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Neuronavigation-guided high-dose repetitive transcranial magnetic stimulation (rTMS) could rapidly treat depressive patients with suicidal ideation. But the mechanism of rTMS still needs to be elucidated. This study aims to investigate if rTMS improves suicidal ideation and depressive symptoms by influencing brain-derived neurotrophic factor (BDNF), tropomysin receptor kinase B (TrkB) and VGF levels.In the present 1-week study, 59 treatment-naive depressive patients with suicidal ideation were randomly assigned to the active (n = 31) or sham (n = 28) rTMS group. The severity of suicidal ideation and depression were measured by the Beck Scale for Suicide Ideation, the Hamilton Depression Rating Scale and Montgomery-Asberg Depression Rating Scale. Fasting venous blood samples were collected at baseline and after treatment. Serum protein concentrations of BDNF, TrkB and VGF were measured by enzyme linked immunosorbent assay.We found after treatment the levels of BDNF in the active rTMS group were higher than the sham group (p = 0.011), TrkB levels were decreased in the active group (p 0.001), VGF levels were increased in the active group (p = 0.005). Post-treatment VGF levels in the active group were higher than the sham group (p = 0.008). However, there were no significant correlation between changes in BDNF, TrkB and VGF levels and the changes in clinical variables.Participants taking medication may affect the results.Our results suggest that the BDNF-TrkB pathway and VGF may be implicated in the mechanisms underlying neuronavigation-guided rTMS for treating depressive patients with suicidal ideation.

Within the past 2 decades, three highly pathogenic human coronaviruses have emerged, namely, severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The health threats and economic burden posed by these tremendously severe coronaviruses have paved the way for research on their etiology, pathogenesis, and treatment. Compared to SARS-CoV and SARS-CoV-2, MERS-CoV genome encoded fewer accessory proteins, among which the ORF4b protein had anti-immunity ability in both the cytoplasm and nucleus. Our work for the first time revealed that ORF4b protein was unstable in the host cells and could be degraded by the ubiquitin proteasome system. After extensive screenings, it was found that UBR5 (ubiquitin protein ligase E3 component N-recognin 5), a member of the HECT E3 ubiquitin ligases, specifically regulated the ubiquitination and degradation of ORF4b. Similar to ORF4b, UBR5 can also translocate into the nucleus through its nuclear localization signal, enabling it to regulate ORF4b stability in both the cytoplasm and nucleus. Through further experiments, lysine 36 was identified as the ubiquitination site on the ORF4b protein, and this residue was highly conserved in various MERS-CoV strains isolated from different regions. When UBR5 was knocked down, the ability of ORF4b to suppress innate immunity was enhanced and MERS-CoV replication was stronger. As an anti-MERS-CoV host protein, UBR5 targets and degrades ORF4b protein through the ubiquitin proteasome system, thereby attenuating the anti-immunity ability of ORF4b and ultimately inhibiting MERS-CoV immune escape, which is a novel antagonistic mechanism of the host against MERS-CoV infection.

In order to identify time-varying transient modal parameters only from nonstationary vibration response measurement signals for slow linear time-varying (SLTV) structures which are weakly damped, a moving window differential evolution (DE) independent component analysis- (ICA-) based operational modal analysis (OMA) method is proposed in this paper. Firstly, in order to overcome the problems in traditional ICA-based OMA, such as easy to go into local optima and difficult-to-identify high-order modal parameters, we combine DE with ICA and propose a differential evolution independent component analysis- (DEICA-) based OMA method for linear time invariant (LTI) structures. Secondly, we combine the moving widow technique with DEICA and propose a moving window differential evolution independent component analysis- (MWDEICA-) based OMA method for SLTV structures. The MWDEICA-based OMA method has high global searching ability, robustness, and complexity of time and space. The modal identification results in a three-degree-of-freedom structure with slow time-varying mass show that this MWDEICA-based OMA method can identify transient time-varying modal parameters effectively only from nonstationary vibration response measurement signals and has better performances than moving window traditional ICA-based OMA.

