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Background: Tumor regression grade (TRG) correlates with prognosis in patients with locally advanced rectal cancer (LARC), but there is controversy regarding the use of magnetic resonance imaging (MRI) for determining TRG. This study to evaluate the diagnostic value of change rate in signal intensity (SI) and volume (V) from MRI to TRG following preoperative chemoradiotherapy (CRT) in patiens with LARC.Materials and methods: This retrospective analysis examined 82 LARC patients who were admitted to our institution between Oct 2017 and Oct 2019. Patients underwent pre- and post-CRT T2-weighted (T2W), diffusion-weighted (DW)/apparent diffusion coefficient (ADC), and contrast-enhanced T1-weighted (ceT1W) MRI. Change rate of volume and relative SI ratio(%△V and %△SIR) from each sequence were determined. All LARCs were confirmed pathologically and classified into TRG 0, 1, 2 and 3. Descriptive statistics and receiver operating characteristic (ROC) analysis, with calculation of area under the curve (AUC), were used to compare the diagnostic performances. Results: Sixteen patients had TRG-0, 15 had TRG-1, 35 had TRG-2, and 16 had TRG-3. Except for ADC-%△SIR, the remaining%△V and %△SIR on T1W, DWI, and ceT1W had significant differences among the four groups. %△V and/or %△SIR did not distinguish TRG-1 from TRG-2 nor TRG-2 from TRG-3, but differences between other TRGs were identified by %△V and/or %△SIR on T2W, DWI, and ceT1W. The combined use of DW-%△V and T2W-%△SIR provided the best diagnostic performance in distinguishing of TRG-0 from TRG-2 (AUC: 0.954) and from TRG-3 (AUC: 1.000).Conclusions: Preoperative MRI of LARC patients can determine TRG and may improve selection of the preoperative therapy.

Mobile apps provide new opportunities to people with disabilities to act independently in the world. Motivated by this trend, researchers have conducted empirical studies by using the inaccessibility issue rate of each page (i.e., screen level) to represent the characteristics of mobile app accessibility. However, there still lacks an empirical investigation directly focusing on the issues themselves (i.e., issue level) to unveil more fine-grained findings, due to the lack of an effective issue detection method and a relatively comprehensive dataset of issues. To fill in this literature gap, we first propose an automated app page exploration tool, named Xbot, to facilitate app accessibility testing and automatically collect accessibility issues by leveraging the instrumentation technique and static program analysis. Owing to the relatively high activity coverage (around 80%) achieved by Xbot when exploring apps, Xbot achieves better performance on accessibility issue collection than existing testing tools such as Google Monkey. With Xbot, we are able to collect a relatively comprehensive accessibility issue dataset and finally collect 86,767 issues from 2,270 unique apps including both closed-source and open-source apps, based on which we further carry out an empirical study from the perspective of accessibility issues themselves to investigate novel characteristics of accessibility issues. Specifically, we extensively investigate these issues by checking 1) the overall severity of issues with multiple criteria, 2) the in-depth relation between issue types and app categories, GUI component types, 3) the frequent issue patterns quantitatively, and 4) the fixing status of accessibility issues.

Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used. In recent years, deep learning methods have rapidly become widespread in the field of video object detection, achieving excellent results compared with those of traditional methods. However, the presence of duplicate information and abundant spatiotemporal information in video data poses a serious challenge to video object detection. Therefore, in recent years, many scholars have investigated deep learning detection algorithms in the context of video data and have achieved remarkable results. Considering the wide range of applications, a comprehensive review of the research related to video object detection is both a necessary and challenging task. This survey attempts to link and systematize the latest cutting-edge research on video object detection with the goal of classifying and analyzing video detection algorithms based on specific representative models. The differences and connections between video object detection and similar tasks are systematically demonstrated, and the evaluation metrics and video detection performance of nearly 40 models on two data sets are presented. Finally, the various applications and challenges facing video object detection are discussed.

Popliteal artery aneurysm (PAA) has been increasingly treated with endovascular intervention in recent years. However, whether transpedal access can be utilized to treat PAA has not been widely reported. We report a case of successful treatment of a PAA with a covered stent via retrograde transpedal approach in an 80-year male with prohibitive surgical risk who initially failed antegrade approach. This case demonstrates the feasibility of treating PAA via a retrograde transpedal access in selected patients.

With the development of the imaging technology of various sensors, multisource image classification has become a key challenge in the field of image interpretation. In this article, a novel classification method, called the deep multiview union learning network (DMULN), is proposed to classify multisensor data. First, an associated feature extractor is designed to process the multisource data by canonical correlation analysis (CCA) in the head of the network. Second, an improved deep learning architecture with two branches is presented to extract high-level view features from the associated features. Third, a novel pooling, called view union pooling, is proposed to fuse the multiview feature from the deep model. Finally, the fused feature is fed into the classifier. The proposed framework is easy to optimize since it is an end-to-end network. Extensive experiments and analysis on the datasets IEEE_grss_dfc_2017 and IEEE_grss_dfc_2018 show that the proposed method achieves comparable results. Our results demonstrate that abundant multisource information can improve the classification performance.

Inspired by hand push whisk blenders that are widely used in the kitchen, we here propose an energy harvester that enables conversion of low-frequency translational vibration to high-speed rotations and further generates electricity via the electromagnetic effect. The whisk-inspired energy harvester (W-EH) is composed of an internal thread sleeve, an external thread driving shaft, a spring, and a buoy. Compared with transmission approaches used by electromagnetic power generators, the proposed design has the superiority of low cost, ultra-low operation frequency (lower than 1 Hz), and high power output. Tests in water indicate that the normalized power density is 300% higher than the state of the art. Excited by mimicking ocean waves at 1.5 Hz, the W-EH prototype generates a maximum power of 64.4 milliwatts and succeeds in lighting up 115 LEDs simultaneously, which power is enough for continuous operations of most wireless sensors.

In this work, the impacts of OFF-state gate bias (VGS,OFF) on dynamic on-resistance (RON) are systematically investigated in commercial Schottky-type p-GaN Gate high-electron-mobility transistors (HEMTs). Double-pulse tester (DPT) and PIV system are adopted to evaluate the dynamic RON with various OFF-state gate and drain bias (VDS,OFF) under hard- and soft-switching conditions. More negative VGS,OFF can aggravate the dynamic RON degradation under both soft- and hard-switching, especially when switching with a high VDS,OFF. The impacts of VGS,OFF on switching transients and OFF-state stress are investigated separately to reveal the underlying mechanisms, which are found to be associated with the generation and movements of holes. OFF-state gate bias of the voltage-driving scheme needs to be carefully considered according to the identified mechanisms in terms of dynamic on-resistance degradation.

