Bioluminescência de fungos: distribuição, função e mecanismo de emissão de luz

The emission of light by living organisms, bioluminescence, has been studied since the nineteenth century. However, some bioluminescent systems, such as fungi, remain poorly understood. The emitter, the two enzymes involved, and the reaction mechanism have not yet been unraveled. Moreover, the ecological role and evolutionary significance for fungal luminescence is also unknown. It is hoped that comprehensive research on fungal bioluminescent systems will generate knowledge and tools for academic and applied sciences. This review discusses the distribution of bioluminescent fungi on Earth, attempts to elucidate the mechanism involved in light emission, and presents preliminary results on the evolution and ecological role of fungal bioluminescence.

Biochemical Modification of Thermomechanical Pulp Fibers

The first objective of this study was to find out reliable laboratory methods to predict the effect of enzymes on specific energy consumption and fiber properties of TMP pulp. The second one was to find with interactive software called “Knowledge discovery in databases” enzymes or other additives that can be used in finding a solution to reduce energy consumption of TMP pulp. The chemical composition of wood and enzymes, which have activity on main wood components were presented in the literature part of the work. The results of previous research in energy reduction of TMP process with enzymes were also highlighted. The main principles of knowledge discovery have been included in literature part too. The experimental part of the work contains the methods description in which the standard size chip, crushed chip and fiberized spruce chip (fiberized pulp) were used. Different types of enzymatic treatment with different dosages and time were tested during the experiments and showed. Pectinase, endoglucanase and mixture of enzymes were used for evaluation of method reliability. The fines content and fiber length of pulp was measured and used as evidence of enzymes' effect. The refining method with “Bauer” laboratory disc refiner was evaluated as not highly reliable. It was not able to provide high repeatability of results, because of uncontrolled feeding capacity and refining consistency. The refining method with Valley refiner did not have a lot of variables and showed stable and repeatable results in energy saving. The results of experiments showed that efficient enzymes impregnation is probably the main target with enzymes application for energy saving. During the work the fiberized pulp showed high accessibility to enzymatic treatment and liquid penetration without special impregnating equipment. The reason was that fiberized pulp has larger wood surface area and thereby the contact area between the enzymatic solution and wood is also larger. Standard size chip and crushed chip treatment without special impregnator of enzymatic solution was evaluated as not efficient and did not show visible, repeatable results in energy consumption decrease. Thereby it was concluded that using of fiberized pulp and Valley refiner for measurements of enzymes' effectiveness in SEC decrease is more suitable than normal size chip and crushed chip with “Bauer” refiner. Endoglucanase with 5 kg/t dosage showed about 20% energy consumption decrease. Mixture of enzymes with 1.5 kg/t dosage showed about 15% decrease of energy consumption during the refining. Pectinase at different dosages and treatment times did not show significant effect on energy consumption. Results of knowledge discovery in databases showed the xylanase, cellulase and pectinase blend as most promising for energy reduction in TMP process. Surfactants were determined as effective additives for energy saving with enzymes.

Tieto ja tietämyksenhallinta projektiympäristössä

Tämän tutkimuksen tavoitteena oli selvittää millaista tietoa sisältyy projektiympäristöön, sekä miten tietoa ja tietämystä projektiympäristössä voidaan hallita. Tutkimuksessa tarkasteltiin myös erilaisia tietämyksenhallinnan työkaluja, sekä tekijöitä jotka vaikuttavat tiedon- ja tietämyksenhallinnan onnistumiseen projektiympäristössä. Tutkimus oli tyypiltään kirjallisuustutkimus. Tutkimuksen tuloksia pyrittiin täydentämään kolmen case-esimerkin avulla, jotka oli poimittu kirjallisuudesta. Projektit sisältävät sekä dokumentoitua että inhimillistä tietoa. Tutkimus osoitti dokumentoinnin keskeisen merkityksen projektiympäristön tiedon- ja tietämyksenhallinnassa. Useat projektiympäristön tiedonhallinnan ongelmakohdat liittyvät tiedon luomiseen, oppimiseen ja jakamiseen. Tiedon- ja tietämyksenhallinnan työkalujen valintaan vaikuttavat mm. työympäristö, tiedon luonne ja projektiorganisaatio. Työkaluja ovat esimerkiksi sähköposti, videoneuvottelu, intranet ja tietokannat.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.

