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GC-MS, LC-MS, and network pharmacology analysis to investigate the chemical profiles and potential pharmacological activities in flower buds and flowers of Lonicera japonica Thunb

Lonicera japonica Thunb. (L. japonica) is an edible-medicinal herb. While the flower buds of L. japonica are commonly utilized for medicinal purposes, the flowers are often overlooked. However, it has been discovered that the flowers contain higher levels of certain active compounds compared to the flower buds. Despite this finding, there have been no reports on the potential differences in pharmacological efficacy between these compounds. Utilizing results from GC-MS and LC-MS, a total of 335 differential compounds were identified, of which 247 complied with Lipinski’s Rule of Five concerning medicinal properties. Among these, 101 compounds were upregulated in the flower buds, while 146 compounds were upregulated in the flowers. Network pharmacology analysis revealed that the upregulated compounds from the flower buds and flowers targeted 143 and 185 core targets, respectively, with 116 being duplicates. The core target proteins among the duplicate targets were primarily involved in pathways related to cancer, lipid and atherosclerosis, hepatitis B, proteoglycans in cancer, and Alzheimer’s disease. Meanwhile, the hub target proteins upregulated in the flowers enriched distinct pathways associated with human T-cell leukemia virus 1 infection, focal adhesion, the thyroid hormone signaling pathway, and fluid shear stress and atherosclerosis. Molecular docking results indicated that the upregulated compounds exhibited strong binding affinity to the core targets. This study provides insights into the differences in active components between the medicinal (flower buds) and non-medicinal (flowers) raw materials predicting the mechanisms of action of these active components and establishing a basis for the more rational utilization of L. japonica flowers.

Genome-wide identification of SABATH gene family in soybean relate to salt, aluminum, chromium toxicity

Exploring turn demands of an English Premier League team across league and knockout competitions over a full season

Turns are key performance actions in soccer, but can also induce high mechanical loads resulting in tissue damage or injury. This study aimed to quantify the turn demands of an elite English Premier League soccer team. Turning data were obtained from 49 soccer matches (2022–23 season), from a single team that played 35 Premier League, 5 UEFA Europa League, 5 League Cup and 4 FA Cup matches using Sportlight LiDAR technology. Turns were analysed from 29 players who were categorised in playing position groups: goalkeeper (GK), central defenders (CD), full-backs (FB), central-midfielders (CM), wide-midfielders (WM), central-forwards (CF). Turn categories: high (120–180°), medium (60–119°) and low (20–60°) angled, and very high (>7.0ms-1), high (5.5–7.0ms-1), medium (3.0–5.5ms-1), and low (<3.0ms-1) entry speed (ES) was analysed. Primary findings show, on average, per match, CM performed more total turns (~35), than all other playing positions. Additionally, CM performed significantly more low and medium entry speed and high angled turns than other outfield positions. There were no significant differences between turn frequencies and turn characteristics in different competitions (p >0.05). The turning demands of soccer appear to vary significantly between player position. These findings may help inform position-specific return-to-play protocols, physical preparation strategies, drill design and rehabilitation programmes.

Mexican axolotl optimization algorithm with a recalling enhanced recurrent neural network for modular multilevel inverter fed photovoltaic system

Tracking the evolution and persistence of antibiotic resistance in the human gut

The relationship between screen time, screen content for children aged 1-3, and the risk of ADHD in preschools

