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Abstract Climate change is currently one of humanity’s greatest threats. To help scholars understand the psychology of climate change, we conducted an online quasi-experimental survey on 59,508 participants from 63 countries (collected between July 2022 and July 2023). In a between-subjects design, we tested 11 interventions designed to promote climate change mitigation across four outcomes: climate change belief, support for climate policies, willingness to share information on social media, and performance on an effortful pro-environmental behavioural task. Participants also reported their demographic information (e.g., age, gender) and several other independent variables (e.g., political orientation, perceptions about the scientific consensus). In the no-intervention control group, we also measured important additional variables, such as environmentalist identity and trust in climate science. We report the collaboration procedure, study design, raw and cleaned data, all survey materials, relevant analysis scripts, and data visualisations. This dataset can be used to further the understanding of psychological, demographic, and national-level factors related to individual-level climate action and how these differ across countries.

Abstract The risk of developing age-related macular degeneration (AMD) is influenced by genetic background. In 2016, the International AMD Genomics Consortium (IAMDGC) identified 52 risk variants in 34 loci, and a polygenic risk score (PRS) from these variants was associated with AMD. The Israeli population has a unique genetic composition: Ashkenazi Jewish (AJ), Jewish non-Ashkenazi, and Arab sub-populations. We aimed to perform a genome-wide association study (GWAS) for AMD in Israel, and to evaluate PRSs for AMD. Our discovery set recruited 403 AMD patients and 256 controls at Hadassah Medical Center. We genotyped individuals via custom exome chip. We imputed non-typed variants using cosmopolitan and AJ reference panels. We recruited additional 155 cases and 69 controls for validation. To evaluate predictive power of PRSs for AMD, we used IAMDGC summary-statistics excluding our study and developed PRSs via clumping/thresholding or LDpred2. In our discovery set, 31/34 loci reported by IAMDGC were AMD-associated (P

What is hope, and how can we measure it? These questions have occupied the minds of hope scholars across disciplines. This article outlines a comprehensive approach to understanding hope: the Bidimensional Model of Hope. Building on the standard definition of hope, the bidimensional model explores hope as the intersection between wishes (desires, aspirations) and expectations (assessment of possibility). Hope is thus located on a bidimensional plane with two perpendicular axes; one corresponds to the levels of wishes to achieve an outcome, and the second to the levels of expectations of achieving the outcome. We claim that the bidimensional approach is comprehensive enough to include existing definitions of hope while being parsimonious, versatile, and applicable to many contexts, including those where the hoped-for outcome is beyond people’s actual or perceived control. We show the model’s theoretical and methodological utility and its use in existing qualitative and quantitative research on hope in the context of intractable international conflicts. We end with suggesting pathways for developing and applying the Bidimensional Model of Hope to assist hope research in a variety of contexts and disciplines.

Abstract Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones and wearable devices, to infer depressive mood. However, most digital phenotype studies suffer from poor replicability, often fail to detect clinically relevant events, and use measures of depression that are not validated or suitable for collecting large and longitudinal data. Here, we report high-quality longitudinal validated assessments of depressive mood from computerized adaptive testing paired with continuous digital assessments of behavior from smartphone sensors for up to 40 weeks on 183 individuals experiencing mild to severe symptoms of depression. We apply a combination of cubic spline interpolation and idiographic models to generate individualized predictions of future mood from the digital behavioral phenotypes, achieving high prediction accuracy of depression severity up to three weeks in advance (R 2 ≥ 80%) and a 65.7% reduction in the prediction error over a baseline model which predicts future mood based on past depression severity alone. Finally, our study verified the feasibility of obtaining high-quality longitudinal assessments of mood from a clinical population and predicting symptom severity weeks in advance using passively collected digital behavioral data. Our results indicate the possibility of expanding the repertoire of patient-specific behavioral measures to enable future psychiatric research.

Abstract Evidence-based interventions to favor more harmonious interactions in difficult relationships remain scarce. This study examined whether compassion training may have beneficial effects in an ongoing tense relationship with a disliked person, by reducing schadenfreude toward them and increasing felt interpersonal closeness. 108 participants were assigned to one of three 5-week trainings in a longitudinal randomized controlled study: compassion training, reappraisal training (emotion regulation control condition), or Italian language training (neutral active control condition). The disliked person was not targeted during the trainings to test potential transfer effects. Misfortune scenarios and a measure of interpersonal closeness were used to test whether schadenfreude and closeness feelings toward a disliked person changed from pre- to post-training, across different experimental and control groups. Only compassion and reappraisal trainees reported a decrease of schadenfreude feelings toward the disliked person compared to their pre-training ratings, no changes were observed in the Italian language training. Importantly, feelings of closeness toward the disliked person increased in the compassion training group compared to the other two groups. This increase of closeness feelings could be a central mechanism for improving social interactions. These transfer effects open new perspectives concerning emotion regulation interventions in conflict resolution.

Cross-party violence – violence between opposing partisans – is a growing concern worldwide. Understanding the predictors of the legitimization of violence against political opponents is thus becoming a vital goal. In this work, we propose two potential explanatory frameworks: affective polarization and low non-violent group efficacy. The first reflects an affective motivation to harm the out-party, while the latter reflects an instrumental motivation to deploy violence. Furthermore, while the former is frequently suggested in scholarly work as a cause for such violence, it has so far been backed only by limited empirical support. On the other hand, the latter is hardly examined in the context of interactions between opposing partisans. We tested the relationship of the two explanatory frameworks with the legitimization of cross-party violence using public opinion surveys in two real-world contexts: in 2020, in Israel (N = 500) and in the US (N = 631). Results from a regression analysis provide support for the instrumental explanation but not for the affective one. We discuss the theoretical implications of our findings for the research of affective polarization, and potential practical implications for attempts to reduce cross-party violence.

