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Sonodynamic therapy (SDT) represents a promising modality that provides the possibility of non-invasively eliminating solid tumors in a site-directed manner. In light of the complexity and heterogeneity of tumors, more and more studies are attempting to combine SDT with other therapeutic methods so as to achieve better tumor treatment effect, which sheds new light on the potential of SDT-based synergistic therapeutics. Herein, the representative studies of SDT-instructed multimodal synergistic cancer therapy are comprehensively presented, such as sono-chemotherapy, sono-radiotherapy, sono-immunotherapy, and sono-chemodynamic therapy, etc., and their incorporate mechanisms are discussed in detail. The current challenges and future prospects to promote the advanced development of SDT-based nanomedicines in this burgeoning research field are highlighted. It is believed that such an emerging synergistic therapeutic modality based on SDT will play a more significant role in the field of tumor precision treatment medicine.

The liver is the main target organ for hepatitis viruses and the vital organ for alcohol metabolism. These two factors of viral hepatitis and alcohol abuse in combination can exert dual harmful actions, leading to enhanced damage to the liver. Epidemiological studies have revealed a higher prevalence of hepatitis C virus (HCV) infection among alcoholics than the general population. The interaction of alcohol with viral hepatitis [e.g., hepatitis B virus (HBV), HCV] and the underlying mechanisms are not fully understood. The effects of alcohol on viral hepatitis include promoted viral replication, weakened immune response, and increased oxidative stress. Clinically, alcohol abuse is correlated with an increased risk of developing end-stage liver cirrhosis and hepatocellular carcinoma in patients with chronic hepatitis B and C, suggesting that the combination of alcohol and HBV/HCV lead to more severe liver damage. The influence of mild to moderate alcohol drinking on the HBV-induced liver fibrosis, cirrhosis, and hepatocellular carcinoma among patients infected with HBV remains unclear. Unlike HBV infected patients, no safe level of alcohol intake has been established for patients with HCV. Even light to moderate alcohol use can exert a synergistic effect with viral hepatitis, leading to the rapid progression of liver disease. Furthermore, interferon-based therapy is less effective in alcohol drinkers than in control patients, even after abstinence from alcohol for a period of time. Therefore, abstaining from alcohol is highly recommended to protect the liver, especially in individuals with HBV/HCV infection, to improve the clinical efficacy of antiviral treatment and prevent the rapid progression of chronic viral hepatitis.

The ground-breaking research on the electricity sector and its effects on energy efficiency (EE) in advanced and emerging economies are crucial in developing a futuristic pathway, leading to self-sustainable energy use. Therefore, this research investigates the functions of sustainable investments to ensure EE in advanced and emerging economies in a decade, ranging from 2008 to 2018, by applying the data envelopment analysis (DEA) method. The findings reveal a lower degree of EE in advanced and emerging economies (0.44), discovering only seven nations, saving energy with DEA and the West Asia and North Africa groups as the top energy savers. Moreover, the mean EE ratio of the advanced and emerging countries plummeted, dating back to 2013, whereas financial investment positively impacts EE. Similarly, the study perceives how open structures in the nations' economic systems can advance ecological conditions by reducing pollution levels and graft enable EE and reduce pollution levels. Simultaneously, natural resources and technological advancements can accelerate the course of EE and ecological dimensions. Therefore, analysis of causality reinforce the response hypotheses between EE, Environmental carbon emissions levels, financial advancements, graft control, proceeds from mineral resources, technological creativity, commerce, and localization of industries, respectively.

Andrographis paniculata (Burm. f.) Wall. ex Nees, a renowned herb medicine in China, is broadly utilized in traditional Chinese medicine (TCM) for the treatment of cold and fever, sore throat, sore tongue, snake bite with its excellent functions of clearing heat and toxin, cooling blood and detumescence from times immemorial. Modern pharmacological research corroborates that andrographolide, the major ingredient in this traditional herb, is the fundamental material basis for its efficacy. As the main component of Andrographis paniculata (Burm. f.) Wall. ex Nees, andrographolide reveals numerous therapeutic actions, such as antiinflammatory, antioxidant, anticancer, antimicrobial, antihyperglycemic and so on. However, there are scarcely systematic summaries on the specific mechanism of disease treatment and pharmacokinetics. Moreover, it is also found that it possesses easily ignored security issues in clinical application, such as nephrotoxicity and reproductive toxicity. Thereby it should be kept a lookout over in clinical. Besides, the relationship between the efficacy and security issues of andrographolide should be investigated and evaluated scientifically. In this review, special emphasis is given to andrographolide, a multifunctional natural terpenoids, including its pharmacology, pharmacokinetics, toxicity and pharmaceutical researches. A brief overview of its clinical trials is also presented. This review intends to systematically and comprehensively summarize the current researches of andrographolide, which is of great significance for the development of andrographolide clinical products. Noteworthy, those un-cracked issues such as specific pharmacological mechanisms, security issues, as well as the bottleneck in clinical transformation, which detailed exploration and excavation are still not to be ignored before achieving integration into clinical practice. In addition, given that current extensive clinical data do not have sufficient rigor and documented details, more high-quality investigations in this field are needed to validate the efficacy and/or safety of many herbal products.

In order to improve the effect of intelligent terminal container management, this paper improves the Internet of Things and big data technology, analyzes the RFID middleware architecture based on the actual needs of container handling management, and proposes a new method of RFID middleware load balancing. Moreover, this paper combines the Internet of Things technology and big data technology to analyze the terminal container loading and unloading process and build a corresponding intelligent system. After constructing a terminal container handling intelligent management system based on the Internet of Things and big data technology, the performance of the system is verified, and multiple sets of simulation data are used to conduct research. The experimental research results show that the terminal container handling management system based on the Internet of Things and big data constructed in this paper basically meets the actual needs of use.

Background Early identification of massive middle cerebral artery infarction (MCAI) at risk for malignant MCAI (m-MCAI) may be useful in selecting patients for aggressive therapies. The aim of this study was to determine whether CYP metabolites may help to predict impending m-MCAI. Methods This is a prospective, two-center observational study in 256 patients with acute massive MCAI. Plasma levels of 20-hydroxyeicosatetraenoic acid (20-HETE), epoxyeicosatrienoic acids, and dihydroxyeicosatrienoic acids were measured at admission. Brain computed tomography (CT) was performed at admission and repeated between day 3 and 7, or earlier if there was neurological deterioration. The primary outcome was m-MCAI. The m-MCAI was diagnosed when follow-up brain CT detected a more than two-thirds space-occupying MCAI with midline shift, compression of the basal cisterns, and neurological worsening. Results In total of 256 enrolled patients, 77 (30.1%) patients developed m-MCAI. Among the 77 patients with m-MCAI, 60 (77.9%) patients died during 3 months of stroke onset. 20-HETE level on admission was significantly higher in patients with m-MCAI than those without m-MCAI. There was an increase in the risk of m-MCAI with increase of 20-HETE levels. The third and fourth quartiles of 20-HETE levels were independent predictors of m-MCAI (OR: 2.86; 95% CI: 1.16 – 6.68; P = 0.025, and OR: 4.23; 95% CI: 1.35 – 8.26; P = 0.002, respectively). Conclusions Incidence of m-MCAI was high in patients with massive MCAI and the prognosis of m-MCAI is very poor. Elevated plasma 20-HETE may be as a predictor for m-MCAI in acute massive MCAI, and it might useful in clinical practice in therapeutic decision making.

