Search Results (298)

The greater wax moth Galleria mellonella (Lepidoptera: Galleriinae) represents a ubiquitous apicultural pest that poses significant threats to global beekeeping industries. The larvae damage honeybee colonies by consuming wax combs and tunneling through brood frames, consequently destroying critical hive infrastructure including brood-rearing areas, honey storage cells, and pollen reserves. Larval feeding behavior is critically dependent on chemosensory input for host recognition and food selection. In this study, we conducted a transcriptome analysis of larval heads and bodies in G. mellonella. We identified a total of 25 chemosensory genes: 9 odorant binding proteins (OBPs), 1 chemosensory protein (CSP), 5 odorant receptors (ORs), 4 gustatory receptors (GRs), 4 ionotropic receptors (IRs) and 2 sensory neuron membrane proteins (SNMPs). TPM normalization was employed to assess differential expression patterns of chemosensory genes between heads and bodies. Nine putative chemosensory genes were detected as differentially expressed, suggesting their potential functional roles. Subsequently, we quantified expression dynamics via reverse transcription quantitative PCR in major chemosensory tissues (larval heads, adult male and female antennae), revealing adult antennal-biased expression for most chemosensory genes in G. mellonella. Notably, two novel candidates (GmelOBP22 and GmelSNMP3) exhibited particularly high expression in larval heads, suggesting their crucial functional roles in larval development and survival. These findings enhance our understanding of the chemosensory mechanisms in G. mellonella larvae and establish a critical foundation for future functional investigations into its olfactory mechanisms. Full article

Among terrestrial animals, spiders exhibit the most striking examples of sexual size dimorphism (SSD) but better understanding of its evolution requires improved taxonomy and phylogeny. Many sexually dimorphic spiders lack adequate description, phylogenetic placement, and natural history observations. In South Africa, we documented the natural history of a poorly known spider, Megaraneus gabonensis (Lucas, 1858), with extreme, female-biased SSD (eSSD, female:male approximately 4:1). Here, we redescribe M. gabonensis, place Megaraneus Lawrence, 1968 phylogenetically for the first time, assess whether the observed eSSD represents an independent evolutionary origin, and test whether the macroevolutionary pattern is better explained by male dwarfism or female gigantism. The recovered phylogenetic placement of Megaraneus in the araneid ‘backobourkiines’, a clade previously considered as restricted to East Asia and Australasia, extends the range of this clade to the Afrotropics. We find that eSSD was present in the common ancestor of the ‘backobourkiines’, with further increases in female body length occurring independently in Megaraneus, Backobourkia Framenau, Dupérré, Blackledge & Vink, 2010, and the currently misplaced Parawixia dehaani (Doleschall, 1859). We conclude that the evolution of eSSD reflects a complex pattern of sex-specific size changes across spider phylogeny, but that in Megaraneus it results from female gigantism. Full article

Large language-model chatbots such as ChatGPT and DeepSeek are quickly gaining traction as an easy, first-stop tool for trip planning because they offer instant, conversational advice that once required sifting through multiple websites or guidebooks. Yet little is known about the biases that shape the destination suggestions these systems provide. This study conducts a controlled, persona-based audit of the two models, generating 6480 recommendations for 216 traveller profiles that vary by origin country, age, gender identity and trip theme. Six observable bias families (popularity, geographic, cultural, stereotype, demographic and reinforcement) are quantified using tourism rankings, Hofstede scores, a 150-term cliché lexicon and information-theoretic distance measures. Findings reveal measurable bias in every bias category. DeepSeek is more likely than ChatGPT to suggest off-list cities and recommends domestic travel more often, while both models still favour mainstream destinations. DeepSeek also points users toward culturally more distant destinations on all six Hofstede dimensions and employs a denser, superlative-heavy cliché register; ChatGPT shows wider lexical variety but remains strongly promotional. Demographic analysis uncovers moderate gender gaps and extreme divergence for non-binary personas, tempered by a “protective” tendency to guide non-binary travellers toward countries with higher LGBTQI acceptance. Reinforcement bias is minimal, with over 90 percent of follow-up suggestions being novel in both systems. These results confirm that unconstrained LLMs are not neutral filters but active amplifiers of structural imbalances. The paper proposes a public-interest re-ranking layer, hosted by a body such as UN Tourism, that balances exposure fairness, seasonality smoothing, low-carbon routing, cultural congruence, safety safeguards and stereotype penalties, transforming conversational AI from an opaque gatekeeper into a sustainability-oriented travel recommendation tool. Full article

