Subgroup Membership Research Articles

Distinct Polygenic Score Profiles in Schizophrenia Subgroups With Different Trajectories of Cognitive Development.

Different cognitive development histories in schizophrenia may reflect variation across dimensions of genetic influence. The authors derived and characterized cognitive development trajectory subgroups within a schizophrenia sample and profiled the subgroups across polygenic scores (PGSs) for schizophrenia, cognition, educational attainment, and attention deficit hyperactivity disorder (ADHD). Demographic, clinical, and genetic data were collected at the National Institute of Mental Health from 540 schizophrenia patients, 247 unaffected siblings, and 844 control subjects. Cognitive trajectory subgroups were derived through cluster analysis using estimates of premorbid and current IQ. PGSs were generated using standard methods. Associations were tested using general linear models and logistic regression. Cluster analyses identified three cognitive trajectory subgroups in the schizophrenia group: preadolescent cognitive impairment (19%), adolescent disruption of cognitive development (44%), and cognitively stable adolescent development (37%). Together, the four PGSs significantly predicted 7.9% of the variance in subgroup membership. Subgroup characteristics converged with genetic patterns. Cognitively stable individuals had the best adult clinical outcomes and differed from control subjects only in schizophrenia PGSs. Those with adolescent disruption of cognitive development showed the most severe symptoms after diagnosis and were cognitively impaired. This subgroup had the highest schizophrenia PGSs and had disadvantageous cognitive PGSs relative to control subjects and cognitively stable individuals. Individuals showing preadolescent impairment in cognitive and academic performance and poor adult outcome exhibited a generalized PGS disadvantage relative to control subjects and were the only subgroup to differ significantly in education and ADHD PGSs. Subgroups derived from patterns of premorbid and current IQ showed different premorbid and clinical characteristics, which converged with broad genetic profiles. Simultaneous analysis of multiple PGSs may contribute to useful clinical stratification in schizophrenia.

Heterogeneity in beliefs about feeding stray animals: the complexity of human–animal interaction

ABSTRACTPeople’s beliefs about feeding stray cats require investigation. Previous studies were based on assumptions about sample homogeneity, potentially obscuring within-group and background differences in beliefs. A latent class analysis was conducted on critical beliefs identified from 167 Malaysian nationals (Kuala Lumpur residents, aged 18-64), based on the Theory of Planned Behavior (TPB). Two distinct subgroups were discerned, revealing heterogeneity in critical beliefs about feeding stray cats. Subgroup membership was associated with multiple background factors (i.e., ethnicity, past feeding experience, pet cat ownership, and religion). Therefore, interventions to reduce the feeding of stray cats (or to change other behaviors) should accommodate subgroup variations in beliefs and background factors. This study provides a novel methodology for investigating the complexity of human variables in human-animal interaction and other behaviors.

Physical Symptom Cluster Subgroups in Chronic Kidney Disease.

Symptom burden associated with chronic kidney disease can be debilitating, with a negative effect on patient health-related quality of life. Latent class clustering analysis is an innovative tool for classifying patient symptom experience. The aim of the study was to identify subgroups of patients at greatest risk for high symptom burden, which may facilitate development of patient-centered symptom management interventions. In this cross-sectional analysis, baseline data were analyzed from 3,921 adults enrolled in the Chronic Renal Insufficiency Cohort Study from 2003 to 2008. Latent class cluster modeling using 11 items on the Kidney Disease Quality of Life symptom profile was employed to identify patient subgroups based on similar observed physical symptom response patterns. Multinomial logistic regression models were estimated with demographic variables, lifestyle and clinical variables, and self-reported measures (Kidney Disease Quality of Life physical and mental component summaries and the Beck Depression Inventory). Three symptom-based subgroups were identified, differing in severity (low symptom, moderate symptom, and high symptom). After adjusting for other variables in multinomial logistic regression, membership in the high-symptom subgroup was less likely for non-Hispanic Blacks and men. Other factors associated with membership in the high-symptom subgroup included lower estimated glomerular filtration rate, history of cardiac/cardiovascular disease, higher Beck Depression Inventory scores, and lower Kidney Disease Quality of Life physical and mental component summaries. Three symptom subgroups of patients were identified among patients with mild-to-moderate chronic kidney disease. Several demographic and clinical variables predicted membership in subgroups. Further research is needed to determine if symptom subgroups are stable over time and can be used to predict healthcare utilization and clinical outcomes.

