Daily micropractice can augment single-session interventions: A randomized controlled trial of self-compassionate touch and examining their associations with habit formation in US college students.

In this pre-registered study, we evaluated the effects of a single-session, self-guided intervention, leveraging daily micropractice (≤20 seconds/day practice) of self-compassionate touch to enhance self-compassion. We randomly assigned undergraduates (N = 135) to one of two conditions: a single-session intervention in which they were taught self-compassionate touch or a finger-tapping active control. Then, we instructed them to practice for 20 seconds/day for one month. At baseline (T1) and one-month follow-up (T2), participants completed assessments of self-compassion, growth mindset, positive affect, stress, psychopathology, habit formation, and more. In confirmatory, intention-to-treat analyses (N = 135), we found no significant effects on these outcomes. However, in confirmatory, per-protocol analyses (comparing the subsets from each condition who practiced>28 times, N = 45), self-compassionate touch, relative to active control, predicted T1-to-T2 increases in self-compassion (ß = 0.71, p = .025), and reductions in stress (ß = -0.62, p = .047) and psychopathology (ß = -0.61, p = .046). In exploratory intention-to-treat analyses (N = 135), we found the same pattern of effects as in the per-protocol analyses among those who practiced self-compassionate touch more frequently relative to active control. We discuss factors associated with habit formation of daily practice. Daily micropractices have the potential for augmenting single-session interventions and for offering help when more time-intensive approaches may be less accessible. CLINICAL TRIAL REGISTRATION NUMBER: NCT05199779.

Elevated depression and anxiety predict future patterns of individualistic and collectivistic cultural values: A cross-lagged longitudinal network analysis.

BACKGROUND: Specific components of independent and interdependent self-construal have been associated with psychopathology. However, most studies on this topic have been cross-sectional, precluding causal inferences. We used contemporaneous and temporal cross-lagged network analysis to establish weak causal effects in understanding the association between self-construal and psychopathology components. METHODS: Middle-aged and older community-dwelling adults (n = 3294) participated in the Midlife Development in the United States study across two time-points, spaced nine years apart. Six self-construal (interdependence: connection to others, commitment to others, receptiveness to influence; independence: behavioral consistency, sense of difference from others, self-reliance) and three psychopathology nodes (major depressive disorder (MDD), generalized anxiety disorder (GAD), and panic disorder (PD) symptom severity) were examined. All network analyses controlled for age, sex, race, and number of chronic illnesses as covariates. RESULTS: Contemporaneous and temporal networks yielded relations between elevated MDD and PD and increased receptiveness to influence. Heightened GAD symptom severity was associated with future increased difference from others and decreased connection to others, commitment to others, and receptiveness to influence. Higher MDD, GAD, and PD severity were associated with future lower self-reliance. Network comparison tests revealed no consistent network differences across sex and race. LIMITATIONS: DSM-III-R measures of MDD, GAD, and PD were used. Results may not generalize to culturally diverse racial groups. CONCLUSIONS: Changes in self-construal may result from increased MDD, GAD, and PD severity. Findings suggest the importance of targeting common mental health symptoms to positively influence how individuals view the self and others in various social contexts.

The SAFE Model: State Authenticity as a Function of Three Types of Fit.

The SAFE model asserts that state authenticity stems from three types of fit to the environment. Across two studies of university students, we validated instruments measuring self-concept, goal, and social fit as unique predictors of state authenticity. In Study 1 (N = 969), relationships between fit and state authenticity were robust to controlling for conceptually similar and distinct variables. Using experience sampling methodology, Study 2 (N = 269) provided evidence that fit and authenticity co-vary at the state (i.e., within-person) level, controlling for between-person effects. Momentary variation in each fit type predicted greater state authenticity, willingness to return to the situation, and state attachment to one's university. Each fit type was also predicted by distinct contextual features (e.g., location, activity, company). Supporting a theorized link to cognitive fluency, situations eliciting self-concept fit elicited higher working memory capacity and lower emotional burnout. We discuss the implications of fit in educational contexts.

Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing.

Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure-function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue-specific deuterium labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB-disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Lastly, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.

Course of Subtypes of Late-Life Depression Identified by Bipartite Network Analysis During Psychosocial Interventions.

