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Abstract Background Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Multiple identified mutations in nexilin (NEXN) have been suggested to be linked with severe DCM. However, the exact association between multiple mutations of Nexn and DCM remains unclear. Moreover, it is critical for the development of precise and effective therapeutics in treatments of DCM. Results In our study, Nexn global knockout mice and mice carrying human equivalent G645del mutation are studied using functional gene rescue assays. AAV-mediated gene delivery is conducted through systemic intravenous injections at the neonatal stage. Heart tissues are analyzed by immunoblots, and functions are assessed by echocardiography. Here, we identify functional components of Nexilin and demonstrate that exogenous introduction could rescue the cardiac function and extend the lifespan of Nexn knockout mouse models. Similar therapeutic effects are also obtained in G645del mice, providing a promising intervention for future clinical therapeutics. Conclusions In summary, we demonstrated that a single injection of AAV-Nexn was capable to restore the functions of cardiomyocytes and extended the lifespan of Nexn knockout and G645del mice. Our study represented a long-term gene replacement therapy for DCM that potentially covers all forms of loss-of-function mutations in NEXN.

Abstract Backgrounds Long nonconding RNAs (lncRNAs) have been found to be a vital regulatory factor in the development process of human cancer, and could regarded as diagnostic or prognostic biomarkers for human cancers. Here, we aim to confirm the expression and molecular mechanism of RP11-171K16.5 (lnc171) in hepatocellular carcinoma (HCC). Methods Screening of differentially expressed lncRNAs by RNA sequencing. Expression level of gene was studied by quantitative real-time PCR (qRT-PCR). The effects of lnc171, mir-873-5p, and ethanol on migration and invasion activity of cells were studied used transwell assay, and luciferase reporter assay was used to confirm the binding site. Results RNA sequencing showed that lnc171 was markedly up-regulated in HCC. siRNA-mediated knockdown of lnc171 repressed the migration and invasion ability of HCC cells. Bioinformatic analysis, dual luciferase reporter assay, and qRT-PCR indicated that lnc171 interacted with mir-873-5p in HCC cells, and Zin-finger E-box binding homeobox (ZEB1) was a downstream target gene of mir-873-5p. In addition, lnc171 could enhance migration and invasion ability of HCC cells by up-regulating ZEB1 via sponging mir-873-5p. More interestingly, ethanol stimulation could up-regulate the increase of lnc171, thereby regulating the expression of competing endogenous RNA (ceRNA) network factors which lnc171 participated in HCC cells. Conclusions Our date demonstrates that lnc171 was a responsive factor of ethanol, and plays a vital role in development of HCC via binding of mir-873-5p.

Abstract Synthetic data generation is being increasingly used as a privacy preserving approach for sharing health data. In addition to protecting privacy, it is important to ensure that generated data has high utility. A common way to assess utility is the ability of synthetic data to replicate results from the real data. Replicability has been defined using two criteria: (a) replicate the results of the analyses on real data, and (b) ensure valid population inferences from the synthetic data. A simulation study using three heterogeneous real-world datasets evaluated the replicability of logistic regression workloads. Eight replicability metrics were evaluated: decision agreement, estimate agreement, standardized difference, confidence interval overlap, bias, confidence interval coverage, statistical power, and precision (empirical SE). The analysis of synthetic data used a multiple imputation approach whereby up to 20 datasets were generated and the fitted logistic regression models were combined using combining rules for fully synthetic datasets. The effects of synthetic data amplification were evaluated, and two types of generative models were used: sequential synthesis using boosted decision trees and a generative adversarial network (GAN). Privacy risk was evaluated using a membership disclosure metric. For sequential synthesis, adjusted model parameters after combining at least ten synthetic datasets gave high decision and estimate agreement, low standardized difference, as well as high confidence interval overlap, low bias, the confidence interval had nominal coverage, and power close to the nominal level. Amplification had only a marginal benefit. Confidence interval coverage from a single synthetic dataset without applying combining rules were erroneous, and statistical power, as expected, was artificially inflated when amplification was used. Sequential synthesis performed considerably better than the GAN across multiple datasets. Membership disclosure risk was low for all datasets and models. For replicable results, the statistical analysis of fully synthetic data should be based on at least ten generated datasets of the same size as the original whose analyses results are combined. Analysis results from synthetic data without applying combining rules can be misleading. Replicability results are dependent on the type of generative model used, with our study suggesting that sequential synthesis has good replicability characteristics for common health research workloads.

Barth syndrome (BTHS) is a mitochondrial lipid disorder caused by mutations in TAFAZZIN (TAZ), required for cardiolipin (CL) remodeling. Cardiomyopathy is a major clinical feature, with no curative therapy. Linoleic acid (LA) supplementation is proposed to ameliorate BTHS cardiomyopathy by enhancing linoleoyl group incorporation into CL. While the beneficial effect of dietary LA supplementation in delaying the development of BTHS cardiomyopathy has been recently tested, its potential to reverse established BTHS cardiomyopathy remains unclear. Our study revealed that LA supplementation cannot rescue established BTHS cardiomyopathy in mice, highlighting the importance of early initiation of LA supplementation for maximum benefits.

In this paper, we introduce MP-GT, a novel Graph Neural Network model that leverages meta-path-guided optimization within the GCN-Transformer framework to enhance application (App) usage prediction accuracy. Our approach addresses issues such as suspended animation and over-smoothing by extracting both local subgraph structures and global graph structures using a combination of GCN and Transformer method. Furthermore, we enhance the capture of semantic information and App usage patterns by incorporating a meta path-guided objective function. Extensive experiments demonstrate that MP-GT outperforms the widely adopted baseline of semantic-aware representation learning via Graph Convolutional Network (SA-GCN) by 13.33% in terms of Accuracy@1. Additionally, MP-GT surpasses the popular baseline of context-aware App usage prediction with heterogeneous graph embedding (CAP) by 74.02% in the same metric. Moreover, MP-GT reduces training time by 79.47% compared to SA-GCN. These findings validate that our approach not only achieves higher prediction accuracy but also converges faster than the baseline models. Therefore, MP-GT proves to be an effective and superior solution for the App usage prediction task.

Background: Health workers (HWs) faced considerable psychosocial hazards during the COVID-19 pandemic, which profoundly affected their occupational health and job performance. The potential indirect relationship between workplace violence (WPV) and burnout among HWs needs to be further explored. The purpose of this study is to examine the chain mediating effects of sleep disturbance and work ability in the relationship between WPV and burnout among HWs. Methods: A cross-sectional study was conducted in a secondary hospital two years after the COVID-19 outbreak in Wuhan, China. A sample of 571 HWs was recruited using a cluster sampling method, achieving a response rate of 80.06%. Participants completed self-report questionnaires that included demographic information and measures of WPV, burnout, sleep disturbance, and work ability. Results: The prevalence rates of burnout and WPV among HWs two years after the COVID-19 outbreak were 37.30% and 31.52%, respectively. WPV was significantly associated with burnout (β = 0.446, p < 0.001). Sleep disturbance was identified as a mediator in the relationship between WPV and burnout (β = 0.063, 95% CI: 0.027–0.105), accounting for 14.13% of the total effect. Similarly, work ability also played a mediating role in this relationship (β = 0.142, 95% CI: 0.065–0.225), accounting for 31.84%. Additionally, both sleep disturbance and work ability exhibited a chain mediation effect on the association between WPV and burnout (β = 0.020, 95% CI: 0.008–0.036), and the total indirect effect accounted for 50.67%. Conclusions: Among Chinese HWs, WPV exerts significant direct and indirect effects on burnout symptoms, mediated by sleep disturbance and work ability. This finding provides valuable empirical insights for designing interventions to mitigate the adverse effects of psychosocial factors such as WPV and burnout among HWs. After exposure to WPV, measures focused on reducing sleep disturbance and enhancing work ability may prove effective in alleviating burnout in subsequent interventions.

