VISH-Pred: an ensemble of fine-tuned ESM models for protein toxicity prediction.

Peptide- and protein-based therapeutics are becoming a promising treatment regimen for myriad diseases. Toxicity of proteins is the primary hurdle for protein-based therapies. Thus, there is an urgent need for accurate in silico methods for determining toxic proteins to filter the pool of potential candidates. At the same time, it is imperative to precisely identify non-toxic proteins to expand the possibilities for protein-based biologics. To address this challenge, we proposed an ensemble framework, called VISH-Pred, comprising models built by fine-tuning ESM2 transformer models on a large, experimentally validated, curated dataset of protein and peptide toxicities. The primary steps in the VISH-Pred framework are to efficiently estimate protein toxicities taking just the protein sequence as input, employing an under sampling technique to handle the humongous class-imbalance in the data and learning representations from fine-tuned ESM2 protein language models which are then fed to machine learning techniques such as Lightgbm and XGBoost. The VISH-Pred framework is able to correctly identify both peptides/proteins with potential toxicity and non-toxic proteins, achieving a Matthews correlation coefficient of 0.737, 0.716 and 0.322 and F1-score of 0.759, 0.696 and 0.713 on three non-redundant blind tests, respectively, outperforming other methods by over $10\%$ on these quality metrics. Moreover, VISH-Pred achieved the best accuracy and area under receiver operating curve scores on these independent test sets, highlighting the robustness and generalization capability of the framework. By making VISH-Pred available as an easy-to-use web server, we expect it to serve as a valuable asset for future endeavors aimed at discerning the toxicity of peptides and enabling efficient protein-based therapeutics.

GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling.

BACKGROUND: Protein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance. RESULTS: In this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set. CONCLUSIONS: GATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol .

Using protein language models for protein interaction hot spot prediction with limited data.

BACKGROUND: Protein language models, inspired by the success of large language models in deciphering human language, have emerged as powerful tools for unraveling the intricate code of life inscribed within protein sequences. They have gained significant attention for their promising applications across various areas, including the sequence-based prediction of secondary and tertiary protein structure, the discovery of new functional protein sequences/folds, and the assessment of mutational impact on protein fitness. However, their utility in learning to predict protein residue properties based on scant datasets, such as protein-protein interaction (PPI)-hotspots whose mutations significantly impair PPIs, remained unclear. Here, we explore the feasibility of using protein language-learned representations as features for machine learning to predict PPI-hotspots using a dataset containing 414 experimentally confirmed PPI-hotspots and 504 PPI-nonhot spots. RESULTS: Our findings showcase the capacity of unsupervised learning with protein language models in capturing critical functional attributes of protein residues derived from the evolutionary information encoded within amino acid sequences. We show that methods relying on protein language models can compete with methods employing sequence and structure-based features to predict PPI-hotspots from the free protein structure. We observed an optimal number of features for model precision, suggesting a balance between information and overfitting. CONCLUSIONS: This study underscores the potential of transformer-based protein language models to extract critical knowledge from sparse datasets, exemplified here by the challenging realm of predicting PPI-hotspots. These models offer a cost-effective and time-efficient alternative to traditional experimental methods for predicting certain residue properties. However, the challenge of explaining why specific features are important for determining certain residue properties remains.

ESM1 enhances fatty acid synthesis and vascular mimicry in ovarian cancer by utilizing the PKM2-dependent warburg effect within the hypoxic tumor microenvironment.

BACKGROUND: The hypoxic tumor microenvironment is a key factor that promotes metabolic reprogramming and vascular mimicry (VM) in ovarian cancer (OC) patients. ESM1, a secreted protein, plays an important role in promoting proliferation and angiogenesis in OC. However, the role of ESM1 in metabolic reprogramming and VM in the hypoxic microenvironment in OC patients has not been determined. METHODS: Liquid chromatography coupled with tandem MS was used to analyze CAOV3 and OV90 cells. Interactions between ESM1, PKM2, UBA2, and SUMO1 were detected by GST pull-down, Co-IP, and molecular docking. The effects of the ESM1-PKM2 axis on cell glucose metabolism were analyzed based on an ECAR experiment. The biological effects of the signaling axis on OC cells were detected by tubule formation, transwell assay, RT‒PCR, Western blot, immunofluorescence, and in vivo xenograft tumor experiments. RESULTS: Our findings demonstrated that hypoxia induces the upregulation of ESM1 expression through the transcription of HIF-1α. ESM1 serves as a crucial mediator of the interaction between PKM2 and UBA2, facilitating the SUMOylation of PKM2 and the subsequent formation of PKM2 dimers. This process promotes the Warburg effect and facilitates the nuclear translocation of PKM2, ultimately leading to the phosphorylation of STAT3. These molecular events contribute to the promotion of ovarian cancer glycolysis and vasculogenic mimicry. Furthermore, our study revealed that Shikonin effectively inhibits the molecular interaction between ESM1 and PKM2, consequently preventing the formation of PKM2 dimers and thereby inhibiting ovarian cancer glycolysis, fatty acid synthesis and vasculogenic mimicry. CONCLUSION: Our findings demonstrated that hypoxia increases ESM1 expression through the transcriptional regulation of HIF-1α to induce dimerization via PKM2 SUMOylation, which promotes the OC Warburg effect and VM.

