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Extracting genes involved in cancer lesions from gene expression data is critical for cancer research and drug development. the method of feature selection has attracted much attention in the field of bioinformatics. Principal Component Analysis (PCA) is a widely used method for learning low-dimensional representation. Some variants of PCA have been proposed to improve the robustness and sparsity of the algorithm. However, the existing methods ignore the high-order relationships between data. In this paper, a new model named Robust Principal Component Analysis via Hypergraph Regularization (HRPCA) is proposed. In detail, HRPCA utilizes L2,1-norm to reduce the effect of outliers and make data sufficiently row-sparse. And the Hypergraph Regularization is introduced to consider the complex relationship between data. Important information hidden in the data are mined, and this method ensures the accuracy of the resulting data relationship information. Extensive experiments on multi-view biological data demonstrate that the feasible and effective of the proposed approach.

The concept of electric-drive-reconstructed onboard charger (EDROC) potentially opens the door to harness the existing drivetrain in electric vehicle (EV) to do the job of battery charger, thus performing a remarkable value in commerce. In this paper, the pre- and post-fault operations of an EDROC is studied, which incorporates an asymmetrical six-phase permanent magnet synchronous machine (ASPMSM) drive. At first, the decomposed currents of machine in healthy and faulty cases are solved and their trajectories are depicted, indicating that a rotating field will exist if open-phase fault (OPF) occurs. Afterwards, a rotating field cancellation scheme based on the minimum copper loss criterion is proposed by rearranging stator currents, with two constraints under consideration that are responsible for generating pulsating field and balanced grid currents, respectively. Following this, a control scheme shared for both pre- and post-fault conditions is proposed, where the carrier-based modulation without any zero-sequence signal injection is employed. Finally, the proposed rotating field cancellation scheme is verified by experimental results.

Traditional predictive control schemes dominated the research field of numerous power electronic applications over the past years, since they usually lead to control solutions with good dynamic and steady results. However, these solutions strongly depend on the available knowledge of the control system (e.g., accurate modeling information), which often results in the lack of robustness in the presence of parametric uncertainties. Furthermore, unnecessary energy loss heavily correlates with high switching frequency, which directly affect efficiency. Aiming to cumbersome the aforementioned scarcities, this letter is concerned with a novel approach to the controller design for modular multilevel converter, which makes use of event-triggered neural predictor-based finite control-set model predictive control methodology under low switching frequency operation. The salient feature of the proposed controller is that the uncertainties and unnecessary energy loss in practical systems can be explicitly dealt with, while keeping the switching frequency in a low value. Finally, steady-state and transient-state performance tests together with the analysis of the experimental results confirm the interest of the proposal, and the results found are promising and motivate further research in this field.

To boost the object grabbing capability of underwater robots for open-sea farming, we propose a new dataset (UDD) consisting of three categories (seacucumber, seaurchin, and scallop) with 2,227 images. To the best of our knowledge, it is the first 4K HD dataset collected in a real open-sea farm. We also propose a novel Poisson-blending Generative Adversarial Network (Poisson GAN) and an efficient object detection network (AquaNet) to address two common issues within related datasets: the class-imbalance problem and the problem of mass small object, respectively. Specifically, Poisson GAN combines Poisson blending into its generator and employs a new loss called Dual Restriction loss (DR loss), which supervises both implicit space features and image-level features during training to generate more realistic images. By utilizing Poisson GAN, objects of minority class like seacucumber or scallop could be added into an image naturally and annotated automatically, which could increase the loss of minority classes during training detectors to eliminate the class-imbalance problem; AquaNet is a high-efficiency detector to address the problem of detecting mass small objects from cloudy underwater pictures. Within it, we design two efficient components: a depth-wise-convolution-based Multi-scale Contextual Features Fusion (MFF) block and a Multi-scale Blursampling (MBP) module to reduce the parameters of the network to 1.3 million. Both two components could provide multi-scale features of small objects under a short backbone configuration without any loss of accuracy. In addition, we construct a large-scale augmented dataset (AUDD) and a pre-training dataset via Poisson GAN from UDD. Extensive experiments show the effectiveness of the proposed Poisson GAN, AquaNet, UDD, AUDD, and pre-training dataset., Comment: 14 pages, 10 figures

The exploration of single cell RNA-sequencing (scRNA-seq) technology generates a new perspective to analyze biological problems. One of the major applications of scRNA-seq data is to discover subtypes of cells by cell clustering. Nevertheless, it is challengeable for traditional methods to handle scRNA-seq data with high level of technical noise and notorious dropouts. To better analyze single cell data, a novel scRNA-seq data analysis model called Maximum correntropy criterion based Non-negative and Low Rank Representation (MccNLRR) is introduced. Specifically, the maximum correntropy criterion, as an effective loss function, is more robust to the high noise and large outliers existed in the data. Moreover, the low rank representation is proven to be a powerful tool for capturing the global and local structures of data. Therefore, some important information, such as the similarity of cells in the subspace, is also extracted by it. Then, an iterative algorithm on the basis of the half-quadratic optimization and alternating direction method is developed to settle the complex optimization problem. Before the experiment, we also analyze the convergence and robustness of MccNLRR. At last, the results of cell clustering, visualization analysis, and gene markers selection on scRNA-seq data reveal that MccNLRR method can distinguish cell subtypes accurately and robustly.

In this article, we propose a novel predictive control framework for modular multilevel converter, which makes use of neural predictor-based low switching frequency finite control-set model predictive control methodology with respect to online weighting factors tuning subject to robustness characteristics. The main objectives of this article are to maintain a low switching frequency operation and to enhance the system robustness. Firstly, a modified cost function together with an online weighting factors auto-tuning technique, which has a good potential to fulfill the low frequency operation, is presented under different operating conditions. Next, we provide a framework to explicitly address parameter mismatches and unknown nonlinear system dynamics in this proposal by introducing the neural network predictor control technique. The novelty of contribution of this work lies not only in incorporating the proposed cost function with a predictor-based neural network design solution for satisfying the requirement of the low switching frequency and robustness, but also in the utilization of the auto-tuning technique for online determining the suitable weighting factors. Finally, we demonstrate the applicability of the proposed predictive control framework in comparison to state-of-the-art finite control-set model predictive control approaches under various operating conditions.

Using a new trademark-based product market competition measure and a novel trademark-merger data set over the period 1983–2016, we show that companies facing greater product market competition are more likely to be acquirers. We further show that postmerger, compared to their nonacquiring peers, acquirers consolidate their product offerings by discontinuing more existing product lines and developing fewer new product lines. Using a quasi-experiment based on bids withdrawn due to exogenous reasons helps us establish the causal effect of deal completion on product-market consolidation. We conclude that acquisitions create product market synergies by cutting overlapping product offerings to achieve cost efficiency.

