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Smart indoor monitoring for disabled individuals using an ensemble of deep learning models in an IoT environment

I climb mountains in search of sustainable agricultural systems

A complex roadside object detection model based on multi-scale feature pyramid network

Seasonal and environmental drivers of antibiotic resistance and virulence in Escherichia coli from aquaculture and their public health implications

Abstract Aquaculture is increasingly impacted by environmental stressors such as temperature and pH fluctuations, which influence the proliferation and antibiotic resistance of Escherichia coli (E. coli). This study investigates the effects of these factors on the prevalence, virulence, and antibiotic resistance of E. coli isolated from aquaculture environments in Egypt, with a focus on public health implications. A total of 328 Oreochromis niloticus (Nile tilapia) samples were collected from Egyptian fish farms over five sampling periods, representing different seasonal conditions. E. coli was isolated and identified using selective culture methods and biochemical tests. Molecular characterization was conducted via polymerase chain reaction (PCR) to detect diarrheagenic E. coli pathotypes (st, lt, eaeA, bfpA, stx1, stx2). Additionally, PCR was utilized to screen for β-lactamase and carbapenemase resistance genes. Water parameters, including temperature and pH, were recorded, and their correlation with bacterial prevalence, virulence, and antibiotic resistance profiles were analyzed. A high prevalence of E. coli (92.68%) was observed, with a significant correlation between bacterial occurrence and elevated water temperatures. Diarrheagenic E. coli was detected in 82.1% of samples, with enterotoxigenic E. coli (ETEC) being the most common pathotype. Some isolates harbored multiple virulence genes, indicating hybrid strains. Resistance genes such as bla TEM, bla CTX-M, and bla OXA-48 were widely distributed, particularly during warmer months and at neutral pH levels. Groups with elevated water temperatures exhibited a higher prevalence of antibiotic-resistant isolates, often harboring multiple resistance genes. This study highlights the significant role of environmental stressors in influencing the prevalence, pathogenicity, and antibiotic resistance profiles of E. coli in aquaculture systems. The findings emphasize the need for continuous monitoring and improved biosecurity measures to mitigate the risks associated with MDR E. coli in aquaculture, ensuring food safety and protecting public health.

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The characteristics of multimodal fundus imaging in AMN patients following COVID infection

Exact solutions of Dirac equation for hydrogen atom using the linear combination of orthogonal Laguerre basis functions

Walking in two worlds: how an Indigenous computer scientist is using AI to preserve threatened languages

Risk factor analysis for cardiac abnormalities in patients with idiopathic scoliosis

Searching for dark photons in the Sun’s atmosphere

Bioimpedance assessment method based on back propagation neural network for irreversible electroporation of liver tissue

Adaptive optimization of natural coagulants using hybrid machine learning approach for sustainable water treatment

Split-belt treadmill training improves gait symmetry and lower limb function in patients with stroke

Abstract Split-belt treadmill training (SBTT) enhances gait symmetry in patients with stroke (PwS) by improving sensorimotor adaptation, However, it remains unclear whether repeated SBTT leads to long-term acquisition of gait adaptation skills and whether it improves functional walking in PwS. This study aimed to investigate the effect of SBTT on gait symmetry and lower limb function in PwS. We designed a parallel-group randomized controlled experiment. PwS who have the ability to stand and walk unassisted for at least 30 min were included in this study. They were randomly assigned to either rehabilitation with SBTT or with tied-belt treadmill training (TBTT). The treatment was provided once per day, five times per week for four weeks(total of 20 treatments). Gait analysis including step length, gait speed, temporal and spatial asymmetry and clinical assessment of lower limb function using Fugl-Meyer Assessment - lower extremity (LEFM), the Berg Balance Scale (BBS), the Wisconsin Gait Scale(WGS), and the Timed Up and Go Test (TUGT) were measured at baseline, 2 weeks post intervention, 4 weeks post intervention, and at follow-up after 4 weeks finishing the exercise. Two-way repeated measures ANOVA was used for intergroup comparisons between the two groups at different time points. In addition, Pearson correlation analysis was used to test the relationship between the improvement of clinical assessment scales and changes in gait parameters. Results showed that SBTT could effectively and efficiently improve the spatial asymmetry and speed of gait, as well as lower limb balance and walking function in PwS. In addition, the improvement of functional walking was positively correlated with the decrease of spatial asymmetry, and the increase of step length of paretic leg. In Conclusion, a 4-week SBTT intervention could effectively improve gait asymmetry and, consequently, enhance walking and lower limb function in PwS with independent walking ability.

Response spectra and design spectrum of ground fissures site under seismic action

Analytical study on steady seepage of a foundation pit adjacent to a structure

Cell therapy with placenta-derived mesenchymal stem cells for secondary progressive multiple sclerosis patients in a phase 1 clinical trial

Piezo1 activation protects against sepsis-induced myocardial dysfunction in a pilot study

Dynamic response of twin parallel tunnels in unsaturated soil under metro train loadings

Storm of seizures in a baby’s brain calms after trial therapy

Cross study transcriptomic investigation of Alzheimer’s brain tissue discoveries and limitations

Abstract Developing effective treatments for Alzheimer’s disease (AD) likely requires a deep understanding of molecular mechanisms. Integration of transcriptomic datasets and developing innovative computational analyses may yield novel molecular targets with broad applicability. The motivation for this study was conceived from two main observations: (a) most transcriptomic analyses of AD data consider univariate differential expression analysis, and (b) insights are often not transferable across studies. We designed a machine learning-based framework that can elucidate interpretable multivariate relationships from multiple human AD studies to discover robust transcriptomic AD biomarkers transferable across multiple studies. Our analysis of three human hippocampus datasets revealed multiple robust synergistic associations from unrelated pathways along with inconsistencies of gene associations across different studies. Our study underscores the utility of developing AI-assisted next-gen metrics for integration, robustness, and generalization and also highlights the potential benefit of elucidating molecular mechanisms and pathways that are important in targeting a single population.