bluuebunny/crossref_metadata_2025_split huggingface dataset:
Linking Subclinical Autistic Traits and Perceptual Category Learning
Authors: Warren Claire V. , Baumert Rebekka , Diermann Kira , Schöttle Daniel , Bayer Janine 
Published: February 2025

ABSTRACTAutism spectrum condition is a neurodevelopmental condition with difficulties in social interaction, communication and repetitive behaviours. Autistic individuals often exhibit difficulties in non‐social cognitive processing, such as grouping items into meaningful categories based on their holistic visual appearance. Underlying mechanisms might be a deficit in abstracting a category's central tendency (i.e., the prototype) or more general atypicalities in visual category learning processes. Milder autistic traits often also extend to a broader autism phenotype in neurotypical individuals. Our study compared adult neurotypical individuals with high or low autistic traits on behavioural performances and neural correlates measured by event‐related functional magnetic resonance imaging (fMRI) during a single‐category perceptual categorization task, based on the well‐known dot‐pattern paradigm. Bayesian computational modelling was used to investigate links between autistic traits and representing category knowledge by the prototype or memorizing single exemplars. We found that a high degree of autistic traits was linked to worse accuracy for endorsing category members. Autistic trait groups also differed in neural correlates during the training phase related to visual processing in occipital regions, decision‐making in midfrontal regions and the posterior cingulate, and feedback processing in the posterior cingulate and the ventral striatum. Model‐based analyses did not support deficits in prototype abstraction but yielded a link between autistic traits and stricter decision policies. In sum, we found no relationship between high autistic traits and difficulties with the prototype strategy but more general atypicalities in visual category learning processes, namely, visual processing, decision‐making and feedback processing.
Rapid Uptake of Nest Boxes by the Endangered Greater Glider (Petauroides volans)
Authors: Gracanin Ana , Hofman Maaike , Willson Stephen , Clough Jordyn B. , Brown Tyler , Mikac Katarina M. 
Published: February 2025

ABSTRACTThe loss of hollow‐bearing trees poses a critical threat to hollow‐dependent species. Nest boxes as supplementary habitat have emerged as a conservation tool for certain species, but their use in providing habitat for Southern Greater Gliders (Petauroides volans), a heat‐sensitive and hollow‐dependent species, remains limited. This study investigates the uptake and use of nest boxes designed specifically for Greater Gliders in a postfire environment. Thirty nest boxes were installed at a site that experienced moderate to severe fire during the 2019/2020 bushfires. Four different nest box designs were investigated for their thermal properties, colonisation rates and usage patterns. The time for a nest box to be used by a Greater Glider was rapid, at an average of 34 days after time of installation. Continuous camera monitoring over the course of a year revealed 100% colonisation, with gliders consistently using nest boxes across seasons regardless of nest box design. We found that nest box design influenced internal temperatures, and the best performing nest box design (constructed of wood and without foil insulation) was on average 1.99°C cooler inside than ambient temperatures (on warm days equal to or above 20°C). In comparison, natural tree hollows in use by Greater Gliders at the study site, had a much greater thermal buffer (on average 7.32°C cooler than ambient temperatures on warm days). Observations of Greater Gliders sitting outside at the entrance hole of nest boxes during the day was linked to warmer temperatures, indicating Greater Gliders likely exited nest boxes to thermoregulate. This study identified a rapid acceptance of nest boxes by the Greater Glider and offered insights into the role of nest boxes as supplementary habitat to assist population recovery following the loss of hollow‐bearing trees.
Supervised machine learning compared to large language models for identifying functional seizures from medical records
Authors: Kerr Wesley T. , McFarlane Katherine N. , Pucci Gabriela Figueiredo , Carns Danielle R. , Israel Alex , Vighetti Lianne , Pennell Page B. , Stern John M. , Xia Zongqi , Wang Yanshan 
Published: February 2025