In a machining process, proper selection of process plans and cutting parameters can effectively reduce energy consumption and shorten production time. Traditionally, studies on process planning and cutting parameter optimization for energy saving are mostly concentrated on electrical energy consumption. Since the preparation process of cutting tools and cutting fluid consumes a considerable amount of energy, conservation of this part of energy consumption, namely, the embodied energy consumption, will achieve a more energy-efficient machining process. In this article, an integrated model for process planning and cutting parameter optimization is proposed to shorten production time and reduce the energy footprint (namely, electrical energy consumption and embodied energy consumption of cutting tools and cutting fluid) of a machining process. Considering that the optimization of process plan and cutting parameters in an integrated manner is a hybrid programming process, simulated annealing and quantum-behaved particle swarm optimization (SA-QPSO) hybrid algorithm is employed to solve the proposed model. Results of the case study show that: 1) embodied energy consumption of cutting tools and cutting fluid accounts for a nonnegligible proportion of energy footprint of the machining process and 2) there is a tradeoff between energy footprint and production time, and the balance of them is achieved through the proposed optimization approach.

The synthesis of materials has been an important task throughout human history, and the ability to perform successful and efficient syntheses has been a factor in industrial revolutions and societal development. Recently, the rapid development of sophisticated techniques such as flow synthesis methods, artificial intelligence, information technology, and robotics, has facilitated the autonomous synthesis of chemicals with minimal effort and at little cost to the economy. This trend has influenced recent materials research and could potentially reduce the time from discovery of materials to their commercialization. In this short review, we focus on the autonomous synthesis of materials. We first discuss how autonomous synthesis could change materials research. Then, we provide some examples of autonomous syntheses in flow reactors. Finally, we present examples of autonomous syntheses using non-flow methods.

Esophageal squamous cell carcinoma (ESCC) is malignant while the carcinogenesis is still unclear. Here, we perform a comprehensive multi-omics analysis of 786 trace-tumor-samples from 154 ESCC patients, covering 9 histopathological stages and 3 phases. Proteogenomics elucidates cancer-driving waves in ESCC progression, and reveals the molecular characterization of alcohol drinking habit associated signatures. We discover chromosome 3q gain functions in the transmit from nontumor to intraepithelial neoplasia phases, and find TP53 mutation enhances DNA replication in intraepithelial neoplasia phase. The mutations of AKAP9 and MCAF1 upregulate glycolysis and Wnt signaling, respectively, in advanced-stage ESCC phase. Six major tracks related to different clinical features during ESCC progression are identified, which is validated by an independent cohort with another 256 samples. Hyperphosphorylated phosphoglycerate kinase 1 (PGK1, S203) is considered as a drug target in ESCC progression. This study provides insight into the understanding of ESCC molecular mechanism and the development of therapeutic targets.

INTRODUCTION The purpose of this study was to assess the application of the early warning score system (EWS-S) and gauge physician awareness, perceptions of necessity and attitudes regarding these tools based on previously experienced unnoticed clinical deterioration (CDET). METHODS A cross-sectional survey was carried out via an online questionnaire at a large 3,500-bed Class 3A general hospital in China. A total of 299 physicians of adult general wards were asked to answer a translated questionnaire that was localised from the original version. Demographic profiles of patients were included as well as three other sections assessing awareness of CDET/EWS-S and gauging attitudes towards and perceptions of the necessity of EWS-S at our hospital. RESULTS A high level of physician awareness of the CDET problem was observed. Most physicians knew about the existence of a systematic assessment tool for clinical application. Physicians with previous experience in reanimation, unplanned transfer to intensive care unit (UTICU) and/or death tended to consider EWS-S necessary in attentive and well-trained staff (p < 0.05). Physicians who had previous experience with UTICU were more likely to recommend implementing EWS-S in their wards compared with those without such experience (p < 0.05). CONCLUSION Most physicians have positive attitudes towards EWS-S. However, their awareness should be further heightened. Physicians who had previous experience with CDET/UTICU were more likely to employ EWS-S in their clinical practices. To better facilitate the implementation of EWS-S in Chinese hospitals, existing facilities, policy supports, standardised managements and the development of information systems should be strengthened.

Prevailing deep methods for image recognition require massive labeled samples in each visual category for training. However, large amounts of data annotations are time-consuming, and some uncommon categories only have rare samples available. For this issue, we focus on more challenging few-shot learning (FSL) task, where just few labeled images are used in the training stage. Existing FSL models are constructed with various convolutional neural networks (CNNs), which are trained on an auxiliary base dataset and evaluated for new few-shot predictions on a novel dataset. The performance of these models is difficult to break through because of the domain shift between base and novel datasets and the monotonous network architectures. Considering that, we propose a novel automatic attribute consistent network called Auto-ACNet to overcome the above problems. On one hand, Auto-ACNet utilizes the attribute information about base and novel categories to guide the procedure of representation learning. It introduces the consistent and non-consistent subnets to capture the common and different attributes of image pair, which helps to mitigate the domain shift problem. On the other hand, the architecture of Auto-ACNet is searched with the popular neural architecture search (NAS) technique DARTS, for obtaining a superior FSL network automatically. And the DARTS’s search space is improved by adding the position-aware module to extract the attribute characteristics better. Extensive experimental results on two datasets indicate that the proposed Auto-ACNet achieves significant improvement over the state-of-the-art competitors in this literature.

Vacuum freeze-drying is a promising technology widely used in pharmaceuticals. Preparing products with prebuilt porosity has attracted considerable attention due to its potential in shortening process duration. However, the design space for the primary drying of initially unsaturated products remains unclear. A novel index, average power, was proposed in this paper to represents the collapse risk. And a multiphase model was employed in this paper to build the design space for the products with initial voids. The simulation results show that both the drying time and average power show higher sensitivity to the temperature variation than pressure. In addition, the initial saturation has significant impacts on the design space, with small initial saturation resulting in vast design space and vice versa, which implies that small initial saturation is more beneficial for the actual production. This paper would be helpful for the development of freeze-drying.

Asthma is a common chronic respiratory disease. The Qufeng Xuanbi formula (QFXBF), a Chinese herbal decoction, has shown efficacy in the management of asthma. The purpose of this study was to investigate the potential therapeutic effects of QFXBF in the treatment of asthma both in vitro and in vivo. Platelet-derived growth factor (PDGF)-induced airway smooth muscle cell (ASMC) proliferation and MTT assays were used to explore the effects of QFXBF on the proliferation of ASMCs. Moreover, 40 female BALB/c mice were randomly divided into five groups: control group, ovalbumin (OVA) group, high QFXBF group, low QFXBF group, and dexamethasone (DEX) group (n = 8 per group). A mouse allergic asthma model was established using the intranasally administered OVA sensitization method. Morphological changes in the lung tissue were examined by hematoxylin and eosin (H&E) staining and Masson’s trichrome staining. Finally, the protein expression of alpha-smooth muscle actin (α-SMA), proliferating cell nuclear antigen (PCNA), phospho-mitogen-activated protein kinase (p-MEK1/2), mitogen-activated protein kinase (MEK1/2), phospho-extracellular signal-regulated kinases (p-ERK1/2), and extracellular signal-regulated kinases (ERK1/2) in ASMCs and lung tissue were determined by western blotting and immunofluorescent staining assays. PDGF significantly increased the viability of ASMCs. Compared with mice in the control group, the airway walls and airway smooth muscle of mice in the OVA group were thickened, and the number of inflammatory cells around the bronchus significantly increased. Moreover, the administration of QFXBF markedly inhibited the proliferation of ASMCs and alleviated the pathological changes induced by OVA. Furthermore, the protein expressions of p-ERK1/2, p-MEK1/2, PCNA, and α-SMA were significantly increased in OVA-treated mice and PDGF-treated ASMCs. Finally, treatment with QFXBF also significantly decreased the protein expression of p-ERK1/2, p-MEK1/2, α-SMA, and PCNA. QFXBF inhibited the proliferation of ASMCs by suppressing MEK/ERK signaling in PDGF-induced ASMCs and OVA-induced mice.

