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Tourette syndrome (TS) is a childhood-onset neuropsychiatric disorder characterized by the presence of multiple motor and vocal tics. Because of its varied clinical expressions and lack of reliable diagnostic biomarker, present TS diagnosis still depends on qualitative descriptions of symptoms. Our study aimed to investigate whether the complexity of resting state brain activity can serve as a potential biomarker for TS diagnosis, since it has been used successfully in various neuropsychiatric disorders, including two common TS comorbidities: attention-deficit hyperactivity disorder (ADHD) and obsessive–compulsive disorder (OCD). In the current study, we used both univariate analysis and multivariate searchlight analysis with both linear and non-linear classification methods to explore the group differences in the complexity of resting state brain blood oxygen level-dependent (BOLD) signals between 25 TS boys without comorbidity and 25 sex, age and educational years matched healthy controls (HCs). We also investigated the relation between symptom severity in TS patients (YGTSS scores) and complexity indices derived from different analysis methods. We found: i) univariate analysis revealed reduced complexity in TS patients in the left cerebellum, left superior frontal gyrus, and left medial frontal gyrus; ii) multivariate analysis with non-linear classification method achieved the highest performance (accuracy: 0.94, sensitivity: 0.96, specificity: 0.92, AUC: 0.95) in bilateral supplementary motor areas; iii) significant correlations were found between complexity index derived from multivariate analysis with non-linear classification method and Tic severity (YGTSS scores) in the left cerebellum (r = 0.523, with YGTSS phonic) and in the right supplementary motor area (r = 0.767, with YGTSS motor). Taken together, these results suggested that complexity of resting state BOLD activity is a highly effective index for differentiating TS patients from normal controls. It has a good potential to be a quantitative biomarker for TS diagnosis. [ABSTRACT FROM AUTHOR]

In soil, organic matter and mineral particles (soil particles; SPs) strongly influence the bio-available fraction of organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), and the metabolic activity of bacteria. However, the effect of SPs as well as comparative approaches to discriminate the metabolic responses to PAHs from those to simple carbon sources are seldom considered in mineralization experiments, limiting our knowledge concerning the dynamics of contaminants in soil. In this study, the metabolic profile of a model PAH-degrading bacterium, Pseudomonas putida G7, grown in the absence and presence of different SPs (i.e., sand, clays and humic acids), using either phenanthrene or glucose as the sole carbon and energy source, was characterized using vibrational spectroscopy (i.e., FT-Raman and FT-IR spectroscopy) and multivariate classification analysis (i.e., PLS-DA). The different type of SPs specifically altered the metabolic profile of P. putida, especially in combination with phenanthrene. In comparison to the cells grown in the absence of SPs, sand induced no remarkable change in the metabolic profile of the cells, whereas clays and humic acids affected it the most, as revealed by the higher discriminative accuracy (R2, RMSEP and sensitivity) of the PLS-DA for those conditions. With respect to the carbon-source (phenanthrene vs. glucose), no effect on the metabolic profile was evident in the absence of SPs or in the presence of sand. On the other hand, with clays and humic acids, more pronounced spectral clusters between cells grown on glucose or on phenanthrene were evident, suggesting that these SPs modify the way cells access and metabolize PAHs. The macromolecular changes regarded mainly protein secondary structures (a shift from α-helices to β-sheets), amino acid levels, nucleic acid conformation and cell wall carbohydrates. Our results provide new interesting evidences that SPs specifically interact with PAHs in defining bacteria metabolic profiles and further emphasize the importance of studying the interaction of bacteria with their surrounding matrix to deeply understand PAHs degradation in soils.

