Indirect Effects of Oral Tolerance Inhibit Pulmonary Granulomas to Schistosoma mansoni Eggs

Parenteral injection of tolerated proteins into orally tolerant mice inhibits the initiation of immunological responses to unrelated proteins and blocks severe chronic inflammatory reactions of immunological origin, such as autoimmune reactions. This inhibitory effect which we have called “indirect effects of oral tolerance” is also known as “bystander suppression.” Herein, we show that i.p. injection of OVA + Al(OH)3 minutes before i.v. injection of Schistosoma mansoni eggs into OVA tolerant mice blocked the increase of pulmonary granulomas. In addition, the expression of ICAM-1 in lung parenchyma in areas outside the granulomas of OVA-orally tolerant mice was significantly reduced. However, at day 18 after granuloma induction there was no difference in immunofluorescency intensity to CD3, CD4, F4/80, andα-SMA per granuloma area of tolerant and control groups. Reduction of granulomas by reexposure to orally tolerated proteins was not correlated with a shift in Th-1/Th-2 cytokines in serum or lung tissue extract

Huánuco : ¿cómo vamos en educación?

Reúne información estadística relevante de la situación actual de la educación de la región Huánuco, ha sido hecho para servir como una guía informativa amigable de todos aquellos actores de la región que puedan influir positivamente en su realidad educativa. Entre 2004 y 2013, la tasa media de crecimiento anual del gasto público educativo por alumno fue de 23.2% en inicial, 16.3% en primaria y 12.3% en secundaria. En los últimos tres años, el gasto por alumno en inicial ha crecido hasta llegar a ser mayor al gasto por alumno nacional, con 2,428 soles en el 2013, aunque el gasto por alumno en primaria y secundaria en Huánuco siguen siendo menores al gasto por alumno nacional. El porcentaje de escuelas con acceso a Internet de Huánuco en primaria pasó de 0.6% en 2005 a 12.4% en 2014. En secundaria el crecimiento fue mayor, pasando de 3.2% en 2005 a 29.6% en 2014. Sin embargo, estos valores siguen siendo menores a los valores nacionales de 27.9% en primaria y 52.0% en secundaria. El indicador en primaria muestra que en la mayoría de distritos el valor es menor a 20%. En secundaria hay mayor heterogeneidad, y son pocos los distritos con porcentajes altos de instituciones con acceso a internet. Entre 2004 y 2013, las tasas de asistencia de Huánuco para los tres niveles de educación básica regular han aumentado. En primaria, la tasa de asistencia pasó de 91.7% en 2004 a 96.3% en 2013, siendo este último valor mayor al nacional. El acceso a educación inicial aumentó en el período, pasando de 30.0% en 2004 a 71.7% en 2013. En el nivel secundaria, la tasa de asistencia pasó de 60.3% en 2004 a 72.2% en 2013. Huánuco presenta mayores tasas de desaprobación, atraso y retiro con respecto a todo el país, tanto en primaria como en secundaria. El porcentaje de desaprobados en primaria en las provincias de Huánuco fluctúa desde 4.8% en Lauricocha hasta 14.2% en Marañón, en tanto que en secundaria oscila entre 7.5% en Lauricocha y 14.9% en Huacaybamba. En ambos casos, la mayoría de distritos tiene menos del 10% de estudiantes con atraso escolar, aunque en secundaria la cantidad de distritos con más del 20% de atraso es mayor. En cuanto al porcentaje de retirados en 2013, la mayoría de distritos en primaria y secundaria se ubican debajo de 8.0%. Sin embargo, en secundaria hay una cantidad significativa de distritos con más de 8% de retiro

SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost