Crucial cytokine interactions in nitric oxide production induced by Mycoplasma arthritidis superantigen

Mycoplasma arthritidis causes autoimmune arthritis in rodents. It produces a superantigen (MAM) that simultaneously activates antigen presenting cells and T cells inducing nitric oxide and cytokine release. Nitric oxide is a key inducer and regulator of the immune system activation. Here, we investigated nitric oxide and cytokine production and interactions of these molecules in MAM-stimulated co-cultures of macrophages (J774A.1 cell line) with spleen lymphocytes. We found that: a) MAM-induced nitric oxide, interferon-gamma, membrane-associated tumor necrosis factor and interleukin-2 production in co-cultures of macrophages with lymphocytes from BALB/c and C3H/HePas but not from C57B1/6 mice; b) production of nitric oxide was dependent on interferon-gamma whereas that of interferon-gamma was dependent on interleukin-2 and membrane-associated tumor necrosis factor; c) these cytokines up regulated MAM-induced nitric oxide production. Unraveling the mechanisms of cell activation induced by MAM might be helpful to design strategies to prevent immune system activation by superantigens and therefore in seeking amelioration of associated immunopathologies. (C) 2008 Elsevier Masson SAS. All rights reserved.

Comparing machine learning classifiers in potential distribution modelling

Species` potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species` potential distribution. (C) 2010 Elsevier Ltd. All rights reserved.

Influence of package, type of apple juice and temperature on the production of patulin by Byssochlamys nivea and Byssochlamys fulva

Although the production of patulin in apple fruits is mainly by Penicillium expansum, there is no information on the ability of heat resistant moulds that may survive pasteurization to produce this mycotoxin in juice packages during storage and distribution. In this study, the production of patulin by Byssochlamys spp (Byssochlamys nivea FRR 4421, B. nivea ATCC 24008 and Byssochlamys fulva IOC 4518) in cloudy and clarified apple juices packaged in laminated paperboard packages or in polyethylene terephthalate bottles (PET) and stored at both 21 degrees C and 30 degrees C, was investigated. The three Byssochlamys strains were able to produce patulin in both cloudy and clarified apple juices. Overall, the lower the storage temperature, the lower the patulin levels and mycelium dry weight in the apple juices (p<0.05). The greatest variations in pH and degrees Brix were observed in the juices from which the greatest mycelium dry weights were recovered. The maximum levels of patulin recovered from the juices were ca. 150 mu g/kg at 21 degrees C and 220 mu g/kg at 30 degrees C. HPLC-UV, HPCL-DAD and mass spectrometry analyses confirmed the ability of B. fulva IOC 4518 to produce patulin. Due to the heat resistance of B. nivea and B. fulva and their ability to produce patulin either in PET bottles or in laminated paperboard packages, the control of contamination and the incidence of these fungi should be a matter of concern for food safety. Control measures taken by juice industries must also focus on controlling the ascospores of heat resistant moulds. (C) 2010 Elsevier B.V. All rights reserved.

Evaluation of the pH and electrical conductivity in milk from jersey cows during the first month of lactation

In order to assess the influence of the colostrum period on pH and, electrical conductivity, we collected 418 milk samples from 127 Jersey cows. The samples were collected from healthy udders that did not present any bacterial growth in the microbiological examination. They were divided into eight groups as follows < 1/2 day; 1/2 and 1 degrees day; 2 degrees day; 3 degrees day; 4 degrees and 5 degrees day; 6 degrees and 7 degrees day; 8 degrees to 15 degrees day; 16 degrees to 30 degrees days of lactation. The samples were collected before milking and the following analyses were conducted: pH, electrical conductivity. In the first 24 hours of lactation, there was an reduction in electrical conductivity value, associated with an increase in pH value. We observed that transition of secretion from colostrum to milk, occurs during the first week of lactation; from 6(rd) day of lactation for pH value and 3(th) day for electrical conductivity value. We recommend the use the following figures as normal ranges for the first 24 hours of lactation (colostrum period): pH <= 6,51 and electrical conductivity <= 6,33 mS/cm; while for the interval between 2(nd) and 7(th) days of lactation (transition from colostrum to milk) we suggest the use of the values as normal ranges: pH <= 6,66 and electrical conductivity <= 5,93 mS/cm.