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While artificial Intelligence (AI) has made significant advancements, the seeming absence of its emotional ability has hindered effective communication with humans. This study explores how ChatGPT (ChatGPT-3.5 Mar 23, 2023 Version) represents affective responses to emotional narratives and compare these responses to human responses. Thirty-four participants read affect-eliciting short stories and rated their emotional responses and 10 recorded ChatGPT sessions generated responses to the stories. Classification analyses revealed the successful identification of affective categories of stories, valence, and arousal within and across sessions for ChatGPT. Classification analyses revealed the successful identification of affective categories of stories, valence, and arousal within and across sessions for ChatGPT. Classification accuracies predicting affective categories of stories, valence, and arousal of humans based on the affective ratings of ChatGPT and vice versa were not significant, indicating differences in the way the affective states were represented., indicating differences in the way the affective states were represented. These findings suggested that ChatGPT can distinguish emotional states and generate affective responses consistently, but there are differences in how the affective states are represented between ChatGPT and humans. Understanding these mechanisms is crucial for improving emotional interactions with AI.

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Metric multidimensional scaling is one of the classical methods for embedding data into low-dimensional Euclidean space. It creates the low-dimensional embedding by approximately preserving the pairwise distances between the input points. However, current state-of-the-art approaches only scale to a few thousand data points. For larger data sets such as those occurring in single-cell RNA sequencing experiments, the running time becomes prohibitively large and thus alternative methods such as PCA are widely used instead. Here, we propose a simple neural network-based approach for solving the metric multidimensional scaling problem that is orders of magnitude faster than previous state-of-the-art approaches, and hence scales to data sets with up to a few million cells. At the same time, it provides a non-linear mapping between high- and low-dimensional space that can place previously unseen cells in the same embedding.

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The COVID-19 pandemic has contributed to significant societal challenges, including increased substance misuse. The COVID stress syndrome is a constellation of interrelated processes that occur in response to pandemics, including danger/contamination fears, fears concerning economic consequences, xenophobia, compulsive checking/reassurance-seeking, and pandemic-related traumatic stress symptoms. In the present study, using a sample of 812 adults collected during the early stages of the COVID-19 pandemic in May 2020, we examined the relations between identified profiles of the COVID Stress Scales (CSS) and behavioral and cognitive aspects of substance misuse. Using profile analysis via multidimensional scaling (PAMS), we identified two core profiles of the CSS, which explained 60 % of the variance in participant responding: 1) High compulsive checking & Low xenophobia and 2) High xenophobia & Low danger/contamination. The first profile is consistent with the COVID stress syndrome, while the second profile aligns with the COVID disregard syndrome, which is a constellation of interrelated processes distinguished by a denial or downplaying of the seriousness of the COVID-19 pandemic and lack of perceived vulnerability to disease. Both profiles demonstrated significant positive correlations with drug and alcohol misuse, respectively. However, only the High xenophobia & Low danger/contamination profile demonstrated relations with cognitive aspects of substance misuse via positive and negative correlations with positive and negative expectancies of alcohol use, respectively. These findings provide further support for the relationship between the COVID stress syndrome and substance misuse and offer insight into how unique profiles of this syndrome may impact pandemic-related mental and public health interventions.

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Migration of nib Cd to the testa during fermentation can be achieved with high temperatures (> 45 °C) and low nib pH values (< 5.0) using spontaneous fermentation. However, this low pH can lead to low flavor quality. This study used three controlled temperature fermentation treatments on three cacao genotypes (CCN 51, ICS 95, and TCS 01) to test its effects on the nib pH, the migration of nib Cd to the testa, and the liquor flavor quality. All treatments were effective in reducing the total nib Cd concentration. Nevertheless, the treatment with the higher mean temperature (44.25 °C) and acidification (pH 4.66) reached the highest mean nib Cd reductions throughout fermentation, a 1.37 factor in TCS 01, promoting the development of fine-flavor cocoa sensorial notes. In unfermented beans, the Cd concentration of nibs was higher than that of the testa, and the Cd migration proceeded down the total concentration gradient. However, Cd migration was observed against the concentration gradient (testa Cd > nib Cd) from the fourth day. Cd migration could increase by extensive fermentation until the sixth day in high temperatures and probably by the adsorbent capacity of the testa. Genotype-by-treatment interactions were present for the nib Cd reduction, and a universal percentage of decrease of Cd for each genotype with fermentation cannot be expected. Selecting genotypes with highly adsorbent testa combined with controlled temperatures would help reduce the Cd concentration in the cacao raw material, improving its safety and quality.

