Browse Articles

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.

Design and enhanced high temperature wear performance of laser clad Co matrix coatings containing NiCrAlY and TiC over a wide temperature

gdf11 is required for pronephros/cloaca development through targeting TGF-β signaling

SwinConvNeXt: a fused deep learning architecture for Real-time garbage image classification

Abstract Waste management handles all kinds of waste, including household, industrial, municipal, organic, biomedical, biological, and radioactive wastes. People still face challenges in proper disposal methods for different types of waste, including landfill-bound items, recyclable materials, and biodegradable waste. Inadequate waste management poses a significant and multifaceted global challenge. The conventional method of segregating waste is a time-consuming and ineffective method that wastes human power and money. To address this issue in real time, sophisticated and sustainable waste management systems need to be implemented. The latest advancements in computer vision and deep learning offer efficient solutions for effective recycling and waste management. Existing deep learning models exhibited various limitations, such as detection accuracy and computational inefficiency, particularly when dealing with objects of varying sizes and exhibiting high degrees of visual similarity. These limitations generate various challenges in effectively capturing and representing the nuanced features of visually similar objects. To address this problem, we proposed the stacking of an enhanced Swin Transformer, improved ConvNeXt, and a spatial attention mechanism. The enhanced Swin transformers incorporate two key components- hierarchical feature extraction and shifting window mechanism to extract the global features from the garbage images effectively. The shifting window mechanism extracts the most important features from various regions of the images to identify the objects. In contrast, the hierarchical feature extraction captures long-range dependencies within the image to effectively identify different types of garbage. The improved ConvNext block with optimized parameterization extracts the local features of the image. This enhanced feature extraction capability enables the model to effectively discern fine-grained details of individual garbage particles, such as shape, texture, and subtle variations in color and appearance, leading to more accurate classification results. When we evaluated the performance of the proposed model using the publicly available Garbage Classification dataset, it attained 98.97% accuracy, 98.42% Precision, and 98.61% Recall. Due to its lightweight and low computational time and power, the proposed model surpasses the existing state-of-the-art deep learning models.

Author Correction: Identification of novel 7-hydroxycoumarin derivatives as ELOC binders with potential to modulate CRL2 complex formation

A deep insight into the sialome of the house fly, Musca domestica, infected with the salivary gland hypertrophy virus (MdSGHV)

Abstract The house fly, Musca domestica, serves as a mechanical vector for numerous pathogens, posing a significant risk to human and animal health. More than two decades ago, the Musca domestica salivary gland hypertrophy virus (MdSGHV) was discovered, infecting both males and females flies and disrupting mating and the reproductive process. While MdSGHV can infect various tissues, its primary replication site is the house fly salivary gland. It is well established that arthropod salivary glands play an important role not only in acquiring food but also in transmitting pathogens. Therefore, understanding the composition of vector salivary glands and the interactions between vector and pathogen components is essential for developing future control strategies. To this end, we conducted a comprehensive RNA-sequencing of salivary glands from both infected and non-infected house flies. Our analysis identified a total of 6,410 putative sequences, with 6,309 originating from M. domestica and 101 from the MdSGHV, categorized into 25 functional groups. Furthermore, differential expression analysis between infected and non-infected salivary glands revealed 2,852 significantly modulated transcripts, highlighting profound transcriptional changes triggered by MdSGHV infection. Overall, these findings not only deepen our understanding of the composition of M. domestica salivary glands but also provide valuable insight into the virus-vector interaction, which could serve as a model to understand other medically relevant interactions.

Numerical simulation and experimental study of suspended particulate matter removal for efficient water recovery and reuse in solid–liquid separation

Association between the oxidative balance score and testosterone deficiency: a cross-sectional study of the NHANES, 2011–2016

A distributed zero-trust scheme for airborne wireless sensor networks using dynamic identity authentication

Physical activity, diet, and social determinants of health associate with health related quality of life and fibrosis in MASLD

Effects of gradation on sandy debris flow behavior

Retraction Note: Partial differential equations of entropy analysis on ternary hybridity nanofluid flow model via rotating disk with hall current and electromagnetic radiative influences

Photonic crystal with a defect layer of silicon containing polymer nanocomposites as radiation detector

The prognostic value of cortical stimulation induced seizures using stereo EEG in presurgical evaluation of focal epilepsies

Abstract The value of stimulation-induced seizures for multimodal determination of the epileptogenic zone in preoperative epilepsy diagnostics has not yet been sufficiently investigated. Patients with focal pharmacorefractory epilepsy who underwent invasive electroencephalography with cortical 50 Hz stimulation at the Epilepsy Center Erlangen between 2018 and 2023, had at least one stimulation-induced seizure, underwent resective epilepsy surgery, and had a postoperative follow-up ≥ 1 year were analyzed. 20 patients were included, 11 (55.0%) with temporal, 7 (35.0%) with frontal and 2 (10.0%) with parietal lobe epilepsy. 12 patients (60.0%) had a good Engel outcome (Engel 1A). Associated with a good vs. poor (Engel 1B-4) surgical outcome were not only the percentage of resected electrode contacts of the spontaneous seizure onset zone, SOZ (p = 0.005), but also the stimulation SOZ (p = 0.022), as well as stimulation-induced seizure with a typical seizure semiology (p = 0.033), the electrodes inducing a stimulation-induced seizure (p = 0.014), electrodes with an identical seizure onset pattern (p = 0.035), and the occurrence of low voltage fast seizure onset pattern, LVFA (p = 0.015). ROC analyses showed that the AUC for the predictors of the spontaneous SOZ were greatest for the stimulation SOZ (AUC 0.876) and stimulation-induced seizures with LVFA (0.860). Analysis of combined predictors showed higher odds of predicting SOZ for combinations including LVFA. Electroclinical stimulation seizures have prognostic value in determining the epileptogenic zone. Characteristics such as the seizure onset zone, seizure pattern and stimulation seizure semiology predict seizure freedom in case of resection of electrode contacts. Electrodes should be resected where both stimulation seizures have been induced or the seizure pattern has been localized and low voltage fast seizure pattern has occurred.