S-system parameter estimation for noisy metabolic profiles using newton-flow analysis.

Biochemical systems are commonly modelled by systems of ordinary differential equations (ODEs). A particular class of such models called S-systems have recently gained popularity in biochemical system modelling. The parameters of an S-system are usually estimated from time-course profiles. However, finding these estimates is a difficult computational problem. Moreover, although several methods have been recently proposed to solve this problem for ideal profiles, relatively little progress has been reported for noisy profiles. We describe a special feature of a Newton-flow optimisation problem associated with S-system parameter estimation. This enables us to significantly reduce the search space, and also lends itself to parameter estimation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems. In addition, we propose an extension of our method that allows the detection of network topologies for small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competing methods for ideal profiles, and that it also allows the determination of parameters for noisy profiles.

Discovering the campus together: a mobile and computer-based learning experience

One of the most relevant difficulties faced by first-year undergraduate students is to settle into the educational environment of universities. This paper presents a case study that proposes a computer-assisted collaborative experience designed to help students in their transition from high school to university. This is done by facilitating their first contact with the campus and its services, the university community, methodologies and activities. The experience combines individual and collaborative activities, conducted in and out of the classroom, structured following the Jigsaw Collaborative Learning Flow Pattern. A specific environment including portable technologies with network and computer applications has been developed to support and facilitate the orchestration of a flow of learning activities into a single integrated learning setting. The result is a Computer-Supported Collaborative Blended Learning scenario, which has been evaluated with first-year university students of the degrees of Software and Audiovisual Engineering within the subject Introduction to Information and Communications Technologies. The findings reveal that the scenario improves significantly students’ interest in their studies and their understanding about the campus and services provided. The environment is also an innovative approach to successfully support the heterogeneous activities conducted by both teachers and students during the scenario. This paper introduces the goals and context of the case study, describes how the technology was employed to conduct the learning scenario, the evaluation methods and the main results of the experience.

Study of common database feeding with results coming from different analytical methods in the framework of illicit drugs chemical profiling

Analytical results harmonisation is investigated in this study to provide an alternative to the restrictive approach of analytical methods harmonisation which is recommended nowadays for making possible the exchange of information and then for supporting the fight against illicit drugs trafficking. Indeed, the main goal of this study is to demonstrate that a common database can be fed by a range of different analytical methods, whatever the differences in levels of analytical parameters between these latter ones. For this purpose, a methodology making possible the estimation and even the optimisation of results similarity coming from different analytical methods was then developed. In particular, the possibility to introduce chemical profiles obtained with Fast GC-FID in a GC-MS database is studied in this paper. By the use of the methodology, the similarity of results coming from different analytical methods can be objectively assessed and the utility in practice of database sharing by these methods can be evaluated, depending on profiling purposes (evidential vs. operational perspective tool). This methodology can be regarded as a relevant approach for database feeding by different analytical methods and puts in doubt the necessity to analyse all illicit drugs seizures in one single laboratory or to implement analytical methods harmonisation in each participating laboratory.

Bayesian networks for evaluating forensic DNA profiling evidence: a review and guide to literature

Almost 30 years ago, Bayesian networks (BNs) were developed in the field of artificial intelligence as a framework that should assist researchers and practitioners in applying the theory of probability to inference problems of more substantive size and, thus, to more realistic and practical problems. Since the late 1980s, Bayesian networks have also attracted researchers in forensic science and this tendency has considerably intensified throughout the last decade. This review article provides an overview of the scientific literature that describes research on Bayesian networks as a tool that can be used to study, develop and implement probabilistic procedures for evaluating the probative value of particular items of scientific evidence in forensic science. Primary attention is drawn here to evaluative issues that pertain to forensic DNA profiling evidence because this is one of the main categories of evidence whose assessment has been studied through Bayesian networks. The scope of topics is large and includes almost any aspect that relates to forensic DNA profiling. Typical examples are inference of source (or, 'criminal identification'), relatedness testing, database searching and special trace evidence evaluation (such as mixed DNA stains or stains with low quantities of DNA). The perspective of the review presented here is not exclusively restricted to DNA evidence, but also includes relevant references and discussion on both, the concept of Bayesian networks as well as its general usage in legal sciences as one among several different graphical approaches to evidence evaluation.

