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We identified data-driven multidimensional physical activity (PA) profiles using several novel accelerometer-derived metrics. Participants aged 75, 80, and 85 (n = 441) wore triaxial accelerometers for 3–7 days. PA profiles were formed with k-means cluster analysis based on PA minutes, intensity, fragmentation, sit-to-stand transitions, and gait bouts for men and women. Associations with physical capacity and life-space mobility were examined using age-adjusted general linear models. Three profiles emerged: "Exercisers" and "actives" accumulated relatively high PA minutes, with actives engaging in lighter intensity PA. "Inactives" had the highest activity fragmentation and lowest PA volume, intensity, and gait bouts. Inactives showed lower scores in physical capacity and life-space mobility compared with exercisers and actives. Exercisers and actives had similar physical capacity and life-space mobility, except female exercisers had higher walking speed in the 6-min walk test. Our findings demonstrate the importance of assessing PA as multidimensional behavior rather than focusing on a single metric. [ABSTRACT FROM AUTHOR]

Cluster randomized trials (CRTs) often enroll large numbers of participants; yet due to resource constraints, only a subset of participants may be selected for outcome assessment, and those sampled may not be representative of all cluster members. Missing data also present a challenge: if sampled individuals with measured outcomes are dissimilar from those with missing outcomes, unadjusted estimates of arm-specific endpoints and the intervention effect may be biased. Further, CRTs often enroll and randomize few clusters, limiting statistical power and raising concerns about finite sample performance. Motivated by SEARCH-TB, a CRT aimed at reducing incident tuberculosis infection, we demonstrate interlocking methods to handle these challenges. First, we extend Two-Stage targeted minimum loss-based estimation to account for three sources of missingness: (i) subsampling; (ii) measurement of baseline status among those sampled; and (iii) measurement of final status among those in the incidence cohort (persons known to be at risk at baseline). Second, we critically evaluate the assumptions under which subunits of the cluster can be considered the conditionally independent unit, improving precision and statistical power but also causing the CRT to behave like an observational study. Our application to SEARCH-TB highlights the real-world impact of different assumptions on measurement and dependence; estimates relying on unrealistic assumptions suggested the intervention increased the incidence of TB infection by 18% (risk ratio [RR]=1.18, 95% confidence interval [CI]: 0.85-1.63), while estimates accounting for the sampling scheme, missingness, and within community dependence found the intervention decreased the incident TB by 27% (RR=0.73, 95% CI: 0.57-0.92).

MOTIVATION: Spatial proteomics can reveal the spatial organization of immune cells in the tumor immune microenvironment. Relating measures of spatial clustering, such as Ripleys K or Besags L, to patient outcomes may offer important clinical insights. However, these measures require pre-specifying a radius in which to quantify clustering, yet no consensus exists on the optimal radius which may be context-specific. RESULTS: We propose a SPatial Omnibus Test (SPOT) which conducts this analysis across a range of candidate radii. At each radius, SPOT evaluates the association between the spatial summary and outcome, adjusting for confounders. SPOT then aggregates results across radii using the Cauchy combination test, yielding an omnibus P-value characterizing the overall degree of association. Using simulations, we verify that the type I error rate is controlled and show SPOT can be more powerful than alternatives. We also apply SPOT to ovarian and lung cancer studies. AVAILABILITY AND IMPLEMENTATION: An R package and tutorial are provided at https://github.com/sarahsamorodnitsky/SPOT.

Background: To identify the clustering characteristics of individual-, family-, and school-level factors, and examine their associations with health-related physical fitness. Methods: A total of 145,893 Chinese children and adolescents aged 9–18 years participated in this cross-sectional study. The 2-step cluster analysis was conducted to identify clusters among individual-, family-, and school-level factors. Physical fitness indicator was calculated through sex- and age-specific z scores of forced vital capacity, standing long jump, sit-and-reach flexibility, body muscle strength, endurance running, and body mass index. Results: Three, 3, and 5 clusters were automatically identified at individual, family, and school levels, respectively. Students with low physical fitness indicator were more likely to be in the "longest sedentary time and skipping breakfast" cluster (odds ratio [OR] = 1.18; 95% confidence interval [CI], 1.12–1.24), and "physical inactivity and insufficient protein consumption" cluster (OR = 1.07; 95% CI, 1.02–1.12) at individual level, the "single children and high parental education level" cluster (OR = 1.15; 95% CI, 1.10–1.21), and "no physical activity support and preference" cluster (OR = 1.30; 95% CI, 1.25–1.36) at family level, and the "physical education occupied" cluster (OR = 1.06; 95% CI, 1.01–1.11), and "insufficient physical education frequency" cluster (OR = 1.16; 95% CI, 1.08–1.24) at school level. Girls were more vulnerable to individual- and school-level clusters, while boys were more susceptible to family clusters; the younger students were more sensitive to school clusters, and the older students were more susceptible to family clusters (P-interaction <.05). Conclusions: This study confirmed different clusters at multilevel factors and proved their associations with health-related physical fitness, thus providing new perspective for developing targeted interventions. [ABSTRACT FROM AUTHOR]

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

BACKGROUND: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. RESULTS: Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html . CONCLUSIONS: Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

The intention-to-treat (ITT) analysis of the Applying Wolbachia to Eliminate Dengue (AWED) trial estimated a protective efficacy of 77.1% for participants resident in areas randomised to receive releases of wMel-infected Aedes aegypti mosquitoes, an emerging dengue preventive intervention. The limiting assumptions of ITT analyses in cluster randomised trials and the mobility of mosquitoes and humans across cluster boundaries indicate the primary analysis is likely to underestimate the full public health benefit. Using spatiotemporally-resolved data on the distribution of Wolbachia mosquitoes and on the mobility of AWED participants (n = 6306), we perform complier-restricted and per-protocol re-examinations of the efficacy of the Wolbachia intervention. Increased intervention efficacy was estimated in all analyses by the refined exposure measures. The complier-restricted analysis returned an estimated efficacy of 80.7% (95% CI 65.9, 89.0) and the per-protocol analysis estimated 82.7% (71.7, 88.4) efficacy when comparing participants with an estimated wMel exposure of ≥ 80% compared to those with

