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In order to greatly improve the photocatalytic properties, corn-like ZnO/ZnS heterojunctions with a particle size of about 60–71 nm have been synthesized by the solvothermal method and the subsequent sulfuration process. A declining trend is found for the specific surface area with increasing sulfuration time. The corn-like ZnO/ZnS heterojunctions exhibit good photocatalytic properties. With increasing sulfuration time, the degradation rate increases first and then decreases. The best degradation rate is observed for the heterojunction sulfurated for 90 min. The strong broad luminescence band is extremely beneficial to the absorption of visible light by multiphoton process. In addition, the energy transfer from ZnS to ZnO contributes to charge separation, forming a type-II heterojunction mechanism. After one cycle of photocatalytic process, except that corns become more broken, variation of particle size and shape is very small. The degradation speed of RhB after a second cycle of photocatalytic process is slower than the first one except when using the sample sulfurated for 360 min. [ABSTRACT FROM AUTHOR]

Acute kidney injury (AKI) is a common complication of acute illnesses with unfavorable outcomes. This cohort study aimed at constructing prediction models for one-year survival in adult AKI patients based on prognostic nutritional index (PNI), platelet-to-lymphocyte ratio (PLR), neutrophil percentage-to-albumin ratio (NPAR), or neutrophil-to-lymphocyte ratio (NLR), respectively. In total, 6050 patients from Medical Information Mart for Intensive Care III (MIMIC-III) were involved. The least absolute shrinkage and selection operator (LASSO) regression was utilized to screen possible covariates. The samples were randomly divided into the training set and the testing set at a ratio of 7.5 : 2.5, and the prediction models were constructed in the training set by random forest. The prediction values of the models were measured via sensitivity, specificity, negative prediction value (NPV), positive prediction value (PPV), area under the curve (AUC), and accuracy. We found that NLR (OR = 1.261, 95% CI: 1.145–1.388), PLR (OR = 1.295, 95% CI: 1.152–1.445), and NPAR (OR = 1.476, 95% CI: 1.261–1.726) were associated with an increased risk, while PNI (OR = 0.035, 95% CI: 0.020–0.059) was associated with a decreased risk of one-year mortality in AKI patients. The AUC was 0.964 (95% CI: 0.959–0.969) in the training set based on PNI, age, gender, length of stay (LOS) in hospital, platelets (PLT), ethnicity, LOS in ICU, systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate, glucose, AKI stage, atrial fibrillation (AF), vasopressor, renal replacement therapy (RRT), and mechanical ventilation. The testing set was applied as the internal validation of the model with an AUC of 0.778 (95% CI: 0.754–0.801). In conclusion, PNI accompanied by age, gender, ethnicity, SBP, DBP, heart rate, PLT, glucose, AF, RRT, mechanical ventilation, vasopressor, AKI stage, LOS in ICU, and LOS in hospital exhibited a good predictive value for one-year mortality of AKI patients. [ABSTRACT FROM AUTHOR]

• We design a novel semantic-refined spatial pyramid network (SSPNet). • The SSPNet can benefit the fusing of multi-scale information. • The SSPNet can enhance the representation ability of deep semantic features. • The multi-scale feature will be adaptively integrated by our method. • The texture features are obtained to enrich the multi-scale features. [Display omitted] In this paper, we propose a novel encoder-decoder model called Semantic-refined Spatial Pyramid Network (SSPNet) for generating high-quality density maps, which aims to build a scale-aware counting network to estimate the number of crowds accurately. The SSPNet consists of the front-end based on VGG-16, spatial pyramid multi-scale module (SPMM), and semantic enhancement module (SEM). First, a series of convolutional neural layers are utilized as the front-end to get deeper features without the extra computational cost. Moreover, the SPMM, which has a spatial pyramid structure with multiple receptive fields, is employed to capture multi-scale features. Furthermore, the SEM is designed to refine the features captured by SPMM, which uses deep semantic information to better integrate multi-scale features. Finally, the shallow texture information is adopted to compensate for the detail of the feature map to enhance the quality of the density map. Extensive experiments and comparisons on three challenge datasets, including ShanghaiTech Part_A & Part_B, UCF_CC_50, and UCF-QNRF, illustrate the superiority of our method. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) has been a challenging task in the field of artificial intelligence. In this paper, we propose a novel model, named regional self-attention convolutional neural network (RSACNN), for FER. Different from the previous methods, RSACNN makes full use of the facial texture of expression salient region, so yields a robust feature representation for FER. The proposed model contains two novel parts: regional local multiple pattern (RLMP) based on the improved K-means algorithm and the regional self-attention module (RSAM). First, RLMP uses the improved K-means algorithm to dynamically cluster the pixels to ensure the robustness of texture features with expression salient variation. Besides, the texture description is enhanced by extending the binary pattern to the multiple patterns and integrating the information of gray difference between pixels in the region. Next, RSAM can adaptively form weights for each region through the self-attention mechanism, and use rank regularization loss (RRLoss) to constrain the weights of different regions. By jointly combining RLMP and RSAM, RSACNN can effectively enhance the feature representation of expression salient regions, so that the performance of expression recognition can be improved. Extensive experiments on public datasets, i.e. CK + , Oulu-CASIA, Fer2013 and SFEW, prove the superiority of our method over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Recently, region proposal network (RPN) has been combined with the Siamese network for tracking and shown excellent accuracy and high efficiency. However, the low correlation between the classification score and localization accuracy in tracking has weakened the performance of the tracking model. In this paper, we propose an IoU-guided Siamese RPN (SiamIG) to address this problem. Specifically, SiamIG predicts the intersection-over-union (IoU) with the ground truth for each regressed anchor by using an IoU predictor. Then the predicted IoU is multiplied with the classification score to compute the final score, which is more suitable for localization accuracy. Furthermore, each anchor in the regression branch is assigned an IoU-based weighting factor such that the tracking accuracy can be further improved. Specifically, anchor boxes with high IoU are given more attention because of the IoU-based weighting factor, which helps the model pay more attention to an anchor box with a high IoU. Experiments demonstrate that the proposed method runs at over 200 fps and achieves up to 2.4%(AUC), 3%(EAO) and 2.3%(AUC) improvements over the baseline on the OTB-100, VOT-2016 and UAV123 datasets respectively. [ABSTRACT FROM AUTHOR]

One primary challenge in liver segmentation is the fuzzy edge contour. Recently, fully convolutional neural networks (FCNs) have been widely used in liver segmentation because of their adequate feature extraction. Nevertheless, the context among liver slices is still ignored by FCN. To address this issue, we first propose a bidirectional convolutional long short-term memory (BiConvLSTM) to explore contextual information. Meanwhile, the attention gate (AG) is utilized to fuse high-dimensional information from BiConvLSTM to remove irrelevant features. Besides, Shape-Net network is proposed to extend the liver shape pattern by latent space information, which will contribute to reduce the interference of fuzzy boundaries. Finally, the improved active contour loss function with L2 norm acts as a feature constraint. Experimental results on public benchmark datasets show that the proposed method slightly outperforms other newly published methods and achieves good performance for liver segmentation. [ABSTRACT FROM AUTHOR]

