Tailored Microarray Platform for the Detection of Marine Toxins

Currently, there are no fast in vitro broad spectrum screening bioassays for the detection of marine toxins. The aim of this study was to develop such an assay. In gene expression profiling experiments 17 marker genes were provisionally selected that were differentially regulated in human intestinal Caco-2 cells upon exposure to the lipophilic shellfish poisons azaspiracid-1 (AZA1) or dinophysis toxin-1 (DTX1). These 17 genes together with two control genes were the basis for the design of a tailored microarray platform for the detection of these marine toxins and potentially others. Five out of the 17 selected marker genes on this dedicated DNA microarray gave dear signals, whereby the resulting fingerprints could be used to detect these toxins. CEACAM1, DDIT4, and TUBB3 were up-regulated by both AZA1 and DTX1, TRIB3 was up-regulated by AZA1 only, and OSR2 by DTX1 only. Analysis by singleplex qRT-PCR revealed the up- and down-regulation of the selected RGS16 and NPPB marker genes by DTX1, that were not envisioned by the new developed dedicated array. The qRT-PCR targeting the DDIT4, RSG16 and NPPB genes thus already resulted in a specific pattern for AZA1 and DTX1 indicating that for this specific case qRT-PCR might a be more suitable approach than a dedicated array.

A novel toxicity fingerprinting method for pollutant identification with lux-marked biosensors

A novel technique is described for the identification and quantification of environmental pollutants based on toxicity fingerprinting with a metabolic lux-marked bacterial biosensor. This method involved characterizing the toxicity-based responses of the biosensor to seven calibration pollutants as acute temporal-dose response fingerprints. An algorithm is described to allow comparisons of responses of an unknown pollutant to be made against the calibration data. This is based on predicting pollutant concentration at each of six different time points over the course of a 5-min assay. If the prediction is consistent between the unknown pollutant and a calibration pollutant at the 95% test level, this is considered to be a positive identification. All seven calibration pollutants could be successfully distinguished from each other with this technique. Environmental samples, individually spiked with single concentrations of pollutants, were compared in this way against the calibration pollutants. An 83% identification success was achieved, with no false positives at the 95% test level. This is a simple and rapid technique that potentially can be applied to monitoring of industrial wastewater or as a screening tool for regulators.

Off-center explosions of Chandrasekhar-mass white dwarfs:An explanation of super-bright type la supernovae?

Context. The recent discovery of a very bright type la supernova, SNLS-03D3bb (=SN 2003fg), in the Supernova Legacy Survey (SNLS) has raised the question of whether super-Chandrasekhar-mass white-dwarf stars are needed to explain such bright explosions. Progenitors of this sort could form by mergers of pairs of rather massive white dwarfs. Binary systems of two white dwarfs in close orbit, where their total mass significantly exceeds the Chandrasekhar mass, have not yet been found. Therefore SNLS-03D3bb could establish the first clear case of a double-degenerate progenitor of a (peculiar) type la supernovae. Moreover, if this interpretation is correct, it casts some doubt on the universality of the calibration relations used to make SNe la distance indicators for cosmology. Aims. We aim to evaluate the case for a super-Chandrasekhar-mass progenitor for SNLS-03D3bb in light of previous theoretical work on super-Chandrasekhar-mass explosions. Furthermore, we propose an alternative scenario involving only a Chandrasekhar-mass progenitor. Methods. We present a theoretically motivated critical discussion of the expected observational fingerprints of super-Chandrasekharmass explosions. As an alternative, we describe a simple class of aspherical Chandrasekhar-mass models in which the products of nuclear burning are displaced from the center. We then perform simple radiative transfer calculations to predict synthetic lightcurves for one such off-center explosion model. Results. In important respects, the expected observational consequences of super-Chandrasekhar-mass explosions are not consistent with the observations of SNLS-03D3bb. We demonstrate that the lopsided explosion of a Chandrasekhar-mass white dwarf could provide a better explanation. © ESO 2007.

