Work-related musculoskeletal disorders are common, especially among women performing repetitive overhead tasks. In a randomized 2 × 2 crossover study with 14 female participants, we investigated the effects of a passive upper-body exoskeleton during an overhead precision task, involving the tightening of 20 bolts into a sensor-based workstation, while muscle activation, task performance, and usability were assessed. The results showed significant reduced M. Trapezius muscle activations during arm lowering (p = .041, 73%), and lower target accuracy (p < .001, 100%) when using the exoskeleton. Subjective strain in the shoulders was significantly lower when using the exoskeleton (p = .035, 15%). The usability was rated as "unacceptable", with users criticizing the complexity and learning effort. While the exoskeleton reduced muscle load, its mechanical limitations impaired precision and usability, especially for women. These results highlight the importance of sex-specific, ergonomic, and adaptive designs to improve exoskeleton effectiveness and acceptance.
Proton Transfer Reaction Mass Spectrometry (PTR-MS) has emerged as a transformative tool in breath analysis, enabling the real-time, high-sensitivity profiling of volatile organic compounds (VOCs) down to the pptv level without sample preparation. This review critically examines the technological evolution of PTR-MS, from fundamental ion-molecule kinetics to advanced configurations, including Time-of-Flight (TOF) analyzers and Switchable Reagent Ion (SRI) technologies. We systematically evaluate the clinical potential of PTR-MS in identifying volatile signatures associated with pulmonary malignancies, infectious diseases (e.g., COVID-19), and systemic metabolic disorders. Even with such technological leaps, the transition from discovery to routine clinical practice remains stalled. We identify the lack of metrological traceability as a primary bottleneck, specifically criticizing the widespread reliance on theoretical quantification and the challenges in isomer discrimination. To bridge this "trust gap," this review proposes a rigorous validation framework. We advocate for the implementation of matrix-matched reference materials for absolute quantification and the development of standardized spectral atlases anchored by GC retention times. This metrological approach is essential to solidify the role of PTR-MS in precision medicine.
Sea lanes of communication (SLOC) are perceived as intricate systems comprising shipping routes, key straits, and canals, which are increasingly vulnerable to external disruptions. This study proposes a holistic framework for resilience assessment on the system by introducing an advanced framework incorporating fuzzy logic, a critic weight calculation approach, and the evidential reasoning (ER) algorithm to assess resilience. Second, a hierarchical influential index is created, assessing the system's capacity to absorb, adjust, and recover from disruptions while incorporating connectivity among key straits and canals to illustrate the risk performance and spatial relationships of the straits and canals within Sea Lanes of Communication. Third, a fuzzy ER algorithm integrates information from diverse sources, taking into account the significance and nonlinear relationships among the influential factors. Finally, we present techniques to assess the resilience performance and validate our models. This proposed framework is implemented through an empirical study of five primary Sea Lanes of Communications that connect the Far East and the rest of the world. This framework provides valuable insights into resilience performance in an environment with high uncertainties and offers guidance for relevant stakeholders.
Climate-induced migration in deltaic environments such as the Indian Sundarbans requires vulnerability assessments that are both spatially explicit and analytically objective. This study advances existing approaches by integrating the distance correlation-based CRITIC (D-CRITIC) technique with village-level environmental and socio-economic indicators to develop an unbiased Migration Risk Index (MRI) for 86 villages in the Pathar Pratima Block. Unlike traditional equal-weighted or expert-based methods (e.g., AHP, TOPSIS), D-CRITIC quantifies interdependence among indicators to derive data-driven weights, minimizing redundancy and improving diagnostic precision. Results indicate that 10.34% and 24.14% of the total area fall into very high and high vulnerability categories, respectively, while incorporating adaptive capacity reduces overall vulnerability by about 10%. The MRI reveals that 59.8% of villages exhibit moderate-to-very high migration risk, with significant hotspots along the southern coast. By quantifying interdependence among indicators, D-CRITIC offers a data-driven alternative to subjective weighting methods, potentially minimizing redundancy and improving consistency in vulnerability assessment. A ground-truth validation using household survey data from ten villages demonstrated a moderate-to-strong agreement between the model predictions and observed migration patterns, with a Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) score of approximately 0.85. This result indicates that the model possesses good discriminatory and descriptive capability in capturing migration dynamics, thereby providing preliminary empirical evidence supporting the applicability and reliability of the proposed framework.. Beyond reaffirming coastal exposure, this study uncovers emerging mid-delta transition zones where exposure and adaptive capacity interact to shape migration pressures. The methodological innovation of D-CRITIC thus provides a replicable, data-driven framework for mapping migration risk and informing resilience planning in deltaic and other climate-sensitive regions.