STRESS AND PSYCHOLOGICAL CHARACTERISTICS PREDICT CLUSTERED HEALTH TRAJECTORIES IN OLDER ADULTS

The first aim of this study was the empirical identification of clustered health trajectories in older individuals, including an expected, more favorable or successful aging (SA) trajectory. The second aim was the identification and analysis of determinants useful for subgroup membership prediction. Particular focus was on early-life and chronic stress, as well as on a broad set of psychological characteristics, such as resilience, personality traits and general affect. A longitudinal survey study with two assessments one year apart has been conducted with older adults (N=224; mean age = 68 years; 72% women). The clustered health trajectories were identified using a longitudinal variant of k-means. For the prediction of subgroup allocation, random forests with conditional interferences were used. The applied machine learning-based approach revealed two latent clustered health trajectories: a ‘constant high health’ (66% of the sample) and a ‘maintaining low health’ trajectory (34%). Chronic stress and positive affect were found to be the most important predictors. Further predictors and their interactions were found to be important for predicting subgroup belonging, including resilience, self-esteem, social support, optimism, as well as negative affect and pessimism. Also, childhood adversities have to be included to predict subgroup belonging. With this study, we were able to show that individuals can be empirically allocated to two separate joint health trajectories in later life over the observation period of one year. In order to understand the current heterogeneity in health in older age, previous and current stress exposure and psychological characteristics have to be taken into account.

PATTERN OF HEALTH-RELATED BEHAVIORS AND RESOURCES AS PREDICTOR OF MEDICAL CARE AND MORTALITY RISK AMONG OLDER ADULTS

This study investigates the patterns and consequences of a wide range of health-related behaviors and resources that include health-compromising behaviors, health-promoting behaviors, preventive health behaviors, and health risks coping resources. We aim to identify the empirically-derived subgroups of individuals with unique profiles of health behaviors and resources to determine how subgroup membership predicts health outcomes and medical care utilization four years later. Data derived from 5,067 respondents in the 2010 and 2014 waves of the Health and Retirement Study. Latent class analysis was used to define classes based on 13 indicators in the 2010 wave, which also provided sociodemographic and health-related covariates. Outcomes were measured over 4 years. Six latent subgroups were identified: “Best Behavior/Resources”, “Low Social Support “, “Low Physical Activity”, “High Substance Abuse”, “Low Preventive Tests”, and “Low Governmental Health Insurance”. Compared with the “Best” group, older adults identified as “Low Physical Activity” and “High Substance Abuse” were found to have higher mortality risks and a lower likelihood of seeing doctors and less nursing home nights; older adults with the lowest level of receiving flu shots, cholesterol and cancer screen test (“Low Preventive Tests”) reported a less likelihood of seeing doctors; respondents in “Low Governmental Health Insurance” subgroup were associated with a lower likelihood of hospital stay and more nursing home nights. Results suggest that distinct groups of older individuals characterized by their health behaviors and resources provide a basis for identifying the high-risk segment of the older population for intervention.

Data-driven identification of subtypes of executive function across typical development, attention deficit hyperactivity disorder, and autism spectrum disorders.

