Mapping Blood Lead Levels in China during 1980-2040 with Machine Learning.

Lead poisoning is globally concerning, yet limited testing hinders effective interventions in most countries. We aimed to create annual maps of county-specific blood lead levels in China from 1980 to 2040 using a machine learning model. Blood lead data from China were sourced from 1180 surveys published between 1980 and 2022. Additionally, regional statistical figures for 15 natural and socioeconomic variables were obtained or estimated as predictors. A machine learning model, using the random forest algorithm and 2973 generated samples, was created to predict county-specific blood lead levels in China from 1980 to 2040. Geometric mean blood lead levels in children (i.e., age 14 and under) decreased significantly from 104.4 µg/L in 1993 to an anticipated 40.3 µg/L by 2040. The number exceeding 100 µg/L declined dramatically, yet South Central China remains a hotspot. Lead exposure is similar among different groups, but overall adults and adolescents (i.e., age over 14), females, and rural residents exhibit slightly lower exposure compared to that of children, males, and urban residents, respectively. Our predictions indicated that despite the general reduction, one-fourth of Chinese counties rebounded during 2015-2020. This slower decline might be due to emerging lead sources like smelting and coal combustion; however, the primary factor driving the decline should be the reduction of a persistent source, legacy gasoline-derived lead. Our approach innovatively maps lead exposure without comprehensive surveys.

[Association of aggressive behaviors of schizophrenia with short tandem repeats loci].

OBJECTIVE: To assess the association of short tandem repeats (STRs) loci with aggressive behaviors of schizophrenia. METHODS: Blood samples from 123 schizophrenic patients with aggressive behaviors and 489 schizophrenic patients without aggressive behaviors were collected. DNA from all samples was amplified with a PowerPlex 21 system and separated by electrophoresis to determine the genotypes and allelic frequencies of 20 STR loci including D3S1368, D1S1656, D6S1043, D13S317, Penta E, D16S639, D18S51, D2S1338, CSF1PO, Penta D, TH01, vWA, D21S11, D7S820, D5S818, TPOX, D8S1179, D12S391, D19S433, and FGA. RESULTS: All of the 20 STR loci have reached Hardy-Weinberg equilibrium in both groups. A significant difference was found in allelic and genotypic frequencies of loci Penta D between the two groups (alleles: P=0.042; genotypes: P=0.014) but not for the remaining 19 loci (P> 0.05). Univariate analysis also showed a significant difference for allele 10 and genotypes 10-12 of Penta D between the two groups (P=0.0027, P=0.0001), with the OR being 1.81 (95%CI: 1.22-2.67) and 4.33 (95%CI: 1.95-9.59), respectively. CONCLUSION: Penta D may be associated with aggressive behaviors of schizophrenia. Allele 10 and genotypes 10-12 of Penta D may confer a risk for the disease.

Low-quality fingerprint recognition using a limited ellipse-band-based matching method.

Current fingerprint recognition technologies are based mostly on the minutia algorithms, which cannot recognize fingerprint images in low-quality conditions. This paper proposes a novel recognition algorithm using a limited ellipse-band-based matching method. It uses the Fourier-Mellin transformation method to improve the limitation of the original algorithm, which cannot resist rotation changes. Furthermore, an ellipse band on the frequency amplitude is used to suppress noise that is introduced by the high-frequency parts of images. Finally, the recognition result is obtained by considering both the contrast and position correlation peaks. The experimental results show that the proposed algorithm can increase the recognition accuracy, particularly of images in low-quality conditions.

Defect detection of castings in radiography images using a robust statistical feature.