Despite the continuously increased requirement on automated synthesis of medicines for distributed manufacturing and personal care, it remains a challenge to realize automated synthesis which requires solid-liquid phase reactions. In this work, we demonstrated an automated solid-liquid synthesis for gadopentetate dimeglumine, the most widely used magnetic resonance imaging (MRI) contrast agent. The high-efficiency reaction was performed in a 3D microfluidic chip which was fabricated by femtosecond laser micromachining. The structure of the chip realized 3D shear flow which was essential for highly efficient mixing and movement of the solid-liquid mixtures. Ultraviolet visible (UV–vis) spectrometer was employed for in-line analysis to help automation of this system. Comparing with the round-bottom flask system, this synthetic system showed significantly higher reaction rate, indicating the advantage of the 3D microfluidic technology in micro chemical engineering.

Pregabalin (PGB) is widely used clinically; however, its pharmacokinetics (PK) has not been studied in pediatric patients with renal impairment (RI). To design optimized PGB regimens for pediatric patients with varying degrees of RI and predict exposure to PGB, physiologically based pharmacokinetic (PBPK) models of PGB were developed and verified, and its disposition was simulated in the healthy population and adults with RI. The simulated results from the PBPK models after single-dose and multi-dose administrations of PGB were consistent with the corresponding observed data based on the fold error values of less than 2. The area under curve ratios were 1.23 ± 0.06, 2.02 ± 0.10, 3.86 ± 0.21, and 9.92 ± 0.79 in pediatric patients with mild, moderate, severe, and end-stage RI, respectively. Based on the predictions for pediatric patients with moderate, severe, and end-stage RI, the maximum dose should not exceed 7, 3.5, and 1.4 mg/kg/day, respectively, among those weighing 30 kg. In conclusion, the developed PBPK model is a valuable tool for predicting PGB dosage for pediatric patients with RI.

Mental health care shortages in the United States are most pronounced in rural areas. This practice-change project aimed to increase access to quality mental health care in rural areas for the treatment of depression and anxiety via telemental health delivery of care. The project yielded improvements across project aims, including an increased percent of enrolled patients from Healthcare Provider Shortage Areas; a decrease in quality measures for depression, anxiety, and disability; and increased patient satisfaction with care. The project can serve as a template for other practices wishing to increase access to mental health care in rural areas.

Pancreatic ductal adenocarcinoma (PDAC) is a common cause of cancer-related death, and most patients are with advanced disease when diagnosed. At present, despite a variety of treatments have been developed for PDAC, few effective treatment options are available; on the other hand, PDAC shows significant resistance to chemoradiotherapy, targeted therapy, and immunotherapy due to its heterogeneous genetic profile, molecular signaling pathways, and complex tumor immune microenvironment. Nevertheless, over the past decades, there have been many new advances in the key theory and understanding of the intrinsic mechanisms and complexity of molecular biology and molecular immunology in pancreatic cancer, based on which more and more diverse new means and reasonable combination strategies for PDAC treatment have been developed and preliminary breakthroughs have been made. With the continuous exploration, from surgical local treatment to comprehensive medical management, the research-diagnosis-management system of pancreatic cancer is improving. This review focused on the variety of treatments for advanced PDAC, including traditional chemotherapy, targeted therapy, immunotherapy, microenvironment matrix regulation as well as the treatment targeting epigenetics, metabolism and cancer stem cells. We pointed out the current research bottlenecks and future exploration directions.

A Ka-band differential variable gain amplifier (VGA) applying symmetrical unit arrays with 6-bit digital-controlled is presented. The proposed VGA, based on the conventional current steering theory, uses symmetrical unit arrays that keep the output impedance constant, while digital-controlled voltage changes. Then it combines neutralizing capacitances to counteract the Miller effect of parasitic capacitances between gain and drain of input transistors. The VGA with symmetrical unit arrays ensures the consistency of phase variation during gain fluctuation. The VGA is fabricated in a 65-nm CMOS technology, and achieves -20.2 to -4.2 dB gain varied with 1.52 dBm IP1dB as well as 8.6 dB noise figure at 36 GHz and less than 4.9° phase variation, while consuming 15.6 mW across 30.5-39 GHz frequency range.

Synthetic aperture radar (SAR) image change detection (CD) is an important application in the field of remote sensing. Due to the lack of labeled data especially in the pixelwise task, it is urgent to develop unsupervised techniques to effectively detect changes. In this article, we propose a novel unsupervised representation learning framework for CD in SAR images, called multiscale self-attention (SA) deep clustering based on octave convolution. The main motivation is that a convolutional neural network (CNN) has the ability to extract significant feature hidden in input images, but it relies heavily on annotated data. Clustering is typically free from supervision; however, SAR images always suffer from speckle noise, which is unfriendly for clustering. Thus, we integrate unsupervised clustering with CNN to learn clustering-friendly feature representations. In the unified framework, CNN feature learning and clustering can be optimized end-to-end without supervision. To better suppress speckle noise and boost the joint optimization for distinguishing changes and unchanges, we use the K-means++ algorithm that is robust to noise as the clustering algorithm. In the meanwhile, we introduce the octave convolution and SA mechanism into the network to fully mine important spatial structure information for enhancing noise resistance of the network. Moreover, multiscale fusion modules are proposed to fuse multiscale input into a complementary feature representation that contains more context and semantic information around each pixel so that it refines the difference feature extraction while reducing speckle noise. Experiments on challenging SAR data sets demonstrate the effectiveness and potential of the proposed model compared with the current state-of-the-art algorithms.

Objective The aim of this study was to evaluate the incidence and duration of delayed hyponatremia and to assess the factors influencing the development of delayed hyponatremia after transsphenoidal surgery (TSS) in pituitary adenomas. Methods We retrospectively analyzed the clinical data of patients with pituitary adenoma who underwent TSS. Univariable and multivariable statistics were carried out to identify factors independently associated with the occurrence of delayed hyponatremia. Results Of the 285 patients with pituitary adenoma who underwent microscopic TSS, 44 (15.4%) developed postoperative-delayed hyponatremia and 241 (84.6%) did not. The onset of delayed hyponatremia occurred an average of 5.84 days post-surgery and persisted for an average of 5.36 days. Logistic regression analysis showed the highest risk of delayed hyponatremia in patients with significant change in tumor cavity height (odds ratio (OR), 1.158; 95% CI, 1.062, 1.262; P = 0.001), preoperative hypothalamus–pituitary–thyroid axis hypofunction (OR, 3.112; 95% CI, 1.481, 6.539; P = 0.003), and significant difference in blood sodium levels before and 2 days after TSS (OR, 1.101; 95% CI, 1.005, 1.206; P = 0.039). Conclusions Preoperative hypothyroidism, difference in blood sodium levels before and 2 days after TSS, and the change in tumor cavity height after TSS played important roles in predicting postoperative-delayed hyponatremia onset in patients with pituitary adenomas.