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This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seven biomarker responses resulting from the exposure of TK6 cells to each of 126 diverse chemicals over a range of concentrations. Obviously, challenges associated with visualizing seven biomarker responses were further complicated whenever there was a desire to represent the entire 126 chemical data set as opposed to results from a single chemical. Scatter plots, spider plots, parallel coordinate plots, hierarchical clustering, principal component analysis, toxicological prioritization index, multidimensional scaling, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are each considered in turn. Our report provides a comparative analysis of these techniques. In an era where multiplexed assays and machine learning algorithms are becoming the norm, stakeholders should find some of these visualization strategies useful for efficiently and effectively interpreting their high-dimensional data.

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Prior studies have extensively examined modality-general representation of affect across various sensory modalities, particularly focusing on auditory and visual stimuli. However, little research has explored the modality-general representation of affect between gustatory and other sensory modalities. This study aimed to investigate whether the affective responses induced by tastes and musical pieces could be predicted within and across modalities. For each modality, eight stimuli were chosen based on four basic taste conditions (sweet, bitter, sour, and salty). Participants rated their responses to each stimulus using both taste and emotion scales. The multivariate analyses including multidimensional scaling and classification analysis were performed. The findings revealed that auditory and gustatory stimuli in the sweet category were associated with positive valence, whereas those from the other taste categories were linked to negative valence. Additionally, auditory and gustatory stimuli in sour taste category were linked to high arousal, whereas stimuli in bitter taste category were associated with low arousal. This study revealed the potential mapping of gustatory and auditory stimuli onto core affect space in everyday experiences. Moreover, it demonstrated that emotions evoked by taste and music could be predicted across modalities, supporting modality-general representation of affect.

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BACKGROUND: Traditional knowledge (TK) in Ladakh encapsulates a repository of experimental wisdom cultivated over millennia. Despite this cultural wealth, dwindling interest among the younger generations in the region's age-old practices underscores the urgency to document TK. The current study investigates the diverse usage of plants in Surru, Wakha and Lower Indus valleys of Western Ladakh exploring the influence of socioeconomic and ecological factors. METHODS: A stratified random sample approach was adopted to select 540 respondents for gathering information of useful plants through interviews and questionnaires. Participant observation, questionnaires, open-ended and semi-structured interviews were conducted for data collection. Free listing was done to create an extensive list of plants and their uses. Ethnobotanical metrics such as relative frequency of citation (RFC), relative importance index (RI), cultural value (CV) index and cultural importance (CI) index were computed to assess species applicability. Additionally, one-way analysis of variance (ANOVA) was utilized to discern significant differences in knowledge levels based on valleys, gender, education and religion using TK as a response variable. RESULTS: Altogether, we recorded 246 plant species under various ethnobotanical uses from Western Ladakh. These include medicinal (126), fodder (124), wild ornamentals (86), food (81), fuel wood (54), dye (20), religious (31) and others (34). Novel plant reports include Berberis brandisiana Ahrendt and Dactylorhiza kafiriana Renz. The dominant plant family is Asteraceae with 35 species. Suru valley exhibits the highest number of cited plants followed by Wakha-chu and Lower Indus valleys (192, 168 and 152 species, respectively). CONCLUSION: Disparities in plant use understanding are evident among different groups, prompting further investigation through intercultural comparisons. Plants such as Arnebia euchroma, Juniperus semiglobosa, and Artemisia species emerge with cultural importance. Gender, valley affiliation, religious background and the remoteness of a village all influence local plant knowledge. These variations are linked to socioeconomic disparities among communities.

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BACKGROUND: Primary progressive aphasia (PPA) diagnostic criteria underestimate the complex presentation of semantic (sv) and logopenic (lv) variants, in which symptoms partially overlap, and mixed clinical presentation (mixed-PPA) and heterogenous profile (lvPPA +) are frequent. Conceptualization of similarities and differences of these clinical conditions is still scarce. METHODS: Lexical, semantic, phonological, and working memory errors from nine language tasks of sixty-seven PPA were analyzed using Profile Analysis based on Multidimensional Scaling, which allowed us to create a distributed representation of patients' linguistic performance in a shared space. Patients had been studied with [18F] FDG-PET. Correlations were performed between metabolic and behavioral data. RESULTS: Patients' profiles were distributed across a continuum. All PPA, but two, presented a lexical retrieval impairment, in terms of reduced production of verbs and nouns. svPPA patients occupied a fairly clumped space along the continuum, showing a preponderant semantic deficit, which correlated to fusiform gyrus hypometabolism, while only few presented working memory deficits. Adjacently, lvPPA + presented a semantic impairment combined with phonological deficits, which correlated with metabolism in the anterior fusiform gyrus and posterior middle temporal gyrus. Starting from the shared phonological deficit side, a large portion of the space was occupied by all lvPPA, showing a combination of phonological, lexical, and working memory deficits, with the latter correlating with posterior temporo-parietal hypometabolism. Mixed PPA did not show unique profile, distributing across the space. DISCUSSION: Different clinical PPA entities exist but overlaps are frequent. Identifying shared and unique clinical markers is critical for research and clinical practice. Further research is needed to identify the role of genetic and pathological factors in such distribution, including also higher sample size of less represented groups.

