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In this article, a semisupervised weighting method for feature dimension based on entropy is proposed for classification, dimension reduction, and correlation analysis. For real-world data, different feature dimensions usually show different importance. Generally, data in the same class are supposed to be similar, so their entropy should be small; and those in different classes are supposed to be dissimilar, so their entropy should be large. According to this, we propose a way to construct the weights of feature dimensions with the whole entropy and the innerclass entropies. The weights indicate the contribution of their corresponding feature dimensions in classification. They can be used to improve the performance of classification by giving a weighted distance metric and can be applied to dimension reduction and correlation analysis as well. Some numerical experiments are given to test the proposed method by comparing it with some other representative methods. They demonstrate that the proposed method is feasible and efficient in classification, dimension reduction, and correlation analysis.

Long-term visual place recognition (VPR) is challenging as the environment is subject to drastic appearance changes across different temporal resolutions, such as time of the day, month, and season. A wide variety of existing methods address the problem by means of feature disentangling or image style transfer but ignore the structural information that often remains stable even under environmental condition changes. To overcome this limitation, this article presents a novel structure-aware feature disentanglement network (SFDNet) based on knowledge transfer and adversarial learning. Explicitly, probabilistic knowledge transfer (PKT) is employed to transfer knowledge obtained from the Canny edge detector to the structure encoder. An appearance teacher module is then designed to ensure that the learning of appearance encoder does not only rely on metric learning. The generated content features with structural information are used to measure the similarity of images. We finally evaluate the proposed approach and compare it to state-of-the-art place recognition methods using six datasets with extreme environmental changes. Experimental results demonstrate the effectiveness and improvements achieved using the proposed framework. Source code and some trained models will be available at http://www.tianshu.org.cn.

Background Previous sleep electroencephalography studies have detected abnormalities in sleep architecture and sleep spindle deficits in schizophrenia (SCZ), but the consistency of these results was not robust, which might be due to the small sample size and the influence of clinical factors such as the various medication therapies and symptom heterogeneity. This study aimed to regard auditory verbal hallucinations (AVHs) as a pointcut to downscale the heterogeneity of SCZ and explore whether some sleep architecture and spindle parameters were more severely impaired in SCZ patients with AVHs compared with those without AVHs. Methods A total of 90 SCZ patients with AVHs, 92 SCZ patients without AVHs, and 91 healthy control subjects were recruited, and parameters of sleep architecture and spindle activities were compared between groups. The correlation between significant sleep parameters and clinical indicators was analyzed. Results Deficits of sleep spindle activities at prefrontal electrodes and intrahemispheric spindle coherence were observed in both AVH and non-AVH groups, several of which were more serious in the AVH group. In addition, deficits of spindle activities at central and occipital electrodes and interhemispheric spindle coherence mainly manifested accompanying AVH symptoms, most of which were retained in the medication-naive first-episode patients, and were associated with Auditory Hallucination Rating Scale scores. Conclusions Our results suggest that the underlying mechanism of spindle deficits might be different between SCZ patients with and without AVHs. In the future, the sleep feature of SCZ patients with different symptoms and the influence of clinical factors, such as medication therapy, should be further illustrated.

Recently, most coverless image steganography (CIS) methods are based on robust mapping rules. However, due to the limited mapping expression relationship between secret information and hash sequence, it is a challenge to further improve the hiding ability of coverless information hiding. Towards this goal, this paper proposes a robust coverless steganography scheme using camouflage image(CI-CIS). For the sender, CI-CIS introduces an camouflage image as the transmission carrier and establishes the correlation between them by Convolutional Neural Network(CNN) features. For the receiver, the camouflage image can retrive the corresponding stego-image to recover the secret information. To this end, we designed a reversible retrieval scheme between stego-image and camouflage image by using image clustering. At the same time, since the semantic features represented by CNN are robust to image attacks, our method can increase the capability of the CIS effectively. Besides, we also build an inverted index to improve retrieval efficiency. Experimental results and analysis show that the CI-CIS has higher robustness and more flexible capacity setting compared with the existing CIS methods.

Summary Background /Objective: As a new immune-nutritional marker, the controlled nutritional status (CONUT) score has been reported to predict the prognosis of cancer patients. We aimed to elucidate the prognostic value of preoperative CONUT score in pancreatic cancer patients undergoing radical surgery, and to construct a nomogram based on CONUT score to predict individual survival. Methods Preoperative CONUT scores were calculated prospectively in 382 patients with pancreatic cancer who underwent radical surgery. Evaluated the relationship between CONUT score and pancreatic cancer prognosis. Cox proportional hazard models were used to determine predictors of survival and a new nomogram was established to predict pancreatic cancer overall survival (OS). Results The area under curve of CONUT score was higher than other immune-nutritional indexes. The OS of the high-CONUT group were significantly lower than that of low-CONUT group. Multivariate analysis showed that CONUT score, gender, AJCC stage, complications and reoperation were independent prognostic factors for OS. Nomogram based on these variables has better discriminant ability in predicting survival compared with other traditional staging systems. Conclusions Preoperative CONUT score is an effective independent predictor of OS in pancreatic cancer patients undergoing radical surgery. This new CONUT based nomogram provides accurate, individualized survival prediction for pancreatic cancer.

Cyber-physical systems (CPSs) seamlessly integrate communication, computing, and control, thus exhibiting tight coupling of their cyber space with the physical world and human intervention. Forming the basis of future smart services, they play an important role in the era of Industry 4.0. However, CPSs also suffer from increasing cyber attacks due to their connections to the Internet. This article investigates resilient control for a class of CPSs subject to actuator attacks, which intentionally manipulate control commands from controllers to actuators. In our study, the supertwisting sliding-mode algorithm is adopted to construct a finite-time converging extended state observer (ESO) for estimating the state and uncertainty of the system in the presence of actuator attacks. Then, for the attacked system, a finite-time converging resilient controller is designed based on the proposed ESO. It integrates global fast terminal sliding-mode and prescribed performance control. Finally, an industrial CPS, permanent magnet synchronous motor control system, is investigated to demonstrate the effectiveness of the composite resilient control strategy presented in this article.

