Gait characterization in golden retriever muscular dystrophy dogs using linear discriminant analysis.

BACKGROUND: Accelerometric analysis of gait abnormalities in golden retriever muscular dystrophy (GRMD) dogs is of limited sensitivity, and produces highly complex data. The use of discriminant analysis may enable simpler and more sensitive evaluation of treatment benefits in this important preclinical model. METHODS: Accelerometry was performed twice monthly between the ages of 2 and 12 months on 8 healthy and 20 GRMD dogs. Seven accelerometric parameters were analysed using linear discriminant analysis (LDA). Manipulation of the dependent and independent variables produced three distinct models. The ability of each model to detect gait alterations and their pattern change with age was tested using a leave-one-out cross-validation approach. RESULTS: Selecting genotype (healthy or GRMD) as the dependent variable resulted in a model (Model 1) allowing a good discrimination between the gait phenotype of GRMD and healthy dogs. However, this model was not sufficiently representative of the disease progression. In Model 2, age in months was added as a supplementary dependent variable (GRMD_2 to GRMD_12 and Healthy_2 to Healthy_9.5), resulting in a high overall misclassification rate (83.2%). To improve accuracy, a third model (Model 3) was created in which age was also included as an explanatory variable. This resulted in an overall misclassification rate lower than 12%. Model 3 was evaluated using blinded data pertaining to 81 healthy and GRMD dogs. In all but one case, the model correctly matched gait phenotype to the actual genotype. Finally, we used Model 3 to reanalyse data from a previous study regarding the effects of immunosuppressive treatments on muscular dystrophy in GRMD dogs. Our model identified significant effect of immunosuppressive treatments on gait quality, corroborating the original findings, with the added advantages of direct statistical analysis with greater sensitivity and more comprehensible data representation. CONCLUSIONS: Gait analysis using LDA allows for improved analysis of accelerometry data by applying a decision-making analysis approach to the evaluation of preclinical treatment benefits in GRMD dogs.

Discrimination of diesel fuels marketed in Morocco using FTIR, GC-MS analysis and chemometrics methods.

The purpose of this study was to perform a discrimination and classification of diesel samples from the four major suppliers of petroleum products in Morocco using Fourier Transform Infrared Spectroscopy (FTIR), Gas Chromatography coupled with Mass Spectrometry (GC-MS) and chemometrics tools. Eighty diesel samples were collected from different gas stations owned by the four biggest brands in the Moroccan market. Principal Component Analysis (PCA) was performed to depict the similarities between the samples and check the presence of outliers. Partial Least Squares Discriminant Analysis (PLS-DA) models were set up for the discrimination and the classification of the four groups of samples (i.e., diesel suppliers). The models proposed in this study, were characterized by good prediction abilities, especially the FTIR-PLSDA model that was characterized by 100% of accurate discrimination of the four groups. The approach of analysis showed that the FTIR spectra can provide a cheap and rapid means for the determination of the diesel origin and to ensure the traceability of diesel products marketed in Morocco with respect for the rules of the green chemistry.

Hierarchical classification of microorganisms based on high-dimensional phenotypic data.

The classification of microorganisms by high-dimensional phenotyping methods such as FTIR spectroscopy is often a complicated process due to the complexity of microbial phylogenetic taxonomy. A hierarchical structure developed for such data can often facilitate the classification analysis. The hierarchical tree structure can either be imposed to a given set of phenotypic data by integrating the phylogenetic taxonomic structure or set up by revealing the inherent clusters in the phenotypic data. In this study, we wanted to compare different approaches to hierarchical classification of microorganisms based on high-dimensional phenotypic data. A set of 19 different species of molds (filamentous fungi) obtained from the mycological strain collection of the Norwegian Veterinary Institute (Oslo, Norway) is used for the study. Hierarchical cluster analysis is performed for setting up the classification trees. Classification algorithms such as artificial neural networks (ANN), partial least-squared discriminant analysis and random forest (RF) are used and compared. The 2 methods ANN and RF outperformed all the other approaches even though they did not utilize predefined hierarchical structure. To our knowledge, the RF approach is used here for the first time to classify microorganisms by FTIR spectroscopy.

Maternal protein restriction during lactation induces early and lasting plasma metabolomic and hepatic lipidomic signatures of the offspring in a rodent programming model.