In predicting building energy (affected by seasons), there are issues like inefficient hyperparameter optimization and inaccurate predictions, it is unclear whether spatial and temporal attention improves performance. This study proposes a method based on Bayesian Optimization (BO), Spatial-Temporal Attention (STA), and Long Short-Term Memory (LSTM). Seven improved LSTM models (BO-LSTM, SA-LSTM, TA-LSTM, STA-LSTM, BO-SA-LSTM, BO-TA-LSTM, and BO-STA-LSTM) are compared with LSTM and the impacts of seasonal variations on BO-STA-LSTM are analysed using different sample types and time domain analysis. To further demonstrate the efficiency of the proposed method, comparisons with convolutional neural network (CNN) and temporal convolutional network (TCN) are performed, and followed by validation with new datasets. The findings indicate that adding STA and BO to LSTM enhances the average R2 of prediction performance by 0.0885. BO alone contributes 0.0717, while adding attention improves 0.0560. BO-STA-LSTM achieves higher prediction accuracy for similar test and training samples or a test sample size of 14,016, effectively capturing seasonal, trend, and peak energy patterns. Additionally, BO-STA-LSTM outperforms CNN and TCN, demonstrating superior prediction accuracy.

Renal interstitial fibrosis (RIF) is a classic pathophysiological process of chronic kidney disease (CKD). However, the mechanisms underlying RIF remain unclear. The present study found that a novel circular RNA, cirInpp5b, might be involved in RIF by high-throughput sequencing. Subsequent experiments revealed that circInpp5b was reduced in UUO mouse kidney tissues and TGF-β1-treated proximal tubular cells. The overexpression of circInpp5b inhibited RIF in UUO mice and prevented extracellular matrix (ECM) deposition in TGF-β1-treated proximal tubular cells. Furthermore, overexpression of circInpp5b down-regulated the protein level of DDX1. Mechanistically, circInpp5b bound to the DDX1 protein and promoted its lysosomal degradation. Collectively, the findings of our study demonstrate that circInpp5b ameliorates RIF by binding to the DDX1 protein and promoting its lysosomal degradation.

Plant glutamate receptor-like channels (GLRs) are homologs of animal ionotropic glutamate receptors. GLRs are critical in various plant biological functions, yet their genomic features and functions in disease resistance remain largely unknown in many crop species. Here, we report the results on a thorough genome-wide study of the GLR family in oilseed rape (Brassica napus) and their role in resistance to the fungal pathogen Sclerotinia sclerotiorum. A total of 61 GLRs were identified in oilseed rape. They comprised three groups, as in Arabidopsis thaliana. Detailed computational analyses, including prediction of domain and motifs, cellular localization, cis-acting elements, PTM sites, and amino acid ligands and their binding pockets in BnGLR proteins, unveiled a set of group-specific characteristics of the BnGLR family, which included chromosomal distribution, motif composition, intron number and size, and methylation sites. Functional dissection employing virus-induced gene silencing of BnGLRs in oilseed rape and Arabidopsis mutants of BnGLR homologs demonstrated that BnGLR35/AtGLR2.5 positively, while BnGLR12/AtGLR1.2 and BnGLR53/AtGLR3.2 negatively, regulated plant resistance to S. sclerotiorum, indicating that GLR genes were differentially involved in this resistance. Our findings reveal the complex involvement of GLRs in B. napus resistance to S. sclerotiorum and provide clues for further functional characterization of BnGLRs.

Abstract Background Renal interstitial fibrosis (RIF) is a common pathway to end-stage renal disease regardless of the initial etiology. Currently, the molecular mechanisms for RIF remains not fully elucidated. Nuclear receptor subfamily 4 group A member 1(Nr4a1), a member of the NR4A subfamily of nuclear receptors, is a ligand-activated transcription factor. The role of Nr4a1 in RIF remains largely unknown. Methods In this study, we determined the role and action mechanism of Nr4a1 in RIF. We used unilateral ureteral obstruction (UUO) mice and transforming growth factor (TGF)-β1-treated human renal proximal tubular epithelial cells (HK-2 cells) as in vivo and in vitro models of RIF. A specific Nr4a1 agonist Cytosporone B (Csn-B) was applied to activate Nr4a1 both in vivo and in vitro, and Nr4a1 small interfering RNA was applied in vitro. Renal pathological changes were evaluated by hematoxylin and eosin and Masson staining, and the expression of fibrotic proteins including fibronectin (Fn) and collagen-I (Col-I), and phosphorylated p38 MAPK was measure by immunohistochemical staining and western blot analysis. Results The results showed that Nr4a1 was upregulated in UUO mouse kidneys, and was positively correlated with the degree of interstitial kidney injury and the levels of fibrotic proteins. Csn-B treatment aggravated UUO-induced renal interstitial fibrosis, and induced p38 MAPK phosphorylation. In vitro, TGF-β induced Nr4a1 expression, and Nr4a1 downregulation prevented TGF-β1-induced expression of Fn and Col-I and the activation of p38 MAPK. Csn-B induced fibrotic proteins expression and p38 MAPK phosphorylation, and moreover Csn-B induced fibrotic proteins expression was abrogated by treatment with p38 MAPK inhibitor SB203580. We provided further evidence that Csn-B treatment promoted cytoplasmic accumulation of Nr4a1. Conclusion The findings in the present study indicate that Nr4a1 promotes renal fibrosis potentially through activating p38 MAPK kinase.

A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

ObjectiveThe purpose of this study was to precisely evaluate the serum Dickkopf-1 (DKK-1) level in patients with ankylosing spondylitis (AS) relative to that in normal controls and to test the causal relationship between DKK-1 and the risk of AS.MethodsEmbase, PubMed, Web of Science, WANFANG DATA, VIP, and China National Knowledge Infrastructure (CNKI) were comprehensively searched until July 2022 for pertinent studies. The pooled standardized mean difference (SMD) with a 95% confidence interval (CI) was calculated by the fixed or random-effect model. In Mendelian randomization (MR) analysis on the causal relationship between serum DKK-1 level and AS risk, the inverse variance weighting method (IVW), MR-Egger regression, weighted median method, and weighted pattern method were applied. Sensitivity analyses, including the horizontal pleiotropy test, heterogeneity test, and leave-one-out test, were also performed.ResultsThe meta-analysis of 40 studies containing 2,371 AS patients and 1,633 healthy controls showed that there was no significant difference in DKK-1 serum level between AS patients and normal controls (pooled SMD=0.207, 95% CI =−0.418-0.832, P=0.516). The subgroup analysis of the CRP ≤ 10 mg/L group showed that AS patients had higher serum DKK-1 concentration than the healthy controls (SMD=2.267, 95% CI = 0.102-4.432, P=0.040). Similarly, MR analysis also demonstrated no significant association between DKK-1 serum level and AS (IVW OR=0.999, 95% CI = 0.989-1.008, P=0.800). All sensitivity analyses revealed consistent results.ConclusionsThere was no significant change in serum DKK-1 concentration between AS patients and healthy controls. In addition, no causal relationship exists between serum DKK-1 levels and AS risk.

ObjectiveEmerging evidence suggests an increased prevalence of coronavirus disease 2019 (COVID-19) in patients with systemic lupus erythematosus (SLE), the prototype of autoimmune disease, compared to the general population. However, the conclusions were inconsistent, and the causal relationship between COVID-19 and SLE remains unknown.MethodsIn this study, we aimed to evaluate the bidirectional causal relationship between COVID-19 and SLE using bidirectional Mendelian randomization (MR) analysis, including MR-Egger, weighted median, weighted mode, and the inverse variance weighting (IVW) method.ResultsThe results of IVW showed a negative effect of SLE on severe COVID-19 (OR = 0.962, p = 0.040) and COVID-19 infection (OR = 0.988, p = 0.025), which disappeared after Bonferroni correction. No causal effect of SLE on hospitalized COVID-19 was observed (OR = 0.983, p = 0.148). In the reverse analysis, no causal effects of severe COVID-19 infection (OR = 1.045, p = 0.664), hospitalized COVID-19 (OR = 0.872, p = 0.109), and COVID-19 infection (OR = 0.943, p = 0.811) on SLE were found.ConclusionThe findings of our bidirectional causal inference analysis did not support a genetically predicted causal relationship between SLE and COVID-19; thus, their association observed in previous observational studies may have been caused by confounding factors.