Metabolomic Profiling of In Vivo Plasma Responses to DioxinAssociated Dietary Contaminant Exposure in Rats: Implications for Identification of Sources of Animal and Human Exposure

Dioxin contamination of the food chain typically occurs when cocktails of combustion residues or polychlorinated biphenyl (PCB) containing oils become incorporated into animal feed. These highly toxic compounds are bioaccumulative with small amounts posing a major health risk. The ability to identify animal exposure to these compounds prior to their entry into the food chain may be an invaluable tool to safeguard public health. Dioxin-like compounds act by a common mode of action and this suggests that markers or patterns of response may facilitate identification of exposed animals. However, secondary co-contaminating compounds present in typical dioxin sources may affect responses to compounds. This study has investigated for the first time the potential of a metabolomics platform to distinguish between animals exposed to different sources of dioxin contamination through their diet. Sprague-Dawley rats were given feed containing dioxin-like toxins from hospital incinerator soot, a common PCB oil standard and pure 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (normalized at 0.1 µg/kg TEQ) and acquired plasma was subsequently biochemically profiled using ultra high performance liquid chromatography (UPLC) quadropole time-of-flight-mass spectrometry (QTof-MS). An OPLS-DA model was generated from acquired metabolite fingerprints and validated which allowed classification of plasma from individual animals into the four dietary exposure study groups with a level of accuracy of 97-100%. A set of 24 ions of importance to the prediction model, and which had levels significantly altered between feeding groups, were positively identified as deriving from eight identifiable metabolites including lysophosphatidylcholine (16:0) and tyrosine. This study demonstrates the enormous potential of metabolomic-based profiling to provide a powerful and reliable tool for the detection of dioxin exposure in food-producing animals.

Development of a novel continuous statistical modelling technique for detecting the adulteration of extra virgin olive oil with hazelnut oil by using spectroscopic data

The adulteration of extra virgin olive oil with other vegetable oils is a certain problem with economic and health consequences. Current official methods have been proved insufficient to detect such adulterations. One of the most concerning and undetectable adulterations with other vegetable oils is the addition of hazelnut oil. The main objective of this work was to develop a novel dimensionality reduction technique able to model oil mixtures as a part of an integrated pattern recognition solution. This final solution attempts to identify hazelnut oil adulterants in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. The proposed Continuous Locality Preserving Projections (CLPP) technique allows the modelling of the continuous nature of the produced in house admixtures as data series instead of discrete points. This methodology has potential to be extended to other mixtures and adulterations of food products. The maintenance of the continuous structure of the data manifold lets the better visualization of this examined classification problem and facilitates a more accurate utilisation of the manifold for detecting the adulterants.

Major and trace elements in milk and Halloumi cheese as markers for authentication of goat feeding regimes and geographical origin

Sixty samples of milk, Halloumi cheese and local grazing plants (i.e. shrubs) were collected over a year from dairy farms located on three different locations of Cyprus. Major and trace elements were quantified using inductively coupled plasma-atomic emission spectroscopy (ICP-AES). Milk and Halloumi cheese produced in different geographical locations presented significant differences in the concentration of some of the elements analysed. Principal component analysis showed grouping of samples according to the region of production for both milk and cheese samples. These findings show that the assay of elements can provide useful fingerprints for the characterisation of dairy products.

Partial Palmprint Matching Using Invariant Local Minutiae Descriptors

In forensic investigations, it is common for forensic investigators to obtain a photograph of evidence left at the scene of crimes to aid them catch the culprit(s). Although, fingerprints are the most popular evidence that can be used, scene of crime officers claim that more than 30% of the evidence recovered from crime scenes originate from palms. Usually, palmprints evidence left at crime scenes are partial since very rarely full palmprints are obtained. In particular, partial palmprints do not exhibit a structured shape and often do not contain a reference point that can be used for their alignment to achieve efficient matching. This makes conventional matching methods based on alignment and minutiae pairing, as used in fingerprint recognition, to fail in partial palmprint recognition problems. In this paper a new partial-to-full palmprint recognition based on invariant minutiae descriptors is proposed where the partial palmprint’s minutiae are extracted and considered as the distinctive and discriminating features for each palmprint image. This is achieved by assigning to each minutiae a feature descriptor formed using the values of all the orientation histograms of the minutiae at hand. This allows for the descriptors to be rotation invariant and as such do not require any image alignment at the matching stage. The results obtained show that the proposed technique yields a recognition rate of 99.2%. The solution does give a high confidence to the judicial jury in their deliberations and decision.