Convolutional neural networks (CNNs) have become the critical technology to realize face detection and face recognition in the face tracking (FT) system. However, traditional CNNs usually have nontrivial computational time and high energy consumption, making them inappropriate to be deployed in the large-scale time-sensitive FT system. To address this challenge, we design an artificial intelligence and Internet of Things (AIoT) empowered edge-cloud collaborative computing (ECCC) system based on the energy-efficient field-programmable gate array (FPGA)-based CNN accelerators for the purpose of realizing a low-latency and low-power FT. First, we present the AIoT-empowered ECCC system architecture, which consists of an intelligent computing subsystem, an Internet-of-Things (IoT) subsystem, an edge-cloud collaborative subsystem, and an application subsystem. In what follows, we investigate the enabling technologies for these subsystems. Thereafter, we develop an FPGA-based hardware accelerator dedicated to the compact MobileNet CNN by using the hardware design techniques, such as systolic array, matrix tiling, fixed-point precision, and parallelism. Furthermore, we integrate the FPGA accelerators with CPUs and GPUs to build a context-aware CPU/GPU/FPGA heterogeneous computing system. Finally, we implement a delay-aware energy-efficient scheduling algorithm dedicated to this heterogeneous system. With the above hardware and software codesign mechanism, the energy cost and execution time of CNNs can be decreased significantly. The real-world experiments on the CPU/GPU/FPGA-based ECCC system proved the effectiveness of the proposed schemes in reducing the latency and improving the power efficiency of the FT system.

As we know, the fractal theory is an effective method for irregular image processing and analysis, which is difficult to implement using other baseline methods. However, Extreme Learning Machine (ELM) algorithm in combination with fractal theory can provide a better solution than the single fractal theory in image processing, transformation, data prediction, and so on. The features including fractal dimension, area ratio, and average perimeter of hematite, magnetite, calcium silicate, calcium ferrite, and stomata in the microstructure of irregular pellet images are extracted by artificial intelligence (AI) technologies. An AL-based ELM model suitable for the features of pellet images was constructed, the ELM model was optimized by regularization, and the feature parameters of pellet images were predicted by improved ELM algorithm, the optimal number of neuron nodes in the hidden layer was obtained for every mineral. The results show that the efficiency of model training and prediction in the feature for irregular pellet images is highly comparable to baseline methods. The method can be used as a reference in the intelligent prediction, control, and optimization of pellet image quality.

Magnetic anomaly detection (MAD) has been widely used for detecting the magnetic targets in many areas. In low signal-to-noise ratio (SNR) situations, the magnetic anomaly is usually buried in the magnetic noise, which leads to a decrease in the detection performance of traditional MAD methods. In order to improve the detection probability of MAD in low SNR, we propose an adaptive method of MAD using support vector machine (SVM). First, we design a detection model using SVM with radial basis function (RBF) kernel function. Meanwhile, we analyze the magnetic anomaly signal and determined the orthonormal basis function (OBF) energy and magnetic entropy as the features of the magnetic anomaly, which are as the input of the model. Then, the model is trained based on the data set with the magnetic signals. Finally, the trained model is applied to the raw magnetic signal to detect the anomaly. Compared to the traditional methods, the proposed method has a better performance and adaptive ability. The proposed method is validated by the real magnetic noise. The results show that the proposed method can reduce the case of false alarm and improve the detection performance in low SNR.

Aim: We investigated the efficacy and safety of hepatic artery infusion chemotherapy (HAIC) combined with anti-PD-1 immunotherapy and tyrosine kinase inhibitors (TKIs) for advanced hepatocellular carcinoma (HCC). Method: This retrospective study included HCC patients treated with HAIC, TKIs and anti-PD-1 antibodies between May 2019 and November 2020 in our hospital. Primary end points were progression-free survival and safety. Results: Twenty-seven advanced HCC patients were analyzed. The median follow-up was 12.9 months (range: 4.0–24.0 months) and the median progression-free survival was 10.6 months. The objective response rate and disease control rate were 63.0 and 92.6%, respectively. No treatment-related deaths occurred. Conclusion: In patients with advanced HCC, treatment with HAIC, anti-PD-1 antibodies and oral TKIs was effective and safe.

Identifying molecular features is an essential component of the management and targeted therapy of brain metastases (BMs). The molecular features are different between primary lung cancers and BMs of lung cancer. Here we report the DNA and RNA mutational profiles of 43 pathological samples of BMs. In addition to previously reported mutational events associated with targeted therapy, PTPRZ1-MET, which was previously exclusively identified in glioma, was present in two cases of BMs of lung cancer. Furthermore, MET exon 14 skipping may be more common (6/37 cases) in BMs of lung cancer than the frequency previously reported in lung cancer. These findings highlight the clinical significance of targeted DNA plus RNA sequencing for BMs and suggest PTPRZ1-MET and MET exon 14 skipping as critical molecular events that may serve as targets of targeted therapy in BMs.

Computational fluid dynamics (CFD) has been used successfully in cardiovascular system research to analyze the physiological processes inside vessels. We evaluated the hydraulic information of urine through the lower urinary tract in a patient with posterior urethral valve (PUV) before and after valve ablation by CFD. A set of models of the lower urinary tract were developed based on geometrical data obtained by cystoscopy and voiding cystourethrography. Simulated assumptions and conditions were applied according to prior studies and urodynamic results. We used Fluent CFD 19.0 (Ansys Inc., USA) to compute the velocity and pressure of the fluid regions. The simplification of Bernoulli's formula was applied afterward to calculate the hydraulic energy of different positions. The urine flow rates of the NORMALst, the PUVst, and the POSTst at 5000 Pa were 18.08 ml/s, 11.14 ml/s, and 12.16 ml/s, respectively. Precipitous pressure change was observed around the valve in the PUVst, and the abnormal change was concentrated in the dilated urethra in the POSTst. Major energy dissipations were generated around the valve and the dilated urethra in the PUVst. The energy loss that occurred in the dilated urethra did not improve after the operation. Our findings are probably indicative of the hydrodynamics changes in the dilated urethra in PUV and need to be confirmed through more improved CFD models in the future. CFD may revolutionize pediatric urologists' perception in the management of urinary disease.

Vibrio vulnificus (V. vulnificus) infection is rare but potentially fatal. This study explored new atypical manifestations and prognostic factors of V. vulnificus-infected patients during hospitalization. We retrospectively reviewed the medical records of 33 patients diagnosed with V. vulnificus infection in Guangdong Province, China between 2010 and 2020. Multiple logistic regression and receiver operating characteristic (ROC) curve analyses were performed. The new atypical manifestations included cholangitis, urinary tract infection, and suppurative otitis media. Eleven of the 33 (33.3%) V. vulnificus-infected patients eventually died. Univariate analysis showed that patients with cardio-cerebrovascular diseases, lower platelet counts, and higher levels of C-reactive protein and procalcitonin (PCT) had statistically higher mortality. However, multivariate analysis showed that only the PCT level (P = 0.036) was statistically significant. In addition, the area under the ROC value estimate for PCT was 0.8816 (95% confidence interval (CI), 0.759-1.000; P = 0.0009). More than half of the patients with V. vulnificus infection died when PCT was > 20 ng/mL, while no patient died when PCT was ≤ 20 ng/mL. This study found new atypical manifestations of V. vulnificus infection. In addition, PCT was an effective and independent predictor of mortality in patients with V. vulnificus infection, allowing clinicians to conduct early risk stratification and determine the best therapeutic strategies.