Diabetic retinopathy (DR) is an eye abnormality caused by chronic diabetes that affected patients worldwide. Hard exudate is an important and observable sign of DR and can be used for early diagnosis. In this paper, an automatic hard exudates segmentation method is proposed in order to aid ophthalmologists to diagnose DR in the early stage. We utilized the SLIC superpixel algorithm to generate sample patches, thus overcoming the difficulty of the limited and imbalanced dataset. Furthermore, a U-net based network architecture with inception modules and residual connections is proposed to conduct end-to-end hard exudate segmentation, and focal loss is utilized as the loss function. Extensive experiments have been conducted on the IDRiD dataset to evaluate the performance of the proposed method. The reported sensitivity, specificity, and accuracy achieve 96.38%, 97.14%, and 97.95% respectively, which demonstrates the effectiveness and superiority of our method. The achieved segmentation results prove the potential of the method for clinical diagnosis.

ObjectivesPatient-centred communication improves patient experiences and patient care outcomes. This study aimed to assess the preference of medical professionals in China towards patient-centred communication under the context of the deteriorating doctor–patient relationship.MethodsA cross-sectional survey of medical professionals was conducted in January and February 2018 in H City of Heilongjiang province, the northeast of China. The Chinese-Revised Patient-Practitioner Orientation Scale (CR-PPOS) was adopted to measure the individual preference of respondents towards patient-centredness in clinical communication. Multivariate logistic regression models were established to identify the sociodemographic (gender, age, marital status and educational attainment) and work experience (years of working, seniority, satisfaction with income, daily workload and perceived doctor–patient relationship) predictors of the preference towards patient-centredness.Patient and public involvementNot applicable.ResultsA total of 618 valid questionnaires were returned. The CR-PPOS demonstrated acceptable reliability and validity. Overall, a low level of preference towards patient-centredness in clinical communication was found. Relatively higher scores on ‘caring for patients’ (20.42±4.42) was found compared with those on ‘information/responsibility sharing’ (15.26±4.21). Younger age, higher educational attainment, lower daily workload and a perception of harmonious doctor–patient relationship were associated with a higher preference towards patient-centredness in clinical communication.ConclusionsA low level of preference towards patient-centredness in clinical communication was found in medical professionals in the northeast of China, which may further jeopardise the efforts to improve doctor–patient relationship.

Objectives The doctor–patient relationship is usually measured in line with patient needs and demands. This study aimed to develop a scale measuring such a relationship from the perspective of doctors. Methods A draft scale was developed and adapted to the hospital context of China based on several existing scales, with an intention to measure how medical doctors view and manage their relationship with patients beyond episodic clinical encounters. Two rounds of Delphi consultations involving 14 experts were conducted to seek their consensus on the inclusion and descriptions of items. This resulted in a 19-item scale measuring four domains of the relationship. The scale was validated through a survey of 1,712 medical doctors selected from 27 public hospitals in Heilongjiang province of China. The internal consistency of the scale was assessed using Cronbach’s α coefficients of the four domains. Confirmatory factor analyses were performed to test the construct validity of the scale. Linear regression analyses were performed to assess the known-group validity of the scale. Results The scale measures four domains. The Cronbach’s α of the scale reached an acceptable level, ranging from 0.61 to 0.78 for its four domains. Good fitness of data into the four-domain structure of the scale was confirmed by the confirmatory factor analysis. Known-group differences were demonstrated in the regression analyses. Conclusion The doctor–patient relationship scale developed in this study is a psychometrically valid tool assessing how medical doctors view and manage their relationship with patients in the hospital setting in China.

