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In the wake of recent advances in machine learning research, the study of pharmacogenomics using predictive algorithms serves as a new paradigmatic application. In this work, our goal was to explore an ensemble machine learning approach which aims to predict probable antidepressant treatment response and remission in major depressive disorder (MDD). To discover the status of antidepressant treatments, we established an ensemble predictive model with a feature selection algorithm resulting from the analysis of genetic variants and clinical variables of 421 patients who were treated with selective serotonin reuptake inhibitors. We also compared our ensemble machine learning framework with other state-of-the-art models including multi-layer feedforward neural networks (MFNNs), logistic regression, support vector machine, C4.5 decision tree, naïve Bayes, and random forests. Our data revealed that the ensemble predictive algorithm with feature selection (using fewer biomarkers) performed comparably to other predictive algorithms (such as MFNNs and logistic regression) to derive the perplexing relationship between biomarkers and the status of antidepressant treatments. Our study demonstrates that the ensemble machine learning framework may present a useful technique to create bioinformatics tools for discriminating non-responders from responders prior to antidepressant treatments.

The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Background: The progressive neurodegenerative disorder Parkinson disease (PD) is well-established as the second most common neurodegenerative disease. Associations between the sequential risk of PD and gout have been addressed in other studies, but findings have been inconclusive. Accordingly, we executed the present study with the purpose of assessing PD risk in patients with gout., Methods: From Taiwan's National Health Insurance Research Database, we identified the data of patients newly diagnosed as having gout between January 1, 2000 and December 1, 2000. A cohort of patients without gout, matched for sex and age, was constructed for comparison. Hazard ratios (HRs) and the incidence rate of subsequent PD were calculated for both cohorts and separately for male and female groups. The gout and comparison cohorts consisted of 7900 patients each., Results: The HR for PD was not significantly higher in the gout cohort compared with the control cohort (HR 1.01, 95% confidence interval [CI], 0.93-1.31, P = .268), even after adjustment for age, urbanization, monthly income, sex, and comorbidities. We did not observe gender differences in the gout-PD association (male: HR 1.01, 95% CI, 0.88-1.36, P = .400; female: HR 1.11, 95% CI, 0.84-1.46, P = .466)., Conclusions: Our study identified that there was no protective effect of gout for the risk of PD in the Taiwanese population.

Despite growing evidence of aberrant neuronal complexity in Alzheimer's disease (AD), it remains unclear how this variation arises. Neural oscillations reportedly comprise different functions depending on their own properties. Therefore, in this study, we investigated details of the complexity of neural oscillations by decomposing the oscillations into frequency, amplitude, and phase for AD patients. We applied resting-state magnetoencephalography (MEG) to 17 AD patients and 21 healthy control subjects. We first decomposed the source time series of the MEG signal into five intrinsic mode functions using ensemble empirical mode decomposition. We then analyzed the temporal complexities of these time series using multiscale entropy. Results demonstrated that AD patients had lower complexity on short time scales and higher complexity on long time scales in the alpha band in temporal regions of the brain. We evaluated the alpha band complexity further by decomposing it into amplitude and phase using Hilbert spectral analysis. Consequently, we found lower amplitude complexity and higher phase complexity in AD patients. Correlation analyses between spectral complexity and decomposed complexities revealed scale-dependency. Specifically, amplitude complexity was positively correlated with spectral complexity on short time scales, whereas phase complexity was positively correlated with spectral complexity on long time scales. Regarding the relevance of cognitive function to the complexity measures, the phase complexity on the long time scale was found to be correlated significantly with the Mini-Mental State Examination score. Additionally, we examined the diagnostic utility of the complexity characteristics using machine learning (ML) methods. We prepared a feature pool using multiple sparse autoencoders (SAEs), chose some discriminating features, and applied them to a support vector machine (SVM). Compared to the simple SVM and the SVM after feature selection (FS + SVM), the SVM with multiple SAEs (SAE + FS + SVM) had improved diagnostic accuracy. Through this study, we 1) advanced the understanding of neuronal complexity in AD patients using decomposed temporal complexity analysis and 2) demonstrated the effectiveness of combining ML methods with information about signal complexity for the diagnosis of AD., (Copyright © 2020 Furutani, Nariya, Takahashi, Noto, Yang, Hirosawa, Kameya, Minabe and Kikuchi.)

Research suggests that the aging relates to variability of resting-state fMRI (rs-fMRI) signal and the functional connectivity. However, the association between the spatial and temporal activity of resting-state fMRI signal was less documented. We recruited 477 healthy Han Chinese participants, who were separated into young, middle and old groups to investigate the relationship between the variability and global functional connectivity (gFC) in different age ranges using standard deviation (SD) of time series and gFC, respectively. Our analysis revealed the changing patterns during healthy aging: 1) 17 brain regions(Olfactory_L, Orbital_L etc.) were identified to have significant association of age with both SD and gFC respectively by linear regression analysis; 2) Two typical associations could be observed between SD and gFC: positive and negative correlations; 3) The variation ratio of SD to gFC was changing with age at the voxel level by using unsupervised clustering method. It is the first time to combine voxel-wise variability and gFC together for the study of age-related changes with rs-fMRI signal. This study may provide a new clue for understanding the synchronization of human brain based on SD and gFC due to the effect of aging., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Sleep is a key factor for health-related quality of life since sleep disturbances are a significant and common problem for patients with various human diseases such as psychiatric disorders. While single nucleotide polymorphisms (SNPs) in circadian clock genes have been indicated to be associated with sleep duration, most of the association studies have been investigated in populations with European ancestry. It is believed that no studies have been conducted to investigate a link between sleep duration and the circadian clock genes RORA and RORB, which play a key role, with NR1D1, in an additional feedback loop for the circadian rhythm machinery., Methods: In this study, we assessed the relationships between sleep duration and SNPs in the circadian clock genes NR1D1, RORA, and RORB in the Taiwan Biobank with a sample of 10,112 Taiwanese subjects., Results: From our data, we revealed a novel significant association in sleep duration with the rs75981965 SNP (P = 9.93 × 10 -5 ) in the RORA gene that has not been previously identified. The association of sleep duration with this SNP remained significant after performing Bonferroni correction. RORA is a potential candidate for sleep duration as RORA has been suggested to play a key role in the regulation of sleep disorders. Additionally, we pinpointed the effects of interactions between RORA rs75981965 and environmental factors such as tea consumption (P = 0.0015), coffee consumption (P = 0.0029), physical activity (P = 0.011), alcohol consumption (P = 0.0146), and smoking (P = 0.0223) in influencing sleep duration. We also found interactions between RORA and NR1D1 (P = 0.0023) as well as between RORA and RORB (P = 0.0061) in affecting sleep duration., Conclusions: Our results indicate that the circadian clock gene RORA may contribute to sleep duration independently as well as through gene-gene and gene-environment interactions in the Taiwanese population., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Tinnitus has been implied as a "soft" sign of neurodegenerative disease, which is characterized by progressive loss of neuronal function, such as Alzheimer's disease (AD) and Parkinson's disease (PD). This study aimed to determine whether the risk of developing AD/PD increases after having tinnitus. We conducted a retrospective matched cohort study with 12,657 tinnitus patients and 25,314 controls from the National Health Insurance Research Database (NHIRD) in Taiwan with almost 10 years follow-up. Tinnitus-related risk on developing AD/PD followingly was determined by the Cox regression to identify potential confounding factors. Through the 10-year follow-up period, 398 individuals with tinnitus (3.1%) and 501 control individuals (2.0%) developed AD (P < 0.001), and 211 tinnitus patients (1.7%) and 249 control patients (1.0%) developed PD (P < 0.001). Compared with controls, patients with tinnitus were 1.54 times more likely to develop AD (95% confidence interval (CI) 1.34-1.78, P < 0.001) and 1.56 times more likely to develop PD (95% CI 1.29-1.89, P < 0.001), after adjusting confounding factors. Our results indicate an association between tinnitus and higher risk of developing AD and PD. Additional physical comorbidities may also increase the risk of developing AD and PD.

