Is visual information use during facial emotion recognition related to eating disorder symptoms in college-aged men and women? An experimental study.

BACKGROUND: Previous studies of emotion recognition abilities of people with eating disorders used accuracy to identify performance deficits for these individuals. The current study examined eating disorder symptom severity as a function of emotion categorization abilities, using a visual cognition paradigm that offers insights into how emotional faces may be categorized, as opposed to just how well these faces are categorized. METHODS: Undergraduate students (N = 87, 50 women, 34 men, 3 non-binary) completed the Bubbles task and a standard emotion categorization task, as well as a set of questionnaires assessing their eating disorder symptomology and comorbid disorders. We examined the relationship between visual information use (assessed via Bubbles) and eating disorder symptomology (EDDS) while controlling for anxiety (STAI), depression (BDI-II), alexithymia (TAS), and emotion regulation difficulties (DERS-sf). RESULTS: Overall visual information use (i.e. how well participants used facial features important for accurate emotion categorization) was not significantly related to eating disorder symptoms, despite producing interpretable patterns for each emotion category. Emotion categorization accuracy was also not related to eating disorder symptoms. CONCLUSIONS: Results from this study must be interpreted with caution, given the non-clinical sample. Future research may benefit from comparing visual information use in patients with an eating disorder and healthy controls, as well as employing designs focused on specific emotion categories, such as anger.

Men and women with severe eating disorder symptoms may find it harder to identify and describe emotions than people with less severe eating disorder symptoms. However, previous work makes it difficult to determine why emotion recognition deficits exist, and what underlying abilities or strategies are actually different due to a deficit. In addition to a typical emotion recognition task (emotion categorization), this study used the Bubbles task, which allowed us to determine which parts of an image are important for emotion recognition, and whether participants used these parts during the task. In 87 undergraduate students (47 female; 49 with clinically-significant eating disorder symptoms), there was no significant relationship between task performance and eating disorder symptom severity, before and after controlling for the relationship with other comorbid disorders. Our results imply that emotion recognition deficits are unlikely to be an important mechanism underlying eating disorder pathology in participants with a range of eating disorders symptoms.

An inter-assessor reliability study on the categorization and staging of pressure injuries.

BACKGROUND: The first step in effective management of pressure injuries (PIs) is to assess, categorize and stage correctly. PURPOSE: This study aims to examine the agreement regarding the classification and staging of PIs among nurse academicians working on chronic wounds and with different stage of expertise. METHODS: Three nurse academicians were assigned as assessors according to Benner's stages of clinical competence (Competent, Proficient, and Expert). The assessors independently evaluated PIs photographs (n = 694). The assessors then met for the wounds where there was disagreement, and a Consensus agreement was reached. Kappa Statistics analysed the agreement between two assessors; Fleiss Kappa Statistics analysed the agreement between Competent, Proficient, Expert, and Consensus. RESULTS: Statistically, almost perfect agreement was obtained between Competent, Proficient, Expert, and Consensus assessments, respectively (Ƙ = 0.871; p < 0.001, Ƙ = 0.842; p < 0.001, Ƙ = 0.937; p < 0.001). The highest agreement between the assessors were Unstageable PIs, Deep Tissue PIs, and Stage 3 PIs respectively. The most common disagreements were between Deep Tissue PIs and Stage 1 PIs, and between Deep Tissue PIs and Stage 2 PIs. CONCLUSION: In the study, it was found that the categorization, and staging of PIs had varying degrees of reliability among the assesors, although at a statistically acceptable level.

Ill persons and capable workers: Constructing work ability in return-to-work negotiations after sickness absence.