Bronchial asthma is a common clinical disease in pediatrics. It affects brain function and causes cognitive impairment due to long-term hypoxia in the brain. Studies have shown attention deficits in asthma children, but lack the basis for brain studies. Functional magnetic resonance imaging (fMRI) based on functional connectivity analysis was used in this study, and 31 asthma children and typically developing children (TDC) were compared in degree centricity (DC), voxel-mirrored homotopic connectivity (VMHC) and continuous performance test (CPT). The study found that the correct number of CPT in the asthma group was significantly lower than the TDC group. And the reaction time in the asthma group was significantly longer than the TDC group. Compared with TDC group, asthma children exhibited lower DC value in the right superior frontal gyrus (after FDR correction, P

Workflow scheduling in cloud environments has become a significant topic in both commercial and industrial applications. However, it is still an extraordinarily challenge to generate effective and economical scheduling schemes under the deadline constraint especially for the large scale workflow applications. To address the issue, this paper investigates the cloud workflow scheduling problem with the aim of minimizing the whole cost of workflow execution whereas maintaining its execution time under a predetermined deadline. A novel knowledge-based adaptive discrete water wave optimization (KADWWO) algorithm is developed based on the problem-specific knowledge of cloud workflow scheduling. In the proposed KADWWO, a discrete propagation operator is designed based on the idle time knowledge of hourly-based cost model to adaptively explore the huge search space. The adaptive refraction operator is employed to avoid stagnation and expand the available resource pool. Meanwhile, the dynamic grouping based breaking operator is designed to exploit the excellent block structure knowledge of task allocation scheme and corresponding resource to intensify the local region and accelerate convergence. Extensive simulation experiments on the well-known scientific workflow demonstrate that the KADWWO approach outperforms several recent state-of-the-art algorithms.

Region-based methods are currently achieving state-of-the-art performance for monocular 3D object tracking. However, they are still prone to fail in cases of partial occlusions and ambiguous colors. We propose a novel region-based method to tackle these problems. The key idea is to derive a pixel-wise weighted region-based cost function using contour constraints. Firstly, we propose a novel region-based cost function using search lines around the object contour, which is more efficient than previous region-based cost functions using signed distance transform, and in the meantime can deal with partial occlusions and ambiguous colors more effectively. Secondly, we propose an optimal searching strategy to search the object contour points in cluttered scenes, and then use the object contour points to detect partial occlusions and ambiguous colors. Thirdly, we propose a pixel-wise weight function based on color and distance constraints of the object contour points, and integrate it into the proposed region-based cost function to reduce the negative impact of partial occlusions and ambiguous colors. We verify the effectiveness and efficiency of our method on challenging public datasets. Experiments demonstrate that our method outperforms the recent state-of-the-art region-based methods in complex scenarios, especially in the presence of partial occlusions and ambiguous colors.

Meta-analysis is a statistical analysis of the data obtained from multiple studies and provides a quantitative synthesis of research results. It can be a key tool for facilitating rapid progress in aquaculture by quantifying what is known and identifying what is not yet known. However, due to the complexity of the environment and problems associated with the use of model in aquaculture, it remain few guidelines for the design, implementation or interpretation of meta-analysis in the field of aquaculture. Here, we first briefly reviewed the history of meta-analysis, then summarized the applications of meta-analysis in terms of major procedures, standards, and methods. Next, we critically reviewed the results of meta-analysis studies in the production chain of aquaculture and identified the potentials for improving yield in both quantity and quality. Overall, there is a large room for improving yield along the production chain. Large contributions can be found in breeding, feed, and farm management. For example, improving breeding can increase yield by 5.6% to 49%, depending on fish species and type of improvements. This study revealed large potentials for improving yield in the production chain of aquaculture and summarized the application of meta-analysis in aquaculture. Some recommendations of standardizing and improving meta-analysis in aquaculture were proposed.

As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine learning and signal processing to combat large outliers. The kernel function in correntropy is usually a zero-mean Gaussian kernel. In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely, a linear combination of several zero-mean Gaussian kernels with different widths. In both correntropy and MC, the center of the kernel function is, however, always located at zero. In the present work, to further improve the learning performance, we propose the concept of multikernel correntropy (MKC), in which each component of the mixture Gaussian kernel can be centered at a different location. The properties of the MKC are investigated and an efficient approach is proposed to determine the free parameters in MKC. Experimental results show that the learning algorithms under the maximum MKC criterion (MMKCC) can outperform those under the original maximum correntropy criterion (MCC) and the maximum MC criterion (MMCC).

Medical practitioners generally rely on multimodal brain images, for example based on the information from the axial, coronal, and sagittal views, to inform brain tumor diagnosis. Hence, to further utilize the 3D information embedded in such datasets, this paper proposes a multi-view dynamic fusion framework (hereafter, referred to as MVFusFra) to improve the performance of brain tumor segmentation. The proposed framework consists of the following three key building blocks. First, a multi-view deep neural network architecture, which represents multi learning networks for segmenting the brain tumor from different views and each deep neural network corresponds to multi-modal brain images from one single view. Second, the dynamic decision fusion method, which is mainly used to fuse segmentation results from multi-views into an integrated method. Then, two different fusion methods (i.e., voting and weighted averaging) are used to evaluate the fusing process. Third, the multi-view fusion loss (comprising segmentation loss, transition loss, and decision loss) is proposed to facilitate the training process of multi-view learning networks, so as to ensure consistency in appearance and space, for both fusing segmentation results and the training of the learning network. We evaluate the performance of MVFusFra on the BRATS 2015 and BRATS 2018 datasets. Findings from the evaluations suggest that fusion results from multi-views achieve better performance than segmentation results from the single view, and also implying effectiveness of the proposed multi-view fusion loss. A comparative summary also shows that MVFusFra achieves better segmentation performance, in terms of efficiency, in comparison to other competing approaches.