Modular multilevel converters have found a large number of applications in high-voltage and high-power DC transmission due to their modularity, scalability, multi-equality and other advantages. The two typical topologies in MMC systems are half-bridge sub-module structure and full-bridge sub-module structure. Half-bridge sub-module is widely used in MMC systems by virtue of its simple control structure, high conversion efficiency and low cost. Insulated gate bipolar transistor (IGBT) failures, especially open-circuit failures, are a major factor in the reliable operation of MMC systems. Fast and accurate positioning of the open circuit fault location and removal of the fault submodule is of great significance for handling MMC system faults. Aiming at the current situation of the fault location of the MMC system, this paper compares the error of the predicted median capacitance voltage with the actual value , and proposes an accurate diagnosis method for MMC lower IGBT tube open circuit faults. This method is suitable for one or more IGBT lower tube open-circuit fault conditions, and can quickly locate the faulty submodule within 5ms. Finally, an MMC simulation system with 8 sub-modules is built based on MATLAB/Simulink, which effectively verifies the accuracy of the proposed method.

BackgroundNew multi-purpose prevention technology (MPT) products are needed to prevent human immunodeficiency virus (HIV) and herpes simplex virus type 2 (HSV2). In this study, we evaluated a fast-dissolve insert that may be used vaginally or rectally for prevention of infection.ObjectiveTo describe the safety, acceptability, multi-compartment pharmacokinetics (PK), and in vitro modeled pharmacodynamics (PD) after a single vaginal dose of an insert containing tenofovir alafenamide (TAF) and elvitegravir (EVG) in healthy women.MethodsThis was a Phase I, open-label, study. Women (n=16) applied one TAF (20mg)/EVG (16mg) vaginal insert and were randomized (1:1) to sample collection time groups for up to 7 days post dosing. Safety was assessed by treatment-emergent adverse events (TEAEs). EVG, TAF and tenofovir (TFV) concentrations were measured in plasma, vaginal fluid and tissue, and TFV-diphosphate (TFV-DP) concentration in vaginal tissue. PD was modeled in vitro by quantifying the change in inhibitory activity of vaginal fluid and vaginal tissue against HIV and HSV2 from baseline to after treatment. Acceptability data was collected by a quantitative survey at baseline and post treatment.ResultsThe TAF/EVG insert was safe, with all TEAEs graded as mild, and acceptable to participants. Systemic plasma exposure was low, consistent with topical delivery, while high mucosal levels were detected, with median TFV vaginal fluid concentrations exceeding 200,000 ng/mL and 1,000 ng/mL for up to 24 hours and 7 days post dosing, respectively. All participants had vaginal tissue EVG concentrations of > 1 ng/mg at 4 and 24 hours post dosing. The majority had tissue TFV-DP concentrations exceeding 1000 fmol/mg by 24 – 72 hours post dosing. Vaginal fluid inhibition of HIV-1 and HSV-2 in vitro significantly increased from baseline and was similarly high at 4 and 24 hours post dosing. Consistent with high tissue TFV-DP concentrations, p24 HIV antigen production from ectocervical tissues infected ex vivo with HIV-1 significantly decreased from baseline at 4 hours post dosing. HSV-2 production from tissue also decreased post treatment.ConclusionsA single dose of TAF/EVG inserts met PK benchmarks, with PK data supporting an extended window of high mucosal protection. PD modeling supports mucosal protection against both HIV-1 and HSV-2. The inserts were safe and highly acceptable.Clinical trial registrationClinicalTrials.gov, identifier NCT03762772.

The presence and extent of cribriform patterned Gleason 4 (G4) glands are associated with poor prognosis following radical prostatectomy. This study used whole-mount prostate histology and multiparametric magnetic resonance imaging (MP-MRI) to evaluate diffusion differences in G4 gland morphology. Fourty-eight patients underwent MP-MRI prior to prostatectomy, of whom 22 patients had regions of both G4 cribriform glands and G4 fused glands (G4CG and G4FG, respectively). After surgery, the prostate was sliced using custom, patient-specific 3D-printed slicing jigs modeled according to the T2-weighted MR image, processed, and embedded in paraffin. Whole-mount hematoxylin and eosin-stained slides were annotated by our urologic pathologist and digitally contoured to differentiate the lumen, epithelium, and stroma. Digitized slides were co-registered to the T2-weighted MRI scan. Linear mixed models were fitted to the MP-MRI data to consider the different hierarchical structures at the patient and slide level. We found that Gleason 4 cribriform glands were more diffusion-restricted than fused glands.

ObjectiveEmerging evidence has highlighted the possible links of environmental pollution with several cardiovascular diseases (CVDs). The current study aimed to explore the impact of short-term air pollution exposure on CHD hospitalization in Hefei.MethodsData about the daily number of CHD admissions (from 2014 to 2021) were retrieved from the First Affiliated Hospital of Anhui Medical University. Air pollutants and meteorological data were obtained from the China Environmental Monitoring Station and the China Meteorological Data Service Center, respectively. The correlation between air pollution and CHD hospitalization was assessed using distributed lag non-linear model (DLNM) and Poisson generalized linear regression.ResultsIn the single-pollutant model, NO2, O3, and CO strongly correlated with CHD hospitalization rate. Specifically, exposure to NO2 (lag0, relative risk [RR]: 1.013, 95%CI: 1.002–1.024, per 10 μg/m3 increase) and CO (lag13, RR: 1.035, 95%CI: 1.001–1.071, per 1 μg/m3 increase) revealed a positive correlation with an increased rate of CHD hospitalization. Interestingly, O3 had a protective association with hospitalization of CHD (lag0, RR: 0.993, 95%CI: 0.988–0.999, per 10 μg/m3 increase). Similar results, to those of the single-pollutant model, were revealed following verification using two-pollutant models. Subgroup analyses indicated that young people, women, and people in hot seasons were more susceptible to NO2 exposure, while the elderly, women, and people in cold seasons were more susceptible to O3. Furthermore, the elderly were more susceptible to CO exposure.ConclusionOverall, exposure to NO2 and CO increases the rate of CHD hospitalization, but exposure to O3 shows a protective association with the rate of CHD hospitalization. Therefore, early preventive measures against air pollutants should be applied to protect vulnerable patients with CHD.

Abstract Background Forest restoration has been considered an effective method to increase soil organic carbon (SOC), whereas it remains unclear whether long-term forest restoration will continuously increase SOC. Such large uncertainties may be mainly due to the limited knowledge on how soil microorganisms will contribute to SOC accumulation over time. Methods We simultaneously documented SOC, total phospholipid fatty acids (PLFAs), and amino sugars (AS) content across a forest restoration gradient with average stand ages of 14, 49, 70, and > 90 years in southern China. Results The SOC and AS continuously increased with stand age. The ratio of fungal PLFAs to bacterial PLFAs showed no change with stand age, while the ratio of fungal AS to bacterial AS significantly increased. The total microbial residue-carbon (AS-C) accounted for 0.95–1.66 % in SOC across all forest restoration stages, with significantly higher in fungal residue-C (0.68–1.19 %) than bacterial residue-C (0.05–0.11 %). Furthermore, the contribution of total AS-C to SOC was positively correlated with clay content at 0–10 cm soil layer but negatively related to clay content at 10–20 cm soil layer. Conclusions These findings highlight the significant contribution of AS-C to SOC accumulation along forest restoration stages, with divergent contributions from fungal residues and bacterial residues. Soil clay content with stand age significantly affects the divergent contributions of AS-C to SOC at two different soil layers.

