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Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a male-to-female prevalence of 4:1. However, the genetic mechanisms underlying this gender difference remain unclear. Mutation burden analysis, a TADA model, and co-expression and functional network analyses were performed on de novo mutations (DNMs) and corresponding candidate genes. We found that the prevalence of putative functional DNMs (loss-of-function and predicted deleterious missense mutations) in females was significantly higher than that in males, suggesting that a higher genetic load was required in females to reach the threshold for a diagnosis. We then prioritized 174 candidate genes, including 60 shared genes, 91 male-specific genes, and 23 female-specific genes. All of the three subclasses of candidate genes were significantly more frequently co-expressed in female brains than male brains, suggesting that compensation effects of the deficiency of ASD candidate genes may be more likely in females. Nevertheless, the three subclasses of candidate genes were co-expressed with each other, suggesting a convergent functional network of male and female-specific genes. Our analysis of different aspects of genetic components provides suggestive evidence supporting the female-protective effect in ASD. Moreover, further study is needed to integrate neuronal and hormonal data to elucidate the underlying gender difference in ASD.

Under the transition towards electrification and intelligence in modern automotive industry, the health status of low-voltage wiring harnesses directly affects vehicle performance and safety. To address the challenge of predicting performance degradation caused by multi-physics coupling effects during wiring harness aging, this study proposes a CNN-BiLSTM-Attention hybrid neural network model. By capturing voltage, current, and temperature parameters during low-voltage system operation, the model combines CNN’s local feature extraction, BiLSTM’s temporal sequence analysis, and attention mechanisms to predict aging levels. Accelerated aging experiments were conducted to obtain wiring harnesses with different degradation levels from new to 720 h aged states, and a dedicated experimental platform was built for data collection and verification. The results show the system achieves a mean absolute error (MAE) of 0.02806, with 32.50% and 62.06% error reduction compared to LSTM and Random Forest models, respectively, demonstrating effective prediction performance.

Recent works have shown that Transformer's excellent performances on natural language processing tasks can be maintained on natural image analysis tasks. However, the complicated clinical settings in medical image analysis and varied disease properties bring new challenges for the use of Transformer. The computer vision and medical engineering communities have devoted significant effort to medical image analysis research based on Transformer with especial focus on scenario‐specific architectural variations. In this paper, we comprehensively review this rapidly developing area by covering the latest advances of Transformer‐based methods in medical image analysis of different settings. We first give introduction of basic mechanisms of Transformer including implementations of selfattention and typical architectures. The important research problems in various medical image data modalities, clinical visual tasks, organs and diseases are then reviewed systemically. We carefully collect 276 very recent works and 76 public medical image analysis datasets in an organized structure. Finally, discussions on open problems and future research directions are also provided. We expect this review to be an up‐to‐date roadmap and serve as a reference source in pursuit of boosting the development of medical image analysis field. With the development of medical image analysis, many successful cases have shown the clinical potential of Transformer. Thus, this paper collected 273 latest (2021–2022) works that based their frameworks on Transformer. To facilitate the readers, 76 public medical image datasets have also been listed. Considering the diversity of these 273 works, we organize this paper as the following sequences: task‐modality‐disease.

Bamboo flute is a traditional Chinese instrument. In the long history of China, the performance of bamboo flute has been used to express and transmit emotions, enriching the spiritual world of the Chinese people. Nowadays, with the continuous progress of spiritual civilization construction and the unprecedented enhancement of Chinese national cultural confidence, the development trend of the bamboo flute art has been more clear. This paper makes an in-depth analysis of the development history and characteristics of bamboo flute ensemble and the significance of its practice, and shares the author’s opinion on the practice of bamboo flute ensemble, so as to enrich the research theory of Chinese bamboo flute ensemble art and make contributions to its development.