This study evaluated the validity of a photoplethysmography (PPG)-based sensor (Polar OH1) for measuring heart rate variability (HRV), compared to an electrocardiography (ECG)-based reference device (Polar H10), considering body position (supine vs. seated), recording duration (2 vs. 5 min), sex, and age (≤40 vs. >40 years). HRV parameters (RMSSD and SDNN) were analyzed in 31 healthy adults using intraclass correlation coefficients (ICCs) and Bland–Altman analyses. Excellent reliability was observed between the devices in the supine position (RMSSD: ICC = 0.955; SDNN: ICC = 0.980), and good to excellent reliability in the seated position (RMSSD: ICC = 0.834; SDNN: ICC = 0.921). Mean biases ranged from −2.1 ms to −8.1 ms, with wider limits of agreement in the seated condition. The change in posture from supine to seated resulted in moderate reliability for both metrics, regardless of the device. Only marginal differences were found between 2- and 5-min recordings. Moreover, agreement was less consistent in older participants and females, suggesting potential effects of age and sex on signal quality. These findings support the use of PPG-based devices for short-term HRV assessment at rest, while highlighting the importance of considering posture, age, and sex when interpreting the results. Full article

Background: Despite more than 25 years of intensive effort following the landmark “To Err Is Human” report, conventional top-down medical error prevention strategies, grounded in the Safety-I paradigm, have largely failed to reduce patient harm. This persistent shortcoming underscores the need for a new prevention model. The medical literature contains an extensive yet systematically underutilized body of physician-generated experiential knowledge on “clinical pitfalls”—specific high-risk scenarios in which errors are likely to occur. This resource presents an opportunity for a novel, physician-driven approach to medical error prevention. The present paper proposes and evaluates such a model, grounded in the principles of Safety-II and translational medicine. Methods: The methodology involved a three-part conceptual analysis: (1) a critical review of the literature assessing the effectiveness of established error prevention strategies, (2) a quantitative bibliometric analysis of the PubMed database to determine the volume and temporal trends of publications on “clinical pitfalls”, and (3) a conceptual synthesis to design a novel physician-driven error prevention model. Each method is described in detail at the beginning of its respective section. Results: The literature review confirms the limited effectiveness of existing top-down safety initiatives, particularly in complex domains such as diagnosis and treatment. The bibliometric analysis identified more than 43,000 publications containing the keyword “pitfall,” with a sustained and significant upward trend in annual publications over the past three decades. The conceptual synthesis demonstrates that a physician-driven system—centered on a “Pitfall Bank”—addresses core weaknesses of current strategies, including unreliable data, heterogeneous knowledge, and cognitive biases. Structured as a circular translational mechanism, the proposed system facilitates a continuous cycle of practice-based problem identification and science-informed solution implementation. Conclusions: A physician-driven prevention system, architected as a translational engine, offers a promising and sustainable strategy to overcome the current impasse in medical error reduction and create a more resilient and adaptive healthcare system. Full article

The satellite-derived climatic variables offer extensive spatial and temporal coverage for research; however, their inherent biases can subsequently reduce their accuracy for water balance estimate. This study evaluates the effectiveness of bias correction in improving the Tropical Rainfall Measuring Mission (TRMM) rainfall and the Global Land Data Assimilation System (GLDAS) land surface temperature (LST) data and illustrates their long-term (2000–2019) hydrological assessment. The novelty lies in coupling the bias-corrected climate variables with the Thornthwaite–Mather water balance model as well as land use land cover (LULC) for improved predictive hydrological modeling. Bias correction significantly improved the agreement with ground observations, enhancing the R² value from 0.89 to 0.96 for temperature and from 0.73 to 0.80 for rainfall, making targeted inputs ready to predict hydrological dynamics. LULC mapping showed a predominance of agricultural land (64.5%) in the area followed by settlements (20.0%), forest (7.3%), barren land (6.5%), and water bodies (1.7%), with soils being silt loam, clay loam, and clay. With these improved datasets, the model found seasonal rise in potential evapotranspiration (PET), peaking at 120.7 mm in June, with actual evapotranspiration (AET) following a similar trend. The annual water balance showed a surplus of 523.8 mm and deficit of 121.2 mm, which proves that bias correction not only enhances the reliability of satellite data but also reinforces the credibility of hydrological indicators, with a direct, positive impact on evidence-based irrigation planning and flood mitigation and drought management, especially in data-scarce regions. Full article