Objective. To introduce a modified transverse process-pedicle puncture technique applied to unilateral extrapedicular percutaneous vertebroplasty (PVP) for the treatment of osteoporotic lumbar vertebral compression fractures. Methods. A retrospective study was performed on 91 patients with osteoporotic vertebral compression fractures (OVCFs) who underwent unilateral extrapedicular PVP from June 2016 to September 2018. Lumbar and back pain was assessed through the visual analogue scale (VAS). Function recovery was assessed through the Oswestry disability index (ODI). Radiologic outcomes were assessed mainly on the basis of bone cement distribution and anterior vertebral height. Results. A total of 101 fractured vertebrae were successfully treated using the extrapedicular technique without any recognized clinical complications. The postoperative VAS and ODI values were significantly lower than the corresponding preoperative values P < 0.01 . Radiologic outcomes in all fractured vertebrae showed that the diffusion of bone cement could exceed the midline of the vertebral body. There was no significant difference between preoperative and postoperative anterior vertebral heights P < 0.05 . Conclusion. The modified transverse process-pedicle approach applied to unilateral extrapedicular percutaneous vertebroplasty is a simple, safe, and effective surgical method.

PurposeThis study aims to examine how job satisfaction (JS) affects two types of knowledge sharing (KS), in-role KS and extra-role KS. It also investigates the mediating effect of knowledge sharing self-efficacy (KSSE) and the moderating effect of team collaborative culture (TCC) between JS and two types of KS.Design/methodology/approachThis study applies attribution theory to develop a cross-level model and validate it through paired data collected from 322 information technology professionals nested within 80 teams. Hierarchical linear modeling is used to test the hypotheses.FindingsJS positively influences in-role and extra-role KS via KSSE and TCC positively moderates the relationship between JS and extra-role KS.Originality/valueThis study is one of the first to investigate the mechanism underlying the influence of JS on two types of KS. It also identifies the mediating and moderating effects of this mechanism.

Anticoagulation response to warfarin during the initial stage of therapy varies among individuals. In this study, we aimed to combine pharmacometabolomic and pharmacogenetic data to predict interindividual variation in warfarin response, and, on this basis, suggest an initial daily dose range. The baseline metabolic profiles, genotypes, and clinical information of 160 patients with heart valve disease served as the variables of the function of the last international normalized ratio measured before a patient's discharge (INRday7 ) to screen for potential biomarkers. The partial least-squares model showed that two baseline metabolites (uridine and guanosine), one single-nucleotide variation (VKORC1), and four clinical parameters (weight, creatinine level, amiodarone usage, and initial daily dose) had good predictive power for INRday7 (R2 = 0.753 for the training set, 0.643 for the test set). With these biomarkers, a machine learning algorithm (two-dimensional linear discriminant analysis-multinomial logit model) was used to predict the subgroups with extremely warfarin-sensitive or less warfarin-sensitive patients with a prediction accuracy of 91% for the training set and 90% for the test set, indicating that individual responses to warfarin could be effectively predicted. Based on this model, we have successfully designed an algorithm,"IniWarD," for predicting an effective dose range in the initial 7-day warfarin therapy. The results indicate that the daily dose range suggested by the IniWarD system is more appropriate than that of the conventional genotype-based method, and the risk of bleeding or thrombus due to warfarin could thus be avoided.

Domain adaptation methods aim at learning transferable model for the unlabeled target domain. Recently, extensive domain adaptation models are proposed to align representations by minimizing distribution distance between different domains or adversarial training manner. However, most existing methods only adapt the different features in feature space and ignore the semantic features that generated by the classifier, which can lead to misclassification of target samples near the source decision boundaries. In this paper, we propose a Semantic Adaptation Network (SAN) for unsupervised domain adaptation. SAN aligns the domain representations at both feature-level and semantic-level. SAN utilizes adversarial training to align different domain representations in the feature space. In the semantic space, SAN obtains the pseudo labels of target samples by nearest source semantic representation centroid, then forces the pseudo labeled target semantic representations close to corresponding source semantic representation centroids. Experiments on ImageCLEF-DA, Office-Home and VisDA datasets validate the effectiveness and superiority of our model.

Objective To compare early clinical effects of the femoral neck system (FNS) and three cannulated screws for the treatment of patients with unstable femoral neck fractures. Methods A retrospective analysis with pair matching of 81 patients who received FNS or cannulated screw internal fixation for Pauwels type‐3 femoral neck fracture in our hospital from January 2019 to December 2019 was conducted. Patients who received FNS were the test group, and those who received cannulated screws comprised the control group. Matching requirements were as follows: same sex, similar age, and similar body mass index (BMI). A total of 30 pairs were successfully matched at a 1:1 ratio, including 12 males and 18 females. The average age of the patients in the FNS group was 54.53 ± 6.71 years. In the cannulated screw group, the average age of the patients was 53.14 ± 7.19 years. The operation time, intraoperative blood loss, hospital stay, hospitalization cost, postoperative visual analog scale (VAS) score, time to walking without crutches, Harris score, femoral head necrosis rate, and complication rate were compared between the groups. Results Postoperative re‐examination of radiographs showed satisfactory reduction in all patients, and all patients were followed up for 10–22 months. Those in the FNS group had lower postoperative VAS scores, earlier times to walking without crutches, higher Harris scores at the last follow‐up, and lower complication rates (P 0.05). Conclusion For patients with unstable femoral neck fractures, FNS has better clinical efficacy than cannulated screws, though it is also more expensive., To compare early clinical effects of the femoral neck system (FNS) and three cannulated screws for the treatment of patients with unstable femoral neck fractures. We designed a retrospective analysis with pair matching. This study found that compared with cannulated screws, FNS is a suitable option for the treatment of Pauwels type‐3 femoral neck fractures. This approach is characterized by its accurate efficacy, simple procedure, reduced trauma, faster recovery, and fewer complications, though it is more expensive. FNS's excellent biomechanical performance and clinical efficacy make it a new choice for the treatment of unstable femoral neck fractures.