Short tandem repeat (STR) sequences are highly variable DNA segments that significantly contribute to human neurodegenerative disorders, highlighting their crucial role in neuropsychiatric conditions. This article examines the pathogenicity of abnormal STRs and classifies tandem repeat expansion disorders(TREDs), emphasizing their genetic characteristics, mechanisms of action, detection methods, and associated animal models. STR expansions exhibit complex genetic patterns that affect the age of onset and symptom severity. These expansions disrupt gene function through mechanisms such as gene silencing, toxic gain-of-function mutations leading to RNA and protein toxicity, and the generation of toxic peptides via repeat-associated non-AUG (RAN) translation. Advances in sequencing technologies—from traditional PCR and Southern blotting to next-generation and long-read sequencing—have enhanced the accuracy of STR variation detection. Research utilizing these technologies has linked STR expansions to a range of neuropsychiatric disorders, including autism spectrum disorders and schizophrenia, highlighting their contribution to disease risk and phenotypic expression through effects on genes involved in neurodevelopment, synaptic function, and neuronal signaling. Therefore, further investigation is essential to elucidate the intricate interplay between STRs and neuropsychiatric diseases, paving the way for improved diagnostic and therapeutic strategies.

It is an objective reality that deaf-mute people have difficulty seeking medical treatment. Due to the lack of sign language interpreters, most hospitals in China currently do not have the ability to interpret sign language. Normal medical treatment is a luxury for deaf people. In this paper, we propose a sign language recognition system: Heart-Speaker. Heart-Speaker is applied to a deaf-mute consultation scenario. The system provides a low-cost solution for the difficult problem of treating deaf-mute patients. The doctor only needs to point the Heart-Speaker at the deaf patient and the system automatically captures the sign language movements and translates the sign language semantics. When a doctor issues a diagnosis or asks a patient a question, the system displays the corresponding sign language video and subtitles to meet the needs of two-way communication between doctors and patients. The system uses the MobileNet-YOLOv3 model to recognize sign language. It meets the needs of running on embedded terminals and provides favorable recognition accuracy. We performed experiments to verify the accuracy of the measurements. The experimental results show that the accuracy rate of Heart-Speaker in recognizing sign language can reach 90.77%.

Background Unresolved inflammation and tissue destruction are considered to underlie the failure of dental pulp repair. As key mediators of the injury response, dental pulp stem cells (DPSCs) play a critical role in pulp tissue repair and regeneration. Resolvin E1 (RvE1), a major dietary omega-3 polyunsaturated fatty-acid metabolite, is effective in resolving inflammation and activating wound healing. However, whether RvE1 facilitates injured pulp-tissue repair and regeneration through timely resolution of inflammation and rapid mobilization of DPSCs is unknown. Therefore, we established a pulp injury model and investigated the effects of RvE1 on DPSC-mediated inflammation resolution and injured pulp repair. Methods A pulp injury model was established using 8-week-old Sprague-Dawley rats. Animals were sacrificed on days 1, 3, 7, 14, 21, and 28 after pulp capping with a collagen sponge immersed in PBS with RvE1 or PBS. Hematoxylin-eosin and Masson’s trichrome staining, immunohistochemistry, and immunohistofluorescence were used to evaluate the prohealing properties of RvE1. hDPSCs were incubated with lipopolysaccharide (LPS) to induce an inflammatory response, and the expression of inflammatory factors after RvE1 application was measured. Effects of RvE1 on hDPSC proliferation, chemotaxis, and odontogenic differentiation were evaluated by CCK-8 assay, transwell assay, alkaline phosphatase (ALP) staining, alizarin red staining, and quantitative PCR, and possible signaling pathways were explored using western blotting. Results In vivo, RvE1 reduced the necrosis rate of damaged pulp and preserved more vital pulps, and promoted injured pulp repair and reparative dentin formation. Further, it enhanced dentin matrix protein 1 and dentin sialoprotein expression and accelerated pulp inflammation resolution by suppressing TNF-α and IL-1β expression. RvE1 enhanced the recruitment of CD146 + and CD105 + DPSCs to the damaged molar pulp mesenchyme. Isolated primary cells exhibited the mesenchymal stem cell immunophenotype and differentiation. RvE1 promoted hDPSC proliferation and chemotaxis. RvE1 significantly attenuated pro-inflammatory cytokine (TNF-α, IL-1β, and IL-6) release and enhanced ALP activity, nodule mineralization, and especially, expression of the odontogenesis-related genes DMP1 , DSPP , and BSP in LPS-stimulated DPSCs. RvE1 regulated AKT, ERK, and rS6 phosphorylation in LPS-stimulated DPSCs. Conclusions RvE1 promotes pulp inflammation resolution and dentin regeneration and positively influences the proliferation, chemotaxis, and differentiation of LPS-stimulated hDPSCs. This response is, at least partially, dependent on AKT, ERK, and rS6-associated signaling in the inflammatory microenvironment. RvE1 has promising application potential in regenerative endodontics.