Genetic diversity of entomopathogenic nematodes (Nematoda: Steinernematidae and Heterorhabditidae) and the nematode Bursaphelenchus xylophilus (Nematoda: Aphelenchoididae) from continental Portugal

“Diversidade genética dos nemátodes entomopatogénicos (Nematoda: Steinernematidae e Heterorhabditidae) e do nemátode Bursaphelenchus xylophilus (Nematoda: Aphelenchoididade) em Portugal continental” Os nematodes entomopatogénicos são utilizados como agentes de controlo biológico. Para compreender a sua diversidade, foi realizada uma prospecção em Portugal. Cinco espécies, nomeadamente Steinernema feltiae, S. intermedium, S. kraussei, Steinernema sp. e Heterorhabditis bacteriophora foram identificadas. As sequências de ITS, região D2D3 do 28S rRNA, COXI e cytb foram utilizadas para estudar a diversidade genética das duas espécies mais abundantes, S. feltiae and H. bacteriophora, não tendo sido encontradas diferenças significativas entre isolados. O nemátode da madeira do pinheiro, Bursaphelenchus xylophilus, provoca doença nos pinheiros tendo sido detectada pela primeira vez na Europa e em Portugal em 1999. Para avaliar a diversidade genética dos isolados Portugueses e identificar o padrão de propagação da doença, foram utilizadas a sequência da região IGS do 5.8S rRNA, e os genes cytb e cellulase, combinados com os padrões ISSR. Os padrões de ISSR mostraram elevada diversidade genética entre os recentes isolados Portugueses, sugerindo a possibilidade de uma nova introdução. As árvores filogenéticas dos genes da celulase e cytb sugeriram uma origem Asiática para os isolados Portugueses; ABSTRACT: Entomopathogenic nematodes are used as biocontrol agents. To understand their diversity, a survey was undertaken in Portugal. Five species, namely Steinernema feltiae, S. intermedium, S. kraussei, Steinernema sp. and Heterorhabditis bacteriophora were identified. The ITS, 28S rRNA D2D3 region, COXI and cytb sequences, used to study the genetic diversity of the two most abundant species, S. feltiae and H. bacteriophora, showed no significant differences among the isolates. Bursaphelenchus xylophilus causes severe disease in pine trees and was detected for the first time in Europe and in Portugal in 1999. To evaluate the genetic diversity of Portuguese isolates and identify disease spread pathways, the sequence of 5.8S rRNA IGS region, cytb and cellulase genes, combined with ISSR fingerprints were used. ISSR fingerprints show a high genetic variability among recent Portuguese isolates, suggesting the possibility of a new introduction. Phylogenetic trees based on cellulase and cytb genes suggests an Asian origin for Portuguese isolates.

Development of infrared spectroscopy for assessing bacterial quality in foods

Rapid and specific detection of foodborne bacteria that can cause food spoilage or illness associated to its consumption is an increasingly important task in food industry. Bacterial detection, identification, and classification are generally performed using traditional methods based on biochemical or serological tests and the molecular methods based on DNA or RNA fingerprints. However, these methodologies are expensive, time consuming and laborious. Infrared spectroscopy is a reliable, rapid, and economic technique which could be explored as a tool for bacterial analysis in the food industry. In this thesis it was evaluated the potential of IR spectroscopy to study the bacterial quality of foods. In Chapter 2, it was developed a calibration model that successfully allowed to predict the bacterial concentration of naturally contaminated cooked ham samples kept at refrigeration temperature during 8 days. In this part, it was developed the methodology that allowed the best reproducibility of spectra from bacteria colonies with minimal sample preparation, which was used in the subsequent work. Several attempts trying different resolutions and number of scans in the IR were made. A spectral resolution of 4 cm-1, with 32 scans were the settings that allowed the best results. Subsequently, in Chapter 3, it was made an attempt to identify 22 different foodborne bacterial genera/species using IR spectroscopy coupled with multivariate analysis. The principal component analysis, used as an exploratory technique, allowed to form distinct groups, each one corresponding to a different genus, in most of the cases. Then, a hierarchical cluster analysis was performed to further analyse the group formation and the possibility of distinction between species of the same bacterial genus. It was observed that IR spectroscopy not only is suitable to the distinction of the different genera, but also to differentiate species of the same genus, with the simultaneous use of principal component analysis and cluster analysis techniques. The utilization of IR spectroscopy and multivariate statistical analysis were also investigated in Chapter 4, in order to confirm the presence of Listeria monocytogenes and Salmonella spp. isolated from contaminated foods, after growth in selective medium. This would allow to substitute the traditional biochemical and serological methods that are used to confirm these pathogens and that delay the obtainment of the results up to 2 days. The obtained results allowed the distinction of 3 different Listeria species and the distinction of Salmonella spp. from other bacteria that can be mistaken with them. Finally, in chapter 5, high pressure processing, an emerging methodology that permits to produce microbiologically safe foods and extend their shelf-life, was applied to 12 foodborne bacteria to determine their resistance and the effects of pressure in cells. A treatment of 300 MPa, during 15 minutes at room temperature was applied. Gram-negative bacteria were inactivated to undetectable levels and Gram-positive showed different resistances. Bacillus cereus and Staphylococcus aureus decreased only 2 logs and Listeria innocua decreased about 5 logs. IR spectroscopy was performed in bacterial colonies before and after HPP in order to investigate the alterations of the cellular compounds. It was found that high pressure alters bands assigned to some cellular components as proteins, lipids, oligopolysaccharides, phosphate groups from the cell wall and nucleic acids, suggesting disruption of the cell envelopes. In this work, bacterial quantification and classification, as well as assessment of cellular compounds modification with high pressure processing were successfully performed. Taking this into account, it was showed that IR spectroscopy is a very promising technique to analyse bacteria in a simple and inexpensive manner.

