Zoom out: An intervention on the virtual learning environment improves minority students' grades in two field experiments in Israel.

Closing the achievement gap for minority students in higher education requires addressing the lack of belonging these students experience. This paper introduces a psychological intervention that strategically targets key elements within the learning environment to foster the success of minority students. The intervention sought to enhance Palestinian minority student's sense of belonging by increasing the presence of their native language. We tested the effectiveness of the intervention in two field experiments in Israel (n > 20,000), at the height of the COVID-19 pandemic when all classes were held via Zoom. Lecturers in the experimental condition added a transcript of their names in Arabic to their default display (English/Hebrew only). Our findings revealed a substantial and positive impact on Palestinian student's sense of belonging, class participation, and overall grades. In experiment 1, Palestinian student's average grade increased by 10 points. In experiment 2, there was an average increase of 4 points among Palestinian students' semester grade. Our intervention demonstrates that small institutional changes when carefully crafted can have a significant impact on minority populations. These results have significant implications for addressing educational disparities and fostering inclusive learning environment.

Disadvantaged group members are prouder of their group when using the language of the dominant group compared to their native language.

In ethnically and linguistically diverse societies, disadvantaged groups often face pressures to acquire and speak the advantaged group's language to achieve social inclusion and economic mobility. This work investigates how using the advantaged group's language affects disadvantaged group members' in-group pride and collective self-esteem, relative to using their native language. Across six experimental studies involving Palestinian citizens of Israel (total N = 1,348), we test two competing hypotheses: Disadvantaged group members may experience greater in-group pride when using a) their native language, due to its emotional significance (the nativity hypothesis), or b) the language of the advantaged group, due to activation of habituated compensatory responses to dominance relations (the identity enhancement hypothesis). We found that respondents reported significantly higher in-group pride when responding to a Hebrew survey when compared to performing the same activity in Arabic (Studies 1a and 1b), regardless of whether the researchers administering the survey were identified as Jewish or Arab (Studies 2a and 2b). Study 3 replicated this effect while employing the "bogus pipeline" technique, suggesting the pride expression was authentic, not merely driven by social desirability. Finally, Study 4 (pre-registered) examined additional measures of positive regard for the in-group, finding that participants described their group more positively in an attribute selection task, and reported greater collective self-esteem, when surveyed in Hebrew, rather than in Arabic. Taken together, these findings suggest that language use influences disadvantaged group members' perceptions and feelings concerning their group when those languages are associated with relative position in an intergroup hierarchy.

Educating for inclusion: Diversity education programs can reduce prejudice toward outgroups in Israel.

Intergroup prejudice is pervasive in many contexts worldwide, leading to discrimination and conflict. Existing research suggests that prejudice is acquired at an early age and that durably improving intergroup relations is extremely challenging, often requiring intense interventions. Building on existing research in social psychology and inspired by the Israeli TV series "You Can't Ask That," which depicts charismatic children from minority groups broaching sensitive topics at the core of intergroup relations, we develop a month-long diversity education program. Our program exposed students to the TV series and facilitated follow-up classroom discussions in which students constructively addressed various sensitive topics at the core of intergroup relations and learned about intergroup similarities, intragroup heterogeneity, and the value of taking others' perspectives. Through two field experiments implemented in Israeli schools, we show that integrating our intervention into school curricula improved Jewish students' attitudes toward minorities and increased some pro-diversity behavior up to 13 wk posttreatment. We further provide suggestive evidence that the intervention was effective by encouraging students to take their outgroups' perspectives and address an element of scalability by delegating implementation responsibilities to classroom teachers in our second study. Our findings suggest that theoretically informed intensive education programs are a promising route to reducing prejudice at a young age.

Protest movements involving limited violence can sometimes be effective: Evidence from the 2020 BlackLivesMatter protests.

SignificanceThe effects of recent protests for racial equality, particularly when they included violence, are currently of public and academic interest. To better understand these effects, we combine a dataset of all 2020 BlackLivesMatter protests with survey data containing measures of prejudice and support for police reform. Protests were not associated with reductions in prejudice, but were associated with increases in support for police reform. Specifically, a mix of nonviolent and violent protests was associated with an increase in police-reform support among conservatives living in liberal areas. This study highlights the importance of considering multiple measures of protest effectiveness and suggests that mass protest (including when it mixes nonviolence and violence) can be effective at advancing the movement's goals.

