[Show abstract][Hide abstract] ABSTRACT: Stomach contents represent complex mixture systems which depend on feeding mode of forager species (carnivores, herbivores) as well as on natural availability/distribution of food resources (preys, plants). Such mixture systems can be considered as small black boxes condensing wide ecological information on (i) feeding behaviors of predator (or herbivore) and (ii) local diversity of preys (or host plants). Feeding behaviors of a hunter species toward different prey taxa represent a complex variability system whose investigation requires multivariate statistical tools. This paper presents a new computational approach which statistically analyzes stomach contents' variability in a predator population leading to graphically highlight different feeding behaviors. This simulation approach is based on iterated combinations between different diet patterns by using a simplex mixture design. Average combinatorial results are graphically visualized to highlight scale-dependent relationships between consumption rates of different food types found in the stomachs. The simplex approach was applied on different subpopulations of Phrynosoma douglassi brevirostre, an insectivore lizard species. These subpopulations were initially defined by different criteria including statistical clusters, gender and sampling periods. Results highlighted successive trade-offs over months of captured potential preys switching from small and less mobile preys to large and flying ones. In these dietary transitions, P. douglassi manifested a systematic memorization of previous preys and a gradual foraging learning of the next ones. This highlighted lightness on dietary flexibility helping this specialist predator to switch between different potential preys-based diets. Adult male and adult female lizards showed different feeding behaviors due to some predation lag-time between them and different dietary ratios toward the same considered preys.
[Show abstract][Hide abstract] ABSTRACT: Wheat represents a principal ingredient in traditional Tunisian diet including couscous, bread, pasta and biscuits. Northen Tunisia is an important growing area of wheat which after harvest is stored in silos and on farm. The cereal grains can become contaminated by post-harvest moulds during storage in silos under unfavorable conditions leading to a decrease in quality, packing and marketing of wheat. In this study, a mycological survey was undertaken to determine the biodiversity of post-harvest moulds on durum wheat stored in silos localized in five regions of Northern Tunisia and to investigate changes during the storage period. A total of 127 samples were obtained from Oued Mliz, Jendouba, Ksar Mezouar, Mateur and Ghezala silos during 2010–2011 and 2011–2012 wheat seasons. After sampling, seeds were placed on Potato Dextrose Agar medium (PDA) for 7 days of incubation at 28 °C. A total of 6035 strains of filamentous fungi were isolated.
The quantitative and qualitative changes on wheat mycoflora during storage were statistically explored by multivariate methods including correspondence and hierarchical cluster analysis. The most predominant post-harvest moulds genera isolated were Alternaria (28%), Fusarium (19%), Penicillium (19%), Aspergillus (14%), Mucor (8%) and Rhizopus (7%). Various genera of fungi imperfecti, including Ulocladium, Geotrichum, Chaetomium, Trichothecium, Paecilomyces, Aureobasidium and Chrysonilia (anamorphic Neurospora), and the Mucorales genera Lichtheiia and Syncephalastrum accounted for the remainder of about 6% of the total. Statistical data analysis revealed six mycological patterns corresponding to six distinct communities as characterized by the prevalence of different moulds. Such patterns clearly showed different spatio-temporal variability indicating that distribution and evolution of moulds during storage was sensitive to geographic location, year of sampling and short or long-term storage.
Journal of Stored Products Research 10/2013; 55:116–123. · 1.35 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Two new tridesmosidic glycosides of (3β,6α,16β,20R,24S)-20,24-epoxycycloartane-3,6,16,25-tetrol (=cycloastragenol), armatosides I and II (1 and 2, resp.), were isolated from the roots of Astragalus armatus (Fabaceae) as well as the known bidesmosidic glycosides of cycloastragenol, trigonoside II (3) and trojanoside H (4). Their structures were elucidated as (3β,6α,16β,20R,24S)-3-O-(2,3-di-O-acetyl-β-D-xylopyranosyl)-20,24-epoxy-25-O-β-D-glucopyranosyl-6-O-β-D-xylopyranosylcycloartane-3,6,16,25-tetrol (1), and (3β,6α,16β,20R,24S)-3-O-(2-O-acetyl-β-D-xylopyranosyl)-20,24-epoxy-25-O-β-D-glucopyranosyl-6-O-β-D-xylopyranosylcycloartane-3,6,16,25-tetrol (2). These structures were established by extensive NMR and MS analyses and by comparison with literature data.
