A comparative study of the anti-fatigue activity of extracts from different parts of Cistanche tubulosa(Schenk)Wight

Objective:To evaluate the anti-fatigue effects of different extracts from Cistanche tubulosa(Schenk)Wight(C.tubulosa,Rou Cong Rong),focusing on central and exercise-induced fatigue in mice.This study investigated the pharmacological effects of the total oligosaccharides,polysaccharides,and phenylethanoid glycosides(CPhGs)extracted from C.tubulosa.Methods: Models of sleep deprivation and forced swimming fatigue were established to simulate central and exercise-induced fatigue.The mice were treated with different extracts of C.tubulosa,and their effects were assessed using behavioral tests to measure exercise capacity,learning,and memory function.Biochemical analyses were performed to evaluate the changes in serum and brain neurotransmitter levels,liver and muscle glycogen storage,and various fatigue-related biomarkers.Results: This study found that treatment with C.tubulosa extract improved exercise capacity,learning,and memory in mice.Total oligosaccharides from C.tubulosa enhanced adrenocorticotropic hormone,cholinesterase,and thyroid-stimulating hormone levels,reduced cortisol levels in central fatigue models,and ameliorated biochemical markers of exercise-induced fatigue,including lowering lactic acid,blood urea nitrogen,and malondialdehyde levels.Among the tested extracts,the total oligosaccharides showed the most comprehensive anti-fatigue effects.Conclusion: The anti-fatigue effects of C.tubulosa,particularly those of its total oligosaccharides,are pronounced in both central and exercise-induced fatigue.These effects are mediated by the regulation of neurotransmitter levels,enhancement of glycogen storage,and improvement of antioxidant enzyme activity,suggesting potential therapeutic benefits in fatigue-related conditions.

Anti-fatigue effects of salidroside in mice

Objective： To study the anti-fatigue effects of salidroside in mice. Methods： Totally 120 normal male Kunming mice were randomized into 5 groups （4 salidroside intervention groups and the control group） based on body weight. The control group was given distilled water and the 4 intervention groups were given various doses of salidroside （60, 180, 360, 720 mg/kg） for 15 consecutive days, respectively. The levels of lactate, serum urea nitrogen, muscle and liver glycogen, the longest swimming time and hemoglobin were determined before and after swimming test. Results： Different doses of salidroside significantly lengthened the swimming time and increased the contents of hemoglobin and muscle and liver glycogen, while reducing that of lactate in blood significantly compared with control group, especially in the 180 mg/kg salidroside group. Conclusion： Salidroside has noticeable anti-fatigue effect on mice. These effects were dose-dependent, and the strongest effect on most biomarkers was seen with an intermediate dose.

Anti-Fatigue Effect of Blended <i>Camellia oleifera Abel</i>Tea Oil and Ge-132 in Mice

Nowadays, people are busier and busier for working and living, and suffer a lot of pressure on their body or mind. Therefore, people are prone to have fatigue activity and decrease their working efficiency and happiness. It was reported that fatigue is a common symptom in the community, with up to half of the general population complaining of fatigue. More and more researchers devoted themselves to studying natural active ingredients in organism as the anti-fatigue drugs to release fatigue symptom. However, these natural ingredients were difficult to obtain from plants, animals and microorganisms by separating and purifying. In addition, some active substances have many side effects. In our study, we employed tea seed oil as main ingredients blended with bis-(carboxyethylgermanium) sesquioxide (Ge-132) to investigate the effects of anti-fatigue on mice by administrating mice with low dose, intermediate dose and high dose of tea seed oil complex for 0, 2 or 4 weeks. The specific tests of studying effects of anti-fatigue were body weight, weight-loaded force swimming, blood urea nitrogen, blood lactic acid and hepaticglycogen. And the results showed that appropriate level of tea seed oil complex could decrease the body weight and prolong the weight-loaded swimming time, and had an active effect on the bloodurea nitrogen, hepatic glycogen and blood lactic acid level mice, which significantly embodied the anti-fatigue activity of tea seed oil complex.

Evaluation of silymarin extract from Silybum marianum in mice: anti-fatigue activity

Silymarin has been used for centuries for its hepatoprotective properties. The specific objective of this study was to evaluate the anti-fatigue properties of silymarin. The silymarin was administered orally at doses of 50, 100, and 200 mg/kg for 4 weeks;the fatigue level and exercise performance were evaluated using exhaustive swimming time and pole-climbing time, as well as levels of plasma lactate, ammonia, glucose, creatine kinase(CK), serum urea nitrogen(SUN), blood lactic acid(BLA), muscle glycogen(MG), and liver glycogen(LG) contents after an intensive swimming session. The results demonstrated that silymarin treatment decreased the BLA and SUN levels while increased the LG and MG levels. In addition, silymarin decreased plasma lactate and ammonia levels and CK activity after swimming test, this is related to the mechanism that increases energy storage(as glycogen) and release(as blood glucose), and decreases plasma levels of lactate, ammonia, and CK. The observation of the skeletal muscle structures of mice also confirmed that skeletal muscles became more damaged in the control group compared with the silymarin-treated mice after prolonged endurance exercise. Therefore, it is reasonable to infer that silymarin may bear potential pharmacological effects in combating fatigue.