In return-to-work (RTW) negotiations after sickness absence, the work ability of an individual employee becomes a shared interest for the multiple stakeholders representing both the healthcare sector and working life. In practice, the employee, employer and occupational health professionals need to reach a shared understanding of the employee's work ability to enable shared decision-making concerning the plans for sustainable RTW. Drawing on 14 video-recorded RTW negotiations, we used conversation analysis-informed membership categorization analysis to examine how the participants of RTW negotiations discuss the work ability of an employee to pursue a shared understanding of the situation. Work ability was constructed in a very situational way, using illness categories to both explain the work ability of the employee and argue for or against their ability or inability to work. Our study contributes to research on RTW by introducing a new perspective to work ability. We show how work ability is realized during RTW negotiations through interaction, and how participants leverage their cultural understanding of illness and capability when negotiating work ability. We also demonstrate how membership categorization analysis can reveal the situational and consequential aspects of illness and work ability categories.

Avoiding unintentional injuries from household chemicals: Comparing the appeal to children from the perspectives of children, caregivers, and experts.

This study investigated whether caregivers and regulatory experts can predict the appeal of household chemicals for children and explored determinants of differences between adults' and children's child-appeal rating. We invited N = 95 4-year-old children and their caregivers to a laboratory study. Both independently ranked 16 household chemicals from the least to the most child-appealing product. In addition to the laboratory study, we sent an online adaptation of this ranking task to N = 46 experts involved in the monitoring and authorizing of household chemicals. Our findings show that the aggregated child-appeal rating of household chemicals was highly similar among caregivers, experts, and children. When comparing child-appeal ratings of household chemicals with and without child-appealing images, caregivers and experts did well in predicting which products might appeal to children. Finally, our findings show that the similarity between individual caregivers' and their own children's child-appeal ratings of household chemicals varied substantially. To conclude, although adults can assess the general child-appeal of household chemicals, they need to consider that the actual appeal of a household chemical can vary considerably among individual children. Instead of regulating specific product attributes, policymakers should prioritize educating caregivers about pitfalls and misconceptions that hinder effective injury prevention.

Delineating Region-Specific contributions and connectivity patterns for semantic association and categorization through ROI and Granger causality analysis.

The neural mechanisms supporting semantic association and categorization are examined in this study. Semantic association involves linking concepts through shared themes, events, or scenes, while semantic categorization organizes meanings hierarchically based on defining features. Twenty-three adults participated in an fMRI study performing categorization and association judgment tasks. Results showed stronger activation in the inferior frontal gyrus during association and marginally weaker activation in the posterior middle temporal gyrus (pMTG) during categorization. Granger causality analysis revealed bottom-up connectivity from the visual cortex to the hippocampus during semantic association, whereas semantic categorization exhibited strong reciprocal connections between the pMTG and frontal semantic control regions, together with information flow from the visual association area and hippocampus to the pars triangularis. We propose that demands on semantic retrieval, precision of semantic representation, perceptual experiences and world knowledge result in observable differences between these two semantic relations.

Ethno-racial categorisations for biomedical studies: the fair selection of research participants and population stratification.

We argue that there are neither scientific nor social reasons to require gathering ethno-racial data, as defined in the US legal regulations if researchers have no prior hypotheses as to how to connect this type of categorisation of human participants of clinical trials with any mechanisms that could explain alleged interracial health differences and guide treatment choice. Although we agree with the normative perspective embedded in the calls for the fair selection of participants for biomedical research, we demonstrate that current attempts to provide and elucidate the criteria for the fair selection of participants, in particular, taking into account ethno-racial categories, overlook important epistemic and normative challenges to implement the results of such race-sorting requirements. We discuss existing arguments for and against gathering ethno-racial statistics for biomedical research and present a new one that refers to the assumption that prediction is epistemically superior to accommodation. We also underline the importance of closer interaction between research ethics and the methodology of biomedicine in the case of population stratifications for medical research, which requires weighing non-epistemic values with methodological constraints.

Categorical misalignment: Making autism(s) in big data biobanking.