[Show abstract][Hide abstract] ABSTRACT: Complexity of metabolic systems can be undertaken at different scales (metabolites, metabolic pathways, metabolic network map, biological population) and under different aspects (structural, functional, evolutive). To analyse such a complexity, metabolic systems need to be decomposed into different components according to different concepts. Four concepts are presented here consisting in considering metabolic systems as sets of metabolites, chemical reactions, metabolic pathways or successive processes. From a metabolomic dataset, such decompositions are performed using different mathematical methods including correlation, stiochiometric, ordination, classification, combinatorial and kinetic analyses. Correlation analysis detects and quantifies affinities/oppositions between metabolites. Stoichiometric analysis aims to identify the organisation of a metabolic network into different metabolic pathways on the hand, and to quantify/optimize the metabolic flux distribution through the different chemical reactions of the system. Ordination and classification analyses help to identify different metabolic trends and their associated metabolites in order to highlight chemical polymorphism representing different variability poles of the metabolic system. Then, metabolic processes/correlations responsible for such a polymorphism can be extracted in silico by combining metabolic profiles representative of different metabolic trends according to a weighting bootstrap approach. Finally evolution of metabolic processes in time can be analysed by different kinetic/dynamic modelling approaches.
Current Drug Metabolism 05/2010; 11(4):315-41. · 4.41 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The flexibility of metabolic systems implies a high variability of metabolic profiles linked to different regulation ratios between metabolites. Such regulations are controlled by several interactive metabolic pathways resulting in multidirectional continuums of metabolic profiles. This article presents a new metabolomic approach helping to graphically analyse the flexibility of metabolic regulation systems. Its principle consists in extracting a metabolic backbone from iterative combinations of metabolic profiles representing different metabolic trends. The iterated combinations were performed on the basis of Scheffe matrix then averaged to calculate a response matrix of smoothed metabolic profiles. From such a smoothed matrix, a graphical analysis of relationships between metabolites highlighted different scale-dependent variation paths responsible for the observed metabolic trends. Such a flexibility favouring some metabolites at the expense of others was indirectly checked by a single kinetic approach by considering both the variation of maximal concentrations and the metabolic trends in time. This kinetic approach highlighted a succession of metabolic trends linked to the variation of maximal concentrations in time. Finally, a delayed regulation of a metabolite was highlighted both by the kinetic approach and by a dynamic application of the metabolomic approach. This new approach was illustrated on a dataset of blood concentrations of levodopa and its metabolites analysed in 34 patients at different times.