Present Situation of the Anti-Fatigue Processing of High-Strength Steel Internal Thread Based on Cold Extrusion Technology:A Review

The adoption of cold-extrusion forming for internal thread net forming becomes an important component of anti-fatigue processing with the development of internal thread processing towards high performance, low cost and low energy consumption. It has vast application foreground in the field of aviation, spaceflight, high speed train and etc. The internal thread processing and anti-fatigue manufacture technology are summarized. In terms of the perspective of processing quality and fatigue serving life, the advantages and disadvantages of the processing methods from are compared. The internal thread cold-extrusion processing technology is investigated for the purpose of improving the anti-fatigue serving life of internal thread. The superiorities of the plastic deformation law and surface integrity of the metal layer in the course of cold extrusion for improving its stability and economy are summed up. The proposed research forecasts the develop- ment tendency of the internal thread anti-fatigue manufacturing technology.

BCCLR:A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

The study of Gongshisong＇ s anti-fatigue active site sifting

Aim To investigate the influence of the Gongshisong extract of different extraction parts on mice sports fatigue index. Method The mice were randomly divided into blank group, model group, the Gongshisong extract group （petroleum ether, chloroform, ethyl acetate, n-butanol, the aqueous layer） and the positive control group. The mice in different groups were fed with corresponding Gongshisong extract liquid 2.5 g kg^-1 d^-1 once per day for consecutively 7 days. The blank group and model group were given 1% sodium carboxymethyl cellulose so- lution, the positive control group was administered with Rhodiola 0.59 g · kg^-1· d^-1 After the last administered each group of mice were determined of weihgt-loading swimming time, liver glycogen and muscle glycogen content, and urea nitrogen, lactic acid and creatine kinase in 90min mice after swimming content in blood. Result Com- pared with the blank group, the petroleum ether extract of Gongshisong could significantly prolong the swimming time of mice （P 〈 0.05）, enhancing the mice muscle glycogen reserves （P 〈 0.05）, and reduce the level of urea nitrogen and creatine kinase in mice after swimming （P 〈 0.05）. Butanol extraction liquid of Gongshisong can ob- viously prolong the swimming time of mice （P 〈 0.05）, increasing the reserves of glycogen in mice （P 〈 0.05）, decreasing the level of urea nitrogen in mice and blood lactate after swimming （P 〈 0.05 ）. Water extract of Gong- shisong could significantly prolong the swimming time of mice （P 〈 0.05） , increasing muscle glycogen storage （P 〈0.05）, reducing the level of blood lactic acid in mice after swimming （P 〈 0.05）. Ethyl acetate extract of Gongshisong could significantly prolong the swimming time of mice （P 〈 0.05） , and increase the reserves of glyco- gen in mice （P 〈0. 05）. Conclusion Comprehensive analysis, the petroleum ether, n-butanol and water site of Gongshisong is better anti fatigue active site.

The Preparation Technology Research and Composition Analysis of Anti-fatigue Yikangshu Granules

[Objectives]To optimize the preparation technology of Yikangshu granule,an anti-fatigue health food.[Methods]The process was studied from three aspects(raw material extraction,excipient selection and forming process),and then it was verified in pilot production,and the content of the three components of the product was determined.[Results]The optimum production process of the formula granules was as follows:raw materials were extracted(adding 1∶9 water,extracting twice,2.5 h each time);vacuum concentration;adding excipients and mixing after vacuum drying,crushing and sieving;granulation of 75%ethanol sifted by a 20-mesh sieve;drying at 60℃,sifting through 14-mesh,and packaging.The average content of products obtained was as follows:crude polysaccharides(3204 mg/100 g),total saponins(2295 mg/100 g),astragaloside A(21 mg/100 g).[Conclusions]The optimized preparation technology is stable and feasible,and has a quality that can be controlled,which can provide a reference for the development of health food in food industry.

Anti-fatigue Effect of Water Extract from Centella asiatica(L.)Urban on Mice

[Objectives]To study the anti-fatigue effect of water extract from Centella asiatica(L.)Urban on mice.[Methods]After intragastric administration of low,medium and high concentration of water extract from Centella asiatica(L.)Urban for 14 d,the rotarod time and the content of serum lactic acid in mice were determined,respectively.[Results]Compared with the control group,the rotarod time of mice in the low and medium concentration groups was significantly prolonged(P<0.05),but there was no significant difference between the control group and the high concentration group;the content of serum lactic acid in the medium concentration group was significantly lower than that in the control group(P<0.01),but there was no significant difference between low concentration group and high concentration group and the control group.[Conclusions]A certain concentration of water extract from Centella asiatica(L.)Urban had a good anti-fatigue effect.

