Air quality forecasting using a spatiotemporal hybrid deep learning model based on VMD-GAT-BiLSTM.

The precise forecasting of air quality is of great significance as an integral component of early warning systems. This remains a formidable challenge owing to the limited information of emission source and the considerable uncertainties inherent in dynamic processes. To improve the accuracy of air quality forecasting, this work proposes a new spatiotemporal hybrid deep learning model based on variational mode decomposition (VMD), graph attention networks (GAT) and bi-directional long short-term memory (BiLSTM), referred to as VMD-GAT-BiLSTM, for air quality forecasting. The proposed model initially employ a VMD to decompose original PM2.5 data into a series of relatively stable sub-sequences, thus reducing the influence of unknown factors on model prediction capabilities. For each sub-sequence, a GAT is then designed to explore deep spatial relationships among different monitoring stations. Next, a BiLSTM is utilized to learn the temporal features of each decomposed sub-sequence. Finally, the forecasting results of each decomposed sub-sequence are aggregated and summed as the final air quality prediction results. Experiment results on the collected Beijing air quality dataset show that the proposed model presents superior performance to other used methods on both short-term and long-term air quality forecasting tasks.

EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism.

EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy, while neglecting further research on the interpretability of models, we are committed to analyzing the impact of different brain regions and signal frequency bands on emotion generation based on graph structure. Therefore, this paper proposes a method named Dual Attention Mechanism Graph Convolutional Neural Network (DAMGCN). Specifically, we utilize graph convolutional neural networks to model the brain network as a graph to extract representative spatial features. Furthermore, we employ the self-attention mechanism of the Transformer model which allocates more electrode channel weights and signal frequency band weights to important brain regions and frequency bands. The visualization of attention mechanism clearly demonstrates the weight allocation learned by DAMGCN. During the performance evaluation of our model on the DEAP, SEED, and SEED-IV datasets, we achieved the best results on the SEED dataset, showing subject-dependent experiments' accuracy of 99.42% and subject-independent experiments' accuracy of 73.21%. The results are demonstrably superior to the accuracies of most existing models in the realm of EEG-based emotion recognition.

[Anxiety disorders as comorbid conditions in neuropsychiatric diseases in children]. / Trevozhnye rasstroistva kak komorbidnye sostoyaniya pri psikhonevrologicheskikh zabolevaniyakh u detei.

The variants of heterotypic comorbidity of anxiety disorders (AD) with attention deficit hyperactivity disorder, autism spectrum disorders, speech and language development disorders, specific learning disabilities (dyslexia, dysgraphia, dyscalculia), migraine, tension type headache in children and adolescents are discussed. In cases of heterotypic comorbidity the patients with AD referrals to specialists may be primarily associated with their emotional problems. Meanwhile, the comorbidity of AD with these diseases leads to a deterioration of their clinical manifestations and a worsening of the prognosis, and anxiety symptoms often not only persist, but also increase with age. It should be borne in mind that AD in children with neurodevelopmental disorders contribute to a decrease in the quality of life, academic failure, have a negative impact on peer relationships and the family environment, and in young adulthood, patients have an increased risk of depression and substance abuse. Therefore, early intervention and a comprehensive therapeutic approach with a dynamic assessment of the patient's condition are becoming important. When choosing pharmacotherapy, it is advisable to choose medictions that have a complex effect on the pathogenetic mechanisms of the underlying disease and concomitant AD, which include Tenoten for children.

An analysis of prescribing data in attention-deficit hyperactivity disorder for adolescents and adults in Scotland.