Impairment of executive function (EF), the goal-directed regulation of thoughts, actions, and emotions, drives negative outcomes and is common across neurodevelopmental disorders including attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). A primary challenge to its amelioration is heterogeneity in symptom expression within and across disorders. Parsing this heterogeneity is necessary to attain diagnostic precision, a goal of the NIMH Research Domain Criteria Initiative. We aimed to identify transdiagnostic subtypes of EF that span the normal to impaired spectrum and establish their predictive and neurobiological validity. Community detection was applied to clinical parent-report measures in 8-14-year-old children with and without ADHD and ASD from two independent cohorts (discovery N=320; replication N=692) to identify subgroups with distinct behavioral profiles. Support vector machine (SVM) classification was used to predict subgroup membership of unseen cases. Preliminary neurobiological validation was obtained with existing functional magnetic resonance imaging (fMRI) data on a subsample (N=84) by testing hypotheses about sensitivity of EF subgroups versus DSM categories. We observed three transdiagnostic EF subtypes characterized by behavioral profiles that were defined by relative weakness in: (a) flexibility and emotion regulation; (b) inhibition; and (c) working memory, organization, and planning. The same tripartite structure was also present in the typically developing children. SVM trained on the discovery sample and tested on the replication sample classified subgroup membership with 77.0% accuracy. Split-half SVM classification on the combined sample (N=1,012) yielded 88.9% accuracy (this SVM is available for public use). As hypothesized, frontal-parietal engagement was better distinguished by EF subtype than DSM diagnosis and the subgroup characterized with inflexibility failed to modulate right IPL activation in response to increased executive demands. The observed transdiagnostic subtypes refine current diagnostic nosology and augment clinical decision-making for personalizing treatment of executive dysfunction in children.

Childhood maltreatment exposure and physical functional limitations in late adulthood: examining subjective sleep quality in midlife as a mediator

Objective: The present study had three major aims: 1) To identify sub-groups of adults with differing combinations of childhood maltreatment exposures, 2) to understand the association of childhood maltreatment sub-group membership with subjective sleep quality in midlife, and 3) to assess poor sleep quality in midlife as a mechanism between childhood maltreatment sub-group membership and physical functional limitations in late adulthood. Design: Data come from the Biomarker project of the Midlife Development in the United States study (n = 1251). Outcome measures: The Pittsburgh Sleep Quality Index (Buysse et al., 1989) was used to assess sleep quality in midlife. Functional limitations in late adulthood were measured using a version of the SF-36 (Brazier et al., 1992). Results: Two vulnerable childhood maltreatment sub-groups emerged (Physical and Emotional Maltreatment Sub-group, n = 49, and Sexual Abuse Sub-group, n = 105) and a normative sub-group (n = 1087; low exposure to childhood maltreatment). Poor sleep quality in midlife mediated the association between both maltreatment sub-groups and functional limitations in late adulthood. Conclusion: Results highlight the role of sleep in linking childhood maltreatment with functional impairments in adulthood and offer a potential target for interventions to improve quality of life in older adults.

179. Predictors of clinical outcome following surgery for lumbar spinal stenosis: a study of postoperative pain and disability trajectories

BACKGROUND CONTEXT On average, patients experience important reductions in pain and disability following surgery for degenerative lumbar spinal stenosis (LSS). However, variability in the postoperative course of symptoms suggests many patients do not fit the average symptom trajectory. PURPOSE To identify patient subgroups defined by trajectories of pain and disability following surgery for LSS and investigate the construct validity of the subgroups by evaluating meaningful differences in clinical outcomes. Identify predictors of belonging to the poor outcomes subgroup for disability, leg pain and back pain. STUDY DESIGN/SETTING Retrospective analysis of prospectively collected data. PATIENT SAMPLE Patients with LSS were recruited from 13 surgical spine centers participating in the Canadian Spine Outcomes and Research Network registry. OUTCOME MEASURES Leg and back pain numeric rating scales and the modified Oswestry disability index were measured before surgery and at 3, 12, and 24 months following surgery. METHODS Patients were fit into pain and disability trajectory subgroups with group-based trajectory modeling. Differences in the proportion of patients achieving minimum clinically important change in pain and disability (30%) and clinical success (50% reduction in disability or Oswestry score ≤22) were examined. Generalized linear mixed models adjusted for age and sex, were used to identify associations between subgroup membership and preoperative demographic, clinical history, health status and surgical factors. The subgroup with excellent outcomes was used as the reference group; associations were reported with relative risk ratios (RR). RESULTS The group-based trajectory models identified 3 unique pain trajectories each (excellent, fair and poor) for leg pain, back pain and disability. Depending on outcome 29% to 42% of patients were classified as members of an outcome trajectory subgroup that experienced little to no benefit from surgery. The construct validity of the trajectory subgroups was supported by differences in the proportion of patients meeting thresholds for minimum clinically important change and clinical success (p CONCLUSIONS The percentage of patients with poor outcomes highlights the need for better methods of treatment selection for patients with LSS. The predictors of having poor outcomes suggests it is time to look beyond pathology/surgical factors only and include mental health variables as an integral part of the surgical decision-making process in an effort to avoid patients at increased risk for surgical failure. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

Roles of biological and psychosocial factors in experiencing a psychoneurological symptom cluster in cancer patients.

