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Thyme and cinnamon essential oils inhibit multidrug resistant Escherichia coli and Klebsiella pneumoniae and alter virulence transcripts
Abstract The increasing incidence of multidrug-resistant (MDR) Gram-negative pathogens, particularly Escherichia coli and Klebsiella pneumoniae , continues to narrow effective treatment options and motivates evaluation of alternative antimicrobial strategies. Here, 33 essential oils (EOs) were screened against six MDR clinical isolates, identifying thyme and cinnamon oils as the most active. Both oils produced large inhibition zones (up to 26 mm) and low minimum inhibitory concentrations (MICs), with cinnamon oil showing a uniform MIC of 0.0488% (v/v) across all strains. For transcriptional analysis, cultures were exposed to cinnamon oil at 0.0244% (v/v) (0.5×MIC). Because this exposure corresponds to a near-MIC/inhibitory condition, observed decreases in virulence-gene transcript levels should be interpreted as inhibitory/stress-associated transcriptional responses rather than definitive sub-MIC anti-virulence effects. GC–MS profiling indicated enrichment of carvacrol (23.4%) and thymol (3.58%) in thyme oil, while cinnamon oil was dominated by cinnamaldehyde (45.8%) and eugenol (10.89%). Cinnamon also showed slightly higher antioxidant capacity than thyme (DPPH RSA 70.57% vs. 67%). Finally, molecular docking (HADDOCK/AutoDock Vina) was used as supportive in-silico screening to explore plausible ligand–target interaction hypotheses. Collectively, thyme and cinnamon EOs demonstrated antibacterial activity and were associated with reduced virulence-gene transcript abundance under near-MIC exposure, supporting further validation using verified sub-MIC conditions and growth controls.
Application of smooth OWA operators to classification of retinitis pigmentosa
Delivery from the sky: investigating visual cues to communicate robot intentions in simulated public spaces
Abstract As robots such as drones begin delivering packages from the sky in public spaces, humans will interact as recipients. Clear communication of a drone’s vertical motion and delivery intentions is essential to reduce feelings of uncertainty and build trust among the public. This study investigates how visual cues, including delivery methods and interfaces, communicate a drone’s drop-off and take-off intentions and affect recipients’ uncertainty in a simulated public environment. Through a video-based online questionnaire, 150 participants viewed scenarios where a drone delivered a package either by landing or via a cable-drop mechanism, each presented with or without visual interfaces such as onboard lights, a display, or ground projection. Participants rated the scenarios for uncertainty, understandability, predictability, trust, and convincingness, and provided qualitative feedback. Results show that visual interfaces improved participants’ ability to predict drone actions, increased certainty in approaching the drop-off spot, and improved trust. While lights posed challenges with visual clarity, both display and projection interfaces conveyed vertical motion and delivery intentions effectively. Projection was particularly recommended, as it marked the drop-off spot on the ground and a safety boundary. We discuss the implications of our findings beyond delivery scenarios, considering broader public space interactions with robots operating in vertical planes, and highlight the need for validation through real-world experiments.