IntroductionData-driven neuropsychological methods can identify mild cognitive impairment (MCI) subtypes with stronger associations to dementia risk factors than conventional diagnostic methods.MethodsCluster analysis used neuropsychological data from participants without dementia (mean age = 71.6 years) in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (n = 26,255) and the "normal cognition" subsample (n = 16,005). Survival analyses examined MCI or dementia progression.ResultsFive clusters were identified: "Optimal" cognitively normal (oCN; 13.2%), "Typical" CN (tCN; 28.0%), Amnestic MCI (aMCI; 25.3%), Mixed MCI-Mild (mMCI-Mild; 20.4%), and Mixed MCI-Severe (mMCI-Severe; 13.0%). Progression to dementia differed across clusters (oCN

BACKGROUND: The clustering of immune repertoire data is challenging due to the computational cost associated with a very large number of pairwise sequence comparisons. To overcome this limitation, we developed Anchor Clustering, an unsupervised clustering method designed to identify similar sequences from millions of antigen receptor gene sequences. First, a Point Packing algorithm is used to identify a set of maximally spaced anchor sequences. Then, the genetic distance of the remaining sequences to all anchor sequences is calculated and transformed into distance vectors. Finally, distance vectors are clustered using unsupervised clustering. This process is repeated iteratively until the resulting clusters are small enough so that pairwise distance comparisons can be performed. RESULTS: Our results demonstrate that Anchor Clustering is faster than existing pairwise comparison clustering methods while providing similar clustering quality. With its flexible, memory-saving strategy, Anchor Clustering is capable of clustering millions of antigen receptor gene sequences in just a few minutes. CONCLUSIONS: This method enables the meta-analysis of immune-repertoire data from different studies and could contribute to a more comprehensive understanding of the immune repertoire data space.

Multiple sclerosis (MS) is a neuro-inflammatory and neurodegenerative disease that is most prevalent in Northern Europe. Although it is known that inherited risk for MS is located within or in close proximity to immune-related genes, it is unknown when, where and how this genetic risk originated1. Here, by using a large ancient genome dataset from the Mesolithic period to the Bronze Age2, along with new Medieval and post-Medieval genomes, we show that the genetic risk for MS rose among pastoralists from the Pontic steppe and was brought into Europe by the Yamnaya-related migration approximately 5,000 years ago. We further show that these MS-associated immunogenetic variants underwent positive selection both within the steppe population and later in Europe, probably driven by pathogenic challenges coinciding with changes in diet, lifestyle and population density. This study highlights the critical importance of the Neolithic period and Bronze Age as determinants of modern immune responses and their subsequent effect on the risk of developing MS in a changing environment.

Neuropsychiatric complications such as neurocognitive impairment and depression are common in people with HIV despite viral suppression on antiretroviral therapy, but these conditions are heterogeneous in their clinical presentations and associated disability. Identifying novel biopsychosocial phenotypes that account for neurocognitive performance and depressive and functional symptoms will better reflect the complexities encountered in clinical practice and may have pathological and therapeutic implications. We classified 1580 people with HIV based on 17 features, including 7 cognitive domains, 4 subscales of the Beck depression inventory-II, 5 components of the patients assessment of own functioning inventory, and dependence in instrumental and basic activities of daily living. A two-stage clustering procedure consisting of dimension reduction with self-organizing maps and Mahalanobis distance-based k-means clustering algorithms was applied to cross-sectional data. Baseline demographic and clinical characteristics were compared between the phenotypes, and their prediction on the biopsychosocial phenotypes was evaluated using multinomial logistic regression. Four distinct phenotypes were identified. Participants in Phenotype 1 overall did well in all domains. Phenotype 2 had mild-to-moderate depressive symptoms and the most substance use disorders. Phenotype 3 had mild-to-moderate cognitive impairment, moderate depressive symptoms, and the worst daily functioning; they also had the highest proportion of females and non-HIV conditions that could affect cognition. Phenotype 4 had mild-to-moderate cognitive impairment but with relatively good mood, and daily functioning. Multivariable analysis showed that demographic characteristics, medical conditions, lifetime cocaine use disorder, triglycerides, and non-antiretroviral therapy medications were important variables associated with biopsychosocial phenotype. We found complex, multidimensional biopsychosocial profiles in people with HIV that were associated with different risk patterns. Future longitudinal work should determine the stability of these phenotypes, assess factors that influence transitions from one phenotype to another, and characterize their biological associations.

Spatial and temporal patterns of water supply and consumptive water use were analyzed from 475 Detailed Analysis Units by County (DAUCOs) spatial units across California during 2002 through 2016 to evaluate spatial and temporal variability and how it might associate with precipitation variability and other factors. Many, but not all, DAUCOs have relatively low total water supply variability compared to that of state-wide precipitation. Such low variability, in DAUCOs having sufficient diversity of water supply sources, is the result of switching between sources as needed to maintain a reliable total water supply. We used multiple approaches to explore these variations which involved four categories of water supply (local, groundwater, imported, and other) and two categories of water use (agricultural and urban). First, a cluster analysis of the volumetric water balance data identified a small set of clusters having similar magnitudes and proportions of water supply sources and water use—some of them composed of only a few DAUCOs but accounting for a disproportionate amount of the state’s water use. Second, a principal components analysis identified leading modes of anomalous water supply and water use among the 475 DAUCOs, capturing most of the time variation during 2002 to 2016. The most prominent mode exhibits a multi-year trend, most strongly involving increasing groundwater supply and agricultural water use, and decreasing urban water use and imported water supply. Over the study period, trends in both supply and use were pronounced, but differed considerably across California DAUCOs. One predominant subset of DAUCOs grew their agricultural water use with increased groundwater supply; in contrast to a widespread group of DAUCOs which reduced their urban water use. An important result for planners is our finding that variation in precipitation—itself important—is amplified by the human response to water supply availability and regulatory policy.

