Early identification of autism spectrum disorder based on machine learning with eye-tracking data.

BACKGROUND: Early identification of autism spectrum disorder (ASD) improves long-term outcomes, yet significant diagnostic delays persist. METHODS: A retrospective cohort of 449 children (ASD: 246, typically developing [TD]: 203) was used for model development. Eye-movement data were collected from the participants watching videos that featured eye-tracking paradigms for assessing social and non-social cognition. Five machine learning algorithms, namely random forest, support vector machine, logistic regression, artificial neural network, and extreme gradient boosting, were trained to classify children with ASD and TD. The best-performing algorithm was selected to build the final model which was further evaluated in a prospective cohort of 80 children. The Shapley values interpreted important eye-tracking features. RESULTS: Random forest outperformed other algorithms during model development and achieved an area under the curve of 0.849 (< 3 years: 0.832, ≥ 3 years: 0.868) on the external validation set. Of the ten most important eye-tracking features, three measured social cognition, and the rest were related to non-social cognition. A deterioration in model performance was observed using only the social or non-social cognition-related eye-tracking features. LIMITATIONS: The sample size of this study, although larger than that of existing studies of ASD based on eye-tracking data, was still relatively small compared to the number of features. CONCLUSIONS: Machine learning models based on eye-tracking data have the potential to be cost- and time-efficient digital tools for the early identification of ASD. Eye-tracking phenotypes related to social and non-social cognition play an important role in distinguishing children with ASD from TD children.

Predicting autism spectrum disorder using maternal risk factors: A multi-center machine learning study.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a complex environmental etiology involving maternal risk factors, which have been combined with machine learning to predict ASD. However, limited studies have considered the factors throughout preconception, perinatal, and postnatal periods, and even fewer have been conducted in multi-center. In this study, five predictive models were developed using 57 maternal risk factors from a cohort across ten cities (ASD:1232, typically developing[TD]: 1090). The extreme gradient boosting model performed best, achieving an accuracy of 66.2 % on the external cohort from three cities (ASD:266, TD:353). The most important risk factors were identified as unstable emotions and lack of multivitamin supplementation using Shapley values. ASD risk scores were calculated based on predicted probabilities from the optimal model and divided into low, medium, and high-risk groups. The logistic analysis indicated that the high-risk group had a significantly increased risk of ASD compared to the low-risk group. Our study demonstrated the potential of machine learning models in predicting the risk for ASD based on maternal factors. The developed model provided insights into the maternal emotion and nutrition factors associated with ASD and highlighted the potential clinical applicability of the developed model in identifying high-risk populations.

Experimental Study on Vibratory Compaction Behavior of Tunneling Rock Wastes Used as Unbound Permeable Aggregate Base Materials.

The tunneling rock wastes (TRW) have been increasingly generated and stockpiled in massive quantities. Recycling them for use as unbound granular pavement base/subbase materials has become an alternative featuring low carbon emission and sustainability. However, the field compaction of such large-size, open-graded materials remains challenging, thus affecting post-construction deformation and long-term stability of such pavement base/subbase layers. This study conducted a series of proctor compaction and new plate vibratory compaction tests to analyze the compaction characteristics of such TRW materials. A total of six different open gradations were designed from particle packing theory. In addition, the effects of gradation and compaction methods on the compaction characteristics, particle breakage of TRW materials, and the optimal combination of vibratory parameters were investigated by normalizing the curves of achieved dry density versus degree of saturation for various combinations of gradations, compaction methods, and compaction energy levels. The post-compaction characteristics of interparticle contact, pore structure, and particle breakage were analyzed from the X-ray computed topography (XCT) scanning results of TRW specimens with different gradations. The findings showed that the gravel-to-sand ratio (G/S) based gradation design method can effectively differentiate distinct types of particle packing structures. There exists an optimal G/S range that could potentially result in the highest maximum dry density, the lowest particle breakage, and the best pore structure of compacted unbound permeable aggregate base (UPAB) materials. The achieved dry density (ρd) of UPAB materials subjected to vibratory plate compaction exhibited three distinct phases with compaction time, from which the optimal excitation frequency range was found to be 25-27 Hz and the optimal combination of vibratory parameters were determined. The normalized compaction curves of degree of saturation versus achieved dry density were found insensitive to changes in material gradations, compaction methods and energy levels, thus allowing for a more accurate evaluation and control of field compaction quality.

