Action Information Research Articles

A mode of action-based probabilistic framework of dose-response assessment for nonmutagenic liver carcinogens: a case study of PCB-126.

A main function of dose-response assessment is to estimate a "safe" dose in the target population to support chemical risk assessment. Typically, a "safe" dose is developed differently for cancer and noncancer effects based on a 2-step procedure, ie, point of departure (POD) derivation and low-dose extrapolation. However, the current dose-response assessment framework is criticized for its dichotomized strategy without integrating the mode of action (MOA) information. The objective of this study was, based on our previous work, to develop a MOA-based probabilistic dose-response framework that quantitatively synthesizes a biological pathway in a dose-response modeling process to estimate the risk of chemicals that have carcinogenic potential. 3,3',4,4',5-Pentachlorobiphenyl (PCB-126) was exemplified to demonstrate our proposed approach. There were 4 major steps in the new modeling framework, including (1) key quantifiable events (KQEs) identification and extraction, (2) essential dose calculation, (3) MOA-based POD derivation, and (4) MOA-based probabilistic reference dose (RfD) estimation. Compared with reported PODs and traditional RfDs, the MOA-based estimates derived from our approach were comparable and plausible. One key feature of our approach was the use of overall MOA information to build the dose-response relationship on the entire dose continuum including the low-dose region. On the other hand, by adjusting uncertainty and variability in a probabilistic manner, the MOA-based probabilistic RfDs can provide useful insights of health protection for the specific proportion of population. Moreover, the proposed framework had important potential to be generalized to assess different types of chemicals other than nonmutagenic carcinogens, highlighting its utility to improve current chemical risk assessment.

High Accuracy Location Information Extraction From Social Network Texts Using Natural Language Processing

Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions. The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves. We will extend the solution to extract dates and action information to achieve the project's goal.

Slow feature subspace: A video representation based on slow feature analysis for action recognition

This paper proposes a new video representation for subspace-based action recognition. Traditional subspace-based methods represent a video as a subspace by applying principal component analysis (PCA) to its frames. However, this subspace might lead to an imprecise representation of actions, as PCA loses the temporal information of frames. Therefore, we introduce the slow feature subspace based on the slow feature analysis (SFA). SFA extracts a set of slow features of an input video by projecting the video frames onto the weight vectors that minimize the data variance over time. Motivated by these properties, several methods based on SFA were proposed for action recognition. However, they do not explicitly consider the distribution of the slow features, which represents essential action information. Therefore, our key idea is to capture this distribution through a low-dimensional subspace called slow feature subspace. Our subspace is generated by applying PCA to several weight vectors corresponding to the slowest components obtained by SFA. Our framework replaces the subspaces of several traditional mutual subspace methods with our slow feature subspace to improve their results in action recognition. This approach transforms the comparison between two videos into the comparison between two slow feature subspaces using the canonical angles between them, avoiding vector concatenation and data aggregation. The effectiveness of our framework is demonstrated through extensive experiments with various datasets. Our results show that our slow feature subspace can improve the traditional subspace-based methods and achieve competitive performance compared to different methods, including state-of-the-art neural networks.

Intuitive mapping between nonsymbolic quantity and observed action across development

Adults’ concurrent processing of numerical and action information yields bidirectional interference effects consistent with a cognitive link between these two systems of representation. This link is in place early in life: infants create expectations of congruency across numerical and action-related stimuli (i.e., a small [large] hand aperture associated with a smaller [larger] numerosity). Although these studies point to a developmental continuity of this mapping, little is known about the later development and thus how experience shapes such relationships. We explored how number–action intuitions develop across early and later childhood using the same methodology as in adults. We asked 3-, 6-, and 8-year-old children, as well as adults, to relate the magnitude of an observed action (a static hand shape, open vs. closed, in Experiment 1; a dynamic hand movement, opening vs. closing, in Experiment 2) to either a small or large nonsymbolic quantity (numerosity in Experiment 1 and numerosity and/or object size in Experiment 2). From 6 years of age, children started performing in a systematic congruent way in some conditions, but only 8-year-olds (added in Experiment 2) and adults performed reliably above chance in this task. We provide initial evidence that early intuitions guiding infants’ mapping between magnitude across nonsymbolic number and observed action are used in an explicit way only from late childhood, with a mapping between action and size possibly being the most intuitive. An initial coarse mapping between number and action is likely modulated with extensive experience with grasping and related actions directed to both arrays and individual objects.