Computerunterstützte Rigiditätsmessung zur Differenzierung psychopathologischer Gruppen [Computer-assisted determination of rigidity in the differentiation of psychopathologic groups]

The measurement of rigidity and perseveration respectively gets increasing importance in clinical psychodiagnostics. Recently we have developed a computer-assisted technique which allows to get information about inadequate persisting in psychic processes and behaviour within shortest time and to differentiate between psychopathological groups. 257 patients of both sexes who came for elucidation of their disorders to the department of clinical psychodiagnostics were investigated. The most significant differences between the groups were found in redundance of second degree (the patient has to press 10 buttons indiscriminately according to the beat of a metronom--standard condition) and in personal speed (the patient has to press 10 buttons as fast as possible--speed condition). Furthermore the psychopathological groups were ranged in the particular variables of rigidity according to their mean values and their average ranges the schizophrenics and effective psychoses were characterized by a high tendency of perseveration while the neurotics, patients with organic brain syndrome and alcohol and drug dependents showed more flexibility.

Intégration de méthodes de data mining dans le renseignement criminel : analyse par des structures issues de la théorie des graphes dans le profilage des stupéfiants

Data mining can be defined as the extraction of previously unknown and potentially useful information from large datasets. The main principle is to devise computer programs that run through databases and automatically seek deterministic patterns. It is applied in different fields of application, e.g., remote sensing, biometry, speech recognition, but has seldom been applied to forensic case data. The intrinsic difficulty related to the use of such data lies in its heterogeneity, which comes from the many different sources of information. The aim of this study is to highlight potential uses of pattern recognition that would provide relevant results from a criminal intelligence point of view. The role of data mining within a global crime analysis methodology is to detect all types of structures in a dataset. Once filtered and interpreted, those structures can point to previously unseen criminal activities. The interpretation of patterns for intelligence purposes is the final stage of the process. It allows the researcher to validate the whole methodology and to refine each step if necessary. An application to cutting agents found in illicit drug seizures was performed. A combinatorial approach was done, using the presence and the absence of products. Methods coming from the graph theory field were used to extract patterns in data constituted by links between products and place and date of seizure. A data mining process completed using graphing techniques is called ``graph mining''. Patterns were detected that had to be interpreted and compared with preliminary knowledge to establish their relevancy. The illicit drug profiling process is actually an intelligence process that uses preliminary illicit drug classes to classify new samples. Methods proposed in this study could be used \textit{a priori} to compare structures from preliminary and post-detection patterns. This new knowledge of a repeated structure may provide valuable complementary information to profiling and become a source of intelligence.

An ultra performance liquid chromatography-tandem MS assay for tamoxifen metabolites profiling in plasma: first evidence of 4'-hydroxylated metabolites in breast cancer patients.

