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Identifying mosquito plant hosts from ingested nectar secondary metabolites

Abstract Establishing how plants contribute food and refuge to insects can be challenging for small species that are difficult to observe in their natural habitat, such as disease vectoring mosquitoes. Currently indirect methods of plant-host identification rely on DNA sequencing of ingested plant material but are often unsuccessful for small insects that feed primarily on plant sugars or have little contact with plant cells. Here we developed an innovative approach to determine species-specific phytophagy by detecting taxon-specific plant secondary metabolites (PSMs) in nectar. Two mosquito species were exposed to three PSMs, each present in the nectar of a known plant host, firstly from dosed sucrose solutions and secondly from flowers. Both experiments yielded high rates of PSM detection in mosquitoes using liquid chromatography-mass spectrometry (LC-MS). PSMs were consistently detected in mosquitoes up to 8 h post-ingestion. In experiments consisting of two or three plant species, multiple PSMs from different host plants could be detected. These positive results demonstrate that PSMs could be useful indicators of insect plant-hosts selection in the wild. With expanded knowledge of nectar-based PSMs across a landscape, improved knowledge of plant-host relationships could be achieved where direct observations in their natural habitat are lacking. Increasing understanding of vector insect ecology will have an important role in tackling vector-borne disease.

Design and motion analysis of a coal mine robot with variable wheel diameter

Diagnosis-related groups study of uterine leiomyoma patients based on E-CHAID

Tendency to overeat predicts an elevated body mass index trajectory across school-age years

Abstract Overeating is a complex appetite trait, cross-sectionally linked to an elevated weight in children. However, little is known about longitudinal associations. Therefore, we studied how a tendency towards overeating predicts weight development between 8 and 16 years of age. In this study among 4517 children from the Finnish Health in Teens cohort, parents reported their child’s tendency to overeat when children were on average 11.2 (SD 0.8) years old. Children were then categorised as overeating, possibly overeating, or not overeating. Height and weight measurements from two data collection periods were combined with growth data from a national health register, and age- and sex-standardised body mass index z-scores (BMIz) were calculated using the International Obesity Task Force reference. Children also reported their lifestyle factors, including food consumption, physical activity, screen time, and sleep patterns. We examined the association between overeating and BMIz using a linear mixed model, adjusting for age, sex, specific food consumption frequencies, physical activity, screen time, and sleep duration. We further analysed whether associations differed by age, food consumption frequencies, or physical activity. The average BMIz in the overeating group was 1.18 (95% CI 1.10─1.26) units higher compared with those without overeating, but remained stable as age increased. Among those without overeating, BMIz increased 0.043 units per year of age (p < 0.001). Physical activity, but not food consumption, modified the association between overeating and BMIz (p for interaction = 0.038). In the lowest third of physical activity (≤ 5 h/week), BMIz was 1.28 units higher (95% CI 1.15─1.41) in the overeating compared with the no overeating group, while in the highest third (≥ 9 h/week) the effect size was 1.08 (95% CI 0.93─1.24). In conclusion, children with a parent-reported tendency to overeat exhibited an elevated, but stable mean BMIz across adolescence. Public health programmes tackling the obesity epidemic should consider the differences in appetite self-regulation among children.

SCCA-YOLO: A Spatial and Channel Collaborative Attention Enhanced YOLO Network for Highway Autonomous Driving Perception System

Polyextremotolerant, opportunistic, and melanin-driven resilient black yeast Exophiala dermatitidis in environmental and clinical contexts

Enterovirus D68 infection in cotton rats results in systemic inflammation with detectable viremia associated with extracellular vesicle and neurologic disease

Association between maternal anemia during pregnancy with low birth weight their infants

Semi-supervised tissue segmentation from histopathological images with consistency regularization and uncertainty estimation

