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Unipolar depression (UD) and bipolar depression (BD) showed convergent and divergent cognitive impairments. Neural oscillations are linked to the foundational cognitive processes. We aimed to investigate the underpinning spectral neuronal power patterns by magnetoencephalography (MEG), which combinates high spatial and temporal resolution. We hypothesized that patients with UD and BD exhibit common and distinct patterns, which may contribute to their cognitive impairments.Group cognitive tests were performed. Eyes closed resting-state MEG data were collected from 61 UD, 55 BD, and 52 healthy controls (HC). Nonparametric cluster-based permutation tests were performed to deal with the multiple comparison problem on channel-frequency MEG data. Correlation analysis of cognitive dysfunction scores and MEG oscillation were conducted by Spearman or partial correlation analysis.Wisconsin Card Sorting Test showed similar cognitive impairment in patients with UD and BD. Moreover, patients with BD exhibited extensive cognitive deficits in verbal executive functions and visuospatial processing. Compare to HC, both patients with UD and BD showed increased frontal-central beta power while high gamma power was decreased in UD groups during the resting-state. The significant correlations between cognitive function and average beta power were observed.Patients with BD had more cognitive impairments on different dimensions than those with UD, involving disrupted beta power modulations. Our investigation provides a better understanding of the neuroelectrophysiological process underlying cognitive impairments in patients with UD and BD.

A supramolecular polymer based on NPyP and CB[8] was constructed via host–guest interactions with the AIE effect for artificial light-harvesting energy transfer and photocatalysis.

BACKGROUND Internet-based cognitive behavioral therapy (ICBT) is an effective remote psychological treatment option. However, its effectiveness is usually assessed based on subjective symptom scales, which may lead to inaccurate reflection of the true symptoms or progress in treatment. Using both subjective symptom scales and objective speech acoustic features to evaluate ICBT has the benefits of being non-invasive and convenient. Utilizing reliable and valid measures of patient response based on objective speech indicators would aid in the advancement of ICBT. OBJECTIVE The objective of this study was to evaluate the effectiveness of ICBT using speech acoustic features for treatment monitoring and predicting treatment response. We examined changes in symptoms and speech, and developed a machine learning-based classification model that could monitor treatment progression and predict treatment outcome based on speech acoustic features. METHODS A four-week randomized controlled trial was conducted to study the use of ICBT in college students with depression. Speech samples and clinical symptoms were collected at the beginning and end of treatment, and the extracted acoustic features were compared between the ICBT and wait-list groups and analyzed for correlations. An artificial neural network (ANN) was also created to predict the efficacy of ICBT and classify treatment response. RESULTS In comparison to the wait-list group, the first formant bandwidth of speech significantly changed in the ICBT group, along with improvements in depressive symptoms following treatment. The efficacy of ICBT and treatment response were predicted using speech features such as the difference in the first and third formants and first formant bandwidth. There was a significant correlation (r=.452, P=.004) between the predicted and true values of the change in PHQ-9 scores from baseline to week 4 of ICBT. Additionally, the classification model built by ANN to identify treatment response and nonresponse had an accuracy rate of 78.37%. CONCLUSIONS This study identified speech formant as objective biological markers of speech that are closely related to depression and the effectiveness of ICBT. The research also showed that classification models based on key speech acoustic features can be a useful method for tracking progress in psychotherapy and predicting efficacy. CLINICALTRIAL The study was registered at ClinicalTrials.gov (ChiCTR2100045542).

Background Biofeedback therapy is mainly based on the analysis of physiological features to improve an individual’s affective state. There are insufficient objective indicators to assess symptom improvement after biofeedback. In addition to psychological and physiological features, speech features can precisely convey information about emotions. The use of speech features can improve the objectivity of psychiatric assessments. Therefore, biofeedback based on subjective symptom scales, objective speech, and physiological features to evaluate efficacy provides a new approach for early screening and treatment of emotional problems in college students. Methods A four-week, randomized, controlled, parallel biofeedback therapy study was conducted with college students with symptoms of anxiety or depression. Speech samples, physiological samples, and clinical symptoms were collected at baseline and at the end of treatment, and the extracted speech features and physiological features were used for between-group comparisons and correlation analyses between the biofeedback and wait-list groups. Based on the speech features with differences between the biofeedback intervention and wait-list groups, an artificial neural network was used to predict the therapeutic effect and response after biofeedback therapy. Results Through biofeedback therapy, improvements in depression (p = 0.001), anxiety (P = 0.001), insomnia(P = 0.013), and stress(P = 0.004) severity were observed in college-going students (n = 52). The speech and physiological features in the biofeedback group also changed significantly compared to the waitlist group (n = 52) and were related to the change in symptoms. The energy parameters and Mel-Frequency Cepstral Coefficients (MFCC) of speech features can predict whether biofeedback intervention effectively improves anxiety and insomnia symptoms and treatment response. The accuracy of the classification model built using the artificial neural network (ANN) for treatment response and non-response was approximately 60%. Conclusions The results of this study provide valuable information about biofeedback in improving the mental health of college-going students. The study identified speech features, such as the energy parameters, and MFCC as more accurate and objective indicators for tracking biofeedback therapy response and predicting efficacy. Trial Registration ClinicalTrials.gov ChiCTR2100045542

BACKGROUND: The purpose of this study was to explore the association between perceived stress and depression among medical students and the mediating role of insomnia in this relationship during the COVID-19 pandemic in China. METHODS: A cross-sectional survey was conducted from March to April 2020 in medical university. Levels of perceived stress, insomnia and depression were measured using Perceived Stress Scale (PSS), Insomnia Severity Index (ISI) and Patient Health Questionnaire 9 (PHQ-9). The descriptive analyses of the demographic characteristics and correlation analyses of the three variables were calculated. The significance of the mediation effect was obtained using a bootstrap approach with SPSS PROCESS macro. RESULTS: The mean age of medical students was 21.46 years (SD=2.50). Of these medical students, 10,185 (34.3%) were male and 19,478 (65.7%) were female. Perceived stress was significantly associated with depression (s=0.513, P

