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Political segregation is an important social problem, increasing polarization and impeding effective governance. Previous work has viewed the central driver of segregation to be political homophily, the tendency to associate with others who have similar views. Here we propose that, in addition to homophily, people's social tie decisions are driven by political acrophily, the tendency to associate with others who have more extreme political views (rather than more moderate). We examined this using a paradigm in which participants share emotions and attitudes on political policies, observe others' responses and choose which others to affiliate with. In four studies (N = 1,235), both liberal and conservative participants' social tie decisions reflected the presence of acrophily. We found that participants who viewed peers who expressed more extreme views as more prototypical of their political group also tended to engage in greater acrophily. These studies identify a previously overlooked tendency in tie formation.

Funding Information: The authors would like to thank Roni Porat for their valuable contribution in the conceptualization stage of the research, and Shir Harpaz, Natali Afrimov, and Dan Heskia for their assistance in the data analysis. This work was supported by an Israel Science Foundation Grant (3105/21) to the first author (BSH). Funding Information: The authors would like to thank Roni Porat for their valuable contribution in the conceptualization stage of the research, and Shir Harpaz, Natali Afrimov, and Dan Heskia for their assistance in the data analysis. This work was supported by an Israel Science Foundation Grant (3105/21) to the first author (BSH). Publisher Copyright: © 2023, The Author(s). 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 (Ntotal = 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.

How does the threat from future violence shape the opinions of those mired in violent intergroup disputes? Two competing rationales seem plausible. During conflict, threat from future violence increases support for dovish policies because the destruction and suffering associated with violence make peace seem more desirable and urgent. At the same time, threat from future violence can decrease support for dovish policies because peace seems inconceivable when violence is on the rise. Indeed, existing studies on the link between threat perceptions and support for conflict-related policies yielded mixed results. We argue that, to some extent, this puzzle could be solved by (1) examining threat through its two core components, namely perceived severity of harm and perceived likelihood of harm, and (2) linking these two components of threat with the two components of hope, namely the wish to attain a goal (in our case peace) and the expectations of attaining it. These arguments were tested using original data collected among 800 Israelis and Palestinians. Results show that the part of threat stemming from perceiving future violence as severe generates support for dovish policies because it increases citizens’ wishes for peace. At the same time, the part of threat that stems from perceiving future violence as likely decreases support for the same policies due to decreased expectations that peace can be achieved. We show that the relative weight of the two components of threat determines citizens’ support for conflict-related policies. Implications for political behavior during conflict are discussed.

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.

We test the notion that the presence of a dual identity group, which partially shares both the ingroup and a relevant outgroup identity, can decrease intergroup prejudice. Previous research has demonstrated that such dual identity groups can act as a possible gateway between the groups that represent the respective sources of the dual identity. The current research applies this notion to the inter-racial context in the United States, focusing on the case of biracial individuals as having dual identities, and thus as potential gateways between blacks and whites. Specifically, we tested the prediction that exposure of whites to biracial individuals would decrease intergroup threat which in turn would decrease prejudice toward blacks. Findings from three studies supported this prediction and showed that the presence of biracial individuals reduced intergroup threat and prejudice among those most likely to harbor them (specifically, those high on social dominance orientation). We discuss the implications of our findings in terms of how this effect corresponds with current theoretical developments on dual identity, and possible practical social implications.

We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. We further propose an additional mode of operation that merely requires a set of known marker genes., 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.

Mindfulness training has been shown to have a beneficial impact on emotions and perceptions. We examined whether it would reduce negative emotions and perceptions and lead to increased support for compromise in the context of prolonged intergroup conflict. We also examined the effect of an intervention that combines mindfulness with cognitive reappraisal, a method that enhances emotion regulation. Israeli students participated in a mindfulness course that either began in the winter semester (mindfulness group) or in the spring semester (control group). After the termination of the mindfulness course, all participants were invited to a laboratory session in which they were randomly assigned to either receive or not a short cognitive reappraisal training. The results showed that after being presented with anger-inducing information related to the Israeli-Palestinian conflict, participants in the mindfulness condition only, the reappraisal condition only or the combined group (mindfulness and reappraisal), were more supportive of conciliatory policies compared to participants that received no mindfulness nor reappraisal training. The increased support for conciliatory policies was mediated by a decrease in negative emotions in all groups, while in the mindfulness group, it was also mediated by reduction in negative perceptions. The combined impact of mindfulness and reappraisal did not reveal any additional effect.

In epigenome-wide association studies (EWAS), different methylation profiles of distinct cell types may lead to false discoveries. We introduce ReFACTor, a method based on principal component analysis (PCA) and designed for the correction of cell type heterogeneity in EWAS. ReFACTor does not require knowledge of cell counts, and it provides improved estimates of cell type composition, resulting in improved power and control for false positives in EWAS. Corresponding software is available at http://www.cs.tau.ac.il/~heran/cozygene/software/refactor.html.