Abstract Everyday expression of prejudice continues to pose a social challenge across societies. We tend to assume that to the extent people are egalitarian, they are more likely to confront prejudice—but this might not necessarily be the case. We tested this assumption in two countries (US and Hungary) among majority members of society, using a behavioral paradigm for measuring confronting. Prejudice was directed at various outgroup minority individuals (African Americans, Muslims and Latinos in the US, and Roma in Hungary). Across four experiments (N = 1116), we predicted and found that egalitarian (anti-prejudiced) values were only associated with hypothetical confronting intentions, but not with actual confronting, and stronger egalitarians were more likely to overestimate their confronting than weaker egalitarians—to the point that while intentions differed, the actual confronting rate of stronger and weaker egalitarians were similar. We also predicted and found that such overestimation was associated with internal (and not external) motivation to respond without prejudice. We also identified behavioral uncertainty (being uncertain how to intervene) as a potential explanation for egalitarians’ overestimation. The implications of these findings for egalitarians’ self-reflection, intergroup interventions, and research are discussed.

Abstract The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.

Abstract Elucidating the sources of a microbiome can provide insight into the ecological dynamics responsible for the formation of these communities. Source tracking approaches to date leverage species abundance information; however, single nucleotide variants (SNVs) may be more informative because of their high specificity to certain sources. To overcome the computational burden of utilizing all SNVs for a given sample, we introduce a novel method to identify signature SNVs for source tracking. Signature SNVs used as input into a previously designed source tracking algorithm, FEAST, can more accurately estimate contributions than species and provide novel insights, demonstrated in three case studies.

Abstract Transforming long-term conflicts into peaceful intergroup relations is one of the most difficult challenges for humanity. Such meaningful social changes are often driven by young people. But do young people living in contexts of long-term conflicts believe that change is even possible? In a series of six studies (N total = 119,671) over two decades and across two unrelated intractable conflicts in Israel/Palestine and Cyprus, we found that younger (compared to older) generations from both respective rival groups have less hope for peace, and consequently less conciliatory attitudes. We also show that this gradual improvement of peace-promoting emotions and attitudes with increasing age can be experimentally accelerated in young people through a virtual reality-based aging simulation. These findings provide a new perspective on the fundamental question of why long-term conflicts are so difficult to resolve and highlight the importance of instilling hope in young generations to advance peace processes.

People tend to feel less empathy toward people who do not belong to their social group (outgroup members). Here, the authors show that leading people to believe that empathy is unlimited increases empathy, support for prosocial actions, and empathic behaviors toward outgroup members.

Abstract Background Large medical centers in urban areas, like Los Angeles, care for a diverse patient population and offer the potential to study the interplay between genetic ancestry and social determinants of health. Here, we explore the implications of genetic ancestry within the University of California, Los Angeles (UCLA) ATLAS Community Health Initiative—an ancestrally diverse biobank of genomic data linked with de-identified electronic health records (EHRs) of UCLA Health patients (N=36,736). Methods We quantify the extensive continental and subcontinental genetic diversity within the ATLAS data through principal component analysis, identity-by-descent, and genetic admixture. We assess the relationship between genetically inferred ancestry (GIA) and >1500 EHR-derived phenotypes (phecodes). Finally, we demonstrate the utility of genetic data linked with EHR to perform ancestry-specific and multi-ancestry genome and phenome-wide scans across a broad set of disease phenotypes. Results We identify 5 continental-scale GIA clusters including European American (EA), African American (AA), Hispanic Latino American (HL), South Asian American (SAA) and East Asian American (EAA) individuals and 7 subcontinental GIA clusters within the EAA GIA corresponding to Chinese American, Vietnamese American, and Japanese American individuals. Although we broadly find that self-identified race/ethnicity (SIRE) is highly correlated with GIA, we still observe marked differences between the two, emphasizing that the populations defined by these two criteria are not analogous. We find a total of 259 significant associations between continental GIA and phecodes even after accounting for individuals’ SIRE, demonstrating that for some phenotypes, GIA provides information not already captured by SIRE. GWAS identifies significant associations for liver disease in the 22q13.31 locus across the HL and EAA GIA groups (HL p-value=2.32×10−16, EAA p-value=6.73×10−11). A subsequent PheWAS at the top SNP reveals significant associations with neurologic and neoplastic phenotypes specifically within the HL GIA group. Conclusions Overall, our results explore the interplay between SIRE and GIA within a disease context and underscore the utility of studying the genomes of diverse individuals through biobank-scale genotyping linked with EHR-based phenotyping.

Transcriptome-wide association studies (TWAS) often ignore the specificity and sharing of effects across contexts (e.g., tissues). Here, the authors describe a method to split genetic effects into context-shared and context-specific terms. They apply their method to tissue and single-cell RNA-seq and show improved power in TWAS.

Developing messaging to encourage minorities to adhere to health recommendations has been a complex task for governments worldwide during the COVID-19 crisis. Here, we propose and tests a new typology of messages among minorities that can be used to mobilize compliance and engagement. This typology comprises three messaging treatments emphasizing personal, ingroup, and intergroup benefits. We examine, via an experimental field study, whether there is a difference in the effect of these messages on two policy outcomes, social distancing and vaccine hesitancy, among the Arab minority living in Israel. The findings suggest that social messages, i.e., ingroup and intergroup messages, positively affect social distancing, while self-messaging harms social distancing compliance. Regarding vaccine intake, within the social messages tested, intergroup-focused messages were more effective than ingroup-focused messages for vaccination intentions only among citizens with low trust in the government. We discuss the findings in detail and propose new avenues in theory and practice to foster health policy compliance among minorities.

