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The association between urinary antibiotics levels and the risk of adolescent depression
Serine protease inhibitor dipetalogastin-like from Galleria mellonella is involved in insect immunity
Abstract A new protein with immune properties was found in Galleria mellonella hemolymph. The so-far putative serine protease inhibitor dipetalogastin-like (GmSPID) was found in one fraction obtained after separation of hemolymph by RP-HPLC. Its amount depended on the immune status of the insect: it significantly increased after oral (10^3 CFU) and intrahemocelic (10 and 50 CFU) infection with entomopathogenic bacteria Pseudomonas entomophila. This was accompanied by up-regulation of the respective gene in the fat body of infected larvae. GmSPID was purified to homogeneity and characterised as a protein with immune properties. Among the three proteases tested, i.e. trypsin, elastase, and thermolysin, the strongest inhibition was observed toward trypsin. No inhibition toward the metalloproteinase thermolysin was detected, confirming that GmSPID is an inhibitor of serine proteases. Additionally, GmSPID was shown to have antimicrobial properties. At the concentration of 7 µM and 15 µM, it acted against Pseudomonas entomophila, Pseudomonas aeruginosa, Bacillus thuringiensis, Escherichia coli, and Candida albicans but not against Staphylococcus aureus. Moreover, with the use of atomic force, scanning, and transmission electron microscopy techniques, we present the effect of the GmSPID protein on the surface properties, shape, and ultrastructure of P. entomophila cells. The protein caused modest perforation of the cellular membrane, contributing to loss of its integrity. The mode of the GmSPID protein action as an antimicrobial compound and its role in G. mellonella immunity are discussed.
Central nervous system and systemic inflammatory networks associated with acute neurological outcomes in COVID-19
Identification of important objectives of networked system based on propagation characteristics
Monitoring water vapor transport in near real-time with low-cost GNSS receiver network
Error analysis of a heating oven for a spin-exchange relaxation-free magnetometer
Abstract We present the design and error analysis of a non-magnetic electric heating oven for spin-exchange relaxation-free (SERF) magnetometers, where precise thermal control and minimal magnetic disturbance are critical. A compact oven using a double-layer polyimide-constantan heating film was developed and evaluated through finite element simulations and experiments. Temperature simulations showed good agreement with measurements using a convective heat transfer coefficient of $$7.6~\mathrm {W/m^2\cdot K}$$ . However, a temperature difference of $$14.94~^\circ$$ C was observed due to heat loss near the vapor cell stem. Adding a thermal shell in the simulation reduced this gradient to $$6.43~^\circ$$ C, indicating improved thermal uniformity. Magnetic field simulations initially showed large discrepancies with experimental results. Including a twisted wire pair improved agreement, but differences remained. To further investigate the remaining discrepancies, Monte Carlo simulations were performed by introducing realistic variations in fabrication tolerances, mounting positions, and wiring configurations. The results revealed that wiring errors had the greatest influence on the measured magnetic field. These findings provide key insights into structural factors affecting magnetic performance and offer practical guidelines for reducing magnetic noise in SERF magnetometer systems.
Survival days of patients with metastatic spinal tumors of lung cancer requiring surgery: a prospective multicenter study
Analysis of coupling coordination development and obstacle factors in the water-energy-carbon-ecological environment nexus across China’s Yellow River basin
Fault detection in electrical power systems using attention-GRU-based fault classifier (AGFC-Net)
Abstract Fault detection is essential in guaranteeing the reliability, security, and productivity of contemporary technological and industrial systems. Faults that go unnoticed may result in disastrous failures as well as prohibitive downtimes in industries as varied as healthcare, manufacturing, and autonomous functioning. Conventional fault detection technologies tend to possess low accuracy rates, weak feature extraction, as well as limitations in generalizability across variegated faults. To overcome these shortcomings, this paper puts forward an Attention-GRU-Based Fault Classifier (AGFC-Net), which employs a sophisticated attention mechanism for improved feature extraction and correlation learning. Through the fusion of attention layers with Gated Recurrent Units (GRU), AGFC-Net is able to focus on key fault features, learn temporal dependencies, and provide better classification performance even under noisy conditions. Experimental results show that AGFC-Net attains a fault detection accuracy of 99.52%, better than conventional machine learning and deep learning algorithms. The suggested method presents a stronger, adaptive, and scalable solution for autonomous fault diagnosis, opening the door to intelligent and trustworthy fault detection systems in future power grids and industrial applications.
Characterization of mechanical properties of graphene nanoplates with vacancy defects using AFEM
Attenuated fast heart rate recovery suggests delayed parasympathetic reactivation after cessation of exercise in uncomplicated type 1 diabetes patients
Prospective associations between 24-h device-measured occupational and leisure-time physical activity and register-based musculoskeletal-related primary healthcare utilization among Danish workers
Lower cortical thickness index does not correlate with increased surgical complications in proximal femoral fractures: a clinical and radiological study
Body weight trajectories from midlife are associated with cognitive decline in advanced age
Abstract Fluctuations in body weight may impact cognitive decline, but current evidence is inconclusive. The aim of this study is to investigate associations between body weight trajectories from midlife to later life and cognitive decline. This retrospective study analyzed harmonized data from two population-based longitudinal studies, the Progetto Veneto Anziani and the Italian Longitudinal Study of Aging, encompassing baseline and two follow-up assessments over 9 years. Weight changes were recorded from baseline to the last available follow-up or from 50 years (self-reported data) to the last available follow-up. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and cognitive decline was defined as experiencing a MMSE change from baseline to the follow-up within the lowest quartile of the change distribution in the total sample. In a sample of 3852 individuals (46% females, age 65–96 years at baseline), we investigated the impact of weight change on cognitive decline with two sets of analyses. First, using weight measurements obtained during old age, growth mixture modelling identified three weight trajectories: decreasing, stable, and increasing. None of these trajectories was significantly associated with cognitive decline. Second, we considered weight at age 50 as the baseline assessment to capture weight changes from midlife. Among the three trajectories detected (increasing, stable, and decreasing), the decreasing trajectory was significantly associated with a higher likelihood of cognitive decline in males (HR 1.44, 95% CI 1.06–1.94) and females (HR = 1.37, 95%CI 1.23–1.67), whereas the increasing trajectory was associated with cognitive decline only in females (HR = 1.33, 95%CI 1.01–1.76). These results suggest that changes in body weight from middle to older age are associated with cognitive decline in advanced age. Since body weight is influenced by multiple factors, a broader assessment of health—including metabolic, vascular, behavioral, and social dimensions—should be considered in both research and clinical settings.
Enhanced cognitive performance in older adults through combined cognitive training and transcranial direct current stimulation
Interpretable machine learning models for survival prediction in prostate cancer bone metastases
Unravelling the impact of linear energy transfer on micronuclei induction from proton and photon irradiation
Abstract Micronucleus (MN) formation has a strong link to radiation damage and is a common bio-dosimeter for acute radiation exposures. The percentage of cells containing MN (PCMN) has a strong relationship with dose, however variation between previous studies has made understanding the effect of linear energy transfer (LET) difficult. This study investigated the PCMN of seven cell lines in response to photon and proton irradiation at two different LETs (0.6 keV/ µm and 6.5 keV/ µm). MN production was scored via the cytokinesis block micronuclei assay. A linear relationship between dose and PCMN was noted for all cell lines and radiation types, with a large variability in the MN yield between cell lines. This dose-dependent increase in PCMN was independent of LET, with most cell lines showing similar responses to the radiation qualities. Overall, this data proposes a more complex relationship between dose, MN and LET.