Current state-of-the-art detectors achieved impressive performance in detection accuracy with the use of deep learning. However, most of such detectors cannot detect objects in real time due to heavy computational cost, which limits their wide application. Although some one-stage detectors are designed to accelerate the detection speed, it is still not satisfied for task in high-resolution remote sensing images. To address this problem, a lightweight one-stage approach based on YOLOv3 is proposed in this paper, which is named Squeeze-and-Excitation YOLOv3 (SE-YOLOv3). The proposed algorithm maintains high efficiency and effectiveness simultaneously. With an aim to reduce the number of parameters and increase the ability of feature description, two customized modules, lightweight feature extraction and attention-aware feature augmentation, are embedded by utilizing global information and suppressing redundancy features, respectively. To meet the scale invariance, a spatial pyramid pooling method is used to aggregate local features. The evaluation experiments on two remote sensing image data sets, DOTA and NWPU VHR-10, reveal that the proposed approach achieves more competitive detection effect with less computational consumption. [ABSTRACT FROM AUTHOR]

Background: Village clinic doctors (VCDs) are part of the health service force in rural China. VCDs' job satisfaction (JS) is important to the stability of the three-tiered health service system. Since 2009, the Chinese government launched a new health care system reform (NHCSR) which affected VCDs significantly. This study aimed to analysing the effect of NHCSR on JS among VCDs.Methods: All the data came from three surveys in Shandong Province conducted in 2012, 2015 and 2018. In 2012, an originally designed questionnaire was used to conduct a baseline survey of 405 VCDs from 27 townships in nine counties. In 2015 and 2018, 519 and 223 VCDs in the same counties were surveyed with the same questionnaire. Descriptive analysis and ANOVA were used to analyse the level and changes in VCDs' JS.Results: The mean scores of VCDs' total JS were 2.664 ± 1.069, 3.121 ± 0.931 and 2.676 ± 1.044 in 2012, 2015 and 2018, respectively, with a significant difference (F = 28.732, P < 0.001). The mean scores of the medical practice environment and the job itself showed a continuous downward trend. The trends of the mean scores for job reward, internal work environment and organizational management were consistent with the trend for total JS.Conclusion: The NHCSR had a partly negative impact on VCDs' JS. Policy-makers should pay more attention to VCDs' job reward and medical practice environment. With the implementation of new reform policies, VCDs' JS should be the subject of more systematic and detailed research. [ABSTRACT FROM AUTHOR]

Introduction: Job burnout is an occupational psychological syndrome with a high prevalence among nurses in China. Hemodialysis (HD) nursing work has the characteristics of high intensity, high technical content, and high risk. The aims of this study were to investigate the prevalence and level of job burnout among HD nurses in China and explore the potential factors associated with burnout among HD nurses. Methods: This was a cross‐sectional study in 2019. Survey data were collected from 2738 HD centers in mainland China. Job burnout was measured by the Chinese version of the Maslach Burnout Inventory. The working atmosphere, interpersonal relationships with colleagues, and intention to leave were each assessed by a single question respectively. Multiple linear regression and structural equation modeling were used for the analysis. Findings: A total of 10,570 surveys were collected. A total of 1199 (11.34%) HD nurses reported a high level of emotional exhaustion, 782 (7.40%) reported a high level of depersonalization, and 6767 (64.02%) reported a low level of personal accomplishment. Job burnout in the Northeastern region of mainland China was higher than that in other regions (p < 0.05). The working atmosphere, interpersonal relationships, region, hospital level, educational level, career planning, age, and number of children were significantly associated with burnout among HD nurses (p < 0.001, adjusted R2 = 0.313). The working environment, individual factors, and specialist nurse training were significantly associated with HD nurse burnout and intention to leave (comparative fit index = 0.907; goodness of fit index = 0.930; root mean square error of approximation = 0.055). Discussion: There were notable regional differences in the burnout of HD nurses. This study contributes to the knowledge of the possible relationship of job burnout and intention to leave in HD nurses. It is suggested that improving the working atmosphere or interpersonal relationships and providing more training opportunities can alleviate job burnout in HD nurses. [ABSTRACT FROM AUTHOR]

With the increase in the morbidity of liver cancer and its high mortality rate, liver segmentation in abdominal computed tomography (CT) scan images has received extensive attention. Segmentation results play an important role in computer-assisted diagnosis and therapy. However, it remains a challenging task because of the complexity of the liver's anatomy, low contrast between the liver and its adjacent organs, and presence of lesions. This study presents an automatic method for liver segmentation from CT scan images based on the convex–concave point for tree structured part model (CCP–TSPM). First, TSPM is utilized as a coarse segmentation tool for capturing the topological shape variation. Then, the proposed CCP is implemented to adjust the position between adjacent points dynamically. As a result, the CCP–TSPM can locate the liver boundary adaptively. Furthermore, color space data provide abundant feature information, which can further improve the method's effectiveness and efficiency. Finally, the curve is evolved by an iteration level set function to obtain the fine segmentation results. The experimental results show that the proposed method can extract the liver boundary successfully. Furthermore, a comparison of the results with those of the state-of-the-art methods demonstrates the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Accurate scale estimation of the target plays an important role in object tracking. Most state-of-the-art methods estimate the target size by employing an exhaustive scale search. These methods can achieve high accuracy but suffer significantly from large computational cost. In this paper, we first propose an adaptive scale search strategy with the scale selection factor instead of an exhaustive scale search. This proposed strategy contributes to reducing computational costs by adaptive sampling. Furthermore, the boundary effects of correlation filters are suppressed by investigating background information so that the accuracy of the proposed tracker can be boosted. Experiments' empirical evaluations of 61 challenging benchmark sequences demonstrate that the overall tracking performance of the proposed tracker is very successfully improved. Moreover, our method obtains the top rank in performance by outperforming 17 state-of-the-art trackers on OTB2013. [ABSTRACT FROM AUTHOR]

Background: Propofol induces short- and long-term neurotoxicity. Our previous study showed that dexmedetomidine (Dex) can attenuate the propofol-induced acute neurotoxicity in rodents by enhancing the PI3K/Akt signaling. However, whether treatment of young rats with Dex could protect them from long-term neurotoxicity induced by propofol is unclear. Materials and methods: Seven-day-old male Sprague Dawley rats were randomized and injected intraperitoneally with saline (100 µL, NS), propofol (100 mg/kg), Dex (75 µg/kg), propofol (100 mg/kg) plus Dex (25, 50 or 75 µg/kg), 10% dimethyl sulfoxide (DMSO, 100 µL) or TDZD-8 (a GSK3β inhibitor, 1 mg/kg), or intracerebroventricularly with DMSO (5 µL) or LY294002 (a PI3K inhibitor, 25 µg/5 µL DMSO). Other rats in the experimental group were injected with the same doses of propofol, Dex and LY294002 or TDZD-8. All the rats were monitored until they were 9 weeks old. Their spatial learning and memory were tested by Morris water maze. The neuronal apoptosis, expression of PSD95, expression and phosphorylation of Akt and GSK3β and synaptic ultrastructures were determined by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling, immunohistochemistry, Western blot and transmission electron microscopy assays, respectively. Results: Compared with the NS control group, young rats injected with intralipid, Dex, TDZD-8, LY294002 or DMSO alone did not show any significant change as they aged. Propofol significantly increased the escape latency time, hippocampal neuroapoptosis and synaptic ultrastructural changes but decreased the relative levels of PSD95 expression, and Akt and GSK3β phosphorylation in the developing hippocampus of the rats. The neuronal toxic effects of propofol were significantly mitigated by the pretreatment with a higher dose of Dex. The neuroprotective effect of Dex was enhanced by the treatment with TDZD-8, but was completely abrogated by the treatment with LY294002. Conclusion: Our results indicated that the pretreatment of young rats with Dex attenuated the propofol-induced long-term neurotoxicity in their developing hippocampus by enhancing the PI3K/Akt signaling. [ABSTRACT FROM AUTHOR]