Scatter targets of complex land covers in polarimetric synthetic aperture radar (PolSAR) images are often randomly oriented and cause randomly fluctuating echoes, which brings a challenge to PolSAR image classification. Therefore, many existing methods have alleviated this problem through orientation compensation. However, there are still two obstacles that limit the improvement of classification accuracy. On the one hand, generally, these methods process PolSAR images with fixed polarization rotation angles, which is experience-dependent and inflexible. On the other hand, for the different land covers of a PolSAR image, the existing methods do not consider these rotation angles separately. For the first obstacle, we design a group of convolution kernels called polarization rotation kernels (PRKs) and utilize them to build the polarimetric convolutional neural network (CNN) (PolCNN). The PolCNN is the base network of our final model, and it can learn polarization rotation angles adaptively. For the second obstacle, we extend the PolCNN into a multipath structure, the final model polarimetric multipath CNN (PolMPCNN). The polarization rotation angles of different land covers are directly related to the networks of different paths within the PolMPCNN. Furthermore, we also put forward the two-scale sampling and the stagewise training algorithm in order that our PolMPCNN can fit different scales of PolSAR targets and pays more attention to difficult training samples. Experiments on real PolSAR images show that the proposed model achieves the best classification results with an extremely low sampling rate of 0.1%.

Although lithium-ion batteries (LIBs) have many advantages, they cannot satisfy the demands of numerous large energy storage industries owing to their high cost, low security, and low resource richness. Aqueous zinc-ion batteries (ZIBs) with low cost, high safety, and high synergistic efficiency have attracted an increasing amount of attention and are considered a promising choice to replace LIBs. However, the existing cathode materials for ZIBs have many shortcomings, such as poor electron and zinc ion conductivity and complex energy storage mechanisms. Thus, it is crucial to identify a cathode material with a stable structure, substantial limit, and suitability for ZIBs. In this review, several typical cathode materials for ZIBs employed in recent years and their detailed energy storage mechanisms are summarized, and various methods to enhance the electrochemical properties of ZIBs are briefly introduced. Finally, the existing problems and expected development directions of ZIBs are discussed.

Since the human body is non-rigid, actions captured in different views always involve action occlusion and information loss. Recently, view-variation-related human action recognition is still a challenging problem. To address the problem, we propose a View Transformation Network (VTN) that realizes the view normalization by transforming arbitrary-view action samples to a base view to seek for a view-invariant representation. an attention learning module is designed to learn a co-attention for action samples of different views, that contributes to output a similar feature representation to erase the view diversity in different views. Extensive and fair evaluations are performed on the UESTC varying-view RGB-D dataset, the NTU RGB-D 60 dataset, and the NTU RGB-D 120 dataset, where three evaluation types,~\ie X-subject, X-view, and A-view recognition, are performed. Experiments illustrate that our VTN model achieves outstanding performance.

Recently, satellite videos provide a new way to dynamically monitor the Earth's surface. The interpretation of satellite videos has attracted more and more attentions. In this article, we focus on the problem of the vehicle tracking in satellite videos. Satellite videos usually own a lower resolution, which leads to the following phenomena: 1) the size of a vehicle target usually includes a few pixels and 2) vehicles are usually with similar appearance which easily results in the wrong tracking within the observing region. General popular tracking methods usually focus on the representation of the target and recognize it from background which are limited in this problem. As a consequence, in this article, we propose to learn motion and background of the target in order to help the trackers recognize the target with higher accuracy. A prediction network is proposed to predict the location probability of the target in each pixel in next frame based on fully convolutional network (FCN) which is learned from previous results. In addition, a segmentation method is introduced to generate the feasible region for target in each frame and assign high probability for such a region. For quantitative comparison, we manually annotate 20 representative vehicle targets from nine satellite videos taken by JiLin-1. In addition, we also selected two public satellite video datasets for experiments. Numerous experimental results demonstrate the superior of the proposed method.

Pedestrian detection has emerged as a fundamental technology for autonomous cars, robotics, pedestrian search, and other applications. Although a lot of progress has been made in pedestrian detection, it is still a challenging problem in wide field-of-view surveillance videos due to the random distribution and dynamic characteristics of pedestrians. Especially in large-scale high-resolution images, there are many pedestrians and vast scale variance, so it is difficult to accurately detect all pedestrians. In order to solve this problem, sliding window is used to crop all original images to obtain pre-detection results firstly in this paper. Then, the original images are cropped again with the object as the center utilizing the label files shared in the same scene to get multi-scale images. Finally, a region NMS algorithm, a fusion strategy about the results of small images mapped into large images, is proposed to remove the redundant shredded boxes caused by cropping image. We verify the effectiveness of the proposed method with Faster R-CNN, Cascade R-CNN, IterDet and Scale-aware Fast R-CNN models on PANDA dataset.

Data-based image classification methods, such as convolutional neural networks (CNNs), have achieved state-of-the-art performance. They usually leverage thousands of labeled samples to train the networks but ignore some prior knowledge. However, labeled samples are difficult to be obtained for synthetic aperture radar (SAR) images. Model-based methods are adept at utilizing the prior information of data, while they have to introduce some restrictions or assumptions during the realization of models. Consequently, to develop the advantages of both methods and improve their disadvantages, we propose a hybrid network by coupling the data-based with model-based methods for SAR image scene classification in this article. First, to fully use the prior information of SAR images and large amounts of unlabeled samples, we improve the G⁰-based variational Bayesian inference model (GVBI) and construct a G⁰-based convolutional variational auto-encoder (GCVAE) for unsupervised learning of the distributional characteristics of SAR images. After that, we further extend the GCVAE by combining it with CNN, resulting in a stronger hybrid network to classify SAR images with a few labeled samples. In addition, considering the abundant structural information is crucial for SAR image classification, we design a sketch fitter and two structural constraints on both pixel and sketch spaces to assist the hybrid network to improve its classification performance. Finally, we evaluate the performance of our method on real-SAR images, and the experimental results demonstrate that the proposed framework outperforms related methods on classification while reducing the manual annotation substantially.

