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Numerous resting-state functional and structural studies have revealed that many brain regions are involved in the pathogenesis of posttraumatic stress disorder (PTSD), but their findings have been inconsistent. Moreover, there has no study explored the functional and structural alterations across languages in PTSD.A meta-analysis of whole-brain on the amplitude of low-frequency fluctuation (ALFF) and voxel-based morphometry (VBM) studies that explored alterations in the spontaneous functional brain activity and grey matter volume (GMV) in PTSD patients across languages by using the Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) software.A total of 15 studies (19 datasets) comprising 577 PTSD patients and 499 HCs for ALFF, and 27 studies (31 datasets) comprising 539 PTSD patients and 693 HCs for VBM were included. Overall, PTSD patients across languages displayed decreased ALFF in the in the left amygdala. For VBM meta-analysis, PTSD patients across languages displayed reduced GMV in the bilateral anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC), striatum, insula, superior temporal gyrus, left postcentral gyrus, and occipital gyrus.The multimodal meta-analysis suggest that PTSD patients showed similar pattern of aberrant resting-state functional brain activity and structure mainly in the amygdala, suggesting that structural deficits might underlie alterations in function. In addition, some regions exhibited only structural abnormalities in PTSD, including the ACC/mPFC, striatum, insula, primary visual, auditory and sensorimotor cortices. Moreover, consistent alterations in PTSD patients across languages may draw attention to the disparity in multi-cultural considerations in psychiatric research and further understanding the neurophysiopathology of PTSD.

Accumulating evidence suggests that hypothalamus-pituitary-thyroid (HPT) axis dysfunction is relevant to the neuropsychological and pathophysiology functions of bipolar disorder (BD). However, no research has investigated the inter-relationships among thyroid hormones disturbance, neurocognitive deficits, and aberrant brain function (particularly in the amygdala) in patients with BD.Data of dynamic resting-state functional connectivity (rs-dFC) were gathered from 59 patients with unmedicated BD II during depressive episodes and 52 healthy controls (HCs). Four seeds were selected (the bilateral lateral amygdala and the bilateral medial amygdala). The sliding-window analysis was applied to investigate dynamic functional connectivity (dFC). Additionally, the serum thyroid hormone (free tri-iodothyronine (FT3), total tri-iodothyronine (TT3), free thyroxin (FT4), total thyroxin (TT4) and thyroid-stimulating hormone (TSH)) levels, and cognitive scores on the MATRICS Consensus Cognitive Battery (MCCB) in patients and HCs were detected.The BD group exhibited increased dFC variability between the left medial amygdala and right medial prefrontal cortex (mPFC) when compared with the HC group. Additionally, the BD group showed lower FT3, TT3, and TSH level, higher FT4 level, and poorer cognitive score. Moreover, a significant negative correlation was observed between the dFC variability of the left medial amygdala-right mPFC and TSH level, or reasoning and problem solving of MCCB score in BD group. Multiple regression analysis showed that the TSH level × dFC variability of the medial amygdala-mPFC was an independent predictor for cognitive processing speed in BD group.This study revealed patients with BD II depression had excessive variability in dFC between the medial amygdala and mPFC. Moreover, both HPT axis dysfunction and abnormal dFC of the amygdala-mPFC might be implicated in cognitive impairment in the early stages of BD.

A one-step reactive extrusion process has been demonstrated to toughen the poly(lactic acid) (PLA) blend without sacrificing its mechanical strength and degradability. Specifically, poly(lactic acid) (PLA), poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), and the peroxide initiator were first introduced into the mixer and then heated at the elevated temperatures for triggering the grafting reaction during the extrusion. During this process, the thermal decomposition of PHBV results in the unsaturated bonds that can directly grafted onto the PLA backbones by free radical reactions. From this study, we found that PLA/PHBV blend with a mass ratio of 80/20 (PLA/PHBV) and 0.3 wt% 2,5-dimethyl-2,5-di(tert-butylperoxy)hexane (DBPH) is an optimized condition, leading to an improved elongation at break of 15.95%, which is 3.1 times of the PLA and 1.5 times of the PLA/PHBV blend without the initiator, while tensile strength exhibited very limited decreased compared to PLA. Overall, this work presents an environmentally friendly and industrial feasible approach to prepare bio-based toughened PLA for improving the elongation at break, while retaining the tensile strength and degradability.

