Relevant Information Research Articles

Unveiling the Latest Breakthroughs: A Comprehensive Review of the Therapeutic Activity and Safety Profile of Aloe vera.

The use of herbal drugs as alternative and complementary medicine has increased in popularity, raising concerns about their safety profile. Aloe vera, a plant with diverse therapeutic properties, has been extensively used for centuries. This review aims to assess the therapeutic activity and safety profile of Aloe vera. A comprehensive literature search was conducted to gather relevant information from various biomedical databases. The chemical composition, mechanism of action, and therapeutic activities of Aloe vera were analyzed. Aloe vera contains numerous active components such as vitamins, enzymes, minerals, sugars, lignin, saponins, and anthraquinones. Its mechanisms of action involve collagen synthesis, anti-inflammatory effects, immune modulation, laxative properties, and antiviral activity. Aloe vera has demonstrated potential therapeutic benefits in wound healing, diabetes management, liver and kidney protection, and glycemic control. However, it is essential to consider potential side effects, such as skin irritation and allergic reactions. This review provides evidence-based information to improve patient safety and promote informed decisions regarding the use of Aloe vera as a therapeutic agent.

Topical Anti-ulcerogenic Effect of the Beta-adrenergic Blockers on Diabetic Foot Ulcers: Recent Advances and Future Prospectives.

Patients with diabetes suffer from major complications like Diabetic Retinopathy, Diabetic Coronary Artery Disease, and Diabetic Foot ulcers (DFUs). Diabetes complications are a group of ailments whose recovery time is especially delayed, irrespective of the underlying reason. The longer duration of wound healing enhances the probability of problems like sepsis and amputation. The delayed healing makes it more critical for research focus. By understanding the molecular pathogenesis of diabetic wounds, it is quite easy to target the molecules involved in the healing of wounds. Recent research on beta-adrenergic blocking drugs has revealed that these classes of drugs possess therapeutic potential in the healing of DFUs. However, because the order of events in defective healing is adequately defined, it is possible to recognize moieties that are currently in the market that are recognized to aim at one or several identified molecular processes. The aim of this study was to explore some molecules with different therapeutic categories that have demonstrated favorable effects in improving diabetic wound healing, also called the repurposing of drugs. Various databases like PubMed/Medline, Google Scholar and Web of Science (WoS) of all English language articles were searched, and relevant information was collected regarding the role of beta-adrenergic blockers in diabetic wounds or diabetic foot ulcers (DFUs) using the relevant keywords for the literature review. The potential beta-blocking agents and their mechanism of action in diabetic foot ulcers were studied, and it was found that these drugs have a profound effect on diabetic foot ulcer healing as per reported literatures. There is a need to move forward from preclinical studies to clinical studies to analyze clinical findings to determine the effectiveness and safety of some beta-antagonists in diabetic foot ulcer treatment.

Molecular Targets of Valeric Acid: A Bioactive Natural Product for Endocrine, Metabolic, and Immunological Disorders.

Postbiotics produced by gut microbiota have exhibited diverse pharmacological activities. Valeric acid, a postbiotic material produced by gut microbiota and some plant species like valerian, has been explored to have diverse pharmacological activities. This narrative review aims to summarise the beneficial role of valeric acid for different health conditions along with its underlying mechanism. In order to get ample scientific evidence, various databases like Science Direct, PubMed, Scopus, Google Scholar and Google were exhaustively explored to collect relevant information. Collected data were arranged and analyzed to reach meaningful a conclusion regarding the bioactivity profiling of valeric acid, its mechanism, and future prospects. Valeric acid belongs to short-chain fatty acids (SCFAs) compounds like acetate, propionate, butyrate, pentanoic (valeric) acid, and hexanoic (caproic) acid. Valeric acid has been identified as one of the potent histone deacetylase (HDAC) inhibitors. In different preclinical in -vitro and in-vivo studies, valeric acid has been found to have anti-cancer, anti-diabetic, antihypertensive, anti-inflammatory, and immunomodulatory activity and affects molecular pathways of different diseases like Alzheimer's, Parkinson's, and epilepsy. These findings highlight the role of valeric acid as a potential novel therapeutic agent for endocrine, metabolic and immunity-related health conditions, and it must be tested under clinical conditions to develop as a promising drug.