A portal for the Canadian Virtual Health Library / Bibliothèque virtuelle canadienne de la santé (CVHL/BVCS): Accomplishments and possibilities, scope and structure - a review of best practices

Introduction: Coordination through CVHL/BVCS gives Canadian health libraries access to information technology they could not offer individually, thereby enhancing the library services offered to Canadian health professionals. An example is the portal being developed. Portal best practices are of increasing interest (usability.gov; Wikipedia portals; JISC subject portal project; Stanford clinical portals) but conclusive research is not yet available. This paper will identify best practices for a portal bringing together knowledge for Canadian health professionals supported through a network of libraries. Description: The portal for Canadian health professionals will include capabilities such as: • Authentication • Question referral • Specialist “branch libraries” • Integration of commercial resources, web resources and health systems data • Cross-resource search engine • Infrastructure to enable links from EHR and decision support systems • Knowledge translation tools, such as highlighting of best evidence Best practices will be determined by studying the capabilities of existing portals, including consortia/networks and individual institutions, and through a literature review. Outcomes: Best practices in portals will be reviewed. The collaboratively developed Virtual Library, currently the heart of cvhl.ca, is a unique database collecting high quality, free web documents and sites relevant to Canadian health care. The evident strengths of the Virtual Library will be discussed in light of best practices. Discussion: Identification of best practices will support cost-benefit analysis of options and provide direction for CVHL/BVCS. Open discussion with stakeholders (libraries and professionals) informed by this review will lead to adoption of the best technical solutions supporting Canadian health libraries and their users.

Développement de modèles prédictifs de la toxicocinétique de substances organiques

Les modèles pharmacocinétiques à base physiologique (PBPK) permettent de simuler la dose interne de substances chimiques sur la base de paramètres spécifiques à l’espèce et à la substance. Les modèles de relation quantitative structure-propriété (QSPR) existants permettent d’estimer les paramètres spécifiques au produit (coefficients de partage (PC) et constantes de métabolisme) mais leur domaine d’application est limité par leur manque de considération de la variabilité de leurs paramètres d’entrée ainsi que par leur domaine d’application restreint (c. à d., substances contenant CH3, CH2, CH, C, C=C, H, Cl, F, Br, cycle benzénique et H sur le cycle benzénique). L’objectif de cette étude est de développer de nouvelles connaissances et des outils afin d’élargir le domaine d’application des modèles QSPR-PBPK pour prédire la toxicocinétique de substances organiques inhalées chez l’humain. D’abord, un algorithme mécaniste unifié a été développé à partir de modèles existants pour prédire les PC de 142 médicaments et polluants environnementaux aux niveaux macro (tissu et sang) et micro (cellule et fluides biologiques) à partir de la composition du tissu et du sang et de propriétés physicochimiques. L’algorithme résultant a été appliqué pour prédire les PC tissu:sang, tissu:plasma et tissu:air du muscle (n = 174), du foie (n = 139) et du tissu adipeux (n = 141) du rat pour des médicaments acides, basiques et neutres ainsi que pour des cétones, esters d’acétate, éthers, alcools, hydrocarbures aliphatiques et aromatiques. Un modèle de relation quantitative propriété-propriété (QPPR) a été développé pour la clairance intrinsèque (CLint) in vivo (calculée comme le ratio du Vmax (μmol/h/kg poids de rat) sur le Km (μM)), de substrats du CYP2E1 (n = 26) en fonction du PC n octanol:eau, du PC sang:eau et du potentiel d’ionisation). Les prédictions du QPPR, représentées par les limites inférieures et supérieures de l’intervalle de confiance à 95% à la moyenne, furent ensuite intégrées dans un modèle PBPK humain. Subséquemment, l’algorithme de PC et le QPPR pour la CLint furent intégrés avec des modèles QSPR pour les PC hémoglobine:eau et huile:air pour simuler la pharmacocinétique et la dosimétrie cellulaire d’inhalation de composés organiques volatiles (COV) (benzène, 1,2-dichloroéthane, dichlorométhane, m-xylène, toluène, styrène, 1,1,1 trichloroéthane et 1,2,4 trimethylbenzène) avec un modèle PBPK chez le rat. Finalement, la variabilité de paramètres de composition des tissus et du sang de l’algorithme pour les PC tissu:air chez le rat et sang:air chez l’humain a été caractérisée par des simulations Monte Carlo par chaîne de Markov (MCMC). Les distributions résultantes ont été utilisées pour conduire des simulations Monte Carlo pour prédire des PC tissu:sang et sang:air. Les distributions de PC, avec celles des paramètres physiologiques et du contenu en cytochrome P450 CYP2E1, ont été incorporées dans un modèle PBPK pour caractériser la variabilité de la toxicocinétique sanguine de quatre COV (benzène, chloroforme, styrène et trichloroéthylène) par simulation Monte Carlo. Globalement, les approches quantitatives mises en œuvre pour les PC et la CLint dans cette étude ont permis l’utilisation de descripteurs moléculaires génériques plutôt que de fragments moléculaires spécifiques pour prédire la pharmacocinétique de substances organiques chez l’humain. La présente étude a, pour la première fois, caractérisé la variabilité des paramètres biologiques des algorithmes de PC pour étendre l’aptitude des modèles PBPK à prédire les distributions, pour la population, de doses internes de substances organiques avant de faire des tests chez l’animal ou l’humain.