There is increasing evidence that the clinical efficacy of tamoxifen, the first and most widely used targeted therapy for estrogen-sensitive breast cancer, depends on the formation of the active metabolites 4-hydroxy-tamoxifen and 4-hydroxy-N-desmethyl-tamoxifen (endoxifen). Large inter-individual variability in endoxifen plasma concentrations has been observed and related both to genetic and environmental (i.e. drug-induced) factors altering CYP450s metabolizing enzymes activity. In this context, we have developed an ultra performance liquid chromatography-tandem mass spectrometry method (UPLC-MS/MS) requiring 100 μL of plasma for the quantification of tamoxifen and three of its major metabolites in breast cancer patients. Plasma is purified by a combination of protein precipitation, evaporation at room temperature under nitrogen, and reconstitution in methanol/20 mM ammonium formate 1:1 (v/v), adjusted to pH 2.9 with formic acid. Reverse-phase chromatographic separation of tamoxifen, N-desmethyl-tamoxifen, 4-hydroxy-tamoxifen and 4-hydroxy-N-desmethyl-tamoxifen is performed within 13 min using elution with a gradient of 10 mM ammonium formate and acetonitrile, both containing 0.1% formic acid. Analytes quantification, using matrix-matched calibration samples spiked with their respective deuterated internal standards, is performed by electrospray ionization-triple quadrupole mass spectrometry using selected reaction monitoring detection in the positive mode. The method was validated according to FDA recommendations, including assessment of relative matrix effects variability, as well as tamoxifen and metabolites short-term stability in plasma and whole blood. The method is precise (inter-day CV%: 2.5-7.8%), accurate (-1.4 to +5.8%) and sensitive (lower limits of quantification comprised between 0.4 and 2.0 ng/mL). Application of this method to patients' samples has made possible the identification of two further metabolites, 4'-hydroxy-tamoxifen and 4'-hydroxy-N-desmethyl-tamoxifen, described for the first time in breast cancer patients. This UPLC-MS/MS assay is currently applied for monitoring plasma levels of tamoxifen and its metabolites in breast cancer patients within the frame of a clinical trial aiming to assess the impact of dose increase on tamoxifen and endoxifen exposure.

Transcriptional profiling of Gram-positive Arthrobacter in the phyllosphere: induction of pollutant degradation genes by natural plant phenolic compounds.

Arthrobacter chlorophenolicus A6 is a Gram-positive, 4-chlorophenol-degrading soil bacterium that was recently shown to be an effective colonizer of plant leaf surfaces. The genetic basis for this phyllosphere competency is unknown. In this paper, we describe the genome-wide expression profile of A.chlorophenolicus on leaves of common bean (Phaseolus vulgaris) compared with growth on agar surfaces. In phyllosphere-grown cells, we found elevated expression of several genes known to contribute to epiphytic fitness, for example those involved in nutrient acquisition, attachment, stress response and horizontal gene transfer. A surprising result was the leaf-induced expression of a subset of the so-called cph genes for the degradation of 4-chlorophenol. This subset encodes the conversion of the phenolic compound hydroquinone to 3-oxoadipate, and was shown to be induced not only by 4-chlorophenol but also hydroquinone, its glycosylated derivative arbutin, and phenol. Small amounts of hydroquinone, but not arbutin or phenol, were detected in leaf surface washes of P.vulgaris by gas chromatography-mass spectrometry. Our findings illustrate the utility of genomics approaches for exploration and improved understanding of a microbial habitat. Also, they highlight the potential for phyllosphere-based priming of bacteria to stimulate pollutant degradation, which holds promise for the application of phylloremediation.

Gene expression profiling of human glioblastomas for the identification of predictive factors and characterisation of molecular mechanism implicated in response to therapy and outcome

