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Abstract Background Hypertension is a common disease, often overlooked in its early stages due to mild symptoms. And persistent elevated blood pressure can lead to adverse outcomes such as coronary heart disease, stroke, and kidney disease. There are many risk factors that lead to hypertension, including various environmental chemicals that humans are exposed to, which are believed to be modifiable risk factors for hypertension. Objective To investigate the role of environmental chemical exposures in predicting hypertension. Methods A total of 11,039 eligible participants were obtained from NHANES 2003–2016, and multiple imputation was used to process the missing data, resulting in 5 imputed datasets. 8 Machine learning algorithms were applied to the 5 imputed datasets to establish hypertension prediction models, and the average accuracy score, precision score, recall score, and F1 score were calculated. A generalized linear model was also built to predict the systolic and diastolic blood pressure levels. Results All 8 algorithms had good predictions for hypertension, with Support Vector Machine (SVM) being the best, with accuracy, precision, recall, F1 scores and area under the curve (AUC) of 0.751, 0.699, 0.717, 0.708 and 0.822, respectively. The R2 of the linear model on the training and test sets was 0.28, 0.25 for systolic and 0.06, 0.05 for diastolic blood pressure. Conclusions In this study, relatively accurate prediction of hypertension was achieved using environmental chemicals with machine learning algorithms, demonstrating the predictive value of environmental chemicals for hypertension.

Abstract Background A short cervix in mid-trimester pregnancy is a risk factor for spontaneous preterm birth. However, there is currently a lack of predictive models and classification systems for predicting spontaneous preterm birth in these patients, especially those without additional risk factors for spontaneous preterm birth. Methods A retrospective observational cohort study of low-risk singleton pregnant women with a short cervix (≤ 25 mm) measured by transvaginal ultrasonography between 22 and 24 weeks was conducted. A multivariate logistic regression model for spontaneous preterm birth 146, respectively. The predicted probabilities of spontaneous preterm birth

Abstract Epilepsies are a group of neurological disorders characterized by abnormal spontaneous brain activity, involving multiscale changes in brain functional organizations. However, it is not clear to what extent the epilepsy-related perturbations of spontaneous brain activity affect macroscale intrinsic dynamics and microcircuit organizations, that supports their pathological relevance. We collect a sample of patients with temporal lobe epilepsy (TLE) and genetic generalized epilepsy with tonic-clonic seizure (GTCS), as well as healthy controls. We extract massive temporal features of fMRI BOLD time-series to characterize macroscale intrinsic dynamics, and simulate microcircuit neuronal dynamics used a large-scale biological model. Here we show whether macroscale intrinsic dynamics and microcircuit dysfunction are differed in epilepsies, and how these changes are linked. Differences in macroscale gradient of time-series features are prominent in the primary network and default mode network in TLE and GTCS. Biophysical simulations indicate reduced recurrent connection within somatomotor microcircuits in both subtypes, and even more reduced in GTCS. We further demonstrate strong spatial correlations between differences in the gradient of macroscale intrinsic dynamics and microcircuit dysfunction in epilepsies. These results emphasize the impact of abnormal neuronal activity on primary network and high-order networks, suggesting a systematic abnormality of brain hierarchical organization.

Abstract Introduction As a novel internal fixation for femoral neck fractures, the femoral neck system has some advantages for young Pauwels type III femoral neck fractures without clear biomechanical effects and mechanisms. Thus, the objection of the study is to realize the biomechanical effects and mechanism of FNS cannulated screws on treating young patients with Pauwels type III femoral neck fractures compared to cannulated screws which are commonly used for femoral neck fractures by finite element analysis. Methods Firstly, the model of young Pauwels type III femoral neck fractures, femoral neck system (FNS), and three cannulated screws (CS) arranged in an inverted triangle were established, and the internal fixations were set up to fix young Pauwels type III femoral neck fractures. Under 2100 N load, the finite element was performed, and the deformation, peak von Mises stress (VMS), and contact at fracture segments were recorded to analyze the biomechanical effects and mechanism of FNS and three-CS fixing young Pauwels type III femoral neck fractures. Results Compared to three-CS, the deformation of the whole model, internal fixation, and fracture segments after FNS fixation were lower, and the peak VMS of the whole model and the internal fixation after FNS were higher with lower peak VMS of the distal femur and the fracture segments. With a sticking contact status, the contact pressure at fracture segments after FNS fixation was lower than that of three-CS. Conclusions FNS can provide better mechanical effects for young patients with Pauwels type III femoral neck fractures, which may be the mechanical mechanism of the clinical effects of FNS on femoral neck fracture. Although there is high stress on FNS, it is still an effective and safe internal fixation for young patients with Pauwels type III femoral neck fractures.

Summary: Under the background of the accelerating speed of urban and rural construction, the geographical environment of overhead transmission lines has also changed greatly. Using unmanned aerial vehicle (UAV) to realize intelligent line inspection can significantly shorten inspection time and improve inspection efficiency. In this paper, the intelligent power inspection of UAVs is studied from two levels: path planning and UAV control, and the insulator is identified through actual image recognition. At the path planning level, the improved swarm intelligence algorithm is used to conduct simulation experiments on the UAV flight path to find a safe and effective route. Insulator identification and defect location of overhead transmission lines are trained on the insulator dataset collected by deep learning technology to achieve accurate insulator identification and improve the efficiency of UAV inspection, which has great application prospects in engineering.

Retinitis pigmentosa (RP) is an inherited retinal disease in which there is a loss of cone-mediated daylight vision. As there are >100 disease genes, our goal is to preserve cone vision in a disease gene-agnostic manner. Previously we showed that overexpressing TXNIP, an α-arrestin protein, prolonged cone vision in RP mouse models, using an AAV to express it only in cones. Here, we expressed different alleles of Txnip in the retinal pigmented epithelium (RPE), a support layer for cones. Our goal was to learn more of TXNIP’s structure-function relationships for cone survival, as well as determine the optimal cell type expression pattern for cone survival. The C-terminal half of TXNIP was found to be sufficient to remove GLUT1 from the cell surface, and improved RP cone survival, when expressed in the RPE, but not in cones. Knock-down of HSP90AB1, a TXNIP-interactor which regulates metabolism, improved the survival of cones alone and was additive for cone survival when combined with TXNIP. From these and other results, it is likely that TXNIP interacts with several proteins in the RPE to indirectly support cone survival, with some of these interactions different from those that lead to cone survival when expressed only in cones.

Abstract Droplets residing on textured oil-impregnated surfaces form a wetting ridge due to the imbalance of interfacial forces at the contact line, leading to a wealth of phenomena not seen on traditional lotus-leaf-inspired non-wetting surfaces. Here, we show that the wetting ridge leads to long-range attraction between millimeter-sized droplets, which coalesce in three distinct stages: droplet attraction, lubricant draining, and droplet merging. Our experiments and model show that the magnitude of the velocity and acceleration at which droplets approach each other horizontally is the same as the vertical oil rise velocity and acceleration in the wetting ridge. Moreover, the droplet coalescence mechanism can be modeled using the classical mass-spring system. The insights gained from this work will inform future fundamental studies on remote droplet interaction on textured oil-impregnated surfaces for optimizing water harvesting and condensation heat transfer.

Abstract Introduction How to reconstruct the damaged fingertip is a clinical problem. Our team propose the theory of equivalent design and use the mini toenail flap pedicled with the hallux transverse artery and toe pulp vein transplantation technique to reconstruct Allen’s type II fingertip injury. Thus, we perform the retrospective study to evaluate the effects of this technique on fingertip injury. Materials and methods A retrospective analysis was performed on 56 patients admitted to our hospital from January 2015 to January 2020 who used equivalently designed miniature hallux toenail flaps for the plastic repair of fingertip damage. We recorded the size of the miniature hallux toenail flap, operation time, intraoperative blood loss, and complications and calculated the survival rate of the transplanted miniature hallux toenail flap. During routine follow-up after surgery, we recorded nail growth time and observed finger appearance. At the last time of follow-up, we recorded Semmes-Weinstein evaluating tactile sensation and Two-point discrimination testing (TPD). The efficacy was evaluated by Zook score evaluation. Results The size of the mini hallux toenail flap was 0.71 cm × 1.22 cm to 0.88 cm × 1.71 cm. The operation time was (3.54 ± 0.58) hours, the intraoperative blood loss was (20.66 ± 4.87) ml, and the survival rate of mini hallux toenail flaps was 100%. The postoperative follow-up time was (30.82 ± 11.21) months, and the total nail growth time was (9.68 ± 2.11) months. The average tactile sensation evaluated by the Semmes-Weinstein test was (0.32 ± 0.14) g, and the average TPD was (7.33 ± 1.02) mm. According to Zook score, the curative effect of fifty-six cases were all excellent or good with 100% excellent and good rate, and all patients had beautiful appearances and good function of damaged fingertips. Conclusions Based on the equivalent design theory, the mini hallux toenail flap pedicled with the hallux transverse artery and toe pulp vein transplantation technique is an effective method to reconstruct Allen’s type II fingertip injury with a beautiful appearance and good function. Type of study/level of evidence Therapeutic IV.

Abstract As an effective and non‐invasive treatment modality for cancer, photodynamic therapy (PDT) has attracted considerable interest. With the recent advances in the photosensitizing agents, the fiber‐optic systems, and other aspects, its application is extended to a wide range of superficial and localized cancers. However, for the few clinically used photosensitizers, most of them suffer from the drawback of causing prolonged photosensitivity after the treatment. As a result, post‐PDT management is also a crucial issue. Herein, a facile bioorthogonal approach is reported that can effectively suppress this common side effect of PDT in nude mice. It involves the use of an antidote that contains a black‐hole quencher BHQ‐3 conjugated with a bicyclo[6.1.0]non‐4‐yne (BCN) moiety and a tetrazine‐substituted boron dipyrromethene‐based photosensitizer. By using tumor‐bearing nude mice as an animal model, it is demonstrated that after PDT with this photosensitizer, the administration of the antidote can effectively quench the photodynamic activity of the residual photosensitizer by bringing the BHQ‐3 quencher close to the photosensitizing unit through a rapid click reaction. It results in substantial reduction in skin damage upon light irradiation. The overall results demonstrate that this simple and facile strategy can provide an effective means for minimizing the photosensitivity after PDT.

Immunotherapy, specifically immune checkpoint inhibitors, has emerged as a promising approach for treating malignant tumors. The gut, housing approximately 70 % of the body's immune cells, is abundantly populated with gut bacteria that actively interact with the host's immune system. Different bacterial species within the intestinal flora are in a delicate equilibrium and mutually regulate each other. However, when this balance is disrupted, pathogenic microorganisms can dominate, adversely affecting the host's metabolism and immunity, ultimately promoting the development of disease. Emerging researches highlight the potential of interventions such as fecal microflora transplantation (FMT) to improve antitumor immune response and reduce the toxicity of immunotherapy. These remarkable findings suggest the major role of intestinal flora in the development of cancer immunotherapy and led us to the hypothesis that intestinal flora transplantation may be a new breakthrough in modifying immunotherapy side effects.

