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BackgroundDiabetes is one of the most common metabolic diseases and continues to be a leading cause of death worldwide. The NLRP3 inflammasome has been shown to exert detrimental effects on diabetic models. However, evidence linking NLRP3 inflammasome and pre-diabetes has been scarcely explored. Herein, we aimed to determine whether the NLRP3 inflammasome correlates with insulin resistance and liver pathology in a cohort of pre-diabetic subjects.Methods50 pre-diabetic subjects were randomly assigned to a Pre-diabetes Control (DC, n=25) and a Pre-diabetes exercise (DEx, n=25) group. 25 Normal subjects (NC) were selected as controls. The DEx group performed a 6-month combined Yijingjing and resistance training intervention, while DC and NC group remained daily routines. Clinical metabolic parameters were determined with an automatic biochemistry analyzer; inflammatory cytokines were quantified by the ELISA assay; the protein expressions of NLRP3 inflammasome components in PBMCs were evaluated by Western Blot.ResultsThe insulin resistance, liver injury and NLRP3 inflammasome activity were higher in pre-diabetic individuals than in normal control group. However, 6-month exercise intervention counteracted this trend, significantly improved insulin sensitivity, reduced liver injury and inhibited the overactivation of NLRP3 inflammasome in pre-diabetic subjects. Moreover, positive correlations between insulin resistance, liver pathology and NLRP3 inflammasome were also found.ConclusionsOur study suggests that exercise training is an effective strategy to alleviate insulin resistance and liver injury in elderly pre-diabetic subjects which is probably associated with the inhibition of NLRP3 inflammasome activity.

The goal of cross-view image based geo-localization is to determine the location of a given street-view image by matching it with a collection of geo-tagged aerial images, which has important applications in the fields of remote sensing information utilization and augmented reality. Most current cross-view image based geo-localization methods focus on the image content and ignore the relations between feature nodes, resulting in insufficient mining of effective information. To address this problem, this study proposes feature relation guided cross-view image based geo-localization. This method first processes aerial remote sensing images using a polar transform to achieve the geometric coarse alignment of ground-to-aerial images, and then realizes local contextual feature concern and global feature correlation modeling of the images through the feature relation guided attention generation module designed in this study. Specifically, the module includes two branches of deformable convolution based multiscale contextual feature extraction and global spatial relations mining, which effectively capture global structural information between feature nodes at different locations while correlating contextual features and guiding global feature attention generation. Finally, a novel feature aggregation module, MixVPR, is introduced to aggregate global feature descriptors to accomplish image matching and localization. After experimental validation, the cross-view image based geo-localization algorithm proposed in this study yields results of 92.08%, 97.70%, and 98.66% for the top 1, top 5, and top 10 metrics, respectively, in CVUSA, a popular public cross-view dataset, and exhibits superior performance compared to algorithms of the same type.

This paper is to study finite horizon linear quadratic (LQ) non-zero sum Nash game for discrete-time infinite Markov jump stochastic systems (IMJSSs). Based on the theory of stochastic analysis, a countably infinite set of coupled generalized algebraic Riccati equations are solved and a necessary and sufficient condition for the existence of Nash equilibrium points is obtained. From a new perspective, the finite horizon mixed robust H2/H∞ control is investigated, and summarize the relationship between Nash game and H2/H∞ control problem. Moreover, the feasibility and validity of the proposed method has been proved by applying it to a numerical example.

The fermentation process can be affected when the starter culture enters the viable but nonculturable (VBNC) state. Therefore, it is of interest to investigate how VBNC cells change physiologically. Lacticaseibacillus (L.) paracasei Zhang is both a probiotic and a starter strain. This study aimed to investigate the metabolomic differences between VBNC and recovered L. paracasei Zhang cells. First, L. paracasei Zhang was induced to enter the VBNC state by keeping the cells in a liquid de Man–Rogosa–Sharpe (MRS) medium at 4 °C for 220 days. Flow cytometry was used to sort the induced VBNC cells, and three different types of culture media (MRS medium, skim milk with 1% yeast extract, and skim milk) were used for cell resuscitation. Cell growth responses in the three types of recovery media suggested that the liquid MRS medium was the most effective in reversing the VBNC state in L. paracasei Zhang. Metabolomics analysis revealed 25 differential metabolites from five main metabolite classes (amino acid, carbohydrate, lipid, vitamin, and purine and pyrimidine). The levels of L-cysteine, L-alanine, L-lysine, and L-arginine notably increased in the revived cells, while cellulose, alginose, and guanine significantly decreased. This study confirmed that VBNC cells had an altered physiology.

Object detection in remote sensing images faces the challenges of a complex background, large object size variations, and high inter-class similarity. To address these problems, we propose an adaptive adjacent layer feature fusion (AALFF) method, which is developed on the basis of RTMDet. Specifically, the AALFF method incorporates an adjacent layer feature fusion enhancement (ALFFE) module, designed to capture high-level semantic information and accurately locate object spatial positions. ALFFE also effectively preserves small objects by fusing adjacent layer features and employs involution to aggregate contextual information in a wide spatial range for object essential features extraction in complex backgrounds. Additionally, the adaptive spatial feature fusion (ASFF) module is introduced to guide the network to select and fuse the crucial features to improve the adaptability to objects with different sizes. The proposed method achieves mean average precision (mAP) values of 77.1%, 88.9%, and 95.7% on the DIOR, HRRSD, and NWPU VHR-10 datasets, respectively. Notably, our approach achieves mAP75 values of 60.8% and 79.0% on the DIOR and HRRSD datasets, respectively, surpassing the state-of-the-art performance on the DIOR dataset.

Feature selection is a typical multiobjective problem including two conflicting objectives. In classification, feature selection aims to improve or maintain classification accuracy while reducing the number of selected features. In practical applications, feature selection is one of the most important tasks in remote sensing image classification. In recent years, many metaheuristic algorithms have attempted to explore feature selection, such as the dragonfly algorithm (DA). Dragonfly algorithms have a powerful search capability that achieves good results, but there are still some shortcomings, specifically that the algorithm’s ability to explore will be weakened in the late phase, the diversity of the populations is not sufficient, and the convergence speed is slow. To overcome these shortcomings, we propose an improved dragonfly algorithm combined with a directed differential operator, called BDA-DDO. First, to enhance the exploration capability of DA in the later stages, we present an adaptive step-updating mechanism where the dragonfly step size decreases with iteration. Second, to speed up the convergence of the DA algorithm, we designed a new differential operator. We constructed a directed differential operator that can provide a promising direction for the search, then sped up the convergence. Third, we also designed an adaptive paradigm to update the directed differential operator to improve the diversity of the populations. The proposed method was tested on 14 mainstream public UCI datasets. The experimental results were compared with seven representative feature selection methods, including the DA variant algorithms, and the results show that the proposed algorithm outperformed the other representative and state-of-the-art DA variant algorithms in terms of both convergence speed and solution quality.

Scene classification is an important and challenging task employed toward understanding remote sensing images. Convolutional neural networks have been widely applied in remote sensing scene classification in recent years, boosting classification accuracy. However, with improvements in resolution, the categories of remote sensing images have become ever more fine-grained. The high intraclass diversity and interclass similarity are the main characteristics that differentiate remote scene image classification from natural image classification. To extract discriminative representation from images, we propose an end-to-end feature fusion method that aggregates features from dual paths (AFDP). First, lightweight convolutional neural networks with fewer parameters and calculations are used to construct a feature extractor with dual branches. Then, in the feature fusion stage, a novel feature fusion method that integrates the concepts of bilinear pooling and feature connection is adopted to learn discriminative features from images. The AFDP method was evaluated on three public remote sensing image benchmarks. The experimental results indicate that the AFDP method outperforms current state-of-the-art methods, with advantages of simple form, strong versatility, fewer parameters, and less calculation.