The opaque relationship between biology and behavior is an intractable problem for psychiatry, and it increasingly challenges longstanding diagnostic categorizations. While various big data sciences have been repeatedly deployed as potential solutions, they have so far complicated more than they have managed to disentangle. Attending to categorical misalignment, this article proposes one reason why this is the case: Datasets have to instantiate clinical categories in order to make biological sense of them, and they do so in different ways. Here, I use mixed methods to examine the role of the reuse of big data in recent genomic research on autism spectrum disorder (ASD). I show how divergent regimes of psychiatric categorization are innately encoded within commonly used datasets from MSSNG and 23andMe, contributing to a rippling disjuncture in the accounts of autism that this body of research has produced. Beyond the specific complications this dynamic introduces for the category of autism, this paper argues for the necessity of critical attention to the role of dataset reuse and recombination across human genomics and beyond.

An improved deep convolutional neural network-based YouTube video classification using textual features.

Video content on the web platform has increased explosively during the past decade, thanks to the open access to Facebook, YouTube, etc. YouTube is the second-largest social media platform nowadays containing more than 37 million YouTube channels. YouTube revealed at a recent press event that 30,000 new content videos per hour and 720,000 per day are posted. There is a need for an advanced deep learning-based approach to categorize the huge database of YouTube videos. This study aims to develop an artificial intelligence-based approach to categorize YouTube videos. This study analyzes the textual information related to videos like titles, descriptions, user tags, etc. using YouTube exploratory data analysis (YEDA) and shows that such information can be potentially used to categorize videos. A deep convolutional neural network (DCNN) is designed to categorize YouTube videos with efficiency and high accuracy. In addition, recurrent neural network (RNN), and gated recurrent unit (GRU) are also employed for performance comparison. Moreover, logistic regression, support vector machines, decision trees, and random forest models are also used. A large dataset with 9 classes is used for experiments. Experimental findings indicate that the proposed DCNN achieves the highest receiver operating characteristics (ROC) area under the curve (AUC) score of 99% in the context of YouTube video categorization and 96% accuracy which is better than existing approaches. The proposed approach can be used to help YouTube users suggest relevant videos and sort them by video category.

Consumers' categorizations of dairy products and plant-based milk, yogurt, and cheese alternatives.

Plant-based dairy alternatives have many benefits in terms of sustainability, animal welfare, and health, but they can only be successful in the market if consumers perceive them as suitable substitutes for conventional dairy. Consumers' expectations for new products are strongly influenced by the food categories into which they place these products. The present study aims to reveal consumers' categorizations of plant-based dairy products to gain insights into their potential as dairy substitutes. In a free sorting task, 100 participants from the German-speaking part of Switzerland sorted a variety of plant-based and conventional animal-based dairy products into groups, indicating their spontaneous similarity perceptions. Additionally, we assessed the participants' characteristics and attitudes toward plant-based dairy products to test potential differences in categorization strategies among consumer groups. Multidimensional scaling and cluster analysis showed that consumers' mental representations of plant-based dairy and conventional animal-based dairy were clearly separated across a wide range of product types. This pattern was even observed among consumers who ate less meat, had higher exposure to vegan dietary styles, and had less negative attitudes toward dairy alternatives. The results suggest that taxonomic distinctions based on plant or animal origin dominate consumers' perceptions and are likely to hinder the substitution of dairy with plant-based dairy. Nevertheless, they also imply that plant-based products that manage to emphasize shared goals and functional properties akin to conventional dairy products are more likely to form a common goal-derived category in consumers' minds and thus have better prospects as substitutes.

Team diversity and team performance: Paths to synergetic and disruptive effects.

Team diversity holds promises and challenges for team performance. The promise of diversity lies in synergy generated from exchange and integration of diverse perspectives; the challenge lies in social categorization processes that give rise to biases favoring similar others over dissimilar others that disrupt team collaboration. I discuss theory capturing these paths to synergetic and disruptive effects and their contingencies as well as the evidence in research in team diversity and performance. I conclude that the evidence is consistent with the theory, but increasingly suffers from proliferation of moderators and mediators without integrative efforts to consolidate accumulating insights. I also identify theoretical parallels between the study of diversity and demographic dissimilarity as opportunity to develop integrative theory.

Distributional learning of bimodal and trimodal phoneme categories in monolingual and bilingual infants.