An Update on Glutathione's Biosynthesis, Metabolism, Functions, and Medicinal Purposes.

Glutathione (GSH) has been the focus of increased scientific interest in the last decades. It plays a crucial role in all major physiological processes by supplying antioxidant defenses through participating in cellular redox reactions in the human body and other living organisms. GSH also participates in detoxifying xenobiotics, protecting protein thiols from crosslinking and oxidation, regulating the cell cycle, storing cysteine, etc. The significant role of GSH in the most important physiological processes has been highlighted, such as maintaining the redox balance and reducing oxidative stress due to its ability to inactivate the reactive oxygen, nitrogen, and sulfur species. It can also enhance metabolic detoxification and regulate the function of the immune system. All of these characteristics make it a universal biomarker since its proper balance is essential for improving health and treating some age-related disorders. This review presents a current concept of the synthesis and metabolism of GSH; its main functions in a living organism, and as a precursor and cofactor; data on the use of GSH for medicinal purposes in the prevention and treatment of some diseases, as well as a nutritional strategy to maintain a normal pool of GSH in the body. The data were gathered by searching relevant information in multiple databases, such as PubMed, Scopus, ScienceDirect, and Google Scholar.

Open-Ended Online Learning for Autonomous Visual Perception.

The visual perception systems aim to autonomously collect consecutive visual data and perceive the relevant information online like human beings. In comparison with the classical static visual systems focusing on fixed tasks (e.g., face recognition for visual surveillance), the real-world visual systems (e.g., the robot visual system) often need to handle unpredicted tasks and dynamically changed environments, which need to imitate human-like intelligence with open-ended online learning ability. Therefore, we provide a comprehensive analysis of open-ended online learning problems for autonomous visual perception in this survey. Based on "what to online learn" among visual perception scenarios, we classify the open-ended online learning methods into five categories: instance incremental learning to handle data attributes changing, feature evolution learning for incremental and decremental features with the feature dimension changed dynamically, class incremental learning and task incremental learning aiming at online adding new coming classes/tasks, and parallel and distributed learning for large-scale data to reveal the computational and storage advantages. We discuss the characteristic of each method and introduce several representative works as well. Finally, we introduce some representative visual perception applications to show the enhanced performance when using various open-ended online learning models, followed by a discussion of several future directions.

Explicit Knowledge Graph Reasoning for Conversational Recommendation

Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests and letting users receive recommendation only passively. Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions. Recently, there has been a rise of using knowledge graphs (KGs) for CRSs, where the core motivation is to incorporate the abundant side information carried by a KG into both the recommendation and conversation processes. However, existing KG-based CRSs are subject to two defects: (1) there is a semantic gap between the learned representations of utterances and KG entities, hindering the retrieval of relevant KG information; (2) the reasoning over KG is mostly performed with the implicitly learned user interests, overlooking the explicit signals from the entities actually mentioned in the conversation. To address these drawbacks, we propose a new CRS framework, namely, the Knowledge Enhanced Conversational Reasoning (KECR) model. As a user can reflect her/his preferences via both attribute- and item-level expressions, KECR jointly embeds the structured knowledge from two levels in the KG. A mutual information maximization constraint is further proposed for semantic alignment between the embedding spaces of utterances and KG entities. Meanwhile, KECR utilizes the connectivity within the KG to conduct explicit reasoning of the user demand, making the model less dependent on the user’s feedback to clarifying questions. As such, the semantic alignment and explicit KG reasoning can jointly facilitate accurate recommendation and quality dialogue generation. By comparing with strong baselines on two real-world datasets, we demonstrate that KECR obtains state-of-the-art recommendation effectiveness, as well as competitive dialogue generation performance.

Multiview Clustering With Propagating Information Bottleneck.