Combining green preparation and high performance is becoming the direction of sustainable development of materials. How to simultaneously overcome the two bottlenecks (poor surface activity and UV resistance) of aramid fibers (AFs) while improving thermal and mechanical properties through a green process is still an interesting issue with big challenge. Herein, new AFs (BL-AFs) were prepared by alternately self-assembling SiO 2 and MgAlFe layered double hydroxide (LDH) on surfaces of AFs, successively, through a green layer-by-layer (LBL) self-assembly technique without using high temperature and organic solvent. The structures and properties of BL-AFs were systematically studied, which are controllable by adjusting the number of self-assembly cycle. The new fibers with three or more self-assembly cycles have remarkably improved surface activity, thermal resistance, mechanical properties and UV resistance compared with AFs. Typically, with three self-assembly cycles, the initial degradation temperature and char yield of the new fiber (3BL-AF) are as high as 552.9 °C and 81.2%, about 92 °C and 25.2% higher than those of AF, respectively; after 168 h-UV irradiation, the retention of tensile performances of 3BL-AF fiber is as high as 91–95%, about 29–14% higher than that of AF, showing the best overall performances among all modified AFs prepared using a green technique reported so far. The origin behind the attractive performances of BL-AFs is revealed through correlating with structures of original and modified fibers. The excellent comprehensive properties of BL-AFs demonstrate that the green method provided in this study is facile and effective to completely solve the bottlenecks of aramid fibers, and developing higher performance organic fibers. [ABSTRACT FROM AUTHOR]

Dibutyl phthalate (DBP) has been extensively used as a plasticizer in many daily products, which is highly toxic to human, notably affecting the reproductive and developmental function. As the previous method is expensive, time-consuming, low sensitivity and just focused on the environment. Present study was aimed to establish an ultra-sensitive and simple method based on good quality monoclonal antibody, applying to evaluate excretion level of DBP in urine samples of Chinese population directly. A monoclonal antibody was generated and characterized after fusion of myeloma cells with spleen cells isolated from BALB/c mouse. The mouse was previously immunized using a specially designed amino derivative of DBP conjugated with bovine serum albumin (BSA) as immunogen. Cross-reactivity values of the monoclonal antibody against DBP, di-isobutyl phthalate (DIBP) were observed 100% and 1.25%, while for dimethyl phthalate (DMP), butyl benzyl phthalate (BBP) and didecyl phthalate (DDP) the values were < 0.06%. The standard curve was constructed at 0–50 ng mL − 1 and good linearity (R 2 = 0.994) was achieved. The observed IC 50 (7.34 ng mL − 1 ) and LOD (0.06 ng mL − 1 ) values was improved 1000-fold to polyclonal antibody and 5-fold to other monoclonal antibodies. A total 1246 urine samples were analyzed and the detection frequency of DBP was observed 72.87% by ic-ELISA. The 95th percentile and mean concentration of DBP were 12.07 and 3.00 ng mL − 1 . Acceptable recovery rates of DBP were 97.8–114.3% and coefficients variation 5.93–11.09%. The concentrations of DBP in females were found significantly higher (p < 0.05) than males. Similarly, the DBP in middle aged and low educated individuals was found higher (p < 0.001) than the others. Considering the adverse health effects, DBP internal exposure in the Chinese population should be reduced. The ic-ELISA method has been proved as a cost effective, specific, and highly sensitive screening tool to detect DBP in urinary samples. [ABSTRACT FROM AUTHOR]

Background Currently, several diagnostic assays for Helicobacter pylori ( H. pylori) are available, but each has some limitations. Further, a high-flux quantitative assay is required to assist clinical diagnosis and monitor the effectiveness of therapy and novel vaccine candidates. Methods Three hundred and eighty-seven adult patients [nonulcer dyspepsia ( NUD) 295, peptic ulcer disease ( PUD) 77, gastric cancer ( GC) 15] were enrolled for gastrointestinal endoscopies. Three biopsy samples from gastric antrum were collected for the following tests: culture, rapid urease test ( RUT), histopathology, conventional polymerase chain reaction ( PCR), and Multiple Genetic Analysis System ( MGAS). The diagnostic capability of H. pylori for all methods was evaluated through the receiver operating characteristic ( ROC) curves. Results Based on the gold standard, the sensitivity and specificity of MGAS were 92.9 and 92.4%, and positive predict value ( PPV) and negative predict value ( NPV) were 96.0 and 87.1%, respectively. All the above parameters of MGAS were higher than that of culture (except its specificity), RUT and histopathology, and nearly closed to that of conventional PCR. The area under curve ( AUC) was 0.7575 (Culture), 0.8870 ( RUT), 0.9000 (Histopathology), 0.9496 (Conventional PCR), and 0.9277 ( MGAS). No significant statistical difference was observed for the H. pylori DNA load in different disease groups ( p = .067). In contrast, a statistically significant difference in the H. pylori DNA copy number was observed based on age ( p = .043) and gender ( p = .021). Conclusions The data showed that MGAS performed well in detecting H. pylori infection. Furthermore, the quantitative analysis showed that the load of H. pylori was significantly different within both age and gender groups. These results suggested that MGAS could be a potential alternative method for clinical detection and monitoring of the effectiveness of H. pylori therapy. [ABSTRACT FROM AUTHOR]

We prove the condition “c is neither 0 nor a negative integer” can be dropped on the boundedness of a class of integral operators Sa,b,c on Lp space, which improves the result by Krues and Zhu. Besides, the exact norm of Sa,b,c on Lp space is also obtained under the assumption c=n+1+a+b. [ABSTRACT FROM AUTHOR]

• We propose a novel multi-task collaboration network (MTCNet) to deal with faces with large rotation faces by cooperation between face detection and face alignment, which avoids the semantic ambiguity among RIP angle groups well. • We incorporate the angle information to optimize the loss function of face alignment to improve the efficiency of model convergence. • We introduce the contextual information to enhance the quality of feature maps for mining those faces with serious rotation change from unconstrained scenarios. • Extensive experiments are conducted to demonstrate the effectiveness of the proposed MTCNet on FDDB and WIDER FACE. Detecting rotated faces is a challenging task with images from uncontrolled environments. The use of deep convolutional neural networks have greatly improved detection performance, but these methods still do not fully exploit face structure information. This leaves faces with more extreme rotation angles undetectable. In this paper, we present a novel Multi-Task Collaboration Network (MTCNet) for rotation-invariance face detection that fully uses facial landmarks to improve the detection performance by means of collaboration between face detection and face alignment. Differing from previous methods that predict rotation angles in a single step, MTCNet employs a cascaded architecture with three stages to predict faces with gradually decreasing rotation-in-plane ranges in a coarse-to-fine process. Accurate facial landmarks further facilitate face detection. We also introduce a new training loss by integrating the geometric angle into the penalization process, which is much more reasonable than measuring the differences of training samples roughly. Our approach also explores contextual information to distinguish challenging faces from unconstrained scenarios. Extensive experimental results were conducted to demonstrate the effectiveness of MTCNet on both the multiple orientation and rotation datasets. Empirical studies show that MTCNet achieves results competitive with state-of-the-art face detectors while being time-efficient. [ABSTRACT FROM AUTHOR]

