Search

Abstract With the development of cloud computing, users are more inclined to outsource complex computing tasks to cloud servers with strong computing capacity, and the cloud returns the final calculation results. However, the cloud is not completely trustworthy, which may leak the data of user and even return incorrect calculations on purpose. Therefore, it is important to verify the results of computing tasks without revealing the privacy of the users. Among all the computing tasks, the polynomial calculation is widely used in information security, linear algebra, signal processing and other fields. Most existing polynomial-based verifiable computation schemes require that the input of the polynomial function must come from a single data source, which means that the data must be signed by a single user. However, the input of the polynomial may come from multiple users in the practical application. In order to solve this problem, the researchers have proposed some schemes for multi-source outsourced data, but these schemes have the common problem of low efficiency. To improve the efficiency, this paper proposes an efficient polynomial-based verifiable computation scheme on multi-source outsourced data. We optimize the polynomials using Horner’s method to increase the speed of verification, in which the addition gate and the multiplication gate can be interleaved to represent the polynomial function. In order to adapt to this structure, we design the corresponding homomorphic verification tag, so that the input of the polynomial can come from multiple data sources. We prove the correctness and rationality of the scheme, and carry out numerical analysis and evaluation research to verify the efficiency of the scheme. The experimental indicate that data contributors can sign 1000 new data in merely 2 s, while the verification of a delegated polynomial function with a power of 100 requires only 18 ms. These results confirm that the proposed scheme is better than the existing scheme.

Abstract Background Although the disturbance of circadian rhythms represents a significant clinical feature of major depressive disorder (MDD), the relationship between biological rhythm disturbances and the severity of suicidal ideation in individuals with MDD remains unclear. We aimed to explore the characteristics of different biological rhythm dimensions in MDD and their association with the severity of depressive symptoms and suicidal ideation. Methods A total of 50 MDD patients and 50 healthy controls were recruited and their general information was collected. The severity of depressive symptoms was assessed with the 17-item Hamilton Depression Rating Scale (HDRS17). The intensity of suicidal ideation was evaluated with the Beck Scale for Suicide Ideation (BSS). The Chinese version of the Biological Rhythms Interview of Assessment in Neuropsychiatry (BRIAN) scale was utilized to assess the participants’ biological rhythm dysregulation. Multiple logistic regression analysis was conducted to explore the relationship between biological rhythm and the risk of MDD. Multiple linear regression analysis was performed in the MDD group to investigate the relationship between different biological rhythm dimensions and suicide ideation. Results Significant differences were observed between the MDD group and the control group in total BRIAN score (Z=-5.41, P

Abstract Astrocytic dysfunction contributes to the molecular pathogenesis of major depressive disorder (MDD). However, the astrocytic subtype that mainly contributes to MDD etiology and whether dysregulated autophagy in astrocytes is associated with MDD remain unknown. Using a single-nucleus RNA sequencing (snRNA-seq) atlas, three astrocyte subtypes were identified in MDD, while C2 State-1Q astrocytes showed aberrant changes in both cell proportion and most differentially expressed genes compared with other subtypes. Moreover, autophagy pathways were commonly inhibited in astrocytes in the prefrontal cortices (PFCs) of patients with MDD, especially in C2 State-1Q astrocytes. Furthermore, by integrating snRNA-seq and bulk transcriptomic data, we found significant reductions in LC3A expression levels in the PFC region of CUMS-induced depressed mice, as well as in postmortem PFC tissues and peripheral blood samples from patients with MDD. These results were further validated by qPCR using whole-blood samples from patients with MDD and healthy controls. Finally, LC3A expression in the whole blood of patients with MDD was negatively associated with the severity of depressive symptoms. Overall, our results underscore autophagy inhibition in PFC astrocytes as a common molecular characteristic in MDD and might reveal a novel potential diagnostic marker LC3A.

The working environment of high-strength steel components produced by wire-arc additive manufacturing (WAAM) often faced challenges of explosion and impact, but the microstructure evolution and dynamic mechanical behavior of WAAM produced high-strength steel were still unclear. In this study, low alloy high-strength steel （HSS） components were produced by WAAM. Split Hopkinson Pressure Bar (SHPB) tests at different strain rates were carried out on WAAM-HSS, and the specimens before and after dynamic compression were characterized by techniques such as SEM, EDS, XRD, EBSD, etc. The results showed that： The as-built WAAM-HSS plate mainly consists of five typical structures: lath martensite (ML), acicular ferrite (AF), polygonal ferrite (PF), martensite-austenite (M-A), and precipitated phase δ. The central area of the precipitated phase is Si and Mn, and the C element is mainly distributed in the surrounding area of the precipitated phase. Under dynamic compression, the as-built WAAM-HSS exhibited obvious yield phenomenon, and the yield stress increased with the increase of strain rate. The yield-strengthening mechanism is mainly contributed by grain refinement strengthening mechanism and dislocation density strengthening mechanism. Under dynamic compression, the martensite transformation occurred in the WAAM-HSS. The deformation mechanism is dislocation slip and twinning, which compete with each other. Dislocation slip deformation is dominant at low strain rate and twinning deformation is more dominant at high strain rate.

