B chromosome of the African cichlid fish Astatotilapia latifasciata: methylation patterns and transcriptional levels of DNA methylation machinery genes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

MxA interacts with and is modified by the SUMOylation machinery

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Development of CAN-based distributed control system for variable rate technology in agricultural machinery

The number of electronic devices connected to agricultural machinery is increasing to support new agricultural practices tasks related to the Precision Agriculture such as spatial variability mapping and Variable Rate Technology (VRT). The Distributed Control System (DCS) is a suitable solution for decentralization of the data acquisition system and the Controller Area Network (CAN) is the major trend among the embedded communications protocols for agricultural machinery and vehicles. The application of soil correctives is a typical problem in Brazil. The efficiency of this correction process is highly dependent of the inputs way at soil and the occurrence of errors affects directly the agricultural yield. To handle this problem, this paper presents the development of a CAN-based distributed control system for a VRT system of soil corrective in agricultural machinery. The VRT system is composed by a tractor-implement that applies a desired rate of inputs according to the georeferenced prescription map of the farm field to support PA (Precision Agriculture). The performance evaluation of the CAN-based VRT system was done by experimental tests and analyzing the CAN messages transmitted in the operation of the entire system. The results of the control error according to the necessity of agricultural application allow conclude that the developed VRT system is suitable for the agricultural productions reaching an acceptable response time and application error. The CAN-Based DCS solution applied in the VRT system reduced the complexity of the control system, easing the installation and maintenance. The use of VRT system allowed applying only the required inputs, increasing the efficiency operation and minimizing the environmental impact.

OntoSDM: an approach to improve quality on spatial data mining algorithms

The increase in new electronic devices had generated a considerable increase in obtaining spatial data information; hence these data are becoming more and more widely used. As well as for conventional data, spatial data need to be analyzed so interesting information can be retrieved from them. Therefore, data clustering techniques can be used to extract clusters of a set of spatial data. However, current approaches do not consider the implicit semantics that exist between a region and an object’s attributes. This paper presents an approach that enhances spatial data mining process, so they can use the semantic that exists within a region. A framework was developed, OntoSDM, which enables spatial data mining algorithms to communicate with ontologies in order to enhance the algorithm’s result. The experiments demonstrated a semantically improved result, generating more interesting clusters, therefore reducing manual analysis work of an expert.

Bills Favor Mining and Threaten Conservation of Brazilian Merganser (Mergus octosetaceus) at Serra da Canastra National Park, Minas Gerais, Brazil

Serra da Canastra National Park (SCNP) is one of the most important protected areas in the Cerrado biome. Despite its importance to the conservation of rare and endangered species like Brazilian Merganser, two bills were approved in 2010 by Brazil's Chamber of Deputies aiming to reduce SCNP's official boundaries and to transform some of its parts into an Environmental Protection Area (EPA). We evaluated whether such changes would facilitate mining areas to be legally exploited within the park's area, and if those mining areas would represent a threat to Brazilian Merganser populations at SCNP. Results showed that 55% of the mining areas currently within the National Park will be located within the new EPA, and six hydrographic micro-basins inhabited by Brazilian Merganser could be affected by environmental impacts caused by mineral exploitation in those areas. For these reasons, we recommend the two bills be refused at the Federal Senate.

Soil compressibility and least limiting water range of a constructed soil under cover crops after coal mining in Southern Brazil