Light microscopy methods have continued to advance allowing for unprecedented analysis of various cell types in tissues including the brain. Although the functional state of some cell types such as microglia can be determined by morphometric analysis, techniques to perform robust, quick, and accurate measurements have not kept pace with the amount of imaging data that can now be generated. Most of these image segmentation tools are further burdened by an inability to assess structures in three-dimensions. Despite the rise of machine learning techniques, the nature of some biological structures prevents the training of several current day implementations. Here we present PrestoCell, a novel use of persistence-based clustering to segment cells in light microscopy images, as a customized Python-based tool that leverages the free multidimensional image viewer Napari. In evaluating and comparing PrestoCell to several existing tools, including 3DMorph, Omipose, and Imaris, we demonstrate that PrestoCell produces image segmentations that rival these solutions. In particular, our use of cell nuclei information resulted in the ability to correctly segment individual cells that were interacting with one another to increase accuracy. These benefits are in addition to the simplified graphically based user refinement of cell masks that does not require expensive commercial software licenses. We further demonstrate that PrestoCell can complete image segmentation in large samples from light sheet microscopy, allowing quantitative analysis of these large datasets. As an open-source program that leverages freely available visualization software, with minimum computer requirements, we believe that PrestoCell can significantly increase the ability of users without data or computer science expertise to perform complex image analysis.

Background: Bladder cancer and therapy responses hinge on immune profiles in the tumor microenvironment (TME) and blood, yet studies linking tumor-infiltrating immune cells to peripheral immune profiles are limited. Methods: DNA methylation cytometry quantified TME and matched peripheral blood immune cell proportions. With tumor immune profile data as the input, subjects were grouped by immune infiltration status and consensus clustering. Results: Immune hot and cold groups had different immune compositions in the TME but not in circulating blood. Two clusters of patients identified with consensus clustering had different immune compositions not only in the TME but also in blood. Conclusion: Detailed immune profiling via methylation cytometry reveals the significance of understanding tumor and systemic immune relationships in cancer patients.

Purpose: Idiopathic Normal Pressure Hydrocephalus (iNPH) is a neurological syndrome defined by gait disturbance, cognitive impairment and urinary incontinence. However, its clinical presentation can vary widely due to overlapping syndromes and common comorbidities in older adults. This study aims to provide practical guidance to aid in the clinical suspicion and support the diagnostic and therapeutic processes for these patients. Methods: Six quantitative variables regarding clinical, functional, and demographic aspects were considered for a large sample of patients with diagnosed iNPH. Principal component analysis (PCA) was adopted to define the main dimensions explaining the variability of the phenomenon. Then, two clusters of iNPH patients were described. Results: 178 patients were included in the analysis. The PCA produced two dimensions covering 61.8% of the total variability. The first one relied mainly on both clinical (mRS, iNPHGs) and functional (TUG, Tinetti) variables, while the second one was represented mainly on the demographic pattern (age and education). Cluster analysis depicted two main groups of patients. Cluster n.1 is composed of individuals who are older, more disabled, with poor functional performances, and highly symptomatic. Cluster n.2 patients are slightly younger, more educated, fitter, and with more nuanced clinical aspects. Conclusions: Profiling iNPH patients using quantitative variables and cluster analysis can help identify distinct characteristics of these patients, aiding in the guidance of both medical and surgical interventions. [ABSTRACT FROM AUTHOR]

Clustering methods are increasingly used in social science research. Generally, researchers use them to infer the existence of qualitatively different types of individuals within a larger population, thus unveiling previously “hidden” heterogeneity. Depending on the clustering technique, however, valid inference requires some conditions and assumptions. Common risks include not only failing to detect existing clusters due to a lack of power but also revealing clusters that do not exist in the population. Simple data simulations suggest that under conditions of sample size, number, correlation and skewness of indicators that are frequently encountered in applied psychological research, commonly used clustering methods are at a high risk of detecting clusters that are not there. Generally, this is due to some violations of assumptions that are not usually considered critical in psychology. The present article illustrates a simple R tutorial and a Shiny app (for those who are not familiar with R) that allow researchers to quantify a priori inferential risks when performing clustering methods on their own data. Doing so is suggested as a much‐needed preliminary sanity check, because conditions that inflate the number of detected clusters are very common in applied psychological research scenarios. [ABSTRACT FROM AUTHOR]

Atlantic salmon aquaculture companies in British Columbia (BC) must report fish health events to Fisheries and Oceans Canada (DFO) as part of their licensing conditions. Our study aimed to summarize these fish health events reported by Atlantic salmon sites in BC to identify spatial and spatio‐temporal clusters. We conducted descriptive, retrospective global, and local cluster analyses using Moran's I and scan statistics. Between 2016 and 2022, 265 fish health events were reported. The annual incidence ranged from 5.60 (95% CI: 3.90–7.80) to 6.86 (95% CI: 4.70–9.60) health events per 100 active site‐months. The most common events were yellow mouth (60.75%; 161/265) and salmonid rickettsial septicaemia (SRS) (15.47%; 41/265). The Moran's I index was positive and significant for yellow mouth, SRS, and overall fish health events at different distance bands. Most of the spatial and spatio‐temporal clusters were identified in the west‐central and southwestern parts of Vancouver Island. Our study hypothesizes that management practices, environmental conditions, and water quality parameters may have influenced the increased reporting of fish health events in these regions. Overall, the study highlights the potential of publicly available data for practical risk mapping in understanding the patterns of farmed Atlantic salmon diseases in BC. [ABSTRACT FROM AUTHOR]