ANGPTL4 accelerates ovarian serous cystadenocarcinoma carcinogenesis and angiogenesis in the tumor microenvironment by activating the JAK2/STAT3 pathway and interacting with ESM1.

BACKGROUND: Ovarian cancer (OC) is a malignant neoplasm that displays increased vascularization. Angiopoietin-like 4 (ANGPTL4) is a secreted glycoprotein that functions as a regulator of cell metabolism and angiogenesis and plays a critical role in tumorigenesis. However, the precise role of ANGPTL4 in the OC microenvironment, particularly its involvement in angiogenesis, has not been fully elucidated. METHODS: The expression of ANGPTL4 was confirmed by bioinformatics and IHC in OC. The potential molecular mechanism of ANGPTL4 was measured by RNA-sequence. We used a series of molecular biological experiments to measure the ANGPTL4-JAK2-STAT3 and ANGPTL4-ESM1 axis in OC progression, including MTT, EdU, wound healing, transwell, xenograft model, oil red O staining, chick chorioallantoic membrane assay and zebrafish model. Moreover, the molecular mechanisms were confirmed by Western blot, Co-IP and molecular docking. RESULTS: Our study demonstrates a significant upregulation of ANGPTL4 in OC specimens and its strong association with unfavorable prognosis. RNA-seq analysis affirms that ANGPTL4 facilitates OC development by driving JAK2-STAT3 signaling pathway activation. The interaction between ANGPTL4 and ESM1 promotes ANGPTL4 binding to lipoprotein lipase (LPL), thereby resulting in reprogrammed lipid metabolism and the promotion of OC cell proliferation, migration, and invasion. In the OC microenvironment, ESM1 may interfere with the binding of ANGPTL4 to integrin and vascular-endothelial cadherin (VE-Cad), which leads to stabilization of vascular integrity and ultimately promotes angiogenesis. CONCLUSION: Our findings underscore that ANGPTL4 promotes OC development via JAK signaling and induces angiogenesis in the tumor microenvironment through its interaction with ESM1.

Heterogeneity of comprehensive clinical phenotype and longitudinal adaptive function and correlation with computational predictions of severity of missense genotypes in KIF1A-associated neurological disorder.

PURPOSE: Pathogenic variants in kinesin family member 1A (KIF1A) are associated with KIF1A-associated neurological disorder. We report the clinical phenotypes and correlate genotypes of individuals with KIF1A-associated neurological disorder. METHODS: Medical history and adaptive function were assessed longitudinally. In-person evaluations included neurological, motor, ophthalmologic, and cognitive assessments. RESULTS: We collected online data on 177 individuals. Fifty-seven individuals were also assessed in-person. Most individuals had de novo heterozygous missense likely pathogenic/pathogenic KIF1A variants. The most common characteristics were hypotonia, spasticity, ataxia, seizures, optic nerve atrophy, cerebellar atrophy, and cognitive impairment. Mean Vineland adaptive behavior composite score (VABS-ABC) was low (M = 62.9, SD = 19.1). The mean change in VABS-ABC over time was -3.1 (SD = 7.3). The decline in VABS-ABC was associated with the age at first assessment and abnormal electroencephalogram/seizure. There was a positive correlation between evolutionary scale model (ESM) score for the variants and final VABS-ABC (P = .003). Abnormal electroencephalogram/seizure, neuroimaging result, and ESM explain 34% of the variance in final VABS-ABC (P < .001). CONCLUSION: In-person assessment confirmed caregiver report and identified additional visual deficits. Adaptive function declined over time consistent with both the neurodevelopmental and neurodegenerative nature of the condition. Using ESM score assists in predicting phenotype across a wide range of unique variants.

