Efeito do uso de aditivos a base de microrganismos vivos na dieta de leitões na fase de creche / Effect of additives live microorganisms-based in the diet of piglets in the nursery phases

Os aditivos a base de DFM (direct fed microbials) são microrganismos vivos utilizados para melhorar a saúde e performance dos animais. Através do uso de DFM na ração, resultados superiores na produtividade, conversão alimentar e ganho de peso tem sido relatados. A introdução de suplementos a base de DFM contribui para a modulação da microbiota e aumento na resposta imune após períodos de estresse como o desmame de leitões. O objetivo do estudo foi avaliar o desempenho animal em uma granja no oeste do Paraná, utilizando o produto a base de DFM (Bacilus subtillis; 100 g/ton) na dieta de leitões nas fases de creche (Pré 1, Pré 2, Inicial 1 e Inicial 2) em substituição a utilização de altas doses de antibióticos (amoxicilina, colistina, clortetraciclina, trimetropim e sulfametazina) na ração dos animais. Neste estudo, o aditivo proporcionou maior ganho de peso dos animais em relação ao grupo Controle (P < 0,05)

Zika Brazilian Cohorts (ZBC) Consortium: Protocol for an Individual Participant Data Meta-Analysis of Congenital Zika Syndrome after Maternal Exposure during Pregnancy.

Despite great advances in our knowledge of the consequences of Zika virus to human health, many questions remain unanswered, and results are often inconsistent. The small sample size of individual studies has limited inference about the spectrum of congenital Zika manifestations and the prognosis of affected children. The Brazilian Zika Cohorts Consortium addresses these limitations by bringing together and harmonizing epidemiological data from a series of prospective cohort studies of pregnant women with rash and of children with microcephaly and/or other manifestations of congenital Zika. The objective is to estimate the absolute risk of congenital Zika manifestations and to characterize the full spectrum and natural history of the manifestations of congenital Zika in children with and without microcephaly. This protocol describes the assembly of the Consortium and protocol for the Individual Participant Data Meta-analyses (IPD Meta-analyses). The findings will address knowledge gaps and inform public policies related to Zika virus. The large harmonized dataset and joint analyses will facilitate more precise estimates of the absolute risk of congenital Zika manifestations among Zika virus-infected pregnancies and more complete descriptions of its full spectrum, including rare manifestations. It will enable sensitivity analyses using different definitions of exposure and outcomes, and the investigation of the sources of heterogeneity between studies and regions

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,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

EQUATIONS TO PREDICT THE METABOLIZABLE ENERGY OF MEAT AND BONE MEAL FOR GROWING PIGS

ABSTRACT The prediction of metabolizable energy (ME) of meat and bone meal (MBM) for pigs is an interesting tool, however, used models to predict these values must be validated in order to garantee higher precision. The aim of this study was to determine the chemical and energetic composition of different types of MBM for pigs and to adjust and validate models to better predict the ME based on the chemical composition. Thirty-two barrows, averaging an initial weight of 26.75 ± 1.45 kg, were individually allotted in a randomized block design with eight treatments and four replicates. The treatments consisted of seven types of MBM that replaced 20% of the basal diet. A stepwise procedure was the statistical procedure used to adjust the prediction equations and the ME was the dependent parameter. The validation of the adjusted models was performed using an independent databank of chemical and energetic composition of theBrazilian and international MBM. The metabolizable energy of different meat and bone meals ranged from 1645 to 2645 kcal kg-1. The equations that provide a good prediction of metabolizable energy of meat and bone meal for swine in Brazil are EM1 = -4233.58 + 0.4134GE + 72CP + 89.62ash - 159.06Ca; EM2 = 2087.49 + 0.3446GE + 31.82ash - 189.18Ca; EM3 = 2140.13 + 0.3845GE - 112.33Ca; EM4 = -346.58 + 0.656GE; EM5 = 3221.27 + 178.96fat - 76.55ash; and EM6 = 5356.45 - 84.75ash

Zika Brazilian Cohorts (ZBC) Consortium: Protocol for an Individual Participant Data Meta-Analysis of Congenital Zika Syndrome after Maternal Exposure during Pregnancy

Despite great advances in our knowledge of the consequences of Zika virus to human health, many questions remain unanswered, and results are often inconsistent. The small sample size of individual studies has limited inference about the spectrum of congenital Zika manifestations and the prognosis of affected children. The Brazilian Zika Cohorts Consortium addresses these limitations by bringing together and harmonizing epidemiological data from a series of prospective cohort studies of pregnant women with rash and of children with microcephaly and/or other manifestations of congenital Zika. The objective is to estimate the absolute risk of congenital Zika manifestations and to characterize the full spectrum and natural history of the manifestations of congenital Zika in children with and without microcephaly. This protocol describes the assembly of the Consortium and protocol for the Individual Participant Data Meta-analyses (IPD Meta-analyses). The findings will address knowledge gaps and inform public policies related to Zika virus. The large harmonized dataset and joint analyses will facilitate more precise estimates of the absolute risk of congenital Zika manifestations among Zika virus-infected pregnancies and more complete descriptions of its full spectrum, including rare manifestations. It will enable sensitivity analyses using different definitions of exposure and outcomes, and the investigation of the sources of heterogeneity between studies and regions