Characterized by complicated backgrounds, various types, large size variations, and arbitrary orientations, the detection and recognition of arbitrary-oriented objects in remote sensing images are challenging. To address the aforementioned problem, an anchor-free arbitrary-oriented object detector using box boundary-aware vectors is proposed. With the idea of CenterNet to detect objects as points, oriented object detection is achieved by predicting the center, the box boundary-aware vectors, the size, and the type of the bounding box. In the feature extraction stage of the designed architecture, Res2Net, a multiscale convolutional neural network, is used to extract feature maps of different scales and adaptively spatial feature fusion is adopted to improve the detector's adaptability to objects of different sizes. In the detector, a context enhancement module with a multibranch network is designed to enhance the contextual information of the objects and improve the detector's robustness to the complicated backgrounds. Experiments are carried on three challenging benchmarks (i.e., HRSC2016, UCAS-AOD, and DOTA) and our method achieves state-of-the-art performance with 90.30%, 89.70%, and 77.18% mAP, respectively.

ObjectiveLife form and body composition may affect the health of college students. This study will explore the relationship between the demographic variables of female college students and their body composition and health promoting lifestyle, so as to provide useful reference for the future design and planning of college students' physical and mental health courses and improving their physical activity level.MethodsUsing the method of questionnaire and bioelectrical resistance measurement, a questionnaire on healthy lifestyle of college students was constructed on the basis of consulting a large number of relevant research literature. Relevant survey samples were obtained through random sampling, and their body composition was measured; use SPSS21.0 statistical analysis software to conduct statistical analysis on relevant indicators.Results and conclusions(1) at present, female college students generally lack leisure activities and sleep, the proportion of regular fitness habits is low, and the number of snacks and average daily online time are generally too high; The overweight rate and body fat rate of female college students are generally too high, while the standard rate of muscle weight is generally too low. (2) Female college students' health promoting lifestyle has the highest score of self-realization, followed by interpersonal support and the worst behavior of sports participation; The older the college students, the worse their sports participation and overall health promotion behavior; The better the Conscious health status, the better the self-realization, exercise participation and nutritional behavior; The more exercise time per day, the higher their participation in sports and the stronger their health responsibility; The more time spent on the Internet every day, the worse the health responsibility and sports participation. (3) The more time female college students spend on the Internet every day, the higher the probability of overweight. Those with regular exercise habits have a lower proportion of overweight and high body fat rate, while the better their sleep and night snack behavior, the lower their body fat rate. The older college students are, the larger their visceral fat area is, the better their exercise habits and sleep behavior are, the smaller their visceral fat area is, and the lower their visceral fat level is.

In this study, we aimed to establish an innovative and efficient preparation method of potato resistant starch (PRS). To achieve this, we prepared type 3 resistant starch (RS3) from native potato starch (PS) using an ultrasonic method combined with autoclave gelatinization and optimized by the response surface method to study the structure and properties of potato RS3 (PRS3) and its effect on the quality of steamed bread. Under optimal treatment conditions, the PRS3 content increased from 7.5% to 15.9%. Compared with PS, the B-type crystal structure of PRS3 was destroyed, and the content of hydroxyl groups was increased, but no new chemical groups were introduced. PRS3 had a rougher surface and a lower crystallinity, gelatinization temperature, viscosity, setback value, and breakdown value. The low content (5%) of PRS3 had a stable viscosity and was easily degraded by bacteria, which can improve the quality of steamed bread to a certain extent. When the PRS3 content was over 10%, it competed with the gluten protein to absorb water, which reduced the contents of β-turn and α-helix in the dough, increased the contents of β-fold, and weakened the structure of the gluten network. It also decreased the specific volume and elasticity of the steamed bread and increased the spreading rate, hardness, and chewiness. Steamed bread prepared with a flour mixture containing 5% PRS3 was similar to the presidential acceptance of control flour. In this study, a new sustainable and efficient PRS3 preparation method was established, which has certain guiding significance for the processing of Functional steamed bread with high-resistant starch.

This paper presents a theoretical and numerical study of angle steel joints for conductors in transmission tower structures in the medium and heavy icing area. The results show that the deformation of the angle steel joint in suspension towers is controlled by the vertical load, and the deformation of the angle steel joint in strain towers is controlled by the tension load. The bolt of the angle steel joint for conductors is tension-controlled. A theoretical method considering the bending stiffness ratio of the angle steel plate and the bolt is derived. The proposed theoretical method quantifies the bending stiffness difference in the angle steel plate and bolt group, and the effects of bolt layout and load cases are considered. The numerical results are in good agreement with theoretical results by validating the internal forces and neutral axis depth. The accuracy of the proposed theoretical method is higher than that of theoretical methods based on steel structure principles. The proposed theoretical method is of great significance to the development of transmission tower structure design.

