APRENDIZADO DE MÁQUINA PARA ROTULAÇÃO AUTOMÁTICA DE USUÁRIOS DE UMA REDE SOCIAL ACADÊMICA

Social networks have become relevant in the Internet due to the great variety of Web sites that use the concept. Its users form databases that provide an important way of sharing, organizing, finding content and making contacts. Thus, Scientia.Net is a social networking site that integrates information from various Internet services (forums, article repositories, websites, blogs and other social networks). Besides, the tool provides the user interaction (students, teachers and researchers) for academic purposes, based on their common interests. This paper presents an application developed to automatically group Scientia.Net users, showing the performance of various machine learning algorithms, offering to Scientia.Net a sorting mechanism that presents a list of other researchers to each user of the network, based on their common interests. With this, we intend to contribute to the interaction among users with similar profiles, allowing an improvement in the productivity of their research efforts. Furthermore, this paper proposes a model that uses a combination of supervised and unsupervised learning algorithms to create groups and identify users based on their relevant attributes.Redes sociais tornaram-se especialmente relevantes na Internet devido à grande variedade de sites Web que utilizam o conceito. Seus usuários formam bases de dados que proveem um importante meio de compartilhar, organizar e encontrar conteúdo, estabelecer contatos com base em interesses comuns. Dessa forma, o Scientia.Net é um site de rede social que integra informações contidas em diversos serviços da Internet (fóruns, repositórios de artigos, sites, blogs e demais redes sociais). Além disso, a ferramenta provê a interação de seus usuários (estudantes, professores e pesquisadores) para fins acadêmicos, com base nos seus interesses em comum. Este artigo apresenta uma aplicação desenvolvida para agrupar de forma automática os usuários do Scientia.Net, mostrando o desempenho de vários algoritmos de aprendizagem de máquina, visando a oferecer ao Scientia.Net um mecanismo de classificação que apresente a cada usuário da rede, uma relação de outros pesquisadores com base nos seus interesses em comum. Com isso, pretende-se contribuir para a interação entre usuários de perfis semelhantes e assim possibilitar que estes melhorem a produtividade de suas pesquisas, ao aumentar sua capacidade de troca de conhecimento. Além disso, o presente artigo propõe um modelo que utiliza uma combinação entre algoritmos com aprendizagem supervisionada e não-supervisionada com o objetivo de criar grupos e identificar quais atributos podem defini-los

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

Transtorno bipolar em crianças: análise de relato de caso 2018-2023

O transtorno bipolar em crianças é uma realidade clínica que demanda atenção especializada. A compreensão dos sintomas, fatores de risco, prevalência e desafios diagnósticos é fundamental para proporcionar intervenções precoces e adequadas, visando melhorar a qualidade de vida desses jovens e reduzir o impacto a longo prazo dessa condição psiquiátrica. Trata-se de um estudo cujo objetivo foi objetivo revisar relatos de caso publicados entre 2018 e 2023 sobre transtorno bipolar em crianças, identificando o estado da arte desses estudos. Para isso, se realizou uma revisão sistemática de literatura utilizando as bases de dados Medical Literature Analysis and Retrieval System Online (MEDLINE), Literatura Latino-Americana e do Caribe em Ciências da Saúde (LILACS) e Scientific Electronic Library Online (SCIELO). Com a análise e interpretação qualitativa dos resultados, a principal conclusão deste estudo é que o transtorno bipolar na infância é uma condição complexa, manifestando-se com comportamentos consistentes com o Transtorno de Conduta e sendo influenciado por fatores ambientais, familiares e genéticos. O tratamento eficaz requer uma abordagem multidisciplinar, integrando intervenções farmacológicas e não farmacológicas, personalizadas conforme as necessidades individuais. A supervisão familiar é crucial para a adesão ao tratamento, mas reconhece-se a necessidade contínua de pesquisa para aprimorar as estratégias terapêuticas diante da diversidade de casos

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

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Measurements of prompt charm production cross-sections in pp collisions at s=5 \sqrt{s}=5 TeV

