Cluster Membership Research Articles

Priorities for health outcomes in glaucoma in an ethnically diverse UK cohort: an observational study

ObjectivesTo assess whether patients from minority ethnic groups have different perceptions about the quality-of-life outcomes that matter most to them.DesignCross-sectional observational study.SettingHigh volume eye centres serving the most ethnically diverse...

BSEC Method for Unveiling Open Clusters and its Application to Gaia DR3: 83 New Clusters

Open clusters (OCs) are common in the Milky Way, but most of them remain undiscovered. There are numerous techniques, including some machine-learning algorithms, available for the exploration of OCs. However, each method has its limitations and therefore, different approaches to discovering OCs hold significant values. We develop a comprehensive approach method to automatically explore the data space and identify potential OC candidates with relatively reliable membership determination. This approach combines the techniques of Hierarchical Density-Based Spatial Clustering of Applications with Noise, Gaussian mixture model, and a novel cluster member identification technique, color excess constraint. The new method exhibits efficiency in detecting OCs while ensuring precise determination of cluster memberships. Because the main feature of this technique is to add an extra constraint (EC) for the members of cluster candidates using the homogeneity of color excess, compared to typical blind search codes, it is called Blind Search-Extra Constraint (BSEC) method. It is successfully applied to the Gaia Data Release 3, and 83 new OCs are found, whose color–magnitude diagrams (CMDs) are fitted well to the isochrones. In addition, this study reports 621 new OC candidates with discernible main sequence or red giant branch. It is shown that BSEC technique can discard some false negatives of previous works, which takes about three percentage of known clusters. It shows that as an EC, the color excess (or two-color) constraint is useful for removing fake cluster member stars from the clusters that are identified from the positions and proper motions of stars, and getting more precise CMDs, when differential reddening of member stars of a cluster is not large (e.g., ΔE(G BP − G RP) < 0.5 mag). It makes the CMDs of 15% clusters clearer (in particular for the region near turnoff) and therefore is helpful for CMD and stellar population studies. Our result suggests that the color excess constraint is more appropriate for clusters with small differential reddening, such as globular clusters or older OCs, and clusters that the distances of member stars cannot be determined accurately.

Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms.

Atrial fibrillation (AF) is a common cardiac arrhythmia with serious health consequences if not detected and treated early. Detecting AF using wearable devices with photoplethysmography (PPG) sensors and deep neural networks has demonstrated some success using proprietary algorithms in commercial solutions. However, to improve continuous AF detection in ambulatory settings towards a population-wide screening use case, we face several challenges, one of which is the lack of large-scale labeled training data. To address this challenge, we propose to leverage AF alarms from bedside patient monitors to label concurrent PPG signals, resulting in the largest PPG-AF dataset so far (8.5 M 30-second records from 24,100 patients) and demonstrating a practical approach to build large labeled PPG datasets. Furthermore, we recognize that the AF labels thus obtained contain errors because of false AF alarms generated from imperfect built-in algorithms from bedside monitors. Dealing with label noise with unknown distribution characteristics in this case requires advanced algorithms. We, therefore, introduce and open-source a novel loss design, the cluster membership consistency (CMC) loss, to mitigate label errors. By comparing CMC with state-of-the-art methods selected from a noisy label competition, we demonstrate its superiority in handling label noise in PPG data, resilience to poor-quality signals, and computational efficiency.

Identification of inflammatory clusters in long-COVID through analysis of plasma biomarker levels.

Mechanisms underlying long COVID remain poorly understood. Patterns of immunological responses in individuals with long COVID may provide insight into clinical phenotypes. Here we aimed to identify these immunological patterns and study the inflammatory processes ongoing in individuals with long COVID. We applied an unsupervised hierarchical clustering approach to analyze plasma levels of 42 biomarkers measured in individuals with long COVID. Logistic regression models were used to explore associations between biomarker clusters, clinical variables, and symptom phenotypes. In 101 individuals, we identified three inflammatory clusters: a limited immune activation cluster, an innate immune activation cluster, and a systemic immune activation cluster. Membership in these inflammatory clusters did not correlate with individual symptoms or symptom phenotypes, but was associated with clinical variables including age, BMI, and vaccination status. Differences in serologic responses between clusters were also observed. Our results indicate that clinical variables of individuals with long COVID are associated with their inflammatory profiles and can provide insight into the ongoing immune responses.