The affective gap: a call for a comprehensive examination of the discrete emotions underlying affective polarization.

Bakker and Lelkes (2024) point at a critical gap in research on affective polarisation: the limited understanding of its affective components, mainly due to the reliance on a unidimensional operationalisation of affect in affective polarisation. They advocate for a broader approach to study affect, integrating explicit and implicit measures, and call on emotion specialists to address this gap. Acknowledging the complexity of affect in the ideological divide, we argue that the lack of a thorough examination of the distinct role of discrete emotions constitutes the primary "affective gap". Drawing from studies on the relationship between ideological groups and utilising a discrete emotions approach, we contest the prevailing assumption that hatred predominantly underlies affective polarisation. Instead, we propose that disappointment, better captures the intricate dynamics between ideological groups. We argue that such nuanced approach, regarding the affective component of affective polarisation enhances our understanding of the phenomenon and shed light on its implications for societies.

Evaluating supervised and unsupervised background noise correction in human gut microbiome data.

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.

Effect of statins on the age of onset of age-related macular degeneration.

BACKGROUND: This study evaluated the relationship between statin use and the age of onset of age-related macular degeneration (AMD). METHODS: Electronic Health Records from 52,840 patients evaluated at University of California Los Angeles (UCLA) Ophthalmology Clinics and 9,977 patients evaluated at University of California San Francisco (UCSF) Ophthalmology Clinics were screened. Survival analysis was performed using Cox proportional hazards regression models and visualized using Kaplan Meier survival curves, with the following covariates-sex, ethnicity, smoking history, fluoxetine use, obesity, diabetes mellitus, and hypertension. RESULTS: 5,498 of 52,840 patients at UCLA were diagnosed with AMD. Statin use was associated with a later AMD onset (HR = 0.8823, p < 0.0001), while female sex (HR = 1.0852, p= 00,035), obesity (HR = 1.4555, p < 0.0001), and fluoxetine (HR = 1.3797, p= 0.0003) were associated with an earlier AMD onset. Non-hispanic black (HR = 0.5687, p < 0.0001) and hispanic ethnicities (HR = 0.8269, p= 0.0028) were associated with a later AMD onset. When stratifying for ethnicity, statins, fluoxetine, sex, and obesity were significant only within non-hispanic white subjects. Statin use was significant among patients with dry AMD (HR = 0.8410, p= 0.0001) but not wet AMD (0.9188, p= 0.0351). In the replication cohort, 526 of 9,977 patients at UCSF had AMD. Associations between statins (HR = 0.7643, p= 0.0033), non-hispanic black ethnicity (HR = 0.5043, p= 0.0035), and obesity (HR = 1.9602, p < 0.0001) on AMD onset were confirmed. CONCLUSIONS: In both cohorts, statin use and non-hispanic black ethnicity are associated with a later AMD onset, while obesity with an earlier AMD onset.

Lay theories of peace and their influence on policy preference during violent conflict.

We often talk about peace as if the concept is self-explanatory. Yet people can have various theories about what peace "is." In this study, we examine the lay theories of peace of citizens embroiled in a prolonged ethnonational conflict. We show that lay theories of peace 1) depend on whether one belongs to the high-power or low-power party and 2) explain citizens' fundamental approaches to conflict resolution. Specifically, we explore the link between power asymmetry, lay theories of peace, and preference for conflict resolution strategies within large-scale samples of Palestinian residents of the West Bank and the Gaza Strip and Jewish residents of Israel. Results reveal that members of the high-power group (in this case Jewish-Israelis) are more likely to associate peace with harmonious relationships (termed "positive peace") than with the attainment of justice (termed "structural peace"), while members of the low-power group (in this case Palestinians) exhibit an opposite pattern. Yet both groups firmly and equally interpret peace as the termination of war and bloodshed (termed "negative peace"). Importantly, across societies, associating peace with negative peace more than with positive or structural peace predicts citizens' desire for a solution that entails the partition of land (the Two-State Solution) whereas associating peace with structural or positive peace more than with negative peace predicts citizens' desire to solve the conflict by sharing the land (the One-State Solution). This study demonstrates the theoretical and policy-relevant utility of studying how those most affected by war understand the concept of peace.

Scalable probabilistic PCA for large-scale genetic variation data.

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.