Objective This study investigates the relationship between screen time, screen content, and the risk of Attention Deficit Hyperactivity Disorder (ADHD) using data from a large sample. Specifically, it examines how different types of screen content (such as educational videos, cartoon videos, and interactive videos) are associated with the risk of ADHD. The aim is to offer a scientific foundation for the rational management of children’s screen time and screen content. Methods We collected data through a questionnaire survey involving a study population of 41,494 children from Longhua District, Shenzhen City, China. The questionnaire recorded the daily screen time and the type of content viewed by the children at ages 1–3 years and assessed their risk of ADHD using the Strengths and Difficulties Questionnaire (SDQ) at ages 4–6 years. Hierarchical logistic regression analysis, controlling for confounding factors, was employed to explore the associations between screen time, screen content, and ADHD risk. Results In the total sample, 6.7% of the participants had screen time exceeding 60 minutes per day, with educational videos predominant type (63.4%). 16.5% of the participants were identified as being at risk for ADHD. Statistically significant positive associations with ADHD were observed across all categories of screen time (P<0.001). Moreover, as screen time increased, the risk of ADHD also rose (OR1~60 mins/d=1.627, 95%CI=1.460~1.813; OR61~120 mins/d=2.838, 95%CI=2.469~3.261; OR>120 mins/d=3.687, 95%CI=2.835~4.796). Significant positive associations with ADHD were observed across all categories of screen time in the educational videos and cartoon videos. For the educational videos group, the odds ratios were as follows: OR1–60 mins/day=1.683 (95% CI=1.481–1.913), OR61–120 mins/day=3.193 (95% CI=2.658–3.835), and OR>120 mins/day=3.070 (95% CI=2.017–4.673). For the cartoon videos group, the odds ratios were: OR1–60 mins/day=1.603 (95% CI=1.290–1.991), OR61–120 mins/day=2.758 (95% CI=2.156–3.529), and OR>120 mins/day=4.097 (95% CI=2.760–6.081). However, no significant associations with ADHD risk were found for any category of screen time in the interactive videos group (OR1~60 mins/d=0.744, 95%CI=0.361~1.534; OR61~120 mins/d=0.680, 95%CI=0.296~1.560; OR>120 mins/d=1.678, 95%CI=0.593~4.748). Conclusion Increased screen time is associated with a higher risk of ADHD, particularly for educational and cartoon videos, while interactive videos show no significant link. To mitigate this risk, parents and educators should implement strategies such as setting time limits, encouraging breaks, and promoting alternative activities. Future research should focus on longitudinal studies and intervention trials to further explore and address this relationship.

Author Correction: Evaluations of dyadic synchrony: observers’ traits influence estimation and enjoyment of synchrony in mirror-game movements

Quantum communication across a 250-kilometre optical-fibre network

Fake leadership influence on organizational destruction in Higher Education Institutions (HEIs)

The role of leadership in Higher Education Institutions (HEIs) is pivotal. While much research emphasizes the beneficial effects of leadership, the negative impacts of destructive leadership styles remain less explored. This paper investigates the concept of fake leadership - a form of destructive leadership characterized by leaders’ intent to engage in harmful behaviors while maintaining a facade of authenticity - and its influence on organizational destruction in HEIs. Drawing on the Toxic Triangle Framework, this study examines how fake leadership undermines intra-organizational trust and job performance, ultimately fostering systemic inefficiencies and organizational decline. The two-step empirical study was conducted with 529 employees from HEIs across Europe (France, Poland, Spain, and the United Kingdom) to verify the hypotheses. Statistical reasoning was based on linear regression analysis with mediators. The results confirmed that fake leadership significantly and positively influences organizational destruction. This effect is mediated by a decline in intra-organizational trust and job performance. This study contributes to the literature by introducing fake leadership as a distinct destructive leadership style in HEIs, providing a tailored framework for understanding its role in enabling organizational destruction, and underscoring the critical role of diminished intra-organizational trust and job performance in deepening these adverse effects. Practical implications emphasize the need for ethical leadership, rigorous selection processes, and proactive measures to safeguard institutional integrity and resilience.

SegMatch: semi-supervised surgical instrument segmentation

Abstract Surgical instrument segmentation is recognised as a key enabler in providing advanced surgical assistance and improving computer-assisted interventions. In this work, we propose SegMatch, a semi-supervised learning method to reduce the need for expensive annotation for laparoscopic and robotic surgical images. SegMatch builds on FixMatch, a widespread semi-supervised classification pipeline combining consistency regularization and pseudo-labelling, and adapts it for the purpose of segmentation. In our proposed SegMatch, the unlabelled images are first weakly augmented and fed to the segmentation model to generate pseudo-labels. In parallel, images are fed to a strong augmentation branch and consistency between the branches is used as an unsupervised loss. To increase the relevance of our strong augmentations, we depart from using only handcrafted augmentations and introduce a trainable adversarial augmentation strategy. Our FixMatch adaptation for segmentation tasks further includes carefully considering the equivariance and invariance properties of the augmentation functions we rely on. For binary segmentation tasks, our algorithm was evaluated on the MICCAI Instrument Segmentation Challenge datasets, Robust-MIS 2019 and EndoVis 2017. For multi-class segmentation tasks, we relied on the recent CholecInstanceSeg dataset. Our results show that SegMatch outperforms fully-supervised approaches by incorporating unlabelled data, and surpasses a range of state-of-the-art semi-supervised models across different labelled to unlabelled data ratios.