Importance: Approximately half of older adults with depression remain symptomatic at treatment end. Identifying discrete clinical profiles associated with treatment outcomes may guide development of personalized psychosocial interventions. Objective: To identify clinical subtypes of late-life depression and examine their depression trajectory during psychosocial interventions in older adults with depression. Design, Setting, and Participants: This prognostic study included older adults aged 60 years or older who had major depression and participated in 1 of 4 randomized clinical trials of psychosocial interventions for late-life depression. Participants were recruited from the community and outpatient services of Weill Cornell Medicine and the University of California, San Francisco, between March 2002 and April 2013. Data were analyzed from February 2019 to February 2023. Interventions: Participants received 8 to 14 sessions of (1) personalized intervention for patients with major depression and chronic obstructive pulmonary disease, (2) problem-solving therapy, (3) supportive therapy, or (4) active comparison conditions (treatment as usual or case management). Main Outcomes and Measures: The main outcome was the trajectory of depression severity, assessed using the Hamilton Depression Rating Scale (HAM-D). A data-driven, unsupervised, hierarchical clustering of HAM-D items at baseline was conducted to detect clusters of depressive symptoms. A bipartite network analysis was used to identify clinical subtypes at baseline, accounting for both between- and within-patient variability across domains of psychopathology, social support, cognitive impairment, and disability. The trajectories of depression severity in the identified subtypes were compared using mixed-effects models, and time to remission (HAM-D score ≤10) was compared using survival analysis. Results: The bipartite network analysis, which included 535 older adults with major depression (mean [SD] age, 72.7 [8.7] years; 70.7% female), identified 3 clinical subtypes: (1) individuals with severe depression and a large social network; (2) older, educated individuals experiencing strong social support and social interactions; and (3) individuals with disability. There was a significant difference in depression trajectories (F2,2976.9 = 9.4; P < .001) and remission rate (log-rank χ22 = 18.2; P < .001) across clinical subtypes. Subtype 2 had the steepest depression trajectory and highest likelihood of remission regardless of the intervention, while subtype 1 had the poorest depression trajectory. Conclusions and Relevance: In this prognostic study, bipartite network clustering identified 3 subtypes of late-life depression. Knowledge of patients' clinical characteristics may inform treatment selection. Identification of discrete subtypes of late-life depression may stimulate the development of novel, streamlined interventions targeting the clinical vulnerabilities of each subtype.

The Gap Between Us: Income Inequality Reduces Social Affiliation in Dyadic Interactions.

In this investigation, we tested the hypothesis that increased income inequality between individuals will reduce social affiliation within dyadic interactions. In three experiments, we examined the effects of income inequality on key indices of affiliation using semi-structured interactions. In the first two experiments, a participant and confederate were randomly assigned to a low- or high-power role and compensated mildly or extremely unequally. In Experiment 3, inequality and inequity were orthogonally manipulated to determine whether inequality's social consequences are moderated by the fairness of the income distribution. We demonstrated that greater inequality produced more negative emotional responses, reduced desire for closeness, and harsher evaluations of one's partner, regardless of one's power role and the equitability of the income distribution. We also obtained evidence that greater inequality reduces behavioral warmth, although this effect was less consistent. Our results begin to unpack the psychological processes through which income inequality worsens societal well-being.

Perception of Social Support and Cognitive Performance in Older Adults With Depression.

This cross-sectional study assesses the association between perceived social support and cognitive performance in older adults with depression.

Cerebellar impulsivity-compulsivity assessment scale.

OBJECTIVE: The cerebellum has been identified as the key brain region that modulates reward processing in animal models. Consistently, we recently found that people with cerebellar ataxia have impulsive and compulsive behaviors (ICBs), the main symptoms related to abnormal reward processing. Due to the lack of a validated scale to quantitatively measure ICBs in cerebellar disorders, we aim to develop and validate a new scale, Cerebellar Impulsivity-Compulsivity Assessment (CIA). METHODS: We recruited 62 cerebellar ataxia cases, categorized into those with ICBs and those without. We developed a preliminary version of CIA, containing 17 questions. We studied the internal consistency, test-retest reliability, and inter-rater reliability to formulate the final version of CIA, which constitutes only 10 questions. The receiver operating characteristic curve (ROC) was generated to assess the sensitivity and specificity of CIA. RESULTS: Cerebellar ataxia cases with ICBs have threefold higher total preliminary CIA scores than those without ICBs (12.06 ± 5.96 vs. 4.68 ± 3.50, p = 0.038). Cronbach's alpha revealed good internal consistency across all items (α > 0.70). By performing the test-retest reliability and inter-rater reliability on the preliminary version of CIA, we excluded seven questions (r < 0.70) and generated the final version of CIA. Based on the ROC, a score of 8.0 in CIA was chosen as the cut-off for ICBs in individuals with cerebellar ataxia with 81% sensitivity and 81% specificity. INTERPRETATION: CIA is a novel tool to assess ICBs in cerebellar ataxia and broaden our understanding of the cerebellum-related cognitive and behavioral symptoms.