Investigating adverse daily life effects following a psychosocial laboratory stress task, and the moderating role of Psychopathology.

Laboratory stress tasks are necessary to closely investigate the stress response in a controlled environment. However, to our knowledge, no study has tested whether participating in such tasks can pose any daily life adverse effect. Fifty-three healthy participants (46 women) took part in a laboratory session where stress was induced using a typical psychosocial stressor: the repeated Montreal Imaging Stress Task (rMIST). Average levels of negative affect (NA), heart rate (HR), root mean square of successive differences (RMSSD), and skin conductance level (SCL), as well as reactivity across all these parameters as measured with the experience sampling method (ESM) in the four days prior to the laboratory session were compared with the four days following the session. We also assessed whether vulnerability to psychopathology moderated these associations. Findings showed that the task did not pose any significant adverse effect on participants. However, there was an unexpected increase in average RMSSD and a decrease in average SCL pre- to post- task. In addition, more vulnerable individuals were more likely to experience an increase in average levels of NA in the days following the task compared to the days preceding it. Our findings suggest that laboratory stress tasks may pose a significant risk to more vulnerable individuals.

Alcohol, Sexual Arousal, and Partner Familiarity as Predictors of Condom Negotiation: An Experience Sampling Study.

Numerous contextual factors contribute to risky sexual decision-making among men who have sex with men (MSM), with experimental laboratory-based studies suggesting that alcohol consumption, sexual arousal, and partner familiarity have the potential to impact condom negotiations during sexual encounters. The purpose of the current study was to extend this line of inquiry outside of the laboratory and into the everyday lives of MSM. We collected six weeks of daily data on alcohol consumption and sexual behaviors from 257 moderate- and heavy-drinking MSM to examine the within- and between-subjects effects of alcohol consumption, average daily sexual arousal, and partner familiarity on condom negotiation processes during sexual encounters. We hypothesized that alcohol consumption, higher levels of average daily sexual arousal, and greater partner familiarity would all contribute to a reduced likelihood of condom negotiation prior to sexual activity, and that they would also affect the difficulty of negotiations. Contrary to hypotheses, none of these three predictors had significant within-subjects effects on condom negotiation outcomes. However, partner familiarity and average daily sexual arousal did exert significant between-subjects effects on the incidence of negotiation and negotiation difficulty. These findings have important implications for risk-reduction strategies in this population. They also highlight the challenges of reconciling results from experimental laboratory research and experience sampling conducted outside of the laboratory on sexual risk behavior.

Comprehensive Research on Druggable Proteins: From PSSM to Pre-Trained Language Models.

Identification of druggable proteins can greatly reduce the cost of discovering new potential drugs. Traditional experimental approaches to exploring these proteins are often costly, slow, and labor-intensive, making them impractical for large-scale research. In response, recent decades have seen a rise in computational methods. These alternatives support drug discovery by creating advanced predictive models. In this study, we proposed a fast and precise classifier for the identification of druggable proteins using a protein language model (PLM) with fine-tuned evolutionary scale modeling 2 (ESM-2) embeddings, achieving 95.11% accuracy on the benchmark dataset. Furthermore, we made a careful comparison to examine the predictive abilities of ESM-2 embeddings and position-specific scoring matrix (PSSM) features by using the same classifiers. The results suggest that ESM-2 embeddings outperformed PSSM features in terms of accuracy and efficiency. Recognizing the potential of language models, we also developed an end-to-end model based on the generative pre-trained transformers 2 (GPT-2) with modifications. To our knowledge, this is the first time a large language model (LLM) GPT-2 has been deployed for the recognition of druggable proteins. Additionally, a more up-to-date dataset, known as Pharos, was adopted to further validate the performance of the proposed model.

A Systematic Review of Momentary Social Predictors of Positive Psychosis Experiences.

BACKGROUND: Social factors can play an important role in the development and maintenance of psychosis. Clarifying this relationship is vital for advancing theoretical understanding and development of targeted interventions. Psychosis is increasingly researched with an experience sampling methodology (ESM), which provides an ecologically valid approach, that reduces recall biases. Studies examining momentary associations between social factors and psychosis have not yet been summarised. METHOD: We identified 29 ESM studies investigating associations between social factors and positive psychotic experiences through a pre-registered systematic search of the published literature. RESULTS: Being alone did not predict increase in psychosis; however, appraisals and feelings associated with being alone such as feeling socially disconnected, lonely and unwanted did. Being with familiar company was found to reduce psychosis experiences but feeling stressed by the current company increased psychosis. CONCLUSIONS: While issues with sample size and generalisability mean these results should be interpreted with caution, some putative conclusions can be made. Individuals with psychosis or emerging symptoms should be offered interventions that improve social networks such as peer support, community participation and engagement skills training. These individuals may also benefit from virtual reality or compassion-based interventions which aim to dampen perceived social threat. Moreover, digital interventions which monitor changes in social variables that predict relapse in symptoms would allow early intervention to prevent mental health crises.