Une nouvelle approche computationnelle pour la découverte des sites de fixation de facteurs de transcription à l’ADN, adaptée aux données de ChIP-chip et de ChIP-séquençage

Les facteurs de transcription sont des protéines spécialisées qui jouent un rôle important dans différents processus biologiques tel que la différenciation, le cycle cellulaire et la tumorigenèse. Ils régulent la transcription des gènes en se fixant sur des séquences d’ADN spécifiques (éléments cis-régulateurs). L’identification de ces éléments est une étape cruciale dans la compréhension des réseaux de régulation des gènes. Avec l’avènement des technologies de séquençage à haut débit, l’identification de tout les éléments fonctionnels dans les génomes, incluant gènes et éléments cis-régulateurs a connu une avancée considérable. Alors qu’on est arrivé à estimer le nombre de gènes chez différentes espèces, l’information sur les éléments qui contrôlent et orchestrent la régulation de ces gènes est encore mal définie. Grace aux techniques de ChIP-chip et de ChIP-séquençage il est possible d’identifier toutes les régions du génome qui sont liées par un facteur de transcription d’intérêt. Plusieurs approches computationnelles ont été développées pour prédire les sites fixés par les facteurs de transcription. Ces approches sont classées en deux catégories principales: les algorithmes énumératifs et probabilistes. Toutefois, plusieurs études ont montré que ces approches génèrent des taux élevés de faux négatifs et de faux positifs ce qui rend difficile l’interprétation des résultats et par conséquent leur validation expérimentale. Dans cette thèse, nous avons ciblé deux objectifs. Le premier objectif a été de développer une nouvelle approche pour la découverte des sites de fixation des facteurs de transcription à l’ADN (SAMD-ChIP) adaptée aux données de ChIP-chip et de ChIP-séquençage. Notre approche implémente un algorithme hybride qui combine les deux stratégies énumérative et probabiliste, afin d’exploiter les performances de chacune d’entre elles. Notre approche a montré ses performances, comparée aux outils de découvertes de motifs existants sur des jeux de données simulées et des jeux de données de ChIP-chip et de ChIP-séquençage. SAMD-ChIP présente aussi l’avantage d’exploiter les propriétés de distributions des sites liés par les facteurs de transcription autour du centre des régions liées afin de limiter la prédiction aux motifs qui sont enrichis dans une fenêtre de longueur fixe autour du centre de ces régions. Les facteurs de transcription agissent rarement seuls. Ils forment souvent des complexes pour interagir avec l’ADN pour réguler leurs gènes cibles. Ces interactions impliquent des facteurs de transcription dont les sites de fixation à l’ADN sont localisés proches les uns des autres ou bien médier par des boucles de chromatine. Notre deuxième objectif a été d’exploiter la proximité spatiale des sites liés par les facteurs de transcription dans les régions de ChIP-chip et de ChIP-séquençage pour développer une approche pour la prédiction des motifs composites (motifs composés par deux sites et séparés par un espacement de taille fixe). Nous avons testé ce module pour prédire la co-localisation entre les deux demi-sites ERE qui forment le site ERE, lié par le récepteur des œstrogènes ERα. Ce module a été incorporé à notre outil de découverte de motifs SAMD-ChIP.