In many practical applications, massive data are observed from multiple sources, each of which contains multiple cohesive views, called hierarchical multiview (HMV) data, such as image-text objects with different types of visual and textual features. Naturally, the inclusion of source and view relationships offers a comprehensive view of the input HMV data and achieves an informative and correct clustering result. However, most existing multiview clustering (MVC) methods can only process single-source data with multiple views or multisource data with single type of feature, failing to consider all the views across multiple sources. Observing the rich closely related multivariate (i.e., source and view) information and the potential dynamic information flow interacting among them, in this article, a general hierarchical information propagation model is first built to address the above challenging problem. It describes the process from optimal feature subspace learning (OFSL) of each source to final clustering structure learning (CSL). Then, a novel self-guided method named propagating information bottleneck (PIB) is proposed to realize the model. It works in a circulating propagation fashion, so that the resulting clustering structure obtained from the last iteration can "self-guide" the OFSL of each source, and the learned subspaces are in turn used to conduct the subsequent CSL. We theoretically analyze the relationship between the cluster structures learned in the CSL phase and the preservation of relevant information propagated from the OFSL phase. Finally, a two-step alternating optimization method is carefully designed for optimization. Experimental results on various datasets show the superiority of the proposed PIB method over several state-of-the-art methods.

RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization

Landmines remain a threat to war-affected communities for years after conflicts have ended, partly due to the laborious nature of demining tasks. Humanitarian demining operations begin by collecting relevant information from the sites to be cleared, which is then analyzed by human experts to determine the potential risk of remaining landmines. In this article, we propose RELand system to support these tasks, which consists of three major components. We (1) provide general feature engineering and label assigning guidelines to enhance datasets for landmine risk modeling, which are widely applicable to global demining routines, (2) formulate landmine presence as a classification problem and design a novel interpretable model based on sparse feature masking and invariant risk minimization, and run extensive evaluation under proper protocols that resemble real-world demining operations to show a significant improvement over the state-of-the-art, and (3) build an interactive web interface to suggest priority areas for demining organizations. We are currently collaborating with a humanitarian demining NGO in Colombia that is using our system as part of their field operations in two areas recently prioritized for demining. The resulting dataset and developed code can be found here .

External Nonfinancial Measures in Substantive Analytical Procedures: Contributions of Weather Information

ABSTRACT Recent studies and new standards suggest that auditors can use information from expanded external sources to verify their clients’ financial information. We propose advanced substantive analytical procedures with external nonfinancial measures derived from weather information to test whether it helps detect misstatements. Using computational simulations and daily store-level sales data, we test whether the proposed procedures with weather indicators outperform the procedures without such indicators in substantive analytical procedures to identify overstated daily store sales. For the multilocation retail firm examined in this study, we find that the models with one or more weather indicators perform better at detecting misstatements than the models without them. When the reliability of relevant internal information is in question, the usefulness of weather indicators is apparent. Overall, our results provide evidence suggesting the potential value of external nonfinancial measures in auditing.

Orthogonal neural representations support perceptual judgements of natural stimuli.

In natural behavior, observers must separate relevant information from a barrage of irrelevant information. Many studies have investigated the neural underpinnings of this ability using artificial stimuli presented on simple backgrounds. Natural viewing, however, carries a set of challenges that are inaccessible using artificial stimuli, including neural responses to background objects that are task-irrelevant. An emerging body of evidence suggests that the visual abilities of humans and animals can be modeled through the linear decoding of task-relevant information from visual cortex. This idea suggests the hypothesis that irrelevant features of a natural scene should impair performance on a visual task only if their neural representations intrude on the linear readout of the task relevant feature, as would occur if the representations of task-relevant and irrelevant features are not orthogonal in the underlying neural population. We tested this hypothesis using human psychophysics and monkey neurophysiology, in response to parametrically variable naturalistic stimuli. We demonstrate that 1) the neural representation of one feature (the position of a central object) in visual area V4 is orthogonal to those of several background features, 2) the ability of human observers to precisely judge object position was largely unaffected by task-irrelevant variation in those background features, and 3) many features of the object and the background are orthogonally represented by V4 neural responses. Our observations are consistent with the hypothesis that orthogonal neural representations can support stable perception of objects and features despite the tremendous richness of natural visual scenes.