Transcranial magnetic stimulation (TMS) is a non-invasive, effective, and safe neuromodulation technique to diagnose and treat neurological and psychiatric disorders. However, the complexity and heterogeneity of the brain composition and structure pose a challenge in accurately determining whether critical brain regions have received the right level of induced electric field. Numerical computation methods, like finite element analysis (FEA), can be used to estimate electric field distribution. However, these methods need exceedingly high computational resources and are time-consuming. In this work, we developed a deep convolutional neural network (DCNN) encoder-decoder model to predict induced electric fields, in real-time, from T1-weighted and T2-weighted magnetic resonance imaging (MRI) based anatomical slices. We recruited 11 healthy subjects and applied TMS to the primary motor cortex to measure resting motor thresholds. Head models were developed from MRIs of the subjects using the SimNIBS pipeline. Head model overall size was scaled to 20 new size scales for each subject to form a total of 231 head models. Scaling was done to increase the number of input data representing different head model sizes. Sim4Life, a FEA software, was used to compute the induced electric fields, which served as the DCNN training data. For the trained network, the peak signal to noise ratios of the training and testing data were 32.83dB and 28.01dB, respectively. The key contribution of our model is the ability to predict the induced electric fields in real-time and thereby accurately and efficiently predict the TMS strength needed in targeted brain regions.

BackgroundNumerous studies of resting-state functional imaging and voxel-based morphometry (VBM) have revealed differences in specific brain regions of patients with bipolar disorder (BD), but the results have been inconsistent.MethodsA whole-brain voxel-wise meta-analysis was conducted on resting-state functional imaging and VBM studies that compared differences between patients with BD and healthy controls using Seed-based d Mapping with Permutation of Subject Images software.ResultsA systematic literature search identified 51 functional imaging studies (1842 BD and 2190 controls) and 83 VBM studies (2790 BD and 3690 controls). Overall, patients with BD displayed increased resting-state functional activity in the left middle frontal gyrus, right inferior frontal gyrus (IFG) extending to the right insula, right superior frontal gyrus and bilateral striatum, as well as decreased resting-state functional activity in the left middle temporal gyrus extending to the left superior temporal gyrus and post-central gyrus, left cerebellum, and bilateral precuneus. The meta-analysis of VBM showed that patients with BD displayed decreased VBM in the right IFG extending to the right insula, temporal pole and superior temporal gyrus, left superior temporal gyrus extending to the left insula, temporal pole, and IFG, anterior cingulate cortex, left superior frontal gyrus (medial prefrontal cortex), left thalamus, and right fusiform gyrus.ConclusionsThe multimodal meta-analyses suggested that BD showed similar patterns of aberrant brain activity and structure in the insula extending to the temporal cortex, fronto-striatal-thalamic, and default-mode network regions, which provide useful insights for understanding the underlying pathophysiology of BD.

Anorexia nervosa (AN) is a complex psychiatric disorder with poorly understood etiology. Numerous voxel-based morphometry (VBM) and resting-state functional imaging studies have provided strong evidence of abnormal brain structure and intrinsic and functional activities in AN, but with inconsistent conclusions. Herein, a whole-brain meta-analysis was conducted on VBM (660 patients with AN, and 740 controls) and resting-state functional imaging (425 patients with AN, and 461 controls) studies that measured differences in the gray matter volume (GMV) and intrinsic functional activity between patients with AN and healthy controls (HCs). Overall, patients with AN displayed decreased GMV in the bilateral median cingulate cortex (extending to the bilateral anterior and posterior cingulate cortex), and left middle occipital gyrus (extending to the left inferior parietal lobe). In resting-state functional imaging studies, patients with AN displayed decreased resting-state functional activity in the bilateral anterior cingulate cortex and bilateral median cingulate cortex, and increased resting-state functional activity in the right parahippocampal gyrus. This multimodal meta-analysis identified reductions of gray matter and functional activity in the anterior and median cingulate in patients with AN, which contributes to further understanding of the pathophysiology of AN.