ObjectiveTo investigate the status of glycemic control and analyze its influencing factors in patients with type 2 diabetes (T2D) in Anhui, China.Methods1,715 T2D patients aged 18–75 years old were selected from 4 counties or districts in Anhui Province in 2018, using a convenience sampling method. All patients have undergone a questionnaire survey, physical examination, and a glycosylated hemoglobin (HbA1c) test. According to the 2022 American Diabetes Association criteria, HbA1c was used to evaluate the glycemic control status of patients, and HbA1c < 7.0% was defined as good glycemic control. The influencing factors of glycemic control were analyzed by multivariate unconditional logistic regression.ResultsThe prevalence of good glycemic control among people with T2D in the Anhui Province was low (22.97%). On univariate analysis, gender, education level, occupation, region, smoking, drinking, waist circumference and disease duration (all P < 0.05) were significantly associated with glycemic control. The factors associated with pool glycemic control were female gender [OR = 0.67, 95%CI (0.52, 0.86), P = 0.001], higher level of education [OR = 0.47, 95%CI (0.27, 0.83), P = 0.001], living in rural areas [OR = 1.77, 95%CI (1.39, 2.26), P < 0.001], central obesity [OR = 1.58, 95%CI (1.19, 2.09), P = 0.001] and longer duration of disease [OR = 2.66, 95%CI (1.91, 3.69), P < 0.001].ConclusionsThe prevalence of good glycemic control in people with T2D in Anhui Province was relatively low, and gender, region, education level, central obesity and course of the disease were influencing factors. The publicity and education on the importance of glycemic control should be further strengthened in T2D patients, and targeted intervention measures should be carried out for risk groups.

Use of the inhalation anesthetic isoflurane may increase the risk of cognitive deficiency and neurotoxicity after birth. A growing body of evidence suggests that melatonin is an effective treatment for various types of oxidative stress damage and neurodegenerative disease. In this study, we aimed to examine the effects of melatonin on isoflurane-induced endoplasmic reticulum (ER) stress, spatial learning and memory impairment during development. The rats were grouped according to whether the rats were exposed to isoflurane or a control gas and whether they were administered melatonin or phosphate buffered saline (PBS). We administered isoflurane to 7-day-old Sprague–Dawley rat pups with intraperitoneal injections of melatonin (20 mg/kg) 15 min before and 3 h after the initiation of anesthesia. Twelve hours after isoflurane anesthesia, rats were randomly selected from each group and sacrificed. The hippocampal tissue and serum were collected to determine the levels of SIRT1, Mfn2, PERK, and other proteins or cytokines related to ER stress, apoptosis, and neuroinflammation. Subsequently, all remaining rats were assessed for spatial learning and memory deficiency 31 days after birth using the Morris water maze test. We found that melatonin attenuated isoflurane-induced ER stress and neuroapoptosis in the hippocampus and decreased the level of neuroinflammatory markers in the serum of newborn rats, resulting in improved spatial learning and memory. In addition, the neuroprotective effect of melatonin was weakened after the SIRT1/Mfn2/PERK signaling pathway was suppressed by lentivirus transfection. Therefore, our findings demonstrate that melatonin ameliorates spatial learning and memory impairment after isoflurane exposure, and these beneficial effects are associated with a reduction in ER stress, neuroapoptosis, and neuroinflammation via the SIRT1/Mfn2/PERK signaling pathway.

With the development of the e-commerce industry, using UAVs (unmanned aerial vehicles) to deliver goods has become more popular in transportation systems. This delivery method can reduce labor costs and improve the distribution efficiency, and UAVs can reach places that are difficult for humans to reach. Because some goods are perishable, the quality of the delivery will have an impact on the customer satisfaction. At the same time, the delivery time should also meet the needs of customers as much as possible. Therefore, this paper takes the distribution distance and customer satisfaction as the objective functions, establishes a bi-objective dynamic programming model, and proposes an improved SPEA2 (strength Pareto evolutionary algorithm 2). The improved algorithm introduces the local search strategy, on the basis of the original algorithm. It conducts a local search for the better non-dominated solutions obtained in each iteration. The new dominated solutions and non-dominated solutions are determined, and the crossover operator is improved, so that the local search ability is improved, on the basis of ensuring its global search ability. The numerical experiment results show that the improved algorithm achieves an excellent performance in three aspects: the Pareto front, generation distance, and spacing, and would have a high application value in UAV cargo delivery and other MOPs (multi-objective optimization problems). The average spacing value of the improved algorithm is more than 20% smaller than SPEA2 + SDE (strength Pareto evolution algorithm 2–shift-based density estimation), which is the second-best algorithm. In the comparison of the average generation distance value, this number reaches 30%.

Image completion methods based on deep learning, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), have succeeded in producing semantically plausible results. However, existing facial image completion methods can either produce only one result or, although they can provide multiple results, cannot attribute particular emotions to the results. We propose EC-GAN, a novel facial Emotion-Controllable GAN-based image completion model that can infer and customize generative facial emotions. We propose an emotion inference module that infers the emotions of faces based on the unmasked regions of the faces. The emotion inference module is trained in a supervised manner and enforces the encoder to disentangle the emotion semantics from the native latent space. We also developed an emotion control module to modify the latent codes of emotions, moving the latent codes of the initial emotion toward the desired one while maintaining the remaining facial features. Extensive experiments were conducted on two facial datasets, CelebA-HQ and CFEED. Quantitative and qualitative results indicate that EC-GAN produces images with diverse desired expressions even when the main features of the faces are masked. On the other hand, EC-GAN promotes semantic inference capability with irregularly masked holes, resulting in more natural facial expressions.

Due to energy constraints and people’s increasing requirements for indoor thermal comfort, improving energy efficiency while ensuring thermal comfort has become the focus of research in the design and operation of HVAC systems. This study took office rooms with few people occupying them in Wuhan as the research object. The EnergyPlus-Fluent co-simulation method was used to study the impact of 12 forms of air distribution on the thermal environment and air-conditioner energy consumption. The results indicate that 3 m/s supply air velocity and 45° supply air angle are more suitable for the case model in this study. The EnergyPlus-Fluent co-simulation method used in this paper provides a reference for the study of indoor environments in offices with few people occupying them.

With the accelerated development of smart cities, the concept of a “smart industrial park” in which unmanned ground vehicles (UGVs) have wide application has entered the industrial field of vision. When faced with multiple tasks and heterogeneous tasks, the task execution efficiency of a single UGV is inefficient, thus the task planning research under multi-UGV cooperation has become more urgent. In this paper, under the anti-collision cooperation mechanism for multi-UGV path planning, an improved algorithm with optimized-weighted-speedy Q-learning (OWS Q-learning) is proposed. The slow convergence speed of the Q-learning algorithm is overcome to a certain extent by changing the update mode of the Q function. By improving the selection mode of learning rate and the selection strategy of action, the relationship between exploration and utilization is balanced, and the learning efficiency of multi-agent in complex environments is improved. The simulation experiments in static environment show that the designed anti-collision coordination mechanism effectively solves the coordination problem of multiple UGVs in the same scenario. In the same experimental scenario, compared with the Q-learning algorithm and other reinforcement learning algorithms, only the OWS Q-learning algorithm achieves the convergence effect, and the OWS Q-learning algorithm has the shortest collision-free path for UGVS and the least time to complete the planning. Compared with the Q-learning algorithm, the calculation time of the OWS Q-learning algorithm in the three experimental scenarios is improved by 53.93%, 67.21%, and 53.53%, respectively. This effectively improves the intelligent development of UGV in smart parks.