The use of cover crops affects the support capacity of soil and least limiting water range to crop growth. The objective of this study was to quantify preconsolidation pressure (sigma(p)), compression index (CI) and least limiting water range (LLWR) of a reclaimed coal mining soil under different cover crops, in Candiota, RS, Brazil. In the experiment, with randomized blocks design and four replicates, the following cover crops (treatments) were evaluated: Hemarthria altissima (Poir.) Stapf & C.E. Hubbard, treatment 1 (T1), Paspalum notatum Flugge, treatment 4 (T4), Cynodon dactilon (L) Pers., treatment 5 (T5), control Brachiaria brizantha (Hochst.) Stapf, treatment 7 (T7) and without cover crop treatment 8 (reference treatment, T8). Soil compression and least limiting water range were evaluated with undisturbed samples at a depth of 0.00-0.05 m. In order to evaluate parameters of soil compressibility, the soil samples were saturated with water and subjected to -10 kPa matric potential and then submitted to a uniaxial compression test under the following pressures: 25, 50, 100, 200, 400, 800 and 1600 kPa. Cover crops decreased the preconsolidation pressure of constructed soils after coal mining and the greatest soil reclamation was obtained with the H. altissima cover crop, where the lowest degree of soil compactness and soil load capacity were observed. Soils cultivated under H. altissima or B. brizantha presented the highest least limiting water range and these two cover crops generated similar soil critical bulk density obtained by least limiting water range and soil load support capacity. (C) 2012 Elsevier B.V. All rights reserved.

Proteomic Analysis of Chloroplast-to-Chromoplast Transition in Tomato Reveals Metabolic Shifts Coupled with Disrupted Thylakoid Biogenesis Machinery and Elevated Energy-Production Components

A comparative proteomic approach was performed to identify differentially expressed proteins in plastids at three stages of tomato (Solanum lycopersicum) fruit ripening (mature-green, breaker, red). Stringent curation and processing of the data from three independent replicates identified 1,932 proteins among which 1,529 were quantified by spectral counting. The quantification procedures have been subsequently validated by immunoblot analysis of six proteins representative of distinct metabolic or regulatory pathways. Among the main features of the chloroplast-to-chromoplast transition revealed by the study, chromoplastogenesis appears to be associated with major metabolic shifts: (1) strong decrease in abundance of proteins of light reactions (photosynthesis, Calvin cycle, photorespiration) and carbohydrate metabolism (starch synthesis/degradation), mostly between breaker and red stages and (2) increase in terpenoid biosynthesis (including carotenoids) and stress-response proteins (ascorbate-glutathione cycle, abiotic stress, redox, heat shock). These metabolic shifts are preceded by the accumulation of plastid-encoded acetyl Coenzyme A carboxylase D proteins accounting for the generation of a storage matrix that will accumulate carotenoids. Of particular note is the high abundance of proteins involved in providing energy and in metabolites import. Structural differentiation of the chromoplast is characterized by a sharp and continuous decrease of thylakoid proteins whereas envelope and stroma proteins remain remarkably stable. This is coincident with the disruption of the machinery for thylakoids and photosystem biogenesis (vesicular trafficking, provision of material for thylakoid biosynthesis, photosystems assembly) and the loss of the plastid division machinery. Altogether, the data provide new insights on the chromoplast differentiation process while enriching our knowledge of the plant plastid proteome.

Determination of trace elements in bovine semen samples by inductively coupled plasma mass spectrometry and data mining techniques for identification of bovine class

The reproductive performance of cattle may be influenced by several factors, but mineral imbalances are crucial in terms of direct effects on reproduction. Several studies have shown that elements such as calcium, copper, iron, magnesium, selenium, and zinc are essential for reproduction and can prevent oxidative stress. However, toxic elements such as lead, nickel, and arsenic can have adverse effects on reproduction. In this paper, we applied a simple and fast method of multi-element analysis to bovine semen samples from Zebu and European classes used in reproduction programs and artificial insemination. Samples were analyzed by inductively coupled plasma spectrometry (ICP-MS) using aqueous medium calibration and the samples were diluted in a proportion of 1:50 in a solution containing 0.01% (vol/vol) Triton X-100 and 0.5% (vol/vol) nitric acid. Rhodium, iridium, and yttrium were used as the internal standards for ICP-MS analysis. To develop a reliable method of tracing the class of bovine semen, we used data mining techniques that make it possible to classify unknown samples after checking the differentiation of known-class samples. Based on the determination of 15 elements in 41 samples of bovine semen, 3 machine-learning tools for classification were applied to determine cattle class. Our results demonstrate the potential of support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) chemometric tools to identify cattle class. Moreover, the selection tools made it possible to reduce the number of chemical elements needed from 15 to just 8.