As a type of central nervous system tumor, meningioma usually compresses the nerve center due to its local expansion, further causing neurological deficits. However, there are limited therapeutic approaches for meningiomas. ITF2357, a potent class I and II histone deacetylase inhibitor (HDACi), has been shown to inhibit cell proliferation, promote apoptosis, and block the cell cycle in various sarcoma cells, including glioblastoma and peripheral T-cell lymphoma. Here, we investigated the potential role of ITF2357 on meningioma cancer cells (IOMM-Lee cells). First, we demonstrated that the half-maximal inhibitory concentration (IC50) of ITF2357 was 1.842 μM by MTT assay. In addition, ITF2357 effectively inhibited the proliferation and colonization ability of IOMM-Lee cells. Flow cytometry analysis showed that ITF2357 induced G0/G1 and G2/M phase cell cycle arrest and cell apoptosis. Mechanically, the RNA sequencing data revealed that ITF2357 could affect the PI3K-Akt signaling pathway and the cell cycle progression. Furthermore, the expression levels of Akt, PI3K, p-Akt, and p-PI3K were determined by western blotting. Collectively, our data revealed that ITF2357 induces G0 G1 and G2/M phase arrest and apoptosis by inhibiting hyperactivation of the PI3K-Akt pathway, ultimately inhibiting cell viability and proliferation of meningioma cells, which developed a new approach to the treatment of meningioma.

ObjectiveDeveloping a neuroimaging-based precision medicine framework for depression.MethodsThe study was conducted in two stages at two sites: development of a neuroimaging-based subtyping and precise repetitive transcranial magnetic stimulation (rTMS) strategy for depression at Center 1 and its clinical application at Center 2. Center 1 identified depression subtypes and subtype-specific rTMS targets based on amplitude of low frequency fluctuation (ALFF) in a cohort of 238 major depressive disorder patients and 66 healthy controls (HC). Subtypes were identified using a Gaussian Mixture Model, and subtype-specific rTMS targets were selected based on dominant brain regions prominently differentiating depression subtypes from HC. Subsequently, one classifier trained per Center 1 findings for subtyping and subtype-specific rTMS targets were employed to deliver two-week precise rTMS to 72 hospitalized, depressed youths at Center 2. MRI and clinical assessments were obtained at baseline, midpoint, and treatment completion for evaluation.ResultsTwo neuroimaging subtypes of depression, archetypal and atypical depression, were identified based on distinct frontal-posterior functional imbalance patterns as measured by ALFF. The dorsomedial prefrontal cortex was identified as the rTMS target for archetypal depression, and the occipital cortex for atypical depression. Following precise rTMS, ALFF alterations were normalized in both archetypal and atypical depressed youths, corresponding with symptom response of 90.00% in archetypal depression and 70.73% in atypical depression.ConclusionsA precision medicine framework for depression was developed based on frontal-posterior functional imbalance and implemented with promising results. Future randomized controlled trials are warranted.Chinese Clinical Trial Registry identifier: ChiCTR2100045391

With the advancements of wireless sensor networks and the Internet of Underwater Things (IoUT), underwater acoustic sensor networks (UASNs) have attracted much attention, which also has been widely used in marine engineering exploration and disaster prevention. However, UASNs still face many challenges, including high propagation latency, limited bandwidth, and energy consumption, influencing the reliability of data transmission. In this paper, we propose a routing protocol based on the on-site architecture (SROA) for UASNs to improve network scalability, energy efficiency, and transmission reliability. The on-site architecture adopted by SROA is different from the most architectures in that the data center is deployed underwater which makes the sink nodes closer to the data source. The SROA is a cluster-based protocol which adapts to the changes of network scale and avoids single-point failure through the decentralized and distributed sensor networks. To seek optimal routing policies, the factors of energy, delay, and link quality are considered jointly in the reward function of Q-Learning. Moreover, the reduction of packet retransmissions and collisions is advocated using a waiting mechanism developed from opportunistic routing (OR). The SROA realizes opportunistic routing to choose candidate nodes and coordinate packet forwarding among candidate nodes. Scalability of the proposed routing protocols is also analyzed by varying the network size and transmission range. According to the evaluation results, with the network scale ranging from 100 to 500, the SROA outperforms the existing routing protocols, extensively increasing packet delivery ratio and decreasing end-to-end delay.

Bipolar disorder type II (BD-II) is linked to an increased suicidal risk. Since a prior suicide attempt (SA) is the single most important risk factor for sequent suicide, the elucidation of involved neural substrates is critical for its prevention. Therefore, we examined the spontaneous brain activity and its temporal variabilities in suicide attempters with bipolar II during a major depressive episode. In this cross-sectional study, 101 patients with BD-II, including 44 suicidal attempters and 57 non-attempters, and 60 non-psychiatric controls underwent a resting-state functional magnetic resonance imaging (fMRI). Participants were assessed with Hamilton Rating Scale for Depression (HAMD) and Nurses, Global Assessment of Suicide Risk (NGASR). The dynamics of low-frequency fluctuation (dALFF) was measured using sliding-window analysis and its correlation with suicidal risk was conducted using Pearson correlation. Compared to non-attempters, suicidal attempters showed an increase in brain activity and temporal dynamics in the anterior cingulate cortex (ACC). In addition, the temporal variabilities of ACC activity positively correlated with suicidal risk (R = 0.45, p = 0.004), while static ACC activity failed to (R = 0.08, p > 0.05). Our findings showed that an aberrant static ALFF and temporal variability could affect suicidal behavior in BD-II patients. However, temporal variability of neuronal activity was more sensitive than static amplitude in reflecting diathesis for suicide in BD-II. Dynamics of brain activity could be considered in developing neuromarkers for suicide prevention.

A kind of double-layer curved compound eye (DLCCE) inspired by natural compound eye was developed and fabricated in two-photon polymerization (TPP) technology to solve the existing problems, such as defocus at the edge, hard to miniaturize, assemble and detect. For each ommatidium, a corresponding modified microlens was designed to refract and focus the incident rays on the photoelectric detector. The diameter of the complete compound eye is $47.9~\mu \text{m}$ . It is integrated by 33 ommatidia with a diameter of $3.9~\mu \text{m}$ and possesses a theoretical FOV angle of 85°. What’s more, extra supports and through-holes were designed to promote to develop the unpolymerized photoresist within the fabricated structure. An imaging experiment was conducted to demonstrate the validity of developed compound eye comparing with the single-layer one. This work was expected to promote compound eyes to be applied in more imaging and sensing fields of microvision for their miniaturization, integration and optimization of optical system.