IntroductionThis study investigated the effect of Yijinjing combined with elastic band exercise on muscle mass and muscle function in patients with prediabetes.MethodsThis study was a randomized controlled trial designed in parallel (Chinese Clinical Trial Registry: ChiCTR2000039049). Participants with prediabetes (n = 47) were randomly divided into control (n = 21, 63.5 ± 4.7 years,16 females) and exercise (n = 26, 62.0 ± 5.0 years, 20 females) groups. The former maintained their original lifestyle, and the latter received Yijinjing combined with elastic band exercise five times a week for 6 months. All the outcome measures were assessed immediately at baseline, after 3- and 6-month intervention.ResultsAfter 6-month of the exercise, the body weight, body mass index, leg fat mass, gynoid fat mass, and total body fat mass in exercise group were significantly decreased compared with those at baseline (p < 0.05). Compared with those at baseline, total lean mass decreased at 3 and 6 months in both groups. The total muscle mass loss in the exercise group was always less than that in control group at all time periods, but the difference was not statistically significant. Handgrip strength, gait speed, reaction time, leg power, eye-closed and single-legged standing, and sit-and-reach were significantly improved for the exercise group at 3 and 6 months (p < 0.05). Gait speed and reaction time between both groups at 3 and 6 months were significant different (p < 0.05), and leg power at 6 months (p < 0.05). Compared with baseline, the reaction time of control group at six months was significantly improved (p < 0.05), and no other significant changes were observed. Compared with those at baseline, fasting plasma glucose, 2-h post-meal plasma glucose, fasting insulin, total cholesterol, and insulin resistance index in exercise group gradually decreased, and growth hormone was gradually increased with significance at 6 months (p < 0.05). 25-hydroxyvitamin D gradually and significantly increased in both groups at 3 and 6 months (p < 0.05). But two groups’ testosterone levels weren’t significant change.ConclusionYijinjing combined with elastic band exercise can substantially reduce the body weight and body fat content of middle-aged and elderly patients with Prediabetes, improve muscle function and growth hormone secretion, and delay muscle mass reduction and diabetes development.Clinical trial registration[http://www.chictr.org.cn/showproj.aspx?proj=62753], identifier [ChiCTR2000039049].

Magnetic interaction and pre-stress effect are two regular ways of introducing nonlinearities for wideband energy harvesting. However, the susceptibility to the fabrication and assembling makes it a tough job to exactly tune the magnetic field distribution and the preload as desired for the purpose of avoiding large deviation from design and model, especially in the micro-electro-mechanical systems scale. The nonlinear generator by introducing a curve fixture to the cantilever beam instead of the rectangular fixture presents a feasible and simple solution with good micro-electro-mechanical system fabrication compatibility. In this article, three prototypes of with different curve fixtures with the same size requirement are fabricated for detailing the influence of the selection of the fixture curve. Experimental and numerical investigations reveal that the generator has better performance when the nonlinear stiffness introduced by the curve fixture is smooth, especially in the case of no linear stiffness segment. [ABSTRACT FROM AUTHOR]

Triamine-based HBPI membranes are known for high gas separation selectivity and physical stability, but their permeabilities are still very low. In this study, we utilized a tetramine monomer called TPDA (N,N,N′,N′-tetrakis(4-aminophenyl)-1,4-benzenediamine) as a crosslinking center and incorporated an additional diamine comonomer called DAM (2,4,6-trimethyl-1,3-diaminobenzene) to enhance gas separation performance, especially gas permeability. The findings demonstrated that the resultant 6FDA−DAM/TPDA membranes based on tetramine TPDA exhibited a greater amount of free volume compared to the triamine-based HBPI membranes, resulting in significantly higher gas permeabilities. Furthermore, the higher concentration of DAM component led to the generation of more fractional free volumes (FFV). Consequently, the gas permeabilities of the 6FDA−DAM/TPDA membranes increased with an increase in DAM content, with a minimal compromise on selectivity. The enhanced gas permeabilities of the 6FDA−DAM/TPDA membranes enabled them to minimize the footprint required for membrane installations in real-world applications. Moreover, the 6FDA−DAM/TPDA membranes exhibited remarkable durability against physical aging and plasticization, thanks to the incorporation of a hyperbranched network structure.