Interpreting gene clusters derived from RNA sequencing (RNA-seq) remains difficult in functional genomics, particularly in antimicrobial resistance studies where mechanistic context is needed for downstream hypothesis generation. We present BIOGEN, an evidence-grounded multi-agent framework for post hoc interpretation of RNA-seq transcriptional modules that integrates biomedical retrieval, structured interpretation, and multi-critic verification. BIOGEN organizes knowledge from PubMed and UniProt into traceable cluster-level explanations with explicit evidence reporting and confidence tiering. On the primary Salmonella enterica dataset, BIOGEN achieved strong grounding and biological coherence, with BERTScore 0.689, Semantic Alignment Score 0.715, KEGG Functional Similarity 0.342, and a non-verifiable identifier rate of 0.000, compared with 0.100 for the LLM-only baseline. Across four additional bacterial RNA-seq datasets, BIOGEN preserved zero ungrounded outputs under the identifier-based criterion. In a controlled multi-dataset comparison against representative open-source agentic AI baselines, BIOGEN was the only framework that consistently produced zero non-verifiable identifier outputs across all five datasets. These results indicate that retrieval access alone is insufficient to ensure reliable biological interpretation. Evidence-grounded orchestration is essential for transparent, source-traceable transcriptomic reasoning under distribution shift.
With the rapid development of Internet of Vehicles (IoV) applications, the demand for serving computation-intensive and delay-sensitive tasks, which are executed in a dynamic mobility environment, continues to grow, while the embedded computing power carried by vehicles remains limited, and they are facing strict requirements in terms of latency. In vehicular edge computing, to offload computation to the nearby roadside units (RSUs) and to enable centralized learning-based offloading, mobility, task, channel, and resource information at the whole system needs to be gathered at a central learner, leading to a higher communication overhead and raw data exposure. This study introduces a privacy-aware federated deep reinforcement learning (FDRL) framework for vehicular edge computing task offloading with RSU assistance. The novelty of the proposed framework does not lie in the common usage of federated learning and deep reinforcement learning (DRL), but rather in the compactness of four coupled mechanisms: generation of a hybrid action representation of federated binary offloading decision and continuous resource allocation for RSUs, a personalized federated aggregation mechanism for non-IID vehicular observations collected on the RSU, a task-criticality-aware deadline reliability model with class-dependent violation penalties, and a handover-aware multi-RSU model that incorporates signaling delay, service-context transfer delay, and processing/authentication delay. In the proposed framework, the model parameters of the local SAC policies are provided to the federated coordinator rather than the locally observed information, such as raw vehicular trajectories, which can instead be used for local training of the SAC-based policies. Controlled simulation experiments are conducted to compare the proposed method with both local execution and edge-offloading methods, two centralized DRL baselines (DQN and DDPG), and three federated DRL baselines (FedAvg-DQN, centralized-SAC, and federated-MADRL). The results indicate that under the adopted simulation settings, the proposed FDRL framework achieves competitive and/or better system cost, delay, energy, deadline-violation performance, and communication overhead compared with other schemes. This privacy usefulness really means having less raw data exposed when federated training is used, and does not mean any formal privacy guarantee against inference attacks against model updates.
Historical theological frameworks, particularly those rooted in Christianity, have conceptualized guilt as a defining component of melancholy. This study compared the network structures of depressive symptoms in guilt-rich and guilt-free presentations of depression using a large-scale cross-national Asian dataset. We analyzed data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants, Phase 3 (REAP-AD3), which included patients with depressive disorders from 11 Asian countries. Network analysis was used to estimate symptom interactions and identify the central symptoms in each group. Participants were dichotomized according to the presence or absence of prominent guilt symptoms, as determined by the "feeling bad about yourself" item of the Patient Health Questionnaire-9 (PHQ-9). The network structures of the two groups differed substantially. In the guilt-rich group, depressed mood was the central node that was closely linked to self-blame. By contrast, the guilt-free group exhibited centralized networks with a loss of energy. Variations in symptom network structures can be partly explained as broadly consonant with early ideas of Richard Baxter and Robert Burton on melancholia. Clinically, symptoms related to moral judgment and self-criticism may indicate potentially different specific therapeutic attention from individuals with significant guilt. Conversely, for those without pronounced guilt, interventions targeting physical and motivational symptoms such as fatigue might inform hypothesis-driven treatment strategies.