Anti-fatigue Effect of L-arginine Complex Preparation on Mice

Objective:To investigate the anti-fatigue effect of L-arginine complex preparation on mice.Methods:The experimental mice were divided into a blank group,low dose group,medium dose group and high dose group.L-arginine complex preparation mice were intragastrically administered for 30 days,and the mice were tested for exhaustive swimming time.At the same time,contents of plasma lactic acid,lactate dehydrogenase,urea nitrogen and hepatic glycogen were measured.Results:Compared with the blank control group,the weight-bearing swimming time and hypoxia-tolerant survival time of the low,middle and high dose groups was significantly increased(P<0.05).Whereas,the serum urea nitrogen levels and lactose content were significantly decreased(P<0.05).However,compared with the blank control group,the liver glycogen content of the middle and high dose groups was increased significantly(P<0.05),and there was no significant difference in the low dose group.Conclusion:The L-arginine complex preparation has an anti-fatigue function in mice.

基于LDA主题模型的GitHub Actions工作流项目推荐算法

在CI/CD实践中,自动化已成为软件开发实践中的一种规范。GitHub引入GitHub Actions为软件维护者提供自动化的持续集成工作流方案,尽管其为开发者提供了诸多便利,GitHub社区也提供了许多第三方的GitHub Actions服务,但仍然只有极少的项目在使用。为了满足开发人员对工作流自动化的需求,减少非开发任务工作量,提出一种基于隐含狄利克雷分布(LDA)主题模型和Jensen-Shannon距离的GitHub Actions工作流项目推荐算法。通过对GitHub Actions存储库的README文件进行主题建模,将GitHub的事件描述文本和用户输入作为模型输入,为正在开发的代码仓库推荐GitHub Actions服务。将该推荐模型与标准的基于余弦相似度的方法进行比较的实验结果表明,该方法能有效改善开源软件仓库的推荐精度。

Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(LPEN)to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios.The LPEN network,comprising three stages—MobileNet,Initial Stage,and Refinement Stage—is employed to swiftly extract image features,detect human key points,and refine them for accurate analysis.Subsequently,a Pose-aware Action Analysis Module(PAAM)captures the positional coordinates of human skeletal points in each frame.Finally,an Abnormal Action Inference Module(AAIM)evaluates whether abnormal fall-down or unauthorized trespass behavior is occurring.For fall-down recognition,three criteria—falling speed,main angles of skeletal points,and the person’s bounding box—are considered.To identify unauthorized trespass,emphasis is placed on the position of the ankles.Extensive experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training.

Unveiling health rights:A call to action for sex workers'HIV care in the Philippines

We are writing in response to the article titled“Addressing the needs and rights of sex workers for HIV healthcare services in the Philippines”[1].The article calls for attention on the significant challenges faced by sex workers in the Philippines in accessing HIV healthcare.We appreciate the article’s effort to examine these issues in depth.We would like to present a constant flow of thoughts in this letter while highlighting the positive aspects,potential obstacles,and additional points that contribute to this ongoing discussion.

Impact of Action Observation Therapy along with Usual Physiotherapy Intervention of Individual with Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairments in the initial stage, which lead to severe cognitive dysfunction in the later stage. Action observation therapy (AOT) is a multisensory cognitive rehabilitation technique where the patient initially observes the actions and then tries to perform. The study aimed to examine the impact of AOT along with usual physiotherapy interventions to reduce depression, improve cognition and balance of a patient with AD. A 67 years old patient with AD was selected for this study because the patient has been suffering from depression, dementia, and physical dysfunction along with some other health conditions like diabetes and hypertension. Before starting intervention, a baseline assessment was done through the Beck Depression Inventory (BDI) tool, the Mini-Cog Scale, and the Berg Balance Scale (BBS). The patient received 12 sessions of AOT along with usual physiotherapy interventions thrice a week for four weeks, which included 45 minutes of each session. After four weeks of intervention, the patient demonstrated significant improvement in depression, cognition, and balance, whereas the BDI score declined from moderate 21/63 to mild 15/63 level of depression. The Mini-Cog score improved from 2/5 to 4/5, and the BBS score increased from 18/56 to 37/56. It is concluded that AOT along with usual physiotherapy intervention helps to reduce depression, improve cognition and balance of people with AD.