International audience; Recent years have witnessed rapid development in the research area of WiFi sensing, which senses human activities in a contactless and non-intrusive manner. One major issue that hinders real-world deployment of these systems is position dependence, i.e., once the human target changes location and orientation, the sensing performance degrades significantly. Existing machine learning based methods aim to solve this problem by either generating high-dimensional features or transfer learning the environment knowledge. However, these methods require significant training effort and yet acquire limited improvement. In this paper, we start by understanding and analyzing the Doppler frequency shift in WiFi sensing. We then develop a WiFi frequency model to quantify the relationship between signal frequency and target position, motion direction and speed for human activities. Based on this theoretical model, we prove that the commonly-used movement speed and motion direction features are position dependent, and further identify movement fragments and relative motion direction changes as two position-independent features. Building upon the frequency model and the position-independent features, we design a suite of position-independent gestures and develop the gesture recognition system accordingly. Evaluation results show that under various conditions (i.e., different locations, orientations, environments, and persons), our system achieves more than 96% recognition accuracy without any training, significantly outperforming state-of-the-art machine learning based solutions.

Typically, through monitoring, collecting, integrating, and analyzing the sport scores of individuals, we can know about the sport physique of the individuals in a real-time manner, so as to further guarantee a high-quality health level of people. However, in the abovementioned sport scores-based sport physique monitoring scenario, two major challenges are raised. First, the collected sport scores for sport physique monitoring are often multi-dimensional, which makes it hard to evaluate the sport physique of individuals fairly and reasonably. Moreover, the multiple dimensions of sport scores are often of different weights or significances, which raise a challenging task to objectively evaluate the sport physique of an individual. Considering these challenges, a novel multi-dimensional sport physique evaluation and ranking method, named MSPER, is proposed in this research work for better healthcare for individuals, which is based on the TOPSIS theory. At last, a case study is presented to show the concrete procedure of MSPER method.

Facing the gaps that the traditional long short-term memory (LSTM) and convolution neural network (CNN) cannot differentially deal with the input data based on the corresponding trend and stage information in remaining useful life (RUL) prediction, a more accurate and robust RUL prediction model is constructed. First, a temporally multi-differential LSTM (TMLSTM) with the multi-trend division unit and multicellular unit is proposed, and a spatially multi-differential CNN (SMCNN) with the multi-stage division unit and differentiated convolutions is designed. Then, by combining TMLSTM and SMCNN, a spatiotemporally multi-differential deep neural network (SMDN) is developed for predicting the equipment RUL, which enhances the ability of feature extraction from the spatiotemporal perspective by using the multi-trend and multi-stage information. Via several evaluation indexes, the Commercial Modular Aero Propulsion System Simulation dataset and the wind turbine gearbox bearing dataset are used to validate the superiority of the proposed method over several existing prediction methods.

Dynamic routing networks, aimed at finding the best routing paths in the networks, have achieved significant improvements to neural networks in terms of accuracy and efficiency. In this paper, we see dynamic routing networks in a fresh light, formulating a routing method as a mapping from a sample space to a routing space. From the perspective of space mapping, prevalent methods of dynamic routing did not take into account how inference paths would be distributed in the routing space. Thus, we propose a novel method, termed CoDiNet, to model the relationship between a sample space and a routing space by regularizing the distribution of routing paths with the properties of consistency and diversity. Specifically, samples with similar semantics should be mapped into the same area in routing space, while those with dissimilar semantics should be mapped into different areas. Moreover, we design a customizable dynamic routing module, which can strike a balance between accuracy and efficiency. When deployed upon ResNet models, our method achieves higher performance and effectively reduces average computational cost on four widely used datasets.

Monocular 3D object detection is a low cost and challenging task for autonomous vehicles and robotics. Utilizing a monocular image for 3D object detection is served as an auxiliary module for autonomous vehicles and is a growing concern recently. Currently, the expensive lidar and stereo cameras have a predominant performance on accurate 3D object detection, whereas monocular based methods are considerably lower in performance. This performance gap is minimized by reforming the monocular based method as a single internal network. We exploit the correlation between 2D and 3D detection spaces, enabling 3D boxes to leverage feature maps generated in image space. The 2D and 3D proposals are extracted through a proposal generation network that is enhanced and utilized for estimating accurate 3D detection and localization. Experimental results on the KITTI dataset demonstrate that in comparison to other monocular object detection methods the proposed method considerably improved the accuracy of 3D object detection. The mean average precision of 3D object detection in front view is improved to 25% and the bird's eye view to 32% for the car class on the moderate difficulty level.

Online medical consult and offline medical check-in have generated a large amount of health-related data in medical forums and patient records. However, exploiting the user-generated content for orienting patients online and assisting medical checkup offline is nontrivial due to the sparsity of symptom-disease associations. The serious sparsity is caused by the informal/chatty expressions of symptoms in the data.

Learning from demonstration (LfD) assists robots to derive a policy from a training set to execute a task. The training set consists of training demonstrations collected from a human who executes the same task. However, due to the human’s varied skill level, the quality of the training set may be bad, which will affect the accuracy of the derived policy. To solve this problem, this paper proposes a novel method to improve the quality of the training set. This method includes two steps, namely detecting and handling bad training demonstrations in the training set. In the detecting step, a reference set containing reference demonstrations is provided by a human teacher. Based on the reference set, we calculate the influence of each training demonstration on the policy derivation. If the influence is negative, the corresponding training demonstration is bad. Afterward, in the handling step, we calculate the proportion of the negative influence with respect to the overall influence and reduce the proportion by iteratively removing bad training demonstrations until it is less than a threshold. The results show that the accuracy of a policy derived from the improved training set increases with up to 19.30%, which verifies the effectiveness of our method.

The great progress of wireless communications breeds the prevalence of connected Internet of Vehicles (CIoV). Naturally, the internal connections among active vehicles also act as an indispensable factor in traffic forecasting. Although much related research work has been conducted during the past few years, they mainly designed and/or developed a single specific forecasting model for this purpose. Existing work may acquire ideal effects in some specific scenarios, yet lacking good robustness to dynamic change of situations. To deal with such a challenge, a graph neural networks-driven traffic forecasting model for CIoV is proposed in this work, deduced as Gra-TF for short. In this paper, we regard the dynamics of traffic data as the situation of temporal evolution. With the assistance of ensemble learning, three typical graph-level prediction methods are employed to construct an integrated and enhanced the forecasting model. Such a design is able to take advantage of several methods to minimize the uncertainty in CIoV. Finally, we select a real-world dataset to build the experimental situation for further assessment. Numerical results indicate that the proposed Gra-TF improves the prediction accuracy by 20% to 30% compared with several baseline methods.