Chemical Biology & Drug Design 01/2010; 75(1):91-105. · 2.47 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Population pharmacokinetic (PK) (or pharmacodynamic (PD)) modelling aims to analyse the variability of drug kinetics (or dynamics) between numerous subjects belonging to a population. Such variability includes inter- and intra-individual sources leading to important differences between the variation ranges, the relative concentrations and the global shapes of PK profiles. These various sources of variability suggest that the distance metrics between the subjects can be examined under different aspects. Some subjects are so distant from the majority that they tend to be atypical or outliers. This paper presents three multivariate statistical methods to diagnose the outliers within a full population PK dataset, prior to any modelling step. Each method combined all the concentration-time variables to analyse the differences between patients by referring to a distance criterion: (a) Correspondence analysis (CA) used the chi-square distance to highlight the most atypical profiles in terms of relative concentrations; (b) Mahalanobis distance was calculated to extract PK profiles showing atypical shapes due to atypical variations in concentration; (c) Andrews method combined all the concentration variables into a Fourier transformation to give sine-cosine curves showing the clustering behaviours of subjects under the Euclidean distance criterion. After identification of outlier subjects, these methods can also be used to extract the concentration values which cause the atypical states of the patients. Therefore, the outliers will incorporate different variability sources of the PK dataset according to each method and independently of any PK modelling. Finally, a significant positive trend was found between the number of times outlier concentrations were detected (by one, two or three diagnostics) and the NPDE metrics of these concentrations (after a PK modelling): NPDE were highest when the corresponding concentration was detected by more diagnostics a priori. The application of a priori outlier diagnostics is illustrated here on two PK datasets: stimulated cortisol by synacthen and capecitabine administrated orally.
Journal of Pharmacokinetics and Pharmacodynamics 05/2008; 35(2):159-83. · 1.81 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The quantitative assessment of plant diversity and its monitoring with time represent a key environmental issue for management
and conservation of natural resources. Assessment of plant diversity could be based on chemical analyses of secondary metabolites
(e.g. flavonoids, terpenoids), because of the substantial quantitative and qualitative between-individual variability in such
compounds. At a geographical scale, the plant populations become widely dispersed, and their monitoring from numerous routine
individual analyses could become restricting. To overcome such constraint, this study develops a multivariate calibration
model giving the relative frequency of a particular taxon from a simple high-performance liquid chromatography (HPLC) analysis
of a plant mixture. The model was built from a complete set of mixtures combining different taxons, according to an experimental
design (Scheffé’s matrix). For each mixture, a reference HPLC pattern was simulated by averaging the individual HPLC profiles
of the constitutive taxons. The calibration models, based on Bayesian discriminant analysis (BDA), gave statistical relationships
between the contributions of each taxon in mixtures and reference HPLC patterns of these mixtures. Finally, these models were
validated on new mixtures by using outside plants. This new biodiversity survey approach is illustrated on four chemical taxons
(four chemotypes) of Astragalus caprinus (Fabaceae). The more differentiated the taxon, the better predicted its contributions (in mixtures) were by BDA calibration
model. This new approach could be very useful for a global routine survey of plant diversity.
Environmental Modeling and Assessment 01/2008; 13(1):17-33. · 0.98 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: HPLC analysis of secondary metabolites represents an efficient tool for the studying of plant chemical diversity under different
aspects: chemotaxonomy, metabolomics, adaptative responses to ecological factors, etc. Statistical analyses of HPLC databases,
e.g. correlation analysis between HPLC peaks, can reliably provide information on the similarity/dissimilarity degrees between
the chemical compounds. The similarities, corresponding to positive correlations, can be interpreted in terms of analogies
between chemical structures, synchronic metabolisms or co-evolution of two compounds under certain environment conditions,
etc. . In terms of metabolism, positive correlations can translate precursor-product relationships between compounds; negative
correlations can be indicative of competitive processes between two compounds for a common precursor(s), enzyme(s) or substrate(s).
Furthermore, the correlation analysis under a metabolic aspect can help to understand the biochemical origins of an observed
polymorphism in a plant species. With the aim of showing this, we present a new approach based on a simplex mixture design,
Scheffé matrix, which provides a correlation network making it possible to graphically visualise and to numerically model
the metabolic trends between HPLC peaks. The principle of the approach consisted in mixing individual HPLC profiles representative
of different phenotypes, then from a complete mixture set, a series of average profiles were calculated to provide a new database
with a small variability. Several iterations of the mixture design provided a smoothed final database from which the relationships
between the secondary metabolites were graphically and numerically analysed. These relationships were scale-dependent, namely
either deterministic or systematic: the first consisted of a monotonic global trend covering the whole variation field of
each metabolites’ pair; the second consisted of repetitive monotonic variations which gradually attenuated or intensified
along a global trend. This new metabolomic approach was illustrated from 404 individual plants of Astragalus caprinus (Leguminoseae), belonging to four chemical phenotypes (chemotypes) on the basis of flavonoids analysed in their leaves. After
smoothing, the relationships between flavonoids were numerically fitted using linear or polynomial models; therefore the co-response
coefficients were easily interpreted in terms of metabolic affinities or competitions between flavonoids which would be responsible
of the observed chemical polymorphism (the four chemotypes). The statistical validation of the approach was carried out by
comparing Pearson correlations to Spearman correlations calculated from the smoothed and the crude HPLC database, respectively.