Background: This systematic review examines the lived experiences of Black students in UK higher education (HE), focusing on their encounters with racism and racial disadvantage, and how institutional and social factors contribute to these experiences. Methods: We conducted a systematic search across seven databases (Academic Search Complete, Education Abstracts, PsycINFO, Race Relations Abstracts, Scopus, Web of Science, and SocINDEX) in April 2023, with periodic updates. The grey literature, which refers to research and information produced outside of traditional academic publishing and distribution channels, was reviewed. This includes reports, policy briefs, theses, conference proceedings, government documents, and materials from organisations, think tanks, or professional bodies that are not commercially published or peer-reviewed but can still offer valuable insights relevant to the topic. Hand searches were also included. Studies were included if they were peer-reviewed, published between 2012 and 2024, written in English, and focused on the experiences of Black students in UK higher education. Both qualitative and quantitative studies with a clear research design were eligible. Studies were excluded if they lacked methodological rigour, did not focus on the UK HE context, or did not disaggregate Black student experiences. Risk of bias was assessed using standard qualitative appraisal tools. Thematic analysis was used to synthesise findings. Results: Nineteen studies were included in the review. Two main themes emerged: (1) diverse challenges including academic barriers and difficulties with social integration, and (2) the impact of racism and institutional factors, such as microaggressions and biased assessments. These issues contributed to mental fatigue and reduced academic performance. Support systems and a sense of belonging helped mitigate some of the negative effects. Discussion: The evidence was limited by potential bias in reporting and variability in study quality. Findings reveal persistent racial inequalities in UK HE that affect Black students’ well-being and outcomes. Institutional reforms, increased representation, and equity-focused policies are needed. Future research should explore effective interventions to reduce the awarding gap and support Black student success Full article

Vision–Language Models (VLMs) have become key contributors to the state of the art in contextual emotion recognition, demonstrating a superior ability to understand the relationship between context, facial expressions, and interactions in images compared to traditional approaches. However, their reliance on contextual cues can introduce unintended biases, especially when the background does not align with the individual’s true emotional state. This raises concerns for the reliability of such models in real-world applications, where robustness and fairness are critical. In this work, we explore the limitations of current VLMs in emotionally ambiguous scenarios and propose a method to overcome contextual bias. Existing VLM-based captioning solutions tend to overweight background and contextual information when determining emotion, often at the expense of the individual’s actual expression. To study this phenomenon, we created synthetic datasets by automatically extracting people from the original images using YOLOv8 and placing them on randomly selected backgrounds from the Landscape Pictures dataset. This allowed us to reduce the correlation between emotional expression and background context while preserving body pose. Through discriminative analysis of VLM behavior on images with both correct and mismatched backgrounds, we find that in 93% of the cases, the predicted emotions vary based on the background—even when models are explicitly instructed to focus on the person. To address this, we propose a multimodal approach (named BECKI) that incorporates body pose, full image context, and a novel description stream focused exclusively on identifying the emotional discrepancy between the individual and the background. Our primary contribution is not just in identifying the weaknesses of existing VLMs, but in proposing a more robust and context-resilient solution. Our method achieves up to 96% accuracy, highlighting its effectiveness in mitigating contextual bias. Full article