Purpose: To determine the safety and clinical outcomes of epidural anaesthesia (EA) relative to internal intercostal nerve block (INB) in Chinese patients with non-small cell lung cancer (NSCLC) who were undergoing non-intubated thoracoscopic lobectomy.Methods: Chinese patients with NSCLC (stage I or II) with no evidence of metastasis were given either EA or INB, with equal number of patients in both groups. The peri-operative outcomes determined were duration of anaesthesia /duration of surgery, SpO2/PaCO2 levels, cases of hypotension, and blood loss. The post-operative outcome indices measured were pain score (determined using visual analogue scale (VAS), post-operative complications, chest drainage, duration of hospital stay, and deaths/mortality.Multiple regression analysis was used to confirm the results obtained in this study by adjusting potential covariates. Peri-operative and post-operative complications were compared between the two groups. The results obtained from 220 patients were subjected to statistical analysis.Results: Peri-operative results showed that patients who underwent INB had shorter duration of anaesthesia (12.3 vs 31.4 min, p < 0.05) and shorter duration of surgery (164.4 vs 197.2 min, p < 0.05) than patients who underwent EA for non-intubated lobectomy. Post-operative results showed that patients who underwent INB had significantly lower number of post-operative complications than those who received EA (29 vs 44 %, p < 0.05). The most common post-operative complications among patients in both treatment groups were nausea, vomiting, emphysema and pulmonary complications. Patients who underwent INB had shorter hospital stay than those who underwent EA (5.1 vs 7.5 days, p < 0.05). These results were confirmed through multiple regression analysis.Conclusion: These findings favour the use of INB for regional anaesthesia in NSCLC patients undergoing non-intubated lobectomy.

Mutations in the Forkhead Box C1 (FOXC1) are known to cause autosomal dominant hereditary Axenfeld-Rieger syndrome, which is a genetic disorder characterized by ocular and systemic features including glaucoma, variable dental defects, craniofacial dysmorphism and hearing loss. Due to late-onset of ocular disorders and lack of typical presentation, clinical diagnosis presents a huge challenge. In this study, we described a pathogenic in-frame variant in FOXC1 in one 5-year-old boy who is presented with hypertelorism, pupil deformation in both eyes, conductive hearing loss, and dental defects. By whole exome sequencing, we identified a 3 bp deletion in FOXC1, c.516_518delGCG (p.Arg173del) as the disease-causing variant, which was de novo and not detected in the parents, and could be classified as a “pathogenic variant” according to the American College of Medical Genetics and Genomics guidelines. After confirmation of this FOXC1 variant, clinical data on Axenfeld-Rieger syndrome-associated clinical features were collected and analyzed. Furthermore, Although the affected individual present hearing loss, however, the hearing loss is conductive and is reversible during the follow-up, which might not linke to the FOXC1 variant and is coincidental. Routine examination of FOXC1 is necessary for the genetic diagnosis of hypertelorism-associated syndrome. These findings may assist clinicians in reaching correct clinical and molecular diagnoses, and providing appropriate genetic counseling.

This letter investigates the possibility of deploying a novel finite control-set model predictive control solution for solving the ongoing research challenges in predictive control regulated modular multilevel converter, i.e., model parameter sensitiveness and excessive computational burden as well as weighting factors selection. Specifically, it is realized by cascading a predictor-based neural network design, which enables a smooth and fast identification of system dynamics, and a computationally efficient finite-set predictive control, which is responsible for simplifying the rolling optimization and reducing the computational complexity. The main contribution of the proposed methodology relies on the fact that no knowledge of any model parameters and weighting factors in whole control process are required, which leads to a significant enhancement in the robustness and reliability of the control system in the presence of parametric uncertainties, while remaining computationally feasible. Finally, the stability analysis is given, and the proposed methodology is experimentally assessed for modular multilevel converter, where steady-state and transient-state performance tests confirm the interest of the proposal.

Background The spread of spinal anesthesia was influenced by many factors, and the effect of body height on spinal anesthesia is still arguable. This study aimed to explore the impact of height on the spread of spinal anesthesia and the stress response in parturients. Methods A total of ninety-seven parturients were allocated into two groups according to their height: the shorter group (body height was shorter than 158 cm) and taller group (body height was taller than 165 cm). Spinal anesthesia was performed with the same amount of 12 mg plain ropivacaine in mothers of different heights. The primary outcome of the study was the success or failure of the spinal anesthesia. The secondary outcomes of the study were stress response, time to T6 sensory level, the incidence of hypotension, the satisfaction of abdominal muscle relaxation and patient VAS scores. Results The rate of successful spinal anesthesia in the shorter group was significantly higher than that in the taller group (p = 0.02). The increase of maternal cortisol level in the shorter group was lower than that in the taller group at skin closure (p = 0.001). The incidence of hypotension (p = 0.013), time to T6 sensory block (p = 0.005), the quality of abdominal muscle relaxation (p p p p = 0.022), amniotic fluid index (p = 0.022) were significantly associated with successful spinal anesthesia. Conclusions It’s difficult to use a single factor to predict the spread of spinal anesthesia. Patient’s vertebral column length, amniotic fluid index and abdominal girth were the high determinant factors for predicting the spread of spinal anesthesia. Trials registration ChiCTR-ROC-17012030 (Chictr.org.cn), registered on 18/07/2017.

Non-negative matrix factorization (NMF) is a dimensionality reduction technique based on high-dimensional mapping. It can effectively learn part-based representations. In this paper, we propose a method called Dual Hyper-graph Regularized Supervised Non-negative Matrix Factorization (HSNMF). To encode the geometric information of the data, the hyper-graph is introduced into the model as a regularization term. The advantage of hyper-graph learning is to find higher order data relationship to enhance data relevance. This method constructs the data hyper-graph and the feature hyper-graph to find the data manifold and the feature manifold simultaneously. The application of hyper-graph theory in cancer datasets can effectively find pathogenic genes. The discrimination information is further introduced into the objective function to obtain more information about the data. Supervised learning with label information greatly improves the classification effect. Furthermore, the real datasets of cancer usually contain sparse noise, so the -norm is applied to enhance the robustness of HSNMF algorithm. Experiments under The Cancer Genome Atlas (TCGA) datasets verify the feasibility of the HSNMF method.