Moreover, the signs of the smoothed relationships were finely supported by analogies and differences between the chemical
structures of flavonoids, leading to fluent interpretation in relation to the pathway architecture.
[Show abstract][Hide abstract] ABSTRACT: A novel oleanane-type triterpene saponin (1) together with two known molecules, soyasapogenol B and astragaloside VIII were isolated from the roots of Astragalus caprinus. Their structural elucidation was performed mainly by 2D NMR techniques (COSY, TOCSY, NOESY, HSQC, HMBC) and mass spectrometry. Compound 1 was determined as 3-O-[alpha-L-rhamnopyranosyl-(1 --> 2)-beta-D-glucuronopyranosyl]-22-O-beta-D-apiofuranosyl-soyasapogenol B.
Magnetic Resonance in Chemistry 07/2006; 44(7):713-6. · 1.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The pharmacokinetics of corticosteroids provides a large set of mathematical models which led to analyse many kinetic profiles corresponding to many clinical and/or physiological situations. In this paper, we present a review on the usefulness, advantages and limits of such models which could find a large application in medicinal chemistry.
Mini Reviews in Medicinal Chemistry 05/2006; 6(4):417-28. · 2.87 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: To be analyzed, the heterogeneity characterizing biological data calls for using appropriate models involving numerous variables. A high variable number could become problematic when one needs to determine a priori the most significant variable combination in order to reduce the inter-individual variability (IIV). Alternatively to multiple introductions of single variables, we propose a single introduction of a multivariate variable. We present cluster analysis as a stratification strategy that combines the initial single covariates to build a multivariate categorical covariate. It is an exploratory multivariate analysis that outlines homogeneous categories of individuals (clusters) according to similarities from the set of covariates. It includes many clustering techniques combining a distance measure and a linkage algorithm, and leading to various stratification patterns. The cluster analysis approach is illustrated by a case study on cortisol kinetics in 82 patients after intravenous bolus administration of synacthen (synthetic corticotropin). Using NONMEM, a basic infusion model was initially achieved for cortisol, and then a classical covariate selection was applied to improve IIV. The best fit was between the elimination rate constant k and the body mass index (BMI), which improved IIV of k. An alternative method is presented consisting in the population into homogeneous and non-overlapping groups by applying a cluster analysis. Such categorization (or clustering) was carried out using Euclidean distance and complete-linkage algorithm. This algorithm gave five dissimilar clusters that differed by increasing BMI, obesity duration, and waist-hip ratio. The dispersion of k according to the five clusters showed three distinctvariation ranges a priori, which corresponded a posteriori(after NONMEM modeling) to three sub-populations of k. After grouping the clusters that had similar variation ranges of k, we obtained three final clusters representing non-obese, intermediate, and extreme obese sub-populations. The pharmacokinetic model based on three clusters was better than the basic model, similar to the classical covariate model, but had a stronger interpretability: It showed that the stimulation and elimination of cortisol were higher in the extreme obese followed by intermediate then non-obese subjects.