BackgroundCurrent therapies for obesity treatment are effective at producing short-term weight loss, but weight loss maintenance remains a significant challenge. Here we investigate the impact of pre-intervention dietary fat intake on the efficacy of a dietary supplement to support weight loss maintenance. Preclinical work demonstrates that a vagal afferent pathway critical for sensing dietary lipids is blunted by a high-fat diet (HFD), resulting in a reduced preference for a low-fat emulsion and severe blunting of the dopamine (DA) response to the gastric infusion of lipids. Infusion of the gut lipid messenger oleoylethanolamide (OEA), which is also depleted by HFD, immediately reverses this DA blunting and restores preference for the low-fat emulsion. Studies of OEA supplementation for weight loss in humans have had limited success. Given the strong effect of HFD on this pathway, we designed a study to test whether the efficacy of OEA as a weight loss treatment is related to pre-intervention habitual intake of dietary fat.Methods/DesignWe employed a randomized, double-blind, placebo-controlled trial in which 100 adults with overweight/obesity (OW/OB) were randomized to receive either OEA or placebo daily for 16 months. Following a baseline evaluation of diet, metabolic health, adiposity, and brain response to a palatable an energy dense food, participants in both groups underwent a 4-month behavioral weight loss intervention (LEARN®) followed by a 1-year maintenance period. The study aims are to (1) determine if pre-intervention dietary fat intake moderates the ability of OEA to improve weight loss and weight loss maintenance after a gold standard behavioral weight loss treatment; (2) identify biomarkers that predict outcome and optimize a stratification strategy; and (3) test a model underlying OEA’s effectiveness.DiscussionFocusing on interventions that target the gut-brain axis is supported by mounting evidence for the role of gut-brain signaling in food choice and the modulation of this circuit by diet. If successful, this work will provide support for targeting the gut-brain pathway for weight loss maintenance using a precision medicine approach that is easy and inexpensive to implement.Clinical Trial Registration[www.ClinicalTrials.gov], identifier [NCT04614233].

National Herbal Drink (NHD) is a functional food approved by the State Administration for Market Regulation for immune enhancement. In this study, the effects of NHD on Cyclophosphamide (CTX)-induced immune regulation and gut microbiota dysregulation in mice were investigated to elucidate whether the attenuation of immunosuppression was related to the regulation of the gut microbiota by NHD. Our study showed that CTX induced immune organ damage in immunosuppressed mice, with reduced levels of serum immunoglobulins IgG, IgM, and complement C3 and disruption of bone marrow hematopoiesis, accompanied by changes in liver toxicity and shortened colonic length. However, all these changes were reversed to varying degrees by NHD intervention. Delayed-type hypersensitivity (DTH) and serum hemolysins in mice also indicated that NHD could effectively regulate cellular and humoral immunity in the body. In addition, it is noteworthy that NHD can also improve intestinal pathology, elevate the expression of intestinal tight junction proteins Occludin and Claudin-1 to restore intestinal barrier function. Furthermore, α-diversity and β-diversity analysis showed that NHD has a positive regulatory effect on the gut microbiota. At the phylum level, NHD could regulate the abundance of three major phyla, namely, Bacteroidetes, Firmicutes, and Actinobacteriota. At the species level, the abundance of the beneficial genus uncultured_bacterium_g__norank_f__ Muribaculaceae increased and the abundance of the harmful genus unclassified_f__Lachnospiraceae decreased after the NHD intervention. These results provide evidence that NHD modulates the immune response in cyclophosphamide-induced immunocompromised mice by restoring intestinal barrier function and regulating gut microbiota disorders.

Summary: COVID-19 outbreaks have crushed our healthcare systems, which requires clinical guidance for the healthcare following the outbreaks. We conducted retrospective cohort studies with Pearson’s pattern-based analysis of clinical parameters of 248 hospitalized patients with COVID-19. We found that dysregulated neutrophil densities were correlated with hospitalization duration before death (p = 0.000066, r = −0.45 for % neutrophil; p = 0.0001, r = −0.47 for neutrophil count). As such, high neutrophil densities were associated with mortality (p = 4.23 × 10−31 for % neutrophil; p = 4.14 × 10−27 for neutrophil count). These findings were further illustrated by a representative “second week crash” pattern and validated by an independent cohort (p = 5.98 × 10−11 for % neutrophil; p = 1.65 × 10−7 for neutrophil count). By contrast, low aspartate aminotransferase (AST) or lactate dehydrogenase (LDH) levels were correlated with quick recovery (p ≤ 0.00005). Collectively, these correlational at-admission findings may provide healthcare guidance for patients with COVID-19 in the absence of targeted therapy.

Background: This study aimed to investigate the seroreactivity of Coronavirus disease 2019 (COVID-19) vaccination and its adverse events among systemic lupus erythematosus (SLE) patients, rheumatoid arthritis (RA) patients, and healthy controls (HCs). Methods: A total of 60 SLE patients, 70 RA patients and 35 HCs, who received a complete inactivated COVID-19 vaccine (Vero cells) regimen, were recruited in the current study. Serum IgG and IgM antibodies against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) were determined by using chemiluminescent microparticle immunoassay (CMIA). Results: There were no significant differences regarding the seroprevalences of IgG and IgM antibodies against SARS-CoV-2, and the self-reported vaccination-related adverse events among SLE patients, RA patients and HCs. The inactivated COVID-19 vaccines appeared to be well-tolerated and moderately immunogenic. In addition, case-only analysis indicated that in SLE patients, the disease manifestation of rash and anti-SSA autoantibody were associated with seroprevalence of IgG antibody against SARS-CoV-2, whereas the uses of ciclosporin and leflunomide had influence on the seroprevalence of IgM antibody against SARS-CoV-2. In RA patients, rheumatoid factor (RF) appeared to be associated with the seroprevalence of IgG antibody against SARS-CoV-2. Conclusion: Our study reveals that the seroprevalences of IgG and IgM antibodies against SARS-CoV-2 and vaccination-related adverse effects are similar among SLE, RA and HCs, suggesting that COVID-19 vaccine is safe and effective for SLE and RA patients to prevent from the pandemic of COVID-19.

Summary: We described a challenge named “Diabetic Retinopathy (DR)—Grading and Image Quality Estimation Challenge” in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations. We discussed details of the top 3 algorithms in each sub-challenges. The weighted kappa for DR grading ranged from 0.93 to 0.82, and the accuracy for image quality evaluation ranged from 0.70 to 0.65. The results showed that image quality assessment can be used as a further target for exploration. We also have released the DeepDRiD dataset on GitHub to help develop automatic systems and improve human judgment in DR screening and diagnosis. The bigger picture: Diabetic retinopathy (DR) is the most common disease caused by diabetes. Challenges are held to address real-world issues encountered in the design of DR automated screening systems to advance the technology in this area. Thus, we described a challenge named ''Diabetic Retinopathy (DR)—Grading and Image Quality Estimation Challenge'' in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI 2020) for fundus image assessment and DR grading. The scientific community responded positively to the challenge. In the challenge, we provided a deep DR image dataset (DeepDRiD) containing regular DR images and ultra-widefield (UWF) DR images, both having image quality and DR grading diagnosis. We discussed details of the three best algorithms in each sub-challenges. The results by the top algorithms showed that image quality assessment can be used as a target for further exploration.

Summary: Emerging evidence shows that metabolic regulation may be a critical mechanism in B cell activation and function. As targets of several most widely used immunosuppressants, Ca2+ signaling and calcineurin may play an important role in regulating B cell metabolism. Here, we demonstrate that IP3R-mediated Ca2+ signaling and calcineurin regulate B cell proliferation and survival by activating metabolic reprogramming in response to B cell receptor (BCR) stimulation. Both IP3R-triple-knockout (IP3R-TKO) and calcineurin inhibition dramatically suppress the metabolic switch in oxidative phosphorylation and glycolysis of stimulated B cells through regulation of glucose uptake, glycolytic enzyme expression, and mitochondrial remodeling, leading to impaired cell-cycle entry and survival. In addition, IP3R-Ca2+ acts as a master regulator of the calcineurin-MEF2C-Myc pathway in driving B cell metabolic adaptations. As genetic defects of IP3Rs were recently identified as a new class of inborn errors of immunity, these results have important implications for understanding the pathogenesis of such diseases.

