Browse Articles

Enhanced state of charge estimation in electric vehicle batteries using chicken swarm optimization with open ended learning

Long-term exposure to PM2.5 exacerbates dopaminergic neuronal loss through CpG hypermethylation induced down-regulation of PINK1 and DJ-1 genes

Optimizing energy systems of livestock farms with computational intelligence for achieving energy autonomy

Methods and exploratory findings of the first Swiss agricultural health cohort FarmCoSwiss

Abstract FarmCoSwiss is Switzerland’s first agricultural cohort study on farmers’ health and wellbeing. It aims to longitudinally describe farmers’ mental and physical health and identify risk and protective factors. Between November 2022 and August 2023, 872 participants (65.9% men) were enrolled in the baseline survey assessing farm characteristics, occupational hazards, lifestyle and wellbeing, and physical and mental health. Selected variables were descriptively compared to the general population using Swiss Health Survey (SHS) data (2022) and the Swiss-wide SAPALDIA cohort (2020–2023). Findings suggest better physical health-related quality of life (HRQoL) in younger participants, and better mental HRQoL in older farmers. Furthermore, descriptive comparisons indicate that female farmers may have higher physical, but lower mental HRQoL than men. Most participants (60.5%) were classified as overweight or obese based on self-reported height and weight. Descriptive comparisons between the SHS and FarmCoSwiss suggest that farmers might spend less hours sitting, consume less alcohol and tobacco, but eat more red and processed meat. FarmCoSwiss participants further reported lower prevalences of most diseases compared to SAPALDIA participants. Occupational accidents were common in the farmers’ cohort. These high accident rates as well as high BMI values and indications for sex-based differences in physical and mental health highlight the need for further research and in-depth studies. Given increasing political, societal, and environmental pressures on agriculture, epidemiological evidence on farmers’ health and wellbeing is crucial to maintain a thriving agricultural workforce.

Transcriptional signature of rapidly responding NK cells reveals S1P5 and CXCR4 as anti-tumor response disruptors

Abstract Natural killer (NK) cells are prototypic cytotoxic innate lymphocytes that can kill target cells, such as tumor cells, in the absence of antigen-restriction. Peripheral NK cells exhibit a high degree of heterogeneity. Here, we set out to broadly assess intrinsic modulators of NK cell degranulation in an unbiased manner. We stimulated human primary blood-borne NK cells pre-treated with different cytokine regimens with the HCT116 human colon cancer cell line and used detection of lysosome-associated membrane glycoprotein 1 (LAMP1) as an identifier of rapid NK cell degranulation. RNA sequencing of FACS-sorted LAMP1hi NK cells showed CXCR4 and S1PR5 were top down-regulated genes. Using compounds that modulate activity of CXCR4 and S1P receptor family members S1P1 and S1P5, we confirmed they play an important immunosuppressive role in NK cell cytotoxicity. Mechanistically, engagement of CXCR4 and S1P1/5 receptors triggered phosphorylation of p42 and Ca2+ influx. CXCR4 activation promoted S1P5 upregulation and vice versa, and joint activation of both receptors amplified the defect NK cell degranulation. Intriguingly, in tumor samples the expression of both receptors and the synthesis of their ligands themselves appear to be coordinately regulated. Together, these data suggest that specifically and simultaneously targeting CXCR4 and S1P5 activity in the tumor microenvironment (TME) could be a beneficial strategy to unleash full cytotoxic potential of cytotoxic NK effector cells in the tumor.

Federally-overlooked flood risk inequities in the conterminous United States

Deproteinization with Papain enzyme improves the bonding performance of self-etch adhesives to eroded dentin

Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete

Synergistic endocrine disruption and cellular toxicity of polyethylene microplastics and bisphenol A in MLTC-1 cells and zebrafish

CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on transformers and information bottleneck theory

A DEM super resolution reconstruction method based on normalizing flow

Publisher Correction: Eye movements of children with and without developmental dyslexia in an alphabetic script during alphabetic and logographic tasks

Substrates bind to residues lining the ring of asymmetrically engaged bacterial proteasome activator Bpa

Abstract Mycobacteria harbor a proteasome that was acquired by Actinobacteria through horizontal gene transfer and that supports the persistence of the human pathogen Mycobacterium tuberculosis within host macrophages. The core particle of the proteasome (20S CP) associates with ring-shaped activator complexes to degrade protein substrates. One of these is the bacterial proteasome activator Bpa that stimulates the ATP-independent proteasomal degradation of the heat shock repressor HspR. In this study, we determine the cryogenic electron microscopy 3D reconstruction of the complex between Bpa and its natural substrate HspR at 4.1 Å global resolution. The resulting maps allow us to identify regions of Bpa that interact with HspR. Using structure-guided site-directed mutagenesis and in vitro biochemical assays, we confirm the importance of the identified residues for Bpa-mediated substrate recruitment and subsequent proteasomal degradation. Additionally, we show that the dodecameric Bpa ring associates asymmetrically with the heptameric α-rings of the 20S CP, adopting a conformation resembling a hinged lid, while still engaging all seven docking sites on the proteasome.

