Identification of male schizophrenia patients using brain morphology based on machine learning algorithms.

BACKGROUND: Schizophrenia is a severe psychiatric disease, and its prevalence is higher. However, diagnosis of early-stage schizophrenia is still considered a challenging task. AIM: To employ brain morphological features and machine learning method to differentiate male individuals with schizophrenia from healthy controls. METHODS: The least absolute shrinkage and selection operator and t tests were applied to select important features from structural magnetic resonance images as input features for classification. Four commonly used machine learning algorithms, the general linear model, random forest (RF), k-nearest neighbors, and support vector machine algorithms, were used to develop the classification models. The performance of the classification models was evaluated according to the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 8 important features with significant differences between groups were considered as input features for the establishment of classification models based on the four machine learning algorithms. Compared to other machine learning algorithms, RF yielded better performance in the discrimination of male schizophrenic individuals from healthy controls, with an AUC of 0.886. CONCLUSION: Our research suggests that brain morphological features can be used to improve the early diagnosis of schizophrenia in male patients.

Investigation of peripheral inflammatory biomarkers in association with violence in schizophrenia.

BACKGROUND: Violent behavior carried out by patients with schizophrenia (SCZ) is a public health issue of increasing importance that may involve inflammation. Peripheral inflammatory biomarkers, such as the systemic immune inflammation index (SII), the neutrophil lymphocyte ratio (NLR), the platelet-lymphocyte ratio (PLR) and the monocyte lymphocyte ratio (MLR) are objective, easily accessible and cost-effective measures of inflammation. However, there are sparse studies investigating the role of peripheral inflammatory biomarkers in violence of patients with SCZ. METHODS: 160 inpatients diagnosed with SCZ between January and December 2022 were recruited into this study. Violent behavior and positive symptoms of all participants were evaluated using Modified Overt Aggression Scale (MOAS) and Positive and Negative Syndrome Scale (PANSS), respectively. The partial correlation analysis was performed to examine the relationship of inflammatory indices and positive symptoms. Based on machine learning (ML) algorithms, these different inflammatory indices between groups were used to develop predictive models for violence in SCZ patients. RESULTS: After controlling for age, SII, NLR, MLR and PANSS positive scores were found to be increased in SCZ patients with violence, compared to patients without violence. SII, NLR and MLR were positively related to positive symptoms in all participants. Positive symptoms partially mediated the effects of peripheral inflammatory indices on violent behavior in SCZ. Among seven ML algorithms, penalized discriminant analysis (pda) had the best performance, with its an area under the receiver operator characteristic curve (AUC) being 0.7082. Subsequently, with the use of pda, we developed predictive models using four inflammatory indices, respectively. SII had the best performance and its AUC was 0.6613. CONCLUSIONS: These findings suggest that inflammation is involved in violent behavior of SCZ patients and positive symptoms partially mediate this association. The models built by peripheral inflammatory indices have a good median performance in predicting violent behavior in SCZ patients.

Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence.

The pursuit of artificial general intelligence (AGI) continuously demands higher computing performance. Despite the superior processing speed and efficiency of integrated photonic circuits, their capacity and scalability are restricted by unavoidable errors, such that only simple tasks and shallow models are realized. To support modern AGIs, we designed Taichi-large-scale photonic chiplets based on an integrated diffractive-interference hybrid design and a general distributed computing architecture that has millions-of-neurons capability with 160-tera-operations per second per watt (TOPS/W) energy efficiency. Taichi experimentally achieved on-chip 1000-category-level classification (testing at 91.89% accuracy in the 1623-category Omniglot dataset) and high-fidelity artificial intelligence-generated content with up to two orders of magnitude of improvement in efficiency. Taichi paves the way for large-scale photonic computing and advanced tasks, further exploiting the flexibility and potential of photonics for modern AGI.

Global evaluation of carbon neutrality and peak carbon dioxide emissions: current challenges and future outlook.

With the acceleration of urbanization and industrialization, carbon neutrality and peak carbon dioxide emissions have become common sustainability goals worldwide. However, there are few literature statistics and econometric analyses targeting carbon neutrality and peak carbon dioxide emissions, especially the publication trends, geographic distribution, citation literature, and research hotspots. To conduct an in-depth analysis of existing research fields and future perspectives in this research area, 1615 publications from the Web of Science Core Collection, between 2010 and 2020, were evaluated by using three analysis tools, under the framework of the bibliometrics method. These publications are distributed between the start-up (2010-2015) and the stable development (2016-2020) phases. Cluster analysis suggests three areas of ongoing research: energy-related carbon emissions, methane emissions, and energy biomass. Overall, future trends in this field include cumulative carbon emissions, the residential building sector, methane emission measurement, nitrogen fertilization, land degradation neutrality, and sciamachy satellite methane measurement. Finally, this paper further examines the most comprehensive coverage of nitrogen fertilization and the most recent research of the residential building sector. In view of the statistical clusters from 1615 publications, this paper provides new insights and perspectives for climate-environment-related researchers and policymakers. Specifically, countries could apply nitrogen fertilizer to crops according to the conditions of different regions. Additionally, experiences from developed countries could be learned from, including optimizing the energy supply structure of buildings and increasing the use of clean energy to reduce CO2 emissions from buildings.

Exploring the potential mechanism of Yixiang capsule immune-enhancing function based on Network Pharmacology, Molecular docking, and Animal function experiment.