SEMA3: A free smartphone platform for daily life surveys.

Traditionally, behavioral, social, and health science researchers have relied on global/retrospective survey methods administered cross-sectionally (i.e., on a single occasion) or longitudinally (i.e., on several occasions separated by weeks, months, or years). More recently, social and health scientists have added daily life survey methods (also known as intensive longitudinal methods or ambulatory assessment) to their toolkit. These methods (e.g., daily diaries, experience sampling, ecological momentary assessment) involve dense repeated assessments in everyday settings. To facilitate research using daily life survey methods, we present SEMA3 ( http://www.SEMA3.com ), a platform for designing and administering intensive longitudinal daily life surveys via Android and iOS smartphones. SEMA3 fills an important gap by providing researchers with a free, intuitive, and flexible platform with basic and advanced functionality. In this article, we describe SEMA3's development history and system architecture, provide an overview of how to design a study using SEMA3 and outline its key features, and discuss the platform's limitations and propose directions for future development of SEMA3.

How mood is affected by environment and upsetting events: The moderating role of psychological flexibility.

OBJECTIVE: Patients suffering from psychological disorders report decreased quality of life and low mood. The relationship of these symptoms to daily upsetting events or environments, and in the context of active coping mechanisms is poorly understood. The present study thus investigates the association between mood, psychological flexibility, upsetting events, and environment in the daily life of outpatients. METHOD: We investigated 80 outpatients at the beginning of treatment, using event sampling methodology (ESM). Patients' mood, occurrence of upsetting events, current environment, and psychological flexibility were sampled six times per day during a one-week intensive longitudinal examination. Data were analyzed using linear mixed models (LMMs). RESULTS: Participants reported worse mood the more upsetting events they experienced. Further, participants reported better mood when in private environments (e.g., with friends), and worse mood when at the hospital, compared to being at home. Higher levels of psychological flexibility, however, were associated with better mood, irrespective of the occurrence of upsetting events or current environment. CONCLUSION: Results suggest that mood is positively associated with psychological flexibility, not despite, but especially during the dynamic and context-specific challenges of daily life. Psychological flexibility may thus potentially act as a buffer against distress-provoking situations as patients go about their daily lives. TRIAL REGISTRATION: ISRCTN.org identifier: ISRCTN11209732.

[Quality control method for phenolic acid components in abandoned stems and leaves of Artemisia selengensis based on UPLC fingerprint combined with quantitative analysis of multi-components by single marker (QAMS)].

This study established an ultra-performance liquid chromatography(UPLC) fingerprint of abandoned stems and leaves of Artemisia selengensis and quantitative analysis of multi-components by single marker(QAMS) for five phenolic acid components. Waters Acquity UPLC BEH C_(18) chromatography column(2.1 mm×100 mm, 1.7 µm) was used. The gradient elution was carried out with the mobile phase composed of 0.1% phosphoric acid water and acetonitrile at a flow rate of 0.3 mL·min~(-1) and a column temperature at 30 ℃. The detection wavelength was 330 nm, and the injection volume was 2 µL. Similarity evaluation and cluster analysis were conducted on the fingerprint data, and 15 common components in 13 batches of abandoned stems and leaves of A. selengensis were identified. The relative correction factors of ferulic acid, isochlorogenic acid A, isochlorogenic acid B, and isochlorogenic acid C were calculated using chlorogenic acid as the internal reference. The QAMS for determining five components in the abandoned stems and leaves of A. selengensis was established. At the same time, the content of these five components was determined using the external standard method(ESM), and the results showed that there were no significant differences in their content determined by the QAMS and the ESM. The results indicated that the content of phenolic acid components in the abandoned stems and leaves of A. selengensis from different varieties and different origins had obvious differences. In addition, the content of phenolic acid components in the abandoned stems and leaves of lignified A. selengensis was significantly higher than that of non-lignified A. selengensis. In summary, QAMS established in this study can be quickly, accurately, and economically used to determine the content of five phenolic acid components in abandoned stems and leaves of A. selengensis, laying a foundation for the resource development and utilization of abandoned stems and leaves of A. selengensis.

