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Inferring plant-bee-microbe associations: Foragers, hive workers, and honey tell complementary stories

Pollinators, both wild and managed, form diverse associations with plants and microbes which affect the wellbeing of the plants and the pollinators. The method by which these associations are sampled impacts our understanding of the system. The common ways to understand pollinator-plant or pollinator-plant-microbe associations are to observe flower visits of insects, or to collect foraging individuals and identify the pollen and microbes they carry. Honey bees offer a test case for methods of sampling these associations. Hives of managed honey bees host thousands of pollinator individuals together with jointly-collected nectar which is turned into honey. Previous studies have used DNA preserved in honey to infer honey bee associations with plants and microbes. Here, we sampled honey, individual bees while they were foraging, and groups of bees from inside the hive. We identified plants and microbes on the surface of the bees or in the honey using DNA metabarcoding – expecting that bees sampled singly or in groups would reveal a subset of the associations recorded in the communal honey deposits. However, we found that each sample type revealed different aspects of the richness and community composition of plants and microbes encountered by bees. Both honey samples and hive bees had more plant and microbial taxa per sample than samples of individual bees. Though individual bees are subsets of the larger colony, pollen and microbe associations recovered from individual bees did not represent a subsample of associations recovered from groups of hive bees or from honey. Thus, while each sampling technique provides information about honey bee ecology, they are not equivalent. DNA in honey represents time-integrated associations between bees and the surrounding ecosystem; the hive bees provide a snapshot of current colony-level associations, and individual foraging bees capture intraspecific variation in foraging preferences and microbe exposure.

Pregnancy after idiopathic granulomatous mastitis does not increase the rate of disease recurrence: a retrospective cohort study on 54 pregnancies

Effect of Cosmos Caudatus supplementation and aerobic exercise on selected neurobehaviour, biochemical profile and histology in rats with mild cognitive impairment (MCI) induced by AlCl3: Study Protocol

Background Cosmos caudatus (C. caudatus) or ‘ulam raja’ is a local plant with antioxidant properties and has the potential to act against oxidative-related conditions found such as in neurodegenerative diseases. Similarly, physical exercise is a consolidated strategy on the prevention of cognitive deficits. Based on the systematic review conducted by Joseph et al. (2023), a study protocol was developed to ensure the combined effect of C. caudatus supplementation and exercise provided improvement against cognitive impairment. There are limitations on studies looking at combined effect of flavonoid and exercise where either one of the interventions provided improvement to the behavioural tests and biomarkers assessed but not when given in combination. Moreover, to our understanding, in the last five years there has been limited research done on the combined effect of flavonoid and exercise against cognitive impairment (based on Pubmed search on 10 June 24; ScienceDirect search on 10 June 24). Therefore, we elucidated a study protocol that looks at the combined effect of C. caudatus supplementation and exercise against AlCl 3 -induced cognitive impairment in rats and the possible mechanisms involved in its neuroprotective effects in male rats. Method Male Wistar rats will be divided into different groups: control, physical exercise (treadmill running), supplemented with C. caudatus or in combination. Consequently, neurobehavioural tests (novel object recognition test, open field test & Y-maze), biochemical tests and histology assessment will be determined to unravel the possible neuroprotective capability against AlCl 3 -induced neurotoxicity. The duration of exercise training is four weeks while C. caudatus is supplemented for 21 days. Outcome The primary outcomes will be neurobehaviour changes at baseline, after 21 days of AlCl 3 -induced rats and at the end of intervention. While the secondary outcomes will be biochemical profile (Oxidative stress markers, inflammatory markers) and brain histology of AlCl 3 -induced rats. Discussion and conclusion Combining exercise training with C. caudatus supplementation will produce synergistic effects, leading to significant improvements in spatial memory impairment and oxidative stress. This combined approach is expected to be more effective than using either intervention alone, potentially restoring spatial memory and antioxidant levels to normal. Consequently, the findings of this study could hold significant value for aging adults, providing safe and cost-effective strategies for managing neurodegenerative disorders.