As a type of central nervous system tumor, meningioma usually compresses the nerve center due to its local expansion, further causing neurological deficits. However, there are limited therapeutic approaches for meningiomas. ITF2357, a potent class I and II histone deacetylase inhibitor (HDACi), has been shown to inhibit cell proliferation, promote apoptosis and block the cell cycle in a variety of sarcoma cells, including glioblastoma and peripheral T-cell lymphoma. Here, we investigated the antitumor potential of ITF2357 on meningioma cells (IOMM). First, we demonstrated that the half-maximal inhibitory concentration (IC50) of ITF2357 was 1.842 µg/ml by MTT assay. In addition, ITF2357 effectively inhibited the proliferation and colonization ability of IOMM cells. Flow cytometry analysis showed that ITF2357 induced G0/G1 cell cycle arrest and cell apoptosis. Mechanically, the RNA sequencing data revealed that ITF2357 could affect the PI3K-Akt signaling pathway and the cell cycle progression. Furthermore, the expression level of cyclin B1, cyclin D1, and CDK1 was determined by western blotting. Collectively, our data revealed that ITF2357 inhibited cell viability and proliferation of meningioma cells by inducing G0/G1 phase arrest and apoptosis, and inhibiting cell cycle-related proteins (CDK/cyclin B1/cyclin D1), which developed a new approach to the treatment of meningioma.

Bipolar disorder (BD) is associated with a high risk of suicidality, and it is challenging to predict suicide attempts in clinical practice to date. Although structural and functional connectivity alterations from neuroimaging studies have been previously reported in BD with suicide attempts, little is known about how abnormal structural and functional connectivity relates to each other. Here, we hypothesize that structure connectivity constrains functional connectivity, and structural–functional coupling is a more sensitive biomarker to detect subtle brain abnormalities than any single modality in BD patients with a current major depressive episode who had attempted suicide. By investigating structural and resting-state fMRI connectivity, as well as their coupling among 191 BD depression patients with or without a history of suicide attempts and 113 healthy controls, we found that suicide attempters in BD depression patients showed significantly decreased central-temporal structural connectivity, increased frontal–temporal functional connectivity, along with decreased structural–functional coupling compared with non-suicide attempters. Crucially, the altered structural connectivity network predicted the abnormal functional connectivity network profile, and the structural–functional coupling was significantly correlated with suicide risk but not with depression or anxiety severity. Our findings suggest that the structural connectome is the key determinant of brain dysfunction, and structural–functional coupling could serve as a valuable trait-like biomarker for BD suicidal predication over and above the intramodality network connectivity. Such a measure can have clinical implications for early identification of suicide attempters with BD depression and inform strategies for prevention.

Purpose This study investigated the effects of resistance training at continuous hypoxia in rat skeletal muscles, with a focus on the modulation of Forkhead box protein O1 (FoxO1) signaling by protein kinase B (PKB/Akt) in this process.Methods In an intervention experiment, male Sprague-Dawley rats were exposed to simulated hypoxia and subjected to resistance training for four weeks. The biceps of forelimb and extensor digitorum longus (EDL) muscle were isolated for histological observation and biochemical analysis. L6 rat myoblasts were differentiated into myotubes in the cell culture experiment and then subjected to hypoxic conditions with the addition of an Akt activator. The cells were harvested to observe their morphology and determine the expression level of FoxO1 and downstream signaling molecules. Results Morphological observation showed the wet weight of the EDL and biceps of the upper limb was significantly higher in the hypoxia-training (HR) group than in the hypoxia (H) group (P < 0.05 and P < 0.05). Resistance training significantly enhanced Akt expression and FoxO1 phosphorylation (P < 0.01 and P < 0.05) in skeletal muscle. In myotubes activated Akt under 1% O2 conditions, FoxO1 phosphorylation at S256 was up-regulated, while expression of MuRF1 and Atrogin-1 was reduced (P < 0.05 and P < 0.05). Conclusion Resistance training alleviated skeletal muscle atrophy induced by hypoxia, in which FoxO1 phosphorylated by Akt at the S256 position and down-regulation of E3 ligase MuRF-1 and Atrogin-1 in skeletal muscle.

Determining the clinical homogeneous and heterogeneous sets among depressive patients is the key to facilitate individual-level treatment decision.The diffusion tensor imaging (DTI) data of 62 patients with major depressive disorder (MDD) and 39 healthy controls were used to construct a Latent Dirichlet Allocation (LDA) Bayesian model. Another 48 MDD patients were used to verify the robustness. The LDA model was employed to identify both shared and unique imaging-derived factors of two typically antidepressant-targeted depressive patients, selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs). Furthermore, we applied canonical correlation analysis (CCA) between each factor loading and Hamilton depression rating scale (HAMD) sub-score, to explore the potential neurophysiological significance of each factor.The results revealed the imaging-derived connectional fingerprint of all patients could be situated along three latent factor dimensions; such results were also verified by the out-of-sample dataset. Factor 1, uniquely expressed by SNRI-targeted patients, was associated with retardation (r = 0.4, p = 0.037) and characterized by coupling patterns between default mode network and cognitive control network. Factor 3, uniquely expressed by SSRI-targeted patients, was associated with cognitive impairment (r = 0.36, p = 0.047) and characterized by coupling patterns within cognitive control and attention network, and the connectivity between threat and reward network. Shared factor 2, characterized by coupling patterns within default mode network, was associated with anxiety (r = 0.54, p = 0.005) and sleep disturbance (r = 0.37, p = 0.032).Our findings suggested that quantification of both homogeneity and heterogeneity within MDD may have the potential to inform rational design of pharmacological therapies.• The shared and unique manifestations guiding pharmacotherapy of depressive patients are caused by the homogeneity and heterogeneity of underlying structural connections of the brain. • Both shared and unique factor loadings were found in different antidepressant-targeted patients. • Significant correlations between factor loading and HAMD sub-scores were found.