The electromagnetic (EM) energy released by electronic devices in the environment is largely wasted and contributes to EM pollution. Here, the authors report the synthesis of staggered circular nanoporous graphene enabling the absorption and conversion of EM waves into electricity via the thermoelectric effect.

Abstract This paper proposes an adaptive control method for an underactuated complex nonlinear system, that is, unmanned aerial vehicle (UAV), to realize high‐precision trajectory tracking under input saturation constraint and other disturbances via artificial neural network and anti‐saturation auxiliary system. First, the rigid body motion theory is applied to establish a simplified nonlinear dynamic model of the UAV. Then a Lyapunov function is designed with the error surface to form the preliminary control law based on dynamic surface control. Afterwards, a radical basis function neural network is developed and used to estimate and compensate the disturbance, while the input saturation is also addressed via the designed anti‐saturation auxiliary system. Thus the stability and signal consistency of the closed‐loop UAV control system can be proved via the Lyapunov stability theory with appropriately tuned parameters. Simulation experiments are performed to verify that the proposed control algorithm can be used for UAV high‐precision trajectory tracking under time‐varying disturbances.

Traffic flow prediction is crucial in intelligent transportation systems. Considering the severe disruptions caused by traffic accidents or congestion, a time series model is developed for traffic flow prediction based on multiple random walks on graphs (MRWG) and the bidirectional spatiotemporal attention mechanism (BSAM), which can adapt to both normal and exceptional situations. The MRWG mechanism is applied to capture spatial features of urban areas during traffic accidents and congestion, especially the spatial dependencies among neighboring regions. Further, a local position attention module is applied to acquire the spatial correlations between different regions to investigate their impact on the global area, while a local temporal attention module is adopted to extract short-term periodic time correlations from traffic flow data. Finally, a spatiotemporal bidirectional attention module is applied to simultaneously extract both the temporal and spatial correlations of the historical traffic flow data in order to generate the output prediction. Experiments have been conducted on NYCTaxi and NYCBike datasets with abnormal events, and the results indicate that the developed model can efficiently predict traffic flow in abnormal events, especially short-term traffic disruptions, outperforming the baseline methods under both abnormal and normal conditions.

ABSTRACT Central nervous system infections (CNSIs) are common complications after neurosurgery with a poor prognosis. The traditional microbiological culture methodology has a low detection rate and time consuming. Metagenomic next-generation sequencing (mNGS) has demonstrated the advantages of being faster, more accurate, and more comprehensive in clinical microbiology. Previous studies had suggested that mNGS had a high sensitivity in the diagnosis of CNSIs. Whether the application of mNGS has health economic value in clinical applications remains to be studied. We designed a prospective, single-center, superiority randomized controlled trial to compare the cost-effectiveness of mNGS with traditional methods for diagnosing CNSIs using a decision tree model. A total of 204 patients will be enrolled and randomly assigned to either the mNGS group or the traditional method group. The two groups of patients entered different decision points according to different clinical manifestations and examination results. They will be then given treatment decisions by a panel of specialists at the corresponding decision point. The primary outcome is the incremental cost-effectiveness ratio, which is the increased cost for every 1% increase in recovery rate. The secondary outcomes are a comparison of time cost, detection cost, and costs associated with antibiotics treatment between the two groups.IMPORTANCEDiagnosing and treating postoperative central nervous system infections (PCNSIs) remains challenging due to the low detection rate and time-consuming nature of traditional methods for identifying microorganisms in cerebrospinal fluid. Metagenomic next-generation sequencing (mNGS) technology provides a rapid and comprehensive understanding of microbial composition in PCNSIs by swiftly sequencing and analyzing the microbial genome. The current study aimed to assess the economic impact of using mNGS versus traditional bacterial culture-directed PCNSIs diagnosis and therapy in post-neurosurgical patients from Beijing Tiantan Hospital. mNGS is a relatively expensive test item, and whether it has the corresponding health-economic significance in the clinical application of diagnosing intracranial infection has not been studied clearly. Therefore, the investigators hope to explore the clinical application value of mNGS detection in PCNSIs after neurosurgery.

ObjectiveThis meta-analysis aimed to compare the relative safety and efficacy of cannulated compression screw (CCS) and femoral neck system (FNS) in treating patients with femoral neck fractures and to provide evidence-based medical evidence for FNS in treating femoral neck fractures.MethodsPubMed, Embase, Cochrane, and China National Knowledge Infrastructure databases were searched to collect outcomes related to femoral neck fractures treated with FNS and CCS, including time to fracture healing, incidence of non-union, incidence of osteonecrosis of the femoral head, incidence of failure of internal fixation, rate of femoral neck shortening, Harris hip score, Barthel index, operative time, intraoperative blood loss, fluoroscopy frequency, and complications. A meta-analysis was performed using RevManv5.4 (The Cochrane Collaboration) and Stata v14.0 software.ResultsThis analysis included 21 studies involving 1,347 patients. The results showed that FNS was superior to CCS in terms of fracture healing time [mean difference (MD) = −0.75, 95% CI = (−1.04, −0.46), P 0.05], Harris hip score [MD = 3.31, 95% CI = (1.99, 4.63), P

