Early Dynamical Instabilities in the Giant Planet Systems

The observed wide eccentricity distribution of extrasolar giant planets is thought to be the result of dynamical instabilities and gravitational scattering among planets. Previously, it has been assumed that the orbits in giant planet systems become gravitationally unstable after the gas nebula dispersal. It was not well understood, however, how these unstable conditions were established in the first place. In this work we numerically simulate the evolution of systems of three planets as the planets sequentially grow to Jupiter's mass, and dynamically interact among themselves and with the gas disk. We use the hydro-dynamical code FARGO that we modified by implementing the $N$-body integrator SyMBA. The new code can handle close encounters and collisions between planets. To test their stability, the planetary systems were followed with SyMBA for up to $10^8$ yr after the gas disk dispersal. We find that dynamics of the growing planets is complex, because migration and resonances raise their orbital eccentricities, and cause dynamical instabilities when gas is still around. If the dynamical instabilities occur early, planets can be removed by collisions and ejections, and the system rearranges into a new, more stable configuration. In this case, the planetary systems emerging from the gas disks are expected to be stable, and would need to be destabilized by other means (low-mass planets, planetesimal disks, etc.). Alternatively, for the giant planet system to be intrinsically unstable upon the gas disk dispersal, a special timing would be required with the growth of (at least some of) the giant planets having to occur near the end of the gas disk lifetime.Comment: 10 pages, 9 figure

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Memory for symmetry and perceptual binding in patients with schizophrenia

The present study investigated the use of perceptual binding processes in schizophrenic (SC) patients and matched healthy controls, by examining their performance on the recall of symmetrical (vertical, horizontal and diagonal) and asymmetrical patterns varying in length between 2 and 9 items. The results showed that, although SC patients were less accurate than controls in all conditions, both groups recalled symmetrical patterns better than asymmetrical ones. The impairment of SC patients was magnified with supra-span symmetrical arrays, and they were more likely to reproduce symmetrical patterns as asymmetrical, particularly at medium and high length levels. Hierarchical regression analyses further indicated that the between-group differences in the recall of supra-span vertical and horizontal arrays, which require a greater involvement of visual pattern processes, remained significant after removing the variance associated with performance on asymmetrical patterns, which primarily reflects intrafigural spatial processes. It is proposed that schizophrenia may be associated with a specific deficit in the formation and retrieval of the global visual images of studied patterns and in the use of the on-line information about the type of symmetry being tested to guide retrieval processes. © 2013 Elsevier B.V

Gamma-Ray Burst observations by the high-energy charged particle detector on board the CSES-01 satellite between 2019 and 2021

In this paper we report the detection of five strong Gamma-Ray Bursts (GRBs) by the High-Energy Particle Detector (HEPD-01) mounted on board the China Seismo-Electromagnetic Satellite (CSES-01), operational since 2018 on a Sun-synchronous polar orbit at a $\sim$ 507 km altitude and 97$^\circ$ inclination. HEPD-01 was designed to detect high-energy electrons in the energy range 3 - 100 MeV, protons in the range 30 - 300 MeV, and light nuclei in the range 30 - 300 MeV/n. Nonetheless, Monte Carlo simulations have shown HEPD-01 is sensitive to gamma-ray photons in the energy range 300 keV - 50 MeV, even if with a moderate effective area above $\sim$ 5 MeV. A dedicated time correlation analysis between GRBs reported in literature and signals from a set of HEPD-01 trigger configuration masks has confirmed the anticipated detector sensitivity to high-energy photons. A comparison between the simultaneous time profiles of HEPD-01 electron fluxes and photons from GRB190114C, GRB190305A, GRB190928A, GRB200826B and GRB211211A has shown a remarkable similarity, in spite of the different energy ranges. The high-energy response, with peak sensitivity at about 2 MeV, and moderate effective area of the detector in the actual flight configuration explain why these five GRBs, characterised by a fluence above $\sim$ 3 $\times$ 10$^{-5}$ erg cm$^{-2}$ in the energy interval 300 keV - 50 MeV, have been detected.Comment: Accepted for publication in The Astrophysical Journal (ApJ

The burden of mental disorders, substance use disorders and self-harm among young people in Europe, 1990-2019 : Findings from the Global Burden of Disease Study 2019

