Apparent digestibility of nutrients, energy, and amino acid of nontoxic and detoxified physic nut cakes for Nile tilapia

Abstract:The objective of this work was to evaluate the apparent digestibility coefficients of nutrients, energy, and amino acids of nontoxic and detoxified physic nut cakes treated with solvent plus posterior extrusion, for Nile tilapia. The apparent digestibility coefficients of crude protein and gross energy were higher for detoxified than for nontoxic physic nut cake. However, the apparent digestibility coefficient of ether extract of the nontoxic physic nut cake was higher than that of the detoxified one. The apparent digestibility coefficient of amino acids of both feed ingredients was superior to 80%, except for glycine, for the nontoxic psychic nut cake, and for threonine, for the detoxified one. Nontoxic and detoxified physic nut cakes show apparent digestibility coefficient values equivalent to those of the other evaluated oilseeds and potential for inclusion in Nile tilapia diets.

CONSUMERS'ACCEPTANCE OF FRESH AND COMBINED METHODS PROCESSED MELON, MANGO AND CASHEW APPLE

Fresh and combined methods processed Cantaloupe melons, mangoes and cashew apples were submitted to consumers' acceptance and scored on a nine-point hedonic scale. Fruits were osmotically treated in sucrose syrup with two different concentrations of SO2. Overall acceptance, appearance, aroma, flavor and texture were evaluated. Fresh cashew apples received lower scores for acceptance than processed cashew apples while fresh mangoes were more acceptable than processed mangoes. Acceptance of fresh melons and processed melons was similar. Treatments of the tropical fruits with two different concentrations of SO2 did not demonstrate significant differences between the fruits tested.

Indol-butyric acid levels on cashew cloning by air-layering process

A study was conducted to determine the possibility of cashew (Anacardium occidentale) cloning by air-layering and influence of IBA (indol-butyric acid) on this process. It was adopted a completely randomized design with 4 treatments, 10 air layers each and 4 replications, reaching 160 air layers. The IBA levels on the treatments were, as follow: 0, 1000, 3000 and 5000 mg.kg-1. It was evaluated: survival, callus and rooting percentage, average number and length of roots. The highest survival rate (67.5%) was registered with no growth regulator and IBA at 1000 mg.kg-1, while the best rooting percentage (82%) referred to 1000 mg.kg-1. In spite of average number and length of roots, the highest results were observed with IBA at 5000 mg.kg-1. IBA concentrations had no influence on cashew air-layering formation.

Pointwise estimates for the Bergman kernel of the weighted Fock space

We prove upper pointwise estimates for the Bergman kernel of the weighted Fock space of entire functions in $L^{2}(e^{-2\phi}) $ where $\phi$ is a subharmonic function with $\Delta\phi$ a doubling measure. We derive estimates for the canonical solution operator to the inhomogeneous Cauchy-Riemann equation and we characterize the compactness of this operator in terms of $\Delta\phi$.

Kernel-PCA data integration with enhanced interpretability

Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. Results We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. Conclusions The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge.

Beurling-Landau densities of weighted Fekete sets and correlation kernel estimates

Let $Q$ be a suitable real function on $C$. An $n$-Fekete set corresponding to $Q$ is a subset ${Z_{n1}},\dotsb, Z_{nn}}$ of $C$ which maximizes the expression $\Pi^n_i_{

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Estimating extreme value cumulative distribution functions using bias-corrected kernel approaches

We propose a new kernel estimation of the cumulative distribution function based on transformation and on bias reducing techniques. We derive the optimal bandwidth that minimises the asymptotic integrated mean squared error. The simulation results show that our proposed kernel estimation improves alternative approaches when the variable has an extreme value distribution with heavy tail and the sample size is small.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

REPEATABILITY OF FRUITS AND SEEDS PRODUCTION AND SELECTION OF BRAZIL NUT GENOTYPES IN NATIVE POPULATIONS IN RORAIMA1

ABSTRACT This study estimates the repeatability coefficients of two production traits in two native populations of Brazil nut trees. It determines the number of years of suitable evaluations for an efficient selection process, determines the permanent phenotypic correlation between production traits and also the selection of promising trees in these populations. Populations, located in the Itã region (ITA) and in the in the Cujubim region (CUJ), are both belonging to the municipality of Caracaraí, state of Roraima - Brazil, and consist of 85 and 51 adult trees, respectively. Each tree was evaluated regarding the number of fruits per plant (NFP) and fresh seed weight per plant (SWP), for eight (ITA) and five consecutive years (CUJ). Statistical analyses were performed according to the mixed model methodology, using Software Selegen-REML/BLUP (RESENDE, 2007). The repeatability coefficients were low for NFP (0.3145 and 0.3269 for ITA and CUJ, respectively) and also for SWP (0.2957 and 0.3436 for ITA and CUJ, respectively). It on average takes nine evaluation years to reach coefficients of determination higher than 80%. Permanent phenotypic correlation values higher than 0.95 were obtained for NFP and SWP in both populations. Although trees with a high number of fruits and seed weight were identified, more evaluation years are needed to perform the selection process more efficiently.