BackgroundMalnutrition is a rising global health issue associated with unfavorable outcomes of a variety of disorders. Currently, the prevalence and prognostic significance of malnutrition to patients with acute coronary syndrome (ACS) and chronic kidney disease (CKD) remained largely unclear.MethodsA total of 705 patients diagnosed with ACS and CKD in the First Affiliated Hospital of Wenzhou Medical University between 2013 and 2021 were included in this retrospective cohort study. Malnutrition was assessed by the Controlling Nutritional Status (CONUT), the Geriatric Nutritional Risk Index (GNRI), and the Prognostic Nutritional Index (PNI), respectively. The relationships between malnutrition and all-cause mortality and major cardiovascular events (MACEs) were analyzed.ResultsDuring a median follow-up of 31 months, 153 (21.7%) patients died, and 165 (23.4%) had MACEs. The prevalence of malnutrition was 29.8, 80.6, and 89.8% for the PNI, CONUT, and GNRI, respectively. All the malnutrition indexes were correlated with each other (r = 0.77 between GNRI and PNI, r = −0.72 between GNRI and CONUT, and r = −0.88 between PNI and CONUT, all p

Abstract Background Dyslipidaemia is a risk factor for abnormal blood glucose. However, studies on the predictive values of lipid markers in prediabetes and diabetes simultaneously are limited. This study aimed to assess the associations and predictive abilities of lipid indices and abnormal blood glucose. Methods A sample of 7667 participants without diabetes were enrolled in this cross-sectional study conducted in 2016, and all of them were classified as having normal glucose tolerance (NGT), prediabetes or diabetes. Blood glucose, blood pressure and lipid parameters (triglycerides, TG; total cholesterol, TC; high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C; non-high-density lipoprotein cholesterol, non-HDL-C; and triglyceride glucose index, TyG) were evaluated or calculated. Logistic regression models were used to analyse the association between lipids and abnormal blood glucose. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to assess the discriminatory power of lipid parameters for detecting prediabetes or diabetes. Results After adjustment for potential confounding factors, the TyG was the strongest marker related to abnormal blood glucose compared to other lipid indices, with odds ratios of 2.111 for prediabetes and 5.423 for diabetes. For prediabetes, the AUCs of the TG, TC, HDL-C, LDL-C, TC/HDL-C, TG/HDL-C, non-HDL-C and TyG indices were 0.605, 0.617, 0.481, 0.615, 0.603, 0.590, 0.626 and 0.660, respectively, and the cut-off points were 1.34, 4.59, 1.42, 2.69, 3.39, 1.00, 3.19 and 8.52, respectively. For diabetes, the AUCs of the TG, TC, HDL-C, LDL-C, TC/HDL-C, TG/HDL-C, non-HDL-C and TyG indices were 0.712, 0.679, 0.440, 0.652, 0.686, 0.692, 0.705, and 0.827, respectively, and the cut-off points were 1.35, 4.68, 1.42, 2.61, 3.44, 0.98, 3.13 and 8.80, respectively. Conclusions The TyG, TG and non-HDL-C, especially TyG, are accessible biomarkers for screening individuals with undiagnosed diabetes.

The optimal path planning for EVs (electric vehicles) has gained great attention during the last decade due to the zero pollution emission characteristics and limited power capacity of EV batteries. In this paper, an optimal route search is proposed considering multiple charging stations in a dynamic urban environment, while it is still applicable when the initial available amount of the battery fails to cover a certain travel range. The TRDP (transit route design problem) and TNDP (transit node design problem) are used to search for the most feasible routes based on time and driving range via the improved route-assisted rapid random tree (RA-RRT*) algorithm. Considering the status of charge of an EV’s battery during optimal routes search, three states are investigated between the destination and the aggregators: (1) bypassing the aggregators, (2) stopping over a single aggregator, and (3) stopping over multiple aggregators. During the states (2) and (3), it is required that the EVs be charged at the charging stations obtained by the RA-RRT* algorithm while approaching the destination. The proposed algorithm is tested on a random dataset under certain conditions, that is, traffic flow with congestion and assigned target locations from a given map data, with comparison experiments for efficacy verification.

This article targets fast indoor positioning and 3D target detection for unmanned aerial vehicle (UAV) real-time task implementation. With the combined direct method and feature method, a method is proposed for fast and accurate position estimation of the UAV. The camera pose is estimated by the visual odometer via the photometric error between the frames. Then the ORB features can be extended from the keyframes for the map consistency improvement by Bundle Adjustment with local and global optimization. A depth filter is also applied to assist the convergence of the map points with depth information updates from multiple frames. Moreover, the convolutional neural network is used to detect the specific target in an unknown space, while YOLOv3 is applied to obtain the semantic information of the target in the images. Thus, the spatial map points of the feature in the keyframes can be associated with the target detection box, while the statistical outlier filter can be simultaneously applied to eliminate the noise points. Experiments with public dataset, and field experiments on the established UAV platform in indoor environments have been carried out for visual based fast localization and object detection in real-time for the efficacy verification of the proposed method.