One of the most commonly used optical methods for defect detection is radiographic inspection. Compared with methods that extract defects directly from the radiography image, model-based methods deal with the case of an object with complex structure well. However, detection of small low-contrast defects in nonuniformly illuminated images is still a major challenge for them. In this paper, we present a new method based on the grayscale arranging pairs (GAP) feature to detect casting defects in radiography images automatically. First, a model is built using pixel pairs with a stable intensity relationship based on the GAP feature from previously acquired images. Second, defects can be extracted by comparing the difference of intensity-difference signs between the input image and the model statistically. The robustness of the proposed method to noise and illumination variations has been verified on casting radioscopic images with defects. The experimental results showed that the average computation time of the proposed method in the testing stage is 28 ms per image on a computer with a Pentium Core 2 Duo 3.00 GHz processor. For the comparison, we also evaluated the performance of the proposed method as well as that of the mixture-of-Gaussian-based and crossing line profile methods. The proposed method achieved 2.7% and 2.0% false negative rates in the noise and illumination variation experiments, respectively.

A novel garment transfer method supervised by distilled knowledge of virtual try-on model.

Garment transfer can wear the garment of the model image onto the personal image. As garment transfer leverages wild and cheap garment input, it has attracted tremendous attention in the community and has a huge commercial potential. Since the ground truth of garment transfer is almost unavailable in reality, previous studies have treated garment transfer as either pose transfer or garment-pose disentanglement, and trained garment transfer in self-supervised learning, However, these implementation methods do not cover garment transfer intentions completely and face the robustness issue in the testing phase. Notably, virtual try-on technology has exhibited superior performance using self-supervised learning, we propose to supervise the garment transfer training via knowledge distillation from virtual try-on. Specifically, the overall pipeline is first to infer a garment transfer parsing, and to use it to guide downstream warping and inpainting tasks. The transfer parsing reasoning model learns the response and feature knowledge from the try-on parsing reasoning model and absorbs the hard knowledge from the ground truth. The progressive flow warping model learns the content knowledge from virtual try-on for a reasonable and precise garment warping. To enhance transfer realism, we propose an arm regrowth task to infer exposed skin. Experiments demonstrate that our method has state-of-the-art performance in transferring garments between persons compared with other virtual try-on and garment transfer methods.

Spatial - temporal mapping of urine cadmium levels in China during 1980 - 2040: Dietary improvements lower exposure amid rising pollution.

Persistent cadmium exposure poses significant health risks to the Chinese population, underscored by its prevalence as an environmental contaminant. This study leverages a machine-learning model, fed with a comprehensive dataset of environmental and socio-economic factors, to delineate trends in cadmium exposure from 1980 to 2040. We uncovered that urinary cadmium levels peaked at 1.09 µg/g Cr in the mid-2000 s. Encouragingly, a decline is projected to 0.92 µg/g Cr by 2025, tapering further to 0.87 µg/g Cr by 2040. Despite this trend, regions heavily influenced by industrialization, such as Hunan and Guizhou, as well as industrial counties in Jilin, report stubbornly high levels of exposure. Our demographic analysis reveals a higher vulnerability among adults & adolescents over 14, with males displaying elevated cadmium concentrations. Alarmingly, the projected data suggests that by 2040, an estimated 41% of the population will endure exposure beyond the safety threshold set by the European Food Safety Authority. Our research indicates disproportionate cadmium exposure impacts, necessitating targeted interventions and policy reforms to protect vulnerable groups and public health in China.

Effects of Treatment with Probiotics on Cognitive Function and Regulatory Role of Cortisol and IL-1ß in Adolescent Patients with Major Depressive Disorder.

The aim of this study was to investigate the effects of probiotics on cognitive function and the regulation of cortisol and IL-1ß in adolescents with depression. All 180 participants were randomly assigned to a study group (treated with probiotics combined with sertraline hydrochloride) and a control group (treated with sertraline hydrochloride). The repetitive Neuropsychological State Test (RBANS) and Hamilton Depression Scale (HAMD) were administered to MDD patients. The levels of serum cortisol and IL-1ß were detected using an ELISA kit. Except for speech function, factors including immediate memory, visual span, attention function, delayed memory, and the RBANS in the study group were significantly higher than those in the control group. The levels of cortisol and interleukin-1ß in the study group were significantly downregulated compared to those in the control group. Except for speech function, the cortisol level was negatively correlated with the RBANS total score and other factors in the study group. Interleukin-1ß was also negatively correlated with the RBANS total score and each factor score. Cortisol and interleukin-1ß were predictors of the RBANS total score, which explained 46.80% of the variance. Cortisol had significant predictive effects on attention function and delayed memory, and interleukin-1ß had significant predictive effects on visual span and speech function. It could be concluded that probiotics could improve cognitive function in adolescents with depression by regulating cortisol and IL-1ß levels.