Scalable hierarchical federated graph-transformer architecture for efficient multi-modal intrusion detection in 6G UAV-assisted vehicular IoT

Abstract The increasing growth of 6G-empowered UAV-assisted vehicular IoT systems brings forth unprecedented scalability issues for distributed intrusion detection, especially in the context of non-IID data distributions and heterogeneous edge environments. Centralized and flat federated systems do not efficiently coordinate large-scale, latency-sensitive and resource-constrained nodes. In this research, we present a scalable hierarchical federated Graph-Transformer architecture for effective multi-modal intrusion detection spanning UAV-edge-cloud tiers. The platform employs hierarchical aggregation among cars, UAVs, and regional edge servers for reducing communication overhead (CO) and speeding up convergence in non-IID scenarios. Meanwhile, a hybrid Graph Neural Network (GNN) and Transformer backbone is adopted to model spatial topology and temporal dynamics, and a lightweight multi-modal fusion is employed to integrate network traffic, telemetry and channel condition information. To improve the efficiency of the system, we propose adaptive aggregation scheduling and communication compression algorithms that considerably reduce bandwidth consumption and training latency. The experimental results on the CIC-IoT-2023, ToN-IoT and Edge-IIoTset datasets exhibit enhanced scalability with over 98.20% detection accuracy and up to 38.00% transmission cost reduction compared to the flat federation baselines. The work presents a scalable and system-efficient approach for next generation distributed intrusion detection in large-scale 6G vehicle ecosystems.

Air quality index prediction using machine learning regression models: A comparative analysis

Air plays a vital role in human life, and poor air quality can lead to respiratory infections. Given the significant impact of air quality on people’s health, monitoring and assessing air quality is crucial. With advancements in machine learning (ML) and artificial intelligence (AI), we now have extensive tools to measure the Air Quality Index (AQI). Air quality is influenced by various pollutants, including carbon monoxide (CO), nitrogen dioxide (NO 2 ), ozone (O 3 ), and sulfur dioxide (SO 2 ), which are prevalent in highly polluted areas and contribute to a wide range of illnesses. Particulate matter, such as PM2.5 (particles with an aerodynamic diameter of less than 2.5 µm) and PM10, poses additional health risks. To address these concerns, this study focuses on predicting AQI values for major cities in Pakistan, specifically Karachi and Peshawar, using four prominent ML algorithms: Random Forest (RF), Gradient Boosting (GB), Linear Regression (LR), and Ridge Regression (RR). The results indicate that the models effectively predicted AQI using evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). This research’s novelty lies in using the latest AQI datasets for Karachi and Peshawar and applying standard scaling for AQI normalization. Additionally, the study compares evaluation metric results across different cities, highlighting the importance of using standard scalers to achieve optimal model performance. This research underscores the value of advanced ML techniques for accurate AQI prediction and analysis.

Effects of hesperidin, nanohesperidin and obeticholic acid on hepatic FXR and SMAD3 in HFD/fructose-fed mice

Intergenerational patterns of digital use: Evidence from a large cross-sectional study

This study aims to determine the level of digital use across six generations (Greatest, Silent, Baby Boomers, and Generations X, Y and Z) within a district in North East England (population ~200,000). A cross-sectional descriptive study was conducted via an online and paper-based survey from February to May 2022, mailed to households in the district (N = 98,260). Respondents estimated their weekly use of digital tools and the internet at home. One-way ANOVA and Tukey-Kramer HSD tests were used to analyse differences in digital resource uses across six generations. To account for underlying sociodemographic characteristics, a follow-up ANCOVA analysis was also performed. Content Analysis was used for qualitative responses from an open-ended survey question. A total of 9,181 completed surveys were analysed. The sample was skewed towards older, homeowner adults (mean age 63, 60% female). Findings revealed that respondents spend less time online than other British cohorts. Baby Boomers and Generation X self-reported statistically significant differences in the level of digital use compared to all other generations. Younger generations (Y and Z) self-reported, on average, a larger amount of time spent on both digital tools and the internet. Members of Greatest and Silent Generations had the lowest hours spent on digital tools and the internet. The results suggest that public health initiatives should prioritise strategies bridging the digital divide between generations. Targeted training programs pairing younger, tech-savvy individuals with older adults could enhance digital literacy. Additionally, integrating user-friendly digital health platforms that cater to varying levels of technological proficiency will encourage wider adoption. These strategies not only foster intergenerational collaboration but also drive successful digital health and care transformations, ensuring equitable access to technological advancements for all age groups.