In this Reply, we address the criticisms raised by Franzini, Valdenassi, and Chirumbolo concerning our study on the effects of ozonized saline solution (O3SS) on microglial polarization and endothelial responses in vitro. We clarify that the primary aim of the original work was mechanistic, relying on rigorously controlled cellular models that are universally recognized as essential preclinical tools in translational medicine. We reaffirm the validity of our experimental approach, including the preparation and characterization of O3SS based on empirically validated methodologies, direct ozone quantification, and standardized protocols consistent with the existing literature and clinical practice. Concerns regarding ozone chemistry, dose relevance, and hypochlorite formation are addressed through analytical validation, biological threshold considerations, and the use of certified assays. We further justify the choice of BV2 microglia and HUVEC cells as established and widely used models for investigating inflammatory and vascular pathways under reproducible conditions. Statistical analyses, gene expression interpretation, and the absence of comparative pharmacological agents are discussed in the context of the study's focused objectives. Finally, we place our findings within the established framework of ozone as an indirect pro-oxidant that elicits adaptive redox signaling ("oxidative eustress"), emphasizing the translational relevance of in vitro systems for elucidating early mechanistic events. Overall, we maintain that our study provides a robust, balanced, and evidence-based contribution to the understanding of ozone-derived redox biology.
Drawing on van Dijk's Ideological Square framework, this paper adopts a corpus-based method to examine the discursive strategies in their responses by the spokespersons for China's Ministry of Foreign Affairs during regular press conferences amid a public health crisis. The analysis focuses on how these discursive strategies shape the national images of China and the other four permanent members of the United Nations Security Council. The results show that (1) the spokespersons actively employed communicative discursive strategy to clarify China's stance and international cooperation initiatives while also using offensive discourse strategy to counter criticisms from US-led Western nations and media regarding the virus and the pandemic; (2) although the spokespersons' discourse generally aligns with van Dijk's Ideological Square of positive self-presentation and negative other-presentation, this model is not fixed but subject to dynamic changes driven by the self-serving principle. It is argued that factors such as diplomatic ideology, geopolitical relations, and traditional Chinese culture underlie the spokespersons' use of discursive strategies and national images representations. This study contributes to reconceptualizing an existing discourse model by offering data-driven insights into the operational mechanisms of ideological discourse in the contexts of global political communication and national image construction.
For the challenge of cooperative optimization of transient performance, energy consumption, and communication resources in multi-agent systems, this paper proposes an event-triggered prescribed time optimal consensus control scheme within the framework of deep reinforcement learning-based optimal backstepping. Firstly, a distributed event-triggered communication mechanism is proposed by designing the output sampling event-driven function with bandwidth sensing characteristics to realize the elastic adjustment of the communication load from the topological dimension. Subsequently, to find the optimal control solution for the co-optimization of stability and energy consumption, an optimal consensus control protocol is constructed based on the actor-critic neural network iterative learning algorithm and Bellman optimality theory. Furthermore, by introducing a time-varying gain scaling function, the Hamilton-Jacobi-Bellman equation analytical framework with explicit time-constrained characteristics is reformulated and an optimal consensus controller with a strict prescribed time convergence guarantee is derived that balances transient performance, energy consumption, and communication resources while attaining multi-agent system optimal consensus. Finally, the effectiveness of the proposed scheme is validated through comparative numerical simulations and a model simulation of a multi-agent electromechanical system.
To address the intensifying water supply-demand imbalance in arid regions under climate change and low-carbon development, this study uses Shihezi City, Xinjiang, as a case study. CMIP6 climate data were used to drive an LSTM model for basin-scale runoff projection, and a multi-objective water resources optimization framework was developed by integrating NSGA-III with CRITIC-TOPSIS, considering water shortage, economic benefits, pollutant emissions, and water-use carbon emissions. Results show that future runoff in the Manas River Basin generally declines, with the most pronounced reduction in September; by 2030, water scarcity in Shihezi City remains substantial. The optimal scheme under SSP1-2.6 performs well in water conservation and carbon reduction; SSP2-4.5 balances economic development and environmental protection, whereas SSP5-8.5 delivers higher economic benefits but greater environmental pressure. The results provide support for efficient water resources allocation and low-carbon utilization in arid oasis cities.