A 68-year-old man presented with 1-day history of increasing right-sided abdominal pain with associated nausea and vomiting. His medical history includes a delayed presentation of empyema of the gallbladder requiring percutaneous cholecystostomy tube and interval laparoscopic subtotal

The development of energy Internet (EI) makes it possible to achieve better utilization of distributed renewable energy sources with the power sharing functionality introduced by energy routers (ERs). In this paper, a bottom-up EI architecture is designed, and a novel data-driven dynamical control strategy is proposed. Intelligent controllers augmented by deep reinforcement learning (DRL) techniques are adopted for the operation of each microgrid independently in the bottom layer. Moreover, the concept of curriculum learning (CL) is integrated into DRL to improve the sample efficiency and accelerate the training process. Based on the power exchange plan determined in the bottom layer, considering the stochastic nature of electricity price in the future power market, the optimal power dispatching scheme in the upper layer is decided via model predictive control. The simulation has shown that, under the bottom-up architecture, compared with the conventional methods such as proportional integral and optimal power flow, the proposed method reduces overall generation cost by more than 7.1% and 37%. Meanwhile, the introduced CL-based training strategy can significantly speed up the convergence during the training of DRL. Last but not least, our method increases the profit of energy trading between ERs and the main grid.

This study was aimed at revising the LI-RADS M category (LR-M) criteria to improve the diagnostic performance categories LR-5 and LR-M of the contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) Version 2017. We enrolled 264 patients (264 nodules) with a risk for hepatocellular carcinoma (HCC). The nodules were assigned specific CEUS LI-RADS categories. Washout onset times for all nodules were noted. The diagnostic performance of LR-5 and LR-M was analyzed based on the different early washout criterion for the LR-M category. The positive predictive values in LR-5, LR-4 and LR-3 were 98.6%, 72.2% and 16.7%, respectively, and that for non-HCC malignancies in LR-M was 25.0%. Patients in the LR-M category were reclassified using 45 s as the early washout criterion. LR-5 had higher sensitivity (65.5% vs. 76.2%, p = 0.012) and area under the receiver operating characteristic curve (0.80 vs. 0.85, p = 0.001) for HCC diagnosis after reclassification. LR-M also had higher specificity (71.4% vs. 81.3%, p = 0.010) in diagnosing non-HCC malignancies after reclassification. Our findings suggest CEUS LR-5 is effective for HCC diagnosis. The use of 45 s as the time criterion of early washout for LR-M can improve LR-5 and LR-M performance in the diagnosis of HCC and non-HCC malignancies, respectively.

AIM: To quantitatively detect aqueous levels of angiopoietin-like (ANGPTL)3, ANGPTL4, and ANGPTL6 and investigate their correlation with optical coherence tomography angiography (OCTA) findings in patients with diabetic macular edema (DME). METHODS: This cross-sectional study included 23 patients (27 eyes) with type 2 diabetes and 16 control subjects (20 eyes). All patients underwent OCTA imaging and ultra-wide field fundus photography. Diabetic patients were categorized into two groups according to the presence or absence of diabetic retinopathy (DME group, 14 patients, 16 eyes); and non-diabetic retinopathy (NDR) group, 9 patients, 11 eyes, respectively. Aqueous levels of ANGPTL3, ANGPTL4, and ANGPTL6 were assessed using suspension array technology, and foveal-centered 3×3 mm2 OCTA scans were automatically graded to determine the central, inner, and full vessel density (CVD, IVD, FVD); central, inner, and full perfusion density (CPD, IPD, FPD), foveal avascular zone (FAZ) area, FAZ perimeter, and FAZ circularity index (FAZ-CI) on superficial capillary plexuses. Additionally, central subfield thickness (CST), cube volume (CV), and cube average thickness (CAT) were measured in a model of macular cube 512×128. RESULTS: Aqueous ANGPTL3 levels were not significantly different among the three groups (P>0.05). ANGPTL4 levels were significantly higher in the DME group than the control and NDR groups (P

Background: Despite the implementation of the universal salt iodization (USI) program for correction of iodine deficiency in China for ∼20 years, the actual iodine nutrition status of Chinese residents and the prevalence of iodine deficiency and iodine excess are issues that need to be addressed. This nationally representative cross-sectional study was conducted across all 31 provinces of mainland China to gather extensive data on iodine nutrition status and the influential factors. Methods: This study included 78,470 participants, aged 18 years or older, who were interviewed and asked to answer a questionnaire. Urine iodine concentration (UIC) was measured by the inductively coupled plasma mass spectrometry method, and goiter was examined by thyroid ultrasonography. In addition, sixty 9-11 years old school children in each province were randomly selected to evaluate the UIC and thyroid ultrasonography. The iodine nutrition status was determined according to the World Health Organization guidelines. Results: The iodized salt coverage was 95.37%. The median urine iodine (MUI) was 177.89 μg/L (interquartile range [IQR], 117.89-263.90 μg/L) and goiter prevalence was 1.17% (confidence interval [95% CI 0.95-1.43]) in the adult population. The MUI was 199.75 μg/L (IQR, 128.41-303.37 μg/L) in school-age children, and goiter prevalence was 3.50% [95% CI, 2.93-4.13]. The percentage of individuals with UIC

Objective This study aimed to investigate the protective effect of astragalus–saffron–rhubarb mixture (Bao’shen recipe, BSR) on diabetic nephropathy (DN) in db/db mice and preliminarily explore the possible underlying mechanism. Methods A total of 125 8-week-old male db/db mice with DN were randomly divided into five groups: model group, irbesartan group and high-, medium- and low doses of BSR group, while 25 male db/m mice were used as a blank control. At 8, 12, 16, 20, and 24 weeks of feeding, the animals were sacrificed and blood as well as urine samples were collected for blood glucose, urea nitrogen, creatinine and urinary albumin excretion rate (UAER) measurement via blood glucose meter or corresponding detection kits, respectively. The renal tissues of each mouse underwent hematoxylin and eosin (H&E), Masson, periodic acid Schiff (PAS) staining. Renal homogenate was used to detect IL-6, TNF-α, TNF-1R and TNF-2R by enzyme-linked immunosorbent assay. Additionally, the data obtained was statistically analyzed via one-way analysis of variance. Results BSR could effectively reduce the body weight, blood glucose, UAER, blood urea nitrogen and creatinine levels, relieve the proliferation of mesangial tissue, and lower the levels of IL-6, TNF-α, TNF-1R, and TNF-2R in renal tissue of db/db mice with DN. Of note, the high-dose BSR treatment group has advantages over irbesartan treatment group in improving above-mentioned aspects. Conclusion BSR could effectively delay the progress of DN, partly related to its anti-inflammation effect.