Background: The high complexity of systemic autoimmune diseases (SADs) has hindered precise management. This study aims to investigate heterogeneity in SADs. Methods: We applied a joint cluster analysis, which jointed multiple correspondence analysis and k-means, to immunomarkers and measured the heterogeneity of clusters by examining differences in immunomarkers and clinical manifestations. The electronic health records of patients who received an antinuclear antibody test and were diagnosed with SADs, namely systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and Sjögren's syndrome (SS), were retrieved between 2001 and 2016 from hospitals in Taiwan. Results: With distinctive patterns of immunomarkers, a total of 11,923 patients with the three SADs were grouped into six clusters. None of the clusters was composed only of a single SAD, and these clusters demonstrated considerable differences in clinical manifestation. Both patients with SLE and SS had a more dispersed distribution in the six clusters. Among patients with SLE, the occurrence of renal compromise was higher in Clusters 3 and 6 (52% and 51%) than in the other clusters (p < 0.001). Cluster 3 also had a high proportion of patients with discoid lupus (60%) than did Cluster 6 (39%; p < 0.001). Patients with SS in Cluster 3 were the most distinctive because of the high occurrence of immunity disorders (63%) and other and unspecified benign neoplasm (58%) with statistical significance compared with the other clusters (all p < 0.05). Conclusions: The immunomarker-driven clustering method could recognise more clinically relevant subgroups of the SADs and would provide a more precise diagnosis basis. [ABSTRACT FROM AUTHOR]

While previous research has documented the unique aspects of Chinese dyslexia as compared to dyslexia in alphabetic scripts, it remains unclear whether the difference in Chinese literacy experiences influences the manifestation of Chinese dyslexia. The present article first reviews the characteristics of Chinese languages and scripts, including important cognitive‐linguistic correlates (rapid automatized naming, phonological, orthographic, and morphological awareness) of Chinese reading development and impairment. The diversity in Chinese literacy experiences of scripts, languages, and instructional practices, and consequently their impact on Chinese literacy acquisition across different Chinese societies are also reviewed. Using an equivalent Chinese assessment battery administered to 91 children with dyslexia from Hong Kong, Beijing, and Taipei, we examined the subtypes of Chinese dyslexia across these three societies concurrently. With the four cognitive‐linguistic skills included as the clustering variable, the hierarchical cluster analysis revealed four cognitive subtypes of dyslexia: 38% mild orthographic deficit subtype (OD), 33% phonological deficit subtype (PD), 18% morphological deficit subtype (MD), and 11% global deficit subtype (GD)—each with their own set of cognitive‐linguistic deficit profiles. Interestingly, all four subtypes of dyslexia manifested poorer orthographic skills as compared to the control group. Bayesian Analysis of Contingency Table further showed that the distribution of dyslexia subtypes remains similar across the three Chinese societies, suggesting invariance of the Chinese dyslexia construct. Findings highlight the importance of assessment in orthographic processing, rapid automatized naming, phonological awareness, and morphological awareness in order to understand Chinese dyslexia, both in a within and cross‐cultural Chinese perspective. [ABSTRACT FROM AUTHOR]

The goal of this study was to create a fermented probiotic fruit puree for lactose‐intolerant people, vegetarians, and infants over 6 months old. Fermented fruit purees were developed using apples, peaches, and bananas with lactic acid bacteria (LAB) strains: Lactobacillus plantarum subsp. plantarum (S5), Lactobacillus fermentum strain w8 (S10), and Lactobacillus pentosus strain ml104 (S14). Different fruit puree formulations were produced using three strains, two inoculation ratios (4% and 5%), and two fermentation durations (24  and 48 h). The physicochemical parameters (pH, total soluble solids, and color), total phenols content (TPC), total antioxidant capacity, bacterial viability, volatile aroma profile (VAP), and individual phenolic compound profile of fruit puree fermented for both 24  and 48 h were compared with the unfermented (control) purees. The results of VAP were evaluated via PROMETHEE and cluster analysis. Time of fermentation and bacterial cultures at varied concentrations improved color values of samples (L*, a*, and b*) compared to controls. The level of bioactive compounds in several samples (S10 and S14) decreased after fermentation in contrast to S5 samples. The bacterial population in the samples ranged from ∼7.00 to 9.50 log CFU/g after 48‐h fermentation. The fruit puree samples exhibited the presence of two different phenolic compounds and a total of 17 distinct volatile aroma compounds. The control sample scored highest for aromatic components in PROMETHEE, while S14‐II was the most unique sample in cluster analysis. In conclusion, fermented probiotic fruit puree shown high promise as a carrier for live probiotics, and the fermentation process boosted the nutritional content of the fruit puree. [ABSTRACT FROM AUTHOR]

We advise a novel measure to determine the unknown number of clusters underlying a designated sample through implementation of the parametric logistic regression model. The regression analysis is carried out to estimate the probabilities of inclusion for every individual member from data, irrespective of its parent distribution, to each of the clusters under existence. The proposed one is shown to be superior to its well-known rivals by means of both synthetic and real-world data sets, while designed to significantly reduce the computational burden serving our desired purpose. [ABSTRACT FROM AUTHOR]

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Highlights The objective of this paper is to analyze the mechanical properties and damage mechanisms of carbon fiber‐reinforced polyamide thermoplastic composite laminates. Four specimens with different ply orientations were designed for open‐hole tensile experiments, and interlaminar toughness experiments including double cantilever beam and end‐notched flexural were carried out. The experimental process was monitored synchronously using acoustic emission, and the strain field changes of the tensile specimens were captured using digital image correlation technology. The unsupervised clustering of the peak frequencies of the acoustic emission signals based on the K‐means++ algorithm was employed to ascertain the peak frequency ranges corresponding to the various damage modes. Typical signals from different specimens were selected, and the gray wolf algorithm was used to optimize the variational modal parameters to decompose the signals. The waveform characteristics, frequency components, and Hilbert spectra of each damage mode were given. The correlation analysis of the intrinsic mode function (IMF) components of the same damage in different specimens demonstrated that the IMF components exhibited high similarity. By analyzing the time series changes in the energy of each damage mode in different specimens, the contribution of different damage modes to the evolution of laminated plate damage was evaluated. The mechanical properties of CF/PA6 laminates were investigated based on open‐hole tensile specimens and pre‐cracked delamination specimens. Unsupervised clustering of AE peak frequencies using K‐means++ to establish the relationship between peak frequencies and damage patterns. AE counts and cumulative energy were used to assess damage evolution. By identifying a single damage signal and providing a more intuitive treatment of the damage energy evolution. [ABSTRACT FROM AUTHOR]