Background Anthropometric characteristics are important factors that affect swimming performance. The aim of this study is to build a discriminant model using anthropometric factors to identify elite short-to-medium-distance freestyle swimmers through an adaptive Lasso approach. Methods The study recruited 254 swimmers (145 males and 109 females) who were divided them into elite (aged 17.9 ± 2.2 years, FINA points 793.8 ± 73.8) and non-elite (aged 17.1 ± 1.3 years, FINA points 560.6 ± 78.7) groups. Data for 73 variables were obtained, including basic information, anthropometric and derivative indicators. After filtering out highly correlated variables, 24 candidate variables were retained to be used in adaptive Lasso to select variables for prediction of elite swimmers. Deviance and area under the curve (AUC) were applied to assess the goodness of fit and prediction accuracy of the model, respectively. Results The adaptive Lasso selected 12 variables using the whole sample, with an AUC being 0.926 (95% CI [0.895–0.956]; P = 2.42 × 10−29). In stratified analysis by gender, nine variables were selected for male swimmers with an AUC of 0.921 (95% CI [0.880–0.963]; P = 8.82 × 10−17), and eight variables were for female swimmers with an AUC of 0.941 (95% CI [0.898–0.984]; P = 7.67 × 10−15). Conclusion The adaptive Lasso showed satisfactory performance in selecting anthropometric characteristics to identify elite swimmers. Additional studies with longitudinal data or data from other ethnicities are needed to validate our findings.

Patients with depressive episodes (PDE), such as unipolar disorder (UD) and bipolar disorder (BD), are often defined as distinct diagnostic categories, but increasing converging evidence indicated shared etiologies and pathophysiological characteristics across different clinical diagnoses. We explored whether these transdiagnostic deficits are caused by the common neural substrates across diseases or disease-sensitive mechanisms, or a combination of both. In this study, we utilized a Bayesian model to decompose the resting-state brain activity into multiple hyper- and hypo-activity patterns (refer to as "factors"), so as to explore the shared and disease-sensitive alteration patterns in PDE. The model was constructed over a total of 259 patients (131 UD and 128 BD) with 100 healthy controls as the reference. The other 32 initial depressive episode BD (IDE-BD) patients who had symptoms of mania or hypomania during follow-up were taken as an independent set to estimate the factor composition using the established model for further analysis. We revealed three transdiagnostic alteration factors in PDE. Based on the distribution of factors and the tendency of factor composition at the group level, these factors were defined as BD sensitive factor, UD sensitive factor and shared basic alteration factor. We further found that the factor composition and the ROIs-based alteration degree (mainly involving in orbitofrontal gyrus and part of parietal lobe) were associated with the bipolar index in IDE-BD patients. Our findings contributed to understanding the core transdiagnostic shared and disease-sensitive alterations in PDE and to predicting the risk of emotional state transition in IDE-BD patients.

Objective Misdiagnosis of bipolar disorder (BD) as unipolar disorder (UD) may cause improper treatment strategy to be chosen, especially in the early stages of disease. The aim of this study was to characterize alterations in specific brain networks for depressed patients who transformed into BD (tBD) from UD. Method The module allegiance from resting-fMRI by applying a multilayer modular method was estimated in 99 patients (33 tBD, 33 BD, 33 UD) and 33 healthy controls (HC). A classification model was trained on tBD and UD patients. HC was used to explore the functional declination patterns of BD, tBD, and UD. Results Based on our classification model, difference mainly reflected in default-mode network (DMN). Compared with HC, both BD and tBD focused on the difference of somatomotor network (SMN), while UD on the abnormity of DMN. The patterns of brain network between patients with BD and tBD were well-overlapped, except for cognitive control network (CCN). Conclusion The functional declination of internal interaction in DMN was suggested to be useful for the identification of BD from UD in the early stage. The higher recruitment of DMN may predispose patients to depressive states, while higher recruitment of SMN makes them more sensitive to external stimuli and prone to mania. Furthermore, CCN may be a critical network for identifying different stages of BD, suggesting that the onset of mania in depressed patients is accompanied by CCN related cognitive impairments.

With the continuous breakthrough and innovation of artificial intelligence technology, the demand for diversified and multi-level compound intelligent manufacturing talents keeps growing. However, the current pace of intelligent manufacturing talent education in colleges and universities is still difficult to keep up with the advances in science and technology in the context of the new generation of artificial intelligence. This work conducted visual research of the literature on artificial intelligence in the field of manufacturing. All the literature was retrieved from the Web of Science Core Collection and divided into three periods (1979–1994, 1995–2007 and 2008–2021) according to the fluctuation of literature volume. Bibliometric and content analysis of the related literature during these periods were conducted to track the hotspots and trend of artificial intelligence in the field of manufacturing. The results showed that the internet of things, deep learning, cyber physical systems and smart manufacturing have been the new research hotspots. Finally, a series of suggestions were given for the sustainable education of intelligent manufacturing talents in the context of the new generation of artificial intelligence. This work may provide references for the construction of sustainable education systems for intelligent manufacturing talents in the context of the new generation of artificial intelligence.

Considering that the physiological mechanism of the anterior cingulate cortex (ACC) in suicide brain remains elusive for bipolar disorder (BD) patients. The study aims to investigate the intrinsic relevance between ACC and suicide attempts (SA) through transient functional connectivity (FC).We enrolled 50 un-medicated BD patients with at least one SA, 67 none-suicide attempt patients (NSA) and 75 healthy controls (HCs). The sliding window approach was utilized to study the dynamic FC of ACC via resting-state functional MRI data. Subsequently, we probed into the temporal properties of dynamic FC and then estimated the relationship between dynamic characteristics and clinical variables using the Pearson correlation.We found six distinct FC states in all populations, with one of them being more associated with SA. Compared with NSA and HCs, the suicide-related functional state showed significantly reduced dwell time in SA patients, accompanied by a significantly increased FC strength between the right ACC and the regions within the subcortical (SubC) network. In addition, the number of transitions was significantly increased in SA patients relative to other groups. All these altered indicators were significantly correlated with the suicide risk.The results suggested that the dysfunction of ACC was relevant to SA from a dynamic FC perspective in BD patients. It highlights the temporal properties in dynamic FC of ACC that could be used as a putative target of suicide risk assessment for BD patients.