To address the manufacturing demands of large-scale aerospace composite components, this study systematically investigates the coordinated motion characteristics of multi-axis systems in fiber placement equipment. This investigation is based on the structural features and process specifications of the equipment. A comprehensive motion control scheme for grid-based fiber placement machines was developed using the Siemens 840D CNC system, integrating filament-winding and tape-laying functionalities on a unified control platform while enabling 10-axis synchronous motion. To mitigate thermal-induced errors, a compensation method incorporating a BP neural network optimized by a genetic algorithm with an enhanced fitness function (GA-BP) was proposed. Experimental results demonstrate significant improvements: the maximum thermal errors of the Z-axis and X3-axis were reduced by 36.7% and 53.3%, respectively, while the core mold placement time was reduced to 61% of the specified duration, with notable enhancements in trajectory accuracy and processing efficiency. This research provides a technical framework for the design of multi-axis cooperative control systems and thermal error compensation in automated fiber placement equipment, offering critical insights for advancing manufacturing technologies in aerospace composite applications. The proposed methodology highlights practical value in balancing precision, efficiency, and system integration for complex composite component production.

The thermal management system (TMS) of electric vehicles (EVs) plays a pivotal role in vehicle performance, driving range, battery lifespan, and passenger comfort. Precise control of compressor speed, informed by real-time sensor data, is essential for improving TMS efficiency and extending EV range. This study proposes a control strategy based on the PID Search Algorithm (PSA), ensuring optimal thermal management for an integrated battery and cabin TMS. A co-simulation platform combining AMESim and Simulink is developed for validation, utilizing various sensors to monitor system performance. Simulations are conducted under target temperatures of 20 °C and 25 °C to replicate various operating conditions. The optimized strategy is compared with the most commonly used PID controllers, fuzzy controllers, and PID fuzzy control strategies. The results demonstrate that the PSA-Optimized control strategy significantly outperforms the other three strategies. For a target of 25 °C, the PSA-Optimized control strategy shows a minimal temperature overshoot of 0.012 °C, with COP improvements of 0.06, 0.04, and 0.03 compared to the other three control strategies, respectively. For a target of 20 °C, the overshoot is further reduced to 0.010 °C, while the coefficient of performance (COP) increases by 0.14, 0.01, and 0.07 relative to the same benchmarks. Overall, the results indicate that the PSA-Optimized control strategy effectively utilizes sensor data to reduce cabin temperature overshoot, stabilize compressor speed fluctuations, slow the decay of the battery’s state of charge (SOC), and enhance the system’s COP.

Intrinsically disordered regions (IDRs), defined as protein segments lacking stable tertiary structures, are ubiquitously present in the human proteome and enriched with disease-associated mutations. IDRs harbor molecular recognition features (MoRFs) and post-translational modification sites (e.g., phosphorylation), enabling dynamic intermolecular interactions through conformational plasticity. Furthermore, IDRs drive liquid–liquid phase separation (LLPS) of biomacromolecules via multivalent interactions such as electrostatic attraction and pi–pi interactions, generating biomolecular condensates that are essential throughout the cellular lifecycle. These condensates separate intracellular space, forming a physical barrier to avoid interference between other molecules, thereby improving reaction specificity and efficiency. As a dynamically equilibrated process, LLPS formation and maintenance are regulated by multiple factors, endowing the condensates with rapid responsiveness to environmental cues and functional versatility in modulating diverse signaling cascades. Consequently, disruption of LLPS homeostasis can derail its associated biological processes, ultimately contributing to disease pathogenesis. Moreover, precisely because liquid–liquid phase separation (LLPS) is co-regulated by multiple factors, it may provide novel insights into the pathogenic mechanisms of disorders such as autism spectrum disorder (ASD), which result from the cumulative effects of multiple etiological factors.