Among those Robert F. Kennedy Jr. recently named to a federal autism committee are people who tout dangerous treatments and say vaccine manufacturers are "poisoning children." US health secretary Robert F. Kennedy Jr. has filled an autism committee with friends, associates, and former colleagues who believe that autism is caused by vaccines. Autism advocates are now worried the group could pave the way for dangerous pseudoscientific treatments going mainstream. Last week, Kennedy announced an entirely new lineup for the Interagency Autism Coordinating Committee (IACC), a group that recommends what types of autism research the government should fund and provides guidance on the services the autism community requires. The group is typically composed of experts in the area of autism research, along with policy experts and autistic people advocating for their own community.

RFK's Overhauled Autism Committee Is Even Worse Than It Looks Kennedy has stacked another HHS panel with his fellow travelers in the anti-vaccine and pseudoscience world. Get your news from a source that's not owned and controlled by oligarchs. Last April, Health and Human Services Secretary Robert F. Kennedy, Jr. promised that his agency would find the cause of autism "by September." That didn't pan out, but this week he appears to be trying again--by stacking a decades-old committee devoted to "innovations in autism research, diagnosis, treatment, and prevention" with his friends and fellow travelers in the anti-vaccine and pseudoscience world. Much like the Centers for Disease Control and Prevention's Advisory Committee on Immunization Practices, which Kennedy overhauled last fall with a full slate of new appointees after firing all the old members, he filled the Interagency Autism Coordinating Committee (IACC), which was first established in 2000 to help set the federal agenda for autism research, with Kennedy's allies in the anti-vaccine movement.

Woke wannabe LA mayor melts down during radio interview, says she deserves job because she's a MOTHER - then gets her own age wrong I got the'taboo' cancer soaring among women. Treatment saved my life... but I can NEVER have sex again. It didn't have to be like this AMANDA PLATELL: This single line in Brooklyn Beckham's nuclear outburst is brutal... but it's made me rethink EVERYTHING about Victoria and David The bitter trademark row at the heart of the Beckham feud: Why'devastated' Victoria bore the brunt of Brooklyn's eviscerating statement that has left her'on the floor in pieces' Cut BACK on breakfast cereal. Nick Reiner is'childlike' in jail and so out of it he cannot process the murders of his parents, insider claims The Osteopenia Plague: Almost HALF of over-50s now have the dreaded bone disease. Dark side of America's favorite vacation hotspot... where women are subjected to the most horrific sex attacks imaginable Disturbing video appears to show former Disney star shoving his ex-fiancée after'hammer threat'... as Matt Prokop is arrested for child pornography Joseph Gordon-Levitt was the hottest actor in Hollywood... then vanished: Unearthing family tragedy that sparked disappearance and has left'lasting' scars Shades-wearing Macron hits back at'bully' Trump and warns'we're shifting to a world without rules' where'international law is trampled underfoot and the only law that matters is that of the strongest' Trump reveals why he leaked world leaders' messages and his secret role in foiling a prison break in Syria: Live updates Brooklyn Beckham and Nicola Peltz's wedding guest speaks out and claims Victoria DID dance inappropriately with her son A person's facial reactions may reveal if they have autism, as scientists have found that those with the condition'speak a different language' with their expressions.

What if the idea of the autism spectrum is completely wrong? For years, we've thought of autism as lying on a spectrum, but emerging evidence suggests that it comes in several distinct types. These three words have become synonymous with autism, yet behind them lies a common misunderstanding. The idea of "the spectrum" suggests that all autistic people share similar experiences and behave in similar ways - only to a greater or lesser extent. The reality couldn't be further from the truth. Some autistic people may not speak at all; others are hyperverbal and extremely fluent.

In many biological and medical contexts, we construct a large labeled corpus by aggregating many sources to use in target prediction tasks. Unfortunately, many of the sources may be irrelevant to our target task, so ignoring the structure of the dataset is detrimental. This work proposes a novel approach, the Multiple Domain Matching Network (MDMN), to exploit this structure. MDMN embeds all data into a shared feature space while learning which domains share strong statistical relationships. These relationships are often insightful in their own right, and they allow domains to share strength without interference from irrelevant data. This methodology builds on existing distribution-matching approaches by assuming that source domains are varied and outcomes multi-factorial. Therefore, each domain should only match a relevant subset. Theoretical analysis shows that the proposed approach can have a tighter generalization bound than existing multiple-domain adaptation approaches. Empirically, we show that the proposed methodology handles higher numbers of source domains (up to 21 empirically), and provides state-of-the-art performance on image, text, and multi-channel time series classification, including clinically relevant data of a novel treatment of Autism Spectrum Disorder.

