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Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images
FARES and Spaso method for anterior shoulder dislocation: a prospective randomized control study demonstrating the benefit of a combined approach
Adaptive data driven multi period power supply recovery method for distribution networks
An end-to-end mass spectrometry data classification model with a unified architecture
Comparative assessment of the Sikun 2000 sequencing platform for whole genome sequencing
On the accurate computation of expected modularity in probabilistic networks
Abstract Modularity is one of the most widely used measures for evaluating communities in networks. In probabilistic networks, where the existence of edges is uncertain and uncertainty is represented by probabilities, the expected value of modularity can be used instead. However, efficiently computing expected modularity is challenging. To address this challenge, we propose a novel and efficient technique ( $$\textrm{FPWP}$$ ) for computing the probability distribution of modularity and its expected value. In this paper, we implement and compare our method and various general approaches for expected modularity computation in probabilistic networks. These include: (1) translating probabilistic networks into deterministic ones by removing low-probability edges or treating probabilities as weights, (2) using Monte Carlo sampling to approximate expected modularity, and (3) brute-force computation. We evaluate the accuracy and time efficiency of $$\textrm{FPWP}$$ through comprehensive experiments on both real-world and synthetic networks with diverse characteristics. Our results demonstrate that removing low-probability edges or treating probabilities as weights produces inaccurate results, while the convergence of the sampling method varies with the parameters of the network. Brute-force computation, though accurate, is prohibitively slow. In contrast, our method is much faster than brute-force computation, but guarantees an accurate result.
Broadly reactive monoclonal antibodies against beta-lactamases for immunodetection of bacterial resistance to antibiotics
Genome-wide identification and characterization of NBS-LRR gene family in tobacco (Nicotiana benthamiana)
Advanced generalized machine learning models for predicting hydrogen–brine interfacial tension in underground hydrogen storage systems
Heavy metals, noradrenaline/adrenaline ratio, and microbiome-associated hormone precursor metabolites: biomarkers for social behaviour, ADHD symptoms, and executive function in children
Abstract The gut microbiome significantly influences physical and mental health, including the synthesis and metabolism of hormones and the detoxification of heavy metals, which are linked to behavioural disorders. This study investigated the associations of these biological factors with the behaviour of primary school children, specifically examining the effects of heavy metals, catecholamines, and microbiome-associated metabolites of dopamine, noradrenaline, adrenaline, and thyroxine precursors. Urine samples from 87 unselected primary school children were analysed to assess heavy metal load (arsenic, cadmium, lead, mercury), noradrenaline/adrenaline ratio, and microbiome-associated metabolites of phenylalanine, tyrosine and L-dopa (3-phenylpropionic acid, p-OH-phenylacetic acid, 4-hydroxybenzoic acid, 3,4-dihydroxyphenylpropionic acid). Three months later, executive functions, ADHD symptoms (inattention, hyperactivity and impulsivity), and social behaviour were evaluated via parent and teacher questionnaires. In a path model, heavy metal load, microbiome-associated metabolites, and the noradrenaline/adrenaline ratio measured in urine accounted for 32% of social behaviours. Microbiome-associated metabolites predicted 11% of the variance in executive functions and 17% in ADHD symptoms. Executive functions shared 55% of the variance with ADHD symptoms and 17% with social behaviours. Children with the lowest social behaviours had a sixfold increase in the odds of high heavy metal loads and a 3.4-fold increase in the odds of elevated microbiome-associated metabolites. Similarly, children with the most compromised executive functions had a threefold increase in the odds of such high metabolite levels. Overall, the results indicate that children’s social behaviours are influenced by heavy metal accumulation, catecholamine balance, and the microbiome-associated metabolism of amino acids, that are crucial for producing stress and thyroid hormones.
Tailored treatment of specific diagnosis improves symptoms and quality of life in patients with myocardial Ischemia and Non-obstructive Coronary Arteries
Mechanism and spatial spillover effect of the digital economy on carbon emission efficiency in Chinese provinces
A global object-oriented dynamic network for low-altitude remote sensing object detection
The a subunit isoforms of V-ATPase are involved in glucose-dependent trafficking of insulin granules
Abstract In pancreatic β cells, insulin granules move toward the plasma membrane to secrete insulin upon glucose stimulation, but the amount of secreted insulin is only a small portion of the total, and many granules do not release insulin. Here, using MIN6 cells derived from mouse pancreatic β cells, we observed that granules that moved toward the plasma membrane returned to the inner area after the stimulation was removed. This back-and-forth trafficking is likely important for strict regulation of insulin secretion in response to the blood glucose level. However, the mechanism was largely unknown. We found that “back” (inward) and “forth” (outward) trafficking was reduced in cells with knockdown of the a2 and a3 subunit isoforms of the proton pump V-ATPase, respectively. Interestingly, the amount of secreted insulin was increased in a2 knockdown cells. Both a2 and a3 interacted with GDP-bound form Rab27A, a member of the Rab small GTPase family that regulates insulin secretion. These results indicate that a2 and a3 are involved in back-and-forth trafficking of insulin granules, respectively. The a subunit isoforms of V-ATPase seem to determine the direction of insulin granule trafficking dependent on the glucose level.