BackgroundInflammation might play a role in bipolar disorder (BD), but it remains unclear the relationship between inflammation and brain structural and functional abnormalities in patients with BD. In this study, we focused on the alterations of functional connectivity (FC), peripheral pro-inflammatory cytokines and their correlations to investigate the role of inflammation in FC in BD depression.MethodsIn this study, 42 unmedicated patients with BD II depression and 62 healthy controls (HCs) were enrolled. Resting-state-functional magnetic resonance imaging was performed in all participants and independent component analysis was used. Serum levels of Interleukin-6 (IL-6) and Interleukin-8 (IL-8) were measured in all participants. Correlation between FC values and IL-6 and IL-8 levels in BD was calculated.ResultsCompared with the HCs, BD II patients showed decreased FC in the left orbitofrontal cortex (OFC) implicating the limbic network and the right precentral gyrus implicating the somatomotor network. BD II showed increased IL-6 (p = 0.039), IL-8 (p = 0.002) levels. Moreover, abnormal FC in the right precentral gyrus were inversely correlated with the IL-8 (r = −0.458, p = 0.004) levels in BD II. No significant correlation was found between FC in the left OFC and cytokines levels.ConclusionsOur findings that serum IL-8 levels are associated with impaired FC in the right precentral gyrus in BD II patients suggest that inflammation might play a crucial role in brain functional abnormalities in BD.

Identifying brain similarities and differences between bipolar disorder (BD) and major depressive disorder (MDD) can help us better understand their pathophysiological mechanisms and develop more effective treatments. However, the features of whole-brain regional cerebral blood flow (CBF) and intrinsic functional connectivity (FC) underlying BD and MDD have not been directly compared.Eighty-eight unmedicated BD II depression patients, 95 unmedicated MDD patients, and 96 healthy controls (HCs) underwent three-dimensional arterial spin labeling (3D ASL) and resting-state functional MRI (rs-fMRI). The functional properties of whole brain CBF and seed-based resting-state FC further performed based on those regions with changed CBF were analyzed between the three groups.The patients with BD and MDD showed commonly increased CBF in the left posterior lobe of the cerebellum and the left middle temporal gyrus (MTG) compared with HCs. The CBF of the left MTG was positively associated with 24-items Hamilton Depression Rating Scale scores in MDD patients. Decreased FC between the left posterior lobe of the cerebellum and the left inferior frontal gyrus (IFG) was observed only in patients with BD compared with HCs.Patients with BD and those with MDD shared common features of CBF in the posterior lobe of the cerebellum and the MTG. The altered posterior lobe of the cerebellum-IFG FC can be considered as a potential biomarker for the differentiation of patients with BD from those with MDD.

Background Systemic inflammation and immune dysregulation have been considered as risk factors in the pathophysiology of mood disorders including bipolar disorder (BD). Previous neuroimaging studies have demonstrated metabolic, structural and functional abnormalities in the insula in BD, proposed that the insula played an important role in BD. We herein aimed to explore neural mechanisms underlying inflammation-induced in the insular subregions functional connectivity (FC) in patients with BD. Methods Brain resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 41 patients with unmedicated BD II (current episode depressed), 68 healthy controls (HCs). Three pairs of insular seed regions were selected: the bilateral anterior insula (AI), the bilateral middle insula (MI) and the bilateral posterior insula (PI), and calculated the whole-brain FC for each subregion. Additionally, the serum levels of pro-inflammatory cytokines in patients and HCs, including IL-6 and TNF-α, were detected. Then the partial correlation coefficients between the abnormal insular subregions FC values and pro-inflammatory cytokines levels in patients with BD II depression were calculated. Results The BD II depression group exhibited decreased FC between the right PI and the left postcentral gyrus, and increased FC between the left AI and the bilateral insula (extended to the right putamen) when compared with the HC group. Moreover, the patients with BD II depression showed higher IL-6 and TNF-α levels than HCs, and IL-6 level was negatively correlated with FC of the right PI to the left postcentral gyrus. Conclusions Our results demonstrated that abnormal FC between the bilateral insula, and between the insula and sensorimotor areas in BD. Moreover, disrupted FC between the insula and sensorimotor areas was associated with elevated pro-inflammatory cytokine levels of IL-6 in BD.