In this study, a basic organic Rankine cycle (ORC) is introduced in an air separation process for waste heat recovery. Conventional and advanced exergy analyses are adopted to investigate the thermodynamic properties of components in the ORC. A comprehensive thermodynamic model is constructed to improve the advanced exergy analysis in the ORC, thereby encompassing real, theoretical, unavoidable, and hybrid cycles. Nine organic working fluids are introduced to investigate the influence on the ORC performance. (1) The conventional exergy analysis reveals the following: (a) The expander constantly demonstrates the maximum exergy efficiency except when R227ea is used. (b) The evaporator constantly exhibits the maximum exergy destruction regardless of the working fluid used. (c) The maximum product exergy is obtained when R114 is used. (d) Key components must focus on the condenser and evaporator to improve the ORC performance. (2) The advanced exergy analysis reveals that the expander demonstrates maximum potential for improvement because its endogenous avoidable exergy destruction accounts for approximately 90% of its real exergy destruction for all working fluids. The expander must be improved to achieve the optimal ORC performance. The advanced exergy analysis can distinguish the source of exergy destruction and the magnitude for possible improvement via the proposed thermodynamic model in this study. The comprehensive thermodynamic model can promote the investigation of the advanced exergy analysis in the ORC. Applying conventional and advanced exergy analyses to investigate the thermodynamic performance of a system or its components is highly recommended.

This study aimed to investigate the effects of repeated preoperative intranasal administration of insulin on the incidence of postoperative delirium (POD) and the levels of serum pro-inflammatory markers in elderly patients undergoing laparoscopic radical gastrointestinal surgery.Prospective, randomized, double-blinded, placebo-controlled clinical study.General Hospital of Western Theater Command from August 2019 to December 2019.Ninety elderly patients underwent laparoscopic radical gastrointestinal tumor resections under general anesthesia.Patients were randomly divided into a control group (0.5 mL saline administered intranasally) or an insulin group (20 U/0.5 mL insulin administered intranasally) for 2 days prior to surgery, with 45 patients in each group.The incidence of delirium was measured at postoperative day 1 (T2), day 3 (T3), and day 5 (T4) using the Confusion Assessment Method for the intensive care unit (CAM-ICU). Plasma levels of interleukin (IL)-1β, IL-6, and tumor necrosis factor (TNF)-α were measured at T0 (before insulin or saline administration), T1 (at the end of surgery), T2, T3, and T4 by enzyme-linked immunosorbent assay.Compared with the control group, the insulin group demonstrated a decreased POD incidence (12.5% vs. 47.5%, p = 0.001) within 5 days after surgery. The incidence of POD was significantly lower in the Ins group than in the Con group at T2 (12.5% vs. 32.5%, p = 0.032) and at T3 (2.5% vs. 20%, p = 0.034). The incidence of POD decreased in both groups over time and was similar at T4 (0% vs 10%, p = 0.116). Compared with the baseline value at T0, serum TNF-α, IL-6 and IL-1β concentrations increased significantly at T1-4 (p0.05). Compared with the control group at the same time point, the expression levels of TNF-α, IL-6 and IL-1β in group I at T2 and T3 were significantly reduced (p0.05). The incidence rates of adverse events were similar in the two groups.Repeated preoperative intranasal administration of insulin prevented the occurrence of delirium after laparoscopic radical gastrointestinal surgery in elderly patients and reduced TNF-α, IL-1β, and IL-6 levels.

Background. The revascularization of small vessels using drug-eluting stents remains challenging. The use of the drug-coated balloon is an attractive therapeutic strategy in de novo lesions in small coronary vessels, particularly in the diabetic group. This study aimed to assess the outcomes of DCB-only angioplasty in small vessel disease. Methods. A total of 1198 patients with small vessel disease treated with DCB-only strategy were followed. Patients were divided into the diabetic and nondiabetic groups. Clinical and angiographical follow-up were organized at 12 months. The primary endpoints were target lesion failure and secondary major adverse cardiac events. Results. There was a significantly higher rate of target lesion failure among diabetic patients compared to nondiabetic [17 (3.9%) vs. 11 (1.4%), P = 0.006 ], taken separately, the rate of target lesion revascularization significantly differed between groups with a higher rate observed in the diabetic group [9 (2%) vs. 4 (0.5%), P = 0.014 ]. Diabetes mellitus remained an independent predictor for TLF (HR: 2.712, CI: 1.254–5.864, P = 0.011 ) and target lesion revascularization (HR: 3.698, CI: 1.112–12.298, P = 0.033 ) after adjustment. However, no significant differences were observed between groups regarding the target vessel myocardial infarction (0.6% vs. 0.1%, P = 0.110 ) and MACE [19 (4.4%) vs. 21 (2.7%), P = 0.120 ]. Conclusion. Drug-coated balloon-only treatment achieved lower incidence rates of TLF and MACE. Diabetes is an independent predictor for target lesion failure and target lesion revascularization at one year following DCB treatment in small coronary vessels. We observed no significant differences between groups regarding MACE in one year.