In São Paulo, Brazil, the first case of coronavirus disease 2019 (CoViD-19) was confirmed on 26 February, the first death due to CoViD-19 was registered on 16 March, and on 24 March, São Paulo implemented the isolation of persons in non-essential activities. A mathematical model was formulated based on non-linear ordinary differential equations considering young (60 years old or less) and elder (60 years old or more) subpopulations, aiming to describe the introduction and dissemination of the new coronavirus in São Paulo. This deterministic model used the data collected from São Paulo to estimate the model parameters, obtaining R0 = 6.8 for the basic reproduction number. The model also allowed to estimate that 50% of the population of São Paulo was in isolation, which permitted to describe the current epidemiological status. The goal of isolation implemented in São Paulo to control the rapid increase of the new coronavirus epidemic was partially succeeded, concluding that if isolation of at least 80% of the population had been implemented, the collapse in the health care system could be avoided. Nevertheless, the isolated persons must be released one day. Based on this model, we studied the potential epidemiological scenarios of release by varying the proportions of the release of young and elder persons. We also evaluated three different strategies of release: All isolated persons are released simultaneously, two and three releases divided in equal proportions. The better scenarios occurred when young persons are released, but maintaining elder persons isolated for a while. When compared with the epidemic without isolation, all strategies of release did not attain the goal of reducing substantially the number of hospitalisations due to severe CoViD-19. Hence, we concluded that the best decision must be postponing the beginning of the release.

The vascular interface of the brain, known as the blood-brain barrier (BBB), is understood to maintain brain function in part via its low transcellular permeability 1-3 . Yet, recent studies have demonstrated that brain ageing is sensitive to circulatory proteins 4,5 . Thus, it is unclear whether permeability to individually injected exogenous tracers-as is standard in BBB studies-fully represents blood-to-brain transport. Here we label hundreds of proteins constituting the mouse blood plasma proteome, and upon their systemic administration, study the BBB with its physiological ligand. We find that plasma proteins readily permeate the healthy brain parenchyma, with transport maintained by BBB-specific transcriptional programmes. Unlike IgG antibody, plasma protein uptake diminishes in the aged brain, driven by an age-related shift in transport from ligand-specific receptor-mediated to non-specific caveolar transcytosis. This age-related shift occurs alongside a specific loss of pericyte coverage. Pharmacological inhibition of the age-upregulated phosphatase ALPL, a predicted negative regulator of transport, enhances brain uptake of therapeutically relevant transferrin, transferrin receptor antibody and plasma. These findings reveal the extent of physiological protein transcytosis to the healthy brain, a mechanism of widespread BBB dysfunction with age and a strategy for enhanced drug delivery.

Supported metal catalysts are extensively used in industrial and environmental applications. To improve their performance, it is crucial to identify the most active sites. This identification is, however, made challenging by the presence of a large number of potential surface structures that complicate such an assignment. Often, the active site is formed by an ensemble of atoms, thus introducing further complications in its identification. Being able to produce uniform structures and identify the ones that are responsible for the catalyst performance is a crucial goal. In this work, we utilize a combination of uniform Pd/Pt nanocrystal catalysts and theory to reveal the catalytic active-site ensemble in highly active propene combustion materials. Using colloidal chemistry to exquisitely control nanoparticle size, we find that intrinsic rates for propene combustion in the presence of water increase monotonically with particle size on Pt-rich catalysts, suggesting that the reaction is structure dependent. We also reveal that water has a near-zero or mildly positive reaction rate order over Pd/Pt catalysts. Theory insights allow us to determine that the interaction of water with extended terraces present in large particles leads to the formation of step sites on metallic surfaces. These specific step-edge sites are responsible for the efficient combustion of propene at low temperature. This work reveals an elusive geometric ensemble, thus clearly identifying the active site in alkene combustion catalysts. These insights demonstrate how the combination of uniform catalysts and theory can provide a much deeper understanding of active-site geometry for many applications., Competing Interests: The authors declare no competing interest.

Objectives: The association between tinnitus and anxiety disorder remains debated. We used a retrospective cohort study to investigate the relationship between anxiety disorder and tinnitus, aiming to decipher possible risk factors for tinnitus in patients with anxiety disorder., Method: Data on a total of 7,525 patients with anxiety disorder and 15,050 patients without (comparison cohort) were extracted from the Longitudinal Health Insurance Database 2005 in Taiwan. The Kaplan-Meier estimator with the log rank test and the Cox proportional-hazard regression model were used to compare the incidence of tinnitus in both groups and to identify risk factors that predicted tinnitus., Results: After adjusting for related covariates, the hazard ratio for the development of tinnitus during the follow-up period was 3.54 (95% confidence interval: 3.11-4.02, P < .001) for anxiety disorder cohort relative to comparison cohort. Age ≧ 60 years, female sex, hypertension, and hyperlipidemia were statistically significant predictive risk factors of tinnitus in patients with anxiety disorder., Conclusion: A significant increase in the lifetime incidence of tinnitus was exhibited in patients with anxiety disorder. Elderly subjects, female sex, hypertension, and hyperlipidemia were risk factors. Clinicians should be alert to the possibility of tinnitus in subjects with anxiety disorder., (Copyright © 2020 Hou, Yang, Tsai, Shen and Lan.)

Schizophrenia and substance involvement frequently co-occur in individuals, and a bidirectional relationship between the two has been proposed; shared underlying genetic factors could be an alternative explanation. This study investigated the genetic overlap between schizophrenia and substance involvement, including tobacco, alcohol and betel nut use. The study subjects were recruited from the Taiwan Biobank, and genome-wide genotyping data was available for 18 327 participants without schizophrenia. We calculated the Psychiatric Genomics Consortium-derived polygenic risk score (PRS) for schizophrenia in each participant. The significance of the schizophrenia PRS associated with substance involvement was evaluated using a regression model with adjustments for gender, age and population stratification components. The modified effect of gender or birth decade was also explored. The schizophrenia PRS was positively associated with lifetime tobacco smoking in women (OR in per SD increase in PRS = 1.12 with 95% CI 1.04-1.20, P = .002), but not in men (OR = 0.99 with 95% CI 0.95-1.04, P = .74), and the gender-PRS interaction reached significance (P = .006). The OR between PRS and lifetime tobacco smoking increased with the birth decade (P of birth decade-PRS interaction = .0002). In women, OR increased from 0.97 (P = .85) for subjects with a birth decade before 1950 to 1.21 (P = .04) for subjects with a birth decade after 1980; in men, the corresponding OR increased from 0.88 (P = .04) to 1.13 (P = .11). There was no association between schizophrenia PRS and alcohol/betel nut use phenotypes. This study provides evidence for the genetic overlap between schizophrenia and tobacco use in women, and this overlap was stronger in the younger population., (© 2020 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.)