Ossicular fixation disturbs the mobility of the ossicular chain and causes conductive hearing loss. To diagnose the lesion area, otologists typically assess ossicular mobility through intraoperative palpation. Quantification of ossicular mobility and evidence-based diagnostic criteria are necessary for accurate assessment of each pathology, because diagnosis via palpation can rely on the surgeons' experiences and skills. In this study, ossicular mobilities were simulated in 92 pathological cases of ossicular fixation as compliances using a finite-element (FE) model of the human middle ear. The validity of the ossicular mobilities obtained from the FE model was verified by comparison with measurements of ossicular mobilities in cadavers using our newly developed intraoperative ossicular mobility measurement system. The fixation-induced changes in hearing were validated by comparison with changes in the stapedial velocities obtained from the FE model with measurements reported in patients and in temporal bones. The 92 cases were classified into four groups by conducting a cluster analysis based on the simulated ossicular compliances. Most importantly, the cases of combined fixation of the malleus and/or the incus with otosclerosis were classified into two different surgical procedure groups by degree of fixation, i.e., malleo-stapedotomy and stapedotomy. These results suggest that pathological characteristics can be detected using quantitatively measured ossicular compliances followed by cluster analysis, and therefore, an effective diagnosis of ossicular fixation is achievable. [ABSTRACT FROM AUTHOR]

Background: The tribe Ampelopsideae plants are important garden plants with both medicinal and ornamental values. The study of codon usage bias (CUB) facilitates a deeper comprehension of the molecular genetic evolution of species and their adaptive strategies. The joint analysis of CUB in chloroplast genomes (cpDNA) offers valuable insights for in-depth research on molecular genetic evolution, biological resource conservation, and elite breeding within this plant family. Results: The base composition and codon usage preferences of the eighteen chloroplast genomes were highly similar, with the GC content of bases at all positions of their codons being less than 50%. This indicates that they preferred A/T bases. Their effective codon numbers were all in the range of 35–61, which indicates that the codon preferences of the chloroplast genomes of the 18 Ampelopsideae plants were relatively weak. A series of analyses indicated that the codon preference of the chloroplast genomes of the 18 Ampelopsideae plants was influenced by a combination of multiple factors, with natural selection being the primary influence. The clustering tree generated based on the relative usage of synonymous codons is consistent with some of the results obtained from the phylogenetic tree of chloroplast genomes, which indicates that the clustering tree based on the relative usage of synonymous codons can be an important supplement to the results of the sequence-based phylogenetic analysis. Eventually, 10 shared best codons were screened on the basis of the chloroplast genomes of 18 species. Conclusion: The codon preferences of the chloroplast genome in Ampelopsideae plants are relatively weak and are primarily influenced by natural selection. The codon composition of the chloroplast genomes of the eighteen Ampelopsideae plants and their usage preferences were sufficiently similar to demonstrate that the chloroplast genomes of Ampelopsideae plants are highly conserved. This study provides a scientific basis for the genetic evolution of chloroplast genes in Ampelopsideae species and their suitable strategies. [ABSTRACT FROM AUTHOR]

Asymmetric timber trade between least-developed countries with large forest resources but backward processing industries and emerging economies has led to an uneven distribution of value in international timber commodity chains. Selling their raw timber with no domestic value added contributes little to socio-economic development and is associated with high rates of deforestation. The Lao PDR has served as a raw timber frontier for advanced timber industries in neighboring countries, leading to forest loss and related environmental problems. In response, since 2015, the Lao government has adopted drastic policy measures to end forest degradation and upgrade its timber industry (e.g., log export ban). Using teak as an example, our study provides empirical ex-post evidence on the effectiveness of state-imposed timber industry upgrading policies. The study relies on expert interviews with key policy actors at national and local levels, as well as an enterprise survey and cluster analysis conducted in Xayyabouly province. These drastic policies had far-reaching implications for the structure of the timber industry and the configuration of the timber value chain and rural development. [ABSTRACT FROM AUTHOR]

In the past 50 years, the transition from adolescence to adulthood has become longer (Arnett, 2000). This transition period is known as emerging adulthood and is characterized by self-identity exploration, particularly in the areas of career planning and romantic relationships. Self-identity is closely linked to quality of life and mental health. Studies on self-identity exploration during emerging adulthood have typically used a variable-centered approach; however, researchers have expressed doubts about this approach. Asendorpf (2015) argued that the variable-centered approach overlooks individual uniqueness and suggested using a person-centered approach for more accurate results regarding behavioral and psychological features. Therefore, this study proposed examining self-identity and psychological adaptation in emerging adulthood by considering both personal identity and relational identity using the person-centered approach (i.e., cluster analysis). An online questionnaire was used with measures including the QIF-III, QII-III, SWLS, RES, SCL-90R, Risk-Taking Behavior Inventory, and Social Alienation Inventory. A total of 777 completed questionnaires were analyzed. Cluster analysis revealed seven types of identity: high achievement, moderate achievement, low achievement, personal alienation, relational alienation, low alienation, and high alienation. These clusters showed varying degrees of psychological adaptation. The high achievement, moderate achievement, and low achievement identity clusters were associated with superior mental adjustment. Personal alienation was a more adaptive identity cluster than were other forms of alienation (i.e., relational alienation, low alienation, and high alienation). The low alienation and high alienation clusters were associated with poor psychological adjustment. The relational alienation cluster was associated with the most impulse control problems. [ABSTRACT FROM AUTHOR]