We completely characterize L p − L q boundedness of two classes of Forelli-Rudin type operators on the unit ball of C n for all (p , q) ∈ [ 1 , ∞ ] × [ 1 , ∞ ]. The results are not only a complement to some previous results on Forelli-Rudin type operators by Kures and Zhu in 2006 and the first author in 2015, but also a high dimension extension of some results by Cheng, Fang, Wang and Yu in 2017. [ABSTRACT FROM AUTHOR]

Background: Pneumoconiosis is the most prevalent occupational disease and displays different patterns in each province of China. Clarifying specific incidence patterns and temporal trends in Zhejiang Province can help provide valuable information on the prevention of pneumoconiosis. Methods: Annual reports of pneumoconiosis for Zhejiang Province from 2006 to 2020 were extracted from the National Occupational Disease and Occupational Health Information Monitoring System. The information of cases included regions, diagnosis ages, genders, exposure durations, pneumoconiosis categories and stages, the first year of exposure, enterprise industries, scales and ownerships. Results: Totally 6037 new cases of pneumoconiosis were reported between 2006 and 2020, which increased at first and then gradually declined since 2013. Among all pneumoconiosis cases, silicosis accounted for the majority (72.17%). Most of the cases occurred in small-scale and domestic-funded enterprises, which accounted for 71.75% and 96.97%, respectively. When analyzing the industry distribution, the cases were mainly concentrated in mining (37.12%), manufacturing (31.11%) and 'public administration and social organization' (23.94%) industry. The average diagnosis age among the pneumoconiosis cases was 55.44 years, and the median exposure duration was 11.00 years. Significantly older diagnosis age and longer exposure duration were found in females, coal workers' pneumoconiosis cases, cases with higher stages, cases with the first year of dust exposure earlier and cases from large-scale companies. In regional distribution, the top three cities reporting the most pneumoconiosis cases in Zhejiang Province were Taizhou, Quzhou and Hangzhou. Conclusion: The current situation of pneumoconiosis in Zhejiang Province was still serious, and government should further strengthen the surveillance of occupational diseases and supervision of enterprises. Moreover, publicity and education regarding pneumoconiosis should be carried out to raise awareness of dust exposure risk and associated health consequences. [ABSTRACT FROM AUTHOR]

Local binary pattern (LBP) operator offers an efficient way to recognize face under varying illumination, while it has the drawback of abandoning some important texture features. Local multiple patterns (LMP) has alleviated the problem by a hierarchical model. However, the LMP method can bring out the rapid expansion of feature dimension, so a special feature encoding method is adopted by this paper. Meanwhile, we find that the LMP features of different layers can be used to recognize face independently so that it would preserve more abundant recognition information. Most importantly, the contribution of the LMP features from different layers is blurry under varying illumination. We propose a fuzzy framework to fuse the recognition result of different layers and use adaptive weights to calculate contribution rates of different layers under varying illumination. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods on four databases such as Yale B, Extended Yale B, CMU PIE and Outdoor. [ABSTRACT FROM AUTHOR]

Background: Family doctors in rural China are the main force for primary health care, but the workforce has not been well stabilized in recent years. Surface acting is an emotional labor strategy with a disparity between inner feelings and emotional displays, provoking negative effects such as emotional exhaustion, occupational commitment reduction, and, consequently, increasing turnover rate. With the Conservation of Resources theory, this study explores how the surface acting of rural family doctors affects turnover intention through emotional exhaustion and investigates what role occupational commitment plays in this relationship. Methods: With a valid response rate of 93.89%, 953 valid data were collected by an anonymous self-administered questionnaire survey in December 2021 in Shandong Province, China. Cronbach's Alpha and confirmatory factor analysis (CFA) were used to estimate reliability and construct validity, respectively. The PROCESS macro in SPSS was performed to analyze the mediating and moderated mediation effects of surface acting, emotional exhaustion, occupational commitment, and turnover intention. Results: Reliability and validity indicated that the measurement instruments were acceptable. Surface acting had a direct positive effect on turnover intention (β = 0.481, 95% CI [0.420, 0.543]). Emotional exhaustion partially mediated the effect of surface acting on turnover intention (indirect effect: 0.214, 95% CI [0.175, 0.256]). Occupational commitment moderated the effect of emotional exhaustion on turnover intention (β = − 0.065, 95% CI [− 0.111, − 0.019]), and moderated the indirect effect of surface acting on turnover intention via emotional exhaustion (index of moderated mediation: − 0.035). Conclusions: Emotional exhaustion partially mediates the relationship between surface acting and turnover intention among family doctors in rural China, and occupational commitment moderates the direct effect of emotional exhaustion on turnover intention and further moderates the mediating effect. Policymakers should pay more attention to the effects of emotional labor and emotional resource depletion on the stability of rural health human resources. [ABSTRACT FROM AUTHOR]

• Illumination-invariant local binary descriptor learning method for face recognition. • The dynamic thresholds strategy is determined to extract LNMLCP features. • EDA and fuzzy fusion framework are further employed to improve face recognition rate. Traditional face recognition method usually faces the challenge of varying lighting condition. In this paper, we propose an illumination-invariant local binary descriptor learning method for face recognition. Unlike local binary descriptor (LBP) and its variants, which usually utilize the rigid sign function for binarization despite of data distributions. We first determine a dynamic thresholds strategy including the information of illumination variation to extract nonlinear multi-layer contrast features. Specially, Exponential Discriminant Analysis (EDA) is designed to act as preprocessing which can contribute to improve the discriminative ability of the face image by enlarging the margin between different classes relative to the same class. To further improve the recognition performance, we combined our preliminary work, the adaptive fuzzy fusion framework, to integrate the recognition results for multi-scale features spaces. Extensive experiments conducted on four face databases validate the effectiveness of the proposed method for illumination face recognition. [ABSTRACT FROM AUTHOR]

Wilson's disease (WD) is an autosomal recessive disorder of copper metabolism caused by mutations in the ATP7B gene encoding a liver active copper transport enzyme. Gene therapy with adeno-associated virus (AAV) carrying full-length ATP7B, which is about 4.4 kb, was shown to rescue copper metabolism disorder in WD mouse model. However, due to its relatively large size, the AAV vector containing full-length ATP7B could be oversized for its packaging capacity, which could lead to inefficient packaging. To this purpose, we engineered a truncated ATP7B mutant (tATP7B) that is about 3.3 kb in length and used for AAV gene therapy for WD mice. In vitro test showed that the excretion of copper outside the cells could be achieved with tATP7B as efficient as the full-length ATP7B. In vivo delivery of tATP7B to WD mice by AAV8 vectors corrected their copper metabolisms and significantly rescued copper accumulation-related syndromes, including reduced urinary copper excretion, increased serum ceruloplasmin, and improved liver damages. Thus, our study demonstrated that AAV gene therapy based on truncated ATP7B is a promising strategy in the treatment of WD. [ABSTRACT FROM AUTHOR]

A correction to the article "A Creative Helicobacter pylori Diagnosis Scheme Based on Multiple Genetic Analysis System: Qualification and Quantitation" is presented.