Objective: To investigate the characteristics of asymmetric hearing loss in left and right ears of patients with occupational noise-induced deafness and to explore the possible causes of asymmetric hearing loss. Methods: A total of 423 cases of occupational noise-induced deafness diagnosed in Guangdong Province Hospital for Occupational Disease Prevention and Treatment from May 2015 to May 2018 were investigated retrospectively. On the basis of three consecutive pure tone audiometry (PTA) with intervals of more than 3 d in hospital, the minimum of hearing threshold of each frequency and the frequency response threshold of auditory steady-state response (ASSR) , and based on the GBZ 49-2014 "Diagnosis of Occupational Noise-induced Deafness". The statistical analysis of Wilcoxon signed rank test and Spearman correlation analysis were carried out by nonparametric test. The hearing threshold weight of PTA and the response threshold of ASSR were tested and compared from different dimensions according to sex, age, length of service of exposure noise, diagnosis grade and so on. Results: There were 369 male (87.23%) and 54 female (12.77%) ; the age was (41.8±7.6) years; the length of service was (10.1±6.2) years. The weighted values of left ear PTA and ASSR were higher than those of right ear ( P <0.001) . The weighted values of left ear PTA and ASSR were higher than those of right ear ( P <0.05) in men, patients with age ≤ 40 and 41-60 years, 3-9 years of service, and patients with mild grade ( P <0.05) . After the equalization of characteristic variables, the weighted values of left ear PTA and ASSR were also higher than those of right ear ( P <0.05) . There was a positive correlation between the weighted value of PTA and the weighted value of ASSR ( P <0.001) . Conclusion: There is asymmetry in hearing loss of patients with occupational noise deafness, and hearing loss in the left ear is higher than that in the right ear.

Previous studies have shown that men and women have different genetic architectures across many traits. However, except waist-to-hip ratio (WHR) and waist circumference (WC), it remains unknown whether the genetic effects of a certain trait are weaker or stronger on men/women. With ~18 000 Taiwan Biobank subjects, we comprehensively investigate sexual heterogeneity in autosomal genetic effects, for traits regarding cardiovascular health, diabetes, kidney, liver, anthropometric profiles, blood, etc. 'Gene-by-sex interactions' (G $\times$ S) were detected in 18 out of 26 traits, each with an interaction P-value (${{P}}&#95;{{INT}}$) less than $0.05/104={0.00048}$, where 104 is the number of tests conducted in this study. The most significant evidence of G $\times$ S was found in WHR (${{P}}&#95;{{INT}}$ = 3.2 $\times{{10}}^{-{55}}$) and WC (${{P}}&#95;{{INT}}$ = 2.3$\times{{10}}^{-{41}}$). As a novel G$\times$S investigation for other traits, we here find that the autosomal genetic effects are weaker on women than on men, for low-density lipoprotein cholesterol (LDL-C), uric acid (UA) and diabetes-related traits such as fasting glucose and glycated hemoglobin. For LDL-C and UA, the evidence of G$\times$S is especially notable in subjects aged less than 50 years, where estrogen can play a role in attenuating the autosomal genetic effects of these two traits. Men and women have systematically distinct environmental contexts caused by hormonal milieu and their specific society roles, which may trigger diverse gene expressions despite the same DNA materials. As many environmental exposures are difficult to collect and quantify, sex can serve as a good surrogate for these factors., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Mental disorders are typically defined as distinct diagnostic entities, but similar patterns of clinical and cognitive impairments are frequently found across diagnostic groups. We investigated whether these transdiagnostic deficits result from common neural substrates across disorders or various illness-specific mechanisms, or a combination of both., Methods: Functional magnetic resonance imaging data were collected from clinically stable patients with major depressive disorder (n = 53), bipolar disorder (n = 78), or schizophrenia (n = 100) and matched healthy control subjects (n = 109) using a single scanner. Group comparisons were conducted to identify transdiagnostic and illness-specific features, and possible confounding effects of medication were considered. A multivariate approach with cross-validation was used to associate dysconnectivity features with shared cognitive deficits., Results: Transdiagnostic dysconnectivities were identified within somatomotor (Cohen's d = 0.50-0.58) and salience (Cohen's d = 0.52-0.58) networks and between subcortical-limbic (Cohen's d = 0.55-0.69) and subcortical-dorsal attention (Cohen's d = 0.56-0.61) networks. The executive control network was found to be illness-specifically disconnected from the prefrontal-limbic-pallidal circuit in major depressive disorder (Cohen's d = 0.57-0.58), prefronto-striato-parietal circuit in bipolar disorder (Cohen's d = 0.48-0.53), and default mode network in schizophrenia (Cohen's d = 0.47-0.56). Working memory deficits were associated with a linear combination of 11 transdiagnostic and 5 illness-specific dysconnectivities (r = .322, p= 9.7 × 10 -4 , n = 340). The associations of the identified dysconnectivities with medication dosage were nonsignificant., Conclusions: Disconnectivity in the somatomotor network was a common transdiagnostic profile, while there were illness-specific patterns in different parts of the prefrontal cortex for different disorders. These findings suggest that prominent psychiatric disorders share common impairments, possibly linked to perception and motor output, as well as unique dysconnectivity profiles that hypothetically mediate the more distinctive features of the disorder-specific psychopathology., (Copyright © 2020 Society of Biological Psychiatry. All rights reserved.)

Brain endothelial cells (BECs) are key constituents of the blood-brain barrier (BBB), protecting the brain from pathogens and restricting access of circulatory factors. Yet, because circulatory proteins have prominent age-related effects on adult neurogenesis, neuroinflammation, and cognitive function in mice, we wondered whether BECs receive and potentially relay signals between the blood and brain. Using single-cell RNA sequencing of hippocampal BECs, we discover that capillary BECs-compared with arterial and venous BECs-undergo the greatest transcriptional changes in normal aging, upregulating innate immunity and oxidative stress response pathways. Short-term infusions of aged plasma into young mice recapitulate key aspects of this aging transcriptome, and remarkably, infusions of young plasma into aged mice exert rejuvenation effects on the capillary transcriptome. Together, these findings suggest that the transcriptional age of BECs is exquisitely sensitive to age-related circulatory cues and pinpoint the BBB itself as a promising therapeutic target to treat brain disease., Competing Interests: Declaration of Interests The authors declare no competing interests., (Published by Elsevier Inc.)

Major depressive disorder (MDD), which is a leading psychiatric illness across the world, severely affects quality of life and causes an increased incidence of suicide. Evidence from animal as well as clinical studies have indicated that increased peripheral or central cytokine interleukin-6 (IL-6) levels play an important role in stress reaction and depressive disorder, especially physical disorders comorbid with depression. Increased release of IL-6 in MDD has been found to be a factor associated with MDD prognosis and therapeutic response, and may affect a wide range of depressive symptomatology. However, study results of the IL6 genetic effects in MDD are controversial. Increased IL-6 activity may cause depression through activation of hypothalamic-pituitary-adrenal axis or influence of the neurotransmitter metabolism. The important role of neuroinflammation in MDD pathogenesis has created a new perspective that the combining of blood IL-6 and other depression-related cytokine levels may help to classify MDD biological subtypes, which may allow physicians to identify the optimal treatment for MDD patients. To modulate the IL-6 activity by IL-6-related agents, current antidepressive agents, herb medication, pre-/probiotics or non-pharmacological interventions may hold great promise for the MDD patients with inflammatory features.