ABSTRACT Poor outcome for glioblastoma patients is largely due to resistance to chemoradiation therapy. While epigenetic inactivation of MGMT mediated DNA repair is highly predictive for benefit from the alkylating agent therapy Temozolomide, additional mechanisms for resistance associated with molecular alterations exist. Furthermore, new concepts in cancer suggest that resistance to treatment may be linked to cancer stem cells that escape therapy and act as source for tumour recurrence. We determined gene expression signatures associated with outcome in glioblastoma patients enrolled in a phase II and phase III clinical trial establishing the new combination therapy of radiation plus concomitant and adjuvant Temozolomide. Correlating stable gene clusters emerging from unsupervised analysis with survival of 42 treated patients identified a number of biological processes associated with outcome. Most prominent, a gene cluster dominated by HOX genes and comprising PROM1, was associated with resistance. PROM1 encodes CD133, a marker for a subpopulation of tumour cells enriched for glioblastoma stem- like cells. The core of this correlated HOX cluster was comprised in the top genes of a "self-renewal signature" defined in a mouse model for MLL-AF9 initiated leukaemia. The association of the HOX gene cluster with tumour resistance was confirmed in two external data sets of 146 malignant glioma As additional resistance factors we identified over-expression of the epidermal growth factor receptor gene, EGFR, while increased gene expression related to biological features of tumour host interaction, including markers for tumour vascular and cell adhesion, and innate immune response, were associated with better outcome. The "self-renewal" signature associated with resistance to the new combination chemoradiation therapy provides first clinical evidence that glioma stem like cells may implicated in resistance in a uniformly treated cohort of glioblastoma patients. This study underlines the need to target the tumour stem cell compartment, and provides some testable hypothesis for biological mechanisms relevant for malignant behaviour of glioblastoma that may be targeted in new treatment approaches. Résumé Le glioblastome, tumeur cérébrale primaire maligne la plus fréquente, est connue pour son mauvais pronostique. Des avancées chimiothérapeutiques récentes avec des agents alkylants comme le témozolomide (TMZ), ont permis une amélioration notable dans la survie de certains patients. Les bénéficiaires ont la caractéristique commune de présenter une particularité génétique, la methylation du MGMT (methylguanine methyltransferase). Néanmoins, d'autres mécanismes de résistance en fonction des aberrations moléculaires existent. Nous avons établi les profils d'expressions génétiques des patients traités par irradiation et TMZ dans des études cliniques de phase II et III. En combinant des méthodes non-supervisées et supervisées, de l'étude de la cohorte des patients traités nous avons découvert des groupes de gènes associés à la survie. Un ensemble de gènes contenant les gènes Hox semble lié au mécanisme de résistance au traitement. Récemment, les gènes Hox ont été décrits comme faisant partie d"une signature d'autorenouvellement (self-renewal) des cellules souches cancéreuses de la leucémie. L'autorenouvellement est un processus grâce auquel les cellules souches se maintiennent tout au long de la vie. Cette association à la résistance est confirmée dans deux autres études indépendantes. Un autre facteur de résistance au traitement est la surexpression du gène EGFR. D'autre part, deux groupes de gènes associés à la relation entre hôte-tumeur tels que les marqueurs des vaisseaux tumoraux et de la réponse immunitaire innée s'avèrent avoir un effet positif sur la survie des patients traités. La découverte de la signature d'autorenouvellement comme facteur de résistance à la nouvelle chimio-radiothérapie offre une preuve clinique que les cellules souches cancéreuses sont impliquées dans la résistance au traitement. If est donc logique de penser que le traitement ciblé contre des cellules souches cancéreuses va dans l'avenir permettre des thérapies anticancéreuses plus performantes.

Current approaches and challenges for the metabolite profiling of complex natural extracts.

Metabolite profiling is critical in many aspects of the life sciences, particularly natural product research. Obtaining precise information on the chemical composition of complex natural extracts (metabolomes) that are primarily obtained from plants or microorganisms is a challenging task that requires sophisticated, advanced analytical methods. In this respect, significant advances in hyphenated chromatographic techniques (LC-MS, GC-MS and LC-NMR in particular), as well as data mining and processing methods, have occurred over the last decade. Together, these tools, in combination with bioassay profiling methods, serve an important role in metabolomics for the purposes of both peak annotation and dereplication in natural product research. In this review, a survey of the techniques that are used for generic and comprehensive profiling of secondary metabolites in natural extracts is provided. The various approaches (chromatographic methods: LC-MS, GC-MS, and LC-NMR and direct spectroscopic methods: NMR and DIMS) are discussed with respect to their resolution and sensitivity for extract profiling. In addition the structural information that can be generated through these techniques or in combination, is compared in relation to the identification of metabolites in complex mixtures. Analytical strategies with applications to natural extracts and novel methods that have strong potential, regardless of how often they are used, are discussed with respect to their potential applications and future trends.

Computer Tips and Tricks Newsletter, May, 2007

Performance & Development Solutions (PDS) publishes a variety of newsletters that include some great information about our programs and services. Some of the topics you may find include: Upcoming Seminars Current events or news related to training Recognition of achievements How-to section

Computer Tips and Tricks Newsletter, December, 2005

Performance & Development Solutions (PDS) publishes a variety of newsletters that include some great information about our programs and services. Some of the topics you may find include: Upcoming Seminars Current events or news related to training Recognition of achievements How-to section