Healthy Life Habits in Caregivers of Children in Vulnerable Populations: A Cluster Analysis.

Intervention programs aimed at mitigating the effects of chronic noncommunicable disease (CNDs) focus on promoting healthy lifestyle habits (HLH), especially in the early stages of life. Because of this, different typologies of caregivers have been identified according to HLH during middle childhood and adolescence. However, the available studies have focused on aspects such as nutrition, physical activity, and rest, ignoring other HLHs that are equally important for children's well-being. Likewise, few studies address HLH during the first five years of life and how caregivers affect children's health. In a sample of 544 caregivers of children aged zero to five years from low-income Colombian communities, we established a typology of attitudes toward different HLHs. The results indicate the presence of three clusters that grouped caregivers with (1) positive attitudes toward all HLHs, (2) toward some HLHs, and (3) relatively low positive attitudes toward all HLHs. Membership in clusters with less positive attitudes toward HLHs was also found to be associated with low educational levels and living in rural areas. This study detected profiles of caregivers who may have unhealthy lifestyles, so the results would allow social workers to design differential interventions on HLHs in non-industrialized countries.

Social inequality in admission chances for prestigious higher education programs in Germany: do application patterns matter?

Abstract Research has shown that admission to prestigious higher education programs varies by students' socio-economic status (SES). Access to these programs is characterized by high competition and often rather complex admission procedures. Thus, access may depend not only on students’ performance and decisions to apply but also on their application patterns: Where and how they apply, which may vary by social background due to differences in educational achievement, aspirations, and constraints. Using applications to highly prestigious medical programs in Germany, we examine whether admission chances are socially selective even among the positively selected group of applicants, and whether this is due to SES differences in application patterns or performance. Based on complete application register data, we identify application patterns through cluster analysis. We then used the resulting cluster model to predict cluster membership in the 2018 applicant cohort, for which we collected survey data with information on applicants’ SES, preferences, and motivations. We find that application patterns vary primarily by applicants’ performance (grades and test scores) and SES-specific geographic constraints. However, our multivariate analyses on admission chances show that application patterns do not mediate SES differences in admission chances. Instead, these differences are entirely due to SES differences in applicants’ performance.

Clusters of lifestyle behavioral risk factors and their associations with depressive symptoms and stress: evidence from students at a university in Finland

BackgroundNo previous research of university students in Finland assessed lifestyle behavioral risk factors (BRFs), grouped students into clusters, appraised the relationships of the clusters with their mental well-being, whilst controlling for confounders. The current study undertook this task.MethodsStudents at the University of Turku (n = 1177, aged 22.96 ± 5.2 years) completed an online questionnaire that tapped information on sociodemographic variables (age, sex, income sufficiency, accommodation during the semester), four BRFs [problematic alcohol consumption, smoking, food consumption habits, moderate-to-vigorous physical activity (MVPA)], as well as depressive symptoms and stress. Two-step cluster analysis of the BRFs using log-likelihood distance measure categorized students into well-defined clusters. Two regression models appraised the associations between cluster membership and depressive symptoms and stress, controlling for sex, income sufficiency and accommodation during the semester.ResultsSlightly more than half the study participants (56.8%) had always/mostly sufficient income and 33% lived with parents/partner. Cluster analysis of BRFs identified three distinct student clusters, namely Cluster 1 (Healthy Group), Cluster 2 (Smokers), and Cluster 3 (Nonsmokers but Problematic Drinkers). Age, sex and MVPA were not different across the clusters, but Clusters 1 and 3 comprised significantly more respondents with always/mostly sufficient income and lived with their parents/partner during the semester. All members in Clusters 1 and 3 were non-smokers, while all Cluster 2 members comprised occasional/daily smokers. Problematic drinking was significantly different between clusters (Cluster 1 = 0%, Cluster 2 = 54%, Cluster 3 = 100%). Cluster 3 exhibited significantly healthier nutrition habits than both other clusters. Regression analysis showed: (1) males and those with sufficient income were significantly less likely to report depressive symptoms or stress; (2) those living with parents/partner were significantly less likely to experience depressive symptoms; (3) compared to Cluster 1, students in the two other clusters were significantly more likely to report higher depressive symptoms; and (4) only students in Cluster 2 were more likely to report higher stress.ConclusionsBRFs cluster together, however, such clustering is not a clear-cut, all-or-none phenomenon. Students with BRFs consistently exhibited higher levels of depressive symptoms and stress. Educational and motivational interventions should target at-risk individuals including those with insufficient income or living with roommates or alone.