Structure, Function, and Physicochemical Properties of Pore-forming Antimicrobial Peptides.

Antimicrobial peptides (AMPs), a class of antimicrobial agents, possess considerable potential to treat various microbial ailments. The broad range of activity and rare complete bacterial resistance to AMPs make them ideal candidates for commercial development. These peptides with widely varying compositions and sources share recurrent structural and functional features in mechanisms of action. Studying the mechanisms of AMP activity against bacteria may lead to the development of new antimicrobial agents that are more potent. Generally, AMPs are effective against bacteria by forming pores or disrupting membrane barriers. The important structural aspects of cytoplasmic membranes of pathogens and host cells will also be outlined to understand the selective antimicrobial actions. The antimicrobial activities of AMPs are related to multiple physicochemical properties, such as length, sequence, helicity, charge, hydrophobicity, amphipathicity, polar angle, and also self-association. These parameters are interrelated and need to be considered in combination. So, gathering the most relevant available information will help to design and choose the most effective AMPs.

High volume online hemodiafiltration: a global perspective and the Brazilian experience.

Online hemodiafiltration (HDF) is a rapidly growing dialysis modality worldwide. In Brazil, the number of patients with private health insurance undergoing HDF has exceeded the number of patients on peritoneal dialysis. The achievement of a high convection volume was associated with better clinical imprand patient - reported outcomes confirming the benefits of HDF. The HDFit trial provided relevant practical information on the implementation of online HDF in dialysis centers in Brazil. This article aims to disseminate technical information to improve the quality and safety of this new dialysis modality.

DualGCN: Exploring Syntactic and Semantic Information for Aspect-Based Sentiment Analysis.

The task of aspect-based sentiment analysis aims to identify sentiment polarities of given aspects in a sentence. Recent advances have demonstrated the advantage of incorporating the syntactic dependency structure with graph convolutional networks (GCNs). However, their performance of these GCN-based methods largely depends on the dependency parsers, which would produce diverse parsing results for a sentence. In this article, we propose a dual GCN (DualGCN) that jointly considers the syntax structures and semantic correlations. Our DualGCN model mainly comprises four modules: 1) SynGCN: instead of explicitly encoding syntactic structure, the SynGCN module uses the dependency probability matrix as a graph structure to implicitly integrate the syntactic information; 2) SemGCN: we design the SemGCN module with multihead attention to enhance the performance of the syntactic structure with the semantic information; 3) Regularizers: we propose orthogonal and differential regularizers to precisely capture semantic correlations between words by constraining attention scores in the SemGCN module; and 4) Mutual BiAffine: we use the BiAffine module to bridge relevant information between the SynGCN and SemGCN modules. Extensive experiments are conducted compared with up-to-date pretrained language encoders on two groups of datasets, one including Restaurant14, Laptop14, and Twitter and the other including Restaurant15 and Restaurant16. The experimental results demonstrate that the parsing results of various dependency parsers affect their performance of the GCN-based models. Our DualGCN model achieves superior performance compared with the state-of-the-art approaches. The source code and preprocessed datasets are provided and publicly available on GitHub (see https://github.com/CCChenhao997/DualGCN-ABSA).

Implemented classification techniques for osteoporosis using deep learning from the perspective of healthcare analytics.

Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neural Networks (NN) to analyze medical imaging data and detect the presence or severity of osteoporosis in patients is known as osteoporosis classification using Deep Learning (DL) algorithms. DL algorithms can extract relevant information from bone images and discover intricate patterns that could indicate osteoporosis. DCNN biases must be initialized carefully, much like their weights. Biases that are initialized incorrectly might affect the network's learning dynamics and hinder the model's ability to converge to an ideal solution. In this research, Deep Convolutional Neural Networks (DCNNs) are used, which have several benefits over conventional ML techniques for image processing. One of the key benefits of DCNNs is the ability to automatically Feature Extraction (FE) from raw data. Feature learning is a time-consuming procedure in conventional ML algorithms. During the training phase of DCNNs, the network learns to recognize relevant characteristics straight from the data. The Squirrel Search Algorithm (SSA) makes use of a combination of Local Search (LS) and Random Search (RS) techniques that are inspired by the foraging habits of squirrels. The method made it possible to efficiently explore the search space to find prospective values while using promising areas to refine and improve the solutions. Effectively recognizing optimum or nearly optimal solutions depends on balancing exploration and exploitation. The weight in the DCNN is optimized with the help of SSA, which enhances the performance of the classification. The comparative analysis with state-of-the-art techniques shows that the proposed SSA-based DCNN is highly accurate, with 96.57% accuracy.

