Actional Mechanism of Trifluoroacetic Acid for the Separation of Biopolymers by Reversed-phase Liquid Chromatography

The present paper covers the actional mechanism of trifluoroacetic acid for the separation of biopolymers investigated by using the parameters of stoichiometric displacement model for retention(SDM-R) in reversed-phase liquid chromatography. It was found that the trifluoroacetic acid(TFA) may participate in, or stimulate the association among displacing agent molecules in mobile phase, and decrease the affinity of both the associate molecules of the displacing agent and the TFA-protein ion-pairing. The former dominates over the separation selectivity of biopolymers as the concentration of TFA is lower than a given value, and the two contrary functions partly offset to each other and the latter dominates as its concentration is greater than the given value.

Going in Homer:The Role of Verb-Inherent Actionality Within Self-Propelled Motion-Event Encoding

The paper aims at investigating the encoding of self-propelled motion events in Homeric Greek in the light of the typology of motion events,taking into account the case of to go.The verbal class of the self-propelled motion refers to those verbs expressing the idea of a simple translational motion,such as to go,to move,without any information about the manner of motion(see,by contrast,the class of the manner-of-motion verbs,such as to run,to swim)or about the path of motion(see,by contrast,the class of the path verbs,such as to enter,to exit).According to Talmy(2000),world languages can be distinguished depending on whether they prototypically express the semantic component of Path in or outside the motion verb.Languages belonging to the S(atellite)-Framed type tend to convey Path outside the motion verb,in a satellite element,such as a particle,an adposition(adpositional phrase),a preverb,an adverb(ial),a nominal case marker.The prototypical encoding pattern of the S-Framed languages,such as Homeric Greek,involves a motion verb conveying Manner and a satellite conveying Path,i.e.,[manner-of-motion verb+Path-satellite].Nonetheless,another pattern is used by this type of languages,albeit less prototypical,which involves a motion verb conveying only Motion and a satellite conveying Path,i.e.,[self-propelled motion verb+Path-satellite].Verb-inherent actionality,namely telicity,turns out to be a strong feature within the ancient Indo-European languages,such as Homeric Greek,playing a role not only in the development of aspectual/tense morphology,but also in the encoding of motion events,at least with reference to manner-of-motion verbs.The present paper aims at verifying the role of inherent telicity within self-propelled motion verbs,through the analysis of Homeric verbs for go.The study takes into account the Homeric suppletive paradigm for go,focusing on ερχομαι“go;come”and ηλθον(aorist)“go;come”(also with reference to the unclear actional opposition between ερχομαι and ειμι“go;come”).From the textual analysis of all the occurrences of both ερχομαι and ηλθον,as well as their co-occurring Path-encoding elements in the Iliad and the Odyssey,data show to what extent verbal inherent telicity plays an important role in motion event encoding also within the class of self-propelled motion verbs.

Entire Sign-Changing Solutions to the Fractional Critical Schrodinger Equation

In this paper,we consider the fractional critical Schrödinger equation(FCSE)(-Δ)^(s)u-|u|2^(*)s-2 u=0,where u∈˙H^(s)(R^(N)),N≥4,0<s<1 and 2^(*)s=2 N/N-2 s is the critical Sobolev exponent of order s.By virtue of the variational method and the concentration compactness principle with the equivariant group action,we obtain some new type of nonradial,sign-changing solutions of(FCSE)in the energy space˙H^(s)(R^(N)).The key component is that we take the equivariant group action to construct several subspace of˙H^(s)(R^(N))with trivial intersection,then combine the concentration compactness argument in the Sobolev space with fractional order to show the compactness property of Palais-Smale sequences in each subspace and obtain the multiple solutions of(FCSE)in˙H^(s)(R^(N)).

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。

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.

Oral fluoroquinolones and the risk of Achilles tendon rupture

Dear editor,Fluoroquinolones(FQs)are a class of antibiotics used to treat bacterial infections,such as lower respiratory,gastrointestinal,and urinary tract infections.Due to its broad-spectrum action and wide availability,it is a commonly prescribed group of antibiotics worldwide.^(1)

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.

