Lipids revert inert Aβ amyloid fibrils to neurotoxic protofibrils that affect learning in mice

Although soluble oligomeric and protofibrillar assemblies of Aβ-amyloid peptide cause synaptotoxicity and potentially contribute to Alzheimer's disease (AD), the role of mature Aβ-fibrils in the amyloid plaques remains controversial. A widely held view in the field suggests that the fibrillization reaction proceeds ‘forward' in a near-irreversible manner from the monomeric Aβ peptide through toxic protofibrillar intermediates, which subsequently mature into biologically inert amyloid fibrils that are found in plaques. Here, we show that natural lipids destabilize and rapidly resolubilize mature Aβ amyloid fibers. Interestingly, the equilibrium is not reversed toward monomeric Aβ but rather toward soluble amyloid protofibrils. We characterized these ‘backward' Aβ protofibrils generated from mature Aβ fibers and compared them with previously identified ‘forward' Aβ protofibrils obtained from the aggregation of fresh Aβ monomers. We find that backward protofibrils are biochemically and biophysically very similar to forward protofibrils: they consist of a wide range of molecular masses, are toxic to primary neurons and cause memory impairment and tau phosphorylation in mouse. In addition, they diffuse rapidly through the brain into areas relevant to AD. Our findings imply that amyloid plaques are potentially major sources of soluble toxic Aβ-aggregates that could readily be activated by exposure to biological lipids

Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019

Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019

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

Exploring the sequence determinants of amyloid structure using position-specific scoring matrices

Protein aggregation results in beta-sheet-like assemblies that adopt either a variety of amorphous morphologies or ordered amyloid-like structures. These differences in structure also reflect biological differences; amyloid and amorphous beta-sheet aggregates have different chaperone affinities, accumulate in different cellular locations and are degraded by different mechanisms. Further, amyloid function depends entirely on a high intrinsic degree of order. Here we experimentally explored the sequence space of amyloid hexapeptides and used the derived data to build Waltz, a web-based tool that uses a position-specific scoring matrix to determine amyloid-forming sequences. Waltz allows users to identify and better distinguish between amyloid sequences and amorphous beta-sheet aggregates and allowed us to identify amyloid-forming regions in functional amyloids

Neurotoxicity of Alzheimer's disease Aβ peptides is induced by small changes in the Aβ42 to Aβ40 ratio

The amyloid peptides Aβ40 and Aβ42 of Alzheimer's disease contribute differentially to the disease process. The findings show here that small changes in the Aβ42/40 ratio have significant effects on the neurotoxicity of Aβ mixtures

GeoInfo: infraestructura de datos espaciales abiertos para la investigación agropecuaria

Made available in DSpace on 2017-12-21T12:29:27Z (GMT). No. of bitstreams: 2 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) 13.pdf: 1291344 bytes, checksum: 88bc95602e9d31d6bcbda97a46402928 (MD5) Previous issue date: 2017Empresa Brasileira de Pesquisa Agropecuária. Informática Agropecuária. Campinas, SP, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Monitoramento por Satélite. Campinas, SP, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Solos. Rio de Janeiro, RJ, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Monitoramento por Satélite. Campinas, RJ, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Informática Agropecuária. Campinas, SP, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Monitoramento por Satélite. Campinas, SP, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Solos. Rio de Janeiro, RJ, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Solos. Rio de Janeiro, RJ, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Monitoramento por Satélite. Campinas, RJ, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Solos. Rio de Janeiro, RJ, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Solos. Rio de Janeiro, RJ, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Florestas. Colombo, PR, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Solos. Rio de Janeiro, RJ, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Cerrados. Brasília, DF, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Meio Ambiente. Jaguariúna, SP, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Algodão. Campina Grande, PB, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Florestas. Colombo, PR, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Clima Temperado. Pelotas, RS, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Clima Temperado. Pelotas, RS, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Agrossilvipastoril. Sinop, MT, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Tabuleiros Costeiros. Aracaju, SE, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Amazônia Oriental. Belém, PA, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Caprinos e Ovinos. Sobral, CE, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Informática Agropecuária. Campinas, SP, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Meio Ambiente. Jaguariúna, SP, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Monitoramento por Satélite. Campinas, SP, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Monitoramento por Satélite. Campinas, SP, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Monitoramento por Satélite. Campinas, SP, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Informática Agropecuária. Campinas, SP, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Florestas. Colombo, PR, Brasil.Empresa Brasileira de Pesquisa Agropecuária. Departamento de Pesquisa e Desenvolvimento. Brasília, DF, Brasil.A geoinformação é essencial para o planejamento e monitoramento das atividades agropecuárias, justificando o emprego de esforços para reuni-la e padronizá-la de acordo com as diretrizes governamentais e possibiltando sua disponibilização à sociedade em geral. O objetivo deste trabalho é apresentar a experiência da Embrapa em construir sua Infraestrutura de Dados Espaciais, denominada GeoInfo, uma iniciativa para organizar, preservar, documentar e ofertar dados geoespaciais abertos produzidos nas pesquisas da empresa, ampliando o potencial de aplicação dessa informação na produção e difusão de conhecimento e inovação. O GeoInfo implementa os padrões da Infraestrutura Nacional de Dados Espaciais e viabiliza a interoperabilidade de dados geoespaciais provenientes de diferentes fontes, inclusive sob o aspecto semântico. Essa iniciativa possibilita a integração das informações geoespaciais produzidas na Embrapa com diversas informações disponíveis em nosso país e permite que a redundância de esforços na obtenção e produção de dados geoespaciais sejam evitados.Geoinformation is essential to plan and monitor agricultural activities, justifying efforts to gather and standardize it according to governmental guidelines and to make it available to decision makers and general public. This work aims on presenting the experience of Embrapa on building its Spatial Data Infrastructure (SDI). This SDI, called GeoInfo, is an initiative to organize, preserve, document and offer geodata produced by the company, in order to increase the application of this information in the production and diffusion of knowledge and innovation. GeoInfo implements the guidelines of the Brazilian National Spatial Data Infrastructure and enables scientific geodata interoperability, encompassing semantics. GeoInfo SDI promotes redundancy of efforts avoidance in obtaining and producing geodata. This initiative enables the integration of geospatial information produced at Embrapa with many other public information sources available worldwide.La geoinformación es esencial para la planificación y monitoreo de las actividades agropecuarias, justificando el empleo de esfuerzos para reunirla y estandarizarla de acuerdo con las directrices gubernamentales, posibilitando su disponibilidad para la sociedad en general y los tomadores de decisión. El objetivo de este trabajo es presentar la experiencia de Embrapa en construir su Infraestructura de Datos Espaciales (IDE), denominada GeoInfo, una iniciativa para organizar, preservar, documentar y ofrecer la geoinformación producida por la empresa, para ampliar el potencial de aplicación de esa información en la producción y difusión de conocimiento e innovación. La IDE-GeoInfo implementa los estándares de la Infraestructura Nacional de Datos Espaciales (INDE) y viabiliza la interoperabilidad de datos e informaciones espaciales provenientes de distintas fuentes, incluso bajo el aspecto semántico. El uso del Geoinfo permite que la redundancia de esfuerzos en la obtención y producción de datos geoespaciales sean evitados. Geoinfo es una estructura que posibilita la integración de las informaciones geoespaciales producidas en la Embrapa con diversas informaciones disponibles en todo el mundo