Integrating Phylogenetic and Network Approaches to Study Gene Family Evolution: The Case of the \u3ci\u3eAGAMOUS\u3c/i\u3e Family of Floral Genes

The study of gene family evolution has benefited from the use of phylogenetic tools, which can greatly inform studies of both relationships within gene families and functional divergence. Here, we propose the use of a network-based approach that in combination with phylogenetic methods can provide additional support for models of gene family evolution. We dissect the contributions of each method to the improved understanding of relationships and functions within the well-characterized family of AGAMOUS floral development genes. The results obtained with the two methods largely agreed with one another. In particular, we show how network approaches can provide improved interpretations of branches with low support in a conventional gene tree. The network approach used here may also better reflect known and suspected patterns of functional divergence relative to phylogenetic methods. Overall, we believe that the combined use of phylogenetic and network tools provide a more robust assessment of gene family evolution

Integrating Phylogenetic and Network Approaches to Study Gene Family Evolution: The Case of the \u3ci\u3eAGAMOUS\u3c/i\u3e Family of Floral Genes

The study of gene family evolution has benefited from the use of phylogenetic tools, which can greatly inform studies of both relationships within gene families and functional divergence. Here, we propose the use of a network-based approach that in combination with phylogenetic methods can provide additional support for models of gene family evolution. We dissect the contributions of each method to the improved understanding of relationships and functions within the well-characterized family of AGAMOUS floral development genes. The results obtained with the two methods largely agreed with one another. In particular, we show how network approaches can provide improved interpretations of branches with low support in a conventional gene tree. The network approach used here may also better reflect known and suspected patterns of functional divergence relative to phylogenetic methods. Overall, we believe that the combined use of phylogenetic and network tools provide a more robust assessment of gene family evolution

Metastable anisotropy orientation of nematic quantum Hall fluids

We analyze the experimental observation of metastable anisotropy resistance orientation at half filled quantum Hall fluids by means of a model of a quantum nematic liquid in an explicit symmetry breaking potential. We interpret the observed ``rotation'' of the anisotropy axis as a process of nucleation of nematic domains and compute the nucleation rate within this model. By comparing with experiment, we are able to predict the critical radius of nematic bubbles, $R_c\sim 2.6 \mu m$. Each domain contains about $10^4$ electrons.Comment: 10 pages, 8 figures, final version as will appear in PR

DISRUPTIVE TECHNOLOGIES OF GENERATION DISTRIBUTED AND ITS FUTURE IMPACTS ON ENERGY COMPANIES DOI: 10.5585/rai.v6i1.284

This paper develops a forecast of the future impact of disruptive technologies for distributed generation on the distribution of electric energy, with use of scenarios. Distributed generation (DG) is defined by the power generation technologies for which production is intended primarily to serve local loads, and disruptive technologies are considered those that, in general, cannot be easily evaluated by a dominant company, but when it is discovered by consumers, the firm changes the initial characteristics of the market and are rapidly incorporated by new consumers and producers. To accomplish the paper, it was used a methodology for the elaboration of scenarios from the use of combined techniques such as Delphi, quantitative extrapolation of data, Analysis and Structuring of Models (AEM) and evaluation of impacts. The results indicate that, with the advent of distributed generation, it will be possible to mitigate any eventual effects of uncertainty on the demand for energy. Thus, as a consequence of less uncertainty in supply, the introduction of distributed generation can contribute with the reliability and efficiency of the system more.Este trabalho faz uma previsão, a partir da construção de cenários, do impacto futuro de tecnologias disruptivas de geração distribuída sobre a distribuição de energia elétrica. Geração Distribuída (GD) é definida por tecnologias de geração de energia cuja produção é destinada a atender predominantemente cargas locais, e tecnologias disruptivas são consideradas aquelas que, em geral, não podem ser facilmente avaliadas por uma empresa dominante, mas, ao serem descobertas pelos consumidores, alteram as características iniciais do mercado e são incorporadas rapidamente por consumidores e novos produtores. Para a realização do trabalho, foi utilizada uma metodologia de elaboração de cenários a partir do uso de técnicas combinadas, como Delphi, extrapolações de dados, Análise e Estruturação de Modelos (AEM) e avaliação de impactos. Os resultados indicam que, com o advento da geração distribuída, será possível mitigar eventuais efeitos da incerteza da demanda de energia. Assim, como consequência da menor incerteza na oferta, a introdução da geração distribuída pode contribuir para tornar o sistema mais confiável e eficiente

Pareceristas ad hoc 2017

Esta seção destina-se a publicar a lista dos nomes dos avaliadores que colaboraram com a PARC em 2017. Esta seção é publicada ao final do quarto fascículo do ano

OTIMIZAÇÃO COM ALGORITMO BIO-INSPIRADO DE CONTROLE DE TRÁFEGO EM SISTEMAS DE GRUPOS DE ELEVADORES

Resumo. Este artigo tem como objetivo apresentar a implementação de uma técnica de otimização bioinspirada como solução ao problema de controle de tráfego em sistemas de grupos de elevadores (EGCS). A técnica de controle usada é o algoritmo de otimização por inteligência de enxame (PSO - swarm optimization particle) de tipo binário. A ideia é que o algoritmo escolha o melhor elevador para um usuário que faz uma chamada de serviço em umsistema de controle destino (DCS ”“ destination control system). Para a escolha do elevador o algoritmo tem uma função custo que considera as variáveis: (1) tempo de espera; (2) tempo de voo; (3) capacidade do elevador; (4) número de paradas alocadas; (5) número de paradas (baseado nas chamadas que são asignadas) para cada elevador. Estes parâmetros são ponderados de acordo com sua importância e inferência na seleção do melhor elevador. Assim, o sistema seleciona de todas as possíveis soluções encontradas a solução que apresenteo melhor valor de aptidão (a solução representa o elevador ou os elevadores selecionado para atender a atual chamada)

Latent cluster analysis of ALS phenotypes identifies prognostically differing groups

BACKGROUND Amyotrophic lateral sclerosis (ALS) is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes. METHODS Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method. RESULTS The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001). Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb) and time from symptom onset to diagnosis (p<0.00001). CONCLUSION The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research