Impact pressure of waves generated by landslides on bank slopes

Impulse waves that are generated by landslides in narrow reservoir areas threaten the stability of buildings and bank slopes.To discuss the action process and evolution law of the wave pressure on bank slopes,a three-dimensional physical model test that considers impulse waves generated by landslides was performed,and factors including landslide width,thickness,slope angles of the sliding surface,and bank slope angle were considered.Based on wave forms on the bank slopes,wave pressure curve characteristics,and peak value,the action process of wave pressure could be divided into the following stages:maximum pulsating pressure stage,wave impact stage(when waves break),and stationary pulsation stage.It was found that wave breaking is dependent on the value of the surf similarity parameterξ.The distribution pattern of impact pressure decays linearly on both sides of the maximum impact pressure point,and the attenuation degree decreases when it attains 40%of the maximum value.Thus,it is proposed that the prediction formula for the maximum effective impact pressure of the bank slope be related to the reciprocal of wave steepness,relative water depth,and slope rate.The prediction formula provides strong theoretical support for early safety warning and for predicting the bank slope under impulse waves generated by landslides.

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.

Towards an actionable One Health approach

Background Despite the increasing focus on strengthening One Health capacity building on global level,challenges remain in devising and implementing real-world interventions particularly in the Asia-Pacific region.Recognizing these gaps,the One Health Action Commission(OHAC)was established as an academic community for One Health action with an emphasis on research agenda setting to identify actions for highest impact.Main text This viewpoint describes the agenda of,and motivation for,the recently formed OHAC.Recognizing the urgent need for evidence to support the formulation of necessary action plans,OHAC advocates the adoption of both bottom-up and top-down approaches to identify the current gaps in combating zoonoses,antimicrobial resistance,addressing food safety,and to enhance capacity building for context-sensitive One Health implementation.Conclusions By promoting broader engagement and connection of multidisciplinary stakeholders,OHAC envisions a collaborative global platform for the generation of innovative One Health knowledge,distilled practical experience and actionable policy advice,guided by strong ethical principles of One Health.

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.

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.

Local Scour Mechanisms and Prediction Methods Around Offshore Wind Turbine Foundations:Insights and Future Directions

Local scour around offshore wind turbine foundations presents a considerable challenge due to its potential influence on structural stability,driven by hydrodynamic forces.While research has made strides in comprehending scouring mechanisms,notable complexities persist,specifically with newer foundation types.Addressing these limitations is vital for advancing our understanding of scour mechanisms and for improving mitigation strategies in offshore wind energy development.This review synthesizes current findings on local scour across various offshore foundations,encompassing field observations,data-driven approaches,turbulence-sediment interactions,scour evolution processes,influencing factors,and numerical model advancements.The objective is to enrich our understanding of local scour mechanisms.In addition,future research directions are outlined,including the development of robust arti-ficial intelligence models for accurate predictions,the exploration of vortex structure characteristics,and the refinement of numerical models to strengthen prediction capabilities while minimizing computational efforts.

Effect of applied electric fields on supralinear dendritic integration of interneuron

Evidences show that electric fields(EFs)induced by the magnetic stimulation could modulates brain activities by regulating the excitability of GABAergic interneuron.However,it is still unclear how and why the EF-induced polarization affects the interneuron response as the interneuron receives NMDA synaptic inputs.Considering the key role of NMDA receptor-mediated supralinear dendritic integration in neuronal computations,we suppose that the applied EFs could functionally modulate interneurons’response via regulating dendritic integration.At first,we build a simplified multi-dendritic circuit model with inhomogeneous extracellular potentials,which characterizes the relationship among EF-induced spatial polarizations,dendritic integration,and somatic output.By performing model-based singular perturbation analysis,it is found that the equilibrium point of fast subsystem can be used to asymptotically depict the subthreshold input–output(sI/O)relationship of dendritic integration.It predicted that EF-induced strong depolarizations on the distal dendrites reduce the dendritic saturation output by reducing driving force of synaptic input,and it shifts the steep change of sI/O curve left by reducing stimulation threshold of triggering NMDA spike.Also,the EF modulation prefers the global dendritic integration with asymmetric scatter distribution of NMDA synapses.Furthermore,we identify the respective contribution of EF-regulated dendritic integration and EF-induced somatic polarization to an action potential generation and find that they have an antagonistic effect on AP generation due to the varied NMDA spike threshold under EF stimulation.