To go beyond the disconnectivity hypothesis of schizophrenia, directed (effective) connectivity was measured between 94 brain regions, to provide evidence on the source of the changes in schizophrenia and a mechanistic model. Effective connectivity (EC) was measured in 180 participants with schizophrenia and 208 controls. For the significantly different effective connectivities in schizophrenia, on average the forward (stronger) effective connectivities were smaller, whereas the backward connectivities tended to be larger. Further, higher EC in schizophrenia was found from the precuneus and posterior cingulate cortex (PCC) to areas such as the parahippocampal, hippocampal, temporal, fusiform, and occipital cortices. These are backward effective connectivities and were positively correlated with the positive symptoms of schizophrenia. Lower effective connectivities were found from temporal and other regions and were negatively correlated with the symptoms, especially the negative and general symptoms. Further, a signal variance parameter was increased for areas that included the parahippocampal gyrus and hippocampus, consistent with the hypothesis that hippocampal overactivity is involved in schizophrenia. This investigation goes beyond the disconnectivity hypothesis by drawing attention to differences in schizophrenia between backprojections and forward connections, with the backward connections from the precuneus and PCC implicated in memory stronger in schizophrenia., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Evidence from electrophysiological, functional, and structural research suggests that abnormal brain connectivity plays an important role in the pathophysiology of schizophrenia. However, most previous studies have focused on single modalities only, each of which is associated with its own limitations. Multimodal combinations can more effectively utilize various information, but previous multimodal research mostly focuses on extracting local features, rather than carrying out research based on network perspective. This study included 135 patients with schizophrenia and 148 sex- and age-matched healthy controls. Functional magnetic resonance imaging, diffusion tensor imaging, and structural magnetic resonance imaging data were used to construct the functional, anatomical, and morphological networks of each participant, respectively. These networks were used in combination with machine learning to identify more consistent biomarkers of brain connectivity and explore the relationships between different modalities. We found that although each modality had divergent connectivity biomarkers, the convergent pattern was that all were mostly located within the basal ganglia-thalamus-cortex circuit. Furthermore, using the biomarkers of these 3 modalities as a feature yielded the highest classification accuracy (91.75%, relative to a single modality), suggesting that the combination of multiple modalities could be effectively utilized to obtain complementary information regarding different mode networks; furthermore, this information could help distinguish patients. These findings provide direct evidence for the disconnection hypothesis of schizophrenia, suggesting that abnormalities in the basal ganglia-thalamus-cortex circuit can be used as a biomarker of schizophrenia., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The neurobiological heterogeneity of schizophrenia is widely accepted, but it is unclear how mechanistic differences converge to produce the observed phenotype. Establishing a pathophysiological model that accounts for both neurobiological heterogeneity and phenotypic similarity is essential to inform stratified treatment approaches. In this cross-sectional diffusion tensor imaging study, we recruited 77 healthy controls, and 70 patients with DSM-IV diagnosis of schizophrenia. We first confirmed the heterogeneity in structural connectivity by showing a reduced between-individual similarity of the structural connectivity in patients compared to healthy controls. Second, at a system level, we found the diversity of the topographic distribution of the strength of structural connectivity was significantly reduced in patients (P = 7.21 × 10 -7 , T 142  = 5.19 [95% CI: 3.37-7.52], Cohen's d = 0.91), and this affected 65 of the 90 brain regions examined (False Discovery Rate <5%). Third, when topographic diversity was used as a discriminant feature to train a model for classifying patients from controls, it significantly improved the accuracy on an independent sample (T 99  = 5.54; P < 0.001). These findings suggest a highly individualized pattern of structural dysconnectivity underlies the heterogeneity of schizophrenia, but these disruptions likely converge on an emergent common pathway to generate the clinical phenotype of the disorder., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Alzheimer's disease is an incurable neurodegenerative disorder in which neuroinflammation has a critical function 1 . However, little is known about the contribution of the adaptive immune response in Alzheimer's disease 2 . Here, using integrated analyses of multiple cohorts, we identify peripheral and central adaptive immune changes in Alzheimer's disease. First, we performed mass cytometry of peripheral blood mononuclear cells and discovered an immune signature of Alzheimer's disease that consists of increased numbers of CD8 + T effector memory CD45RA + (T EMRA ) cells. In a second cohort, we found that CD8 + T EMRA cells were negatively associated with cognition. Furthermore, single-cell RNA sequencing revealed that T cell receptor (TCR) signalling was enhanced in these cells. Notably, by using several strategies of single-cell TCR sequencing in a third cohort, we discovered clonally expanded CD8 + T EMRA cells in the cerebrospinal fluid of patients with Alzheimer's disease. Finally, we used machine learning, cloning and peptide screens to demonstrate the specificity of clonally expanded TCRs in the cerebrospinal fluid of patients with Alzheimer's disease to two separate Epstein-Barr virus antigens. These results reveal an adaptive immune response in the blood and cerebrospinal fluid in Alzheimer's disease and provide evidence of clonal, antigen-experienced T cells patrolling the intrathecal space of brains affected by age-related neurodegeneration.

Evidence suggests that the neuro-inflammation mechanisms associated with interleukin-12 (IL-12) may be linked to Alzheimer's diseases and cognitive aging. In this study, we speculate that single nucleotide polymorphisms (SNPs) in IL-12-associated genes, such as IL12A, IL12B, IL12RB1 , and IL12RB2 genes, could be associated with cognitive aging individually and/or via complicated interactions in the elder Taiwanese population. There were totally 3,730 Taiwanese individuals with age ≥60 years from the Taiwan Biobank. Mini-Mental State Examination (MMSE) was analyzed for all participants. We employed MMSE scores to assess cognitive functions. Our analysis revealed that the IL12A gene (including rs116910715, rs78902931, and rs78569420), the IL12B gene (including rs730691), and the IL12RB2 gene (including rs3790558, rs4655538, rs75699623, and rs1874396) were associated with cognitive aging. Among these SNPs, the association with the IL12RB2 rs3790558 SNP remained significant after performing Bonferroni correction ( P = 6.87 × 10 -4 ). Additionally, we found that interactions between the IL12A and IL12RB2 genes influenced cognitive aging ( P = 0.022). Finally, we pinpointed the effects of interactions between IL12A, IL12B , and IL12RB2 with physical activity ( P < 0.001, = 0.002, and < 0.001, respectively). Our study suggests that the IL-12-associated genes may contribute to susceptibility to cognitive aging independently as well as through gene-gene and gene-physical activity interactions., (Copyright © 2019 Lin, Kuo, Liu, Yang and Tsai.)

The rapid emergence of antibiotic-resistant infections is prompting increased interest in phage-based antimicrobials. However, acquisition of resistance by bacteria is a major issue in the successful development of phage therapies. Through natural evolution and structural modeling, we identified host-range-determining regions (HRDRs) in the T3 phage tail fiber protein and developed a high-throughput strategy to genetically engineer these regions through site-directed mutagenesis. Inspired by antibody specificity engineering, this approach generates deep functional diversity while minimizing disruptions to the overall tail fiber structure, resulting in synthetic "phagebodies." We showed that mutating HRDRs yields phagebodies with altered host-ranges, and select phagebodies enable long-term suppression of bacterial growth in vitro, by preventing resistance appearance, and are functional in vivo using a murine model. We anticipate that this approach may facilitate the creation of next-generation antimicrobials that slow resistance development and could be extended to other viral scaffolds for a broad range of applications., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Indonesians constituted 79% of foreign care workers for long-term care in Taiwan in 2015. Therefore, it is crucial to explore the effect of work stress and quality of life (QoL) on this population., Purpose: This study aims to explore stress levels, social support requirements, and perceived QoL among Indonesian care workers (ICWs) who work in home care settings., Methods: A cross-sectional design was used. Data were collected between 2014 and 2016 from a convenience sample of ICWs working in eastern Taiwan using a self-administered questionnaire that comprised the Stress Perception Scale (SPS), Social Support Scale (SSS), and World Health Organization Quality of Life-Brief scale. One hundred fifty-seven valid questionnaires were collected. The response rate was 80.51%. Data were analyzed using descriptive statistics, one-way analysis of variance, Pearson's correlation coefficient, and multiple regression analysis., Results: The results found that the average SPS of the sample was 70.50, with low QoL scores and requirements for more social support. In addition, significant and positive correlations were found between QoL and SPS, SSS, duration of patient care, and hours of care per week. Stepwise regression analysis showed that the most predictive variables for QoL were time spent caring, preservice training, psychological stress, and emotional support, which together accounted for 60.9% of the total variance., Conclusions/implications for Practice: This study facilitated understanding of the stress on ICWs and the impact of social support on their QoL. The findings suggest that new immigrants in Taiwan should be introduced to foreign care workers or nursing attendants. Moreover, long-term-care-related teachers should work with home care institutions or agencies to develop a teaching model for innovative care skills to relieve the perceived stress of ICWs.

