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A modified transformer based on adaptive frequency enhanced attention, large kernel convolution, and multiscale implementation for bearing fault diagnosis
Prognostic signature and tumor microenvironment infiltration based on amino acid metabolism-related genes and lncRNAs of colorectal cancer
Adjacent-differential network with shallow attention for polyp segmentation in colonoscopy images
Multifunctional poly (lactic acid)/vanillin coatings on drug loaded titanium nanotube arrays for improved biocompatibility, corrosion resistance, and antibacterial characteristics
Living things are showing increasing anomalies in their seasonal activity, which could disrupt the dynamics of biodiversity and ecosystems
Abstract The IPCC reported that the 2011–2020 decade has been the warmest on record worldwide. The frequency of climate extremes has increased and the future rates of warming will be well above historical averages. Climate warming has already modified species seasonal activity with cascading effects on species interactions, geographical ranges, ecosystems productivity and feedback to the atmosphere. However, we lack information on how the new climate regime will translate in terms of impacts for human populations and ecosystems. Here we report on abnormal seasonal activities of plants and animals, which took place in Europe and other countries worldwide since 2015. We show that they are unpreceded, related to warmer fall and winter as well as drier summer conditions. These anomalies are projected to increase in frequency in a near future and might have dramatic consequences for biodiversity dynamics, ecosystems functioning and human activities.
Smoke-dried mummies pre-date Egypt’s embalmed bodies
Single-Cell transcriptomic profiles of peripheral blood immune cells reveal early monocyte and platelet activation in the transition from high-risk states to clinical sepsis
Improved Inception-Capsule deep learning model with enhanced feature selection for early prediction of heart disease
Abstract Heart disease continues to rank among the world’s top causes of death, underscoring the pressing need for precise and accurate prediction techniques. Performance issues with traditional machine learning techniques have been identified, particularly when working with high-dimensional and unbalanced medical datasets. We present a new deep learning-based framework called IDLHICNet, or an Improved Deep Learning-based Hybrid Inception-Capsule Network, which is combined with an Enhanced Whale Optimization Algorithm (EWOA) for feature selection in order to overcome this issue. Using Improved K-Means Clustering (IKC) to remove outliers, Min-Max normalization to scale features, SMOTE oversampling to balance classes, and EWOA to select essential features are some of the crucial steps in the proposed process. The IDLHICNet model, which makes use of Capsule Networks’ spatial awareness and the Inception architecture’s feature extraction capabilities, is then used to classify the processed information. The effectiveness of our approach is demonstrated by experiments carried out on three benchmark datasets, including the Faisalabad, CVD, and heart failure datasets. The proposed model surpasses existing state-of-the-art methods by achieving high performance metrics, with accuracy values of 99.51%, 98.76%, and 99.07% across different test scenarios, as well as superior precision, recall, and F1-scores. This research demonstrates how well a hybrid deep learning architecture and sophisticated feature selection work together to predict heart disease accurately and early, allowing for prompt medical intervention and better patient outcomes.
Determining the minimum urban fleet for a valet style autonomous mobility service using real trip data
Effect of emotional migration on Cooperation for public goods games on continuous two-dimensional space
Genome-wide identification of essential genes in the invasive Streptococcus anginosus strain
Abstract Streptococcus anginosus, part of the Streptococcus anginosus group (SAG), is a human commensal increasingly recognized as an opportunistic pathogen responsible for abscesses formation and infections, also invasive ones. Despite its growing clinical importance, the genetic determinants of its pathogenicity remain poorly understood. This study aimed to identify essential genes in S. anginosus 980/01, a bloodstream isolate, under nutrient-rich laboratory conditions using a transposon mutagenesis combined with Transposon-Directed Insertion Site Sequencing (TraDIS). A mutant library was generated using the ISS1 transposon delivered via the thermosensitive plasmid pGh9:ISS1. Following transposition, insertions were mapped using Illumina sequencing and subsequently analyzed. Essential genes were identified based on the absence of insertions and statistical filtering. The library exhibited 98% genome saturation with over 130,000 unique insertion sites. Among 1825 genes, 348 (19.1%) were essential, 1446 non-essential, and 30 non-conclusive. Comparative analyses were performed with S. pyogenes MGAS5005 and S. agalactiae A909. Similarly to the latter, essential genes were enriched in functions related to translation, transcription, and cell wall biosynthesis. However, 40 genes uniquely essential to S. anginosus 980/01 were identified, suggesting unique survival strategies in S. anginosus. This study presents the first genome-wide identification of essential genes for S. anginosus 980/01, highlighting conserved and unique essential genes. These findings provide a basis for understanding its physiology and key genetic determinants of bacterial viability, and may help to uncover the pathogenic potential of S. anginosus in future studies.