Hanying Shen,1 Lingling Jiang,1 Linlin Nie,1 Xiaohui Fan,1 Yanqiu Xu,1 Lingling Yin,2 Linyan Xu,1,3 Jieyun Xia,2 Zhenyu Li,1– 3 Feng Zhu,1– 3 Kailin Xu1– 3 1Blood Disease Institute, Xuzhou Medical University, Xuzhou, Jiangsu, 221000, People’s Republic of China; 2Department of Hematology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221002, People’s Republic of China; 3Key Laboratory of Bone Marrow Stem Cell, Xuzhou, Jiangsu, 221000, People’s Republic of ChinaCorrespondence: Feng Zhu; Jieyun XiaDepartment of Hematology, The Affiliated Hospital of Xuzhou Medical University, 99, West Huaihai Road, Quanshan District, Xuzhou, Jiangsu, 221002, People’s Republic of ChinaTel +86 516 8580 6985Fax +86 516 8580 1527Email frankfeng_2004@126.com; jieyunxia0315@163.comBackground: Originating from extranodal organs or tissues, primary extranodal lymphoma (PENL) acts in different primary sites with diverse clinical performances and PENL has remarkable geographical differences and lacks the relevant reports in each region.Patients and Methods: Two hundred and twenty PENL patients were enrolled, and the relevant clinical and laboratory indicators were analyzed. In addition, statistical methods were applied to analyze the effects of different factors on overall survival (OS) and progression-free survival (PFS) of patients.Results: The three most frequent primary sites of PENL are the digestive system, head and neck, and central nervous system. The patients were classified into groups based on their risk status, resulting in low-risk, medium-low-risk, medium-high-risk, and high-risk, and their respective 3-year OS values were calculated, which showed that 121 patients (55%) were in the low-risk group and 3-year OS was 85.2% (25.9% medium-low-risk, 3-year OS 66.6%; 15% medium-high-risk, 3-year OS 61.9%; 4.09% high risk, 3-year OS 45.7%). A multivariate analysis of the Cox regression demonstrated that serum beta 2-microglobulin (β2-MG) and lactate dehydrogenase (LDH) were independent prognostic factors for OS and PFS, respectively. Both the performance status and pathological subtypes were independent prognostic factors for OS and PFS.Conclusion: The correlated independent risk factors such as β2-MG, LDH, performance status, and pathological subtypes, were helpful for effectively determining the prognosis of PENL patients and guiding treatment.Keywords: beta 2-microglobulin, lactate dehydrogenase, performance status, pathological subtype, primary extranodal lymphoma, prognostic analysis

Sepsis-induced myocardial dysfunction (SIMD) is ubiquitous in septic shock patients and is associated with high morbidity and mortality rates. Heat shock protein 22 (Hsp22), which belongs to the small HSP family of proteins, is involved in several biological functions. However, the function of Hsp22 in lipopolysaccharide (LPS)-induced myocardial injury is not yet established. This study was aimed at investigating the underlying mechanistic aspects of Hsp22 in myocardial injury induced by LPS. In this study, following the random assignment of male C57BL/6 mice into control, LPS-treated, and LPS + Hsp22 treated groups, relevant echocardiograms and staining were performed to scrutinize the cardiac pathology. Plausible mechanisms were proposed based on the findings of the enzyme-linked immunosorbent assay and Western blotting assay. A protective role of Hsp22 against LPS-induced myocardial injury emerged, as evidenced from decreased levels of creatinine kinase-MB (CK-MB), lactate dehydrogenase (LDH), and enhanced cardiac function. The post-LPS administration-caused spike in inflammatory cytokines (IL-1β, IL-6, TNF-α and NLRP3) was attenuated by the Hsp22 pre-treatment. In addition, superoxide dismutase (SOD) activity and B-cell lymphoma-2 (Bcl2) levels were augmented by Hsp22 treatment resulting in lowering of LPS-induced oxidative stress and cardiomyocyte apoptosis. In summary, the suppression of LPS-induced myocardial injury by Hsp22 overexpression via targeting of inflammation, oxidative stress, and apoptosis in cardiomyocytes paves the way for this protein to be employed in the therapy of SIMD.

Perovskite/silicon tandem solar cells (PSTSCs) have exhibited huge technological potential for breaking the Shockley-Queisser limit of single-junction solar cells. The efficiency of P-I-N type PSTSCs has surpassed the single-junction limit, while the performance of N-I-P type PSTSCs is far below the theoretical value. Here, we developed a composite electron transport layer for N-I-P type monolithic PSTSCs with enhanced open-circuit voltage (VOC) and power conversion efficiency (PCE). Lithium chloride (LiCl) was added into the tin oxide (SnO2) precursor solution, which simultaneously passivated the defects and increased the electron injection driving force at the electron transfer layer (ETL)/perovskite interface. Eventually, we achieved monolithic PSTSCs with an efficiency of 25.42% and VOC of 1.92 V, which is the highest PCE and VOC in N-I-P type perovskite/Si tandem devices. This work on interface engineering for improving the PCE of monolithic PSTSCs may bring a new hot point about perovskite-based tandem devices.

Keloids are disfiguring pathological scars that could cause pain and pruritus. The conventional treatments, such as bolus injection of drugs or surgery, are invasive and require a personal visit to clinic. Microneedle (MN) technology has great potential to offer a self-administered and minimally invasive treatment of keloids. However, drugs delivered using MNs suffer from limited penetration in keloid tissue. This study demonstrates enhanced drug penetration in human keloid scar tissue by combining MN and sonophoresis.

Affiliation with a financial conglomerate may provide hedge funds with superior information about the conglomerate’s lending, investment banking, and brokerage clients; such affiliation can also lead to potential conflicts with the other units of the conglomerate and exacerbate the conflict between hedge fund companies and hedge fund investors. We find that affiliated funds significantly underperform unaffiliated funds. A difference-in-difference analysis confirms the negative relation between financial industry affiliation and hedge fund performance. Affiliated funds pursue asset-gathering strategies, overweight their conducted initial public offerings/seasoned equity offerings clients’ stocks, are more likely to commit legal and regulatory violations, and tend to exhibit a greater number of internal conflicts. Our results are consistent with conflict of interest exerting a negative impact on the performance of affiliated hedge funds. However, it is possible that lack of skill also contributes to the underperformance of affiliated funds. This paper was accepted by Karl Diether, finance.

Introduction Physical activity is recommended for healthy pregnant women. Clinical practice guidelines vary with regard to initial time, duration, or type of physical activity that is recommended, which is confusing for health care professionals and pregnant women alike. This study aimed to appraise clinical guidelines relevant to physical activity for pregnant women and summarize consensus and discrepant recommendations. Methods The websites of organizations that develop guidelines including those of obstetricians, gynecologists, midwives, and medical sports associations were searched in addition to 9 literature databases for the period from January 2010 through November 2020. Guidelines were included if they were published in a journal or on a website in English or Chinese; were labeled as a recommendation guideline, position paper, practice parameter, or consensus statement; and addressed physical activity for pregnant women. Two authors independently extracted recommendations. Four reviewers independently assessed guideline quality using the AGREE II instrument. Results Thirteen guidelines met the inclusion criteria. The guidelines developed by the World Health Organization and the National Institute for Health and Clinical Excellence were deemed to have the highest methodological quality. The recommendations were consistent with regard to recommended exercise duration, frequency, intensity, and type of physical activity. The main discrepant recommendations included best tools for conducting pre-exercise screening and assessing intensity of exertion. Guidelines also differ on optimal heart rate during exercise, when in pregnancy to initiate an exercise regimen, and duration or frequency of strengthening exercises. Discussion Of the 13 guidelines, 2 were found to have good methodological quality. The recommendations that were consistent across the differing guidelines can support health care providers in counseling women about physical activity during pregnancy. The recommendations that are discrepant among these guidelines may contribute to confusion and a reluctance to recommend exercise during pregnancy. Research is needed to clarify discrepant recommendations.