AbstractObjectiveThe Functional Seizures Likelihood Score (FSLS) is a supervised machine learning–based diagnostic score that was developed to differentiate functional seizures (FS) from epileptic seizures (ES). In contrast to this targeted approach, large language models (LLMs) can identify patterns in data for which they were not specifically trained. To evaluate the relative benefits of each approach, we compared the diagnostic performance of the FSLS to two LLMs: ChatGPT and GPT‐4.MethodsIn total, 114 anonymized cases were constructed based on patients with documented FS, ES, mixed ES and FS, or physiologic seizure‐like events (PSLEs). Text‐based data were presented in three sequential prompts to the LLMs, showing the history of present illness (HPI), electroencephalography (EEG) results, and neuroimaging results. We compared the accuracy (number of correct predictions/number of cases) and area under the receiver‐operating characteristic (ROC) curves (AUCs) of the LLMs to the FSLS using mixed‐effects logistic regression.ResultsThe accuracy of FSLS was 74% (95% confidence interval [CI] 65%–82%) and the AUC was 85% (95% CI 77%–92%). GPT‐4 was superior to both the FSLS and ChatGPT (p <.001), with an accuracy of 85% (95% CI 77%–91%) and AUC of 87% (95% CI 79%–95%). Cohen's kappa between the FSLS and GPT‐4 was 40% (fair). The LLMs provided different predictions on different days when the same note was provided for 33% of patients, and the LLM's self‐rated certainty was moderately correlated with this observed variability (Spearman's rho2: 30% [fair, ChatGPT] and 63% [substantial, GPT‐4]).SignificanceBoth GPT‐4 and the FSLS identified a substantial subset of patients with FS based on clinical history. The fair agreement in predictions highlights that the LLMs identified patients differently from the structured score. The inconsistency of the LLMs' predictions across days and incomplete insight into their own consistency was concerning. This comparison highlights both benefits and cautions about how machine learning and artificial intelligence could identify patients with FS in clinical practice.
Predictive Model for Hypoglycaemia Risk in Type 2 Diabetes Mellitus Patients During the Peri‐Colonoscopy Period: A Retrospective Cohort Study
Authors: Yang Haiyan , Zhang Linlin , Liu Huiling , Hu Shuqiao , Yang Qiuping , Wu Jianan , Xu Mingming , Chu Shufang 
Published: February 2025

ABSTRACTAimsTo identify factors influencing hypoglycaemia in patients with type 2 diabetes mellitus (T2DM) undergoing colonoscopy and to construct a predictive model for assessing hypoglycaemia risk.DesignA retrospective cohort study.MethodsWe retrospectively collected data on 598 T2DM patients who underwent colonoscopy and randomised them into a developmental cohort and a validation cohort in a 7:3 ratio. We used multivariate logistic regression to develop a predictive model for hypoglycaemia during colonoscopy and identify independent predictors in pre‐ and post‐colonoscopy hypoglycaemia groups.ResultsWe identified 112 of 598 (18.7%) T2DM patients who experienced hypoglycaemia during the peri‐colonoscopy period: 43 pre‐colonoscopy, 61 post‐colonoscopy and 8 at both junctures. Ultimately, five predictors—insulin, SGLT2 inhibitors, fasting after colonoscopy, fasting C‐peptide and estimated glomerular filtration rate (eGFR)—were integrated into the predictive model. The AUC for predicting hypoglycaemia was 0.78 (95% CI, 0.71–0.84) and 0.82 (95% CI, 0.74–0.90) in the development and validation cohort, respectively. Variables associated with pre‐colonoscopy hypoglycaemia included SGLT2 inhibitors, fasting C‐peptide and eGFR, whereas the post‐colonoscopy hypoglycaemia group was associated with metformin, duration of diabetes, fasting C‐peptide and fasting after the examination.ConclusionThis study successfully developed and validated a predictive model for assessing hypoglycaemia risk in T2DM patients during peri‐colonoscopy.Implications for the Profession and/or Patient CareEarly identification of patients at high risk for peri‐colonoscopy hypoglycaemia allows nurses to implement personalised preventive strategies. The predictive model enables clinical nurses to deliver tailored interventions based on individual risk factors, potentially reducing hypoglycaemia‐related complications and improving patient safety outcomes.ImpactThis study provides nurses with a validated risk prediction tool for identifying high‐risk type 2 diabetes patients during colonoscopy, enabling targeted blood glucose monitoring protocols and preventive interventions in clinical practice.Reporting MethodThis study follows the STROBE guidelines for reporting cohort studies.Patient or Public ContributionDiabetes patients contributed electronic health record datasets.
Harshing the Vibes: Behavioural Responses to Predation and Deterrent Vibrational Playback in Scolypopa Australis (Hemiptera: Ricaniidae)
Authors: MacDougall Mark S. , Nelson Ximena J. , Stringer Lloyd D. 
Published: February 2025

