Experimental Identification of Actinobacillus pleuropneumoniae Strains L20 and JL03 Heptosyltransferases, Evidence for a New Heptosyltransferase Signature Sequence.

We experimentally identified the activities of six predicted heptosyltransferases in Actinobacillus pleuropneumoniae genome serotype 5b strain L20 and serotype 3 strain JL03. The initial identification was based on a bioinformatic analysis of the amino acid similarity between these putative heptosyltrasferases with others of known function from enteric bacteria and Aeromonas. The putative functions of all the Actinobacillus pleuropneumoniae heptosyltrasferases were determined by using surrogate LPS acceptor molecules from well-defined A. hydrophyla AH-3 and A. salmonicida A450 mutants. Our results show that heptosyltransferases APL_0981 and APJL_1001 are responsible for the transfer of the terminal outer core D-glycero-D-manno-heptose (D,D-Hep) residue although they are not currently included in the CAZY glycosyltransferase 9 family. The WahF heptosyltransferase group signature sequence [S(T/S)(GA)XXH] differs from the heptosyltransferases consensus signature sequence [D(TS)(GA)XXH], because of the substitution of D(261) for S(261), being unique.

Identification of Paintings from Camera-Phone Images

Peer-reviewed

Framework for Traffic Pattern Identification: Required Step for Short-term Forecasting

In the world of transport management, the term ‘anticipation’ is gradually replacing ‘reaction’. Indeed, the ability to forecast traffic evolution in a network should ideally form the basis for many traffic management strategies and multiple ITS applications. Real-time prediction capabilities are therefore becoming a concrete need for the management of networks, both for urban and interurban environments, and today’s road operator has increasingly complex and exacting requirements. Recognising temporal patterns in traffic or the manner in which sequential traffic events evolve over time have been important considerations in short-term traffic forecasting. However, little work has been conducted in the area of identifying or associating traffic pattern occurrence with prevailing traffic conditions. This paper presents a framework for detection pattern identification based on finite mixture models using the EM algorithm for parameter estimation. The computation results have been conducted taking into account the traffic data available in an urban network.

Identification of molecular subtypes and gene expression patterns of breast cancer analysing RNA-seq data

Breast cancer is the most common diagnosed cancer and the leading cause of cancer death among females worldwide. It is considered a highly heterogeneous disease and it must be classified into more homogeneous groups. Hence, the purpose of this study was to classify breast tumors based on variations in gene expression patterns derived from RNA sequencing by using different class discovery methods. 42 breast tumors paired-samples were sequenced by Illumine Genome Analyzer and the data was analyzed and prepared by TopHat2 and htseq-count. As reported previously, breast cancer could be grouped into five main groups known as basal epithelial-like group, HER2 group, normal breast-like group and two Luminal groups with a distinctive expression profile. Classifying breast tumor samples by using PAM50 method, the most common subtype was Luminal B and was significantly associated with ESR1 and ERBB2 high expression. Luminal A subtype had ESR1 and SLC39A6 significant high expression, whereas HER2 subtype had a high expression of ERBB2 and CNNE1 genes and low luminal epithelial gene expression. Basal-like and normal-like subtypes were associated with low expression of ESR1, PgR and HER2, and had significant high expression of cytokeratins 5 and 17. Our results were similar compared with TGCA breast cancer data results and with known studies related with breast cancer classification. Classifying breast tumors could add significant prognostic and predictive information to standard parameters, and moreover, identify marker genes for each subtype to find a better therapy for patients with breast cancer.

Identification of metabolites and thermal transformation products of quinolones in raw cow milk by liquid chromatography coupled to high resolution mass spectrometry.

The presence of residues of antibiotics, metabolites, and thermal transformation products (TPs), produced during thermal treatment to eliminate pathogenic microorganisms in milk, could represent a risk for people. Cow"s milk samples spiked with enrofloxacin (ENR), ciprofloxacin (CIP), difloxacin (DIF), and sarafloxacin (SAR) and milk samples from cows medicated with ENR were submitted to several thermal treatments. The milk samples were analyzed by liquid chromatography-mass spectrometry (LC-MS) to find and identify TPs and metabolites. In this work, 27 TPs of 4 quinolones and 24 metabolites of ENR were found. Some of these compounds had been reported previously, but others were characterized for the first time, including lactose-conjugated CIP, the formamidation reaction for CIP and SAR, and hydroxylation or ketone formation to produce three different isomers for all quinolones studied.

Identification of paintings from camera-phone images

Peer-reviewed