Abstract Inference of clinical phenotypes is a fundamental task in precision medicine, and has therefore been heavily investigated in recent years in the context of electronic health records (EHR) using a large arsenal of machine learning techniques, as well as in the context of genetics using polygenic risk scores (PRS). In this work, we considered the epigenetic analog of PRS, methylation risk scores (MRS), a linear combination of methylation states. We measured methylation across a large cohort (n = 831) of diverse samples in the UCLA Health biobank, for which both genetic and complete EHR data are available. We constructed MRS for 607 phenotypes spanning diagnoses, clinical lab tests, and medication prescriptions. When added to a baseline set of predictive features, MRS significantly improved the imputation of 139 outcomes, whereas the PRS improved only 22 (median improvement for methylation 10.74%, 141.52%, and 15.46% in medications, labs, and diagnosis codes, respectively, whereas genotypes only improved the labs at a median increase of 18.42%). We added significant MRS to state-of-the-art EHR imputation methods that leverage the entire set of medical records, and found that including MRS as a medical feature in the algorithm significantly improves EHR imputation in 37% of lab tests examined (median R 2 increase 47.6%). Finally, we replicated several MRS in multiple external studies of methylation (minimum p-value of 2.72 × 10−7) and replicated 22 of 30 tested MRS internally in two separate cohorts of different ethnicity. Our publicly available results and weights show promise for methylation risk scores as clinical and scientific tools.

Abstract Manuscripts that have successfully used machine learning (ML) to predict a variety of perioperative outcomes often use only a limited number of features selected by a clinician. We hypothesized that techniques leveraging a broad set of features for patient laboratory results, medications, and the surgical procedure name would improve performance as compared to a more limited set of features chosen by clinicians. Feature vectors for laboratory results included 702 features total derived from 39 laboratory tests, medications consisted of a binary flag for 126 commonly used medications, procedure name used the Word2Vec package for create a vector of length 100. Nine models were trained: baseline features, one for each of the three types of data Baseline + Each data type, (all features, and then all features with feature reduction algorithm. Across both outcomes the models that contained all features (model 8) (Mortality ROC-AUC 94.32 ± 1.01, PR-AUC 36.80 ± 5.10 AKI ROC-AUC 92.45 ± 0.64, PR-AUC 76.22 ± 1.95) was superior to models with only subsets of features. Featurization techniques leveraging a broad away of clinical data can improve performance of perioperative prediction models.

Understanding collective behaviour is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviours and support for public health policies in the context of the pandemic.

Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss in individuals over 55 years old in the United States. One of the late-stage manifestations of AMD, and a major cause of vision loss, is the development of exudative macular neovascularization (MNV). Optical Coherence Tomography (OCT) is the gold standard to identify fluid at different levels within the retina. The presence of fluid is considered the hallmark to define the presence of disease activity. Anti-vascular growth factor (anti-VEGF) injections can be used to treat exudative MNV. However, given the limitations of anti-VEGF treatment, as burdensome need for frequent visits and repeated injections to sustain efficacy, limited durability of the treatment, poor or no response, there is a great interest in detecting early biomarkers associated with a higher risk for AMD progression to exudative forms in order to optimize the design of early intervention clinical trials. The annotation of structural biomarkers on optical coherence tomography (OCT) B-scans is a laborious, complex and time-consuming process, and discrepancies between human graders can introduce variability into this assessment. To address this issue, a deep-learning model (SLIVER-net) was proposed, which could identify AMD biomarkers on structural OCT volumes with high precision and without human supervision. However, the validation was performed on a small dataset, and the true predictive power of these detected biomarkers in the context of a large cohort has not been evaluated. In this retrospective cohort study, we perform the largest-scale validation of these biomarkers to date. We also assess how these features combined with other EHR data (demographics, comorbidities, etc) affect and/or improve the prediction performance relative to known factors. Our hypothesis is that these biomarkers can be identified by a machine learning algorithm without human supervision, in a way that they preserve their predictive nature. The way we test this hypothesis is by building several machine learning models utilizing these machine-read biomarkers and assessing their added predictive power. We found that not only can we show that the machine-read OCT B-scan biomarkers are predictive of AMD progression, we also observe that our proposed combined OCT and EHR data-based algorithm outperforms the state-of-the-art solution in clinically relevant metrics and provides actionable information which has the potential to improve patient care. In addition, it provides a framework for automated large-scale processing of OCT volumes, making it possible to analyze vast archives without human supervision.

ABSTRACT Microbial source tracking analysis has emerged as a widespread technique for characterizing the properties of complex microbial communities. However, this analysis is currently limited to source environments sampled in a specific study. In order to expand the scope beyond one single study and allow the exploration of source environments using large databases and repositories, such as the Earth Microbiome Project, a source selection procedure is required. Such a procedure will allow differentiating between contributing environments and nuisance ones when the number of potential sources considered is high. Here, we introduce STENSL (microbial Source Tracking with ENvironment SeLection), a machine learning method that extends common microbial source tracking analysis by performing an unsupervised source selection and enabling sparse identification of latent source environments. By incorporating sparsity into the estimation of potential source environments, STENSL improves the accuracy of true source contribution, while significantly reducing the noise introduced by noncontributing ones. We therefore anticipate that source selection will augment microbial source tracking analyses, enabling exploration of multiple source environments from publicly available repositories while maintaining high accuracy of the statistical inference. IMPORTANCE Microbial source tracking is a powerful tool to characterize the properties of complex microbial communities. However, this analysis is currently limited to source environments sampled in a specific study. In many applications there is a clear need to consider source selection over a large array of microbial environments, external to the study. To this end, we developed STENSL (microbial Source Tracking with ENvironment SeLection), an expectation-maximization algorithm with sparsity that enables the identification of contributing sources among a large set of potential microbial environments. With the unprecedented expansion of microbiome data repositories such as the Earth Microbiome Project, recording over 200,000 samples from more than 50 types of categorized environments, STENSL takes the first steps in performing automated source exploration and selection. STENSL is significantly more accurate in identifying the contributing sources as well as the unknown source, even when considering hundreds of potential source environments, settings in which state-of-the-art microbial source tracking methods add considerable error.