Background: Research suggests an association between metabolic disorders, such as type 2 diabetes mellitus (T2DM), and schizophrenia. However, the risk of metabolic disorders in the unaffected siblings of patients with schizophrenia remains unclear., Methods: Using the Taiwan National Health Insurance Research Database, 3135 unaffected siblings of schizophrenia probands and 12,540 age-/sex-matched control subjects were included and followed up to the end of 2011. Individuals who developed metabolic disorders during the follow-up period were identified., Results: The unaffected siblings of schizophrenia probands had a higher prevalence of T2DM (3.4% vs. 2.6%, p = 0.010) than the controls. Logistic regression analyses with the adjustment of demographic data revealed that the unaffected siblings of patients with schizophrenia were more likely to develop T2DM (odds ratio [OR]: 1.39, 95% confidence interval [CI]: 1.10-1.75) later in life compared with the control group. Moreover, only female siblings of schizophrenia probands had an increased risk of hypertension (OR: 1.47, 95% CI: 1.07-2.01) during the follow-up compared with the controls.DiscussionThe unaffected siblings, especially sisters, of schizophrenia probands had a higher prevalence of T2DM and hypertension compared with the controls. Our study revealed a familial link between schizophrenia and T2DM in a large sample. Additional studies are required to investigate the shared pathophysiology of schizophrenia and T2DM.

Obesity is a worldwide health problem that is closely linked to many metabolic disorders. Regular physical exercise has been found to attenuate the genetic predisposition to obesity. However, it remains unknown what kinds of exercise can modify the genetic risk of obesity. This study included 18,424 unrelated Han Chinese adults aged 30-70 years who participated in the Taiwan Biobank (TWB). A total of 5 obesity measures were investigated here, including body mass index (BMI), body fat percentage (BFP), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR). Because there have been no large genome-wide association studies on obesity for Han Chinese, we used the TWB internal weights to construct genetic risk scores (GRSs) for each obesity measure, and then test the significance of GRS-by-exercise interactions. The significance level throughout this work was set at 0.05/550 = 9.1x10-5 because a total of 550 tests were performed. Performing regular exercise was found to attenuate the genetic effects on 4 obesity measures, including BMI, BFP, WC, and HC. Among the 18 kinds of self-reported regular exercise, 6 mitigated the genetic effects on at least one obesity measure. Regular jogging blunted the genetic effects on BMI, BFP, and HC. Mountain climbing, walking, exercise walking, international standard dancing, and a longer practice of yoga also attenuated the genetic effects on BMI. Exercises such as cycling, stretching exercise, swimming, dance dance revolution, and qigong were not found to modify the genetic effects on any obesity measure. Across all 5 obesity measures, regular jogging consistently presented the most significant interactions with GRSs. Our findings show that the genetic effects on obesity measures can be decreased to various extents by performing different kinds of exercise. The benefits of regular physical exercise are more impactful in subjects who are more predisposed to obesity., Competing Interests: The authors have declared that no competing interests exist.

The chronic autoimmune disease myasthenia gravis (MG) is characterized by fluctuating muscle weakness, which can lead to a large amount of stress in the patient. The current investigation plans to assess the risk of depressive disorders in MG patients. A retrospective cohort study of patients ageing 20 years and older and also newly diagnosed with MG between January 1, 2000, and December 31, 2008, was conducted from the National Health Insurance Research Database (NHIRD) in Taiwan. Observations of all 349 MG patients and 1,396 control individuals were made until a diagnosis of a depressive disorder by a psychiatrist, until death, or until December 31, 2013. A range of comorbidities were found, such as coronary artery disease, hypertension, diabetes mellitus, and dyslipidemia, with cerebrovascular disease being reported more frequently in MG patients in comparison with control subjects. After adjustment of patients' sex, age, urbanization, comorbidities, and monthly income, results indicated that MG individuals are 1.94 times more at risk (95% confidence interval [CI], 1.15-3.27, P = 0.014) of developing depressive disorders than are controls. This showed an increased risk in the development of depressive disorders in people with MG. Thus, depressive symptoms in MG patients should be regularly assessed.

An aged circulatory environment can activate microglia, reduce neural precursor cell activity and impair cognition in mice. We hypothesized that brain endothelial cells (BECs) mediate at least some of these effects. We observe that BECs in the aged mouse hippocampus express an inflammatory transcriptional profile with focal upregulation of vascular cell adhesion molecule 1 (VCAM1), a protein that facilitates vascular-immune cell interactions. Concomitantly, levels of the shed, soluble form of VCAM1 are prominently increased in the plasma of aged humans and mice, and their plasma is sufficient to increase VCAM1 expression in cultured BECs and the hippocampi of young mice. Systemic administration of anti-VCAM1 antibody or genetic ablation of Vcam1 in BECs counteracts the detrimental effects of plasma from aged individuals on young brains and reverses aging aspects, including microglial reactivity and cognitive deficits, in the brains of aged mice. Together, these findings establish brain endothelial VCAM1 at the blood-brain barrier as a possible target to treat age-related neurodegeneration.

Evidence indicates that the age-related neuropathological mechanisms associated with DNA repair genes may contribute to cognitive aging and Alzheimer's disease. In this study, we hypothesize that single nucleotide polymorphisms (SNPs) within 155 DNA repair genes may be linked to cognitive aging independently and/or through complex interactions in an older Taiwanese population. A total of 3,730 Taiwanese subjects aged over 60 years from the Taiwan Biobank were analyzed. Mini-Mental State Examination (MMSE) was administered to all subjects, and MMSE scores were used to measure cognitive functions. Our data showed that out of 1,652 SNPs, the rs1776181 (P = 1.47 × 10 -5 ), rs1776177 (P = 8.42 × 10 -7 ), rs1635510 (P = 7.97 × 10 -6 ), and rs2526698 (P = 7.06 × 10 -6 ) SNPs in the EXO1 gene were associated with cognitive aging. The association with these SNP remained significant after performing Bonferroni correction. Additionally, we found that interactions between the EXO1 and RAD51C genes influenced cognitive aging (P = 0.002). Finally, we pinpointed the influence of interactions between EXO1 and physical activity (P < 0.001) as well as between DCLRE1C and physical activity (P < 0.001). Our study indicated that DNA repair genes may contribute to susceptibility in cognitive aging independently as well as through gene-gene and gene-physical interactions., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Horizontal docking assembly is a fundamental process in the aerospace assembly, where intelligent measurement and adjustable support systems are urgently needed to achieve higher automation and precision. Thus, a laser scanning approach is employed to obtain the point cloud from a laser scanning sensor. And a method of section profile fitting is put forward to solve the pose parameters from the data cloud acquired by the laser scanning sensor. Firstly, the data is segmented into planar profiles by a series of parallel planes, and ellipse fitting is employed to estimate each center of the section profiles. Secondly, the pose of the part can be obtained through a spatial straight line fitting with these profile centers. However, there may be some interference features on the surface of the parts in the practical assembly process, which will cause negative effects to the measurement. Aiming at the interferences, a robust method improved from M-estimation and RANSAC is proposed to enhance the measurement robustness. The proportion of the inner points in a whole profile point set is set as a judgment criterion to validate each planar profile. Finally, a prototype is fabricated, a series of experiments have been conducted to verify the proposed method., Competing Interests: The authors declare no conflict of interest.