Temozolomide and flavonoids against glioma: from absorption and metabolism to exosomal delivery.

Patients with glioblastoma multiforme and anaplastic astrocytoma are treated with temozolomide. Although it has been demonstrated that temozolomide increases GBM patient survival, it has also been connected to negative immune-related adverse effects. Numerous research investigations have shown that flavonoids have strong antioxidant and chemo-preventive effects. Consequently, it might lessen chemotherapeutic medicines' side effects while also increasing therapeutic effectiveness. The need for creating innovative, secure, and efficient drug carriers for cancer therapy has increased over time. Recent research indicates that exosomes have enormous potential to serve as carriers and cutting-edge drug delivery systems to the target cell. In recent years, researchers have been paying considerable attention to exosomes because of their favorable biodistribution, biocompatibility, and low immunogenicity. In the present review, the mechanistic information of the anti-glioblastoma effects of temozolomide and flavonoids coupled with their exosomal delivery to the targeted cell has been discussed. In addition, we discuss the safety aspects of temozolomide and flavonoids against glioma. The in-depth information of temozolomide and flavonoids action via exosomal delivery can unravel novel strategies to target Glioma.

Molecular Characterization and Antibacterial Activities of Photorhabdus and Xenorhabdus from Mizoram, North-East India

Photorhabdus and Xenorhabdus are the bacterial symbionts of insect pathogenic nematodes, Heterorhabditis and Steinernema, respectively. This study aims to characterize the bacterial symbionts from Mizoram, North-east India and to evaluate their antibacterial potential. The bacterial isolates were characterized using recA and gyrB gene regions. The ethyl acetate extract of bacterial isolates was tested against pathogenic bacterial strains, viz. Escherichia coli (ATCC 10536), Klebsiella pneumoniae (ATCC 10031), Pseudomonas aeruginosa (ATCC 10145), and Bacillus subtilis (ATCC 11774) using disk diffusion method. Analysis of recA and gyrB genes revealed that the Photorhabdus isolates were P. hindustanensis, and P. namnaonensis. This study constitutes the first documentation of P. namnaonensis from India. The two isolated Xenorhabdus belong to X. vietnamensis and X. stockiae. The ethyl acetate extracts of the studied bacteria suppressed the development of all the microorganisms tested. Based on MIC and MBC values, the highest activity was exhibited by TS (P. hindustanensis) and TD (P. namnaonensis) isolates against P. aeruginosa and K. pneumoniae respectively. The lowest inhibitory activity was observed on both Xenorhabdus isolates (RF and PTS) against B. subtilis. This study focuses on the existence and identification of symbiotic bacteria from Mizoram, an Indo-Burma biodiversity hotspot region, and details their activity against different pathogenic bacteria. Since these metabolites could be potent antibiotics, further research is required to better understand the genetic information, chemical composition, and method of action against other microorganisms.

Learning high-level robotic manipulation actions with visual predictive model

Learning visual predictive models has great potential for real-world robot manipulations. Visual predictive models serve as a model of real-world dynamics to comprehend the interactions between the robot and objects. However, prior works in the literature have focused mainly on low-level elementary robot actions, which typically result in lengthy, inefficient, and highly complex robot manipulation. In contrast, humans usually employ top–down thinking of high-level actions rather than bottom–up stacking of low-level ones. To address this limitation, we present a novel formulation for robot manipulation that can be accomplished by pick-and-place, a commonly applied high-level robot action, through grasping. We propose a novel visual predictive model that combines an action decomposer and a video prediction network to learn the intrinsic semantic information of high-level actions. Experiments show that our model can accurately predict the object dynamics (i.e., the object movements under robot manipulation) while trained directly on observations of high-level pick-and-place actions. We also demonstrate that, together with a sampling-based planner, our model achieves a higher success rate using high-level actions on a variety of real robot manipulation tasks.