Abstract Background Major depressive disorder (MDD) with atypical features, namely depression with atypical features (AFD), is one of the most common clinical specifiers of MDD, closely associated with bipolar disorder (BD). However, there is still a lack of clinical guidelines for the diagnosis, treatment, and prognosis of AFD. Our study mainly focuses on three issues about how to identify AFD, what is the appropriate individualized treatment for AFD, and what are the predictive biomarkers of conversion to BD. Methods The Study of Individualized Diagnosis and Treatment for Depression with Atypical Features (iDoT-AFD) is a multicenter, prospective, open-label study consisting of a 12-week randomized controlled trial (RCT) and a continued follow-up until 4 years or reaching the study endpoint. It is enrolling 480 patients with AFD (120 per treatment arm), 100 patients with BD, and 100 healthy controls (HC). Multivariate dimension information is collected including clinical features, cognitive function, kynurenine pathway metabolomics, and multimodal magnetic resonance imaging (MRI) data. Firstly, multivariate informatics analyses are performed to recognize patients with AFD from participants including the first-episode and recurrent atypical depression, patients with BD, and patients with HC. Secondly, patients with atypical depression are randomly allocated to one of the four treatment groups including “single application of selective serotonin reuptake inhibitor (SSRI) or serotonin-noradrenaline reuptake inhibitor (SNRI)”, “SSRI/SNRI combined with mood stabilizer,” “SSRI/SNRI combined with quetiapine (≥ 150 mg/day),” or “treatment as usual (TAU)” and then followed up 12 weeks to find out the optimized treatment strategies. Thirdly, patients with atypical depression are followed up until 4 years or switching to BD, to explore the risk factors of conversion from atypical depression to BD and eventually build the risk warning model of conversion to BD. Discussion The first enrolment was in August 2019. The iDoT-AFD study explores the clinical and biological markers for the diagnosis, treatment, and prognosis of AFD and further provides evidence for clinical guidelines of AFD. Trial registration ClinicalTrials.gov NCT04209166. Registered on December 19, 2019.

Abstract Background Retroperitoneal liposarcoma (RPLPS), a rare tumor, is often treated using surgical procedures as the first choice for treatment. However, there is no consensus on the scope of surgical resection. In addition, the treatment outcomes of conventional radiotherapy and chemotherapy have not been satisfactory, particularly for specific types of LPS, such as dedifferentiated LPS. In this case study, we present a brief review of other cases of RPLPS, highlighting the selection of surgical scope for RPLPS and related adjuvant treatment for advanced RPLPS. Case presentation A case study is reported regarding an extremely rare recurrent and metastatic retroperitoneal dedifferentiated LPS. The primary RPLPS tumor, with a diameter of 20 cm and a weight of 2.5 kg, occupied the whole left abdomen and adhered to the left kidney. A surgical tumor resection combined with a left nephrectomy is performed. During the 6th -month postoperative follow-up examination, we observed the local recurrence of the tumor in the operation area, in addition to multiple metastatic tumors in both lungs. Further, the prescribed 3-month targeted treatment with anlotinib significantly reduced the size of the metastatic pulmonary tumors. However, the recurrent retroperitoneal tumors showed no significant change in size. Eventually, we observed no substantial evidence of tumor progression, with the patient’s condition under control. Conclusion The case demonstrated that the postoperative recurrence of widespread RPLPS required R0 resection to cure the disease, considering targeted therapy for advanced RPLPS control.