(a) To identify subgroups with unique psychoneurological symptom-cluster experience (depression, cognitive impairment, fatigue, sleep disturbance, and pain) and (b) to examine whether the selected demographic, clinical, psychological, and biological factors determine a symptom-cluster experience in cancer patients. The sample included 203 patients with diverse cancer types recruited from a Korean university hospital. Latent profile analyses were conducted to identify subgroups. Influencing factors of subgroup membership (demographic/clinical variables, hemoglobin level, social support, and psychological stress) were included as covariates in latent profile analysis and analyzed by multinomial logistic regression. Latent profile analyses classified patients into two subgroups with a unique symptom cluster experience: patients experiencing high intensity in all symptoms within the cluster (the all-high-symptom subgroup, 71%) and patients experiencing low intensity in all symptoms within the cluster (all-low-symptom subgroup, 29%). The validity of the two subgroups was confirmed by the group classification accuracy (97% of the all-low-symptom subgroup and 99% of the all-high-symptom subgroup) and by significant Wald's mean equality tests, showing each symptom (depression, cognitive impairment, fatigue, sleep disturbance, and pain) significantly differentiated the two subgroups (ps < .001). Psychological stress independently determined the subgroup membership. Patients with high levels of stress were more likely to be in the all-high-symptom group (OR = 4.69, p < .0001). Hemoglobin level, cancer diagnosis, social support, and previous chemotherapy experience did not influence group membership. A large number of patients experience five psychoneurological symptoms simultaneously due to psychological stress. Interventions targeted to stress would be beneficial for those patients.

Preferences of patients with rheumatoid arthritis regarding disease-modifying antirheumatic drugs: a discrete choice experiment.

BackgroundAlthough patients have different treatment preferences, these individual preferences could often be grouped in subgroups with shared preferences. Knowledge of these subgroups as well as factors associated with subgroup membership supports health care professionals in the understanding of what matters to patients in clinical decision-making.ObjectivesTo identify subgroups of patients with rheumatoid arthritis (RA) based on their shared preferences toward disease-modifying antirheumatic drugs (DMARDs), and to identify factors associated with subgroup membership.MethodsA discrete choice experiment to determine DMARD preferences of adult patients with RA was designed based on a literature review, expert recommendations, and focus groups. In this multicenter study, patients were asked to state their preferred choice between two different hypothetical treatment options, described by seven DMARD characteristics with three levels within each characteristic. Latent class analyses and multinomial logistic regressions were used to identify subgroups and the characteristics (patient characteristics, disease-related variables, and beliefs about medicines) associated with subgroup membership.ResultsAmong 325 participating patients with RA, three subgroups were identified: an administration-driven subgroup (45.6%), a benefit-driven subgroup (29.7%), and a balanced subgroup (24.7%). Patients who were currently using biologic DMARDs were significantly more likely to belong to the balanced subgroup than the administration-driven subgroup (relative risk ratio (RRR): 0.50, 95% CI: 0.28–0.89). Highly educated patients were significantly more likely to belong to the benefit-driven subgroup than the balanced subgroup (RRR: 11.4, 95% CI: 0.97–133.6). Patients’ medication-related concerns did not contribute significantly to subgroup membership, whereas a near-significant association was found between patients’ beliefs about medication necessity and their membership of the benefit-driven subgroup (RRR: 1.12, 95% CI: 1.00–1.23).ConclusionThree subgroups with shared preferences were identified. Only biologic DMARD use and educational level were associated with subgroup membership. Integrating patient’s medication preferences in pharmacotherapy decisions may improve the quality of decisions and possibly medication adherence.

Polygenic and environmental influences on the course of African Americans' alcohol use from early adolescence through young adulthood.