Background With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA–disease associations (MDAs) is expensive and time-consuming. Practitioners have shown growing interest in methods for predicting potential MDAs. In recent years, an increasing number of computational methods for predicting novel MDAs have been developed, making a huge contribution to the research of human diseases and saving considerable time. In this paper, we proposed an efficient computational method, named bipartite graph-based collaborative matrix factorization (BGCMF), which is highly advantageous for predicting novel MDAs. Results By combining two improved recommendation methods, a new model for predicting MDAs is generated. Based on the idea that some new miRNAs and diseases do not have any associations, we adopt the bipartite graph based on the collaborative matrix factorization method to complete the prediction. The BGCMF achieves a desirable result, with AUC of up to 0.9514 ± (0.0007) in the five-fold cross-validation experiments. Conclusions Five-fold cross-validation is used to evaluate the capabilities of our method. Simulation experiments are implemented to predict new MDAs. More importantly, the AUC value of our method is higher than those of some state-of-the-art methods. Finally, many associations between new miRNAs and new diseases are successfully predicted by performing simulation experiments, indicating that BGCMF is a useful method to predict more potential miRNAs with roles in various diseases.

Extreme learning machine (ELM) has fast learning speed and perfect performance, at the same time, ELM provides a unified learning framework with a widespread type of feature mappings which can be applied in multiclass classification applications directly. These advantages make ELM become one of the best classification algorithms, and ELM has attracted great attention in supervised learning and semi-supervised learning. However, noise and outliers of data are usually existed in the real world, which will affect the performance of ELM. To improve the robustness and classification performance of ELM, we propose the Kernel Risk-Sensitive Mean p-power Loss Based Hyper-graph Regularized Robust Extreme Learning Machine (KRP-HRELM) method. On the one side, as a nonlinear similarity measure defined in the reproducing kernel space, the kernel risk-sensitive mean p-power loss (KRP) can effectively weaken the negative effects caused by noise and outliers. Therefore, the KRP is introduced into ELM to enhance its robustness. Then, the application of hyper-graph can help the ELM to explore higher-order geometric structures among more sampling points, thereby obtaining more comprehensive data information. In addition, to obtain a more sparsity network model, the L2,1-norm is used to constrain the output weight. On the other side, improving the practical application ability of KRP-HRELM is also the focus of our research, so KRP-HRELM is extended to semi-supervised learning, which is called the semi-supervised KRP-HRELM (SS-KRP-HRELM). Notably, the results of the robustness experiment have proved that our method has extraordinary robustness. At the same time, by using four evaluation measures such as accuracy, recall, precision, and F1-measure, to evaluate the classification results, we can find that our method has obtained better classification performance than other advanced methods.

Purpose This paper aims to investigate the influence of over-allocation and under-allocation of family board seats on the corporate investment efficiency. Design/methodology/approach Based on the perspective of altruistic behavior, this paper theoretically analyzes the relationship between the preference of family board seats allocation and corporate investment efficiency, and designs the research. On this basis, we use STATA14.0 as an analysis tool to empirically test the relationship between the preference of family family board seats allocation and corporate investment efficiency, and consider the impact of different governance scenarios. Findings This study finds that firms with a higher over-allocation degree of family board seats invest more efficiently, evidenced by significantly suppressed over-investment rather than mitigated under-investment. However, we do not find evidence that the higher degree of under-allocation of family board seats contribute to lower corporate investment efficiency. Additionally, this study finds that the positive relationship between the over-allocation degree of family board seats and corporate investment efficiency is more pronounced for firms with higher separation of cash flow rights and control rights, and weaker regional law system environment. Our mechanism discussion shows that the higher over-allocation level of family board seats contributes to the mitigation of agency costs for family firms by reducing the tendency for non-family boards to vote “against board proposals” and the appropriation behavior of the controlling family, and eventually improving corporate investment efficiency. Originality/value This paper examines the relationship between the preference of family board seats allocation and corporate investment efficiency from the perspective of altruistic behavior. Unlike previous studies, this paper distinguishes the governance effects arising from over-allocation and under-allocation of family board seats. Additionally, different governance scenarios are incorporated into the decision-making mechanism of the board of family firms, and the influences of the divergence of cash-flow and control rights and a weaker regional law system on the governance effect of the preference of family board seat allocation are analyzed.

Purpose Stigma toward people with epilepsy (PWE) is common around the globe. Perceived stigma produced by mental or physical disorders may represent a significant risk factor for suicide.This study examines whether and how perceived stigma, unemployment and depression interact to influence suicidal risk in PWE. Methods A consecutive cohort of people with epilepsy (PWE) was recruited from the First Affiliated Hospital of Chongqing Medical University. Each patient completed the Stigma Scale for Epilepsy (SSE), the Neurological Disorders Depression Inventory for Epilepsy scale (NDDI-E) and the suicidality module of Mini-International Neuropsychiatric Interview(MINI) v.5.0.0. Spearman's correlation and moderated mediation analysis were used to examine the associations among perceived stigma, depression, unemployment and suicidal risk. Results Perceived stigma was positively associated with depression severity and suicidal risk. Depression severity mediated the association between perceived stigma and suicidal risk. The indirect effect of perceived stigma on suicidal risk through depression severity was positively moderated by unemployment. Conclusions The effect of perceived stigma on suicidal risk can be explained by the mediation of depression severity, At the same time, getting out from the shadow of perceived stigma may help reducing suicidal risk in PWE. In addition, improving employment status of PWE may attenuate the indirect effect of perceived stigma on suicidal risk through depression severity.

Background Cerebral fat embolism (CFE) is a subtype of fat embolism syndrome which tends to cause ischemic cerebral infarction. Fat embolism in the cerebral venous system have not been reported. We hereby present a rare case of fat embolus formed in the cerebral venous system 10 days after cosmetic surgery, and describe our management of this patient. Case presentation A 26-year-old woman with the disturbance of consciousness and recurrent convulsions of the right upper extremity over a 21-h period was admitted to our hospital. The patient was initially diagnosed with haemorrhagic infarction, and cerebral venous thrombosis (CVT) was suspected based on computed tomography (CT). A diagnosis of CFE was confirmed based on surgical findings. Breast and hip augmentation performed 10 days ago was considered the underlying cause. Drug-induced hypothermia, low molecular weight heparin, atorvastatin, dexamethasone, piperacillin/tazobactam, valproic acid, and mannitol were applied. On hospital day 30, she was discharged with a Montreal Cognitive Assessment score of 25. Conclusions Fat embolism can occur in the cerebral venous system, and may mimic CVT symptoms rather than CFE symptoms. Early identification of the nature of the embolus is essential. The use of heparin may prevent secondary thrombus formation, and accelerate fat embolus decomposition.

Background and aims Hyperhomocysteinemia (HHcy) is an independent risk factor for atherosclerosis and plaque vulnerability. The macrophage apoptosis mediated by endoplasmic reticulum (ER) stress plays an important role in the pathogenesis of HHcy-aggravated atherosclerosis. Endoplasmic reticulum oxidoreductase 1α (Ero1α) is critical for ER stress-induced apoptosis. We hypothesize that Ero1α may contribute to ER-stress induced macrophage apoptosis and plaque stability in advanced atherosclerotic lesions by HHcy. Methods Apoe−/− mice were maintained on drinking water containing homocysteine (Hcy, 1.8 g/L) to establish HHcy atherosclerotic models. The role of Ero1α in the atherosclerotic plaque stability, macrophage apoptosis and ER stress were monitored in the plaque of aortic roots in HHcy Apoe−/− mice with or without silence or overexpression of Ero1α through lentivirus. Mouse peritoneal macrophages were used to confirm the regulation of Ero1α on ER stress dependent apoptosis in the presence of HHcy. Results The atherosclerotic plaque vulnerability and macrophage apoptosis were promoted in Apoe−/− mice by high Hcy diet, accompanied by the upregulation of Ero1α expression and ER stress. Inhibition of Ero1α prevented macrophage apoptosis and atherosclerotic plaque vulnerability, and vice versa. Consistently, in mouse peritoneal macrophages, ER stress and apoptosis were attenuated by Ero1α deficiency, but enhanced by Ero1α overexpression. Conclusions Hcy via upregulating Ero1α expression activates ER stress-dependent macrophage apoptosis, so as to promote vulnerable plaque formation in atherosclerosis. Ero1α may be a potential therapeutic target for atherosclerosis induced by Hcy.