The mammalian gut microbiota is considered to be determined mostly by diet, while the effect of genotype is still controversial. Here, we examined the effect of genotype on the gut microbiota in normal populations, exhibiting only natural polymorphisms, and evaluated this effect in comparison to the effect of sex. DNA fingerprinting approaches were used to profile the gut microbiota of eight different recombinant inbred mouse lines of the collaborative cross consortium, whose level of genetic diversity mimics that of a natural human population. Analyses based on automated ribosomal internal transcribed spacer analysis demonstrated significant higher similarity of the gut microbiota composition within mouse lines than between them or within same-gender groups. Thus, genetic background significantly impacts the microbiota composition and is a stronger determinant than gender. These findings imply that genetic polymorphisms help shape the intestinal microbiota of mammals and consequently could affect host susceptibility to diseases.

We conducted genome-wide association studies of non-Hodgkin lymphoma using Illumina HumanHap550 BeadChips to identify subtype-specific associations in follicular, diffuse large B-cell and chronic lymphocytic leukemia/small lymphocytic lymphomas. We found that rs6457327 on 6p21.33 was associated with susceptibility to follicular lymphoma (FL; N = 189 cases, 592 controls) with validation in another 456 FL cases and 2,785 controls (combined allelic P = 4.7 x 10(-11)). The region of strongest association overlapped C6orf15 (STG), located near psoriasis susceptibility region 1 (PSORS1).

Haplotype phasing is a well studied problem in the context of genotype data. With the recent developments in high-throughput sequencing, new algorithms are needed for haplotype phasing, when the number of samples sequenced is low and when the sequencing coverage is blow. High-throughput sequencing technologies enables new possibilities for the inference of haplotypes. Since each read is originated from a single chromosome, all the variant sites it covers must derive from the same haplotype. Moreover, the sequencing process yields much higher SNP density than previous methods, resulting in a higher correlation between neighboring SNPs. We offer a new approach for haplotype phasing, which leverages on these two properties. Our suggested algorithm, called Perfect Phlogeny Haplotypes from Sequencing (PPHS) uses a perfect phylogeny model and it models the sequencing errors explicitly. We evaluated our method on real and simulated data, and we demonstrate that the algorithm outperforms previous methods when the sequencing error rate is high or when coverage is low.

Background Recent advances in sequencing technologies set the stage for large, population based studies, in which the ANA or RNA of thousands of individuals will be sequenced. Currently, however, such studies are still infeasible using a straightforward sequencing approach; as a result, recently a few multiplexing schemes have been suggested, in which a small number of ANA pools are sequenced, and the results are then deconvoluted using compressed sensing or similar approaches. These methods, however, are limited to the detection of rare variants. Results In this paper we provide a new algorithm for the deconvolution of DNA pools multiplexing schemes. The presented algorithm utilizes a likelihood model and linear programming. The approach allows for the addition of external data, particularly imputation data, resulting in a flexible environment that is suitable for different applications. Conclusions Particularly, we demonstrate that both low and high allele frequency SNPs can be accurately genotyped when the DNA pooling scheme is performed in conjunction with microarray genotyping and imputation. Additionally, we demonstrate the use of our framework for the detection of cancer fusion genes from RNA sequences.

Papers describing genetic associations with common diseases are currently being published at a rapid rate. These new papers add to an already large body of literature which includes candidate gene studies, genome wide association studies, review papers, and meta-analyses. Related papers describe the basic epidemiology of these common diseases, as well as interactions between genes and environment, other genes, and drugs (pharmacogenomics), all of which may affect disease predisposition and management. As the field of personalized genomics continues to grow and mature, this body of literature is being synthesized in various ways. One aspect of utmost importance is to track gene-disease associations over time to see if they are replicated in different populations by different authors. This has historically^1^ been a limitation of genetic association studies and if not addressed, can be a major barrier to the adoption of personalized genomics. Another key need is to systematically collect data on the magnitude of the gene-disease effect (typically an odds ratio), variant identifier, allele frequency, risk and non-risk alleles, and other key information from the papers. This is often made more complicated the way that the data is presented by authors (in particular, it is often difficult to tell which is the risk allele), as well as by DNA strand issues. We have built a literature curation database that addresses these two key needs, using Ruby on Rails with MySQL. Lack of consistent standards for the reporting of gene-disease associations, either by journal editors or other consortia or agencies, make automated computer curation infeasible at this time. Thus, Navigenics does all curation manually, employing a team of epidemiologists and human geneticists. To minimize human error, we have incorporated quality control measures, including two independent literature reviews, into the data collection system. Methods for the collection, interpretation, storage, and retrieval of genetic association data from large numbers of papers will be discussed.