Abstract In two-thirds of intensive care unit (ICU) patients and 90% of surgical patients, arterial blood pressure (ABP) is monitored non-invasively but intermittently using a blood pressure cuff. Since even a few minutes of hypotension increases the risk of mortality and morbidity, for the remaining (high-risk) patients ABP is measured continuously using invasive devices, and derived values are extracted from the recorded waveforms. However, since invasive monitoring is associated with major complications (infection, bleeding, thrombosis), the ideal ABP monitor should be both non-invasive and continuous. With large volumes of high-fidelity physiological waveforms, it may be possible today to impute a physiological waveform from other available signals. Currently, the state-of-the-art approaches for ABP imputation only aim at intermittent systolic and diastolic blood pressure imputation, and there is no method that imputes the continuous ABP waveform. Here, we developed a novel approach to impute the continuous ABP waveform non-invasively using two continuously-monitored waveforms that are currently part of the standard-of-care, the electrocardiogram (ECG) and photo-plethysmogram (PPG), by adapting a deep learning architecture designed for image segmentation. Using over 150,000 min of data collected at two separate health systems from 463 patients, we demonstrate that our model provides a highly accurate prediction of the continuous ABP waveform (root mean square error 5.823 (95% CI 5.806–5.840) mmHg), as well as the derived systolic (mean difference 2.398 ± 5.623 mmHg) and diastolic blood pressure (mean difference − 2.497 ± 3.785 mmHg) compared to arterial line measurements. Our approach can potentially be used to measure blood pressure continuously and non-invasively for all patients in the acute care setting, without the need for any additional instrumentation beyond the current standard-of-care.

Abstract One of the core challenges in applying machine learning and artificial intelligence to medicine is the limited availability of annotated medical data. Unlike in other applications of machine learning, where an abundance of labeled data is available, the labeling and annotation of medical data and images require a major effort of manual work by expert clinicians who do not have the time to annotate manually. In this work, we propose a new deep learning technique (SLIVER-net), to predict clinical features from 3-dimensional volumes using a limited number of manually annotated examples. SLIVER-net is based on transfer learning, where we borrow information about the structure and parameters of the network from publicly available large datasets. Since public volume data are scarce, we use 2D images and account for the 3-dimensional structure using a novel deep learning method which tiles the volume scans, and then adds layers that leverage the 3D structure. In order to illustrate its utility, we apply SLIVER-net to predict risk factors for progression of age-related macular degeneration (AMD), a leading cause of blindness, from optical coherence tomography (OCT) volumes acquired from multiple sites. SLIVER-net successfully predicts these factors despite being trained with a relatively small number of annotated volumes (hundreds) and only dozens of positive training examples. Our empirical evaluation demonstrates that SLIVER-net significantly outperforms standard state-of-the-art deep learning techniques used for medical volumes, and its performance is generalizable as it was validated on an external testing set. In a direct comparison with a clinician panel, we find that SLIVER-net also outperforms junior specialists, and identifies AMD progression risk factors similarly to expert retina specialists.

Civilian casualties contribute to the perpetuation of intergroup conflicts through increased radicalization and hostilities, but little is known on the psychological processes that affect responses to outgroup civilian casualties. The goal of the present research was to explore two factors expected to lead group members to act more cautiously, thereby reducing civilian casualties: perceived accountability and forecast group-based moral emotions. In two studies, Jewish–Israeli civilians (Study 1) and soldiers (Study 2) were asked to forecast their group-based moral emotions in case of Palestinian (i.e., outgroup) civilian casualties, then exposed to accountability manipulations. Participants who expected to feel low levels of shame and were primed with accountability made more cautious decisions than those in the control condition. Participants who expected to feel high levels of shame were unaffected by accountability primes. Theoretical and practical implications regarding forecast moral emotions and accountability as an intervention in intergroup conflicts are discussed.

Alzheimer’s disease (AD) is the most common form of dementia and a growing public health burden in the United States. Significant progress has been made in identifying genetic risk for AD, but limited studies have investigated how AD genetic risk may be associated with other disease conditions in an unbiased fashion. In this study, we conducted a phenome-wide association study (PheWAS) by genetic ancestry groups within a large academic health system using the polygenic risk score (PRS) for AD. PRS was calculated using LDpred2 with genome-wide association study (GWAS) summary statistics. Phenotypes were extracted from electronic health record (EHR) diagnosis codes and mapped to more clinically meaningful phecodes. Logistic regression with Firth’s bias correction was used for PRS phenotype analyses. Mendelian randomization was used to examine causality in significant PheWAS associations. Our results showed a strong association between AD PRS and AD phenotype in European ancestry (OR = 1.26, 95% CI: 1.13, 1.40). Among a total of 1,515 PheWAS tests within the European sample, we observed strong associations of AD PRS with AD and related phenotypes, which include mild cognitive impairment (MCI), memory loss, and dementias. We observed a phenome-wide significant association between AD PRS and gouty arthropathy (OR = 0.90, adjusted p = 0.05). Further causal inference tests with Mendelian randomization showed that gout was not causally associated with AD. We concluded that genetic predisposition of AD was negatively associated with gout, but gout was not a causal risk factor for AD. Our study evaluated AD PRS in a real-world EHR setting and provided evidence that AD PRS may help to identify individuals who are genetically at risk of AD and other related phenotypes. We identified non-neurodegenerative diseases associated with AD PRS, which is essential to understand the genetic architecture of AD and potential side effects of drugs targeting genetic risk factors of AD. Together, these findings expand our understanding of AD genetic and clinical risk factors, which provide a framework for continued research in aging with the growing number of real-world EHR linked with genetic data.

