Consórcio sorgo-feijão: efeito de arranjos de fileiras no rendimento de grãos.

Com o objetivo de estudar os efeitos de arranjos de fileiras no consorcio sorgo-feijao, instalou-se um experimento (marco de 1992) no CNPMS/EMBRAPA, Sete Lagoas, MG, Brasil. usaram-se duas linhagens de sorgo granifero (BR 007B e CMSXS 210B) em monocultura e em tres arranjos de fileiras (plantio na mesma fileira, plantio em fileiras alternadas e duas fileiras de feijao nas entrelinhas do sorgo), mais um tratamento adicional (feijao em monocultura), no esquema fatorial (2 x 4) + 1 e delineamento de blocos inteiramente casualizados, com quatro repeticoes. Foi utilizado cultivar de feijao Ouro, de habito de crescimento indeterminado (tipo II). Os rendimentos individuais das culturas foram maiores no arranjo em fileiras alternadas, mas suas medias nao diferiram das obtidas nos seus respectivos monocultivos. Independentemente da linhagem, os maiores rendimentos totais foram obtidos nos arranjos de fileiras alternadas e de duas fileiras de feijao nas entrelinhas do sorgo. Quando o consorcio foi feito usando a CMSXS 210B no arranjo de duas fileiras de feijao nas entrelinhas do sorgo, houve reducao do rendimento de feijao e dos indices de equivalencia de area (IEAs), devido a reducao do numero de vagens por planta. Concluiu-se que os arranjos em fileira alternadas e de duas fileiras de feijao nas entrelinhas do sorgo podem ser usados no consorcio sorgo-feijao, todavia este ultimo arranjo seria adequado apenas com cultivares de sorgo menos competitivos

Practical probabilistic programming with monads

The machine learning community has recently shown a lot of interest in practical probabilistic programming systems that target the problem of Bayesian inference. Such systems come in different forms, but they all express probabilistic models as computational processes using syntax resembling programming languages. In the functional programming community monads are known to offer a convenient and elegant abstraction for programming with probability distributions, but their use is often limited to very simple inference problems. We show that it is possible to use the monad abstraction to construct probabilistic models for machine learning, while still offering good performance of inference in challenging models. We use a GADT as an underlying representation of a probability distribution and apply Sequential Monte Carlo-based methods to achieve efficient inference. We define a formal semantics via measure theory. We demonstrate a clean and elegant implementation that achieves performance comparable with Anglican, a state-of-the-art probabilistic programming system.The first author is supported by EPSRC and the Cambridge Trust.This is the author accepted manuscript. The final version is available from ACM via http://dx.doi.org/10.1145/2804302.280431

Pollen mother cells of Tradescantia clone 4430 and Tradescantia pallida var. purpurea are equally sensitive to the clastogenic effects of X-rays.

The Tradescantia micronucleus test is a sensitive bioassay for mutagenesis that may be employed both under field and laboratory conditions. This test has been standardized mostly on the basis of the results obtained with clone 4430. However, this clone is not well adapted to tropical weather, frequently showing problems with growth and flowering. In addition, it is attacked by parasites and insects, a fact that limits its use in field studies aiming at the biomonitoring of air pollution. In the city of São Paulo, Tradescantia pallida (Rose) Hunt. var. purpurea Boom is widely distributed as an ornamental plant in gardens and along roadsides and streets, mostly because of its natural resistance and its easy propagation. In this report, we present dose-response curves indicating that the sensitivity of T. pallida and clone 4430 to X-radiation (1, 10, 25 and 50 cGy) is similar. The results confirm our previous suggestion that T. pallida represents a good alternative for in situ mutagenesis testing in tropical regions, especially biomonitoring studies in which the exposure conditions may not be fully controllable

Moving Toward a Strategy for Addressing Climate Displacement of Marine Resources: A Proof-of-Concept

Realistic predictions of climate change effects on natural resources are central to adaptation policies that try to reduce these impacts. However, most current forecasting approaches do not incorporate species-specific, process-based biological information, which limits their ability to inform actionable strategies. Mechanistic approaches, incorporating quantitative information on functional traits, can potentially predict species- and population-specific responses that result from the cumulative impacts of small-scale processes acting at the organismal level, and can be used to infer population-level dynamics and inform natural resources management. Here we present a proof-of-concept study using the European anchovy as a model species that shows how a trait-based, mechanistic species distribution model can be used to explore the vulnerability of marine species to environmental changes, producing quantitative outputs useful for informing fisheries management. We crossed scenarios of temperature and food to generate quantitative maps of selected mechanistic model outcomes (e.g., Maximum Length and Total Reproductive Output). These results highlight changing patterns of source and sink spawning areas as well as the incidence of reproductive failure. This study demonstrates that model predictions based on functional traits can reduce the degree of uncertainty when forecasting future trends of fish stocks. However, to be effective they must be based on high spatial- and temporal resolution environmental data. Such a sensitive and spatially explicit predictive approach may be used to inform more effective adaptive management strategies of resources in novel climatic conditions

Epidemic processes in complex networks

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio

An analysis of protein patterns present in the saliva of diabetic patients using pairwise relationship and hierarchical clustering

Molecular diagnosis is based on the quantification of RNA, proteins, or metabolites whose concentration can be correlated to clinical situations. Usually, these molecules are not suitable for early diagnosis or to follow clinical evolution. Large-scale diagnosis using these types of molecules depends on cheap and preferably noninvasive strategies for screening. Saliva has been studied as a noninvasive, easily obtainable diagnosis fluid, and the presence of serum proteins in it enhances its use as a systemic health status monitoring tool. With a recently described automated capillary electrophoresis-based strategy that allows us to obtain a salivary total protein profile, it is possible to quantify and analyze patterns that may indicate disease presence or absence. The data of 19 persons with diabetes and 58 healthy donors obtained by capillary electrophoresis were transformed, treated, and grouped so that the structured values could be used to study individuals’ health state. After Pairwise Relationships and Hierarchical Clustering analysis were observed that amplitudes of protein peaks present in the saliva of these individuals could be used as differentiating parameters between healthy and unhealthy people. It indicates that these characteristics can serve as input for a future computational intelligence algorithm that will aid in the stratification of individuals that manifest changes in salivary proteins.info:eu-repo/semantics/acceptedVersio