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HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding. In addition, we apply multi-scale FTVP modules to propagate the rich spatial information of low-level features to mitigate spatial deviation of the predicted object location. Experiments on public benchmarks show that our method achieves various tasks on road layout estimation, vehicle occupancy estimation, and multi-class semantic estimation, at a performance level comparable to the state-of-the-arts, while maintaining superior efficiency.

Nuclei segmentation and classification play a crucial role in pathology diagnosis, enabling pathologists to analyze cellular characteristics accurately. Overlapping cluster nuclei, misdetection of small-scale nuclei, and pleomorphic nuclei-induced misclassification have always been major challenges in the nuclei segmentation and classification tasks. To this end, we introduce an auxiliary task of nuclei boundary-guided contrastive learning to enhance the representativeness and discriminative power of visual features, particularly for addressing the challenge posed by the unclear contours of adherent nuclei and small nuclei. In addition, misclassifications resulting from pleomorphic nuclei often exhibit low classification confidence, indicating a high level of uncertainty. To mitigate misclassification, we capitalize on the characteristic clustering of similar cells to propose a locality-aware class embedding module, offering a regional perspective to capture category information. Moreover, we address uncertain classification in densely aggregated nuclei by designing a top-k uncertainty attention module that leverages deep features to enhance shallow features, thereby improving the learning of contextual semantic information. We demonstrate that the proposed network outperforms the off-the-shelf methods in both nuclei segmentation and classification experiments, achieving the state-of-the-art performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Semantic segmentation is one of the directions in image research. It aims to obtain the contours of objects of interest, facilitating subsequent engineering tasks such as measurement and feature selection. However, existing segmentation methods still lack precision in class edge, particularly in multi-class mixed region. To this end, we present the Feature Enhancement Network (FE-Net), a novel approach that leverages edge label and pixel-wise weights to enhance segmentation performance in complex backgrounds. Firstly, we propose a Smart Edge Head (SE-Head) to process shallow-level information from the backbone network. It is combined with the FCN-Head and SepASPP-Head, located at deeper layers, to form a transitional structure where the loss weights gradually transition from edge labels to semantic labels and a mixed loss is also designed to support this structure. Additionally, we propose a pixel-wise weight evaluation method, a pixel-wise weight block, and a feature enhancement loss to improve training effectiveness in multi-class regions. FE-Net achieves significant performance improvements over baselines on publicly datasets Pascal VOC2012, SBD, and ATR, with best mIoU enhancements of 15.19%, 1.42% and 3.51%, respectively. Furthermore, experiments conducted on Pole&Hole match dataset from our laboratory environment demonstrate the superior effectiveness of FE-Net in segmenting defined key pixels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Graph neural networks (GNNs) are widely used for analyzing graph-structural data and solving graph-related tasks due to their powerful expressiveness. However, existing off-the-shelf GNN-based models usually consist of no more than three layers. Deeper GNNs usually suffer from severe performance degradation due to several issues including the infamous "over-smoothing" issue, which restricts the further development of GNNs. In this article, we investigate the over-smoothing issue in deep GNNs. We discover that over-smoothing not only results in indistinguishable embeddings of graph nodes, but also alters and even corrupts their semantic structures, dubbed semantic over-smoothing. Existing techniques, e.g., graph normalization, aim at handling the former concern, but neglect the importance of preserving the semantic structures in the spatial domain, which hinders the further improvement of model performance. To alleviate the concern, we propose a cluster-keeping sparse aggregation strategy to preserve the semantic structure of embeddings in deep GNNs (especially for spatial GNNs). Particularly, our strategy heuristically redistributes the extent of aggregations for all the nodes from layers, instead of aggregating them equally, so that it enables aggregate concise yet meaningful information for deep layers. Without any bells and whistles, it can be easily implemented as a plug-and-play structure of GNNs via weighted residual connections. Last, we analyze the over-smoothing issue on the GNNs with weighted residual structures and conduct experiments to demonstrate the performance comparable to the state-of-the-arts.

Seed vigor is an essential quality evaluation index for seed selection. However, accurately detecting the vigor of a single corn seed is challenging. In this study, we constructed a single-fiber spatially resolved detection device using visible/near-infrared spectroscopy to investigate the patterns and correlations between spatially resolved spectroscopy (SRS) at 500-1000 nm and seed vigor. The device collected spectral data at a light source-detector distance of 5-6.6 mm on the embryo side (S1) and endosperm side (S2) of the corn seeds. The proposed spectral ratio method based on SRS and spectral combination analysis achieved an improvement in the detection accuracy of different corn seed vigor. Modeling by SG-CARS-PLSDA using the ratio method showed further improvement in the prediction ability. The highest accuracy for both S1 and S2 in the Zhengdan 958 variety was 91.67 %, while those of S1 and S2 for the Shaandan 650 variety were 86.67 % and 88.33 %, respectively. In addition, SRS was found to be more advantageous in S2 acquisition, verifying the potential of SRS in the non-destructive testing of seed vigor. This provides a favorable reference for the comprehensive evaluation of other internal quality indices of seeds., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In this study, a fortified Daqu (FF Daqu) was prepared using high cellulase-producing Bacillus subtilis , and the effects of in situ fortification on the physicochemical properties, flavor, active microbial community and metabolism of Daqu were analyzed. The saccharification power, liquefaction power, and cellulase activity of the FF Daqu were significantly increased compared with that of the traditional Daqu (CT Daqu). The overall differences in flavor components and their contents were not significant, but the higher alcohols were lower in FF Daqu. The relative abundance of dominant active species in FF Daqu was 85.08% of the total active microbiota higher than 63.42% in CT Daqu, and the biomarkers were Paecilomyces variotii and Aspergillus cristatus , respectively. The enzymes related to starch and sucrose metabolic pathways were up-regulated and expressed in FF Daqu. In the laboratory level simulation of baijiu brewing, the yield of baijiu was increased by 3.36% using FF Daqu., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

This paper introduces a method for high-resolution lattice image reconstruction and dislocation analysis based on diffraction extinction. The approach primarily involves locating extinction spots in the Fourier transform spectrum (reciprocal space) and constructing corresponding diffraction wave functions. By the coherent combination of diffraction and transmission waves, the lattice image of the extinction planes is reconstructed. This lattice image is then used for dislocation localization, enabling the observation and analysis of crystal planes that exhibit electron diffraction extinction effects and atomic jump arrangements during high-resolution transmission electron microscopy (HRTEM) characterization. Furthermore, due to the method's effectiveness in localizing dislocations, it offers a unique advantage when analyzing high-resolution images with relatively poor quality. The feasibility of this method is theoretically demonstrated in this paper. Additionally, the method was successfully applied to observed edge dislocations, such as 1/6[211-], 1/6[2-11-], and 1/2[01-1], which are not easily observable in conventional HRTEM characterization processes, in electro-deposited Cu thin films. The Burgers vectors were determined. Moreover, this paper also attempted to observe screw dislocations that are challenging to observe in high-resolution transmission electron microscopy. By shifting a pair of diffraction extinction spots and superimposing the reconstructed images before and after the shift, screw dislocations with a Burgers vector of 1/2[011-] were successfully observed in electro-deposited Cu thin films.

Agriculture is the primary source of human survival, which provides the most basic living and survival conditions for human beings. As living standards continue to improve, people are also paying more attention to the quality and safety of agricultural products. Therefore, the detection of agricultural product quality is very necessary. In the past decades, the spectroscopy technique has been widely used because of its excellent results in agricultural quality detection. However, traditional spectral inspection methods cannot accurately describe the internal information of agricultural products. With the continuous research and development of optical properties, it has been found that the internal quality of an object can be better reflected by separating the properties of light, such as its absorption and scattering properties. In recent years, spatially resolved spectroscopy has been increasingly used in the field of agricultural product inspection due to its simple compositional structure, low-value cost, ease of operation, efficient detection speed, and outstanding ability to obtain information about agricultural products at different depths. It can also separate optical properties based on the transmission equation of optics, which allows for more accurate detection of the internal quality of agricultural products. This review focuses on the principles of spatially resolved spectroscopy, detection equipment, analytical methods, and specific applications in agricultural quality detection. Additionally, the optical properties methods and direct analysis methods of spatially resolved spectroscopy analysis methods are also reported in this paper., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xia, Liu, Meng, Hu, Liu, Kang, Luo, Zhang and Tang.)