The successful applications of two-dimensional (2D) transition metal dichalcogenides highly rely on rational regulation of their electronic properties. The nondestructive and controllable doping strategy is of great importance to implement 2D materials in electronic devices. Herein, we propose a straightforward and effective method to realize controllable n-type doping in WSe2 monolayer by electron beam irradiation. Electrical measurements and photoluminescence (PL) spectra verify the strong n-doping in electron beam-treated WSe2 monolayers. The n-type doping arises from the generation of Se vacancies and the doping degree is precisely controlled by irradiation fluences. Due to the n-doping-induced narrowing of the Schottky barrier, the current of back-gated monolayer WSe2 is enhanced by an order of magnitude and a ∼8× increase in the electron filed-effect mobility is observed. Remarkably, it is a moderate method without significant reduction in electrical performance and severe damage to lattice structures even under ultra-high doses of irradiation.

Background D3 or complete mesocolic excision (CME) surgery has become a common surgical procedure for the treatment of colon cancer metastasis. Clinical misuse and overuse of lymph node dissection bring unnecessary burdens to patients. A detailed guidance for lymph node dissection in patients with T3 and T4 stage right colon cancer at different locations is urgently needed. Methods A retrospective study was performed. Patients received D3 or CME surgery were divided into ileocecal group, ascending colon group, and hepatic flexure group according to the 9th edition of the Japanese Society for Cancer of the Colon and Rectum guidelines. The distributions of lymph node metastases were analyzed according to tumor infiltration depth (T stage) and tumor location. Results The incidence of metastases in the paracolic area (or station), intermediate area, and main (or central) area was 38.4% (139/362), 12.7% (46/362), and 9.7% (35/362), respectively. The proportion of patients having No.206 and terminal ileum lymph nodes metastases was 7.7% (14/181) and 3.7% (9/244), respectively. No.206 lymph node metastasis is related to tumor location (χ2 = 7.955, p = 0.019) and degree of differentiation (χ2 = 18.99, p = 0.000), and terminal ileum lymph node metastasis is related to tumor location (χ2 = 6.273, p = 0.043). Patients with T3/T4 hepatic flexure cancer received radical right hemicolectomy in addition to No.206 lymph node dissection. Conclusion Radical right hemicolectomy and No.206 group lymph node dissection are necessary for T3 and T4 stage colon cancer therapy.

Epilepsy is a neurological disorder characterized by excessive neuronal discharge which results in many problems in terms of behavior, state of mind, consciousness, and can threaten the lives of patients. An automatic epileptic seizure detection method with graph-regularized non-negative matrix factorization (GNMF) and Bayesian linear discriminate analysis (BLDA) is presented in this paper. First, discrete wavelet decomposition is applied to analyze raw electroencephalogram (EEG) signals, and the normalization based on differential operator is used to guarantee the nonnegative constraint and reinforce the distinction between seizure and non-seizure signals. Then, GNMF is employed to dimensionality reduction and feature extraction for EEG data, which could capture a parts-based representation of samples and obtain more discriminative features. The EEG features are calculated and entered into the BLDA classifier for categorized results. The public Freiburg EEG database is used to evaluate the performance of the proposed seizure detection method. The results showed event-based sensitivity of 95.24%, epoch-based sensitivity of 93.20%, and a false-alarm rate of 0.5/h. These results demonstrate the potential clinical value of this method for automatic seizure detection.

Fault detection and diagnosis have always been the key techniques for safe and reliable operation of industrial processes. However, the high dimension and noise of process variables have brought gr...

The molecular subtyping of diffuse gliomas is important. The aim of this study was to establish predictive models based on preoperative multiparametric MRI. A total of 1016 diffuse glioma patients were retrospectively collected from Beijing Tiantan Hospital. Patients were randomly divided into the training (n = 780) and validation (n = 236) sets. According to the 2016 WHO classification, diffuse gliomas can be classified into four binary classification tasks (tasks I–IV). Predictive models based on radiomics and deep convolutional neural network (DCNN) were developed respectively, and their performances were compared with receiver operating characteristic (ROC) curves. Additionally, the radiomics and DCNN features were visualized and compared with the t-distributed stochastic neighbor embedding technique and Spearman’s correlation test. In the training set, areas under the curves (AUCs) of the DCNN models (ranging from 0.99 to 1.00) outperformed the radiomics models in all tasks, and the accuracies of the DCNN models (ranging from 0.90 to 0.94) outperformed the radiomics models in tasks I, II, and III. In the independent validation set, the accuracies of the DCNN models outperformed the radiomics models in all tasks (0.74–0.83), and the AUCs of the DCNN models (0.85–0.89) outperformed the radiomics models in tasks I, II, and III. DCNN features demonstrated more superior discriminative capability than the radiomics features in feature visualization analysis, and their general correlations were weak. Both the radiomics and DCNN models could preoperatively predict the molecular subtypes of diffuse gliomas, and the latter performed better in most circumstances. • The molecular subtypes of diffuse gliomas could be predicted with MRI. • Deep learning features tend to outperform radiomics features in large cohorts. • The correlation between the radiomics features and DCNN features was low.

With the rapid development of immunotherapy, the efficacy and feasibility of neoadjuvant immunotherapy for early resectable non–small-cell lung cancer (NSCLC) has been demonstrated. However, there are still difficulties and controversies in evaluating the efficacy of neoadjuvant immunotherapy. In our report, we described a 43-year-old female patient who was diagnosed with stage IIIA (cT1N2M0) pulmonary adenocarcinoma. After two cycles of neoadjuvant immunotherapy (sintilimab) combined with chemotherapy, according to imaging evaluation, the efficacy of the primary lesion was evaluated as stable disease and the mediastinal lymph nodes were evaluated as partial response. However, the postoperative pathological evaluation showed the primary lesion was pathological complete response and the mediastinal lymph nodes were major pathological response. This indicated that neoadjuvant chemo-immunotherapy was effective for both primary and mediastinal lymph nodes, but regression of the lesions was not synchronous. This study provided a complete process of neoadjuvant treatment, illustrating the effectiveness and safety of neoadjuvant chemo-immunotherapy to a certain extent. It is also suggested that the evaluation of neoadjuvant immunotherapy should be combined with imaging and pathology, and the primary tumor and lymph nodes should be evaluated, respectively.