Microglia maintain homeostasis in the central nervous system through phagocytic clearance of protein aggregates and cellular debris. This function deteriorates during ageing and neurodegenerative disease, concomitant with cognitive decline. However, the mechanisms of impaired microglial homeostatic function and the cognitive effects of restoring this function remain unknown. We combined CRISPR-Cas9 knockout screens with RNA sequencing analysis to discover age-related genetic modifiers of microglial phagocytosis. These screens identified CD22, a canonical B cell receptor, as a negative regulator of phagocytosis that is upregulated on aged microglia. CD22 mediates the anti-phagocytic effect of α2,6-linked sialic acid, and inhibition of CD22 promotes the clearance of myelin debris, amyloid-β oligomers and α-synuclein fibrils in vivo. Long-term central nervous system delivery of an antibody that blocks CD22 function reprograms microglia towards a homeostatic transcriptional state and improves cognitive function in aged mice. These findings elucidate a mechanism of age-related microglial impairment and a strategy to restore homeostasis in the ageing brain.

We describe an approach to multivariate analysis, termed structured kernel principal component regression (sKPCR), to identify associations in voxel-level connectomes using resting-state functional magnetic resonance imaging (rsfMRI) data. This powerful and computationally efficient multivariate method can identify voxel-phenotype associations based on the whole-brain connectivity pattern of voxels, and it can detect linear and non-linear signals in both volume-based and surface-based rsfMRI data. For each voxel, sKPCR first extracts low-dimensional signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations by an adaptive regression model. The method's power is derived from appropriately modelling the spatial structure of the data when performing dimension reduction, and then adaptively choosing an optimal dimension for association testing using the adaptive regression strategy. Simulations based on real connectome data have shown that sKPCR can accurately control the false-positive rate and that it is more powerful than many state-of-the-art approaches, such as the connectivity-wise generalized linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). Moreover, since sKPCR can reduce the computational cost of non-parametric permutation tests, its computation speed is much faster. To demonstrate the utility of sKPCR for real data analysis, we have also compared sKPCR with the above methods based on the identification of voxel-wise differences between schizophrenic patients and healthy controls in four independent rsfMRI datasets. The results showed that sKPCR had better between-sites reproducibility and a larger proportion of overlap with existing schizophrenia meta-analysis findings. Code for our approach can be downloaded from https://github.com/weikanggong/sKPCR., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: EEG mu-desynchronization is an index of motor resonance (MR) and is used to study social interaction deficiencies, but finding differences in mu-desynchronization does not reveal how nonlinear brain dynamics are affected during MR. The current study explores how nonlinear brain dynamics change during MR. We hypothesized that the complexity of the mu frequency band (8-13 Hz) changes during MR, and that this change would be frequency specific. Additionally, we sought to determine whether complexity at baseline and changes in complexity during action observation would predict MR and changes in network dynamics. Methods: EEG was recorded from healthy participants ( n = 45) during rest and during an action observation task. Baseline brain activity was measured followed by participants observing videos of hands squeezing stress balls. We used multiscale entropy (MSE) to quantify the complexity of the mu rhythm during MR. We then performed post-hoc graph theory analysis to explore whether nonlinear dynamics during MR affect brain network topology. Results: We found significant mu-desynchronization during the action observation task and that mu entropy was significantly increased during the task compared to rest, while gamma, beta, theta, and delta bands showed decreased entropy. Moreover, resting-state entropy was significantly predictive of the degree of mu desynchronization. We also observed a decrease in the clustering coefficient in the mu band only and a significant decrease in global alpha efficiency during action observation. MSE during action observation was strongly correlated with alpha network efficiency. Conclusions: The current findings suggest that the desynchronization of the mu wave during MR results in a local increase of mu entropy in sensorimotor areas, potentially reflecting a release from alpha inhibition. This release from inhibition may be mediated by the baseline MSE in the mu band. The dynamical complexity and network analysis of EEG may provide a useful addition for future studies of MR by incorporating measures of nonlinearity.

Alzheimer's disease (AD) is a progressive brain disorder with gradual memory loss that correlates to cognitive deficits in the elderly population. Recent studies have shown the potentials of machine learning algorithms to identify biomarkers and functional brain activity patterns across various AD stages using electroencephalography (EEG). In this study, we aim to discover the altered spatio-temporal patterns of EEG complexity associated with AD pathology in different severity levels. We employed the multiscale entropy (MSE), a complexity measure of time series signals, as the biomarkers to characterize the nonlinear complexity at multiple temporal scales. Two regularized logistic regression methods were applied to extracted MSE features to capture the topographic pattern of MSEs of AD cohorts compared to healthy baseline. Furthermore, canonical correlation analysis was performed to evaluate the multivariate correlation between EEG complexity and cognitive dysfunction measured by the Neuropsychiatric Inventory scores. 123 participants were recruited and each participant was examined in three sessions (length = 10 seconds) to collect resting-state EEG signals. MSE features were extracted across 20 time scale factors with pre-determined parameters ( m = 2, r = 0.15). The results showed that comparing to logistic regression model, the regularized learning methods performed better for discriminating severe AD cohort from normal control, very mild and mild cohorts (test accuracy ~ 80%), as well as for selecting significant biomarkers arcoss the brain regions. It was found that temporal and occipitoparietal brain regions were more discriminative in regard to classifying severe AD cohort vs. normal controls, but more diverse and distributed patterns of EEG complexity in the brain were exhibited across individuals in early stages of AD.

