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Behavioural phenotypes of autism in autistic and nonautistic gender clinic-referred youth and their caregivers
Get PDFIn recent years, referrals of youth to specialised gender services have risen sharply, with ~11% of these youth diagnosed as autistic compared with a general population rate of ~1%. In two preregistered studies, we addressed this insufficiently understood intersection. In Study 1, we examined the number and developmental trajectory of autism traits in autistic and nonautistic gender clinic-referred and cisgender youth (aged 7–16 years) using both screening measures (Autism-Spectrum Quotient Children’s Version and Autism-Spectrum Quotient Adolescent Version, Social Communication Questionnaire–Lifetime) and diagnostic tools (Autism Diagnostic Interview–Revised, Brief Observation of Symptoms of Autism). In Study 2, we examined autism traits among the caregivers of participants from each group using the Autism-Spectrum Quotient Adolescent Version. Study 1 results showed the autism phenotype in autistic gender clinic-referred youth closely resembled that of their cisgender autistic peers. In addition, after addressing methodological limitations in previous research, we found no evidence of elevated autism traits in nonautistic gender clinic-referred youth, challenging findings of some earlier studies. Study 2 provided evidence of familial aggregation of both autism traits and diagnoses among caregivers of both autistic gender clinic-referred and cisgender participants. Taken together, these findings challenge the hypothesis that autism in gender-diverse youth is merely a ‘phenomimic’ of autism and provide valuable clinical insights into the presentation of autism in this population
Real-time, high-fidelity face identity swapping with a vision foundation model
Get PDFMany recent face-swapping methods based on generative adversarial networks (GANs) or autoencoders achieve strong performance under constrained conditions but degrade significantly in high-resolution or extreme pose scenarios. Moreover, most existing models generate outputs at limited resolutions ( 128×128 ), which fall short of modern visual standards. Diffusion-based approaches have shown promise in handling such challenges, but are computationally intensive and unsuitable for real-time applications. In this work, we propose FaceChanger, a real-time face identity swap framework designed to enhance robustness across various poses and outputs at 256×256 (double the linear resolution of typical 128×128 baselines). While maintaining compatibility with conventional GAN- and autoencoder-based pipelines, FaceChanger uniquely incorporates a vision foundation model (VFM) to extract richer semantic features, which can enhance identity preservation, attribute control, and robustness to variations. In this work, we employ the Contrastive Language-Image Pre-training (CLIP) model to obtain the features. These features guide identity preservation and attribute control through newly designed VFM-based visual and textual semantic contrastive losses. Extensive evaluations on benchmarks such as the FaceForensics++ (FF++) dataset, the Multiple Pose, Illumination, and Expression (MPIE) dataset, and the large-pose Flickr face (LPFF) dataset demonstrate that FaceChanger matches or exceeds state-of-the-art performance under standard conditions and significantly outperforms them in high-resolution, pose-intensive scenarios
Oxford Handbook of Prescribing for Nurses and Allied Health Professionals
No full textThe new revised third edition of the Oxford Handbook of Prescribing for Nurses and Allied Health Professionals, provides concise, practical, and expert guidance on all aspects of non-medical prescribing. Now thoroughly updated, key advice has been organised by practice specialty, to aid learning and decision making in prescribing practice, and ensure the vital information is always at your fingertips.
Using information from the authors' years of experience as nurse practitioners and teachers, this practical handbook is packed with a wealth of recommendations, guidance, and support. Containing the practical principles nurses and other non-medical prescribers need to practice safely, effectively and cost-consciously whatever the situation, it provides evidence-based advice on a wide selection of subjects to give a complete picture of the role of the prescriber. The topics covered range from basic pharmacology, to the legal aspects and processes of prescribing practice, as well as prescribing for specific conditions and special groups, such as the older person and the very young.
This new edition features the most recent legislation and changes to prescribing standards, giving nurses and non-medical prescribers access to the most up-to-date information to inform their practice. This new edition has been reorganised to focus on the most common scopes of practice, for both acute and long conditions frequently managed by NMPs, to ensure ease-of-access to essential information.