BACKGROUND: Medication, combined with environmental and psychosocial support, can mitigate adverse outcomes in attention-deficit hyperactivity disorder (ADHD). There is a need for research into regional and national prescription volumes and patterns, especially among adults. AIMS: This study analysed prescribing patterns for medications commonly used to treat ADHD in adolescents and adults. METHOD: Data was extracted from the NHS Scotland Prescribing Information System on prescriptions for 7806 adolescents (aged 10-19 years) and 4998 adults (aged 20-59 years) in 2019. This included medications listed under Section 4.4 of the British National Formulary. We explored 2019 prescription patterns across different regions and estimated ADHD prevalence levels. Additionally, we assessed changes in dispensed prescriptions, defined daily dose and costs, compared with figures from 2010. RESULTS: Between 2010 and 2019, prescriptions for ADHD medications increased (dispensed prescriptions +233.2%, defined daily dose +234.9%, cost +216.6%). Despite these increases, analysis indicated that in 2019, considering a 5% estimated ADHD prevalence among adolescents, 73% were not prescribed medication, increasing to 81% at a 7% estimated prevalence. Similarly, among adults with a 2% estimated prevalence, 91% were not prescribed medication, rising to 96% at a 4% estimated prevalence. Regional disparities were evident, with 41-96% of adolescents and 85-100% of adults, based on ADHD prevalence estimates, not receiving a prescription, depending on area. CONCLUSIONS: Although prescription rates for ADHD medication have increased over time, the data do not indicate excessive use of medication. Instead, they suggest that for some groups there is a lower use of medication compared with expected prevalence figures, especially among adults.

Clonal hematopoiesis of indeterminate potential is associated with reduced risk of cognitive impairment in patients with chronic kidney disease.

INTRODUCTION: Clonal hematopoiesis of indeterminate potential (CHIP) and dementia disproportionately burden patients with chronic kidney disease (CKD). The association between CHIP and cognitive impairment in CKD patients is unknown. METHODS: We conducted time-to-event analyses in up to 1452 older adults with CKD from the Chronic Renal Insufficiency Cohort who underwent CHIP gene sequencing. Cognition was assessed using four validated tests in up to 6 years mean follow-up time. Incident cognitive impairment was defined as a test score one standard deviation below the baseline mean. RESULTS: Compared to non-carriers, CHIP carriers were markedly less likely to experience impairment in attention (adjusted hazard ratio [HR] [95% confidence interval {CI}] = 0.44 [0.26, 0.76], p = 0.003) and executive function (adjusted HR [95% CI] = 0.60 [0.37, 0.97], p = 0.04). There were no significant associations between CHIP and impairment in global cognition or verbal memory. DISCUSSION: CHIP was associated with lower risks of impairment in attention and executive function among CKD patients. HIGHLIGHTS: Our study is the first to examine the role of CHIP in cognitive decline in CKD. CHIP markedly decreased the risk of impairment in attention and executive function. CHIP was not associated with impairment in global cognition or verbal memory.

Prevalence of Seizures in Children Diagnosed With Neurodevelopmental Disorders.

Introduction Neurodevelopmental disorders (NDDs) typically emerge in early childhood and have a profound impact on the development of the nervous system, leading to various neurological challenges in cognition, communication, social interaction, motor skills, and behavior. These disorders arise from disruptions in brain development mechanisms. NDDs include conditions such as cerebral palsy (CP), global developmental delay (GDD), intellectual disability (ID), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), with ADHD and ASD being the most prevalent. However, there is a lack of comprehensive research on the causes of NDDs in children receiving care at tertiary hospitals in Saudi Arabia. Therefore, in this study, we aim to investigate the characteristics of patients with NDDs and explore the association between NDDs and seizures. It also focuses on identifying specific risk factors that may influence the relationship between NDDs and seizures. Methods We conducted a retrospective cross-sectional study at the pediatric neurology and developmental assessment clinic of King Abdulaziz University Hospital in Jeddah, Saudi Arabia. The study involved a review of electronic medical records from January 2021 to May 2023 for 200 pediatric patients who attended the clinic for NDD and seizures. Descriptive statistics summarized the data, using frequencies and percentages for categorical variables, and mean ± standard deviation for quantitative variables. The chi-square test identified differences between qualitative variables, with a significance threshold of p < 0.05. Results The study sample comprised 200 children ranging in age from one month to 14 years, with the majority of patients being from Jeddah city. Participants were categorized into four age groups: 17.0% (n=34) were aged between one month and three years, 18.5% (n=37) were aged between three and six years, 55.0% (n=110) were aged between six and 12 years old, and 9.5% (n=19) were aged between 12 and 14 years. The NDD subtypes identified were ASD 9.5%, ADHD 16.0%, CP 8.5%, GDD 30.5%, ID 5.5%, and 30% had multiple types of NDD. Generalized tonic-clonic seizures were the most common type observed. Conclusion Children with NDDs exhibit a high prevalence of seizures, with the age of the patient and consanguinity emerging as significant influencing factors in this correlation. Among the key findings is an emphasis on the importance of early detection and intervention for children with NDDs at higher risk of developing seizures. Overall, the study sheds light on the characteristics of NDD patients and their association with seizures, contributing to a better understanding of the complex relationship between NDDs and seizure occurrence. It also emphasizes the need for comprehensive assessment and management strategies that consider seizures in children with NDDs.

