Effects of mini-basketball training program on social communication impairments and regional homogeneity of brain functions in preschool children with autism spectrum disorder.

BACKGROUND: Social communication impairments (SCI) is a core symptom of autism spectrum disorder (ASD) and is marked by challenges in social interaction. Although physical exercise has been shown to improve SCI, this finding has not been supported by comprehensive scientific evidence. Existing research has established a strong link between the SCI in children with ASD and abnormalities in regional homogeneity (ReHo). Therefore, investigating the effects of physical exercise on SCI and Reho in patients with ASD may help to elucidate the neurological mechanisms involved. METHODS: The present study included 30 preschool children diagnosed with ASD, with 15 participants in each group (experimental and control). The experimental group underwent a 12-week mini-basketball training program (MBTP) based on routine behavioral rehabilitation, while the control group only received routine behavioral rehabilitation. The Social Responsiveness Scale-Second Edition (SRS-2) was employed to assess SCI in both groups. Resting-state functional magnetic resonance imaging technology was used to evaluate ReHo in both groups. RESULTS: After 12-week of MBTP, significant group × time interactions were observed between the experimental and control groups in total SRS-2 scores (F = 14.514, p < 0.001, ηp2 = 0.341), as well as in the domains of social cognition (F = 15.620, p < 0.001, ηp2 = 0.358), social communication (F = 12.460, p < 0.01, ηp2 = 0.308), and autistic mannerisms (F = 9.970, p < 0.01, ηp2 = 0.263). No statistical difference was found in the scores for the social awareness subscale and social motivation subscale in the group × time interaction (all p > 0.05). The experimental group exhibited increased ReHo in the right Cerebellum_Crus1 and right parahippocampal gyrus, coupled with decreased ReHo in the left middle frontal gyrus (orbital part), left superior frontal gyrus (dorsolateral), left postcentral gyrus, and right superior parietal gyrus. Furthermore, a decrease in ReHo in the left postcentral gyrus positively correlated with changes in social communication scores in SCI behaviors (p < 0.05). CONCLUSIONS: Our study underscores the effectiveness of a 12-week MBTP in ameliorating SCI and abnormalities in ReHo among preschool children with ASD. TRIAL REGISTRATION: The trial is retrospectively registered on the Chinese Clinical Trial Registry (ChiCTR1900024973; August 5, 2019).

The effects of football juggling learning on executive function and brain functional connectivity.

This study aimed to explore the relationship between motor skill learning and executive function (EF), with an emphasis on the potential effects of football juggling learning. A randomized controlled trial involving 111 participants aged 17-19 years was conducted. Participants were randomly assigned to either the football juggling learning (FJL) group or a control group. The FJL group underwent 70 sessions of football juggling learning, while the control group engaged in their normal daily activities without any exercise intervention during the same time frame. Both groups were assessed for EF performance and underwent functional magnetic resonance imaging (fMRI) scans before and after the experiment. The executive function test included three tasks, namely, inhibition, working memory, and shifting. The results showed significant improvement in inhibition and shifting in both groups, and the FJL group showed greater improvement in these aspects of EF compared to the control group. Additionally, in comparison to the control group, the FJL group exhibited increased functional connectivity within the frontal, temporal, and cerebellar regions from the pre-test to the post-test. Notably, enhanced functional connectivity between the right superior temporal gyrus (posterior division) and left cerebellum 6 was identified in the FJL group and was associated with improved EF performance induced by football juggling learning. These findings shed light on the potential causal relationship between motor skill learning, EF, and brain plasticity. Importantly, our study provides preliminary evidence supporting the use of motor skill learning, such as football juggling, as a potential avenue for cognitive enhancement.

Living, Heat-Killed Limosilactobacillus mucosae and Its Cell-Free Supernatant Differentially Regulate Colonic Serotonin Receptors and Immune Response in Experimental Colitis.