Distributional learning has been proposed as a mechanism for infants to learn the native phonemes of the language(s) to which they are exposed. When hearing two speech streams, bilingual infants may find other strategies more useful and rely on distributional learning less than monolingual infants. A series of studies examined how bilingual language experience affects the application of the distributional learning to novel phoneme distributions. Monolingual and bilingual infants between 6 and 8 months old performed a distributional learning task using palatal consonant stimuli grouped into one of three distributions based on voice onset time. Performance after exposure to a unimodal distribution was compared to performance after both a bimodal (Experiment 1) and trimodal distribution (Experiment 2) of the same voice onset time cue. Results indicated that monolingual and bilingual infants performed similarly on all tasks, and infants were able to learn both bimodal and trimodal phoneme distributions. The universality of the distributional learning mechanism is suggested by these results, but future research would need to test the two groups and distributions for equivalence of performance.

Tailor-made ammonia nitrogen risk management with machine learning models for aquatic environments in the Mainland of China.

Efficient management of pollutant risks in water bodies is crucial for public health and aquatic ecosystem sustainability. However, the toxicities of pollutants, such as ammonia nitrogen (NH3-N), are often affected by multiple water quality factors, including the pH and water temperature. Extensive spatial and temporal variability in these factors hinders tailor-made management of risk. This study used high-frequency monitoring data collected over 1 year to evaluate the long-term NH3-N risk in China's aquatic ecosystems. High accuracy and interpretability were achieved by decomposing NH3-N risk into the contributions of key influencing factors using random forest models and Shapley Additive Explanations. Two distinct types of NH3-N risk hotspots were identified across 18 cities: 15 cities with high NH3-N concentrations and 3 cities with low environmental carrying capacity due to high pH levels or elevated water temperatures. For the former, rapid NH3-N abatement measures are necessary to bring NH3-N concentrations back below the environmental capacity. For the latter, it is recommended that NH3-N related industries are relocated to regions with high environmental capacities because fragile environments are not suitable for such industries. Importantly, this study investigated methods for attributing pollutant risks in the context of non-linear influencing factors, and the risk of NH3-N was predicted to increase by 6.1 % by the end of 2100 in the context of increasing temperatures under the SSP 2-4.5 scenario. The methodology is also adaptable and suitable for integration into global ecosystem risk management efforts to balance development and aquatic ecological sustainability.

Biomarkers and Data Visualization of Insulin Resistance and Metabolic Syndrome: An Applicable Approach.

Type 2 diabetes, prediabetes, and insulin resistance (IR) are widespread yet often undetected in their early stages, contributing to a silent epidemic. Metabolic Syndrome (MetS) is also highly prevalent, increasing the chronic disease burden. Annual check-ups are inadequate for early detection due to conventional result formats that lack specific markers and comprehensive visualization. The aim of this study was to evaluate low-budget biochemical and hematological parameters, with data visualization, for identifying IR and MetS in a community-based laboratory. In a cross-sectional study with 1870 participants in Patras, Greece, blood samples were analyzed for key cardiovascular and inflammatory markers. IR diagnostic markers (TyG-Index, TyG-BMI, Triglycerides/HDL ratio, NLR) were compared with HOMA-IR. Innovative data visualization techniques were used to present metabolic profiles. Notable differences in parameters of cardiovascular risk and inflammation were observed between normal-weight and obese people, highlighting BMI as a significant risk factor. Also, the inflammation marker NHR (Neutrophils to HDL-Cholesterol Ratio) Index was successful at distinguishing the obese individuals and those with MetS from normal individuals. Additionally, a new diagnostic index of IR, combining BMI (Body Mass Index) and NHR Index, demonstrated better performance than other well-known indices. Lastly, data visualization significantly helped individuals understand their metabolic health patterns more clearly. BMI and NHR Index could play an essential role in assessing metabolic health patterns. Integrating specific markers and data visualization in routine check-ups enhances the early detection of IR and MetS, aiding in better patient awareness and adherence.

A novel technology for rapid identification of hemp fibers by terahertz spectroscopy.