Perinatal undernutrition affects not only fetal and neonatal growth but also adult health outcome, as suggested by the metabolic imprinting concept. However, the exact mechanisms underlying offspring metabolic adaptations are not yet fully understood. Specifically, it remains unclear whether the gestation or the lactation is the more vulnerable period to modify offspring metabolic flexibility. We investigated in a rodent model of intrauterine growth restriction (IUGR) induced by maternal protein restriction (R) during gestation which time window of maternal undernutrition (gestation, lactation or gestation-lactation) has more impact on the male offspring metabolomics phenotype. Plasma metabolome and hepatic lipidome of offspring were characterized through suckling period and at adulthood using liquid chromatography-high-resolution mass spectrometry. Multivariate analysis of these fingerprints highlighted a persistent metabolomics signature in rats suckled by R dams, with a clear-cut discrimination from offspring fed by control (C) dams. Pups submitted to a nutritional switch at birth presented a metabolomics signature clearly distinct from that of pups nursed by dams maintained on a consistent perinatal diet. Control rats suckled by R dams presented transiently higher branched-chain amino acid (BCAA) oxidation during lactation besides increased fatty acid (FA) ß-oxidation, associated with preserved insulin sensitivity and lesser fat accretion that persisted throughout their life. In contrast, IUGR rats displayed permanently impaired ß-oxidation, associated to increased glucose or BCAA oxidation at adulthood, depending on the fact that pups experienced slow postnatal or catch-up growth, as suckled by R or C dams, respectively. Taken together, these findings provide evidence for a significant contribution of the lactation period in metabolic programming.

Breast Milk Lipidome Is Associated with Early Growth Trajectory in Preterm Infants.

Human milk is recommended for feeding preterm infants. The current pilot study aims to determine whether breast-milk lipidome had any impact on the early growth-pattern of preterm infants fed their own mother's milk. A prospective-monocentric-observational birth-cohort was established, enrolling 138 preterm infants, who received their own mother's breast-milk throughout hospital stay. All infants were ranked according to the change in weight Z-score between birth and hospital discharge. Then, we selected infants who experienced "slower" (n = 15, -1.54 ± 0.42 Z-score) or "faster" (n = 11, -0.48 ± 0.19 Z-score) growth; as expected, although groups did not differ regarding gestational age, birth weight Z-score was lower in the "faster-growth" group (0.56 ± 0.72 vs. -1.59 ± 0.96). Liquid chromatography-mass spectrometry lipidomic signatures combined with multivariate analyses made it possible to identify breast-milk lipid species that allowed clear-cut discrimination between groups. Validation of the selected biomarkers was performed using multidimensional statistical, false-discovery-rate and ROC (Receiver Operating Characteristic) tools. Breast-milk associated with faster growth contained more medium-chain saturated fatty acid and sphingomyelin, dihomo-γ-linolenic acid (DGLA)-containing phosphethanolamine, and less oleic acid-containing triglyceride and DGLA-oxylipin. The ability of such biomarkers to predict early-growth was validated in presence of confounding clinical factors but remains to be ascertained in larger cohort studies.

Understanding aroma release from model cheeses by a statistical multiblock approach on oral processing.

For human beings, the mouth is the first organ to perceive food and the different signalling events associated to food breakdown. These events are very complex and as such, their description necessitates combining different data sets. This study proposed an integrated approach to understand the relative contribution of main food oral processing events involved in aroma release during cheese consumption. In vivo aroma release was monitored on forty eight subjects who were asked to eat four different model cheeses varying in fat content and firmness and flavoured with ethyl propanoate and nonan-2-one. A multiblock partial least square regression was performed to explain aroma release from the different physiological data sets (masticatory behaviour, bolus rheology, saliva composition and flux, mouth coating and bolus moistening). This statistical approach was relevant to point out that aroma release was mostly explained by masticatory behaviour whatever the cheese and the aroma, with a specific influence of mean amplitude on aroma release after swallowing. Aroma release from the firmer cheeses was explained mainly by bolus rheology. The persistence of hydrophobic compounds in the breath was mainly explained by bolus spreadability, in close relation with bolus moistening. Resting saliva poorly contributed to the analysis whereas the composition of stimulated saliva was negatively correlated with aroma release and mostly for soft cheeses, when significant.

Prediction of the PCDD/F and dl-PCB 2005-WHO-TEQ content based on the contribution of six congeners: toward a new screening approach for fish samples?

