Shiga toxin-producing Escherichia coli and atypical enteropathogenic Escherichia coli strains isolated from healthy sheep of different populations in Sao Paulo, Brazil

Aims: Sheep are important carriers of Shiga toxin-producing Escherichia coli (STEC) in several countries. However, there are a few reports about ovine STEC in American continent. Methods and Results: About 86 E. coli strains previously isolated from 172 healthy sheep from different farms were studied. PCR was used for detection of stx(1), stx(2), eae, ehxA and saa genes and for the identification of intimin subtypes. Restriction fragment length polymorphism (RFLP)-PCR was performed to investigate the variants of stx(1) and stx(2), and the flagellar antigen (fliC) genes in nonmotile isolates. Five isolates were eae(+) and stx(-), and belonged to serotypes O128:H2/beta-intimin (2), O145:H2/gamma, O153:H7/beta and O178:H7/epsilon. Eighty-one STEC isolates were recovered, and the stx genotypes identified were stx(1c)stx(2d-O118) (46.9%), stx(1c) (27.2%), stx(2d-O118) (23.4%), and stx(1c)stx(2dOX3a) (2.5%). Pulsed-field gel electrophoresis (PFGE) revealed 27 profiles among 53 STEC and atypical enteropathogenic Escherichia coli (EPEC) isolates. Conclusions: This study demonstrated that healthy sheep in Sao Paulo, Brazil, can be carriers of potential human pathogenic STEC and atypical EPEC. Significance and Impact of the Study: As some of the STEC serotypes presently found have been involved with haemolytic uraemic syndrome (HUS) in other countries, the important role of sheep as sources of STEC infection in our settings should not be disregarded.

Characterization of spliced leader genes of Trypanosoma (Megatrypanum) theileri: phylogeographical analysis of Brazilian isolates from cattle supports spatial clustering of genotypes and parity with ribosomal markers

Trypanosoma (Megatrypanum) theileri from cattle and trypanosomes of other artiodactyls form a clade of closely related species in analyses using ribosomal sequences. Analysis of polymorphic sequences of a larger number of trypanosomes from broader geographical origins is required to evaluate the Clustering of isolates as suggested by previous studies. Here, we determined the sequences of the spliced leader (SL) genes of 21 isolates from cattle and 2 from water buffalo from distant regions of Brazil. Analysis of SL gene repeats revealed that the 5S rRNA gene is inserted within the intergenic region. Phylogeographical patterns inferred using SL sequences showed at least 5 major genotypes of T. theileri distributed in 2 strongly divergent lineages. Lineage TthI comprises genotypes IA and IB from buffalo and cattle, respectively, from the Southeast and Central regions, whereas genotype IC is restricted to cattle from the Southern region. Lineage Tth II includes cattle genotypes IIA, which is restricted to the North and Northeast, and IIB, found in the Centre, West, North and Northeast. PCR-RFLP of SL genes revealed valuable markers for genotyping T. theileri. The results of this study emphasize the genetic complexity and corroborate the geographical structuring of T. theileri genotypes found in cattle.

Structural, electronic, and magnetic entropy contributions of the orbital order-disorder transition in LaMnO(3)

Measurements of X-ray diffraction, electrical resistivity, and magnetization are reported across the Jahn-Teller phase transition in LaMnO(3). Using a thermodynamic equation, we obtained the pressure derivative of the critical temperature (T(JT)), dT(JT)/dP = -28.3 K GPa(-1). This approach also reveals that 5.7(3)J(mol K)(-1) comes from the volume change and 0.8(2)J(mol K)(-1) from the magnetic exchange interaction change across the phase transition. Around T(JT), a robust increase in the electrical conductivity takes place and the electronic entropy change, which is assumed to be negligible for the majority of electronic systems, was found to be 1.8(3)J(mol K)(-1).

Thermo-optics parameters measurements in photorefractive sillenite Bi(12)SiO(20) crystals by thermal lens technique

The present work reports on the thermo-optical properties of photorefractive sillenite Bi(12)SiO(20) (BSO) crystals obtained by applying the Thermal Lens Spectrometry technique (TLS). This crystals presents one high photorefractive sensitivity in the region blue-green spectra, since the measurements were carried out at two pump beam wavelengths (514.5 nm and 750 nm) to study of the light-induced effects in this material (thermal and/or photorefractive). We determine thermo-optical parameters like thermal diffusivity (D), thermal conductivity (K) and temperature coefficient of the optical path length change (ds/dT) in sillenite crystals. These aspects, for what we know, not was studied in details up to now using the lens spectrometry technique and are very important against of the promising potentiality of applications these crystals in non linear optics, real time holography and optical processing data.

