORAM than for SMOC and TCM in the unamended and WS-amended soils at 14 °C and 24 °C. The application of WS to soils increased the DT50 values from 1.1 to 11.2 times for all the herbicides at both incubation temperatures due to their higher adsorption and lower bioavailability. The herbicides recorded a faster degradation at 24 °C (1.2-3.9 times) than at 14 °C, according to Q10 values >1. SMOC metabolites were more persistent in WS-amended soils than in unamended ones, in agreement with the DT50 values recorded for the parent compound. The results indicate that the effect of the mulch applied to soils as an organic amendment was different depending on the herbicide and incubation temperature. The outcomes of this research can give key suggestions for reducing the effects of residual herbicides following sustainable agricultural practices by avoiding soil and groundwater contamination, which is one of the challenges involved in the application of chemical inputs.

Improved selection of canonical proteins for reference proteomes.

The 'canonical' protein sets distributed by UniProt are widely used for similarity searching, and functional and structural annotation. For many investigators, canonical sequences are the only version of a protein examined. However, higher eukaryotes often encode multiple isoforms of a protein from a single gene. For unreviewed (UniProtKB/TrEMBL) protein sequences, the longest sequence in a Gene-Centric group is chosen as canonical. This choice can create inconsistencies, selecting >95% identical orthologs with dramatically different lengths, which is biologically unlikely. We describe the ortho2tree pipeline, which examines Reference Proteome canonical and isoform sequences from sets of orthologous proteins, builds multiple alignments, constructs gap-distance trees, and identifies low-cost clades of isoforms with similar lengths. After examining 140 000 proteins from eight mammals in UniProtKB release 2022_05, ortho2tree proposed 7804 canonical changes for release 2023_01, while confirming 53 434 canonicals. Gap distributions for isoforms selected by ortho2tree are similar to those in bacterial and yeast alignments, organisms unaffected by isoform selection, suggesting ortho2tree canonicals more accurately reflect genuine biological variation. 82% of ortho2tree proposed-changes agreed with MANE; for confirmed canonicals, 92% agreed with MANE. Ortho2tree can improve canonical assignment among orthologous sequences that are >60% identical, a group that includes vertebrates and higher plants.

ProtVar: mapping and contextualizing human missense variation.

Genomic variation can impact normal biological function in complex ways and so understanding variant effects requires a broad range of data to be coherently assimilated. Whilst the volume of human variant data and relevant annotations has increased, the corresponding increase in the breadth of participating fields, standards and versioning mean that moving between genomic, coding, protein and structure positions is increasingly complex. In turn this makes investigating variants in diverse formats and assimilating annotations from different resources challenging. ProtVar addresses these issues to facilitate the contextualization and interpretation of human missense variation with unparalleled flexibility and ease of accessibility for use by the broadest range of researchers. By precalculating all possible variants in the human proteome it offers near instantaneous mapping between all relevant data types. It also combines data and analyses from a plethora of resources to bring together genomic, protein sequence and function annotations as well as structural insights and predictions to better understand the likely effect of missense variation in humans. It is offered as an intuitive web server https://www.ebi.ac.uk/protvar where data can be explored and downloaded, and can be accessed programmatically via an API.

Reduced mitochondrial pyruvate carrier expression in hearts with heart failure and reduced ejection fraction patients: ischemic vs. non-ischemic origin.

Introduction and objectives: Mitochondrial pyruvate carrier (MPC) mediates the entry of pyruvate into mitochondria, determining whether pyruvate is incorporated into the Krebs cycle or metabolized in the cytosol. In heart failure (HF), a large amount of pyruvate is metabolized to lactate in the cytosol rather than being oxidized inside the mitochondria. Thus, MPC activity or expression might play a key role in the fate of pyruvate during HF. The purpose of this work was to study the levels of the two subunits of this carrier, named MPC1 and MPC2, in human hearts with HF of different etiologies. Methods: Protein and mRNA expression analyses were conducted in cardiac tissues from three donor groups: patients with HF with reduced ejection fraction (HFrEF) with ischemic cardiomyopathy (ICM) or idiopathic dilated cardiomyopathy (IDC), and donors without cardiac pathology (Control). MPC2 plasma levels were determined by ELISA. Results: Significant reductions in the levels of MPC1, MPC2, and Sirtuin 3 (SIRT3) were observed in ICM patients compared with the levels in the Control group. However, no statistically significant differences were revealed in the analysis of MPC1 and MPC2 gene expression among the groups. Interestingly, Pyruvate dehydrogenase complex (PDH) subunits expression were increased in the ICM patients. In the case of IDC patients, a significant decrease in MPC1 was observed only when compared with the Control group. Notably, plasma MPC2 levels were found to be elevated in both disease groups compared with that in the Control group. Conclusion: Decreases in MPC1 and/or MPC2 levels were detected in the cardiac tissues of HFrEF patients, with ischemic or idiopatic origen, indicating a potential reduction in mitochondrial pyruvate uptake in the heart, which could be linked to unfavorable clinical features.

