Las comunidades vegetales de la sabana del Parque Nacional del Palmar (Argentina)

El Parque Nacional El Palmar, Entre Ríos, preserva desde 1965 el mayor remanente de la sabana de Butia yatay, un ecosistema rico en especies amenazado por la actividad humana. Para estudiar el funcionamiento de este ecosistema y gestionar su preservación, es necesario contar con una adecuada caracterización de la heterogeneidad de su vegetación. En este trabajo presentamos una clasificación fitosociológica de las comunidades vegetales de la sabana del Parque, describimos su estructura, composición y diversidad florística y detallamos su distribución en el paisaje. Con el fin de estratificar la obtención de datos, delimitamos primero las principales unidades geomorfológicas del paisaje. En la primavera de 1999 y en el verano siguiente, realizamos censos florísticos completos en 73 "stands" de sabana con diferentes historias de incendio, distribuidos en las diferentes unidades geomorfológicas. Utilizamos técnicas de análisis multivariado y clasificación numérica para agrupar los censos en comunidades y las especies en grupos florísticos. Finalmente, resumimos las diferencias entre las comunidades en un escalamiento multidimensional métrico. Obtuvimos 7 comunidades, descriptas por 20 grupos florísticos. El patrón de similitudes entre estas comunidades sugiere que la heterogeneidad florística se ordena según dos componentes, uno asociado con diferencias en la textura del suelo y el otro con diferencias en el régimen de humedad del suelo. Tres de las comunidades, afines entre sí, se distribuyen en las terrazas altas e interfluvios que conforman la matriz del paisaje, en correspondencia con diferencias graduales de topografía y suelos. Estas comunidades cambian sucesionalmente por el avance de la lignificación desencadenada por la exclusión del ganado y mantenida por la escasez de incendios naturales. Las restantes cuatro comunidades aparecen estrictamente asociadas con afloramientos rocosos, depósitos arenosos o vías de escurrimiento que interrumpen la matriz del paisaje; en ellas no es evidente la lignificación.Fil: Batista, William B.. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Rolhauser, Andrés Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Biganzoli, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Burkart, Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Goveto, Liliana. Administración de Parques Nacionales; ArgentinaFil: Maranta, Aristobulo. Administración de Parques Nacionales; ArgentinaFil: Pignataro, Ana Genoveva. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Morandeira, Natalia Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Rabadan, Mirta Mabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentin

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (USMR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71.2 deaths per 1000 livebirths (95% uncertainty interval WI] 68.3-74-0) in 2000 to 37.1 (33.2-41.7) in 2019 while global NMR correspondingly declined more slowly from 28.0 deaths per 1000 live births (26.8-29-5) in 2000 to 17.9 (16.3-19-8) in 2019. In 2019,136 (67%) of 204 countries had a USMR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030,154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9.65 million (95% UI 9.05-10.30) in 2000 and 5.05 million (4.27-6.02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3.76 million 95% UI 3.53-4.021) in 2000 to 48% (2.42 million; 2.06-2.86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0.80 (95% UI 0.71-0.86) deaths per 1000 livebirths and U5MR to 1.44 (95% UI 1-27-1.58) deaths per 1000 livebirths, and in 2019, there were as many as 1.87 million (95% UI 1-35-2.58; 37% 95% UI 32-43]) of 5.05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve USMR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

The flooding pampa grassland: alternatives states.

[email protected] analítica--semestra

Keystone meeting summary: \u27Adipogenesis, obesity, and inflammation\u27 and \u27Diabetes mellitus and the control of cellular energy metabolism, \u27 January 21-26, 2006, Vancouver, Canada

The dysregulation of specific cellular functions in adipocytes, muscle cells, beta cells, and the liver leads to changes in systemic metabolic processes and ultimately to the pathophysiological manifestations that cause type 2 diabetes. The underlying cellular mechanisms are complex. The two meetings summarized here aimed to highlight the recent advances in our understanding of the molecular basis of feeding and nutrient storage and on the molecular consequences of obesity in terms of promoting risk for type 2 diabetes and cardiovascular disease

Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health : all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (USMR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71.2 deaths per 1000 livebirths (95% uncertainty interval WI] 68.3-74-0) in 2000 to 37.1 (33.2-41.7) in 2019 while global NMR correspondingly declined more slowly from 28.0 deaths per 1000 live births (26.8-29-5) in 2000 to 17.9 (16.3-19-8) in 2019. In 2019,136 (67%) of 204 countries had a USMR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030,154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9.65 million (95% UI 9.05-10.30) in 2000 and 5.05 million (4.27-6.02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3.76 million [95% UI 3.53-4.021) in 2000 to 48% (2.42 million; 2.06-2.86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0.80 (95% UI 0.71-0.86) deaths per 1000 livebirths and U5MR to 1.44 (95% UI 1-27-1.58) deaths per 1000 livebirths, and in 2019, there were as many as 1.87 million (95% UI 1-35-2.58; 37% [95% UI 32-43]) of 5.05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve USMR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe

Prognostication, Survival and Treatment-Related Outcomes in HIV-Associated Burkitt Lymphoma (HIV-BL): A US and UK Collaborative Analysis

Introduction: There are few data about prognostication and outcomes in patients (pts) with HIV-BL treated in the cART era. Optimal treatment strategies to minimize treatment-related mortality (TRM) remain unclear and current recommendations are based on small studies. We conducted a multicenter international analysis to identify prognostic factors and outcomes in pts with HIV-BL treated in the cART era. Methods: This retrospective analysis included a subcohort from a recent study across 30 US sites (Evens et al. Blood 2020) augmented by data from 5 UK centers treated 2009-2018. Progression-free (PFS) and overall survival (OS) were estimated by Kaplan-Meier & differences assessed by log-rank test. Univariate (UVA) associations were derived via Cox model and multivariable (MVA) models were constructed by forward selection of significant variables with P 3x upper limit of normal (ULN) 49% & >5xULN 39%); >1 extranodal (EN) site: 60%; any CNS involvement (CNSinv) 25%; and +bone marrow (BM) 46%. MYC rearrangement was reported in 93% of pts with t(8;14) in 49%, break-apart probe in 41% and MYC-light chain in 3%; the rest had classical BL with negative MYC testing (4%) or missing result (3%) (otherwise classical BL). Median CD4 count was 217 (IQR 90-392 cells/µL) with 68% pts having CD4>100 cells/µL. At BL diagnosis, HIV viral load was detectable in 55%; 39% of pts were on cART. Baseline features were similar between the US & UK cohorts with significant differences only in ECOG PS 2-4 (32% vs 65%; P100 cells/µL had better 3-yr PFS (Fig B) & OS (68% vs 57% P=0.03). We observed significantly worse outcomes in pts with baseline CNSinv (3-yr PFS 36% vs 69%, P1 EN, +BM, baseline CNSinv, LDH>ULN, CD4 5xULN (HR 2.09, P1 EN sites (HR 1.58 P=0.043). The same variables were significant on MVA for OS. Adjusting for all of the prognostic variables, Tx with Magrath was associated with longer PFS (adjusted HR, 0.45, P=0.005). Conclusions: These data represent the largest analysis of HIV-BL to date. There were favorable tolerance and outcomes with intensive R-containing regimens with Magrath being associated with lower TRM and the highest PFS. In addition, prognostic factors for pt outcomes were associated with lymphoma characteristics rather than with HIV-related features. Pts with baseline CNSinv represent a high-risk group with unmet therapeutic needs. Disclosures Alderuccio: Oncinfo: Honoraria; Puma Biotechnology: Other: Family member; ADC Therapeutics: Membership on an entity's Board of Directors or advisory committees; OncLive: Honoraria; Inovio Pharmaceuticals: Other: Family member; Foundation Medicine: Other: Family member; Forma Therapeutics: Other: Family member; Agios Pharmaceuticals: Other: Family member. Olszewski:Spectrum Pharmaceuticals: Research Funding; TG Therapeutics: Research Funding; Adaptive Biotechnologies: Research Funding; Genentech, Inc.: Research Funding. Evens:Epizyme: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria; Merck: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Mylteni: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria, Research Funding; MorphoSys: Consultancy, Honoraria; Research To