Spatio-temporal variation and influencing factors of aero-sports tourism internet attention in Shaanxi Province, China.

Despite China's building into a leading sporting nation and sport-tourism integration high-quality development strategy in the 20th National Congress of the Communist Party of China, existing tourism studies seldom concern sport-tourism integration, especially their spatial hot spots and evolutional trend based on geospatial big data. This study aims to probe into the spatiality and the underlying mechanism of sport tourism through internet attention data in 2015, 2018, and 2021 by social network analysis, with a specific focus on aero-sports tourism. Shaanxi Province is chosen as the study site given its advantages of rich aero-sports tourism resources and various aero-sports modes (e.g., sky diving, paragliding, etc.). The results are concluded: (1) At the provincial scale, the aero-sports tourism internet attention shows a pattern of "strong in the middle and weak in the north and south". (2) At the regional scale, the sub-group clusters within the three specific regions (Shanbei, Guanzhong, and Shannan) of Shaanxi Province turn to be inter-regional clusters. Guanzhong region, especially with Xi'an as the core, is dominant in connecting its peripheral area. Since 2016, the radiation effects of the Guanzhong Region have shown a homogeneous trend of yearly growth and effect strengthening, yet become loosely connected with a heterogeneous trend in 2021 due to the COVID-19 epidemic. (3) At the city scale, the core area of aero-sports tourism internet attention expanded from Xi'an and Xianyang to Yulin and Baoji from 2015 to 2021, resulting from urban economic strength and aviation flight camp club development. (4) The number of general aviation manufacturers, tourist attendance, and tourism revenue significantly affect aero-sports tourism internet attention.

Altered Functional Connectivity of the Thalamus Subregions Associated with Impaired Attention After Sleep Deprivation.

Objective: The thalamus plays a critical role in attentional maintenance. Previous studies have revealed the dysfunction of the thalamus in attention decline after acute sleep deprivation (SD). However, the functional connectivity (FC) between the thalamus subregions and cortical regions underlying attentional impairment after acute SD remains unclear. Here, we aimed to probe the relationship between attentional function and the altered thalamocortical FC after acute SD. Methods: In this study, 25 healthy participants with regular sleep conducted an attentional network test and received a resting-state fMRI scan before and after 24 hours of SD. Then, we analyzed the FC between the thalamus and cerebrum and relationships with attentional function in the enrolled subjects. Results: Our results showed that the participants showed a significantly lower alerting effect, a higher executive effect, and lower accuracy after acute SD. Compared to the rested wakefulness state, we observed decreased FCs between the "somatosensory" thalamic seed and left frontal pole, right frontal pole, left middle temporal gyrus (posterior division), and right middle temporal gyrus (posterior division). Furthermore, the reduced FC between the right middle temporal gyrus and "somatosensory" thalamic seed was negatively associated with the change in orienting effect of the participants. Conclusion: Our findings reveal that the disrupted FC between thalamus subregions and cortical regions may contribute to impaired attention after SD.

An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design.