The intrinsic composition and functional relevance of resting-state blood oxygen level-dependent signals are fundamental in research using functional magnetic resonance imaging (fMRI). Using the Hilbert-Huang Transform to estimate high-resolution time-frequency spectra, we investigated the instantaneous frequency and amplitude modulation of resting-state fMRI signals, as well as their functional relevance in a large normal-aging cohort (n = 420, age = 21-89 years). We evaluated the cognitive function of each participant and recorded respiratory signals during fMRI scans. The results showed that the Hilbert-Huang Transform effectively categorized resting-state fMRI power spectra into high (0.087-0.2 Hz), low (0.045-0.087 Hz), and very-low (≤0.045 Hz) frequency bands. The high-frequency power was associated with respiratory activity, and the low-frequency power was associated with cognitive function. Furthermore, within the cognition-related low-frequency band (0.045-0.087 Hz), we discovered that aging was associated with the increased frequency modulation and reduced amplitude modulation of the resting-state fMRI signal. These aging-related changes in frequency and amplitude modulation of resting-state fMRI signals were unaccounted for by the loss of gray matter volume and were consistently identified in the default mode and salience network. These findings indicate that resting-state fMRI signal modulations are dynamic during the normal aging process. In summary, our results refined the functionally related blood oxygen level-dependent frequency band in a considerably narrow band at a low-frequency range (0.045-0.087 Hz) and challenged the current method of resting-fMRI preprocessing by using low-frequency filters with a relatively wide range below 0.1 Hz., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Abnormal brain lateralization has been implicated in schizophrenia but few studies have focused on the variability of resting-state fMRI signal and its lateralization in schizophrenia. Here we utilized standard deviations (SD) to quantify the variability of resting-state fMRI signal and measured the lateralization index (LI), on the basis of SD of the resting-state fMRI signal in order to assess the difference of brain signal variability across the hemispheres. We recruited 180 patients with schizophrenia and 358 age- and sex-matched healthy volunteers. Between-group comparison revealed that in comparison to healthy volunteers, schizophrenia patients have significantly higher SD of resting-state fMRI activity in left inferior temporal, left fusiform, and right superior medial frontal cortex, and lower SD in right precuneus, posterior cingulum on both sides, right lingual, and left calcarine in the occipital region. Using region of interest approach, most brain regions showed increased leftward lateralization in patients with schizophrenia, as compared with healthy controls. SD and LI were also found to be correlated to age of onset or duration of illness. These results provide further evidence that abnormal variability and lateralization exist in schizophrenia patients, and abnormality in fusiform, lingual and inferior temporal could have potential help to identify the dysfunctional brain lateralization in schizophrenia., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Diffusion tensor imaging and its distinct capability to detect micro-structural changes in vivo allows the exploration of white matter (WM) abnormalities in patients who have been diagnosed with schizophrenia; however, the results regarding the anatomical positions and degree of abnormalities are inconsistent. In order to obtain more robust and stable findings, we conducted a multi-level analysis to investigate WM disruption in a relatively large sample size (142 schizophrenia patients and 163 healthy subjects). Specifically, we evaluated the univariate fractional anisotropy (FA) in voxel level; the bivariate pairwise structural connectivity between regions using deterministic tractography as the network node defined by the Human Brainnetome Atlas; and the multivariate network topological properties, including the network hub, efficiency, small-worldness, and strength. Our data demonstrated callosal and subcortical WM alterations in patients with schizophrenia. These disruptions were evident in both voxel and connectivity levels and further supported by associations between FA values and illness duration. Based on the findings regarding topological properties, the structural network showed weaker global integration in patients with schizophrenia than in healthy subjects, while brain network hubs showed decreased functionality. We replicated these findings using an automated anatomical labeling atlas to define the network node. Our study indicates that callosal and subcortical WM disruptions are biomarkers for chronic schizophrenia.

Inference of causality in time series has been principally based on the prediction paradigm. Nonetheless, the predictive causality approach may underestimate the simultaneous and reciprocal nature of causal interactions observed in real-world phenomena. Here, we present a causal-decomposition approach that is not based on prediction, but based on the covariation of cause and effect: cause is that which put, the effect follows; and removed, the effect is removed. Using empirical mode decomposition, we show that causal interaction is encoded in instantaneous phase dependency at a specific time scale, and this phase dependency is diminished when the causal-related intrinsic component is removed from the effect. Furthermore, we demonstrate the generic applicability of our method to both stochastic and deterministic systems, and show the consistency of causal-decomposition method compared to existing methods, and finally uncover the key mode of causal interactions in both modelled and actual predator-prey systems.

Introduction: Previous studies have suggested that both bipolar disorder and atopy are associated with an increased risk of stroke. However, the role of atopic diseases/diathesis in the risk of stroke among patients with bipolar disorder remains unclear., Methods: Using Taiwan's National Health Insurance Research Database, we selected 55,593 patients with bipolar disorder between 2002 and 2008, divided them into patients with atopic diseases/diathesis (n = 21,050) and patients without atopic diseases/diathesis (n = 34,543), and observed them until the end of 2011. Patients who experienced a stroke during the follow-up period were identified., Results: Patients with bipolar disorder and atopic diseases/diathesis had an elevated risk of ischemic stroke (hazard ratio [HR]: 1.44, 95% confidence interval [CI]: 1.25-1.59) compared with patients with only bipolar disorder; a dose-dependent relationship was observed between the number of allergic comorbidities and the risk of ischemic stroke (1 atopic disease, HR: 1.30, 95% CI: 1.13-1.49; 2 atopic diseases, HR: 1.59, 95% CI: 1.33-1.91; ≥ 3 atopic diseases, HR: 2.09, 95% CI: 1.50-2.91).The role of atopic diseases in the risk of hemorrhagic stroke among patients with bipolar disorder was nonsignificant (HR: 0.84, 95% CI: 0.64-1.09)., Conclusions: Patients with bipolar disorder and atopic diseases/diathesis are more prone to ischemic stroke later in life than are those without atopic diseases/diathesis. Further study is required to investigate the underlying mechanism linking atopy, bipolar disorder, and stroke., Competing Interests: The authors have declared that no competing interests exist.

Aim: To develop a risk stratification model for the early diagnosis of borderline personality disorder (BPD) using Taiwan National Health Insurance Research Database., Methods: We conducted a retrospective case-control study of 6132 patients (292 BPD patients and 5840 control subjects) who were selected from the National Health Insurance Research Database. Psychiatric co-morbidities including depressive disorder, bipolar disorder, anxiety disorder, substance-use disorder, personality disorders other than BPD, sleep disorder, eating disorder, autistic spectrum disorder, mental retardation and attention-deficit hyperactivity disorder, which were diagnosed within 3 years before enrolment, were collected. A logistic regression was used to calculate the odds ratio of psychiatric co-morbidities between subjects with and without BPD. The classification and regression tree method was used to generate a risk stratification model., Results: The odds ratios for depressive disorder, bipolar disorder, anxiety disorder, substance-use disorder, personality disorders other than BPD, sleep disorder, eating disorder, mental retardation and attention-deficit hyperactivity disorder were greater for BPD patients than for the control subjects. Furthermore, the risk of BPD can be reliably estimated using age and psychiatric co-morbidities including bipolar disorder, substance-use disorder and depressive disorder., Conclusions: Most psychiatric disorders were more common in BPD patients than in the control subjects. Using psychiatric co-morbidities, we identified four variables as significant risk predictors of BPD and permitted identification of subjects with low, intermediate or high risk for BPD. The accuracy of the risk stratification model is high and can be easily applied in clinical practice., (© 2016 John Wiley & Sons Australia, Ltd.)

Purpose: Previous studies suggested that patients with attention-deficit hyperactivity disorder (ADHD) were prone to health-risk behaviors and accidents. However, the relationship of ADHD with the risk of traumatic brain injury (TBI) remained uncertain., Methods: Using the Taiwan National Health Insurance Research Database, 72,181 children (aged 3-11 years), adolescents (12-17 years), and young adults (18-29 years) with ADHD and 72,181 age-/sex-matched controls were enrolled between 2001 and 2009, and followed up to the end of 2011 in our study. Those who developed any TBI during the follow-up period were identified., Results: Children, adolescents, and young adults with ADHD had a higher incidence of developing any TBI (9.8% vs. 2.2%, p < .001), such as skull fracture (.2% vs. .1%, p < .001) and concussion (4.3% vs. 1.0%, p < .001), than the controls did. Cox regression analysis with the adjustment of demographic data, psychiatric comorbidities, and ADHD medications showed that ADHD was related to an increased risk of subsequent TBI (hazard ratio: 4.57, 95% confidence interval: 4.31-4.85), and indicated that long-term use of ADHD medication was associated with a reduced likelihood of subsequent TBI (hazard ratio: .93, 95% confidence interval: .87-.99)., Conclusions: Patients with ADHD had an increased risk of developing any TBI compared with the controls. Long-term use of ADHD medications would reduce this risk. Our findings suggested that the public health government and clinicians should pay more attention to the TBI risk among patients with ADHD, and further indicated the importance of the optimal treatment for ADHD., (Copyright © 2018 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.)