Chemical investigation of polycyclic aromatic hydrocarbon sources and associated health risks in PM2.5 from Eastern India
Source identification of sudden water pollution events in the Dongliao River using a hybrid machine learning framework
Finite element analysis of stress in removable lower complete denture under vertical and oblique occlusal forces
Sex-dependent dysregulation of the gut-brain NPYergic system in a mouse model of autism spectrum disorder
Abstract The microbiome-gut-brain axis has been increasingly recognized for its role in the pathophysiology of autism spectrum disorder (ASD), yet the underlying molecular mechanisms remain poorly understood. Neuropeptide Y (NPY), a key modulator of gut-brain communication, may play a pivotal role in this axis. This study investigated the sex-specific molecular profile of the NPY system in gut-brain communication via a genetic mouse model of ASD, the Nf1 + /- mice. Quantitative real-time PCR was performed to assess the expression of NPY and its receptor transcripts in the amygdala, hippocampus, prefrontal cortex and intestinal tissue of juvenile male and female Nf1 + /- mice. Additionally, gut microbiota analysis focused on Lactobacillus species in stool samples. Special emphasis was placed on sex differences, an area underexplored in ASD research. Sex-specific differences in NPY and its receptor expression were observed in both the brain and intestinal tissues of Nf1 + /- mice. In mutant females, estrous cycle fluctuations were partly associated with changes in the NPY system. Notably, distinct correlations between the brain and intestinal NPY systems were identified in both sexes of wild-type (WT) and Nf1 + /- mice. Microbiota analysis revealed sex-dependent alterations in Lactobacillus abundance, which correlated with the intestinal NPY system. Importantly, the Y2 receptor exhibited sex-specific expression patterns in both the gut and brain of Nf1 + /- mice. This study provides novel evidence that the NPY system may play a critical role in gut-brain communication in ASD, with sex-dependent alterations in both the brain and gut. The intestinal Y2 receptor has emerged as a potential molecular biomarker for ASD, underscoring the importance of incorporating sex as a biological variable in future ASD research.
Interpretable ESG–sentiment hybrid deep learning for asset return forecasting with quantified interactions and latency-aware deployment
Abstract Accurate forecasting of financial time series increasingly relies on alternative data such as environmental, social and governance (ESG) scores and news-based sentiment, yet the way these signals interact and when they actually improve forecasts is still poorly understood. We introduce an interpretable hybrid framework for asset return forecasting that combines a Temporal Fusion Transformer (TFT) with a lightweight Support Vector Regression (SVR) residual corrector and an explicit gated late fusion of ESG features with aspect-based financial sentiment (FinBERT-based ABSA). The gating mechanism learns when to emphasize sustainability versus sentiment signals, while SHAP interaction values and Friedman’s H quantify ESG–sentiment interactions across assets and regimes. A finance-grade, leak-proof walk-forward protocol (252 trading days train / 10 days test, within-fold scaling, ABSA items strictly before 16:00 ET; ESG effective T+3; macro T+1, HAC-robust Diebold–Mariano tests) is applied to US large-cap technology equities, major global indices, and BTC/ETH over 2020–2024. Across $$n=5$$ independent seeds, the hybrid achieves aggregate mean absolute error of $$2.77\times 10^{-3}$$ and RMSE of $$5.18\times 10^{-3}$$ on next-day log returns, with directional accuracy $$94.5\%$$ , IC 0.39, and ICIR 0.82, significantly outperforming tuned deep-learning and machine-learning baselines (HAC-robust per-asset Diebold–Mariano tests with BH-FDR $$q=0.05$$ ; Fisher aggregation yields $$p<0.01$$ ). Simple long-only, thresholded simulations indicate higher risk-adjusted performance and lower maximum drawdown under conservative transaction-cost assumptions. Ablation studies show that removing either ESG or sentiment features yields the largest degradations, and that the SVR corrector stabilizes errors under regime shifts. To directly address market-cycle sensitivity, we evaluate stability across event-defined stress windows (COVID-19 crash, 2022 tightening cycle, and 2023 banking stress) and volatility-defined regimes using terciles of 20-day realized volatility. We report regime-split forecasting and strategy metrics with block-bootstrap confidence intervals, HAC-robust Diebold–Mariano tests within each regime, and residual-stabilization diagnostics that quantify the SVR variance and skewness reduction under stress. ESG–sentiment interactions are statistically non-zero and regime-dependent, with sentiment gaining importance in turbulent periods and ESG in calmer markets. A latency-optimized variant that removes auxiliary BiLSTMs retains over $$90\%$$ of the accuracy gains while reducing inference time by approximately $$55\%$$ of the full model (i.e., a reduction of about $$45\%$$ ), supporting near-real-time deployment.