Faults are inevitable in building energy systems, such as heating, ventilation, air conditioning and refrigeration systems. With the increasing utilization of these systems, their fault detection and diagnosis has become more significant for maintaining their operation and performance. The rapid development of data analytics has led to fault detection and diagnosis using data-based algorithms considering building intelligence. Feature engineering, which focuses on generating near minimum and optimal model inputs from the original dataset, is a fundamental technique to balance efficiency and accuracy of fault detection and diagnosis models. The generated inputs are fault features which ensures a better description of fault operations owing to their stronger fault indicative capability than original data. However, since most feature engineering research has been implemented alongside the development of data-based algorithms, most studies fail to effectively distinguish and understand their roles in fault detection and diagnosis models. To fill this research gap, this study presented the problems and mechanisms associated with feature engineering, comprehensively reviewed about 200 papers on the topic of feature engineering for heating, ventilation, air conditioning and refrigeration system fault detection and diagnosis tasks over the past 15 years. Firstly, feature engineering studies are divided according to their feature-generation technique, into manual and automated techniques. Next, feature engineering studies are analysed in terms of data type, system type, fault type, operation statue, methods and fault features. The desirable characteristics of feature engineering works are then discussed in terms of their capability of rapidly generating indicative, decoupled, interpretable and new fault features to enhance fault detection and diagnosis accuracy; the efficiency, reliability and robustness of feature engineering process; the mutual adaptability between feature engineering and fault detection and diagnosis; and their widespread applicability for more fault types and system operations. The challenges affecting feature engineering studies are discussed in terms of data volume, data diversity and quality, along with the performance evaluation of feature engineering with respect to feature engineering algorithms, fault detection and diagnosis models, fault features and online application constraints. Future work should aim to design a hybrid, heuristic, robust and automated feature engineering strategy under the guidance of expert knowledge and practical application constraints that consider the relationships between fault-related features and real-time fault impacts with energy consumption, occupants' thermal comfort, etc.

This paper describes the flight path planning for unmanned aerial vehicles (UAVs) based on the advanced swarm optimization algorithm of the bat algorithm (BA) in a static environment. The main purpose of this work is that the UAVs can obtain an accident-free, shorter, and safer flight path between the starting point and the endpoint in the complex three-dimensional battlefield environment. Based on the characteristics of the standard BA and the artificial bee colony algorithm (ABC), a new modification of the BA algorithm is proposed in this work, namely, the improved bat algorithm integrated into the ABC algorithm (IBA). The IBA mainly uses ABC to modify the BA and solves the problem of poor local search ability of the BA. This article demonstrates the convergence of the IBA and performs simulations in MATLAB environment to verify its effectiveness. The simulations showed that the time required for the IBA to obtain the optimum solution is approximately 50% lower than the BA, and that the quality of the optimum solution is about 14% higher than the ABC. Furthermore, by comparing with other traditional and improved swarm intelligent path planning algorithms, the IBA can plan a faster, shorter, safer, accident-free flight path for UAVs. Finally, this article proves that IBA also has good performance in optimizing functions and has broad application potential.

BackgroundA regular task by developers and users of synthetic data generation (SDG) methods is to evaluate and compare the utility of these methods. Multiple utility metrics have been proposed and used to evaluate synthetic data. However, they have not been validated in general or for comparing SDG methods. ObjectiveThis study evaluates the ability of common utility metrics to rank SDG methods according to performance on a specific analytic workload. The workload of interest is the use of synthetic data for logistic regression prediction models, which is a very frequent workload in health research. MethodsWe evaluated 6 utility metrics on 30 different health data sets and 3 different SDG methods (a Bayesian network, a Generative Adversarial Network, and sequential tree synthesis). These metrics were computed by averaging across 20 synthetic data sets from the same generative model. The metrics were then tested on their ability to rank the SDG methods based on prediction performance. Prediction performance was defined as the difference between each of the area under the receiver operating characteristic curve and area under the precision-recall curve values on synthetic data logistic regression prediction models versus real data models. ResultsThe utility metric best able to rank SDG methods was the multivariate Hellinger distance based on a Gaussian copula representation of real and synthetic joint distributions. ConclusionsThis study has validated a generative model utility metric, the multivariate Hellinger distance, which can be used to reliably rank competing SDG methods on the same data set. The Hellinger distance metric can be used to evaluate and compare alternate SDG methods.

PurposeDrawing upon the stimulus-organism-response (S-O-R) model, this paper aims to investigate the effects of stores’ environmental components (i.e., lighting, cleanliness, scent, design, layout, music, and employee interaction) on Chinese consumers’ emotions and intentions to purchase luxury brands.Design/Methodology/ApproachData were collected from Chinese consumers who have purchased luxury brands from retail stores. Partial least squares-structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were employed for data analysis.FindingsThe results of PLS-SEM indicated that three dimensions of the store environment (i.e., music, scent, and employee interaction) directly and significantly influenced Chinese consumers’ emotions. However, fsQCA revealed greater heterogeneity among respondents by highlighting stores’ environmental components and Chinese consumers’ emotions.Originality/ValueThis study makes significant contributions to the marketing literature by examining the S-O-R model alongside fsQCA approach to conditionally predict outcomes in a luxury brand context. The present study may be one of the first to examine the effects of stores’ environmental components on Chinese consumers’ emotions and purchase intentions, applying both symmetrical test (PLS-SEM) and asymmetrical test (fsQCA) approaches to determine significant components of the store environment as well as component combinations that predict Chinese consumers’ emotions and purchase intentions.

For heating, ventilation and air-conditioning systems (HVACs), their operating performance depends largely on sensors and their accurate measurements. Various types of faults are inevitable to sensors amounted in HVACs during the system lifespan, which eventually makes system deviated from the normal operation. To explore sensor fault impacts on different HVACs, the thermostat offset fault was taken as an example. For the same office building in Wuhan, this study compared its influences on operational performance of three HVACs including the ground source heat pump (GSHP) system, the variable refrigerant flow (VRF) system and the chiller + boiler (CB) system. The three HVACs were simulated simultaneously by EnergyPlus for the same target building with same outdoor meteorological environment to make a relatively fair comparison. The thermostat offset fault was introduced into the three HVACs with bias amplitudes ranging from −5–5 °C at the interval of 1 °C. Further, the fault impacts were quantitatively analyzed from three aspects in terms of system energy consumption, operating performance and indoor thermal comfort. Results indicated that the thermostat offset fault with different bias amplitudes and offset directions have different impacts on the three systems. Compared with the other two systems, GSHP is less affected by the introduced thermostat offset fault.

There is lack of prospective evidence regarding vagal nerve stimulator (VNS) in younger children with intractable epilepsy. Here, we report the outcomes of using VNS in two pre-school patients for pediatric intractable epilepsy (VNS-PIE) study. Medical treatment was ineffective in both the patients, and they underwent VNS implantation. Seizure frequency, score on the Gesell scale, and heart rate variability (HRV) were assessed following VNS therapy. After 6 months VNS treatment, the seizure frequency in the two patients decreased by 50% from that at baseline, based on the records in their epileptic diary. Video electroencephalography (EEG) examinations showed that abnormal fast waves diminished in the background in Patient 1, and captured seizure frequency in Patient 2 remarkably decreased. The adaptability, language, and individual and social interaction on their Gesell scales increased slightly, suggesting that VNS had a positive effect on the development of these two children. Moreover, the changes in the different HRV indices indicated improved cardiac autonomic function. In conclusion, these two cases indicated that VNS may not only be a superior therapy for pre-school children with intractable epilepsy, but also may exert a positive effect on their mental development and cardiac autonomic function.