Exploiting the enzymatic machinery of Arthrobacter atrocyaneus for oxidative kinetic resolution of secondary alcohols

We evaluated Arthrobacter atrocyaneus (R1AF57) as producer of oxidoreductases for oxidative kinetic resolution of racemic secondary alcohols via oxidation reaction. This bacterium was isolated from Amazon soil samples using medium enriched with (RS)-1-(4-methylphenyl)ethanol as a carbon source. The kinetic resolution of several secondary alcohols through enantioselective oxidation mediated by resting cells and growing cells of A. atrocyaneus was efficiently achieved for the most alcohols. In general, it was possible to obtain only the (S)-enantiomer from (RS)-1-arylethanols.

Multi-element determination in Brazilian honey samples by inductively coupled plasma mass spectrometry and estimation of geographic origin with data mining techniques

Multi-element analysis of honey samples was carried out with the aim of developing a reliable method of tracing the origin of honey. Forty-two chemical elements were determined (Al, Cu, Pb, Zn, Mn, Cd, Tl, Co, Ni, Rb, Ba, Be, Bi, U, V, Fe, Pt, Pd, Te, Hf, Mo, Sn, Sb, P, La, Mg, I, Sm, Tb, Dy, Sd, Th, Pr, Nd, Tm, Yb, Lu, Gd, Ho, Er, Ce, Cr) by inductively coupled plasma mass spectrometry (ICP-MS). Then, three machine learning tools for classification and two for attribute selection were applied in order to prove that it is possible to use data mining tools to find the region where honey originated. Our results clearly demonstrate the potential of Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Random Forest (RF) chemometric tools for honey origin identification. Moreover, the selection tools allowed a reduction from 42 trace element concentrations to only 5. (C) 2012 Elsevier Ltd. All rights reserved.

Preimplantation development regulatory pathway construction through a text-mining approach

Background: The integration of sequencing and gene interaction data and subsequent generation of pathways and networks contained in databases such as KEGG Pathway is essential for the comprehension of complex biological processes. We noticed the absence of a chart or pathway describing the well-studied preimplantation development stages; furthermore, not all genes involved in the process have entries in KEGG Orthology, important information for knowledge application with relation to other organisms. Results: In this work we sought to develop the regulatory pathway for the preimplantation development stage using text-mining tools such as Medline Ranker and PESCADOR to reveal biointeractions among the genes involved in this process. The genes present in the resulting pathway were also used as seeds for software developed by our group called SeedServer to create clusters of homologous genes. These homologues allowed the determination of the last common ancestor for each gene and revealed that the preimplantation development pathway consists of a conserved ancient core of genes with the addition of modern elements. Conclusions: The generation of regulatory pathways through text-mining tools allows the integration of data generated by several studies for a more complete visualization of complex biological processes. Using the genes in this pathway as “seeds” for the generation of clusters of homologues, the pathway can be visualized for other organisms. The clustering of homologous genes together with determination of the ancestry leads to a better understanding of the evolution of such process.

MR-Radix: a multi-relational data mining algorithm

Abstract Background Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

QuMinS: fast and scalable querying, mining and summarizing multi-modal databases

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.

Ecucational data mining (EDM) para la determinación de comportamientos en estudiantes de ingeniería en la modalidad virtual de UDE

[ES]En este artículo se describe la experiencia de la aplicación de técnicas de EDM (clustering) a un curso disponible en la plataforma Ude@ de la Universidad de Antioquia. El objetivo es clasificar los patrones de interacción de los estudiantes a partir de la información almacenada en la base de datos de la plataforma Moodle. Para ello, se generan informes sobre el uso de los recursos y la autoevaluación que permiten analizar el comportamiento y los patrones de navegación de los estudiantes durante el uso del LMS (Learning Management System).