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Identification and validation of palmitoylation-related biomarkers in gestational diabetes mellitus
Ameliorative effect of rhizobacteria Bradyrhizobium japonicum on antioxidant enzymes, cell viability and biochemistry in tomato plant under nematode stress
Modeling Intraday Aedes-human exposure dynamics enhances dengue risk prediction
Abstract Cities are the hot spots for global dengue transmission. The increasing availability of human movement data obtained from mobile devices presents a substantial opportunity to address this prevailing public health challenge. Leveraging mobile phone data to guide vector control can be relevant for numerous mosquito-borne diseases, where the influence of human commuting patterns impacts not only the dissemination of pathogens but also the daytime exposure to vectors. This study utilizes hourly mobile phone records of approximately 3 million urban residents and daily dengue case counts at the address level, spanning 8 years (2015–2022), to evaluate the importance of modeling human-mosquito interactions at an hourly resolution in elucidating sub-neighborhood dengue occurrence in the municipality of Rio de Janeiro. The findings of this urban study demonstrate that integrating knowledge of Aedes biting behavior with human movement patterns can significantly improve inferences on urban dengue occurrence. The inclusion of spatial eigenvectors and vulnerability indicators such as healthcare access, urban centrality measures, and estimates for immunity as predictors, allowed a further fine-tuning of the spatial model. The proposed concept enabled the explanation of 77% of the deviance in sub-neighborhood DENV infections. The transfer of these results to optimize vector control in urban settings bears significant epidemiological implications, presumably leading to lower infection rates of Aedes-borne diseases in the future. It highlights how increasingly collected human movement patterns can be utilized to locate zones of potential DENV transmission, identified not only by mosquito abundance but also connectivity to high incidence areas considering Aedes peak biting hours. These findings hold particular significance given the ongoing projection of global dengue incidence and urban sprawl.
Dynamic niche technology based hybrid breeding optimization algorithm for multimodal feature selection
Wideband filtering antenna loading U-shaped parasitic patches based on characteristic mode analysis
Mitigation of salt stress in Camelina sativa by epibrassinolide and salicylic acid treatments
Research on “theory of force-center” based on failure characteristics of different roadway sections and its application
Changes in feeding behavior, milk yield, serum indexes, and metabolites of dairy cows in three weeks postpartum
Exploring the prevalence and risks of eating disorders in Lebanon’s athletic community
Characterization of the ceramic coating formed on 2024 al alloy by scanning plasma electrolytic oxidation
Recent metaheuristic algorithms for solving some civil engineering optimization problems
Abstract In this study, a novel hybrid metaheuristic algorithm, termed (BES–GO), is proposed for solving benchmark structural design optimization problems, including welded beam design, three-bar truss system optimization, minimizing vertical deflection in an I-beam, optimizing the cost of tubular columns, and minimizing the weight of cantilever beams. The performance of the proposed BES–GO algorithm was compared with ten state-of-the-art metaheuristic algorithms: Bald Eagle Search (BES), Growth Optimizer (GO), Ant Lion Optimizer, Tuna Swarm Optimization, Tunicate Swarm Algorithm, Harris Hawk Optimization, Artificial Gorilla Troops Optimizer, Dingo Optimizer, Particle Swarm Optimization, and Grey Wolf Optimizer. The hybrid algorithm leverages the strengths of both BES and GO techniques to enhance search capabilities and convergence rates. The evaluation, based on the CEC’20 test suite and the selected structural design problems, shows that BES–GO consistently outperformed the other algorithms in terms of convergence speed and achieving optimal solutions, making it a robust and effective tool for structural Optimization.
Genomic relatedness of colonizing and invasive disease Klebsiella pneumoniae isolates in South African infants
Abstract Klebsiella pneumoniae (KPn) colonizes multiple anatomical sites and is a leading cause of invasive disease and death in African children; however, there is no comparative genomic analysis between colonizing and invasive strains. This study investigated the genomic relatedness of KPn colonizing and invasive isolates in South African infants; and evaluated the relative invasiveness of KPn isolates based on sequence types (ST), capsular (KL), and lipopolysaccharide (O) loci by calculating case-carrier ratios (CCRs). There was less genomic diversity amongst invasive (22 ST, 17 K-loci) than colonizing isolates (31 ST, 29 K-loci), with invasive isolates being 8.59-fold and 3.49-fold more likely to harbour genes encoding for multi-drug resistance and yersiniabactin production compared with colonizing isolates. The CCRs for KL102 and O1/O2v2 were > 1, and < 1 for KL8, ST1414, and O1O2v1. Identifying high-risk strains, including KL102 and O1O2v2, that may have a higher potential to cause invasive disease, could enhance risk assessment and management strategies in vulnerable populations.
