Autoencoder-based Image Recommendation for Lung Cancer Characterization

Neste projeto, temos como objetivo desenvolver um sistema de IA que recomende um conjunto de casos relativos (passados) para orientar a tomada de decisão do médico. Objetivo: A ambição é desenvolver um modelo de aprendizado baseado em IA para caracterização de câncer de pulmão, a fim de auxiliar na rotina clínica. Considerando a complexidade dos fenômenos biológicos que ocorrem durante o desenvolvimento do câncer, as relações entre eles e as manifestações visuais capturadas pela tomografia computadorizada (CT) têm sido exploradas nos últimos anos. No entanto, devido à falta de robustez dos métodos atuais de aprendizado profundo, essas correlações são frequentemente consideradas espúrias e se perdem quando confrontadas com dados coletados a partir de distribuições alteradas: diferentes instituições, características demográficas ou até mesmo estágios de desenvolvimento do câncer.In this project, we aim to develop an AI system that recommends a set of relative (past) cases to guide the decision-making of the clinician. Objective: The ambition is to develop an AI-based learning model for lung cancer characterization in order to assist in clinical routine. Considering the complexity of the biological phenomenat hat occur during cancer development, relationships between these and visual manifestations captured by CT have been explored in recent years; however, given the lack of robustness of current deep learning methods, these correlations are often found spurious and get lost when facing data collected from shifted distributions: different institutions, demographics or even stages of cancer development

Monitoring land use and plant cover on an integrated agroecological production system through GIS.

The objective of this paper is to study in detail the land use and plant cover of an Integrated Agroecological Production System (IAPS) from 2003 through 2005. Four quarterly updating visits were performed on the 26 land units of the System from January 2003 to December 2005. Cartographic documents and QuickBird satellite images were also used to generate the final index maps for agrodiversity, fallow intensity and green manure use intensity. A high diversity of crops was observed. In some land units up to 40 plant species were recorded. However, this diversity was not uniformly distributed throughout the terrain. A high intensity of land use, mostly with annuals was also observed in a large part of the area. In most cases, fallow periods were up to 3 months in 3 years. Since annual crops demand intense tillage, minimum or no tillage practices are recommended for those areas to improve soil conservation. The use of legumes was less frequent on the land units used for annual crops. They were not uniformly distributed throughout the terrain. The results of this research are useful not only for those who are interested in the system itself, but also to validate the hypothesis that through GIS it is possible to summarize complex agroecological information into a visually friendly format, allowing easy interpretation of systemic analyses

PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIAS DO ENVELHECIMENTO DA UNIVERSIDADE SÃO JUDAS TADEU: TRAJETÓRIA E PANORAMA ATUAL

O Programa de Pós-Graduação em Ciências do Envelhecimento (PGCE), nível mestrado, foi aprovado pela CAPES em 2009 e iniciou suas atividades em 2010. É o único programa de pós-graduação de uma universidade privada com foco em Gerontologia na cidade de São Paulo. Atualmente, o PGCE está organizado em uma área de concentração denominada Ciências do Envelhecimento e em três linhas de pesquisa: (1) Aspectos educacionais, psicológicos e socioculturais do envelhecimento; (2) Doenças associadas ao envelhecimento; e (3) Saúde e funcionalidade no envelhecimento. Esta revisão narrativa apresenta a descrição dos atuais projetos de pesquisa do PGCE, conforme apresentado no relatório para avaliação de 2017 a 2020, recentemente submetido à CAPES. No período acima referido, a produção científica do PGCE correspondeu a 331 produções: 54 artigos em periódicos, 15 capítulos de livros, 36 trabalhos em anais de congressos, 91 apresentações de trabalhos em congressos e 135 produções técnicas. Ao longo de sua trajetória, algumas mudanças destacaram as características interdisciplinares do PGCE, o que pode ser evidenciado pelo aumento: na qualidade dos artigos publicados, no número de alunos matriculados, de dissertações apresentadas e de projetos de pesquisa e extensão desenvolvidos no período de 2017 a 2020, em comparação com o período de 2013 a 2016. O PGCE é um programa dinâmico que se adapta às necessidades emergentes da sociedade, integra pesquisa e extensão e, ao mesmo tempo, apresenta uma produção robusta para a comunidade científica

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

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