Calibration of angles of nozzles and deposition of an axial-flow sprayer on dwarf cashew

The study aimed to determine an optimum angle for the nozzles axial-flow sprayers a deposition for better vertical distribution focused on cashew. In laboratory tests were conducted adjusting the angle of the nozzle axial-flow sprayers. The experimental design was randomized blocks in a 2x3 factorial with four replications. The treatment for this test were two settings (with and without the adjustment of the angles of the nozzles ) and tree application volumes 273, 699 and 954 L ha-¹.The study was conducted in an orchard of dwarf cashew, with eight years of age. It was concluded that the volumetric distribution profile showed better vertical distribution uniformity when the angles of the nozzles were regulated for the canopy, the adjustment of the angles of the nozzles for the canopy provided greater deposition of droplets, the increased volume of application resulted in higher depositions in the leaves of the crop.

Economic analysis of cashew early dwarf Crop BRS - 189 depending on water levels and doses of potassium fertilization

Water and fertilizer among the production factors are the elements that most restrict the production of cashew. The precise amount of these factors is essential to the success of the crop yield. This research aimed to determine the best factor-product ratio and analyze technical and economic indicators, of productivity of the cashew clone BRS 189 (Anacardium occidentale) to production factors water and potassium. The experiment was conducted from May 2009 to December 2009 in an experimental area of 56.0 m x 112.0 m in the irrigated Curu - Pentecoste, located in the municipality of Pentecoste, Ceará, Brazil. Production factors water (W) and potassium (K) were the independent variables and productivity (Y), the dependent variable. Ten statistical models that have proven satisfactory for obtaining production function were tested. The marginal rate of substitution was obtained through the ratio of the potassium marginal physical product and the water marginal physical product. The most suited model to the conditions of the experiment was the quadratic polynomial without intercept and interaction. Considering that the price of the water was 0.10 R$ mm -1, the price of the potassium 2.19 R$ kg -1 and the price of the cashew 0.60 R$ kg-1, the amounts of water and K2O to obtain the maximum net income were 6,349.1 L plant-1 of water and 128.7 g plant -1year, -1 respectively. Substituting the values obtained in the production function, the maximum net income was achieved with a yield of 7,496.8 kg ha-1 of cashew.

Germination and early growth of physic nut submitted to levels of salinity

Two experiments were conducted to evaluate the effect of salinity on early physic nut plant development. In the first trial, physic nut seeds were exposed to seven levels of salinity (0, 2, 4, 6, 8, 10 and 12dS m-1) with eight repetitions, using a substrate of paper soaked with solutions of CaCl2 and KCl. The treatments were evaluated based on the initial germination, total percentage of germination, and time necessary to germination of 50% of the seeds. Increased salinity reduced the first germination count and delayed the time to 50% germination. From 10dS m-1, there was a reduction in germination percentage. The second trial evaluated the effect of salinity on the growth of physic nut seeds. This trial, carried out inside a greenhouse, with a completely randomized design, was composed of five salinity treatments (0.02, 2, 4, 6 and 8dS m-1) with 5 replications. It was observed that salinity levels above 2dS m-1 affected plant development. The current study suggests that salinity management is an important factor to be considered to achieve the potential productivity of physic nut.

Directed-spray application of paraquat and diuron in physic nut plants

There is little information about the selectivity of herbicides in physic nut (Jatropha curcas) in Brazil. Therefore, this study aimed to evaluate the selectivity of different doses and mixtures of paraquat and diuron in direted-spray applications in physic nut plants in greenhouse conditions. The study used a randomized block design, with five replicates. The treatments were: paraquat (200 and 600 g ha-1), diuron (1,000 and 2,000 g ha-1), paraquat + diuron (200 + 1,000 g ha-1), paraquat + diuron (200 + 2,000 g ha-1), paraquat + diuron (600 + 1,000 g ha-1), paraquat + diuron (600 + 2,000 g ha-1) and a control (no application). Directed-spray application was performed at 70 days after sowing by the lower third of the plants. The treatments of diuron and paraquat + diuron mixtures affected the growth and photosynthetic activity of physic nut plants, injuries being more pronounced at doses of diuron of 2,000 g ha‑1, while the isolated application of paraquat at doses of 200 and 600 g ha-1 showed good selectivity potential for physic nut plants.