Digital image noise may be introduced during acquisition, transmission, or processing and affects readability and image processing effectiveness. The accuracy of established image processing techniques, such as segmentation, recognition, and edge detection, is adversely impacted by noise. There exists an extensive body of work which focuses on circumventing such issues through digital image enhancement and noise reduction, but this work is limited by a number of constraints including the application of non-adaptive parameters, potential loss of edge detail information, and (with supervised approaches) a requirement for clean, labeled, training data. This paper, developed on the principle of Noise2Void, presents a new unsupervised learning approach incorporating a pseudo-siamese network. Our method enables image denoising without the need for clean images or paired noise images, instead requiring only noise images. Two independent branches of the network utilize different filling strategies, namely zero filling and adjacent pixel filling. Then, the network employs a loss function to improve the similarity of the results in the two branches. We also modify the Efficient Channel Attention module to extract more diverse features and improve performance on the basis of global average pooling. Experimental results show that compared with traditional methods, the pseudo-siamese network has a greater improvement on the ADNI dataset in terms of quantitative and qualitative evaluation. Our method therefore has practical utility in cases where clean images are difficult to obtain.

COVID-19 is mainly associated with respiratory distress syndrome, but a subset of patients often present gastrointestinal (GI) symptoms. Imbalances of gut microbiota have been previously linked to respiratory virus infection. Understanding how the gut–lung axis affects the progression of COVID-19 can provide a novel framework for therapies and management. In this study, we examined the gut microbiota of patients with COVID-19 (n = 47) and compared it to healthy controls (n = 19). Using shotgun metagenomic sequencing, we have identified four microorganisms unique in COVID-19 patients, namely Streptococcus thermophilus, Bacteroides oleiciplenus, Fusobacterium ulcerans, and Prevotella bivia. The abundances of Bacteroides stercoris, B. vulgatus, B. massiliensis, Bifidobacterium longum, Streptococcus thermophilus, Lachnospiraceae bacterium 5163FAA, Prevotella bivia, Erysipelotrichaceae bacterium 6145, and Erysipelotrichaceae bacterium 2244A were enriched in COVID-19 patients, whereas the abundances of Clostridium nexile, Streptococcus salivarius, Coprococcus catus, Eubacterium hallii, Enterobacter aerogenes, and Adlercreutzia equolifaciens were decreased (p < 0.05). The relative abundance of butyrate-producing Roseburia inulinivorans is evidently depleted in COVID-19 patients, while the relative abundances of Paraprevotella sp. and the probiotic Streptococcus thermophilus were increased. We further identified 30 KEGG orthology (KO) modules overrepresented, with 7 increasing and 23 decreasing modules. Notably, 15 optimal microbial markers were identified using the random forest model to have strong diagnostic potential in distinguishing COVID-19. Based on Spearman’s correlation, eight species were associated with eight clinical indices. Moreover, the increased abundance of Bacteroidetes and decreased abundance of Firmicutes were also found across clinical types of COVID-19. Our findings suggest that the alterations of gut microbiota in patients with COVID-19 may influence disease severity. Our COVID-19 classifier, which was cross-regionally verified, provides a proof of concept that a set of microbial species markers can distinguish the presence of COVID-19.

Cyanotoxins are the underlying cause of the threat that globally pervasive Cyanobacteria Harmful algal blooms (CyanoHABs) pose to humans. Major attention has been focused on the cyanobacterial hepatotoxin microcystins (MCs), however, there is a dearth of studies on cyanobacterial neurotoxin anatoxins. In this study, we explored how an anatoxin-producing Cuspidothrix issatschenkoi strain responded to culture with inorganic and organic nitrogen sources in terms of growth and anatoxins production. The results of our study revealed that ʟ- alanine could greatly boost cell growth, and was associated with the highest cell productivity, while urea significantly stimulated anatoxin production with the maximum anatoxin yield reaching 25.86 &mu, g/mg dry weight, which was 1.56-fold higher than that in the control group (BG11). To further understand whether the carbon/nitrogen balance in C. issatschenkoi would affect anatoxin production, we explored growth and toxin production in response to different carbon/nitrogen ratios (C/N). Anatoxin production was mildly promoted when the C/N ratio was within low range, and significantly inhibited when the C/N ratio was within high range, showing approximately a three-fold difference. Furthermore, the transcriptional profile revealed that anaC gene expression was significantly up-regulated over 2&ndash, 24 h when the C/N ratio was increased, and was significantly down-regulated after 96 h. Overall, our results further enriched the evidence that urea can stimulate cyanotoxin production, and ʟ-alanine could boost C. issatschenkoi proliferation, thus providing information for better management of aquatic systems. Moreover, by focusing on the intracellular C/N metabolic balance, this study explained the anatoxin production dynamics in C. issatschenkoi in response to different N sources.