Lactobacillus species have been shown to alleviate gut inflammation and oxidative stress. However, the effect of different lactobacilli components on gut inflammation has not been well studied. This study aims to identify the differences in the effect and mechanisms of different forms and components of Limosilactobacillus mucosae (LM) treatment in the alleviation of gut inflammation using a colitis mouse model that is induced by dextran sodium sulfate (DSS). Seventy-two C57BL/6 mice were divided into six groups: control, DSS, live LM+DSS (LM+DSS), heat-killed LM+DSS (HKLM+DSS), LM cell-free supernatant + DSS (LMCS+DSS), and MRS medium + DSS (MRS+DSS). The mice were treated with different forms and components of LM for two weeks before DSS treatment. After that, the mice were sacrificed for an assessment of their levels of inflammatory cytokines, serotonin (5-HT) receptors (HTRs), and tryptophan metabolites. The results showed that, compared to other treatments, LMCS was more effective (p < 0.05) in the alleviation of DSS-induced body weight loss and led to an increase in the disease activity index score. All three forms and components of LM increased (p < 0.05) the levels of indole-3-acetic acid but reduced (p < 0.05) the levels of 5-HT in the colon. HKLM or LMCS reduced (p < 0.05) the percentages of CD3+CD8+ cytotoxic T cells but increased (p < 0.05) the percentages of CD3+CD4+ T helper cells in the spleen. LM or HKLM increased (p < 0.05) abundances of CD4+Foxp3+ regulatory T cells in the spleen. The LM and LMCS treatments reduced (p < 0.05) the expression of the pro-inflammatory cytokines Il6 and Il17a. The mice in the HKLM+DSS group had higher (p < 0.05) mRNA levels of the anti-inflammatory cytokine Il10, the cell differentiation and proliferation markers Lgr5 and Ki67, the 5-HT degradation enzyme Maoa, and HTRs (Htr1a, Htr2a, and Htr2b) in the colon. All three forms and components of LM reduced the phosphorylation of STAT3. The above findings can help to optimize the functionality of probiotics and develop new dietary strategies that aid in the maintenance of a healthy gut.

Pooled CRISPR Screening Identifies P-Bodies as Repressors of Cancer Epithelial-Mesenchymal Transition.

Epithelial-mesenchymal transition (EMT) is a fundamental cellular process frequently hijacked by cancer cells to promote tumor progression, especially metastasis. EMT is orchestrated by a complex molecular network acting at different layers of gene regulation. In addition to transcriptional regulation, posttranscriptional mechanisms may also play a role in EMT. Here, we performed a pooled CRISPR screen analyzing the influence of 1,547 RNA-binding proteins on cell motility in colon cancer cells and identified multiple core components of P-bodies (PB) as negative modulators of cancer cell migration. Further experiments demonstrated that PB depletion by silencing DDX6 or EDC4 could activate hallmarks of EMT thereby enhancing cell migration in vitro as well as metastasis formation in vivo. Integrative multiomics analysis revealed that PBs could repress the translation of the EMT driver gene HMGA2, which contributed to PB-meditated regulation of EMT. This mechanism is conserved in other cancer types. Furthermore, endoplasmic reticulum stress was an intrinsic signal that induced PB disassembly and translational derepression of HMGA2. Taken together, this study has identified a function of PBs in the regulation of EMT in cancer. SIGNIFICANCE: Systematic investigation of the influence of posttranscriptional regulation on cancer cell motility established a connection between P-body-mediated translational control and EMT, which could be therapeutically exploited to attenuate metastasis formation.

A novel amidine-based fluorescent probe TPE-4+ for rapid detection of anionic surfactant sodium dodecyl sulfate.

An accurate, fast, and simple surfactant detection method is of great significance for monitoring surfactants pollution. Sodium dodecyl sulfate (SDS) is one of the most commonly used anionic surfactants and has been listed as an important monitoring pollutant for surfactant residues. Herein, a novel fluorescent probe named TPE-4+ with four amidines as the recognition functional group and tetraphenylethene as the fluorophore was fabricated. Due to the special intramolecular environment, the probe showed selectively identification towards SDS which made an aggregation induced fluorescence enhencement. Under the optimum conditions, the fluorescence enhencement of TPE-4+ is linearly related to the concentration of SDS in the range of 5.0-60.0 µM with limit of detection (LOD) of 0.010 µM and limit of quantification (LOQ) of 0.034 µM. Relative to the reported methods, the probe in our work showed better selectivity and sensitivity. The proposed method was successfully applied for the SDS determination of disinfecting bowls.

