Publications by authors named "Eran Halperin"

171 Publications

Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning.

Sci Rep 2021 Aug 3;11(1):15755. Epub 2021 Aug 3.

Department of Computer Science, University of California, Los Angeles, CA, USA.

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.
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http://dx.doi.org/10.1038/s41598-021-94913-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8333060PMC
August 2021

Awareness of the Psychological Bias of Naïve Realism Can Increase Acceptance of Cultural Differences.

Pers Soc Psychol Bull 2021 Jul 1:1461672211027034. Epub 2021 Jul 1.

ARTIS International, Saint Michaels, MD, USA.

Acceptance of cultural differences can contribute to diversity. However, naïve realism-the conviction that one's views are objective whereas others' are biased-might hinder intercultural coexistence. We tested, in three experimental studies, whether a cognitive strategy based on raising awareness of the naïve realism, without any reference to culture and free of emotional involvement, can have a beneficial effect on cultural acceptance. Results revealed that participants showed more acceptance of cultural differences once they were aware of this bias (Study 1). The intervention had an indirect effect on acceptance via openness, especially for participants higher in prejudice (Study 2). Participants aware of this bias could not maintain an enhanced self-view, which mediated the effect of the manipulation on acceptance (Study 3). These findings suggest that strategies based on "cold" cognition, without an explicit emphasis on culture, might be beneficial for increasing the acceptance of cultural differences in an era of xenophobia.
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http://dx.doi.org/10.1177/01461672211027034DOI Listing
July 2021

Expressive suppression as an obstacle to social change: Linking system justification, emotion regulation, and collective action.

Motiv Emot 2021 Jun 12:1-22. Epub 2021 Jun 12.

Department of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel.

Research on system justification theory suggests that justifying the societal status quo decreases negative emotions, leading to less collective action. In this investigation, we propose that the degree to which negative emotions mediate the link between system justification and collective action may depend upon whether individuals tend to suppress the expression of their negative emotions. We tested this hypothesis in the diverse socio-political contexts of Turkey, Israel, and the U.S. In one correlational study (Study 1) and three experimental studies (Studies 2-4), we observed that the link between system justification and willingness to participate in collective action through anger (Studies 1-2 and 4) and guilt (Study 3) was moderated by expressive suppression. We found that negative emotions mediated the association between system justification and collective action among those who suppress the expression of their emotions less frequently, but not those who use expressive suppression more frequently. These findings suggest that emotion regulation may undermine, rather than facilitate, efforts to engage in collective action even among people who are low in system justification.

Supplementary Information: The online version contains supplementary material available at 10.1007/s11031-021-09883-5.
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http://dx.doi.org/10.1007/s11031-021-09883-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8196296PMC
June 2021

Bladder Cancer Immunotherapy by BCG Is Associated with a Significantly Reduced Risk of Alzheimer's Disease and Parkinson's Disease.

Vaccines (Basel) 2021 May 11;9(5). Epub 2021 May 11.

Department of Microbiology and Molecular Genetics, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.

Bacillus Calmette-Guerin (BCG) is a live attenuated form of 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; -value = 0.0473). While in a hospital-based cohort, BCG treatment resulted in an HR of 0.416 (95% CI: 0.203-0.853; -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.
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http://dx.doi.org/10.3390/vaccines9050491DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151667PMC
May 2021

Bladder Cancer Immunotherapy by BCG Is Associated with a Significantly Reduced Risk of Alzheimer's Disease and Parkinson's Disease.

Vaccines (Basel) 2021 May 11;9(5). Epub 2021 May 11.

Department of Microbiology and Molecular Genetics, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.

Bacillus Calmette-Guerin (BCG) is a live attenuated form of 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; -value = 0.0473). While in a hospital-based cohort, BCG treatment resulted in an HR of 0.416 (95% CI: 0.203-0.853; -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.
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http://dx.doi.org/10.3390/vaccines9050491DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151667PMC
May 2021

I Care About Your Plight, But Only If I Like Your Leader: The Effect of National Leaders' Perceived Personality on Empathy and Pro-Social Behavior Towards Their Citizenry.

Pers Soc Psychol Bull 2021 Apr 13:146167220987989. Epub 2021 Apr 13.

The Hebrew University of Jerusalem, Israel.