Single cell RNA-sequencing can be a powerful approach to characterizing cell composition in a population of cells but is thought to be too expensive for population-scale analyses. Here, the authors show how lower coverage of more samples can increase the power to detect cell-type-specific eQTL.

Traditional methods for determining cell type composition lack scalability, while single-cell technologies remain costly and noisy compared to bulk RNA-seq. Here, the authors present a highly efficient tool to measure cellular heterogeneity in bulk expression through robust integration of single-cell information.

How complex microbial communities assemble through the animal’s life, and how predictable the assembly process is, remains poorly understood. Here, the authors profile the cow gut microbiome from birth to adulthood in animals born in C-section or natural birth and show that chance events early in life have a strong impact on microbiome development.

The current research examined whether for a message that is based on the paradoxical thinking principles—i.e., providing extreme, exaggerated, or even absurd views, that are congruent with the held views of the message recipients—to be effective, it needs to hit a ‘sweet spot’ and lead to a contrast effect. That is, it moderates the view of the message's recipients. In the framework of attitudes toward African refugees and asylum seekers in Israel by Israeli Jews, we found that compared to more moderate messages, an extreme, but not too extreme, message was effective in leading to unfreezing for high morally convicted recipients. The very extreme message similarly led to high levels of surprise and identity threat as the extreme message that was found to be effective. However, it was so extreme and absurd that it was rejected automatically. This was manifested in high levels of disagreement compared to all other messages, rendering it less effective compared to the extreme, paradoxical thinking, message. We discuss these findings’ practical and theoretical implications for the paradoxical thinking conceptual framework as an attitude change intervention, and for social judgment theory.

The ability to predict human phenotypes and identify biomarkers of disease from metagenomic data is crucial for the development of therapeutics for microbiome-associated diseases. However, metagenomic data is commonly affected by technical variables unrelated to the phenotype of interest, such as sequencing protocol, which can make it difficult to predict phenotype and find biomarkers of disease. Supervised methods to correct for background noise, originally designed for gene expression and RNA-seq data, are commonly applied to microbiome data but may be limited because they cannot account for unmeasured sources of variation. Unsupervised approaches address this issue, but current methods are limited because they are ill-equipped to deal with the unique aspects of microbiome data, which is compositional, highly skewed, and sparse. We perform a comparative analysis of the ability of different denoising transformations in combination with supervised correction methods as well as an unsupervised principal component correction approach that is presently used in other domains but has not been applied to microbiome data to date. We find that the unsupervised principal component correction approach has comparable ability in reducing false discovery of biomarkers as the supervised approaches, with the added benefit of not needing to know the sources of variation apriori. However, in prediction tasks, it appears to only improve prediction when technical variables contribute to the majority of variance in the data. As new and larger metagenomic datasets become increasingly available, background noise correction will become essential for generating reproducible microbiome analyses.

Climate change attributable to human activities has created a global threat to humanity and the natural world. However, there is a tendency for people to view climate change as a threat primarily affecting those in far-away places and there is reluctance to engage in pro-environmental action, which is often costly. It is therefore crucial to understand the factors that shape willingness to engage in pro-environmental behavior. Existing research suggests that personal experience with the consequences of climate change may increase pro-environmental action, however it is unknown whether personal experiences in other non-environmental domains may have similar effects. The circumstances of the Covid-19 pandemic allowed us to conduct a quasi-natural experiment to examine the effects of personal experience with a different global threat, namely Covid-19, on environmental responses. Across two studies conducted among UK and US participants, we found that personal experience of Covid-19 was associated with pro-environmental behavioral intentions, and that this relationship was mediated by increased environmental concern. We found that personal experience with Covid-19 was associated with stronger self-transcendence values of universalism and benevolence, which played a further mediating role between personal experience with the virus and environmental concern. These findings suggest that personal experience with at least some global threats, even when not directly related to climate change, may increase concern for distant others and also sensitize people to environmental issues and motivate pro-environmental action.

Calling differential methylation at a cell-type level from tissue-level bulk data is a fundamental challenge in genomics that has recently received more attention. These studies most often aim at identifying statistical associations rather than causal effects. However, existing methods typically make an implicit assumption about the direction of effects, and thus far, little to no attention has been given to the fact that this directionality assumption may not hold and can consequently affect statistical power and control for false positives. We demonstrate that misspecification of the model directionality can lead to a drastic decrease in performance and increase in risk of spurious findings in cell-type-specific differential methylation analysis, and we discuss the need to carefully consider model directionality before choosing a statistical method for analysis.

Compared to bulk data, cell-type-specific DNA methylation data provide higher resolution of epigenetic variation. Here, the authors introduce Tensor Composition Analysis, a novel computational approach for learning cell-type-specific DNA methylation from tissue-level bulk data, and show its application in epigenome-wide association studies.