In recent years, deep saliency models have made significant progress in predicting human visual attention. However, the mechanisms behind their success remain largely unexplained due to the opaque nature of deep neural networks. In this paper, we present a novel analytic framework that sheds light on the implicit features learned by saliency models and provides principled interpretation and quantification of their contributions to saliency prediction. Our approach decomposes these implicit features into interpretable bases that are explicitly aligned with semantic attributes and reformulates saliency prediction as a weighted combination of probability maps connecting the bases and saliency. By applying our framework, we conduct extensive analyses from various perspectives, including the positive and negative weights of semantics, the impact of training data and architectural designs, the progressive influences of fine-tuning, and common error patterns of state-of-the-art deep saliency models. Additionally, we demonstrate the effectiveness of our framework by exploring visual attention characteristics in various application scenarios, such as the atypical attention of people with autism spectrum disorder, attention to emotion-eliciting stimuli, and attention evolution over time.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition whose rising prevalence places increasing demands on a lengthy diagnostic process. Machine learning (ML) has shown promise in automating ASD diagnosis, but most existing models operate as black boxes and are typically trained on a single dataset, limiting their generalizability. In this study, we introduce a transparent and interpretable ML approach that leverages BioBERT, a state-of-the-art language model, to analyze unstructured clinical text. The model is trained to label descriptions of behaviors and map them to diagnostic criteria, which are then used to assign a final label (ASD or not). We evaluate transfer learning, the ability to transfer knowledge to new data, using two distinct real-world datasets. We trained on datasets sequentially and mixed together and compared the performance of the best models and their ability to transfer to new data. We also created a black-box approach and repeated this transfer process for comparison. Our transparent model demonstrated robust performance, with the mixed-data training strategy yielding the best results (97 % sensitivity, 98 % specificity). Sequential training across datasets led to a slight drop in performance, highlighting the importance of training data order. The black-box model performed worse (90 % sensitivity, 96 % specificity) when trained sequentially or with mixed data. Overall, our transparent approach outperformed the black-box approach. Mixing datasets during training resulted in slightly better performance and should be the preferred approach when practically possible. This work paves the way for more trustworthy, generalizable, and clinically actionable AI tools in neurodevelopmental diagnostics.

Neurodivergent women in Software Engineering (SE) encounter distinctive challenges at the intersection of gender bias and neurological differences. To the best of our knowledge, no prior work in SE research has systematically examined this group, despite increasing recognition of neurodiversity in the workplace. Underdiagnosis, masking, and male-centric workplace cultures continue to exacerbate barriers that contribute to stress, burnout, and attrition. In response, we propose a hybrid methodological approach that integrates InclusiveMag's inclusivity framework with the GenderMag walkthrough process, tailored to the context of neurodivergent women in SE. The overarching design unfolds across three stages, scoping through literature review, deriving personas and analytic processes, and applying the method in collaborative workshops. We present a targeted literature review that synthesize challenges into cognitive, social, organizational, structural and career progression challenges neurodivergent women face in SE, including how under/late diagnosis and masking intensify exclusion. These findings lay the groundwork for subsequent stages that will develop and apply inclusive analytic methods to support actionable change.

ASD is a complicated neurodevelopmental disorder marked by variation in symptom presentation and neurological underpinnings, making early and objective diagnosis extremely problematic. This paper presents a Graph Convolutional Network (GCN) model, incorporating Chebyshev Spectral Graph Convolution and Graph Attention Networks (GAT), to increase the classification accuracy of ASD utilizing multimodal neuroimaging and phenotypic data. Leveraging the ABIDE I dataset, which contains resting-state functional MRI (rs-fMRI), structural MRI (sMRI), and phenotypic variables from 870 patients, the model leverages a multi-branch architecture that processes each modality individually before merging them via concatenation. Graph structure is encoded using site-based similarity to generate a population graph, which helps in understanding relationship connections across individuals. Chebyshev polynomial filters provide localized spectral learning with lower computational complexity, whereas GAT layers increase node representations by attention-weighted aggregation of surrounding information. The proposed model is trained using stratified five-fold cross-validation with a total input dimension of 5,206 features per individual. Extensive trials demonstrate the enhanced model's superiority, achieving a test accuracy of 74.82\% and an AUC of 0.82 on the entire dataset, surpassing multiple state-of-the-art baselines, including conventional GCNs, autoencoder-based deep neural networks, and multimodal CNNs.

Social media data has become a vital resource for studying mental health, offering real-time insights into thoughts, emotions, and behaviors that traditional methods often miss. Progress in this area has been facilitated by benchmark datasets for mental health analysis; however, most existing benchmarks have become outdated due to limited data availability, inadequate cleaning, and the inherently diverse nature of social media content (e.g., multilingual and harmful material). We present a new benchmark dataset, \textbf{MindSET}, curated from Reddit using self-reported diagnoses to address these limitations. The annotated dataset contains over \textbf{13M} annotated posts across seven mental health conditions, more than twice the size of previous benchmarks. To ensure data quality, we applied rigorous preprocessing steps, including language filtering, and removal of Not Safe for Work (NSFW) and duplicate content. We further performed a linguistic analysis using LIWC to examine psychological term frequencies across the eight groups represented in the dataset. To demonstrate the dataset utility, we conducted binary classification experiments for diagnosis detection using both fine-tuned language models and Bag-of-Words (BoW) features. Models trained on MindSET consistently outperformed those trained on previous benchmarks, achieving up to an \textbf{18-point} improvement in F1 for Autism detection. Overall, MindSET provides a robust foundation for researchers exploring the intersection of social media and mental health, supporting both early risk detection and deeper analysis of emerging psychological trends.