Browse Articles
Discover research articles across all indexed journals
A multi-disciplinary approach to identify spillover interfaces of bat coronaviruses to pig farms in Italy
Bats are recognized reservoirs of diverse coronaviruses (CoVs), but little is known about the pathways enabling their spillover into livestock. This study applied a multidisciplinary approach, combining bioacoustic surveys, landscape analysis and molecular virology, to assess the risk of CoV transmission from bats to pigs in intensive farming systems of Northern Italy. Between 2021 and 2022, we carried out bioacoustic monitoring in 14 pig farms to assess bat presence, diversity and behaviour. We also analysed landscape and farm-level variables to identify predictors of bat activity and richness. Additionally, we investigated CoV circulation in three populations of Pipistrellus kuhlii through active longitudinal surveillance, performing whole-genome sequencing on new and archival CoV strains detected in P. kuhlii and Hypsugo savii. Using these data, we explored the viral biodiversity potentially present at this interface via genetic and phylogenetic analyses. We identified eight bat species across farms, with P. kuhlii, P. pipistrellus and H. savii being the most widespread and active. Landscape and structural analysis revealed that farm features attracting insects were associated with higher bat activity, while the surrounding habitat showed little effect. Crucially, we found frequent absence of physical barriers preventing contact between bats or their droppings and pig enclosures, increasing exposure risks. Focusing on the most common bat species, we detected active CoV circulation in P. kuhlii, including colonies located near pig facilities. Two distinct CoV species were identified in P. kuhlii, suggesting potential for viral recombination. CoVs were detected throughout the active season, with amplification peaks in May and August. Phylogenetic analysis indicated that pigs could be exposed to at least eight bat CoV species in Italy. Notably, CoVs appeared to be shared between P. kuhlii and H. savii, further increasing recombination risks. Our study outlines a potential transmission route of bat CoVs to swine and highlights key risk factors, including farm structures, biosecurity gaps, bat species involved, viral diversity and seasonal patterns of virus circulation.
Enhancing multilevel inverter performance through open-end winding motors and SVPWM series compensation
Abstract This study proposes a unique control approach to improve the power quality of a multilevel inverter architecture based on an Open-End Winding Induction Motor (OEWIM) drive system. To obtain an open-end induction motor, two different voltage-source inverters are fed from both ends of the star-connected primary winding. The OEWIM is supplied from one end by a main three-level inverter that is created by cascading dual two-level inverters which are controlled using space-vector modulation (SVM). To offset the harmonics generated by the three-level inverter, a supplemental inverter that operates as a series compensator is connected to the other end of the OEWIM. An active filtering control method is utilized to govern the supplemental two-level inverter to reduce the voltage harmonic distortions brought on by the three-level inverter. The strategy leverages the inherent advantages of OEWIMs, such as enhanced fault tolerance and reduced common-mode voltage, making the system suitable for high-performance industrial applications. Detailed analysis of the inverter’s switching patterns, simulations, and experimental validation demonstrate significant improvements in power quality for different modulation indices. As the motor voltage and current waveforms’ harmonic distortions diminish, the drive’s efficiency will rise.
The degraded contingency test fails to detect habit induction in humans
In experimental psychology and behavioral neuroscience, habits are considered stimulus-response (S-R) associations formed through extended reward training. Accordingly, habits are assessed using one of two tests: 1) Outcome devaluation, in which the value of the outcome (reward) is reduced, making it less desirable, and 2) Contingency degradation, in which the response-outcome association is reversed so that responding prevents the delivery of a reward. If a behavior is controlled by S-R links, then it should remain mostly insensitive by these two manipulations. Animal research using the outcome devaluation test has shown that initially goal-directed actions can become habitual after extended operant training. However, replicating this transition in human research has proven challenging, representing a significant problem for translational research. Notably, the contingency degradation test has rarely been used in human research. In this study, we aimed to demonstrate a shift from goal-directed to habitual control through three pre-registered experiments. Participants were trained in two S-R-O (stimulus-response-outcome) mappings for three days, with one condition (the ‘overtrained’) occurring four times more frequently than the other (‘standard’). Importantly, we assessed the habitualization of both responses by using a degraded contingency test. Overall, we found no evidence of an overtraining effect — that is, the ‘overtrained’ condition did not lead to increased habitual responding. We discuss the theoretical and applied implications of these findings and explore further directions for studying habitual behavior.
