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).

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.

Spatiotemporal successions of N, S, C, Fe, and As cycling genes in groundwater of a wetland ecosystem: Enhanced heterogeneity in wet season.

Microorganisms in wetland groundwater play an essential role in driving global biogeochemical cycles. However, largely due to the dynamics of spatiotemporal surface water-groundwater interaction, the spatiotemporal successions of biogeochemical cycling in wetland groundwater remain poorly delineated. Herein, we investigated the seasonal coevolution of hydrogeochemical variables and microbial functional genes involved in nitrogen, carbon, sulfur, iron, and arsenic cycling in groundwater within a typical wetland, located in Poyang Lake Plain, China. During the dry season, the microbial potentials for dissimilatory nitrate reduction to ammonium and ammonification were dominant, whereas the higher potentials for nitrogen fixation, denitrification, methane metabolism, and carbon fixation were identified in the wet season. A likely biogeochemical hotspot was identified in the area located in the low permeable aquifer near the lake, characterized by reducing conditions and elevated levels of Fe2+ (6.65-17.1 mg/L), NH4+ (0.57-3.98 mg/L), total organic carbon (1.02-1.99 mg/L), and functional genes. In contrast to dry season, higher dissimilarities of functional gene distribution were observed in the wet season. Multivariable statistics further indicated that the connection between the functional gene compositions and hydrogeochemical variables becomes less pronounced as the seasons transition from dry to wet. Despite this transition, Fe2+ remained the dominant driving force on gene distribution during both seasons. Gene-based co-occurrence network displayed reduced interconnectivity among coupled C-N-Fe-S cycles from the dry to the wet season, underpinning a less complex and more destabilizing occurrence pattern. The rising groundwater level may have contributed to a reduction in the stability of functional microbial communities, consequently impacting ecological functions. Our findings shed light on microbial-driven seasonal biogeochemical cycling in wetland groundwater.

Deterministic factors modulating assembly of groundwater microbial community in a nitrogen-contaminated and hydraulically-connected river-lake-floodplain ecosystem.

The river-lake-floodplain system (RLFS) undergoes intensive surface-groundwater mass and energy exchanges. Some freshwater lakes are groundwater flow-through systems, serving as sinks for nitrogen (N) entering the lake. Despite the threat of cross-nitrogen contamination, the assembly of the microbial communities in the RLFS was poorly understood. Herein, the distribution, co-occurrence, and assembly pattern of microbial community were investigated in a nitrogen-contaminated and hydraulically-connected RLFS. The results showed that nitrate was widely distributed with greater accumulation on the south than on the north side, and ammonia was accumulated in the groundwater discharge area (estuary and lakeshore). The heterotrophic nitrifying bacteria and aerobic denitrifying bacteria were distributed across the entire area. In estuary and lakeshore with low levels of oxidation-reduction potential (ORP) and high levels of total organic carbon (TOC) and ammonia, dissimilatory nitrate reduction to ammonium (DNRA) bacteria were enriched. The bacterial community had close cooperative relationships, and keystone taxa harbored nitrate reduction potentials. Combined with multivariable statistics and self-organizing map (SOM) results, ammonia, TOC, and ORP acted as drivers in the spatial evolution of the bacterial community, coincidence with the predominant deterministic processes and unique niche breadth for microbial assembly. This study provides novel insight into the traits and assembly of bacterial communities and potential nitrogen cycling capacities in RLFS groundwater.

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.

Integration of Cobalt Phthalocyanine, Acetylene Black and Cu2 O Nanocubes for Efficient Electroreduction of CO2 to C2 H4.