ABSTRACTThe use of substrate borne vibrational playback to control insect pests is a rapidly growing line of research targeting many problematic species. Among the control approaches currently being tested, vibrational deterrent playbacks that aim to prevent feeding and to drive pests away from the target area are comparatively under‐explored. In experiments involving an introduced pest of New Zealand orchards, the passion vine hopper (PVH, Scolypopa australis), we investigated PVH anti‐predator behaviour and alarm call responses and the potential use of alarm calls as vibration‐based deterrent stimuli. We used laser‐Doppler vibrometry and video recordings to assess the vibrational and behavioural responses of PVH to the presence of a predator (Maratus griseus jumping spider), serendipitously discovering a new female call possibly involved in mate rejection. In our tests, we did not find evidence of any vibrational alarm calls in PVH, which showed a significantly reduced rate of general vibration‐based signalling and reduced propensity to call overall compared to control recordings. Instead, PVH responded to a predator threat with a rapid jumping escape response in 87.5% of attacks. We also tested the efficacy of three deterrent vibrational playbacks on driving PVH movement away from a source point, finding that a broad‐band white noise playback spanning 301–688 Hz induced significant movement away from the playback source compared with silent controls. As preliminary proof of concept, we hope these results will assist in developing successful vibrational deterrence for PVH.
Palaeobiology and osteohistology of South African sauropodomorph dinosaurs
Authors: Toefy Fay‐yaad , Krupandan Emil , Chinsamy Anusuya 
Published: February 2025

AbstractSeveral sauropodomorph dinosaurs have been excavated from the Elliot Formation (EF) of Southern Africa which include important taxa such as Massospondylus, Melanorosaurus and Antetonitrus. The study of the bone microstructure of smaller, bipedal Sauropodomorpha and larger, quadrupedal Sauropoda allow us to infer how the growth dynamics changed during the evolution of gigantism. Historically, osteohistological studies of Sauropodomorpha tended to have focused on either early diverging taxa (e.g. Plateosaurus & Massospondylus) or on derived taxa (diplodocids & titanosaurs), whereas studies on the growth dynamics of the transitionary groups (i.e. Sauropodiformes & early Sauropoda) are poorly known. Here, we assess the palaeobiology of two sauropodiformes and an early sauropod by analysing their bone histology. Thin sections of the long bones of two indeterminate sauropodiformes NMQR 3314 and NMQR 1551, and an indeterminate sauropod SAM–PK–K382 were prepared. The general histology of the long bones of all three dinosaurs were similar. Rapid growth through the deposition of fibrolamellar bone tissue characterised their respective ontogenies. Lines of arrested growth (LAGs) were commonly located in the mid and outer cortex signalling the onset of uninterrupted growth. Differences in the histology of these dinosaurs were principally related to the pathological bone tissue evident in the femur of the sauropodiform NMQR 1551 and to the formation of annuli around LAGs in Sauropoda indet., as well as in the location of LAGs in the compacta. The number of LAGs in the cortex varied among the taxa but generally the outer regions of the cortex showed an accumulation of LAGs. The growth dynamics of our three sauropodomorph dinosaurs are similar to early sauropods such as Antetonitrus. It appears that the abundance of fibrolamellar bone tissue and uninterrupted growth at later ontogenetic stages are likely key traits in the early evolution of gigantism in Sauropoda, which supports the occurrence of a mosaic of growth dynamic patterns among transitionary Sauropodomorpha.
The Efficacy of Thermal Mechanical Fractional Injury System for Facial Rejuvenation: Subjective, Objective and Image Analysis Study
Authors: Erlich Guy , Skorochod Ron , Sabo Edmond , Saigal Rishi , Dahan Eliran , Wolf Yoram 
Published: February 2025