Effects of dietary gluten on body weight and gut microbiota in BALB-C mice using 16 S rRNA-Based analysis
Abstract Despite the widespread adoption of gluten-free diets for weight management, the relationship between gluten intake and obesity remains unclear because of the limited number of controlled studies available in the literature. Furthermore, there is ongoing debate regarding the impact of gluten-containing diets on the gut microbiota. This study aimed to investigate the effects of gluten consumption on the body weight and intestinal microbiota of mice fed a high-fat diet. Twenty-four Bagg albino laboratory-bred mice (BALB/c) were randomly divided into four groups for oral gavage feeding: standard diet control (SDC), standard diet + 5 mg/day gluten (SD + gluten), high-fat diet control (HFDC), and high-fat diet + 5 mg/day gluten (HFD + gluten). Each subject’s body weight was measured and recorded weekly. For microbiota analysis, fecal samples were collected weekly from the cages after overnight cage changes. The microbiota was analyzed using via the 16 S ribosomal ribonucleic acid (rRNA) method. Compared with the control diet, both gluten consumption and a high fat diet significantly increased weight gain (p < 0.05). No significant difference was observed in the total mesophilic aerobic bacterial count among the groups (p > 0.05). However, the addition of gluten to the diet positively affected Lactobacillus bulgaricus (p < 0.05). Conversely, gluten-containing diets negatively impacted the total coliform bacteria and Escherichia coli counts in the gut (p < 0.05). These findings suggest that gluten, when combined with either a normal diet or a high-fat diet, contributes to weight gain while exerting positive effects on the intestinal microbiota.
Neural network backstepping control of OWC wave energy system
Singularity transformation through single-pass phase modulation of light
Carbon ion stimulation therapy reverses iron deposits and microglia driven neuroinflammation and induces cognitive improvement in an Alzheimer’s disease mouse model
Social networks and loneliness differ between LGBTIA and cis-heterosexual persons: results from a two-wave survey in Germany
Abstract Current research suggests LGBTIA persons to be lonelier than cis-heterosexual persons. While they rely more on friends than on family as support network, their social network and weekly contact to family and friends, as well as the association between the social network and loneliness have not been fully explored yet. The aim of this analysis was to examine differences in the social network between LGBTIA and cis-heterosexual persons in Germany, and how these differences affect loneliness. Data was collected through an online survey conducted in two independent waves in March/April 2020 and in January/February 2021. Linear regression analyses were performed to examine the influence of the social network on loneliness. Of 6784 participants, 5442 identified as LGBTIA. Weekly contact to family was lower in the LGBTIA group than in cis-heterosexuals. LGBTIA were less likely to be in a relationship. Identifying as LGBTIA increased social and emotional loneliness. Differences in social network partly explained the risk for social loneliness of LGBTIA persons and, to a lower degree, the risk for emotional loneliness. We encourage health care professionals to inquire about sexual orientation, gender identity, and relationship status to raise awareness for feelings of loneliness and related health problems.
The linguistic and emotional effects of weather on UK social media users
Abstract Weather significantly impacts mood and happiness, yet observing this at scale and differentiating across weather types is challenging. This study examines the variation in public sentiment related to different weather conditions, as reflected in the vocabulary used in UK-based social media (Twitter) content. We introduce a novel context-sensitive sentiment metric to construct scales that rank words and emojis by both weather severity and emotional intensity, controlling for linguistic variations that naturally occur in different discussion topics. Our findings reveal that emotional responses to weather are complex, influenced by combinations of weather variables and regional language differences. For five weather conditions (temperature, precipitation, humidity, wind speed and barometric pressure) we first identify the sentiment and weather severity associated with words commonly used to discuss them, highlighting the distinct vocabulary used to express positive and negative emotions for each weather type. Next, we demonstrate that language used in weather discussions predicts the severity of each condition and varies across different weather combinations. These findings highlight the importance of context-sensitive sentiment methods for better understanding public mood in response to weather. This approach reveals systematic relationships between weather conditions and public mood, offering insights for impact-based weather forecasting and risk communication.