After a decade of rapid development, mobile devices have become an essential part of people’s daily life and mobile applications generate rich contents and provide important information access. Contents inside mobile applications can provide valuable insights for social sensing and application development. However, unlike Web search, there is a lack of efficient methods to sense and index information from mobile applications. This article proposes an automated mobile application content sensing (AMACS) framework, which can be the fundamental tool to extract effectively the contents and conducting measurements in mobile applications. AMACS uses a content-aware discovery model to extract effectively the contents from a variety of mobile applications without any manual intervention. Its modular design with independent building blocks can be readily expanded into a distributed system for large-scale deployment. AMACS is compared with the existing relevant mobile automated analysis methods to evaluate its performance. The results show that the crawler can cover more contents efficiently in mobile applications with low overheads, and it can be used to extract contents for application scenarios like social sensing and network measurements.

Background: Resting-state functional MRI (fMRI) studies have provided much evidence for abnormal intrinsic brain activity in schizophrenia, but results have been inconsistent. Methods: We conducted a meta-analysis of whole-brain, resting-state fMRI studies that explored differences in amplitude of low-frequency fluctuation (ALFF) between people with schizophrenia (including first episode and chronic) and healthy controls. Results: A systematic literature search identified 24 studies comparing a total of 1249 people with schizophrenia and 1179 healthy controls. Overall, patients with schizophrenia displayed decreased ALFF in the bilateral postcentral gyrus, bilateral precuneus, left inferior parietal gyri and right occipital lobe, and increased ALFF in the right putamen, right inferior frontal gyrus, left inferior temporal gyrus and right anterior cingulate cortex. In the subgroup analysis, patients with first-episode schizophrenia demonstrated decreased ALFF in the bilateral inferior parietal gyri, right precuneus and left medial prefrontal cortex, and increased ALFF in the bilateral putamen and bilateral occipital gyrus. Patients with chronic schizophrenia showed decreased ALFF in the bilateral postcentral gyrus, left precuneus and right occipital gyrus, and increased ALFF in the bilateral inferior frontal gyri, bilateral superior frontal gyrus, left amygdala, left inferior temporal gyrus, right anterior cingulate cortex and left insula. Limitations: The small sample size of our subgroup analysis, predominantly Asian samples, processing steps and publication bias could have limited the accuracy of the results. Conclusion: Our comprehensive meta-analysis suggests that findings of aberrant regional intrinsic brain activity during the initial stages of schizophrenia, and much more widespread damage with the progression of disease, may contribute to our understanding of the progressive pathophysiology of schizophrenia.

Background: The pathophysiological mechanisms of mood disorders including bipolar disorder (BD) are not completely known, and systemic inflammation and immune dysregulation are considered as risk factors of it. Previous neuroimaging studies have proved metabolic, structural and functional abnormalities of the amygdala in BD patients, suggesting the vital role of amygdala in BD patients. This study aimed to test the underlying neural mechanism of inflammation-induced functional connectivity (FC) in the amygdala subregions of BD patients.Methods: Resting-state functional MRI (rs-fMRI) was used to delineate the amygdala FC from two pairs of amygdala seed regions (the bilateral lateral and medial amygdala) in 51 unmedicated BD patients and 69 healthy controls (HCs). The levels of pro-inflammatory cytokines including interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF)-α were measured in the serum. The correlation between abnormal levels of pro-inflammatory cytokines and FC values were calculated in BD patients.Results: The BD group exhibited decreased FC between the right medial amygdala and bilateral medial frontal cortex (MFC), and decreased FC between the left medial amygdala and the left temporal pole (TP), right orbital inferior frontal gyrus compared with HCs. The BD patients had higher levels of TNF-α than HCs. Correlation analysis showed negative correlation between the TNF-α level and abnormal FC of the right medial amygdala-bilateral MFC; and negative correlation between TNF-α levels and abnormal FC of the left medial amygdala-left TP in BD group.Conclusions: These findings suggest that dysfunctional and immune dysregulation between the amygdala and the frontotemporal circuitry might play a critical role in the pathogenesis of BD.