A novel neighbor selection scheme based on dynamic evaluation towards recommender systems.

Collaborative filtering is a kind of widely used and efficient technique in various online environments, which generates recommendations based on the rating information of his/her similar-preference neighbors. However, existing collaborative filtering methods have some inadequacies in revealing the dynamic user preference change and evaluating the recommendation effectiveness. The sparsity of input data may further exacerbate this issue. Thus, this paper proposes a novel neighbor selection scheme constructed in the context of information attenuation to bridge these gaps. Firstly, the concept of the preference decay period is given to describe the pattern of user preference evolution and recommendation invalidation, and thus two types of dynamic decay factors are correspondingly defined to gradually weaken the impact of old data. Then, three dynamic evaluation modules are built to evaluate the user's trustworthiness and recommendation ability. Finally, A hybrid selection strategy combines these modules to construct two neighbor selection layers and adjust the neighbor key thresholds. Through this strategy, our scheme can more effectively select capable and trustworthy neighbors to provide recommendations. The experiments on three real datasets with different data sizes and data sparsity show that the proposed scheme provides excellent recommendation performance and is more suitable for real applications, compared to the state-of-the-art methods.

Effects of Health-Promoting Lifestyle on Late-Onset Depression in Older Adults: Mediating Effect of Meaning in Life and Interleukin-6 (IL-6).

Purpose: Late-onset depression (LOD) with poor treatment response has high incidence and mortality in the China's aged people, this study aims to explore the correlation between health-promoting lifestyle, meaning in life, interleukin-6 (IL-6) and LOD for providing scientific basis of LOD prevention and rehabilitation. Patients and Methods: A total of 496 LOD patients (study group) and healthy older adults (control group) were enrolled and investigated by using the Health-promoting lifestyle Profile-II, revised (HPLP-IIR), Meaning in Life Questionnaire-Chinese Version (MLQ-C), and Hamilton Depression Scale (HAMD). The interleukin-6 (IL-6) in the circulating blood was detected by utilizing ELISA kit. Results: The results showed that the scores of all factors in HPLP-IIR and MLQ were significantly lower and IL-6 level was higher in the study group than the control group. Scores of most factors in HPLP-IIR and MLQ negatively and IL-6 positively correlated with scores of subscales and total HAMD score. Meaning in life and IL-6 partially mediated the relationship between health-promoting lifestyles and depression severity in the study group, with the mediating effect explains 15.76% and 22.64% of the total effect, respectively. Conclusion: Health-promoting lifestyles, meaning in life, and IL-6 are predictors of LOD, and an unhealthy lifestyle could induce LOD through the mediating effect of meaning in life and IL-6 in older adults.

Robustness optimization for rapid prototyping of functional artifacts based on visualized computing digital twins.

This study presents a robustness optimization method for rapid prototyping (RP) of functional artifacts based on visualized computing digital twins (VCDT). A generalized multiobjective robustness optimization model for RP of scheme design prototype was first built, where thermal, structural, and multidisciplinary knowledge could be integrated for visualization. To implement visualized computing, the membership function of fuzzy decision-making was optimized using a genetic algorithm. Transient thermodynamic, structural statics, and flow field analyses were conducted, especially for glass fiber composite materials, which have the characteristics of high strength, corrosion resistance, temperature resistance, dimensional stability, and electrical insulation. An electrothermal experiment was performed by measuring the temperature and changes in temperature during RP. Infrared thermographs were obtained using thermal field measurements to determine the temperature distribution. A numerical analysis of a lightweight ribbed ergonomic artifact is presented to illustrate the VCDT. Moreover, manufacturability was verified based on a thermal-solid coupled finite element analysis. The physical experiment and practice proved that the proposed VCDT provided a robust design paradigm for a layered RP between the steady balance of electrothermal regulation and manufacturing efficacy under hybrid uncertainties.