Abstract Background Soil and vegetation have a direct impact on the process and direction of plant community succession, and determine the structure, function, and productivity of ecosystems. However, little is known about the synergistic influence of soil physicochemical properties and vegetation features on vegetation restoration. The aim of this study was to investigate the co-evolution of soil physicochemical properties and vegetation features in the process of vegetation restoration, and to distinguish the primary and secondary relationships between soil and vegetation in their collaborative effects on promoting vegetation restoration in a subtropical area of China. Methods Soil samples were collected to 40 cm in four distinct plant communities along a restoration gradient from herb (4–5 years), to shrub (11–12 years), to Pinus massoniana coniferous and broadleaved mixed forest (45–46 years), and to evergreen broadleaved forest (old growth forest). Measurements were taken of the soil physicochemical properties and Shannon–Wiener index (SD), diameter at breast height (DBH), height (H), and biomass. Principal component analysis, linear function analysis, and variation partitioning analysis were then performed to prioritize the relative importance of the leading factors affecting vegetation restoration. Results Soil physicochemical properties and vegetation features showed a significant trend of improvement across the vegetation restoration gradient, reflected mainly in the high response rates of soil organic carbon (SOC) (140.76%), total nitrogen (TN) (222.48%), total phosphorus (TP) (59.54%), alkaline hydrolysis nitrogen (AN) (544.65%), available phosphorus (AP) (53.28%), species diversity (86.3%), biomass (2906.52%), DBH (128.11%), and H (596.97%). The soil properties (pH, SOC, TN, AN, and TP) and vegetation features (biomass, DBH, and H) had a clear co-evolutionary relationship over the course of restoration. The synergistic interaction between soil properties and vegetation features had the greatest effect on biomass (55.55%–72.37%), and the soil properties contributed secondarily (3.30%–31.44%). The main impact factors of biomass varied with the restoration periods. Conclusions In the process of vegetation restoration, soil and vegetation promoted each other. Vegetation restoration was the cumulative result of changes in soil fertility and vegetation features.

This paper considers the problem of the multiple traveling salesperson problem (MTSP) with multiple depots and closed path. We analyzed the advantages and disadvantages of Partheno-Genetic algorithm (PGA) in solving the MTSP. By integrating the reproduction mechanism in the invasive weed algorithm (IWO), we have solved the defect that the local information of individuals in the PGA population may be missing, and obtained the improved Partheno-Genetic algorithm with reproduction mechanism (RIPGA), which greatly improves the solution performance. By comparing the RIPGA with other GAs and other intelligent algorithms on a large number of traveling salesperson problem (TSP) test functions, we have well verified the solution performance of the RIPGA on the optimal solution, indicating that the algorithm can effectively avoid local convergence. At the same time, the algorithm is less affected by parameters and has good stability.

Aimed at the bottleneck of Firework Algorithm's performance and the problem that it is easy to fall into the local extremum, this paper introduces a chaotic map approach and analyzes its statistical characteristics. New hybrid chaotic systems and a chaotic perturbation operator are designed. Based on different chaotic map approaches, a new Chaotic Firework Algorithm (CFWA) is proposed and simulated by typical test functions. The results show that performance of the Chaotic Firework Algorithm is generally better than the original algorithms, and the chaotic hybrid approaches are superior to single chaotic approaches. In order to further verify the application of the proposed algorithm in practical engineering problems, CFWA is applied to Blind Source Separation (BSS) of radar signals. As a result, it does well in sorting observed signals, and has faster convergence and better sorting performance than the traditional algorithms. In this way, the CFWA extends its engineering application.

Optical orthogonal frequency-division multiplexing offset quadrature amplitude modulation (O-OFDM/OQAM) system relaxes the orthogonal condition of the sub-carriers from the complex domain to the real field. Inter-symbol-interference (ISI) and inter-carrier-interference (ICI) could be suppressed by using filter banks with promising time-frequency-localization (TFL) properties. Therefore, cyclic prefix (CP) inserted between consecutive OFDM blocks could be removed for O-OFDM/OQAM system to improve system spectral efficiency. When passing through fiber channel, O-OFDM/OQAM faces serious intrinsic imaginary interference (IMI) induced by chromatic dispersion (CD), and fiber nonlinear effect, which would deteriorate system performance evidently. Fiber nonlinear effect induced interference is a great impairment for long haul transmission O-OFDM/OQAM system, and can not be equalized directly by using nonlinear equalization method designed for traditional optical OFDM. There is still much room for improvement of nonlinear equalization method for O-OFDM/OQAM. In this paper, we systematically study Volterra expansion based nonlinear equalization method (VENE) for O-OFDM/OQAM. We theoretically deduce simplified Volterra series expansion nonlinear transmission matrix (SVEM) for O-OFDM/OQAM based on mathematic deduction. With SVEM, intra channel nonlinear effect induced distortions could be modeled and estimated. By using specially designed pilot blocks, we obtain approximate solution of SVEM and perform effective VENE with very limited complexities. As shown in multiple Montel Carlo simulation results, nonlinear robustness for O-OFDM/OQAM has been improved significantly thanks to VENE, with various transmission distances and system parameters.

Obstacle avoidance path planning capability, as one of the key capabilities of UAV (Unmanned Aerial Vehicle) to achieve safe autonomous flight, has always been a hot research topic in UAV research filed. As a commonly used obstacle avoidance path planning algorithm, RRT (Rapid-exploration Random Tree) algorithm can carry out obstacle avoidance path planning in real time and online. In addition, it can obtain the asymptotically optimal obstacle avoidance path on the premise of ensuring the completeness of probability. However, it has some problems, such as high randomness, slow convergence speed, long transit time, and curved flight trajectory, so that it cannot meet the flight conditions of the actual UAV. To solve these problems, the paper proposes an improved RRT algorithm. In the process of extending the random tree, ACO (ant colony optimization) is introduced to make the planning path asymptotically optimal. The optimized algorithm can set pheromones on the path obtained by RRT and select the next extension point according to the pheromone concentration. And then through a certain number of iterations, it converges to an ideal path scheme. In addition, this paper also uses MATLAB to verify the effectiveness and superiority of the algorithm: Although RRT is easy to fall into local optimization, since the optimization method in this paper can almost certainly converge to the optimal solution, when it is necessary to preplan the path before UAV takeoff, it can be used.

Aim: Modified Suanzaoren Decoction (MSZRD) is obtained by improving Suanzaoren Decoction (SZRT), a traditional Chinese herbal prescription that has been used to treat insomnia for more than thousands of years. Our previous study showed that MSZRD can improve the gastrointestinal discomfort related insomnia by regulating Orexin-A. This study is the first study to evaluate the effects and possible mechanisms of MSZRD in mice with insomnia caused by p-chlorophenylalanine (PCPA) combined with multifactor random stimulation. Methods: After 14 days of multifactor stimulation to ICR mice, a PCPA suspension (30 mg/mL) was injected intraperitoneally for two consecutive days to establish an insomnia model. Three different doses of MSZRD (3.6, 7.2, and 14.4 g/kg/day) were given to ICR mice for 24 days. The food intake and back temperature were measured, and behavioral tests and pentobarbital sodium-induced sleep tests were conducted. The levels of Orexin-A, corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and adrenocortical hormones (CORT) in the serum and 5-hydroxytryptamine (5-HT), dopamine (DA), and norepinephrine (NE) in hypothalamus were measured using enzyme-linked immunosorbent assay (ELISA) kits. The levels of γ-aminobutyric acid (GABA) and glutamic acid (Glu) were measured by high-performance liquid chromatography (HPLC). The expression of 5HT1A receptor (5-HTRIA) and orexin receptor 2 antibody (OX2R) was measured by Western blot (WB) and immunohistochemical staining (ICH). Hematoxylin and eosin (H&E) staining and Nissl staining were used to assess the histological changes in hypothalamus tissue. Results: Of note, MSZRD can shorten the sleep latency of insomnia mice (P