Background: Schizophrenia and bipolar disorder have been linked to alterations in the functional activity and grey matter volume of some brain areas, reflected in impaired regional homogeneity and aberrant voxel-based morphometry. However, because of variable findings and methods used across studies, identifying patterns of brain alteration in schizophrenia and bipolar disorder has been difficult. Methods: We conducted a meta-analysis of differences in regional homogeneity and voxel-based morphometry between patients and healthy controls for schizophrenia and bipolar disorder separately, using seed-based d mapping. Results: We included 45 publications on regional homogeneity (26 in schizophrenia and 19 in bipolar disorder) and 190 publications on voxel-based morphometry (120 in schizophrenia and 70 in bipolar disorder). Patients with schizophrenia showed increased regional homogeneity in the frontal cortex and striatum and the supplementary motor area; they showed decreased regional homogeneity in the insula, primary sensory cortex (visual and auditory cortices) and sensorimotor cortex. Patients with bipolar disorder showed increased regional homogeneity in the frontal cortex and striatum; they showed decreased regional homogeneity in the insula. Patients with schizophrenia showed decreased grey matter volume in the superior temporal gyrus, inferior frontal gyrus, cingulate cortex and cerebellum. Patients with bipolar disorder showed decreased grey matter volume in the insula, cingulate cortex, frontal cortex and thalamus. Overlap analysis showed that patients with schizophrenia displayed decreased regional homogeneity and grey matter volume in the left insula and left superior temporal gyrus; patients with bipolar disorder displayed decreased regional homogeneity and grey matter volume in the left insula. Limitations: The small sample size for our subgroup analysis (unmedicated versus medicated patients and substantial heterogeneity in the results for some regions could limit the interpretability and generalizability of the results. Conclusion: Patients with schizophrenia and bipolar disorder shared a common pattern of regional functional and structural alterations in the insula and frontal cortex. Patients with schizophrenia showed more widespread functional and structural impairment, most prominently in the primary sensory motor areas.

Background Previous studies demonstrated that patients with bipolar disorder (BD) exhibited abnormal neural activity in multiple brain regions. However, no study has been conducted to identify regional intrinsic neural activity changes in BD II. In the present study, we used the regional homogeneity (ReHo) approach to explore the regional abnormal neural activity in bipolar II disorder Methods One hundred unmedicated patients with BD II depression and 100 healthy controls (HC) underwent the resting-state functional magnetic resonance imaging. The ReHo values of each voxel was calculated in the whole brain. The two-sample t- test and threshold-free cluster enhancement (TFCE) correction were applied for the ReHo analysis. Results Compared with the HC group, the BD II group showed significantly decreased ReHo in the left orbitofrontal cortex, and increased ReHo in the right precentral gyrus, right supplementary motor area and bilateral middle occipital gyrus ( P Limitations This study lacks the evidence of brain structural changes, and used the cross-sectional design which did not explore local alterations of remitted and manic patients. Conclusion Our findings revealed abnormal local intrinsic neural activity during resting state which may contribute to the pathophysiology of bipolar II disorder. Particularly the disrupted balance between the prefrontal cortex and primary sensorimotor regions provides evidence for the unique pathological mechanism underlying BD.

With the rapid development of cloud computing technology, more and more users choose to outsource image data to clouds. To protect data's confidentiality, images need to be encrypted before being outsourced to clouds, but this brings difficulties to some basic yet important data services, such as content-based image retrieval. Existing secure image retrieval methods generally have some problems such as low retrieval accuracy and low retrieval efficiency, which cannot meet requirements for large-scale image retrieval in cloud environment. In this paper, we propose a large-scale secure image retrieval method in cloud environment. The Hamming embedding algorithm is utilized to generate binary signatures of image descriptors. A frequency histogram combined with binary signatures is generated to provide a more precise representation of image features in an image and thus the retrieval accuracy is improved. Visual words are selected from the histogram by the random sampling method before the min-Hash algorithm is performed on binary signatures of selected visual words to generate a secure index. The random sampling method and min-Hash algorithm can not only ensure the security of the search index, but also greatly improve the image retrieval efficiency. This method achieves the balance among security, accuracy and efficiency of large-scale secure image retrieval in public clouds. The security analysis and experimental results show the effectiveness of the proposed method.