Nutrients profiling for investigating variation and its effect on heterosis and combining ability of cucumber (Cucumis sativus)

An experiment was conducted at Vegetable Research Centre, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, during 2021 and 2022 to determine the nutritional quality, variation and its effect on cucumber (Cucumis sativus L.). Heterosis for nutritional quality in 30 F1 of cucumber obtained by L×T method involving 10 gynoecious and 3 monoecious cucumber, was studied and significant difference was found in all the characters. Based on per se performance, PPCUC-6 (gynoecious) followed by PPCUC-3 (gynoecious) are best performing parents and PPCUC-7 × PCUC-8 (monoecious) is best performing hybrid for most of characters. The cross combination PPCUC-10 × PCUC-28 (monoecious) based on mid parent, PPCUC-10 × PCUC-28 (monoecious) and PPCUC-12 × PCUC-28 (monoecious) based on better parent and PPCUC-12 × PCUC-28 (monoecious) based on standard parent are found best hybrids for maximum traits. PPCUC-3 (gynoecious) and PCUC-28 (monoecious) were best general combiner for most of characters, which can be used as a parents in improvement of nutritional quality. PPCUC-12 × PCUC-28 (monoecious) was best specific combiner for most of characters. Information of gene actions obtained from the study indicates the predominance of dominant gene action which is highly desirable to develop hybrids in cucumber with better nutritional quality.

Decentralized signal control for multi-modal traffic network: A deep reinforcement learning approach

Managing traffic flow at intersections in a large-scale network remains challenging. Multi-modal signalized intersections integrate various objectives, including minimizing the queue length and maintaining constant bus headway. Inefficient traffic signals and bus headway control strategies may cause severe traffic jams, high delays for bus passengers, and bus bunching that harms bus line operations. To simultaneously improve the level of service for car traffic and the bus system in a multi-modal network, this paper integrates bus priority and holding with traffic signal control via decentralized controllers based on Reinforcement Learning (RL). The controller agents act and learn from a synthetic traffic environment built with the microscopic traffic simulator SUMO. Action information is shared among agents to achieve cooperation, forming a Multi-Agent Reinforcement Learning (MARL) framework. The agents simultaneously aim to minimize vehicles' total stopping time and homogenize the forward and backward space headways for buses approaching intersections at each decision step. The Deep Q-Network (DQN) algorithm is applied to manage the continuity of the state space. The tradeoff between the bus transit and car traffic objectives is discussed using various numerical experiments. The introduced method is tested in scenarios with distinct bus lane layouts and bus line deployments. The proposed controller outperforms model-based adaptive control methods and the centralized RL method regarding global traffic efficiency and bus transit stability. Furthermore, the remarkable scalability and transferability of trained models are demonstrated by applying them to several different test networks without retraining.

Exploration With Task Information for Meta Reinforcement Learning.

Meta reinforcement learning (meta-RL) is a promising technique for fast task adaptation by leveraging prior knowledge from previous tasks. Recently, context-based meta-RL has been proposed to improve data efficiency by applying a principled framework, dividing the learning procedure into task inference and task execution. However, the task information is not adequately leveraged in this approach, thus leading to inefficient exploration. To address this problem, we propose a novel context-based meta-RL framework with an improved exploration mechanism. For the existing exploration and execution problem in context-based meta-RL, we propose a novel objective that employs two exploration terms to encourage better exploration in action and task embedding space, respectively. The first term pushes for improving the diversity of task inference, while the second term, named action information, works as sharing or hiding task information in different exploration stages. We divide the meta-training procedure into task-independent exploration and task-relevant exploration stages according to the utilization of action information. By decoupling task inference and task execution and proposing the respective optimization objectives in the two exploration stages, we can efficiently learn policy and task inference networks. We compare our algorithm with several popular meta-RL methods on MuJoco benchmarks with both dense and sparse reward settings. The empirical results show that our method significantly outperforms baselines on the benchmarks in terms of sample efficiency and task performance.