ABSTRACTBackgroundFacial aging is the result of a number of factors, including the resorption of skeletal tissue, the atrophy of fat pads, and the laxity of connective tissue. These factors contribute to a reduction in facial volume and the development of skin changes. Thermal mechanical fractional injury (TMFI) represents a minimally invasive solution, stimulating collagen production and enhancing the delivery of topical formulations via epidermal microcraters.AimThis study seeks to evaluate the effects of TMFI on skin quality and introduce quantifiable methods to assess improvements.Patients and MethodsAdult patients seeking facial rejuvenation underwent three TMFI sessions, with a one‐month interval between each session. The assessment of skin quality was conducted using the Scientific Assessment Scale of Skin Quality (SASSQ), which was completed by blinded experts. Patient satisfaction was evaluated using the FACEQ scale. Additionally, objective texture analysis was performed using the MAZDA program, which quantified surface irregularities.ResultsThe objective assessments demonstrated statistically significant improvements in the following parameters: roughness, wrinkles, pore size, elasticity, pigmentation, and erythema (p < 0.001). The FACE‐Q results demonstrated high patient satisfaction, with a response rate of 68.4%. The MAZDA analysis confirmed significant texture improvements across most facial zones, with the exception of the medial forehead.ConclusionsThe findings of this study indicate that TMFI is an effective intervention for improving skin quality in a diverse range of patients. This study contributes to the existing literature on TMFI, which supports its use as a valuable tool in the field of skin rejuvenation.
The effects of a personalized sleep improvement app in subclinical poor sleepers: A randomized controlled trial
Authors: Munt Oliver , Schredl Michael , Gottlieb Elie , Fischer Susanne , Gautschi Daniela , Gahan Luke , Schoebel Christoph 
Published: February 2025

SummaryThe aim of this study was to determine the effects of a personalized smartphone application on subjective sleep quality, wellbeing and stress in participants with subclinical sleep problems. Healthy participants with subclinical threshold sleep issues that did not meet diagnostic criteria for insomnia were randomly assigned to use the app or be on a waiting list for app usage (control group). Subjective data on sleep quality (sleep quality component of Sleep Questionnaire B [primary endpoint] and the Pittsburgh Sleep Quality Index), feeling refreshed after the sleep component of Sleep Questionnaire B, Short Form‐12 physical and mental components, and stress (Perceived Stress Scale) were compared between the app and control groups at baseline and after 6 and 12 weeks. Depending on their usage, app users were defined as “frequent”, “occasional” or “rare/never” users. Two‐hundred and six and 205 app users and 286 and 285 control participants completed the 6‐ and 12‐week assessments, respectively. Sleep Questionnaire B scores improved in frequent (effect size 0.595; F = 13.7) and occasional users (0.653; F = 9.4) after 6 weeks; effects persisted at 12 weeks, and were similar for the Pittsburgh Sleep Quality Index. Feeling refreshed after the sleep component of Sleep Questionnaire B improved over time in all groups, with no significant difference between app users and controls. There were no changes in Short Form‐12 physical component, Short Form‐12 mental component, and Perceived Stress Scale scores. A personalized app positively affected subjective sleep quality in a population of users with subclinical threshold sleep issues, and could serve as a low‐burden digital intervention to potentially prevent the exacerbation of sleep pathologies.
Genomic Signatures of Adaptation Across a Precipitation Gradient From Niche Centre to Niche Edge
Authors: Van Deurs Samantha , Reutimann Oliver , Luqman Hirzi , Lifshitz Dikla , Mayzlish‐Gati Einav , Alexander Jake , Fior Simone 
Published: February 2025

ABSTRACTEvaluating the potential for species to adapt to changing climates relies on understanding current patterns of adaptive variation and selection, which might vary in intensity across a species' niche, hence affecting our inference of where adaptation might be most important in the future. Here, we investigate the genetic basis of adaptation in Lactuca serriola along a steep precipitation gradient in Israel approaching the species' arid niche limit and use candidate loci to inform predictions of its past and future adaptive evolution. Environmental association analyses combined with generalised dissimilarity models revealed 108 candidate genes showing nonlinear shifts in allele frequencies across the gradient, with 66% of these genes under strong selection near the dry niche edge. We detected selection acting on genes with separate suites of biological functions, specifically related to phenology and responses to environmental stressors, including osmotic stress, at the dry niche edge, and related to biotic interactions and defence closer to the niche centre. The adaptive genetic composition of populations, as inferred through polygenic risk scores, point to intensified selection operating towards the dry niche edge. However, inference of past and future evolutionary change predicts larger adaptive shifts occurring in the mesic part of the range, which is most affected by climate change. Our study reveals that adaptive shifts in response to climate change can be heterogeneous across a species' range and not necessarily strongest near its niche edge.
An optimized and precise road crack segmentation network in complex scenarios
Authors: Wang Gang , He MingFang , Liu Genhua , Li Liujun , Liu Exian , Zhou Guoxiong 
Published: February 2025

