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.

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.

Amazonian malaria: Asymptomatic human reservoirs, diagnostic challenges, environmentally driven changes in mosquito vector populations, and the mandate for sustainable control strategies

Across the Americas and the Caribbean, nearly 561,000 slide-confirmed malaria infections were reported officially in 2008. The nine Amazonian countries accounted for 89% of these infections; Brazil and Peru alone contributed 56% and 7% of them, respectively. Local populations of the relatively neglected parasite Plasmodium vivax, which currently accounts for 77% of the regional malaria burden, are extremely diverse genetically and geographically structured. At a time when malaria elimination is placed on the public health agenda of several endemic countries, it remains unclear why malaria proved so difficult to control in areas of relatively low levels of transmission such as the Amazon Basin. We hypothesize that asymptomatic parasite carriage and massive environmental changes that affect vector abundance and behavior are major contributors to malaria transmission in epidemiologically diverse areas across the Amazon Basin. Here we review available data supporting this hypothesis and discuss their implications for current and future malaria intervention policies in the region. Given that locally generated scientific evidence is urgently required to support malaria control interventions in Amazonia, we briefly describe the aims of our current field-oriented malaria research in rural villages and gold-mining enclaves in Peru and a recently opened agricultural settlement in Brazil. (C) 2011 Elsevier B.V. All rights reserved.

Synthetic Phytochelatin Surface Display in Cupriavidus metallidurans CH34 for Enhanced Metals Bioremediation

This work describes the effects of the cell surface display of a synthetic phytochelatin in the highly metal tolerant bacterium Cupriavidus metallidurans CH34. The EC20sp synthetic phytochelatin gene was fused between the coding sequences of the signal peptide (SS) and of the autotransporter beta-domain of the Neisseria gonorrhoeae IgA protease precursor (IgA beta), which successfully targeted the hybrid protein toward the C. metallidurans outer membrane. The expression of the SS-EC20sp-IgA beta gene fusion was driven by a modified version of the Bacillus subtilis mrgA promoter showing high level basal gene expression that is further enhanced by metal presence in C. metallidurans. The recombinant strain showed increased ability to immobilize Pb2+, Zn2+, Cu2+, Cd2+, Mn2+, and Ni2+ ions from the external medium when compared to the control strain. To ensure plasmid stability and biological containment, the MOB region of the plasmid was replaced by the E. coli hok/sok coding sequence.

Mapping the lignin distribution in pretreated sugarcane bagasse by confocal and fluorescence lifetime imaging microscopy

Abstract Background Delignification pretreatments of biomass and methods to assess their efficacy are crucial for biomass-to-biofuels research and technology. Here, we applied confocal and fluorescence lifetime imaging microscopy (FLIM) using one- and two-photon excitation to map the lignin distribution within bagasse fibers pretreated with acid and alkali. The evaluated spectra and decay times are correlated with previously calculated lignin fractions. We have also investigated the influence of the pretreatment on the lignin distribution in the cell wall by analyzing the changes in the fluorescence characteristics using two-photon excitation. Eucalyptus fibers were also analyzed for comparison. Results Fluorescence spectra and variations of the decay time correlate well with the delignification yield and the lignin distribution. The decay dependences are considered two-exponential, one with a rapid (τ1) and the other with a slow (τ2) decay time. The fastest decay is associated to concentrated lignin in the bagasse and has a low sensitivity to the treatment. The fluorescence decay time became longer with the increase of the alkali concentration used in the treatment, which corresponds to lignin emission in a less concentrated environment. In addition, the two-photon fluorescence spectrum is very sensitive to lignin content and accumulation in the cell wall, broadening with the acid pretreatment and narrowing with the alkali one. Heterogeneity of the pretreated cell wall was observed. Conclusions Our results reveal lignin domains with different concentration levels. The acid pretreatment caused a disorder in the arrangement of lignin and its accumulation in the external border of the cell wall. The alkali pretreatment efficiently removed lignin from the middle of the bagasse fibers, but was less effective in its removal from their surfaces. Our results evidenced a strong correlation between the decay times of the lignin fluorescence and its distribution within the cell wall. A new variety of lignin fluorescence states were accessed by two-photon excitation, which allowed an even broader, but complementary, optical characterization of lignocellulosic materials. These results suggest that the lignin arrangement in untreated bagasse fiber is based on a well-organized nanoenvironment that favors a very low level of interaction between the molecules.

Impact of the Level of Disclosure of Financial Information on the Pricing of Shares in the Context of Adverse Selection: an experimental research

Managers know more about the performance of the organization than investors, which makes the disclosure of information a possible strategy for competitive differentiation, minimizing adverse selection. This paper's main goal is to analyze whether or not an entity's level of diclosure may affect the risk perception of individuals and the process of evaluating their shares. The survey was carried out in an experimental study with 456 subjects. In a stock market simulation, we investigated the pricing of the stocks of two companies with different levels of information disclosure at four separate stages. The results showed that, when other variables are constant, the level of disclosure of an entity can affect the expectations of individuals and the process of evaluating their shares. A higher level of disclosure by an entity affected the value of its share and the other company's.

CID: an efficient complexity-invariant distance for time series

The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While dozens of classification algorithms have been applied to time series, recent empirical evidence strongly suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm is important, and depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping, and cardiology data requires invariance to the baseline (the mean value). Similarly, recent work suggests that for time series clustering, the choice of clustering algorithm is much less important than the choice of distance measure used.In this work we make a somewhat surprising claim. There is an invariance that the community seems to have missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where some complex objects may be incorrectly assigned to a simpler class. Similarly, for clustering this effect can introduce errors by “suggesting” to the clustering algorithm that subjectively similar, but complex objects belong in a sparser and larger diameter cluster than is truly warranted.We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification and clustering accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series mining experiments ever attempted in a single work, and show that complexity-invariant distance measures can produce improvements in classification and clustering in the vast majority of cases.