Background: Daqu is an essential starter for baijiu brewing in China. However, the microbial enrichment and metabolic characteristics of Daqu formed at different fermentation temperatures are still unclear., Methods: High-throughput sequencing technology and the non-targeted metabolomics were used to compare the microbial communities and metabolites of Taorong-type high-temperature Daqu and middle-temperature Daqu. In this study, the relationship between microorganisms and metabolites was established., Results: The study found that the composition and metabolites of the microbial community differed due to the difference in Daqu-making temperature. The bacterial diversity of Taorong-type high-temperature Daqu was higher than that of middle-temperature Daqu, while the fungal community diversity of Taorong-type middle-temperature Daqu was higher than that of high temperature Daqu. A total of 1,034 differential metabolites were screened from the two types of Daqu, and 76 metabolites with significant differences were detected ( P < 0.001 and variable importance in projection (VIP) > 1.15). Tetraacetylethylenediamine is the metabolite with the largest differential fold among the 76 differential metabolites, which can be used as a potential marker metabolite of high-temperature Daqu., Conclusion: This study helps elucidate the microbial assembly mechanisms and functional expression under different processing conditions through a further understanding of the composition and metabolic profile differences of different types of Daqu microflora in Taorong-type baijiu., Competing Interests: Zhenke Zhang, Suna Han and Jianguang Hou are employed by Henan Yangshao Distillery Co., Ltd., (© 2024 Liu et al.)

Traditional monocular depth estimation assumes that all objects are reliably visible in the RGB color domain. However, this is not always the case as more and more buildings are decorated with transparent glass walls. This problem has not been explored due to the difficulties in annotating the depth levels of glass walls, as commercial depth sensors cannot provide correct feedbacks on transparent objects. Furthermore, estimating depths from transparent glass walls requires the aids of surrounding context, which has not been considered in prior works. To cope with this problem, we introduce the first Glass Walls Depth Dataset (GW-Depth dataset). We annotate the depth levels of transparent glass walls by propagating the context depth values within neighboring flat areas, and the glass segmentation mask and instance level line segments of glass edges are also provided. On the other hand, a tailored monocular depth estimation method is proposed to fully activate the glass wall contextual understanding. First, we propose to exploit the glass structure context by incorporating the structural prior knowledge embedded in glass boundary line segment detections. Furthermore, to make our method adaptive to scenes without structure context where the glass boundary is either absent in the image or too narrow to be recognized, we propose to derive a reflection context by utilizing the depth reliable points sampled according to the variance between two depth estimations from different resolutions. High-resolution depth is thus estimated by the weighted summation of depths by those reliable points. Extensive experiments are conducted to evaluate the effectiveness of the proposed dual context design. Superior performances of our method is also demonstrated by comparing with state-of-the-art methods. We present the first feasible solution for monocular depth estimation in the presence of glass walls, which can be widely adopted in autonomous navigation.

Aim: Our study aimed to construct a practical risk prediction model for metabolic syndrome (MetS) based on the longitudinal health check-up data, considering both the baseline level of physical examination indicators and their annual average cumulative exposure, and to provide some theoretical basis for the health management of Mets., Methods: The prediction model was constructed in male and female cohorts, separately. The shared set of predictive variables screened out from 49 important physical examination indicators by the univariate Cox model, Lasso-Cox model and the RSF algorithm collectively was further screened by Cox stepwise regression method. The screened predictors were used to construct prediction model by the Cox proportional hazards regression model and RSF model, respectively. Subsequently, the better method would be selected to develop final MetS predictive model according to comprehensive comparison and evaluation. Finally, the optimal model was validated internally and externally by the time-dependent ROC curve (tdROC) and concordance indexes (C-indexes). The constructed predictive model was converted to a web-based prediction calculator using the "shiny" package of the R4.2.1 software., Results: A total of 15 predictors were screened in the male cohort and 9 predictors in the female cohort. In both male and female cohorts, the prediction error curve of the RSF model was consistently lower than that of the Cox proportional hazards regression model, and the integrated Brier score (IBS) of the RSF model was smaller, therefore, the RSF model was used to develop the final prediction model. Internal validation of the RSF model showed that the area under the curve (AUC) of tdROC for 1 year, 3 years and 5 years in the male cohort were 0.979, 0.991, and 0.983, and AUCs in the female cohort were 0.959, 0.975, and 0.978, respectively, the C-indexes calculated by 500 bootstraps of the male and female cohort RSF models are above 0.7. The external validation also showed that the model has good predictive ability., Conclusion: The risk predictive model for MetS constructed by RSF in this study is more stable and reliable than Cox proportional hazards regression model, and the model based on multiple screening of routine physical examination indicators has performed well in both internal and external data, and has certain clinical application value., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Liu, Tang, Cui, Zhao and Song.)

Stitched fluorescence microscope images inevitably exist in various types of stripes or artifacts caused by uncertain factors such as optical devices or specimens, which severely affects the image quality and downstream quantitative analysis. Here, we present a deep learning-based Stripe Self-Correction method, so-called SSCOR. Specifically, we propose a proximity sampling scheme and adversarial reciprocal self-training paradigm that enable SSCOR to utilize stripe-free patches sampled from the stitched microscope image itself to correct their adjacent stripe patches. Comparing to off-the-shelf approaches, SSCOR can not only adaptively correct non-uniform, oblique, and grid stripes, but also remove scanning, bubble, and out-of-focus artifacts, achieving the state-of-the-art performance across different imaging conditions and modalities. Moreover, SSCOR does not require any physical parameter estimation, patch-wise manual annotation, or raw stitched information in the correction process. This provides an intelligent prior-free image restoration solution for microscopists or even microscope companies, thus ensuring more precise biomedical applications for researchers., (© 2023. Springer Nature Limited.)

This study investigated the effects of a dance-based exergaming on Chinese college students' energy expenditure, self-efficacy, and enjoyment in comparison with the traditional aerobic dance exercise. Forty young adults (33 females; Mage = 21.55 years, SD = 2.06) completed two separate 20 min exercise sessions with 10 min intervals on the same day: (1) Xbox 360 Kinect Just Dance exergaming session; and (2) a traditional instructor-led aerobic dance exercise session. Participants' energy expenditure (Kcal/session) was measured by the ActiGraph GT9X Link accelerometers, and their perceived self-efficacy and enjoyment were assessed via validated surveys following each session. Dependent t-test indicated significant differences in participants' enjoyment ( t  = -1.83, p  = 0.04). Specifically, participants in the dance-based exergaming session reported a higher level of enjoyment (M = 3.96, SD = 0.65) as compared to the aerobic dance session (M = 3.61, SD = 0.54). However, there was no significant difference in energy expenditure and self-efficacy between the two sessions. Findings suggest that college students had comparable energy expenditure as the traditional aerobic dance session while experiencing more fun and enjoyment. This suggests that exergaming can be a fun exercise alternative for promoting physical activity among young adults., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Gu, Gu, Oginni, Ryu, Liu, Li and Gao.)