Recently, the gradient‐based neural architecture search has made remarkable progress with the characteristics of high efficiency and fast convergence. However, two common problems in the gradient‐based NAS algorithms are found. First, with the increase in the raining time, the NAS algorithm tends to skip‐connect operation, leading to performance degradation and instability results. Second, another problem is no reasonable allocation of computing resources on valuable candidate network models. The above two points lead to the difficulty in searching the optimal sub‐network and poor stability. To address them, the trick of pre‐training the super‐net is applied, so that each operation has an equal opportunity to develop its strength, which provides a fair competition condition for the convergence of the architecture parameters. In addition, a skip‐controller is proposed to ensure each sampled sub‐network with an appropriate number of skip‐connects. The experiments were performed on three mainstream datasets CIFAR‐10, CIFAR‐100 and ImageNet, in which the improved method achieves comparable results with higher accuracy and stronger robustness.

BACKGROUND: Among patients with stable coronary disease and moderate or severe ischemia, whether clinical outcomes are better in those who receive an invasive intervention plus medical therapy than in those who receive medical therapy alone is uncertain. METHODS: We randomly assigned 5179 patients with moderate or severe ischemia to an initial invasive strategy (angiography and revascularization when feasible) and medical therapy or to an initial conservative strategy of medical therapy alone and angiography if medical therapy failed. The primary outcome was a composite of death from cardiovascular causes, myocardial infarction, or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest. A key secondary outcome was death from cardiovascular causes or myocardial infarction. RESULTS: Over a median of 3.2 years, 318 primary outcome events occurred in the invasive-strategy group and 352 occurred in the conservative-strategy group. At 6 months, the cumulative event rate was 5.3% in the invasive-strategy group and 3.4% in the conservative-strategy group (difference, 1.9 percentage points; 95% confidence interval [CI], 0.8 to 3.0); at 5 years, the cumulative event rate was 16.4% and 18.2%, respectively (difference, -1.8 percentage points; 95% CI, -4.7 to 1.0). Results were similar with respect to the key secondary outcome. The incidence of the primary outcome was sensitive to the definition of myocardial infarction; a secondary analysis yielded more procedural myocardial infarctions of uncertain clinical importance. There were 145 deaths in the invasive-strategy group and 144 deaths in the conservative-strategy group (hazard ratio, 1.05; 95% CI, 0.83 to 1.32). CONCLUSIONS: Among patients with stable coronary disease and moderate or severe ischemia, we did not find evidence that an initial invasive strategy, as compared with an initial conservative strategy, reduced the risk of ischemic cardiovascular events or death from any cause over a median of 3.2 years. The trial findings were sensitive to the definition of myocardial infarction that was used. (Funded by the National Heart, Lung, and Blood Institute and others; ISCHEMIA ClinicalTrials.gov number, NCT01471522.).

For guiding natural language generation, many semantic-driven methods have been proposed. While clearly improving the performance of the end-to-end training task, these existing semantic-driven methods still have clear limitations: for example, (i) they only utilize shallow semantic signals (e.g., from topic models) with only a single stochastic hidden layer in their data generation process, which suffer easily from noise (especially adapted for short-text etc.) and lack of interpretation; (ii) they ignore the sentence order and document context, as they treat each document as a bag of sentences, and fail to capture the long-distance dependencies and global semantic meaning of a document. To overcome these problems, we propose a novel semantic-driven language modeling framework, which is a method to learn a Hierarchical Language Model and a Recurrent Conceptualization-enhanced Gamma Belief Network, simultaneously. For scalable inference, we develop the auto-encoding Variational Recurrent Inference, allowing efficient end-to-end training and simultaneously capturing global semantics from a text corpus. Especially, this article introduces concept information derived from high-quality lexical knowledge graph Probase, which leverages strong interpretability and anti-nose capability for the proposed model. Moreover, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence concept dependence. Experiments conducted on several NLP tasks validate the superiority of the proposed approach, which could effectively infer meaningful hierarchical concept structure of document and hierarchical multi-scale structures of sequences, even compared with latest state-of-the-art Transformer-based models.

Automatic image crack detection is a critical task for ensuring the safety of various facilities. However, it remains a challenging topic due to the complex background from long and sharp crack topologies. Inspired by recent advances on computer vision applications in deep learning, we propose a novel network architecture with richer feature fusion and attention mechanism and mixed pooling module for crack detection. The proposed network uses the mixed pooling module to replace the conventional spatial pooling. Moreover, we first extract the richer convolutional features to better characterize cracks. Then, we use a spatial attention (SA) in low level feature maps to capture the spatial structure information of cracks. Besides, we use a channel-wise attention (CA) to capture the features of high-level context. Finally, we fuse them together for the final crack prediction. A large crack dataset is used for training and testing. We evaluate our method on a large scale crack dataset, and experimental results on the DeepCrack dataset have demonstrate the effectiveness of the proposed method against state-of-the-art crack detection methods, which achieves Precision(P) 87.3%, Recall(R) 88.5%, and F-score over 88%.

Rapid single-flux-quantum (RSFQ) circuits are developing rapidly, but there are still many layouts that need to be completed manually, greatly reducing the design efficiency. We report the shape constraints of Josephson transmission line (JTL), and then propose automatic placement and routing methods for RSFQ circuits. First, for the pipeline structure of concurrent-flow clocking, a detailed placement algorithm based on simulated annealing is proposed, which use D Flip-Flop (DFF) insertion, column placement, and intra-column perturbation strategies to ensure the synchronization of clock phase while completing the placement of logic cells. More importantly, for the shape constraints of JTL, a new two-step JTL routing method is proposed. The first step is dummy wire routing, and the shape violations and routing congestion caused by shape constraints are solved to search legal paths for JTL routing; in the second step, timing optimization is performed when replacing dummy wire with JTL cells. Experimental results show that the proposed automatic methods achieve a 5% area-reduction and a 20× speed-up compared to manual design.

Background Effectively reducing the expression of certain aversive memories (fear or trauma memories) with extinction training is generally viewed to be therapeutically important. A deeper understanding of the biological basis for a more effective extinction process is also of high scientific importance. Methods Our study involved intraventricular injection or local injection into the dorsal dentate gyrus of anti-neuregulin 1 antibodies (anti-NRG1) before fear extinction training, followed by testing the expression of fear memory 24 hours afterward or 9 days later. We used local injection of chemogenetic or optogenetic viruses into the dorsal dentate gyrus to manipulate the activity of the dorsal dentate gyrus and test the expression of fear memory. We also examined the effect of deep brain stimulation in the dorsal dentate gyrus on the expression of fear memory. Results Mice that received intraventricular injection with anti-NRG1 antibodies exhibited lower expression of fear memory and increased density of activated excitatory neurons in the dorsal dentate gyrus. Injection of anti-NRG1 antibodies directly into the dorsal dentate gyrus also led to lower expression of fear memory and more activated neurons in the dorsal dentate gyrus. Inhibiting the activity of dorsal dentate gyrus excitatory neurons using an inhibitory designer receptor exclusively activated by designer drugs (DREADD) eliminated the effects of the anti-NRG1 antibodies. Enhancing the activity of the dorsal dentate gyrus with an excitatory DREADD or optogenetic stimulation resulted in lower expression of fear memory in mice that did not receive infusion of anti-NRG1 antibodies. Deep brain stimulation in the dorsal dentate gyrus effectively suppressed expression of fear memory, both during and after fear extinction training. Limitations The mechanism for the contribution of the dorsal dentate gyrus to the expression of fear memory needs further exploration. Conclusion Activation of the dorsal dentate gyrus may play an important role in modulating the expression of fear memory; its potential use in fear memory extinction is worthy of further exploration.