Lumpy skin disease (LSD) is a serious, transboundary disease that affects cattle all over the world and results in considerable productivity losses. Although Türkiye's first outbreak of LSD was reported in August 2013, there is very little information available about the outbreak's spatiotemporal distribution or severity. GIS-based data analysis provides crucial tools for describing the spatial epidemiology of the disease by assessing the spatial distribution of LSD across time. This study used information on outbreaks reported to the the World Animal Health Organization (WOAH-OIE) between 2013 and 2021 to conduct a retrospective study on the epidemiology of LSD in Türkiye. Differences in the number of reported outbreaks and cases across different regions, provinces, months, and years were evaluated and descriptive statistics were calculated. In addition, spatial statistical tests (Local Moran's I and Getis-Ord Gi*) and Geographical Information Systems (GIS) were used to assess LSD outbreaks that had taken place at the province level in Türkiye. Possible epidemiological clusters of LSD were identified. A total of 1787 outbreaks and 10109 cases of LSD were reported from 75 out of 81 provinces of Türkiye during the course of the nine-year period. Hotspots for the circulation of LSD were identified in the Aegean, Southeastern and Eastern regions using spatial cluster analyses and it was observed that the spatial autocorrelation of LSD cases is positive across the country. The findings from this study, it may help us comprehend the disease's spatial character and offer authorities the beneficial information for surveillance efforts. [ABSTRACT FROM AUTHOR]

Statistical morphological variations at the population level serve as a robust foundation for species delimitation and taxonomic treatment. We employed this approach to investigate species delimitation within the Ptilagrostis dichotoma species complex (Poaceae), which is characterized by open panicles. While three species have been described, the boundaries between these species and the morphological distributions of all taxonomic traits remain unclear. In this study, we examined the geographical variability of 19 morphological traits based on at least six individuals from each of the 28 populations across the entire distributional range of the species complex. Our statistical analyses identified five distinct phenotypic clusters with specific geographical preferences. Three of these clusters corresponded to the previously described species, while the remaining two clusters represented previously undescribed species. Furthermore, our findings highlight several traits, such as basal leaf scabrosity, panicle branch pulvinae, and glume length, which exhibit distinct gaps between phenotypic units and prove highly valuable for species delimitation. Finally, two new species are described and a key is provided to differentiate the five species. This morphological revision establishes a basis for future studies on species boundaries and divergences of the complex based on other evidence. [ABSTRACT FROM AUTHOR]

Previous studies have shown that therapists' performance varies, known as therapist effects, and have indicated that therapists who excel in one treatment outcome may not necessarily be effective in other outcomes. This observational naturalistic study aimed to enhance our understanding of therapist effects and the assessment of therapists' performance in different areas. The study included 68 therapists and 5,582 clients from a large mental health facility. Information about their learning activities was available for a subsample of 49 therapists. Separate multilevel analyses were conducted for treatment outcomes, including case mix-corrected OQ-45 change scores, dropout rates, referrals to other facilities, treatment duration, and client satisfaction ratings. A hierarchical cluster analysis was performed to identify groups of therapists based on their performance across various treatment outcomes. Additionally, differences in therapist characteristics among the clusters identified were examined. Therapist effects varied across different outcomes, ranging from small (2.6% for OQ-45 change) to moderate (6.5% for number of sessions). The cluster analysis revealed four distinct clusters of therapists with specific profiles. They had performance differences in certain areas but not in others. This exploratory study supports the notion that therapists exhibit diverse profiles regarding treatment outcomes. These findings are significant for future investigations of therapist effects that aim to identify the characteristics of effective therapists and in the context of personalizing treatment for clients. [ABSTRACT FROM AUTHOR]

Wu, Yu, Wenqi Zhao, Bao Liu, Jianyang Zhang, Zhifeng Zhong, Simin Zhou, Jiaxin Xie, Yuqi Gao, Peng Li, and Jian Chen. Assessment of Acute Mountain Sickness: Comparing the Chinese Ams Score to the Lake Louise Score. High Alt Med Biol 25:164–173, 2024. Objective: To compare the ability of the Chinese AMS Score (CAS) to detect acute mountain sickness (AMS) using the 2018 version of the Lake Louise Score (LLS) as reference. Methods: After flying from Chengdu (altitude: 500 m) to Lhasa (3,658 m), 2,486 young men completed a questionnaire. The questionnaire contained LLS and CAS items. An LLS ≥3 and/or a CAS ≥cutoff were used as the criteria for AMS. Hierarchical cluster analysis and two-step cluster analysis were used to investigate relationships between the symptoms. Results: AMS incidence rates were 33.8% (n = 840) with the LLS and 59.3% (n = 1,473) with the CAS (χ2 = 872.5, p < 0.001). The LLS and CAS had a linear relationship (orthogonal regression, Pearson r = 0.91, p < 0.001). With the LLS as the standard, the CAS had high diagnostic accuracy (area under the curve = 0.95, 95% confidence interval: 0.94–0.96). However, with the CAS, 25.5% (n = 633) more participants were labeled as having AMS than with the LLS (false positives). Two clusters were identified: one with headache only (419 participants, 66.2%) and one without headache but with other symptoms (214 participants, 33.8%). Reducing the weight of headache in the CAS allowed to align CAS and LLS. Conclusion: In comparison to the LLS, the CAS has a sensitivity close to 100% but lacks specificity given the high rate of false positives. The different weight of headaches may be the main reason for the discrepancy. [ABSTRACT FROM AUTHOR]