Mindfulness-based stress reduction training supplemented with physiological signals from smartwatch improves mindfulness and reduces stress, but not anxiety and depression

Introduction Mindfulness-Based Stress Reduction (MBSR) helps counteract the negative consequences of stress. An essential aspect of mind-body therapies is learning to be mindful of emotional reactions and bodily sensations, a process defined as interoceptive awareness. This awareness can also be enhanced by providing physiological feedback from a smartwatch. However, the impact of using smartwatch-generated physiological signals during mindfulness training has not been studied yet. The study aims at verifying, whether physiological signals from a smartwatch would support the MBSR. Methods We conducted a mixed-design randomized controlled trial to investigate the effects of MBSR training, with and without monitoring physiological signals via a smartwatch, on mental functioning parameters, with measurements taken at baseline and after 8 weeks. Participants were classified into three groups (N = 72): the MBSR group, the MBSR + smartwatch group, and the control group. Between measurement sessions, two groups of participants were engaged in MBSR training, while the third group did not participate in any training. Results Results showed a significant reduction in subjectively perceived stress levels, eating disorder symptoms, and intrusive ruminations in both groups participating in MBSR, compared to the control group. However, a notable difference emerged between the two MBSR groups: in the group with smartwatches, a significant increase in mindfulness was observed. In contrast, in the MBSR group without smartwatches, there was a significant decrease across multiple stress-related components, including: anxiety, cognitive impairment, addictions, sleep disorders symptoms, behaviors indicating lack of entertainment, and poor functioning. Conclusions The results suggest that supplementing MBSR with monitoring interoceptive signals by a smartwatch enhances mindfulness, and maintains the effect of stress and eating disorders symptoms reduction but does not decrease anxiety nor improve general mental functioning. This imposes the need for further research to investigate mechanisms involved when observing interoceptive signals by a smartwatch.

Ambiguity-aware semi-supervised learning for leaf disease classification

Abstract In deep learning, Semi-Supervised Learning is a highly effective technique to enhances neural network training by leveraging both labeled and unlabeled data. This process involves using a trained model to generate pseudo labels to the unlabeled samples, which are then incorporated to further train the original model, resulting in a new model. However, if these pseudo labels contain substantial errors, the resulting model’s accuracy may drop, potentially falling below the performance of the initial model. To tackle the problem, we propose an Ambiguity-Aware Semi-Supervised Learning method for Leaf Disease Classification. Specifically, we present a per-disease ambiguity rejection algorithm that eliminates ambiguous results, thereby enhancing the precision of pseudo labels for the subsequent semi-supervised training step and improving the precision of the final classifier. The proposed method is evaluated on two public leaf disease datasets of coffee and banana across various data scenarios, including supervised and semi-supervised settings, with varying proportions of labeled data. The results indicate that our semi-supervised method reduces the reliance for fully labeled datasets while preserving high accuracy by utilizing the ambiguity rejection algorithm. Additionally, the rejection algorithm significantly boosts precision of final classifier on both coffee and banana datasets, achieving rates of 99.46% and 100.0%, respectively, while using only 50% labeled data. The study also presents a thorough set of experiments and analyses to validate the effectiveness of the proposed method, comparing its performance against state-of-the-art supervised approaches. The results demonstrate that our method, despite using only 50% of the labeled data, achieves competitive performance compared to fully supervised models that use 100% of the labeled data.

Relative income and its relationship with mental health in UK employees: A systematic review

Purpose: The relative income hypothesis theorises that one’s earnings relative to others exert a greater influence on subjective wellbeing than absolute income. Understanding the relationship between relative income and mental health could contribute to employee wellbeing. This review aimed to summarise the defining features of relative income in relation to mental health and how it is measured in the literature. In addition, it aimed to explore the relationship between relative income and mental health in those currently employed in the UK. Methods: Nine electronic databases were searched using a pre-defined search strategy: PubMed (including MEDLINE and PubMed Central), PsycINFO, Scopus, Web of Science, Global Health, JSTOR, Business Source Complete (EBSCO), ScienceDirect and Emerald. The protocol was pre-registered on PROSPERO (CRD42023408657). Quantitative and qualitative studies and grey literature, which described the defining features and measurement of relative income and its impact on mental health among UK employees, were included. Results: After screening, 13 studies were included in the review. A conceptualisation of relative income revealed that an income comparison is either researcher-defined using averages or self-assessed based on a person’s perception. Having a lower income than the reference group was commonly associated with diminished wellbeing, though moderating factors (gender, income inequality and composition of reference group) were identified. Conclusions: Having a lower income than the reference group is associated with poorer wellbeing. Implications for practice and policy are considered amidst the UK’s ‘cost of living crisis’ and ongoing pay disputes in various sectors.

