Does pre-training on brain-related tasks results in better deep-learning-based brain age biomarkers?

Brain age prediction using neuroimaging data has shown great potential as an indicator of overall brain health and successful aging, as well as a disease biomarker. Deep learning models have been established as reliable and efficient brain age estimators, being trained to predict the chronological age of healthy subjects. In this paper, we investigate the impact of a pre-training step on deep learning models for brain age prediction. More precisely, instead of the common approach of pre-training on natural imaging classification, we propose pre-training the models on brain-related tasks, which led to state-of-the-art results in our experiments on ADNI data. Furthermore, we validate the resulting brain age biomarker on images of patients with mild cognitive impairment and Alzheimer's disease. Interestingly, our results indicate that better-performing deep learning models in terms of brain age prediction on healthy patients do not result in more reliable biomarkers.Comment: Accepted at BRACIS 202

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

Evolução da saúde do trabalhador na perícia médica previdenciária no Brasil Evolution of worker's health in the social security medical examination in Brazil

Com o objetivo de analisar a prática da Perícia Médica Previdenciária a partir da introdução dos paradigmas da Saúde do Trabalhador, coletaram-se informações sobre a concessão de benefícios por incapacidade, avaliando o adoecimento pela geração da Comunicação de Acidente de Trabalho no Polo Cimenteiro do Rio de Janeiro. Entre 2007 e 2009 foi encontrada apenas uma notificação envolvendo o manuseio de resíduos tóxicos utilizados como substitutos de matriz energética embora a análise mostrasse fontes e mecanismos de adoecimento não considerados pela Perícia Médica, ainda centrada na lógica unicausal da Medicina do Trabalho. Para alcançar os paradigmas da Saúde do Trabalhador são necessárias mudanças na atuação da Perícia Médica, com o restabelecimento de parcerias, formação de recursos humanos, adoção de indicadores epidemiológicos, estabelecendo e avaliando metas que avancem para além da simples concessão de benefícios por incapacidade.<br>In order to analyze the practice of the social security medical examination starting from the introduction of the worker's health paradigms, data was gathered on the granting of social security disability benefits to assess worker illness based on notification of work-related accidents in the cement industries of Rio de Janeiro. From 2007 to 2009 there was only one notification, which involved a worker handling toxic waste instead of the energy matrix. However, the analysis revealed sources and mechanisms of illness overlooked in the social security medical examination, which is still focused on the one-cause-only logic of occupational medicine. To achieve the worker's health paradigms, changes are required to alter the way of conducting the social security medical examination, by re-establishing partnerships, training human resources, adopting epidemiological indicators, as well as setting and assessing social security goals that transcend the mere granting of disability benefits

Nos caminhos da história urbana, a presença das figueiras-bravas

RESUMO Este texto trata das chamadas figueiras-bravas, espécies nativas em florestas tropicais e subtropicais que, plantadas ou nascendo espontaneamente em certos locais, propiciaram, sob suas imensas copas, espaços de sociabilidade em muitos núcleos urbanos oitocentistas. As figueiras-bravas foram também importantes marcos paisagísticos, influindo muitas vezes na configuração de espaços urbanos de várias cidades brasileiras. Neste artigo, três casos relativos ao estado de São Paulo são apresentados: o caso de Lorena, no Vale do Paraíba, onde ao menos quatro logradouros importantes foram formados a partir da existência de figueiras-bravas e outros dois casos relativos à capital paulista, a saber, a figueira-brava da chácara da Marquesa de Santos, na várzea do Carmo, atual Parque D. Pedro II, e a figueira conhecida como Árvore das Lágrimas, ainda existente no Ipiranga

Resumos concluídos - Saúde Coletiva

Resumos concluídos - Saúde Coletiv

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data