Background and aims: Previously identified national drinking patterns in Europe lack comparability and might be no longer be valid due to changes in economic conditions and policy frameworks. We aimed to identify the most recent alcohol drinking patterns in Europe based on comparable alcohol exposure indicators using a data‐driven approach, as well as identifying temporal changes and establishing empirical links between these patterns and indicators of alcohol‐related harm. Design: Data from the World Health Organization's monitoring system on alcohol exposure indicators were used. Repeated cross‐sectional hierarchical cluster analyses were applied. Differences in alcohol‐attributable harm between clusters of countries were analyzed via linear regression. Setting: European Union countries, plus Iceland, Norway and Ukraine, for 2000, 2010, 2015 and 2019. Participants/Cases: Observations consisted of annual country data, at four different time points for alcohol exposure. Harm indicators were only included for 2019. Measurements Alcohol exposure indicators included alcohol per capita consumption (APC), beverage‐specific consumption and prevalence of drinking status indicators (lifetime abstainers, current drinkers, former drinkers and heavy episodic drinking). Alcohol‐attributable harm was measured using age‐standardized alcohol‐attributable Disability‐Adjusted Life Years (DALYs) lost and deaths per 100 000 people. Findings The same six clusters were identified in 2019, 2015 and 2010, mainly characterized by type of alcoholic beverage and prevalence drinking status indicators, with geographical interpretation. Two‐thirds of the countries remained in the same cluster over time, with one additional cluster identified in 2000, characterized by low APC. The most recent drinking patterns were shown to be significantly associated with alcohol‐attributable deaths and DALY rates. Compared with wine‐drinking countries, the mortality rate per 100 000 people was significantly higher in Eastern Europe with high spirits and 'other' beverage consumption [ β^ = 90, 95% confidence interval (CI) = 55–126], and in Eastern Europe with high lifetime abstainers and high spirits consumption (β^ = 42, 95% CI = 4–78). Conclusions: European drinking patterns appear to be clustered by level of beverage‐specific consumption, with heavy episodic drinkers, current drinkers and lifetime abstainers being distinguishing factors between clusters. Despite the overall stability of the clusters over time, some countries shifted between drinking patterns from 2000 to 2019. Overall, patterns of drinking in the European Union seem to be stable and partly determined by geographical proximity. [ABSTRACT FROM AUTHOR]

Purpose: The heterogeneity of autism is well documented, but few studies have studied the heterogeneity of gesture production ability in autistic children. The present study aimed to identify subgroups of autistic children who displayed heterogeneous gesture production abilities and explore the underlying factors, including autism characteristics, intellectual ability, and language ability, that were associated with the heterogeneity. Methods: A total of 65 Chinese autistic children (mean age = 5;3) participated. Their autism characteristics and intellectual ability were assessed by standardized measurements. Language output and gesture production were captured from a parent-child interaction task. Results: We conducted a hierarchical cluster analysis and identified four distinct clusters. Cluster 1 and Cluster 2 both had low gesture production whereas Cluster 3 and Cluster 4 had high gesture production. Both Clusters 1 and 2 had relatively strong autism characteristics, in comparison to Clusters 3 and 4. Conclusions: Our findings revealed that children with stronger autism characteristics may gesture less often than those with weaker characteristics. However, the relationship between language ability and intellectual ability and gesture production was not clear. These findings shed light on the directions of intervention on gesture production for autistic children, especially those with stronger autism characteristics. [ABSTRACT FROM AUTHOR]

The main purpose of the present study was to examine the typology of Palestinian student-teachers living in Israel according to four variables: (a) the type of motivation, (b) perceived suitability, (c) career choice satisfaction and (d) student-teacher commitment. Data were collected from 113 first-year Palestinian Arab students studying at teacher training colleges in Israel. questionnaires and interviews served as methods to collect data. cluster analysis was performed in order to distinguish between different types of student teachers. Results indicated that there were four different types of student-teachers: the 'prophets', the 'convinced', the 'realist' and the 'sufferers'. [ABSTRACT FROM AUTHOR]

This study examines hotel water management in Madeira, Portugal and Tenerife, Spain, which differ in water stress and tourism pressure, both much higher in Tenerife. Determinants of hotel water use and water-saving measures are evaluated using correlations and cluster analyses. In Tenerife, more decisive implementation of water-saving measures results in much lower consumption. Conversely, in Madeira, hotel size and category appear to overshadow the effect of water-saving measures. The novelty of this study lies in applying the same methodology to two different destinations, highlighting the role of external factors in shaping hotel water management, while exploring water-saving measures in depth. [ABSTRACT FROM AUTHOR]

Background: The use of molecular HIV cluster analysis to supplement public health contact tracing has shown promise in addressing HIV outbreaks. However, the potential of HIV cluster analysis as an adjunct to daily, person-by-person HIV prevention efforts remains unknown. We documented lessons learned within a unique public health-academic partnership while guiding workaday HIV prevention efforts with near-real-time molecular cluster analysis. Setting: A public health-academic partnership in the State of Rhode Island, the United States. Methods: We recorded perceptions of our team of academicians and public health practitioners that were encountered in an 18-month study evaluating the integration of molecular cluster analysis with HIV contact tracing for public health benefit. The focus was on monthly conferences where molecular clustering of each new statewide diagnosis was discussed to facilitate targeted interventions and on attempted reinterviews of all newly HIV-diagnosed persons statewide whose HIV sequences clustered to increase partner naming. Results: Three main themes emerged: First, multidisciplinary conferences are substantially beneficial for gleaning actionable inferences from integrating molecular cluster analysis and public health data. Second, universal reinterviews were perceived to potentially have negative consequences but may be selectively beneficial. Third, the translation of cluster analysis into public health action is hampered by jurisdictional surveillance boundaries and within-jurisdictional data silos, across which data sharing is problematic. Conclusions: Insights from a statewide public health-academic partnership support integration of molecular HIV cluster analyses with public health efforts, which can guide public health activities to prevent transmission while identifying substantial barriers to integration, informing continued research. [ABSTRACT FROM AUTHOR]