A Review of Labeling Approaches Used in Small Extracellular Vesicles Tracing and Imaging.

Small extracellular vesicles (sEVs), a subset of extracellular vesicles (EVs) originating from the endosomal compartment, are a kind of lipid bilayer vesicles released by almost all types of cells, serving as natural carriers of nucleic acids, proteins, and lipids for intercellular communication and transfer of bioactive molecules. The current findings suggest their vital role in physiological and pathological processes. Various sEVs labeling techniques have been developed for the more advanced study of the function, mode of action, bio-distribution, and related information of sEVs. In this review, we summarize the existing and emerging sEVs labeling techniques, including fluorescent labeling, radioisotope labeling, nanoparticle labeling, chemical contrast agents labeling, and label-free technique. These approaches will pave the way for an in-depth study of sEVs. We present a systematic and comprehensive review of the principles, advantages, disadvantages, and applications of these techniques, to help promote applications of these labeling approaches in future research on sEVs.

AGPN: Action Granularity Pyramid Network for Video Action Recognition

Video action recognition is a fundamental task for video understanding. Action recognition in complex spatio-temporal contexts generally requires fusing of different multi-granularity action information. However, existing works do not consider spatio-temporal information modeling and fusion from the perspective of action granularity. To address this problem, this paper proposes an Action Granularity Pyramid Network (AGPN) for action recognition, which can be flexibly integrated into 2D backbone networks. The core module is the Action Granularity Pyramid Module (AGPM), a hierarchical pyramid structure with residual connections, which is established to fuse multi-granularity action spatio-temporal information. From top to bottom level in the designed pyramid structure, the receptive field decreases and action granularity becomes more refined. To enrich temporal information of the inputs, a Multiple Frame Rate Module (MFM) is proposed to mix different frame rates at a fine-grained pixel-wise level. Moreover, a Spatio-temporal Anchor Module (SAM) is employed to fix spatio-temporal feature anchors to promote the effectiveness of feature extraction. We conduct extensive experiments on three large-scale action recognition datasets, Something-Something V1 & V2 and Kinetics-400. The results demonstrate that our proposed AGPN outperforms the state-of-the-art methods for the tasks of video action recognition.

Climate action information disclosure in Colombian companies: A regional and sectorial analysis

This study analyzes Sustainable Development Goal 13 (SDG 13) which urges action to combat climate change and its impacts. This study focuses mainly on goal 13.2 which establishes the incorporation of measures related to climate change policies, strategies, and plans. This objective includes information on climate change adaptation strategies, greenhouse gas emissions, and their scopes 1 (direct emissions), 2 (indirect emissions), and 3 (Other Emissions). This study is based on the legitimacy theory and the approach of symbolic and substantive information disclosure and the greenwashing and SDG-washing concepts. We designed two disclosure quality indexes for SDG 13 (qualitative SDG13 index and quantitative SDG13 index) through content analysis of the sustainability reports of 60 listed Colombian companies. Our findings suggest that there is a significant gap between the disclosure quality of symbolic (qualitative) and substantive (quantitative) information, thus generating impacts in relation to climate action at the regional and sectoral levels. The sectors with the highest disclosure quality are environmentally sensitive industries. At the regional level, there is evidence of heterogeneous behavior due to the region's social, economic, and political characteristics and their little connection with government climate change plans. Our results are useful to understand and identify the territorial context through business behavior factors related to climate change. These results also reveal necessary elements so that territorial governments can efficiently prevent, mitigate, and adapt measures that guarantee public health and the life of their inhabitants by managing resilient and habitable territories. This study has implications that go beyond the business sphere. The results should be understood as a contribution to the country's low carbon strategies, low greenhouse gas emissions, and carbon neutrality by the year 2050.