The CluMPR galaxy cluster-finding algorithm and DESI legacy survey galaxy cluster catalogue

ABSTRACT Galaxy clusters enable unique opportunities to study cosmology, dark matter, galaxy evolution, and strongly lensed transients. We here present a new cluster-finding algorithm, CluMPR (Clusters from Masses and Photometric Redshifts), that exploits photometric redshifts (photo-z’s) as well as photometric stellar mass measurements. CluMPR uses a 2D binary search tree to search for overdensities of massive galaxies with similar redshifts on the sky and then probabilistically assigns cluster membership by accounting for photo-z uncertainties. We leverage the deep DESI Legacy Survey grzW1W2 imaging over one-third of the sky to create a catalogue of $\sim 300\, 000$ galaxy cluster candidates out to z = 1, including tabulations of member galaxies and estimates of each cluster’s total stellar mass. Compared to other methods, CluMPR is particularly effective at identifying clusters at the high end of the redshift range considered (z = 0.75–1), with minimal contamination from low-mass groups. These characteristics make it ideal for identifying strongly lensed high-redshift supernovae and quasars that are powerful probes of cosmology, dark matter, and stellar astrophysics. As an example application of this cluster catalogue, we present a catalogue of candidate wide-angle strongly lensed quasars in Appendix C. The nine best candidates identified from this sample include two known lensed quasar systems and a possible changing-look lensed QSO with SDSS spectroscopy. All code and catalogues produced in this work are publicly available (see Data Availability).

The Emerging Stellar Complex in Mon R2: Membership and Optical Variability Classification

Monoceros R2 (Mon R2) is one of the closest large active star-forming regions. This extremely young and partially embedded region provides an excellent laboratory for studying star formation and the early evolution of young stellar objects (YSOs). In this paper, we conduct an optical study of the greater Mon R2 region. Beginning with 1690 previously identified candidate YSOs, we used 496 sources with good proper motions and parallaxes from Gaia Data Release (DR) 3 to determine the astrometric properties for likely members of Mon R2. We then used both astrometric and photometric (isochronal and variability) criteria to determine that 308 of these stars are highly probable members. Using the same criteria, we considered a broad area search around Mon R2 in Gaia DR3 and separated candidate members from field stars. In total, we selected 651 likely new cluster members that had been missed in the previous X-ray and infrared excess selection techniques used in the past to establish cluster membership. Revised astrometric properties of the cluster were found using the combined sample of ∼959 highly probable member stars. For the literature plus the new candidate member list, optical light curves were compiled from the Zwicky Transient Facility. For 470 identified variable sources, we attempted classification based on the flux asymmetry (M) and quasiperiodicity (Q) metrics. We find that Mon R2 is dominated by quasiperiodic symmetric variables, with aperiodic sources also a significant population. A few tens of large-amplitude variables are identified that may be of interest for further study.

