Diet Affects the Temperature-Size Relationship in the Blowfly Aldrichina grahami.

In warmer environments, most ectotherms exhibit a plastic reduction in body size (the temperature-size rule, TSR). However, in such environments, growth is usually accelerated and would be expected to result in maturation at a larger body size, leading to increases in fecundity, survival, and mating success, compared to maturation at a smaller size (the 'life-history puzzle'). To explore these mechanisms, we reared Aldrichina grahami at 20 °C, 25 °C, and 30 °C, and added a nutritional challenge by using dilutions of pork liver paste to provide diets that ranged in quality from high (undiluted) to moderate (1/8), low (1/16), and poor (1/24). Larvae were randomly sampled for weighing from hatching. Growth curves were fitted to the relationships between growth rate and weight for the third instar larvae. Our results showed that body size was affected by an interaction between temperature and diet, and that following or not following the TSR can vary depending on underfeeding. Moreover, when the TSR was followed as temperature increased, there was a cross-over point that divided the two growth curves into early and later stages, which could be used to help understand the life-history puzzle in warmer temperatures, with the instantaneous growth rate being faster in the early stages of development and then slower in later stages. This study reminds us that animals have evolved to cope with multiple simultaneous environmental changes, and it has thus offered a better understanding of life-history puzzles.

Long-term saline water irrigation affected soil carbon and nitrogen cycling functional profiles in the cotton field.

Saline water irrigation (SWI) plays an important role in alleviating water resource shortages. At the same time, the salt input of irrigation water affects soil microorganisms which participate in various ecological processes of terrestrial ecosystems. However, the responses of soil microbial functional potential to long-term SWI remains unclear. Therefore, Metagenomics method was utilized in cotton fields under long-term SWI to reveal the microbial functional profiles associated with soil carbon and nitrogen cycles. Results indicated that SWI impacted the microbial functional profiles of soil carbon and nitrogen cycles in the cotton fields significantly. Especially, irrigation water salinity inhibited the relative abundances of sacC and vanB, which are soil carbon degradation genes. SWI also affected the functional gene abundance of nitrogen degradation, dissimilatory nitrate reduction, and nitrification. Moreover, SWI significantly increased the abundance of Candidatus_Cloacimonetes in both carbon and nitrogen cycles. In the discussion, we used person analysis found that soil salinity, pH, and ammonium nitrogen were important factors affecting the abundance of functional genes and microbial taxa. Overall, this study indicated that long-term SWI significantly influenced specific microbial functional genes and taxa abundance, which may lead to predictable outcomes for soil carbon and nitrogen cycling, and is of great importance in exploring the impact of SWI on soil environments.

Insufficient Sleep is Associated With Increasing Trends in Adolescent Suicidal Behaviors.

PURPOSE: Youth suicide has been increasing and became a public health concern worldwide. Identifying insufficient sleep as the potential risk factor is critical to reducing suicide risk and increasing trends. This study aimed to determine whether insufficient sleep is associated with increasing trends in suicidal behaviors and disparities by sex, age, and race/ethnicity among school adolescents. METHODS: The present study used biennial data from the US nationally representative Youth Risk Behavior Survey from 2007 to 2019. Joinpoint regression models were used to estimate biennial percent changes (BPCs) and average BPCs (ABPCs) of suicidal behaviors by sleep duration. Logistic regression models were used to examine the association between insufficient sleep and suicidal behaviors. RESULTS: Of 73,356 adolescent students included (mean [standard deviation] age, 16.11 [1.23] years), 50.03% were female. Suicidal ideation and suicide plan among insufficient sleep group increased from 2007 to 2019 (BPC = 2.88% [95% confidence interval {CI}: 1.65%, 4.13%]; BPC = 3.42% [95% CI: 2.09%, 4.77%]), but were nonsignificant among sufficient sleep group. Trends in suicidal ideation (ABPC = 3.03% [95% CI: 1.35%, 4.73%]) and suicide plan (ABPC = 4.03% [95% CI: 2.47%, 5.62%]) among female adolescents with insufficient sleep increased, but nonsignificant among male adolescents with insufficient sleep. Suicidal ideation (ABPC = 1.73% [95% CI: 0.51%, 2.97%]) and suicide plan (ABPC = 2.31% [95% CI: 0.70%, 3.95%]) increased among younger adolescents only with insufficient sleep, whereas suicide trends by sleep duration were similar among older adolescents. Suicide plan among insufficient sleep group increased across the four racial groups, with BPC highest for the White (BPC = 3.48% [95% CI: 1.31%, 5.69%]), and lowest for the Hispanic/Latino (BPC = 1.18% [95% CI: 0.15%, 2.23%]), but were nonsignificant among sufficient sleep group except for the White (BPC = 2.83% [95% CI: 0.62%, 5.09%]). DISCUSSION: Insufficient sleep was disproportionately associated with increasing trends in suicidal behaviors among female, younger, and non-White adolescent students. Ensuring sufficient sleep can potentially reduce suicide among school adolescents.