Evaluation of seven Theobroma cacao clones grown in Terai region of India for nutritional composition and bioactive compounds
Association between community-based resource collection site use and functional disability risk among older adults: A Quasi-experimental study
This study aimed to investigate the longitudinal association between users versus non-users of MEGURU STATION, a community-based resource collection site, and the risk of functional disability among older adults in Japan. This quasi-experimental study included 973 older adults aged ≥65 years from three communities in Japan. Baseline and follow-up surveys were conducted 1 year apart to measure the risk scores for functional disability (RSFD) as the primary outcome. The main explanatory measure was self-reported MEGURU STATION use, with participants categorized as users or non-users. Mixed-effects linear regression models accounted for community-level variability and were adjusted for covariates, including sex, age, activities of daily living (ADL), education, subjective economic status, residential status, employment, and social participation. An additional analysis examined changes in going out, social interaction, and participation in community activities associated with MEGURU STATION use. Of the participants, 19.2% reported MEGURU STATION use. MEGURU STATION use was associated with a lower RSFD (B = −1.20, 95% confidence interval: −2.27, −0.12). Users reported increased opportunities for social interaction, participation in community activities, and going out compared with non-users. In summary, MEGURU STATION, a community-based intervention that integrates social interaction into daily routines, lowers the risk of functional disability among older adults. This scalable and socially inclusive model holds promise for promoting healthy aging. Future research should investigate its long-term impact and cultural adaptability.
Settlement prediction of undercrossing semicircular tunnel-pile set up in soft clay soil
Exploring the relationship between housing conditions and risk perception in a disaster context
The relationship between housing conditions and risk perception is overlooked commonly in disaster studies. Correspondingly, this research study helps in filling this research gap by answering the two main following research questions, 1) Does individuals’ perception of hurricane risk vary based on their housing conditions?, and 2) Does this risk perception, in turn, influence their intention to take a hurricane protective action? For data collection, a quantitative approach was utilized, involving an online questionnaire that was filled by 816 subjects from five cities in Florida: Miami, Tallahassee, Jacksonville, Gainesville, and Ocala. In order to answer the first research question, many housing physical characteristics were statistically tested through variance analyses based on the survey responses collected; however, the only statistically significant variance found in risk perception among the survey subjects was based on two housing conditions; 1) Required Dwelling Repairs, & 2) If the Dwelling is on Ground-Floor or not. The variance had a medium strength for Threat Possibility, but was very weak for Threat Severity. Similarly, to answer the second research question, correlation and regression analysis were conducted to test the relationship between Threat Possibility and Threat Severity and the intention of preparing a supply emergency kit, an evacuation plan, and a communication plan. Risk perception had a weak correlation to the intentions of hurricane protective behaviors. Across all regression models, neither threat possibility nor threat severity showed statistically significant associations (p > 0.01) with preparedness intentions. By identifying specific housing conditions that influence risk perception, this research study has the potential to inform targeted interventions and educational campaigns to improve disaster preparedness among vulnerable populations. This can lead to better resource allocation and more effective community outreach programs. Moreover, the findings can guide policymakers and urban planners in designing and implementing building codes and housing regulations that enhance safety and resilience against hurricanes. This can result in improved living conditions and reduced vulnerability for residents in hurricane-prone areas.