Esports for development? Exploring esports player profiles and their development and well-being outcomes

PurposeThis study aimed to identify subgroups of esports players based on their gaming behavior patterns across game genres and compare self-efficacy, social efficacy, loneliness and three dimensions of quality of life between these subgroups.Design/methodology/approach324 participants were recruited from prolific academic to complete an online survey. We employed latent profile analysis (LPA) to identify subgroups of esports players based on their behavioral patterns across genres. Additionally, a one-way multivariate analysis of covariance (MANCOVA) was conducted to test the association between cluster memberships and development and well-being outcomes, controlling for age and gender as covariates.FindingsLPA analysis identified five clusters (two single-genre gamer groups, two multigenre gamer groups and one all-genre gamer group). Univariate analyses indicated the significant effect of the clusters on social efficacy, psychological health and social health. Pairwise comparisons highlighted the salience of the physical enactment-plus-sport simulation genre group in these outcomes.Originality/valueThis study contributes to the understanding of the development and well-being benefits experienced by various esports consumers, as well as the role of specific gameplay in facilitating targeted outcomes among these consumer groups.

Distinct profiles of mental health need and high need overall among New Zealand adolescents - Cluster analysis of population survey data.

The objective was to identify clinically meaningful groups of adolescents based on self-reported mental health and wellbeing data in a population sample of New Zealand secondary school students. We conducted a cluster analysis of six variables from the Youth19 Rangatahi Smart Survey (n = 7721, ages 13-18 years, 2019): wellbeing (World Health Organization Well-Being Index), possible anxiety symptoms (Generalized Anxiety Disorder 2-item, adapted), depression symptoms (short form of the Reynolds Adolescent Depression Scale) and past-year self-harm, suicide ideation and suicide attempt. Demographic, contextual and behavioural predictors of cluster membership were determined through multiple discriminant function analysis. We performed cross-validation analyses using holdout samples. We identified five clusters (n = 7083). The healthy cluster (n = 2855, 40.31%) reported positive mental health across indicators; the anxious cluster (n = 1994, 28.15%) reported high possible anxiety symptoms and otherwise generally positive results; the stressed and hurting cluster (n = 667, 9.42%) reported sub-clinical depression and possible anxiety symptoms and some self-harm; the distressed and ideating cluster (n = 1116, 15.76%) reported above-cutoff depression and possible anxiety symptoms and high suicide ideation; and the severe cluster (n = 451; 6.37%) reported the least positive mental health across indicators. Female, rainbow, Māori and Pacific students and those in higher deprivation areas were overrepresented in higher severity clusters. Factors including exposure to sexual harm and discrimination were associated with increasing cluster severity. We identified high prevalence of mental health challenges among adolescents, with distinct clusters of need. Youth mental health is not 'one size fits all'. Future research should explore youth behaviour and preferences in accessing support and consider how to best support the mental health of each cluster.

The role of clusters in advancing Industry 4.0 solutions: insights from the Polish automotive context