Objective: The purpose of this study was to investigate the associations between mobile phone dependency, bedtime procrastination, FoMO, and sleep quality among college students during the COVID-19 outbreak. Specifically, we examined whether bedtime procrastination and FoMO mediate the relationship between mobile phone dependency and sleep quality., Methods: A total of 881 college students completed an online survey in May 2022 in Shanghai, China. Mobile Phone Involvement Questionnaire, Bedtime Procrastination Scale, Fear of Missing Out Scale and Pittsburgh Sleep Quality Index were used to assess mobile phone dependency, bedtime procrastination, fear of missing out, and sleep quality, respectively. Multiple linear regression and mediation analysis were conducted., Results: The correlation analyses indicated mobile phone dependency was positively associated with fear of missing out, bedtime procrastination, and poor sleep quality among college students. The structural equation modeling analyses revealed that mobile phone dependency had significant indirect effects on sleep quality through bedtime procrastination (indirect effect: 0.030, 95%CI: 0.022-0.041) and fear of missing out (indirect effect: 0.013, 95%CI: 0.003-0.023)., Conclusion: The findings indicated that bedtime procrastination and fear of missing out are mediators mediating the relationship between mobile phone dependency with sleep quality. Bedtime procrastination and fear of missing out should be considered as potential intervention targets for reducing mobile phone dependency and improving sleep quality in college students., (© 2023. The Author(s).)

Many studies have explored the efficacy of probiotics on autism spectrum disorder (ASD) in children, but there is no consensus on the curative effect. This systematic review and meta-analysis aimed to comprehensively investigate whether probiotics could improve behavioral symptoms in children with ASD. A systematic database search was conducted and a total of seven studies were included in the meta-analysis. We found a nonsignificant overall effect size of probiotics on behavioral symptoms in children with ASD (SMD = -0.24, 95% CI: -0.60 to 0.11, p = 0.18). However, a significant overall effect size was found in the subgroup of the probiotic blend (SMD = -0.42, 95% CI: -0.83 to -0.02, p = 0.04). Additionally, these studies provided limited evidence for the efficacy of probiotics due to their small sample sizes, a shorter intervention duration, different probiotics used, different scales used, and poor research quality. Thus, randomized, double-blind, and placebo-controlled studies following strict trial guidelines are needed to precisely demonstrate the therapeutic effects of probiotics on ASD in children.

Event cameras, or dynamic vision sensors, have recently achieved success from fundamental vision tasks to high-level vision researches. Due to its ability to asynchronously capture light intensity changes, event camera has an inherent advantage to capture moving objects in challenging scenarios including objects under low light, high dynamic range, or fast moving objects. Thus event camera are natural for visual object tracking. However, the current event-based trackers derived from RGB trackers simply modify the input images to event frames and still follow conventional tracking pipeline that mainly focus on object texture for target distinction. As a result, the trackers may not be robust dealing with challenging scenarios such as moving cameras and cluttered foreground. In this paper, we propose a distractor-aware event-based tracker that introduces transformer modules into Siamese network architecture (named DANet). Specifically, our model is mainly composed of a motion-aware network and a target-aware network, which simultaneously exploits both motion cues and object contours from event data, so as to discover motion objects and identify the target object by removing dynamic distractors. Our DANet can be trained in an end-to-end manner without any post-processing and can run at over 80 FPS on a single V100. We conduct comprehensive experiments on two large event tracking datasets to validate the proposed model. We demonstrate that our tracker has superior performance against the state-of-the-art trackers in terms of both accuracy and efficiency.

Objective : To evaluate how different physical activity (PA) interventions (traditional, exergaming, and teacher/parent education) impacted children's motor skills (object control, locomotor, and gross motor). Design : Systematic review and network meta-analysis. Data sources : PubMed, Medline, Scopus, Web of Science, EMBASE, and PsycINFO. Eligibility criteria : (1) Participants comprised 1708 children 3-12 years; (2) PA or exercise-based interventions were investigated; (3) only studies using a Test of Gross Motor Skills assessment were included; (4) RCT were chosen as the study design to assess the impact of PA interventions on children's motor skills; and (5) culture-based PA studies with English language only were included. Data were analyzed using a Bayesian network meta-analysis. Results : The results were reported as standardized mean differences (SMDs) with associated 95% credible intervals (CrIs). For object control, aerobic intervention (SMD 6.90, 95% Crl 1.39 to 13.50); for locomotor, exergaming intervention (SMD 12.50, 95% Crl 0.28 to 24.50); and for gross motor, aerobic intervention (SMD 7.49, 95% Crl 0.11 to 15.70) were the most effective treatments. Conclusion : Children's FMSs have been improved through different PA interventions. Among them, aerobic interventions seem to be the most effective intervention in enhancing object control skills and overall gross motor skills.

Purpose : Current evidence regarding the use of commercially available immersive virtual reality (VR) headsets and compatible VR exercise apparatus in the promotion of individuals' physical activity (PA) is lacking. This exploratory study investigated the acute effects of a VirZoom VR exercise bike (vBike) on college students' physical responses compared to an exergaming bike (eBike) and a traditional stationary exercise bike (tBike). Method : Forty-eight college students (34 females, X age  = 23.58 ± 3.39; X BMI  = 23.81 ± 3.57) completed three separate 20-minute cycling sessions on the vBike, eBike, and tBike in a counterbalanced order. Outcomes included rating of perceived exertion (RPE), assessed at 4-minute intervals throughout each cycling session, and overall PA, operationalized as pedal revolution counts (PRC) during each session. Results : Linear models indicated significant group differences between exercise modalities for RPE ( F (2,138) = 14.76-27.30, all p < .001, η 2  = 0.17-0.27) and PRC ( F (2,138) = 74.862, p < .001, η 2  = 0.48). Pairwise comparisons revealed that RPE was reported significantly higher at each time point (i.e., 4th minute, 8th minute, 12th minute, 16th minute, and 20th minute) during the tBike session vs. the vBike and eBike sessions ( p < .001) and that participants had higher PRC during the vBike session vs. the eBike and tBike sessions ( p < .001). Conclusion : A commercially available VR exercise bike would trigger greater overall PA as compared to exergaming and traditional cycling while being perceived less intense. Future intervention studies conducted in real-world settings to determine cause and effect relationship are warranted.

In this paper, we introduce a novel yet challenging research problem, interactive crowd video generation, committed to producing diverse and continuous crowd video, and relieving the difficulty of insufficient annotated real-world datasets in crowd analysis. Our goal is to recursively generate realistic future crowd video frames given few context frames, under the user-specified guidance, namely individual positions of the crowd. To this end, we propose a deep network architecture specifically designed for crowd video generation that is composed of two complementary modules, each of which combats the problems of crowd dynamic synthesis and appearance preservation respectively. Particularly, a spatio-temporal transfer module is proposed to infer the crowd position and structure from guidance and temporal information, and a point-aware flow prediction module is presented to preserve appearance consistency by flow-based warping. Then, the outputs of the two modules are integrated by a self-selective fusion unit to produce an identity-preserved and continuous video. Unlike previous works, we generate continuous crowd behaviors beyond identity annotations or matching. Extensive experiments show that our method is effective for crowd video generation. More importantly, we demonstrate the generated video can produce diverse crowd behaviors and be used for augmenting different crowd analysis tasks, i.e., crowd counting, anomaly detection, crowd video prediction. Code is available at https://github.com/Icep2020/CrowdGAN.