Comparison of Surgical and Colonoscopy Tissue to Establish Colorectal Patient-derived Organoids.

Patient-derived organoids (PDOs) are ex vivo models that retain the functions and characteristics of individualized source tissues, including a simulated tumor microenvironment. However, the potential impact of undiscovered differences between tissue sources on PDO growth and progression remains unclear. This study aimed to compare the growth and condition of PDO models originating from surgical resection and colonoscopy and to provide practical insights for PDO studies. Tissue samples and relevant patient clinical information were collected to establish organoid models. PDOs were derived from both surgical and colonoscopy tissues. The growth of the organoids, including their state, size, and success rate of establishment, was recorded and analyzed. The activity of the organoids at the end stage of growth was detected using calcein-AM fluorescence staining. The results showed that the early growth phase of 2/3 colonoscopy-derived organoids was faster compared to surgical PDOs, with a growth difference observed within 11-13 days of establishment. However, colonoscopy-derived organoids exhibited a diminished growth trend after this time. There were no significant differences observed in the terminal area and quantity between the two types of tissue-derived organoids. Immunofluorescence assays of the PDOs revealed that the surgical PDOs possessed a denser cell mass with relatively higher viability than colonoscopy-derived PDOs. In the establishment of colorectal patient-derived organoids, surgically derived organoids require a slightly longer establishment period, while colonoscopy-derived organoids should be passaged prior to growth inhibition to preserve organoid viability.

Over comparative study of text summarization techniques based on graph neural networks

Due to the enormous content of text available online through emails, social media, and news articles, it has become complicated to summarize the textual information from multiple documents. Text summarization automatically creates a comprehensive description of the document that retains its informative contents through the keywords, where Multi-Document Summarization (MDS) is a productive tool for data accumulation that creates a concise and informative summary from the documents. In order to extract the relevant information from the documents, Graph neural networks (GNNs) is the neural structure that detains the interrelation of the graph by progressing the messages between the graphical nodes. In the current years, the advanced version of GNNs, such as graph attention network (GAN), graph recurrent network, and graph convolutional network (GCN) provides a remarkable performance in text summarization with the advantage of deep learning techniques. Hence, in this survey, graph approaches for text summarization has been analyzed and discussed, where the recent text summarization model based on Deep learning techniques are highlighted. Further, the article provides the taxonomy to abstract the design pattern of Neural Networks and conducts a comprehensive of the existing text summarization model. Finally, the review article enlists the future direction of the researcher, which would motivate the enthusiastic and novel contributions in text summarizations.

Authorization of Aadhar data using Diffie Helman key with enhanced security concerns

In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time.

Whole-genome sequencing in prenatally detected congenital malformations: prospective cohort study in clinical setting.