Progressive or chronic renal diseases arise from a process of destructive renal fibrosis. Therefore, the molecular basis of renal fibrosis has attracted increasing attention. In this investigation, we set out to elucidate the potential interaction among long non-coding RNA ENST00000453774.1 (lncRNA 74.1), microRNA-324-3p (miR-324-3p), and NRG1, and to investigate their roles in the context of cellular autophagy and renal fibrosis. We collected 30 renal fibrosis tissue samples for analysis. In other studies, HK-2 cells were stimulated with TGF-β1 to induce a cell model of renal fibrosis, followed by alteration on the expression of lncRNA 74.1, miR-324-3p, or NRG1, or by the addition of AKT activator SC79 in the HK-2 cells. The expression levels of lncRNA 74.1, miR-324-3p, NRG1, autophagy-related proteins (ATG5, ATG7, LC3II/I, and P62), and the corresponding fibrosis markers (Collagen I, Fibronectin, and α-SMA) were subsequently determined using various assay methods. In addition, the proportion of LC3 positive cells and number of autophagosomes were recorded. Results revealed that lncRNA 74.1 and NRG1 were poorly expressed and miR-324-3p was highly expressed in renal fibrosis tissues and modeled cells. LncRNA 74.1 could bind to miR-324-3p, which led to upregulated NRG1 expression and inhibition of the PI3K/AKT signaling pathway. Meanwhile, overexpression of lncRNA 74.1 or down-regulation of miR-324-3p increased the levels of ATG5, ATG7, LC3II, and LC3I, and decreased levels of P62, Collagen I, Fibronectin, and α-SMA, accompanied by elevated proportions of LC3 positive cells and autophagosomes. Findings concur in showing that lncRNA 74.1 could induce cellular autophagy and alleviate renal fibrosis by regulating the miR-324-3p-mediated NRG1/PI3K/AKT axis. This axis may thus present a potential molecular target in renal fibrosis treatment.

Dysregulation of cardiac transcription programs has been identified in patients and families with heart failure, as well as those with morphological and functional forms of congenital heart defects. Mediator is a multi-subunit complex that plays a central role in transcription initiation by integrating regulatory signals from gene-specific transcriptional activators to RNA polymerase II (Pol II). Recently, Mediator subunit 30 (MED30), a metazoan specific Mediator subunit, has been associated with Langer-Giedion syndrome (LGS) Type II and Cornelia de Lange syndrome-4 (CDLS4), characterized by several abnormalities including congenital heart defects. A point mutation in MED30 has been identified in mouse and is associated with mitochondrial cardiomyopathy. Very recent structural analyses of Mediator revealed that MED30 localizes to the proximal Tail, anchoring Head and Tail modules, thus potentially influencing stability of the Mediator core. However, in vivo cellular and physiological roles of MED30 in maintaining Mediator core integrity remain to be tested. Here, we report that deletion of MED30 in embryonic or adult cardiomyocytes caused rapid development of cardiac defects and lethality. Importantly, cardiomyocyte specific ablation of MED30 destabilized Mediator core subunits, while the kinase module was preserved, demonstrating an essential role of MED30 in stability of the overall Mediator complex. RNAseq analyses of constitutive cardiomyocyte specific Med30 knockout (cKO) embryonic hearts and inducible cardiomyocyte specific Med30 knockout (icKO) adult cardiomyocytes further revealed critical transcription networks in cardiomyocytes controlled by Mediator. Taken together, our results demonstrated that MED30 is essential for Mediator stability and transcriptional networks in both developing and adult cardiomyocytes. Our results affirm the key role of proximal Tail modular subunits in maintaining core Mediator stability in vivo.

Cardiolipin (CL) is a unique, tetra-acylated diphosphatidylglycerol lipid that mainly localizes in the inner mitochondria membrane (IMM) in mammalian cells and plays a central role in regulating mitochondrial architecture and functioning. A deficiency of CL biosynthesis and remodeling perturbs mitochondrial functioning and ultrastructure. Clinical and experimental studies on human patients and animal models have also provided compelling evidence that an abnormal CL content, acyl chain composition, localization, and level of oxidation may be directly linked to multiple diseases, including cardiomyopathy, neuronal dysfunction, immune cell defects, and metabolic disorders. The central role of CL in regulating the pathogenesis and progression of these diseases has attracted increasing attention in recent years. In this review, we focus on the advances in our understanding of the physiological roles of CL biosynthesis and remodeling from human patients and mouse models, and we provide an overview of the potential mechanism by which CL regulates the mitochondrial architecture and functioning.

Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices.Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model.Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1).Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.

Objective: Intractable epilepsy and uncontrolled seizures could affect cardiac function and the autonomic nerve system with a negative impact on children's growth. The aim of this study was to investigate the variability and complexity of cardiac autonomic function in pre-school children with pediatric intractable epilepsy (PIE).Methods: Twenty four-hour Holter electrocardiograms (ECGs) from 93 patients and 46 healthy control subjects aged 3–6 years were analyzed by the methods of traditional heart rate variability (HRV), multiscale entropy (MSE), and Kurths–Wessel symbolization entropy (KWSE). Receiver operating characteristic (ROC) curve analysis was used to estimate the overall discrimination ability. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) models were also analyzed.Results: Pre-school children with PIE had significantly lower HRV measurements than healthy controls in time (Mean_RR, SDRR, RMSSD, pNN50) and frequency (VLF, LF, HF, LF/HF, TP) domains. For the MSE analysis, area 1_5 in awake state was lower, and areas 6_15 and 6_20 in sleep state were higher in PIE with a significant statistical difference. KWSE in the PIE group was also inferior to that in healthy controls. In ROC curve analysis, pNN50 had the greatest discriminatory power for PIE. Based on both NRI and IDI models, the combination of MSE indices (wake: area1_5 and sleep: area6_20) and KWSE (m = 2, τ = 1, α = 0.16) with traditional HRV measures had greater discriminatory power than any of the single HRV measures.Significance: Impaired HRV and complexity were found in pre-school children with PIE. HRV, MSE, and KWSE could discriminate patients with PIE from subjects with normal cardiac complexity. These findings suggested that the MSE and KWSE methods may be helpful for assessing and understanding heart rate dynamics in younger children with epilepsy.

Abstract Background Epithelial ovarian cancer (EOC) is the majority ovarian cancer (OC) type with a poor prognosis. This present study aimed to investigate potential prognostic factors including albumin-to-fibrinogen ratio (AFR) for advanced EOC patients with neoadjuvant chemotherapy (NAC) followed by debulking surgery. Methods A total of 313 advanced EOC patients with NAC followed by debulking surgery from 2010 to 2017 were enrolled. The predictive value of AFR for the overall survival (OS) was evaluated by receiver operating characteristic (ROC) curve analysis. The univariate and multivariate Cox proportional hazards regression analyses were applied to investigate prognostic factors for advanced EOC patients. The association between preoperative AFR and progression free survival (PFS) or OS was determined via the Kaplan–Meier method using log-rank test. Results The ROC curve analysis showed that the cutoff value of preoperative AFR in predicting OS was determined to be 7.78 with an area under the curve (AUC) of 0.773 (P

When the machine is at high speed, serious problems occur, such as high frequency loss, difficult thermal management, and the rotor structural strength insufficiency. In this paper, the performances of two high-speed permanent magnet generators (HSP- MGs) with different rotational speeds and the same torque are compared and analyzed. The two-dimensional finite element model (FEM) of the 117 kW, 60 000 rpm HSPMG is established. By comparing a calculation result and test data, the accuracy of the model is verified. On this basis, the 40 kW, 20 000 rpm HSPMG is designed and the FEM is established. The relationship between the voltage regulation sensitivity and power factor of the two HSPMGs is determined. The influence mechanism of the voltage regulation sensitivity is further revealed. In addition, the air-gap flux density is decomposed by the Fourier transform principle, and the influence degree of different harmonic orders on the HSPMG performance is determined. The method to reduce the harmonic content is further proposed. Finally, the method to improve the HSPMG overload capacity is obtained by studying the maximum power. The research showed that the HSPMG at low speed (20 000 rpm) has high sensitivity of the voltage regulation, while the HSPMG at high speed (60 000 rpm) is superior to the HSPMG at low speed in reducing the harmonic content and increasing the overload capacity.