Detection of virulence-associated genes of Pasteurella multocida isolated from cases of fowl cholera by multiplex-PCR

The current systems of breeding poultry, based on high population density, increase the risk of spreading pathogens, especially those causing respiratory diseases and those that have more than one host. Fowl Cholera (FC) is one such pathogen, and even though it represents one of several avian diseases that should be considered in the differential diagnosis of notifiable diseases that present with sudden death, the pathogenesis and virulence factors involved in FC are still poorly understood. The objective of this study was to investigate twelve genes related to virulence in 25 samples of Pasteurella multocida isolated from FC cases in the southern region of Brazil through the development of multiplex PCR protocols. The protocols developed were capable of detecting all of the proposed genes. The ompH, oma87, sodC, hgbA, hgbB, exBD-tonB and nanB genes were present in 100% of the samples (25/25), the sodA and nanH genes were present in 96% (24/25), ptfA was present in 92% (23/25), and pfhA was present in 60% (15/25). Gene toxA was not identified in any of the samples studied (0/25). Five different genetic profiles were obtained, of which P1 (negative to toxA) was the most common. We concluded that the multiplex-PCR protocols could be useful tools for rapid and simultaneous detection of virulence genes. Despite the high frequency of the analyzed genes and the fact that all samples belonged to the same subspecies of P. multocida, five genetic profiles were observed, which should be confirmed in a study with a larger number of samples.

Detection of Horizontal Two-Phase Flow Patterns Through a Neural Network Model

One of the main problems related to the transport and manipulation of multiphase fluids concerns the existence of characteristic flow patterns and its strong influence on important operation parameters. A good example of this occurs in gas-liquid chemical reactors in which maximum efficiencies can be achieved by maintaining a finely dispersed bubbly flow to maximize the total interfacial area. Thus, the ability to automatically detect flow patterns is of crucial importance, especially for the adequate operation of multiphase systems. This work describes the application of a neural model to process the signals delivered by a direct imaging probe to produce a diagnostic of the corresponding flow pattern. The neural model is constituted of six independent neural modules, each of which trained to detect one of the main horizontal flow patterns, and a last winner-take-all layer responsible for resolving when two or more patterns are simultaneously detected. Experimental signals representing different bubbly, intermittent, annular and stratified flow patterns were used to validate the neural model.

DETECTION OF GLYPHOSATE-RESISTANT PALMER AMARANTH (Amaranthus palmeri) IN AGRICULTURAL AREAS OF MATO GROSSO, BRAZIL

ABSTRACT The recent introduction of Palmer amaranth (Amaranthus palmeri) in Brazilian agricultural areas may promote several changes on weed management, especially in no-till systems and in glyphosate-resistant crops, since glyphosate-resistant biotypes of A. palmerihave been frequently selected in other countries. Therefore, this research was developed in order to evaluate the glyphosate susceptibility of a Palmer amaranth biotype recently identified in the State of Mato Grosso, Brazil. For this purpose, glyphosate susceptibility of three Amaranthusbiotypes was compared: A.hybridus var. patulus, collected in the State of Rio Grande do Sul - Brazil; A.hybridus var. patulus, collected in the State of São Paulo - Brazil; and A.palmeri, collected in the State of Mato Grosso - Brazil. Dose-response curves were generated for all biotypes, considering eight rates of glyphosate and six replicates. All the experiments were repeated twice. Both A.hybridus biotypes were satisfactorily controlled by glyphosate, demanding rates equal to or lower than 541.15 g a.e. ha-1 for 80% control (LD80). The A.palmeri biotype was not controlled by glyphosate in any of the assessments and required rates greater than 4,500 g a.e. ha-1 to reach LD80, which are economically and environmentally unacceptable. Comparison of the Brazilian A.palmeri biotype to the A. hybridus biotypes, as well as, to the results available in scientific international literature, led to the conclusion that the Brazilian Palmer amaranth biotype is resistant to glyphosate.

Detection of anthraquinones and identification of 1,4-naphthohydroquinone in cell suspension cultures of Rudgea jasminoides (Cham.) Müll. Arg. (Rubiaceae)

In Rubiaceae, anthraquinones and naphthoquinones are secondary metabolites characteristic of the subfamily Rubioideae, in which Rudgea jasminoides is included. Thin-layer chromatography using specific solvent systems and spray reagents indicated the presence of anthraquinones constitutively produced by cell suspension cultures of R. jasminoides. GC/MS analysis detected 1,4-naphthohydroquinone as a product of biosynthesis only after elicitation of the cells with yeast extract (Saccharomyces cerevisiae). The latter compound is probably a phytoalexin produced by suspension cultures of R. jasminoides.

Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis

Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.

Detection of adventitious presence of genetically modified seeds in lots of non transgenic soybean

The difficulty on identifying, lack of segregation systems and absence of suitable standards for coexistence of non trangenic and transgenic soybean are contributing for contaminations that occur during productive system. The objective of this study was to evaluate the efficiency of two methods for detecting mixtures of seeds genetically modified (GM) into samples of non-GM soybean, in a way that seed lots can be assessed within the standards established by seed legislation. Two sizes of soybean samples (200 and 400 seeds), cv. BRSMG 810C (non-GM) and BRSMG 850GRR (GM), were assessed with four contamination levels (addition of GM seeds, for obtaining 0.0%, 0.5%, 1.0%, and 1.5% contamination), and two detection methods: immunoassay of lateral flux (ILF) and bioassay (pre-imbibition into 0.6% herbicide solution; 25 ºC; 16 h). The bioassay is efficient in detecting presence of GM seeds in seed samples of non-GM soybean, even for contamination lower than 1.0%, provided that seeds have high physiological quality. The ILF was positive, detecting the presence of target protein in contaminated samples, indicating test effectiveness. There was significant correlation between the two detection methods (r = 0.82; p < 0.0001). Sample size did not influence efficiency of the two methods in detecting presence of GM seeds.