People's default levels of empathy toward members of a distant group tend to be low. The current research shows that favorable perceptions regarding the personality of a group's leader can stimulate empathy and pro-social behavior toward his or her countrymen. In four experimental studies ( = 884), we found that exposure to a news article that positively (vs. negatively) characterizes a foreign national leader (vs. non-national leader) led to (a) increased levels of empathy toward distressed citizens of that leader's nation, (b) willingness to help those citizens, (c) motivation to invest time in inspecting additional information elucidating the circumstances that led to this adversity, and (d) an actual monetary donation for the benefit of those people. This effect turned out to be prominent when the national leader's domestic popularity was perceived as high. The results show that national leaders are in a position to contribute to more empathetic inter-society relations and enhance pro-social behavior.
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http://dx.doi.org/10.1177/0146167220987989DOI Listing
April 2021

Automated identification of clinical features from sparsely annotated 3-dimensional medical imaging.

NPJ Digit Med 2021 Mar 8;4(1):44. Epub 2021 Mar 8.

Department of Computer Science, University of California, Los Angeles, CA, USA.

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.
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http://dx.doi.org/10.1038/s41746-021-00411-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7940637PMC
March 2021

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

J Gen Intern Med 2021 Apr 2;36(4):1017-1022. Epub 2021 Feb 2.

Division of Infectious Diseases, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.

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.
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http://dx.doi.org/10.1007/s11606-020-06494-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7853705PMC
April 2021

With or without you: The paradoxical role of identification in predicting joint and ingroup collective action in intergroup conflict.

Eur J Soc Psychol 2020 Oct 23;50(6):1334-1343. Epub 2020 Jun 23.

The Hebrew University of Jerusalem Jerusalem Israel.

While we have a rich understanding of the motivations of disadvantaged group members to act collectively with their group, especially the important role played by identification, we know less about the disadvantaged's motivations to engage in joint action with the advantaged. This research examines the role of identification in predicting joint and ingroup collective action in intergroup conflicts. Since joint action inherently diffuses the perception of "us versus them", we propose that identification predicts ingroup action, but not joint action. We also examine conflict intensity as a moderator, and examine how changing identification is linked to change in support for joint action. We test these hypotheses in a three-wave longitudinal study in the Palestinian-Israeli conflict. Results support our hypotheses, demonstrating that identification positively predicts ingroup action but not necessarily joint action, and that when conflict intensifies, changes in identification are negatively related to joint action with outgroup members.
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http://dx.doi.org/10.1002/ejsp.2677DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7754399PMC
October 2020

Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis.

Nat Commun 2020 10 30;11(1):5504. Epub 2020 Oct 30.

Department of Computer Science, University of California Los Angeles, 404 Westwood Plaza, Los Angeles, CA, 90095, USA.

Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We use simulations starting from one of the largest available real single-cell RNA-Seq data from 120 individuals to also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool.
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http://dx.doi.org/10.1038/s41467-020-19365-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599215PMC
October 2020

A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity.

PLoS One 2020 22;15(9):e0239474. Epub 2020 Sep 22.

Faculty Practice Group, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0239474PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508387PMC
October 2020

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

PLoS Genet 2020 09 14;16(9):e1009018. Epub 2020 Sep 14.

Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.

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.
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http://dx.doi.org/10.1371/journal.pgen.1009018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515203PMC
September 2020

Context-aware dimensionality reduction deconvolutes gut microbial community dynamics.

Nat Biotechnol 2021 02 31;39(2):165-168. Epub 2020 Aug 31.

Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.

The translational power of human microbiome studies is limited by high interindividual variation. We describe a dimensionality reduction tool, compositional tensor factorization (CTF), that incorporates information from the same host across multiple samples to reveal patterns driving differences in microbial composition across phenotypes. CTF identifies robust patterns in sparse compositional datasets, allowing for the detection of microbial changes associated with specific phenotypes that are reproducible across datasets.
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http://dx.doi.org/10.1038/s41587-020-0660-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7878194PMC
February 2021

Disrupting the system constructively: Testing the effectiveness of nonnormative nonviolent collective action.

J Pers Soc Psychol 2020 Aug 13. Epub 2020 Aug 13.

Department of Psychology, The Hebrew University of Jerusalem.