Development of Artificial-Neural-Network-Based Permanent Deformation Prediction Model of Unbound Granular Materials Subjected to Moving Wheel Loading.

The majority of existing regression models for unbound granular materials (UGMs) consider only the effects of the number of loading cycles and stress levels on the permanent deformation characteristics and are thus unable to account for the complexity of plastic deformation accumulation behavior influenced by other factors, such as dry density, moisture content and gradation. In this study, research efforts were made to develop artificial-neural-network (ANN)-based prediction models for the permanent deformation of UGMs. A series of laboratory repeated load triaxial tests were conducted on UGM specimens with varying gradations to simulate realistic stress paths exerted by moving wheel loads and study permanent deformation characteristics. On the basis of the laboratory testing database, the ANN prediction models were established. Parametric sensitivity analyses were then performed to evaluate and rank the relative importance of each factor on permanent deformation behavior. The results indicated that the developed ANN prediction model is more accurate and reliable as compared to previously published regression models. The two major factors influencing the magnitude of accumulated plastic deformation of UGMs are the shear stress ratio (SSR) and the number of loading cycles, of which the calculated influence coefficients are 38% and 41%, respectively, while the degree of influence of gradation is twice that of the confining pressure.

Resilient Modulus Behavior and Prediction Models of Unbound Permeable Aggregate Base Materials Derived from Tunneling Rock Wastes.

Tunneling rock wastes (TRWs), which are often open- or gap-graded in nature, have been increasingly recycled and reused for sustainable construction of unbound permeable aggregate base (UPAB) courses with high porosity and desired drainability. However, there is still a lack of sufficient understanding of long-term mechanical stability of such TRW materials subjected to repeated applications of moving wheel loads. This paper aimed to characterize and predict resilient modulus (Mr) behavior of the TRW materials used in unbound permeable aggregate base applications. To achieve this goal, five different UPAB gradations were designed based on the gravel-to-sand ratio (G/S) concept. In order to study their Mr behavior, the laboratory repeated load triaxial tests were conducted under different combinations of confining pressure and deviator stress as controlled by the levels of the shear stress ratio (SSR). The prediction accuracy of fourteen classical Mr prediction models was comparatively analyzed, from which the improved Mr prediction model incorporating gradation and stress variables was proposed for TRW-derived UPAB materials and further validated by external database accordingly. The results show that under the same G/S value and confining pressure level, the higher the SSR is, the greater the final Mr values are, and the more significant the effect of G/S on Mr is. Under the same SSR level, the increase of confining pressure alleviates the effect of G/S on Mr. There appears to exist an optimal G/S value of around 1.6-1.8 that yields the best Mr behavior of the TRW-derived UPAB materials studied. The improved Mr prediction model was verified extensively to be universally applicable. It can potentially contribute to balancing long-term mechanical stability and drainability of TRW-derived UPAB materials through gradation optimization. The findings could provide a theoretical basis and technical reference for cost-effective and sustainable applications of UPAB materials derived from TRWs.

Investigating Morphology and Breakage Evolution Characteristics of Railroad Ballasts over Distinct Supports Subjected to Impact Loading.