Abstract Methylation datasets are affected by innumerable sources of variability, both biological (cell-type composition, genetics) and technical (batch effects). Here, we propose a reference-free method based on sparse canonical correlation analysis to separate the biological from technical sources of variability. We show through simulations and real data that our method, CONFINED, is not only more accurate than the state-of-the-art reference-free methods for capturing known, replicable biological variability, but it is also considerably more robust to dataset-specific technical variability than previous approaches. CONFINED is available as an R package as detailed at https://github.com/cozygene/CONFINED .

Summary: Coronavirus disease 2019 (COVID-19) has exposed health care disparities in minority groups including Hispanics/Latinxs (HL). Studies of COVID-19 risk factors for HL have relied on county-level data. We investigated COVID-19 risk factors in HL using individual-level, electronic health records in a Los Angeles health system between March 9, 2020, and August 31, 2020. Of 9,287 HL tested for SARS-CoV-2, 562 were positive. HL constituted an increasing percentage of all COVID-19 positive individuals as disease severity escalated. Multiple risk factors identified in Non-Hispanic/Latinx whites (NHL-W), like renal disease, also conveyed risk in HL. Pre-existing nonrheumatic mitral valve disorder was a risk factor for HL hospitalization but not for NHL-W COVID-19 or HL influenza hospitalization, suggesting it may be a specific HL COVID-19 risk. Admission laboratory values also suggested that HL presented with a greater inflammatory response. COVID-19 risk factors for HL can help guide equitable government policies and identify at-risk populations.

Reverse causality has made it difficult to establish the causal directions between obesity and prediabetes and obesity and insulin resistance. To disentangle whether obesity causally drives prediabetes and insulin resistance already in non-diabetic individuals, we utilized the UK Biobank and METSIM cohort to perform a Mendelian randomization (MR) analyses in the non-diabetic individuals. Our results suggest that both prediabetes and systemic insulin resistance are caused by obesity (p = 1.2×10-3 and p = 3.1×10-24). As obesity reflects the amount of body fat, we next studied how adipose tissue affects insulin resistance. We performed both bulk RNA-sequencing and single nucleus RNA sequencing on frozen human subcutaneous adipose biopsies to assess adipose cell-type heterogeneity and mitochondrial (MT) gene expression in insulin resistance. We discovered that the adipose MT gene expression and body fat percent are both independently associated with insulin resistance (p≤0.05 for each) when adjusting for the decomposed adipose cell-type proportions. Next, we showed that these 3 factors, adipose MT gene expression, body fat percent, and adipose cell types, explain a substantial amount (44.39%) of variance in insulin resistance and can be used to predict it (p≤2.64×10-5 in 3 independent human cohorts). In summary, we demonstrated that obesity is a strong determinant of both prediabetes and insulin resistance, and discovered that individuals' adipose cell-type composition, adipose MT gene expression, and body fat percent predict their insulin resistance, emphasizing the critical role of adipose tissue in systemic insulin resistance.

Summary: Single-cell RNA-sequencing (scRNA-seq) is a set of technologies used to profile gene expression at the level of individual cells. Although the throughput of scRNA-seq experiments is steadily growing in terms of the number of cells, large datasets are not yet commonly generated owing to prohibitively high costs. Integrating multiple datasets into one can improve power in scRNA-seq experiments, and efficient integration is very important for downstream analyses such as identifying cell-type-specific eQTLs. State-of-the-art scRNA-seq integration methods are based on the mutual nearest neighbor paradigm and fail to both correct for batch effects and maintain the local structure of the datasets. In this paper, we propose a novel scRNA-seq dataset integration method called BATMAN (BATch integration via minimum-weight MAtchiNg). Across multiple simulations and real datasets, we show that our method significantly outperforms state-of-the-art tools with respect to existing metrics for batch effects by up to 80% while retaining cell-to-cell relationships.

Dynamic changes in microbial communities play an important role in human health and disease. Specifically, deciphering how microbial species in a community interact with each other and their environment can elucidate mechanisms of disease, a problem typically investigated using tools from community ecology. Yet, such methods require measurements of absolute densities, whereas typical datasets only provide estimates of relative abundances. Here, we systematically investigate models of microbial dynamics in the simplex of relative abundances. We derive a new nonlinear dynamical system for microbial dynamics, termed "compositional" Lotka-Volterra (cLV), unifying approaches using generalized Lotka-Volterra (gLV) equations from community ecology and compositional data analysis. On three real datasets, we demonstrate that cLV recapitulates interactions between relative abundances implied by gLV. Moreover, we show that cLV is as accurate as gLV in forecasting microbial trajectories in terms of relative abundances. We further compare cLV to two other models of relative abundance dynamics motivated by common assumptions in the literature-a linear model in a log-ratio transformed space, and a linear model in the space of relative abundances-and provide evidence that cLV more accurately describes community trajectories over time. Finally, we investigate when information about direct effects can be recovered from relative data that naively provide information about only indirect effects. Our results suggest that strong effects may be recoverable from relative data, but more subtle effects are challenging to identify.

Principal component analysis (PCA) is a key tool for understanding population structure and controlling for population stratification in genome-wide association studies (GWAS). With the advent of large-scale datasets of genetic variation, there is a need for methods that can compute principal components (PCs) with scalable computational and memory requirements. We present ProPCA, a highly scalable method based on a probabilistic generative model, which computes the top PCs on genetic variation data efficiently. We applied ProPCA to compute the top five PCs on genotype data from the UK Biobank, consisting of 488,363 individuals and 146,671 SNPs, in about thirty minutes. To illustrate the utility of computing PCs in large samples, we leveraged the population structure inferred by ProPCA within White British individuals in the UK Biobank to identify several novel genome-wide signals of recent putative selection including missense mutations in RPGRIP1L and TLR4.

Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic and laboratory features to serve as a screening tool at hospitals where testing is scarce or unavailable. We used retrospectively collected data from the UCLA Health System in Los Angeles, California. We included all emergency room or inpatient cases receiving SARS-CoV-2 PCR testing who also had a set of ancillary laboratory features (n = 1,455) between 1 March 2020 and 24 May 2020. We tested seven machine learning models and used a combination of those models for the final diagnostic classification. In the test set (n = 392), our combined model had an area under the receiver operator curve of 0.91 (95% confidence interval 0.87-0.96). The model achieved a sensitivity of 0.93 (95% CI 0.85-0.98), specificity of 0.64 (95% CI 0.58-0.69). We found that our machine learning algorithm had excellent diagnostic metrics compared to SARS-CoV-2 PCR. This ensemble machine learning algorithm to diagnose COVID-19 has the potential to be used as a screening tool in hospital settings where PCR testing is scarce or unavailable.

Standard approaches for heritability and set testing in statistical genetics rely on parametric models that might not hold in reality and give inflated p-values. Here, the authors develop a fast method for permutation-based testing of marker sets and of heritability that does not suffer from model misspecification.

Abstract We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer cell counts without methylation reference only capture linear combinations of cell counts rather than provide one component per cell type. Our approach allows the construction of components such that each component corresponds to a single cell type, and provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.

In three studies across three cultures (U.S., Sweden, and Israel), we examine whether implicit theories about groups are associated with political identity and whether this relationship is mediated by Social Dominance Orientation (SDO). Study 1 found that raising the salience of entity beliefs leads to increased right-wing political self-identification on social issues, although no such effect was found regarding general or economic political identity. In Study 2, we found that the more participants endorsed entity beliefs about groups (vs. incremental beliefs about groups), the more they identified as political rightists (vs. leftists) in the U.S., Sweden, and Israel. SDO mediated this relationship in the U.S. and Swedish samples, but not in the Israeli sample – a political setting in which political identity is largely determined by attitudes regarding the Israeli-Palestinian conflict. Study 3 showed that SDO mediated the relationship between implicit theories about groups and Israelis’ political identity regarding social/economic issues, but did not have such a mediating role with respect to political identity regarding the Israeli-Palestinian conflict. Theoretical and practical implications are discussed.

Given the highly dynamic and complex nature of the human gut microbial community, the ability to identify and predict time-dependent compositional patterns of microbes is crucial to our understanding of the structure and functions of this ecosystem. One factor that could affect such time-dependent patterns is microbial interactions, wherein community composition at a given time point affects the microbial composition at a later time point. However, the field has not yet settled on the degree of this effect. Specifically, it has been recently suggested that only a minority of taxa depend on the microbial composition in earlier times. To address the issue of identifying and predicting temporal microbial patterns we developed a new model, MTV-LMM (Microbial Temporal Variability Linear Mixed Model), a linear mixed model for the prediction of microbial community temporal dynamics. MTV-LMM can identify time-dependent microbes (i.e., microbes whose abundance can be predicted based on the previous microbial composition) in longitudinal studies, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on real and synthetic time series datasets, and found that MTV-LMM outperforms commonly used methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance of taxa at later time points is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM, we demonstrate that a considerable portion of the human gut microbiome, both in infants and adults, has a significant time-dependent component that can be predicted based on microbiome composition in earlier time points. This suggests that microbiome composition at a given time point is a major factor in defining future microbiome composition and that this phenomenon is considerably more common than previously reported for the human gut microbiome.

Perspective-taking is essential for improving intergroup relations. However, it is difficult to implement, especially in violent conflicts. Given that immersive virtual reality (VR) can simulate various points of view (POV), we examined whether it can lead to beneficial outcomes by promoting outgroup perspective-taking, even in armed conflicts. In two studies, Jewish-Israelis watched a 360° VR scene depicting an Israeli-Palestinian confrontation from different POVs-outgroup's, ingroup's while imagining outgroup perspective or ingroup's without imagined perspective-taking. Participants immersed in the outgroup's POV, but not those who imagined the outgroup's perspective, perceived the Palestinians more positively than those immersed in the ingroup's POV. Moreover, participants in the outgroup's POV perceived the Palestinian population in general more favorably and judged a real-life ingroup transgression more strictly than those in the ingroup's POV, even five months after VR intervention. Results suggest that VR can promote conflict resolution by enabling effective perspective-taking.

Bacillus Calmette–Guerin (BCG) is a live attenuated form of Mycobacterium bovis that was developed 100 years ago as a vaccine against tuberculosis (TB) and has been used ever since to vaccinate children globally. It has also been used as the first-line treatment in patients with nonmuscle invasive bladder cancer (NMIBC), through repeated intravesical applications. Numerous studies have shown that BCG induces off-target immune effects in various pathologies. Accumulating data argue for the critical role of the immune system in the course of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). In this study, we tested whether repeated exposure to BCG during the treatment of NMIBC is associated with the risk of developing AD and PD. We presented a multi-center retrospective cohort study with patient data collected between 2000 and 2019 that included 12,185 bladder cancer (BC) patients, of which 2301 BCG-treated patients met all inclusion criteria, with a follow-up of 3.5 to 7 years. We considered the diagnosis date of AD and nonvascular dementia cases for BC patients. The BC patients were partitioned into those who underwent a transurethral resection of the bladder tumor followed by BCG therapy, and a disjoint group that had not received such treatment. By applying Cox proportional hazards (PH) regression and competing for risk analyses, we found that BCG treatment was associated with a significantly reduced risk of developing AD, especially in the population aged 75 years or older. The older population (≥75 years, 1578 BCG treated, and 5147 controls) showed a hazard ratio (HR) of 0.726 (95% CI: 0.529–0.996; p-value = 0.0473). While in a hospital-based cohort, BCG treatment resulted in an HR of 0.416 (95% CI: 0.203–0.853; p-value = 0.017), indicating a 58% lower risk of developing AD. The risk of developing PD showed the same trend with a 28% reduction in BCG-treated patients, while no BCG beneficial effect was observed for other age-related events such as Type 2 diabetes (T2D) and stroke. We attributed BCG’s beneficial effect on neurodegenerative diseases to a possible activation of long-term nonspecific immune effects. We proposed a prospective study in elderly people for testing intradermic BCG inoculation as a potential protective agent against AD and PD.