Background Bipolar disorder (BD) has been linked to abnormalities in the communication and gray matter volume (GMV) of large-scale brain networks, as reflected by impaired resting-state functional connectivity (rs-FC) and aberrant voxel-based morphometry (VBM). However, identifying patterns of large-scale network abnormality in BD has been elusive. Methods Whole-brain seed-based rs-FC and VBM studies comparing individuals with BD and healthy controls (HCs) were retrieved from multiple databases. Multilevel kernel density analysis was used to identify brain networks in which BD was linked to hyper-connectivity or hypo-connectivity with each prior network and the overlap between dysconnectivity and GMV changes. Results Thirty-six seed-based rs-FC publications (1526 individuals with BD and 1578 HCs) and 70 VBM publications (2715 BD and 3044 HCs) were included in the meta-analysis. Our results showed that BD was characterized by hypo-connectivity within the default network (DN), hyper-connectivity within the affective network (AN), and ventral attention network (VAN) and hypo- and hyper-connectivity within the frontoparietal network (FN). Hyper-connectivity between-network of AN-DN, AN-FN, AN-VAN, AN-thalamus network (TN), VAN-TN, VAN-DN, VAN-FN, and TN-sensorimotor network were found. Hypo-connectivity between-network of FN and DN was observed. Decreased GMV was found in the insula, inferior frontal gyrus, and anterior cingulate cortex. Limitations Differential weights in the number of included studies and sample size of FC and VBM might have a disproportionate influence on the meta-analytic results. Conclusions These results suggest that BD is characterized by both structural and functional abnormalities of large-scale neurocognitive networks, especially in the DN, AN, VAN, FN, and TN.

A central question in financial economics concerns the degree of informational efficiency. Violations of informational efficiency represent capital miss-allocations and potentially profitable trading opportunities. Market efficiency analyses have evolved to incorporate increasingly rich public information and innovative statistical methods to analyze this information. We propose an Automatic Crawling and Prediction System (ACPS) to 1) automatically crawl online media, 2) extract useful information from a rich set of financial news, and 3) predict future stock price movements. ACPS consists of a feature selection pipeline to select an optimal set of predictive features and a sentiment analysis model to measure sentence-level news sentiment. Generated features and news sentiment data are further processed via an ensemble model based on several machine learning and deep learning algorithms to generate forecasts. Results demonstrate the robustness of our proposed model in predicting the directional movement of daily stock prices. Specifically, the model consistently outperforms existing methods on single stock prediction and it performs well across all S&P 500 stocks. Our results indicate the potential value of rich text analysis and ensemble learning methods in a real-time trading context.

Background: Bipolar disorder is associated with a high risk of suicide. Routine neuroimaging examination exhibited that bipolar disorder with suicidality was associated with brain structural and functional changes. However, the alterations of brain dynamics have still remained elusive. Purpose: To investigate the alterations of brain dynamics in unmedicated bipolar disorder II depression with suicidality and predict the severity of suicidality. Materials and Methods: This prospective study included 106 bipolar disorder II participants (20 with suicidal attempt, 35 with suicidal ideation, 51 without suicidal ideation) and 50 healthy controls who underwent resting-state functional magnetic resonance imaging between February 2016 and December 2017. We first used sliding window analysis to evaluate the dynamic amplitude of low-frequency fluctuations. Then, we predicted the severity of suicidality using a multivariate regression model. Results: One-way analysis of covariance revealed that the dynamic amplitude of low-frequency fluctuations in the right temporal pole, inferior temporal gyrus, superior temporal gyrus and the bilateral precuneus/posterior cingulate cortex was significantly different among the four groups. Post hoc pairwise comparisons revealed that dynamic amplitude of low-frequency fluctuations was remarkably decreased in the bilateral precuneus/posterior cingulate cortex in the three bipolar disorder II groups compared with that in healthy controls group. Increased dynamic amplitude of low-frequency fluctuations was found in the right superior temporal gyrus and inferior temporal gyrus in the suicidal attempt group compared with that in the other groups, and in the right temporal pole in the suicidal attempt group compared with that in the suicidal ideation and healthy controls groups. Importantly, these temporal variabilities could be used to predict the severity of suicidality ( r = 0.330, p = 0.036), whereas static amplitude of low-frequency fluctuations couldn’t ( r = –0.050, p = 0.532). Conclusion: Our findings indicated that alterations of temporal variability in the precuneus/posterior cingulate cortex are such a common feature of bipolar disorder patients. Besides, the severity of suicidality could be predicted by the dynamic amplitude of low-frequency fluctuations abnormalities rather than static amplitude of low-frequency fluctuations abnormalities, which is the first evidence of dynamic brain alterations in bipolar disorder patients with suicidality. The proposed predictive model may be advantageous for clinical applications.