Background: Almost one-third of patients with major depressive disorder (MDD) do not respond to conventional antidepressants, and new treatments for MDD are urgently needed. Objectives: This phase IIb clinical trial was designed to evaluate the efficacy and safety of Anyu Peibo capsules in the treatment of adults with MDD. Design: A multicenter, randomized, double-blind, placebo-controlled, parallel-group, fixed-dose study. Methods: A total of 172 patients with MDD from nine study centers were randomized (1:1) to receive placebo ( n = 86) or oral Anyu Peibo capsules (0.8 g) twice per day ( n = 86) for 6 weeks. The primary endpoint was the change in the Montgomery Åsberg Depression Rating Scale (MADRS) total score from baseline to week 6, analyzed using an analysis of covariance (ANCOVA) approach with the baseline MADRS score, center effect and center by group interaction as the covariates. Other efficacy endpoints and variables included clinical response and remission rates according to the MADRS and the 17-item Hamilton Depression Rating Scale (HAMD-17) scores, the change in the HAMD-17, Clinical Global Impression – Severity scale and Clinical Global Impression – Improvement scale scores and the reduction in the Hamilton Anxiety Scale from baseline to week 6. Results: The mean baseline MADRS total scores were 29.20 and 29.72 in the Anyu Peibo ( n = 82) and placebo groups ( n = 81), respectively. The least squares mean change in the MADRS score from baseline to week 6 was 16.59 points in the Anyu Peibo group and 14.51 points in the placebo group. Although there were greater reductions in the MADRS score from baseline to week 6 in the Anyu Peibo capsule group compared to the placebo group, the difference did not reach statistical significance (least-squares mean difference, 2.07 points; 95% confidence interval, −0.27 to 4.41; p = 0.0819). The results of sensitivity analyses by ANCOVA with the last observation carried forward method for missing data indicated that the administration of Anyu Peibo capsules may lead to a significant reduction in depressive symptoms compared to the placebo (least-squares mean difference: 3.29 points; 95% confidence interval: 0.64–5.93; p = 0.0152). Furthermore, Anyu Peibo capsules showed significant benefits over placebo when the change in the HAMD-17 score from baseline to week 6 was evaluated as the secondary analysis ( t = 2.01; 95% confidence interval, 0.03–4.23; p = 0.0464). Conclusion: Anyu Peibo capsules may have an effective and safe antidepressant effect, which warrants further research.

Abstract Binary code similarity detection (BCSD) plays a big role in the process of binary application security test. It can be applied in several fields, such as software plagiarism detection, malware analysis, vulnerability detection. Most research is based on recurrent neural networks, which is difficult to get the overall or long-distance semantic information of functions. Besides, exiting works simply extract high-level semantic features, lacking in-depth investigations on the potential mechanisms for fusing low-level and high-level semantic features. In this paper we propose a multi-semantic feature fusion attention network (MFFA-Net) for BCSD. MFFA-Net contains two critical modules: semantic feature fusion (SFF) and attention feature fusion (AFF). The SFF module concatenates multiple semantic features to represent the semantics of the function, which helps to obtain the overall semantic information of the function. The AFF module is designed to find useful information from various features, which assigns an attention matrix to research the relationship between features. In order to evaluate the proposed method, we made extensive experiments on two datasets. MFFA-Net can achieve a high degree of AUC at 99.6% and 98.3% respectively on the two datasets. The experimental results show that MFFA-Net has better performance for BCSD.

To provide a pathway to improve the tensile properties of wire arc additive manufactured Ti6Al4V, the microstructures and properties of Ti6Al4V-0.1Si produced by the wire arc additive manufacturing were investigated in this study. It was demonstrated that the tensile strength of Ti6Al4V-0.1Si improved more than 100 MPa compared with traditional WAAM Ti6Al4V. The microstructures of WAAM Ti6Al4V and Ti6Al4V-0.1Si mainly consists of basket-weave structures but the width of the α-phase lamellae of Ti6Al4V-0.1Si is significantly lower.The added Si is solid-soluble on the α phase matrix of Ti6Al4V, which reduces the β phase transition temperature. Si as well produces the high growth restriction effect, then the α phase size is refined. The fine grain and solid solution strengthening are the main reasons for the increase in the strength of titanium alloy after adding trace Si.

Existing secure two-party Euclidean distance computation schemes are mostly performed based on a semi-honest model, which faces bottlenecks in computation efficiency and security. In view of this, this study proposed a secure two-party euclidean distance computation (SEDC) scheme under a covert adversarial model based on Paillier encryption. In the scheme, the secure two-party Euclidean distance was computed by using technologies, such as Paillier encryption, and the computation results were verified with each other to ensure that both parties sent real data and had no cheating in the process of parameter interaction. Through verification, the scheme is correct, confidential and unforgeable under chosen-plaintext attack. Scheme comparison and efficiency analysis show that the scheme achieves a balance in the security model, which not only has relatively high security, but also is more in line with the value of Euclidean distance computation in practice. In the meanwhile, this ensures the slight communication complexity and time complexity in the computation of the scheme and enables the scheme to be of high application prospect.