PurposeThis study is exploratory in nature and designed to address poorly documented issues in the literature. The dimensions of regional distribution or spatial organisation of Industry 4.0 (I4.0), including the potential role of clusters, have only recently been addressed, with most available studies focusing on advanced, mainly Western European countries. Although developing fast, the literature on I4.0 in other countries, such as the Central and Eastern European or post-transition economies like Poland, needs to pay more attention to the spatial distribution or geographical and organisational aspects. In response to the identified knowledge gap, this paper aims to identify the role of clusters in the transformation towards I4.0. This explains why clusters may matter for advancing the fourth digital transformation, how advanced in implementing I4.0 solutions are the residents of Polish clusters and how they perceive the advantages of cluster membership for such implementation. Finally, it seeks to formulate policy recommendations based on the evidence gathered.Design/methodology/approachThe methodology used in this study combines quantitative analysis of secondary data from a cluster benchmarking survey with a case study approach. The benchmarking survey, conducted by the polish agency for enterprise development in 2021, gathered responses from 435 cluster members and 41 cluster managers, representing an estimated 57% of the current clusters in Poland. In addition to quantitative analysis, a case study approach was used, incorporating primary sources such as interview with cluster managers and surveys of cluster members, as well as secondary sources like company documents and information from cluster organisation websites. Statistical analysis involved assessing the relationship between technology implementation and the adoption of management systems, as well as exploring potential correlations between technology use and company characteristics such as revenue, export revenue share and number of employees using Pearson correlation coefficient.FindingsIn Poland, implementing I4.0 technologies by cluster companies is still modest. The cluster has influenced the use of I4.0 technologies in 23% of surveyed companies. Every second surveyed company declared a positive impact of a cluster on technological advancement. The use of I4.0 technologies is not correlated with the revenue of clustered companies. A rather bleak picture emerges from the results, revealing a need for more interest among cluster members in advancing I4.0 technologies. This may be due to a comfortable situation in which firms still enjoy alternative competitive advantages that do not force them to seek new advanced advantages brought about by I4.0. It also reflects the sober approach and awareness of associated high costs and necessary investments, which are paramount and prevent successful I4.0 implementation.Research limitations/implicationsThe limitations inherent in this study reflect the scarcity of the available data. This paper draws on the elementary survey administered centrally and is confined by the type of questions asked. The empirical section focuses on an important, though only one selected sector of the economy – the automotive industry. Nevertheless, the diagnosis of the Polish cluster’s role in advancing I4.0 should complement the existing literature.Practical implicationsThe exploratory study concludes with policy recommendations and sets the stage for more detailed studies. Amidst the research’s limitations, this study pioneers a path for future comprehensive investigations, enabling a deeper understanding of Polish clusters’ maturity in I4.0 adoption. By comparing the authors’ analysis of the Polish Automotive Group (PGM) cluster with existing literature, the authors uncover a distinct disparity between the theoretical prominence of cluster catalysis and the current Polish reality. Future detailed dedicated enquiries will address these constraints and provide a more comprehensive map of Polish clusters’ I4.0 maturity.Originality/valueThis study identifies patterns of I4.0 implementation and diagnoses the role of clusters in the transformation towards I4.0. It investigates how advanced is the adoption of I4.0 solutions among the residents of Polish clusters and how they perceive the advantages of cluster membership for such transformation. Special attention was paid to the analysis of the automotive sector. Comparing the conclusions drawn from the analysis of the Polish PGM cluster in this case study to those from the literature on the subject, it becomes clear that the catalytic role of clusters in the implementation of I4.0 technologies by enterprises, as emphasised in the literature, is not yet fully reflected in the Polish reality.

Effectiveness of digital marketing and its value in new ventures

This paper analyzes the effect of digital marketing and cluster membership on the entrepreneurship and performance of dental clinics in Colombia. The use of different digital marketing tools in dental clinics is evaluated, and the effect of these tools on the creation of new markets, and clinics’ services and general performance, are analyzed. In addition, the cluster effect on the learning and implementation of digital tools in individual clinics is studied. The results show the most effective digital tools to create new markets, as well as the tools that improve clinics’ performance through the Internet image and online visibility. The combined use of corporate Facebook and Instagram, a robust website with wide online visibility and YouTube develops the image of companies on the Internet in the most effective way. In addition, the importance of belonging to a cluster in the learning and implementation of digital tools in individual clinics was verified.

Characterizing the US Market for Salad Mixes through the Lens of Environmental Preferences