How do drunk-driving events escalate into drunk-driving crashes? An empirical analysis of Beijing from a spatiotemporal perspective.

Drunk-driving events often escalate into drunk-driving crashes, however, the contributing factors of this progression remain elusive. To mitigate the likelihood of crashes stemming from drunk-driving events, this paper introduces the notion of 'the severity of drunk-driving event' and examines the complex relationship between the severity and its contributing factors, considering spatiotemporal heterogeneity. The study utilizes a Geographically and Temporally Weighted Binary Logistic Regression (GTWBLR) model to conduct spatiotemporal analysis based on police-reported drunk-driving events in Beijing, China. The results show that most factors passed the non-stationary test, indicating their effects on the severity of drunk-driving event vary significantly across different spatial and temporal domains. Notably, during non-workday, drunk-driving events in northeast of Beijing are more likely to escalate into crashes. Furthermore, severe weather during winter in the northwest of Beijing is associated with high risk of drunk-driving crashes. Based on these insights, the authorities can strengthen drunk-driving checks in the northeast region of Beijing, particularly during non-workdays. And it is crucial to promptly clear accumulated snow on the roads during severe winter weather to improve road safety. These insights and recommendations are highly valuable for reducing the risk of drunk-driving crashes.

Predicting academic achievement from the collaborative influences of executive function, physical fitness, and demographic factors among primary school students in China: ensemble learning methods.

BACKGROUND: Elevated levels of executive function and physical fitness play a pivotal role in shaping future quality of life. However, few studies have examined the collaborative influences of physical and mental health on academic achievement. This study aims to investigate the key factors that collaboratively influence primary school students' academic achievement from executive function, physical fitness, and demographic factors. Additionally, ensemble learning methods are employed to predict academic achievement, and their predictive performance is compared with individual learners. METHODS: A cluster sampling method was utilized to select 353 primary school students from Huai'an, China, who underwent assessments for executive function, physical fitness, and academic achievement. The recursive feature elimination cross-validation method was employed to identify key factors that collaboratively influence academic achievement. Ensemble learning models, utilizing eXtreme Gradient Boosting and Random Forest algorithms, were constructed based on Bagging and Boosting methods. Individual learners were developed using Support Vector Machine, Decision Tree, Logistic Regression, and Linear Discriminant Analysis algorithms, followed by the establishment of a Stacking ensemble learning model. RESULTS: Our findings revealed that sex, body mass index, muscle strength, cardiorespiratory function, inhibition, working memory, and shifting were key factors influencing the academic achievement of primary school students. Moreover, ensemble learning models demonstrated superior predictive performance compared to individual learners in predicting academic achievement among primary school students. CONCLUSIONS: Our results suggest that recognizing sex differences and emphasizing the simultaneous development of cognition and physical well-being can positively impact the academic development of primary school students. Ensemble learning methods warrant further attention, as they enable the establishment of an accurate academic early warning system for primary school students.

Understanding key contributing factors on the severity of traffic violations by elderly drivers: a hybrid approach of latent class analysis and XGBoost based SHAP.

Traffic violation is one of the leading causes of traffic crashes. In the context of global aging, it is important to study traffic violations by elderly drivers for improving traffic safety in preparation for a worldwide aging population. In this study, a hybrid approach of Latent Class Analysis (LCA) and XGBoost based SHAP is proposed to identify hidden clusters and to understand the key contributing factors on the severity of traffic violations by elderly drivers, based on the police-reported traffic violation dataset of Beijing (China). First, LCA is applied to segment the dataset into several latent homogeneous clusters, then XGBoost based SHAP is established on each cluster to identify feature contributions and the interaction effects of the key contributing factors on the severity of traffic violations by elderly drivers. Two comparison groups were set up to analyze factors, which are responsible for the different severities of traffic violations. The results show that elderly drivers can be classified into four groups by age, urban or not, license, and season; factors such as less annual number of traffic violations, national & provincial highway, night and winter are key contributing factors for higher severity of traffic violations, which are consistent with common cognition; key contributing factors for all clusters are similar but not identical, for example, more annual number of traffic violations contribute to more severe violation for all clusters except for Cluster 2; some factors which are not key contributing factors may affect the severity of traffic violations when they are combined with other factors, for example, the combination of lower annual number of traffic violations and county & township highway contributes to more severe violation for Cluster 1. These findings can help government to formulate targeted countermeasures to decrease the severity of traffic violations by specific elderly groups and improve road service for the driving population.