The ballast bed constantly degrades under the repeated applications of impact loading exerted by passing trains in terms of the particle size, shape, breakage, fouling, etc., thus significantly jeopardizing the in-service performance and operational safety of ballasted tracks. In this study, the morphology and breakage evolution characteristics of railroad ballasts of single- and multiple-size ranges were investigated from laboratory impact-load tests. Both a concrete block and sand layer were placed to mimic the distinct under-ballast supports. The degradation trends of the typical shape and breakage indices were comparatively quantified for different combinations of ballast particle sizes and shapes, under-ballast supports, impact energies, and number of impact-load applications (N). The results show that both shape and size affect ballast particle breakage, with shape being more influential. The breakage severity of flake-like particles is about 1.5-1.66 times and 1.25-1.5 times higher than those of regular and needle-like particles, respectively. Under impact loading, large and small single-size ballasts degrade mainly by breakage and abrasion, respectively. The modified fouling index (FI) of flake-like particles within 31.5-40 mm is about 3.6 times that of regular particles within 50-63 mm. The shape indices of the ballast particles within 31.5-40 mm exhibit the most profound changes. The severities of the ballast breakage and fines generation (or modified FI) increased by 50% and 74%, respectively, due to the increase in the under-ballast support stiffness by 100 times and the drop height of 80 cm, respectively. The convexity and ballast breakage index (BBI) are promising for quantifying particle-degradation trends, and their statistical correlation found herein is potentially useful for the transition of ballast-bed-maintenance management from the current plan-based scheduling to condition-based upgrading.

Characterizing Particle-Scale Acceleration of Mud-Pumping Ballast Bed of Heavy-Haul Railway Subjected to Maintenance Operations.

Fouling and mud-pumping problems in ballasted track significantly degrade serviceability and jeopardize train operational safety. The phenomenological approaches for post hoc forensic investigation and remedies of mud pumps have relatively been well studied, but there still lacks studies on inherent mechanisms and ex ante approaches for early-age detection of mud pumps. This paper was aimed to exploring the feasibility of using particle acceleration responses to diagnose and identify early-age mud-pumping risks in real-world field applications. The innovative wireless sensors with 3D-printed shells resembling real shape of ballast particles were instrumented in the problematic railway section to monitor ballast particle movement prior to, during, and after maintenance operations, respectively. The real-time particle-scale acceleration data of ballast bed under both degraded and maintenance-restored clean conditions were recorded. The time histories, power spectra, and marginal spectra of 3D acceleration were comparatively analyzed. The results showed the 3D acceleration of ballast particles underneath rail-supporting tie plates displayed relatively clear periodicity of about 0.8 s with adjacent bogies regarded as a loading unit. The tamping operation was effective for compacting ballast bed laterally and improving the lateral interlocking of ballast particles, whereas the stabilizing operation was effective mainly in the lateral direction and for ballast particles underneath the sleepers. The mud pumps caused intensive particle-scale acceleration, and ballast particles underneath the sleepers were affected more severely than those in between adjacent sleepers. The ballast particles directly underneath tie plates exhibit dramatic acceleration variations due to maintenance operations as compared to those in other positions studied; hence, it seems promising to use particle-scale acceleration underneath tie plates as readily-implementable indicators for smart in-service track health monitoring.

Extracellular vesicles derived from astrocyte-treated with haFGF14-154 attenuate Alzheimer phenotype in AD mice.

