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The gut microbiota of bees affects nutrition, immunity and host fitness, yet the roles of diet, sociality and geographical variation in determining microbiome structure, including variant-level diversity and relatedness, remain poorly understood. Here, we use full-length 16S rRNA amplicon sequencing to compare the crop and gut microbiomes of two incipiently social carpenter bee species, Xylocopa sonorina and Xylocopa tabaniformis, from multiple geographical sites within each species' range. We found that Xylocopa species share a set of core taxa consisting of Bombilactobacillus, Bombiscardovia and Lactobacillus, found in95% of all individual bees sampled, and Gilliamella and Apibacter were also detected in the gut of both species with high frequency. The crop bacterial community of X. sonorina comprised nearly entirely Apilactobacillus with occasionally abundant nectar bacteria. Despite sharing core taxa, Xylocopa species' microbiomes were distinguished by multiple bacterial lineages, including species-specific variants of core taxa. The use of long-read amplicons revealed otherwise cryptic species and population-level differentiation in core microbiome members, which was masked when a shorter fragment of the 16S rRNA (V4) was considered. Of the core taxa, Bombilactobacillus and Bombiscardovia exhibited differentiation in amplicon sequence variants among bee populations, but this was lacking in Lactobacillus, suggesting that some bacterial genera in the gut may be structured by different processes. We conclude that these Xylocopa species host a distinctive microbiome, similar to that of previously characterized social corbiculate apids, which suggests that further investigation to understand the evolution of the bee microbiome and its drivers is warranted.

O objetivo desta pesquisa é verificar a relação entre as variáveis macroeconômicas e o índice Ibovespa, no período de janeiro de 2000 a fevereiro de 2019. Para tanto, utiliza-se como metodologia a estimação de um modelo Vetor de Correção de Erros (VEC), a função impulso resposta e o teste de causalidade de Granger. Os resultados mostram que a taxa básica de juros está negativamente associada com o Ibovespa e possui causalidade no sentido de Granger, com um nível de significância de 10%, assim como uma relação positiva foi observada em relação à confiança dos agentes. O Produto Interno Bruto (PIB) não impactou positivamente o Ibovespa, diferentemente de outras aplicações empíricas, o que pode ser resultado do contexto macroeconômico do Brasil observado entre 2015 e 2019, de alta do mercado acionário ao meio de uma crise econômica. As estimativas mostraram também que as variáveis macroeconômicas respondem aos estímulos do mercado acionário, mostrando que o aumento deste possui impactos negativos no Câmbio e positivos no PIB e nas reservas internacionais. Conclui-se que o mercado de capitais é um provedor de recursos permanentes para a economia, uma vez que, a relação entre as variáveis macroeconômicas e o mercado acionário é estatisticamente significante. Dessa forma, este estudo contribui para a academia no sentido de identificar as melhores variáveis de controle nos modelos econométricos e pela determinação do Ibovespa como uma variável explicativa para o crescimento econômico dos países.

Dairy cattle are globally important agricultural animals. Central to their biology is the rumen, which houses an essential microbial community, or microbiome, important for providing nutrition from otherwise host-inaccessible dietary components. The rumen environment is noted for its substantial spatial heterogeneity, as illustrated by the stratification into ruminal solid and liquid phases. A third microbiota found directly attached to the ruminal epithelium (the epimural microbiota) also exists but is less well understood because of challenges in sampling the ruminal epithelium. As a result, our understanding of the epimural microbiota is based on analyses of cannulated animals sampled at a single location—the ventral sac—and does not account for other ruminal locations, which may have importance for overall rumen function. To address this knowledge gap, we hypothesize that the epimural microbiota at different ruminal locations differs due to known morphological, physiological, and functional differences across the geographic spread of the rumen epithelium. Here, we characterized bacterial epimural communities at different sites within 8 lactating Holstein dairy cows using 16S rRNA gene sequencing. Four different sites were sampled via rumen tissue biopsy: cranial sac (CS), ventral sac (VS), caudodorsal blind sac (CDBS), and caudoventral blind sac (CVBS). We found that locations differed in both epimural bacterial community structure and composition, with the CDBS community displaying the greatest diversity. Across all sampling sites, epimural bacterial communities were dominated by members of the phyla Bacteroidetes, Firmicutes, and Proteobacteria. Bacteria within Prevotellaceae, Butyrivibrio, Campylobacter, Mogibacterium, and Desulfobulbus all showed high relative sequence abundance and differential distributions according to sample location. There appears to be a core epimural microbiota present across all locations in all cows, although relative abundance was highly variable. The difference in relative abundance in epimural microbial communities, perhaps influenced by host physiology and the diversity within rumen contents, likely has important consequences for nutrition acquisition and general health. To the best of our knowledge, this work represents the first characterization of the ruminal epimural microbiota across different epithelial locations for any bovine ruminant.