Using a Randomised Controlled Trial to Test the Effectiveness of a Simplified Notice of Police Bail

Abstract Purpose To test whether a simplified notice of police bail can increase court attendance in New Zealand by increasing defendant awareness of upcoming court hearings. Methods We designed a simplified notice of police bail using principles from behavioural science. The simplified notice reduced the required reading age of the front page by 2 years and included a clear call to action and simplified information. We rolled the notice out across six police stations in New Zealand and tested the impact with a cluster randomised controlled trial (RCT) (n = 1542, with clustering by custody officer). We also conducted interviews with defendants and staff, and conducted a survey with staff, to gain additional insights into the barriers to court attendance. Results Our results suggest the simplified notice increased court attendance by 3.6 percentage points (p < 0.1). If scaled throughout New Zealand, this would translate to around 1400 more defendants attending court each year due to the new notice. Our qualitative findings highlight a number of barriers to attendance not addressed by the simplified notice, including transport barriers, childcare barriers, and waiting times at court. Conclusions A simplified notice of police bail can increase court attendance by increasing defendants’ awareness of their court requirements. There are a range of additional barriers to attendance not overcome by our intervention which future interventions could address. Trial Registration AEARCTR-0007018.

A potential mechanism for Gibsonian resonance: behavioral entrainment emerges from local homeostasis in an unsupervised reservoir network.

While the cognitivist school of thought holds that the mind is analogous to a computer, performing logical operations over internal representations, the tradition of ecological psychology contends that organisms can directly "resonate" to information for action and perception without the need for a representational intermediary. The concept of resonance has played an important role in ecological psychology, but it remains a metaphor. Supplying a mechanistic account of resonance requires a non-representational account of central nervous system (CNS) dynamics. Towards this,we present a series of simple models in which a reservoir network with homeostatic nodes is used to control a simple agent embedded in an environment. This network spontaneously produces behaviors that are adaptive in each context, including (1) visually tracking a moving object, (2) substantially above-chance performance in the arcade game Pong, (2) and avoiding walls while controlling a mobile agent. Upon analyzing the dynamics of the networks, we find that behavioral stability can be maintained without the formation of stable or recurring patterns of network activity that could be identified as neural representations. These results may represent a useful step towards a mechanistic grounding of resonance and a view of the CNS that is compatible with ecological psychology.

Dual-attention Network for View-invariant Action Recognition

View-invariant action recognition has been widely researched in various applications, such as visual surveillance and human–robot interaction. However, view-invariant human action recognition is challenging due to the action occlusions and information loss caused by view changes. Modeling spatiotemporal dynamics of body joints and minimizing representation discrepancy between different views could be a valuable solution for view-invariant human action recognition. Therefore, we propose a Dual-Attention Network (DANet) aims to learn robust video representation for view-invariant action recognition. The DANet is composed of relation-aware spatiotemporal self-attention and spatiotemporal cross-attention modules. The relation-aware spatiotemporal self-attention module learns representative and discriminative action features. This module captures local and global long-range dependencies, as well as pairwise relations among human body parts and joints in the spatial and temporal domains. The cross-attention module learns view-invariant attention maps and generates discriminative features for semantic representations of actions in different views. We exhaustively evaluate our proposed approach on the NTU-60, NTU-120, and UESTC large-scale challenging datasets with multi-type evaluation metrics including Cross-Subject, Cross-View, Cross-Set, and Arbitrary-view. The experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art approaches in view-invariant action recognition.