A novel method for the rapid identification of hemp fibers is proposed in this paper, utilizing terahertz time-domain spectroscopy (THz-TDS) combined with the LargeVis (LV) dimensionality reduction technique. This approach takes advantage of the strengths of THz-TDS while enhancing classification accuracy through LV. To verify the efficacy of this method, terahertz absorption spectral data from three types of hemp fibers were processed. The THz absorption spectra were initially preprocessed using Hanning filtering. Following this, the filtered data underwent dimensionality reduction through three distinct methods: linear Principal Component Analysis (PCA), nonlinear t-Distributed Stochastic Neighbor Embedding (t-SNE), and the LV method. This sequence of steps resulted in a two-dimensional feature data matrix derived from the THz source spectral data. The resultant feature data matrices were then input into both K-Nearest Neighbors (KNN) and Decision Tree (DT) classifiers for analysis. The classification accuracies of six models were evaluated, revealing that the LV-KNN model achieved a 86.67% accuracy rate for the three hemp fiber types. Impressively, the LV-DT model achieved a perfect 100.00% accuracy rate for the same fibers. The LV-DT model, when integrated with THz spectroscopy technology, offers a quick and precise method for identifying various types of hemp fibers. This development introduces an innovative optical measurement scheme for the characterization of textile materials.

Optimizing the detection of N-nitrosamine mutagenicity in the Ames test.

Accurately determining the mutagenicity of small-molecule N-nitrosamine drug impurities and nitrosamine drug substance-related impurities (NDSRIs) is critical to identifying mutagenic and cancer hazards. In the current study we have evaluated several approaches for enhancing assay sensitivity for evaluating the mutagenicity of N-nitrosamines in the bacterial reverse mutagenicity (Ames) test. Preincubation assays were conducted using five activation conditions: no exogenous metabolic activation and metabolic activation mixes employing both 10% and 30% liver S9 from hamsters and rats pretreated with inducers of enzymatic activity. In addition, preincubations were conducted for both 60 min and 30 min. These test variables were evaluated by testing 12 small-molecule N-nitrosamines and 17 NDSRIs for mutagenicity in Salmonella typhimurium tester strains TA98, TA100, TA1535, and TA1537, and Escherichia coli strain WP2 uvrA (pKM101). Eighteen of the 29 N-nitrosamine test substances tested positive under one or more of the testing conditions and all 18 positives could be detected by using tester strains TA1535 and WP2 uvrA (pKM101), preincubations of 30 min, and S9 mixes containing 30% hamster liver S9. In general, the conditions under which NDSRIs were mutagenic were similar to those found for small-molecule N-nitrosamines.

Does Stimulus Category Coherence Influence Visual Working Memory? A Rational Analysis.

Visual working memory (VWM) refers to the temporary storage and manipulation of visual information. Although visually different, objects we view and remember can share the same higher-level category information, such as an apple, orange, and banana all being classified as fruit. We study the influence of category information on VWM, focusing on the question of whether stimulus category coherence (i.e., whether all to-be-remembered items belong to the same semantic category) influences VWM performance. This question is addressed in two behavioral experiments using a change-detection paradigm and a rational analysis using an ideal observer based on a Bayesian model. Both experimental participants and the ideal observer often, but not always, performed numerically better on coherent trials (i.e., when all stimuli belonged to the same category). We hypothesize that the influence of category coherence information on VWM may be task-dependent and/or stimulus-dependent. In conditions when category coherence information is highly valuable for task performance, as indicated by the ideal observer, then participants tended to make use of it. However, when the ideal observer suggested this information was not crucial to performance, participants did not. In addition, both participants and the ideal observer showed a bias toward responding "same," and often showed a stronger influence of category coherence on change trials. The consistencies between participant and ideal observer responses suggest participants often behaved as they did because these behaviors are optimal (or approximately so) for maximizing task performance. This may help explain conflicting results reported in the scientific literature.

A library for innovative category exemplars (ALICE) database: Streamlining research with printable 3D novel objects.