BackgroundAccumulating studies have found structural and functional abnormalities of the striatum in bipolar disorder (BD) and major depressive disorder (MDD). However, changes in intrinsic brain functional connectivity dynamics of striato-cortical circuitry have not been investigated in BD and MDD. This study aimed to investigate the shared and specific patterns of dynamic functional connectivity (dFC) variability of striato-cortical circuitry in BD and MDD.MethodsBrain resting-state functional magnetic resonance imaging data were acquired from 128 patients with unmedicated BD II (current episode depressed), 140 patients with unmedicated MDD, and 132 healthy controls (HCs). Six pairs of striatum seed regions were selected: the ventral striatum inferior (VSi) and the ventral striatum superior (VSs), the dorsal-caudal putamen (DCP), the dorsal-rostral putamen (DRP), and the dorsal caudate and the ventral-rostral putamen (VRP). The sliding-window analysis was used to evaluate dFC for each seed.ResultsBoth BD II and MDD exhibited increased dFC variability between the left DRP and the left supplementary motor area, and between the right VRP and the right inferior parietal lobule. The BD II had specific increased dFC variability between the right DCP and the left precentral gyrus compared with MDD and HCs. The MDD had increased dFC variability between the left VSi and the left medial prefrontal cortex compared with BD II and HCs.ConclusionsThe patients with BD and MDD shared common dFC alteration in the dorsal striatal-sensorimotor and ventral striatal-cognitive circuitries. The patients with MDD had specific dFC alteration in the ventral striatal-affective circuitry.

Objectives Inflammation might play a role in bipolar disorder (BD), but it remains unclear the relationship between inflammation and brain structural and functional abnormalities in patients with BD. In this study, we focused on the alterations of functional connectivity (FC), peripheral pro-inflammatory cytokines and their correlations to investigate the role of inflammation in FC in BD depression.Methods In this study, 42 unmedicated patients with BD II depression and 62 healthy controls (HCs) were enrolled. Resting-state-functional magnetic resonance imaging (rs-fMRI) was performed in all participants and independent component analysis (ICA) was used. Serum levels of Interleukin-6 (IL-6) and Interleukin-8 (IL-8) were measured in all participants. Correlation between FC values and IL-6 and IL-8 levels in BD was calculated.Results Compared with the HCs, BD II patients showed decreased FC in the left orbitofrontal cortex (OFC) implicating the limbic network and the right precentral gyrus implicating the somatomotor network (SMN). BD II showed increased IL-6 (P = 0.039), IL-8 (P = 0.002) levels. Moreover, abnormal FC in the right precentral gyrus were inversely correlated with the IL-8 (r=-0.458, P = 0.004) levels in BD II. No significant correlation was found between FC in the left OFC and cytokines levels.Conclusions Our findings that serum IL-8 levels is associated with impaired FC in the right precentral gyrus in BD II patients suggest that inflammation might play a crucial role in brain functional abnormalities in BD.

Background When bipolar disorder (BD) presents as the depressive state, it is often misdiagnosed as major depressive disorder (MDD). However, few studies have focused on dynamic differences in local brain activity and connectivity between BD and MDD. Therefore, the present study explored shared and specific patterns of abnormal dynamic brain segregation and integration in BD and MDD patients. Methods BD Patients (n = 106), MDD patients (n = 114), and 130 healthy controls (HCs) underwent resting state functional magnetic resonance imaging (fMRI). We first used a sliding window analysis to evaluate the dynamic amplitude of low-frequency fluctuations (dALFF) and, based on the altered dALFF, further analyzed the dynamic functional connectivity (dFC) using a seed-based approach. Results Both the BD and MDD groups showed decreased temporal variability of the dALFF (less dynamic segregation) in the bilateral posterior cingulate cortex (PCC)/precuneus compared with the HCs. The MDD group showed increased temporal variability of the dALFF (more dynamic segregation) in the left putamen compared with the controls, but there was no significant difference between the BD and HCs. The dFC analysis also showed that both the BD and MDD groups had reduced dFC (less dynamic integration) between the bilateral PCC/ precuneus and the left inferior parietal lobule compared with the HCs. Limitations This study was cross-sectional and did not examine data from remitted BD and MDD patients. Conclusion Our findings indicated disrupted dynamic balance between segregation and integration within the default mode network in both BD and MDD. Moreover, we found MDD-specific abnormal brain dynamics in the putamen.