At present, three problems exist in searchable encryption in cloud storage services: firstly, most traditional searchable encryption schemes only support single-keyword search while fail to perform Boolean searches; even if a few schemes support Boolean searching, the storage efficiency is also unsatisfactory. Secondly, most existing schemes do not support dynamic keyword updates, so the update efficiency is low. Thirdly, most existing schemes cannot meet all demands of users, to perform rankable searching over search files according to the importance of keywords. To solve these problems, a rankable Boolean searchable encryption scheme supporting dynamic updates in a cloud environment (RBDC) is proposed. By using Paillier and GM encryption algorithms, secure indices supporting dynamic updating are established. Based on applicable knowledge gleaned from cryptography and set theory, the indices of keyword intersections and the intersection search trapdoors are constructed to achieve multi-keyword Boolean search. With assistance of the SCP, score indices of each file are constructed according to the TF-IDF index, which allow ranking of files. Security analysis proofs that our scheme can ensure security in the known ciphertext model and the known background model. Experimental results prove that the scheme improves the search efficiency and the index storage efficiency.

Abstract Background Although disturbances in biological rhythms are closely related to the onset of major depressive disorder (MDD), they are not commonly assessed in Chinese clinical practice. The Biological Rhythms Interview of Assessment in Neuropsychiatry (BRIAN) has been used to evaluate disturbances in biological rhythms in MDD. We aimed to assess and confirm the reliability and validity of the Chinese version of the BRIAN (C-BRIAN) in patients with MDD. Methods A total of 120 patients with MDD and 40 age- and sex-matched controls were recruited consecutively. Reliability was estimated using Cronbach’s alpha, the split-half coefficient, and the test-retest coefficient; test-retest reliability was assessed using Spearman’s correlation coefficient. A confirmatory factor analysis was used to determine the construct validity of the scale. The Pittsburgh Sleep Quality Index (PSQI) and the Morningness-Eveningness Questionnaire (MEQ) were used to check concurrent validity by evaluating the correlation between the C-BRIAN, PSQI, and MEQ. Results The overall Cronbach’s α value was 0.898, indicating good internal consistency. The Guttman split-half coefficient was 0.792, indicating good split-half reliability. Moreover, the test-retest reliability for both the total and individual item score was excellent. Confirmatory factor analysis revealed that construct validity was acceptable (χ2/df = 2.117, GFI = 0.80, AGFI = 0.87, CFI = 0.848, and RMSEA = 0.097). Furthermore, total BRIAN scores were found to be negatively correlated with MEQ (r = − 0.517, P

ObjectiveCardioembolic stroke (CE stroke, also known as cardiogenic cerebral embolism, CCE) has the highest recurrence rate and fatality rate among all subtypes of ischemic stroke, the pathogenesis of which was unclear. Autophagy plays an essential role in the development of CE stroke. We aim to identify the potential autophagy-related molecular markers of CE stroke and uncover the potential therapeutic targets through bioinformatics analysis.MethodsThe mRNA expression profile dataset GSE58294 was obtained from the GEO database. The potential autophagy-related differentially expressed (DE) genes of CE stroke were screened by R software. Protein–protein interactions (PPIs), correlation analysis, and gene ontology (GO) enrichment analysis were applied to the autophagy-related DE genes. GSE66724, GSE41177, and GSE22255 were introduced for the verification of the autophagy-related DE genes in CE stroke, and the differences in values were re-calculated by Student’s t-test.ResultsA total of 41 autophagy-related DE genes (37 upregulated genes and four downregulated genes) were identified between 23 cardioembolic stroke patients (≤3 h, prior to treatment) and 23 healthy controls. The KEGG and GO enrichment analysis of autophagy-related DE genes indicated several enriched terms related to autophagy, apoptosis, and ER stress. The PPI results demonstrated the interactions between these autophagy-related genes. Moreover, several hub genes, especially for CE stroke, were identified and re-calculated by Student’s t-test.ConclusionWe identified 41 potential autophagy-related genes associated with CE stroke through bioinformatics analysis. SERPINA1, WDFY3, ERN1, RHEB, and BCL2L1 were identified as the most significant DE genes that may affect the development of CE stroke by regulating autophagy. CXCR4 was identified as a hub gene of all types of strokes. ARNT, MAPK1, ATG12, ATG16L2, ATG2B, and BECN1 were identified as particular hub genes for CE stroke. These results may provide insight into the role of autophagy in CE stroke and contribute to the discovery of potential therapeutic targets for CE stroke treatment.