The causal effect of obesity on prediabetes and insulin resistance reveals the important role of adipose tissue in insulin resistance.

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.

FEAST: fast expectation-maximization for microbial source tracking.

A major challenge of analyzing the compositional structure of microbiome data is identifying its potential origins. Here, we introduce fast expectation-maximization microbial source tracking (FEAST), a ready-to-use scalable framework that can simultaneously estimate the contribution of thousands of potential source environments in a timely manner, thereby helping unravel the origins of complex microbial communities ( https://github.com/cozygene/FEAST ). The information gained from FEAST may provide insight into quantifying contamination, tracking the formation of developing microbial communities, as well as distinguishing and characterizing bacteria-related health conditions.

Remote blood collection from older adults in the Brain Health Registry for plasma biomarker and genetic analysis.

INTRODUCTION: Use of online registries to efficiently identify older adults with cognitive decline and Alzheimer's disease (AD) is an approach with growing evidence for feasibility and validity. Linked biomarker and registry data can facilitate AD clinical research. METHODS: We collected blood for plasma biomarker and genetic analysis from older adult Brain Health Registry (BHR) participants, evaluated feasibility, and estimated associations between demographic variables and study participation. RESULTS: Of 7150 participants invited to the study, 864 (12%) enrolled and 629 (73%) completed remote blood draws. Participants reported high study acceptability. Those from underrepresented ethnocultural and educational groups were less likely to participate. DISCUSSION: This study demonstrates the challenges of remote blood collection from a large representative sample of older adults. Remote blood collection from > 600 participants within a short timeframe demonstrates the feasibility of our approach, which can be expanded for efficient collection of plasma AD biomarker and genetic data.

The Dual Effect of COVID-19 on Intergroup Conflict in the Korean Peninsula.

The coronavirus pandemic has fundamentally shifted the way human beings interact, both as individuals and groups, in the face of such a widespread outbreak. This paper seeks to investigate the effects of COVID-19 on intergroup emotions and attitudes within an intractable intergroup conflict, specifically, through the lens of the Korean conflict. Using a two-wave, cross-sectional design, this study was able to track the profound psychological changes in intergroup emotions and attitudes both prior to the pandemic and during its onslaught. Results of these two wave representative samples show that South Korean citizens demonstrated higher levels of fear of their neighbors in North Korea after the outbreak of COVID-19 than before. In turn, this led to increased societal support of hostile government policies towards North Koreans. Conversely, the same participants exhibited higher levels of empathy towards North Koreans during the pandemic, which led to a higher willingness to collaborate with their outgroup. This dual effect on intergroup emotions within intractable conflicts brings forth new avenues from which societies may be able to restrain the destructive influence of the COVID-19 threat on intergroup relations - as well as harvesting its constructive potential for reconciling warring intergroup relations.

I hate you when I am anxious: Anxiety during the COVID-19 epidemic and ideological hostility.

Most previous studies that examined the effect of anxiety on hostility towards a distinct group have focused on cases in which we hate those we are afraid of. The current study, on the other hand, examines the relationship between anxiety in one domain and hostility towards a distinct group that is not the source of that anxiety. We focus here on symptoms of anxiety during the COVID-19 pandemic, which have become increasingly frequent, and show that the implications of such mental difficulties are far-reaching, posing a threat to relationships between ideological groups. In two studies conducted in both Israel and the United States, we found that high levels of anxiety during the COVID-19 epidemic are associated with higher levels of hatred towards ordinary people from the respective political outgroups, lower levels of willingness to sustain interpersonal relations with these people (i.e., greater social distancing), and greater willingness to socially exclude them. This relationship was mediated by the perception of threat posed by the political outgroup. This study is the first to show that mental difficulty driven by an external threat can be a fundamental factor that explains levels of intergroup hostility.

A social virus: Intergroup dehumanization and unwillingness to aid amidst COVID-19 - Who are the main targets?