Production cross-sections of prompt charm mesons are measured using data from $pp$ collisions at the LHC at a centre-of-mass energy of $5\,$TeV. The data sample corresponds to an integrated luminosity of $8.60\pm0.33\,$pb$^{-1}$ collected by the LHCb experiment. The production cross-sections of $D^0$, $D^+$, $D_s^+$, and $D^{*+}$ mesons are measured in bins of charm meson transverse momentum, $p_{\text{T}}$, and rapidity, $y$. They cover the rapidity range $2.0 < y < 4.5$ and transverse momentum ranges $0 < p_{\text{T}} < 10\, \text{GeV}/c$ for $D^0$ and $D^+$ and $1 < p_{\text{T}} < 10\, \text{GeV}/c$ for $D_s^+$ and $D^{*+}$ mesons. The inclusive cross-sections for the four mesons, including charge-conjugate states, within the range of $1 < p_{\text{T}} < 8\, \text{GeV}/c$ are determined to be \begin{equation*} \sigma(pp\rightarrow D^0 X) = 1190 \pm 3 \pm 64\,\mu\text{b} \end{equation*} \begin{equation*} \sigma(pp\rightarrow D^+ X) = 456 \pm 3 \pm 34\,\mu\text{b} \end{equation*} \begin{equation*} \sigma(pp\rightarrow D_s^+ X) = 195 \pm 4 \pm 19\,\mu\text{b} \end{equation*} \begin{equation*} \sigma(pp\rightarrow D^{*+} X)= 467 \pm 6 \pm 40\,\mu\text{b} \end{equation*} where the uncertainties are statistical and systematic, respectively.Production cross-sections of prompt charm mesons are measured using data from pp collisions at the LHC at a centre-of-mass energy of 5 TeV. The data sample corresponds to an integrated luminosity of 8.60 ± 0.33 pb$^{−1}$ collected by the LHCb experiment. The production cross-sections of D$^{0}$, D$^{+}$, D$_{s}^{+}$ , and D$^{∗+}$ mesons are measured in bins of charm meson transverse momentum, p$_{T}$, and rapidity, y. They cover the rapidity range 2.0 < y < 4.5 and transverse momentum ranges 0 < p$_{T}$ < 10 GeV/c for D$^{0}$ and D$^{+}$ and 1 < p$_{T}$ < 10 GeV/c for D$_{s}^{+}$ and D$^{∗+}$ mesons. The inclusive cross-sections for the four mesons, including charge-conjugate states, within the range of 1 < p$_{T}$ < 8 GeV/c are determined to be $\begin{array}{l}\sigma \left( pp\to {D}^0X\right)=1004\pm 3\pm 54\mu \mathrm{b},\\ {}\sigma \left( pp\to {D}^{+}X\right)=402\pm 2\pm 30\mu \mathrm{b},\\ {}\sigma \left( pp\to {D}_s^{+}X\right)=170\pm 4\pm 16\mu \mathrm{b},\\ {}\sigma \left( pp\to {D}^{\ast +}X\right)=421\pm 5\pm 36\mu \mathrm{b},\end{array}$ where the uncertainties are statistical and systematic, respectively.Production cross-sections of prompt charm mesons are measured using data from $pp$ collisions at the LHC at a centre-of-mass energy of $5\,$TeV. The data sample corresponds to an integrated luminosity of $8.60\pm0.33\,$pb$^{-1}$ collected by the LHCb experiment. The production cross-sections of $D^0$, $D^+$, $D_s^+$, and $D^{*+}$ mesons are measured in bins of charm meson transverse momentum, $p_{\text{T}}$, and rapidity, $y$. They cover the rapidity range $2.0<y<4.5$ and transverse momentum ranges $0 < p_{\text{T}} < 10\, \text{GeV}/c$ for $D^0$ and $D^+$ and $1 < p_{\text{T}} < 10\, \text{GeV}/c$ for $D_s^+$ and $D^{*+}$ mesons. The inclusive cross-sections for the four mesons, including charge-conjugate states, within the range of $1 < p_{\text{T}} < 8\, \text{GeV}/c$ are determined to be \sigma(pp\rightarrow D^0 X) = 1004 \pm 3 \pm 54\,\mu\text{b} \sigma(pp\rightarrow D^+ X) = 402 \pm 2 \pm 30\,\mu\text{b} \sigma(pp\rightarrow D_s^+ X) = 170 \pm 4 \pm 16\,\mu\text{b} \sigma(pp\rightarrow D^{*+} X)= 421 \pm 5 \pm 36\,\mu\text{b} where the uncertainties are statistical and systematic, respectively

