Suicide in Brazilian indigenous communities: clustering of cases in children and adolescents by household

OBJECTIVE: To estimate age and sex-specific suicide rates, compare suicide rates between indigenous communities, and quantify the frequency of intrafamilial suicide clustering. METHODS: We performed a retrospective cohort study involving 14,666 indigenous individuals in reservations in Dourados, state of Mato Grosso do Sul, Brazil, from 2003 through 2013 using national and local census. RESULTS: The overall suicide rate was 73.4 per 100,000 person-years. Adolescent males aged 15–19 and girls aged 10–14 had the highest rates for each sex at 289.3 (95%CI 187.5–391.2) and 85.3 (95%CI 34.9–135.7), respectively. Comparing the largest reservations, Bororo had a higher suicide rate than Jaguapiru (RR = 4.83, 95%CI 2.85–8.16) and had significantly lower socioeconomic indicators including income and access to electricity. Nine of 19 suicides among children under 15 occurred in household clusters. Compared with adult suicides, a greater proportion of child (OR = 5.12, 95%CI 1.89–13.86, p = 0.001) and adolescent (OR = 3.48, 95%CI 1.29–9.44, p = 0.017) suicides occurred within household clusters. CONCLUSIONS: High rates of suicide occur among children and adolescents in these indigenous reservations, particularly in poor communities. Nearly half of child suicides occur within household clusters. These findings underscore the need for broad public health interventions and focused mental health interventions in households following a suicide

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

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development