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Background: Suicide is the second leading cause of death among 15- to 29-year-olds in China, and 60% of suicidal patients have a history of depression. Previous brain imaging studies have shown that depression and suicide may be associated with abnormal activity in default mode network (DMN) regions. However, no study has specifically investigated the relationship between DMN functional activity and suicidal behavior in depressed individuals. Therefore, in the present study, we directly investigated features of DMN brain activity in adolescent patients with histories of depression and attempted suicide. Methods: A total of 35 sex- and age-matched suicidal depressed patients were compared with 18 non-suicidal depressed patients and 47 healthy controls. We explored functional activity changes in DMN regions that could be associated with suicidal behavior by comparing resting-state functional magnetic resonance imaging (rs-fMRI) signals using independent component analysis (ICA). Scores on six clinical scales that measure depression severity (Hamilton Depression Scale (HDRS) and Beck Depression Inventory (BDI)) and suicidal traits (Barratt Impulsiveness Scale (BIS-11), Suicide Attitude Questionnaire (SAQ), Beck Hopelessness Scale (BHS), and Scale for Suicide Ideation (SSI)) were compared in the three groups. Results: Compared with the healthy controls, all of the evaluated depressed patients showed increased functional connectivity in select DMN regions. The suicidal patients showed increased connectivity in the left cerebellum and decreased connectivity in the right posterior cingulate cortex (PCC), whereas the non-suicidal depressed patients showed increased connectivity in the left superior frontal gyrus, left lingual gyrus and right precuneus and decreased connectivity in the left cerebellum. Compared to the non-suicidal patients, the suicidal patients showed increased connectivity in the left cerebellum and the left lingual gyrus and decreased connectivity in the right precuneus. No differences in the scores of any clinical scales were found between the suicidal and non-suicidal depressed patients. Conclusions: Collectively, our results highlight the importance of the DMN in the pathophysiology of depression and suggest that suicidal behavior in depressed adolescents may be related to abnormal functional connectivity in the DMN. In particular, abnormal connectivity in the PCC/precuneus and left cerebellum might be a predictor of suicidal behavior in depressed adolescent patients. [ABSTRACT FROM AUTHOR]

• The AdaBoost algorithm is adopted to predict the compressive strength of concrete. • 1030 sets of data is collected to train the model and reaches an accuracy of 98%. • Different algorithms are compared to show the superior of the proposed model. • Key factors and in the AdaBoost and influence of input variables are investigated. In this paper, an intelligent approach based on the machine learning technique is proposed for predicting the compressive strength of concrete. This approach employs the adaptive boosting algorithm to construct a strong learner by integrating several weak learners, which can find the mapping between the input data and output data. The weak learner whose predicting error is small will have a larger weight in the entire system, thus the overall accuracy of the strong learner will be enhanced. A total of 1030 sets of concrete compressive strength tests is collected to train and test the learners, in which the concrete mixture components (e.g., coarse/fine aggregates, cement, water, additive, etc.) and the curing time are set as the input data while the compressive strength value is set as the output data. The proposed approach is validated through a 10-fold cross validation method, and reaches an average accuracy of over 95% in sense of determination coefficient. In addition, a new dataset of 103 samples for concrete compressive strength is also adopted to demonstrate the generalization power of the proposed mode. The proposed approach is also compared to some other individual machine learning techniques that are already applied in this field, e.g., artificial neural network (ANN) and support vector machine (SVM), and shows superior advantages over these methods. Finally, the influence of some key factors in the adaptive boosting approach is also investigated, e.g., the amount of training data, the choice of weak learner, and the influence of the sensitivity and number of the input parameters. It is shown that using 80% of the total data for training can obtain acceptable prediction results and decision tree is the best choice for the weak learner in the boosting framework. Also, the importances of different input variables are obtained based on the sensitivity analysis results. [ABSTRACT FROM AUTHOR]