Facial expression recognition for emotion perception: A comprehensive science mapping.

Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

Cognitive domains related to attention and executive functions (a set of cognitive processes that regulate, control, and manage other cognitive abilities) seem to influence the recognition of facial expressions in people with Alzheimer's disease (AD). We examined the relationship between facial expression recognition, global cognition and executive function in people with AD according to their cognitive level. In a cross-sectional design, we included 130 participants with AD divided into three groups based on their Mini-Mental State Examination (MMSE) scores: MMSE 1 (scores 23-28), MMSE 2 (scores 17-22), and MMSE 3 (scores 11-16). Facial expression recognition ability was analyzed using the Faces Test. Executive function was analyzed using the Trail Making Test (TMT), the Verbal Fluency Test (VFT), the Semantic Fluency Test (SFT), the Digit Span Forward (DSF) and Backward (DSB) tests, and the Clock Drawing Test (CDT). In MMSE 1 group difficulties in divided attention and cognitive flexibility impacted the accuracy of facial expression recognition. In the MMSE 2 group, facial expression recognition was related to impairment in working memory. In the MMSE 3 group, the impact on facial expression recognition was directly related to visuoconstructive abilities. We observed that the executive resources involved in each evaluated group differed in terms of facial recognition task performance efficacy. Interventions at stimulating executive and visuoconstructive abilities in people with AD may contribute to better preservation of facial expression recognition.

Crustacean aquaculture is expected to be a major source of fishery commodities in the near future. Hemocytes are key players of the immune system in shrimps; however, their classification, maturation, and differentiation are still under debate. To date, only discrete and inconsistent information on the classification of shrimp hemocytes has been reported, showing that the morphological characteristics are not sufficient to resolve their actual roles. Our present study using single-cell RNA sequencing revealed six types of hemocytes of Marsupenaeus japonicus based on their transcriptional profiles. We identified markers of each subpopulation and predicted the differentiation pathways involved in their maturation. We also predicted cell growth factors that might play crucial roles in hemocyte differentiation. Different immune roles among these subpopulations were suggested from the analysis of differentially expressed immune-related genes. These results provide a unified classification of shrimp hemocytes, which improves the understanding of its immune system.

Facial expressions recognition and synthesis are important research fields to study how human beings reflect to environments in affective computing. With the rapid development of mathematical theory on multivariate statistics and multi-media technology especially image processing, facial expressions recognition researchers have achieved many useful results. Recently studies show that approaches to facial modeling and expressions recognition and synthesis analysis could be adapted to control security or even real-time health monitoring in the real world. Similarity, how to achieve facial expressions recognition and synthesis for independent-free mechanism is a central design in general. Based on the cognitive analysis of independent user's affective facial recognition researches we proposed an emotions composition model to dope out what are the user's new affective facial expressions. At this point, principal component, cluster and discriminate analysis were applied to show and verify independent user's affective expressions can be composited for synthesis by basic facial expressions such as happy, neutral and unhappy. Experiments were conducted to prove our models to be very significant.

Objectives The gut microbiome–gut–brain axis (MGBA) has been associated in the pathophysiology of depression; however, the expanding literature remains fragmented across metabolic signalling, immune–inflammatory pathways, stress physiology and dysbiosis outcomes. Methods s were retrieved from bibliographic databases (Lens.org, PubMed, DOAJ, Europe PMC) for studies published between 2014 and 2024 investigating associations between the gut microbiome and depression using 16S rRNA sequencing. Following text preprocessing, Latent Dirichlet Allocation (LDA) was applied to identify latent thematic topics. Topic proportions were subsequently embedded using principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation and projection (UMAP). Results Topic modelling revealed distinct interconnected thematic domains within the gut microbiome depression literature, encompassing metabolic and short chain fatty acid pathways, immune inflammatory mechanisms, stress and hypothalamic pituitary adrenal (HPA) axis regulation, probiotic and interventional work, microbial diversity and compositional metrics, neurochemical and neuroplasticity, developmental cohorts, and sequencing- or methodology-focused research. Conclusions Computational synthesis indicates that research on the gut microbiome depression axis is structured around multiple convergent mechanistic themes. This thematic landscape highlights dominant areas of mechanistic focus, providing a conceptual framework to guide future experimental design, mechanistic validation and translational research.