Background: aFGF content in serum and cerebrospinal fluid is increased in Alzheimer's disease (AD) patients and attenuates the activation of astrocytes. Extracellular vesicles (EVs) are a major mediator in astrocyte-neuron communications. Since excessive or persistent reactive astrocytes lead to chronic inflammation and neuronal dysfunction, and the activation of astrocytes can be inhibited by aFGF, we proposed that the cargoes of astrocyte-derived EVs (AEVs) might be modified by aFGF stimulation, playing an important role in AD progression. However, the mechanisms underlying the role of aFGF remain unclear. Methods: AEVs were isolated from damaged astrocytes, treated with or without aFGF in Aß-loading condition, and were intranasally administered to AD mice. We determined the ability of AEVs to enter the brain, ameliorate cognitive behavior deficits, alleviate the Aß burden in the brain, and improve synapse ultrastructure. Subsequently, the miRNAs enriched in AEVs were sequenced to identify the key molecules specifically modified by aFGF. Finally, we explored the protective effects of miR-206-3p inhibition on cognitive deficiency and its regulatory mechanism and determined its role as a specific biomarker for potential AD diagnosis. Results: AEVs stimulated by aFGF (defined as AEVs-Aß+H) had favorable neuroprotection in AD pathology by enhancing neurite growth and reduction of Aß loading on neurons in vitro. Following intranasal administration, AEVs-Aß+H ameliorated cognitive behavior deficits, promoted synaptic plasticity, and alleviated brain Aß burden in the APP/PS1 and Aß brain-injected mice. AEVs-Aß+H showed beneficial effects on AD similar to AEVs produced in normal situations (AEVs-Ctrl). aFGF stimulation modified the cargoes in EVs derived from Aß damaged astrocytes, the most significant of which being the down-regulation of miR-206-3p. The miR-206-3p level was specifically high in the plasma of AD mice and patients, and miR-206-3p antagomir reversed the Alzheimer phenotype in AD mice. The brain-derived neurotrophic factor (BDNF) gene was negatively regulated by miR-206-3p and upregulated by AEVs-Aß+H and miR-206-3p antagomir in AD mice. AEVs-Aß+H inhibited δ-secretase (Asparagine endopeptidase, AEP) activation via the miR-206-3p/BDNF axis to alleviate Aß burden in the AD brain. Conclusion: Our findings highlight the role of aFGF in the modification of AEVs cargoes, especially miR-206-3p that can potentially serve as a biomarker for AD diagnosis and therapeutic target.

Discrete Element Modeling of Instability Mechanisms of Unbound Permeable Aggregate Base Materials in Triaxial Compression.

Unbound permeable aggregate base (UPAB) materials with strong load-transmitting skeleton yet adequate inter-connected pores are desired for use in the sponge-city initiative. However, the micro-scale fabric evolution and instability mechanism of macroscopic strength behavior of such UPAB materials still remain unclear. In this study, virtual monotonic triaxial compression tests were conducted by using the discrete element method (DEM) modeling approach on specimens with different gradations quantified by the parameter of gravel-to-sand ratio (G/S). The realistic aggregate particle shape and inter-particle contact behavior were properly considered in the DEM model. The micromechanical mechanisms of the shearing failure of such UPAB materials and their evolution characteristics with G/S values were disclosed from contact force chains, microstructures, and particle motion. It was found that the proportion of rotating particles in the specimens decreased and the proportion of relative sliding between particles increased as the content of fine particles decreased. The plastic yielding of the specimens originated from the failure of contact force chains and the occurrence of the relative motion between particles, while the final instability was manifested by the large-scale relative motion among particles along the failure plane (i.e., changes in the internal particle topology). By comparing the macroscopic strength, microstructure evolution, and particle motion characteristics of the specimens with different G/S values, it was found that the specimens with G/S value of 1.8 performed the best, and that the G/S value of 1.8 could be regarded as the threshold for separating floating dense and skeletal gap type packing structures. The variation of Euler angles of rotating particles was significantly reduced in the particle size range of 4.75 mm to 9.50 mm, indicating that this size range separates most of the particles from rolling and sliding. Since particle rolling and sliding behavior are directly related to shear strength, this validates the rationality of the parameter G/S for controlling and optimizing gradations from the perspective of particle movement. The findings could provide theoretical basis and technical guidance for the effective design and efficient utilization of UPAB materials.

Excitatory Crossmodal Input to a Widespread Population of Primary Sensory Cortical Neurons.

Crossmodal information processing in sensory cortices has been reported in sparsely distributed neurons under normal conditions and can undergo experience- or activity-induced plasticity. Given the potential role in brain function as indicated by previous reports, crossmodal connectivity in the sensory cortex needs to be further explored. Using perforated whole-cell recording in anesthetized adult rats, we found that almost all neurons recorded in the primary somatosensory, auditory, and visual cortices exhibited significant membrane-potential responses to crossmodal stimulation, as recorded when brain activity states were pharmacologically down-regulated in light anesthesia. These crossmodal cortical responses were excitatory and subthreshold, and further seemed to be relayed primarily by the sensory thalamus, but not the sensory cortex, of the stimulated modality. Our experiments indicate a sensory cortical presence of widespread excitatory crossmodal inputs, which might play roles in brain functions involving crossmodal information processing or plasticity.