Collective action research tends to focus on motivations of the disadvantaged group, rather than on which tactics are effective at driving the advantaged group to make concessions to the disadvantaged. We focused on the potential of nonnormative nonviolent action as a tactic to generate support for concessions among advantaged group members who are resistant to social change. We propose that this tactic, relative to normative nonviolent and to violent action, is particularly effective because it reflects constructive disruption: a delicate balance between disruption (which can put pressure on the advantaged group to respond) and perceived constructive intentions (which can help ensure that the response to action is a conciliatory one). We test these hypotheses across 4 contexts (total N = 3650). Studies 1-3 demonstrate that nonnormative nonviolent action (compared with inaction, normative nonviolent action, and violent action) is uniquely effective at increasing support for concessions to the disadvantaged among resistant advantaged group members (compared with advantaged group members more open to social change). Study 3 shows that constructive disruption mediates this effect. Study 4 shows that perceiving a real-world ongoing protest as constructively disruptive predicts support for the disadvantaged, whereas Study 5 examines these processes longitudinally over 2 months in the context of an ongoing social movement. Taken together, we show that nonnormative nonviolent action can be an effective tactic for generating support for concessions to the disadvantaged among those who are most resistant because it generates constructive disruption. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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http://dx.doi.org/10.1037/pspi0000333DOI Listing
August 2020

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

Proc Natl Acad Sci U S A 2020 08 20;117(31):18378-18384. Epub 2020 Jul 20.

Department of Psychology, Hebrew University, 9190501 Jerusalem, Israel.

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.
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http://dx.doi.org/10.1073/pnas.2005928117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414054PMC
August 2020

Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM.

Sci Rep 2020 07 3;10(1):11019. Epub 2020 Jul 3.

Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.

Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. We observe that snRNA-seq is commonly subject to contamination by high amounts of ambient RNA, which can lead to biased downstream analyses, such as identification of spurious cell types if overlooked. We present a novel approach to quantify contamination and filter droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: (1) human differentiating preadipocytes in vitro, (2) fresh mouse brain tissue, and (3) human frozen adipose tissue (AT) from six individuals. All three data sets showed evidence of extranuclear RNA contamination, and we observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq, our clustering strategy also successfully filtered single-cell RNA-seq data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.
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http://dx.doi.org/10.1038/s41598-020-67513-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7335186PMC
July 2020

BATMAN: Fast and Accurate Integration of Single-Cell RNA-Seq Datasets via Minimum-Weight Matching.

iScience 2020 Jun 20;23(6):101185. Epub 2020 May 20.

Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Institute of Precision Health, University of California, Los Angeles, CA, USA. Electronic address:

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.
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http://dx.doi.org/10.1016/j.isci.2020.101185DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7276436PMC
June 2020

Publisher Correction: Accurate estimation of cell composition in bulk expression through robust integration of single-cell information.

Nat Commun 2020 06 3;11(1):2891. Epub 2020 Jun 3.

Department of Human Genetics, David Geffen School ofMedicine at UCLA, Los Angeles, CA, 90095, USA.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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http://dx.doi.org/10.1038/s41467-020-16607-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7270096PMC
June 2020

Scalable probabilistic PCA for large-scale genetic variation data.

PLoS Genet 2020 05 29;16(5):e1008773. Epub 2020 May 29.

Department of Computer Science, University of California, Los Angeles, California, United States of America.

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.
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http://dx.doi.org/10.1371/journal.pgen.1008773DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7286535PMC
May 2020

Compositional Lotka-Volterra describes microbial dynamics in the simplex.

PLoS Comput Biol 2020 05 29;16(5):e1007917. Epub 2020 May 29.

Department of Computer Science, Columbia University, New York, New York, United States of America.

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.
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http://dx.doi.org/10.1371/journal.pcbi.1007917DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325845PMC
May 2020

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information.

Nat Commun 2020 04 24;11(1):1971. Epub 2020 Apr 24.

Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.

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.
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http://dx.doi.org/10.1038/s41467-020-15816-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7181686PMC
April 2020

Stochasticity constrained by deterministic effects of diet and age drive rumen microbiome assembly dynamics.

Nat Commun 2020 04 20;11(1):1904. Epub 2020 Apr 20.

Department of Life Sciences, Ben-Gurion University of the Negev and the National Institute for Biotechnology in the Negev, Marcus Family Campus, Beer-Sheva, Israel.