Background: Public health guidelines have called for innovative and flexible physical activity (PA) intervention strategies to promote PA and health amid the coronavirus disease 2019 (COVID-19) pandemic. Therefore, this study's purpose was to examine the effects of a home-based, YouTube-delivered PA intervention grounded in self-determination theory on young adults' free-living PA, sedentary behavior, and sleep quality (NCT04499547)., Methods: Sixty-four young adults (48 females; age = 22.8 ± 3.4 years, mean ± SD; body mass index = 23.1 ± 2.6 kg/m 2 ) were randomized (1:1) into the intervention group, which received weekly aerobic and muscle-strengthening PA videos, or control group, which received weekly general health education videos, for 12 weeks. Our primary outcome was free-living moderate-to-vigorous PA (MVPA) and our secondary outcomes were sedentary behavior, light PA, and sleep quality (measured using ActiGraph accelerometers) along with muscle-strengthening PA frequency, self-determination theory-related motivation (non-regulation, external regulation, introjected regulation, identified regulation, integrated regulation, and intrinsic regulation), and perceived PA barriers (assessed using validated questionnaires). Repeated measures analysis of variances (ANOVAs) examined between-group differences at an adjusted significance level of 0.004 and effect sizes as partial eta-squared (η p 2 )., Results: We observed statistically significant interaction effects for MVPA, sleep efficiency, muscle-strengthening PA frequency, non-regulation, integrated regulation, intrinsic regulation, and perceived PA barriers (F(1, 62) = 10.75-77.67, p < 0.001-0.002, η p 2  = 0.15-0.56) with all outcomes favoring the intervention group. We observed no statistically significant differences in either group for sedentary behavior, light PA, sleep duration, or external, introjected, and identified regulations after 12 weeks (F(1, 62) = 1.11-3.64, p = 0.06-0.61)., Conclusion: With national COVID-19 restrictions still in place and uncertainty regarding post-pandemic PA environments and behaviors, a remote, YouTube-delivered PA intervention may help foster clinically meaningful improvements in young adults' free-living MVPA, muscle-strengthening PA frequency, sleep efficiency, PA-related intrinsic motivation, and perceived PA barriers., (Copyright © 2021. Production and hosting by Elsevier B.V.)

Objective: Given the low levels of physical activity (PA) among U.S. college students, the use of exergaming as a supplement to traditional exercise may promote higher levels of motivation and PA. Therefore, this study’s purpose was to examine the effect of two different exergames on college students’ situational interest (SI), self-efficacy (SE), and equilibrium change (EQC) compared to traditional treadmill walking. Methods: Sixty college students (30 female; Mage = 23.6 ± 4.1 years; MBMI = 23.9 ± 4.0 kg/m2) participated in three separate 20 min exercise sessions: (1) Xbox 360 Kinect Just Dance; (2) Xbox 360 Kinect Reflex Ridge; and (3) traditional treadmill walking at 4.0 mph. Participants’ SI, SE, and EQC were measured after each session using a series of validated surveys. Results: A mixed model analysis of covariance (ANCOVA) with repeated measures evaluated mean differences between exercise sessions for all outcomes. Significant main effects were observed between the three exercise sessions (all p < 0.01). Specifically, Just Dance and Reflex Ridge sessions yielded significantly higher SI scores than treadmill exercise, F (10, 49) = 54.61, p < 0.01, η2 = 0.92. In addition, participants experienced significantly lower EQC in Reflex Ridge than in treadmill exercise, F (2, 58) = 4.26, p = 0.02, η2 = 0.13. No differences were identified for SE. Conclusion: The integration of exergaming into traditional exercise routines may help to promote higher levels of SI but not SE amongst college students. RR exergaming also demonstrated low EQC as compared to traditional exercise. Experimental study designs are warranted to provide additional evidence on the efficacy of exergaming.

Data representation learning is one of the most important problems in machine learning. Unsupervised representation learning becomes meritorious as it has no necessity of label information with observed data. Due to the highly time-consuming learning of deep-learning models, there are many machine-learning models directly adapting well-trained deep models that are obtained in a supervised and end-to-end manner as feature abstractors to distinct problems. However, it is obvious that different machine-learning tasks require disparate representation of original input data. Taking human action recognition as an example, it is well known that human actions in a video sequence are 3-D signals containing both visual appearance and motion dynamics of humans and objects. Therefore, the data representation approaches with the capabilities to capture both spatial and temporal correlations in videos are meaningful. Most of the existing human motion recognition models build classifiers based on deep-learning structures such as deep convolutional networks. These models require a large quantity of training videos with annotations. Meanwhile, these supervised models cannot recognize samples from the distinct dataset without retraining. In this article, we propose a new 3-D deconvolutional network (3DDN) for representation learning of high-dimensional video data, in which the high-level features are obtained through the optimization approach. The proposed 3DDN decomposes the video frames into spatiotemporal features under a sparse constraint in an unsupervised way. In addition, it also can be regarded as a building block to develop deep architectures by stacking. The high-level representation of input sequential data can be used in multiple downstream machine-learning tasks, we evaluate the proposed 3DDN and its deep models in human action recognition. The experimental results from three datasets: 1) KTH data; 2) HMDB-51; and 3) UCF-101, demonstrate that the proposed 3DDN is an alternative approach to feedforward convolutional neural networks (CNNs), that attains comparable results.

Physical behaviors (e.g., physical activity and sedentary behavior) have been the focus among many researchers in the biomedical and behavioral science fields. The recent shift from hip- to wrist-worn accelerometers in these fields has signaled the need to develop novel approaches to process raw acceleration data of physical activity and sedentary behavior. However, there is currently no consensus regarding the best practices for analyzing wrist-worn accelerometer data to accurately predict individuals' energy expenditure and the times spent in different intensities of free-living physical activity and sedentary behavior. To this end, accurately analyzing and interpreting wrist-worn accelerometer data has become a major challenge facing many clinicians and researchers. In response, this paper attempts to review different methodologies for analyzing wrist-worn accelerometer data and offer cutting edge, yet appropriate analysis plans for wrist-worn accelerometer data in the assessment of physical behavior. In this paper, we first discuss the fundamentals of wrist-worn accelerometer data, followed by various methods of processing these data (e.g., cut points, steps per minute, machine learning), and then we discuss the opportunities, challenges, and directions for future studies in this area of inquiry. This is the most comprehensive review paper to date regarding the analysis and interpretation of free-living physical activity data derived from wrist-worn accelerometers, aiming to help establish a blueprint for processing wrist-derived accelerometer data.

Background: Motor skill competence (MSC) and perceived competence (PC) are primary correlates that are linked with physical activity (PA) participation, yet there is limited evidence of the mutual longitudinal or temporal associations between these variables in preschoolers. Therefore, this study's purpose was to examine the bidirectional relationships between MSC and PA, MSC and PC, and PC and PA in preschoolers over time., Methods: The final sample were 61 preschoolers (M age = 4.45 years, ranging from 4 to 5) from two underserved schools. MSC was assessed using the Test of Gross Motor Development, Second Edition (TGMD-2). PC was assessed using the Pictorial Scale of Perceived Competence and Social Acceptance for Young Children. PA was assessed using ActiGraph GT9X Link accelerometers during three consecutive school days. All assessments of MSC, PC, and PA were measured in identical conditions at schools at the baseline (T1) and the end of the eighth week (T2). We employed a cross-lagged model approach to understand the bidirectional relationships between MSC, PC, and PA., Results: The results showed that T1 MSC significantly predicted T2 MSC ( p < 0.01) and T1 MSC significantly predicted T2 PA only in girls ( p = 0.03). Additionally, a cross-lagged effect of T1 MSC and T2 PC was only observed in boys ( p = 0.03). Lastly, a significant association for T1 moderate-to-vigorous physical activity (MVPA) and T2 PC was only observed in girls ( p = 0.04)., Conclusions: Bidirectional relationships between the variables were not observed in preschoolers. However, significant gender differences were observed in each cross-lagged model.