A remaining useful life prediction method based on PSR-former.

The non-linear and non-stationary vibration data generated by rotating machines can be used to analyze various fault conditions for predicting the remaining useful life(RUL). It offers great help to make prognostic and health management(PHM) develop. However, the complexity of the mechanical working environment makes the vibration data collected easily affected, so it is hard to form an appropriate health index(HI) to predict the RUL. In this paper, a PSR-former model is proposed including a Phase space reconstruction(PSR) layer and a Transformer layer. The PSR layer is utilized as an embedding to deepen the understanding of vibration data after feature fusion. In the Transformer layer, an attention mechanism is adopted to give different assignments, and a layer-hopping connection is used to accelerate the convergence and make the structure more stable. The effectiveness of the proposed method is validated through the Intelligent Maintenance Systems (IMS) bearing dataset. Through analysis, the prediction accuracy is judged by the parameter RMSE which is 1.0311. Some state-of-art methods such as LSTM, GRU, and CNN were also analyzed on the same dataset to compare. The result indicates that the proposed method can effectively establish a precise model for RUL predictions.

Springback angle prediction of circular metal tube considering the interference of cross-sectional distortion in mandrel-less rotary draw bending.

The springback directly affects the forming accuracy and quality of metal bent-tube, and accurate springback prediction is the key to the springback compensation and control. This paper investigates the springback of mandrel-less rotary draw bending (MLRDB) of circular metal tubes, and an innovative method, springback angle prediction considering the interference of cross-sectional distortion (IoCSD-SAP), is proposed. The digit decomposition condition variational auto-encoder generative adversarial network (D2CVAE-GAN) is developed to augment the data samples. Considering the nonlinear interference of the cross-sectional distortion on springback, auxiliary extended radial basis function (AE-RBF) is proposed. It establishes the mapping relationship between the characteristic parameters and cross-sectional distortion. By extracting the information encode of cross-sectional distortion as the condition input, this model realizes the condition prediction of springback angle. Taking MLRDB of 6060-T6 Al-alloy circular tube as a case study, the proposed method, IoCSD-SAP, is verified. According to the experimental results, the mean absolute percentage error (MAPE) for the springback angle of our proposed method is 4.73%, and three different analytical models are 38.92%, 14.39%, and 14.22%, respectively. It can be seen that our proposed method significantly improves the prediction accuracy of springback angle. For the springback angle prediction of circular metal tube in MLRDB, the data augmentation can effectively reduce the generalization error and improve the prediction accuracy. The nonlinear interference of the cross-sectional distortion on springback should be taken into account to improve the accuracy and robustness of the springback prediction model.

Support Diminution Design for Layered Manufacturing of Manifold Surface Based on Variable Orientation Tracking.

This article proposes a support diminution design method for layered manufacturing of manifold surface based on variable orientation tracking (VOT). We aim at reducing the external support or upholders to a minimum with maximum possibility theoretically to save material and diminish material stripping effect (MSE), thereby improving the bilateral surface precision either exterior or interior. The cosmic gravity effect criterion is first used to extract surface need support from manifold surface with various materials by considering the balance force involving material characteristics and inclination angle. In the light of this criterion theory, varying the substrate normal orientation (SNO), namely workbench, for each layer in printing coordinate system, may break the balance between gravity and its equilibrium force. Therefore, the optimal SNO can be rigorously calculated using mathematical harmonic analysis among the continuous domain. To serve for the multidegree of freedom (DOF) on account of SNO, a reconfigurable VOT robot with six-axis DOF is developed for 3D printing (3DP). The matched servo controller is successfully implemented to accurate tracking of both orientation and Cartesian coordinates, using forward kinematic chains as well as reverse kinematic tracking. What is more, the end-effector (extruder) is holding perpendicular to the substrate workbench. The physical experiment that takes human external ear auricle, for example, using a layer-based process is implemented via VOT. The MSE due to supporting material can be clearly observed and diminished using an optical microscope. The stripped material from external support via diminution design can be evaluated quantitatively by electronic weighting balance. All of which indicate the findings that external support in 3DP can be virtually reckoned and diminished using VOT rather than the so-called build orientation traversal method. The VOT method upon which we touched can be widely applied to various layered manufacturing of accurate structure, for instance, cantilever, sandwich, and scaffolds in the occasion needing precise curtailment of outer support multimaterial.