Iso-stress architecture from mineral foliation patterns

Blood cancer driven by ‘one hit’ mutation event undergoes rapid growth

Retraction: Fine-granularity inference and estimations to network traffic for SDN

Gut microbiota diversity in obese rats treated with intermittent fasting, probiotic-fermented camel milk with or without dates and their combinations

Factors associated with the presence and intensity of ongoing symptoms in Long COVID

Objective Identification of modifiable factors associated with symptom intensity among people seeking care for Post-Acute Sequelae of SARS-CoV-2 infection (PASC) could help guide the development of comprehensive, whole-person care pathways to alleviate symptoms irrespective of potential underlying pathophysiologies. We aimed to better define the key contributors to PASC, and sought the factors associated with PASC symptom presence and intensity. Methods In this cross-sectional study, 249 patients presenting for PASC care at a dedicated Post-COVID-19 clinic completed a standardized screening assessment prior to initial visit and evaluation by a general internist or nurse practitioner. We measured 46 symptoms based on the WHO’s Global COVID-19 Clinical Platform Case Report Form for Post COVID Condition and performed a factor analysis and item response theory based 2-parameter logistic model to develop a population-based t-score to measure PASC symptom presence and intensity (PASC-SPI). A multivariable linear regression analysis was used to assess factors associated with PASC-SPI, accounting for demographics, comorbidities, COVID-19 infection duration and severity, and mental health. Results Greater PASC-SPI was associated with greater symptoms of anxiety, a longer duration of COVID-19 infection, and hypercholesterolemia. Lower PASC-SPI was associated with older age, self-reported 1–3 units of alcohol per week, and self-reported clinician confirmation of COVID-19 diagnosis. Symptoms of anxiety accounted for a considerably higher proportion of variation in PASC-SPI than other variables. Conclusion Symptoms of anxiety were the strongest correlate of PASC-SPI, highlighting it as both a potential neuroinflammatory marker of PASC and a modifiable component of the illness. This emphasizes the need for comprehensive, whole person treatment strategies that integrate evidence-based interventions to address the multifaceted nature of PASC.

Development of intelligent controller for high performance electric drives with hybrid CSA and RERNN technique

Abdominal subcutaneous fat area can predict 2-year survival in patients with end-stage renal disease initiating hemodialysis

Obesity and adipose tissue are commonly regarded as detrimental factors linked to adverse outcomes, including cardiovascular and metabolic diseases. However, the obesity paradox is obesity that may provide survival benefits for chronic diseases including patients undergoing hemodialysis. Fat mass can be a surrogate marker for nutrition status in patients undergoing hemodialysis. Thus, this study evaluated subcutaneous fat and all-cause mortality in patients initiating hemodialysis. A total of 123 patients initiating hemodialysis were included in this study. MATLAB (version R2014a) was used to identify subcutaneous fat area (SFA) and visceral fat area (VFA) in computed tomography images for the analysis of body composition. The survival rate was calculated using Cox regression analysis. The Kaplan–Meier survival rates were 70.0% and 85.7% in the low and high subcutaneous fat area (SFA) groups, respectively (log rank, p = 0.021). In Cox analysis, the low SFA group showed high risk for all-cause mortality than the high SFA group (hazard ratio (HR) 3.541, 95% CI 1.358–9.235, p = 0.010). In subgroup univariate analysis, the risk for all-cause mortality was higher in patients with low SFA and diabetes than those with high SFA and diabetes (HR 3.541, 95% CI 1.358–9.235, p = 0.010). In multivariate analysis, the risk for all-cause mortality was higher in patients with low SFA and diabetes than those with high SFA and diabetes (HR 4.615, 95% CI 1.484–14.351, p = 0.008). Conclusively, low SFA increases the risk of 2-year all-cause mortality, and SFA analysis can provide information for risk evaluation for patients initiating hemodialysis.