Saikosaponins comprise a large group of chemical components present in the Bupleurum species that have attracted attention in the field of medicine because of their significant biological activities. Due to the high polarity, structural similarity, and the presence of several isomers of this class of components, their structural identification is extremely challenging. In this study, the mass spectrometric fragmentation pathways, UV spectral features, and chromatographic behavior of different types of saikosaponins were investigated using 24 standard substances. Saikosaponins containing carbonyl groups (C=O) in the aglycone produced fragment ions by loss of 30 Da, and in addition, type IV saikosaponins could produce [aglycone-CH 2 OH-OH-H] - and [aglycone-H 2 O-H] - fragment ions through neutral losses at positions C 16 and C 17 . The above characteristic ions can be used to identify saikosaponins. More notably, the identification process of saikosaponins was systematically summarized, and using this method, 109 saikosaponins were identified or tentatively characterized from the saikosaponins extract of Bupleurum marginatum var. stenophyllum (BMS) using UPLC-PDA-Q/TOF-MS with both data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes, of which 25 were new compounds and 60 were first discovered from BMS. Further studies revealed that the saikosaponins profiles of BMS, Bupleurum chinense DC (BC), and Bupleurum marginatum Wall. ex DC (BMW) were very similar. This work is of great significance for the basic research of the Bupleurum species and provides strong technical support to solve the resource problems associated with Radix Bupleuri., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Liu, Cheng, Kang, Wang, Guo, Jing, Wei and Ma.)

Purpose: The bidirectional associations among children's motor skill competence (MSC), perceived competence (PC), physical activity (PA), and cardiorespiratory fitness (CRF) over time remain unanswered. This study is aimed at discerning the bidirectional relationships among elementary school children's MSC, PC, PA and, CRF over the course of one school year., Methods: A total of 261 second and third grade children (127 boys, 134 girls; mean age = 8.27 years; BMI = 18.22 ± 3.71) were recruited from two Texas elementary schools. Approximately 73.56% of participants were White American. Children's baseline data were assessed in September/October in 2012 (Time1), and identical assessments were conducted in April/May in 2013 (Time2). MSC was assessed using product-oriented skill tests (e.g., throw, kick, and jump). PC was assessed via the Pictorial Scale of Perceived Competence for Children. Minutes spent in moderate-to-vigorous PA (MVPA) was assessed using ActiGraph GT3X+ accelerometers for five days, and CRF was assessed by the PACER test. Six age- and body mass index-adjusted cross-lagged panel models were used to test the relationships between the variables., Results: We observed that T1 MSC significantly predicted T2 MSC ( β = 0.59; p < 0.01), T2 CRF ( β = 0.28; p < 0.01), and T2 MVPA ( β = 0.18; p < 0.01). Children's CRF was a positive predictor for T2 CRF ( β = 0.56; p < 0.01) and T2 MSC ( β = 0.13; p < 0.05) Additionally, T1 MVPA significantly predicted T2 MVPA ( β = 0.30; p < 0.01) and T2 PC ( β = -0.14; p < 0.05)., Conclusion: Findings suggested a fully bidirectional relationship between elementary children's MSC and CRF. Other bidirectional relationships among the variables were only partially supported. Educators and health professionals need to emphasize the importance of developing both MSC and CRF to maintain physical health over time., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2021 Suryeon Ryu et al.)

Design: A systematic review. Data Sources . 114 studies were gathered using the following search descriptors: ("mobile phone" OR "smartphone" OR "cell phone" OR "mobile device" OR "mobile apps" OR "mHealth") AND ("exercise" OR "physical activity" OR "physical fitness" OR "motor activity") AND ("physiological outcomes" OR "weight outcomes" OR "psychological outcomes" OR "health" OR "health behavior"). Seven databases were used including databases such as Academic Search Premier and PubMed. PRISMA guidelines were followed in this review. Eligibility Criteria for Selecting Studies . The 20 articles included in this review met the following inclusion criteria: (1) randomized and controlled trials, (2) involving an outcome variable measured by accelerometer, and (3) intervention enforced by a smartphone application., Results: Overall, 56% of the studies reviewed in this paper resulted in successful interventions. Of the 19 articles that examined the first individual health outcome of physical and physiological outcomes, 11 interventions resulted in a positive effect on one of the following parameters: MVPA/step count, sedentary behavior, cardiorespiratory fitness, and blood pressure. Six interventions examined the effects on the second individual health outcome, weight-related outcomes. Five of these interventions observed significant positive effects from mobile application interventions on weight and waist circumference. Six articles evaluated the effectiveness of smartphone-based physical activity interventions on the third and final individual health outcome, psychological outcomes, with four resulting in significant positive outcomes in self-efficacy, life enjoyment/satisfaction, and intrinsic PA motivation., Conclusion: The findings in this review suggest that mobile application physical activity interventions, compared to unguided exercise activities, can effectively improve certain health outcomes for individuals such as physical/physiological and weight-related outcomes. It was found that research in the area of effectiveness of mobile application interventions on specific psychosocial health outcomes such as self-efficacy, life enjoyment, and intrinsic PA motivation is limited. Thus, the effect of mobile health applications remains unclear for psychosocial outcomes. Due to this limitation, more research is warranted to confirm the findings of this review., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2021 Mia A. Emberson et al.)

Streptomyces produces many valuable and important biomolecules with clinical and pharmaceutical applications. The development of simple and highly efficient gene editing tools for genetic modification of Streptomyces is highly desirable. In this study, we developed a screening system for targeted gene knockout using a uracil auxotrophic host (Δ pyrF ) resistant to the highly toxic uracil analog of 5-fluoroorotic acid (5-FOA) converted by PyrF, and a non-replicative vector pKC1132-pyrF carrying the complemented pyrF gene coding for orotidine-5'-phosphate decarboxylase. The pyrF gene acts as a positive selection and counterselection marker for recombinants during genetic modifications. Single-crossover homologous integration mutants were selected on minimal medium without uracil by reintroducing pyrF along with pKC1132-pyrF into the genome of the mutant Δ pyrF at the targeted locus. Double-crossover recombinants were generated, from which the pyrF gene, plasmid backbone, and targeted gene were excised through homologous recombination exchange. These recombinants were rapidly screened by the counterselection agent, 5-FOA. We demonstrated the feasibility and advantage of using this pyrF- based screening system through deleting the otcR gene, which encodes the cluster-situated regulator that directly activates oxytetracycline biosynthesis in Streptomyces rimosus M4018. This system provides a new genetic tool for investigating the genetic characteristics of Streptomyces species.

Purpose: Sedentary behavior (SB), sleep efficiency (SE), sleep duration (SD), and body mass index (BMI) are crucial determinants of an individual's health. However, empirical evidence regarding associations between these factors in young adults living in China remains unknown. Therefore, the purpose of this study was to examine the relationships between accelerometer-measured SB, SE, SD, and BMI in Chinese college students., Methods: Two-hundred and twenty college students (115 females, Mean age = 20.29 years, SD = 2.37) were recruited from a south-central Chinese university. Participants' SB (daily % time spent in SB), SE (number of minutes of sleep duration/number of minutes in bed), and SD were assessed via wrist-worn ActiGraph GT9X Link accelerometers for one week. Body weight was measured using a digital weight scale, height was measured using a stadiometer, and BMI was calculated as weight (kg)/height (m 2 )., Results: Participants' average time spent in SB was 76.52% (SD = 10.03), SE was 84.12% (SD = 4.79), and BMI was 20.67 kg/m 2 (SD = 3.12), respectively. Regression analyses indicated that SB (β = -0.17, p = 0.01) and BMI (β = -0.20, p < 0.01) negatively predicted SE. In addition, BMI negatively predicted SD (β = -0.22, p < 0.01)., Conclusion: Prolonged SB (e.g., screen viewing, smartphone use, and computer playing) and higher BMI may link to shorter sleep duration and lower sleep efficiency in Chinese young adults. Future randomized controlled trials are needed to further confirm these findings. Given that increased BMI status and SB may relate to adverse health outcomes, more population-based intervention strategies seeking to lower BMI and reduce SB (e.g., nutrition education and physical activity promotion) are needed in this population.