Bridging in vitro bioactivities and in silico insights of Rubia cordifolia L. (Rubiaceae) leaf extracts for therapeutic applications

Deep-Penetrating Transdermal Lipopeptide Liposomes for Sustained IL-17 Inhibition and Prevention of Psoriatic Recurrence

Mediating role of depression medication on association between Body Mass Index and cigarette smoking among US adults: Insights from the NHIS

Depression in people who are obese, and smoke cigarettes is often complicated by the possibility of using smoking as a tool for coping with stress. This study sought to determine the mediating role of depression medication on smoking among obese, overweight, normal weight and underweight adults. Data from the 2023 National Health Interview Survey, an annual public health survey of adults from 18–65 years of age in the United States of America, was analyzed using a Generalized Structural Equation Model. Underweight participants on depression medication were more likely to smoke compared to obese participants (reference group: obese; aOR = 0.49, 95% CI: [0.38, 0.63]). There was an indirect association between obesity and depression medication on smoking and obese participants on depression medication had 1.32 times higher odds of smoking when using depression medication compared to underweight individuals (aOR = 1.32, 95% CI: [1.09, 1.56]). Use of depression medication had the highest mediating role on smoking among underweight and the lowest role among obese participants (aOR = 1.64, 95% CI: [1.05, 2.22]). The findings suggest that body mass index should be considered in planning smoking cessation interventions in health care settings.

Stylopine as multi-target anti-Alzheimer agent

Age-range–matched external validation of standard liver volume equations: Methodological re-evaluation of 13 regression models

Background and aim Accurate estimation of standard liver volume (SLV) is essential in hepatobiliary surgery, particularly in living donor liver transplantation and hepatic resection. Numerous and substantially divergent SLV equations have been proposed, yet it remains unclear which are methodologically valid. Although several comparative and validation studies have been reported, these have typically evaluated equations beyond their original derivation ranges, thereby conflating external validity with extrapolation and ignoring physiologically distinct processes across the lifespan. As a result, a proper external validation of existing SLV equations has not been systematically performed. Methods Thirteen previously proposed SLV formulae were selected for methodologically constrained external validation in individuals without known liver disease (n = 1152). Each equation was evaluated strictly within its original derivation age range using age-range–matched cohorts. Agreement between estimated SLV and computed tomography–measured total liver volume (TLV) was assessed using Bland–Altman analysis and intraclass correlation coefficients (ICC). Model performance was further examined in older adults and across subgroups defined by sex, obesity, and emaciation. Results Urata’s (proportional function of body surface area) and Noda’s (power function of body weight) formulae showed the highest agreement within their derivation age ranges. When extrapolated beyond these ranges, systematic age-dependent bias emerged, consistent with age-related hepatic involution rather than model structure. TLV remained stable between 20 and 49 years of age but declined linearly after 50 years at a rate of 2.7–2.8% per five years. Obesity was associated with increased TLV due to steatosis, whereas sex and emaciation had minimal effects. Conclusions This study demonstrates that failure to respect derivation age ranges leads to misleading assessments of SLV equation performance. Age-range–matched external validation clarifies prior inconsistencies and provides a generalizable framework for evaluating regression-based prediction models, with implications for both clinical epidemiology and quantitative medical research.