Gastric cancer (GC) is a kind of malignancy with a high mortality and recurrence. An effective prediction model based on ideal biomarkers to assess prognosis could benefit patients for optimization of treatment. Bioinformatics has played an increasingly important role in the study of cancer diseases. Therefore, this study started with bioinformatics to establish a reliable prognostic model of gastric cancer. The gene expression data and clinical data of GC tissues and normal tissues were obtained from the Gene Expression Omnibus (GEO), Genotype‐Tissue Expression (GTEx), and The Cancer Genome Atlas (TCGA) profile database. We finally identified a four gene signature and constructed a prognostic model. The results of internal and external validation showed that the model is highly reliable. In addition, we also constructed a nomogram based on the model, which was verified by a calibration curve to show its predicted accuracy. Comprehensive analysis indicated that the four genes in the model are related to the occurrence and development of tumors, perhaps they are potential targets for tumor treatment. Generally, this prognostic model can bring potential benefits to patients with gastric cancer., The model based on multi‐database bioinformatics analysis was used to predict the prognosis of gastric cancer.

To improve corrosion-resistance of shallow-buried concrete urban utility tunnels (UUTs), basalt fiber reinforced polymer (BFRP) bars are applied to reinforce UUTs. As the UUT must have excellent survival capability under accidental explosions, a shallow-buried BFRP bars reinforced UUT (BBRU) was designed and constructed. Repetitive blast experiments were carried out on this BBRU. Dynamic responses, damage evolutions and failure styles of the BBRU under repetitive explosions were revealed. The tunnel roof is the most vulnerable component and longitudinal cracks develop along the tunnel. When the scaled distance is larger than 1.10 m/kg1/3, no cracks are observed in the experiments. When the BBRU is severely damaged, there are five cracks forming and developing along the roof. The roof is simplified as a clamped-supported one-way slab, proved by the observation that the maximum strain of the transverse bar is much larger than that of the longitudinal bar. Dynamic responses of the roof slab are predicted by dynamic Euler beam theory, which can consistently predict the roof displacement under large-scaled-distance explosion. Compared with the UUT reinforced with steel bars, the BBRU has advantages in blast resistance with smaller deflections and more evenly-distributed cracks when the scaled distance is smaller than 1.260 m/kg1/3 and the steel bars enter plastic state. Longer elastic defamation of the BFRP bars endows the UUT more excellent blast resistance under small-scaled-distance explosions.

Most studies focused on the temporal trend of mortality risk associated with temperature exposure. The relative role of heat, cold, and temperature variation (TV) on morbidity and its temporal trends are explored insufficiently. This study aims to investigate the temporal trends of emergency ambulance dispatch (EAD) risk and the attributable burden of heat, cold, and hourly temperature variation (HTV). We collected time-series data of daily EAD and ambient temperature in Shenzhen from 2010 to 2017. HTV was calculated as the standard deviation of the hourly temperatures between 2 consecutive days. Quasi-Poisson generalized additive models (GAM) with a time-varying distributed lag nonlinear model (DLNM) were applied to examine temporal trends of the HTV-, heat-, and cold-EAD association. The temporal variation of the attributable fraction (AF%) and attributable number (AN) for different temperature exposures was also calculated. The largest RR was observed in extreme cold [1.30 (95% CI: 1.18, 1.43)] and moderate cold [1.25 (95% CI: 1.17, 1.34)]. Significant increasing trends in HTV-related effects and burden were observed, especially for the extreme HTV effects (P for interaction < 0.05). Decreasing trends were observed in the heat-related effect and burden, though it showed no significance (P for interaction = 0.46). There was no clear change pattern of cold-related effects and burdens. Overall, the three temperature exposure caused 13.7% of EAD, of which 4.1%, 4.3%, and 5.3% were attributed to HTV, heat, and cold, respectively. All the temperature indexes in this study, especially the cold effect, are responsible for the increased risk of EAD. People have become more susceptible to HTV over the recent decade. However, there is no clear evidence to support the temporal change of the population's susceptibility to heat and cold. Thus, in addition to heat and cold, the emergency ambulance service department should pay more attention to HTV under climate change.

To investigate the possible role of physical activity (PA) on sleep disturbance in breast cancer patients. Literature in PubMed, Embase, and the Cochrane Library was systematically searched until January 30, 2020. Randomized controlled trials that focused on the role of PA interventions on sleep disturbance were selected. The main outcome measures included the global Pittsburgh Sleep Quality Index (PSQI) score and PSQI subscales. Subgroup analysis was performed based on the study area and intervention time. The stability and authenticity of the results were measured by sensitivity analysis and publication bias analysis, respectively. Six articles were included in this meta-analysis. There were no significant differences in global PSQI scores between the PA intervention group and the usual care group (P = 0.057). As for PSQI subscales, PA intervention could improve sleep quality (weighted mean difference = 0.22; 95% confidence interval 0.04–0.40; P = 0.018). There were no significant differences in sleep duration, sleep medication, sleep latency, habitual sleep efficiency, and daytime dysfunction between the two groups (all P > 0.05). PA serves as an effective intervention to improve sleep quality.

Recently, Retinex theory based low-light image enhancement (LLIE) algorithms have achieved impressive results in controlled environment. However, the majority of deep learning based LLIE algorithms leverage relighting by enhancing the illumination components that directly determines the image brightness, regretfully, they ignore the information of reflectance components, which may cause problems such as image noise and color distortion in reconstructed images. To tackle this problem, in this letter, we propose a component enhancement network based on Generative Adversarial Network (Component-GAN) for recovering clear images from low-light ones. Specifically, the network is composed of the decomposition part for dividing the paired low/normal-light images into illumination components and reflectance components, and the enhancement part for generating high-quality images. It is worth to note that we provide two branches of component enhancement network, which are parallel to improve the two components simultaneously. Hereby, we treat the reconstruction part as the generative network and adopt discriminative network to boost image reconstruction performance. Through extensive experiments, the proposed approach outperforms some state-of-the-art LLIE methods in terms of visual and subjective qualities.

The PL, ASE and photo-generated carrier recombination of quasi-2D PBA2(MAPbBr3)n−1PbBr4 perovskite films have been studied in detail, confirming that these photo-physical characters are sensitive to layer number.