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1-3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy.

White matter hyperintensities (WMHs) are prevalent in the older adult and are often accompanied by cognitive decline and an increased risk of dementia. However, the roles of WMHs in the periventricular white matter and deep white matter regions in the aging process remain controversial. This study aimed to investigate the WMH burden across the adult lifespan and determine the interrelationships among age, WMH, and cognition. The present study included 312 healthy individuals aged 21-89 years who received structural magnetic resonance imaging and cognitive assessments. Periventricular WMH (PVWMH) and deep WMH (DWMH) volumes were computed and fitted using different regression models to evaluate the trajectory of WMH changes across the lifespan. Our findings support that the changes in WMH volume in the healthy population follow a nonlinear pattern with age, especially in PVWMH. With 2-mediator mediation analysis, we further suggest that the effect of age on the cognitive performance is mediated only by PVWMH. Conclusively, the increased PVWMH, but not DWMH, plays a major role in predicting cognitive aging in healthy adults., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Objective: This pilot study evaluated the effects of Tai Chi training on sleep quality (primary outcomes), and depression and social functioning levels (secondary outcomes) among patients with depression., Participants: Sixteen depressed Chinese patients., Methods: Participants received 1-hr Tai Chi training sessions 2 times per week for 10 weeks. Patients' subjective sleep quality ratings, objective sleep quality measurements, and depression and social functioning levels were measured before, during, and after the intervention., Results: Sleep quality and depression outcomes improved significantly. Patients reported improved Pittsburgh Sleep Quality Index (PSQI) scores (9.6 ± 3.3 to 6.6 ± 5.2, p = 0.016), and cardiopulmonary coupling (CPC) analysis of electrocardiogram (ECG) showed decreased stable sleep onset latency (75.7 ± 100.6 to 20.9 ± 18.0, p = 0.014), increased stable sleep percentages (31.5 ± 18.7 to 46.3 ± 16.9, p = 0.016), and decreased unstable sleep percentages (45.3 ± 20.1 to 30.6 ± 16.5, p = 0.003). Patients also reported decreased Hamilton Rating Scale for Depression (HAM-D-17; 20.1 ± 3.7 to 7.8 ± 5.9, p < 0.001) and Beck Depression Inventory (BDI) scores (22.3 ± 9.1 to 11.1 ± 10.6, p = 0.006). Significant correlations were found between the changes in subjective sleep assessments ΔPSQI and ΔHAM-D-17 (r = 0.6, p = 0.014), and ΔPSQI and ΔBDI (r = 0.62, p = 0.010). Correlations between changes in objective sleep measurements and changes in depression symptoms were low and not significant., Conclusions: Tai Chi training improved sleep quality and mood symptoms among depressed patients.

Bioelectrical impedance analysis technology was used tomeasure whole body parameters and electrical resistance and reactance of specific body regions in schizophrenia patients after long-term medication use. Patient data was compared to that of a control group of subjects to analyze metabolic differences. Our experimental group was composed of 60 schizophrenia patients, and the control group was composed of 30 normal subjects. T-test and ANOVA analyses were conducted to compared differences between groups, with confidencev intervals set at 95%. T-test results showed significant differences in body fat levels between groups (F=4.753, p=0.031<0.05). Additionally, we also found significant differences in upper body resistance and reactance (F=78.433, p=0.000<0.05, F=67.488, p=0.000<0.05), which could be related to accumulation of body fat. These results show correlation between schizophrenia and development of metabolic syndrome.

Bioorthogonal tools enable cell-type-specific proteomics, a prerequisite to understanding biological processes in multicellular organisms. Here we report two engineered aminoacyl-tRNA synthetases for mammalian bioorthogonal labeling: a tyrosyl ( ScTyr Y43G ) and a phenylalanyl ( MmPhe T413G ) tRNA synthetase that incorporate azide-bearing noncanonical amino acids specifically into the nascent proteomes of host cells. Azide-labeled proteins are chemoselectively tagged via azide-alkyne cycloadditions with fluorophores for imaging or affinity resins for mass spectrometric characterization. Both mutant synthetases label human, hamster, and mouse cell line proteins and selectively activate their azido-bearing amino acids over 10-fold above the canonical. ScTyr Y43G and MmPhe T413G label overlapping but distinct proteomes in human cell lines, with broader proteome coverage upon their coexpression. In mice, ScTyr Y43G and MmPhe T413G label the melanoma tumor proteome and plasma secretome. This work furnishes new tools for mammalian residue-specific bioorthogonal chemistry, and enables more robust and comprehensive cell-type-specific proteomics in live mammals.

Complexity analysis of resting-state blood oxygen level-dependent (BOLD) signals using entropy methods has attracted considerable attention. However, investigation on the bias of entropy estimates in resting-state functional magnetic resonance imaging (fMRI) signals and a general strategy for selecting entropy parameters is lacking. In this paper, we present a minimizing error approach to reduce the bias of sample entropy (SampEn) and multiscale entropy (MSE) in resting-state fMRI data. The strategy explored a range of parameters that minimized the relative error of SampEn of BOLD signals in cerebrospinal fluids where minimal physiologic information was present, and applied these parameters to calculate SampEn of BOLD signals in gray matter regions. We examined the effect of various parameters on the results of SampEn and MSE analyses of a large normal aging adult cohort (354 healthy subjects aged 21-89 years). The results showed that a tradeoff between pattern length m and tolerance factor r was necessary to maintain the accuracy of SampEn estimates. Furthermore, an increased relative error of SampEn was associated with an increased coefficient of variation in voxel-wise statistics. Overall, the parameters m = 1 and r = 0.20-0.45 provided reliable MSE estimates in short resting-state fMRI signals. For a single-scale SampEn analysis, a wide range of parameters was available with data lengths of at least 97 time points. This study provides a minimization error strategy for future studies on the non-linear analysis of resting-state fMRI signals to account for the bias of entropy estimates.

To analyse the functioning of the amygdala in depression, we performed the first voxel-level resting state functional-connectivity neuroimaging analysis of depression of voxels in the amygdala with all other voxels in the brain, with 336 patients with major depressive disorder and 350 controls. Amygdala voxels had decreased functional connectivity (FC) with the orbitofrontal cortex, temporal lobe areas, including the temporal pole, inferior temporal gyrus and the parahippocampal gyrus. The reductions in the strengths of the FC of the amygdala voxels with the medial orbitofrontal cortex and temporal lobe voxels were correlated with increases in the Beck Depression Inventory score and in the duration of illness measures of depression. Parcellation analysis in 350 healthy controls based on voxel-level FC showed that the basal division of the amygdala has high FC with medial orbitofrontal cortex areas, and the dorsolateral amygdala has strong FC with the lateral orbitofrontal cortex and related ventral parts of the inferior frontal gyrus. In depression, the basal amygdala division had especially reduced FC with the medial orbitofrontal cortex, which is involved in reward; and the dorsolateral amygdala subdivision had relatively reduced FC with the lateral orbitofrontal cortex, which is involved in non-reward.