Local climate zones (LCZ) describe urban surface structures, supporting studies of urban heat islands, sustainable urbanization, and energy balance. The existing studies mapped LCZs from satellite images using scene-based classification, which trained deep-learning classifiers by labeled image patches, segmented satellite images into patches by sliding windows to match the size of training data, and finally classified the segmented patches to obtain LCZ maps. However, sliding windows are different from the real footprints of LCZs, which leads to large errors in classification. To address this problem, this article proposes a parcel-based method for LCZ classification using Sentinel-2 images, road networks, and elevation data. First, the Sentinel-2 images are segmented by the road network to obtain the land parcels as classification units. Second, each image parcel is standardized to match the training dataset, So2Sat LCZ42. Third, the trained convolutional neural network (CNN) is used to classify the standardized parcels into LCZs. Finally, the building height information derived from elevation data is used to refine the LCZs by a rule-based classifier. The results of the four test sites show that the overall accuracy of our method is 0.75, higher than the sliding-window-based method's accuracy of 0.47. Additional simulation experiments demonstrated that parcels derived from road networks can reduce the mixture effect in image patches, and parcel standardization can ensure the transferability of the CNN model trained by regular image patches. Considering that the road network and elevation data are widely available, the proposed method has the potential of mapping LCZs in large areas.

During task execution, the autonomous robots would likely pass through many narrow corridors along with mobile obstacles in dynamically complex environments. In this case, the off-line path planning algorithm is rather difficult to be directly implemented to acquire the available path in real-time. Hence, this article proposes a probabilistic roadmap algorithm based on the obstacle potential field sampling strategy to tackle the online path planning, called Obstacle Potential field-Probabilistic Roadmap Method (OP-PRM). The obstacle potential field is introduced to determine the obstacle area so as to construct the potential linked roadmap. Then the specific range around the obstacle boundary is justified as the target sampling area. Based on this obstacle localization, the effectiveness of the sampling points falling into the narrow corridors can be increased greatly for feasible roadmap construction. Furthermore, an incremental heuristic D* Lite algorithm is applied to search the shortest paths between the starting point and the target point on the roadmap. Simulation experiments demonstrate that the OP-PRM path planning algorithm can enable robots to search the optimal path fast from the starting point to the destination and effectively cross narrow corridors in complex dynamic environments.

A fast‐running human detection system for the unmanned aerial vehicle (UAV) based on optical flow and deep convolution networks is proposed in this study. In the system, running humans can be detected in real‐time at the speed of 15 frames per second (fps) with an 81.1% detection accuracy. To fast locate the candidate targets, optical flow representing the motion information is calculated with every two successive frames. A series of prior‐processing operations, including spatial average filtering, morphological expansion and outer contour extraction, are performed to extract the regions of interest. A classification model based on small‐kernel convolution networks is proposed to achieve the accurate recognition of the running people in various backgrounds. In the model, small convolutional filters are adopted to accelerate the speed of the data representation. Moreover, a total of 60,000 samples are collected to enhance the robustness of the model to adapt to the complex outdoor UAV scenes. The proposed method is compared with other deep learning frameworks for object detection. Field experiments on UAV videos are performed to verify that the proposed system can effectively detect the running people targets in real‐time.

Summary: Background: Ultrasound is a critical non-invasive test for preoperative diagnosis of ovarian cancer. Deep learning is making advances in image-recognition tasks; therefore, we aimed to develop a deep convolutional neural network (DCNN) model that automates evaluation of ultrasound images and to facilitate a more accurate diagnosis of ovarian cancer than existing methods. Methods: In this retrospective, multicentre, diagnostic study, we collected pelvic ultrasound images from ten hospitals across China between September 2003, and May 2019. We included consecutive adult patients (aged ≥18 years) with adnexal lesions in ultrasonography and healthy controls and excluded duplicated cases and patients without adnexa or pathological diagnosis. For DCNN model development, patients were assigned to the training dataset (34 488 images of 3755 patients with ovarian cancer, 541 442 images of 101 777 controls). For model validation, patients were assigned to the internal validation dataset (3031 images of 266 patients with ovarian cancer, 5385 images of 602 with benign adnexal lesions), external validation datasets 1 (486 images of 67 with ovarian cancer, 933 images of 268 with benign adnexal lesions), and 2 (1253 images of 166 with ovarian cancer, 5257 images of 723 benign adnexal lesions). Using these datasets, we assessed the diagnostic value of DCNN, compared DCNN with 35 radiologists, and explored whether DCNN could augment the diagnostic accuracy of six radiologists. Pathological diagnosis was the reference standard. Findings: For DCNN to detect ovarian cancer, AUC was 0·911 (95% CI 0·886–0·936) in the internal dataset, 0·870 (95% CI 0·822–0·918) in external validation dataset 1, and 0·831 (95% CI 0·793–0·869) in external validation dataset 2. The DCNN model was more accurate than radiologists at detecting ovarian cancer in the internal dataset (88·8% vs 85·7%) and external validation dataset 1 (86·9% vs 81·1%). Accuracy and sensitivity of diagnosis increased more after DCNN-assisted diagnosis than assessment by radiologists alone (87·6% [85·0–90·2] vs 78·3% [72·1–84·5], p

Background and Objectives: Job burnout is prevalent among primary care providers (PCPs) in different countries, and the factors that can alleviate burnout in these countries have been explored. However, no study has addressed the prevalence and the correlates of job burnout among Togolese PCPs. Therefore, we aimed to examine the prevalence of burnout and its association with social support and psychological capital among PCPs in Togo. Material and Methods: We conducted a cross-sectional study in Togo from 5 to 17 November 2020 among 279 PCPs of 28 peripheral care units (PCUs). Participants completed the Maslach Burnout Inventory, Job Content Questionnaire, and Psychological Capital Questionnaire. Data were analyzed using the Mann–Whitney U test, Kruskal–Wallis H test, Pearson correlation analysis, and multiple linear regression. Results: We received 279 responses, out of which 37.28% experienced a high level of emotional exhaustion (EE), 13.62% had a high level of depersonalization (DP), and 19.71% experienced low levels of personal accomplishment (PA). EE had a significant negative correlation with the supervisor’s support. In contrast, self-efficacy, hope, optimism, and resilience had a significant negative correlation with DP and a significant positive correlation with PA. Furthermore, supervisors’ support significantly predicted lower levels of EE. Optimism significantly predicted lower levels of DP and higher levels of PA. Conclusions: Burnout is common among Togolese PCPs, and self-efficacy, optimism, and supervisors’ support significantly contribute to low levels of job burnout among Togolese PCPs. This study provided insight into intervention programs to prevent burnout among PCPs in Togo.