Multiple myeloma (MM) is a hematologic tumor with monoclonal proliferation of malignant plasma cells in the bone marrow. Fascin (FSCN) is an actin-binding protein that plays a crucial role in cell migration and invasion, contributing to tumor metastasis. There are three members (FSCN1-3) in FSCN family. However, the prognostic role of FSCN family in MM remains unclear. In this study, we used four independent Gene Expression Omnibus (GEO) datasets to explore the relationships between FSCN1-3 expression profiles and patient survival in MM. We found that FSCN1 was dramatically down-regulated in MM compared to normal donors (p < 0.001) and monoclonal gammopathy of undetermined significance (MGUS) (p = 0.032). Patients with high expression of FSCN1 and FSCN2 had significantly longer OS (p = 0.023 and 0.028, respectively). Univariate and multivariate analysis showed that FSCN1 (p = 0.003, 0.002) and FSCN2 (p = 0.018, 0.013) were independent favorable prognostic factors for OS in MM. Moreover, the combination of high expression of FSCN1 and FSCN2 could effectively predict both longer EFS (p = 0.046) and OS (p = 0.015). Our study suggested that FSCN1 and FSCN2 can be used as favorable biomarkers for predicting clinical outcomes in MM.

Background Hepatocellular carcinoma (HCC) is a common digestive tumor with great heterogeneity and different overall survival (OS) time, causing stern problems for selecting optimal treatment. Here we aim to establish a nomogram to predict the OS in HCC patients. Methods International Cancer Genome Consortium (ICGC) database was searched for the target information in our study. Lasso regression, univariate and multivariate cox analysis were applied during the analysis process. And a nomogram integrating model scoring and clinical characteristic was drawn. Results Six mRNAs were screened out by Lasso regression to make a model for predicting the OS of HCC patients. And this model was proved to be an independent prognostic model predicting OS in HCC patients. The area under the ROC curve (AUC) of this model was 0.803. TCGA database validated the significant value of this 6-mRNA model. Eventually a nomogram including 6-mRNA risk score, gender, age, tumor stage and prior malignancy was set up to predict the OS in HCC patients. Conclusions We established an independent prognostic model of predicting OS for 1–3 years in HCC patients, which is available to all populations. And we developed a nomogram on the basis of this model, which could be of great help to precisely individual treatment measures.

Although face verification algorithms have made great success under controlled conditions in recent years, there’s plenty of room at its performance under uncontrolled real-world due to lack of discriminative feature representation ability. From the perspective of metric learning, we proposed a context-aware based Siamese neural network (CASNN) to learn a simple yet powerful network for face verification task to enhance its discriminative feature representation ability. Firstly, a context-aware module is used to automatically focus on the key area of the input facial images without irrelevant background area. Then we design a Siamese network equipped with center-classification loss to compress intra-class features and enlarge between-class ones for discriminative metric learning. Finally, we propose a quantitative indicator named “D-score” to show the discriminative representation ability of the learnt features from different methods. The extensive experiments are conducted on LFW dataset, YouTube Face dataset (YTF) and real-world dataset. The results confirm that CASNN outperforms some state-of-the-art deep learning-based face verification methods.

To ensure adequate oil supply and mitigate geopolitical uncertainties, China has diversified its sources of crude oil imports in recent years. Central Asia is a neighbor region of China, rich in oil and natural gas reserves, which can play an important role in China’s strategy to reduce its dependence on energy supplies from the Middle East. The geopolitical attributes of energy and the geopolitical situation in Central Asia determine that Central Asia’s energy development and cooperation are disturbed by domestic and foreign factors, and also face the risks of complex energy structural evolution and geopolitical games, which create a unique energy geopolitical pattern in Central Asia. This study proposes an analysis framework for energy geopolitics in Central Asia, arguing that the complexity of energy geopolitics in Central Asia can be demonstrated from the perspectives of both the main countries of energy development (game actors) and the whole-industry-chain of energy development (game themes). The most obvious feature of Central Asian energy geopolitics is the fierce game that is played between key countries and regions, Russia, the United States, China, the European Union (EU), Japan, India, Iran, and Turkey. Strategic geopolitical considerations and resource requirements necessitate the active participation of these players in Central Asian energy development and mean that the foreign policy agendas of states in this region are likely to become ever more crowded. Therefore, the energy cooperation between China and Central Asia faces the geopolitical risks of the great power games. It is necessary to develop reasonable and effective policies to establish guarantee mechanisms to minimize the risks of energy cooperation. This study characterizes the energy geopolitical strategies of Russia, the United States, China, and other related powers in Central Asia. It also assesses the country risks faced by energy cooperation between China and Central Asian countries. To withstand possible geopolitical and country risks, this study develops a series of policy frameworks which can be used to fortify the stability of the energy cooperation between China and Central Asian countries, and can also be effective in responding to the array of risks that might be encountered in the coming years.

Cancer immune plays a critical role in cancer progression. Tumour immunology and immunotherapy are one of the exciting areas in bladder cancer research. In this study, we aimed to develop an immune‐related gene signature to improve the prognostic prediction of bladder cancer. Firstly, we identified 392 differentially expressed immune‐related genes (IRGs) based on TCGA and ImmPort databases. Functional enrichment analysis revealed that these genes were enriched in inflammatory and immune‐related pathways, including in ‘regulation of signaling receptor activity’, ‘cytokine‐cytokine receptor interaction’ and ‘GPCR ligand binding’. Then, we separated all samples in TCGA data set into the training cohort and the testing cohort in a ratio of 3:1 randomly. Data set GSE13507 was set as the validation cohort. We constructed a prognostic six‐IRG signature with LASSO Cox regression in the training cohort, including AHNAK, OAS1, APOBEC3H, SCG2, CTSE and KIR2DS4. Six IRGs reflected the microenvironment of bladder cancer, especially immune cell infiltration. The prognostic value of six‐IRG signature was further validated in the testing cohort and the validation cohort. The results of multivariable Cox regression and subgroup analysis revealed that six‐IRG signature was a clinically independent prognostic factor for bladder cancer patients. Further, we constructed a nomogram based on six‐IRG signature and other clinicopathological risk factors, and it performed well in predict patients' survival. Finally, we found six‐IRG signature showed significant difference in different molecular subtypes of bladder cancer. In conclusions, our research provided a novel immune‐related gene signature to estimate prognosis for patients' survival with bladder cancer.

Molecules with fast-acting antidepressant effects have potentials to become new antidepressants. Here we report the fast-acting (1hr) antidepressant effects of ketamine (10 mg/kg, i.p.) in chronic adreno-cortico-tropic-hormone (ACTH)-induced and chronic unpredictable mild stress (CUMS)-induced depression mouse models. These behavioral anti-depressant effects are associated with normalized expression of glutamate transporter-1(GLT-1), glial fibrillary acidic protein (GFAP), brain-derived neurotrophic factor (BDNF) and eukaryotic elongation factor 2 phosphorylation (p-eEF2) in the prelimbic prefrontal cortex (PrL-PFC). Excitatory neurons in PrL also showed reduced ambient glutamate responses to synaptic stimulation, and reduced ambient NMDA receptor responses after ketamine injection. Interestingly, ketamine induced biochemical and electrophysiological changes still occurred with GLT-1 knockdown in PrL, suggesting that elevated GLT-1 level is not required for ketamine to exert its antidepressant effect. At the same time, ketamine did not elevate GLT-1 level in the isolated astrocytes, suggesting distinct contributions from neurons and astrocytes to ketamine-induced changes.