With the further development of the social economy, people pay more attention to spiritual and cultural needs. As the main place of people’s daily life, the family is very important to the creation of its cultural atmosphere. In fact, China has fully entered the era of interior decoration, and people are paying more and more attention to decorative effects and the comfort and individual characteristics of decoration. Therefore, it is of practical significance to develop the application of decorative art in interior space design. However, the transfer effect of current interior decoration art design tends to be artistic, which leads to image distortion, and image content transfer errors are easy to occur in the process of transfer. The application of image style transfer in interior decoration art can effectively solve such problems. This paper analyzes the basic theory of image style transfer through image style transfer technology, Gram matrix, and Poisson image editing technology and designs images from several aspects such as image segmentation, content loss, enhanced style loss, and Poisson image editing constrained image spatial gradient. The application process of style transfer in interior decoration art realizes the application of image style transfer in interior decoration art. The experimental results show that the application of image style transmission in interior decoration art design can effectively avoid the contents of the interior decoration errors and distortions and has a good style transfer effect.

Zusammenfassung Ziel der Studie Zur Aufrechterhaltung der in der psychosomatischen Rehabilitationsmaßnahme erzielten Effekte ist eine psychosomatische Reha-Nachsorge bei den meisten Rehabilitanden indiziert. Auf Grund der derzeitig geringen wohnortnahen Versorgung mit durch die Deutsche Rentenversicherung zur Nachsorge zugelassenen Psychotherapeuten (sog. Nachsorgetherapeuten) sind webbasierte Nachsorge-Angebote eine gleichwertige Alternative zu analogen Angeboten. Die vorliegende Arbeit klärt, bei welchen Merkmalen eine webbasierte Nachsorge empfohlen werden kann und wie diese webbasierten Formate insbesondere in Bezug auf die therapeutische Beziehung durch die Teilnehmenden bewertet werden. Methodik 142 psychosomatische Rehabilitanden wurden bei Verfügbarkeit eines wohnortnahen Angebotes der analogen Nachsorge oder der webbasierten Nachsorge randomisiert zugewiesen (Äquivalenzstudiendesign). Die Testvariablen wurden durch Fragebögen erhoben und zum Teil stratifiziert nach Alter und Geschlecht analysiert. Ergebnisse Für männliche Teilnehmende ergeben sich keine signifikanten Unterschiede zwischen den beiden Nachsorge-Formaten (webbasierte vs. analoge Nachsorge). Frauen scheinen langfristig geringere Werte für Depressivität aufzuweisen, wenn sie an der webbasierten Nachsorge teilnehmen im Vergleich zur analogen Nachsorge. Teilnehmende bis 50 Jahren profitieren signifikant mehr von der webbasierten Nachsorge als Teilnehmende über 50 Jahre. Die Qualität der therapeutischen Beziehung wird in beiden Nachsorge-Formaten gleich gut beurteilt. Diskussion Webbasierte Nachsorge-Angebote bieten insbesondere angesichts der zunehmenden Digitalisierung des Gesundheitswesens im Rahmen der Corona-Pandemie die Möglichkeit, Patienten wohnortunabhängig und mit gleichem Nutzen wie analoge Angebote zu versorgen. Eine therapeutische Beziehung kann aus Patientensicht in beiden Formaten hergestellt werden. Bei der Indikationsstellung sind demografische Faktoren wie das Alter und Geschlecht zu beachten. Schlussfolgerung Therapeuten sollten die Empfehlung einer webbasierten Nachsorge besonders für jüngere Patienten und für Frauen aussprechen, während Männern und älteren Patienten beide Formate gleichermaßen empfohlen werden können. Therapeuten, die eine webbasierte Nachsorge anbieten, sollten vorab, wie in der vorliegenden Arbeit geschehen, zu technischen und inhaltlichen Aspekten geschult werden.

Good structural performance and reliability are two key objectives in design of wind turbine blade. To improve the natural frequency of blades to avoid resonance effectively, but it also increases the weight and direct manufacturing cost of blades. To achieve the best trade-off between mass and first-order frequency of blades, this paper presents a method of structural optimization based on multi-objective genetic algorithm NSGA-II. A small wind turbine blade is taken as case study. The CFD computation is performed to obtain the aerodynamic load, and the FEM is applied to obtain structure responses. With the weight minimization and the first-order frequency maximization of the blade as two goals, the optimization model is constructed and the Pareto solution set of two goals is obtained. Compared with original scheme, the weight of the optimized blade is reduced by about 15%. On the premise of avoiding blade resonance, the solution with the weight as light as possible is selected as the design scheme. The delamination failure, buckling failure, strength failure, and other failure modes of the blade with thin airfoil are analyzed, and the results show that the delamination failure and buckling failure of the blade can be ignored temporarily. Time-varying wind speed and some mechanical parameters of composite materials are taken as random input variables. The reliabilities of the design scheme with multiple failure modes are evaluated, and the reliability sensitivity is analyzed by combining FEM, BP neural network method and Monte Carlo Simulation (MCS) method. The results show that wind speed and longitudinal elastic modulus of uniaxial fiberglass are critical influence factors for affecting reliabilities of the blade. Meanwhile, the results can also provide some references about the timing of shutting down wind turbines and the selection of composite materials.

Interfacial solar-driven evaporation provides one of the most promising green and sustainable technologies to deal with the knotty water crisis by extracting vapor from a variety of water sources powered by solar energy. Advanced photothermal materials play critical roles in interfacial solar-driven evaporation by photothermal conversion and heat localization. Herein, inspired by the unique hierarchical structure and light-harvesting function of diatoms, we propose a novel photothermal material with a diatom-like hierarchical nanostructure derived from TiO2-PANi-decorated bilayer melamine foam (TiO2-PANi@MF) for solar-driven clean water generation. The diatom-like hierarchical nanostructured TiO2-PANi@MF can realize full-spectrum light absorption and photothermal conversion by enhancing multiple light reflection and light scattering. Thanks to the diatom-like hierarchical nanostructure, TiO2-PANi@MF not only impressively achieves an evaporation rate of 2.12 kg m-2 h-1 under 1 sun irradiation but also shows a high solar steam conversion efficiency up to 88.9%. Notably, the TiO2-PANi composite also endows TiO2-PANi@MF with photocatalytic degradation capability. Apart from the excellent steam generation capability, optimized TiO2-PANi@MF also provides the high photocatalytic efficiency of dye degradation and maintains a high evaporation rate of more than 2 kg m-2 h-1. We believe that the proposed photothermal material with a diatom-like hierarchical nanostructure can envision promising practical applications in seawater desalination and sewage purification.

Site planning not only involves the arrangement of a large number of elements but also considers the complexity of urban systems; hence, the design process involves large workloads and is time consuming. Retrieving similar existing cases helps architects optimize or accelerate the design process. This paper proposes a computational approach that provides valuable references by retrieving similar cases. Three types of attributes are extracted to represent a given parcel: land-use attributes, geometric attributes (area, orientation, etc.), and environmental attributes (greening rate, surrounding facilities, etc.). The complete hierarchical retrieval process is divided into three phases. The first phase selects cases whose land-use attributes are consistent with the target parcel. Then, the similarity distances between the given target parcel and the selected cases are calculated using geometric attributes. The eXtreme Gradient Boosting (XGBoost) classifier is adopted to determine which case is similar to the target parcel. Finally, similarity scores of the retrieved cases are calculated based on the environmental attributes to provide more options during the actual design. In total, 1189 cases with different land-use types in Beijing were collected for the case base. The comparative experimental results confirmed that the proposed ensemble learning-based hierarchical retrieval of similar cases approach improves the accuracy of retrieval results. Furthermore, we use a real-world target parcel to demonstrate the superiority and flexibility of the retrieval process.