The study examined (a) whether alcohol use subgroups could be identified among African Americans assessed from adolescence through early adulthood, and (b) whether subgroup membership was associated with the interaction between internalizing symptoms and antisocial behavior polygenic risk scores (PRSs) and environmental characteristics (i.e., parental monitoring, community disadvantage). Participants (N = 436) were initially recruited for an elementary school-based prevention trial in a Mid-Atlantic city. Youths reported on the frequency of their past year alcohol use from ages 14-26. DNA was obtained from participants at age 21. Internalizing symptoms and antisocial behavior PRSs were created based on a genome-wide association study (GWAS) conducted by Benke et al. (2014) and Tielbeek et al. (2017), respectively. Parental monitoring and community disadvantage were assessed at age 12. Four classes of past year alcohol use were identified: (a) early-onset, increasing; (b) late-onset, moderate use; (c) low steady; and (d) early-onset, decreasing. In high community disadvantaged settings, participants with a higher internalizing symptoms PRS were more likely to be in the early-onset, decreasing class than the low steady class. When exposed to elevated community disadvantage, participants with a higher antisocial behavior PRS were more likely to be in the early-onset, increasing class than the early-onset, decreasing and late-onset, moderate use classes.

Musculoskeletal Pain in Individuals With Inflammatory Bowel Disease Reflects Three Distinct Profiles.

Pain affects over 70% of individuals with inflammatory bowel disease (IBD), with abdominal and musculoskeletal pain representing the most common symptoms. Musculoskeletal pain in IBD is reported to be associated with multiple clinical features, however the scope and nature of pain is not well understood. Primary aims were to identify subgroups of musculoskeletal pain in individuals with IBD based on clinical features of pain and assess how these subgroups differ in aspects of demographics, comorbidity, and IBD characteristics. Cross-sectional online survey was performed on adults with IBD. Domains included: demographics, comorbidity, and clinical IBD and pain features. Latent class analysis was used to identify subgroups with similar attributes of: pain (severity, location, interference, and quality), IBD (activity, quality of life, and abdominal pain), and symptoms related to central sensitization. Correlation and regression analyses were used to profile identified subgroups. Of 305 included participants, 208 indicated the presence of musculoskeletal pain. Three identified subgroups were characterized as "mixed mechanism," "central mechanism," and "regional and remission." Between subgroup differences included: total comorbidity score (P=0.005), osteoarthritis (P=0.027), osteoporosis (P=0.045), depression (P=0.001), anxiety (P=0.025), and chronic fatigue syndrome (P=0.020). Sex and age were identified as confounders for depression and anxiety. Study results suggest multiple mechanisms contributing to pain experiences in IBD, to include central mechanisms. Features related to demographics, extraintestinal manifestations, IBD subtype, and clinical IBD features were not predictors of subgroup membership. However, total comorbidity demonstrated association with pain subgroups in this population.

Identifying a typology of high schools based on their orientation toward STEM: A latent class analysis of HSLS:09

AbstractThe purpose of this study is to investigate the extent that there is a typology of high schools based on their orientation toward science, technology, engineering, and mathematics (STEM), as well as the extent to which school‐level demographic variables and student high school outcomes are associated with subgroup membership in the typology, by analyzing data from a large nationally representative sample of high schools (n = 940) from the High School Longitudinal Study of 2009 (HSLS:09) using latent class analysis (LCA). We used a three‐step LCA approach to identify significantly different subgroups of STEM‐oriented high schools, what covariates predict subgroup membership, and how subgroup membership predicts observed distal outcomes. We find that there are four significantly different subgroups of STEM‐oriented high schools based on their principal's perceptions: Abundant (12.3%), Support (23.3%), Bounded (10.1%), and Comprehensive (54.3%). In addition, we find that these subgroups are associated with school demographics, such as the percent of students eligible for free and reduced‐price lunch, school locale, and control (public or private). Subgroup membership is also associated with student outcomes, such as postsecondary program enrollment and intent to pursue a STEM degree.

Effects of measurement timing on subgroup identification using growth mixture modeling: An empirical application to alcohol use.