Evaluating the Effect of Rail Fastener Failure on Dynamic Responses of Train-Ballasted Track-Subgrade Coupling System for Smart Track Condition Assessment.

Rail fasteners are among the key components of ballasted track of high-speed railway due to their functionality of fixing rails to sleepers. The failure of rail fastening system hinders the transmission of train loads to underlying track substructure and therefore endangers the operation safety and longevity of ballasted track. This paper first established a three-dimensional (3D) numerical model of the train-ballasted track-subgrade coupling system by integrating multibody dynamics (MBD) and finite element method (FEM). Numerical simulations were then performed to investigate the effects of different patterns of rail fastener failure (i.e., consecutive single-side, alternate single-side, and consecutive double-side) on critical dynamic responses of track structures, train running stability, and operation safety. The results show that the resulting influences of different patterns of rail fastener failure descend in the order of consecutive double-side failure, consecutive single-side failure, and alternate single-side failure. As the number of failed fasteners increases, the range where dynamic responses of track structures are influenced extends, and the failure of two consecutive single-side fasteners exerts a similar influence as that of four alternate single-side fasteners. The failure of single-side fasteners affects dynamic responses of the intact side of track structures relatively insignificantly. The influence of rail fastener failure on track structures exhibits hysteresis, thus indicating that special attention needs to be paid to locations behind failed fasteners during track inspection and maintenance. The occurrence of the failure of two or more consecutive fasteners demands timely maintenance work in order to prevent aggravated deterioration of track structures. The findings of this study could provide useful reference and guidance to smart track condition assessment and condition-based track maintenance.

Characterizing and Predicting the Resilient Modulus of Recycled Aggregates from Building Demolition Waste with Breakage-Induced Gradation Variation.

Building demolition waste (BDW) has been massively stockpiled due to increasingly rapid urbanization and modernization. The use of recycled BDW as unbound granular base/subbase materials is among the sustainable, cost-effective, and environmentally friendly pavement construction alternatives. The resilient modulus is an important mechanical property of BDW-derived aggregates and mechanistic design input of pavements incorporating BDW. This paper presents the results of a comprehensive laboratory study on the shear strength and resilient modulus characteristics of BDW-derived aggregate materials. A series of monotonic triaxial compression tests and repeated-load triaxial (RLT) tests were conducted with five different gradations representing particle breakage and different stress paths. The apparent cohesion and internal friction angle of recycled BDW aggregates under consolidated drained conditions ranged from 35.3 to 57.5 kPa and from 30.2° to 54.3°, respectively. The apparent cohesion and internal friction angle also increased and decreased non-linearly with the increasing relative content of fine particles, respectively. The resilient modulus of recycled BDW aggregates gradually decreased with increasing relative content of fine particles at the same stress level. Both the deviator stress and confining pressure exhibited significant influences on the resilient modulus, while the effect of confining pressure was more profound. Based on laboratory testing data, a mechanistic-empirical model was developed to predict the resilient modulus of recycled BDW aggregates from gradation and stress-state variables. The findings could be useful for extended engineering applications of BDW in unbound granular pavement base/subbase construction.

Bruceae Fructus Oil Inhibits Triple-Negative Breast Cancer by Restraining Autophagy: Dependence on the Gut Microbiota-Mediated Amino Acid Regulation.