Evaluating morpho-physio-biochemical and yield performance of six commercial potato cultivars under a semi-arid agroecosystem
Role of oxygen vacancies on the structural, electronic, optical, and photocatalytic properties of Ba2CeMO6 (M = Bi, Sb) double perovskites: a DFT study
Investigation of TID-induced capacitance variation in GaAs edge-lift capacitors and its effect on RF impedance matching
Abstract This paper investigates the total ionizing dose response of passive components fabricated in a commercial GaAs process, with a focus on dose-dependent capacitance variation in fringe-field–dominant structures. Measurements indicate that the edge-lift capacitor exhibits the highest total ionizing dose(TID) sensitivity, with its effective capacitance increasing from 7.65 pF to 22.96 pF after 300 krad(Si) irradiation at 10 GHz. This behavior is consistently reproduced in electromagnetic simulations by increasing the relative permittivity of the SiN dielectric from its nominal value of 6.9 to 8.5, enabling a radiation-equivalent dielectric modeling approach. When applied to an RF amplifier matching network, the TID-induced capacitance variation shifts the input impedance from 43.75 + j27.15 Ω to 33.8 + j5.35 Ω, accompanied by degradation in gain and noise performance. These results demonstrate that TID-induced dielectric property changes in GaAs passive components can be effectively captured using radiation-equivalent electromagnetic modeling and can significantly impact RF circuit performance.
The persistence of behavioral disparities post-pandemic: Insights from activity time series data
Paradoxical oncogenic effects of hepatic Brca1 through modulating Bhmt
Particle swarm optimized deep learning for jamming detection and throughput enhancement in cognitive radio networks
Predicting the risk for distant metastasis in hypopharyngeal squamous cell carcinoma and assessing the survival benefit of induction therapy
Ultra-high-speed holographic data storage system based on extending data page size
White matter microstructure differences in obstructive sleep apnea severity groups assessed by diffusion tensor metrics and biophysical modeling
Research on precision prediction of heavy-duty lathes based on hybrid PINNS neural network
Global, regional, and national burden of nonalcoholic fatty liver disease among adults aged ≥ 45 years: A comprehensive analysis of epidemiological trends and projections to 2035
Background Nonalcoholic fatty liver disease (NAFLD) has emerged as the leading cause for chronic liver diseases around the globe, disproportionately affecting aging populations. This research focused on the global burden of NAFLD in adults aged 45 and older from 1990 to 2021, with projections extending to 2035. Methods Using data from the Global Burden of Disease (GBD) Study between 1990 and 2021, we assessed the incidence, prevalence, mortality and disability-adjusted life years (DALYs) related to NAFLD in adults aged 45 and older in 204 countries and territories. To evaluate the underlying drivers including demographics and lifestyle, Bayesian age-period-cohort (BAPC) modeling was employed. Results In 2021, the worldwide prevalence of NAFLD has reached 48.35 million cases (with a 95% uncertainty interval of 44.23 to 52.36 million). Among individuals aged ≥ 45 years, age-standardized incidence rose by 18.3% (EAPC = 0.53) from 1990 to 2021, while prevalence increased by 24.5% (EAPC = 0.74). Mortality and DALYs also climbed, with Egypt, Mongolia, and Andean Latin America bearing the highest burdens. A bell-shaped Socio-Demographic Index (SDI) correlation emerged, peaking in medium-SDI regions (e.g., North Africa, Middle East). Projections indicate persistent female predominance, with ASIR expected to rise to 826.11 (women) vs. 665.72 (men) per 100,000 by 2035. Conclusions This analysis explored the global burden of NAFLD in people aged 45 years and older from 1990 to 2021, demonstrating significant epidemiological changes. Age-standardized incidence and prevalence rates rose by 18.3% and 24.5%, respectively, with the most pronounced burden observed in middle-to-high SDI regions attributable to aging populations. Although women exhibited higher incidence rates, mortality rates remained consistently elevated among men, underscoring unmet intervention needs. Projections to 2035 indicate increasing incidence (particularly in women) alongside moderate declines in mortality and DALYs, underlining the requirement for prevention strategies that are specific to age and gender.