Diagnosing autism severity associated with physical fitness and gray matter volume in children with autism spectrum disorder: Explainable machine learning method.

PURPOSE: This study aimed to investigate the relationship between physical fitness, gray matter volume (GMV), and autism severity in children with autism spectrum disorder (ASD). Besides, we sought to diagnose autism severity associated with physical fitness and GMV using machine learning methods. METHODS: Ninety children diagnosed with ASD underwent physical fitness tests, magnetic resonance imaging scans, and autism severity assessments. Diagnosis models were established using extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), and decision tree (DT) algorithms. Hyperparameters were optimized through the grid search cross-validation method. The shapley additive explanation (SHAP) method was employed to explain the diagnosis results. RESULTS: Our study revealed associations between muscular strength in physical fitness and GMV in specific brain regions (left paracentral lobule, bilateral thalamus, left inferior temporal gyrus, and cerebellar vermis I-II) with autism severity in children with ASD. The accuracy (95 % confidence interval) of the XGB, RF, SVM, and DT models were 77.9 % (77.3, 78.6 %), 72.4 % (71.7, 73.2 %), 71.9 % (71.1, 72.6 %), and 66.9 % (66.2, 67.7 %), respectively. SHAP analysis revealed that muscular strength and thalamic GMV significantly influenced the decision-making process of the XGB model. CONCLUSION: Machine learning methods can effectively diagnose autism severity associated with physical fitness and GMV in children with ASD. In this respect, the XGB model demonstrated excellent performance across various indicators, suggesting its potential for diagnosing autism severity.

Hyperbolic exciton polaritons in a van der Waals magnet.

Exciton polaritons are quasiparticles of photons coupled strongly to bound electron-hole pairs, manifesting as an anti-crossing light dispersion near an exciton resonance. Highly anisotropic semiconductors with opposite-signed permittivities along different crystal axes are predicted to host exotic modes inside the anti-crossing called hyperbolic exciton polaritons (HEPs), which confine light subdiffractionally with enhanced density of states. Here, we show observational evidence of steady-state HEPs in the van der Waals magnet chromium sulfide bromide (CrSBr) using a cryogenic near-infrared near-field microscope. At low temperatures, in the magnetically-ordered state, anisotropic exciton resonances sharpen, driving the permittivity negative along one crystal axis and enabling HEP propagation. We characterize HEP momentum and losses in CrSBr, also demonstrating coupling to excitonic sidebands and enhancement by magnetic order: which boosts exciton spectral weight via wavefunction delocalization. Our findings open new pathways to nanoscale manipulation of excitons and light, including routes to magnetic, nonlocal, and quantum polaritonics.

Single cell multi-omics reveal intra-cell-line heterogeneity across human cancer cell lines.

Human cancer cell lines have long served as tools for cancer research and drug discovery, but the presence and the source of intra-cell-line heterogeneity remain elusive. Here, we perform single-cell RNA-sequencing and ATAC-sequencing on 42 and 39 human cell lines, respectively, to illustrate both transcriptomic and epigenetic heterogeneity within individual cell lines. Our data reveal that transcriptomic heterogeneity is frequently observed in cancer cell lines of different tissue origins, often driven by multiple common transcriptional programs. Copy number variation, as well as epigenetic variation and extrachromosomal DNA distribution all contribute to the detected intra-cell-line heterogeneity. Using hypoxia treatment as an example, we demonstrate that transcriptomic heterogeneity could be reshaped by environmental stress. Overall, our study performs single-cell multi-omics of commonly used human cancer cell lines and offers mechanistic insights into the intra-cell-line heterogeneity and its dynamics, which would serve as an important resource for future cancer cell line-based studies.

Coexistence of Gaussian and non-Gaussian statistics in vector integrable turbulence.