Optimizing learning paths to improve learning outcomes and learner engagement has always been a challenge in the field of personalized learning and online education. Traditional recommendation systems often suffer from limitations such as data sparsity and poor interpretability, which restrict the effectiveness of personalized recommendations. To address these issues, this paper proposes a novel course recommendation model-Reinforced Heterogeneous Knowledge Graph Reasoning for Course Recommendation (RHCR). Specifically, RHCR introduces a heterogeneous course knowledge graph to mitigate issues like sparse data and weak interactions, and formulates course path reasoning as a Markov Decision Process (MDP). By utilizing the Asynchronous Advantage Actor-Critic (A3C) algorithm enhanced with Multi-Head Attention and Bidirectional Long Short-Term Memory (MHA-BiLSTM), the model optimizes recommendation paths based on learners' profiles and historical course data. Experimental results show that RHCR increases Normalized Discounted Cumulative Gain (NDCG) by 8.16% and the Precision by 16.29% on the same dataset, outperforming traditional neural network-based methods. Moreover, it alleviates data sparsity and improves recommendation interpretability, providing an effective solution for personalized learning path optimization.
The home environment has been a central topic in the discipline of environmental gerontology. Home becomes the place where inhabitants' emotions and senses intertwine with their physical environment. As time spent at home increases with age, so does the importance of home environments for inhabitants' wellbeing. However, contemporary understandings of wellbeing within environmental gerontology have been criticized for their individualistic and human-centered approaches, treating the home environment primarily as a passive determinant of subjective wellbeing and neglecting the relationality of wellbeing in material, spatial and temporal contexts. This paper adopts a material gerontology perspective to conceptualize the relationship between aging inhabitants and their home environment as "person-home-assemblages". The aim of this study is to understand wellbeing in relation to aging and the home environment through these assemblages. To do so, we discuss the assemblage analysis of three cases. These cases were selected based on entanglements of transitional life events and changes in architectural environments. Each case combines data from an interview concerning an inhabitant's housing biography, an interview with the architect who designed the inhabitant's current dwelling, and material representations of the home environment. Our findings pull together aging persons, architectural spaces, personal objects, evolving landscapes and socially constructed housing ideals in relation to aging and wellbeing. Two primary perspectives emerged. Firstly, wellbeing emerges in fundamentally specific and situated ways from person-home-assemblages. Secondly, wellbeing emerges from person-home-assemblages while also actively shaping the evolution of these assemblages. These perspectives highlight how the home environment becomes an active actor and how home environments and inhabitants co-evolve and age together over time.
Because mutation rates vary widely across genomes and environments, natural selection is typically presented with highly biased variation. Yet, the idea that mutational tendencies can influence adaptation is still controversial. While mutation-driven adaptation has been observed in diverse taxa, critics contend it reflects small populations or weak-effect mutations. Therefore, the importance and generality of this phenomenon remain unclear, largely due to a lack of empirical tests across broad population-size gradients and multiple fitness-relevant traits. Here, we address this gap using a system in which two Escherichia coli mutator lineages evolve antibiotic resistance via two mutationally favoured, yet genetically distinct, routes. Simulations and experiments show that the scaling of mutation-biased adaptation with population size is complex, highly dependent on biological details, and - most critically - on how closely mutation bias aligns with selection. Contrary to the common view, we find that mutation-biased adaptation may not wane in large populations, but instead intensify depending on the bias. Crucially, we demonstrate that distinct mutation biases produce markedly different collateral sensitivity profiles to multiple antibiotics, even at large population sizes. Our findings suggest that mutation-biased adaptation may be widespread, with far-reaching and unpredictable consequences both within and beyond the original selective context.
When people reflect on past interpersonal conflicts (e.g., being attacked, criticized, or excluded), they often replay events in their minds, recalling what actually happened and imagining how things could have gone differently, thus engaging in counterfactual thinking. We investigated the types of counterfactuals they generate and whether counterfactual thinking influences their willingness to forgive. Results from Study 1 showed that, when recalling a past offense, victims generated more additive ("If only X had") than subtractive ("If only X had not") counterfactuals. Study 2 found that additive (vs. subtractive) counterfactuals focused on the perpetrator were associated with greater (vs. lesser) willingness to take the perpetrator's perspective and to forgive. Study 3 provided further evidence supporting the effects of additive (vs. subtractive) counterfactuals when the counterfactuals were focused on the victim. No direct evidence of moderation by offense severity or temporal distance was found (Studies 2 and 3), while the effects of victim-focused counterfactuals were moderated by responsibility attribution to the perpetrator or the victim (Study 3). (PsycInfo Database Record (c) 2026 APA, all rights reserved).