Thermal lens spectrometry in pyroelectric lithium niobate crystals

In this work, the light-induced lens effect due to thermal and/or photorefractive processes was studied in pyroelectric (undoped and Fe(2+)-doped) lithium niobate crystals (LiNbO(3)) using thermal lens spectrometry with a two-beam (pump-probe) mode-mismatched configuration. The measurements were carried out at two pump beam wavelengths (514.5 and 750 nm) to establish a full understanding of the present effects in this material (thermal and/or photorefractive). We present an easy-to-implement method to determine quantitative values of the pyroelectric coefficient (dPs/dT), its contribution to the thermal effect and other thermo-optical parameters like thermal diffusivity (D), thermal conductivity (K) and temperature coefficient of the optical path length change (ds/dT). These measurements were performed in LiNbO(3) and LiNbO(3): Fe (0.1 ppm Fe(2+)) crystals with c axis along the direction of laser propagation.

Prigogine-Defay Ratio for an Ionic Glass-Former: Molecular Dynamics Simulations

The pressure dependence of the glass-transition temperature, T(g)(P), of the ionic glass-former 2Ca(NO(3))(2) center dot 3KNO(3), CKN, has been obtained by molecular dynamics (MD) simulations The liquid-glass difference of thermal expansivity, Delta alpha, heat capacity, Delta C(p), and isothermal compressibility, Delta kappa, have been calculated as a function of pressure. It has been found that the Ehrenfest relation dT(g)/dP = TV Delta alpha/Delta C(p) predicts the pressure dependence of T, but the other Ehrenfest relation, dT(g)/dP = Delta kappa/Delta alpha, does not. Consequently, the Prigogine-Defay ratio, Pi = Delta C(p)Delta kappa/TV Delta alpha(2), is Pi similar to 1.2 at low pressures, but increases 1 order of magnitude at high pressures. The pressure dependence of the Prigogine-Defay ratio is interpreted in light of recent explanations for the finding Pi > 1.

Compatibility and decomposition kinetics studies of prednicarbate alone and associated with glyceryl stearate

In this work, TG/DTG and DSC techniques were used to the determination of thermal behavior of prednicarbate alone and associated with glyceryl stearate excipient ( 1: 1 physical mixture). TG/DTG curves obtained for the binary mixture showed a reduction of approximately 37 degrees C to the thermal stability of drug (T(dm/dt-0) (Max)(DTG)). The disappearance of stretching band at 1280 cm(-1) (nu(as) C-O, carbonate group) and the presence of streching band with less intensity at 1750 cm(-1) (nu(s) C-O, ester group) in IR spectrum obtained to the binary mixture submitted at 220 degrees C, when compared with IR spectrum of drug submitted to the same temperature, confirmed the chemical interaction between these substances due to heating. Kinetics parameters of decomposition reaction of prednicarbate were obtained using isothermal (Arrhenius equation) and non-isothermal (Ozawa) methods. The reduction of approximately 45% of activation energy value (E(a)) to the first step of thermal decomposition reaction of drug in the 1:1 (mass/mass) physical mixture was observed by both kinetics methods.

Regionalekonomisk betydelse av travsporten : exemplet Dalatravet Rättvik

Tidigare studier har visat att hästnäringen inklusive spridningseffekter årligen omsätter ca 46 miljarder kronor och bidrar till sysselsättning för ca 30 000 helårsverken. Motsvarande beräkningar för travsportens del av hästnäringen eller för en mindre region finns däremot inte. Denna rapport syftar till att uppskatta regionalekonomisk betydelse av travsport i Sverige med utgångspunkt i en mindre travbanas verksamhet. Dalatravet Rättvik (DT Rättvik) används som en fallstudie. Resultaten baseras dels på registerdata över faktiskt registrerade hästar med koppling till DT Rättvik dels på enkätmaterial insamlat från ett urval av besökare vid tävlingar under sommaren 2014. Totalt används svar från 444 besökare i analysen. Resultaten från beräkningarna visar att den direkta ekonomiska effekten av DT Rättviks 333 travhästar i amatör- eller proffsträning genererar 20 miljoner i travhästtrelaterad konsumtion. DT Rättviks travsportevenemang uppskattas generera ca 29 miljoner varav 11,1 miljoner ärett tillskott till den regionala ekonomin i form av turistekonomisk omsättning. Travsportens sammanlagda regionalekonomiska effekt beräknas således uppgå till minst 31,1 miljonerkronor. Inkluderas även utbildning, avel och uppfödning samt sysselsättning vid ATG-ombudså uppskattas travsporten generera ca 40 helårsverken inom DT Rättviksregionen.  

Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.

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