This paper introduces the exergy efficiency that takes into account both the quantity and quality of energy, and constructs a bi-level planning optimization model of the regional integrated energy system. Factors such as equipment exergy efficiency are considered by the upper-level planning model. Through matter-element information theory and Frequent Patterm-growth algorithm, the energy structure of the regional integrated energy system planning is determined by quantitative means. The lower-level planning model is intended for economy and exergy efficiency, and the tabu search algorithm is embedded in the solving algorithm of the multi-objective genetic algorithm for solving, determining the capacity of each equipment in the energy structure. In the case study, three comparison schemes are used to verify the proposed model and method through the regional integrated energy system in a resort town in northern China. Among them, Scheme 1 is devised for economy, Scheme 2 is proposed for economy and energy efficiency, and Scheme 3 is intended for economy and exergy efficiency. The results show that compared with the Scheme 1 and Scheme 2, the system exergy efficiency of Scheme 3 corresponding to this model has increased by 17.34% and 11.17%, respectively. Despite an increase of 10.61% in total annualized cost in Scheme 3 compared to Scheme 1, there is a decrease of 6.37% compared to Scheme 2. Despite the conflict in improving exergy efficiency and economy, there is still a balance to draw in the proposed model in this paper.

With the outbreak of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), coronaviruses have begun to attract great attention across the world. Of the known human coronaviruses, however, Middle East respiratory syndrome coronavirus (MERS-CoV) is the most lethal. Coronavirus proteins can be divided into three groups: nonstructural proteins, structural proteins, and accessory proteins. While the number of each of these proteins varies greatly among different coronaviruses, accessory proteins are most closely related to the pathogenicity of the virus. We found for the first time that the ORF3 accessory protein of MERS-CoV, which closely resembles the ORF3a proteins of severe acute respiratory syndrome coronavirus and SARS-CoV-2, has the ability to induce apoptosis in cells in a dose-dependent manner. Through bioinformatics analysis and validation, we revealed that ORF3 is an unstable protein and has a shorter half-life in cells compared to that of severe acute respiratory syndrome coronavirus and SARS-CoV-2 ORF3a proteins. After screening, we identified a host E3 ligase, HUWE1, that specifically induces MERS-CoV ORF3 protein ubiquitination and degradation through the ubiquitin-proteasome system. This results in the diminished ability of ORF3 to induce apoptosis, which might partially explain the lower spread of MERS-CoV compared to other coronaviruses. In summary, this study reveals a pathological function of MERS-CoV ORF3 protein and identifies a potential host antiviral protein, HUWE1, with an ability to antagonize MERS-CoV pathogenesis by inducing ORF3 degradation, thus enriching our knowledge of the pathogenesis of MERS-CoV and suggesting new targets and strategies for clinical development of drugs for MERS-CoV treatment.

In this paper, we propose a new semantic segmentation network called ’E-Res U-Net’, to achieve better segmentation results of deep and superficial muscles in ultrasonic muscle images. This model is based on U-Net, and its structure has been modified to improve the performance of the algorithm. There are three aspects of improvement based on U-Net, including E-Res layer, dilated convolution module, and E-Res path. Additional experiments demonstrate that each designed module in our proposed network is effective, can improve the accuracy compared to the original U-Net. When compared with other algorithms which are state-of-the-art, the experimental result under the overall network structure is even more excellent.