Objective To study the risk factors leading to type II/III cesarean scar pregnancy and evaluate the efficacy of hysteroscopic treatment and subsequent reproductive outcomes. Design Retrospective study from 2013 to 2018. Setting Inpatient samples. Patient(s) A total of 439 patients with cesarean scar pregnancy received hysteroscopic treatment. They were grouped according to the type of surgery as hysteroscopy combined with dilation and curettage, systemic methotrexate followed by hysteroscopy combined with dilation and curettage, and uterine artery embolization or laparoscopic ligation of bilateral uterine arteries followed by hysteroscopy combined with dilation and curettage. Cesarean scar pregnancy was classified as types I, II, and III on the basis of the relationship between the gestational sac and myometrial thickness by ultrasound. Intervention(s) Not applicable. Main Outcome Measure(s) Risk factors leading to type II/III cesarean scar pregnancy. The success of hysteroscopic treatment as well as favorable reproductive outcomes. Result(s) The significant variables were multiple parity, prior cesarean section (CS), hysteroscopic therapy, and dilation and curettage after the last CS between type I and type II/III. There were significant differences among the three groups in symptoms, largest diameter of the gestational sac, presence of fetal heartbeat, myometrial thickness, type of cesarean scar pregnancy, blood loss, length of hospital stay, and expense. The rates of complications and success were 8.2% and 93.6%, respectively. Thirty-seven women conceived again, and 22 women completed a term pregnancy with no uterine rupture. The recurrence rate of cesarean scar pregnancy was 10.8%. Conclusions The type of cesarean scar pregnancy is related to the rates of multiple parity, cesarean deliveries, and dilation and curettage procedures after the last CS. Hysteroscopic therapy is a safe and effective surgical approach with a low risk of subsequent ectopic pregnancy.

In this study, we investigated the clinical efficacy and poor prognosis of patients with limb disorders after the operation of hypertensive cerebral hemorrhage, using the treatment of removing blood stasis and Shengxin acupuncture combined with early rehabilitation training using a randomized trial. A total of 100 patients with the hypertensive intracerebral hemorrhage who underwent surgery in our hospital from May 2019 to May 2021 were selected and divided into control group were treated with early rehabilitation training, and the intervention group was treated with acupuncture for removing blood stasis and promoting new acupuncture based on the control group. We observed that there was no significant difference in the evaluation of limb function recovery between the two groups of patients (P>0.05). After the intervention, the modified Ashworth score, FMA score, and B1 index of the two groups were significantly different in the ADL scores and the scores of families and friends support were remarkably improved. The clinical treatment efficiency of the intervention group was 94.00%, significantly higher than that of the control group of 80.00%, and the difference was statistically significant (P

Resource shortages and environmental pollution of the world are becoming more and more serious, wind Turbine generation as a kind of clean energy power generation has attracted much attention. The difficulty of wind power generation lies in the randomness of natural wind energy, which leads to the instability of the output power of Wind Turbine Generators (WTGs). The traditional variable pitch controller cannot realize global precision control of the wind power system. To solve the problem, this paper combines Takagi-Sugeno (T-S) fuzzy model with optimal control theory to design a robust controller. The unique novelty of the controller is that when the wind speed is higher than the rated, no matter how much the wind speed is, even if it is far from the balance point of the system, the controller can make the output power at a constant value, that is, increase the global stability of the wind power system. Specifically, the wind power system was divided into several linear subsystems, and the state feedback controller of each subsystem was designed through optimal control. Then, the subsystem controllers were merged by superposing membership functions, forming a controller of the entire system. Finally, wind turbine generators (WTGs) were modeled and simulated in Matlab/Simulink. The results show that the proposed controller kept the output power of WTGs constant under the global step wind of 13–25 m/s and the stochastic wind of 14–15 m/s. Our design enables the precision control at any wind speed, and improves the stability of power output of WTGs.

Microplastic pollution is a growing global concern and a serious threat to aquatic life. Microplastics (MPs) have been found in the natural environment in the last few years. However, levels of microplastic pollution in aquaculture areas of Hainan Island have been less studied than in wild environments. Xincun bay (XC) and Gangmen bay (GM) are lagoons that are rich in fishery resources. Here, we investigated microplastic pollution in 8 surface seawater samples, 17 species of fish, and mussel samples from XC bay and GM bay, Hainan, China. The MPs were isolated from surface seawater, organisms and identified by microscopic, Raman spectroscopic and micro-Fourier Transform Infrared spectrometer methods. The mean abundance of MPs in seawater samples was 523 items/m3 and that of fish was 7.1 items/individual and the percentages of microplastics detected in the gastrointestinal tracts (GITs) and gill were 99% and 1%, respectively. MPs were also observed in the soft tissues of mussel samples with a mean abundance of 0.36 ± 0.81 items/individual. Most MPs were identified as polystyrene and their sizes ranged between 0.5 and 5 mm in both seawater and organism samples. Our results indicated that the uptake of microplastics by organisms was closely related to microplastic pollution in the aquaculture environment. Foam pollution is common in organisms and surface seawater in the aquaculture area of our study region, which provides a way for human exposure.

Esophageal squamous cell carcinoma (ESCC) is a major histological subtype of esophageal cancer with a poor prognosis. Although several serum metabolomic investigations have been reported, ESCC tumor-associated metabolic alterations and predictive biomarkers in sera have not been defined. Here, we enrolled 34 treatment-naive patients with ESCC and collected their pre- and post-esophagectomy sera together with the sera from 34 healthy volunteers for a metabolomic survey. Our comprehensive analysis identified ESCC tumor-associated metabolic alterations as represented by a panel of 12 serum metabolites. Notably, postoperative abrosia and parenteral nutrition substantially perturbed the serum metabolome. Furthermore, we performed an examination using sera from carcinogen-induced mice at the dysplasia and ESCC stages and identified three ESCC tumor-associated metabolites conserved between mice and humans. Notably, among these metabolites, the level of pipecolic acid was observed to be progressively increasing in mouse sera from dysplasia to cancerization, and it could be used to accurately discriminate between mice at the dysplasia stage and healthy control mice. Furthermore, this metabolite is essential for ESCC cells to restrain oxidative stress-induced DNA damage and cell proliferation arrest. Together, this study revealed a panel of 12 ESCC tumor-associated serum metabolites with potential for monitoring therapeutic efficacy and disease relapse, presented evidence for refining parenteral nutrition composition, and highlighted serum pipecolic acid as an attractive biomarker for predicting ESCC tumorigenesis.