Facial expressions have significant communicative functions; any changes in the facial muscles help disentangle meaning, control the conversational flow, provide information as to the speaker/listener’s emotional state and inform about intention. Abnormalities in the recognition of facial expressions have been associated with psychiatric disorders. This review focuses on facial recognition abilities in children and adolescents with Attention Deficit Hyperactivity Disorder (ADHD). Using PRISMA guidelines original articles published prior to August 2019 were identified focusing on the emotion recognition skills of children and adolescents with ADHD and the measures administered. 25 studies were identified with the majority (18) showing some deficits on emotion recognition in children/adolescents with ADHD compared to typically developing (TD) children. The results are synthesized in terms of the type of stimuli implicated (static vs. dynamic), the measures/tasks administered, whether authors differentiated among specific emotion dimensions in the analysis of results, the effect of comorbidity on emotion recognition, and whether greater deficits have been reported for some emotions compared to others. Studies on facial emotion recognition in children and adolescents with ADHD focused mainly on the recognition accuracy of facial emotions, showing inconsistent results and a heterogenous use of measures. It is unknown whether the studies’ participants followed therapeutic plans (other than pharmacotherapy) at the time of the study or before, a factor that may potentially have influenced the review’s results.

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">As a common seismic facies visualization analysis method, SOM projects the waveform or seismic attribute vectors into a two-dimensional topological plane in a nonlinear way, which can effectively and efficiently discover the topological structure of a dataset. SOM does not need to set the number of classes in prior, and has friendly visualization characteristics and excellent generalization, which are conducive to seismic facies interpretation using unlabeled data. However, due to the competitive learning used in SOM and the imbalance of data distribution in real world, the samples from majority classes are expanded on the topological plane and the minority classes are compressed. As a result, the plane cannot accurately describe the global structure of data distribution. To improve the visualization precision by modifying the topological relationship of the prototype vectors of SOM, we utilize UMAP (Uniform Manifold Approximation and Projection), a novel manifold learning technique for dimension reduction, to correct the prototype vectors generated by SOM. By combing the advantages of SOM and UMAP in the representation of data topological structure, the global relationship between seismic data samples can be properly established, and the internal relative spatial structure of majority class samples can be retained as much as possible, resulting in a more reliable classification. Meanwhile, the framework maintains the advantages of SOM in visualization. In the modeling tests and real data experiments, we have demonstrated the effectiveness and rationality of UMAP-SOM on the spatial structure representation of three-dimensional seismic data</i> .

To examine the impact of music as a cross-modal prime on facial emotion recognition ability in autistic children, this study compares the priming effect of music with that of faces as an intramodal prime and nonverbal sounds as the same cross-modal prime. The response time and accuracy of facial emotion recognition (happy and sad) were compared among 21 neurotypical children and 17 autistic children under various priming stimuli. A data analysis revealed that autistic children exhibited worse recognition of facial emotional expressions and demonstrated longer reaction time than neurotypical children. Unlike the other two stimuli (facial expressions and nonverbal sounds), music as a cross-modal prime resulted in slightly higher response accuracy for emotionally congruent conditions compared with emotionally incongruent conditions among autistic children. Furthermore, reaction time was significantly prolonged under emotionally congruent conditions than under emotionally incongruent conditions. This suggests that autistic children demonstrate a greater propensity to allocate additional time to improve the accuracy of their judgments under congruent conditions compared with incongruent conditions. Therefore, music intervention has significant potential for supporting autistic children to empathize with faces by playing emotionally congruent music and improving their facial emotion recognition abilities.

To overcome the challenges of high signal-to-noise ratio requirements and the poor applicability of pulse waveform features in partial discharge (PD) blind source separation for cable accessories, this paper proposes a pulse source separation technique based on Uniform Manifold Approximation and Projection (UMAP). This method enables pulse signal separation in the presence of various noise interferences by using the time-frequency spectrogram of PD pulses as input and applying the UMAP algorithm for dimensionality reduction and feature extraction of the spectrogram's global characteristics. Unlike traditional feature extraction methods, this approach eliminates the need for defining and selecting specific features. The reduced-dimensional features effectively capture the relative differences between different discharge pulses, enabling their separation. Experimental validation using data from typical cable accessory defects shows that this method can effectively extract and separate features from random pulses. Compared to similar manifold approximation algorithms, UMAP is less sensitive to hyperparameters, and the reduced-dimensional features are more stable, which facilitates the execution of clustering algorithms.

Numerical modelling increasingly generates massive, high-dimensional spatio-temporal datasets. Exploring such datasets relies on effective visualization. This study presents a generic workflow to (i) project high-dimensional spatio-temporal data on a two-dimensional (2D) plane accurately (ii) compare dimensionality reduction techniques (DRTs) in terms of resolution and computational efficiency (iii) represent 2D projection spatially using a 2D perceptually uniform background color map. Machine learning (ML) based DRTs for data visualization i.e., principal component analysis (PCA), generative topographic mapping (GTM), t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are compared in terms of accuracy, resolution and computational efficiency to handle massive datasets. The accuracy of visualization is evaluated using a quality metric based on a co-ranking framework. The workflow is applied to an output of an Australian Water Resource Assessment (AWRA) model for Tasmania, Australia. The dataset consists of daily time series of nine components of the water balance at a 5 km grid cell resolution for the year 2017. The case study shows that PCA allows rapid visualization of global data structures, while t-SNE and UMAP allows more accurate representation of local trends. Furthermore, UMAP is computationally more efficient than t-SNE and least affected by the outliers compared to GTM.

Learning from potentially infinite and high-dimensional data streams poses significant challenges in the classification task. For instance, k-Nearest Neighbors (kNN) is one of the most often used algorithms in the data stream mining area that proved to be very resource-intensive when dealing with high-dimensional spaces. Uniform Manifold Approximation and Projection (UMAP) is a novel manifold technique and one of the most promising dimension reduction and visualization techniques in the non-streaming setting because of its high performance in comparison with competitors. However, there is no version of UMAP that copes with the challenging context of streams. To overcome these restrictions, we propose a batch-incremental approach that pre-processes data streams using UMAP, by producing successive embeddings on a stream of disjoint batches in order to support an incremental kNN classification. Experiments conducted on publicly available synthetic and real-world datasets demonstrate the substantial gains that can be achieved with our proposal compared to state-of-the-art techniques.

Identifying collective variables (CVs) for chemical reactions is essential to reduce the 3N-dimensional energy landscape into lower dimensional basins and barriers of interest. However, in condensed phase processes, the nonmeaningful motions of bulk solvent often overpower the ability of dimensionality reduction methods to identify correlated motions that underpin collective variables. Yet solvent can play important indirect or direct roles in reactivity, and much can be lost through treatments that remove or dampen solvent motion. This has been amply demonstrated within principal component analysis (PCA), although less is known about the behavior of nonlinear dimensionality reduction methods, e.g., uniform manifold approximation and projection (UMAP), that have become recently utilized. The latter presents an interesting alternative to linear methods though often at the expense of interpretability. This work presents distance-attenuated projection methods of atomic coordinates that facilitate the application of both PCA and UMAP to identify collective variables in the presence of explicit solvent and further the specific identity of solvent molecules that participate in chemical reactions. The performance of both methods is examined in detail for two reactions where the explicit solvent plays very different roles within the collective variables. When applied to raw molecular dynamics data in solution, both PCA and UMAP representations are dominated by bulk solvent motions. On the other hand, when applied to data preprocessed by our attenuated projection methods, both PCA and UMAP identify the appropriate collective variables (though varying sensitivity is observed due to the presence of explicit solvent that results from the projection method). Importantly, this approach allows identification of specific solvent molecules that are relevant to the CVs and their importance.

This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with three temperature conditions: no heat (RT), boiling (100 °C), and frying (180 °C). Gas chromatography–mass spectrometry (GC-MS) analysis showed that odorants in the beef varied under different conditions. Compounds like acetoin and 1-hexanol changed significantly with the storage days, while pyrazines and furans were more detectable at higher temperatures. The odor sensing system data were visualized using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). PCA and unsupervised UMAP clustered beef odors by storage days but struggled with the processing temperatures. Supervised UMAP accurately clustered different temperatures and dates. Machine learning analysis using six classifiers, including support vector machine, achieved 57% accuracy for PCA-reduced data, while unsupervised UMAP reached 49.1% accuracy. Supervised UMAP significantly enhanced the classification accuracy, achieving over 99.5% with the dimensionality reduced to three or above. Results suggest that the odor sensing system can sufficiently enhance non-destructive beef quality and safety monitoring. This research advances electronic nose applications and explores data downscaling techniques, providing valuable insights for future studies.

Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.

Visualization is widely used to help explore and interpret high dimensional single cell (sc) omics data, such as scRNA-seq expression data. In particular, uniform manifold approximation and projection (UMAP) has become nearly ubiquitous in scientific publications that apply single cell omics technologies. Some experts have expressed concerns that the global cell-cell relationship, especially the spatial distances among cell clusters in a dataset, may not be faithfully depicted in a 2-dimensional (2D) UMAP. To help users to better appreciate this issue with their own data, we created an online server for the community to upload their single cell data and interactively make UMAPs with different hyper-parameters to witness how the distribution of cell clusters changes. The server thus can help promote proper usages of UMAP, especially to avoid the common pitfalls in misinterpretation of inter-cluster relationships in single cell studies.Availability and ImplementationShinyUMAP is freely available as an online Shiny server implemented in Python athttps://scviewer.shinyapps.io/shinyUMAP/.