AbstractRoad cracks pose a serious threat to the stability of road structures and traffic safety. Therefore, this paper proposes an optimized accurate road crack segmentation network called MBGBNet, which can solve the problems of complex background, tiny cracks, and irregular edges in road segmentation. First, multi‐scale domain feature aggregation is proposed to address the interference of complex background. Second, bidirectional embedding fusion adaptive attention is proposed to capture the features of tiny cracks, and finally, Gaussian weighted edge segmentation algorithm is proposed to ensure the accuracy of crack edge segmentation. In addition, this paper uses the preheated bat optimization algorithm, which can quickly determine the optimal learning rate to converge the equilibrium. In the validation experiments on the self‐built dataset, mean intersection over union reaches 80.54% and precision of 86.38%. MBGBNet outperforms the other seven state‐of‐the‐art crack segmentation networks on the three classical crack datasets, highlighting its advanced segmentation capabilities. Therefore, MBGBNet is an effective auxiliary method for solving road safety problems.
Investigating Asthma After Coccidioidomycosis Among Patients With Commercial Health Insurance, United States, 2017–2022
Authors: Benedict Kaitlin , Smith Dallas J. , Haczku Angela , Zeki Amir A. , Hsu Joy , Toda Mitsuru , Kenyon Nicholas J. , Thompson George R. 
Published: February 2025

ABSTRACTBackgroundThe relationship between asthma and coccidioidomycosis has not been fully described. We have hypothesised that Coccidioides could trigger inflammatory airway responses, similar to other fungi. Objectives: To estimate the frequency of new‐onset asthma‐related symptoms after coccidioidomycosis and identify potentially associated factors.Patients/MethodsWe used a large health insurance claims database to identify patients with coccidiomycosis with and without an asthma diagnosis code or a short‐acting β2 agonist prescription in the year after diagnosis.ResultsThirteen per cent of 1657 patients with an asthma diagnosis code or a short‐acting β2 agonist prescription (median 2.5 months later).ConclusionsIncreased healthcare provider awareness of asthma as a potential coccidioidomycosis complication could benefit patients, especially female patients and patients with severe pulmonary infection.
Investigating the Impact of Açai (Euterpe oleracea) on Lipid Profile: A Comprehensive Systematic Review and Meta‐Analysis
Authors: Candeloro Bruno Moreira , Bitencourt Túlio H. B. , Elias Sergio F. R. , Rauli Maria Clara , Brandão Isabela B. , Barbalho Sandra Maria , Garner David M. , Raimundo Rodrigo D. , Porto Andrey A. , Valenti Vitor E. 
Published: February 2025

ABSTRACTConditions such as obesity and cardiovascular diseases represent significant global public health challenges, driven by their increasing prevalence and profound effects on morbidity and mortality. Dyslipidemia, defined by abnormal lipid concentrations, performs a pivotal role in the onset of cardiovascular diseases, especially atherosclerosis. Dietary strategies and other nonpharmacological interventions are crucial for the management of dyslipidemia. Açaí (Euterpe oleracea), a fruit native to the Amazon, is recognised for its antioxidant properties and potential impact on lipid metabolism. This systematic review and meta‐analysis aimed to assess the impact of açaí consumption on lipid profile markers. A systematic search was performed across several databases, including MEDLINE/PubMed, EMBASE, Web of Science and Scopus. We included non‐randomised, randomised, no blind, blind and control trials involving adult participants who received açaí in any form. The outcome measures included triglycerides, total cholesterol, LDL‐c and HDL‐c. The screening of studies and data extraction were independently conducted by four reviewers. The risk of bias was evaluated using the Cochrane Risk of Bias tool, and meta‐analyses were completed where applicable. Out of 188 identified studies, 8 met the inclusion criteria. Meta‐analysis of 411 participants displayed no significant effect of açaí on LDL‐c (MD = 6.06 mg/dL 95% CI: −0.03, 12.48, p = 0.06), HDL‐c (MD = 0.30 mg/dL 95% CI: −1.54, 2.13, p = 0.75), total cholesterol (MD = 2.94 mg/dL 95% CI: −6.44, 12.31, p = 0.54) and triglycerides (MD = 2.05 mg/dL 95% CI: 1.79, 2.28, p = 0.59). Additionally, açaí consumption was related to a significant reduction in total lipids (MD = −9.80 g, 95% CI: −13.94 to −5.66, p < 0.001). GRADE evaluation pointed to low/very low certainty of evidence. Açaí consumption may lessen total lipid levels, but no significant effect was noted for cholesterol parameters and triglycerides. Further trials with more demanding research methodologies are required to clarify the effects of açaí on lipid profiles.Trial Registration: CRD42024549531