Abnormal Action Detection Based on Parameter-Efficient Transfer Learning in Laboratory Scenarios

Laboratory safety is a critical area of broad societal concern,particularly in the detection of abnormal actions.To enhance the efficiency and accuracy of detecting such actions,this paper introduces a novel method called TubeRAPT(Tubelet Transformer based onAdapter and Prefix TrainingModule).Thismethod primarily comprises three key components:the TubeR network,an adaptive clustering attention mechanism,and a prefix training module.These components work in synergy to address the challenge of knowledge preservation in models pretrained on large datasets while maintaining training efficiency.The TubeR network serves as the backbone for spatio-temporal feature extraction,while the adaptive clustering attention mechanism refines the focus on relevant information.The prefix training module facilitates efficient fine-tuning and knowledge transfer.Experimental results demonstrate the effectiveness of TubeRAPT,achieving a 68.44%mean Average Precision(mAP)on the CLA(Crazy LabActivity)small-scale dataset,marking a significant improvement of 1.53%over the previous TubeR method.This research not only showcases the potential applications of TubeRAPT in the field of abnormal action detection but also offers innovative ideas and technical support for the future development of laboratory safety monitoring technologies.The proposed method has implications for improving safety management systems in various laboratory environments,potentially reducing accidents and enhancing overall workplace safety.

General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline.Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve.Considering the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system.Furthermore,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and adaptability.Extensive simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants.Moreover,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

HgaNets:Fusion of Visual Data and Skeletal Heatmap for Human Gesture Action Recognition

Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data,failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility toefficiently process both uniformand disparate input patterns.Thus, in this paper, an attention-enhanced pseudo-3Dresidual model is proposed to address the GAR problem, called HgaNets. This model comprises two independentcomponents designed formodeling visual RGB (red, green and blue) images and 3Dskeletal heatmaps, respectively.More specifically, each component consists of two main parts: 1) a multi-dimensional attention module forcapturing important spatial, temporal and feature information in human gestures;2) a spatiotemporal convolutionmodule that utilizes pseudo-3D residual convolution to characterize spatiotemporal features of gestures. Then,the output weights of the two components are fused to generate the recognition results. Finally, we conductedexperiments on four datasets to assess the efficiency of the proposed model. The results show that the accuracy onfour datasets reaches 85.40%, 91.91%, 94.70%, and 95.30%, respectively, as well as the inference time is 0.54 s andthe parameters is 2.74M. These findings highlight that the proposed model outperforms other existing approachesin terms of recognition accuracy.

Adjustment mechanism of blasting dynamic-static action in the water decoupling charge

Water decoupling charge blasting excels in rock breaking,relying on its uniform pressure transmission and low energy dissipation.The water decoupling coefficients can adjust the contributions of the stress wave and quasi-static pressure.However,the quantitative relationship between the two contributions is unclear,and it is difficult to provide reasonable theoretical support for the design of water decoupling blasting.In this study,a theoretical model of blasting fracturing partitioning is established.The mechanical mechanism and determination method of the optimal decoupling coefficient are obtained.The reliability is verified through model experiments and a field test.The results show that with the increasing of decoupling coefficient,the rock breaking ability of blasting dynamic action decreases,while quasi-static action increases and then decreases.The ability of quasi-static action to wedge into cracks changes due to the spatial adjustment of the blast hole and crushed zone.The quasi-static action plays a leading role in the fracturing range,determining an optimal decoupling coefficient.The optimal water decoupling coefficient is not a fixed value,which can be obtained by the proposed theoretical model.Compared with the theoretical results,the maximum error in the model experiment results is 8.03%,and the error in the field test result is 3.04%.

Developments in the study of Chinese herbal medicine's assessment index and action mechanism for diabetes mellitus

In traditional Chinese medicine(TCM),based on various pathogenic symptoms and the‘golden chamber’medical text,Huangdi Neijing,diabetes mellitus falls under the category‘collateral disease’.TCM,with its wealth of experience,has been treating diabetes for over two millennia.Different antidiabetic Chinese herbal medicines re-duce blood sugar,with their effective ingredients exerting unique advantages.As well as a glucose lowering effect,TCM also regulates bodily functions to prevent diabetes associated complications,with reduced side effects compared to western synthetic drugs.Chinese herbal medicine is usually composed of polysaccharides,saponins,al-kaloids,flavonoids,and terpenoids.These active ingredients reduce blood sugar via various mechanism of actions that include boosting endogenous insulin secretion,enhancing insulin sensitivity and adjusting key enzyme activity and scavenging free radicals.These actions regulate glycolipid metabolism in the body,eventually achiev-ing the goal of normalizing blood glucose.Using different animal models,a number of molecular markers are available for the detection of diabetes induction and the molecular pathology of the disease is becoming clearer.Nonetheless,there is a dearth of scientific data about the pharmacology,dose-effect relationship,and structure-activity relationship of TCM and its constituents.Further research into the efficacy,toxicity and mode of action of TCM,using different metabolic and molecular markers,is key to developing novel TCM antidiabetic formulations.