Background Major depressive disorder (MDD) is often accompanied with classic diurnal mood variation (DMV) symptoms. Patients with DMV symptoms feel a mood improvement and prefer activities at dusk or in the evening, which is consistent with the evening chronotype. Their neural alterations are unclear. In this study, we aimed to explore the neuropathological mechanisms underlying the circadian rhythm of mood and the association with chronotype in MDD. Methods A total of 126 depressed patients, including 48 with DMV, 78 without, and 67 age/gender-matched healthy controls (HC) were recruited and underwent a resting-state functional magnetic resonance imaging. Spontaneous neural activity was investigated using amplitude of low-frequency fluctuation (ALFF) and region of interest (ROI)-based functional connectivity (FC) analyses were conducted. The Morningness-Eveningness Questionnaire (MEQ) was utilized to evaluate participant chronotypes and Pearson correlations were calculated between altered ALFF/FC values and MEQ scores in patients with MDD. Results Compared with NMV, DMV group exhibited lower MEQ scores, and increased ALFF values in the right orbital superior frontal gyrus (oSFG). We observed that increased FC between the left suprachiasmatic nucleus (SCN) and supramarginal gyrus (SMG). ALFF in the oSFG and FC of rSCN-SMG were negatively correlated with MEQ scores. Limitation Some people's chronotypes information is missing. Conclusion Patients with DMV tended to be evening type and exhibited abnormal brain functions in frontal lobes. The synergistic changes between frontotemporal lobe, SCN-SMG maybe the characteristic of patients with DMV symptoms.

In microchannels, microstructure-induced acoustic streaming can be achieved at low frequencies, providing simple platforms for biomedicine and microfluidic manipulation. Nowadays, microstructures are generally fabricated by photolithography or soft photolithography. Existing studies mainly focused on the projection plane, while ignoring the side profile including microstructure’s sidewall and channel’s upper wall. Based on the perturbation theory, the article focuses on the effect of microstructure’s sidewall errors caused by machining and the viscous dissipation of upper wall on the streaming. We discovered that the side profile parameters, particularly the gap (gap g between the top of the structure and the upper wall of the channel), have a significant impact on the maximum velocity, mode, and effective area of the streaming.To broaden the applicability, we investigated boundary layer thickness parameters including frequency and viscosity. Under different thickness parameters, the effects of side profile parameters on the streaming are similar. But the maximum streaming velocity is proportional to the frequency squared and inversely proportional to the viscosity. Besides, the ratio factor θ of the maximum streaming velocity to the vibration velocity is affected by the side profile parameter gap g and sidewall profile angle α.

Disruption of circadian rhythms is related to disorders of glucose metabolism, and the molecular clock also exists in skeletal muscle. The ChIP-derived repressor of network oscillator (Chrono) and brain and muscle ARNT-like 1 (Bmal1) are core circadian components. Chrono is considered to be the repressor of Bmal1, and the Chrono–Bmal1 pathway is important in regulating the circadian rhythm; it has been speculated that this pathway could be a new mechanism for regulating glucose metabolism. The purpose of this study was to investigate the effects of Chrono on glucose metabolism in skeletal muscle and exercise capacity by using mice with skeletal-muscle-specific overexpression of Chrono (Chrono TG) and wild-type (WT) mice as the animal models. The results of this cross-sectional study indicated that the Chrono TG mice had an impaired glucose tolerance, lower exercise capacity, and higher levels of nonfasted blood glucose and glycogen content in skeletal muscle compared to WT mice. In addition, the Chrono TG mice also showed a significant increase in the amount of Chrono bound to Bmal1 according to a co-IP analysis; a remarkable decrease in mRNA expression of Tbc1d1, Glut4, Hk2, Pfkm, Pdp1, Gbe1, and Phka1, as well as in activity of Hk and protein expression of Ldhb; but higher mRNA expression of Pdk4 and protein expression of Ldha compared with those of WT mice. These data suggested the skeletal-muscle-specific overexpression of Chrono led to a greater amount of Chrono bound to Bmal1, which then could affect the glucose transporter, glucose oxidation, and glycogen utilization in skeletal muscle, as well as exercise capacity.

Background: The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. This study aims to identify subgroups of medical students based on mental health status and explore the influencing factors during the COVID-19 epidemic in China. Methods: A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Latent class analysis of the mental health of medical students was performed using M-plus software to identify subtypes of medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. Results: In this study, three distinct subgroups were identified, namely, the high-risk group, the low-risk group and the normal group. Therefore, medical students can be divided into three latent classes, and the number of students in each class is 4325, 9321 and 16,017. The multinomial logistic regression results showed that compared with the normal group, the factors influencing mental health in the high-risk group were insomnia, perceived stress, family psychiatric disorders, fear of being infected, drinking, individual psychiatric disorders, sex, educational level and knowledge of COVID-19, according to the intensity of influence from high to low. Conclusions: Our findings suggested that latent class analysis can be used to categorize different medical students according to their mental health subgroup during the outbreak of COVID-19. The main factors influencing the high-risk group and low-risk group are basic demographic characteristics, disease history, COVID-19 related factors and behavioral lifestyle, among which insomnia and perceived stress have the greatest impact. School administrative departments could utilize more specific measures on the basis of different subgroups, and provide targeted measures.

Objective: Alcohol use disorder (AUD) is a serious issue worldwide and frequently co-occurs with depression. However, the quality of life (QOL) of AUD patients with and without depression is not well studied in the Chinese Han population. The aim of this study was to investigate QOL and its correlates in AUD patients with and without depression in China.Methods: Five hundred and fifteen psychiatric patients diagnosed with AUD were recruited. All these patients completed the Beck Depression Inventory (BDI) to assess depression, the Medical Outcome Study 36-Item Short Form Health Survey (SF-36) to evaluate QOL and the Alcohol Use Disorders Identification Test (AUDIT) to measure the severity of drinking.Results: Compared with AUD patients without depression, those with depression had a lower QOL in all eight domains of the SF-36 (all P < 0.001), but were more willing to have alcohol-related treatment (P < 0.05). Negative correlations were noted between (i) the BDI total score and all eight domains of the SF-36 (all P < 0.001); and (ii) between the AUDIT total score and six domains of the SF-36 (all P < 0.05).Conclusions: Depression impairs QOL in patients with AUD in China. Early intervention in comorbid depression to improve QOL is needed.