The increasing prevalence of smartphone use has raised concerns about its impact on human psychological functioning. This literature review provides a comprehensive overview of the psychological dimensions influenced by smartphone use, spanning health psychology, individual differences, social psychology, and cognitive functioning. The review draws on findings from numerous studies, primarily conducted in highly developed Western and Asian countries, where cultural factors may influence usage patterns and psychological outcomes. Key limitations in the current body of research include geographical biases and methodological challenges such as sample homogeneity and reliance on self-report measures. Evidence suggests that excessive smartphone use can lead to addiction and is associated with negative psychological and health consequences. The review also highlights how individual differences—such as personality traits, age, and gender—affect smartphone usage. Social implications, both positive (e.g., increased connectivity) and negative (e.g., interpersonal conflict), are explored in depth. Cognitive effects are considered, particularly in relation to attention and memory, where findings suggest potential impairments in sustained focus and information retention. While the literature often emphasizes risks, this review also points to the need for further exploration of the potential benefits of smartphone use. In summary, the review offers valuable insights into the complex psychological effects of smartphones and underscores the importance of future research to better understand their nuanced impact on well-being. Full article

Synthetic Aperture Radar (SAR) technology has the characteristics of all-day and all-weather functionality; accordingly, it is not affected by rainy weather, overcoming the limitations of optical remote sensing, and it provides irreplaceable technical support for efficient water body extraction. To address the issues of low accuracy and unstable results in water body extraction from Sentinel-1 SAR images using a single method, a water body extraction method fusing the Sentinel-1 dual-polarized water index and random forest is proposed. This novel method enhances water extraction accuracy by integrating the results of two different algorithms, reducing the biases associated with single-method water body extraction. Taking Dalu Lake, Yinfu Reservoir, and Huashan Reservoir as the study areas, water body information was extracted from SAR images using the dual-polarized water body index, the random forest method, and the fusion method. Taking the normalized difference water body index extraction results obtained via Sentinel-2 optical images as a reference, the accuracy of different water body extraction methods when used with SAR images was quantitatively evaluated. The experimental results show that, compared with the dual-polarized water body index and the random forest method, the fusion method, on average, increased overall water body extraction accuracy and Kappa coefficients by 3.9% and 8.2%, respectively, in the Dalu Lake experimental area; by 1.8% and 3.5%, respectively, in the Yinfu Reservoir experimental area; and by 4.1% and 8.1%, respectively, in the Huashan Reservoir experimental area. Therefore, the fusion method of the dual-polarized water index and random forest effectively improves the accuracy and reliability of water body extraction from SAR images. Full article

Attentional biases seem to play an important role in anorexia nervosa (AN). The objective of this study was to measure visual attention patterns toward female bodies in adolescents and adults with and without AN in order to explore developmental and disease-specific aspects. Female adult and adolescent patients with AN (n = 38) and control participants (n = 39) viewed standardized photographic stimuli showing women’s bodies from five BMI categories. The fixation times on the bodies and specific body parts were analyzed. Differences between participants with and without AN did not emerge: All participants showed increased attention toward the body, while adolescents displayed shorter fixation times on specific areas of the body than adults. Increased visual attention toward areas indicative of weight (e.g., hips, thighs, abdomen, buttocks) and a shorter fixation time on unclothed body parts were observed in all participants. There is evidence for the developmental effect of differential viewing patterns when looking at women’s bodies. The attention behavior of patients with AN seems to be similar to that of the control groups, which is partly consistent with, and partly contradictory to, previous studies. Full article

Optical sensor technologies are reshaping obesity detection by enabling non-invasive, dynamic analysis of biomechanical and morphological biomarkers. This review synthesizes recent advances in three key areas: optical gait analysis, vision-based pose estimation, and depth-sensing voxel modeling. Gait analysis leverages optical sensor arrays and video systems to identify obesity-specific deviations, such as reduced stride length and asymmetric movement patterns. Pose estimation algorithms—including markerless frameworks like OpenPose and MediaPipe—track kinematic patterns indicative of postural imbalance and altered locomotor control. Human voxel modeling reconstructs 3D body composition metrics, such as waist–hip ratio, through infrared-depth sensing, offering precise, contactless anthropometry. Despite their potential, challenges persist in sensor robustness under uncontrolled environments, algorithmic biases in diverse populations, and scalability for widespread deployment in existing health workflows. Emerging solutions such as federated learning and edge computing aim to address these limitations by enabling multimodal data harmonization and portable, real-time analytics. Future priorities involve standardizing validation protocols to ensure reproducibility, optimizing cost-efficacy for scalable deployment, and integrating optical systems with wearable technologies for holistic health monitoring. By shifting obesity diagnostics from static metrics to dynamic, multidimensional profiling, optical sensing paves the way for scalable public health interventions and personalized care strategies. Full article