BackgroundPain is a common complication after stroke with a high incidence and mortality rate. Many studies in the field of pain after stroke have been published in various journals. However, bibliometric analysis in the domain of pain after stroke is still lacking. This study aimed to deliver a visual analysis to analyze the global trends in research on the comorbidity of pain after stroke in the last 12 years.MethodsThe publications from the Web of Science (WoS) in the last 12 years (from 2010 to 2021) were collected and retrieved. CiteSpace software was used to analyze the relationship of publication year with countries, institutions, journals, authors, references, and keywords.ResultsA total of 322 publications were included in the analysis. A continuous but unstable growth in the number of articles published on pain after stroke was observed over the last 12 years. The Peoples' R China (65), Chang Gung University (10), and Topic in Stroke Rehabilitation (16) were the country, institution, and journal with the highest number of publications, respectively. Analysis of keywords showed that shoulder pain after stroke and central post-stroke pain were the research development trends and focus in this research field.ConclusionThis study provides a visual analysis method for the trend and frontiers of pain research after stroke. In the future, large sample, randomized controlled trials are needed to identify the potential treatments and pathophysiology for pain after stroke.

The 6FDA-based network PI has attracted significant attention for gas separation. A facile strategy to tailor the micropore structure within the network PI membrane prepared by the in situ crosslinking method is extremely significant for achieving an advanced gas separation performance. In this work, the 4,4′-diamino-2,2′-biphenyldicarboxylic acid (DCB) or 3,5-diaminobenzoic acid (DABA) comonomer was incorporated into the 6FDA-TAPA network polyimide (PI) precursor via copolymerization. The molar content and the type of carboxylic-functionalized diamine were varied in order to easily tune the resulting network PI precursor structure. Then, these network PIs containing carboxyl groups underwent further decarboxylation crosslinking during the following heat treatment. Properties involving thermal stabilities, solubility, d-spacing, microporosity, and mechanical properties were investigated. Due to the decarboxylation crosslinking, the d-spacing and the BET surface areas of the thermally treated membranes were increased. Moreover, the content of DCB (or DABA) played a key role in determining the overall gas separation performance of the thermally treated membranes. For instance, after the heating treatment at 450 °C, 6FDA-DCB:TAPA (3:2) showed a large increment of about ~532% for CO2 gas permeability (~266.6 Barrer) coupled with a decent CO2/N2 selectivity~23.6. This study demonstrates that incorporating the carboxyl-containing functional unit into the PI backbone to induce decarboxylation offers a practical approach with which to tailor the micropore structure and corresponding gas transport properties of 6FDA-based network PIs prepared by the in situ crosslinking method.

Abstract Background Surgical treatment has been recommended by most surgeons to treat pseudarthrosis in ankylosing spondylitis (AS). However, there is still some debate on the necessity of anterior fusion. There is very limited literature on the treatment and surgical outcomes of thoracolumbar pseudarthrosis in AS patients treated through a posterior-only approach. Methods From January 1, 2012 to December 31, 2017, a total of 42 cases diagnosed with thoracolumbar pseudarthrosis in AS patients with moderate kyphosis were included in this study. All of the patients received posterior-only kyphosis correction, internal fixation and fusion without anterior fusion, and underwent at least 2 years of follow-up. Clinical and radiographic results and complications were assessed. Results All of the patients were followed up for an average of 35.3 months (range, 24–48 months), and they achieved successful bone graft fusion at the pseudarthrosis sites. Satisfactory radiographic changes were achieved in these patients. The Cobb angles of global kyphosis (GK) were corrected from 53.2 ± 5.4 degrees preoperatively to 33.2 ± 4.3 degrees postoperatively, and to 36.1 ± 5.3 degrees at the latest follow-up. The Cobb angles of local kyphosis (LK) were corrected from 43.3 ± 4.6 degrees preoperatively to 26.8 ± 3.3 degrees postoperatively, and to 28.2 ± 3.6 degrees at the latest follow-up. The mean sagittal vertical axis (SVA) were corrected from 7.6 ± 4.2 cm preoperatively to 4.3 ± 2.1 cm postoperatively, and to 4.8 ± 2.3 cm at the latest follow-up. No serious neurological complication or deep wound infection was found in these 42 patients. Conclusion Posterior-only kyphosis correction and fixation without anterior fusion can achieve excellent bone fusion and satisfactory improvement in AS patients with thoracolumbar pseudarthrosis. This method may be a good choice for treating thoracolumbar pseudarthrosis in AS patients with moderate kyphosis.