Virtual Reality Utilized for Safety Skills Training for Autistic Individuals: A Review.

In recent years, virtual reality technology, which is able to simulate real-life environments, has been widely used in the field of intervention for individuals with autism and has demonstrated distinct advantages. This review aimed to evaluate the impact of virtual reality technology on safety skills intervention for individuals with autism. After searching and screening three databases, a total of 20 pertinent articles were included. There were six articles dedicated to the VR training of street-crossing skills for individuals with autism, nine articles focusing on the training of driving skills for individuals with ASD, and three studies examining the training of bus riding for individuals with ASD. Furthermore, there were two studies on the training of air travel skills for individuals with ASD. First, we found that training in some complex skills (e.g., driving skills) should be selected for older, high-functioning individuals with ASD, to determine their capacity to participate in the training using scales or questionnaires before the intervention; VR devices with higher levels of immersion are not suitable for younger individuals with ASD. Second, VR is effective in training safety skills for ASD, but there is not enough evidence to determine the relationship between the level of VR immersion and intervention effects. Although the degree of virtual reality involvement has an impact on the ability of ASD to be generalized to the real world, it is important to ensure that future virtual reality settings are realistic and lifelike. Again, adaptive models that provide personalized training to individuals with ASD in VR environments are very promising, and future research should continue in this direction. This paper also discusses the limitations of these studies, as well as potential future research directions.

Long-term saline water irrigation has the potential to balance greenhouse gas emissions and cotton yield in North China plain.

Saline water has proven to be one of the alternative sources of freshwater for agricultural irrigation in water-scarce areas. However, the changes in farmland ecology caused by saline water irrigation remain unclear. In this study, six irrigation water salinities (CK: 1.3 dS m-1, S1: 3.4 dS m-1, S2: 7.1 dS m-1, S3: 10.6 dS m-1, S4: 14.1 dS m-1, S5: 17.7 dS m-1) were set in a three-year (2019, 2021-2022) experiment to investigate their effects on soil environment and greenhouse gas emissions in cotton fields under long-term saline water irrigation. Results show that soil salinity in the same layer increased as increasing water salinity. Soil moisture of S3-S5 increased significantly by 4.99-12.94%. There was no significant difference in soil organic matter content between CK and S1. Saline water irrigation increased soil ammonium nitrogen content by 0.57-49.26%, while decreasing nitrate nitrogen content by 1.43-32.03%. Soil CO2 and N2O emissions and CH4 uptake were lower in S1-S5 than in CK at different cotton growth stages. In addition, saline water irrigation reduced the global warming potential by 6.93-53.86%. A structural equation model was developed to show that soil salinity, moisture, and ammonium nitrogen content were negatively correlated with global warming potential, while organic matter and nitrate nitrogen had positive effects on global warming potential. Considering the comprehensive perspectives of gas emissions and cotton yield, irrigation water with salinity less than 10.6 dS m-1 could effectively reduce greenhouse gas emissions from cotton fields while maintaining stable cotton yields in the experimental area and similar region.

Neodb: a comprehensive neoantigen database and discovery platform for cancer immunotherapy.

