Valor nutritivo do mata-pasto (Senna obtusifolia (L.) Irwin & Barneby) em diferentes idades.

O mata-pasto (Senna obtusifolia (L.) Irwin & Barneby), leguminosa de larga ocorrência natural no Nordeste, apesar de não apreciada pelos ruminantes quando verde, é muito consumida quando naturalmente seca. Apresenta, portanto, a possibilidade de ser usada como feno, para diminuir a carência alimentar da época seca. Este trabalho teve como objetivo a determinação dos percentuais de proteína bruta (PB), fibra bruta (FB), extrato etéreo IEE), cálcio (Ca) e fósforo (P), na planta inteira, caule e folhas do mata-pasto, em diferentes idades. Coletaram-se, mensalmente, dos 30 aos 180 dias de idade, cinco amostras de 0,5 x 0,5 m para a análise da planta inteira e outras cinco para caules e folhas. A PB diminuiu com a idade das plantas, sendo maior nas folhas (11,75% - 28,63%) e menor nos caules (2,44% - 22,46%). A FB foi maior nos caules (29,50% - 48,50%) do que nas folhas (8,28% - 10,52%), aumentando com a idade. O EE pouco variou em relação às partes da planta, como tambem com a idade (2,91% - 3,34% nas folhas e 0,83% - 1,04% nos caules). O P decresceu com a idade, sendo de 0,16% - 0,28% nas folhas e 0,11% - 0,20% nos caules. O Ca foi também mais alto nas folhas (1,33% - 3,07%), onde aumentou com a idade, enquanto decresceu nos caules e na planta inteira. Todas as variáveis tiveram, na planta inteira, valores intermediários entre os observados nas folhas e nos caules. O mata-pasto é uma planta nutricionalmente adequada, com seu corte para fenação por volta de 120 dias.bitstream/item/35799/1/BP33.pd

Catingueira - forrageira nativa para fenação.

bitstream/item/44533/1/Catingueira0001.pd

Deep Discrete Hashing with Self-supervised Pairwise Labels

Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, which require labels. In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. In the proposed framework, we address two main problems: 1) how to directly learn discrete binary codes? 2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? We resolve these problems by introducing an intermediate variable and a loss function steering the learning process, which is based on the neighborhood structure in the original space. Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17) demonstrate that our DDH significantly outperforms existing hashing methods by large margin in terms of~mAP for image retrieval and object recognition. Code is available at \url{https://github.com/htconquer/ddh}

Subprodutos da agroindústria da soja na alimentação de ruminantes.

bitstream/item/38616/1/Circular37.pd

Produção e valor nutritivo de feno de duas gramíneas tropicais em solo de baixa fertilidade natural.

Em Teresina, PI, a produtividade e o valor nutritivo do feno de Andropogon gayanus cv. Planaltina e de Brachiaria brizantha, cv. Marandu foram avaliados. O delineamento experimental foi inteiramente casualizado, em arranjo fatorial, combinando duas gramíneas e dois cortes, com dez repetições. Em relação ao valor nutritivo foram analisados os teores de proteína bruta (PB), fibra em detergente neutro (FDN), fibra em detergente ácido (FDA), lignina, celulose, cálcio (Ca), fósforo (P) e digestibilidade in situ da matéria seca (DISMS). O primeiro corte para fenação foi realizado aos 60 dias após um fogo acidental e o segundo aos 60 dias após o primeiro, ambos no período chuvoso de 1998. A unidade experimental constou de uma área de 1 m x 1 m, cuja forragem foi cortada a 20 cm de altura e fenada naturalmente a campo. Na média das duas coletas, a braquiária teve maior produção de feno (2.294 kg MS/ha), com maior PB (7,65%), menor porcentagem de FDA, e menor porcentagem de celulose que andropogon. Nos fenos de ambas as gramíneas, a porcentagem de Ca e porcentagem de P foram baixas, porém a porcentagem DISMS foi satisfatória. A braquiária mostrou maior potencial para a produção de feno que o andropogon.bitstream/item/35812/1/BP35.pd

Produção e valor nutritivo de gramíneas forrageiras tropicais em solo de baixa fertilidade natural.