Written by practising nurses and checked by subject experts and pharmacologists, the Oxford Handbook of Prescribing for Nurses and Allied Health Professionals, third edition continues to be an essential companion for all nurse and non-medical prescribers. It is ideal for students taking prescribing courses as a detailed and practical guide to practice as it covers all the competency areas, through to established practitioners who would like a handy reference to dip into when needed, as well as for other non-medical prescribers
Advanced Hypergraph Mining for Web Applications Using Sphere Neural Networks
Get PDFWeb-based applications often involve analyzing complex multi-relational data generated by various domains, including social platforms, bibliographic networks, recommendation systems, and e-commerce platforms. Traditional graph-based methods struggle to model interactions beyond simple pairwise relationships, such as higher-order dependencies and the underlying geometric and structural properties of the data. This paper presents a novel application of hyperspherical deep learning to hypergraphs, integrating geometric hypergraph mining with a Sphere Neural Network (SNN) to model and analyze these intricate relationships effectively. Using real-world datasets, including Reddit, DBLP, MovieLens, and Amazon Co-purchase, our framework embeds hypergraphs into hyperspherical spaces, preserving both relational and geometric properties. Experimental results demonstrate that our method significantly improves performance on tasks such as recommendation, co-purchase prediction, and user behavior analysis, outperforming state-of-the-art techniques. This work highlights the potential of integrating geometric hypergraphs and hyperspherical deep learning to advance the analysis of web-based data
Accounting for population structure in genomic prediction of strawberry sweetness at a global scale
Get PDFGenomic prediction models that fit multiple environments globally are valuable tools for assessing cultivar performance across diverse and variable growing conditions. We analyzed 2,064 strawberry (Fragaria × ananassa) accessions genotyped with 12,591 SNP markers. Soluble solids content (SSC) was measured in multi-year trials conducted at seven locations spanning the U.S., Europe, and Australia. Population structure analysis grouped accessions into two major clusters corresponding to subtropical and temperate origins, which was confirmed by significant differences in allele frequency distributions. To improve prediction accuracy across environments, we developed factor analytic models focusing on genotype-by-environment interactions rather than covariance between sub-populations. We compared three genomic prediction approaches: (i) a standard GBLUP model (Gfa), (ii) a GBLUP model incorporating principal component analysis eigenvalues and re-parameterization (Pfa), and (iii) a multi-population GBLUP model that fits sub-population genomic relationship matrices (Wfa). The Pfa and Wfa models achieved the highest prediction accuracy (r = 0.8) for SSC, outperforming individual environment models and the standard GBLUP. These findings demonstrate that accounting for population structure and genotype-by-environment interactions enhances multi-environment genomic prediction and supports practical implementation of genomic selection in global strawberry improvement programs
Relationship between Blastocystis infection and clinical outcomes: A scoping review protocol [version 1; peer review: 4 approved]
Get PDFBlastocystis, a common protist in the human gastrointestinal tract, exhibits substantial genetic diversity and has been linked to varying clinical outcomes. However, its role in human health remains debated, with studies suggesting both commensal and pathogenic interactions. This scoping review aims to systematically map the existing evidence on the association between Blastocystis presence and human clinical outcomes. Herein, we present our proposed protocol, where, using systematic search methods, studies will be identified from multiple databases, focusing on diagnostic procedures, clinical outcomes, and treatment options. Findings will provide a comprehensive evidence map, highlighting knowledge gaps and guiding future research. The resulting data is intended to inform clinical and public health perspectives on Blastocystis and its potential implications for human health
Tune in to the prebunking network! Development and validation of six inoculation videos that prebunk manipulation tactics and logical fallacies in misinformation
