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Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time‐varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross‐sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research.

In clinical research, populations are often selected on the sum-score of diagnostic criteria such as symptoms. Estimating statistical models where a subset of the data is selected based on a function of the analyzed variables introduces Berkson's bias, which presents a potential threat to the validity of findings in the clinical literature. The aim of the present paper is to investigate the effect of Berkson's bias on the performance of the two most commonly used psychological network models: the Gaussian Graphical Model (GGM) for continuous and ordinal data, and the Ising Model for binary data. In two simulation studies, we test how well the two models recover a true network structure when estimation is based on a subset of the data typically seen in clinical studies. The network is based on a dataset of 2807 patients diagnosed with major depression, and nodes in the network are items from the Hamilton Rating Scale for Depression (HRSD). The simulation studies test different scenarios by varying (1) sample size and (2) the cut-off value of the sum-score which governs the selection of participants. The results of both studies indicate that higher cut-off values are associated with worse recovery of the network structure. As expected from the Berkson's bias literature, selection reduced recovery rates by inducing negative connections between the items. Our findings provide evidence that Berkson's bias is a considerable and underappreciated problem in the clinical network literature. Furthermore, we discuss potential solutions to circumvent Berkson's bias and their pitfalls.

Scientific theories reflect some of humanity's greatest epistemic achievements. The best theories motivate us to search for discoveries, guide us towards successful interventions, and help us to explain and organize knowledge. Such theories require a high degree of specificity, which in turn requires formal modeling. Yet, in psychological science, many theories are not precise and psychological scientists often lack the technical skills to formally specify existing theories. This problem raises the question: How can we promote formal theory development in psychology, where there are many content experts but few modelers? In this paper, we discuss one strategy for addressing this issue: a Many Modelers approach. Many Modelers consists of mixed teams of modelers and non-modelers that collaborate to create a formal theory of a phenomenon. Here, we report a proof of concept of this approach, which we piloted as a three-hour hackathon at the Society for the Improvement of Psychological Science conference in 2021. After surveying the participants, results suggest that (a) psychologists who have never developed a formal model can become (more) excited about formal modeling + and theorizing; (b) a division of labor in formal theorizing is possible where only one or a few team members possess the prerequisite modeling expertise; and (c) first working prototypes of a theoretical model can be created in a short period of time. These results show some promise for the many modelers approach as a team science tool for theory development.

Many people have flipped coins but few have stopped to ponder the statistical and physical intricacies of the process. We collected \\(350{,}757\\) coin flips to test the counterintuitive prediction from a physics model of human coin tossing developed by Diaconis, Holmes, and Montgomery (DHM; 2007). The model asserts that when people flip an ordinary coin, it tends to land on the same side it started -- DHM estimated the probability of a same-side outcome to be about 51\\%. Our data lend strong support to this precise prediction: the coins landed on the same side more often than not, \\(\\text{Pr}(\\text{same side}) = 0.508\\), 95\\% credible interval (CI) [\\(0.506\\), \\(0.509\\)], \\(\\text{BF}_{\\text{same-side bias}} = 2359\\). Furthermore, the data revealed considerable between-people variation in the degree of this same-side bias. Our data also confirmed the generic prediction that when people flip an ordinary coin -- with the initial side-up randomly determined -- it is equally likely to land heads or tails: \\(\\text{Pr}(\\text{heads}) = 0.500\\), 95\\% CI [\\(0.498\\), \\(0.502\\)], \\(\\text{BF}_{\\text{heads-tails bias}} = 0.182\\). Furthermore, this lack of heads-tails bias does not appear to vary across coins. Additional analyses revealed that the within-people same-side bias decreased as more coins were flipped, an effect that is consistent with the possibility that practice makes people flip coins in a less wobbly fashion. Our data therefore provide strong evidence that when some (but not all) people flip a fair coin, it tends to land on the same side it started.

Many people have flipped coins but few have stopped to ponder the statistical and physical intricacies of the process. In a preregistered study we collected \\(350{,}757\\) coin flips to test the counterintuitive prediction from a physics model of human coin tossing developed by Diaconis, Holmes, and Montgomery (DHM; 2007). The model asserts that when people flip an ordinary coin, it tends to land on the same side it started -- DHM estimated the probability of a same-side outcome to be about 51%. Our data lend strong support to this precise prediction: the coins landed on the same side more often than not, \\(\\text{Pr}(\\text{same side}) = 0.508\\), 95% credible interval (CI) [\\(0.506\\), \\(0.509\\)], \\(\\text{BF}_{\\text{same-side bias}} = 2359\\). Furthermore, the data revealed considerable between-people variation in the degree of this same-side bias. Our data also confirmed the generic prediction that when people flip an ordinary coin -- with the initial side-up randomly determined -- it is equally likely to land heads or tails: \\(\\text{Pr}(\\text{heads}) = 0.500\\), 95% CI [\\(0.498\\), \\(0.502\\)], \\(\\text{BF}_{\\text{heads-tails bias}} = 0.182\\). Furthermore, this lack of heads-tails bias does not appear to vary across coins. Additional exploratory analyses revealed that the within-people same-side bias decreased as more coins were flipped, an effect that is consistent with the possibility that practice makes people flip coins in a less wobbly fashion. Our data therefore provide strong evidence that when some (but not all) people flip a fair coin, it tends to land on the same side it started. Our data provide compelling statistical support for the DHM physics model of coin tossing.

The severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) disease (COVID-19) pandemic has caused millions of deaths worldwide. Genome-wide association studies identified the 3p21.31 region as conferring a twofold increased risk of respiratory failure. Here, using a combined multiomics and machine learning approach, we identify the gain-of-function risk A allele of an SNP, rs17713054G>A, as a probable causative variant. We show with chromosome conformation capture and gene-expression analysis that the rs17713054-affected enhancer upregulates the interacting gene, leucine zipper transcription factor like 1 ( LZTFL1 ). Selective spatial transcriptomic analysis of lung biopsies from patients with COVID-19 shows the presence of signals associated with epithelial–mesenchymal transition (EMT), a viral response pathway that is regulated by LZTFL1 . We conclude that pulmonary epithelial cells undergoing EMT, rather than immune cells, are likely responsible for the 3p21.31-associated risk. Since the 3p21.31 effect is conferred by a gain-of-function, LZTFL1 may represent a therapeutic target. SNP rs17713054 in the 3p21.31 COVID-19 risk locus is identified as a probable causative variant for disease association. Chromatin conformation and gene expression data indicate that LZTFL1 is impacted by rs17713054 in pulmonary epithelial cells.

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individualdifference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationshipspecific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

Background Atopic dermatitis (AD) is a chronic inflammatory skin disease that affects 10 to 20% of children and between 2 and 15% of the adults in Western Europe. Since 2000, therapeutic clothing or functional textiles based on silver or chitosan as antibacterial agents were introduced for AD. These agents aim to reduce skin colonization with Staphylococcus ( S .) aureus . Increased colonization with S. aureus is correlated with increased AD severity. The antimicrobial effects of silver and chitosan have been demonstrated before. At this point, there is insufficient evidence for the effectiveness of antibacterial therapeutic clothing in patients with AD. Methods This is a pragmatic randomized controlled double-blind multi-center trial comparing the effectiveness of antibacterial therapeutic clothing based on silver or chitosan as compared with non-antibacterial therapeutic clothing in patients with moderate to severe AD. A total of 165 participants, aged 0 to 80, diagnosed with moderate to severe AD are included. The study is performed in the Erasmus MC University Medical Center, University Medical Center Groningen, University Medical Center Utrecht, Amsterdam University Medical Centers, and St. Antonius Hospital Nieuwegein. Patients will be randomized 1:1:1 into one of the three intervention groups: group A will receive therapeutic clothing without antimicrobial agents, group B will receive microbial growth reducing therapeutic clothing based on chitosan, and group C will receive antimicrobial clothing based on silver. All therapeutic clothing is to be worn at night during the 12-month intervention period. Usual care is continued. The primary objective is to assess the effectiveness of antibacterial clothing (silver and chitosan group) as compared to non-antibacterial clothing assessed with the Eczema Area and Severity Index at 12 months compared to baseline. Secondary outcomes include between-group differences in physician- and patient-reported outcome measures, topical therapy use, S. aureus skin colonization, and safety. Data will be collected at baseline and after 1 month, 3 months, 6 months, and 12 months. A cost-effectiveness analysis will be performed. Discussion This trial will provide data on the effectiveness, cost-effectiveness, and safety of antibacterial therapeutic clothing for patients with AD. Trial registration ClinicalTrials.gov NCT04297215. Registered on 5 March 2020

Objective To examine childhood cancer risks associated with exposure to diagnostic radiation and ultrasound scans in utero and in early infancy (age 0-100 days).Design Case-control study.Setting England and Wales.Participants 2690 childhood cancer cases and 4858 age, sex, and region matched controls from the United Kingdom Childhood Cancer Study (UKCCS), born 1976-96.Main outcome measures Risk of all childhood cancer, leukaemia, lymphoma, and central nervous system tumours, measured by odds ratios.Results Logistic regression models conditioned on matching factors, with adjustment for maternal age and child’s birth weight, showed no evidence of increased risk of childhood cancer with in utero exposure to ultrasound scans. Some indication existed of a slight increase in risk after in utero exposure to x rays for all cancers (odds ratio 1.l4, 95% confidence interval 0.90 to 1.45) and leukaemia (1.36, 0.91 to 2.02), but this was not statistically significant. Exposure to diagnostic x rays in early infancy (0-100 days) was associated with small, non-significant excess risks for all cancers and leukaemia, as well as increased risk of lymphoma (odds ratio 5.14, 1.27 to 20.78) on the basis of small numbers.Conclusions Although the results for lymphoma need to be replicated, all of the findings indicate possible risks of cancer from radiation at doses lower than those associated with commonly used procedures such as computed tomography scans, suggesting the need for cautious use of diagnostic radiation imaging procedures to the abdomen/pelvis of the mother during pregnancy and in children at very young ages.