Aim: To investigate the effects of penehyclidine hydrochloride combined with dexmedetomidine on pulmonary function in patients undergoing heart valve surgery with cardiopulmonary bypass (CPB). Methods: A total of 180 patients undergoing elective heart valve surgery with CPB were randomly divided into four groups: 45 in group P (intravenous penehyclidine hydrochloride 0.02 mg/kg 10 min before anesthesia induction and at the beginning of CPB, total 0.04 mg/kg); 43 in group D (dexmedetomidine 0.5 μg/kg/h after induction of anesthesia until the end of anesthesia); 44 in group PD (penehyclidine hydrochloride 0.04 mg/kg combined with dexmedetomidine 0.5 μg/kg/h intravenously during anesthesia); and 43 in group C (same amount of normal saline 10 min before and after anesthesia induction, to the end of anesthesia, and at the beginning of CPB). The main outcomes were the incidence and severity of postoperative pulmonary complications (PPCs). The secondary outcomes were: (1) extubation time, length of stay in intensive care, and postoperative hospital stay, and adverse events; and (2) pulmonary function evaluation indices (oxygenation index and respiratory index) and plasma inflammatory factor concentrations (tumor necrosis factor-α, interleukin-6, C-reactive protein and procalcitonin) during the perioperative period. Results: The incidence of PPCs in groups P, D and PD after CPB was lower than that in group C (P < 0.05), and the incidence in group PD was significantly lower than that in groups P and D (P < 0.05). The scores for PPCs in groups P, D and PD were lower than those in group C (P < 0.05). Conclusion: Combined use of penehyclidine hydrochloride and dexmedetomidine during anesthesia reduced the occurrence of postoperative pulmonary dysfunction, and improved the prognosis of patients undergoing heart valve surgery with CPB. Trial registration: The trial was registered in the Chinese Clinical Trial Registry on 3/11/2020 (Registration No.: ChiCTR2000039610). [ABSTRACT FROM AUTHOR]

To identify the association between quantified sleep factors and the incidence of cardiovascular disease (CVD) through a 10-year prospective cohort study. A total of 45,919 individuals were recruited in this population-based prospective study. The healthy sleep score was constructed by four sleep measures (sleep duration, insomnia symptoms, snoring and daytime sleepiness), which were collected by questionnaire. The hazard ratio (HR) and 95% confidence intervals (CIs) were calculated by the multivariate-adjusted Cox proportional hazards model. Restricted cubic spline analysis was used to examine the dose‒response relationships between healthy sleep scores and outcomes. During a median follow-up of 10.73 years (interquartile range: 10.08–11.72 years), 10,523 cases of total CVD incidence, 3766 cases of CHD, and 3967 cases of stroke incidence were documented. Our results found that participants who maintained four healthy sleep measures (including no insomnia, snoring, or frequent daytime sleepiness and sleeping 7–8 h/d) had a 12% (HR: 0.88, 95% CI: 0.84–0.93) and 16% (HR: 0.84, 95% CI: 0.78–0.92) lower risk of developing CVD and CHD, respectively, but not stroke. There was a dose‒response relationship between sleep scores and the risk of cardiovascular events. With an increasing healthy sleep score, the risk of cardiovascular events decreases. Compared to those with a sleep score of 0–1, participants with a score of 4 had 27% (HR: 0.73, 95% CI: 0.67–0.79), 25% (HR: 0.75, 95% CI: 0.65–0.87), and 24% (HR: 0.76, 95% CI: 0.66–0.86) reduced risks of CVD, CHD, and stroke, respectively. In this large prospective cohort study, a healthy sleep pattern effectively reduced the risk of CVD, CHD, and stroke. • There was a dose‒response relationship between sleep scores and the risk of cardiovascular events. • Higher healthy sleep score reduces risk of CVD, CHD, and stroke. • Healthy sleep behaviors reduce the risk of CVD, CHD, and stroke, especially in those between 30 and 60 years old. [ABSTRACT FROM AUTHOR]

The image classification precision is vastly enhanced with the growing complexity of convolutional neural network (CNN) structures. However, the uneven visual separability between categories leads to various difficulties in classification. The hierarchical structure of categories can be leveraged to deal with it, but a few CNNs pay attention to the character of data. Besides, a network model with a hierarchical structure is promising to extract more specific features from the data than current CNNs, since, for the latter, all categories have the same fixed number of layers for feed-forward computation. In this paper, we propose to use category hierarchies to integrate ResNet-style modules to form a hierarchical network model in a top-down manner. To extract abundant discriminative features and improve the computation efficiency, we adopt residual block selection based on coarse categories to allocate different computation paths. Each residual block works as a switch to determine the JUMP or JOIN mode for an individual coarse category. Interestingly, since some categories need less feed-forward computation than others by jumping layers, the average inference time cost is reduced. Extensive experiments show that our hierarchical network achieves higher prediction accuracy with similar FLOPs on CIFAR-10 and CIFAR-100, SVHM, and Tiny-ImageNet datasets compared to original residual networks and other existing selection inference methods. • The hierarchy of classes is used to form a hierarchical network. • The selective computation based on coarse category is proposed for the first time. • The proposed network model helps inference acceleration and accuracy improvement. [ABSTRACT FROM AUTHOR]

This article aims to explore hub genes related to different clinical types of cases with COVID-19 and predict the therapeutic drugs related to severe cases. The expression profile of GSE166424 was divided into four data sets according to different clinical types of COVID-19 and then calculated the differential expression genes (DEGs). The specific genes of four clinical types of COVID-19 were obtained by Venn diagram and conducted enrichment analysis, protein–protein interaction (PPI) networks analysis, screening hub genes, and ROC curve analysis. The hub genes related to severe cases were verified in GSE171110, their RNA-specific expression tissues were obtained from the HPA database, and potential therapeutic drugs were predicted through the DGIdb database. There were 536, 266, 944, and 506 specific genes related to asymptomatic infections, mild, moderate, and severe cases, respectively. The hub genes of severe specific genes were AURKB, BRCA1, BUB1, CCNB1, CCNB2, CDC20, CDC6, KIF11, TOP2A, UBE2C, and RPL11, and also differentially expressed in GSE171110 (P < 0.05), and their AUC values were greater than 0.955. The RNA tissue specificity of AURKB, CDC6, KIF11, UBE2C, CCNB2, CDC20, TOP2A, BUB1, and CCNB1 specifically enhanced on lymphoid tissue; CCNB2, CDC20, TOP2A, and BUB1 specifically expressed on the testis. Finally, 55 drugs related to severe COVID-19 were obtained from the DGIdb database. Summary, AURKB, BRCA1, BUB1, CCNB1, CCNB2, CDC20, CDC6, KIF11, TOP2A, UBE2C, and RPL11 may be potential diagnostic biomarkers for severe COVID-19, which may affect immune and male reproductive systems. 55 drugs may be potential therapeutic drugs for severe COVID-19. [ABSTRACT FROM AUTHOR]