Experimental verification of the six sectors neural DTC approach of squirrel cage induction motors

Abstract The direct torque control (DTC) approach is one of the suitable solutions for controlling squirrel cage induction machines (SCIMs) due to its distinctive performance compared to other strategies and its simplicity. However, using this approach has several drawbacks and problems. This paper presents an experimental work using real equipment of an innovative method that combines six sectors of DTC technique and neural networks (NNs). The use of an NN algorithm allows for overcoming problems of the DTC approach, such as reducing torque ripples. Using the NN technique, the operation of the SCIM inverter is controlled, as the NN technique provides the pulses necessary to run the inverter, which allows for improving the quality of the current. Therefore, the presented approach is based on the usual method, using the same estimation equations. First, the validity of the designed approach was tested using MATLAB, comparing the results with the DTC approach. The results obtained showed a high ability of the six sectors’ NN-DTC approach to significantly enhance the quality of torque and current, which confirms the competence of using NNs. Secondly, real equipment was used to verify the simulation results and the extent of the efficiency and competence of the six sectors NN-DTC approach compared to the DTC technique in terms of improving the quality of current and torque. These experimental results obtained are of great value in the field of control, as they give a clear picture of the advantage of the six sectors of the NN-DTC approach in improving the features of the control system, which makes it more suitable for different applications in the future.

A genome-wide cross-trait analysis characterizes the shared genetic architecture between lung and gastrointestinal diseases

Machine learning for risk prediction of acute kidney injury in patients with diabetes mellitus combined with heart failure during hospitalization

Programmed enhancement of endogenous iron-mediated lysosomal membrane permeabilization for tumor ferroptosis/pyroptosis dual-induction

Machine learning assisted Co3O4/NiO popsicle sticks-infused electrospun nanofibers for efficient oxygen evolution reaction

Abstract Wide range of noble metal free bimetallic and trimetallic based electrocatalysts have been synthesized to develop efficient oxygen evolution reaction (OER) systems to-date, however, to determine which metal part of bimetallic and trimetallic electrocatalysts plays a significant role in controlling OER efficacy remains very challenging. To address this issue, herein we have employed machine learning (ML) for the first time to determine OER efficacy controlling metal element, thus leading to the development of an optimized bimetallic electrocatalyst. Briefly, we have designed a novel, simple ML optimized sustainable OER electrocatalyst based on Co3O4/NiO popsicle sticks (CNPS) infused polyaniline/cellulose acetate (a biopolymer) (PNCA) electrospun nanofibers supported on nickel foam (NF). ML optimized CNPS infused PNCA (CNPS@PNCA) electrode offers maximum and homogenous exposition of active sites and shows high OER activity by exhibiting low onset potential (1.41 V vs. RHE), overpotential (237 mV at 10 mA cm−2) and Tafel slope of 62.1 mV dec−1. Additionally, it shows a better stability of more than 100 h and is consistent with the reported literature.

PD-L1+ plasma cells suppress T lymphocyte responses in patients with sepsis and mouse sepsis models

Abstract Sepsis, a leading cause of death in intensive care units, is associated with immune alterations that increase the patients’ risk of secondary infections and mortality, so better understandings of the pathophysiology of sepsis-induced immunosuppression is essential for the development of therapeutic strategies. In a murine model of sepsis that recapitulates immune alterations observed in patients, here we demonstrate that PD-L1+CD44+B220LowCD138+IgM+ regulatory plasma cells are induced in spleen and regulate ex vivo proliferation and IFNɣ secretion induced by stimulation of T splenocytes. This effect is mediated both by cell-cell contact through increased PD-L1 expression on plasma cells and by production of a soluble factor. These observations are recapitulated in three cohorts of critically ill patients with bacterial and viral sepsis in association with increased mortality. Our findings thus reveal the function of regulatory plasma cells in the pathophysiology of sepsis-induced immune alterations, and present a potential therapeutic target for improving immune cell function impaired by sepsis.

Automated liver and spleen segmentation for MR elastography maps using U-Nets

Abstract To compare pretrained and trained U-Nets for liver and spleen segmentation in multifrequency magnetic resonance elastography (MRE) magnitude images for automated quantification of shear wave speed (SWS). Seventy-two healthy participants (34 ± 11 years; BMI, 23 ± 2 kg/m2; 51 men) underwent multifrequency MRE at 1.5T or 3T. Volumes of interest (VOIs) of liver and spleen were generated from MRE magnitude images with mixed T2-T2* image contrast and then transferred to SWS maps. Pretrained and trained 2D and 3D U-Nets were compared with ground truth values obtained by manual segmentation using correlation analysis, intraclass correlation coefficients (ICCs), and Dice scores. For both VOI and SWS values, pairwise comparison revealed no statistically significant difference between ground truth and pretrained and trained U-Nets (all p ≥ 0.95). There was a strong positive correlation for SWS between ground truth and U-Nets with R = 0.99 for liver and R = 0.81–0.84 for spleen. ICC was 0.99 for liver and 0.90–0.92 for spleen, indicating excellent agreement for liver and good agreement for spleen for all U-Nets investigated. Dice scores showed excellent segmentation performance for all networks with the 2D U-Net achieving slightly higher values for the liver (0.95) and spleen (0.90), though the differences between the three tested U-Nets were minimal. The excellent performance we found for automated liver and spleen segmentation when applying 2D and 3D U-Nets to MRE magnitude images suggests that fully automated quantification of MRE parameters within anatomical regions is feasible by leveraging the previously unexploited anatomical information conveyed in MRE magnitude images.