Abstract As the important transmission approach of the perception data, the security and privacy of the perception layer nodes have been paid more and more attention. To evaluate the credibility of the perception nodes and protect autologous identity privacy, a remote attestation mechanism using group signature (GS-RAM) for the perception layer of centralized networking is proposed in this paper. First, a RAM, based on the computational Diffie–Hellman (CDH) problem and GS, is established for the data source of the perception nodes. Second, the specific construction of the proposed GS-RAM is given to identify the trusted state of data sources without exposing the privacy of the perception nodes. Then, a strict security certification was carried out to verify the correctness, unforgeability, anonymity, traceability, unrelatedness, non-framing, anti-joint aggression and forward security of the proposed GS-RAM. Finally, simulation experiments are carried out to verify that the proposed scheme has better security and dynamic adaptability.

Introduction Major depressive disorder (MDD) represents a worldwide burden on healthcare and the response to antidepressants remains limited. Systems biology approaches have been used to explore the precision therapy. However, no reliable biomarker clinically exists for prognostic prediction at present. The objectives of the Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) study are to predict the efficacy of antidepressants by integrating multidimensional omics and performing validation in a real-world setting. As secondary aims, a series of potential biomarkers are explored for biological subtypes.Methods and analysis iMore is an observational cohort study in patients with MDD with a multistage design in China. The study is performed by three mental health centres comprising an observation phase and a validation phase. A total of 200 patients with MDD and 100 healthy controls were enrolled. The protocol-specified antidepressants are selective serotonin reuptake inhibitors and serotonin–norepinephrine reuptake inhibitors. Clinical visits (baseline, 4 and 8 weeks) include psychiatric rating scales for symptom assessment and biospecimen collection for multiomics analysis. Participants are divided into responders and non-responders based on treatment response (>50% reduction in Montgomery-Asberg Depression Rating Scale). Antidepressants’ responses are predicted and biomarkers are explored using supervised learning approach by integration of metabolites, cytokines, gut microbiomes and immunophenotypic cells. The accuracy of the prediction models constructed is verified in an independent validation phase.Ethics and dissemination The study was approved by the ethics committee of Shanghai Mental Health Center (approval number 2020-87). All participants need to sign a written consent for the study entry. Study findings will be published in peer-reviewed journals.Trial registration number NCT04518592.

Abstract Background Cognitive deficits are common in patients with schizophrenia (SCZ). Abnormal serum total bilirubin (TBIL) levels have been involved in cognitive deficits associated with neuropsychiatric diseases such as mild cognitive impairment and subcortical ischemic vascular disease. However, this relationship has not yet been fully investigated in patients with SCZ. Therefore, the aim of this study was to investigate the association between the serum TBIL concentration and cognitive deficits in SCZ patients and to determine whether a sex difference exists in the association. Methods A total of 455 participants were eligible and included in this cross-sectional study. Cognition was evaluated using the Montreal Cognitive Assessment. Serum TBIL concentration was measured with an automatic biochemistry analyzer according to the routine protocol in the hospital medical laboratory. Results Serum TBIL levels were lower in the cognition impairment group than in the cognition normal group in male patients. In contrast, serum TBIL levels tended to be increased in the cognition impairment group in female patients, although the difference was not significant. Further stepwise multiple regression analysis stratified by sex showed that serum TBIL was independently and positively associated with cognitive function in male patients but not in female patients. Moreover, the association between serum TBIL level and cognitive function was also identified by the propensity score matching (PSM) method in male patients, but not in female patients. Conclusion These findings suggest that lower serum TBIL levels may be associated with cognitive impairment in male SCZ patients.

Background: Discontinuation of antipsychotic treatment is a common problem in patients with schizophrenia and could reduce the effectiveness of treatment. Time to discontinuation (TTD) is one of the indicators of compliance and may also be an effective indicator of medication efficacy. The aim of the study was to compare the clinical effectiveness of quetiapine, olanzapine, risperidone, and aripiprazole in the real-world treatment of schizophrenia with 3-years follow-up.Method: A multi-center, open, cohort, prospective, real-world study was conducted. 706 patients were analyzed without intervention in medication selection and use, followed up for 3 years. Kaplan-Meier survival curves were used to draw the treatment discontinuation rates (TDR) curves at each time point. Cox proportional hazard regression model was used to assess the relative risk of TTD of antipsychotics.Results: There was a significant difference among monotherapy groups in all-cause antipsychotic treatment discontinuation (p = 0.0057). Among the four medications, the TDR of risperidone was the highest. Compared with polypharmacy, except for aripiprazole, the TDR of other three monotherapy medications were lower than that of polypharmacy, and olanzapine was statistically different (p = 0.0325). The cox regression analysis showed that after correction of Hochberg with multiple tests, only olanzapine had a relative risk lower than risperidone (p < 0.0083).Conclusions: The findings indicated that risperidone monotherapy and polypharmacy had the highest TDR and the shortest TTD. Olanzapine monotherapy had a relative risk lower than risperidone and was superior to polypharmacy.