The consumption of salad mixes has increased because of their convenience and nutritional value, resulting in significant sales increases during the past decade. Conversely, the uses of pest-control chemicals, long-distance transportation of salad mixes, and plastic packaging have raised environmental concerns among “green consumers.” Because proenvironmental products are becoming more widespread, this study delved into market segments of salad mix consumers based on their preferences for proenvironmental labels. Data for this study were collected via a 2020 web-based survey of 2100 salad mix consumers in the United States. We performed a comprehensive two-stage cluster analysis that integrated both hierarchical and partitioning methods. This analysis was based on consumers’ preferences and evaluations of production (low energy use, low fertilizer use, low greenhouse gas emissions, low water use, and pollinator-friendly) and marketing-related (biodegradable packaging, low carbon footprint, and low food miles) proenvironmental labels. Three segments were identified. We used ordered probit regression to assess the impact of consumer demographic characteristics, market preferences, and environmental perceptions on cluster membership. The deep-rooted segment, which represented 36% of the sample, highly valued all proenvironmental labels related to salad mixes and had a particular preference for labels that included low fertilizer use, pollinator-friendly production methods, and low greenhouse gas emissions. The indecisive segment comprised 40% of the sample and moderately valued all proenvironmental labels; this group mainly comprised individuals with lower income levels and those living in rural areas. The skeptic segment represented 23% of the sample and valued environmental labels less than the deep-rooted and indecisive segments did; additionally, they reported the lowest consumption of salad mixes. These findings can help retailers and policymakers communicate information about proenvironmental labels more effectively to each segment of salad mix consumers.

Investigating 56 High Galactic Latitude Open Cluster Candidates in Gaia DR3

Using Gaia DR3 data, we revisit 56 high Galactic latitude (∣b∣ ≥ 30°) open cluster (OC) candidates with poor shapes of color–magnitude diagrams (CMDs), including unclear and paired main sequences (MSs). We aim to confirm their physical reality and explore whether the special MS morphology is intrinsic to genuine OCs. Initially, we redetermine cluster memberships by integrating five outlier detection algorithms into pyUPMASK. However, this work fails to reproduce the 56 clusters. Instead, we find an alternative set of 56 clusters, six of which are non-duplicates. To ascertain whether the six clusters are real OCs, we build synthetic CMDs to derive reliable cluster properties, including fundamental parameters, binary fraction, and mass of the cluster. Subsequently, we investigate the structural parameters and the age–mass and mass–radius relations of the six candidate OCs. Finally, we utilize a multidimensional approach, incorporating cluster properties, spatial structure, kinematic attributes, and CMD verification, to assess their physical reality as genuine OCs further. Our results suggest that the six candidates should be physical OCs, exhibiting well-defined CMD characteristics. Moreover, we discover two of the six OCs as potential binary clusters.

A Deep Dynamic Latent Block Model for Co-clustering of Zero-Inflated Data Matrices

The simultaneous clustering of observations and features of datasets (known as co-clustering) has recently emerged as a central machine learning application to summarize massive datasets. However, most existing models focus on continuous and dense data in stationary scenarios, where cluster assignments do not evolve over time. This work introduces a novel latent block model for the dynamic co-clustering of data matrices with high sparsity. To properly model this type of data, we assume that the observations follow a time and block dependent mixture of zero-inflated distributions, thus, combining stochastic processes with the time-varying sparsity modeling. To detect abrupt changes in the dynamics of both cluster memberships and data sparsity, the mixing and sparsity proportions are modeled through systems of ordinary differential equations. The inference relies on an original variational procedure whose maximization step trains fully connected neural networks in order to solve the dynamical systems. Numerical experiments on simulated and real world datasets demonstrate the effectiveness of the proposed methodology in the context of count data.

Neighborhood disadvantage and parenting predict longitudinal clustering of uncinate fasciculus microstructural integrity and clinical symptomatology in adolescents

Parenting behaviors and neighborhood environment influence the development of adolescents’ brains and behaviors. Simultaneous trajectories of brain and behavior, however, are understudied, especially in these environmental contexts. In this four-wave study spanning 9–18 years of age (N=224 at baseline, N=138 at final assessment) we used longitudinal k-means clustering to identify clusters of participants with distinct trajectories of uncinate fasciculus (UF) fractional anisotropy (FA) and anxiety symptoms; we examined behavioral outcomes and identified environmental factors that predicted cluster membership. We identified three clusters of participants: 1) high UF FA and low symptoms (“low-risk”); 2) low UF FA and high symptoms (“high-risk”); and 3) low UF FA and low symptoms (“resilient”). Adolescents in disadvantaged neighborhoods were more likely to be in the resilient than high-risk cluster if they also experienced maternal warmth. Thus, neighborhood disadvantage may confer neural risk for psychopathology that can be buffered by maternal warmth, highlighting the importance of considering multiple environmental influences in understanding emotional and neural development in youth.