Neoantigens derived from somatic deoxyribonucleic acid alterations are ideal cancer-specific targets. However, integrated platform for neoantigen discovery is urgently needed. Recently, many scattered experimental evidences suggest that some neoantigens are immunogenic, and comprehensive collection of these experimentally validated neoantigens is still lacking. Here, we have integrated the commonly used tools in the current neoantigen discovery process to form a comprehensive web-based analysis platform. To identify experimental evidences supporting the immunogenicity of neoantigens, we performed comprehensive literature search and constructed the database. The collection of public neoantigens was obtained by using comprehensive features to filter the potential neoantigens from recurrent driver mutations. Importantly, we constructed a graph neural network (GNN) model (Immuno-GNN) using an attention mechanism to consider the spatial interactions between human leukocyte antigen and antigenic peptides for neoantigen immunogenicity prediction. The new easy-to-use R/Shiny web-based neoantigen database and discovery platform, Neodb, contains currently the largest number of experimentally validated neoantigens. In addition to validated neoantigen, Neodb also includes three additional modules for facilitating neoantigen prediction and analysis, including 'Tools' module (comprehensive neoantigen prediction tools); 'Driver-Neo' module (collection of public neoantigens derived from recurrent mutations) and 'Immuno-GNN' module (a novel immunogenicity prediction tool based on a GNN). Immuno-GNN shows improved performance compared with known methods and also represents the first application of GNN model in neoantigen immunogenicity prediction. The construction of Neodb will facilitate the study of neoantigen immunogenicity and the clinical application of neoantigen-based cancer immunotherapy. Database URL https://liuxslab.com/Neodb/.

Copy number alteration features in pan-cancer homologous recombination deficiency prediction and biology.

Homologous recombination deficiency (HRD) renders cancer cells vulnerable to unrepaired double-strand breaks and is an important therapeutic target as exemplified by the clinical efficacy of poly ADP-ribose polymerase (PARP) inhibitors as well as the platinum chemotherapy drugs applied to HRD patients. However, it remains a challenge to predict HRD status precisely and economically. Copy number alteration (CNA), as a pervasive trait of human cancers, can be extracted from a variety of data sources, including whole genome sequencing (WGS), SNP array, and panel sequencing, and thus can be easily applied clinically. Here we systematically evaluate the predictive performance of various CNA features and signatures in HRD prediction and build a gradient boosting machine model (HRDCNA) for pan-cancer HRD prediction based on these CNA features. CNA features BP10MB[1] (The number of breakpoints per 10MB of DNA is 1) and SS[ > 7 & <=8] (The log10-based size of segments is greater than 7 and less than or equal to 8) are identified as the most important features in HRD prediction. HRDCNA suggests the biallelic inactivation of BRCA1, BRCA2, PALB2, RAD51C, RAD51D, and BARD1 as the major genetic basis for human HRD, and may also be applied to effectively validate the pathogenicity of BRCA1/2 variants of uncertain significance (VUS). Together, this study provides a robust tool for cost-effective HRD prediction and also demonstrates the applicability of CNA features and signatures in cancer precision medicine.

Accurate prediction of pan-cancer types using machine learning with minimal number of DNA methylation sites.

DNA methylation analysis has been applied to determine the primary site of cancer; however, robust and accurate prediction of cancer types with a minimum number of sites is still a significant scientific challenge. To build an accurate and robust cancer type prediction tool with a minimum number of DNA methylation sites, we internally benchmarked different DNA methylation site selection and ranking procedures, as well as different classification models. We used The Cancer Genome Atlas dataset (26 cancer types with 8296 samples) to train and test models and used an independent dataset (17 cancer types with 2738 samples) for model validation. A deep neural network model using a combined feature selection procedure (named MethyDeep) can predict 26 cancer types using 30 methylation sites with superior performance compared with the known methods for both primary and metastatic cancers in independent validation datasets. In conclusion, MethyDeep is an accurate and robust cancer type predictor with the minimum number of DNA methylation sites; it could help the cost-effective clarification of cancer of unknown primary patients and the liquid biopsy-based early screening of cancers.

TLimmuno2: predicting MHC class II antigen immunogenicity through transfer learning.

Major histocompatibility complex (MHC) class II molecules play a pivotal role in antigen presentation and CD4+ T cell response. Accurate prediction of the immunogenicity of MHC class II-associated antigens is critical for vaccine design and cancer immunotherapies. However, current computational methods are limited by insufficient training data and algorithmic constraints, and the rules that govern which peptides are truly recognized by existing T cell receptors remain poorly understood. Here, we build a transfer learning-based, long short-term memory model named 'TLimmuno2' to predict whether epitope-MHC class II complex can elicit T cell response. Through leveraging binding affinity data, TLimmuno2 shows superior performance compared with existing models on independent validation datasets. TLimmuno2 can find real immunogenic neoantigen in real-world cancer immunotherapy data. The identification of significant MHC class II neoantigen-mediated immunoediting signal in the cancer genome atlas pan-cancer dataset further suggests the robustness of TLimmuno2 in identifying really immunogenic neoantigens that are undergoing negative selection during cancer evolution. Overall, TLimmuno2 is a powerful tool for the immunogenicity prediction of MHC class II presented epitopes and could promote the development of personalized immunotherapies.