This paper introduces A Library for Innovative Category Exemplars (ALICE) database, a resource that enhances research efficiency in cognitive and developmental studies by providing printable 3D objects representing 30 novel categories. Our research consists of three experiments to validate the novelty and complexity of the objects in ALICE. Experiment 1 assessed the novelty of objects through adult participants' subjective familiarity ratings and agreement on object naming and descriptions. The results confirm the general novelty of the objects. Experiment 2 employed multidimensional scaling (MDS) to analyze perceived similarities between objects, revealing a three-dimensional structure based solely on shape, indicative of their complexity. Experiment 3 used two clustering techniques to categorize objects: k-means clustering for creating nonoverlapping global categories, and hierarchical clustering for allowing global categories that overlap and have a hierarchical structure. Through stability tests, we verified the robustness of each clustering method and observed a moderate to good consensus between them, affirming the strength of our dual approach in effectively and accurately delineating meaningful object categories. By offering easy access to customizable novel stimuli, ALICE provides a practical solution to the challenges of creating novel physical objects for experimental purposes.

Ocular Disease Detection with Deep Learning (Fine-Grained Image Categorization) Applied to Ocular B-Scan Ultrasound Images.

INTRODUCTION: The aim of this work is to develop a deep learning (DL) system for rapidly and accurately screening for intraocular tumor (IOT), retinal detachment (RD), vitreous hemorrhage (VH), and posterior scleral staphyloma (PSS) using ocular B-scan ultrasound images. METHODS: Ultrasound images from five clinically confirmed categories, including vitreous hemorrhage, retinal detachment, intraocular tumor, posterior scleral staphyloma, and normal eyes, were used to develop and evaluate a fine-grained classification system (the Dual-Path Lesion Attention Network, DPLA-Net). Images were derived from five centers scanned by different sonographers and divided into training, validation, and test sets in a ratio of 7:1:2. Two senior ophthalmologists and four junior ophthalmologists were recruited to evaluate the system's performance. RESULTS: This multi-center cross-sectional study was conducted in six hospitals in China. A total of 6054 ultrasound images were collected; 4758 images were used for the training and validation of the system, and 1296 images were used as a testing set. DPLA-Net achieved a mean accuracy of 0.943 in the testing set, and the area under the curve was 0.988 for IOT, 0.997 for RD, 0.994 for PSS, 0.988 for VH, and 0.993 for normal. With the help of DPLA-Net, the accuracy of the four junior ophthalmologists improved from 0.696 (95% confidence interval [CI] 0.684-0.707) to 0.919 (95% CI 0.912-0.926, p < 0.001), and the time used for classifying each image reduced from 16.84 ± 2.34 s to 10.09 ± 1.79 s. CONCLUSIONS: The proposed DPLA-Net showed high accuracy for screening and classifying multiple ophthalmic diseases using B-scan ultrasound images across mutiple centers. Moreover, the system can promote the efficiency of classification by ophthalmologists.

Adaptively triggered comparisons enhance perceptual category learning: evidence from face learning.

Categorical learning is important and often challenging in both specialized domains, such as medical image interpretation, and commonplace ones, such as face recognition. Research has shown that comparing items from different categories can enhance the learning of perceptual classifications, particularly when those categories appear highly similar. Here, we developed and tested novel adaptively triggered comparisons (ATCs), in which errors produced during interactive learning dynamically prompted the presentation of active comparison trials. In a facial identity paradigm, undergraduate participants learned to recognize and name varying views of 22 unknown people. In Experiment 1, single-item classification trials were compared to a condition in which ATC trials were generated whenever a participant repeatedly confused two faces. Comparison trials required discrimination between simultaneously presented exemplars from the confused categories. In Experiment 2, an ATC condition was compared to a non-adaptive comparison condition. Participants learned to accuracy and speed criteria, and completed immediate and delayed posttests. ATCs substantially enhanced learning efficiency in both experiments. These studies, using a novel adaptive procedure guided by each learner's performance, show that adaptively triggered comparisons improve category learning.