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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 [ 21 1 − ] , 1 / 6 [ 2 − 1 1 − ] , and 1 / 2 [ 0 1 − 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 [ 01 1 − ] were successfully observed in electro-deposited Cu thin films. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Domain Generalization (DG) aims to generalize a model trained on multiple source domains to an unseen target domain. The source domains always require precise annotations, which can be cumbersome or even infeasible to obtain in practice due to the vast amount of data involved. Web data, namely web-crawled images, offers an opportunity to access large amounts of unlabeled images with rich style information, which can be leveraged to improve DG. From this perspective, we introduce a novel paradigm of DG, termed as Semi-Supervised Domain Generalization (SSDG) , to explore how the labeled and unlabeled source domains can interact, and establish two settings, including the close-set and open-set SSDG. The close-set SSDG is based on existing public DG datasets, while the open-set SSDG, built on the newly-collected web-crawled datasets, presents a novel yet realistic challenge that pushes the limits of current technologies. A natural approach of SSDG is to transfer knowledge from labeled data to unlabeled data via pseudo labeling, and train the model on both labeled and pseudo-labeled data for generalization. Since there are conflicting goals between domain-oriented pseudo labeling and out-of-domain generalization, we develop a pseudo labeling phase and a generalization phase independently for SSDG. Unfortunately, due to the large domain gap, the pseudo labels provided in the pseudo labeling phase inevitably contain noise, which has negative affect on the subsequent generalization phase. Therefore, to improve the quality of pseudo labels and further enhance generalizability, we propose a cyclic learning framework to encourage a positive feedback between these two phases, utilizing an evolving intermediate domain that bridges the labeled and unlabeled domains in a curriculum learning manner. Extensive experiments are conducted to validate the effectiveness of our method. It is worth highlighting that web-crawled images can promote domain generalization as demonstrated by the experimental results. • Developing the close-set and open-set semi-supervised domain generalization (SSDG). • Constructing two web-crawled datasets are for the open-set SSDG. • Establishing strong baselines for SSDG using existing deep learning methods. • Proposing a framework with evolving intermediate domain for SSDG. • The framework outperforms the strong baselines in both close-set and open-set SSDG. [ABSTRACT FROM AUTHOR]

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 SO 3 H 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 M w and more broad molar mass distribution in size exclusion chromatography combined with laser light scattering (SEC-LLS) analysis. Results of M W – 〈 S 2 〉 z 1 / 2 showed a decrease in fractal dimension ( d f ) 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, SLBG 2 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 SO 3 H groups improved significantly the stiffness of the chains due to the electrostatic effect. [ABSTRACT FROM AUTHOR]

With NH3·H2O, NH3·H2O-NH4HCO3 buffer solution and NaOH solution as coprecipitation agents, NiO–ZrO2, anode powder of solid oxide fuel cell, was synthesized by coprecipitation methods. Composition accuracy was analyzed by comparing the original composition with the mass of the eventually obtained powder. It was shown that cubic NiO and cubic YSZ with a cascade powder distribution were obtained after the powder was calcined for 1h at different temperatures. Composition analysis of the powder synthesized with NH3·H2O as coprecipitation agent showed that NiO–YSZ powder greatly deviated from the original composition due to the loss of Ni2+ in the form of [Ni(NH3)4]2+. When NH3·H2O–NH4HCO3 buffer solution was used, productive output of nickel was increased. The loss of nickel can be totally avoided and better control of the powder composition could be realized when NaOH was used as coprecipitation agent. [Copyright &y& Elsevier]

In recent years, data imbalance in the conventional classification problem has raised great interest in the industry. However, concerning the rule-based systems, this problem has been rarely investigated. We propose a Belief Rule-Based (BRB) reasoning model based on the evidential reasoning algorithm to fill in the gap by solving the problem caused by imbalanced data with regards to the rule-based systems. It utilizes the data-driven characteristics of Extended Belief Rule-Based (EBRB) and incorporates the data class rebalancing technique with the ensemble learning method of EBRB. In order to increase the number of rules that can promote the correct classification of minority classes in EBRB, we apply an adaptive data resampling strategy to expand the proportion of minority classes. On this basis, the data is divided into several EBRB base learners with different classification characteristics. During the iterative process, the rule weight of the base learner is dynamically adjusted according to the correct and incorrect classification, such that the classifier can pay more attention to the minority classes and reduce the bias of classification. Finally, the optimized EBRB base learner is selected to build an ensemble classifier with excellent classification performance. Experiments on 27 binary class and 12 multi-class imbalanced datasets show that our approach significantly improves F-value and MACC compared with other imbalanced classification algorithms. By building a cascaded EBRB model, the activation weight is taken as the basis of model evolution, the application limitations of the EBRB model in the ensemble learning are solved innovatively, and the classification performance of the EBRB model for imbalanced data is improved significantly. [ABSTRACT FROM AUTHOR]

• Deep compression of these deterministic models has been proposed in last few years to reduce the number of connections and nodes, while remaining the classification accuracy of these models. This paper is the first attempt to combine deep probabilistic graphical networks (PGNs) and deep compression techniques together to derive sparse versions of the deep probabilistic models. • The developed pruning approach is a layer-by-layer and pre-pruning one. That is, the final PGNs grow in a layer-by-layer and compressed manner. It is very efficient in redundancy reduction. The whole pruning only takes a few seconds to conduct, while other complex methods take much more time to select sparse architectures or conduct model compression, such as regularization and low-rank factorization approaches. These model compression techniques are more time-consuming compared with pruning due to the complexity of regularization and factorization. • The correctness and efficiency of the proposed compression approach are evaluated by a number of PGNs with different datasets. It can be used to compress trained PGNs to achieve a lightweight network which can be embedded in on-chip memory of small mobile devices. It is also flexible to implement and can be generalized to other deep PGNs. Probabilistic Graphical Models (PGMs) are important and active research areas in machine learning and artificial intelligence. The well-known representatives of PGMs include Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), Deep Boltzmann Machines (DBMs), and their variants. These PGMs open a new dimension of machine learning and directly lead to the revival of deep learning since 2006. Compared with deep deterministic models, such as deep convolutional neural networks (CNNs), PGMs are more flexible, robust and powerful for both unsupervised and supervised learning problems. As the generative models on the one hand, PGMs have the good capability of generating samples after extracting informative features of input data. On the other hand, they also can provide a good initialization of hierarchical structures for supervised learning. The PGMs with deep structures are called deep Probabilistic Graphical Networks (PGNs) due to their hierarchical structures and close relationships with deep neural networks. There are a large number of CNN-based deep deterministic networks proposed for computer vision tasks. However, these deep deterministic models are shown to possess extremely large number of model parameters, and have serious redundancy problems. Therefore, deep compression of these deterministic models have been proposed in the last few years to reduce the number of connections and nodes, while preserving the classification accuracy. This paper is the first attempt to combine deep PGNs and deep compression techniques together to derive sparse versions of the deep probabilistic models. We firstly examine that whether deep PGNs have serious problem of parameter redundancy, and compress them if necessary. From the experimental results on MNIST, Fashion-MNIST and CIFAR-10 dataset, deep PGNs can be compressed by at least 50% of their parameters through a deep pruning and retraining approach, while keeping their capabilities in both generative and discriminative tasks. [ABSTRACT FROM AUTHOR]

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.)

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.)

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.)

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.

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.)

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.

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.

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.