A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose

Food product safety is one of the most promising areas for the application of electronic noses. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a fuzzy-wavelet neural network model which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modeling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results indicated that the proposed modeling scheme could be considered as a valuable detection methodology in food microbiology

A Rapid Detection of Meat Spoilage using an Electronic Nose and Fuzzy-Wavelet systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. The proposed model incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results against neural networks and neurofuzzy systems indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage

Identification of pyridine analogs as new predator-derived kairomones.

In the wild, animals have developed survival strategies relying on their senses. The individual ability to identify threatening situations is crucial and leads to increase in the overall fitness of the species. Rodents, for example have developed in their nasal cavities specialized olfactory neurons implicated in the detection of volatile cues encoding for impending danger such as predator scents or alarm pheromones. In particular, the neurons of the Grueneberg ganglion (GG), an olfactory subsystem, are implicated in the detection of danger cues sharing a similar chemical signature, a heterocyclic sulfur- or nitrogen-containing motif. Here we used a "from the wild to the lab" approach to identify new molecules that are involuntarily emitted by predators and that initiate fear-related responses in the recipient animal, the putative prey. We collected urines from carnivores as sources of predator scents and first verified their impact on the blood pressure of the mice. With this approach, the urine of the mountain lion emerged as the most potent source of chemical stress. We then identified in this biological fluid, new volatile cues with characteristic GG-related fingerprints, in particular the methylated pyridine structures, 2,4-lutidine and its analogs. We finally verified their encoded danger quality and demonstrated their ability to mimic the effects of the predator urine on GG neurons, on mice blood pressure and in behavioral experiments. In summary, we were able to identify here, with the use of an integrative approach, new relevant molecules, the pyridine analogs, implicated in interspecies danger communication.

Variabilité des souches de Escherichia coli provenant de divers poulaillers au Québec

La technique d’empreinte génétique par rep-PCR, qui utilise des séquences d’ADN répétitives, a été utilisée pour mettre en évidence la présence de groupes d’Escherichia coli signatures pour divers poulaillers et d’évaluer leur évolution suite au détassement. L’amorce (GTG)5 a été utilisée pour générer des empreintes d’ADN de 522 isolats provenant de 7 poulaillers échantillonnés deux fois : juste avant et 5 jours après le détassement. Les empreintes d’ADN ont été analysées selon l’algorithme de correspondance de bandes de Jaccard. Les analyses de Jackknife des coefficients de similitude ont révélé qu’entre 73% et 93% des isolats ont pu être correctement regroupés selon leur poulailler d’origine. Un dendrogramme construit à partir des coefficients de similitude de Jaccard a groupé les isolats dans 42 grappes avec près de la moitié dans une seule grappe. Environ 80% des isolats ont été groupés dans les 6 plus grosses grappes. Quatre de ces grappes été constituées majoritairement d’isolats provenant d’un seul site. Ces grappes pourraient être des grappes signatures qui permettraient d’identifier des poulaillers en particulier. La comparaison des nombres de grappes présentes avant et après le détassement a révélé une variabilité de l’impact du détassement sur les populations fécales d’E. coli. Pour certains sites, il y avait peu d’agrégats présents tant avant qu’après le détassement alors que pour d’autres sites c’était le contraire. Quoique plus de recherches soient nécessaires afin de valider les conclusions, nos résultats suggèrent la présence de sous-populations signatures d’E. coli pour certains poulaillers et une réponse variable à l’effet du détassement.