Background: The effects of school-based exergaming interventions on adolescents' physical activity (PA) and psychosocial outcomes have been mixed. Researchers speculate this may be attributed to design issues. Therefore, this study examined differences in urban minority adolescents' PA, enjoyment, and self-efficacy during small-groups and full-class exergaming., Methods: Forty-seven urban minority adolescents (83% black; X¯age=11.8+1.3 y) completed two 15-minute exergaming sessions on the Xbox One Kinect Just Dance: (1) small groups (n = 3-4) and (2) full class (n = 23-24). Participants' time in sedentary behavior, light PA, and moderate to vigorous PA and steps were retrieved from ActiGraph GT3X+ accelerometers with enjoyment and self-efficacy assessed using validated surveys., Results: Participants spent significantly more time in sedentary behavior (5.9 [5.2] min vs 3.5 [2.7] min, respectively: P < .001, d = 0.57) and less time in moderate-to-vigorous PA (2.1 [2.8] min vs 5.5 [2.2] min, respectively: P < .001, d = 0.85) during the full-class versus the small-groups session. Moreover, small-groups exergaming resulted in significantly higher steps than the full-class exergaming (504.2 [132.1] vs 387.8 [122.1], respectively: P = .01, d = 0.50) and significantly greater enjoyment (3.5 [1.1] vs 3.2 [1.0], respectively: P = .02, d = 0.37). There were no significant differences between sessions for time in light PA and self-efficacy., Conclusions: Small-groups exergaming appears ideal for promoting enjoyable PA at higher intensities and lower sedentary time in underserved minority adolescents.

Synthesizing high dynamic range (HDR) images from multiple low-dynamic range (LDR) exposures in dynamic scenes is challenging. There are two major problems caused by the large motions of foreground objects. One is the severe misalignment among the LDR images. The other is the missing content due to the over-/under-saturated regions caused by the moving objects, which may not be easily compensated for by the multiple LDR exposures. Thus, it requires the HDR generation model to be able to properly fuse the LDR images and restore the missing details without introducing artifacts. To address these two problems, we propose in this paper a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images. To our best knowledge, this work is the first GAN-based approach for fusing multi-exposed LDR images for HDR reconstruction. By incorporating adversarial learning, our method is able to produce faithful information in the regions with missing content. In addition, we also propose a novel generator network, with a reference-based residual merging block for aligning large object motions in the feature domain, and a deep HDR supervision scheme for eliminating artifacts of the reconstructed HDR images. Experimental results demonstrate that our model achieves state-of-the-art reconstruction performance over the prior HDR methods on diverse scenes.

Objective: This systematic review synthesized current randomized controlled trials (RCTs) examining casual evidence regarding the effects of traditional and exergaming-based physical activity (PA) interventions on motor skill development in typically developed children (i.e., those aged 6-12 years)., Methods: We adhered to the PRISMA-P statement and searched electronic databases (Medline, PsycInfo, Web of Science, PubMed, ERIC, Scopus, and SportDiscus) from inception through July 2020. We screened for peer reviewed RCTs published in English between 2000 and 2020 examining the effect of PA on motor skill development in healthy children., Results: A total of 25 RCTs were included, 20 (80%) of which reported significant improvements in children's motor skill performance. Specifically, 18 studies examined traditional PA interventions and 7 studies examined exergaming-based PA interventions, 83% and 71% of which observed statistically significant improvements in children's motor skill development, respectively., Conclusions: Findings support the causal evidence regarding the effects of PA on motor skill development in children. Notable limitations of this review included heterogeneity of measurement protocols and assessment tools used to test children's motor skills across studies, a wide range of PA intervention dose across studies, and the lack of power analyses and long-term follow-up assessments in individual studies to discern appropriate sample sizes and long-term effectiveness, respectively. To further strengthen the evidence in this emerging field, we advocate for future RCTs to employ a priori power analyses, long-term follow-up measurements, and more exergaming-based interventions to allow for comparisons with traditional PA interventions, to explore the dose response and moderating relationships between PA and motor skill development in childhood, and to utilize homogenous assessment instruments to allow for more rigorous, quantitative syntheses., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2020 Daniel J. McDonough et al.)

Objective : The present study aimed to synthesize the most updated literature regarding the casual evidence of the effects of active video games (AVGs) on fundamental motor skills (FMS; locomotor skills and object control skills) and physical fitness among healthy children. Methods : Electronic databases were searched through October 2020. Peer-reviewed randomized control trials (RCTs) and quasi-experimental designs examining the effectiveness of AVGs on FMS and physical fitness development among healthy children (3-12 years) were screened. Results : A total of nine RCTs and one quasi-experimental study were included. Of the five studies examining the effect of AVGs on FMS, two reported significant improvements, while three reported no significant improvements in motor skills development as compared to control. Of the five studies assessing the effects of AVGs on physical fitness, four reported significant improvements in physical fitness such as balance, agility, and speed, whereas one reported significant improvements in skill-related executive function, but not in physical competence. Conclusions : Overall, the current available evidence supports AVGs as an effective means to improve physical fitness, such as balance, postural stability, and agility, among healthy children. However, the findings of AVGs on healthy children's object control and locomotor skills remain inconclusive.

Mobile devices usually mount a depth sensor to resolve ill-posed problems, like salient object detection on cluttered background. The main barrier of exploring RGBD data is to handle the information from two different modalities. To cope with this problem, in this paper, we propose a boundary-aware cross-modal fusion network for RGBD salient object detection. In particular, to enhance the fusion of color and depth features, we present a cross-modal feature sampling module to balance the contribution of the RGB and depth features based on the statistics of their channel values. In addition, in our multi-scale dense fusion network architecture, we not only incorporate edge-sensitive losses to preserve the boundary of the detected salient region, but also refine its structure by merging the estimated saliency maps of different scales. We accomplish the multi-scale saliency map merging using two alternative methods which produce refined saliency maps via per-pixel weighted combination and an encoder-decoder network. Extensive experimental evaluations demonstrate that our proposed framework can achieve the state-of-the-art performance on several public RGBD-based datasets.

Existing video salient object detection (VSOD) methods focus on exploring either short-term or long-term temporal information. However, temporal information is exploited in a global frame-level or regular grid structure, neglecting interframe structural dependencies. In this paper, we propose to learn long-term structural dependencies with a structure-evolving graph convolutional network (GCN). Particularly, we construct a graph for the entire video using a fast supervoxel segmentation method, in which each node is connected according to spatio-temporal structural similarity. We infer the inter-frame structural dependencies of salient object using convolutional operations on the graph. To prune redundant connections in the graph and better adapt to the moving salient object, we present an adaptive graph pooling to evolve the structure of the graph by dynamically merging similar nodes, learning better hierarchical representations of the graph. Experiments on six public datasets show that our method outperforms all other state-of-the-art methods. Furthermore, We also demonstrate that our proposed adaptive graph pooling can effectively improve the supervoxel algorithm in the term of segmentation accuracy.

Objective: Integrating novel technologies, such as virtual reality (VR), into traditional exercise apparatuses (e.g., stationary bikes) may assist in promoting physical activity (PA) participation among young adults. Therefore, this study's purpose was to examine young adults' systolic and diastolic blood pressure (BP) change (BP post - BP pre ), rating of perceived exertion (RPE), enjoyment, and self-efficacy during VR, exergaming, and traditional stationary cycling sessions. Materials and Methods: Forty-nine college students (34 females; M age  = 23.6 ± 3.4 years; M BMI  = 23.8 ± 3.1 kg/m 2 ) participated in three separate 20-minute stationary cycling sessions: (1) PlayStation 4 VR; (2) Xbox 360 exergaming; and (3) traditional stationary cycling. Participants' systolic and diastolic BP change was measured by using an Omron HEM-705CP digital BP cuff. Further, RPE was assessed by using the modified Borg RPE Scale and enjoyment and self-efficacy were evaluated by using validated questionnaires. Results: A multivariate analysis of variance indicated significant differences for systolic BP change, RPE, enjoyment, and self-efficacy between the three cycling sessions ( F (2, 144) = 3.3-32.4, P  < 0.05, [Formula: see text] = 0.04-0.3). Specifically, participants had significantly higher enjoyment and self-efficacy and lower RPE during VR cycling compared with the other two cycling sessions despite similar or higher change systolic BP during the VR cycling session. There was no statistically significant change in diastolic BP between the three cycling sessions ( P  > 0.05). Conclusion: Incorporating VR equipment with traditional stationary cycle ergometers may be favorable when seeking to promote enjoyable PA in college students. To further support VR exercise's efficacy, future studies with more rigorous research designs are warranted.