Journal of Pharmacokinetics and Pharmacodynamics 09/2005; 32(3-4):333-58. · 1.81 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In previous work, 14 flavonol glycosides were characterised in the leaves of Astragalus caprinus, an endemic Fabaceae, widely distributed in North West Africa. These compounds included quercetin and kaempferol derivatives, glycosylated by two, three or four sugars with an aliphatic or aromatic acyl moiety. In this paper, we describe the flavonoid patterns of 404 individual plants investigated by HPLC and subjecting the resulting data to cluster analysis. This analysis highlighted four chemotypes associated with a high accumulation of a kaempferol triglycoside, a kaempferol tetraglycoside with its acylated derivatives, two quercetin tetraglycoside derivatives and methylated diglycoside derivatives, respectively. A discriminant analysis validated the chemotaxonomic value of the four chemical polymorphisms. These chemotypes were more or less abundant according to geographical sites which corresponded to different climatic conditions.
[Show abstract][Hide abstract] ABSTRACT: A new flavonol tetraglycoside, together with four acylated derivatives, were isolated from the leaves of Astragalus caprinus. Their structures were elucidated by spectroscopic methods, mainly 2D NMR, as kaempferol-3-O-[[beta-D-xylopyranosyl(1-->3)-alpha-L-rhamnopyranosyl(1-->6)][alpha-L-rhamnopyranosyl(1-->2)]]-beta-D-galactopyranoside (1), its 3(Gal)-p-coumaric (2) and 3(Gal)-ferulic (3) esters, and its 4(Gal)-p-coumaric (4) and 4(Gal)-ferulic (5) esters.
CHEMICAL & PHARMACEUTICAL BULLETIN 07/2002; 50(7):981-4. · 1.56 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Four new flavonol 3-O-glycosides were isolated from the leaves of Astragalus caprinus. Their structures were elucidated by spectroscopic methods as rhamnocitrin-3-O-[3-hydroxy-3-methylglutaroyl(1-->6)][beta-D-apiofuranosyl(1-->2)]-beta-D-galactopyranoside (1), rhamnetin-3-O-[3-hydroxy-3-methylglutaroyl(1-->6)][beta-D-apiofuranosyl(1-->2)]-beta-D-galactopyranoside (2), kaempferol-3-O-[beta-D-xylopyranosyl(1-->3)-alpha-L-rhamnopyranosyl(1-->6)]-beta-D-galactopyranoside (3), and quercetin-3-O-[beta-D-xylopyranosyl(1-->3)-alpha-L-rhamnopyranosyl(1-->6)][beta-D-apiofuranosyl(1-->2)]-beta-D-galactopyranoside (4).
Journal of Natural Products 04/2002; 65(4):576-9. · 3.29 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Flavonoid glycosides of Astragalus caprinus (Leguminosae) were investigated; more than 30 glycosides were found in leaf material, based on the aglycones kaempferol, quercetin and their methylated derivatives. Among them 14 compounds were found in significant amounts and showed a contrasting distribution. They could be ordered into three groups: polyglycosides, acylated polyglycosides and methylated polyglycosides. The distribution of these compounds was studied within a large collection of individual plants harvested in Tunisia; the results showed a relationship between metabolic trends and ecological diversification.
Biochemical Systematics and Ecology 08/2001; 29(7):727-738. · 1.15 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: A new glycoside of flavonol (1) and a new glycoside of a cycloartane-type triterpene (2) were isolated from the leaves and the roots of Astragalus caprinus, respectively. Their structures were elucidated in turn by spectroscopic data interpretation as 3-O-[[beta-D-xylopyranosyl(1-->3)-alpha-L-rhamnopyranosyl(1-->6)][beta-D-apiofuranosyl(1-->2)]]-beta-D-galactopyranosyl kaempferol (1) and 3-O-(beta-D-xylopyranosyl)-24-O-(beta-D-glucopyranosyl)-20,25-epoxycycloartane-3beta,6alpha,16beta,24alpha-tetrol (2).
Journal of Natural Products 05/2001; 64(5):656-8. · 3.29 Impact Factor