Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Assessment of the high-dose concept and level of control provided by MON 87701 x MON 89788 soybean against Anticarsia gemmatalis and Pseudoplusia includens (Lepidoptera: Noctuidae) in Brazil

BACKGROUND: Genetically modified MON 87701 X MON 89788 soybean (Glycine max), which expresses the Cry1Ac and EPSP-synthase proteins, has been registered for commercial use in Brazil. To develop an Insect Resistance Management (IRM) program for this event, laboratory and field studies were conducted to assess the high-dose concept and level of control it provides against Anticarsia gemmatalis and Pseudoplusia includens. RESULTS: The purified Cry1Ac protein was more active against A. gemmatalis [LC50 (FL 95%) = 0.23 (0.150.34) mu g Cry1Ac mL-1 diet] than P. includens [LC50 (FL 95%) = 3.72 (2.654.86) mu g Cry1Ac mL-1 diet]. In bioassays with freeze-dried MON 87701X MON 89788 soybean tissue diluted 25 times in an artificial diet, there was 100% mortality of A. gemmatalis and up to 95.79% mortality for P. includens. In leaf-disc bioassays and under conditions of high artificial infestation in the greenhouse and natural infestation in the field, MON 87701X MON 89788 soybean showed a high level of efficacy against both target pests. CONCLUSIONS: The MON 87701X MON 89788 soybean provides a high level of control against A. gemmatalis and P. includes, but a high-dose event only to A. gemmatalis. Copyright (c) 2012 Society of Chemical Industry

Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

High prevalence of OXA-143 and alteration of outer membrane proteins in carbapenem-resistant Acinetobacter spp. isolates in Brazil

Carbapenem resistance amongst Acinetobacter spp. has been increasing in the last decade. This study evaluated the outer membrane protein (OMP) profile and production of carbapenemases in 50 carbapenem-resistant Acinetobacter spp. isolates from bloodstream infections. Isolates were identified by API20NE. Minimum inhibitory concentrations (MICs) for carbapenems were determined by broth microdilution. Carbapenemases were studied by phenotypic tests, detection of their encoding gene by polymerase chain reaction (PCR) amplification, and imipenem hydrolysis. Nucleotide sequencing confirming the enzyme gene type was performed using MegaBACE 1000. The presence of OMPs was studied by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) and PCR. Molecular typing was performed using pulsed-field gel electrophoresis (PFGE). All isolates were resistant to carbapenems. Moreover, 98% of the isolates were positive for the gene encoding the enzyme OXA-51-like, 18% were positive for OXA-23-like (only one isolate did not show the presence of the insertion sequence ISAba1 adjacent to this gene) and 76% were positive for OXA-143 enzyme. Five isolates (10%) showed the presence of the IMP-1 gene. Imipenem hydrolysing activity was detected in only three strains containing carbapenemase genes, comprising two isolates containing the bla(IMP) gene and one containing the bla(OXA-51/OXA-23-like) gene. The OMP of 43 kDa was altered in 17 of 25 strains studied, and this alteration was associated with a high meropenem MIC (256 mu g/mL) in 5 of 7 strains without 43 kDa OMP. On the other hand, decreased OMP 33-36 kDa was found in five strains. The high prevalence of OXA-143 and alteration of OMPs might have been associated with a high level of carbapenem resistance. (C) 2012 Elsevier B.V. and the International Society of Chemotherapy. All rights reserved.

LALP: a language to program custom FPGA-based acceleration engines

Field-Programmable Gate Arrays (FPGAs) are becoming increasingly important in embedded and high-performance computing systems. They allow performance levels close to the ones obtained with Application-Specific Integrated Circuits, while still keeping design and implementation flexibility. However, to efficiently program FPGAs, one needs the expertise of hardware developers in order to master hardware description languages (HDLs) such as VHDL or Verilog. Attempts to furnish a high-level compilation flow (e.g., from C programs) still have to address open issues before broader efficient results can be obtained. Bearing in mind an FPGA available resources, it has been developed LALP (Language for Aggressive Loop Pipelining), a novel language to program FPGA-based accelerators, and its compilation framework, including mapping capabilities. The main ideas behind LALP are to provide a higher abstraction level than HDLs, to exploit the intrinsic parallelism of hardware resources, and to allow the programmer to control execution stages whenever the compiler techniques are unable to generate efficient implementations. Those features are particularly useful to implement loop pipelining, a well regarded technique used to accelerate computations in several application domains. This paper describes LALP, and shows how it can be used to achieve high-performance computing solutions.