Marine microalgae extracts as plant biostimulant to boost baby leaf lettuce production
Comparison of deep LSTM and machine learning models for predicting compressive strength of fly ash/slag-based geopolymer concrete
Abstract In the production of geopolymer concrete (GPC), using ground granulated blast furnace slag (GGBFS) and fly ash (FA) can reduce the carbon dioxide footprint and decrease the amount of waste materials released into the environment. Finding the compressive strength (fc) of GPC through experiments is time-consuming and costly; thus, applying artificial intelligence models can expedite this process. This study aims to compare the performance of deep Long Short-Term Memory (LSTM) and the machine learning (ML)-based algorithms in predicting the fc of FA/GGBFS-based GPC. Artificial neural networks (ANN), Bootstrap aggregating (Bagging), Least-Squares Boosting (LSBoost) and K-Nearest-Neighbours (kNN) were used for ML-based algorithms. For this goal, data were collected from the previous studies in the literature. The selected input characteristic variables included the chemical composition and quantities of FA and GGBFS, fine and coarse aggregates, sodium hydroxide molarity, alkaline activators, superplasticizer dosage, and curing temperature. Based on sensitivity analysis, the most influential parameter in the fc of FA/GGBFS-based GPC was the fine aggregate content. Performance metrics, error percentage distribution, and Taylor diagrams indicate that the highest accuracy was achieved by LSTM, which had an R-squared value of 0.98. This was followed by ANN, LSBoost, Bagging, and kNN. Notably, LSBoost and ANN also demonstrated strong performance, with R-squared values of 0.94 and 0.95, respectively. Also, Bagging showed acceptable ability for fc estimation of FA/GGBFS-based GPC due to having an R-squared value of 0.88, but kNN had very poor performance.
Characterization of a novel halophilic and thermostable multifunctional cellulase from Ebinur Salt Lake
Meta-analysis of comprehensive prognostic evaluation in patients with atrial fibrillation complicated by heart failure after catheter ablation
Relationship between neuropsychiatric symptoms and social support in older adults with mild behavioral impairment: a moderated mediation model
A novel method to assess motor planning deficits in patients with parkinson’s disease and mild cognitive impairment
Abstract It is well established that patients with Parkinson’s disease (PD) show deficits with movement execution, however experiences of motor planning dysfunction, and how they relate to the severity of motor symptoms, remains unclear. To investigate motor planning in PD, we designed a novel precision-grip task. PD patients showed significantly higher uncertainty in task performance compared to healthy controls, indicative of motor planning deficits. Performance of PD patients did not correlate with indicators of disease severity or subtype, yet patients on a higher daily levodopa dosage showed reduced motor planning deficits. Interestingly, these deficits were present even in recently diagnosed patients, implying that this measure may have potential as an early marker of motor planning impairment. These results suggest that the motor planning deficits revealed by our task may arise from separate pathological processes to that of motor execution dysfunction in PD, though might be alleviated with higher treatment dosages.
LIGO is 10 years old: black-hole breakthroughs will ‘only get better’
Role of glycolysis related genes in the pathogenesis of hemorrhoids and immune cell infiltration analysis
Abstract Hemorrhoids are a prevalent condition affecting the anorectal area. Recent studies have highlighted glycolysis as a crucial metabolic pathway in numerous diseases. However, systematic studies exploring the distinct functions of the seven glycolysis-related genes (GRGs) during hemorrhoid development are limited. This investigation sought to elucidate the function of GRGs in hemorrhoid development and their correlation with immune cell infiltration. Using bioinformatics methodologies, we performed differential expression analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, gene set enrichment analysis (GSEA), establishment of protein–protein interaction (PPI) networks, and analysis of immune infiltration. We identified 34 glycolysis-related differentially expressed genes (GRDEGs) in the GSE154650 dataset, including PCK1, ALDOB, and PCK2. GO and KEGG analyses showed a considerable increase of GRDEGs in monosaccharide and glucose metabolic processes and AMPK signaling cascades. PPI network analysis identified seven hub genes (HGs) that may act as essential regulatory nodes and potential drug targets. Additionally, we found notable associations between the infiltration patterns of monocytes and plasma cells and particular HGs, highlighting the significance of the immune microenvironment. This study established a foundation for subsequent functional validation and exploration of innovative therapeutic strategies targeting glycolysis-related pathways in hemorrhoids.