Abstract This paper presents a novel mixed reality based navigation system for accurate respiratory liver tumor punctures in radiofrequency ablation (RFA). Our system contains an optical see-through head-mounted display device (OST-HMD), Microsoft HoloLens for perfectly overlaying the virtual information on the patient, and a optical tracking system NDI Polaris for calibrating the surgical utilities in the surgical scene. Compared with traditional navigation method with CT, our system aligns the virtual guidance information and real patient and real-timely updates the view of virtual guidance via a position tracking system. In addition, to alleviate the difficulty during needle placement induced by respiratory motion, we reconstruct the patient-specific respiratory liver motion through statistical motion model to assist doctors precisely puncture liver tumors. The proposed system has been experimentally validated on vivo pigs with an accurate real-time registration approximately 5-mm mean FRE and TRE, which has the potential to be applied in clinical RFA guidance.

Unstructured point clouds are a representative shape representation of real-world scenes in 3D vision and graphics. Incompletion inevitably arises, due to the way the set of unorganized points is captured, e.g., as fusion of depth images, merged laser scans, or structure-from-x. In this paper, an end-to-end sparse-to-dense multi-encoder neural network (termed an SDME-Net) is proposed for uniformly completing an unstructured point cloud with its shape details preserved. Unlike most existing learning-based shape completion methods that are enforced on the representations of 2D images and 3D voxelization of point clouds, and require priors of the underlying shape's structures, topologies and annotations, the SDME-Net is implemented on the incomplete and even noisy point cloud without any transformation, and makes no specific assumptions about the incompletion distribution and geometry features in the input. Specifically, the defective point cloud is completed and optimized in a sparse-to-dense manner of two-stages. In the first stage, we generate a sparse but complete point cloud based on a bistratal PointNet, and in the second stage, we yield a dense and high-fidelity point cloud by encoding and decoding the sparse result in the first stage using PointNet++. Meanwhile, we combine the distance loss and repulsion loss to generate more uniformly distributed output point clouds closer to the ground-truth counterparts. Qualitative and quantitative experiments on the public ShapeNet dataset illustrate that our approach outperforms the state-of-art learning-based point cloud shape completion methods in terms of real structure recovery, uniformity, and noise/partiality robustness.

Cell images, which have been widely used in biomedical research and drug discovery, contain a great deal of valuable information that encodes how cells respond to external stimuli and intentional perturbations. Meanwhile, to discover rarer phenotypes, cell imaging is frequently performed in a high-content manner. Consequently, the manual interpretation of cell images becomes extremely inefficient. Fortunately, with the advancement of deep-learning technologies, an increasing number of deep learning-based algorithms have been developed to automate and streamline this process. In this study, we present an in-depth survey of the three most critical tasks in cell image analysis: segmentation, tracking, and classification. Despite the impressive score, the challenge still remains: most of the algorithms only verify the performance in their customized settings, causing a performance gap between academic research and practical application. Thus, we also review more advanced machine learning technologies, aiming to make deep learning-based methods more useful and eventually promote the application of deep-learning algorithms.