BACKGROUND: Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors-based FDSs have become the mainstream of research, which can be categorized into threshold-based FDSs using experience, machine learning-based FDSs using manual feature extraction, and deep learning (DL)-based FDSs using automatic feature extraction. However, most FDSs focus on the global information of sensor data, neglecting the fact that different segments of the data contribute variably to fall detection. This shortcoming makes it challenging for FDSs to accurately distinguish between similar human motion patterns of actual falls and fall-like actions, leading to a decrease in detection accuracy. OBJECTIVE: This study aims to develop and validate a DL framework to accurately detect falls using acceleration and gyroscope data from wearable sensors. We aim to explore the essential contributing features extracted from sensor data to distinguish falls from activities of daily life. The significance of this study lies in reforming the FDS by designing a weighted feature representation using DL methods to effectively differentiate between fall events and fall-like activities. METHODS: Based on the 3-axis acceleration and gyroscope data, we proposed a new DL architecture, the dual-stream convolutional neural network self-attention (DSCS) model. Unlike previous studies, the used architecture can extract global feature information from acceleration and gyroscope data. Additionally, we incorporated a self-attention module to assign different weights to the original feature vector, enabling the model to learn the contribution effect of the sensor data and enhance classification accuracy. The proposed model was trained and tested on 2 public data sets: SisFall and MobiFall. In addition, 10 participants were recruited to carry out practical validation of the DSCS model. A total of 1700 trials were performed to test the generalization ability of the model. RESULTS: The fall detection accuracy of the DSCS model was 99.32% (recall=99.15%; precision=98.58%) and 99.65% (recall=100%; precision=98.39%) on the test sets of SisFall and MobiFall, respectively. In the ablation experiment, we compared the DSCS model with state-of-the-art machine learning and DL models. On the SisFall data set, the DSCS model achieved the second-best accuracy; on the MobiFall data set, the DSCS model achieved the best accuracy, recall, and precision. In practical validation, the accuracy of the DSCS model was 96.41% (recall=95.12%; specificity=97.55%). CONCLUSIONS: This study demonstrates that the DSCS model can significantly improve the accuracy of fall detection on 2 publicly available data sets and performs robustly in practical validation.

DEAF-Net: Detail-Enhanced Attention Feature Fusion Network for Retinal Vessel Segmentation.

Retinal vessel segmentation is crucial for the diagnosis of ophthalmic and cardiovascular diseases. However, retinal vessels are densely and irregularly distributed, with many capillaries blending into the background, and exhibit low contrast. Moreover, the encoder-decoder-based network for retinal vessel segmentation suffers from irreversible loss of detailed features due to multiple encoding and decoding, leading to incorrect segmentation of the vessels. Meanwhile, the single-dimensional attention mechanisms possess limitations, neglecting the importance of multidimensional features. To solve these issues, in this paper, we propose a detail-enhanced attention feature fusion network (DEAF-Net) for retinal vessel segmentation. First, the detail-enhanced residual block (DERB) module is proposed to strengthen the capacity for detailed representation, ensuring that intricate features are efficiently maintained during the segmentation of delicate vessels. Second, the multidimensional collaborative attention encoder (MCAE) module is proposed to optimize the extraction of multidimensional information. Then, the dynamic decoder (DYD) module is introduced to preserve spatial information during the decoding process and reduce the information loss caused by upsampling operations. Finally, the proposed detail-enhanced feature fusion (DEFF) module composed of DERB, MCAE and DYD modules fuses feature maps from both encoding and decoding and achieves effective aggregation of multi-scale contextual information. The experiments conducted on the datasets of DRIVE, CHASEDB1, and STARE, achieving Sen of 0.8305, 0.8784, and 0.8654, and AUC of 0.9886, 0.9913, and 0.9911 on DRIVE, CHASEDB1, and STARE, respectively, demonstrate the performance of our proposed network, particularly in the segmentation of fine retinal vessels.

A Kernel Attention-based Transformer Model for Survival Prediction of Heart Disease Patients.

Survival analysis is employed to scrutinize time-to-event data, with emphasis on comprehending the duration until the occurrence of a specific event. In this article, we introduce two novel survival prediction models: CosAttnSurv and CosAttnSurv + DyACT. CosAttnSurv model leverages transformer-based architecture and a softmax-free kernel attention mechanism for survival prediction. Our second model, CosAttnSurv + DyACT, enhances CosAttnSurv with Dynamic Adaptive Computation Time (DyACT) control, optimizing computation efficiency. The proposed models are validated using two public clinical datasets related to heart disease patients. When compared to other state-of-the-art models, our models demonstrated an enhanced discriminative and calibration performance. Furthermore, in comparison to other transformer architecture-based models, our proposed models demonstrate comparable performance while exhibiting significant reduction in both time and memory requirements. Overall, our models offer significant advancements in the field of survival analysis and emphasize the importance of computationally effective time-based predictions, with promising implications for medical decision-making and patient care.