BackgroundTransfer delay provokes prolongation of prehospital time, which contributes to treatment delay that endangers patients with ST-segment elevation myocardial infarction (STEMI). A key constraint in reducing transfer delay is the shortage of emergency healthcare workers. This study was to explore the influence of the quality and quantity of healthcare professionals at emergency medical stations on transfer delay and in-hospital mortality among STEMI patients.MethodsA cross-sectional study using mixed methods was conducted at 89 emergency stations in 9 districts in China's Shenzhen province. Based on a sample of 31 hospitals, 1,255 healthcare professionals, and 3,131 patients with STEMI, a generalized linear model was used to explore the associations between the quality and quantity of healthcare professionals and transfer delay and in-hospital mortality among STEMI patients. Qualitative data were collected and analyzed to explore the reasons for the lack of qualified healthcare professionals at emergency medical stations.ResultsThe analysis of the quantity of healthcare professionals showed that an increase of one physician per 100,000 individuals was associated with decreased transfer delay for patients with STEMI by 5.087 min (95% CI −6.722, −3.452; P < 0.001). An increase of one nurse per 100,000 individuals was associated with decreased transfer delay by 1.471 min (95% CI −2.943, 0.002; P=0.050). Analysis of the quality of healthcare professionals showed that an increase of one physician with an undergraduate degree per 100,000 individuals was associated with decreased transfer delay for patients with STEMI by 8.508 min (95% CI −10.457, −6.558; P < 0.001). An increase of one nurse with an undergraduate degree per 100,000 individuals was associated with decreased transfer delay by 6.645 min (95% CI −8.218, −5.072; P < 0.001). Qualitative analysis illustrated that the main reasons for low satisfaction of healthcare professionals at emergency medical stations included low income, limited promotion opportunities, and poor working environment.ConclusionsThe quantity and quality of emergency healthcare professionals are key factors influencing transfer delay in STEMI patients. The government should increase the quantity of healthcare professionals at emergency medical stations, strengthen the training, and improve their performance by linking with clinical pathways to enhance job enthusiasm among emergency healthcare professionals.

Diabetes is prevalent worldwide, but ideally intensive therapeutic strategy in clinical diabetes and diabetic nephropathy (DN) is still lack. Pyruvate is protective from glucometabolic disturbances and kidney dysfunction in various pathogenic insults. Present studies focused on oral pyruvate effects on diabetes status and DN with 0.35% pyruvate in pyruvate-enriched oral rehydration solution (Pyr-ORS) and 1% pyruvate as drinking water for 8 weeks, using the model of diabetic db/db mice. Both Pyr-ORS and 1% pyruvate showed comparable therapeutic effectiveness with controls of body weight and blood sugar, increases of blood insulin levels, and improvement of renal function and pathological changes. Aberrant key enzyme activities in glucometabolic pathways, AR, PK, and PDK/PDH, were also restored; indexes of oxidative stress and inflammation, NAD+/NADH ratio, and AGEs in the kidneys were mostly significantly preserved after pyruvate treatments. We concluded that oral pyruvate delayed DN progression in db/db mice and the modified Pyr-ORS formula might be an ideal novel therapeutic drink in clinical prevention and treatment of type 2 diabetes and DN.

Determining how to reasonably evaluate the production performance of bulk cargo terminals has always been a concern of the terminal managers. Aiming at the problem of production performance evaluat...

Background The pathogenesis of chronic obstructive pulmonary disease (COPD) is complex. Our study aimed to investigate the clinical value of N-acetylcysteine (NAC) combined with terbutaline sulfate in the treatment of COPD in elderly people, and its effect on the apoptosis/anti-apoptosis mechanism. Methods A total of 126 elderly COPD patients in our hospital from December 2017 to June 2019 were recruited and divided into 3 groups. On the basis of conventional treatment, control group A was treated with NAC, control group B with terbutaline sulfate, and combined group with both drugs. Lung function, apoptosis/anti-apoptosis related indexes, oxidative stress indexes, COPD assessment test (CAT) score, 6-min walk distance (6MWD), blood gas indexes, and adverse reactions were measured. Results The levels of forced vital capacity (FVC), maximum mid-expiratory flow rate (MMF), peak expiratory flow (PEF), oxygenation index (OI), and blood oxygen saturation (SaO2) in 3 groups were increased after treatment, and were the highest in the combined group. The level of carbon dioxide partial pressure (PaCO2) was decreased, and was the lowest in the combined group. After 2 weeks of treatment, the 6MWD had increased in all 3 groups and was longest in the combined group. The CAT score was decreased and the extent of decrease was the highest in the combined group. After treatment, the levels of Fas receptor/apoptosis antigen 1 (Fas/APO-1), soluble Fas (sFas), malondialdehyde (MDA), and reactive oxygen species (ROS) in the three groups were decreased, and their levels were decreased most markedly in the combined group. Meanwhile, the levels of superoxide dismutase (SOD) and glutathione peroxide enzyme (GSH-PX) were increased after treatment, and their levels were the highest in the combined group. The incidence of dizziness, chest tightness, constipation, and nasal congestion in the combination group were not significantly different from the other two groups. Conclusions The combined use of terbutaline sulfate and NAC in the treatment of elderly patients with COPD can effectively improve their lung function and blood gas status, which can strengthen athletic ability, reduce the oxidative stress response, and regulate apoptotic cytokines.

Chronic neuropathic pain is frequently accompanied by memory impairment, yet the underlying mechanisms remain unclear. Here, we showed that mice displayed memory impairment starting at 14 days and lasting for at least 21 days after chronic constriction injury (CCI) of unilateral sciatic nerve in mice. Systemic administration of the pan histone deacetylase (HDAC) inhibitor sodium butyrate attenuated this memory impairment. More specifically, we found that hippocampus HDAC3 was involved in this process because the levels of its mRNA and protein increased significantly in the hippocampus at 14 and 21 days after CCI, but not sham surgery. Systemic administration of the selective HDAC3 antagonist RGFP966 attenuated CCI-induced memory impairment, improved hippocampal long-term potentiation impairment, and rescued reductions of dendritic spine density and synaptic plasticity-associated protein in the hippocampus. In addition, HDAC3 overexpression in the hippocampus led to memory impairment without affecting basal nociceptive responses in naive mice. Our findings suggest that HDAC3 contributes to memory impairment after CCI by impairing synaptic plasticity in hippocampus. Histone deacetylase 3 might serve as a potential molecular target for therapeutic treatment of memory impairment under neuropathic pain conditions.