Objective To evaluate the influence of different transcutaneous electrical acupoint stimulation (TEAS) modes on ovarian responses and pregnancy outcomes in patients with infertility undergoing in vitro fertilization and embryo transfer (IVF-ET). Methods Two hundred infertility patients undergoing IVF-ET were divided randomly into experimental groups (TEAS groups: E-I, E-II, E-III, and E-IV, 40 cases each group) and a control group (mock TEAS group, 40 patients) using the random number method. The patients in the experimental groups received TEAS treatment of 20, 30, 40 and 50 mA for the E-I, E-II, E-III and E-IV groups, respectively. The control group received a treatment of 5 mA. TEAS was applied at acupoints of Guanyuan (RN 4), Zhongji (RN 3), Sanyinjiao (SP 6), Zigong (EX-CA 1), and Taixi (KI 13), once a day for 30 min each time for a treatment period of 10-13 d. Treatment effect was assessed using the following indicators: endometrial thickness on the 6th day of gonadotropin treatment (GN6 day), endometrial thickness on the day on chorionic gonadotropin administration (HCG day), number of ovarian follicles on HCG day, number of ova captured, amount of estrogen required for each harvested ova, number of mature ova divided by the total number of ova, percentage of high-quality embryos, and clinical pregnancy. Results Endometrial thickness in the experimental groups on the HCG day was significantly better than that of the control group after TEAS stimulation (P=0.01). TEAS exhibited a greater impact on the number of ova captured (P=0.003). However, the effect of TEAS stimulation on the high-quality embryo rate and clinical pregnancy in patients was not statistically significant (P>0.05). Conclusions TEAS is an effective method in improving the ovarian state. When the stimulus intensity was at 40 mA and above, it could be helpful to improve the patient's endometrial condition and endometrial receptivity and to retrieve more oocytes. (Trial registration No. ChiCTR-TRC-11001780).

Background and Objective: Pre-eclampsia (PE) is a hypertensive gestational disease appearing during second trimester of pregnancy. Free radicals are released by the placenta in this condition that may cause oxidative damage. This study was designed to determine the serum ferritin (SF) levels in maternal blood, fetal umbilical cord blood and placenta and the changes associated with oxidative stress as well as cell apoptosis to understand the pathogenesis of PE. Methods: This cross-sectional analytical study recruited 60 pregnant females with severe PE and assigned into early and late onset PE groups. Another n = 60 cases of normal pregnant females with similar gestational weeks were selected in the control group. Maternal serum and fetal umbilical cord blood ferritin levels were determined by automatic biochemical immunoassay system. Reverse transcription real-time fluorescence, Western blot and colorimetry were used to determine mRNA expression levels of ferritin and ferritin heavy chain, relative expression of ferritin and superoxide dismutase, malondialdehyde (MDA), and glutathione peroxidase (GSH-Px) levels, respectively. Results: Mean age was 30.89 ± 5.65 and 31.79 ± 5.06 years in early and late onset PE groups respectively. Serum uric acid and creatinine levels of both PE groups were significantly higher than the normal pregnant females. SF levels were also higher in the participants of PE groups. However, the mRNA and ferritin protein levels in placental tissue were significantly lower in PE groups while comparing to controls. The cleaved caspase-3 protein, GSH-Px and MDA levels were significantly higher in both PE groups (p < 0.05). Conclusion: The alterations in factors related to oxidative stress and cell apoptosis in placental tissue may be helpful to understand the pathogenesis of PE and may provide potential biomarkers for the diagnosis of PE.

Allergic asthma is a heterogeneous inflammatory disorder triggered by inhaled allergens, leading to airflow obstruction, bronchial inflammation, and airway hyperresponsiveness (AHR). T helper (Th) 2 cell-mediated immune response and airway inflammation are the key features of allergic asthma. Bruceine D (BD) is a bioactive compound extracted from the seeds of Brucea javanica. The present study aimed to investigate the effects of increased doses of BD on AHR, secretion of Th1-/Th2-associated cytokines, and inflammatory cell infiltration in ovalbumin (OVA)-induced allergic asthma mice. The results showed that BD reduced OVA-induced inflammatory cell infiltration and bronchial hyperresponsiveness into the peribronchial tissues and perivascular areas. Mice treated with BD also showed significantly decreased expressions of Th2-associated cytokines (i.e., interleukin (IL)-4, IL-5, and IL-13) and elevated production of Th1-associated cytokines (i.e., interferon gamma and IL-2) following OVA stimulation. BD treatment dose-dependently inhibited OVA-induced accumulation of inflammatory cells in asthmatic mice. Further analysis revealed that OVA exposure upregulated pulmonary expressions of NOTCH signaling receptors, a group of transmembrane proteins that communicate signals upon binding to transmembrane ligands expressed on adjacent cells, while BD treatment significantly abolished OVA-induced activation of the NOTCH pathway. In conclusion, BD protected mice against OVA-induced allergic asthma by reducing AHR and restoring the Th1/Th2 balance through the NOTCH signaling pathway. Our findings highlighted the potential of BD as a therapeutic agent for allergic asthma.

BACKGROUND & AIMS Early determination of the prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is important to guide clinical management and decrease mortality. The aim of this study was to develop a new simplified prognostic score to accurately predict outcomes in patients with HBV-ACLF. METHODS Prospective clinical data from 2,409 hospitalized patients with acute deterioration of HBV-related chronic liver disease were used to develop a new prognostic score that was validated in an external group. RESULTS A total of 954 enrolled patients with HBV-ACLF were diagnosed based on the Chinese Group on the Study of Severe Hepatitis B-ACLF (COSSH-ACLF) criteria. Six predictive factors were significantly related to 28-day mortality and constituted a new prognostic score (=1.649×ln(international normalized ratio)+0.457×hepatic encephalopathy score+0.425×ln(neutrophil)+0.396×ln(total bilirubin)+0.576×ln(serum urea)+0.033×age). The C-indices of the new score for 28-/90-day mortality (0.826/0.809) were significantly higher than those of 4 other scores (COSSH-ACLF, 0.793/0.784; CLIF-C ACLF, 0.792/0.770; MELD, 0.731/0.727; MELD-Na, 0.730/0.726; all p

In a wide range of applications such as healthcare treatment, environmental monitoring, food processing and storage, and semiconductor chip manufacturing, relative humidity (RH) sensing is required. However, traditional fiber-optic humidity sensors face the challenges of miniaturization and indirectly obtaining humidity values. Here, we propose and demonstrate an optical barcode technique by cooperating with RH meta-tip, which can predict the humidity values directly. Such RH meta-tip is composed of fiber-optic sensor based on surface plasmon resonance (SPR) effect and graphene oxide film as humidity sensitizer. While SPR sensor is composed of multimode fiber (MMF) integrated with metallic metasurface. Dynamic time warping (DTW) algorithm is used to obtain the warp path distance (WPD) sequence between the measured reflection spectrum and the spectra of the precalibrated database. The distance sequence is transformed into a pseudo-color barcode, and the humidity value is corresponded to the lowest distance, which can be read by human eyes. The RH measurement depends on the collective changes of the reflection spectrum rather than tracking a single specific resonance peak/dip. This work can open up new doors to the development of a humidity sensor with direct RH recognition by human eyes.