As global public health is under threat by the 2019-nCoV and a potential new wave of large-scale epidemic outbreak and spread is looming, an imminent question to ask is what the optimal strategy of epidemic prevention and control (P&C) measures would be, especially in terms of the timing of enforcing aggressive policy response so as to maximize health efficacy and to contain pandemic spread. Based on the current global pandemic statistic data, here we developed a logistic probability function configured SEIR model to analyse the COVID-19 outbreak and estimate its transmission pattern under different “anticipate- or delay-to-activate” policy response scenarios in containing the pandemic. We found that the potential positive effects of stringent pandemic P&C measures would be almost canceled out in case of significantly delayed action, whereas a partially procrastinatory wait-and-see control policy may still be able to contribute to containing the degree of epidemic spread although its effectiveness may be significantly compromised compared to a scenario of early intervention coupled with stringent P&C measures. A laissez-faire policy adopted by the government and health authority to tackling the uncertainly of COVID19-type pandemic development during the early stage of the outbreak turns out to be a high risk strategy from optimal control perspective, as significant damages would be produced as a consequence.

Periodical changes in bioimpedance caused by blood perfusion are relatively fast and weak; therefore, a fast, high-precision imaging system is required. A novel electrical impedance tomography (EIT) system is proposed for real-time perfusion imaging. Synchronous parallel sampling is combined with timely excitation switching control to achieve fast acquisition and maintain high sampling accuracy. To further improve the overall precision of the system, a bipolar-mirror feedback voltage controlled current source and an internal calibration unit was used to improve the current precision and measurement accuracy, respectively. Test results of the resistor model show that the system has a signal-to-noise ratio of 81 dB at 50 kHz and achieves an imaging rate of 100 frames/ s. A preliminary test shows that the reconstructed EIT images reveal a complete cycle of blood exchange between the heart and lungs. Our system may provide a useful research platform for real-time and non-invasive blood perfusion imaging, which could be used in in vivo studies.

Objective: The evaluation of cultivated land quality from the regional perspective and the clear level and change in cultivated land quality in the whole region can better guide the construction of cultivated land quality, carry out scientific optimization allocation, improve grain production capacity, and promote the sustainable use of cultivated land. Research methods: The utilization of local resources and natural conditions were comprehensively considered, and the evaluation was carried out from the four dimensions of ecology, quantity, spatial structure and scale to comprehensively reflect the level of regional cultivated land utilization. Results: (1) The cultivated land quality level of the Horqin Zuoyihou Banner is low, and the pressure of regional water resources is great. Some cultivated land is located in the area with poor natural conditions, and there are many thin and narrow cultivated areas of land. The area of cultivated land that can be carried by regional water resources under current irrigation is 184,492.17 hm2, and that under water-saving irrigation is 259,703.72 hm2, which are lower than the current cultivated land areas. (2) During the study period, the total amount and spatial distribution of cultivated land changed greatly, and there were good natural conditions and utilization conditions of newly added cultivated land, but the quality of cultivated land from the regional perspective showed a downward trend. The dynamic attitude of cultivated land change was 0.99%. (3) According to the evaluation results, the cultivated land was divided into the following four categories: priority protection type, optimization and coordination type, gradual conversion type and priority conversion type, which accounted for 34.18%, 30.59%, 28.83% and 6.40% of the total cultivated land area, respectively. Conclusions: There is a sharp contradiction between the supply and demand of water resources, and the quality of cultivated land in the region is low and shows a downward trend, which is not conducive to the sustainable use of regional land resources. In this regard, the Horqin Zuoyihou Banner should actively curb the growth of cultivated land, develop water-saving irrigation processes, strengthen ecological environment protection, and systematically optimize the layout of cultivated land on the premise of ensuring ecological security according to the actual conditions of different regions.

Objective This study aimed to examine whether addition of cervical elastographic parameters measured by ElastoScan for the cervix (E-cervix) improves the predictive value of cervical length (CL) in induction of labor at term by dinoprostone. Methods We conducted a prospective, observational study between January 2020 and June 2020 in term primiparous women (n = 73) who were scheduled for labor induction by a 10-mg dinoprostone vaginal insert. The time intervals from the start of labor induction to regular uterine contractions and to vaginal delivery were calculated as the primary outcomes. We divided subjects into two groups using a threshold of 24 hours. Ultrasound measurements were compared between the two groups and the area under the curve (AUC) of the prediction model was calculated. Results Women who delivered vaginally within 24 hours had a shorter CL and softer cervix than those who delivered after 24 hours. The combination of CL and elastographic parameters increased the AUC to 0.672 compared with CL alone (AUC = 0.637). Conclusions Measurement by E-cervix is relatively reproducible. Addition of cervical strain elastography slightly improves the predictive performance of CL in vaginal delivery within 24 hours. This technique is a promising ancillary tool for use with ultrasound.