Stress distribution characteristics and oriented fracturing mechanism of ultra-thick overlying strata in extra-thick coal seams

Abstract To address the problem of strong mine pressure caused by ultra-thick overlying strata (UTOS), this paper analyzes the distribution law of mining stress field in different layers of UTOS, and the expression of mining stress concentration coefficient of UTOS is given. The law of hydraulic fracture propagation in stress concentration area and original stress area of stope roof is expounded. The stress-oriented fracturing mechanism of UTOS is revealed. The results show that: (1) Along the advacned direction, the stress concentration value in the UTOS increases first and then decreases, exhibiting a “core” distribution pattern. As the buried depth of the roof increases, the range of the stress concentration core gradually decreases; (2) When hydraulic fracturing is carried out in the stress concentration area and the original stress area respectively, vertical hydraulic fractures and horizontal hydraulic fractures will be formed in the roof respectively; (3) The fracture propagation pressure in the stress concentration area is significantly greater than that in the original stress area. After the stress-oriented fracturing of the UTOS, the support resistance of the working face is significantly reduced, which is beneficial to the mine pressure management of the working face.

Towards new animal models of pure hypoxic Lance-Adams syndrome: Negative results

Lance-Adams syndrome is a severe and disabling posthypoxic myoclonus in humans. Its underlying mechanisms are still unknown. We aimed to develop a new animal model of Lance-Adams syndrome. We performed our procedures on Sprague-Dawley rats monitored for basic cardiorespiratory parameters. The Group 1 was sedated and submitted to hypoxia in a dedicated cage where oxygen was replaced with nitrogen. The Groups 2 and 3 were sedated, intubated, ventilated, and submitted to anoxia with the replacement of oxygen by nitrogen in the ventilatory circuit, either continuously (Group 2) or intermittently (Group 3). Rats benefited from cardiopulmonary resuscitation. Each rat was evaluated daily for spontaneous, wandering-induced, and auditory stimuli-induced myoclonic jerks, for the latter using a quantitative myoclonus score. In the Group 1 (n = 19), the duration of hypoxia was 25 ± 20 min for the surviving animals (n = 14/19) and minimal partial pressure of oxygen in the cage was 7.2 ± 0.8%. In the Group 2 (n = 38), the duration of anoxia was 5.6 ± 1.9 min for the surviving animals (n = 23/38). In the Group 3 (n = 30), the total duration of anoxia was 15 ± 3.5 min in the surviving animals (n = 15/30). These three procedures did not allow for generating a myoclonic phenotype. Some refinements of hypoxic/anoxic procedures are still needed to develop an animal model of posthypoxic myoclonus that would be a precious tool to understand better the pathophysiology of Lance-Adams syndrome.

Gender disparities in central obesity in Cape Verde: analysis of the 2020 WHO STEPS survey

Establishment of a large-scale oral disease registry (NDCS-ODR) in a national specialty center

This study describes the establishment of the National Dental Centre Singapore Oral Disease Registry (NDCS-ODR), a large-scale, electronic health records registry designed to capture real-world data on oral diseases. The NDCS-ODR was developed to standardize and integrate oral health data within Singapore Health Services, the country’s largest healthcare cluster. Its development, governance, and data architecture are described, with an overview of individuals with oral diseases recorded in the registry. Data collection from 2013 to June 2025 has been completed. As of June 2025, the NDCS-ODR comprises 229,249 unique patients, with a mean (SD) age of 49.1 (19.5) years and an approximately equal sex distribution. Most were of Chinese ethnicity (77.6%), and Singapore citizens (92.5%). Clinical variables indicated substantial disease and treatment burden, with a mean of 7.9 (7.7) missing teeth, 4.8 (6.2) restored surfaces, and 2.8 (3.4) restored teeth per patient. Among 108,517 recorded periodontal diagnoses, Stage III periodontitis (2018 EFP/AAP Classification) and severe chronic periodontitis (1999 Classification of Periodontal Diseases and Conditions) were most common. The NDCS-ODR represents Singapore’s first large-scale, real-world oral disease registry embedded within a national specialty center, demonstrating the feasibility of leveraging electronic health record data for research and service evaluation.