The spread of carbapenem-resistant Gram-negative bacteria poses a substantial threat to morbidity and mortality worldwide, which is mainly attributed to the overuse of carbapenem. This study aimed to evaluate the use of a quality control circle (QCC) in controlling the overuse of carbapenems and improving the state of carbapenem resistance at a Chinese tertiary teaching hospital.A pharmacist-led multidisciplinary QCC project was carried out and the plan-do-check-act (PDCA) method was applied for 12 months. The data on carbapenem consumption, bacterial identification, and antibacterial susceptibility testing were collected to evaluate the effect of this project.The antibiotics use density (AUD) of carbapenems exhibited a decreasing trend over time (P0.001), and the AUD of meropenem, imipenem, and biapenem decreased by 30.20%, 42.45%, and 78.05% after the intervention, respectively. The total AUD of carbapenems decreased from 7.37 to 3.96, which included the decrease in the irrational use of carbapenems by 1.61, accounting for 47.21% of the total. Moreover, the positive correlations were discovered between the resistance rate of carbapenem-resistant Klebsiella pneumonia (CRKP)/Acinetobacter baumannii (CRAB) and the AUD of carbapenems (P0.05). The resistance rate of CRKP and CRAB decreased from 51.93% and 89.21% to 32.94% and 60.66%, respectively, following QCC project implementation.This is the first study to highlight the success of a multifaceted intervention QCC project and PDCA method, which led to a significant reduction in the AUD and resistance rate of carbapenems. QCC is a feasible and effective management tool for improving the quality of carbapenem use in medical institutions.

The superplastic forming process has been used extensively in aerospace applications for its advantages in manufacture of complicated components. But it has been limited to the long production cycle and the non-uniform thickness distribution of the final formed part. The two-stage SPF process in opposite directions for 5083 aluminum alloy door frames was investigated. A finite element model in PAMSTAMP was used to simulate the forming process. Analyzed the effect of pre-forming die parameters on the minimum thickness of the final formed part by the Response Surface Methodology and optimized the pre-forming die parameters. Moreover, the influence of different rated pressure on the forming quality and forming time of the door frames under the same strain rate were investigated to determine the ideal pressure profile. The result was that the minimum thickness meets the requirements (thinning not exceeding 40%) while reducing the production cycle. Finally, the two-stage superplastic forming experiment in opposite directions was conducted to verify the accuracy of the numerical simulation and produce the door frames with high quality.

Currently, many low-carbon approaches are being applied in the power dispatching process, such as dynamic economic emission dispatch (DEED), wind power integration and carbon trading. Furthermore, these approaches may influence the carbon emission from electricity. To investigate such joint influence, this paper firstly builds a stochastic DEED model in the forms of chance-constrained programming related to wind power uncertainties. After that, the established model is transformed to a deterministic form by discussing relevant statistical characteristics. Next, an auxiliary decision-making method is designed to draw the optimal dispatching solution upon real-time price of carbon trading. Finally, empirical analysis shows that: (1) the minimization of carbon emissions and fuel costs comes at the expense of the reliability of wind power integrated system, for reasons that the decrement rate of thermal power output is about 60.8% from higher reliability level to lower level, (2) the decrement rate of carbon emissions (0.01%–0.012%) is superior to the growth rate of fuel costs (0.0011%–0.0022%) as the carbon trading price increases, which proves that carbon emission reduction can be effectively realized by carbon trading rather than substantial increase in fuel costs, and (3) carbon trading is conducive to the improvement of carbon emission efficiency.

Pannexin 1 (PANX1) channel is a critical ATP-releasing pathway that modulates tumor immunity, progression, and prognosis. However, the roles of PANX1 in different cancers remain unclear. We analyzed the expression of PANX1 in human pan-cancer in the Oncomine and GEPIA2.0 databases. The prognostic value of PANX1 expression was determined using Kaplan-Meier plotter and OncoLnc tools. The correlation between PANX1 and tumor-infiltrating immune cells was investigated using the TIMER 2.0. In addition, the relationship between PANX1 and immunomodulators was explored using TISIDB. Finally, gene set enrichment analysis (GSEA) was performed utilizing LinkedOmics. The results indicated that PANX1 was overexpressed in most cancers compared to normal tissues. The high expression of PANX1 was associated with poor prognosis in multiple tumors, especially in pancreatic adenocarcinoma (PAAD). In addition, PANX1 was correlated with a variety of immunomodulators, such as CD274, IL10, CD276, IL2RA, TAP1, and TAP2. PANX1 expression level was significantly related to infiltration of multiple immune cells in many cancers, including cancer associated fibroblast, macrophage, and neutrophil cells. Further analysis revealed that PANX1 was significantly associated with T cells CD8+ (rho = 0.524, P = 1.94e-13) and Myeloid dendritic cell (rho = 0.564, P = 9.45e-16). GSEA results showed that PANX1 was closely associated with leukocyte cell-cell adhesion, endoplasmic reticulum lumen, ECM-receptor interaction, and Focal adhesion pathways in PAAD. PANX1 expression was higher in pan-cancer samples than in normal tissues. The high expression of PANX1 was associated with poor outcome and immune infiltration in multiple cancers, especially in PAAD.

Antipsychotic-induced metabolic dysfunction (AIMD) is an intractable clinical challenge worldwide. The situation is becoming more critical as second-generation antipsychotics (SGAs), to a great extent, have replaced the role of first-generation antipsychotics in managing major psychiatric disorders. Although the exact mechanisms for developing AIMD is intricate, emerging evidence has indicated the involvement of the microbiota-gut-brain axis in AIMD. SGAs treatment may change the diversity and compositions of intestinal flora (e.g., decreased abundance of Bacteroidetes and Akkermansia muciniphila, and increased Firmicutes). Short-chain fatty acids and other metabolites derived from gut microbiota, on the one hand, can regulate the activity of intestinal endocrine cells and their secretion of satiety hormones (e.g., glucagon-like peptide 1, peptide YY, cholecystokinin and ghrelin); on the other hand, can activate the vagus nerve or transport into the brain to exert a central modulation of foraging behaviors via binding to neuropeptide receptors. Interestingly, metformin, a classical antidiabetic agent, is capable of alleviating AIMD possibly by regulating the microbiota-gut-brain axis. That is, metformin can not only partially reverse the alterations of gut microbial communities due to SGAs treatment, but also play a positive role in rectifying the disturbances of peripheral and central satiety-related neuropeptides. Current evidence has indicated a promising role for metformin on ameliorating AMID, but further verifications in well-designed clinical trials are still warranted.