Measurement of the B0s→μ+μ− Branching Fraction and Effective Lifetime and Search for B0→μ+μ− Decays

A search for the rare decays Bs0→μ+μ- and B0→μ+μ- is performed at the LHCb experiment using data collected in pp collisions corresponding to a total integrated luminosity of 4.4  fb-1. An excess of Bs0→μ+μ- decays is observed with a significance of 7.8 standard deviations, representing the first observation of this decay in a single experiment. The branching fraction is measured to be B(Bs0→μ+μ-)=(3.0±0.6-0.2+0.3)×10-9, where the first uncertainty is statistical and the second systematic. The first measurement of the Bs0→μ+μ- effective lifetime, τ(Bs0→μ+μ-)=2.04±0.44±0.05  ps, is reported. No significant excess of B0→μ+μ- decays is found, and a 95% confidence level upper limit, B(B0→μ+μ-)<3.4×10-10, is determined. All results are in agreement with the standard model expectations.A search for the rare decays $B^0_s\to\mu^+\mu^-$ and $B^0\to\mu^+\mu^-$ is performed at the LHCb experiment using data collected in $pp$ collisions corresponding to a total integrated luminosity of 4.4 fb$^{-1}$. An excess of $B^0_s\to\mu^+\mu^-$ decays is observed with a significance of 7.8 standard deviations, representing the first observation of this decay in a single experiment. The branching fraction is measured to be ${\cal B}(B^0_s\to\mu^+\mu^-)=\left(3.0\pm 0.6^{+0.3}_{-0.2}\right)\times 10^{-9}$, where the first uncertainty is statistical and the second systematic. The first measurement of the $B^0_s\to\mu^+\mu^-$ effective lifetime, $\tau(B^0_s\to\mu^+\mu^-)=2.04\pm 0.44\pm 0.05$ ps, is reported. No significant excess of $B^0\to\mu^+\mu^-$ decays is found and a 95 % confidence level upper limit, ${\cal B}(B^0\to\mu^+\mu^-)<3.4\times 10^{-10}$, is determined. All results are in agreement with the Standard Model expectations

Abordagem Semissupervisionada usando Deep Learning Aplicada á Rotulação e Classificação de Dados

Large-scale data generation has brought the need for the developmentof intelligent techniques capable of analyzing this data automatically. In thissense, this paper proposes a semisupervisioned classification model capable oflabeling unlabeled data from a few labeled examples. For this, a deep neuralnetwork was trained with labeled and unlabeled examples, simutaneally. Theexperiments performed show that the model is efficient in labeling data andpredicting new examples

Brazilian carbon footprint calculators: comparative approaches and implications of using these tools

The increasing emergence of carbon calculators and the absence of specific standards to regulate these tools, may lead to inconsistencies in the results. This article evaluates individual carbon calculators that are publicly available in Brazil. Qualitative and quantitative analyses of 15 calculators were performed. Input and output data, conversion factors, as well as the costs associated with the possibilities of mitigation were evaluated. The analyses showed that there is a great discrepancy between the Emission Factors (EF), which have been highlighted by the coefficient of variation of EF adopted for the emission of liquefied petroleum gas (146.5%). The values of carbon stock also showed a large amplitude (139.45 to 359.84 kgCO2 tree−1). Furthermore, it was observed that tips on the possibility of reducing emissions are poorly provided by the calculators (27%). However, most tools (67%) make it possible to offset quantified emissions, and carbon offset plantations are widely suggested. The discrepancies found may affect calculators reliability, their potential for raising environmental awareness and their influence on decision-making. Thus, the diffusion of the calculators should be accompanied by more specific guidelines in order to minimize the uncertainties associated with the estimates