Multiple temporal scale aggregation graph convolutional network for skeleton-based action recognition

Skeleton-based human action recognition is attracting increasing attention and is widely applied in virtual reality, human–computer interaction system and other cases. Nevertheless, the performance of recent methods on actions with similar appearance features is still barely satisfactory due to their inherent weakness in modeling the discriminative temporal dynamics. Besides, previous methods have limitation in mining the global information of action. To this end, a multiple temporal scale aggregation graph convolutional network is proposed. Firstly, taking advantage of the varying temporal resolutions offered by different layers in the graph convolutional network, we develop a multiple temporal scale aggregation module to extract discriminative temporal feature. Secondly, a new skeleton feature representation method termed as relative joint across frames is proposed, which provides more global structure clue than the absolute coordinates. Furthermore, we propose a five-stream structure that comprehensively models complementary features and eventually achieves a significant performance boost. In the empirical experiments, our method shows an improvement of 2.38% and 4.08% compared to the baseline method on the cross-subject evaluation benchmark of NTU-RGB+D 60 and NTU-RGB+D 120, respectively.

Review of Multiple Human Action Recognition Techniques

During the last decades topics such as video analysis and image understanding have acquired a big importance due to its inclusion in applications such as security, intelligent spaces, assistive living and focused marketing. Human action detection is investigated in utilization of artificial intelligence and computer vision. Numerous effective action recognition strategies have demonstrated and the action information are successfully gained from motion videos and still pictures. In order to get equivalent actions, the proper activity information gained from various kind of media like video or picture might be connected. The majority of existing video activity action identification strategies experience the ill effects of inadequate recordings. In this review article we are going to discussed about some earlier human action recognition techniques. In past numbers of researchers, developers dose a nicest work in same specific domain, in this review article/Paper we are going to review their work. During this we are going to find out and conclude the result and working of different Human action recognition techniques. In this process we are going to serve our focus on some specific action resignation techniques like: Action Recognition System using 3D Convolutional Neural Networks, Using Deep Learning, Deep Convolutional Neural Networks, Image Processing.

Model-Free Control in Wireless Cyber-Physical System With Communication Latency: A DRL Method With Improved Experience Replay.

This article explores the model-free remote control problem in a wireless networked cyber-physical system (CPS) composed of spatially distributed sensors, controllers, and actuators. The sensors sample the states of the controlled system to generate control instructions at the remote controller, while the actuators maintain the system's stability by executing control commands. To realize the control under a model-free system, the deep deterministic policy gradient (DDPG) algorithm is adopted in the controller to enable model-free control. Unlike the traditional DDPG algorithm, which only takes the system state as input, this article incorporates historical action information as input to extract more information and achieve precise control in the case of communication latency. Additionally, in the experience replay mechanism of the DDPG algorithm, we incorporate the reward into the prioritized experience replay (PER) approach. According to the simulation results, the proposed sampling policy improves the convergence rate by determining the sampling probability of transitions based on the joint consideration of temporal difference (TD) error and reward.

Novel Motion Patterns Matter for Practical Skeleton-Based Action Recognition

Most skeleton-based action recognition methods assume that the same type of action samples in the training set and the test set share similar motion patterns. However, action samples in real scenarios usually contain novel motion patterns which are not involved in the training set. As it is laborious to collect sufficient training samples to enumerate various types of novel motion patterns, this paper presents a practical skeleton-based action recognition task where the training set contains common motion patterns of action samples and the test set contains action samples that suffer from novel motion patterns. For this task, we present a Mask Graph Convolutional Network (Mask-GCN) to focus on learning action-specific skeleton joints that mainly convey action information meanwhile masking action-agnostic skeleton joints that convey rare action information and suffer more from novel motion patterns. Specifically, we design a policy network to learn layer-wise body masks to construct masked adjacency matrices, which guide a GCN-based backbone to learn stable yet informative action features from dynamic graph structure. Extensive experiments on our newly collected dataset verify that Mask-GCN outperforms most GCN-based methods when testing with various novel motion patterns.