How complex communities assemble through the animal's life, and how predictable the process is remains unexplored. Here, we investigate the forces that drive the assembly of rumen microbiomes throughout a cow's life, with emphasis on the balance between stochastic and deterministic processes. We analyse the development of the rumen microbiome from birth to adulthood using 16S-rRNA amplicon sequencing data and find that the animals shared a group of core successional species that invaded early on and persisted until adulthood. Along with deterministic factors, such as age and diet, early arriving species exerted strong priority effects, whereby dynamics of late successional taxa were strongly dependent on microbiome composition at early life stages. Priority effects also manifest as dramatic changes in microbiome development dynamics between animals delivered by C-section vs. natural birth, with the former undergoing much more rapid species invasion and accelerated microbiome development. Overall, our findings show that together with strong deterministic constrains imposed by diet and age, stochastic colonization in early life has long-lasting impacts on the development of animal microbiomes.
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http://dx.doi.org/10.1038/s41467-020-15652-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7170844PMC
April 2020

Developing ReApp: an emotion regulation mobile intervention for intergroup conflict.

Cogn Emot 2020 11 8;34(7):1326-1342. Epub 2020 Apr 8.

Psychology Department, Hebrew University, Jerusalem, Israel.

People living in areas of intractable conflicts experience extreme negative emotions which ultimately lead to support of aggressive policies. Emotion regulation and particularly cognitive reappraisal has been found to be effective in reducing negative emotional experiences and shifting policy preferences. Therefore, it is important to develop scalable, evidence-based interventions aimed at regulating negative emotions in such contexts. In this paper, we introduce ReApp - a mobile game, aimed at training people to regulate their emotions using cognitive reappraisal. We examine the game's effectiveness in reducing negative emotions and support for aggressive policies in the context of the Israeli-Palestinian conflict. Results indicate that people who played ReApp experienced lower levels of anger and disgust, and were less supportive of aggressive political policies targeted at the outgroup. We believe that games such as ReApp could potentially influence mass audiences and by that promote conflict resolution.
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http://dx.doi.org/10.1080/02699931.2020.1747400DOI Listing
November 2020

Exposure to Analogous Harmdoing Increases Acknowledgment of Ingroup Transgressions in Intergroup Conflicts.

Pers Soc Psychol Bull 2020 12 19;46(12):1649-1664. Epub 2020 Mar 19.

Interdisciplinary Center Herzliya, Israel.

A major barrier to the resolution of intergroup conflicts is the reluctance to acknowledge transgressions committed by one's ingroup toward the outgroup. Existing research demonstrates that individuals are generally motivated to justify ingroup conduct and avoid experiencing guilt and shame about ingroup harmdoing. The current work explores the use of an analogy-based intervention to attenuate motivated reasoning in evaluations of ingroup harmdoing. Overall, across six studies, we find support for our hypothesis that considering a case of harmdoing in a removed context increases acknowledgment of an analogous case of ingroup harmdoing. We further explore why, and under what conditions, the analogy is effective in leading to increased acknowledgment of an ingroup transgression. We find that the effect of the analogy is mediated by the endorsement of moral principles specific to the domain of the transgression, suggesting that the mechanism involves a cognitive process of analogical reasoning.
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http://dx.doi.org/10.1177/0146167220908727DOI Listing
December 2020

Be Afraid, Be Very Afraid! Motivated Intergroup Emotion Regulation.

Pers Soc Psychol Bull 2020 11 19;46(11):1596-1613. Epub 2020 Mar 19.

The Hebrew University of Jerusalem, Israel.

Group-based emotions can shape group members' behaviors and intergroup relations. Therefore, we propose that people may try to regulate emotions of outgroup members to attain ingroup goals. We call this phenomenon "motivated intergroup emotion regulation." In four studies, conducted in both hypothetical and real-world contexts, we show that deterrence and reconciliation goals influence how fearful or calm people want outgroup members to feel, respectively. We further show that such motivated intergroup emotion regulation can guide behavior toward the outgroup, influencing how outgroup members feel (Studies 1, 2, and 4) and behave (Study 4). We demonstrate how affiliation with the ingroup, which renders ingroup goals more salient, shapes what ingroup members want outgroup members to feel (Studies 3 and 4) and subsequently how outgroup members feel and behave (Study 4). Finally, we discuss how motivated intergroup emotion regulation might contribute to understanding motivation in emotion regulation, group-based emotions, and intergroup relations.
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http://dx.doi.org/10.1177/0146167220910833DOI Listing
November 2020

Association of a Variant in VWA3A with Response to Anti-Vascular Endothelial Growth Factor Treatment in Neovascular AMD.

Invest Ophthalmol Vis Sci 2020 02;61(2):48

,.