Novel specific anti-ESM1 antibodies overcome tumor bevacizumab resistance by suppressing angiogenesis and metastasis.

Suppressing tumors through anti-angiogenesis has been established as an effective clinical treatment strategy. Bevacizumab, a monoclonal antibody, is commonly used in various indications. However, two major challenges limit the long-term efficacy of bevacizumab: drug resistance and side effects. Bevacizumab resistance has been extensively studied at the molecular level, but no drug candidates have been developed for clinical use to overcome this resistance. In a previous study conducted by our team, a major finding was that high expression of ESM1 in bevacizumab-resistant tumors is associated with an unfavorable response to treatment. In particular, an increase in ESM1 expression contributes to heightened lung metastasis and microvascular density, which ultimately decreases the tumor's sensitivity to bevacizumab. In contrast, the silencing of ESM1 results in reduced angiogenesis and suppressed tumor growth in tumors resistant to bevacizumab. We put forward the hypothesis that targeting ESM1 could serve as a therapeutic strategy in overcoming bevacizumab resistance. In this study, a variety of anti-ESM1 antibodies with high affinity to human ESM1 were successfully prepared and characterized. Our in vivo study confirmed the establishment of a bevacizumab-resistant human colorectal cancer model and further demonstrated that the addition of anti-ESM1 monoclonal antibodies to bevacizumab treatment significantly improved tumor response while downregulating DLL4 and MMP9. In conclusion, our study suggests that anti-hESM1 monoclonal antibodies have the potential to alleviate or overcome bevacizumab resistance, thereby providing new strategies and drug candidates for clinical research in the treatment of bevacizumab-resistant colorectal cancer.

A smartphone-based intervention for young people who self-harm ('PRIMARY'): study protocol for a multicenter randomized controlled trial.

BACKGROUND: Self-harm in young people is a public health concern connected with severe mental health problems, such as personality pathology. Currently, there are no specific evidence-based interventions available for young people who self-harm. Therefore, we developed PRe-Intervention Monitoring of Affect and Relationships in Youth (PRIMARY), a smartphone-based intervention, co-designed by clinicians and young people with lived experience of mental ill-health. PRIMARY combines the Experience Sampling Method (ESM) with weekly report sessions. The study aims to examine the effectiveness of PRIMARY with regard to reducing self-harm, and improving emotion regulation and quality of relationships. METHODS: This study is a multicenter, parallel groups, randomized controlled trial (RCT) comparing the PRIMARY intervention to a waiting list control group. PRIMARY comprises 28 consecutive days of questionnaires five times each day (i.e., ESM) and four weekly report sessions. Participants will comprise 180 young people referred for treatment to the participating Dutch mental healthcare institutions and (1) are aged 12 to 25 years, and (2) engaged in ≥ 1 act of self-harm in the past year. Participants are randomly allocated to a study group after screening in a 1:1 ratio by an independent researcher using computer-generated randomization sequences with stratified block randomization by age (12 to 15 years / 16 to 25 years). Staff will conduct assessments with all participants at baseline (Wave 1), after 28 days (Wave 2), and in a subsample after 10 weeks of subsequent specialized treatment (Wave 3). The primary outcomes are self-harm, emotion regulation, and quality of relationships. Secondary outcomes include patient and clinician satisfaction. Exploratory analyses of ESM data will examine the relationship between emotions, social relationships, and self-harm. DISCUSSION: The results of this trial will clarify whether an innovative smartphone-based intervention is effective for reducing self harm and improving emotion regulation and the quality of social relationships. It has the potential to fill a treatment gap of interventions specifically targeting self-harm. If proven effective, it would provide an accessible, easy-to-implement, low-cost intervention for young people. Furthermore, the ESM-data will allow detailed analyses into the processes underlying self-harm, which will contribute to theoretical knowledge regarding the behavior. TRIAL REGISTRATION: ISRCTN42088538 ( https://doi.org/10.1186/ISRCTN42088538 ), retrospectively registered on the 26th of October 2022.

Associations between daily affective experiences, trait and daily rumination on negative and positive affect: a diary study.