The repertoire of copy number alteration signatures in human cancer.

Copy number alterations (CNAs) are a predominant source of genetic alterations in human cancer and play an important role in cancer progression. However comprehensive understanding of the mutational processes and signatures of CNA is still lacking. Here we developed a mechanism-agnostic method to categorize CNA based on various fragment properties, which reflect the consequences of mutagenic processes and can be extracted from different types of data, including whole genome sequencing (WGS) and single nucleotide polymorphism (SNP) array. The 14 signatures of CNA have been extracted from 2778 pan-cancer analysis of whole genomes WGS samples, and further validated with 10 851 the cancer genome atlas SNP array dataset. Novel patterns of CNA have been revealed through this study. The activities of some CNA signatures consistently predict cancer patients' prognosis. This study provides a repertoire for understanding the signatures of CNA in cancer, with potential implications for cancer prognosis, evolution and etiology.

Early Onset of Impairments of Interpersonal Motor Synchrony in Preschool-Aged Children with Autism Spectrum Disorder.

Characteristics of interpersonal motor synchrony in individuals with autism spectrum disorder (ASD) have been investigated only in older children and adolescents, which calls for investigations in younger samples. The interpersonal motor synchrony was compared between preschool-aged children with (n = 23) and without ASD (n = 24) during free plays with familiar teachers. Children with ASD exhibited reduced synchrony of the upper body and trunk compared with typically developing (TD) children. Moreover, the degree of synchrony in ASD group was not above than chance. For autistic children, interpersonal motor synchrony was negatively correlated with aspects of autistic traits. The results suggest that the impairment of interpersonal motor synchrony has an onset earlier than school age and is a potential pathway for understanding autistic traits.

Can human-machine feedback in a smart learning environment enhance learners' learning performance? A meta-analysis.

Objective: The human-machine feedback in a smart learning environment can influences learners' learning styles, ability enhancement, and affective interactions. However, whether it has stability in improving learning performance and learning processes, the findings of many empirical studies are controversial. This study aimed to analyze the effect of human-machine feedback on learning performance and the potential boundary conditions that produce the effect in a smart learning environment. Methods: Web of Science, EBSCO, PsycINFO, and Science Direct were searched for publications from 2010 to 2022. We included randomized controlled trials with learning performance as outcome. The random effects model was used in the meta-analysis. The main effect tests and the heterogeneity tests were used to evaluate the effect of human-machine feedback mechanism on learning performance, and the boundary conditions of the effect were tested by moderating effects. Moreover, the validity of the meta-analysis was proved by publication bias test. Results: Out of 35 articles identified, 2,222 participants were included in this study. Human-machine interaction feedback had significant effects on learners' learning process (d = 0.594, k = 26) and learning outcomes (d = 0.407, k = 42). Also, the positive effects of human-machine interaction feedback were regulated by the direction of feedback, the form of feedback, and the type of feedback technique. Conclusion: To enhance learning performance through human-machine interactive feedback, we should focus on using two-way and multi-subject feedback. The technology that can provide emotional feedback and feedback loops should be used as a priority. Also, pay attention to the feedback process and mechanism, avoid increasing students' dependence on machines, and strengthen learners' subjectivity from feedback mechanism.

Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction.

Neoantigens derived from somatic DNA alterations are ideal cancer-specific targets. In recent years, the combination therapy of PD-1/PD-L1 blockers and neoantigen vaccines has shown clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations result in immunogenicity among cancer cells and efficient tools to predict the immunogenicity of neoepitopes are still urgently needed. Here, we present the Seq2Neo pipeline, which provides a one-stop solution for neoepitope feature prediction using raw sequencing data. Neoantigens derived from different types of genome DNA alterations, including point mutations, insertion deletions and gene fusions, are all supported. Importantly, a convolutional neural network (CNN)-based model was trained to predict the immunogenicity of neoepitopes and this model showed an improved performance compared to the currently available tools in immunogenicity prediction using independent datasets. We anticipate that the Seq2Neo pipeline could become a useful tool in the prediction of neoantigen immunogenicity and cancer immunotherapy. Seq2Neo is open-source software under an academic free license (AFL) v3.0 and is freely available at Github.

Eye Tracking Study of Social Intensity on Social Orientation of Autistic Children.