1 A high-throughput method was established for detecting esophageal squamous cell carcinoma (ESCC) using patient serum and plasma by transmission FTIR method. 2 For early-stage cancer discrimination, the method PC-LDA can differentiate ESCC from the normal group with an accuracy of 97.08%, a sensitivity of 91.43% and a specificity of 100%. 3 For late-stage cancer, the established model PC-LDA can differentiate ESCC from normal group with an accuracy of 99.26%, a sensitivity of 98.5%, and a specificity of 100%. Fourier transform infrared (FTIR) spectroscopy, as a platform technology for cancer detection, must be up to the challenge of clinical transformation. To this end, detection of esophageal squamous cell carcinoma (ESCC) was hereby explored using serum and plasma scrape-coated on barium fluoride (BaF 2) disk by transmission FTIR method, and the classification model was built using six multivariate statistical analyses, including support vector machine (SVM), principal component linear discriminant analysis (PC-LDA), decision tree (DT), k-nearest neighbor (KNN) classification, ensemble algorithms (EA) and partial least squares for discriminant analysis (PLS-DA). All statistical analyses methods demonstrated that late-stage cancer could be well classified from healthy people employing either serum or plasma with different anticoagulants. Resulting PC-LDA model differentiated late-stage cancer from normal group with an accuracy of 99.26%, a sensitivity of 98.53%, and a specificity of 100%. The accuracy and sensitivity reached 97.08% and 91.43%, respectively for early-stage cancer discrimination from normal group. This pilot exploration demonstrated that transmission FTIR provided a rapid, cost effective and sensitive method for ESCC diagnosis using either serum or plasma. [Display omitted] [ABSTRACT FROM AUTHOR]

In two replication studies we examined response bias and sequential dependencies in binary decisions. We applied a linear classifier (SVM) to predict spontaneous decisions as well as hidden intentions from responses in preceding trials and achieve similar prediction accuracies as multivariate pattern classification of voxel activities in frontopolar cortex. We discuss implications of our findings and suggest a simple way to improve analyses of fMRI BOLD signals to contain effects of sequential dependencies between trials.

Since working memory deficits in schizophrenia have been linked to negative symptoms, we tested whether features of the one could predict the treatment outcome in the other. Specifically, we hypothesized that working memory-related functional connectivity at pre-treatment can predict improvement of negative symptoms in antipsychotic-treated patients. Fourteen antipsychotic-naive patients with first-episode schizophrenia were clinically assessed before and after 7 months of quetiapine monotherapy. At baseline, patients underwent functional magnetic resonance imaging while performing a verbal n-back task. Spatial independent component analysis identified task-modulated brain networks. A linear support vector machine was trained with these components to discriminate six patients who showed improvement in negative symptoms from eight non-improvers. Classification accuracy and significance was estimated by leave-one-out cross-validation and permutation tests, respectively. Two frontoparietal and one default mode network components predicted negative symptom improvement with a classification accuracy of 79% (p = 0.003). Discriminating features were found in the frontoparietal networks but not the default mode network. These preliminary data suggest that functional patterns at baseline can predict negative symptom treatment–response in schizophrenia. This information may be used to stratify patients into subgroups thereby facilitating personalized treatment. [ABSTRACT FROM AUTHOR]

In soil, organic matter and mineral particles (soil particles; SPs) strongly influence the bio-available fraction of organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), and the metabolic activity of bacteria. However, the effect of SPs as well as comparative approaches to discriminate the metabolic responses to PAHs from those to simple carbon sources are seldom considered in mineralization experiments, limiting our knowledge concerning the dynamics of contaminants in soil. In this study, the metabolic profile of a model PAH-degrading bacterium, Pseudomonas putida G7, grown in the absence and presence of different SPs (i.e., sand, clays and humic acids), using either phenanthrene or glucose as the sole carbon and energy source, was characterized using vibrational spectroscopy (i.e., FT-Raman and FT-IR spectroscopy) and multivariate classification analysis (i.e., PLS-DA). The different type of SPs specifically altered the metabolic profile of P. putida , especially in combination with phenanthrene. In comparison to the cells grown in the absence of SPs, sand induced no remarkable change in the metabolic profile of the cells, whereas clays and humic acids affected it the most, as revealed by the higher discriminative accuracy ( R 2 , RMSEP and sensitivity) of the PLS-DA for those conditions. With respect to the carbon-source (phenanthrene vs. glucose), no effect on the metabolic profile was evident in the absence of SPs or in the presence of sand. On the other hand, with clays and humic acids, more pronounced spectral clusters between cells grown on glucose or on phenanthrene were evident, suggesting that these SPs modify the way cells access and metabolize PAHs. The macromolecular changes regarded mainly protein secondary structures (a shift from α-helices to β-sheets), amino acid levels, nucleic acid conformation and cell wall carbohydrates. Our results provide new interesting evidences that SPs specifically interact with PAHs in defining bacteria metabolic profiles and further emphasize the importance of studying the interaction of bacteria with their surrounding matrix to deeply understand PAHs degradation in soils.