ANÁLISE DE MÉTODOS DE DETECÇÃO E RECONHECIMENTO DE FACES UTILIZANDO VISÃO COMPUTACIONAL E ALGORITMOS DE APRENDIZADO DE MÁQUINA

O avanço da tecnologia das últimas décadas tem proporcionado muitas facilidades para a humanidade em várias aplicações, e a tecnologia de reconhecimento facial é uma delas. Existem vários problemas a serem resolvidos para se realizar o reconhecimento de faces a partir de imagens digitais, como variação de iluminação do ambiente, mudança das características físicas do rosto e resolução das imagens utilizadas. Este trabalho buscou realizar uma análise comparativa entre alguns dos métodos de detecção e reconhecimento facial, assim como o tempo de execução dos mesmos. Foram utilizados os algoritmos de reconhecimento facial Eigenface, Fisherface e LBPH em conjunto com o algoritmo de detecção facial Haar Cascade, todos da biblioteca OpenCV. Também foi explorado o uso de uma rede neural CNN para reconhecimento facial em conjunto com o algoritmo de detecção facial HOG, estes da biblioteca Dlib. O trabalho almejou, além de analisar os algoritmos com relação a taxas de acertos, fatores como grau de confiabilidade e tempo de execução também foram considerados

Uma abordagem acerca da Influenza A-H1N1 e a pandemia de Covid-19 no contexto brasileiro: uma revisão integrativa / An approach to Influenza A-H1N1 in the Brazilian context: an integrative review

O presente estudo tem como objetivo relatar os aspectos da influenza A-H1N1, dentre eles a caracterização viral, diagnóstico e prevenção. Ademais, objetivou-se na abordagem o papel dos suínos na transmissão interespécie e a epidemiologia da doença no Brasil, bem como as semelhanças da doença com a COVID-19, que apresentam um desafio para a saúde pública do país. Trata-se de uma revisão integrativa da literatura realizada através da Biblioteca Virtual em Saúde (BVS), com o auxílio das bases de dados Literatura Latino-Americana e do Caribe em Ciências (LILACS), Scientific Library Eletronic Library Online (SciELO) e Base de Dados de Enfermagem (BDENF), sendo utilizados os descritores: Brasil; COVID-19; Epidemiologia; Patologia e Vírus da influenza A subtipo H1N1, localizados no DeCS. Tais descritores foram cruzados utilizando o operador booleano AND. Foram identificados inicialmente 736 estudos nas bases elencas e, após aplicação dos critérios de inclusão e exclusão, apenas 06 estudos foram selecionados para composição e análise do estudo. Os estudos apontam a sintomatologia ocasionada pelos vírus da Influenza A-H1N1 e COVID-19é semelhante, assim como a forma de transmissão e o agravamento ocasionado pela presença eventual de doenças prévias. A diferenciação se encontra no período de incubação, sendo de 3 a 7 dias no caso da influenza e de 2 a 14 dias na COVID-19. Conclui-se que no contexto pandêmico atual envolvendo o vírus SARS-CoV-2 exige um maior monitoramento, uma vez que as enfermidades possuem semelhanças que podem levar a confusão e dificultar o atendimento

Manifestações psiquiátricas na reumatologia: uma revisão sistemática / Psychiatric manifestations in rheumatology: a systematic review

Doenças reumatológicas, são doenças crônicas que geralmente apresentam múltiplas causas. São representadas principalmente pela artrite reumatoide e lúpus eritematoso sistêmico. Sabe-se, no entanto, que existe uma ligação entre os processos autoimunes subjacentes às doenças reumáticas e aos transtornos mentais. O objetivo desta revisão foi avaliar as manifestações psiquiátricas em pacientes com condições reumatológicas. Uma busca sistemática na literatura foi realizada, nos portais BIREME e PubMed de estudos publicados nos últimos 10 anos. A busca foi realizada utilizando descritores em português e seus correspondentes em inglês: “artrite reumatoide”, “lúpus eritematoso sistêmico”, “esclerose sistêmica” e “síndrome de Sjögren” e “manifestações psiquiátricas”. Os artigos que foram incluídos após leitura na íntegra, tiveram seus dados coletados em instrumento padronizado e elaborado antes do início da busca. foram identificados e adicionados através da estratégia de busca 14 artigos. Ansiedade, depressão, incapacidade cognitiva e insônia estão entre as manifestações psiquiátricas mais prevalentes. A frequente presença de manifestações psiquiátricas na reumatologia acende um alerta entre os profissionais para priorizar a qualidade de vida de seus pacientes, reduzindo suas limitações

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

Consistent patterns of common species across tropical tree communities

Trees structure the Earth’s most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations1,2,3,4,5,6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth’s 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories7, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world’s most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees.Publisher PDFPeer reviewe

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

ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest

Epiphytes are hyper-diverse and one of the frequently undervalued life forms in plant surveys and biodiversity inventories. Epiphytes of the Atlantic Forest, one of the most endangered ecosystems in the world, have high endemism and radiated recently in the Pliocene. We aimed to (1) compile an extensive Atlantic Forest data set on vascular, non-vascular plants (including hemiepiphytes), and lichen epiphyte species occurrence and abundance; (2) describe the epiphyte distribution in the Atlantic Forest, in order to indicate future sampling efforts. Our work presents the first epiphyte data set with information on abundance and occurrence of epiphyte phorophyte species. All data compiled here come from three main sources provided by the authors: published sources (comprising peer-reviewed articles, books, and theses), unpublished data, and herbarium data. We compiled a data set composed of 2,095 species, from 89,270 holo/hemiepiphyte records, in the Atlantic Forest of Brazil, Argentina, Paraguay, and Uruguay, recorded from 1824 to early 2018. Most of the records were from qualitative data (occurrence only, 88%), well distributed throughout the Atlantic Forest. For quantitative records, the most common sampling method was individual trees (71%), followed by plot sampling (19%), and transect sampling (10%). Angiosperms (81%) were the most frequently registered group, and Bromeliaceae and Orchidaceae were the families with the greatest number of records (27,272 and 21,945, respectively). Ferns and Lycophytes presented fewer records than Angiosperms, and Polypodiaceae were the most recorded family, and more concentrated in the Southern and Southeastern regions. Data on non-vascular plants and lichens were scarce, with a few disjunct records concentrated in the Northeastern region of the Atlantic Forest. For all non-vascular plant records, Lejeuneaceae, a family of liverworts, was the most recorded family. We hope that our effort to organize scattered epiphyte data help advance the knowledge of epiphyte ecology, as well as our understanding of macroecological and biogeographical patterns in the Atlantic Forest. No copyright restrictions are associated with the data set. Please cite this Ecology Data Paper if the data are used in publication and teaching events. © 2019 The Authors. Ecology © 2019 The Ecological Society of Americ

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others