Direct Current grid technology based on flexible Direct Current transmission is one of the key means to solve large-scale grid integration and consumption of new energy. The flexible Direct Current transmission technology developed in recent years has superior control performance, strong flexibility, and independent control of active power. And reactive power, can be connected to the weak Alternating Current system. This article derives the power flow equation of the Direct Current transmission network by establishing a mathematical model of the Direct Current transmission network and combining different system-level control strategies in the Direct Current transmission network. The Newton-Raphson method is used to solve the power flow distribution of the Direct Current network when the master-slave control and droop control are adopted. The influence of the two control methods on the power flow distribution of the Direct Current network is compared through a calculation example.

This paper proposes a comprehensive energy expansion planning method based on multi-energy coupling, and establishes a mixed integer linear programming model with the goal of minimizing the sum of investment, operation, power shortage, and wind curtailment costs. The model can be used on the basis of ensuring computational efficiency. In the above, the uncertainty of wind power is accurately explored, and a two-stage planning and solving strategy based on the scenario method is established to ensure the efficiency and feasibility of the model solution. GAMS is used to optimize the calculation of the IEEE14-NGS14 example system, and the result of the optimal expansion plan proves the correctness and effectiveness of the method proposed in this paper.

Deoxynivalenol (DON) is a common contaminant of grain worldwide and is often detected in the human diet and animal feed. Selenium is an essential trace element in animals. It has many biological functions. The role of selenium in the body is mainly orchestrated by selenoprotein. Glutathione peroxidase (GPx) also exists widely in the body and has attracted much attention due to its high antioxidant capacity. In order to explore the effect of the GPx1 gene on toxicity of DON, in this study, we overexpressed or knockdown GPx1 in porcine splenic lymphocytes, then added different concentrations of DON (0.1025, 0.205, 0.41, and 0.82 μg/mL) and sodium selenite (2 μmol/L) to the culture system. Using various techniques, we detected antioxidant function, free radical content, cell apoptosis, and methylation-related gene expression to explore the effect of GPx1 expression on DON-induced cell damage. We also explored whether selenium can antagonize the toxicity of DON in these two cell models and revealed the protective effect of sodium selenite on DON-induced cell damage in GPx1-overexpressing or knockdown splenic lymphocytes. Finally, our findings revealed the following: (1) GPx1 can regulate the antioxidant capacity, apoptosis rate, and expression of DNA methylation-related genes in pig splenic lymphocytes. (2) Na2SeO3 (2 μmol/L) can regulate the antioxidant capacity, apoptosis rate, and expression of DNA methylation-related genes in pig splenic lymphocytes, and this effect is more significant in GPx1-overexpressing cells than in GPx1-knockdown cells. (3) DON can cause oxidative damage, apoptosis, and methylation injury in GPx1-overexpressing or knockdown pig splenic lymphocytes in a concentration-dependent manner. (4) Na2SeO3 (2 μmol/L) can antagonize the toxic effect of DON on GPx1-overexpressing or knockdown pig splenic lymphocytes. Our findings may have important implications for food/feed safety, human health, and environmental protection.

The economy of the Regional Integrated Energy System (RIES) scheduling scheme is affected by the parameter accuracy of different energy conversion equipment models. The traditional static energy hub (EH) model regards the equipment efficiency as a constant and ignores the variable condition characteristics of the equipment efficiency changing with the load rate. This paper presents an optimal scheduling method of RIES considering the characteristics of equipment under variable operating conditions. Firstly, the architecture of the integrated energy system is analyzed. Secondly, based on the DEH model, an optimal scheduling method of RIES was proposed considering the characteristics of equipment under variable operating conditions. Finally, an optimal scheduling analysis is performed for a typical RIs. The results show that the proposed method can improve the accuracy of the equipment model and reduce the cost prediction error by considering the variable operating conditions of the equipment, thus providing a more economical scheduling scheme for Ries.