Background: Village doctors in China are not only the gatekeepers of rural residents' health but also the net bottom of the medical security system. However, emotional labour is increasingly threatening the stability of the rural primary medical system. In addition, the ongoing coronavirus disease 2019 (COVID-19) pandemic has further exposed the vulnerability of human resources in China's rural health system. This study aims to evaluate the current situation of emotional labour among village doctors and explore the impact of emotional labour on job burnout during the COVID-19 pandemic in China. Methods: A cross-sectional survey was conducted in December 2021 in Shandong Province. We used structured questionnaires to collect data, including sociodemographic characteristics, emotional labour, and job burnout. Data were analysed by t test, analysis of variance (ANOVA), Pearson correlation analysis, and hierarchical multiple linear regression. Results: A total of 1,093 village doctors from Shandong Province participated in the study. More than half of the participants were male (62.40%) and were between 41 and 50 years old (53.43%). The total mean score of emotional labour was 3.17 ± 0.67, of which the surface acting (SA) score was 2.28 ± 0.90, and the deep acting (DA) score was 3.91 ± 0.93. There were significant differences in SA according to gender and work content (P < 0.05) and in DA according to gender, age, education level, and work content (P < 0.05). Pearson correlation analysis showed that SA was positively correlated with job burnout (P < 0.001), and DA was negatively correlated with job burnout (P < 0.001). Hierarchical multiple linear regression analysis revealed that 29% of the variance in job burnout is attributable to SA (β = 0.530, P < 0.001) and DA (β = -0.154, P < 0.001). Conclusion: Village doctors in Shandong Province performed moderate levels of emotional labour during the COVID-19 pandemic. SA had a significant positive effect on job burnout, while DA had a significant negative effect on job burnout among village doctors. Administrators should enhance training on emotional labour for village doctors to play a positive role in alleviating their job burnout. [ABSTRACT FROM AUTHOR]

Background: Causal inference is a crucial element within medical decision-making. There have been many methods for investigating potential causal relationships between disease and treatment options developed in recent years, which can be categorized into two main types: observational studies and experimental studies. However, due to the nature of experimental studies, financial resources, human resources, and patients' ethical considerations, researchers cannot fully control the exposure of the research participants. Furthermore, most existing observational research designs are limited to determining causal relationships and cannot handle observational data, let alone determine the dosages needed for medical research. Results: This paper presents a new experimental strategy called quasi-intervention for quantifying the causal effect between disease and treatment options in observed data by using a causal inference method, which converts the potential effect of different treatment options on disease into computing differences in the conditional probability. We evaluated the accuracy of the quasi-intervention by quantifying the impact of adjusting Chinese patients' neutrophil-to-lymphocyte ratio (NLR) on their overall survival (OS) (169 lung cancer patients and 79 controls).The results agree with the literature in this study, consisting of nine papers on cohort studies on the NLR and the prognosis of lung cancer patients, proving that our method is correct. Conclusion: Taken together, the results imply that quasi-intervention is a promising method for quantifying the causal effect between disease and treatment options without clinical trials, and it could improve confidence about treatment options' efficacy and safety. [ABSTRACT FROM AUTHOR]

Background: Cisplatin-based concurrent chemoradiotherapy has been identified as the primary and standard treatment for locally advanced nasopharyngeal carcinoma (NPC). However, the side effects of cisplatin affect the compliance to therapy. Thus, the search for a platinum-based substitute for NPC has always been a research focus. However, there is a variability in the efficacy of different platinum-based chemotherapies in the treatment of NPC. We performed a meta-analysis to compare the efficacy and safety of cisplatin-based regimens and other platinum-based derivatives (carboplatin, nedaplatin, and lobaplatin) for locally advanced NPC.Methods: PubMed, EMBASE, Cochrane Library, Web of Science, and ClinicalTrials.gov were systematically searched for all potentially eligible clinical trials as of February 15, 2022. The pooled hazard ratios, risk ratio, and 95% confidence interval were calculated using Review Manager Software version 5.4.Results: A total of 1,907 patients with locally advanced NPC were eligible from the 1,265 retrieved records. This systematic review included eight articles, six of which were randomized controlled clinical trials. There was no significant difference in the 3- and 5-year overall survival, progression-free survival, distant metastasis-free survival, and locoregional relapse-free survival between cisplatin-based chemotherapy and other platinum-based chemotherapy. Severe acute hematological side effects (≥ grade 3) during treatment, such as neutropenia, leukopenia, and thrombocytopenia, were equivalent in both groups. However, the incidence of anemia was higher in patients receiving other platinum-based chemotherapies. The risk of nausea, vomiting and weight loss was higher in the cisplatin group; however, there was no significant difference in the other non-hematological and late side effects between the two groups.Conclusions: Other types of platinum-based chemotherapies are as effective as cisplatin-based chemotherapy in the treatment of locally advanced NPC, thus acting as potential alternatives to cisplatin. Further studies providing high-level evidence are needed. [ABSTRACT FROM AUTHOR]

The recognition of protospacer adjacent motif (PAM) is a key factor for the CRISPR (i.e. clustered regularly interspaced short palindromic repeats)/CRISPR‐associated 9 (Cas9) system to distinguish foreign DNAs from the host genome, and also significantly restricts the targeting scope of the system during genome‐editing applications. Structurally, the PAM interacting (PI) domain, which usually is located in the C‐terminus of Cas9 proteins, directly binds to PAM and plays a key role in determining the recognition specificity. However, several lines of evidence showed that other regions of Cas9 protein beyond the PI domain might also play roles in PAM interaction. Here, we constructed a mosaic SpCas9 protein (xCas9‐NG) by fusing the PI domain of SpCas9 PAM variant, Cas9‐NG with the non‐PI fragment of xCas9 protein that contains multiple amino acid substitutions. We found that non‐PI fragment of xCas9 expanded PAM recognition of the Cas9‐NG PI domain. In addition, xCas9‐NG showed an improved editing efficiency in the majority of targets harboring xCas9 and Cas9‐NG PAMs. Importantly, this finding was also successfully extended to other Cas9 variants, including SpRY and the non‐G SpCas9 series. Together, our work expands the target scope of SpCas9 editing system and demonstrates the notion that the non‐PI domain fragment plays an important role in PAM restriction. [ABSTRACT FROM AUTHOR]

There has been no severity evaluation model for pediatric patients with hemophagocytic lymphohistiocytosis (HLH) that uses readily available parameters. This study aimed to develop a novel model for predicting the early mortality risk in pediatric patients with HLH using easily obtained parameters whatever etiologic subtype. Patients from one center were divided into training and validation sets for model derivation. The developed model was validated using an independent validation cohort from the second center. The prediction model with nomogram was developed based on logistic regression. The model performance underwent internal and external evaluation and validation using the area under the receiver operating characteristic curve (AUC), calibration curve with 1000 bootstrap resampling, and decision curve analysis (DCA). Model performance was compared with the most prevalent severity evaluation scores, including the PELOD-2, P-MODS, and pSOFA scores. The prediction model included nine variables: glutamic-pyruvic transaminase, albumin, globulin, myohemoglobin, creatine kinase, serum potassium, procalcitonin, serum ferritin, and interval between onset and diagnosis. The AUC of the model for predicting the 28-day mortality was 0.933 and 0.932 in the training and validation sets, respectively. The AUC values of the HScore, PELOD-2, P-MODS and pSOFA were 0.815, 0.745, 0.659 and 0.788, respectively. The DCA of the 28-day mortality prediction exhibited a greater net benefit than the HScore, PELOD-2, P-MODS and pSOFA. Subgroup analyses demonstrated good model performance across HLH subtypes. The novel mortality prediction model in this study can contribute to the rapid assessment of early mortality risk after diagnosis with readily available parameters. [ABSTRACT FROM AUTHOR]