Intersectional implicit bias: Evidence for asymmetrically compounding bias and the predominance of target gender.

Little is known about implicit evaluations of complex, multiply categorizable social targets. Across five studies (N = 5,204), we investigated implicit evaluations of targets varying in race, gender, social class, and age. Overall, the largest and most consistent evaluative bias was pro-women/anti-men bias, followed by smaller but nonetheless consistent pro-upper-class/anti-lower-class biases. By contrast, we observed less consistent effects of targets' race, no effects of targets' age, and no consistent interactions between target-level categories. An integrative data analysis highlighted a number of moderating factors, but a stable pro-women/anti-men and pro-upper-class/anti-lower-class bias across demographic groups. Overall, these results suggest that implicit biases compound across multiple categories asymmetrically, with a dominant category (here, gender) largely driving evaluations, and ancillary categories (here, social class and race) exerting relatively smaller additional effects. We discuss potential implications of this work for understanding how implicit biases operate in real-world social settings. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Individual differences in social power: Links with beliefs about emotion and emotion regulation.

OBJECTIVE: People differ in how they regulate their emotions, and how they do so is guided by their beliefs about emotion. We propose that social power-one's perceived influence over others-relates to one's beliefs about emotion and to emotion regulation. More powerful people are characterized as authentic and uninhibited, which should translate to the belief that one should not have to control one's emotions and, in turn, less suppression and more acceptance. More powerful people are also characterized as self-efficacious and confident, which should translate to the belief that one can control one's emotions and, in turn, more reappraisal and acceptance. METHOD: Two preregistered studies using four samples (Ntotal  = 1286) tested these hypotheses using cross-sectional and longitudinal surveys as well as diaries. RESULTS: In Study 1, power related to beliefs about emotion and emotion regulation in hypothesized ways. Study 2 also largely supported the hypotheses: The belief that one should not have to control one's emotions accounted for the links between power and suppression and acceptance, whereas the belief that one can control one's emotions accounted for the link between power and reappraisal. CONCLUSION: Power and emotion regulation are interconnected, in part because of their links with beliefs about emotions.

Evaluating the utility of a global webinar for mentoring medical students and OBGYN residents in REI.

BACKGROUND: In medical school and residency, clinical experiences influence trainee's decisions on what medical specialty they choose. Most trainees have limited access to opportunities to engage in the field of reproductive endocrinology and infertility (REI). Due to the COVID-19 pandemic and the shutdown of away electives, exposure to REI was especially limited. This study aims to evaluate the effectiveness of a live Q and A webinar on improving trainees' access to mentorship and knowledge of the path to becoming a reproductive endocrinology and infertility (REI) physician. MATERIALS AND METHODS: This study is a prospective paired cohort study. Medical students and OBGYN residents participated in a global Q and A webinar featuring REI physicians and fellows. 70 pre- and post-webinar surveys were included in the analysis. Paired nonparametric tests (Wilcoxon signed-rank test) were performed to assess whether post-webinar knowledge was significantly different from pre-webinar knowledge. RESULTS: Of the 268 registrants, 162 (60%) attended the live webinar. A majority of the respondents who completed both surveys were female (90%) and allopathic medical students (80%). Seventy-seven percent reported receiving only minimal advice about an REI career from their medical school or residency program, while 22% reported receiving some advice, and 1% extensive advice. Thirty-four percent had previously shadowed an REI physician and 23% had rotated in an REI office. Post-webinar significantly more trainees had a better understanding of the REI field, the path required to become an REI physician, opportunities to find mentors in the field, opportunities that are conducive to learning more about REI, and applying for rotations in the REI field (p = <.00001). Eighty-two percent agreed that their interest in REI increased due to this webinar. CONCLUSIONS: A webinar featuring REI physicians and fellows was effective in providing mentorship and career advisement for prospective REI trainees who otherwise expressed having limited access to the field.

Understanding interactions between biomolecules and two-dimensional nanomaterials using in silico microscopes.