Growth mixture modeling (GMM) identifies latent classes exhibiting distinct longitudinal patterns on an outcome. Subgroups identified by GMM may be artifactually influenced by measurement timing (e.g., timing of the initial assessment, length of the interval from the first to the last assessment, and total number of assessments) as well as the theoretically posited developmental patterns of the behavior. The current study investigated this possibility using alcohol data from the 1997 National Longitudinal Survey of Youth (n = 2686; 49.44% female; 71.84% White). Three assessment configurations were examined: all 12 waves, first 6 waves, and last 7 waves. Five subgroups were identified using all 12 waves: Normative (71.33%), Low-Increasing (8.45%), Low-Steady (8.97%), High-Slowly Decreasing (7.67%), and Extreme-Sharply Decreasing (3.57%). When comparing participants' subgroup membership for all 12 waves to the first six waves, 14% of the sample was differentially classified. When comparing all 12 waves to the last seven waves, 62% of the sample was differentially classified. Alterations in the timing of the initial assessment had a substantial impact on latent class estimation, underscoring the importance of selecting the developmental window a priori based on theory and empirical knowledge. The time-bounded nature of mixture modeling solutions (i.e., a selected developmental window within the course of a phenomenon) suggests that the latent subgroups should not be interpreted as representing subgroups that are present in the population. Future directions and strategies for testing alternative interpretations are presented. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Person-Centered Profiles Among Treatment-Seeking Children and Adolescents with Anxiety Disorders

Latent profile analysis (LPA) was used to derive homogeneous subgroups within the Child/Adolescent Anxiety Multimodal Study sample (N = 488; 7–17 years, M = 10.69, SD = 2.80) and examine whether class membership predicted or moderated treatment response. Subgroups were identified on baseline multi-informant measures of variables most consistently associated with outcome (youth anxiety/diagnosis, impairment, family psychopathology/functioning). Subgroup membership was examined as a predictor/moderator of outcome across the four treatment conditions (CBT, Sertraline, CBT+Sertraline, pill placebo) at posttreatment (12 weeks) and open-extension follow-up (24 weeks). Four subgroups emerged: mild symptoms/impairment, moderate symptoms/impairment, moderate symptoms/impairment with family dysfunction/parental psychopathology, and severe symptoms/impairment. There were significant between-class differences on socioeconomic status (SES; lower reported SES in the moderate with family dysfunction/parental psychopathology class compared to the mild and moderate class) and age (older age in the severe symptoms class compared to the other three classes). Youth in the mild symptoms/impairment class showed lower posttreatment anxiety across conditions but reported significantly lower symptom severity at baseline. Controlling for demographic differences, response to treatment type did not differ across classes. Analyses indicate that elevated family dysfunction/parental psychopathology clusters primarily within one subgroup of anxious youth rather than mapping onto symptom severity, highlighting the utility of LPA for clarifying within-person combinations of predictor/moderator variables. Implications for development of interventions targeting class-relevant variables are discussed.

Six Month Abstinence Heterogeneity in the Best Quit Study.

Understanding the characteristics of smokers who are successful in quitting may help to increase smoking cessation rates. To examine heterogeneity in cessation outcome at 6 months following smoking cessation behavioral counseling with or without weight management counseling. 2,540 smokers were recruited from a large quitline provider and then randomized to receive proactive smoking cessation behavioral counseling without or with two versions of weight management counseling. A Classification and Regression Tree (CART) analysis was conducted to identify the individual pretreatment and treatment characteristics of groups of smokers with different quitting success (as measured by point prevalence of self-reported smoking of any amount at 6 months). CART analysis identified 10 subgroups ranging from 25.5% to 70.2% abstinent. The splits in the CART tree involved: the total number of counseling and control calls received, whether a smoking cessation pharmacotherapy was used, and baseline measures of cigarettes per day, confidence in quitting, expectation that the study would help the participant quit smoking, the motivation to quit, exercise minutes per week, anxiety, and lack of interest or pleasure in doing things. Costs per quitter ranged from a low of $US270 to a high of $US630. Specific treatment recommendations are made for each group. These results indicate the presence of a substantial variation in abstinence following treatment, and that the total extent of contact via counseling calls of any type and baseline characteristics, rather than assigned treatment, were most important to subgroup membership and abstinence. Tailored treatments to subgroups who are at high risk for smoking following a quit attempt could increase successful smoking cessation.