Pyroptosis is recently identified as an inflammatory form of programmed cell death. However, the roles of pyroptosis-related genes (PS genes) in major depressive disorder (MDD) remain unclear. This study developed a novel diagnostic model for MDD based on PS genes and explored the pathological mechanisms associated with pyroptosis. First, we obtained 23 PS genes that were differentially expressed between healthy controls and MDD cases from GSE98793 dataset. There were obvious variation in immune cell infiltration profiles and immune-related pathway enrichment between healthy controls and MDD cases. Then, a novel diagnostic model consisting of eight PS genes (GPER1, GZMA, HMGB1, IL1RN, NLRC4, NLRP3, UTS2, and CAPN1) for MDD was constructed by random forest (RF) and least absolute shrinkage and selection operator (LASSO) analyses. ROC analysis revealed that our model has good diagnostic performance, AUC = 0.795 (95% CI 0.721–0.868). Subsequently, the consensus clustering method based on 23 differentially expressed PS genes was constructed to divide all MDD cases into two distinct pyroptosis subtypes (cluster A and B) with different immune and biological characteristics. Principal component analysis (PCA) algorithm was performed to calculate the pyroptosis scores (“PS-scores”) for each sample to quantify the pyroptosis regulation subtypes. The MDD patients in cluster B had higher “PS-scores” than those in cluster A. Furthermore, we also found that MDD patients in cluster B showed lower expression levels of 11 interferon (IFN)-α isoforms. In conclusion, pyroptosis may play an important role in MDD and can provide new insights into the diagnosis and underlying mechanisms of MDD.

Imbalanced class has been a common problem encountered in the modeling process, and has attracted more and more attention from scholars. Biased classifiers, which limit the classifiers’ performance for minority classes, will be produced if the imbalanced ratio between the number of positive labels and negative labels is ignored. The synthetic minority over-sampling technique (SMOTE) is a very classic and popular over-sampling method, which is widely used to address this problem. However, SMOTE increases label noise and the training time during the over-sampling process. To improve the detection rate of minority classes while ensuring efficiency, we propose a cost-sensitive XGBoost (CS-XGB) for the imbalanced data problem. The CS-XGB method can reduce the classifiers’ preference for most classes without changing the distribution of the original data. 600000 Uniform Resource Locators (URLs) were collected to validate the CS-XGB method. We compare XGBoost (XGB), SMOTE+XGB and CS-XGB, and the experimental results confirm that the CS-XGB is robust and efficient for imbalanced cases.

Based on our previous studies and other evidence, miR-124 is an important biomarker and therapeutic target for major depressive disorder (MDD). The aim of this study was to clarify the role of miR-124 methylation in MDD and antidepressant effects from the perspective of epigenetics. MethylTarget™ was used to detect methylation levels of the three miR-124 precursor genes (MIR124-1, MIR124-2, and MIR124-3) in 33 pre- and post-treatment MDD patients and 33 healthy controls. A total of 11 cytosine-phosphate-guanine (CpG) islands in the three miR-124 precursor genes, including 222 CpG sites, were detected. All CpG islands were hypomethylated in MDD patients when compared to healthy controls and seven CpG regions were still identified with a statistically significant difference after Bonferroni correction. In addition, 137 of 222 CpG sites were found a statistical difference between MDD patients and controls, and 40 CpG sites were still statistically significant after Bonferroni correction. After performing the LASSO regression model, seven biomarkers with differential methylation among 40 CpG sites were identified. Mean methylation score was lower in MDD patients (z = −5.84, p = 5.16E-9). The AUC value reached 0.917 (95% CI: 0.854–0.981) to discriminate MDD and controls. No changes in methylation of the three miR-124 precursor genes were found in MDD patients following antidepressant treatment. The methylation of miR-124 could be a promising diagnostic biomarker for MDD.