This paper studies the design of clean electric energy alternatives based on electric energy alternative equipment. First, sort out the current research status of energy substitution at home and abroad, and determine the research focus of electric energy substitution; list the electric energy substitution equipment that is suitable for construction, industrial, commercial and other fields, analyze its technical characteristics and specific application scenarios; list each device To analyze the performance of the equipment, establish a multi-factor output model for energy storage and production equipment according to the key influencing factors of the equipment performance coefficient; finally, based the above research, consider the substitutability of each equipment With complementary characteristics, the design is suitable for high-clean energy electric energy to replace typical structures.

Traditional demand response technologies optimize the load profile by cutting or shifting the electrical load, but they affect the customer’s energy experience, and thus the customer’s motivation to participate in demand response and load controllability are limited. For community integrated energy operators, the study of integrated customer demand response can explore their dispatch potential. Based on consumer psychology, we study the alternative energy use behavior of residential customers, establish a customer optimization model and a community integrated energy operator optimization model, and set up a Stackelberg master-slave game model with the operator as the leader and the customer as the follower. The simulation case verifies that the utilization of load-side multi-energy complementary characteristics and integrated demand response behavior can effectively improve the utilization of renewable energy and the efficiency of integrated energy operators.

Servant leadership, which prioritizes the interests of followers and other shareholders, is considered as a unique approach that offers much potential to enrich our understanding of leadership. Des...

With the development of renewable energy technology, integrating a variety of renewable energy integrated energy systems can effectively solve the problem of optimizing the scheduling of buildings with high energy consumption and fast growth rate. Based on the modeling and analysis of various energy equipment in the system, the integrated energy system of building buildings, based on the demand response compensation price, with the lowest construction operating cost as the goal function, establishes the optimization scheduling model of building-level integrated energy system based on demand response, and uses the particle group algorithm based on cloud model improvement to optimize the solution of the model. The study is introduced for simulation to compare the two different modes of participation in demand response, and the optimal performance of cloud model particle group algorithm and elementary particle group algorithm. The results show that the cloud model particle group algorithm model based on demand response can effectively save the operating cost of the building-level integrated energy system, and reduce the power grid side load peak and valley difference.

Due to the limitations of a single power load forecasting model, the power load forecasting cannot be performed well. In order to obtain a greater closeness to predict results with actual data, this paper presents the power load forecasting model based on gray neural network combined return to Guangzhou, 2010 - 2019 on actual data for example, the results show that: As used herein, the combined model method has high accuracy and strong use value.

Objectives Assessing medical students’ attitudes toward patient-centred care is essential to bettering medical education. Based on doctor-patient relationships and the medical system in China, it is important to explore the impact of gender differences and other background factors on patient-centred attitudes and to provide references for medical education reform. Methods A cross-sectional study was conducted on fourth-year medical undergraduate students from November 2017 to March 2018 in Heilongjiang Province, Northeast China. The Chinese-revised Patient-Practitioner Orientation Scale (CR-PPOS), which has been validated in previous research, was used to measure the medical students’ attitudes. The medical students’ demographic data was collected, including their gender, age, information on whether they have siblings, family residence location, doctor(s) for parents, year in which the student first experienced clinical practice, and student category. Results A total of 513 students (91.12%) completed the survey. The Chinese medical students scored considerably higher for ‘Caring’ (including patients’ preferences into the decision-making process) than for ‘Sharing’ (sharing information/responsibility with patients). These students tended to have patient-centred attitudes, as measured by an average overall CR-PPOS score of 3.63 (scores higher than 3.5 indicate patient-centred attitudes), which is higher than Malian (3.38) and Pakistani (3.40) medical students but lower than American (4.57) and Brazilian (4.66) students. Female students (P Student category (P Clinical hospital students (P Students without siblings (p Conclusions In China, gender has a significant impact on medical students’ patient-centred attitudes, which is similar to findings from other countries. If medical schools want to raise patient-centred attitudes across the board and bridge the gap between male and female patient-centred attitudes, gender, student category, and other factors should be incorporated into medical education.