Avaliação de Uma Rede Neural Artificial Como Modelo Regressor Para Séries Temporais

Técnicas de predição de demanda são utilizadas em inúmeros ramos da indústria, com o objetivo de agregar valor ao negócio das empresas, especialmente por meio da busca pelo dimensionamento ótimo dos recursos de produção. A predição de demanda em refeitórios, com o intuito de balancear a quantidade de alimento produzido, buscando um melhor aproveitamento dos ingredientes, é um desafio, pois fatores como a quantidade de usuários, o tempo de atendimento e o tipo de alimento utilizado podem ser bastante variáveis neste tipo de problema. O estudo das filas, neste contexto, é de primordial importância, dado que, conhecendo suas características, podem-se estimar, por meio de previsão, informações que podem melhorar a qualidade de atendimento. O presente trabalho teve por finalidade utilizar modelos baseados em Redes Neurais Artificiais (RNA) para realizar regressões em uma série temporal personalizada, gerada por meio de metodologia própria, mediantes os dados coletados in loco no restaurante do IFMG - Campus Bambuí. Teve-se por principal objetivo desenvolver um modelo computacional que fosse capaz de descrever o comportamento para os intervalos de tempo no atendimento dos usuários. Por meio deste recurso, pôde-se gerar informações importantes para a tomada de decisão, como os horários de maior e menor pico de atendimento.Palavras-chave: Redes neurais artificiais, regressão, séries temporais.==================================================================================Demand prediction techniques are used in numerous industry sectors with the aim of adding value to the business of the companies, especially through the search for optimal sizing of production resources. The prediction of demand in restaurants with the intention of balancing the quantity food produced looking for better use of ingredients is a challenge, since factors like the quantity of users, the time of service and the kind of food can be quite variable in this type of problem. The study of queue, in this context, is of paramount importance, given that, knowing its characteristics, it is possible to estimate, by means of prediction, information that can improve service quality. Present work had the purpose of using models based on Artificial Neural Networks (ANN) to perform regressions in a personalized time series, generated through its own methodology with data collected in the restaurant of the IFMG - Campus Bambuí. The main objective was to develop a computational model that would be able to describe the behavior for the time intervals in the restaurant customer service. Through this resource, it was possible to generate important information for decision making, such as the peak times of higher and lower demands.Keywords: Artificial neural networks, regression, time series

Doença de Cushing - aspectos epidemiológicos, fisiopatológicos e manejo terapêutico

A Síndrome de Cushing (SC) representa um conjunto de sinais e sintomas causados pelo excesso de hormônios glicocorticóides no organismo, podendo ter origem exógena ou endógena. Nesse sentido, a Doença de Cushing (DC) corresponde a uma forma específica de SC e é causada por um tumor hipofisário produtor do hormônio adrenocorticotrófico (ACTH). Ademais, tem uma prevalência mundial estimada de 4 casos em cada 100.000 pessoas, com uma incidência anual de 0,12 a 0,24 casos em cada 100.000 pessoas. No entanto, é possível que esses números estejam subestimados, já que alguns portadores da doença podem não estar cientes do diagnóstico. Em relação aos adultos, a DC afeta três vezes mais mulheres do que homens, e os sintomas geralmente começam a aparecer entre a terceira e a sexta década de vida. Quanto aos sintomas, a DC se manifesta com características clínicas específicas, como obesidade, hipertensão arterial sistêmica (HAS) e alterações cutâneas, e pode levar a complicações cardiovasculares graves - correspondendo o infarto agudo do miocárdio (IAM) e o acidente vascular encefálico (AVE) as complicações de maior mortalidade relacionadas à doença. O diagnóstico da DC é desafiador, sendo confirmado através de exames de imagem que identificam tumores hipofisários secretores de ACTH. O tratamento de primeira linha para a DC é a cirurgia transesfenoidal para remoção do tumor, mas a recorrência é possível mesmo após a remissão inicial, tornando o acompanhamento regular essencial para monitorar a condição e possibilitar novas intervenções cirúrgicas se necessário. A reoperação apresenta maior risco de complicações devido ao tecido cicatricial prévio na hipófise

Um refúgio de Mata Úmida no interior do Nordeste brasileiro: estrutura e diversidades alfa e beta