Two-dimensional (2D) nanomaterials such as graphene are increasingly used in research and industry for various biomedical applications. Extensive experimental and theoretical studies have revealed that 2D nanomaterials are promising drug delivery vehicles, yet certain materials exhibit toxicity under biological conditions. So far, it is known that 2D nanomaterials possess strong adsorption propensities for biomolecules. To mitigate potential toxicity and retain favorable physical and chemical properties of 2D nanomaterials, it is necessary to explore the underlying mechanisms of interactions between biomolecules and nanomaterials for the subsequent design of biocompatible 2D nanomaterials for nanomedicine. The purpose of this review is to integrate experimental findings with theoretical observations and facilitate the study of 2D nanomaterial interaction with biomolecules at the molecular level. We discuss the current understanding and progress of 2D nanomaterial interaction with proteins, lipid membranes, and DNA based on molecular dynamics (MD) simulation. In this review, we focus on the 2D graphene nanosheet and briefly discuss other 2D nanomaterials. With the ever-growing computing power, we can image nanoscale processes using MD simulation that are otherwise not observable in experiment. We expect that molecular characterization of the complex behavior between 2D nanomaterials and biomolecules will help fulfill the goal of designing effective 2D nanomaterials as drug delivery platforms.

Characterization of multilevel influences of mental health care transitions: a comparative case study analysis.

PURPOSE: Mental health care transitions are increasingly prioritized given their potential to optimize care delivery and patient outcomes, especially those focused on the transition from inpatient to outpatient mental health care. However, limited efforts to date characterize such mental health transition practices, especially those spanning multiple service setting contexts. Examination of key influences of inpatient to outpatient mental health care transitions across care contexts is needed to inform ongoing and future efforts to improve mental health care transitions. The current work aims to characterize multilevel influences of mental health care transitions across three United States-based mental health system contexts. METHODS: A comparative multiple case study design was used to characterize transition practices within the literature examining children's, non-VA adult, and VA adult service contexts. Andersen's (1995) Behavioral Health Service Use Model was applied to identify and characterize relevant distinct and common domains of focus in care transitions across systems. RESULTS: Several key influences to mental health care transitions were identified spanning the environmental, individual, and health behavior domains, including: community capacity or availability, cross-system or agency collaboration, provider training and experience related to mental health care transitions, client care experience and expectations, and client clinical characteristics or complexity. CONCLUSIONS: Synthesis illustrated several common factors across system contexts as well as unique factors for further consideration. Our findings inform key considerations and recommendations for ongoing and future efforts aiming to plan, expand, and better support mental health care transitions. These include timely information sharing, enhanced care coordination and cross setting and provider communication, continued provider/client education, and appropriate tailoring of services to improve mental health care transitions.

'I have high self-compassion': A face-valid single-item self-compassion scale for resource-limited research contexts.

The original 26-item Self-Compassion Scale (SCS; Neff, 2003) and 12-item Short-Form Self-Compassion Scale (SF-SCS; Raes et al., 2011) are scales commonly used in cross-sectional and longitudinal research to assess the global self-compassion construct and its six facets. We introduce the Single-Item Self-Compassion Scale (SISC; 'I have high self-compassion') to measure the global self-compassion construct in time-, space- and resource-limited contexts (e.g., daily diaries, experience sampling and nationally representative surveys). Additionally, the SISC will expand knowledge about self-compassion by providing researchers whose primary interest is not self-compassion with a convenient, face-valid option to measure self-compassion. Across 10 samples (four cross-sectional, four longitudinal and two 7-day daily diary; N = 2,477), we demonstrated that the SISC has acceptable psychometric properties. Specifically, the SISC was temporally consistent, correlated adequately with the SCS and SF-SCS, exhibited nearly identical correlational patterns when compared with the SCS and SF-SCS with a wide range of criterion measures (e.g., self-esteem, personality, affective and social functioning, mental health and demographic variables) and saved 12 min over a 7-day diary. Results replicated among students, community samples and across the United States, Turkey and Malaysia. Thus, we provide the field with an alternative measure of the global self-compassion construct that complements the SCS and SF-SCS.

Psychological Science in the Wake of COVID-19: Social, Methodological, and Metascientific Considerations.

The COVID-19 pandemic has extensively changed the state of psychological science from what research questions psychologists can ask to which methodologies psychologists can use to investigate them. In this article, we offer a perspective on how to optimize new research in the pandemic's wake. Because this pandemic is inherently a social phenomenon-an event that hinges on human-to-human contact-we focus on socially relevant subfields of psychology. We highlight specific psychological phenomena that have likely shifted as a result of the pandemic and discuss theoretical, methodological, and practical considerations of conducting research on these phenomena. After this discussion, we evaluate metascientific issues that have been amplified by the pandemic. We aim to demonstrate how theoretically grounded views on the COVID-19 pandemic can help make psychological science stronger-not weaker-in its wake.