Neural evidence of core foundations and conceptual change in preschool numeracy.

Symbolic numeracy first emerges as children learn the meanings of number words and how to use them to precisely count sets of objects. This development starts before children enter school and forms a foundation for lifelong mathematics achievement. Despite its importance, exactly how children acquire this basic knowledge is unclear. Here we test competing theories of early number learning by measuring event-related brain potentials during a novel number word-quantity comparison task in 3-4-year-old preschool children (N = 128). We find several qualitative differences in neural processing of number by conceptual stage of development. Specifically, we find differences in early attention-related parietal electrophysiology (N1), suggesting that less conceptually advanced children process arrays as individual objects and more advanced children distribute attention over the entire set. Subsequently, we find that only more conceptually advanced children show later-going frontal (N2) sensitivity to the numerical-distance relationship between the number word and visual quantity. The nature of this response suggested that exact rather than approximate numerical meanings were being associated with number words over frontal sites. No evidence of numerical distance effects was observed over posterior scalp sites. Together these results suggest that children may engage parallel individuation of objects to learn the meanings of the first few number words, but, ultimately, create new exact cardinal value representations for number words that cannot be defined in terms of core, nonverbal number systems. More broadly, these results document an interaction between attentional and general cognitive mechanisms in cognitive development. RESEARCH HIGHLIGHTS: Conceptual development in numeracy is associated with a shift in attention from objects to sets. Children acquire meanings of the first few number words through associations with parallel attentional individuation of objects. Understanding of cardinality is associated with attentional processing of sets rather than individuals. Brain signatures suggest children attribute exact rather than approximate numerical meanings to the first few number words. Number-quantity relationship processing for the first few number words is evident in frontal but not parietal scalp electrophysiology of young children.

IEA-Net: Internal and External Dual-Attention Medical Segmentation Network with High-Performance Convolutional Blocks.

Currently, deep learning is developing rapidly in the field of image segmentation, and medical image segmentation is one of the key applications in this field. Conventional CNN has achieved great success in general medical image segmentation tasks, but it has feature loss in the feature extraction part and lacks the ability to explicitly model remote dependencies, which makes it difficult to adapt to the task of human organ segmentation. Although methods containing attention mechanisms have made good progress in the field of semantic segmentation, most of the current attention mechanisms are limited to a single sample, while the number of samples of human organ images is large, ignoring the correlation between the samples is not conducive to image segmentation. In order to solve these problems, an internal and external dual-attention segmentation network (IEA-Net) is proposed in this paper, and the ICSwR (interleaved convolutional system with residual) module and the IEAM module are designed in this network. The ICSwR contains interleaved convolution and hopping connection, which are used for the initial extraction of the features in the encoder part. The IEAM module (internal and external dual-attention module) consists of the LGGW-SA (local-global Gaussian-weighted self-attention) module and the EA module, which are in a tandem structure. The LGGW-SA module focuses on learning local-global feature correlations within individual samples for efficient feature extraction. Meanwhile, the EA module is designed to capture inter-sample connections, addressing multi-sample complexities. Additionally, skip connections will be incorporated into each IEAM module within both the encoder and decoder to reduce feature loss. We tested our method on the Synapse multi-organ segmentation dataset and the ACDC cardiac segmentation dataset, and the experimental results show that the proposed method achieves better performance than other state-of-the-art methods.

Inter-subject gamma oscillation synchronization as a biomarker of abnormal processing of social interaction in ADHD.

Children with attention-deficit hyperactivity disorder (ADHD) have difficulties in social interactions. Studying brain activity during social interactions is difficult with conventional artificial stimuli. This pioneering study examined the neural correlates of social perception in children with ADHD and matched controls using naturalistic stimuli. We presented 20 children with ADHD and 20 age-and-sex-matched controls with tailored movies featuring high- or low-level social interactions while recording electroencephalographic signals. Both groups exhibited synchronized gamma-band oscillations, but controls demonstrated greater inter-subject correlations. Additionally, the difference in inter-subject correlations between high- and low-interaction movies was significantly larger in controls compared to ADHD patients. Between 55 and 75 Hz comparing viewing high interaction movies with low interaction moves, controls had a significantly larger weighting in the right parietal lobe, while ADHD patients had a significantly smaller weighting in the left occipital lobe. These findings reveal distinct spatiotemporal neural signatures in social interaction processing among children with ADHD and controls using naturalistic stimuli. These neural markers offer potential for group differentiation and assessing intervention efficacy, advancing our understanding ADHD-related social interaction mechanisms.

Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study.

BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric condition that typically emerges during childhood but often persists into adulthood, significantly impacting individuals' functioning, relationships, productivity, and overall quality of life. However, the current diagnostic process exhibits limitations that can significantly affect its overall effectiveness. Notably, its face-to-face and time-consuming nature, coupled with the reliance on subjective recall of historical information and clinician subjectivity, stand out as key challenges. To address these limitations, objective measures such as neuropsychological evaluations, imaging techniques and physiological monitoring of the Autonomic Nervous System functioning, have been explored. METHODS: The main aim of this study was to investigate whether physiological data (i.e., Electrodermal Activity, Heart Rate Variability, and Skin Temperature) can serve as meaningful indicators of ADHD, evaluating its utility in distinguishing adult ADHD patients. This observational, case-control study included a total of 76 adult participants (32 ADHD patients and 44 healthy controls) who underwent a series of Stroop tests, while their physiological data was passively collected using a multi-sensor wearable device. Univariate feature analysis was employed to identify the tests that triggered significant signal responses, while the Informative k-Nearest Neighbors (KNN) algorithm was used to filter out less informative data points. Finally, a machine-learning decision pipeline incorporating various classification algorithms, including Logistic Regression, KNN, Random Forests, and Support Vector Machines (SVM), was utilized for ADHD patient detection. RESULTS: Results indicate that the SVM-based model yielded the optimal performance, achieving 81.6% accuracy, maintaining a balance between the experimental and control groups, with sensitivity and specificity of 81.4% and 81.9%, respectively. Additionally, integration of data from all physiological signals yielded the best results, suggesting that each modality captures unique aspects of ADHD. CONCLUSIONS: This study underscores the potential of physiological signals as valuable diagnostic indicators of adult ADHD. For the first time, to the best of our knowledge, our findings demonstrate that multimodal physiological data collected via wearable devices can complement traditional diagnostic approaches. Further research is warranted to explore the clinical applications and long-term implications of utilizing physiological markers in ADHD diagnosis and management.

Clinical and genetic characteristics of a child with Sotos syndrome and attention-deficit/hyperactivity disorder: A case report.

BACKGROUND: Sotos syndrome is an autosomal dominant disorder, whereas attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition. This report aimed to summarize the clinical and genetic features of a pediatric case of Soros syndrome and ADHD in a child exhibiting precocious puberty. CASE SUMMARY: The patient presented with accelerated growth and advanced skeletal maturation; however, she lacked any distinct facial characteristics related to specific genetic disorders. Genetic analyses revealed a paternally inherited heterozygous synonymous mutation [c.4605C>T (p.Arg1535Arg)]. Functional analyses suggested that this mutation may disrupt splicing, and bioinformatics analyses predicted that this mutation was likely pathogenic. After an initial diagnosis of Sotos syndrome, the patient was diagnosed with ADHD during the follow-up period at the age of 8 years and 7 months. CONCLUSION: The potential for comorbid ADHD in Sotos syndrome patients should be considered to avoid the risk of a missed diagnosis.

Controllability in attention deficit hyperactivity disorder brains.

The role of network metrics in exploring brain networks of mental illness is crucial. This study focuses on quantifying a node controllability index (CA-scores) and developing a novel framework for studying the dysfunction of attention deficit hyperactivity disorder (ADHD) brains. By analyzing fMRI data from 143 healthy controls and 102 ADHD patients, the controllability metric reveals distinct differences in nodes (brain regions) and subsystems (functional modules). There are significantly atypical CA-scores in the Rolandic operculum, superior medial orbitofrontal cortex, insula, posterior cingulate gyrus, supramarginal gyrus, angular gyrus, precuneus, heschl gyrus, and superior temporal gyrus of ADHD patients. A comparison with measures of connection strength, eigenvector centrality, and topology entropy suggests that the controllability index may be more effective in identifying abnormal regions in ADHD brains. Furthermore, our controllability index could be extended to investigate functional networks associated with other psychiatric disorders. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-023-10063-z.