Cooperative activation of sodium channels for downgrading the energy efficiency in neuronal information processing

The Hodgkin–Huxley model assumes independent ion channel activation,although mutual interactions are common in biological systems.This raises the problem why neurons would favor independent over cooperative channel activation.In this study,we evaluate how cooperative activation of sodium channels affects the neuron’s information processing and energy consumption.Simulations of the stochastic Hodgkin–Huxley model with cooperative activation of sodium channels show that,while cooperative activation enhances neuronal information processing capacity,it greatly increases the neuron’s energy consumption.As a result,cooperative activation of sodium channel degrades the energy efficiency for neuronal information processing.This discovery improves our understanding of the design principles for neural systems,and may provide insights into future designs of the neuromorphic computing devices as well as systematic understanding of pathological mechanisms for neural diseases.

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.

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.

Review on dietary supplements as an effective improvement of Alzheimer's disease:focus on structures and mechanisms

Alzheimer’s disease(AD),the major form of neurodegenerative diseases that can severely impede normal cognitive function,makes it one of the most common fatal diseases.There are currently over 50 million AD patients worldwide.The neuropathology of AD is perplexing and there is a scarcity of disease-modifying treatments.Currently,early diagnosis of AD has been made possible with the discovery of biological markers associated with pathology,providing strong support for the improvement of the disease status.The search for inhibitors of AD markers from dietary supplements(DSs)has become a major hot topic.Especially with the widespread use of DSs,DSs containing polyphenols,alkaloids,terpenes,polysaccharides and other bioactive components can prevent AD by reducing Aβdeposition,inhibiting tau protein hyperphosphorylation,reconstructing synaptic dysfunction,weakening cholinesterase activity,regulating mitochondrial oxidative stress,neuronal inflammation and apoptosis.This review summarizes the anti-AD effects of the main DSs and their bioactive constituents,as well as the potential molecular mechanisms covers from 2017 to 2023.Additionally,we discussed the opportunities and challenges faced by DSs in the process of AD prevention and treatment,aiming to further provide new perspectives for functional food development.

Experimental study on the movement of oil spill under freeze-thaw action

Oil leakages cause environmental pollution,economic losses,and even engineering safety accidents.In cold regions,researchers urgently investigate the movement of oil spill in soils exposed to freeze-thaw cycles.In this study,a series of laboratory model experiments were carried out on the migration of oil leakage under freeze-thaw action,and the distributions of the soil temperature,unfrozen water content,and displacement were analyzed.The results showed that under freeze-thaw action,liquid water in soils migrated to the freezing front and accumulated.After the pipe cracked,oil pollutants first gathered at one side of the leak hole,and then moved around.The pipe wall temperature affected the soil temperature field,and the thermal influence range below and transverse the pipe wall(35–40 cm)was larger than that above the pipe wall(8 cm)owing to the soil surface temperature.The leaked oil's temperature would make the temperature of the surrounding soil rise.Oil would inhibit the cooling of the soils.Besides,oil migration was significantly affected by the gravity and water flow patterns.The freeze-thaw action would affect the migration of the oil,which was mainly manifested as inhibiting the diffusion and movement of oil when soils were frozen.Unfrozen water transport caused by freeze-thaw cycles would also inhibit oil migration.The research results would provide a scientific reference for understanding the relationship between the movement of oil pollutants,water,and soil temperature,and for establishing a waterheat-mass transport model in frozen soils.