Some previous studies indicate that impaired social attention mainly results in social disorders in autistic children. In the social attention mode of autistic children, social orientation and joint attention are particularly important. The influence of different social intensity and ecological validity on them are worthy of further study. This study used realistic paintings with moderate ecological validity as experimental materials, to design isolated individual scene and social interaction scene, and to explore the impact of social interaction on the social orientation of autistic children. It found that in the scenes without social interaction, the attention patterns of autistic children and typical developing children were the same, while the attention patterns of autistic children were abnormal in the scenes with social interaction. From the eye tracking data, it was shown that the gaze processing process of autistic children was not as smooth as that of typical developing children. Compared with cartoons and other social scenes with low ecological validity, realistic painting could better restore the proportion of real scenes. Moreover, it could reduce the complexity of information which could not be done in real scenes. The findings of this study provide support for training and education of autistic children. Intervention with realistic paintings is conducive to the migration of autistic children.

Different Responses in Root Water Uptake of Summer Maize to Planting Density and Nitrogen Fertilization.

Modifying farming practices combined with breeding has the potential to improve water and nutrient use efficiency by regulating root growth, but achieving this goal requires phenotyping the roots, including their architecture and ability to take up water and nutrients from different soil layers. This is challenging due to the difficulty of in situ root measurement and opaqueness of the soil. Using stable isotopes and soil coring, we calculated the change in root water uptake of summer maize in response to planting density and nitrogen fertilization in a 2-year field experiment. We periodically measured root-length density, soil moisture content, and stable isotopes δ18O and δD in the plant stem, soil water, and precipitation concurrently and calculated the root water uptake based on the mass balance of the isotopes and the Bayesian inference method coupled with the Markov Chain Monte Carlo simulation. The results show that the root water uptake increased asymptotically with root-length density and that nitrogen application affected the locations in soil from which the roots acquired water more significantly than planting density. In particular, we find that reducing nitrogen application promoted root penetration to access subsoil nutrients and consequently enhanced their water uptake from the subsoil, while increasing planting density benefited water uptake of the roots in the topsoil. These findings reveal that it is possible to manipulate plant density and fertilization to improve water and nutrient use efficiency of the summer maize and the results thus have imperative implications for agricultural production.

The mediating role of gaze patterns in the association of child sleep disturbances and core symptoms of autism spectrum disorder.

Children with autism spectrum disorder (ASD) are at high risk for sleep disturbances, but the mechanism underlying the association between sleep disturbances and ASD core symptoms is largely unknown. This study examined the relationship between sleep disturbances and ASD core symptoms, and the mediating role of gaze patterns during the facial emotion recognition (FER) task. The study included 57 children with ASD and 59 age- and intelligence-matched typically developing (TD) controls aged 3-7 years. Parents reported their children's sleep disturbances and ASD core symptoms using the Children's Sleep Habits Questionnaire (CSHQ) and Social Communication Questionnaire (SCQ). Children's gaze patterns during the FER task were recorded by an eye tracking method. We found (1) ASD children had more severe sleep disturbances than TD children; (2) ASD children had atypical gaze patterns and poor FER task performance as determined by lower accuracy and longer reaction time; (3) sleep disturbances were significantly associated with ASD core symptoms of social interaction, communication, and restricted, repetitive and stereotyped patterns of behavior; and (4) atypical gaze patterns partially mediated the association between sleep disturbances and ASD core symptoms. These findings suggest the need for more comprehensive clinical interventions and more effective sleep interventions to improve ASD core symptoms. LAY SUMMARY: Sleep disturbances are very common in children with autism spectrum disorder (ASD). The current study found that sleep disturbances were significantly associated with ASD core symptoms, and gaze patterns during facial emotion recognition task could partially mediate this relationship.

Quantification of Neoantigen-Mediated Immunoediting in Cancer Evolution.

Immunoediting includes three temporally distinct stages, termed elimination, equilibrium, and escape, and has been proposed to explain the interactions between cancer cells and the immune system during the evolution of cancer. However, the status of immunoediting in cancer remains unclear, and the existence of neoantigen depletion in untreated cancer has been debated. Here we developed a distribution pattern-based method for quantifying neoantigen-mediated negative selection in cancer evolution. The method can provide a robust and reliable quantification for immunoediting signal in individual patients with cancer. Moreover, this method demonstrated the prevalence of immunoediting in the immunotherapy-naive cancer genome. The elimination and escape stages of immunoediting can be quantified separately, where tumor types with strong immunoediting-elimination exhibit a weak immunoediting-escape signal, and vice versa. The quantified immunoediting-elimination signal was predictive of clinical response to cancer immunotherapy. Collectively, immunoediting quantification provides an evolutionary perspective for evaluating the antigenicity of neoantigens and reveals a potential biomarker for precision immunotherapy in cancer. SIGNIFICANCE: Quantification of neoantigen-mediated negative selection in cancer progression reveals distinct features of cancer immunoediting and can serve as a potential biomarker to predict immunotherapy response.