Este estudo teve como objetivo analisar as interações discursivas entre a professora e seus alunos em uma sequência didática sobre fermentação. A análise enfoca o processo mental construído pelo aluno durante a elaboração do conhecimento. Nessa pesquisa foram analisados episódios de uma sequência didática com alunos do 7º ano do Ensino Fundamental de uma escola particular do município de Maringá – Paraná. Os dados foram coletados durante as aulas no laboratório da escola, mediante gravações em áudio com um grupo de 10 alunos. Para a análise dos conteúdos de aprendizagem da sequência didática aplicada e das interações discursivas construídas, utilizou-se a estrutura analítica proposta por Zabala (1998) e as categorias idealizadas por Mortimer e Scott (2002) respectivamente, visto que nos dão suporte no sentido de compreender a importância do ambiente, do diálogo e das interações entre os indivíduos. Os resultados obtidos evidenciam os padrões discursivos durante as intervenções da professora, bem como foi possível identificar elementos potenciais e limitantes que favorecem ou impedem a elaboração do conhecimento por parte dos alunos, no sentido da re(estruturação) de suas ideias e na construção de novos significados. Palavras-chave: Ensino de Ciências; Interações Discursivas; Sequência Didática

The gut microbiota of bees affect nutrition, immunity, and host fitness, yet the role of diet, sociality, and geographic variation in determining microbiome structure, including strain-level diversity and relatedness, remain poorly understood. Here, we use full-length 16S amplicon sequencing to compare the crop and gut microbiomes of two incipiently social carpenter bee species, Xylocopa sonorina and Xylocopa tabaniformis, from multiple geographic sites within each species’ range. We found that Xylocopa species share a set of core taxa consisting of Bombilactobacillus, Bombiscardovia, and Lactobacillus apis, found in >95% of all individual bees sampled, and Gilliamella and Apibacter were also detected in the gut of both species with high frequency. The crop bacterial community of both species was comprised nearly entirely of Apilactobacillus with occasionally abundant nectar bacteria. Despite sharing core taxa, Xylocopa species’ microbiomes were distinguished by multiple bacterial lineages, including species-specific strains of core taxa. In both bee species, bacterial species exhibited geographic patterns in the presence of specific sequence variants. The use of long-read amplicons revealed otherwise cryptic species and population-level differentiation in core microbiome members which was masked when a shorter fragment of the 16S (V4) was considered. We conclude that these Xylocopa species host a distinctive microbiome, similar to that of previously characterized social apids, which suggests that further investigation to understand the evolution of bee microbiome and its drivers is warranted.

Aggregation through binning is a commonly used technique for visualizing large, dense, and overplotted two-dimensional data sets. However, aggregation can hide nuanced data-distribution features and complicates the display of multiple data-dependent variables, since color mapping is the primary means of encoding. In this paper, we present novel techniques for enhancing hexplots with spatialization cues while avoiding common disadvantages of three-dimensional visualizations. In particular, we focus on techniques relying on preattentive features that exploit shading and shape cues to emphasize relative value differences. Furthermore, we introduce a novel visual encoding that conveys information about the data distributions or trends within individual tiles. Based on multiple usage examples from different domains and real-world scenarios, we generate expressive visualizations that increase the information content of classic hexplots and validate their effectiveness in a user study., Session II, Thomas Trautner, Maximilian Sbardellati, Sergej Stoppel, and Stefan Bruckner

Aggregation through binning is a commonly used technique for visualizing large, dense, and overplotted two-dimensional data sets. However, aggregation can hide nuanced data-distribution features and complicates the display of multiple data-dependent variables, since color mapping is the primary means of encoding. In this paper, we present novel techniques for enhancing hexplots with spatialization cues while avoiding common disadvantages of three-dimensional visualizations. In particular, we focus on techniques relying on preattentive features that exploit shading and shape cues to emphasize relative value differences. Furthermore, we introduce a novel visual encoding that conveys information about the data distributions or trends within individual tiles. Based on multiple usage examples from different domains and real-world scenarios, we generate expressive visualizations that increase the information content of classic hexplots and validate their effectiveness in a user study. publishedVersion

O objetivo desta pesquisa e verificar se a divulgacao do guidance das companhias que compoe o indice IBOVESPA influencia o gerenciamento de resultados, por meio de escolhas contabeis com accruals discricionarios, para atingir ou minimizar erros de projecoes de resultados. A amostra do estudo compreende 43 companhias que constituem o indice IBOVESPA. O periodo analisado foi de 2011 a 2016, totalizando 258 observacoes. Os dados foram analisados por meio de dados em painel balanceado. A proxy do gerenciamento de resultados foi o accrual discricionario (AD), estimados por meio do modelo Jones Modificado; as variaveis de controle foram tamanho, crescimento, retorno sobre o ativo (ROA) e alavancagem financeira. Os resultados dos testes estatisticos indicam que a divulgacao do guidance influencia o gerenciamento de resultados, quando as projecoes incluem variaveis relacionadas ao resultado (lucros ou earnings before interest, taxes, depreciation and amortization ). Divulgacoes sobre investimentos e CAPEX nao tem relacao com o gerenciamento de resultados. As limitacoes da pesquisa sao: utilizacao de informacoes nao auditadas; amostra nao probabilistica; curto periodo de analise; falta de comparacao internacional e o modelo econometrico empregado. Esse estudo traz implicacoes praticas para stakeholders que utilizam informacao contabil-financeira para analises. A principal contribuicao e que o tipo de informacao divulgada no guidance determina sua ligacao com as escolhas contabeis e praticas de gerenciamento de resultados. Guidance; Escolhas Contabeis; Gerenciamento de Resultados; Projecoes de Resultados; Empresas Brasileiras.