Regional Public Universities (RPUs) advance public good missions to foster postsecondary access, student-centeredness, and regional wellbeing. Despite the important contributions that RPUs make to their regions and students, scholars and the media often view the sector as amorphous and undistinguished, or they define RPUs by what they are not (e.g., elite, selective, community colleges, etc.). This sequential mixed methods research study grounded in organizational ecology theory and involving content analysis and cluster analysis proposes a typology of RPUs. Specifically, five RPU types are proposed: accessible, midsized master's-degree granting RPUs; regionally focused Historically Black Colleges and Universities; rural-serving and CUNY RPUs; large, urban regional-serving RPUs; and Puerto Rican Hispanic-serving RPUs. This study significantly advances conceptual understanding about RPUs by disrupting deficit-based understandings about the sector being shapeless and amorphous by showing that the sector contains distinct institutional types that represent unique approaches to fulfilling the RPU public good mission. This typology is also among the first to document the institutional diversity of RPUs. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a complex, heterogeneous disease. Categorizing the disorder into phenotypes can help us better understand its pathology and guide us toward more personalized treatment approaches. Nevertheless, most of the previous cluster analysis (CA) studies in OSA predominantly included middle-aged to older men and may not adequately represent the heterogeneity of OSA phenotypes in women. Our aim is to identify these phenotypes in women using an extensive, exclusively female cohort. Cross-sectional study of 1886 women diagnosed with OSA (apnea-hypopnea index >5 events/h) by PSG (polysomnography) and RP (respiratory polygraphy) at a tertiary hospital Sleep Unit. A CA was performed including general data, clinical variables, comorbidities and sleep study results. Four phenotypic subtypes were identified: Cluster 1 "Middle-aged paucisymptomatic women without cardiovascular risk factors" (507 patients, 27 %); Cluster 2 "Older paucisymptomatic women with established cardiovascular disease and severe OSA" (228 patients, 12 %); Cluster 3 "Middle-aged women with "classic" symptoms and cardiovascular risk factors" (892 patients, 47 %), and Cluster 4 ″Middle-aged women with mood disorders, nonrestorative sleep and cardiovascular risk factors" (259 patients, 14 %). Conducting a CA exclusively within a female cohort reveals a heterogeneous presentation of OSA in women, similar to what has been previously reported in the literature for men. The "classical" presentation is notably the most prevalent, while the "atypical" presentation, which was previously more frequently associated with women, is less prevalent. Additionally, paucisymptomatic presentations, with or without associated comorbidities, are also present. [Display omitted] • Cluster analysis in mainly male cohorts may not reflect female OSA diversity. • Analyzing a female cohort reveals a heterogeneous presentation of OSA in women. • The "classical" OSA phenotype characterized by marked sleepiness is the most common. • The "atypical" presentation previously associated with women is less prevalent. • Paucisymptomatic presentations, with or without comorbidities, are also observed. [ABSTRACT FROM AUTHOR]

Understanding background factors is beneficial for interpreting general movements (GMs). This study examines the factors involved in preterm-writhing GMs by comparing twins and singletons. The subjects were 107 infants cared for at Oita University. The cohort consisted of very-low-birth-weight infants, including twins with a birth weight < 2000 g. The median gestational age (GA) was 29 weeks 1 day. The subjects consisted of 75 singletons, 32 twins (16 pairs), 20 monochorionic twins (M−twins), and 12 dichorionic twins (D-twins). GMs were scored according to the GMs optimality score (GMOS) and integrated into 6 items: the quality, neck-trunk and space, amplitude-speed, rotation, onset-offset and cramped, and tremulous score at 32–34 weeks, 35–36 weeks, and 37–42 weeks' GA. A hierarchical cluster analysis was performed using integrated GMOS, and the characteristics of clusters were examined according to clinical backgrounds. Three clusters were identified. Cluster 1 was characterized by good-quality GMs, cluster 2 by a poor repertoire but optimal space and rotatory components, and cluster 3 by overall poor-quality GMs, respectively. The mean GMOSs were 36.6, 31.8 and 24.3 in clusters 1, 2, and 3, respectively. There were no marked differences in proportions within clusters with respect to sex and twins. Small-for-gestational age (SGA) was significantly more frequent in cluster 3 at 32–34 weeks' GA than in other clusters. Perinatal brain injury had a significantly lower proportion in cluster 1 and a higher proportion in cluster 3 at 35–36 weeks' GA and 37–42 weeks' GA. M−twin pairs tended to belong to the same clusters at 35–36 weeks' GA. Preterm writhing GMs are associated with SGA and perinatal brain injury. Cluster matching in M−twins suggests that certain genetic factors may substantially influence GMs. [ABSTRACT FROM AUTHOR]

Objective: The present study examined client profiles in observed emotion to explore possible subgroups among clients given that there is substantial heterogeneity in clients' expression of emotion in therapy. Subgroups were identified using the sequential model of emotional processing, which posits that global distress, shame/fear and rejecting anger represent 'early expressions of distress', whereas assertive anger, self‐compassion and grief/hurt are conceptualised to be 'adaptive states' that predict good outcomes. The present study also offers a unique strategy for studying differences in clients' emotional presentation with implications for within‐session assessment by therapists. Method: Sections of videotaped therapy sessions for 34 participants in emotion‐focussed therapy were coded for each emotional state. Through cluster analysis, participants were grouped based on the relative magnitude of each emotion state in the model. Results: In the first set of analyses, two naturalistic groups were formed: Cluster 2 had higher levels of adaptive emotion and rejecting anger than Cluster 1. There was a significantly greater proportion of good within‐session outcomes in Cluster 2 than in Cluster 1. In the second set of analyses, a separate cluster analysis categorised participants based on their early expressions of distress. Four groups were described as follows: Distressed, Protesters, Fearful & Ashamed and Minimally Distressed. Only the Minimally Distressed group showed significantly higher rates of good within‐session outcome. Discussion: These findings provide support for the emotional processing model by using novel methods and analyses. Although the findings are preliminary, they have implications for clinicians' assessments of clients' emotional needs and characteristic presentations. [ABSTRACT FROM AUTHOR]