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Although black rhinoceros Diceros bicornis are mostly solitary in the wild, the Hiroshima City Asa Zoological Park (Asa Zoo) has kept a family group together during the daytime, with good reproductive performance over five decades. Management procedures at the zoo include temporary single housing of the mother before and after giving birth, which facilitates maintenance of a compatible family group. We recorded intra-group spatial relationships for 4 years and 4 months, during which time an adult female reared two consecutive calves. During daytime she remained in an enclosure with her new calf, one or two older offspring, and an adult male, the sire of all her offspring. Proximity (within two adult body-lengths) scores between the mother and her two calves were especially high during the first year after birth, and only slightly lower for her older offspring. The adult male had the lowest proximity scores. The spatial relationships were visualized by applying multidimensional scaling (MDS) to the proximity scores. Mother and calves were plotted close to each other, with older offspring slightly farther apart on the two-dimensional MDS representation; the adult male was more distant from the other group members. These findings indicate clear follower-type characteristics in the mother-calf pair and also older immature offspring, albeit to a lesser degree. Although black rhinoceros are generally solitary in the wild, our results duplicate observations of some wild black rhinoceros groups containing an adult female, her calf, and an older immature, with adult males being largely solitary.

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We propose a method for imaging in scattering media when large and diverse datasets are available. It has two steps. Using a dictionary learning algorithm the first step estimates the true Green's function vectors as columns in an unordered sensing matrix. The array data comes from many sparse sets of sources whose location and strength are not known to us. In the second step, the columns of the estimated sensing matrix are ordered for imaging using the multidimensional scaling algorithm with connectivity information derived from cross-correlations of its columns, as in time reversal. For these two steps to work together, we need data from large arrays of receivers so the columns of the sensing matrix are incoherent for the first step, as well as from sub-arrays so that they are coherent enough to obtain connectivity needed in the second step. Through simulation experiments, we show that the proposed method is able to provide images in complex media whose resolution is that of a homogeneous medium.

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Time series clustering is a usual task in many different areas. Algorithms such as K-means and model-based clustering procedures are used relating to multivariate assumptions on the datasets, as the consideration of Euclidean distances, or a probabilistic distribution of the observed variables. However, in many cases the observed time series are of unequal length and/or there is missing data or, simply, the time periods observed for the series are not comparable between them, which does not allow the direct application of these methods. In this framework, dynamic time warping is an advisable and well-known elastic dissimilarity procedure, in particular when the analysis is accomplished in terms of the shape of the time series. In relation to a dissimilarity matrix, K-means clustering can be performed using a particular procedure based on classical multidimensional scaling in full dimension, which can result in a clustering problem in high dimensionality for large sample sizes. In this paper, we propose a procedure robust to dimensionality reduction, based on an auxiliary configuration estimated from the squared dynamic time warping dissimilarities, using an alternating least squares procedure. The performance of the model is compared to that obtained using classical multidimensional scaling, as well as to that of model-based clustering using this related auxiliary linear projection. An extensive Monte Carlo procedure is employed to analyze the performance of the proposed method in which real and simulated datasets are considered. The results obtained indicate that the proposed K-means procedure, in general, slightly improves the one based on the classical configuration, both being robust in reduced dimensionality, making it advisable for large datasets. In contrast, model-based clustering in the classical projection is greatly affected by high dimensionality, offering worse results than K-means, even in reduced dimension.