BACKGROUND: Rumination has mostly been studied in relation to depression, however, it may also occur in response to positive emotions (i.e., positive rumination) and therefore may be a protective factor related to the maintenance of positive mood. OBJECTIVE: We hypothesized that daily positive and negative affect would be associated with daily positive and negative rumination even after controlling for trait-level rumination. METHOD: We carried out a diary study with university students (n = 178), where participants had to answer short surveys online about their daily affect and daily rumination every evening for 10 days. We analyzed our data with multilevel regression in R. RESULTS: Daily positive and negative affect were significantly associated with daily negative and positive rumination, while trait-level rumination scores were not. Daily and trait-level rumination were moderately correlated (r = 0.333-0.440). CONCLUSIONS: Our findings highlight that daily rumination plays a more significant role in daily emotional experiences than trait rumination across positive and negative valence domains. Daily negative affect appears to be more closely related to higher daily negative rumination than the lack of daily positive rumination, which could be relevant for intervention strategies.

Never miss a beep: Using mobile sensing to investigate (non-)compliance in experience sampling studies.

Given the increasing number of studies in various disciplines using experience sampling methods, it is important to examine compliance biases because related patterns of missing data could affect the validity of research findings. In the present study, a sample of 592 participants and more than 25,000 observations were used to examine whether participants responded to each specific questionnaire within an experience sampling framework. More than 400 variables from the three categories of person, behavior, and context, collected multi-methodologically via traditional surveys, experience sampling, and mobile sensing, served as predictors. When comparing different linear (logistic and elastic net regression) and non-linear (random forest) machine learning models, we found indication for compliance bias: response behavior was successfully predicted. Follow-up analyses revealed that study-related past behavior, such as previous average experience sampling questionnaire response rate, was most informative for predicting compliance, followed by physical context variables, such as being at home or at work. Based on our findings, we discuss implications for the design of experience sampling studies in applied research and future directions in methodological research addressing experience sampling methodology and missing data.

How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa.

Intensive longitudinal data (ILD) have become popular for studying within-person dynamics in psychological constructs (or between-person differences therein). Before investigating the dynamics, it is crucial to examine whether the measurement model (MM) is the same across subjects and time and, thus, whether the measured constructs have the same meaning. If the MM differs (e.g., because of changes in item interpretation or response styles), observations cannot be validly compared. Exploring differences in the MM for ILD can be done with latent Markov factor analysis (LMFA), which classifies observations based on the underlying MM (for many subjects and time points simultaneously) and thus shows which observations are comparable. However, the complexity of the method or the fact that no open-source software for LMFA existed until now may have hindered researchers from applying the method in practice. In this article, we provide a step-by-step tutorial for the new user-friendly software package lmfa, which allows researchers to easily perform the analysis LMFA in the freely available software R to investigate MM differences in their own ILD.

Feedback About a Person's Social Context - Personal Networks and Daily Social Interactions.

The social context of a person, meaning their social relationships and daily social interactions, is an important factor for understanding their mental health. However, personalised feedback approaches to psychotherapy do not consider this factor sufficiently yet. Therefore, we developed an interactive feedback prototype focusing specifically on a person's social relationships as captured with personal social networks (PSN) and daily social interactions as captured with experience sampling methodology (ESM). We describe the development of the prototype as well as two evaluation studies: Semi-structured interviews with students (N = 23) and a focus group discussion with five psychotherapy patients. Participants from both studies considered the prototype useful. The students considered participation in our study, which included social context assessment via PSN and ESM as well as a feedback session, insightful. However, it remains unclear how much insight the feedback procedure generated for the students beyond the insights they already gained from the assessments. The focus group patients indicated that in a clinical context, (social context) feedback may be especially useful to generate insight for the clinician and facilitate collaboration between patient and clinician. Furthermore, it became clear that the current feedback prototype requires explanations by a researcher or trained clinician and cannot function as a stand-alone intervention. As such, we discuss our feedback prototype as a starting point for future research and clinical implementation.

Personalized Prediction of Behaviors and Experiences: An Idiographic Person-Situation Test.

A longstanding goal of psychology is to predict the things that people do and feel, but tools to accurately predict future behaviors and experiences remain elusive. In the present study, we used intensive longitudinal data (N = 104 college-age adults at a midwestern university; total assessments = 5,971) and three machine-learning approaches to investigate the degree to which three future behaviors and experiences-loneliness, procrastination, and studying-could be predicted from past psychological (i.e., personality and affective states), situational (i.e., objective situations and psychological situation cues), and time (i.e., trends, diurnal cycles, time of day, and day of the week) phenomena from an idiographic, person-specific perspective. Rather than pitting persons against situations, such an approach allows psychological phenomena, situations, and time to jointly predict future behaviors and experiences. We found (a) a striking degree of prediction accuracy across participants, (b) that a majority of participants' future behaviors are predicted by both person and situation features, and (c) that the most important features vary greatly across people.