Recently, extreme learning machine (ELM) has attracted a lot of attention due to its high performance and extreme speed. However, how to improve the robustness of ELM has always been a problem. Considering the problems of noise and outliers in the experimental data, in this paper, we introduce the kernel risk-sensitive mean p-power error (KRP) into ELM and propose a robust ELM method named kernel risk-sensitive mean p-power error based robust extreme learning machine (KRPELM), on the basis of the high efficiency and robustness of KRP. In KRPELM, KRP function instead of square loss is integrated into ELM as the loss function, which can improve the robustness of ELM to noise and outliers. We also propose an efficient iterative adjustment strategy to optimize KRPELM. Nine benchmark datasets are utilized to verify the classification performance of the proposed KRPELM. In addition, we apply the proposed method to the classification of cancer samples. The experimental results on five cancer gene expression datasets show that KRPELM can identify different cancer types more accurately.

Non-negative matrix factorization (NMF) has become one of the most powerful methods for clustering and feature selection. However, the performance of the traditional NMF method severely degrades when the data contain noises and outliers or the manifold structure of the data is not taken into account. In this article, a novel method called correntropy-based hypergraph regularized NMF (CHNMF) is proposed to solve the above problem. Specifically, we use the correntropy instead of the Euclidean norm in the loss term of CHNMF, which will improve the robustness of the algorithm. And the hypergraph regularization term is also applied to the objective function, which can explore the high-order geometric information in more sample points. Then, the half-quadratic (HQ) optimization technique is adopted to solve the complex optimization problem of CHNMF. Finally, extensive experimental results on multi-cancer integrated data indicate that the proposed CHNMF method is superior to other state-of-the-art methods for clustering and feature selection.

Machine learning techniques have been widely adopted to assist in data analysis in a variety of Internet of Things (IoT) systems. To enable flexible use of trained learning models, one viable solution is to leverage all categories of data from different applications to train a general model, which can be further tuned for applications through the tuning process. This process incurs additional overhead at the start, but makes later revision and iteration faster and more flexible. Nonetheless, due to limited computing capabilities, IoT devices cannot handle the training process of large data sets. To address this issue, in this article, we propose a general framework to adopt transfer learning in Industrial IoT (IIoT) systems. In our study, we categorize the application space of applying transfer learning to IIoT systems into four generic scenarios: 1) centralized transfer learning with large data sets; 2) distributed transfer learning with large data sets; 3) centralized transfer learning with small data sets; and 4) distributed transfer learning with small data sets. According to the characteristics of each scenario, we design workflows to apply the transfer learning technique. To demonstrate the efficacy of the approach, we apply our transfer learning technique to the task of IIoT component recognition. We use the known VGG-16 model and leverage T-Less industrial data sets to evaluate the performance of our approach in different scenarios. Via performance evaluation, our experimental results confirm the efficacy of our approach, which can not only reduce training time but also achieve higher accuracy, compared with the classical convolutional neural network (CNN) approach.

BACKGROUND There is a lack of data on drug-related problems (DRPs) occurring in nephrology department in China. The objective of this study was to identify and categorize the types and causes of DRPs and to assess their severity. DRPs were examined by clinical pharmacists and the results of their interventions were rated. METHODS Clinical pharmacists reviewed all medication orders for patients and documented clinical pharmacy services within a nine-month study period. The Pharmaceutical Care Network Europe (PCNE) classification (Version 9.00) was used to identify DRPs. Our Primary outcomes measured the number, causes, types, potential hazards of DRPs and the types and success rate of intervention. RESULTS Admission medication reconciliation data of 113 patients with chronic kidney disease (CKD) were collected and all of the medications were reviewed retrospectively. Exclude 26 patients who did not occurred DRPs, 87 patients (77%) identified 101 DRPs. The average DRP number per patient was 1.16. The most common type of problem was "treatment effectiveness P1" (84.16%; 85/101). The most common causes were "drug selection C1" (36.00%; 45/125), "dose selection C3" (29.60%; 37/125), and "patient related C7" (26.40%; 33/125). Clinical pharmacists totally proposed 249 interventions, of which 190 (76.31%) were fully accepted and implemented. CONCLUSIONS DRPs are common among CKD patients in the nephrology department. Hence the necessity for pharmaceutical care to be improved to ensure the ongoing safety of patients.

Hepatitis B virus (HBV) infection is a significant global health problem and > 42–52% of patients are infected during perinatal period. Tenofovir alafenamide fumarate (TAF) and tenofovir disoproxil fumarate (TDF) have been widely recognized as the main compounds used for antiviral treatment of hepatitis B. The present study evaluated the efficacy and safety of TAF in reducing HBV vertical transmission. A total of 72 pregnant women, who met the inclusion criteria, were randomly divided into the TDF (300 mg/day, n = 36) and TAF (25 mg/day, n = 36) groups. Clinical and laboratory data were analyzed and compared between the two groups. No significant differences in alanine aminotransferase, total bilirubin, blood creatinine and blood urea nitrogen levels were noted between the two groups after treatment. The serum HBV DNA viral load and hepatitis B e antigen (HBeAg) levels of the two groups were significantly decreased following treatment, whereas the difference between the two groups was not statistically significant. The levels of urine retinol-binding protein and β2-microglobulin had no significant change after TAF treatment (p > 0.05), but increased significantly after TDF treatment (p

Prospective data are limited regarding dynamic adulthood weight changes and hepatocellular carcinoma (HCC) risk. We included 77,238 women (1980–2012) and 48,026 men (1986–2012), who recalled young-adult weight [age 18 years (women); 21 years (men)], and provided biennially updated information regarding weight, body mass index (BMI), and comorbidities. Overall adulthood weight change was defined as the difference in weight (kilograms) between young-adulthood and present. Using Cox proportional hazards models, we calculated multivariable adjusted HRs (aHR) and 95% confidence intervals (CI). Over 3,676,549 person-years, we documented 158 incident HCC cases. Elevated HCC risk was observed with higher BMI in both young-adulthood and later-adulthood [continuous aHRs per each 1 unit = 1.05; 95% CI = 1.02–1.09 (Ptrend = 0.019), and 1.08; 95% CI = 1.06–1.10 (Ptrend = 0.004), respectively]. Moreover, overall adulthood weight gain was also significantly associated with increased HCC risk (aHR per each 1-kg increase = 1.03; 95% CI = 1.01–1.08; Ptrend = 0.010), including after further adjusting for young-adult BMI (Ptrend = 0.010) and later-adult BMI (Ptrend = 0.008). Compared with adults with stable weight (±5 kg), the multivariable-aHRs with weight gain of 5– Prevention Relevance: Our data suggest that maintaining a stable weight during adulthood, specifically by preventing weight gain, could represent an important public health strategy for the prevention of hepatocellular carcinoma.