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Low-level features are typically continuous (e.g., the gamut between two colors), but semantic information is often categorical (there is no corresponding gradient between dog and turtle) and hierarchical (animals live in land, water, or air). To determine the impact of these differences on cognitive representations, we characterized the geometry of perceptual spaces of five domains: a domain dominated by semantic information (animal names presented as words), a domain dominated by low-level features (colored textures), and three intermediate domains (animal images, lightly texturized animal images that were easy to recognize, and heavily texturized animal images that were difficult to recognize). Each domain had 37 stimuli derived from the same animal names. From 13 participants (9F), we gathered similarity judgments in each domain via an efficient psychophysical ranking paradigm. We then built geometric models of each domain for each participant, in which distances between stimuli accounted for participants' similarity judgments and intrinsic uncertainty. Remarkably, the five domains had similar global properties: each required 5-7 dimensions, and a modest amount of spherical curvature provided the best fit. However, the arrangement of the stimuli within these embeddings depended on the level of semantic information: dendrograms derived from semantic domains (word, image, and lightly texturized images) were more "tree-like" than those from feature-dominated domains (heavily texturized images and textures). Thus, the perceptual spaces of domains along this feature-dominated to semantic-dominated gradient shift to a tree-like organization when semantic information dominates, while retaining a similar global geometry.

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The graphical representation of the correlation matrix by means of different multivariate statistical methods is reviewed, a comparison of the different procedures is presented with the use of an example data set, and an improved representation with better fit is proposed. Principal component analysis is widely used for making pictures of correlation structure, though as shown a weighted alternating least squares approach that avoids the fitting of the diagonal of the correlation matrix outperforms both principal component analysis and principal factor analysis in approximating a correlation matrix. Weighted alternating least squares is a very strong competitor for principal component analysis, in particular if the correlation matrix is the focus of the study, because it improves the representation of the correlation matrix, often at the expense of only a minor percentage of explained variance for the original data matrix, if the latter is mapped onto the correlation biplot by regression. In this article, we propose to combine weighted alternating least squares with an additive adjustment of the correlation matrix, and this is seen to lead to further improved approximation of the correlation matrix.

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The possibility of plants growing on serpentine soils and the ability of serpentine minerals to accumulate significant amounts of metals was the basis for developing a method for using serpentine-containing materials to restore vegetation in areas with a high level of metal pollution. Serpentine-containing products obtained from phlogopite mining overburden (Kovdor, Murmansk region, Russia) with and without thermal activation were used in a field experiment on the remediation of industrially polluted peat soil. According to the geochemical mobility of the components, one of four fractions was allocated depending on the acidic (HCl) concentration of the solution used for the material treatment: readily mobile (0.001 mol/L), mobile (0.01 mol/L), potentially mobile (0.1 mol/L), and acid-soluble (1.0 mol/L). This study showed that the addition of serpentinites to peat soil changed the fraction composition. The most significant changes were noted for serpentinite components such as Ca and Mg: their concentrations increased 2-3 times even in the smallest portion of serpentine material. On the contrary, the contents of metals in the readily mobile fraction decreased 3-18, 3-23, 5-26, and 2-42 times for Cu, Ni, Fe, and Al, respectively. The main factor causing the decrease in metal mobility was the pH rise due to the release of Ca and Mg compounds into the soil solution. This study showed that the addition of serpentine-containing material at 25 vol.% to peat soil was sufficient to create a geochemical barrier with a stable-functioning vegetation cover. All serpentine-containing materials are recommended for the remediation of large industrially polluted areas.

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Custom and tradition played essential roles in developing the built environment among the Balinese Hindu society for centuries. The wisdom in managing the environment has passed through generations, as demonstrated by some ancient remnants and Old Balinese inscriptions. We observed that glorifying the mountains in this society has long been a part of protecting the hydrologic cycle. This practice even started in the pre-Hindu era, as shown by sacred megalithic features adjacent to the natural reservoir, water spring, and mountainous forest. At some point, the behavior has successfully passed through times and even developed into a more complex socio-cultural system such as subak. Nevertheless, Balinese society is now facing the threat of hydrological disasters, primarily due to a rapid change in land use. Here, we describe the importance of revealing the value behind the ancient Balinese water-management system to gain people's resilience and sustainability. Multidimensional scaling (MDS) was used to measure the sustainability index and the leverage factors affecting water tradition in Bali. We reveal that the inheritance of the Balinese people's water management tradition is generally weak in each dimension. The socio-cultural dimension score is relatively high, with a sustainability score of 71.11%, which signifies that the society still trusts their culture of water resources management to protect nature. However, the economy and ecology dimensions' score low, with sustainability scores of 56.12 and 63.34%, respectively, indicating the need for improvement in economic matters through the policy strategy. Moreover, our study also suggests that the noble value of the cultural heritage related to the water management system needs to be revitalized and disseminated to the public. Hence, the implication is not limited to conserving the natural environment and cultural heritage. It also provides a reference for the current society regarding a built environment in harmony with sustainability principles.