Globalization has attracted much attention to universities over the past decade. The aim of this study was to evaluate the effect of the United States-China international pharmacy education programs (IPEP) in China.Kirkpatrick's model of evaluation was used to evaluate IPEP from reaction and learning levels. In the reaction level, a questionnaire survey with a Likert scale was used. In the learning level, data from "Assessment Record of Advanced Clinical Pharmacy Practice of Peking University" were collected. Cronbach' α coefficient of reliability was calculated, principal component analysis and independent t-test were conducted.All of the students who attended IPEP (n = 36) completed the questionnaire survey. The scores of benefits were increased in 4 categories, including "Clinical practice competency improvement" (mean ranking [MR] = 3.11 points), "Understanding of doctor of Pharmacy education mode" (MR = 3.48 points), "English competency improvement" (MR = 3.64 points) and "International collaboration" (MR = 3.92 points). Meanwhile, the overall satisfaction was relatively high with the IPEP (MR = 4.22 points). In the learning level, a total of 22 records was obtained. Students who attended (n = 5) the IPEP achieved higher scores than those did not attend (n = 17) in the assessment records, although no statistical significant differences were observed. Personal in-depth interviews further supported the overall benefit of IPEP.The Kirkpatrick model of evaluation can be used for IPEP. The benefit and satisfaction of students attended the IPEP were high in the reaction level; even though no statistically significant difference was shown in the learning level, higher scores were still demonstrated.

Purpose: This feasibility study investigated whether a year-long combined fitness wristband-based and personalized exercise prescription intervention improves Chinese breast cancer survivors' (BCSs) health outcomes., Methods: Ninety-five BCSs (X̅ age = 44.81 ± 7.94; X̅ BMI = 22.18 ± 3.48) were recruited from Southern region of China and were delivered the exercise intervention across 12 months, using a single group pretest-posttest design. Participants' lipid profile (e.g., total cholesterol , high-density lipoprotein, low-density lipoprotein, and triglycerides), blood glucose, breast cancer biomarkers (e.g., carcinoembryonic antigen and cancer antigen 15-3), and functional fitness (e.g., strength in arms and legs, endurance, balance, agility, and flexibility) were assessed at baseline and 12-month post-intervention., Results: Thirty-three BCSs successfully completed the intervention. A significant change in blood glucose (mean difference (MD): -0.22, 95% confidence interval (CI): -0.41--0.03, t = -2.25, p = 0.028) was observed, with participants demonstrating lower levels of blood glucose at the 12-month post-intervention versus the baseline assessment. Notable changes in functional fitness were also discerned, including agility and balance (MD: -0.47, 95% CI: -0.68--0.26, t = -4.336, p < 0.001), aerobic endurance (MD: 89.25, 95% CI: 73.82-104.68, t = 11.336, p < 0.001), lower-body flexibility (left) (MD: 4.58, 95% CI: -4.4-13.56, t = 4.653, p < 0.001), and lower-body flexibility (right) (MD: 4.84, 95% CI: -4.65-14.33, t = 4.092, p < 0.001)., Conclusion: The observations suggested that our behavioral change program might promote certain health outcomes in Chinese BCSs, yet we are unable to recommend such a program given existing limitations. Future randomized control trials with diverse samples are warranted to confirm our findings.

Background: Various pharmacokinetic (PK) equations and software have been developed to individualize vancomycin dosing. However, the benefit of using any PK information to guide vancomycin dosing has not been fully elucidated. Objective: To appraise available evidence on the effectiveness and safety of individualized vancomycin dosing via PK tools. Methods: PubMed, EMBASE, the Cochrane Library, and 2 Chinese literature databases were searched through August 1, 2019. Randomized controlled trials (RCTs) and cohort studies that reported the PK and clinical outcomes of individualized vancomycin dosing versus empirical dosing were included. Pooled risk ratios (RRs) and mean differences were calculated for dichotomous and continuous outcomes, respectively. Results: A total of 21 studies involving 4346 patients were finally included, of which 3 were RCTs and 18 were cohort studies. Meta-analysis revealed that PK-guided vancomycin dosing significantly increased the attainment of target trough concentration (RR = 1.59; 95% CI = 1.49-1.70) and decreased the incidence of nephrotoxicity (RR = 0.57; 95% CI = 0.46-0.71). Additionally, the available evidence showed that target area under the curve/minimum inhibitory concentration attainment rate and time to target concentration could improve. However, the evidence on clinical outcomes was scarce, and no significant differences were detected in clinical response rate, microbiological eradication rate, mortality, and length of hospital stay between PK-guided vancomycin dosing and empirical dosing strategies. Conclusion and Relevance: Individualized vancomycin dosing via PK tools significantly increases the attainment of target trough concentration and decreases the incidence of nephrotoxicity. Evidence on clinical effectiveness was limited and showed no significant benefit. Further well-designed studies are warranted to assess its clinical effectiveness and inform routine care.

Supervoxel segmentation algorithm has been applied as a preprocessing step for many vision tasks. However, existing supervoxel segmentation algorithms cannot generate hierarchical supervoxel segmentation well preserving the spatiotemporal boundaries in real time, which prevents the downstream applications from accurate and efficient processing. In this paper, we propose a real-time hierarchical supervoxel segmentation algorithm based on the minimum spanning tree (MST), which achieves state-of-the-art accuracy meanwhile at least 11× faster than existing methods. In particular, we present a dynamic graph updating operation into the iterative construction process of the MST, which can geometrically decrease the numbers of vertices and edges. In this way, the proposed method is able to generate arbitrary scales of supervoxels on the fly. We prove the efficiency of our algorithm that can produce hierarchical supervoxels in the time complexity of O(n) , where n denotes the number of voxels in the input video. Quantitative and qualitative evaluations on public benchmarks demonstrate that our proposed algorithm significantly outperforms the state-of-the-art algorithms in terms of supervoxel segmentation accuracy and computational efficiency. Furthermore, we demonstrate the effectiveness of the proposed method on a downstream application of video object segmentation.

The development of innovative technology, such as virtual reality (VR), has provided opportunities for promoting physical activity (PA) in a fun and engaging manner. The purpose of this study was to examine differences in young adults' situational motivation (SM) among immersive VR, non-immersive VR, and traditional stationary cycling sessions. In all, 49 healthy college students (35 females; M age = 23.6 years, SD = 3.4; M %BF = 24.0%, SD = 7.5) completed three separate 20 min cycling sessions: (1) immersive VR cycling; (2) non-immersive VR cycling; and (3) traditional cycling. Participants' SM was assessed via the situational motivation scale, which included four subconstructs: intrinsic motivation, identified regulation, external regulation, and amotivation. Repeated measures ANOVAs indicated significant differences for situational motivation between cycling sessions ( F (2, 96) = 4.74-53.04, p < 0.01, η p 2 = 0.090-0.525). Specifically, participants elicited the highest level of intrinsic motivation in immersive VR cycling compared to the other two sessions. Moreover, participants in both immersive VR and traditional cycling showed greater identified regulation than the non-immersive VR session. Furthermore, participants showed greater external regulation compared to the immersive VR session. In addition, greater amotivation was observed in non-immersive VR compared to the immersive VR session. Findings suggested that immersive VR exercise has the potential to be an attractive exercise alternative, possibly promoting greater PA participation and adherence among young adults.