Suppressing side reactions and simultaneously enriching key intermediates during CO2 reduction reaction (CO2 RR) has been a challenge. Here, we propose a tandem catalyst (Cu2 O NCs-C-Copc) consisting of acetylene black, cobalt phthalocyanine (Copc) and cuprous oxide nanocubes (Cu2 O NCs) for efficient CO2 -to-ethylene conversion. Density-functional theory (DFT) calculation combined with experimental verification demonstrated that Copc can provide abundant CO to nearby copper sites while acetylene black successfully reduces the formation energies of key intermediates, leading to enhanced C2 H4 selectivity. X-ray photoelectron spectroscopy (XPS) and potentiostatic tests indicated that the catalytic stability of Cu2 O NCs-C-Copc was significantly enhanced compared with Cu2 O NCs. Finally, the industrial application prospect of the catalyst was evaluated using gas diffusion electrolyzers. The F E C 2 H 4 ${{\rm { F}}{{\rm { E}}}_{{{\rm { C}}}_{{\rm { 2}}}{{\rm { H}}}_{{\rm { 4}}}}}$ of Cu2 O NCs-C-Copc can reach to 58.4 % at -1.1 V vs. RHE in 0.1 M KHCO3 and 70.3 % at -0.76 V vs. RHE in 1.0 M KOH. This study sheds new light on the design and development of highly efficient CO2 RR tandem catalytic systems.

Cu2O Nanoparticles Wrapped by N-Doped Carbon Nanotubes for Efficient Electroreduction of CO2 to C2 Products.

Electroreduction of carbon dioxide (CO2) to C2 products (ethylene and ethanol) using efficient catalysts is a feasible approach to alleviate the climate crisis. Cuprous oxide nanoparticles (Cu2O NPs) are a promising catalyst for C2 production but suffer from inherent selectivity and durability. To address this challenge, a Cu2O NPs-nitrogen-doped carbon nanotube (Cu2O NPs-NCNT) composite was prepared with carbon nanotubes (CNTs), Cu2O NPs, and phthalocyanine (Pc). The results indicate that Cu2O NPs-NCNT has excellent Faradic efficiency of C2 products (77.61%) at -1.1 V vs RHE, which is 103.43% higher than that of Cu2O NPs. In the potentiostatic electrolysis combined with Raman spectroscopy and X-ray photoelectron spectroscopy (XPS) measurements, Cu2O NPs-NCNT exhibited structural and catalytic current stability over 10 h. Finally, density functional theory calculations combined with XPS demonstrated that the NCNT in Cu2O NPs-NCNT can selectively absorb CO2 through specific N-CO2 interactions. Our work provides a unique strategy to promote the selectivity of Cu2O NPs for C2 production by introducing N-doped linear carbon materials to fabricate composite.

Spatial variability of source contributions to nitrate in regional groundwater based on the positive matrix factorization and Bayesian model.

Groundwater nitrate (NO3-) pollution has attracted widespread attention; however, accurately evaluating the sources of NO3- and their contribution patterns in regional groundwater is difficult in areas with multiple sources and complex hydrogeological conditions. In this study, 161 groundwater samples were collected from the Poyang Lake Basin for hydrochemical and dual NO3- isotope analyses to explore the sources of NO3- and their spatial contribution using the Positive Matrix Factorization (PMF) and Bayesian stable isotope mixing (MixSIAR) models. The results revealed that the enrichment of NO3- in groundwater was primarily attributed to sewage/manure (SM), which accounted for more than 50 %. The contributions of nitrogen fertilizer and soil organic nitrogen should also be considered. Groundwater NO3- sources showed obvious spatial differences in contributions. Regions with large contributions of SM (>90 %) were located in the southeastern part of the study area and downstream of Nanchang, which are areas with relatively high population density. Nitrogen fertilizer and soil organic nitrogen showed concentrated contributions in paddy soil in the lower reaches of the Gan and Rao Rivers, and these accumulations were mainly driven by the soil type, land use type, and topography. This study provides insight into groundwater NO3- contamination on a regional scale.

Nitrogen species and microbial community coevolution along groundwater flowpath in the southwest of Poyang Lake area, China.