Abstract Background There are hundreds of phenotypically distinguishable domestic chicken breeds or lines with highly specialized traits worldwide, which provide a unique opportunity to illustrate how selection shapes patterns of genetic variation. There are many local chicken breeds in China. Results Here, we provide a population genome landscape of genetic variations in 86 domestic chickens representing 10 phenotypically diverse breeds. Genome-wide analysis indicated that sex chromosomes have less genetic diversity and are under stronger selection than autosomes during domestication and local adaptation. We found an evidence of admixture between Tibetan chickens and other domestic population. We further identified strong signatures of selection affecting genomic regions that harbor genes underlying economic traits (typically related to feathers, skin color, growth, reproduction and aggressiveness) and local adaptation (to high altitude). By comparing the genomes of the Tibetan and lowland fowls, we identified genes associated with high-altitude adaptation in Tibetan chickens were mainly involved in energy metabolism, body size maintenance and available food sources. Conclusions The work provides crucial insights into the distinct evolutionary scenarios occurring under artificial selection for agricultural production and under natural selection for success at high altitudes in chicken. Several genes were identified as candidates for chicken economic traits and other phenotypic traits.

Security of Internet of things network is difficult to manage because Internet of things devices are heterogeneous and always deployed in remote places. Based on these features, attacks against Internet of things network like distributed denial-of-service and system intrusion are easier than traditional network. In order to enhance Internet of things network’s security, in this article we propose the concept of intrinsic security to establish a trusted and autonomy Internet of things network. Intrinsic security emphasizes that network itself must be trusted and security autonomous to address the security problems which are caused by remote and heterogeneous. Based on this thought, we designed the architecture of Trusted Autonomy Security System. Trusted Autonomy Security System maintains a LAN characterized by trusted and security autonomy, in which all Internet of things network gateways formed a decentralized autonomy system to evaluate if other gateways or Internet of things devices are secure. Compared to traditional Internet of things networks, this architecture uses trusted computing technique to build the basic of security, and makes Internet of things network available to manage security automatically. In addition, the decentralized architecture could reduce security risks caused by compromise of single node in Internet of things network. With these advantages, it can be expected that this novel idea will have more development in Internet of things scenarios with high security requirements.

Major depressive disorder (MDD) is a leading cause of disability worldwide, although its etiology and mechanism remain unknown. The aim of our study was to identify hub genes associated with MDD and to illustrate the underlying mechanisms. A weighted gene co-expression network analysis (WGCNA) was performed to identify significant gene modules and hub genes associated with MDD in peripheral blood mononuclear cells (PBMCs) (n = 45). In the blue module (R2 = 0.95), five common hub genes in both co-expression network and protein–protein interaction (PPI) network were regarded as “real” hub genes. In another independent dataset, GSE52790, four genes were still significantly down-regulated in PBMCs from MDD patients compared with the controls. Furthermore, these four genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR) in PBMCs from 33 MDD patients and 41 healthy controls. The qRT-PCR analysis showed that ATP synthase membrane subunit c locus 1 (ATP5G1) was significantly down-regulated in samples from MDD patients than in control samples (t = −2.89, p-value = 0.005). Moreover, this gene was significantly differentially expressed between patients and controls in the prefrontal cortex (z = −2.83, p-value = 0.005). Highly significant differentially methylated positions were identified in the Brodmann area 25 (BA25), with probes in the ATP5G1 gene being significantly associated with MDD: cg25495775 (t = 2.82, p-value = 0.008), cg25856120 (t = −2.23, p-value = 0.033), and cg23708347 (t = −2.24, p-value = 0.032). These findings indicate that the ATP5G1 gene is associated with the pathogenesis of MDD and that it could serve as a peripheral biomarker for MDD.

Currently, the choice of medical treatment for major depressive disorder (MDD) is primarily based on a trial-and-error process. Thus, identification of individual factors capable of predicting treatment response is of great clinical relevance. Recent work points towards beclin-1 and inflammatory factors as potential biomarkers of antidepressant treatment response. The primary aim of the study was to investigate whether pre-treatment serum levels of beclin-1 and inflammatory factors could predict antidepressant treatment response in Chinese Han patients with MDD. Forty patients with MDD were treated with either a selective serotonin reuptake inhibitor (SSRI) (paroxetine in 20 cases) or a serotonin–norepinephrine reuptake inhibitor (SNRI) (duloxetine in 13 cases and venlafaxine in 7 cases). Depression scores and serum levels of beclin-1 were measured at the baseline and after 8 weeks of antidepressant treatment. Serum C-reactive protein (CRP), interleukin (IL)-1B, and IL-6 levels were determined using enzyme-linked immunosorbent assay kits at the baseline. Twenty-seven patients were identified as treatment responders, whereas 13 were identified as non-responders after 8 weeks of antidepressant treatment. Baseline serum beclin-1 levels were significantly higher in non-responders than in responders (p = 0.001), whereas no differences were found in baseline serum CRP, IL-1B, or IL-6 levels between responders and non-responders. There were no significant correlations between baseline levels of beclin-1 and baseline IL-1β, IL-6, and CRP levels—neither in the total sample nor in responder and non-responder groups. Moreover, logistic regression models and a random forest model showed that baseline serum beclin-1, but not inflammatory factors, was an independent and the most important predictor for antidepressant treatment response. Furthermore, serum beclin-1 levels were significantly increased in responders (p = 0.027) but not in non-responders after 8 weeks of treatment (p = 0.221). Baseline serum beclin-1 levels may be a predictive biomarker of antidepressant response in patients with MDD. Moreover, beclin-1 may be involved in the therapeutic effect of antidepressant drugs.

