Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Conditions and constraints for astrocyte calcium signaling in the hippocampal mossy fiber pathway.

The spatiotemporal activities of astrocyte Ca²⁺ signaling in mature neuronal circuits remain unclear. We used genetically encoded Ca²⁺ and glutamate indicators as well as pharmacogenetic and electrical control of neurotransmitter release to explore astrocyte activity in the hippocampal mossy fiber pathway. Our data revealed numerous localized, spontaneous Ca²⁺ signals in astrocyte branches and territories, but these were not driven by neuronal activity or glutamate. Moreover, evoked astrocyte Ca²⁺ signaling changed linearly with the number of mossy fiber action potentials. Under these settings, astrocyte responses were global, suppressed by neurotransmitter clearance, and mediated by glutamate and GABA. Thus, astrocyte engagement in the fully developed mossy fiber pathway was slow and territorial, contrary to that frequently proposed for astrocytes within microcircuits. We show that astrocyte Ca²⁺ signaling functionally segregates large volumes of neuropil and that these transients are not suited for responding to, or regulating, single synapses in the mossy fiber pathway

Cardiomyocyte PDGFR-β signaling is an essential component of the mouse cardiac response to load-induced stress

PDGFR is an important target for novel anticancer therapeutics because it is overexpressed in a wide variety of malignancies. Recently, however, several anticancer drugs that inhibit PDGFR signaling have been associated with clinical heart failure. Understanding this effect of PDGFR inhibitors has been difficult because the role of PDGFR signaling in the heart remains largely unexplored. As described herein, we have found that PDGFR-β expression and activation increase dramatically in the hearts of mice exposed to load-induced cardiac stress. In mice in which Pdgfrb was knocked out in the heart in development or in adulthood, exposure to load-induced stress resulted in cardiac dysfunction and heart failure. Mechanistically, we showed that cardiomyocyte PDGFR-β signaling plays a vital role in stress-induced cardiac angiogenesis. Specifically, we demonstrated that cardiomyocyte PDGFR-β was an essential upstream regulator of the stress-induced paracrine angiogenic capacity (the angiogenic potential) of cardiomyocytes. These results demonstrate that cardiomyocyte PDGFR-β is a regulator of the compensatory cardiac response to pressure overload–induced stress. Furthermore, our findings may provide insights into the mechanism of cardiotoxicity due to anticancer PDGFR inhibitors