A geography of indoors for analyzing global ways of living using computer vision
Automated software bug severity classification using ensemble machine learning scheme: A real case study
Software bug report classification is one of the most significant processes in software development for determining the nature and severity of faults based on their causes and effects. In many projects, software experts implement this process manually, which requires exorbitant time and effort. Although there are a few studies on automatic bug report classification using machine learning techniques, they mainly focus on structured open-source datasets. This paper presents an ensemble learning approach utilizing various multiclass machine learning, text classification, and natural language processing techniques for automated software bug severity classification, with an application in the Persian language. This language, due to its unique characteristics, requires the adoption of different approaches from those applicable to the English language for text classification. The proposed approach utilizes a real bug dataset extracted from a case study containing unstructured bug reports. This dataset contains 4429 bug reports about the software product of the studied company, which is used by thousands of users in government and private organizations. These bug reports were recorded in Persian text by the testing team or software users, and then classified based on their severity through meetings of development team managers in the company. Results demonstrate that the developed appraoch is highly accurate and significantly faster than manual classification, which can dramatically decrease software development time and cost.
Insight into the influence of various cultivation regions on the identification of metabolites from Capsella bursa-pastoris via a clustering algorithm
Informal care in different European care systems: Effects of caregiving on mental health over time
Background The study explores the impact of informal caregiving on mental health within different European care systems, recognizing the significant role of informal care due to demographic changes and the shortage of formal care options. The growing necessity for informal care is opposed to labor market demands and geographic mobility. A distinction is made between the “family effect” and the “care effect” on mental health, emphasizing the need to explore these impacts across different care systems longitudinally. Methods Utilizing data from the Survey of Health, Aging, and Retirement in Europe (SHARE) across six waves, this study includes respondents aged 30 and older who participated in at least three waves. Participating countries were classified according to support services – outpatient care, payments for nursing care, obligation to support relatives – into the care systems implicit familism, explicit familism and optional familism. We employ least squares dummy variable (LSDV) regression followed by two-stage least squares (TSLS) regression to investigate intra-individual changes and the relationship between informal caregiving and mental health. Results The sample comprises 5,761 individuals, with 2,800 individuals involved in informal caregiving across three defined care systems. LSDV-results show that caregiving significantly affects mental health in explicit familism for both genders and in implicit familism for women, increasing depressive symptoms as measured by the EURO-D score. These findings are not confirmed by TSLS-results. Instead TSLS-results show positive significant influence of informal care on mental health for both genders in implicit familism which include a reduction of EURO-D score and no significant results in explicit familism. Conclusion The study highlights the differential impacts of informal caregiving on mental health across European care systems. The policy frameworks in implicit familism appear to benefit informal caregivers. Future research should further explore the dynamics of care systems and the role of policy interventions in supporting caregivers’ mental health.
The relationship between perceived social support and quality of life among hospitalized patients with schizophrenia
Risk of atopic dermatitis in periodontitis patients with and without dental scaling: A retrospective cohort study
Background Both atopic dermatitis (AD) and periodontitis are common chronic inflammatory diseases. However, the association between AD and periodontitis remains poorly understood. This study aimed to evaluate the effects of dental scaling (DS) on the risk of AD among patients with periodontitis. Methods In this retrospective cohort study using health insurance data, we identified individuals aged ≥20 years with periodontitis and a matched cohort without a history of periodontitis in Taiwan from 2011 to 2015. Age- and sex-matching was applied to select controls (ratio = 1:1). Both cohorts were followed until the end of 2017 to monitor atopic dermatitis (AD) incidence. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for AD risk associated with periodontitis were estimated using multivariate Cox regression. Among patients with periodontitis, we compared the risk of AD between those who received DS and those who did not. Results During the follow-up period, patients with periodontitis had an increased risk of AD compared with those without periodontitis (HR 2.47, 95% CI 2.25–2.71). The association between periodontitis and increased risk of AD was significant in men (HR 2.68, 95% CI 2.33–3.08), women (HR 2.35, 95% CI 2.07–2.66), and people in every age group. Among patients with periodontitis (n = 38,943), DS was associated with a reduced risk of AD (HR 0.33, 95% CI 0.30–0.37), and there was a dose-response relationship (p < 0.0001). The beneficial effects of DS on the risk of AD were observed across subgroups. The risk of AD was lowest in patients with periodontitis who received DS more than four times compared with those without DS (HR 0.14, 95% CI 0.08–0.25). Conclusions In conclusion, our study revealed a significant association between periodontitis and increased risk of atopic dermatitis in Taiwanese adults. Moreover, regular dental scaling may lower this risk, underscoring the value of integrating oral care into managing systemic inflammation.