Integrable turbulence studies the complex dynamics of random waves for the nonlinear integrable systems, and it has become an important element in exploring the sophisticated turbulent phenomena. In the present work, based on the coupled nonlinear Schrödinger models, we have shown the coexistence of Gaussian and non-Gaussian single-point statistics in multiple wave components, which might be viewed as an exclusive feature for the vector integrable turbulence. This coexistent statistic can relate to different distributions of the vector solitonic excitations depending on the time-invariant nonlinear spectra. Our results are expected to shed light on a deeper understanding of the turbulent behaviors of vector waves and may motivate relevant experiments in the coupled optical or atomic systems.

SpeCamX: mobile app that turns unmodified smartphones into multispectral imagers.

We present the development of SpeCamX, a mobile application that enables an unmodified smartphone into a multispectral imager. Multispectral imaging provides detailed spectral information about objects or scenes, but its accessibility has been limited due to its specialized requirements for the device. SpeCamX overcomes this limitation by utilizing the RGB photographs captured by smartphones and converting them into multispectral images spanning a range of 420 to 680 nm without a need for internal modifications or external attachments. The app also includes plugin functions for extracting medical information from the resulting multispectral data cube. In a clinical study, SpeCamX was used to implement an augmented smartphone bilirubinometer, predicting blood bilirubin levels (BBL) with superior performance in accuracy, efficiency and stability compared to default smartphone cameras. This innovative technology democratizes multispectral imaging, making it accessible to a wider audience and opening new possibilities for both medical and non-medical applications.

Revealing the frequency-dependent oscillations in the nonlinear terahertz response induced by the Josephson current.

Nonlinear responses of superconductors to intense terahertz radiation has been an active research frontier. Using terahertz pump-terahertz probe spectroscopy, we investigate the c-axis nonlinear optical response of a high-temperature superconducting cuprate. After excitation by a single-cycle terahertz pump pulse, the reflectivity of the probe pulse oscillates as the pump-probe delay is varied. Interestingly, the oscillatory central frequency scales linearly with the probe frequency, a fact widely overlooked in pump-probe experiments. By theoretically solving the nonlinear optical reflection problem on the interface, we show that our observation is well explained by the Josephson-type third-order nonlinear electrodynamics, together with the emission coefficient from inside the material into free space. The latter results in a strong enhancement of the emitted signal whose physical frequency is around the Josephson plasma edge. Our result offers a benchmark for and new insights into strong-field terahertz spectroscopy of related quantum materials.

Visualizing moiré ferroelectricity via plasmons and nano-photocurrent in graphene/twisted-WSe2 structures.

Ferroelectricity, a spontaneous and reversible electric polarization, is found in certain classes of van der Waals (vdW) materials. The discovery of ferroelectricity in twisted vdW layers provides new opportunities to engineer spatially dependent electric and optical properties associated with the configuration of moiré superlattice domains and the network of domain walls. Here, we employ near-field infrared nano-imaging and nano-photocurrent measurements to study ferroelectricity in minimally twisted WSe2. The ferroelectric domains are visualized through the imaging of the plasmonic response in a graphene monolayer adjacent to the moiré WSe2 bilayers. Specifically, we find that the ferroelectric polarization in moiré domains is imprinted on the plasmonic response of the graphene. Complementary nano-photocurrent measurements demonstrate that the optoelectronic properties of graphene are also modulated by the proximal ferroelectric domains. Our approach represents an alternative strategy for studying moiré ferroelectricity at native length scales and opens promising prospects for (opto)electronic devices.

Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder.

The individual differences among children with autism spectrum disorder (ASD) may make it challenging to achieve comparable benefits from a specific exercise intervention program. A new method for predicting the possible outcomes and maximizing the benefits of exercise intervention for children with ASD needs further exploration. Using the mini-basketball training program (MBTP) studies to improve the symptom performance of children with ASD as an example, we used the supervised machine learning method to predict the possible intervention outcomes based on the individual differences of children with ASD, investigated and validated the efficacy of this method. In a long-term study, we included 41 ASD children who received the MBTP. Before the intervention, we collected their clinical information, behavioral factors, and brain structural indicators as candidate factors. To perform the regression and classification tasks, the random forest algorithm from the supervised machine learning method was selected, and the cross validation method was used to determine the reliability of the prediction results. The regression task was used to predict the social communication impairment outcome following the MBTP in children with ASD, and explainable variance was used to evaluate the predictive performance. The classification task was used to distinguish the core symptom outcome groups of ASD children, and predictive performance was assessed based on accuracy. We discovered that random forest models could predict the outcome of social communication impairment (average explained variance was 30.58%) and core symptom (average accuracy was 66.12%) following the MBTP, confirming that the supervised machine learning method can predict exercise intervention outcomes for children with ASD. Our findings provide a novel and reliable method for identifying ASD children most likely to benefit from a specific exercise intervention program in advance and a solid foundation for establishing a personalized exercise intervention program recommendation system for ASD children.

