From connection to stigmatization: The influence of storytelling networks on perceived stigma and quality of life among migrant domestic workers in Hong Kong.

Integration of advanced deep neural networks in analysis codes and monitoring systems for Identification of multiphase flow patterns

Aberrant hippocampal-cortical connectivity and network coupling in facial emotion recognition-based subtypes of depression.

Inclusion-exclusion integral neural network with monotone regularization terms

Shrimp shell protein as a functional wheat flour substitute: modulating gluten networks and dough quality.

Exploring the therapeutic potential of Radix Puerariae isoflavonoids against atherosclerosis through integrative network pharmacology and machine learning.

How does disaster context shape emergency response coordination? Using an embedded comparative design, we examine how shock intensity and threat familiarity jointly reshape network structures and task sequencing across six Chinese provinces responding to Typhoons Yagi and Bebinca in September 2024. Familiar high-intensity disasters are associated with more integrated, centralized networks, whereas unfamiliar disasters exhibit fragmented coordination characterized by continuous information feedback. Shock intensity shows a consistent relationship with network integration, whereas threat familiarity enables larger mobilization scale without necessarily increasing integration. Recovery operations maintain stability in familiar contexts but show reduced temporal persistence in unfamiliar settings. These findings suggest that disaster governance design may benefit from distinguishing between exploitation-based coordination for familiar threats and exploration-based coordination for unfamiliar events, with implications for regions facing novel hazard exposure under changing climatic conditions.

Integrative network toxicology and multi-omics analyses reveal divergent molecular programs underlying pyrethroid-induced ovarian dysfunction.

Nitric oxide and phytohormones molecular crosstalk regulating fruit quality and postharvest management.

Disrupted cerebello-cerebral functional integration of triple core networks in major depressive disorder: A resting-state fMRI study.

Integrating Contrast Colour Correction (CACC) and Convolutional Neural Networks (CNN) can help fish breeders in earlier classification and identification of Cryptocaryon fish disease (protozoan white spot disease). Disease identification accuracy is enhanced through such method by adaptive colour changes and CNN feature extracting ability, thereby boosting underwater image clarity. Unlike traditional rule-based systems that relies on expert knowledge despite being error-prone, existing methods focus on visual quality without classifying impact influence. Early disease identification is hampered in terms of efficiency due to machine learning methods reliant on abundant human expertise other than efficient feature extracting. An artificial intelligence (AI)-oriented computer model is introduced for existing research in overcoming limitations and eliminating subjectivity. The model employs a proprietary method of diagnosing fish disease through underwater images examination that yields objective outcome. Several CNN structures such as GoogleNet, ResNet-101, AlexNet, ResNet-50 as well as VGG-16 are tested on its performance. The current study shows integration of CACC with CNN through a set of 15000 images boosting up model performance in Cryptocaryon fish disease detection. The introduced novel method significantly enhances performance with 99.53% accuracy, 99.08% precision along with 100.00% recall. This efficient, accurate approach can significantly reduce the workload of experts and fish farmers while promoting sustainable aquaculture and healthier aquatic ecosystems. • Contrast-Adaptive Colour Correction (CACC) and Convolutional Neural Network (CNN) enhance fish images for accurate Cryptocaryon fish disease detection. • 6500 image datasets from National Fish Health Research aids AI training and testing. • One of the CNN architectures, ResNet50 outperforms others with 99.52 % accuracy. • 99 % accuracy across key metrics ensures early disease detection. • AI-driven approach supports sustainable aquaculture and reduces losses.

Mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), shows pronounced clinical heterogeneity poorly explained by pathology burden, representing a gap complicating prognosis. As the brain operates as a complex network for information integration, we hypothesized that connectome architecture mediates the link between AD pathology and clinical expression. We developed a framework integrating structural and functional connectomes from multi-center cohorts, performing connectome-based subtyping in MCI, with analyses of upstream pathology, downstream phenotypes, and transcriptomic associations. This approach identified an "MCI-compromised" (MCI-C) subgroup characterized by extensive structural-functional connectomic disruption and an "MCI-preserved" (MCI-P) subgroup with relatively preserved connectome integrity. Despite comparable pathology, MCI-C demonstrated more severe neurodegeneration, accelerated cognitive decline, and elevated progression risk. Multiscale analyses linked these patterns to transcriptomic profiles of mitochondrial, synaptic, and neuroimmune processes. These findings demonstrate that the connectome acts as a critical mediator, rather than a passive endophenotype, shaping AD clinical expression.

Connection density affects the behavior of functional brain network metrics.

Fluid–structure interaction training of integral conservation physics-informed neural networks for blood flow in elastic vessels

Characterizing nursing home care team communication via text messaging: A social network analysis.

Opioids are among the most widely prescribed treatments for pain; however, prolonged use leads to adverse effects, including tolerance and opioid-induced hyperalgesia. Neurons that detect noxious stimuli (nociceptors) within the dorsal root ganglion (DRG) are critical for both the analgesic and adverse effects of opioids. Although post-transcriptional RNA control is critical for DRG function, the role of circular RNAs (circRNAs), an evolutionarily conserved and highly stable class of RNA, in nociceptive processes remains largely unexplored. Furthermore, the effect of opioids on the circRNA landscape of human DRG (hDRG) is unknown. To address these gaps, we performed high-coverage total RNA sequencing on hDRG tissue obtained from opioid-exposed donors and compared circRNA and linear transcriptomic profiles to age- and sex-matched controls. Opioid exposure was associated with a global reduction in circRNA abundance, as well as specific alterations in 43 differentially expressed circRNAs. Parallel analysis of linear transcripts (mRNAs) revealed 349 differentially expressed transcripts, including genes involved in neuronal signaling and immune responses. Integrative circRNA-mRNA network analyses suggest that opioid-responsive circRNAs may modulate nociceptor gene expression through miRNA- and RNA-binding protein-binding mechanisms. Among opioid-associated circRNAs, circNTRK2 , derived from NTRK2 , the tyrosine kinase B receptor gene, was downregulated in both opioid-exposed hDRG tissue and a repeat morphine exposure paradigm in nociceptor-like cell line. Collectively, these findings establish the landscape of circRNAs in hDRG, reveal opioid-associated circRNA dysregulation, and highlight circRNAs as potential modulators of post-transcriptional gene expression in nociceptors.

Integrative multi-omics and network perturbation analysis in human airway organoids reveals product-specific toxicity profiles of heated tobacco products.

Resistant cotton maintains vascular health via an integrative defence network against Verticillium dahliae.

Prostaglandin D2 reinforces mitochondrial quality control to enhance ovarian and embryonic competence.

Circadian-dependent neural mechanisms of lighting optimization in underground transit environments: Evidence from fNIRS network analysis.