Application of Machine Learning Techniques to Detect the Children with Autism Spectrum Disorder.

Early detection of autism spectrum disorder (ASD) is highly beneficial to the health sustainability of children. Existing detection methods depend on the assessment of experts, which are subjective and costly. In this study, we proposed a machine learning approach that fuses physiological data (electroencephalography, EEG) and behavioral data (eye fixation and facial expression) to detect children with ASD. Its implementation can improve detection efficiency and reduce costs. First, we used an innovative approach to extract features of eye fixation, facial expression, and EEG data. Then, a hybrid fusion approach based on a weighted naive Bayes algorithm was presented for multimodal data fusion with a classification accuracy of 87.50%. Results suggest that the machine learning classification approach in this study is effective for the early detection of ASD. Confusion matrices and graphs demonstrate that eye fixation, facial expression, and EEG have different discriminative powers for the detection of ASD and typically developing children, and EEG may be the most discriminative information. The physiological and behavioral data have important complementary characteristics. Thus, the machine learning approach proposed in this study, which combines the complementary information, can significantly improve classification accuracy.

Extrachromosomal DNA formation enables tumor immune escape potentially through regulating antigen presentation gene expression.

Extrachromosomal DNA (ecDNA) is a type of circular and tumor specific genetic element. EcDNA has been reported to display open chromatin structure, facilitate oncogene amplification and genetic material unequal segregation, and is associated with poor cancer patients' prognosis. The ability of immune evasion is a typical feature for cancer progression, however the tumor intrinsic factors that determine immune evasion remain poorly understood. Here we show that the presence of ecDNA is associated with markers of tumor immune evasion, and obtaining ecDNA could be one of the mechanisms employed by tumor cells to escape immune surveillance. Tumors with ecDNA usually have comparable TMB and neoantigen load, however they have lower immune cell infiltration and lower cytotoxic T cell activity. The microenvironment of tumors with ecDNA shows increased immune-depleted, decreased immune-enriched fibrotic types. Both MHC class I and class II antigen presentation genes' expression are decreased in tumors with ecDNA, and this could be the underlying mechanism for ecDNA associated immune evasion. This study provides evidence that ecDNA formation is an immune escape mechanism for cancer cells.

Eye Tracking Research on the Influence of Spatial Frequency and Inversion Effect on Facial Expression Processing in Children with Autism Spectrum Disorder.

Facial expression processing mainly depends on whether the facial features related to expressions can be fully acquired, and whether the appropriate processing strategies can be adopted according to different conditions. Children with autism spectrum disorder (ASD) have difficulty accurately recognizing facial expressions and responding appropriately, which is regarded as an important cause of their social disorders. This study used eye tracking technology to explore the internal processing mechanism of facial expressions in children with ASD under the influence of spatial frequency and inversion effects for improving their social disorders. The facial expression recognition rate and eye tracking characteristics of children with ASD and typical developing (TD) children on the facial area of interest were recorded and analyzed. The multi-factor mixed experiment results showed that the facial expression recognition rate of children with ASD under various conditions was significantly lower than that of TD children. TD children had more visual attention to the eyes area. However, children with ASD preferred the features of the mouth area, and lacked visual attention and processing of the eyes area. When the face was inverted, TD children had the inversion effect under all three spatial frequency conditions, which was manifested as a significant decrease in expression recognition rate. However, children with ASD only had the inversion effect under the LSF condition, indicating that they mainly used a featural processing method and had the capacity of configural processing under the LSF condition. The eye tracking results showed that when the face was inverted or facial feature information was weakened, both children with ASD and TD children would adjust their facial expression processing strategies accordingly, to increase the visual attention and information processing of their preferred areas. The fixation counts and fixation duration of TD children on the eyes area increased significantly, while the fixation duration of children with ASD on the mouth area increased significantly. The results of this study provided theoretical and practical support for facial expression intervention in children with ASD.