In this paper, we propose a novel method to generate stereoscopic images from light-field images with the intended depth range and simultaneously perform image super-resolution. Subject to the small baseline of neighboring subaperture views and low spatial resolution of light-field images captured using compact commercial light-field cameras, the disparity range of any two subaperture views is usually very small. We propose a method to control the disparity range of the target stereoscopic images with linear or nonlinear disparity scaling and properly resolve the disocclusion problem with the aid of a smooth energy term previously used for texture synthesis. The left and right views of the target stereoscopic image are simultaneously generated by a unified optimization framework, which preserves content coherence between the left and right views by a coherence energy term. The disparity range of the target stereoscopic image can be larger than that of the input light field image. This benefits many light field image-based applications, e.g., displaying light field images on various stereo display devices and generating stereoscopic panoramic images from a light field image montage. An extensive experimental evaluation demonstrates the effectiveness of our method.

Purpose : To better promote college students' physical activity (PA) and quality of life (QoL), it is imperative to understand this population's PA correlates, such as self-determined motivation and perceived competence. However, few studies existed in this area of inquiry among Chinese college students. Thus, the purpose of this study was to examine the relationships among Chinese college students' self-determined motivation, PA, and QoL. Method: A total of 220 college students (115 females; M age = 20.29 years, SD = 2.37; M BMI = 20.67) were recruited from one university in south-central China. Participants were instructed to wear the ActiGraph GT9X Link (ActiGraph, Pensacola, FL, USA) accelerometers for 7 days. A minute-by-minute stepping rate methodology was used to determine participants sedentary behaviors, light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA). Participants' self-determined motivation (autonomous, controlled, and amotivation), perceived competence, and QoL (physical function, stress, depression, fatigue, sleep, and social issues) were assessed by a battery of validated surveys in June 2017. Results : Participants reported moderate-high levels of PA correlates and QoL as the means ranged from 5.5 to 6 (out of 7) for PA correlates and 2.75 to 4 (out of 4) for QoL. The minute-by-minute stepping rate revealed participants had average 580.51 min/day in sedentary, 134.77 min/day in LPA, and 1.57 min/day in MVPA. Regression analyses for physical function, stress, depression, and social issues suggested that the models explained 4%-8% of the variances. Specifically, perceived competence was the negative predictor of the problems with physical function (β = -0.17, p < 0.05) and depression (β = -0.18, p < 0.01), amotivation was positively associated with depression and stress ( p < 0.05). Additionally, controlled motivation predicted the ability to participate in social roles and activities (β = 0.22, p < 0.05). No significant predictors emerged for fatigue or for sleep. Conclusions : Findings suggest Chinese college students' perceived competence and social support are critical for improving PA and QoL. In addition, strategies are needed to motivate Chinese college students to engage in PA participation and improve overall well-being.

The tracking-by-detection framework receives growing attention through the integration with the convolutional neural networks (CNNs). Existing tracking-by-detection-based methods, however, fail to track objects with severe appearance variations. This is because the traditional convolutional operation is performed on fixed grids, and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions. In this paper, we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework. We aim to capture the target appearance variations via deformable convolution, which adaptively enhances its original features. In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance. The enriched feature representation through deformable convolution facilitates the discrimination of the CNN classifier on the target object and background. The extensive experiments on the standard benchmarks show that the proposed tracker performs favorably against the state-of-the-art methods.

Rationale: Heparin-induced thrombocytopenia (HIT) is a common antibody-mediated adverse reaction that occurs after heparin exposure. However, few case reports exist regarding nonantibody-mediated HIT., Patient Concerns and Diagnoses: An 81-year-old female diagnosed with rapidly progressive glomerulonephritis (RPGN) presented with atypical presentation of non antibody-meditated HIT after using heparin during hemodialysis., Interventions and Outcomes: Patient was initiated on hemodialysis and presented with thrombocytopenia following administration of heparin during dialysis. After ruling out all other causes of thrombocytopenia, HIT was suspected to be the cause. Patient's 4Ts score was 6 points, and Naranjo adverse drug reaction probability scale was a score of 10. However, enzyme-linked immunoassay for platelet factor 4 (PF4)/heparin antibodies was negative, indicating non-antibody mediated HIT. Patient eventually continued hemodialysis without heparin., Lessons: This patient case presented a rare presentation of HIT type I reaction due to heparin and demonstrated the importance of timely recognition of thrombocytopenia, appropriate diagnosis and management, and possible existence of a new atypical or subtype of HIT reaction.

Polygonum multiflorum Thunb. and its processed products have been used in China for centuries due to their multiple beneficial effects to human body. Currently, liver injuries caused by taking P. multiflorum have been reported worldwide, but the potential toxic components and possible mechanism that caused hepatotoxicity remain unclear. It is worth noting that the processing procedure could significantly decrease the toxicity of raw P. multiflorum and the processed products of P. multiflorum are considered to be relatively safe. However, the processing mechanism is still ambiguous, and there is the lack of a scientific approach to control the quality of P. multiflorum praeparata. This study is the first review that summarizes the recently advances (from 2007 to 2017) in the chemical analysis of P. multiflorum , and provides comprehensive information on the quantitative and qualitative analysis of P. multiflorum as well as its related species. In addition, the processing mechanism and quality evaluation of processed P. multiflorum are discussed. Moreover, the toxicity of P. multiflorum is analyzed from the perspectives of exploration of the proposed toxic ingredients, metabolite identification, metabolomics studies, and exogenous contaminant determination. Furthermore, trends and perspectives for future research of this medicine are discussed.

Aim: This study examined the relationships between energy balance-related behaviours (EBRBs) outside school hours and obesity in Chinese primary school students. We also explored the influence of gender on those relationships., Methods: The study sample was a cross-sectional cohort of 5032 Chinese children who were enrolled in grades 1-6 in primary schools in five Chinese cities and whose mean ages ranged from seven years and three months to 11.9 years. The children's parents completed a survey on their child's height, weight and EBRBs outside school hours., Results: The response rate was 97%, and the reported rates of overweight and obesity were 13.6% and 13.8%, respectively. The obesity rates were higher in boys and lower grade children. Most EBRBs varied between boys and girls and with increased grade levels. The amount of time spent on academic-related activities, screen viewing, outdoor activities and sleep was mostly associated with obesity on weekdays and varied by gender., Conclusion: Rate of obesity was alarmingly high in the primary school Chinese children in this cohort, especially in younger children. Excessive time spent on academic-related activities outside school hours, inadequate sleep, physical inactivity and higher levels of screen viewing were major contributors to obesity in these Chinese children., (©2016 The Authors. Acta Paediatrica published by John Wiley & Sons Ltd on behalf of Foundation Acta Paediatrica.)

Sulfated derivatives of galactomannan from locust bean gum (LBG) with the degree of substitution (DS) of 0.34-1.07 were synthesized using chlorosulfonic acid/pyridine (CSA/Py) method. Box-Behnken design (BBD) of response surface methodology (RSM) was employed to optimize the reaction conditions. Results of FT-IR and X-ray photoelectron spectroscopy (XPS) indicated that SO3H groups were widely present in sulfated LBG (SLBG). (13)C NMR result revealed that sulfation had occurred and C-6 substitution was predominant in SLBG. All sulfated samples showed a decrease in Mw and more broad molar mass distribution in size exclusion chromatography combined with laser light scattering (SEC-LLS) analysis. Results of MW - [Formula: see text] showed a decrease in fractal dimension (df) value. Laser light scattering results also showed a conformation transition from a compact chain conformation of branched clusters to a random coil conformation of SLBG. Compared to LBG and SLBG with low DS and molecular weight, SLBG2 exhibited an internal structure of random coil with a DS of 1.07. DS and molecular weight had great influence on its conformation in aqueous solution. Our results confirmed that the degradation of polysaccharide and SO3H groups improved significantly the stiffness of the chains due to the electrostatic effect., (Copyright © 2014 Elsevier Ltd. All rights reserved.)