Modular multilevel converters (MMCs) are characterized by the multi-harmonic coupling interactions among internal dynamics. In this work, multiple d-q references and multi-harmonic frames, which are developed through dynamic phasor and harmonic state-space, are used to derive the generalized impedance models of MMC in the d-q and modified sequence domain, respectively. Although some previous researches have been devoted to revealing the equivalence of different impedance models, this paper attempts to elaborate the relation of different multi-frequency modeling approaches, and to provide a more comprehensive review of MMC impedance models. A rigorous comparison of modeling procedure, impedance response and stability analysis result between different MMC models is conducted. It is demonstrated that the MMC impedance models in the two domains can be related by a generic linear transformation matrix. Furthermore, the stability analysis results given by generalized Nyquist criterion are identical in these two domains. Application recommendations for different impedance models are given as well. The developed impedance models and their effectiveness for stability and resonance analysis are validated by a 501-level aggregated model in MATLAB/SIMULINK.

Background The purpose is to compare the efficacy and safety of mini percutaneous nephrolithotomy (mini-PCNL) versus standard percutaneous nephrolithotomy (standard-PCNL) in patients with renal stones >2cm. Materials and Methods A systematic literature search was conducted in PubMed, Web of Science, Scopus, and the Cochrane Library databases to identify relevant studies before March 8, 2021. Stone-free rate (SFR), operation time, fever rate, hemoglobin drop, blood transfusion rate, and hospitalization time were used as outcomes to compare mini-PCNL and standard-PCNL. The meta-analysis was performed using the Review Manager version 5.4. Results Seven randomized controlled trials were included in our meta-analysis, involving 1407 mini-PCNL cases and 1436 standard-PCNL cases. Our results reveal that, for renal stones >2cm, mini-PCNL has a similar SFR (risk ratio (RR)=1.01, 95% confidence interval (CI): 0.98 to 1.04, p=0.57) and fever rate (RR=1.22, 95% CI: 0.97-1.51, p=0.08). Standard-PCNL was associated with a significantly shorter operating time (weighted mean difference (WMD)=8.23, 95% CI: 3.44 to 13.01, p 2cm renal stones, mini-PCNL should be considered an effective and reliable alternative to standard-PCNL (30F). It achieves a comparable SFR to standard-PCNL, but with less blood loss, lower transfusion rate, and shorter hospitalization. However, the mini-PCNL does not show a significant advantage over the 24F standard-PCNL. On the contrary, this procedure takes a longer operation time. Trial registration This meta-analysis was reported consistent with the PRISMA statement and was registered on PROSPERO, with registration number 2021CRD42021234893.

Representing nodes in a network as low-dimensional dense vectors can facilitate the analysis of complex networks, which is a challenging task and has attracted increasing attention. However, in the real world, networks are changing over time, such as cooperation in citation networks and communication in email networks. Most of the recent embedding methods only focus on static networks. Thus they ignore the critical temporal information, which serves as a supplement to structure information and has been proved to improve the quality of node embedding. In this work, we propose an unsupervised deep learning model called DTINE, which explores temporal information for further enhancing the robustness of node representations in dynamic networks. To preserve network topology, we pertinently design a temporal weight and sampling strategy to extract features from the neighborhood. An attention mechanism will be applied on the recurrent neural network to measure the contributions of historical information and capture the evolution of the networks. Experimental results on four real-world networks demonstrate that the proposed method achieves better performance than state-of-the-art methods.

Objective To explore the genetic changes in the progression of castration-resistant prostate cancer (CRPC) and neuroendocrine prostate cancer (NEPC) and the reason why these cancers resist existing therapies. Methods We employed our CRPC cell line microarray and other CRPC or NEPC datasets to screen the target gene NEIL3. Lentiviral transfection and RNA interference were used to construct overexpression and knockdown cell lines. Cell and animal models of radiotherapy were established by using a medical electron linear accelerator. Flow cytometry was used to detect apoptosis or cell cycle progression. Western blot and qPCR were used to detect changes in the protein and RNA levels. Results TCGA and clinical patient datasets indicated that NEIL3 was downregulated in CRPC and NEPC cell lines, and NEIL3 was correlated with a high Gleason score but a good prognosis. Further functional studies demonstrated that NEIL3 had no effect on the proliferation and migration of PCa cells. However, cell and animal radiotherapy models revealed that NEIL3 could facilitate the radiotherapy sensitivity of PCa cells, while loss of NEIL3 activated radiotherapy resistance. Mechanistically, we found that NEIL3 negatively regulated the expression of ATR, and higher NEIL3 expression repressed the ATR/CHK1 pathway, thus regulating the cell cycle. Conclusions We demonstrated that NEIL3 may serve as a diagnostic or therapeutic target for therapy-resistant patients.

With an growing number of web services available on the Internet, an increasing burden is imposed on the use and management of service repository. Service clustering has been employed to facilitate a wide range of service-oriented tasks, such as service discovery, selection, composition and recommendation. Conventional approaches have been proposed to cluster web services by using explicit features, including syntactic features contained in service descriptions or semantic features extracted by probabilistic topic models. However, service implicit features are ignored and have yet to be properly explored and leveraged. To this end, we propose a novel heuristics-based framework DeepWSC for web service clustering. It integrates deep semantic features extracted from service descriptions by an improved recurrent convolutional neural network and service composability features obtained from service invocation relationships by a signed graph convolutional network, to jointly generate integrated implicit features for web service clustering. Extensive experiments are conducted on 8,459 real-world web services. The experiment results demonstrate that DeepWSC outperforms state-of-the-art approaches for web service clustering in terms of multiple evaluation metrics.

In Internet of Things (IoT) applications with multihop networking, not only traditional energy efficiency but also many distinct features should be considered when designing routing protocols, including different security requirements, heterogeneity, and scalability. In this paper, an energy efficient multi-level secure routing (EEMSR) protocol in IoT networks is proposed. Considering that clustering is a reasonable solution of conserving energy, a cluster-based multi-hop routing protocol is utilized to reduce the high communication overhead due to the scalability of IoT networks. In particular, more reasonable analytic hierarchy process and genetic algorithms are adopted to assign accurate weight and optimize inter-cluster routing in which heterogeneous IoT networks are considered to support large amount of heterogeneous IoT entities and services. Moreover, multiple trust levels are adopted to defend the different attacks by calculating the trust factor on the clustering and routing, including data perception trust, data fusion trust, and communication trust. It is shown that the proposed algorithm outperforms the existing algorithms in terms of network lifetime, throughput, packet delivery ratio, network energy balance, and adaptability.