The vegetation in climatically heterogeneous regions exhibits significant spatial variability and temporal succession characteristics. It is crucial to obtain consistent vegetation characteristics in this region over time. Traditional single indices such as NDVI (Normalized Difference Vegetation Index), LAI (Leaf Area Index), and NPP (Net Primary Productivity) each have their own advantages, but they often show inconsistent trends when applied to complex vegetation. To effectively capture spatial heterogeneity and enhance the ecological interpretability, we propose a Dynamic Spatially Variable Weighted Synthesis Vegetation Index (DWS-SVI). Based on four Global Land Surface Satellite Dataset (GLASS) vegetation parameters (FVC (Fractional Vegetation Cover), LAI, NDVI, and NPP) and land cover types, this method employs the CRITIC method to perform dynamic weighting and generate continuous weight surfaces, ultimately synthesizing a comprehensive vegetation index at the pixel level. It combines "global trend and local adaptation" by integrating spatial heterogeneity modeling and multi-variable dynamic weighting, thereby overcoming the limitations of traditional methods in terms of spatial heterogeneity and ecological interpretability. Results show that over the past two decades, more than 69.4% of the area in the YRB has witnessed a significant improvement in vegetation conditions. The improvement was most notable in the summer, and it was mainly attributed to the improvement in the temperature and humidity conditions in this region. Compared with a single indicator, the DWS-SVI index can reflect the coordinated evolution of ecosystem structure and function, and can effectively suppress the observation errors caused by the bias of a single vegetation index, especially the "false greening" signals in transition zones and arid areas. Furthermore, the dominant factor map constructed based on DWS-SVI further reveals the differentiated driving mechanisms of ecosystems such as farmland, grassland, and forest, demonstrating that it has superior interpretability. This study provides a transferable framework for constructing spatially adaptive vegetation indices, enabling more reliable monitoring of ecosystem changes in large river basins and other climatically heterogeneous regions.
This paper investigates secure and low-latency communications in UAV-mounted simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted urban vehicular networks, where severe blockage, high vehicle mobility, eavesdropping threats, and delay-sensitive traffic services coexist. In the considered system, the UAV is used not only as an aerial carrier for the STAR-RIS but also as a mobile intelligent control node that can dynamically adjust its horizontal aerial position according to vehicle distribution, blockage conditions, and eavesdropping threats. First, a UAV-STAR-RIS-assisted vehicular communication system model is developed by jointly considering urban blockage, vehicle mobility, passive eavesdropping attacks, queueing dynamics, and UAV flight constraints. Then, a high-dimensional, non-convex, and strongly coupled dynamic optimization problem is formulated to maximize the long-term average secure and low-latency utility through the joint optimization of the UAV trajectory, the STAR-RIS transmission-reflection partition ratio, the phase-shift matrices, and the transmit power allocation. Furthermore, the problem is modeled as a Markov decision process with continuous state and action spaces, and a hierarchical constrained soft actor-critic (HC-SAC)-based joint control algorithm is proposed to enable adaptive UAV movement, STAR-RIS configuration, and power control in complex dynamic environments. Simulation results demonstrate that the proposed method outperforms DDPG and several structural benchmark schemes. In the representative evaluation, the proposed HC-SAC achieves an average delay of 10.85 slots and a secrecy outage probability of 0.7160, compared with 11.72 slots and 0.8501 for PPO, and 11.94 slots and 0.8599 for DDPG. Although PPO provides the highest average secrecy rate and successful service ratio, the proposed method still maintains a competitive secure communication capability and service reliability. A normalized composite utility analysis further shows that HC-SAC attains the highest utility value of 0.9254, indicating a more favorable security-latency trade-off in complex urban vehicular scenarios.