Purpose: Anti-vascular endothelial growth factor (VEGF) therapy for neovascular AMD (nvAMD) obtains a variable outcome. We performed a genome-wide association study for anti-VEGF treatment response in nvAMD to identify variants potentially underlying such a variable outcome.

Methods: Israeli patients with nvAMD who underwent anti-VEGF treatment (n = 187) were genotyped on a whole exome chip containing approximately 500,000 variants. Genotyping was correlated with delta visual acuity (deltaVA) between baseline and after three injections of anti-VEGF. Top principal components, age, and baseline VA were included in the analysis. Two lead associated variants were genotyped in an independent validation set of patients with nvAMD (n = 108).

Results: Linear regression analysis on 5,353,842 variants revealed five exonic variants with an association P value of less than 6 × 10-5. The top variant in the gene VWA3A (P = 1.77 × 10-6) was tested in the validation cohort. The minor allele of the VWA3A variant was associated with worse response to treatment (P = 0.02). The average deltaVA of discovery plus validation was -0.214 logMAR (≈ a gain of 10.7 Early Treatment Diabetic Retinopathy Study letters) for homozygote for the major allele, 0.172 logMAR for heterozygotes (≈ a loss of 8.6 Early Treatment Diabetic Retinopathy Study letters), and 0.21 logMAR for homozygote for the minor allele (≈ a loss of 10.5 Early Treatment Diabetic Retinopathy Study letters). Minor allele carriers had a higher frequency of macular hemorrhage at baseline.

Conclusions: An VWA3A gene variant was associated with worse response to anti-VEGF treatment in Israeli patients with nvAMD. The VWA3A protein is a precursor of the multimeric von Willebrand factor which is involved in blood coagulation, a system previously associated with nvAMD.
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http://dx.doi.org/10.1167/iovs.61.2.48DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7329947PMC
February 2020

Group-based emotion regulation: A motivated approach.

Emotion 2020 Feb;20(1):16-20

Department of Psychology, The Hebrew University of Jerusalem.

The regulation of group-based emotions has gained scholarly attention only in recent years. In this article, we review research on group-based emotion regulation, focusing on the role of motivation and distinguishing between different emotion regulation motives in the group context. For that purpose, we first define group-based emotions and their effects on both intragroup and intergroup processes. We then review motives for group-based emotion regulation, suggesting 3 classes of group-based motives: (a) intragroup motives pertaining to what I want to be in relation to the group (e.g., increase sense of belongingness), (b) intergroup motives pertaining to what I want my group's relationship with other groups to be (e.g., preserve the status quo), and (c) meta group motives pertaining to what I want my group to be (e.g., perceive the ingroup more positvely). We discuss the implications of these different motives for group-based emotion regulation and how they might inform scholars in the field. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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http://dx.doi.org/10.1037/emo0000639DOI Listing
February 2020

An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

Br J Anaesth 2019 12 15;123(6):877-886. Epub 2019 Oct 15.

Department of Computer Science, University of California, Los Angeles, CA, USA; Department of Anaesthesiology and Perioperative Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Human Genetics, University of California, Los Angeles, CA, USA; Department of Biomathematics, University of California, Los Angeles, CA, USA.

Background: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level or utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart.

Methods: We report on the use of machine learning algorithms, specifically random forests, to create a fully automated score that predicts postoperative in-hospital mortality based solely on structured data available at the time of surgery. Electronic health record data from 53 097 surgical patients (2.01% mortality rate) who underwent general anaesthesia between April 1, 2013 and December 10, 2018 in a large US academic medical centre were used to extract 58 preoperative features.

Results: Using a random forest classifier we found that automatically obtained preoperative features (area under the curve [AUC] of 0.932, 95% confidence interval [CI] 0.910-0.951) outperforms Preoperative Score to Predict Postoperative Mortality (POSPOM) scores (AUC of 0.660, 95% CI 0.598-0.722), Charlson comorbidity scores (AUC of 0.742, 95% CI 0.658-0.812), and ASA physical status (AUC of 0.866, 95% CI 0.829-0.897). Including the ASA physical status with the preoperative features achieves an AUC of 0.936 (95% CI 0.917-0.955).

Conclusions: This automated score outperforms the ASA physical status score, the Charlson comorbidity score, and the POSPOM score for predicting in-hospital mortality. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period.
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http://dx.doi.org/10.1016/j.bja.2019.07.030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6883494PMC
December 2019
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