Previous studies have shown that external threats, such as financial crises and natural disasters, might fuel negative attitudes, emotions, and behaviors towards outgroup members. However, it is unclear what types of outgroups are likely to be targeted when an external threat is taking its toll. In this study, we examine two types of outgroups that might be at risk of becoming victims of intergroup hostility. The first is the "ultimate scapegoat" outgroup which has a long history of negative relations with the ingroup. The second is the "context-dependent" outgroup which is viewed as an outgroup only in certain contexts. We utilized the COVID-19 crisis and the highly diverse social makeup of Israeli society to explore the extent to which each type of outgroup would be targeted. Results from our study (N = 664), conducted during the first peak of COVID-19 in Israel, show that higher levels of exposure to COVID-19 predicted lower willingness to aid outgroups and that outgroup dehumanization mediated this association. However, this held true only when the target outgroup was a context-dependent outgroup. When the target group was the ultimate scapegoat, exposure to COVID-19 did not predict ingroup willingness to aid them. Our findings contribute to our theoretical and practical knowledge on how intergroup hostility proliferates during external threats and, as such, are valuable to scholars, practitioners, and policymakers working to reduce intergroup tensions during large-scale crises.

Discharge Clinical Characteristics and Post-Discharge Events in Patients with Severe COVID-19: A Descriptive Case Series.

BACKGROUND: As the SARS-CoV-2 pandemic continues, little guidance is available on clinical indicators for safely discharging patients with severe COVID-19. OBJECTIVE: To describe the clinical courses of adult patients admitted for COVID-19 and identify associations between inpatient clinical features and post-discharge need for acute care. DESIGN: Retrospective chart reviews were performed to record laboratory values, temperature, and oxygen requirements of 99 adult inpatients with COVID-19. Those variables were used to predict emergency department (ED) visit or readmission within 30 days post-discharge. PATIENTS (OR PARTICIPANTS): Age ≥ 18 years, first hospitalization for COVID-19, admitted between March 1 and May 2, 2020, at University of California, Los Angeles (UCLA) Medical Center, managed by an inpatient medicine service. MAIN MEASURES: Ferritin, C-reactive protein, lactate dehydrogenase, D-dimer, procalcitonin, white blood cell count, absolute lymphocyte count, temperature, and oxygen requirement were noted. KEY RESULTS: Of 99 patients, five required ED admission within 30 days, and another five required readmission. Fever within 24 h of discharge, oxygen requirement, and laboratory abnormalities were not associated with need for ED visit or readmission within 30 days of discharge after admission for COVID-19. CONCLUSION: Our data suggest that neither persistent fever, oxygen requirement, nor laboratory marker derangement was associated with need for acute care in the 30-day period after discharge for severe COVID-19. These findings suggest that physicians need not await the normalization of laboratory markers, resolution of fever, or discontinuation of oxygen prior to discharging a stable or improving patient with COVID-19.

Compositional Lotka-Volterra describes microbial dynamics in the simplex.

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.

Testing the impact and durability of a group malleability intervention in the context of the Israeli-Palestinian conflict.

Fostering perceptions of group malleability (teaching people that groups are capable of change and improvement) has been shown to lead to short-term improvements in intergroup attitudes and willingness to make concessions in intractable conflicts. The present study, a field intervention involving 508 Israelis from three locations in Israel, replicated and substantially extended those findings by testing the durability of a group malleability intervention during a 6-month period of frequent violence. Three different 5-hour-long interventions were administered as leadership workshops. The group malleability intervention was compared with a neutral coping intervention and, importantly, with a state-of-the-art perspective-taking intervention. The group malleability intervention proved superior to the coping intervention in improving attitudes, hope, and willingness to make concessions, and maintained this advantage during a 6-month period of intense intergroup conflict. Moreover, it was as good as, and in some respects superior to, the perspective-taking intervention. These findings provide a naturalistic examination of the potential of group malleability interventions to increase openness to conflict resolution.

Unravelling plasmidome distribution and interaction with its hosting microbiome.

Horizontal gene transfer via plasmids plays a pivotal role in microbial evolution. The forces that shape plasmidomes functionality and distribution in natural environments are insufficiently understood. Here, we present a comparative study of plasmidomes across adjacent microbial environments present in different individual rumen microbiomes. Our findings show that the rumen plasmidome displays enormous unknown functional potential currently unannotated in available databases. Nevertheless, this unknown functionality is conserved and shared with published rat gut plasmidome data. Moreover, the rumen plasmidome is highly diverse compared with the microbiome that hosts these plasmids, across both similar and different rumen habitats. Our analysis demonstrates that its structure is shaped more by stochasticity than selection. Nevertheless, the plasmidome is an active partner in its intricate relationship with the host microbiome with both interacting with and responding to their environment.

Modeling the temporal dynamics of the gut microbial community in adults and infants.

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.