Current European Union regulation regarding polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans (PCDD/Fs) and dioxin-like polychlorinated biphenyls (dl-PCBs) in food and feed is based on Toxic Equivalent Quotient (TEQ) concept. For confirmatory purpose, the isotope-dilution method associated to a measurement by gas chromatography coupled with high resolution mass spectrometry is usually the method of choice for precisely measuring the 29 target congeners in three separated fractions. Time and cost related to these analyses are very significant. Various kinds of screening concepts can be considered. In the present study, we elaborated and validated a prediction model for the 2005 World Health Organization TEQ in fish, based on the measurement of 4 PCDD/F and 2 non-ortho dl-PCB congeners, potentially analyzable in a single extracted fraction by gas chromatography coupled with mass spectrometry. Large independent datasets have been used for model elaboration (n=108) and validation (n=363, n=357 and n=6).

Studying variations in the PCDD/PCDF profile across various food products using multivariate statistical analysis.

Polychlorinated dibenzo-p-dioxins (PCDD) and polychlorinated dibenzofurans (PCDF) are widely recognized by the scientific community as persistent organic pollutants due to their toxicity and adverse effects on wildlife and human health. The actual regulation dedicated to the monitoring of dioxins in food is based on the measurement of 17 congener concentrations. The final result is reported as a toxic equivalent value that takes into account the relative toxicity of each congener. This procedure can minimize the qualitative information available from the abundances of each PCDD/PCDF congener: the characteristic contamination profile of the sample. Multivariate statistical techniques, such as principal component analysis (PCA) or linear discriminant analysis (LDA), represent an interesting way to investigate this qualitative information. Nevertheless, they have only been applied to the analysis of contamination data from food products and biological matrices infrequently. The objective of the present study was to analyze a large data set from dioxin analyses performed on various food products of animal origin. The results demonstrate the existence of differences in congener-specific patterns between the analyzed samples. Variability was first demonstrated in terms of the food type (fish, meat, milk, fatty products). Then a variability was observed that was related to the specific animal species for meat and milk samples (bovine, ovine, porcine, caprine and poultry). Some practical applications of these results are discussed. The origin(s) of the observed differences, as well as their significance, now remain to be investigated, both in terms of environmental factors and transfer through living organisms. A better knowledge of the relation between a contamination profile and its specific source and/or food product should be of great interest to scientists working in the fields of contaminant analysis, toxicology and metabolism, as well as to regulatory bodies and risk assessors in charge of final decisions regarding the eventual hazards associated with theses substances.

Multidimensional statistical analysis applied to electron ionization mass spectra to determine steroid stereochemistry.

The differentiation of stereoisomers on the basis of their mass spectra only is usually a difficult challenge even when an informative ionization technique such as electron ionization is used; this is particularly the case for steroids. In this study, multivariate statistical techniques have been applied to the mass spectra of derivatized 5xi-androstane-3xi,17xi-diols (xi = alpha,beta) in order to investigate the possibility of discrimination among the different isomers. After collection of the data from the mass spectra (20 replicates for each of the 8 isomers), each ion was considered as a statistical variable and each mass spectrum as an observation. The more discriminative variables (42 out of the 160 initial ones) were selected using the analysis of variance technique (ANOVA). Thereafter, a linear discriminant analysis (LDA) allowed us to set up a predictive model for stereochemistry determination. The two-dimensional graphical display of the 160 observations on the basis of the canonical variables derived from LDA made it possible to separate the eight isomers. The discrimination of 5alpha and 5beta isomers as well as 3alpha and 3beta was unambiguous, whereas, the discrimination of 17alpha and 17beta epimers was less obvious. The robustness of the model was checked with 40 mass spectra recorded over a 6-month period on different quadrupole mass spectrometers and under different signal acquisition conditions. The percentage of correct assignment of these 'unknown' stereoisomers was 92%; only three 17alpha and 17beta epimers were not correctly plotted in the expected zone. Nevertheless, the performance score was better than those observed with traditional mass spectral libraries. Furthermore, this statistical approach allowed us to identify the main fragment ions involved in the discrimination between isomers: m/z 256 and 421 for isomers 5a-5b; m/z 241 and 331 for isomers 5alpha3alpha-5alpha3beta; m/z 143 and 162 for isomers 5beta3alpha-5beta3beta; and m/z 255 for epimers 17alpha-17beta.