Abstract Pathologists have depended on their visual experience to assess tissue structures in smear images, which was time-consuming, error-prone, and inconsistent. Deep learning, particularly Convolutional Neural Networks (CNNs), offers the ability to automate this procedure by recognizing patterns in tissue images. However, training these models necessitates huge amounts of labeled data, which can be difficult to come by due to the skill required for annotation and the unavailability of data, particularly for rare diseases. This work introduces a new semi-supervised method for tissue structure semantic segmentation in histopathological images. The study presents a CNN based teacher model that generates pseudo-labels to train a student model, aiming to overcome the drawbacks of conventional supervised learning approaches. Self-supervised training is used to improve the teacher model’s performance on smaller datasets. Consistency regularization is integrated to efficiently train the student model on labeled data. Further, the study uses Monte Carlo dropout to estimate the uncertainty of proposed model. The proposed model demonstrated promising results by achieving an mIoU score of 0.64 on a public dataset, highlighting its potential to improve segmentation accuracy in histopathological image analysis.

Intolerance of uncertainty and mental health in patients with IBD: the mediating role of maladaptive coping

Detection of illegal wells using advanced GIS analysis through Landsat 8 and Sentinel-2 image fusion in Bastam, Iran

Insights into the role of hnRNPK in spermatogenesis via the piRNA pathway

A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity

A hybrid time series forecasting approach integrating fuzzy clustering and machine learning for enhanced power consumption prediction

Predictive methods for CO2 emissions and energy use in vehicles at intersections

A superresolution-enhanced spectrometer beyond the Cramer–Rao bound in phase sensitivity

Genomics of novel influenza A virus (H18N12) in bats, Caribe Colombia

The relationship between red blood cell distribution width and long-term prognosis of asthma: a population-based study

Feasibility and comparison of 3D modified rosette ultra-short echo time (PETALUTE) with conventional weighted acquisition in 31P-MRSI

Abstract Phosphorus-31 magnetic resonance spectroscopic imaging ( 31 P-MRSI) provides valuable non-invasivein vivoinformation on tissue metabolism but is burdened by poor sensitivity and prolonged scan duration. Ultra-short echo time (UTE) acquisitions minimize signal loss when probing signals with relatively short spin-spin relaxation time (T 2 ), while also preventing first-order dephasing. Here, a three-dimensional (3D) UTE sequence with a rosette k-space trajectory (PETALUTE) is applied to 31 P-MRSI at 3T. Conventional weighted MRSI employs highly regular Cartesian k-space sampling, susceptible to substantial artifacts when accelerated via undersampling. In contrast, this novel sequence’s “petal-like” pattern offers incoherent sampling more suitable for compressed sensing (CS). These results showcase the competitive performance of PETALUTE against conventional weighted 31 P-MRSI with simulation, phantom, and in vivo leg muscle comparisons.

Association between systemic immune inflammation index and adolescent obesity in a cross-sectional analysis

Abstract Obesity is a prevalent health issue among adolescents, characterized by chronic low-grade inflammation, which increases the risk of developing various chronic diseases in the future. The systemic immune-inflammation index (SII) serves as an indicator of inflammation and immune response. This study conducted a cross-sectional analysis using data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2016, including 5,676 participants. A multivariate logistic regression model, Generalized Additive Models (GAM), and subgroup analysis were used to examine the relationship between obesity and SII. The multivariate logistic regression results revealed a significant positive correlation between log SII and adolescent obesity (1.254 [1.024–1.537]). Furthermore, the risk of obesity increased with higher quartiles of SII. Subgroup analysis and interaction tests showed that this positive association persisted across various factors, including female gender, race (Non-Hispanic White and Mexican American), non-hyperlipidemia, normal white blood cell count, and PIR < 1. Additionally, a U-shaped relationship between log SII and obesity was observed, with a turning point at 6.410. The findings suggest that an increase in the systemic immune-inflammation index is significantly associated with obesity in adolescents. However, further validation through large-scale prospective studies is needed.