Application of Machine Learning Techniques to an Agent-Based Model of Pantoea.

Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea. We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py. Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R 2 coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

Evolution of Thyroglobulin Loop Kinetics in EpCAM.

Epithelial cell-activating molecule (EpCAM) is an important cancer biomarker and therapeutic target given its elevated expression in epithelial cancers. EpCAM is a type I transmembrane protein that forms cis-dimers along the thyroglobulin type-1A-like domain (TYD) in the extracellular region. The thyroglobulin loop (TY loop) within the TYD is structurally dynamic in the monomer state of human EpCAM, binding reversibly to a TYD site. However, it is not known if this flexibility is prevalent across different species. Here, we conduct over 17 µs of all-atom molecular dynamics simulations to study EpCAM TY loop kinetics of five different species, including human, mouse, chicken, frog, and fish. We find that the TY loop remains dynamic across evolution. In addition to the TYD binding site, we discover a second binding site for the TY loop in the C-terminal domain (CTD). Calculations of the dissociation rate constants from the simulation trajectories suggest a differential binding pattern of fish EpCAM and other organisms. Whereas fish TY loop has comparable binding for both TYD and CTD sites, the TY loops of other species preferably bind the TYD site. A hybrid construct of fish EpCAM with human TY loop restores the TYD binding preference, suggesting robust effects of the TY loop sequence on its dynamic behavior. Our findings provide insights into the structural dynamics of EpCAM and its implication in physiological functions.

Toward Guided Mutagenesis: Gaussian Process Regression Predicts MHC Class II Antigen Mutant Binding.

Antigen-specific immunotherapies (ASI) require successful loading and presentation of antigen peptides into the major histocompatibility complex (MHC) binding cleft. One route of ASI design is to mutate native antigens for either stronger or weaker binding interaction to MHC. Exploring all possible mutations is costly both experimentally and computationally. To reduce experimental and computational expense, here we investigate the minimal amount of prior data required to accurately predict the relative binding affinity of point mutations for peptide-MHC class II (pMHCII) binding. Using data from different residue subsets, we interpolate pMHCII mutant binding affinities by Gaussian process (GP) regression of residue volume and hydrophobicity. We apply GP regression to an experimental data set from the Immune Epitope Database, and theoretical data sets from NetMHCIIpan and Free Energy Perturbation calculations. We find that GP regression can predict binding affinities of nine neutral residues from a six-residue subset with an average R2 coefficient of determination value of 0.62 ± 0.04 (±95% CI), average error of 0.09 ± 0.01 kcal/mol (±95% CI), and with an receiver operating characteristic (ROC) AUC value of 0.92 for binary classification of enhanced or diminished binding affinity. Similarly, metrics increase to an R2 value of 0.69 ± 0.04, average error of 0.07 ± 0.01 kcal/mol, and an ROC AUC value of 0.94 for predicting seven neutral residues from an eight-residue subset. Our work finds that prediction is most accurate for neutral residues at anchor residue sites without register shift. This work holds relevance to predicting pMHCII binding and accelerating ASI design.

Molecular mechanism of secreted amyloid-ß precursor protein in binding and modulating GABABR1a.

A recent phenomenal study discovered that the extension domain of secreted amyloid-ß precursor protein (sAPP) can bind to the intrinsically disordered sushi 1 domain of the γ-aminobutyric acid type B receptor subunit 1a (GABABR1a) and modulate its synaptic transmission. The work provided an important structural foundation for the modulation of GABABR1a; however, the detailed molecular interaction mechanism, crucial for future drug design, remains elusive. Here, we further investigated the dynamical interactions between sAPP peptides and the natively unstructured sushi 1 domain using all-atom molecular dynamics simulations, for both the 17-residue sAPP peptide (APP 17-mer) and its minimally active 9 residue segment (APP 9-mer). We then explored mutations of the APP 9-mer with rigorous free energy perturbation (FEP) calculations. Our in silico mutagenesis studies revealed key residues (D4, W6, and W7) responsible for the binding with the sushi 1 domain. More importantly, one double mutation based on different vertebrate APP sequences from evolution exhibited a stronger binding (ΔΔG = -1.91 ± 0.66 kcal mol-1), indicating a potentially enhanced GABABR1a modulator. These large-scale simulations may provide new insights into the binding mechanism between sAPP and the sushi 1 domain, which could open new avenues in the development of future GABABR1a-specific therapeutics.