Memristive patch attention neural network for facial expression recognition and edge computing.

Facial expression recognition has made a significant progress as a result of the advent of more and more convolutional neural networks (CNN). However, with the improvement of CNN, the models continues to get deeper and larger so as to a greater focus on the high-level features of the image and the low-level features tend to be lost. Because of the reason above, the dependence of low-level features between different areas of the face often cannot be summarized. In response to this problem, we propose a novel network based on the CNN model. To extract long-range dependencies of low-level features, multiple attention mechanisms has been introduced into the network. In this paper, the patch attention mechanism is designed to obtain the dependence between low-level features of facial expressions firstly. After fusion, the feature maps are input to the backbone network incorporating convolutional block attention module (CBAM) to enhance the feature extraction ability and improve the accuracy of facial expression recognition, and achieve competitive results on three datasets CK+ (98.10%), JAFFE (95.12%) and FER2013 (73.50%). Further, according to the PA Net designed in this paper, a hardware friendly implementation scheme is designed based on memristor crossbars, which is expected to provide a software and hardware co-design scheme for edge computing of personal and wearable electronic products.

Exposure to anti-seizure medication during pregnancy and the risk of autism and ADHD in offspring: a systematic review and meta-analysis.

Background: Evidence of an association between maternal use of anti-seizure medication (ASM) during pregnancy and the risk of autism spectrum disorder (ASD) or attention-deficit/hyperactivity disorder (ADHD) in children is conflicting. This systematic review and meta-analysis aimed to summarize the relationship between fetal exposure to ASM and the development of ASD or ADHD in offspring. Methods: A comprehensive literature search was conducted in PubMed and other databases to identify relevant epidemiological studies published from inception until 1 March 2024. Results: Seven cohort studies were included in the meta-analysis. The results showed that maternal exposure to ASMs during pregnancy was associated with an increased risk of ASD [odds ratio (OR): 2.1, 95% confidence interval (CI): 1.63-2.71; p < 0.001] in the general population. This association became weaker (ASD: OR: 1.38, 95% CI: 1.11-1.73; p = 0.004) when the reference group was mothers with a psychiatric disorder or epilepsy not treated during pregnancy. Furthermore, an increased risk of ADHD was observed when the study data adjusted for drug indications were pooled (OR: 1.43, 95% CI: 1.07-1.92; p = 0.015). In subgroup analyses based on individual ASM use, only exposure to valproate preconception was significantly associated with an increased risk of ASD or ADHD. Conclusion: The significant association between maternal ASM use during pregnancy and ASD or ADHD in offspring may be partially explained by the drug indication or driven by valproate.

Transformer-based decoder of melanoma classification using hand-crafted texture feature fusion and Gray Wolf Optimization algorithm.

Melanoma is a type of skin cancer that poses significant health risks and requires early detection for effective treatment. This study proposing a novel approach that integrates a transformer-based model with hand-crafted texture features and Gray Wolf Optimization, aiming to enhance efficiency of melanoma classification. Preprocessing involves standardizing image dimensions and enhancing image quality through median filtering techniques. Texture features, including GLCM and LBP, are extracted to capture spatial patterns indicative of melanoma. The GWO algorithm is applied to select the most discriminative features. A transformer-based decoder is then employed for classification, leveraging attention mechanisms to capture contextual dependencies. The experimental validation on the HAM10000 dataset and ISIC2019 dataset showcases the effectiveness of the proposed methodology. The transformer-based model, integrated with hand-crafted texture features and guided by Gray Wolf Optimization, achieves outstanding results. The results showed that the proposed method performed well in melanoma detection tasks, achieving an accuracy and F1-score of 99.54% and 99.11% on the HAM10000 dataset, and an accuracy of 99.47%, and F1-score of 99.25% on the ISIC2019 dataset. • We use the concepts of LBP and GLCM to extract features from the skin lesion images. • The Gray Wolf Optimization (GWO) algorithm is employed for feature selection. • A decoder based on Transformers is utilized for melanoma classification.