To investigate the diagnostic yield of trio whole-genome sequencing (WGS) in fetuses with various congenital malformations referred to a tertiary center for prenatal diagnosis. In this prospective study, 50 pregnancies with different congenital malformations, negative for trisomies and causative copy-number variants, were analyzed further with fetal-parental trio WGS analysis. Parents were eligible for inclusion if they accepted further investigation following the detection of isolated or multiple malformations on prenatal ultrasound. Cases with isolated increased nuchal translucency, gamete donation or multiple pregnancy were excluded. WGS with the Illumina Inc. 30× polymerase-chain-reaction-free short-read sequencing included analysis of single-nucleotide variants, insertions and deletions, structural variants, short tandem repeats and copy-number identification of SMN1 and SMN2 genes. A molecular diagnosis was achieved in 13/50 (26%) cases. Causative sequence variants were identified in 12 genes: FGFR3 (n = 2), ACTA1 (n = 1), CDH2 (n = 1), COL1A2 (n = 1), DHCR7 (n = 1), EYA1 (n = 1), FBXO11 (n = 1), FRAS1 (n = 1), L1CAM (n = 1), OFD1 (n = 1), PDHA1 (n = 1) and SOX9 (n = 1). The phenotypes of the cases were divided into different groups, with the following diagnostic yields: skeletal malformation (4/9 (44%)), multisystem malformation (3/7 (43%)), central nervous system malformation (5/15 (33%)) and thoracic malformation (1/10 (10%)). Additionally, two cases carried variants that were considered potentially clinically relevant, even though they were assessed as variants of uncertain significance, according to the guidelines provided by the American College of Medical Genetics and Genomics. Overall, we identified a causative or potentially clinically relevant variant in 15/50 (30%) cases. We demonstrate a diagnostic yield of 26% with clinical WGS in prenatally detected congenital malformations. This study emphasizes the benefits that WGS can bring to the diagnosis of fetal structural anomalies. It is important to note that causative chromosomal aberrations were excluded from our cohort before WGS. As chromosomal aberrations are a well-known cause of prenatally detected congenital malformations, future studies using WGS as a primary diagnostic test, including assessment of chromosomal aberrations, may show that the detection rate exceeds the diagnostic yield of this study. WGS can add clinically relevant information, explaining the underlying cause of the fetal anomaly, which will provide information concerning the specific prognosis of the condition, as well as estimate the risk of recurrence. A genetic diagnosis can also provide more reproductive choice for future pregnancies. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Increased Ability to Perceive Relevant Sensory Information Minimizes Low Back Exposures in Lifting.

We have previously shown evidence that some individuals seem to consistently minimize low back loads when lifting, while others do not. However, it is unknown why. Individual differences in ability to perceive relevant sensory information may explain differences in minimization of low back loads during lifting, consistent with considering load reduction in one's movement objective in an optimal feedback control theory framework. The purpose of this study was to investigate whether individuals' ability to perceive proprioceptive information (both force- and posture-senses) at the low back was associated with peak low back loads when performing generic or occupation-specific lifts. Seventy-two participants were recruited to perform 10 barbell (generic) and backboard (occupation-specific) lifts, while whole-body kinematics and ground reaction forces were collected. Peak low back compression and anteroposterior shear forces normalized to body mass were calculated as dependent variables. Both posture matching ability and force matching ability at the heavier force targets were associated with lower means and variability of peak low-back loads in both lift types, albeit with small effect sizes (R2 ≤ .17). These findings support the utility of an optimal feedback control theory framework to explore factors explaining interindividual differences in low back loads during lifting. Further, this evidence suggests improving proprioceptive ability may be a useful strategy in lift training programs designed for workplace injury prevention.

What makes a word a good representative of the category of "emotion"? The role of feelings and interoception.

The words we use to describe emotions vary in terms of prototypicality; that is, some of these words may be more representative of the semantic category of emotion than others (e.g., anger refers more clearly to an emotion than boredom). Based on a multicomponential conception of emotions, the aim of the present study was to examine the contribution of several variables to emotion prototypicality. Some of those variables are related to the distinct components of emotions: evaluation, action, body expression, internal body sensations (interoception), and feelings. Other variables are related to the concreteness/abstractness distinction: sensory experience, social interaction, thought, and morality. We collected ratings for these variables for a large set of words (1,286) which varied in emotion prototypicality. A regression analysis revealed that the variables that most contributed to emotion prototypicality were feelings and interoception. Furthermore, a factor analysis identified two underlying factors: socioemotional polarity and emotional experience. The scores of each word in both factors were used to create a two-dimensional space and a density plot which provides relevant information about the organization of emotion concepts in memory. (PsycInfo Database Record (c) 2024 APA, all rights reserved).