With the increasing number of electric vehicles (EVs), the charging demand of EVs has brought many new research hotspots, i.e., charging path planning and charging pricing strategy of the charging stations. In this paper, an integrated framework is proposed for multiobjective EV path planning with varied charging pricing strategies, considering the driving distance, total time consumption, energy consumption, charging fee such factors, while the charging pricing strategy is designed based on the objectives of maximizing the total revenues of the charging stations and balancing the profits of the charging stations. First, the energy consumption model of EVs, the M/M/S queuing model of charging stations, and the charging model of charging piles are established. A novel charging path planning algorithm is proposed based on bidirectional Martins’ algorithm, which can assist EV users to select charging stations and plan charging paths. Then, a particle swarm optimization (PSO) algorithm is applied to solve the optimal solution of charging station pricing designation. Finally, the method proposed in the paper is simulated on the street map of Shenzhen to verify the efficacy of the multiobjective charging path planning for EVs and the feasibility of the charging pricing strategy.

Since the outbreak of the novel coronavirus disease (COVID-19) at the beginning of December 2019, there have been more than 28.69 million cumulative confirmed cases worldwide as of 12th September 2020, affecting over 200 countries and regions with more than 920,463 deaths. The COVID-19 pandemic has been sweeping worldwide with unexpected rapidity. In this paper, a hybrid modelling strategy based on tessellation structure- (TS-) configured SEIR model is adopted to estimate the scale of the pandemic spread. Building on the data pertaining to the global pandemic transmission over the last six months around the world, key impact factors in the transmission and control procedure have been analysed, including isolation rate, number of the infected cases before taking prevention measures, degree of contact scope, and medical level, so as to capture the fundamental factor influencing the pandemic. The quantitative evaluation allowed us to illustrate the magnitude of risks of pandemic and to recommend appropriate national health policy of prevention measures for effectively controlling both intra- and interregional pandemic spread. Our modelling results clearly indicate that the early-stage preventive measures are the most effective action to be taken to contain the pandemic spread of the highly contagious nature of the COVID-19.

In this paper, a remote sensing method for heart rate (HR) detection is proposed via nearinfrared facial video data, which can be used for the online monitoring of the physiological parameters of the drivers and further analysis so as to enhance the driving safety. The proposed HR detection method is based on an automatic facial tracking algorithm, i.e., the Kanade-Lucas-Tomasi, to transform the facial images into time-series signals. Empirical mode decomposition, bandpass filtering and fast Fourier transformation are applied to the time-series signals for the HR extraction. Indoor experiments in different scenarios are conducted to discuss the involved impact factors, i.e., the distance between the near-infrared camera and the participant, the illumination intensity and the video duration, to acquire the optimal settings for HR measurement. It is more robust and convenient than those of the current commercial devices and webcambased HR measurement approaches, especially in complex illumination environments, such as wearing hat, glasses or makeup, with measurement accuracy 95%. Field experiments are also performed for the prognosis of physical state of the driver.

There are a large number of high-dimensional data in the Internet of Brain Things, and the data is reduced from the high-dimensional to low-dimensional to maintain the similarity between the data, thereby effectively ensuring the operating speed of the Internet of Brain Things. In the traditional method, the distributed dimensionality reduction reconstruction algorithm of the High dimensional data has poor dimensionality reduction effect and serious data distortion after reconstruction. A method for dimensionality reconstruction of high-dimensional data in the Internet of Brain Things is proposed. By using the algorithm of linear discriminant analysis, the projection matrix of high dimensional data is constructed to solve it. According to the solution results, using improved implicit variable model to establish a high dimensional large data dimensionality reduction model for the Internet of Brain Things. The fitness value of data after dimensionality reduction is calculated by quantum immune clonal algorithm, and the optimal individual and optimal solution are determined. The data reconfiguration is realized through the optimal solution marshalling. Experimental results show that the proposed algorithm can effectively improve the dimensionality reduction of High dimensional data in the Internet of Brain Things. After reconstruction, the reconstructed data retain accurate data information, the reliability of reconstructed data is high, and the computational complexity is not high, the need for small storage space, and the advantages of strong promotion ability.

Using unmanned aerial vehicle as movable base stations is a promising approach to enhance network coverage. Moreover, movable unmanned aerial vehicle–base stations can dynamically move to the target devices to expand the communication range as relays in the scenario of the Internet of things. In this article, we consider a communication system with movable unmanned aerial vehicle–base stations in millimeter-Wave. The movable unmanned aerial vehicle–base stations are equipped with antennas and multiple sensors for channel tracking. The cylindrical array antenna is mounted on the movable unmanned aerial vehicle–movable base stations, making the beam omnidirectional. Furthermore, the attitude estimation method using the deep neural network can replace the traditional attitude estimation method. The estimated unmanned aerial vehicle attitude information is combined with beamforming technology to realize a reliable communication link. Simulation experiments have been performed, and the results have verified the effectiveness of the proposed method.

In the paper, the effect of the charging behaviours of electric vehicles (EVs) on the grid load is discussed. The residential traveling historical data of EVs are analyzed and fitted to predict their probability distribution, so that the models of the traveling patterns can be established. A nonlinear stochastic programming model with the maximized comprehensive index is developed to analyze the charging schemes, and a heuristic searching algorithm is used for the optimal parameters configuration. With the comparison of the evaluation criteria, the multiobjective strategy is more appropriate than the single-objective strategy for the charging, i.e., electricity price. Furthermore, considering the characteristics of the normal batteries and charging piles, user behaviour and EV scale, a Monte Carlo simulation process is designed to simulate the large-scale EVs traveling behaviours in long-term periods. The obtained simulation results can provide prediction for the analysis of the energy demand growth tendency of the future EVs regulation. As a precedent of open-source simulation system, this paper provides a stand-alone strategy and architecture to regulate the EV charging behaviours without the unified monitoring or management of the grid.