In den letzten Jahren hat die Verwendung von Modellen des maschinellen Lernens (MLModelle) und insbesondere von tiefen neuronalen Netzen außerhalb der Forschungswelt stark zugenommen. Eine der größten Herausforderungen bei der Arbeit mit ML-Modellen ist die korrekte und effiziente Interpretation der von einem Modell gelieferten Ergebnisse.Außerdem ist das Verständnis wie das Modell zu Schlussfolgerungen gekommen ist selbst für Experten auf dem Gebiet des maschinellen Lernens eine sehr komplizierte Aufgabe.Für Laien sind ML-Modelle oft nur Black-Boxes, die nicht nachvollziehbare Ergebnisseliefern. Das mangelnde Verständnis eines Modells und seiner Argumentation führt dazu,dass Benutzer den Modellvorhersagen oft nicht vertrauen.In dieser Arbeit arbeiten wir mit einem ML-Modell, das auf Eventorganisationsdaten trainiert wurde. Das Ziel ist es, ein exploratives visuelles Eventorganisationssystem zuerstellen, das es Event-Organisatoren ermöglicht, effizient mit dem Modell zu arbeiten.Die Hauptziele des Benutzers in diesem Szenario sind die Gewinnmaximierung und die Möglichkeit, sich auf die vorhergesagte Besucherzahl vorzubereiten. Um diese Ziele zu erreichen, müssen Nutzer in der Lage sein, Aufgaben, wie das Interpretieren der aktuellen Vorhersage und das Durchführen von was-wäre-wenn Analysen um die Auswirkungen von Parameteränderungen zu verstehen, zu erfüllen. Das vorgeschlagene System beinhaltet angepasste Versionen mehrerer moderner modell-agnostischerI nterpretationsmethoden wie Partial Dependency Plots und Case-based Reasoning. Da modell-agnostische Methoden unabhängig vom ML-Modell sind, bieten sie eine hoheFlexibilität.Viele State-of-the-Art Ansätze zur Erklärung von ML-Modellen sind oftmals zu komplex um von Laien verstanden zu werden. Bei unserer Zielgruppe, den Event-Organisatoren,kann nicht davon ausgegangen werden, dass sie über außreichendes technisches Wissen im Bereich des maschinellen Lernens verfügen. In dieser Arbeit wollen wir Antworten auf die folgenden Fragen finden: Wie können wir ML-Vorhersagen für Laien verständlich visualisieren? Wie können die Vorhersagen miteinander verglichen werden? Wie können wir Nutzer dabei unterstützen, Vertrauen in das ML-Modell zu gewinnen? Unser Eventorganisationssystem wird mit Hilfe eines Human-Centred Design Ansatzes erstellt,wobei während des gesamten Entwicklungszyklus mehrere Fallstudien mit potenziellen Nutzern durchgeführt wurden., In recent years, the usage of machine learning (ML) models and especially deep neural networks in many different domains has increased rapidly. One of the major challenges when working with ML models is to correctly and efficiently interpret the results given by a model. Additionally, understanding how the model came to its conclusions can be a very complicated task even for domain experts in the field of machine learning. For laypeople, ML models are often just black-boxes. The lack of understanding of a model and its reasoning often leads to users not trusting the model’s predictions. In this thesis, we work with an ML model trained on event-organisation data. The goal is to create an exploratory visual event-organisation system that enables event organisers to efficiently work with the model. The main user goals in this scenario are to maximise profits and to be able to prepare for the predicted number of visitors. To achieve these goals users need to be able to perform tasks like: interpreting the prediction of the current input and performing what-if analyses to understand the effects of changing parameters. The proposed system incorporates adapted versions of multiple state-of-the-art model-agnostic interpretation methods like partial dependence plots and case-based reasoning. Since model-agnostic methods are independent of the ML model, they provide high flexibility. Many state-of-the-art approaches to explain ML models are too complex to be understood by laypeople. Our target group of event organisers cannot be expected to have a sufficient amount of technical knowledge in the field of machine learning. In this thesis, we want to find answers to the questions: How can we visualise ML predictions to laypeople in a comprehensible way? How can predictions be compared against each other? How can we support users in gaining trust into the ML model? Our event-organisation system is created using a human-centred design approach performing multiple case studies with potential users during the whole development circle.