BACKGROUND: The Yixiang capsule with traditional Chinese herbs Cistanches Herba, Hedysarum multijugum Maxim,Fructus Ligustri Lucidi,and Fructus lycii are the main raw materials in a modern herbal medicine preparation containing corresponding bioactive ingredients after extraction and processing. This study aimed to investigate the immune-enhancing function of the Yixiang capsule and explore its potential mechanism. METHODS: After oral administration of the Yixiang capsule to mice for 30 days, the spleen lymphocyte transformation activity, NK cell activity, DTH, macrophage phagocytosis, HC50 value, amounts of antibody-producing cells, carbon particle clearance rate, spleen, and thymus index were determined to evaluate the regulation effect of the Yixiang capsule on immune function. The potential mechanism underlying the immune-enhancing function of the Yixiang capsule was studied based on network pharmacology and molecular docking. RESULTS: The results showed that the Yixiang capsules can obviously increase the HC50 values in all groups, significantly enhance the DTH reaction of mice, the carbon clearance function in medium and high dose groups,and the phagocytic function of macrophages in high dose groups, but had no significant effect on NK cell activity, T lymphocyte proliferation, body weight and immune organ weight of mice. A total of 78 compounds of the Yixiang capsule may affect 123 metabolic pathways and 1022 GO terms by regulating 232 targets, such as the cancer signaling pathway ad the signal pathways of response to lipopolysaccharide, and the key targets predicted could stably bind to the core active components of the Yixiang capsule. CONCLUSION: These results suggested that the Yixiang capsule could enhance the immune function of mice and have the characteristics of a multi-component and multi-target synergistic effect on the enhanced immunity function, which provides the basis for the follow-up study.

Prediction of violence in male schizophrenia using sMRI, based on machine learning algorithms.

BACKGROUND: Violent behavior in patients with schizophrenia (SCZ) is a major social problem. The early identification of SCZ patients with violence can facilitate implementation of targeted intervention. METHODS: A total of 57 male SCZ patients were recruited into this study. The general linear model was utilized to compare differences in structural magnetic resonance imaging (sMRI) including gray matter volume, cortical surface area, and cortical thickness between 30 SCZ patients who had exhibited violence and 27 SCZ patients without a history of violence. Based on machine learning algorithms, the different sMRI features between groups were integrated into the models for prediction of violence in SCZ patients. RESULTS: After controlling for the whole brain volume and age, the general linear model showed significant reductions in right bankssts thickness, inferior parietal thickness as well as left frontal pole volume in the patients with SCZ and violence relative to those without violence. Among seven machine learning algorithms, Support Vector Machine (SVM) have better performance in differentiating patients with violence from those without violence, with its balanced accuracy and area under curve (AUC) reaching 0.8231 and 0.841, respectively. CONCLUSIONS: Patients with SCZ who had a history of violence displayed reduced cortical thickness and volume in several brain regions. Based on machine learning algorithms, structural MRI features are useful to improve predictive ability of SCZ patients at particular risk of violence.

The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms.

Background: Early to identify male schizophrenia patients with violence is important for the performance of targeted measures and closer monitoring, but it is difficult to use conventional risk factors. This study is aimed to employ machine learning (ML) algorithms combined with routine data to predict violent behavior among male schizophrenia patients. Moreover, the identified best model might be utilized to calculate the probability of an individual committing violence. Method: We enrolled a total of 397 male schizophrenia patients and randomly stratified them into the training set and the testing set, in a 7:3 ratio. We used eight ML algorithms to develop the predictive models. The main variables as input features selected by the least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were integrated into prediction models for violence among male schizophrenia patients. In the training set, 10 × 10-fold cross-validation was conducted to adjust the parameters. In the testing set, we evaluated and compared the predictive performance of eight ML algorithms in terms of area under the curve (AUC) for the receiver operating characteristic curve. Result: Our results showed the prevalence of violence among male schizophrenia patients was 36.8%. The LASSO and LR identified main risk factors for violent behavior in patients with schizophrenia integrated into the predictive models, including lower education level [0.556 (0.378-0.816)], having cigarette smoking [2.121 (1.191-3.779)], higher positive syndrome [1.016 (1.002-1.031)] and higher social disability screening schedule (SDSS) [1.081 (1.026-1.139)]. The Neural Net (nnet) with an AUC of 0.6673 (0.5599-0.7748) had better prediction ability than that of other algorithms. Conclusion: ML algorithms are useful in early identifying male schizophrenia patients with violence and helping clinicians take preventive measures.

Sustainable development and health assessment model of higher education in India: A mathematical modeling approach.

The Coronavirus Disease 2019 has resulted in a transition from physical education to online learning, leading to a collapse of the established educational order and a wisdom test for the education governance system. As a country seriously affected by the pandemic, the health of the Indian higher education system urgently requires assessment to achieve sustainable development and maximize educational externalities. This research systematically proposes a health assessment model from four perspectives, including educational volume, efficiency, equality, and sustainability, by employing the Technique for Order Preference by Similarity to an Ideal Solution Model, Principal Component Analysis, DEA-Tobit Model, and Augmented Solow Model. Empirical results demonstrate that India has high efficiency and an absolute health score in the higher education system through multiple comparisons between India and the other selected countries while having certain deficiencies in equality and sustainability. Additionally, single-target and multiple-target path are simultaneously proposed to enhance the Indian current education system. The multiple-target approach of the India-China-Japan-Europe-USA process is more feasible to achieve sustainable development, which would improve the overall health score from .351 to .716. This finding also reveals that the changes are relatively complex and would take 91.5 years considering the relationship between economic growth rates and crucial indicators. Four targeted policies are suggested for each catching-up period, including expanding and increasing the social funding sources, striving for government expenditure support to improve infrastructures, imposing gender equality in education, and accelerating the construction of high-quality teachers.