Cloud storage attracts a lot of clients to join the paradise. For a high data availability, some clients require their files to be replicated and stored on multiple servers. Because clients are generally charged based on the redundancy level required by them, it is critical for clients to obtain convincing evidence that all replicas are stored correctly and are updated to the up-to-date version. In this paper, we propose a dynamic proof of data possession and replication (DPDPR) scheme, which is proved to be secure in the defined security model. Our scheme shares a single authenticated tree across multiple replicas, which reduces the tree's storage cost significantly. Our scheme allows for batch verification for multiple challenged leaves and can verify multiple replicas in a single batch way, which considerably save bandwidth and computation resources during audit process. We also evaluate the DPDPR's performance and compare it with the most related scheme. The evaluation results show that our scheme saves almost 66% tree's storage cost for three replicas, and obtains almost 60% and 80% efficiency improvements in terms of the overall bandwidth and computation costs, respectively, when three replicas are checked and each challenged with 460 blocks.

Global warming caused by the use of fossil fuels is a common concern of the world today. It is of practical importance to conduct in-depth fundamental research and optimal design for modern engine combustors through high-fidelity computational fluid dynamics (CFD), so as to achieve energy conservation and emission reduction. However, complex hydrocarbon chemistry, an indispensable component for predictive modeling, is computationally demanding. Its application in simulation-based design optimization, although desirable, is quite limited. To address this challenge, we propose a methodology for representing complex chemistry with artificial neural networks (ANNs), which are trained with a comprehensive sample dataset generated by the Latin hypercube sampling (LHS) method. With a given chemical kinetic mechanism, the thermochemical sample data is able to cover the whole accessible pressure/temperature/species space in various turbulent flames. The ANN-based model consists of two different layers: the self-organizing map (SOM) and the back-propagation neural network (BPNN). The methodology is demonstrated to represent a 30-species methane chemical mechanism. The obtained ANN model is applied to simulate both a non-premixed turbulent flame (DLR_A) and a partially premixed turbulent flame (Flame D) to validate its applicability for different flames. Results show that the ANN-based chemical kinetics can reduce the computational cost by about two orders of magnitude without loss of accuracy. The proposed methodology can successfully construct an ANN-based chemical mechanism with significant efficiency gain and a broad scope of applicability, and thus holds a great potential for complex hydrocarbon fuels.

Relief demand forecasting is vital to the success of disaster relief operations, but it is associated with challenges including insufficient training samples, incomplete and imprecise inputs, and inaccurate demands. This paper presents a co-evolutionary fuzzy deep transfer learning (CoFDTL) method for relief demand forecasting where different types of disasters (e.g., earthquake, typhoon, and flood) are considered as different tasks. CoFDTL consists of three stages. First, a deep fuzzy learning model is used to learn latent representation of the shared inputs of all tasks. Second, a co-evolutionary algorithm is used to simultaneously learn task-specific features and the shared regressor. Third, the shared regressor is re-trained based on the best solutions obtained for different tasks in the second stage. Experiments demonstrate that CoFDTL exhibits significant performance improvements over the selected popular fuzzy learning, deep learning, and transfer learning models. This paper also reports the application of CoFDTL to two real-world disasters in China, 2018. The proposed CoFDTL that integrates fuzzy deep learning, transfer learning, and co-evolutionary learning can be used for many other complex multi-task transfer learning problems with insufficient samples and uncertain information.

Implanted biomaterials have transformed healthcare and the treatment of injury and disease, but their influence on the local immune landscape remains unclear. Here we discovered that degradation-resistant titanium-based implants establish an immunosuppressive microenvironment by recruiting myeloid cells, including monocytes, macrophages, neutrophils, and myeloid-lineage dendritic cells. Unlike normal tissues, the tissues nearby implants exhibit an chronic inflamed and immunosuppressive status characterised by myeloid-rich, T cell-exhaustion gene signature by single-cell RNA sequencing. Vitamin C treatment provides an effective strategy to rescue the immunosuppressive microenvironment, which can be used as a regular supplement to reduce the risk of malignant cell survival around the implants.

Abstract: Estrogen receptors (ERs) are steroid hormone receptors, which belong to a large nuclear receptor family. Endocrine diseases correlate strongly with dysregulated ER signaling. Traditional therapies continue to rely on small molecule inhibitors, including aromatase inhibitors (AIs) and selective estrogen receptor modulators (SERMs), all of which permit acquired resistance to endocrine therapy. Proteolytic targeting chimeras (PROTACs) offer unprecedented potential for solving acquired endocrine resistance. ARV-471, an ER-targeting PROTAC developed by Arvinas, was approved by the US FDA to entered clinical trials in 2019 to treat patients suffering from locally advanced or metastatic ER-positive/HER2-negative breast cancer, and has since been approved by the US FDA. In this review, we will focus on progress in developing ER-targeting PROTACs from publications and patents aimed at the treatment of endocrine diseases.

In recent years, an increasing number of competing firms have started to integrate their product attribute development and advertising strategies by targeted advertising of the best-developed attribute of their product. With a focus on such integration and its potential effects, we establish an analytical model in which two competing firms sell multi-attribute products. Each firm can choose which attribute to focus on in its development strategy and which attribute to emphasize in its advertising strategy. By optimizing the development and advertising strategies of these firms, we first verify the integration of these two strategies as each firm advertises only its developed attribute. Second, we find that advertising, according to the firms’ development choices, plays distinctive roles on profit generation: the difference-alleviating or difference-strengthening effect. Therefore, when determined endogenously, the advertising intensities show qualitatively different changing trends: a monotonous curve in the same-attribute-development case but an inverted-U shaped curve in the different-attribute-development case. Third, we find advertising establishes a connection between firms’ attribute development choices, making them depending not only on their own development cost. A smaller difference between the development costs of these firms or a higher advertising efficiency increases the firms’ willingness to develop a same attribute. We also extend our analysis to the flexible quality level of the non-developed attribute, asymmetric substitution degrees between attributes and prices, or the endogenous attribute development levels, and study how the interactions between the product development and advertising are affected. This paper highlights the importance of making development and advertising strategies jointly.