Objectives: The nature of the diagnostic classification of mood disorder is a typical dichotomous data problem and the method of combining different dimensions of evidences to make judgments might be more statistically reliable. In this paper, we aimed to explore whether peripheral neurotrophic factors could be helpful for early detection of bipolar depression.Methods: A screening method combining peripheral biomarkers and clinical characteristics was applied in 30 patients with major depressive disorder (MDD) and 23 patients with depressive episode of bipolar disorder. By a model-based algorithm, some information was extracted from the dataset and used as a “model” to approach penalized regression model for stably differential diagnosis for bipolar depression.Results: A simple and efficient model of approaching the diagnosis of individuals with depressive symptoms was established with a fitting degree (90.58%) and an acceptable cross-validation error rate. Neurotrophic factors of our interest were successfully screened out from the feature selection and optimized model performance as reliable predictive variables.Conclusion: It seems to be feasible to combine different types of clinical characteristics with biomarkers in order to detect bipolarity of all depressive episodes. Neurotrophic factors of our interest presented its stable discriminant potentiality in unipolar and bipolar depression, deserving validation analysis in larger samples.

N6-methyladenosine (m6A) is a ubiquitous reversible epigenetic RNA modification that plays an important role in the regulation of post-transcriptional protein coding gene expression. Liver is a vital organ and plays a major role in metabolism with numerous functions. Information concerning the dynamic patterns of mRNA m6A methylation during postnatal development of liver has been long overdue and elucidation of this information will benefit for further deciphering a multitude of functional outcomes of mRNA m6A methylation. Here, we profile transcriptome-wide m6A in porcine liver at three developmental stages: newborn (0 day), suckling (21 days) and adult (2 years). About 33% of transcribed genes were modified by m6A, with 1.33 to 1.42 m6A peaks per modified gene. m6A was distributed predominantly around stop codons. The consensus motif sequence RRm6ACH was observed in 78.90% of m6A peaks. A negative correlation (average Pearson's r = -0.45, P < 10-16) was found between levels of m6A methylation and gene expression. Functional enrichment analysis of genes consistently modified by m6A methylation at all three stages showed genes relevant to important functions, including regulation of growth and development, regulation of metabolic processes and protein catabolic processes. Genes with higher m6A methylation and lower expression levels at any particular stage were associated with the biological processes required for or unique to that stage. We suggest that differential m6A methylation may be important for the regulation of nutrient metabolism in porcine liver.

MicroRNAs (miRNAs) are non-coding small RNA ∼22 nucleotides in length that can regulate the expression of a wide range of coding genes at the post-transcriptional level. Visceral adipose tissues (VATs) and subcutaneous adipose tissues (SATs), the two main fat compartments in mammals, are anatomically, physiologically, metabolically, and clinically distinct. Various studies of adipose tissues have focused mainly on DNA methylation, and mRNA and protein expression, nonetheless little research sheds directly light on the miRNA transcriptome differences between these two distinct adipose tissue types. Here, we present a comprehensive investigation of miRNA transcriptomes across six variant porcine adipose tissues by small RNA-sequencing. We identified 219 known porcine miRNAs, 97 novel miRNA*s, and 124 miRNAs that are conserved to other mammals. A set of universally abundant miRNAs (i.e., miR-148a-3p, miR-143-3p, miR-27b-3p, miR-let-7a-1-5p, and miR-let-7f-5p) across the distinct adipose tissues was found. This set of miRNAs may play important housekeeping roles that are involved in adipogenesis. Clustering analysis indicated significant variations in miRNA expression between the VATs and SATs, and highlighted the role of the greater omentum in responding to potential metabolic risk because of the observed enrichment in this tissue of the immune- and inflammation-related miRNAs, such as the members of miR-17-92 cluster and miR-181 family. Differential expression of the miRNAs between the VATs and SATs, and miRNA target prediction analysis revealed that the VATs-specific enriched miRNAs were associated mainly with immune and inflammation responses. In summary, the differences of miRNA expression between the VATs and SATs revealed some of their intrinsic differences and indicated that the VATs might be closely associated with increased risk of metabolic disorders.