In traditional sequencing techniques, the different functions of cells and the different roles they play in differentiation are often ignored. With the advancement of single-cell RNA sequencing (scRNA-seq) techniques, scientists can measure the gene expression value at the single-cell level, and it is helping to understand the heterogeneity hidden in cells. One of the most powerful ways to find heterogeneity is using the unsupervised clustering method to get separate subpopulations. In this paper, we propose a novel clustering method Similarity and Dissimilarity Regularized Nonnegative Matrix Factorization (SDCNMF) that simultaneously impose similarity and dissimilarity constraints on low-dimensional representations. SDCNMF both considers the similarity of closer cells and the dissimilarity of cells that are farther away. It can not only keep the similar cells getting closer in low-dimensional space, but also can push the dissimilar cells away from each other. We test the validity of our proposed method on five scRNA-seq datasets. Clustering results show that SDCNMF is better than other comparative methods, and the gene markers we find are also consistent with previous studies. Therefore, we can conclude that SDCNMF is effective in scRNA-seq data analysis. This paper proposes a novel clustering method Similarity and Dissimilarity Regularized Nonnegative Matrix Factorization (SDCNMF) that simultaneously impose similarity and dissimilarity constraints on low-dimensional representations. SDCNMF both considers the similarity of closer cells and the dissimilarity of cells that are farther away. It can not only keep the similar cells getting closer in low-dimensional space, but also can push the dissimilar cells away from each other. Clustering results show that SDCNMF is better than other comparative methods, and the gene markers we find are also consistent with previous studies.

Wen-Guang Zhang,1 Jia-Wei Liu,2 Su-Yun Yang,3 Qiao-Qian Wang,4 Chen-Xing Liu,2 Yao Li2 1Department of Nursing, The First Hospital of Shanxi Medical University, Taiyuan, 030000, People’s Republic of China; 2School of Nursing, Shanxi Medical University, Taiyuan, 030000, People’s Republic of China; 3Department of Nuclear Medicine, The First Hospital of Shanxi Medical University, Taiyuan, 030000, People’s Republic of China; 4Department of Hypertension, The First Hospital of Shanxi Medical University, Taiyuan, 030000, People’s Republic of ChinaCorrespondence: Su-Yun YangDepartment of Nuclear Medicine, The First Hospital of Shanxi Medical University, Room 601, Unit 8, Building 1, No. 8 Wenyuan Lane, Yingze District, Taiyuan, 030000, People’s Republic of ChinaTel +8613603512029Email syy_dr09sun@163.comObjective: This study aimed to evaluate the effect of closed-loop management on nursing disruption risk.Methods: Using a quasi-experimental research method, convenient sampling was used to extract 20 nurses working at our hospital as the research objects. The control group members were selected from January to March 2018 via the traditional method, and the experimental group members were selected from April to June 2018 via the closed-loop management method. At three months before and after the implementation of the management model, a self-designed quantitative test form and satisfaction questionnaire were used to analyze the frequency of nursing disruption events, the accuracy rate of doctors’ advice, the average drug delivery time of the static distribution center, the implementation rate of personal digital assistant (PDA) code scanning, and the report rate of risk-outcome nursing disruption events.Results: After the implementation of the management model, the frequency of nursing disruptions and average drug delivery time of the static distribution center were significantly lower than before, and the differences were statistically significant (p < 0.05). Moreover, the accuracy rate of doctors’ advice, the implementation rate of PDA code scanning, and the reporting rate of risk-outcome nursing disruption events were significantly higher than before, and these differences were statistically significant as well (p < 0.05).Conclusion: The application of a closed-loop management model could significantly reduce the occurrence and optimize the outcomes of nursing disruption events and improve the work processes of medical care.Keywords: closed-loop management, nursing interruption, risk control, personal digital assistant, PDA, management mode

Skeleton based human action recognition has attracted more and more attentions recently thanks to the accessibility of depth sensors and the development of pose estimation techniques. Conventional approaches such as convolutional neural networks usually model skeletons with grid-shaped representations, which cannot explicitly explore the dependency between two correlated joints. In this paper, we treat the skeleton as a single graph with joints as nodes and bones as edges. Based on the skeleton graph, the improved graph convolutional network called adaptive multi-view graph convolutional networks(AMV-GCNs) is proposed to deal with skeleton based action recognition. We firstly construct a novel skeleton graph and two kinds of graph nodes are defined to model the spatial configuration and temporal dynamics respectively. Then the generated graphs along with feature vectors on graph nodes are fed into AMV-GCNs. In AMV-GCNs, an adaptive view transformation module is designed to reduce the impact of view diversity. Proposed module can automatically determine suitable viewpoints and transform skeletons to new representations under those viewpoints for better recognition. Further, we employ multiple GCNs based streams to utilize and learn action information from different viewpoints. Finally, the classification scores from multiple streams are fused to provide the recognition result. Extensive experimental evaluations on four challenging datasets, NTU RGB+D 60, NTU RGB+D 120, Northwestern-UCLA and UTD-MHAD, demonstrate the superiority of our proposed network.

Background:. The neoadjuvant chemotherapy is increasingly used in advanced gastric cancer, but the effects on safety and survival are still controversial. The objective of this meta-analysis was to compare the overall survival and short-term surgical outcomes between neoadjuvant chemotherapy followed by surgery (NACS) and surgery alone (SA) for locally advanced gastric cancer. Methods:. Databases (PubMed, Embase, Web of Science, Cochrane Library, and Google Scholar) were explored for relative studies from January 2000 to January 2021. The quality of randomized controlled trials and cohort studies was evaluated using the modified Jadad scoring system and the Newcastle-Ottawa scale, respectively. The Review Manager software (version 5.3) was used to perform this meta-analysis. The overall survival was evaluated as the primary outcome, while perioperative indicators and post-operative complications were evaluated as the secondary outcomes. Results:. Twenty studies, including 1420 NACS cases and 1942 SA cases, were enrolled. The results showed that there were no significant differences in overall survival (P = 0.240), harvested lymph nodes (P = 0.200), total complications (P = 0.080), and 30-day post-operative mortality (P = 0.490) between the NACS and SA groups. However, the NACS group was associated with a longer operation time (P

Background/AimsThis study aims to assess the contribution of biallelicCPAMD8variants in patients with different forms of glaucoma, especially primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG), based on a systematic analysis of exome sequencing (ES).MethodsPotentially pathogenicCPAMD8variants were selected from the ES data of 5307 subjects with various eye conditions through multiple bioinformatics analyses. Of the 5307 subjects, 1221 probands had different forms of primary glaucoma. The genotype–phenotype correlation was assessed by a systematic review of biallelicCPAMD8variants that including our data and data from the literature. The expression profile ofCPAMD8in human tissues was determined at the mRNA and protein levels.ResultsBiallelicCPAMD8variants, including one frameshift and six missense variants, were exclusively present and significantly enriched in patients with glaucoma (one with juvenile open-angle glaucoma (JOAG), two with POAG and two with PACG) compared with none of the 4086 probands with other eye conditions in this cohort (p=4.1E-07). The effect of variants in these patients is relatively mild compared with that reported in patients with anterior segment dysgenesis or primary congenital glaucoma.CPAMD8mRNA was highly expressed in the optic nerve, ciliary body, retina and iris, whereas the CPAMD8 protein was mainly detected in the nonpigmented epithelium of the iris and ciliary process, determined by immunohistochemistry.ConclusionsThe data from this study not only provide further evidence to support the association of biallelicCPAMD8variants with JOAG but also suggest that biallelicCPAMD8variants might be associated with POAG and PACG.