Background Mental health is a public health issue for European young people, with great heterogeneity in resource allocation. Representative population-based studies are needed. The Global Burden of Disease (GBD) Study 2019 provides internationally comparable information on trends in the health status of populations and changes in the leading causes of disease burden over time. Methods Prevalence, incidence, Years Lived with Disability (YLDs) and Years of Life Lost (YLLs) from mental disorders (MDs), substance use disorders (SUDs) and self-harm were estimated for young people aged 10-24 years in 31 European countries. Rates per 100,000 population, percentage changes in 1990-2019, 95% Uncertainty Intervals (UIs), and correlations with Sociodemographic Index (SDI), were estimated. Findings In 2019, rates per 100,000 population were 16,983 (95% UI 12,823 - 21,630) for MDs, 3,891 (3,020 4,905) for SUDs, and 89.1 (63.8 - 123.1) for self-harm. In terms of disability, anxiety contributed to 647.3 (432 -912.3) YLDs, while in terms of premature death, self-harm contributed to 319.6 (248.9-412.8) YLLs, per 100,000 population. Over the 30 years studied, YLDs increased in eating disorders (14.9%;9.4-20.1) and drug use disorders (16.9%;8.9-26.3), and decreased in idiopathic developmental intellectual disability (-29.1%;23.8-38.5). YLLs decreased in self-harm (-27.9%;38.3-18.7). Variations were found by sex, age-group and country. The burden of SUDs and self-harm was higher in countries with lower SDI, MDs were associated with SUDs. Interpretation Mental health conditions represent an important burden among young people living in Europe. National policies should strengthen mental health, with a specific focus on young people. Funding The Bill and Melinda Gates Foundation Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Peer reviewe

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Profiling the different needs and expectations of patients for population-based medicine: a case study using segmentation analysis

Abstract Background This study illustrates an evidence-based method for the segmentation analysis of patients that could greatly improve the approach to population-based medicine, by filling a gap in the empirical analysis of this topic. Segmentation facilitates individual patient care in the context of the culture, health status, and the health needs of the entire population to which that patient belongs. Because many health systems are engaged in developing better chronic care management initiatives, patient profiles are critical to understanding whether some patients can move toward effective self-management and can play a central role in determining their own care, which fosters a sense of responsibility for their own health. A review of the literature on patient segmentation provided the background for this research. Method First, we conducted a literature review on patient satisfaction and segmentation to build a survey. Then, we performed 3,461 surveys of outpatient services users. The key structures on which the subjects’ perception of outpatient services was based were extrapolated using principal component factor analysis with varimax rotation. After the factor analysis, segmentation was performed through cluster analysis to better analyze the influence of individual attitudes on the results. Results Four segments were identified through factor and cluster analysis: the “unpretentious,” the “informed and supported,” the “experts” and the “advanced” patients. Their policies and managerial implications are outlined. Conclusions With this research, we provide the following: – a method for profiling patients based on common patient satisfaction surveys that is easily replicable in all health systems and contexts; – a proposal for segments based on the results of a broad-based analysis conducted in the Italian National Health System (INHS). Segments represent profiles of patients requiring different strategies for delivering health services. Their knowledge and analysis might support an effort to build an effective population-based medicine approach.</p

Why non-urgent patients choose emergency over primary care services? Empirical evidence and managerial implications

Objective To investigate structural and psychological factors that lead non-urgent patients to choose the Accidents & Emergency Department (A&ED) rather than primary care services. Data Sources Data were collected through interviews by means of a structured questionnaire. Data regarding the A&ED sample were also drawn from the database of the department. Study Design Hypotheses were tested in a survey comparing A&ED non-urgent patients and patients using GP surgeries. Different perceptions of the characteristics of A&ED and primary care services were measured and a perceptual map was created using the linear discriminant analysis (LDA). Data Collection Emergency services users were interviewed in the A&ED of the General Hospital of the Province of Macerata (Italy). Primary care users were interviewed in 4 GP surgeries. 527 patients were interviewed between December 2006 and February 2007. Principal Findings A&ED and primary care patients look for different characteristics as diagnostic and therapeutic potentialities, empathy and competence, quick access or long-lasting relationship. Information asymmetry explains part of the behaviour. Conclusions Use of A&ED services for non urgent care can be reduced. The understanding of reasons underlying the choice and a change in access, timing and contents of care/services provided by GPs might provide incentives for shifting from A&ED to GPs surgeries

Why non-urgent patients choose emergency over primary care services? Empirical evidence and managerial implications

Objective To investigate structural and psychological factors that lead non-urgent patients to choose the Accidents & Emergency Department (A&ED) rather than primary care services.Data sources Data were collected through interviews by means of a structured questionnaire. Data regarding the A&ED sample were also drawn from the database of the department.Study design Hypotheses were tested in a survey comparing A&ED non-urgent patients and patients using GP surgeries. Different perceptions of the characteristics of A&ED and primary care services were measured and a perceptual map was created using the linear discriminant analysis (LDA).Data collection Emergency services users were interviewed in the A&ED of the General Hospital of the Province of Macerata (Italy). Primary care users were interviewed in four GP surgeries. 527 patients were interviewed between December 2006 and February 2007.Principal findings A&ED and primary care patients look for different characteristics as diagnostic and therapeutic potentialities, empathy and competence, quick access or long-lasting relationship. Information asymmetry explains part of the behaviour.Conclusions Use of A&ED services for non-urgent care can be reduced. The understanding of reasons underlying the choice and a change in access, timing and contents of care/services provided by general practitioners (GPs) might provide incentives for shifting from A&ED to GPs surgeries.Health care services Organization Primary care Emergency