With the rapid development of society, economy, politics and science, there is a vast amount of collected daily news reports. How to detect news events and discover the underlying event evolution pattern has become an urgent problem. There have been many existing works to solve this problem, but most just use TF-IDF or LDA features to extract the limited semantic information, and the structural information of documents is also potential to be exploited. In this paper, we propose a novel Graph Convolutional Network based event detection model, named as NED-GCN, for news stream. The proposed model utilizes ConceptGraph to represent a document and fully takes semantic information and structural information of a document into account. Further, a Siamese Graph Convolutional Network (SiamGCN) is presented to calculate the similarity between document pair via shared weights for document embedding learning, and finally the learned document embeddings are clustered to generate events. Experimental evaluation on two real datasets shows that our method outperforms the state-of-art approaches in event detection.

Supplemental material, Supplementary_material for Characteristics of temporal dynamics of intrinsic brain activity in unmedicated bipolar disorder with suicidality by Jiaying Gong, Guanmao Chen, Mengyao Zhou, Yanbin Jia, Shuming Zhong, Feng Chen, Shunkai Lai, Zhenye Luo, Jurong Wang, Hao Xu, Lu Wang, Li Huang and Ying Wang in Australian & New Zealand Journal of Psychiatry

We propose a zero-shot learning relation classification (ZSLRC) framework that improves on state-of-the-art by its ability to recognize novel relations that were not present in training data. The zero-shot learning approach mimics the way humans learn and recognize new concepts with no prior knowledge. To achieve this, ZSLRC uses advanced prototypical networks that are modified to utilize weighted side (auxiliary) information. ZSLRC's side information is built from keywords, hypernyms of name entities, and labels and their synonyms. ZSLRC also includes an automatic hypernym extraction framework that acquires hypernyms of various name entities directly from the web. ZSLRC improves on state-of-the-art few-shot learning relation classification methods that rely on labeled training data and is therefore applicable more widely even in real-world scenarios where some relations have no corresponding labeled examples for training. We present results using extensive experiments on two public datasets (NYT and FewRel) and show that ZSLRC significantly outperforms state-of-the-art methods on supervised learning, few-shot learning, and zero-shot learning tasks. Our experimental results also demonstrate the effectiveness and robustness of our proposed model., Comment: 10 pages, 8 figures, published in CIKM 2021

With the rapid development of cloud computing technology, more and more users choose to outsource image data to clouds. To protect users’ privacy and guarantee data’s confidentiality, images need to be encrypted before being outsourced to CSP, but this brings new difficulties to some basic yet important data services, such as content-based image retrieval (CBIR). In this paper, a privacy-preserving image retrieval method based on an improved BoVW model is proposed. An improved BoVW method based on Hamming embedding can provide binary signatures that refine the matching based on visual words; therefore, retrieval precision is improved significantly; orthogonal transformation is utilized to implement privacy-preserving image retrieval, where image features are divided into two different fields with orthogonal decomposition, for which encryption and distance comparison are executed separately, and two kinds of operation results are fused in the final vector with orthogonal composition. As a result, c...

Solved the challenge of the privacy-preserving CBIR under cloud environment.Honest-but-curious CSP can retrieve image directly without violating data privacy.The proposed method is based on orthogonal decomposition.Our method can adapt different applications for no restriction of cipher algorithms. In order to protect data privacy, image with sensitive or private information needs to be encrypted before being outsourced to a cloud service provider. However, this causes difficulties in image retrieval and data management. A privacy-preserving content-based image retrieval method based on orthogonal decomposition is proposed in the paper. The image is divided into two different components, for which encryption and feature extraction are executed separately. As a result, cloud server can extract features from an encrypted image directly and compare them with the features of the queried images, so that users can thus obtain the image. Different from other methods, the proposed method has no special requirements to encryption algorithms, which makes it more universal and can be applied in different scenarios. Experimental results prove that the proposed method can achieve better security and better retrieval performance.