Inflammatory bowel disease induces pathological α-synuclein aggregation in the human gut and brain.

AIMS: According to Braak's hypothesis, it is plausible that Parkinson's disease (PD) originates in the enteric nervous system (ENS) and spreads to the brain through the vagus nerve. In this work, we studied whether inflammatory bowel diseases (IBDs) in humans can progress with the emergence of pathogenic α-synuclein (α-syn) in the gastrointestinal tract and midbrain dopaminergic neurons. METHODS: We have analysed the gut and the ventral midbrain from subjects previously diagnosed with IBD and form a DSS-based rat model of gut inflammation in terms of α-syn pathology. RESULTS: Our data support the existence of pathogenic α-syn in both the gut and the brain, thus reinforcing the potential role of the ENS as a contributing factor in PD aetiology. Additionally, we have analysed the effect of a DSS-based rat model of gut inflammation to demonstrate (i) the appearance of P-α-syn inclusions in both Auerbach's and Meissner's plexuses (gut), (ii) an increase in α-syn expression in the ventral mesencephalon (brain) and (iii) the degeneration of nigral dopaminergic neurons, which all are considered classical hallmarks in PD. CONCLUSION: These results strongly support the plausibility of Braak's hypothesis and emphasise the significance of peripheral inflammation and the gut-brain axis in initiating α-syn aggregation and transport to the substantia nigra, resulting in neurodegeneration.

Development and validation of a software application to analyze thermal and kinematic multimodels of Stirling engines.

The work developed presents, for the first time, a tool to analyze all the thermodynamic models used in the study and development of Stirling engines: isothermal, ideal adiabatic and adiabatic with losses, combined adiabatic thermodynamic with finite speed (CAFS), thermodynamic with finite speed (FST), ideal polytropic and polytropic with losses (PSVL), allowing a comparative study of them. This software (ASCE-UMA), designed and implemented in a Matlab GUI® allows to obtain the operating parameters of these engines, calculating the thermodynamic parameters, power output and efficiency. Additionally, the thermodynamic models can be evaluated with different mechanical configurations, for which different drive mechanisms are implemented: Sinusoidal, Alfa Ross yoke types, Alfa Ross V yoke, Beta rhombic type and free piston Stirling engine (FPSE). Thermoacoustic and other, models could be analyzed by virtue of their similarity of movement with some of the implemented models. In the same way, ASCE-UMA allows the study of various exchanger configurations, as well as various regenerator models. The versatility of ASCE-UMA allows the development analysis of all the fundamental elements of a new prototype as well as the analysis of experimental data by performing a customized and detailed calculation. To test the effectiveness of ASCE-UMA, its performance is verified by analyzing Ross Yoke D-90 models and a GM GPU-3 engine. This is a tool that allows to analyze and comparing the different models and the different existing mechanisms for the multiple configurations of Stirling engines in an easy and intuitive application with a high-quality graphical interface.

The effects of pathogenic and likely pathogenic variants for inherited hemostasis disorders in 140 214 UK Biobank participants.

Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants.

Transfer learning for drug-target interaction prediction.

MOTIVATION: Utilizing AI-driven approaches for drug-target interaction (DTI) prediction require large volumes of training data which are not available for the majority of target proteins. In this study, we investigate the use of deep transfer learning for the prediction of interactions between drug candidate compounds and understudied target proteins with scarce training data. The idea here is to first train a deep neural network classifier with a generalized source training dataset of large size and then to reuse this pre-trained neural network as an initial configuration for re-training/fine-tuning purposes with a small-sized specialized target training dataset. To explore this idea, we selected six protein families that have critical importance in biomedicine: kinases, G-protein-coupled receptors (GPCRs), ion channels, nuclear receptors, proteases, and transporters. In two independent experiments, the protein families of transporters and nuclear receptors were individually set as the target datasets, while the remaining five families were used as the source datasets. Several size-based target family training datasets were formed in a controlled manner to assess the benefit provided by the transfer learning approach. RESULTS: Here, we present a systematic evaluation of our approach by pre-training a feed-forward neural network with source training datasets and applying different modes of transfer learning from the pre-trained source network to a target dataset. The performance of deep transfer learning is evaluated and compared with that of training the same deep neural network from scratch. We found that when the training dataset contains fewer than 100 compounds, transfer learning outperforms the conventional strategy of training the system from scratch, suggesting that transfer learning is advantageous for predicting binders to under-studied targets. AVAILABILITY AND IMPLEMENTATION: The source code and datasets are available at https://github.com/cansyl/TransferLearning4DTI. Our web-based service containing the ready-to-use pre-trained models is accessible at https://tl4dti.kansil.org.