Objective. Nasopharyngeal carcinoma is particularly prevalent in Guangdong and Guangxi (southern China); the economic burden of nasopharyngeal cancer patients is heavy in China. This study is aimed at retrospectively analyzing the basic features and economic burden of newly diagnosed nasopharyngeal carcinoma patients admitted to the First Affiliated Hospital of Guangxi Medical University and at providing a scientific basis for nasopharyngeal carcinoma prevention and control strategies. Methods. The data of 3,727 nasopharyngeal carcinoma inpatients diagnosed from January 2012 to December 2020 were extracted from the Guangxi Nasopharyngeal Carcinoma Healthcare Big Data Management Information Platform. Basic demographic characteristics, duration of hospital stay, and hospitalization cost of nasopharyngeal carcinoma patients were collected and analyzed statistically. Results. The incidence period of nasopharyngeal carcinoma was primarily from 30 to 69 years of age, with the 40–49-year age group comprising the largest proportion of nasopharyngeal carcinoma patients, accounting for 34.18% of the patients with newly diagnosed nasopharyngeal carcinoma in the hospital. The male-to-female ratio was 2.87 : 1. There were 2,223 cases from rural areas, 2,153 from the Han ethnic group, and 1,460 from the Zhuang ethnic group, accounting for 59.65%, 55.77%, and 39.17% of the total number of cases, respectively. The average duration of hospitalization decreased whereas the average hospitalization cost increased annually. Multivariate analysis of hospitalization cost showed that the duration of hospital stay, rural/urban, and ethnicity was the main influencing factors: the longer the duration of hospital stay, the higher the hospitalization cost; patients from rural incurred lower costs than from urban; ethnic Zhuang patients incurred significantly lower costs than patients from other ethnicities. Conclusion. Early diagnosis and treatment should be actively carried out to reduce the incidence of nasopharyngeal carcinoma, especially for rural, ethnic Zhuang, and males in the 40–49-year age group patients. The future research on nasopharyngeal carcinoma will focus on exploring the pathogenesis of nasopharyngeal carcinoma, improving the screening system, and reducing the burden on patients, in order to further improve the survival rate and quality of life of patients with nasopharyngeal carcinoma. [ABSTRACT FROM AUTHOR]

Background: Arteriovenous fistulas (AVF) have been the main vascular accesses for haemodialysis patients, but the maintenance after maturation poses serious challenges. Arm exercises promote the maturation of AVFs. However, few studies have evaluated the effect of arm exercise on matured AVF and addressed the intervention for late fistula failure. Objectives: The study was conducted to explore the effect of dumbbell exercise on mature AVF. Methods: 86 participants undergoing haemodialysis with AVFs were randomized into the control group and experimental group. The experimental group held 6-pound dumbbells on non-dialysis days for 3 months, while the control group squeezed rubber balls. Results: For blood flow of draining vein (DV; primary outcome), the between-group effects, interaction effect and time effect showed significant differences. A significant increase in blood flow of DV was observed in the dumbbell group at the 3rd month (mean difference, 359.50 [111.90–829.05] mL/min; p = 0.001). The difference in blood flow of AVF proximal artery, blood flow of brachial artery, the diameter of DV and the incidence of adverse events at 3 months (secondary outcomes) between the 2 groups was insignificant. Conclusion: Prolonged training with arm exercises is essential for patients with AVFs though the fistula has matured. The designed dumbbell exercise is an economical, effective intervention to maintain the function of AVF, especially for patients with potential reduction of access blood flow and no percutaneous transluminal angioplasty indication. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) is a widely used tool for dimensionality reduction and feature extraction in the field of computer vision. Traditional PCA is sensitive to outliers which are common in empirical applications. Therefore, in recent years, massive efforts have been made to improve the robustness of PCA. However, many emerging PCA variants developed in the direction have some weaknesses. First, few of them pay attention to the 2D structure of error matrix. Second, to estimate data mean from sample set with outliers by averaging is usually biased. Third, if some elements of a sample are disturbed, to extract principal components (PCs) by directly projecting data with transformation matrix causes incorrect mapping of sample to its genuine location in low-dimensional feature subspace. To alleviate these problems, we present a novel robust method, called nuclear norm-based on PCA (N-PCA) to take full advantage of the structure information of error image. Meanwhile, it is developed under a novel unified framework of PCA to remedy the bias of computing data mean and the low-dimensional representation of a sample both of which are treated as unknown variables in a single model together with projection matrix. To solve N-PCA, we propose an iterative algorithm, which has a closed-form solution in each iteration. Experimental results on several open databases demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Background: Pesticide poisoning in children has been a serious public health issue around the world, especially in the developing countries where agriculture is still one of the largest economic sectors. The purpose of this study was to analyze epidemiological characteristics of acute pesticide poisoning in children from Zhejiang province, China.Methods: The pesticide poisoning cases for children were retrieved from Occupational Disease Surveillance and Reporting System, Zhejiang Provincial Center for Disease Control and Prevention, China. The incident cases, deaths, and fatality rate of child pesticide poisoning from 2006 through 2015 were calculated.Results: During the study period, totally 2952 children were poisoned by pesticides, with 66 deaths, resulting in a fatality rate of 2.24%. Among them, there were 1607 male cases with 28 deaths, and 1345 female cases with 38 deaths. Most of the cases occurred in preschool children (1349) and adolescent age group (1269). Organophosphate and carbamate insecticides were the cause of most poisonings (1130), leading to 34 deaths. The highest fatality rate (3.13%) was due to poisoning by herbicides and fungicides, causing 14 deaths out of 448 cases. Poisoning occurred mostly in rural areas (78%). And most pesticide poisoning occurred in the summer (896) and fall (811), while fewest poisoning cases in the winter (483) but with the highest fatality rate (3.52%).Conclusions: This study shows that pesticide poisoning of children is a major health problem in Zhejiang, suggesting preventive strategies should be conducted to control childhood pesticide poisoning. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated that propofol causes neurodegeneration in developing brains. Evidence has shown that dexmedetomidine has neuroprotective effects. However, whether dexmedetomidine can reduce propofol-induced neuroapoptosis and by what mechanisms it acts remain unclear. We investigated whether dexmedetomidine can attenuate propofol-induced neuroapoptosis by disturbing the PI3K/Akt/GSK3β pathway during brain development. Seven-day-old rats were randomly exposed to 100 mg/kg propofol and 100 mg/kg propofol plus different doses of dexmedetomidine or 100 mg/kg propofol and 75 μg/kg dexmedetomidine plus PI3K inhibitor LY294002 or GSK3β inhibitor TDZD-8. TEM and TUNEL were used to detect neuronal structure changes and apoptosis. The expression of phospho-Akt, phospho-GSK3β, Akt and GSK3β were quantified using western blots and immunofluorescence. Pretreatment with different doses of dexmedetomidine protected against propofol-induced neuroapoptosis. Furthermore, propofol decreased the levels of phospho-Akt and phospho-GSK3β, whereas dexmedetomidine partially reversed this inhibition. In addition, treatment with LY294002 inhibited the neuroprotection of dexmedetomidine, whereas TDZD-8 enhanced neuroprotection. Our results indicate that dexmedetomidine prevents propofol-induced neuroapoptosis by increasing the levels of phospho-Akt and phospho-GSK3β. [ABSTRACT FROM AUTHOR]

Bag of Words (BoW) model has been widely used in conventional object recognition tasks. Different from the existing methods, this paper proposed a method for object recognition based on Region of Interest (ROI) and Optimal Bag of Words model. It includes the following steps: (1) ROI extraction in combination with the Shi–Tomasi corner and Itti saliency map; (2) The SIFT feature descriptors are detected and described on images of interest; (3) A visual codebook is generated through utilizing the Gaussian mixture models, which relies on the clustering results of k-means++; (4) The similarities between each visual word and corresponding local feature are computed by posterior pseudo probabilities discriminative to construct a visual word soft histogram for image representation; (5) The Support vector machine (SVM) is used to perform image classification and recognition. The experiments are performed on the MSRC 21-class database. The results show that the proposed method can be more accurately recognize images. [ABSTRACT FROM AUTHOR]