This study analyses algorithmic biases in AI-generated images, focusing on aesthetic violence, gender stereotypes, and weight discrimination. By examining images produced by the DALL-E Nature and Flux 1 systems, it becomes evident how these tools reproduce and amplify hegemonic beauty standards, excluding bodily diversity. Likewise, gender representations reinforce traditional roles, sexualising women and limiting the presence of non-normative bodies in positive contexts. The results show that training data and the algorithms used significantly influence these trends, perpetuating exclusionary visual narratives. The research highlights the need to develop more inclusive and ethical AI models, with diverse data that reflect the plurality of bodies and social realities. The study concludes that artificial intelligence (AI), far from being neutral, actively contributes to the reproduction of power structures and inequality, posing an urgent challenge for the development and regulation of these technologies. Full article

Background: We assessed the frequency and causes of discrepancies in CT dose indices such as dose-length product (DLP), size-specific dose estimate (SSDE), and effective dose (ED), as calculated by CT dose-monitoring systems. Our secondary aim was to demonstrate the estimation of size-specific ED (SED) from the patients’ dose records. Methods: The retrospective study included dosimetric data of 79,383 consecutive CT exams performed on two CT scanners. The following dose values were recorded from both the locally developed dose-monitoring system (DMS) and a commercial dose-monitoring program (DW^TM): DLP, SSDE, and ED. Only the DMS provided bodyweight-corrected effective dose (SED_DMS) and the SED based on previous published data. Results: Without body-region-specific analysis, there were no tendentious differences between the DLP, ED, or SSDE values obtained from DW^TM and DMS. However, the body region-based correlation revealed substantial differences between ED_DMS and ED_DW, primarily related to inadequate identification of the body. SSDE showed strong correlation to each anatomical category and CT device, except for the head region, where inadequate consideration of CT inclination was the reason for the biased SSDE_DW value. Furthermore, by analyzing the SED_DMS, SSDE, and SED correlations, we concluded that SED_DMS is a promising figure for estimating the SED value. Conclusions: SED provides suitable supplementary size-specific dose data to SDDE and may be a preferable choice for estimating cumulative doses in routine radiological practice. Full article

Background: The increased presence of metabolic syndrome (MetS) indicators globally is considered a public health problem, and the dose-response of exercise is not clarified. Objectives: This purpose of this study was to analyze the effectiveness of changes in biochemical, physiological, and anthropometric indicators of MetS based on distinct types of exercise in adults. Methods: Based on PRISMA guidelines, we searched the following databases—PubMed, Cochrane, LILACS, Springer Link, and Science Direct— for clinical trials investigating the effect of exercise in MetS indicators, without date or language restrictions. The quality of evidence and risk of biases were assessed using the PEDro scale. The impact of aerobic training “AT,” resistance training “An-T”, concurrent training “CT”) on MetS indicators (body mass index (BMI), waist circumference (WC), total body weight (TBW), total cholesterol (TC), LDL-c, HDL-c, fasting blood glucose (FBG), triglycerides (TG), and systolic and diastolic blood pressure (SBP, DBP)) were included in this analysis. Results: A total of 10 clinical trials was selected. Results demonstrate a heterogeneity of over 50% (p < 0.001). A mean difference was found for TC by AT (−23.70 mg/dL, p= 0.003) and An-T (3.91 mg/dL, p= 0.003); on HDL by CT (0.12 mg/dL, p = 0.004); on FBG by AT (−0.66 mg/dL, p = 0.02), CT (−1.42 mg/dL, p = 0.01); on DBP by AT (−0.79 mmHg, p = 0.02). Conclusions: There is a dominance of concurrent exercise over other types of exercise, considering the greater effectiveness and significance of the effect of seven MetS indicators, including HDL and fasting blood glucose, with a major effect size. Full article