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.

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.

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.

Comparação do sinal EMG e das características da passada em diferentes protocolos de corrida incremental

O objetivo do presente estudo foi comparar o sinal eletromiográfi co, a frequência e a amplitude de passada entre diferentes intensidades de corrida: 60%, 80% e 100% da velocidade máxima em dois protocolos incrementais. Participaram deste estudo 11 corredores do sexo masculino. Os protocolos de corrida foram realizados com velocidades iniciais de 10 km.hr-1, com incrementos de 1 km.hr-1 a cada três minutos até a exaustão, que diferiram em relação ao intervalo entre cada estágio incremental: 30 e 120 segundos. Foram analisados valores RMS dos músculos iliocostal lombar, reto femoral, vasto lateral, vasto medial, bíceps femoral, tibial anterior, e gastrocnêmio, e a amplitude e frequência de passada. Os valores RMS mostraram aumento entre as intensidades para quase todos os músculos, e não foram influenciados pelo tipo de protocolo utilizado para maioria dos músculos. A frequência e amplitude de passada apresentaram contribuições percentuais diferenciadas para o aumento da velocidade de corrida.

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.