Salbutamol (SAL) is known as quick-acting β-2 receptor agonist and its use in humans for pulmonary diseases and/or in animal feed is limited because of associated potential hazardous effects on health. Several techniques are available for the detection of SAL, but are expensive and time consuming. Here for the first time, a novel pre-assembled DNA immunoprobe-based immuno-PCR assay was developed to investigate the levels of SAL in human urine samples and compared the proposed immuno-PCR assay for the detection of SAL with that of direct competitive ELISA. Under the optimized conditions, the assay showed a linear range over seven orders of magnitude, whereas the limit of detection of SAL in buffer was found to be 21 fg/mL. Current immuno-PCR assay exhibited a 300-fold better detection limit for SAL as compared to one achieved with direct competitive ELISA using the same antibody. In conclusion, it has been found that the developed method is highly sensitive and simple method that can significantly enhance the detection sensitivity for small molecules. Moreover it can be modified and used for detecting other small molecules and/or chemicals that exist in the environment and are responsible for the environmental pollution. [ABSTRACT FROM AUTHOR]

Abstract: Partial occlusions in face images pose a great challenge for most existing face recognition approaches. Although algorithms based on sparse representation and linear regression have demonstrated promising results about handling occlusion, the performance strongly depends on the way partition scheme is performed. In the present paper, we propose a novel method for face recognition against contiguous occlusion without using partition scheme. The general idea is to eliminate the impact of occlusions on the linear regression-based classification (LRC) method. In this approach, we first analyze that error image derived from the LRC is a better choice than original image for identifying occluded regions. Inspired by the level set methods that can provide smooth and closed contours as segmentation results which fit for the assumption of spatially continuity about occlusion, we present how to effectively use the spatial continuity of corrupted pixels to determine the occluded regions. By incorporating the idea of level set based image segmentation into the LRC, the proposed approach is capable of reliably determining the occluded regions and removing them from LRC framework. Extensive experiments on several publicly available databases (Extended Yale B, outdoor and AR) show the efficacy of the proposed approach against different types of occlusion. [Copyright &y& Elsevier]

Antimicrobial resistance (AMR) threatens global public health and modern surgical medicine. Expression of β-lactamase genes is the major mechanism by which pathogens become antibiotic resistant. Pathogens expressing extended spectrum β-lactamases (ESBL) and carbapenemases (CP) are especially difficult to treat and are associated with increased hospitalization and mortality rates. Despite considerable effort, identification of ESBLs and CPs in a clinically relevant timeframe remains challenging. In this study, a two-dimensional AMR profiling assay strategy was developed employing panels of antibiotics (penicillins, cephamycins, oximino-cephalosporins and carbapenems) and β-lactamases inhibitors (avibactam and EDTA). The assay required the development of a novel biosensor that employed New Delhi metallo-β-lactamase-1 (NDM-1) as the sensing element. Functionally probing β-lactamase activity using substrates and inhibitors combinatorically increased the informational content that enabled the development of assays capable of simultaneous, differential identification of multiple β-lactamases expressed in a single bacterial isolate. More specifically, the assay enabled the simultaneous identification of ESBL and CP in mock samples, as well as in an engineered construct which co-expressed these β-lactamases. The NDM-1 biosensor assay was 16 times and 8 times more sensitive than the ESBL Nordmann/Dortet/Poirel (NDP) and Carba Nordmann/Poirel (NP) assays, respectively. In a retrospective study, NDM-1 biosensor assays were able to differentially identify ESBLs, metallo-CPs and serine-CPs β-lactamases in 23 clinical isolates with 100% accuracy. An assay algorithm was developed which accelerated data analytics reducing turnaround to <1 h. The assay strategy integrated with AI-based data analytics has the potential to provide physicians with a comprehensive readout of patient AMR status. [Display omitted] • Two-dimensional antibiotic/inhibitor profiling creates a universal AMR detection strategy. • Elimination of culturing and sample pretreatment simplifies assay & lowers costs. • 2D profiling enables simultaneous differential determination of multiple β-lactamases. • Patient AMR β-lactamase status determined in 60 min enables personalized treatment. • Validated using clinical bacterial isolates expressing ESBL and/or carbapenemase. [ABSTRACT FROM AUTHOR]

Abstract: A semi-continuous crystallizer configuration that combines continuous seeding using a dual impinging jet with growth rate control in a stirred tank was experimentally demonstrated for the manufacture of l-asparagine monohydrate (LAM) crystals with the objective of obtaining a target flattop size distribution. The dual impinging jets combined hot and cold saturated solutions to generate highly uniform 20-μm crystals that were further grown to a desired size in the stirred tank with suppressed nucleation that was instrumented with attenuated total reflection–Fourier transform infrared (ATR–FTIR) spectroscopy and focused beam reflectance measurement (FBRM). The construction of calibration models and the measurement of solubility and metastable limit were obtained by an automated system that followed preset supersaturation profiles using feedback control. The experiments confirm that greatly enhanced control of the crystal size distribution can be achieved using continuous seeding. [Copyright &y& Elsevier]

Varying illumination is a huge challenge of face recognition. The variation caused by varying illumination in the face appearance can be much larger than the variation caused by personal identity. The high frequency signal component in image represents the detail characteristic of the face, and for the reason of being influenced scarcely by varying illumination, this signal component can be used as illumination invariance features in face recognition. However, the definition of the high frequency signal component is blurry, and it is impossible to separate this component from the face image exactly. Because of using the different decomposition methods and different decomposition parameters, high frequency component has been dispersed in decomposed detail images that characterize themselves by containing different scale frequency signal component. This paper proposes a framework to fuse that high frequency signal components in multi-scale detail images using adaptive weight. This novel framework is an open structure, and any method of getting illumination invariance feature can be applied on this framework. The experiment based on three open face databases shows the framework proposed by this paper can get remarkable performance. [ABSTRACT FROM AUTHOR]

• Hierarchically porous carbon networks (HPCN) were prepared through TMB-induced self-assembly and carbonization strategy. • The introduction of TMB results in changed morphology, enriched pore structure, and improved defect degree. • The O 2 − sensor HPCN/SPCE has the potential to distinguish between cancer and normal cells by detecting cell-released O 2 −. Realizing the quantitative detection of superoxide anions (O 2 −) is of great significance to the pathological research based on the correlation between O 2 − and many diseases. Currently, the main challenge for O 2 − electrochemical detection in the actual sample is the development of effective nano-mimetic materials to replace natural enzymes to achieve high-selectivity detection of O 2 −. In this work, hierarchically porous carbon networks (HPCN) were successfully prepared through 1,3,5-trimethylbenzene (TMB)-induced self-assembly and carbonization strategy. Owing to the introduction of TMB, HPCN obtains the changed morphology, enriched pore structure, and improved defect degree, which promotes mass transfer and leads to faster electron transfer efficiency. Therefore, the non-enzymatic O 2 − electrochemical sensor fabricated with HPCN (denoted as HPCN/SPCE) exhibits high sensitivity (607.4 μA cm−2 mM-1) and excellent selectivity for detecting O 2 −. Importantly, the sensor successfully achieved the dynamic monitor of O 2 − released from living cells and has the potential to distinguish between cancer and normal cells. [ABSTRACT FROM AUTHOR]