Microbiological quality of tomato (<i>Solanum lycopersicum</i> L.) produced under greenhouse conditions in five Municipalities of the State of Mexico

The aim of the current research was to determine tomato (Solanum lycopersicum L.) microbiological quality produced under greenhouse conditions in 5 municipalities of the State of Mexico. Studies were conducted during the 2013 production cycle to know the risks and apply prevention strategies prior to its consumption. A microbiological analysis of samples of irrigation water, soil and 100 tomato fruits variety cid was performed to determine Aerobic Mesophiles, Total Coliforms and Fecal Coliforms. The methodology used were those according to the Official Mexican Standards NOM- 109-SSA1-1994, NOM-110-SSA1-1994, NOM-092-SSA1-1994, NOM-113-SSA1-1994, and the Regulations of the National French Organization for Standardization (AFNOR) NF V08-60, and NOM-093-SSA1-1994, which establish the allowable limits for the study microorganisms. The results showed a zero level of pollution in water and soil samples. For fruits, levels of Aerobic Mesophilic were within the maximum limits permitted by the standards. The municipality of Texcaltitlan showed the highest average for these microorganisms (10083.80 CFU/mL). Huixquilucan showed 2266.84 CFU/mL for Total Coliforms. For Fecal Coliforms, municipalities of Coatepec and Texcaltitlan exceeded the allowed limit.     

Physiology engages with functional genomics - at last

A report on the XXXV International Congress of Physiological Sciences, held together with Experimental Biology 2005, San Diego, USA, 31 March - 6 April 2005.     

Measuring the Effect of Inter-Study Variability on Estimating Prediction Error

Background

The biomarker discovery field is replete with molecular signatures that have not translated into the clinic despite ostensibly promising performance in predicting disease phenotypes. One widely cited reason is lack of classification consistency, largely due to failure to maintain performance from study to study. This failure is widely attributed to variability in data collected for the same phenotype among disparate studies, due to technical factors unrelated to phenotypes (e.g., laboratory settings resulting in “batch-effects”) and non-phenotype-associated biological variation in the underlying populations. These sources of variability persist in new data collection technologies.

Methods

Here we quantify the impact of these combined “study-effects” on a disease signature’s predictive performance by comparing two types of validation methods: ordinary randomized cross-validation (RCV), which extracts random subsets of samples for testing, and inter-study validation (ISV), which excludes an entire study for testing. Whereas RCV hardwires an assumption of training and testing on identically distributed data, this key property is lost in ISV, yielding systematic decreases in performance estimates relative to RCV. Measuring the RCV-ISV difference as a function of number of studies quantifies influence of study-effects on performance.

Results

As a case study, we gathered publicly available gene expression data from 1,470 microarray samples of 6 lung phenotypes from 26 independent experimental studies and 769 RNA-seq samples of 2 lung phenotypes from 4 independent studies. We find that the RCV-ISV performance discrepancy is greater in phenotypes with few studies, and that the ISV performance converges toward RCV performance as data from additional studies are incorporated into classification.

Conclusions

We show that by examining how fast ISV performance approaches RCV as the number of studies is increased, one can estimate when “sufficient” diversity has been achieved for learning a molecular signature likely to translate without significant loss of accuracy to new clinical settings.     

Antibody VH and VL recombination using phage and ribosome display technologies reveals distinct structural routes to affinity improvements with VH-VL interface residues providing important structural diversity

In vitro selection technologies are an important means of affinity maturing antibodies to generate the optimal therapeutic profile for a particular disease target. Here, we describe the isolation of a parent antibody, KENB061 using phage display and solution phase selections with soluble biotinylated human IL-1R1. KENB061 was affinity matured using phage display and targeted mutagenesis of VH and VL CDR3 using NNS randomization. Affinity matured VHCDR3 and VLCDR3 library blocks were recombined and selected using phage and ribosome display protocol. A direct comparison of the phage and ribosome display antibodies generated was made to determine their functional characteristics.     

<Emphasis Type="Italic">Escherichia coli</Emphasis> phylogenetic group determination and its application in the identification of the major animal source of fecal contamination

Background  

Escherichia coli strains are commonly found in the gut microflora of warm-blooded animals. These strains can be assigned to one of the four main phylogenetic groups, A, B1, B2 and D, which can be divided into seven subgroups (A₀, A₁, B1, B2₂, B2₃, D₁ and D₂), according to the combination of the three genetic markers chuA, yjaA and DNA fragment TspE4.C2. Distinct studies have demonstrated that these phylo-groups differ in the presence of virulence factors, ecological niches and life-history. Therefore, the aim of this work was to analyze the distribution of these E. coli phylo-groups in 94 human strains, 13 chicken strains, 50 cow strains, 16 goat strains, 39 pig strains and 29 sheep strains and to verify the potential of this analysis to investigate the source of fecal contamination.     

The top-scoring 'N' algorithm: a generalized relative expression classification method from small numbers of biomolecules

ABSTRACT: BACKGROUND: Relative expression algorithms such as the top-scoring pair (TSP) and the top-scoring triplet (TST) have several strengths that distinguish them from other classification methods, including resistance to overfitting, invariance to most data normalization methods, and biological interpretability. The top-scoring 'N' (TSN) algorithm is a generalized form of other relative expression algorithms which uses generic permutations and a dynamic classifier size to control both the permutation and combination space available for classification. RESULTS: TSN was tested on nine cancer datasets, showing statistically significant differences in classification accuracy between different classifier sizes (choices of N). TSN also performed competitively against a wide variety of different classification methods, including artificial neural networks, classification trees, discriminant analysis, k-Nearest neighbor, naive Bayes, and support vector machines, when tested on the Microarray Quality Control II datasets. Furthermore, TSN exhibits low levels of overfitting on training data compared to other methods, giving confidence that results obtained during cross validation will be more generally applicable to external validation sets. CONCLUSIONS: TSN preserves the strengths of other relative expression algorithms while allowing a much larger permutation and combination space to be explored, potentially improving classification accuracies when fewer numbers of measured features are available.