Dynamic sign language can be described by its trajectory and key hand types. Most of the commonly used sign language can be recognized by trajectory curve matching. Therefore, In this paper, a new dynamic sign language recognition method is proposed, which uses trajectory and key hand type to extract features, adopts a key frame weighted DTW (dynamic time warping) algorithm to implement hierarchical matching strategy, and gradually matches sign language gestures from two levels of trajectory and key hand type, so as to effectively improve the accuracy and efficiency of sign language recognition.

Sign language recognition especially finger language recognition facilitates the life of deaf people in China. It overcomes many difficulties and provides convenience for deaf people’s life. In this paper, we used the advanced convolutional neural network to extract the different characteristics of the input. We created an optimized seven-layer CNN, including five convolution layers for feature extraction and two fully connected layers for classification to enhance the original signal function and reduce noise after operation. Some advanced techniques such as batch normalization, ReLu and dropout were employed to optimize the neural network. Meanwhile, we adopted data augmentation technology, which not only expanded the data set and improve the performance of machine learning algorithm, but also avoided the over-fitting problem. The experimental results show that the average recognition accuracy reaches 91.99 ± 1.21%, which indicate an excellent property.

Background High-fat diet (HFD)-induced obesity causes immune cells to infiltrate adipose tissue, leading to chronic inflammation and metabolic syndrome. Brown adipose tissue (BAT) can dissipate the energy produced by lipid oxidation as heat, thereby counteracting obesity. Aerobic exercise activates BAT, but the specific underlying mechanism is still unclear. Methods Male C57BL/6 J mice were divided into a normal diet control group (NC group) and HFD group (H group). After becoming obese, the animals in the H group were subdivided into a control group (HC group) and an exercise group (HE group, with treadmill training). After 4 weeks, the mRNA profile of BAT was determined, and then differentially expressed key genes and pathways were verified in vitro. Results Relative to the NC group, the genes upregulated in the HC group coded mainly for proteins involved in immune system progression and inflammatory and immune responses, while the downregulated genes regulated lipid metabolism and oxidation–reduction. Relative to the HC group, the genes upregulated in the HE group coded for glycolipid metabolism, while those that were downregulated were involved in cell death and apoptosis. VEGF and other signaling pathways were enhanced by aerobic exercise. Interaction analysis revealed that the gene encoding cyclooxygenase 2 (COX2) of the VEGF signaling pathway is central to this process, which was verified by a sympathetic activator (isoprenaline hydrochloride) and COX2 inhibitor (NS-398). Conclusions In mice with HFD-induced obesity, four weeks of aerobic exercise elevated BAT mass and increased the expression of genes related to glycolipid metabolism and anti-inflammatory processes. Several pathways are involved, with COX2 in the VEGF signaling pathway playing a key role.

Bipolar disorder type II (BD-II) is linked to an increased suicidal risk. Since a prior suicide attempt (SA) is the single most important risk factor for sequent suicide, the elucidation of involved neural substrates is critical for its prevention. Therefore, we examined the spontaneous brain activity and its temporal variabilities in suicide attempters with bipolar II during a major depressive episode. In this cross-sectional study, 101 patients with BD-II, including 44 suicidal attempters and 57 non-attempters, and 60 non-psychiatric controls underwent a resting-state functional magnetic resonance imaging (fMRI). Participants were assessed with Hamilton Rating Scale for Depression (HAMD) and Nurses

Diurnal mood variation (DMV), a common symptom of major depressive disorder (MDD), is associated with circadian related genes and dysregulation of the suprachiasmatic nucleus (SCN). Previous research confirmed that the RORA gene is involved in the regulation of circadian rhythms. In this study, we hypothesized that polymorphisms of RORA may affect DMV symptoms of MDD through functional changes in the SCN. A total of 208 patients diagnosed with depression and 120 control subjects were enrolled and underwent a resting-state functional magnetic resonance imaging (rs-fMRI). Blood samples were collected and genotyping of 9 RORA gene SNPs were performed using next-generation sequencing technology. Patients were categorized as an AA genotype or C allele carriers based on RORA rs72752802 polymorphism. SCN-seed functional connectivity (FC) was compared between the two groups and correlation with severity of DMV was analyzed. Finally, a mediation analysis was performed to further determine FC intermediary effects. We observed that rs72752802 was significantly associated with patients' DMV symptoms. C allele carriers of rs72752802 showed significantly decreased FC between the right SCN and right superior temporal gyrus (rSTG). This was also correlated with DMV symptoms. In addition, the rs72752802 SNP influenced DMV symptoms through intermediary effects of SCN-rSTG connectivity. The study presented here provides a neurological and genetic basis for understanding depressed patients experiencing DMV.

Objective The physiological mechanism of suicide attempt (SA) in bipolar II disorder (BD-II) remains only partially understood. The study seeks to identify the dysfunction pattern in suicide brain for BD-II patients. Methods Graph theory was utilized to explore topological properties at whole-brain, module and region levels based on resting-state functional MRI (rs-fMRI) data, which acquired from 38 un-medicated BD-II patients with at least one SA, 60 none SA (NSA) patients and 69 healthy controls (HCs). Finally, the correlation relationship between graph metrics and clinical variables were estimated. Results Compared with NSA patients and HCs, the functional connectivity strength between limbic/sub-cortical (LIMB/SubC) and frontoparietal network (FPN) were significantly weakened. Nodal strength in left head of caudate nucleus (HCN), raphe nucleus (RN), right nucleus accumbens (NAcc), right subgenual anterior cingulate cortex (sgACC) and nodal efficiency in right sgACC, right HCN for SA patients were significantly reduced relative to NSA and HCs. In particular, nodal strength in RN and nodal efficiency in right sgACC showed a significant negative correlation with Nurses’ Global Assessment of Suicide Risk (NGASR) scores. Limitations This is a single-mode cross-sectional study, the results were not verified by multi-center data. Conclusions The abnormal disrupted FC between LIMB/SubC and FPN is associated with SA in BD-II patients, which increased the susceptibility of suicide. Especially, the dysfunction in RN and right sgACC predict a higher suicide risk in BD-II patients.The results can help us to understand the suicide mechanism and early judgment of suicidal behaviors for BD-II patients.

