Diagnoses of Ovine Infection by the Serotype-4 Bluetongue Virus on Minas Gerais, Brazil

Background: Bluetongue (BT) is a viral disease transmitted by hematophagous vectors of the genus Culicoides. In Brazil, the identifcation of antibodies against the virus has been held for over thirty years, however clinicopathological diagnosis of the disease are scarce. The frst reported case occurred in the state of Paraná in 2001, confrmed by isolation and identifcation of serotype 12 of BTV. In the state of Rio Grande do Sul, in 2009, two outbreaks confrmed and was identifed the serotype 12. Serotype 4 was isolated during an outbreak in the state of Rio de Janeiro in 2013. This study describes the Diagnoses of ovine infection by the serotype-4 bluetongue virus in Minas Gerais, Brazil. Case: In a farm in the Southern region of the state of Minas Gerais, a group of 28 male sheep, was sent for necropsy at the Setor de Patologia Veterinária of Universidade Federal de Lavras (SPV-UFLA). In a flock of 80 male sheep 28 died with clinical signs of respiratory distress, whereas other showed signs of anemia and hypoproteinemia, cough, sneezing, prostration, fever, mucopurulent nasal discharge, anemia and submandibular edema. At necropsy, the main changes observed were cranioventral pulmonary consolidation, hemorrhage at the base of the pulmonary artery and ulcerating lesions in the hard palate mucosa, rumen and reticulum. The histological changes consisted of bacterial bronchopneumonia, papillary necrosis associated with bacterial structures, multifocal vasculitis in the submucosa and thrombi in blood vessels of the serosa in the rumen and reticulum, hyaline and flocculate necrosis in esophageal muscle, skeletal and cardiac muscle fbers were also observed, associated with moderate mononuclear inflammatory infltrate between fbers and around blood vessels. Discussion: The diagnosis of BT was confrmed by the identifcation of nucleic acids of the virus in blood samples and from tissues of animals from the herd by RT-PCR and by the detection of antibodies against Bluetongue virus with the agar gel immunodiffusion (AGID) test using serum samples from the remaining herd animals. Serotype 4 was identifed in three of the samples inoculated into KC cells. The hemorrhage at the base of the pulmonary artery, one characteristic fndings, was found in three of the necropsied sheep. The pulmonary lesions observed in the present study strongly suggest the occurrence of pneumonia caused by opportunistic bacteria, especially Mannheimia haemolytica, which is commonly associated with pneumonia in sheep infected with BTV. This work is the frst in the state of Minas Gerais and the fourth in Brazil to report an outbreak of the disease with clinical signs. The economic impact of bluetongue results not only from the direct losses of animals to the disease, but also to the correlation among BTV infection and other problems including pneumonia, abortion and verminoses. In a study focused on the characteristics of ovine farming in Minas Gerais, farmers from the center-southwest reported that abortion and pneumonia were among their main problems. Thus, further epidemiological studies on BTV may improve the level of identifcation of infected herds and may help promote prophylactic measures. Necropsies and histopathology exams constitute crucial tools for diagnosis, because most cases present at a sub-clinical stage or in association with other, concomitant diseases. Keywords: BTV 4, viral diseases, sheep, RT-PCR e IDG

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

BioTIME 2.0 : expanding and improving a database of biodiversity time series

Funding: H2020 European Research Council (Grant Number(s): GA 101044975, GA 101098020).Motivation: Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables: Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain: Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain: The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement: The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format: csv and. SQL.Peer reviewe

BioTIME 2.0 : expanding and improving a database of biodiversity time series

Motivation. Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included. The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain. Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain. The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement. The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format. csv and. SQL

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

Análise de polimorfismos dos genes KIR e HLA classe I em pacientes com câncer colorretal

O câncer colorretal (CCR) pode ocorrer em qualquer parte do cólon ou do reto e representa o terceiro câncer mais comum no mundo em ambos os sexos. As células Natural Killer (NK) fazem parte do sistema imune inato reconhecendo moléculas de HLA de classe I em células alvo, através de seus receptores de membrana killer cell immunoglobulin-like receptors (KIR). O objetivo deste estudo foi avaliar a associação entre os genes KIR e os ligantes HLA em pacientes com câncer colorretal e controles saudáveis. Examinamos o polimorfismo de 16 genes KIR e seus ligantes HLA em 154 pacientes caucasóides com CCR e 216 controles saudáveis pela técnica de PCR-SSO e PCR-SSP. Quando comparamos os dois grupos, não foram encontradas diferenças significativas para os ligantes HLA e os genes KIR após correção de Bonferroni. Entretanto, o grupo de genótipos Bx (heterozigoto e homozigoto para o haplótipo B) foi mais frequente nos controles, quando comparados com os pacientes. Estes achados sugerem que altos níveis de ativação de sinais KIR aparecem como proteção para o câncer colorretal.Colorectal cancer (CRC) can occur anywhere in the colon or rectum and represents the third most common cancer in the world in both sexes. Natural killer cells (NK) are part of the innate immune system recognizing class I HLA molecules on target cells through their membrane receptors, called killer cell immunoglobulin-like receptors (KIR). The aim of our study was to evaluate the association between the KIR genes and HLA ligands in patients with colorectal cancer and healthy controls. We examined the polymorphism of 16 KIR genes and their HLA ligands in 154 caucasoid CRC patients and 216 healthy controls by PCR-SSO and PCR-SSP. When both groups were compared, no significant differences were found for HLA ligands and KIR genes after Bonferroni correction. However, the Bx group genotypes (heterozygous and homozygous for the haplotype B) were more frequent in controls, when compared with patients. These findings suggest that individuals with Bx genotypes could have some protection to colorectal cancer. These findings suggest that higher levels of activating KIR signals appear as protective to colorectal cancer