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When selecting fillers to include in a police lineup, one must consider the level of similarity between the suspect and potential fillers. In order to reduce misidentifications, an innocent suspect should not stand out. Therefore, it is important that the fillers share some degree of similarity. Importantly, increasing suspect-filler similarity too much will render the task too difficult reducing correct identifications of a guilty suspect. Determining how much similarity yields optimal identification performance is the focus of the proposed study. Extant research on lineup construction has provided somewhat mixed results. In part, this is likely due to the subjective nature of similarity, which forces researchers to define similarity in relative terms. In the current study, we manipulate suspect-filler similarity via a multidimensional scaling model constructed using objective facial measurements. In doing so, we test the "propitious heterogeneity" and the diagnostic-feature-detection hypotheses which predict an advantage of lineups with low-similarity fillers in terms of discriminability. We found that filler similarity did not affect discriminability. We discuss limitations and future directions.

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Research on processes of multiple-cue judgments usually uses artificial stimuli with predefined cue structures, such as artificial bugs with four binary features like back color, belly color, gland size, and spot shape. One reason for using artifical stimuli is that the cognitive models used in this area need known cues and cue values. This limitation makes it difficult to apply the models to research questions with complex naturalistic stimuli with unknown cue structure. In two studies, building on early categorization research, we demonstrate how cues and cue values of complex naturalistic stimuli can be extracted from pairwise similarity ratings with a multidimensional scaling analysis. These extracted cues can then be used in a state-of-the-art hierarchical Bayesian model of numerical judgments. In the first study, we show that predefined cue structures of artificial stimuli are well recovered by an MDS analysis of similarity judgments and that using these MDS-based attributes as cues in a cognitive model of judgment data from an existing experiment leads to the same inferences as when the original cue values were used. In the second study, we use the same procedure to replicate previous findings from multiple-cue judgment literature using complex naturalistic stimuli.

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Characterizing judgments of similarity within a perceptual or semantic domain, and making inferences about the underlying structure of this domain from these judgments, has an increasingly important role in cognitive and systems neuroscience. We present a new framework for this purpose that makes very limited assumptions about how perceptual distances are converted into similarity judgments. The approach starts from a dataset of empirical judgments of relative similarities: the fraction of times that a subject chooses one of two comparison stimuli to be more similar to a reference stimulus. These empirical judgments provide Bayesian estimates of underling choice probabilities. From these estimates, we derive three indices that characterize the set of judgments, measuring consistency with a symmetric dis-similarity, consistency with an ultrametric space, and consistency with an additive tree. We illustrate this approach with example psychophysical datasets of dis-similarity judgments in several visual domains and provide code that implements the analyses.

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Conventional dimensionality reduction methods like Multidimensional Scaling (MDS) are sensitive to the presence of orthogonal outliers, leading to significant defects in the embedding. We introduce a robust MDS method, called DeCOr-MDS (Detection and Correction of Orthogonal outliers using MDS), based on the geometry and statistics of simplices formed by data points, that allows to detect orthogonal outliers and subsequently reduce dimensionality. We validate our methods using synthetic datasets, and further show how it can be applied to a variety of large real biological datasets, including cancer image cell data, human microbiome project data and single cell RNA sequencing data, to address the task of data cleaning and visualization.

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The Na+/K+-ATPase (NKA) α1-isoforms were examined by in situ hybridization chain reaction (ISHCR) using short hairpin DNAs, and we showed triple staining of NKA α1a, α1b, and α1c transcripts in the gill of chum salmon acclimated to freshwater (FW) and seawater (SW). The NKA α1-isoforms have closely resembled nucleotide sequences, which could not be differentiated by conventional in situ hybridization. The ISHCR uses a split probe strategy to allow specific hybridization using regular oligo DNA, resulting in high specificity at low cost. The results showed that NKA α1c was expressed ubiquitously in gill tissue and no salinity effects were observed. FW lamellar ionocytes (type-I ionocytes) expressed cytoplasmic NKA α1a and nuclear NKA α1b transcripts. However, both transcripts of NKA α1a and α1b were present in the cytoplasm of immature type-I ionocytes. The developing type-I ionocytes increased the cytoplasmic volume and migrated to the distal region of the lamellae. SW filament ionocytes (type-II ionocytes) expressed cytoplasmic NKA α1b transcripts as the major isoform. Results from morphometric analysis and nonmetric multidimensional scaling indicated that a large portion of FW ionocytes was NKA α1b-rich, suggesting that isoform identity alone cannot mark the ionocyte types. Both immature or residual type-II ionocytes and type-I ionocytes were found on the FW and SW gills, suggesting that the chum salmon retains the potential to switch the ionocyte population to fit the ion-transporting demands, which contributes to their salinity tolerance and osmoregulatory plasticity.

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