Digital Control Method and Performance Analysis of the Double-Compound Axial Piston Pump.

The flow control range of the double-compound axial piston pump with the traditional mechanical-hydraulic feedback servo control is limited and the accuracy is poor. Accordingly, this paper proposes a digital control scheme and its control strategy using a linear stepper motor direct drive servo valve for the precise control and double pumps cooperation of the double-compound axial piston pump. A numerical model of the digital control double-compound axial piston pump is established, and the validity of the model is verified by experimental tests. The performance advantages of the digital control method relative to the mechanical-hydraulic feedback servo control method are analyzed, as is the performance of the control strategy for double pumps. The results show that the digital control method can achieve a wider range of flow control than the traditional mechanical-hydraulic feedback servo control method and avoid the torque impact on the prime mover caused by the active control. The combination of the flow control and the power control including four control modes can meet the performance requirements of the double-compound axial piston pump. The highest priority is given to the energy-saving control, which can reduce the displacement of the main pump in the nonworking state to reduce the additional power loss. The study provides a basis for the accurate matching and optimization of power to load and flow to operating speed of the double-compound axial piston pump.

Impact of Trisulfide on the Structure and Function of Different Antibody Constructs.

Trisulfide is a post-translational modification (PTM) commonly found in recombinant antibodies. It has been demonstrated that trisulfide had no impact on the bioactivity of mono-specific antibodies (MsAbs). However, the impact of trisulfide on multi-specific antibodies has not been evaluated. In this study, two mass spectrometric methods were developed for comprehensive trisulfide characterization. The non-reduced peptide mapping method combined with the unique electron activated dissociation (EAD) provided signature fragments for confident trisulfide identification as well as trisulfide quantitation at individual sites. A higher throughput method using Fab mass analysis was also developed and qualified to support routine monitoring of trisulfide during process development. Fab mass analysis features simpler sample preparation and shorter analysis time but provides comparable results to the non-reduced peptide mapping method. In this study, a bi-specific (BsAb) and a tri-specific antibody (TsAb) were compared side-by-side with a MsAb to evaluate the impact of trisulfide on the structure and function of multi-specific antibodies. Results indicated that trisulfide dominantly formed at similar locations across different antibody constructs and had no impact on the size heterogeneity, charge heterogeneity, or bioactivities of any assessed antibodies. Together with the in vitro stability under heat stress (25 °C and 40 °C for up to four weeks) and rapid conversion from trisulfide to disulfide during in vivo circulation, trisulfide could be categorized as a non-critical quality attribute (non-CQA) for antibody products.

A hybrid approach of random forest and random parameters logit model of injury severity modeling of vulnerable road users involved crashes.

Vulnerable road users (VRUs) involved crashes are a major road safety concern due to the high likelihood of fatal and severe injury. The use of data-driven methods and heterogeneity models separately have limitations in crash data analysis. This study develops a hybrid approach of Random Forest based SHAP algorithm (RF-SHAP) and random parameters logit modeling framework to explore significant factors and identify the underlying interaction effects on injury severity of VRUs-involved crashes in Shenyang (China) from 2015 to 2017. The results show that the hybrid approach can uncover more underlying causality, which not only quantifies the impact of individual factors on injury severity, but also finds the interaction effects between the factors with random parameters and fixed parameters. Seven factors are found to have significant effect on crash injury severity. Two factors, including primary roads and rural areas produce random parameters. The interaction effects reveal interesting combination features. For example, even though rural areas and primary roads increase the likelihood of fatal crash occurrence individually, the interaction effect of the two factors decreases the likelihood of being fatal. The findings form the foundation for developing safety countermeasures targeted at specific crash groups for reducing fatalities in future crashes.