Investigation of fatigue life and active vibration control via piezoelectric elements in vehicle suspension
Who gets sicker and why? Parents’ perceptions of COVID-19 disparities and how they would explain them to their children
The COVID-19 pandemic revealed substantial health disparities, disproportionately impacting Black individuals, individuals of lower socioeconomic status, and older adults in the US. Little is known as to whether and how adults discuss these disparities with their children, an essential first step toward determining when and how children come to understand these differences. To address these questions, we recruited parents with at least one child aged 5–12 (N = 443, 61% White) from CloudResearch Prime Panels. We asked participants to report their likelihood of discussing these disparities with their children, how they would explain them, their own beliefs regarding these disparities, and a series of group perception and attitudinal measures. An ordinal mixed-effects regression revealed that parents were significantly more likely to say they would discuss the age disparity than the race and class disparities, with no difference between the latter. Parents of older children reported being more likely to discuss race and age disparities than parents of younger children. Ordinal logistic regressions revealed that parents reported they would discuss the race disparity significantly more when they held stronger racial essentialist beliefs, held stronger racial social constructionist beliefs, and perceived Black people as warmer and less competent. Parents also reported that they would discuss the social class disparity significantly more when they held stronger essentialist beliefs about social class. Qualitative coding revealed that parents’ potential explanations for the disparity and reasons to discuss the disparities (or not) with their children differed by dimension. Finally, parents’ own beliefs about the existence, nature, and causes of these disparities predicted the likelihood that they would discuss them with their children -- though differently for the different dimensions. Overall, our findings suggest that parents’ likelihood of discussing health disparities reflects three key factors: their own beliefs about whether/how such disparities exist, their attitudes toward the affected groups, and their comfort in discussing social issues with their children.
Predicting carotid plaques in metabolic dysfunction-associated steatotic liver disease using machine learning and SHAP interpretation
Abstract Cardiovascular disease (CVD) remains the most common cause of death worldwide. Carotid plaque is an indicator of subclinical CVDs. Metabolic dysfunction-associated steatotic liver disease (MASLD) is a risk factor for atherosclerotic CVDs. We aimed to develop and validate a predictive model for carotid plaque occurrence in annual health check-up populations, to integrate health check-up indicators with machine learning (ML) algorithms and LASSO-based feature selection and leverage advanced interpretability frameworks to elucidate the contribution of individual risk factors. In this retrospective cohort study, we enrolled 4,973 MASLD patients, among whom 1,178 were diagnosed with carotid plaques using carotid ultrasound. Collected baseline data included demographic indicators, clinical histories, blood biochemical parameters, and liver function test indicators. A predictive model for carotid plaques was developed and validated using five ML algorithms. Model performance was evaluated based on the area under the curve, sensitivity, specificity, accuracy, and F1 Score. For model interpretability, we adopted the Shapley Additive Explanations (SHAP) framework to quantify the contribution of individual features to the prediction outcomes. Among the five ML algorithm models, the support vectors machine model demonstrated superior discriminative capability, higher goodness-of-fit, and greater clinical utility compared to other ML algorithm models. Moreover, age, systolic blood pressure, total cholesterol, sex, and fasting plasma glucose were the most important risk factors associated with carotid plaques in the MASLD population. This study demonstrated the feasibility of constructing a predictive model for carotid plaques in MASLD populations using health check-up indicators combined with ML algorithms. The application of SHAP methods enhanced model interpretability by quantifying the contribution of individual risk factors to prediction outcomes, enabling clinicians to identify high risk MASLD patients prone to carotid plaque development, so that they can adjust interventions accordingly.