Nitrate and ammonia overload in groundwater can lead to eutrophication of surface water in areas where surface water is recharged by groundwater. However, this process remained elusive due to the complicated groundwater N cycling, which is governed by the co-evolution of hydrogeochemical conditions and N-cycling microbial communities. Herein, this process was studied along a generalized groundwater flowpath in Ganjing Delta, Poyang Lake area, China. From groundwater recharge to the discharge area near the lake, oxidation-reduction potential (ORP), NO3-N, and NO2-N decreased progressively, while NH3-N, total organic carbon (TOC), Fe2+, sulfide, and TOC/NO3- ratio accumulated in the lakeside samples. The anthropogenic influences such as sewage and agricultural activities drove the nitrate distribution, as observed by Cl- vs. NO3-/Cl- ratio and isotopic composition of nitrate. The hydrogeochemical evolution was intimately coupled with the changes in microbial communities. Variations in microbial community structures was significantly explained by Fe2+, NH3-N, and sulfide, while TOC/NO3- controlled the distribution of predicted N cycling gene. The absence of NH3-N in groundwater upstream was accompanied by the enrichment in Acinetobacter capable of nitrification and aerobic denitrification. These two processes were also supported by Ca2+ + Mg2+ vs. HCO3- ratio and isotopic composition of NO3-. The DNRA process downstream was revealed by both the presence of DNRA-capable microbes such as Arthrobacter and the isotopic composition of NH4+ in environments with high concentrations of NH3-N, TOC/NO3-, Fe2+, and sulfide. This coupled evolution of N cycling and microbial community sheds new light on the N biogeochemical cycle in areas where surface water is recharged by groundwater.

Electrochemiluminescence Distance and Reactivity of Coreactants Determine the Sensitivity of Bead-Based Immunoassays.

Herein we report the study of electrochemiluminescence (ECL) generation by tris(2,2'-bipyridyl)ruthenium (Ru(bpy)3 2+ ) and five tertiary amine coreactants. The ECL distance and lifetime of coreactant radical cations were measured by ECL self-interference spectroscopy. And the reactivity of coreactants was quantitatively evaluated in terms of integrated ECL intensity. By statistical analysis of ECL images of single Ru(bpy)3 2+ -labeled microbeads, we propose that ECL distance and reactivity of coreactant codetermine the emission intensity and thus the sensitivity of immunoassay. 2,2-bis(hydroxymethyl)-2,2',2''-nitrilotriethanol (BIS-TRIS) can well balance ECL distance-reactivity trade-off and enhance the sensitivity by 236 % compared with tri-n-propylamine (TPrA) in the bead-based immunoassay of carcinoembryonic antigen. The study brings an insightful understanding of ECL generation in bead-based immunoassay and a way of maximizing the analytical sensitivity from the aspect of coreactant.

A Facile yet Versatile Strategy to Construct Liquid Hybrid Energy-Saving Windows for Strong Solar Modulation.

Smart windows with light management and indoor solar heating modulation capacities are of paramount importance for building energy conservation. Thermochromic poly(N-isopropylacrylamide) (PNIPAm) hydrogel smart windows exhibit advantages of the relatively suitable transition temperature of 32 °C, high cost-effective and automatic passive sunlight regulation, but sustain slow response rate and unsatisfactory solar modulation efficiency. Herein, a strategy of one-step copolymerization of NIPAm and different olefine acids (OA) using reverse atom transfer radical polymerization method is developed to fabricate various chain/microparticle hybrids (CMH) for liquid energy-saving windows. Synergetic mechanisms of thermal-induced dissolution and aggregation of linear polymer chains integrated with water capture and release of microgel particles contribute to tunable light-scattering behaviors and adaptive solar modulation. Without any post-treatment, the as-prepared poly(N-isopropylacrylamide-co-acrylic acid) (P(NIPAm-co-AA))-based CMH suspension is injected into sandwich glass to construct energy-saving windows, which exhibits appreciated near-room-temperature transition (26.7 °C), rapid response (5 s), extraordinary luminous transmittance (91.5%), and solar modulation efficiency (85.8%), resulting in a substantial decline of indoor temperature of 24.5 °C in simulation experiment. Combining the versatile strategy with flexible adjustment on transition temperature, multifarious P(NIPAm-co-OA)-based CMH windows with eminent light management capacity are obtained. This work will powerfully promote the development and renovation of energy-efficient windows.