Genome-wide study of key genes and scoring system as potential noninvasive biomarkers for detection of suicide behavior in major depression disorder.

Although some progress has been made in the molecular biological detection of major depression disorder (MDD), its specificity and accuracy are still insufficient. This study is aimed to find hub genes, which could contribute to MDD related suicide and provide potential therapeutic targets for diagnosis and treatment. We downloaded RNA expression and clinical information from Gene Expression Omnibus (GEO) Dataset. Then, weighted gene co-expression network analysis (WGCNA) was applied to find core modules. Logistic regression was performed to identify the independent risk factors, and a scoring system was constructed based on these independent risk factors. As a result, a total of 16487 genes were selected to further conducted WGCNA analysis. We found that tan and green functional modules were exhibited high correlation with suicide behavior. 309 genes were identified in tan modules that were the strongest positively correlated with suicide behavior. Functional analysis in tan module indicated that activation of enzymes including nitric-oxide synthase and endoribonuclease, estrogen signaling pathway, glucagon signaling pathway, and legionellosis pathway were most enriched in MDD. Furthermore, we applied protein-protein interaction (PPI) analysis to select the hub genes and 10 genes were found in the core area of network. Then, we identified three-gene base independent risk signature by logistic regression model, including HSPA1A, RASEF, TBC1D8B. In conclusion, our study suggests that the tan module genes are closely related to suicide behaviors, which is mainly caused by multiple signaling pathway activation. The three-genes-based signature could provide a better efficacy to predict suicidal behavior in MDD patients.

Biomechanical performance design of joint prosthesis for medical rehabilitation via generative structure optimization.

This paper proposes a biomechanical performance design method of joint prosthesis for medical rehabilitation via Generative Structure Optimization (GSO). Firstly, the 3D reconstruction of manifold structure involving hard bone and cartilage is sequentially and progressively implemented from heterogeneous medical images such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) via iteration. On the basis of reconstructed mesh structure, the finite element method (FEM) is hereby employed to verify the structure by evaluating the mechanical force distribution. The biomechanical performance design model for 3 D printing (3DP) is then built using multi-objective optimization (MOO) by considering adaptive layer thickness, infill patterns and infill trajectories, etc. The GSO outlets a generative data-driven system which covers various stages such as personalized CT, subsequent 3 D reconstruction, further finite element analysis (FEA) and even structural parameter optimization. The physical experiment of Additive manufacturing (AM) proves that, the relative density, surface topography and wear-resisting performance of joint prosthesis can be improved by GSO which helps to improve biomechanical performance, including kinematics and dynamics. The proposed method may arouse the huge attention in the prosthesis applications to promote patients' high-end customization well-being.

An EEG Study of a Confusing State Induced by Information Insufficiency during Mathematical Problem-Solving and Reasoning.