Background: Facial paralysis (FP) is a common symptom after stroke, which influences the quality of life and prognosis of patients. Recently, peripheral magnetic stimulation (PMS) shows potential effects on peripheral and central nervous system damage. However, the effect of PMS on FP after stroke is still unclear. Methods: In this study, we applied PMS on the facial nerve of nine stroke patients with FP. At the same time, laser speckle contrast imaging (LSCI) was used to explore the facial skin blood flow (SkBF) in 19 healthy subjects and nine stroke patients with FP before and after the PMS intervention. The whole face was divided into 14 regions to compare the SkBF in different sub-areas. Results: In baseline SkBF, we found that there were no significant differences in the SkBF between the left and right faces in the healthy subjects. However, there was a significant difference in the SkBF between the affected and unaffected faces in Region 7 (Chin area, p = 0.046). In the following five minutes after the PMS intervention (Pre_0–5 min), the SkBF increased in Region 5 (p = 0.014) and Region 7 (p = 0.046) and there was an increasing trend in Region 3 (p = 0.088) and Region 6 (p = 0.069). In the five to ten minutes after the intervention (Post_6–10 min), the SkBF increased in Region 5 (p = 0.009), Region 6 (p = 0.021) and Region 7 (p = 0.023) and there was an increasing trend in Region 3 (p = 0.080) and left and right whole face (p = 0.051). Conclusions: These pilot results indicate that PMS intervention could increase facial skin blood flow in stroke patients with FP. A further randomized controlled trial can be performed to explore its possible clinical efficacy.

Remote sensing for image object detection has numerous important applications. However, complex backgrounds and large object-scale differences pose considerable challenges in the detection task. To overcome these issues, we proposed a one-stage remote sensing image object detection model: a multi-feature information complementary detector (MFICDet). This detector contains a positive and negative feature guidance module (PNFG) and a global feature information complementary module (GFIC). Specifically, the PNFG is used to refine features that are beneficial for object detection and explore the noisy features in a complex background of abstract features. The proportion of beneficial features in the feature information stream is increased by suppressing noisy features. The GFIC uses pooling to compress the deep abstract features and improve the model’s ability to resist feature displacement and rotation. The pooling operation has the disadvantage of losing detailed feature information; thus, dilated convolution is introduced for feature complementation. Dilated convolution increases the receptive field of the model while maintaining an unchanged spatial resolution. This can improve the ability of the model to recognize long-distance dependent information and establish spatial location relationships between features. The detector proposed also improves the detection performance of objects at different scales in the same image using a dual multi-scale feature fusion strategy. Finally, classification and regression tasks are decoupled in space using a decoupled head. We experimented on the DIOR and NWPU VHR-10 datasets to demonstrate that the newly proposed MFICDet achieves competitive performance compared to current state-of-the-art detectors.

Background: In epidemiological surveillance of mental health there is good reason to also include scales that measure the presence of well-being rather than merely symptoms of ill health. The Mental Health Continuum-Short Form (MHC-SF) is a self-reported scale to measure emotional, psychological and social well-being and conduct categorical diagnosis of positive mental health. This particular instrument includes the three core components of the World Health Organization's definition of mental health and had previously not been psychometrically evaluated on adolescents in China.Methods: In total 5,399 students (51.1% female) from schools in the urban areas of Weifang in China were included in the study (mean age = 15.13, SD = 1.56). Participants completed a comprehensive questionnaire with several scales, among them the MHC-SF. Statistical analyses to evaluate reliability, structural validity, measurement invariance, presence of floor and ceiling effects and to some extent external validity of the MHC-SF were carried out.Results: The Cronbach's α coefficients for sub-scales as well as the total scale were all above 0.80 indicating good reliability. Confirmative factor analysis confirmed the three-dimensional structure of the Chinese version of MHC-SF and supported the configural and metric invariance across gender and age. Noteworthy ceiling effects were observed for single items and sub-scales although not for the total scale. More importantly, observed floor effects were negligible. The stronger correlation found between MHC-SF and Minneapolis-Manchester Quality of Life Instrument (as measure of positive mental health) than between MHC-SF and Hospital Anxiety Depression Scale (as measure of mental illness and distress) yielded support for external validity.Conclusion: In conclusion, the main findings of this study are in line with studies from other countries that evaluated the psychometric properties of the MHC-SF and show that this instrument, that includes the three core components of the WHO definition of mental health, is useful in assessing positive adolescent mental health also in China. [ABSTRACT FROM AUTHOR]