Self-compassion is a supportive self-relating style linked to emotional well-being. Acceptance and Commitment Therapy (ACT) targets psychological flexibility and may influence self-compassion, yet evidence is inconsistently quantified. This systematic review investigated changes in self-compassion following ACT or ACT-based interventions in adults with mental health concerns and summarized findings for self-criticism and emotional well-being. We followed PRISMA 2020 and preregistered the protocol on the Open Science Framework (10.17605/OSF.IO/8u7e9). PubMed, Embase, Web of Science, and the Cochrane Library were searched to August 5, 2024. Two reviewers (A.C., D.L.) independently screened studies and extracted data. C.F. adjudicated discrepancies, with substantial agreement at title and abstract screening (κ = 0.72) and full-text eligibility (κ = 0.78). RoB 2 and ROBINS-I were used for randomized and non-randomized studies. We synthesized results narratively following SWiM and reported effect estimates with uncertainty when available. Meta-analysis was not performed. Ten studies were included (five randomized controlled trials and five non-randomized, experimental, or qualitative studies). Self-compassion was most often measured with the self-compassion scale. In trials, within-group improvements were common, but between-group effects were mixed, ranging from negligible contrasts to large gains in selected samples (one trial reported a large within-group change, d ≈ 1.5, versus a smaller change in the waitlist). A pre-post chronic pain evaluation reported a small improvement in self-compassion (d ≈ 0.21). Self-criticism was reported in one trial with minimal post-treatment separation, and emotional well-being outcomes were variably defined and mixed. Most randomized controlled trials had some concerns of bias (one high risk), and most non-randomized studies were at serious risk or provided insufficient information. ACT may improve self-compassion, but comparative effects are inconsistent and certainty is limited. Standardized measurement and complete reporting are needed to clarify durability and clinical relevance.
Water quality monitoring networks require strategic resource allocation to maximize effectiveness while managing budget constraints. This study presents an integrated multi-criteria decision analysis (MCDA) framework combining CRITIC (CRiteria Importance Through Intercriteria Correlation), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), and linear programming (LP) optimization for prioritizing monitoring stations and allocating resources. Using real data from 347 monitoring stations in California extracted from the United States Environmental Protection Agency (EPA) Water Quality Portal (WQP) spanning January 2023 through December 2024 (16,750 measurements across seven water quality parameters), we demonstrate a reproducible methodology for evidence-based monitoring network optimization. Prior to analysis, physical plausibility filters removed 1106 impossible values (2.4% of raw data), ensuring analytical integrity. Dissolved oxygen was treated as an optimal-range criterion (|DO - 8 mg/L|) rather than a monotonic benefit, correcting a common misclassification in MCDA water quality studies. Spearman's rank correlation replaced Pearson's throughout the CRITIC computation to account for skewed parameter distributions. The CRITIC method determined objective criteria weights, with dissolved oxygen deviation (0.1813) and phosphorus (0.1804) as the most informative parameters. TOPSIS analysis identified top-performing stations with closeness coefficients ranging from 0.9813 to 0.3799, with a mean of 0.712 (MAD = 0.063). VIKOR analysis confirmed ranking consistency, yielding Spearman ρ = 0.912 ( p < 0.001) between TOPSIS and VIKOR rankings. LP optimization concentrated resources efficiently, achieving 36.9% improvement over uniform allocation ( Z ∗ = 0.9749 vs. C ¯ i = 0.712 under uniform distribution). This integrated MCDA-optimization framework provides water resource managers with a transparent, data-driven tool for strategic planning, enabling efficient allocation of limited monitoring resources while maintaining comprehensive environmental surveillance.
The Lancet Commission on the Future of Care and Clinical Research in Autism proposed the construct of "profound autism" as a recognizable subtype of autism. Supporters argue that this classification is necessary to ensure that autistic persons with severe impairment receive appropriate research attention and policy support, whereas critics contend that the construct lacks scientific validity and may reflect social or political considerations more than biological distinction. To inform this debate, we evaluate whether the proposed "profound autism" category represents a distinct genetic phenotype using multiple molecular data types collected in a large cohort. Across genomic, transcriptomic, and regulatory analyses, we find no evidence supporting "profound autism" as a biologically distinct phenotypic group. Instead, differences emerge primarily in inferred gene regulatory networks distinguishing nonspeaking from speaking autistic children, suggesting potential regulatory mechanisms contributing to speech ability. These findings suggest that future research into severe impairment may be more productive if focused on specific traits-such as speech impairment-rather than attempting to define a distinct biological subtype within the multidimensional phenomenon of autism.