Prevalence and risk factors of Influenza Avian Virus in backyard pigeons, ducks, and chickens in Toba Tek Singh District, Pakistan
Influenza Avian virus (IAV) is a zoonotic pathogen that can be transmitted from birds to humans. Multiple IAV pandemics have had a devastating impact on the poultry industry and backyard birds (including ducks, chickens, and pigeons) worldwide, notably in Europe, United States, Africa, and Asia. In Pakistan, numerous outbreaks of H7, H5, and H9 subtypes have been documented in both commercial and rural areas, resulting in significant financial losses. However, the epidemiological status of various IAV subtypes in backyard birds in rural areas remains largely unknown. This study aimed to evaluate the prevalence of IAV and associated risk factors among domesticated birds in the Toba Tek Singh District, Pakistan. A cross-sectional study was conducted between 2017 and 2019 using multistage cluster sampling approach. Pooled tracheal and cloacal swab samples were collected and tested for IAV. Positive pooled swab samples were subsequently evaluated at the individual level. RNA was extracted using theTrizol method, followed by multiplex RT‒PCR with specific primers and probes to detect the IAV M-gene and its subtypes. Statistical analysis was performed using a multivariable logistic regression model. Overall, the prevalence of IAV in backyard chickens, pigeons, and ducks was 13.4%, 7.7%, and 11.4%, respectively. The most commonly detected IAV subtypes included H7, H9, and HA/Untyped. No statistically significant difference (p > 0.05) in IAV prevalence was observed across cities for any bird species. In the multivariable analysis, species type (particularly chickens and pigeons) was significantly associated with IAV prevalence, while fighting cocks showed a borderline association. Enhanced surveillance, improved biosecurity protocols, targeted educational initiatives, and the adoption of better farming practices are recommended to mitigate IAV transmission and safeguard both poultry production and public health in Pakistan.
Seasonal trophic controls drive population variability in a foundational marine copepod
Abstract Understanding the trophic drivers of zooplankton population variability is critical for predicting ecosystem responses to climate change. In the Gulf of Maine, the copepod Calanus finmarchicus is a foundational species linking primary producers to higher trophic levels, yet the biotic drivers shaping its seasonal and interannual abundances remain incompletely understood. Here, we assess how predators impact C. finmarchicus abundances using over four decades of survey data. We find strong evidence for seasonally-structured trophic control, with spring C. finmarchicus abundances driving mid-year predator increases, which subsequently imposes significant top-down pressure on fall C. finmarchicus populations. This interplay is especially pronounced in the deep, retentive inner basins of the Gulf of Maine, where predator-prey dynamics tend to dominate over advective exchange. Our results reveal shifting interactions between bottom-up and top-down controls, highlighting the need to incorporate seasonal trophic mechanisms into ecosystem models to improve projections under further environmental change.
Synergistic antibacterial effect of hydroxyl radicals generated by the combination of hypochlorous acid and UV irradiation
Livestock farms are at risk of exposure to various environmental pollutants, particularly airborne viruses that can cause infectious diseases. Hydroxyl radicals (•OH) are well-known for their strong bactericidal and virucidal properties and are widely applied in disinfection processes. However, their efficacy is significantly diminished in the presence of organic substances. This study investigated the bactericidal effects of hydroxyl radicals generated from hypochlorous acid (HOCl) under UV irradiation and evaluated their resistance to quenching by airborne organic matter. Rose Bengal (RNO) dye was used as a probe to detect •OH radical generation, while yeast extract served as a representative organic contaminant. RNO bleaching efficiency increased in a concentration-dependent manner under UV irradiation, confirming the formation of hydroxyl radicals. However, in the presence of yeast extract, this bleaching effect was drastically reduced, indicating that organic compounds can interfere with radical activity. The bactericidal effects of UV light and HOCl were independently evaluated using Salmonella as a model organism. The presence of organic matter significantly reduced the bactericidal efficacy of both UV and HOCl treatments when applied separately. In contrast, combined exposure to HOCl and UV irradiation demonstrated a 10% increase in bacterial reduction and halved the required exposure time, regardless of HOCl concentration. These findings highlight the synergistic bactericidal potential of HOCl and UV irradiation and support their applicability in airborne bacterial disinfection under realistic environmental conditions.