A predictive model approach to forecast consumers’ cluster membership in the green fast moving consumer goods sector

Predictive models are increasingly crucial in navigating heterogeneous markets. This study develops a predictive model approach to forecast consumer cluster membership in the green fast-moving consumer goods sector, focusing on bio-based products like adhesives and plasters. Through two online surveys in Germany, we identified key factors acting as drivers and barriers, demonstrating their effectiveness in distinguishing similar consumer segments across both product categories. Utilizing multinomial logistic regression, we crafted a prediction model that accurately forecasts cluster membership, providing novel insights into consumer behavior towards non-food bio-based products. This facilitates the development of targeted business and marketing strategies, optimizing resource allocation in market research activities. Our findings offer significant contributions to understanding the dynamics influencing consumer choices in the bio-based product market.

Cognitive Decline After Divorce and Widowhood: Is Marital Loss Always a Loss?

We used longitudinal data to determine whether the type of marital loss is associated with the rate of cognitive change before and after divorce or widowhood. Previous research found that relationship status was associated with older adults' cognitive performance: married persons performed better on memory assessments and had lower dementia risk than unmarried-cohabitating, never-married, divorced, and widowed persons. However, the end of a marriage may cause distress or reduce distress because a stressor disappears. Questions thus remain about the mechanisms by which marital change affects cognitive outcomes and, specifically, whether termination of marriage can improve cognitive performance for some. Using data from the 1998-2016 waves of the Health and Retirement Study (N = 23,393), we conducted two analyses. First, we used trajectory analysis to create clusters of participants with similar cognitive trajectories and tested the association between participants' cluster membership and marital loss type. Second, we used multilevel modeling to analyze the relationship between participants' cognitive scores while married and following divorce or widowhood and linked these to marital features. Participants who divorced showed no difference in trajectory distribution; widowed participants were more likely to be in the lower-performing and more quickly declining groups. Participants had lower rates of decline following divorce (β = .136, p < .001), while widowed participants had accelerated decline following spousal death (β = -0.183, p < .001) and an immediate decline following spousal death (β = -0.113, p = .028). We found that the type of marital loss was important, and predicted improvements in cognition for some and decrements for others, with individuals who were divorced performing best while those who were widowed or separated but not divorced performing worse.

A New Approach based on Fuzzy Clustering and Enhancement Operator for Medical Image Contrast Enhancement.

Image enhancement is a very significant topic in image processing that improves the quality of the images. The methods of image enhancement are classified into 3 categories. They include histogram method, fuzzy logic method and optimal method. Studies on image enhancement are often based on rules: if it's bright then it's brighter, if it's dark then it's darker and using the global approach. So, it's hard to enhance objects in all dark and light areas, as in the medical images. Input data is downloaded from the link: http://www.med.harvard.edu/AANLIB. This paper introduces a new algorithm for enhancing medical images that is called the medical image enhancement based on cluster enhancement (MIECE). Firstly, the input image is clustered by the algorithm of fuzzy clustering. Then, the upper bound, and lower bound are calculated according to cluster. Next, the sub-algorithm is implemented for clustering enhancement using an enhancement operator. For each pixel, the gray levels for each channel (R, G, B) are transformed with this sub-algorithm to generate new corresponding gray levels. Because after clustering, each pixel belongs to each cluster with the corresponding membership value. Therefore, the output gray level value will be aggregated from the enhanced gray levels by the sub-algorithm with the weight of the corresponding cluster membership value. This paper experiences the method MIECE with input data downloaded from the link: http://www.med.harvard.edu/AANLIB. The experimental results are compared with some recent methods that include: SGHIE (2017), Ying (2017) and KinD++ (2021). This paper introduces the new algorithm which is based on cluster enhancement (MIECE) to enhance the medical image contrast. The experimental results show that the output images of the proposed algorithm are better than some other recent methods for enhancing dark objects.