Confusion is a complex cognitive state that is prevalent during learning and problem-solving. The aim of this study is to explore the brain activity reflected by electroencephalography (EEG) during a confusing state induced by two kinds of information insufficiencies during mathematical problem-solving, namely, an explicit situation that clearly lacked information and an implicit situation in which the missing information was hidden in the problem itself, and whether there is an EEG difference between these two situations. Two experimental tasks and three control tasks were created. Short time Fourier transformation (STFT) was used for time-frequency analysis; then the alpha task-related-power (TRP) changes and distributions, which are closely related to cognitive processing, were calculated, and repeated measures ANOVA were performed to find the significant difference between task conditions. The results showed that the alpha power decreased significantly in the regions related to calculation when the participants encountered both explicit and implicit information insufficiency tasks compared to the control tasks, suggesting that confusion can cause more brain activity in the cortical regions related to the tasks that induce confusion. In addition, the implicit information insufficiency task elicited more activity in the parietal and right temporal regions, whereas the explicit information insufficiency task elicited additional activity in the frontal lobe, which revealed that the frontal region is related to the processing of novel or unfamiliar information and the parietal-temporal regions are involved in sustained attention or reorientation during confusing states induced by information insufficiency. In conclusion, this study has preliminarily investigated the EEG characteristics of confusion states, suggests that EEG is a promising methodology to detect confusion, and provides a basis for future studies aiming to achieve automatic recognition of confusing states.

The association of 22 Y chromosome short tandem repeat loci with initiative-aggressive behavior.

Aggressive behavior represents an important public concern and a clinical challenge to behaviorists and psychiatrists. Aggression in humans is known to have an important genetic basis, so to investigate the association of Y chromosome short tandem repeat (Y-STR) loci with initiative-aggressive behavior, we compared allelic and haplotypic distributions of 22 Y-STRs in a group of Chinese males convicted of premeditated extremely violent crimes (n = 271) with a normal control group (n = 492). Allelic distributions of DYS533 and DYS437 loci differed significantly between the two groups (P < 0.05). The case group had higher frequencies of DYS533 allele 14, DYS437 allele 14, and haplotypes 11-14 of DYS533-DYS437 compared with the control group. Additionally, the DYS437 allele 15 frequency was significantly lower in cases than controls. No frequency differences were observed in the other 20 Y-STR loci between these two groups. Our results indicate a genetic role for Y-STR loci in the development of initiative aggression in non-psychiatric subjects.

Linking Y-chromosomal short tandem repeat loci to human male impulsive aggression.

Introduction: Men are more susceptible to impulsive behavior than women. Epidemiological studies revealed that the impulsive aggressive behavior is affected by genetic factors, and the male-specific Y chromosome plays an important role in this behavior. In this study, we investigated the association between the impulsive aggressive behavior and Y-chromosomal short tandem repeats (Y-STRs) loci. Methods: The collected biologic samples from 271 offenders with impulsive aggressive behavior and 492 healthy individuals without impulsive aggressive behavior were amplified by PowerPlexRY23 PCR System and the resultant products were separated by electrophoresis and further genotyped. Then, comparisons in allele and haplotype frequencies of the selected 22 Y-STRs were made in the two groups. Results: Our results showed that there were significant differences in allele frequencies at DYS448 and DYS456 between offenders and controls (p < .05). Univariate analysis further revealed significant frequency differences for alleles 18 and 22 at DYS448 (0.18 vs 0.27, compared to the controls, p = .003, OR=0.57,95% CI=0.39-0.82; 0.03 vs 0.01, compared to the controls, p = .003, OR=7.45, 95% CI=1.57-35.35, respectively) and for allele 17 at DYS456 (0.07 vs 0.14, compared to the controls, p = .006, OR=0.48, 95% CI =0.28-0.82) between two groups. Interestingly, the frequency of haploid haplotype 22-15 on the DYS448-DYS456 (DYS448-DYS456-22-15) was significantly higher in offenders than in controls (0.033 vs 0.004, compared to the control, p = .001, OR = 8.42, 95%CI =1.81-39.24). Moreover, there were no significant differences in allele frequencies of other Y-STRs loci between two groups. Furthermore, the unconditional logistic regression analysis confirmed that alleles 18 and 22 at DYS448 and allele 17 at DYS456 are associated with male impulsive aggression. However, the DYS448-DYS456-22-15 is less related to impulsive aggression. Conclusion: Our results suggest a link between Y-chromosomal allele types and male impulsive aggression.