The recent discovery of circular RNAs (circRNAs) in serum exosomes suggests a novel and potentially useful tool for noninvasive cancer diagnosis. However, there are currently no substantial studies addressing this topic. RNA-sequencing analysis was performed between three pairs of non-small-cell lung cancer (NSCLC) patients and controls. The diagnostic values of exosomal circRNAs were assessed in a training set, validation set 1, and validation set 2. A total of 46 abnormally expressed circRNAs were identified. Among these, circ_0047921, circ_0056285, and circ_0007761 were found to display significant diagnostic validity for NSCLC. In distinguishing NSCLC cases from healthy controls, the panel of aforementioned circRNAs exhibited area under the curve (AUC) values of 0.926 (95% CI, 0.895-0.956) in the training set and 0.919 (95% CI, 0.877-0.962) in validation set 1. Circ_0047921 could distinguish NSCLC cases from chronic obstructive pulmonary disease controls (AUC, 0.890; 95%CI, 0.831-0.948), whereas the circ_0056285 and circ_0007761 combination could distinguish NSCLC cases from tuberculosis controls (AUC, 0.820; 95% CI, 0.739-0.902). For an early NSCLC diagnosis, similar results were observed for these circRNAs in distinguishing early-stage NSCLC cases from healthy controls, chronic obstructive pulmonary disease controls, or tuberculosis controls. Circ_0056285 expression was correlated with clinical stages and lymph node metastasis. The exosomal circRNAs, circ_0047921, circ_0056285, and circ_0007761, are promising biomarkers for NSCLC diagnosis in the Chinese population.

The study aimed to investigate the role of exosomes derived from rat bone marrow mesenchymal stem cells (rBMSCs) in acute soft tissue injury and its related mechanisms. Exosomes were isolated from rBMSCs and characterized by Nanosight NS300 particle size analyser (NTA), transmission electron microscopy (TEM), and western blot. Twenty four rats were randomly divided into four groups (n = 6): control group, strike group, rBMSCs group, and rBMSCs-exo group. Haematoxylin-eosin (HE) staining was used to observe the morphology. Real-time quantification PCR (RT-qPCR) and western blot were used to analyse the expression of IL-1A, IL-12A, COL11A1, COL4A4, and Wnt4. NTA, TEM and western blot results showed that exosomes isolated from rBMSCs were cup-shaped morphology with a size of about 100 nm. HE staining showed that there was severe soft tissue inflammation in strike group, and the symptoms were alleviated after rBMSCs and rBMSCs-exo treatment. RT-qPCR and western blot indicated that in the strike group, the expression levels of IL-1A and IL-12A were significantly increased, and their expressions were decreased markedly by exosomes treatment. In addition, after treatment, the expression levels of COL11A1 and Wnt4 were up-regulated, while the expression of COL4A4 was down-regulated. Exosomes isolated from rBMSCs could improve acute soft tissue injury, and may be used as a new therapeutic strategy acute soft tissue injury. SIGNIFICANCE OF THE STUDY: Acute soft tissue injury is a common clinical exercise injury, which has a significant impact on people's health and work ability. Exosomes have been attracting increasing attention as a media of cell-to-cell communication. This study showed that exosomes isolated from rBMSCs could improve acute soft tissue injury by inhibiting inflammatory response, regulating the levels of COL11A1 and COL4A4, and up-regulating the expression of Wnt4. These will provide a new therapy strategy of acute soft tissue injury, and improve our understanding of the occurrence and development in acute soft tissue injury.

The internal structural information of words has proven to be very effective for learning Chinese word embeddings. However, most previous attempts made a single form extraction of internal feature to learn representations, ignoring the comprehensive combination of such information. And they focused only on explicit feature of internal structures, even though these structures still have the implicit semantics of words. In this paper, we propose Radical and Stroke-enhanced Word Embeddings (RSWE), a novel method based on neural networks for learning Chinese word embeddings with joint guidance from semantic and morphological internal information. RSWE enables an embedding model to learn simultaneously from (1) implicit semantic information that is exploited from the radicals, and (2) stroke n-grams information that can be explicitly obtained from Chinese words. In the learning process, RSWE uses stroke n-grams to capture the local structural feature of words, and integrates the implicit information exploited from radicals to enhance the semantic of embeddings. Through this combination procedure, semantics of Chinese words are effectively transferred into the learned embeddings. We evaluate the effectiveness of RSWE on word similarity computation, word analogy reasoning, performance over dimensions, performance over learning corpus size, and named entity recognition tasks, the experimental results show that our model outperforms existing state-of-the-art approaches.

Purpose The aim of this study was to screen the predisposed population and explore possible interactions between genetic polymorphisms and risk factors involved in the tumorigenesis and progression of ESCC (esophageal squamous cell carcinoma), in hope of identifying possible therapeutic targets along the way. Patients and methods Cases (1043) and controls (1315) were enrolled to evaluate the possible association between MAP3K1 SNPs and ESCC risk. Subgroup analyses include MAP3K1 variants, gender, age, smoking and drinking status. Results Among all three single locus polymorphisms of MAP3K1, only the heterozygote genotype of rs702689 AG is shown to be associated with increased risk for developing ESCC (OR=1.272, 95% confidence interval=1.061-1.525, p=0.009). Moreover, stratified analysis results observed altered susceptibility among patients with exposure to risk factors combined with certain genetic variant to ESCC. Conclusion This study reveals that MAP3K1 rs702689 AG genotype might facilitate the tumorigenesis in ESCC, particularly among women, patients who were over 63y and those who never drink nor smoke.

The intelligent poly N,N-dimethylacrylamide hydrogel material system with high mechanical strength and the 3D printable property was prepared via in situ free radical polymerization under vacuum successfully. With the increase in nanofibrillated cellulose (NFC) content, stress and strain of hydrogels increased gradually. As the effective reinforcement, NFC enhanced the crosslinking density, which realized the controllable regulation of rheology behaviors including viscosity, storage modulus, and loss modulus of hydrogels. Combined with the swelling rate and the existence of the gel–sol transition point, a hydrogel with 10 mg/mL NFC was treated as the 3D printing ink of hydrogel actuators. Variation of printing parameters significantly affected self-driven deformations. The hydrogel actuators with 90°/0° and 45°/135° configurations owned bending and spiral deformations, respectively. Actuators with a larger length–width ratio owned a lower pitch value. The precise anisotropic swelling property of the printed bilayer structure was the self-driven deformation mechanism of hydrogel actuators, which provided material candidates for the preparation of soft robots and actuators.

The pressure-dependent optoelectronic properties of all-inorganic perovskite CsPbBr3 nanocrystals (NCs) are investigated with steady-state and transient spectroscopy. The steady-state absorption an...