Triple-negative breast cancer (TNBC) has been acknowledged as an aggressive disease with worst prognosis, which requires endeavor to develop novel therapeutic agents. Bruceae fructus oil (BO), a vegetable oil derived from the fruit of Brucea javanica (L.) Merr., is an approved marketable drug for the treatment of cancer in China for several decades. Despite that the anti-breast cancer activity of several quassinoids derived from B. javanica has been found, it was the first time that the potential of BO against TNBC was revealed. Although BO had no cytotoxicity on TNBC cell lines in vitro, the oral administration of BO exhibited a gut microbiota-dependent tumor suppression without toxicity on the non-targeted organs in vivo. By metagenomics and untargeted metabolomics, it was found that BO not only altered the composition and amino acid metabolism function of gut microbiota but also regulated the host's amino acid profile, which was in accordance with the metabolism alternation in gut microbiota. Moreover, the activity of mTOR in tumor was promoted by BO treatment as indicated by the phosphorylation of 4E-binding protein 1 (4E-BP1) and ribosomal protein S6, and hyper-autophagy was consequently restrained. By contrast, the failure of tumor suppression by BO under pseudo germ-free (PGF) condition came with indistinctive changes in autophagy and mTOR activity, implying the critical role of the gut microbiota in BO's anticancer activity. The present study highlighted a promising application of BO against breast cancer with novel efficacy and safety.

Evaluating Gyratory Compaction Characteristics of Unbound Permeable Aggregate Base Materials from Meso-Scale Particle Movement Measured by Smart Sensing Technology.

The quality of compaction of unbound aggregate materials with permeable gradation plays a vital role in their field performance; however, there are currently few unanimously accepted techniques or quality control criteria available for ensuring adequate compaction of such materials in either laboratory or field applications. This paper presented testing results of a laboratory gyratory compaction study where the combinations of gyratory parameters were properly designed using the orthogonal array theory. Innovative real-time particle motion sensors were employed to record particle movement characteristics during the compaction process and provide a meso-scale explanation about compaction mechanisms. Particle abrasion and breakage were also quantified from particle shape digitized from the three-dimensional (3D) laser scanner before and after compaction. The optimal combination of gyratory parameters that yields the best compaction performance was determined from the orthogonal testing results with the relative importance of major influencing parameters ranked accordingly. Meso-scale particle movement at the upper center and center side positions of the specimen are promising indicators of compaction quality. The gyratory compaction process can be consistently divided into three distinct stages according to both macro-scale performance indicators and meso-scale particle movement characteristics. A statistically significant bi-linear relationship was found to exist between relative breakage index and maximum abrasion depth, whereas the quality of compaction and the extent of particle breakage appear to be positively correlated, thus necessitating the cost-effective balance between them. The results of this study could provide technical insights and guidance to field compaction of unbound permeable aggregates.

The correlation between stroke characteristics and stroke effect of young table tennis players.

BACKGROUND: An optimal stroke is essential for winning table tennis competition. The main purpose of this study was to examine the correlations between the stroke characteristics and the stroke effect. METHODS: Forty-two young table tennis players were randomly selected from China Table Tennis College (Mage=14.21; Mheight=1.57m; Mweight=46.05 kg, right-hand racket, shake-hands grip, no injuries in each joint of the body). The high-speed infrared motion capture system was used to collect the data of stroke characteristics, and the high-speed camera was used to measure the spin speed of the stroke. The influence of stroke characteristics on stroke effect was analyzed. RESULTS: The time duration of backswing and forward motion were significantly correlated with ball speed (r=-0.403, P<0.01; r=-0.390, P<0.01, respectively) and spin speed (r=-0.244, P=0.027; r=-0.369, P<0.01, respectively). The ball speed was positively correlated with the linear velocity of right wrist joint (r=0.298, P<0.01), and the angular velocity of right elbow joint (r=0.219, P=0.013), right hip joint (r=0.427, P<0.01) and right ankle joint (r=0.443, P<0.01). The spin speed was positively correlated with the linear velocity of right wrist joint (r=0.238, P=0.031), and the angular velocity of right elbow joint (r=0.172, P=0.048) and right hip joint (r=0.277, P=0.012). The placement had a negative correlation with the angular velocity of right knee joint (r=-0.246, P=0.026). CONCLUSIONS: The time allocation of the three phases of backspin forehand stroke had an important correlation with stroke effect, especially the ball speed and spin speed. The movement of the right wrist joint and right ankle joint were mainly correlated with the ball speed of the stroke. The spin speed of the stroke was mainly correlated with the movement of the right wrist joint. The placement of the stroke was mainly correlated with the rotation of the right knee joint.