OBJECTIVES/GOALS: Health care systems are complex, dynamic, and varied. Advances in artificial intelligence (AI) are enabling healthcare systems to use their own data to elicit patterns and design suitable interventions. To realize this potential, computer scientists and clinicians need an effective, practical, and replicable approach to collaboration METHODS/STUDY POPULATION: In this study, computer scientists partnered with clinicians to investigate predictors of avoidable emergency department use. The team sought an approach to computational medicine that could increase the relevance and impact of prediction to solve pressing problems in the health system. The team adopted an emergent architecture that engaged system leaders, computer scientists, data scientists, health services researchers, and practicing clinicians with deep ambulatory and inpatient knowledge to form the initial questions that shaped the prediction model; to understand nuances of coding and recording in source data and the implications for models; and to generate insights for promising points of intervention. The team recorded decisions and challenges as it progressed to analyze its function. RESULTS/ANTICIPATED RESULTS: Most avoidance models focus on a narrow time period around target events, or on high cost patients and events. This interdisciplinary team used their insights into the health system’s workflows and patient population to adopt a longitudinal approach to their prediction models. They used AI to build models of behavior in the system and consider prevention points across clinical units, time, and place. The holistic, systemwide focus enabled the team to generate insights that the system leaders and subsequently specific clinical units could apply to improve value and outcomes. A facilitated team process using learning system and cooperative network principles allowed a large and modular interdisciplinary team to build a transparent AI modeling process that yielded actionable insights into hypercomplex workflows. DISCUSSION/SIGNIFICANCE OF IMPACT: An architecture for involving diverse stakeholders in computational medicine projects can increase the relevance and impact of AI for solving care delivery problems in complex health systems. Translational science and computational medicine programs can foster this type of engagement and encourage a whole system perspective.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

ABSTRACT Ruminants sustain a long-lasting obligatory relationship with their rumen microbiome dating back 50 million years. In this unique host-microbiome relationship, the host’s ability to digest its feed is completely dependent on its coevolved microbiome. This extraordinary alliance raises questions regarding the dependent relationship between ruminants’ genetics and physiology and the rumen microbiome structure, composition, and metabolism. To elucidate this relationship, we examined the association of host genetics with the phylogenetic and functional composition of the rumen microbiome. We accomplished this by studying a population of 78 Holstein-Friesian dairy cows, using a combination of rumen microbiota data and other phenotypes from each animal with genotypic data from a subset of 47 animals. We identified 22 operational taxonomic units (OTUs) whose abundances were associated with rumen metabolic traits and host physiological traits and which showed measurable heritability. The abundance patterns of these microbes can explain high proportions of variance in rumen metabolism and many of the host physiological attributes such as its energy-harvesting efficiency. Interestingly, these OTUs shared higher phylogenetic similarity between themselves than expected by chance, suggesting occupation of a specific ecological niche within the rumen ecosystem. The findings presented here suggest that ruminant genetics and physiology are correlated with microbiome structure and that host genetics may shape the microbiome landscape by enriching for phylogenetically related taxa that may occupy a unique niche. IMPORTANCE Dairy cows are an essential nutritional source for the world’s population; as such, they are extensively farmed throughout our planet and subsequently impact our environment. The microbial communities that reside in the upper digestive tract of these animals in a compartment named the rumen degrade and ferment the plant biomass that the animal ingests. Our recent efforts, as well as those of others, have shown that this microbial community’s composition and functionality are tightly linked to the cow’s capacity to harvest energy from its feed, as well as to other physiological traits. In this study, we identified microbial groups that are heritable and also linked to the cow’s production parameters. This finding could potentially allow us to apply selection programs on specific rumen microbial components that are linked to the animal’s physiology and beneficial to production. Hence, it is a steppingstone toward microbiome manipulation for increasing food availability while lowering environmental impacts such as methane emission.

The modern era of globalization has been accompanied by a massive growth in interconnections between groups, and has led to the sharing of multiple identities by individuals and groups. Following these developments, research has focused on the issue of multiple identities, and has shed important light on how individuals who hold these complex forms of identity feel and behave, and on the reactions they elicit from members of other groups. However, the potential of groups with such multiple identities (e.g., biracials, immigrants, etc.) to affect the intergroup relations between the groups that represent the respective sources of the different identities (e.g., Blacks and Whites, country of origin and country of residence, etc.) has not been examined to date. Accordingly, in this paper, we first systematically explore the potential of groups in which people identify with multiple social categories, or groups that are perceived as such by others, to play a role in intergroup dynamics. Next, we offer a theoretical framework outlining what functions groups of people with shared multiple identities may serve (as bridges or barriers) by proposing how their presence may facilitate or deteriorate intergroup relations. Finally, we present recent empirical research examining how groups of people with shared multiple identities can act as gateways and bridge the cleft between two separate groups that represent the respective sources of their different identities, and discuss the theoretical and practical implications for the field of intergroup relations.