The purpose of this systematic review and meta-analysis was to investigate the relationship between liver transplantation and kidney cancer. Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines were followed. PubMed, the Web of Science, and the Cochrane databases were searched for peer-reviewed cohort studies in which standardized incidence of kidney cancer post-transplant was compared to the general population by means of standardized incidence ratio (SIR) with 95% confidence interval (CI). No limits were placed on language or year of publication. A fixed-effects model was used for pooling the data. Of the 937 citations identified from the electronic databases, we included nine cohort studies with 53913 liver transplant patients, a male percentage of 56.8% and a minimum follow-up of 12.4 months and more. The meta-analysis revealed that liver transplant recipients faced a significantly higher risk of developing kidney cancer than the general population with the pooled SIR of 2.02 (95% CI, 1.64-2.50; P < 0.001). No significant between-study heterogeneity was observed (I = 0, Phet = 0.553). On sensitivity analysis after removing the study by Engles et al. with the largest sample size (37 888 liver transplant recipients), the SIR remained stable (SIR 2.75; 95% CI, 1.85-4.10; P < 0.001). Overall, our synthesis of the literature indicates that an increased risk of kidney cancer exists after liver transplantation. Future studies should evaluate the potential risk factors associated with kidney cancer.

In this study, several estimation methods of fatigue properties based on different monotonic mechanical parameters were first discussed. The advantages and disadvantages of the Hardness Method proposed by Roessle and Fatemi were investigated and improved through the analysis of a total of 92 fatigue test data. A new Segment Fitting Method from Brinell hardness was then proposed for the fatigue properties estimation, and a total of 96 pieces of fatigue test data under axial, torsional, and multiaxial in-phase loading were collected to verify the applicability of the new proposal. Finally, the prediction accuracy of the new proposal and three exciting estimation methods was compared with the predictions based on the experimental fatigue properties. Based on the results obtained, the newly proposed estimation method has a significant improvement on the relation between fatigue ductility coefficient and Brinell hardness, which consequently improves the fatigue life prediction accuracy with the scatter band of 2, particularly for the materials with low Brinell hardness. The present study can provide a simplified analysis of the preliminary fatigue design of engineering structures.

During severe earthquakes, the ductility and energy dissipation capacity of shear links in Y-shaped eccentrically braced frames (EBFs) are crucial to achieve desirable seismic performance and structural safety. In this study, an innovative SSL-SSBC combining the advantages of a shear slotted bolted connection (SSBC) and a very short shear link is developed to improve the seismic performance of Y-shaped EBFs. Finite element (FE) analysis methods are first validated through comparison with existing experimental results for slotted bolted connection and very short shear links. Further, FE simulations of the SSBC, very short shear link with shear slotted bolted connection (SSL-SSBC), and Y-shaped EBF are conducted to determine their mechanical behavior systematically. The FE results demonstrate that a reasonable SSBC can effectively increase the ductility and energy dissipation capacity through friction slipping, allowing the SSBC to maintain elastic behavior throughout the entire range of seismic loading, while the very short shear link in the SSL-SSBC could undergo severe damage to dissipate seismic energy. As expected, the SSL-SSBC is capable of working as both a shear dissipation damper and a metallic ductile damper in the Y-shaped EBF. This study also clearly identifies that no yielding or damage of the very short shear links occurs during small and medium earthquakes, and the deformation and damage of the very short shear link can be minimized during severe earthquakes, allowing the seismic resilience capacity of the Y-shaped EBF to be dramatically enhanced.

Detection of visual markers, such as circular markers or quick response codes, is a commonly used approach to the positioning of wall-climbing robots. However, when the camera is far from the wall-climbing robot (e.g., 20 m), these markers become extremely blurred and difficult to detect. In this paper, a convolutional neural network-based positioning scheme comprised of a global bounding box detector and local wheel detector is proposed. The light-weight local wheel detector can quickly and accurately detect the four wheel points of a distant wall-climbing robot, and the detected wheel points can be used for calculating its position and direction angle. Our wheel detector has a single-frame processing time of 72.2 ms on a CPU and 7.1 ms on a GPU, where the latter meets the real-time positioning requirements of the wall-climbing robot. We also developed an efficient cost function for wheel matching between video frames. Simulation results and multiple test videos confirmed that the proposed cost function can match wheels between video frames perfectly. The high performance of this positioning system indicates that it may be used in a variety of industrial applications.

The temperature-dependent optoelectronic properties of FAPbI3 nanocrystals’ (NCs) film are studied by steady photoluminescence (PL), transient PL, and photocurrent measurements. Steady PL shows tha...

Study design Retrospective analysis. Objective Our goal was to provide a predictive model and a risk classification system that predicts cancer-specific survival (CSS) from spinal and pelvic tumors. Summary of background data Primary bone tumors of the spinal and pelvic are rare, thus limiting the understanding of the manifestations and survival from these tumors. Nomograms are the graphical representation of mathematical relationships or laws that accurately predict individual survival. Methods A total of 1033 patients with spinal and pelvic bone tumors between 2004 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate Cox analysis was used on the training set to select significant predictors to build a nomogram that predicted 3- and 5-year CSS. We validate the precision of the nomogram by discrimination and calibration, and the clinical value of nomogram was assessed by making use of a decision curve analyses (DCA). Results Data from 1033 patients with initially-diagnosed spinal and pelvic tumors were extracted from the SEER database. Multivariate analysis of the training cohort, predictors included in the nomogram were age, pathological type, tumor stage, and surgery. The value of C-index was 0.711 and 0.743 for the internal and external validation sets, respectively, indicating good agreement with actual CSS. The internal and external calibration curves revealed good correlation of CSS between the actual observation and the nomogram. Then, the DCA showed greater net benefits than that of treat-all or treat-none at all time points. A novel risk grouping system was established for CSS that can readily divide all patients into three distinct risk groups. Conclusion The proposed nomogram obtained more precision prognostic prediction for patients with initially-diagnosed primary spinal and pelvic tumors. Level of evidence 3.

Cloud computing gives resource-constrained clients great conveniences to outsource exorbitant computations to a public cloud. The extended Euclidean algorithm with large-scale polynomials over finite fields is fundamental and widespread in computer science and cryptography, yet it is computationally overloaded for quantities of lightweight devices emerged with the dawn of internet of things (IoT). In this respect, we design an efficient outsourcing algorithm that enables a lightweight client to achieve this heavy computation with the assistance of a remote cloud server. By comprehensively employing the variable substitution, random polynomial-based blind technique and unimodular matrix transformation , our algorithm achieves the goals of cheating resistance, privacy preservation , and high efficiency. Concretely, our algorithm is based on single untrusted program model which avoids the too strong security assumption between multiple servers, and it enables the client to detect the cloud’s misbehaviors with (optimal) probability 1. Also, Thorough theoretical analysis indicates that the algorithm provides robust input/output privacy. Moreover, the algorithm only needs one round interaction between the client and the cloud. Strict theoretical analysis and extensive experimental evaluations demonstrate our algorithm’s practical efficiency and effectiveness. Finally, an important application of our algorithm on securely outsourcing the expensive key generation step of NTRU cryptosystem is given.