Em Teresina, PI, foram avaliados o rendimento forrageiro e os teores de protelna bruta (PB), fibra em detergente neutro (FDN), fibra em detergente ácido (FDA), lignina, celulose. cálcio (Ca), fósforo (P) e digestibilidade in situ da matéria seca (DISMS) de Brachiaria brizantha Stapf cv. Marandu e de Andropogon gayanus Kunth cv. Planaltina. De cada gramínea coletaram-se, em uma área de 1 ha, dez amostras de 1 m x 1 m em duas épocas do período chuvoso de 1998. O primeiro corte foi realizado aos 60 dias após um fogo acidental e o segundo corte, aos 60 dias após o primeiro. No total dos dois cortes, o rendimento forrageiro da braquiária (13.046 kg MS/ha) foi o dobro do verificado em andropógon. As variáveis relacionadas ao valor nutritivo não diferiram muito entre as gramíneas, e mantiveram-se entre médias a baixas, o que pode ser explicado pela reduzida fertilidade do solo.bitstream/item/35805/1/BP34.pd

Improving Orbit Estimates for Incomplete Orbits with a New Approach to Priors -- with Applications from Black Holes to Planets

We propose a new approach to Bayesian prior probability distributions (priors) that can improve orbital solutions for low-phase-coverage orbits, where data cover less than approximately 40% of an orbit. In instances of low phase coverage such as with stellar orbits in the Galactic center or with directly-imaged exoplanets, data have low constraining power and thus priors can bias parameter estimates and produce under-estimated confidence intervals. Uniform priors, which are commonly assumed in orbit fitting, are notorious for this. We propose a new observable-based prior paradigm that is based on uniformity in observables. We compare performance of this observable-based prior and of commonly assumed uniform priors using Galactic center and directly-imaged exoplanet (HR 8799) data. The observable-based prior can reduce biases in model parameters by a factor of two and helps avoid under-estimation of confidence intervals for simulations with less than about 40% phase coverage. Above this threshold, orbital solutions for objects with sufficient phase coverage such as S0-2, a short-period star at the Galactic center with full phase coverage, are consistent with previously published results. Below this threshold, the observable-based prior limits prior influence in regions of prior dominance and increases data influence. Using the observable-based prior, HR 8799 orbital analyses favor lower eccentricity orbits and provide stronger evidence that the four planets have a consistent inclination around 30 degrees to within 1-sigma. This analysis also allows for the possibility of coplanarity. We present metrics to quantify improvements in orbital estimates with different priors so that observable-based prior frameworks can be tested and implemented for other low-phase-coverage orbits.Comment: Published in AJ. 23 pages, 14 figures. Monte Carlo chains are available in the published article, or are available upon reques

Simple clinical and laboratory predictors to improve empirical treatment strategies in areas of high scrub typhus and dengue endemicity, central Vietnam

BACKGROUND: Dengue fever is highly endemic in Vietnam, but scrub typhus-although recognized as an endemic disease-remains underappreciated. These diseases together are likely to account for more than half of the acute undifferentiated fever burden in Vietnam. Scrub typhus (ST) is a bacterial disease requiring antimicrobial treatment, while dengue fever (DF) is of viral etiology and does not. The access to adequate diagnostics and the current understanding of empirical treatment strategies for both illnesses remain limited. In this study we aimed to contribute to the clinical decision process in the management of these two important etiologies of febrile illness in Vietnam. METHODS: Using retrospective data from 221 PCR-confirmed scrub typhus cases and 387 NS1 protein positive dengue fever patients admitted to five hospitals in Khanh Hoa province (central Vietnam), we defined predictive characteristics for both diseases that support simple clinical decision making with potential to inform decision algorithms in future. We developed models to discriminate scrub typhus from dengue fever using multivariable logistic regression (M-LR) and classification and regression trees (CART). Regression trees were developed for the entire data set initially and pruned, based on cross-validation. Regression models were developed in a training data set involving 60% of the total sample and validated in the complementary subsample. Probability cut points for the distinction between scrub typhus and dengue fever were chosen to maximise the sum of sensitivity and specificity. RESULTS: Using M-LR, following seven predictors were identified, that reliably differentiate ST from DF; eschar, regional lymphadenopathy, an occupation in nature, increased days of fever on admission, increased neutrophil count, decreased ratio of neutrophils/lymphocytes, and age over 40. Sensitivity and specificity of predictions based on these seven factors reached 93.7% and 99.5%, respectively. When excluding the "eschar" variable, the values dropped to 76.3% and 92.3%, respectively. The CART model generated one further variable; increased days of fever on admission, when eschar was included, the sensitivity and specificity was 95% and 96.9%, respectively. The model without eschar involved the following six variables; regional lymphadenopathy, increased days of fever on admission, increased neutrophil count, increased lymphocyte count, platelet count >/= 47 G/L and age over 28 years as predictors of ST and provided a sensitivity of 77.4% and a specificity of 90.7%. CONCLUSIONS: The generated algorithms contribute to differentiating scrub typhus from dengue fever using basic clinical and laboratory parameters, supporting clinical decision making in areas where dengue and scrub typhus are co-endemic in Vietnam