BACKGROUND:Subsyndromal symptomatic depression (SSD) is a subtype of subthreshold depressive and can lead to significant psychosocial functional impairment. Although the pathogenesis of major depressive disorder (MDD) and SSD still remains poorly understood, a set of studies have found that many same genetic factors play important roles in the etiology of these two disorders. Nowadays, the differential gene expression between MDD and SSD is still unknown. In our previous study, we compared the expression profile and made the classification with the leukocytes by using whole-genome cRNA microarrays among drug-free first-episode subjects with SSD, MDD and matched healthy controls (8 subjects in each group), and finally determined 48 gene expression signatures. Based on these findings, we further clarify whether these genes mRNA was different expressed in peripheral blood in patients with SSD, MDD and healthy controls (60 subjects respectively). METHOD:With the help of the quantitative real-time reverse transcription-polymerase chain reaction (RT-qPCR), we gained gene relative expression levels among the three groups. RESULTS:We found that there are three of the forty eight co-regulated genes had differential expression in peripheral blood among the three groups, which are CD84, STRN, CTNS gene (F = 3.528, p = 0.034; F = 3.382, p = 0.039; F = 3.801, p = 0.026, respectively) while there were no significant differences for other genes. CONCLUSION:CD84, STRN, CTNS gene may have significant value for performing diagnostic functions and classifying SSD, MDD and healthy controls.

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

Based on the second-order moments, the analytical expression of the beam propagation factor of a cosh-Airy beam has been derived. The beam propagation factor was determined by the decay factor and the cosh parameter. Because the beam propagation factors in the x- and y-directions of the cosh-Airy beam have the same form, only the beam propagation factor in the x- direction was selected as the object of numerical calculation and analysis. The effects of the decay factor and the cosh parameter on the beam propagation factor were investigated. When the decay factor was greater than 1, the beam propagation factor first increased and then decreased with the increase of the cosh parameter, and finally, tended to a minimum value. Under the condition that the decay factor was less than 1, the beam propagation factor always increased with the increase of the cosh parameter. As the decay factor increased, the beam propagation factor decreased and tended to a minimum value. Finally, the effects of the decay factor and the cosh parameter on the squares of the beam waist and the divergence were analyzed in more detail.

The characteristics of partially coherent circular flattened Gaussian vortex beams in turbulent biological tissues are investigated, and the analytical formula for the cross-spectral density of this beam is derived. According to the cross-spectral density matrix, the average intensity and degree of polarization can be obtained. By numerical simulation, the distributions of the normalized average intensity and degree of polarization of partially coherent circular flattened Gaussian vortex beams are demonstrated on the research plane of turbulent biological tissues. The effects of the two beam parameters, the topological charge, the two transverse coherent lengths, and the structural constant of biological turbulence on the normalized average intensity and degree of polarization are analyzed. This study is of great significance for the potential application of partially coherent circular flattened Gaussian vortex beams in medical imaging and medical diagnosis.

In this paper, precise fault location in electrical traction network systems is discussed in the high-frequency domain. Based on the analysis of the equivalent impedance at the measurement terminal, the relationship between the fault distance and the frequency spectrum extreme points of the system impedance are derived. The exact fault location can be obtained from the relationship curves accordingly. MATLAB simulation experiments have been performed to verify the effectiveness of the proposed algorithm.

In this paper, we are concerned with the existence of positive solutions of the following nonlinear elliptic system involving critical Hardy-Sobolev exponent \begin{equation*}\label{eq:1}(*) \left\{ \begin{array}{lll} -\Delta u= \frac{2\alpha}{\alpha+\beta}\frac{u^{\alpha-1}v^\beta}{|x|^s}-\lambda u^p, & \quad {\rm in}\quad \Omega,\\[2mm] -\Delta v= \frac{2\beta}{\alpha+\beta}\frac{u^\alpha v^{\beta-1}}{|x|^s}-\lambda v^p, & \quad {\rm in}\quad \Omega,\\[2mm] u\gt 0, v\gt 0, &\quad {\rm in}\quad \Omega,\\[2mm] u=v=0, &\quad {\rm on}\quad \partial\Omega, \end{array} \right. \end{equation*} where \(N\geq 4\) and \(\Omega\) is a \(C^1\) bounded domain in \(\mathbb{R}^N\) with \(0\in\partial\Omega\). \(0\lt s \lt 2\), \(\alpha+\beta=2^*(s)=\frac{2(N-s)}{N-2}\), \(\alpha,\beta\gt 1\), \(\lambda\gt 0\) and \(1 \lt p\lt \frac{N+2}{N-2}\). The case when 0 belongs to the boundary of \(\Omega\) is closely related to the mean curvature at the origin on the boundary. We show in this paper that problem \((*)\) possesses at least a positive solution.

In this paper, methods of fault location are discussed in electrical traction single-end direct power supply network systems. Based on the distributed parameter model of the system, the position of the short-circuit fault can be located with the aid of the current and voltage value at the measurement end of the electrical traction line. Furthermore, the influence of the transient resistance, the position of the locomotive, locomotive load for fault location are also discussed. MATLAB simulation tool is used for the simulation experiments. Simulation results are proved the effectiveness of the proposed algorithms.