Prediction of Writing True Scores in Automated Scoring of Essays by Best Linear Predictors and Penalized Best Linear Predictors

AbstractMany assessments of writing proficiency that aid in making high‐stakes decisions consist of several essay tasks evaluated by a combination of human holistic scores and computer‐generated scores for essay features such as the rate of grammatical errors per word. Under typical conditions, a summary writing score is provided by a linear combination of the holistic scores and the feature scores. The best linear predictor (BLP) is used to approximate the true composite writing score by a linear combination of holistic scores and scores of essay features. However, because the relationship between computer‐generated feature score and human scores may depend on subgroup membership and the same scoring rules must normally be applied to all test takers, Yao, Haberman, and Zhang proposed a modified methodology of the penalized best linear predictor (PBLP) by incorporating a quadratic penalty function into the conventional BLP method. This research report contains full accounts of the BLP results as well as supplementary PBLP results to Yao et al. for three assessments of writing that aid in making high‐stakes decisions: the TOEFL iBT® Writing test, the GRE® General Analytical Writing subject test, and the Praxis® Core Academic Skills for Educators: Writing assessment. Results obtained indicate the added value in using machine features for prediction of composite true scores of essay writings and effectiveness of the penalty function in suppressing the lack of population invariance.

Trajectories of Depression and Anxiety in Latina Breast Cancer Survivors.

To identify subgroups of Latina breast cancer survivors with unique trajectories of depression and anxiety and examine predictors associated with these subgroups. Secondary analysis of Latina breast cancer survivors (N = 293) from three psychosocial intervention studies. Depression and anxiety were assessed at intake and at weeks 8 and 16. Group-based growth mixture modeling was used to identify subgroups who followed distinct trajectories of depression and anxiety. Multinomial logistic regression models were used to identify predictors of trajectory-based subgroup membership. Three trajectories emerged for depression. Latina women treated for breast cancer are at an elevated risk for depression and anxiety and follow distinct trajectories of these symptoms. Psychosocial interventions are needed to manage these symptoms, particularly for subgroups in which depression and anxiety persist or worsen.

The Association of Psychological Well-Being with Disablement Processes in a National Sample.

Objectives were to explore subgroups of individuals with differential disability trajectories and evaluate the protective effects of psychological well-being (i.e. hedonic and eudaimonic) in the presence of multiple disease conditions (or multimorbidity) and sociodemographic disadvantages. Data come from the prospective longitudinal cohort study the Midlife Development in the United States (n=3,904). Three waves of data spanning a 20-year period were used to identify subgroups with different disability trajectories. Subgroup membership was then modelled as a function of psychological well-being assessed at wave 1 of the study using multinomial logistic regression. Three unique groups were identified: a normative group with initially low and slowly increasing levels of disability; a group with high levels of disability that was stable over time; and a group with moderate initial levels of disability that increased over time. Hedonic well-being at wave 1 was associated with membership in the risk groups relative to the normative group. Individuals may follow one of three disability pathways mostly as a function of multimorbidity. However, hedonic well-being was associated with having an advantageous disability trajectory regardless of multimorbidity status. Cultivating psychological well-being may improve disability outcomes in aging individuals.

Dynamin-like proteins in plasmodium falciparum: Molecular characterisation

Members of the dynamin superfamily are big GTPases that have remained mostly unchanged throughout evolution and are primarily characterised as mechanochemical enzymes engaged in membrane scission processes. Due to its large GTPase domain, conserved dynamin M and GED domains, and dynamin-like protein subgroup membership, PfDYN2 is a member of the dynamin superfamily. PfDYN2 partially co-localized with markers for the parasite endoplasmic reticulum, Golgi apparatus, and apicoplast, indicating that it may be involved in vesicular trafficking and/or organelle fission events that have been associated with the final stages of the parasite&#39;s development in erythrocytes. The P. falciparum genome only contains two members of the dynamin superfamily, PfDYN2 and PfDYN1.