INTRODUCTION Numerous studies have demonstrated that end-stage renal disease (ESRD) patients undergoing hemodialysis (HD) have high myocardial fibrosis (MF) levels. Circulating fibrocytes are bone marrow-derived circulating mesenchymal progenitors, and new evidence suggests a vital role for fibrocytes in the development of MF. This study aimed to investigate whether fibrocyte levels are elevated in patients undergoing HD and its influence factors. METHODS We carried out a flow cytometry analysis to measure the proportion of peripheral blood circulating fibrocytes in a cohort of 126 healthy control individuals and 161 subjects with HD. Cardiac function and morphology were assessed by electrocardiogram and transthoracic echocardiogram. FINDINGS Compared to healthy controls, individuals with ESRD had significantly higher levels of circulating fibrocytes. There was a strong correlation between the frequency of fragmented QRS (fQRS) and circulating fibrocytes in HD patients. Furthermore, higher fibrocytes correlated to increasing age, dialysis age, left ventricular mass index (LVMI), left ventricular ejection fraction (LVEF), and hypertension complication. On multivariate analysis, the dialysis age [odds ratio (OR) 1.011, 95% confidence interval (CI) 1.003-1.019, p = 0.006], LVMI (OR 1.012, 95% CI 1.002-1.022, p = 0.016), hypertension (OR 4.303, 95% CI 1.129-16.406, p = 0.033), and fQRS (OR 2.439, 95% CI 1.049-5.262, p = 0.038) were significant independent predictors of fibrocytes percentage. DISCUSSION We concluded that bone marrow-derived circulating fibrocytes were significantly increased in ESRD patients with HD compared with controls. Our data suggested that these cells might play essential roles during MF in HD patients.

After fitting a topic model to 40,927 COVID-19–related paragraphs in 3,581 earnings calls over the period Jan. 22–Apr. 30, 2020, we obtain firm-level measures of exposure and response related to COVID-19 for 2,894 U.S. firms. We show that despite the large negative impact of COVID-19 on their operations, firms with a strong corporate culture outperform their peers without a strong culture. Moreover, these firms are more likely to support their community, embrace digital transformation, and develop new products than those peers. We conclude that corporate culture is an intangible asset designed to meet unforeseen contingencies as they arise.

Abnormal bowel movements have been related to a variety of hepatocellular carcinoma (HCC) risk factors such as dyslipidemia, diabetes and altered metabolism of bile acids and gut microbiota. However, little is known about whether bowel movement frequency affects the risk of developing HCC. We followed 88 123 women in the Nurses' Health Study (NHS) and 28 824 men in the Health Professionals Follow-up Study (HPFS) for up to 24 years. The Cox proportional hazards regression model was used to calculate multivariable hazard ratios (HRs) and confidence intervals (95%CI). We documented 101 incident HCC cases. Compared to those with daily bowel movements, participants with bowel movement more than once per day had a multivariable HR of 1.93 (95%CI: 1.18 to 3.16) in the pooled cohorts. For the same comparison, the positive association appeared stronger for men (2.72, 95% CI: 1.14 to 6.44) than for women (1.63, 95% CI: 0.87 to 3.06) but there was no statistically significant heterogeneity by sex (P-value = .31). We found null associations between bowel movement every 2 days or less and the risk of HCC (HR = 1.05, 95%CI: 0.62 to 1.79). The HR (95%CI) for participants who used laxatives regularly relative to those who never used laxatives was 1.00 (0.64 to 1.55). Our results suggest participants with bowel movement more than once daily is associated with a higher risk of developing HCC compared to those with daily bowel movements. These findings need to be confirmed and potential mechanisms underlying this association need to be elucidated.

Blockchain has been regarded as an emerging global technological phenomenon. This study uses the patent analysis method to compare the development of blockchain technology in China and the USA. An overview of blockchain policies in China and the USA is presented. Our analyses suggest that policies related to the blockchain stimulate the number of blockchain patent applications and create regional innovation in China. The innovation capability of Chinese enterprises has been more affected by these policies than that of the USA, which is reflected in the fact that Chinese enterprises have become key players in China and actively carry out patent layout in the USA. Although the developmental trend of blockchain technology in China and the USA is almost identical, the USA attaches more importance to safety technology, whereas China pays more attention to the application technology based on the differences in policies.

High-throughput sequencing of single-cell gene expression reveals a complex mechanism of individual cell's heterogeneity in a population. An important purpose for analyzing single-cell RNA sequencing (scRNA-seq) data is to identify cell subtypes and functions by cell clustering. To deal with high levels of noise and cellular heterogeneity, we introduced a new single cell data analysis model called Adaptive Total-Variation Regularized Low-Rank Representation (ATV-LRR). In scRNA-seq data, ATV-LRR can reconstruct the low-rank subspace structure to learn the similarity of cells. The low-rank representation can not only segment multiple linear subspaces, but also extract important information. Moreover, adaptive total variation also can remove cell noise and preserve cell feature details by learning the gradient information of the data. At the same time, to analyze scRNA-seq data with unknown prior information, we introduced the maximum eigenvalue method into the ATV-LRR model to automatically identify cell populations. The final clustering results show that the ATV-LRR model can detect cell types more effectively and stably.

Background. High power shorter duration (HPSD) ablation may lead to safe and rapid lesion formation. However, the optimal radio frequency power to achieve the desired ablation index (AI) or lesion size index (LSI) is insubstantial. This analysis aimed to appraise the clinical safety and efficacy of HPSD guided by AI or LSI (HPSD-AI or LSI) in patients with atrial fibrillation (AF). Methods. The Medline, PubMed, Embase, Web of Science, and the Cochrane Library databases from inception to November 2020 were searched for studies comparing HPSD-AI or LSI and low power longer duration (LPLD) ablation. Results. Seven trials with 1013 patients were included in the analysis. The analyses verified that HPSD-AI or LSI revealed benefits of first-pass pulmonary vein isolation (PVI) (RR: 1.28; 95% CI: 1.05–1.56, P = 0.01) and acute pulmonary vein reconnection (PVR) (RR: 0.65; 95% CI: 0.48–0.88, P = 0.005) compared with LPLD. HPSD-AI or LSI showed higher freedom from atrial tachyarrhythmia (AT) (RR = 1.32, 95% CI: 1.14–1.53, P = 0.0002) in the subgroup analysis of studies with PVI ± (with or without additional ablation beyond PVI). HPSD-AI or LSI could short procedural time (WMD: −22.81; 95% CI, −35.03 to −10.60, P = 0.0003), ablation time (WMD: −10.80; 95% CI: −13.14 to −8.46, P