Although structural and functional brain abnormalities have been observed in depressed suicide attempters (DS), structural deficits and functional impairments together with their relationship in DS remain unclear. To clarify this issue, we aimed to examine the differences in gray matter (GM) alteration, corresponding functional connectivity (FC) change, and their relationship between DS and depressed non-suicide attempters (NDS). Sixty-eight DS, 119 NDS and 103 healthy controls were enrolled and subjected to magnetic resonance imaging scans. The patients were evaluated using the 17-item Hamilton Rating Scale for Depression (HRSD) and Nurses' Global Assessment of Suicide Risk (NGASR) scale. Both voxel-based morphometry and resting-state FC analyses were performed based on functional and structural imaging data. Compared with NDS, the DS group showed reduced GM volume in the right inferior frontal orbital gyrus (IFOG) and left caudate (CAU) but increased GM volume in the left calcarine fissure, weaker negative right IFOG-left rectus gyrus (REG) FC, and weaker positive right IFOG-left inferior parietal lobule (IPL) FC. In DS, the GM volume of the right IFOG and left CAU was negatively correlated with NGASR and HRSD scores, respectively; the right IFOG-left IPL FC was negatively correlated with cognitive factor scores; and the GM volume of the right IFOG was positively correlated with IFOG-REG and IFOG-IPL FC. Our findings indicate that structural deficit with its related functional alterations in brain circuits converged in right IFOG centralized pathways and may play a central role in suicidal behaviors in depression.

A superparamagnetic graphene oxide (GO)/Fe3O4 nanocomposite (MGO) was prepared by a facile in situ co-precipitation strategy, resulting in a prospective material for the application of graphene oxide in wastewater treatment. MGO was characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), x-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). The prepared adsorbent showed a high adsorption efficiency relevant to the purification of dye-contaminated wastewater and could be readily magnetically separated. The maximum adsorption capacity was ca. 546.45 mg g−1 for the common cationic dye methylene blue (MB) and ca. 628.93 mg g−1 for the anionic dye Congo red (CR). The adsorption processes fit the pseudo-second-order kinetic model well, which revealed that these processes may involve the chemical interaction between adsorbate and adsorbent. The thermodynamic parameters indicated that the adsorption reaction was an endothermic and spontaneous process. Furthermore, the prepared magnetic adsorbent had a wide effective pH range from 5 to 11 and showed good stability after five reuse cycles. The synthetic MGO showed great potential as a promising adsorbent for organic contaminant removal in wastewater treatment.

In this paper, a method of obtaining four elastic constants of orthotropic composites by single tensile test is proposed. The principle of this method is to obtain the full field strain on the surface of the specimen through the tensile test of the specimen with a specific geometry, and to invert the measured in-plane strain field into the virtual fields method (VFM) to obtain the elastic constants. Based on the deformation field data generated by finite element numerical simulation, the effects of design parameters such as effective length, notch position, notch size and fillet radius on the identification results of elastic parameters of V-notch tensile specimen are studied. The results show that the inversion results of symmetrical V-notch specimen with 50 mm length, 12 mm notch size and 3.1 mm fillet radius are the best, and the sum of absolute relative errors of four elastic constants is less than 3%, which verifies the feasibility of the method.

In the process of manufacture and use, the composite stiffened plate will degrade and produce different types of cracks and damage, which limits its application. Therefore, it is significant to study the composite stiffened plate with cracks. Three-dimensional Hashin failure criterion and the gradual damage model of TAN parameter degradation are applied to simulate composite stiffened plate with long crack under uniaxial compression and pure shear load. The effects of crack angle on the ultimate compressive strength, shear strength and progressive damage of composite stiffened plates were investigated. The results show that the effect of crack angle on the compression performance of the stiffened plate is not simply linear, but the effect on the shear performance of the stiffened plate is more complicated, and The effect of crack angle on shear behavior is also related to the sequence of layer.

Photogenerated electron/hole recombination greatly limits the catalytic efficiency of TiO 2 , and recently modification with graphene substance has been regarded as an effective way to enhance the photocatalytic performance of TiO 2 . When referring to the fabrication of graphene based materials, the reduction process of graphene oxide has been demonstrated to be a key step. Therefore, it is highly required to develop an efficient and simple route for the GO reduction and the formation of TiO 2 @rGO composites. In this study, we have demonstrated a facile and environmentally friendly strategy for in-situ preparation of the TiO 2 @rGO “dyade” hybrid and systematically investigated the photodegradation efficiency of the resultant composite by utilizing rhodamine 6G as the model pollutant. The obtained TiO 2 @rGO has a significant enhancement in photo energy adsorption leading to the effective photocatalytic degradation reactions. The results indicated that the best performance was conducted by the TiO 2 @rGO (10 wt%, 120 min’s irradiation), which exhibited more than triple the higher photodegradation rate than commercial TiO 2 (P25) nanoparticles mainly due to two aspects, the rapid separation of h + / e − and to improve adsorption. This work provides new insight into the synthesis of TiO 2 @rGO composites as a high performance photocatalyst for the degradation of organic contaminant.