Get PDFMeta-analyses have demonstrated how inoculation interventions increase the detection of misinformation, but their scalability has remained elusive. To address this, Study 1 (pre-registered; N = 1,583) tested the efficacy of three short inoculation videos (prebunks) against three common manipulation tactics used in misinformation: (1) polarization, (2) conspiracy theories, and (3) fake experts. Results indicated that all three inoculation videos (vs. control) increased the detection of relevant manipulative content without altering perceptions of non-manipulative content, but only the polarization inoculation video increased manipulation discernment (i.e., increased ability to distinguish between manipulative and non-manipulative content). In Study 2 (pre-registered; N = 1,603), we tested the efficacy of three more inoculation videos containing logic-based prebunks against logical fallacies commonly used in misinformation: (1) whataboutism, (2) the moving the goalposts fallacy, and (3) the strawman fallacy. Detection of the relevant fallacious content was higher in all conditions (vs. control), but only the strawman fallacy inoculation video increased fallacy discernment. The moving the goalposts fallacy inoculation video appeared to increase overall distrust of relevant content, whereas the other two videos did not alter perceptions of relevant non-fallacious content. We discuss the implications and limitations of these findings
Claiming deservingness: The durability of social security claimant discourses during the Covid-19 pandemic
Get PDFThe Covid-19 pandemic created extraordinary conditions for social protection systems globally, with both material and discursive implications. In the UK, these unprecedented circumstances led to an influx of (first-time) social security claims, expectations of increased social solidarity and more positive public discussion around benefits. One might expect this to affect attitudes towards claiming. This article focuses on the accounts of claimants themselves , and how they conceived of their own claims during the pandemic. We analyse in-depth interviews conducted during the Covid-19 pandemic with a large, diverse sample of social security benefit claimants, and draw on concepts of deservingness to show how social security claimants negated stigma through appealing to specific deservingness frames. We show how frames relating to the normative criteria of need, control, contribution and identity were deployed by those who began claiming during the pandemic, as well as those whose claim began pre-pandemic. Despite important points of variation, especially in relation to the categories of control and identity, we find that these deservingness frames did not appear to be disrupted in a major way by the pandemic context, suggesting their notable durability in extraordinary circumstances, with implications for the conditions that can (and cannot) precipitate discursive change or rupture
Predictive model for live birth outcomes in single euploid frozen embryo transfers: a comparative analysis of logistic regression and machine learning approaches.
No full textTo develop and validate a predictive model for live birth (LB) outcomes in single euploid frozen embryo transfers (seFET) based on patient's characteristics and embryo parameters. A retrospective cohort study was performed including 1979 seFET performed between March 2017 and December 2023. Prediction models were built using logistic regression (LR), random forest classifier (RFC), support vector machines (SVM), and a gradient booster (XGBoost). Considered variables associated with LB outcomes were blastocyst expansion, blastocyst inner cell mass (ICM) and TE quality, day (D) of TE biopsy (D5, D6, and D7), female age and body mass index (BMI), distance from the uterine fundus at embryo transfer, endometrial preparation as natural cycles (NC) or hormonal replacement therapy (HRT), and endometrial thickness. Model performance was evaluated using area under the precision-recall curve and calibration metrics. Variables that were negatively associated with LB rate were BMI (OR = 0.79 [0.64-0.96], P = 0.020 for overweight and OR = 0.76 [0.60-0.95], P = 0.015 for obese class I/II), ICM grade B (OR = 0.72 [0.57-0.90], P = 0.005) or C (OR = 0.21 [0.15-0.30], P < 0.001), TE grade C (OR = 0.32 [0.24-0.43], P < 0.001), and blastocyst biopsied on D6 (OR = 0.66 [0.55-0.80], P < 0.001 or D7 (OR = 0.19[0.09-0.37], P < 0.001). The LR model was the best in terms of overall classification performance (C-statistics: 0.626 ± 0.018 vs. 0.606 ± 0.018, 0.581 ± 0.018, 0.601 ± 0.017, LR vs. RFC, XGBoost, and SVM, respectively, P < 0.001). A prediction model of LB outcome was developed and is free to access: https://artfertilityclinics.shinyapps.io/ABLE/ . LR demonstrated a stable validation performance and superior LB prediction, aiding as a predictive tool for patient counselling and assessing success in seFET cycles
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