Impact of room configuration on energy use intensity in high-density pilgrim accommodations: a case study in Makkah, Saudi Arabia

Identification of the oxidation stress-related gene signatures and functional verification of MINK1 in prostate cancer cells

Background A major challenge facing prostate cancer (PCa) cells is oxidative stress, yet the precise role and underlying mechanisms remain inadequately elucidated. The study sought to investigate the association between oxidative stress and PCa prognosis, as well as to identify potential regulatory pathways involved. Methods Oxidative stress-related genes and data were sourced from the Genecards, TCGA-PRAD, and GSE16560 databases. A risk model was developed using machine learning, including random forest and LASSO analyses. Survival analysis, functional and immune infiltrate analysis, as well as immunotherapy analysis were performed. The expression of MINK1 was examined, and the effects of MINK1 silencing on cell biological activities (including proliferation, migration, and invasion) were investigated, alongside analyses of MINK1-related pathways. Results Four genes (BCO1, MINK1, TAF1C, and MIS18BP1) were identified and utilized to develop an oxidative stress-risk score (OS-score). The high-OS-score predicted a poor prognosis, and OS-score was an independent prognostic factor for PCa. The OS-score demonstrated a positive correlation with CD8 + T cells, activated CD4 + T cells, and macrophages. Patients classified within the high-OS-score group were more effective in anti-PD-1 therapy (Nominal P  = 0.001, Bonferroni corrected P  = 0.011). A significant disparity was observed in the efficacy of immune checkpoint inhibitor treatment between the high- and low-OS-score groups ( P  = 1.1 × 10 −9 ), with a higher proportion of responders in the high-OS-score group compared to non-responders. The key oxidative stress gene, MINK1, was experimentally validated and found to be highly expressed in PC-3 and DU145 cell lines. Silencing MINK1 resulted in decreased proliferation, migration, and invasion activity. Additionally, MINK1 knockdown induced G0/G1 phase arrest and inhibited nuclear translocation of NF-κB. Conclusion In summary, oxidative stress is associated with a poor prognosis in PCa. Oxidative stress-related gene MINK1 may regulate the cell biological activity through cell cycle and NF-κB signaling pathway. This study may provide new clues for the identification and development of new markers for the diagnosis and prognosis of PCa patients.

FTO-mediated m6A modification alleviates diabetic nephropathy progression by downregulating the CYP2J3/Smurf2 axis

Can LLMs improve the accuracy of behavior prediction for personnel in high-stakes scenarios by predicting emotions? — A fine-tuning study based on scarce homogeneous cultural data in high-pressure environments

Research on human behavior prediction using LLMs is increasingly prevalent. This study addresses a key question in the interdisciplinary fields of behavioral science and artificial intelligence: Can LLMs enhance the accuracy of predicting personnel behavior by predicting emotions in high-pressure scenarios? We rigorously screened homogeneous cultural data for personnel in high-stakes roles, constructing the first multidimensional dataset of “scenario-emotion-behavior” under these conditions. Following the fine-tuning of the LLM based on this dataset, we evaluated its behavior prediction accuracy. Experimental results reveal that emotion-predicting LLMs outperform baseline LLMs and behavior-predicting LLMs on multiple metrics, emphasizing the crucial roles of the Emotion-Imbued Choice Model and Behavioral Decision Theory in enhancing LLM behavior prediction capabilities. This study promotes the interdisciplinary integration of AI with cognitive and behavioral science, offering fresh insights into high-risk behavior domains and establishing a novel paradigm where LLMs improve behavior prediction accuracy through emotion prediction.