Introduction Skeletal muscle is the major producing and metabolizing site of lactic acid. A family of monocarboxylate transporter (MCT) proteins, especially MCT1 and MCT4, are involved in the lactate–pyruvate exchange and metabolism. Nuclear factor erythroid 2-related factor 2 (Nrf2) is a pivotal coordinator of antioxidant response and energy metabolism, and has been reported to associate with the physiological functions of the skeletal muscle. Methods In this study, C57BL/6 J mice were administrated with an Nrf2 activator, sulforaphane (SFN) before taking incremental treadmill exercise to exhaustion under hypoxia; then the effects of SFN on exercise endurance and molecular/biochemical makers of the skeletal muscle were evaluated. Results The results indicated that SFN pretreatment enhanced the exercise endurance under hypoxia. SFN not only increased the expressions of antioxidant genes and activity of antioxidant enzymes, but also significantly increased the mRNA and protein levels of MCT1 and CD147, but not MCT4. Moreover, the expressions of LDH-B and LDH activity of converting lactate into pyruvate, as well as citrate synthase activity were significantly higher, whereas the LDH activity of converting pyruvate into lactate and blood lactate level were remarkably lower in the SFN-exercise mice than those of the phosphate-buffered saline–exercise group. Furthermore, Atf3Δzip2 (the alternatively spliced isoform of activating transcription factor-3) mRNA was increased by the exercise and further potentiated by SFN. Conclusion These results show, for the first time, that SFN increases MCT1 expression in the skeletal muscle under acute hypoxic exercise and suggest that Nrf2 activation is a promising strategy to enhance exercise performance under hypoxia.

Background The fundamental pathophysiology of major depressive disorder (MDD) could be characterized by functional brain networks which tightly and dynamically connect into groups as communities, making the flexible brain possible to external multifarious demands. We aim to scrutinize what brain dynamics go awry in MDD and antidepressants effects on multi-dimensional symptoms. Methods Thirty-five patients and thirty-five controls underwent resting-state functional magnetic resonance imaging (MRI). Patients were scanned before and after 8 or 12 weeks of pharmacotherapy. Group independent component analysis decomposed resting-state images to instinct networks and networks' integrated flexibility was calculated. Network flexibility between patients at baseline and after therapy were compared. Results All patients completed the clinical trial and MRI scans. Following antidepressants treatment, we found significant normalization of reduced network flexibility in default mode network (DMN) and cognitive control network (CCN) of MDD patients. Selectively significant correlations between network flexibility and multi-dimensional symptoms such as anxiety/somatization and hysteresis factor were also found. Conclusions “Hypoflexible” CCN may involve in anxiety syndrome. Low flexibility in DMN may be indicative of hysteresis. These suggest an important pathophysiology of depressive manifestation of MDD. The antidepressant-induced normalization of the “hypoflexibility” suggests a selective pathway through which antidepressants may alleviate symptoms in depression.

Background: The treatment outcomes of the depression remain unsatisfactory. The timely selection of the optimal treatment at early-stage is a critical issue. Despite of significant findings in neuroimaging biomarkers, few studies included neuroimaging assessments in early-phases of treatment and a single biomarker is hard to guide personal treatment selection. Thus, a data-driven approach combined with functional circuits and clinical symptom was applied to predict effective treatment selection by identifying treatment-related subgroups. Methods: Our study was a naturalistic study and 489 participants were enrolled from three sites. We identified symptom-guided brain connectome subgroups and built an early-stage treatment prediction model in the discovery dataset (n=228). Subgroups were compared in the aspects of effective intervention, symptomatic improvement, demographics, brain connectomes and clinical symptoms. Notably, we employed two independent datasets (n=89) to externally validate model. New patients' effective intervention was then expected to be consistent with the pre-defined subgroup. Results: Three subgroups with distinct treatment recommendations emerged: (1) an SSRIs-oriented subgroup with more symptomatic improvements of apathy. (2) a physiotherapy-oriented subgroup with high improvement of suicide symptom. (3) an SNRI-oriented subgroup, unique decreased interactions within salience network (SN), between SN and cognition control network, together with the high baseline depressive severity. Specially, our model had a high sensitivity of 83.6% in improvement prediction, with an overall accuracy of 69.7%. Furthermore, our model was more effective for SSRIs prediction with 87.5% accuracy. Conclusion: Our study offers guidance of multiple treatment strategies in early-stage treatment and extrapolates neuroimaging findings to 'real-world' practice. Funding Statement: The work was supported by the National Natural Science Foundation of China [grant numbers: 81871066，81571639]; Jiangsu Provincial Medical Innovation Team of the Project of Invigorating Health Care through Science, Technology and Education [grant number: CXTDC2016004]; Jiangsu Provincial key research and development program [grant number: BE2018609]. Declaration of Interests: The authors state that they have no conflict of interest with the content of this article. Ethics Approval Statement: A complete description of the study was informed to all subjects and written informed consents were obtained from all participants as approved by the Research Ethics Review Board of the Affiliated Brain Hospital of Nanjing Medical University, Nanjing Drum Tower Hospital and Peking University Sixth Hospital.

Major depressive disorder (MDD) is associated with a heavy disease burden due to the difficulty in diagnosing the disorder and the uncertainty of treatment outcomes. Previous studies have demonstrated the value of functional connectivity (FC) between the dorsolateral prefrontal cortex (DLPFC) and the subgenual anterior cingulate cortex (sgACC) in the identification of MDD and the prediction of antidepressant efficacy. In the present study, we aimed to investigate whether FC is helpful in discriminating patients from healthy controls and in predicting treatment outcome.Seventy-six medication-free patients with MDD and 28 healthy controls were enrolled in the study. Magnetoencephalography (MEG) and the Hamilton Rating Score for Depression (HRSD-17) were administered at baseline. Then, the HRSD-17 was assessed weekly until each patient met the remission criteria, defined as a total HRSD-17 score ≤ 7. Time-dependent Cox regression analysis was used to evaluate the association between FC and the incidence of remission.Healthy controls and MDD patients had opposite FC patterns; this may be helpful for identifying MDD (AUC = 0.8, p 0.001, sensitivity 85.7%, specificity 67.9%). Alpha connectivity between the DLPFC and sgACC (HR 1.858, 95%CI 1.013-3.408, p = 0.045) was found to be an independent factor associated with better final antidepressant outcome.This study was conducted in a small sample of subjects. Further, the direction of regulation between the DLPFC and sgACC was not considered.FC may help identify depression and may be related to the severity of depressive symptoms and predict the efficacy of antidepressant treatment.