Inside the Brazilian semi-arid region (Caatinga biome) there are vegetation refuges from other biomes that are present mainly at greater elevations in ancient geological formations such as Chapada do Araripe. This study aimed to understand the structure, diversity and floristic similarity of a fragment of humid forest in order to support conservation strategies for this community in Chapada do Araripe, southern Ceará (708m Alt.), an area of relevant ecological, cultural and scenic value. In 13 parcels of 25x25m (0.8 ha) all individuals with DBH ≥ 5cm were inventoried, observing total height. Floristic similarity was observed using the Jaccard method, comparing it with seven other areas in different regions of the Brazil. Sixty-six species were found distributed in 34 families, in a total of 1,544 individuals with DA=1,997.30 ind.ha-1 and AB =32,618 m²ha-1. Fabaceae (25.75%) and Myrtaceae (6%) had the highest number of species and Brosimum gaudichaudii had the highest IVI. The Shannon index (H') was 3.13 and the Pielou equability (J') was 0.75. The beta diversity, compared with other areas of humid forest (core and disjunct), was considered high and the statistics showed greater similarity with the area of the mesoregion of Pernambuco forest. The results allow the characterization of the area as a vegetational refuge of humid forest, corroborating the Refuge Theory and showing strong penetration of savanna species (“Cerradão”, Cerrado, Caatinga/“Carrasco”), emphasizing the action of climatic impulses that occurred in the Quaternary and pointing to the need to conservation strategies due to accelerated anthropism.No interior da região semiárida brasileira (bioma Caatinga), há ocorrência de refúgios de vegetação de outros biomas que se apresentam principalmente em topos de formações geológicas do período Pré-Cambriano, como a Chapada do Araripe. Este trabalho objetivou o conhecimento da estrutura, diversidade e similaridade florística de um fragmento de mata úmida na Chapada do Araripe, sul do Ceará (708m Alt.), visando subsidiar estratégias de conservação dessa comunidade, área de relevante valor ecológico, cultural e paisagístico. Em 13 parcelas de 25x25m (0,8 ha), foram inventariados todos os indivíduos com DAP ≥ 5cm, observando-se altura total. A similaridade florística foi observada pelo método de Jaccard a partir da comparação com outras sete áreas em diferentes regiões do Brasil. Foram encontradas 66 espécies distribuídas em 34 famílias, num total de 1.544 indivíduos com DA=1.997,30 ind.ha-1 e AB=32,618 m²ha-1. Fabaceae (25,75%) e Myrtaceae (6%) apresentaram maior número de espécies e Brosimum gaudichaudii apresentou maior IVI. O índice de Shannon (H’) foi 3,13 e a equabilidade de Pielou (J’) foi 0,75. A diversidade beta, comparada com outras áreas de mata úmida (core e disjuntas), foi considerada alta e a estatística apontou maior similaridade com área da mesorregião da mata pernambucana. Os resultados permitem caracterizar a área como refúgio vegetacional de mata úmida, corroborando com a teoria dos refúgios e evidenciam forte penetração de espécies savânicas (cerradão, cerrado, caatinga/carrasco) enfatizando a atuação de impulsos climáticos ocorridos no Quaternário e apontando para a necessidade de estratégias de conservação devido ao antropismo acelerado

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

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

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

Zika Virus Envelope Protein Domain III Produced in <i>K. phaffii</i> Has the Potential for Diagnostic Applications

Zika virus (ZIKV) represents a global human health threat and it is related to severe diseases such as congenital Zika syndrome (CZS) and Guillain-Barré syndrome (GBS). There is no vaccine available nor specific antiviral treatment, so developing sensitive, specific, and low-cost diagnostic tests is necessary. Thus, the objective of this work was to produce the Zika virus envelope protein domain III (ZIKV-EDIII) in Komagataella phaffii KM71H and evaluate its potential for diagnostic applications. After the K. phaffii had been transformed with the pPICZαA-ZIKV-EDIII vector, an SDS-PAGE and Western Blot were performed to characterize the recombinant protein and an ELISA to evaluate the antigenic potential. The results show that ZIKV-EDIII was produced in the expected size, with a good purity grade and yield of 2.58 mg/L. The receiver operating characteristic (ROC) curve showed 90% sensitivity and 87.5% specificity for IgM, and 93.33% sensitivity and 82.76% specificity for IgG. The ZIKV-EDIII protein was efficiently produced in K. phaffi, and it has the potential for diagnostic applications

Assessment of intrinsic capacity in the Brazilian older population and the psychometric properties of the WHO/ICOPE screening tool: a multicenter cohort study protocol

INTRODUCTION: The World Health Organization (WHO) has proposed to monitor intrinsic capacity (IC) in the older population as a public health strategy through the Integrated Care for Older People (ICOPE) program. Although the program has been developed based on solid concepts, scientific evidence on its practical applicability is still scarce. OBJECTIVES: To evaluate IC in Brazilian older adults, its progress over time, and its association with sociodemographic and health factors and outcomes. To evaluate the psychometric properties of the WHO/ICOPE screening tool. METHODS: This is a prospective multicenter cohort study with a 36-month follow-up. We will recruit 3838 people aged &ge; 60 years, registered in the health care units included in the study by the participating centers. We will collect sociodemographic and health data and will administer tools to assess IC domains, both those provided for in the ICOPE screening tool and the sequence of confirmatory assessments provided for in the program. Participants will be reassessed every 6 months for 36 months. EXPECTED RESULTS: To establish the profile of IC in the study population and to understand its progress and the variables associated with the clinical outcomes of interest. To reveal the diagnostic and psychometric properties of the WHO/ICOPE screening tool. The project is funded by the Brazilian National Council for Scientific and Technological Development (CNPq). RELEVANCE: Understanding the potential use of the ICOPE public health strategy proposed by the WHO within the scope of the Brazilian Unified Health System (SUS) by integrating several research centers in the field of Geriatrics and Gerontology throughout Brazil.</p