Practice: Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria. Collins:Gilead: Consultancy, Honoraria, Speakers Bureau; BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; MSD: Consultancy, Honoraria, Research Funding; Taekda: Consultancy, Honoraria, Other: travel, accommodations, expenses, Speakers Bureau; BeiGene: Consultancy; Roche: Consultancy, Honoraria, Other: travel, accommodations, expenses , Speakers Bureau; Celleron: Consultancy, Honoraria, Research Funding; ADC Therapeutics: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Speakers Bureau; Celgene: Research Funding; Amgen: Research Funding; Pfizer: Honoraria. Danilov:Astra Zeneca: Consultancy, Research Funding; Verastem Oncology: Consultancy, Research Funding; Takeda Oncology: Research Funding; Gilead Sciences: Research Funding; Bayer Oncology: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; TG Therapeutics: Consultancy; Nurix: Consultancy; Celgene: Consultancy; Aptose Biosciences: Research Funding; Bristol-Myers Squibb: Research Funding; Rigel Pharmaceuticals: Consultancy; Karyopharm: Consultancy; Pharmacyclics: Consultancy; BeiGene: Consultancy; Abbvie: Consultancy. Jagadeesh:Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Debiopharm Group: Research Funding; MEI Pharma: Research Funding; Verastem: Membership on an entity's Board of Directors or advisory committees; Regeneron: Research Funding. Reddy:Genentech: Research Funding; Abbvie: Consultancy; BMS: Consultancy, Research Funding; Celgene: Consultancy; KITE Pharma: Consultancy. Farooq:Kite, a Gilead Company: Honoraria. Bond:Seattle Genetics: Honoraria. Khan:Celgene: Research Funding; Janssen: Honoraria; Pharmacyclics: Honoraria; Bristol Myers Squibb: Research Funding; Seattle Genetics: Research Funding. Yazdy:Bayer: Honoraria; Genentech: Research Funding; Octapharma: Consultancy; Abbvie: Consultancy. Karmali:Karyopharm: Honoraria; Takeda: Research Funding; AstraZeneca: Speakers Bureau; BeiGene: Speakers Bureau; BMS/Celgene/Juno: Honoraria, Other, Research Funding, Speakers Bureau; Gilead/Kite: Honoraria, Other, Research Funding, Speakers Bureau. Martin:Janssen: Consultancy; Regeneron: Consultancy; Bayer: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy; Beigene: Consultancy; Cellectar: Consultancy; Incyte: Consultancy; Kite: Consultancy; Morphosys: Consultancy; Celgene: Consultancy; Teneobio: Consultancy; Karyopharm: Consultancy, Research Funding. Diefenbach:Bristol-Myers Squibb: Consultancy, Research Funding; Denovo: Research Funding; Genentech, Inc.: Consultancy, Research Funding; Incyte: Research Funding; LAM Therapeutics: Research Funding; MEI: Research Funding; Merck: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Millenium/Takeda: Research Funding; Trillium: Research Funding. Klein:Takeda: Membership on an entity's Board of Directors or advisory committees. Haverkos:Viracta THerapeutics: Consultancy. Epperla:Verastem Oncology: Speakers Bureau; Pharmacyclics: Honoraria. Caimi:Amgen: Other: Advisory Board; Bayer: Other: Advisory Board; Kite Pharma: Other: Advisory Board; ADC Therapeutics: Other: Advisory Board, Research Funding; Celgene: Speakers Bureau; Verastem: Other: Advisory Board. Kamdar:Roche: Research Funding. Feldman:Eisai: Research Funding; Pfizer: Research Funding; Kyowa Kirin: Consultancy, Research Funding; Portola: Research Funding; Janssen: Speakers Bureau; AstraZeneca: Consultancy; Trillium: Research Funding; Cell Medica: Research Funding; Amgen: Research Funding; Pharmacyclics: Honoraria, Other, Speakers Bureau; Abbvie: Honoraria; Bayer: Consultancy, Honoraria; Viracta: Research Funding; Rhizen: Research Funding; Corvus: Research Funding; BMS: Consultancy, Honoraria, Research Funding; Kite: Honoraria, Other: Travel expenses, Speakers Bureau; Celgene: Honoraria, Research Funding; Takeda: Honoraria, Other: Travel expenses; Seattle Genetics, Inc.: Consultancy, Honoraria, Other: Travel expenses, Research Funding, Speakers Bureau. Smith:AstraZeneca: Consultancy; Millenium/Takeda: Consultancy; Karyopharm: Consultancy; Beigene: Consultancy; Seattle Genetics: Research Funding; Ayala: Research Funding; Bayer: Research Funding; AstraZeneca: Research Funding; Acerta Pharma BV: Research Funding; Bristol Meyers Squibb: Research Funding; Portola: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Incyte: Research Funding; Ignyta: Research Funding; Genentech: Research Funding; De Novo Biopharma: Research Funding. Portell:Amgen: Consultancy