SPATIAL CLUSTERING ALGORITHMS-AN OVERVIEW.

An Overview of known spatial clustering algorithms The space of interest can be the two-dimensional abstraction of the surface of the earth or a man-made space like the layout of a VLSI design, a volume containing a model of the human brain, or another 3d-space representing the arrangement of chains of protein molecules. The data consists of geometric information and can be either discrete or continuous. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations) which are used by spatial data mining algorithms. Therefore, spatial data mining algorithms are required for spatial characterization and spatial trend analysis. Spatial data mining or knowledge discovery in spatial databases differs from regular data mining in analogous with the differences between non-spatial data and spatial data. The attributes of a spatial object stored in a database may be affected by the attributes of the spatial neighbors of that object. In addition, spatial location, and implicit information about the location of an object, may be exactly the information that can be extracted through spatial data mining

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis

Hierarchies of conceptual scales

Formal Concept Analysis allows to derive conceptual hierarchies from data tables. Formal Concept Analysis is applied in various domains, e.g., data analysis, information retrieval, and knowledge discovery in databases. In order to deal with increasing sizes of the data tables (and to allow more complex data structures than just binary attributes), conceputal scales habe been developed. They are considered as metadata which structure the data conceptually. But in large applications, the number of conceptual scales increases as well. Techniques are needed which support the navigation of the user also on this meta-level of conceptual scales. In this paper, we attack this problem by extending the set of scales by hierarchically ordered higher level scales and by introducing a visualization technique called nested scaling. We extend the two-level architecture of Formal Concept Analysis (the data table plus one level of conceptual scales) to many-level architecture with a cascading system of conceptual scales. The approach also allows to use representation techniques of Formal Concept Analysis for the visualization of thesauri and ontologies.

Dual retrieval in conceptual information systems

Conceptual Information Systems provide a multi-dimensional conceptually structured view on data stored in relational databases. On restricting the expressiveness of the retrieval language, they allow the visualization of sets of realted queries in conceptual hierarchies, hence supporting the search of something one does not have a precise description, but only a vague idea of. Information Retrieval is considered as the process of finding specific objects (documents etc.) out of a large set of objects which fit to some description. In some data analysis and knowledge discovery applications, the dual task is of interest: The analyst needs to determine, for a subset of objects, a description for this subset. In this paper we discuss how Conceptual Information Systems can be extended to support also the second task.

Efficient data mining based on formal concept analysis

Formal Concept Analysis is an unsupervised learning technique for conceptual clustering. We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are designed for analyzing very large databases. In particular they serve as a condensed representation of frequent patterns as known from association rule mining. In order to show the interplay between Formal Concept Analysis and association rule mining, we discuss the algorithm TITANIC. We show that iceberg concept lattices are a starting point for computing condensed sets of association rules without loss of information, and are a visualization method for the resulting rules.

Off to new shores

In the last years, the main orientation of Formal Concept Analysis (FCA) has turned from mathematics towards computer science. This article provides a review of this new orientation and analyzes why and how FCA and computer science attracted each other. It discusses FCA as a knowledge representation formalism using five knowledge representation principles provided by Davis, Shrobe, and Szolovits [DSS93]. It then studies how and why mathematics-based researchers got attracted by computer science. We will argue for continuing this trend by integrating the two research areas FCA and Ontology Engineering. The second part of the article discusses three lines of research which witness the new orientation of Formal Concept Analysis: FCA as a conceptual clustering technique and its application for supporting the merging of ontologies; the efficient computation of association rules and the structuring of the results; and the visualization and management of conceptual hierarchies and ontologies including its application in an email management system.