Metal-Ion-Binding Oligonucleotides: High-Affinity Probes for Nucleic Acid Sequences

Small non-coding RNAs have numerous biological functions in cell and are divided into different classes such as: microRNA, snoRNA, snRNA and siRNA. MicroRNA (miRNA) is the most studied non-coding RNA to date and is found in plants, animals and some viruses. miRNA with short sequences is involved in suppressing translation of target genes by binding to their mRNA post-transcriptionally and silencing it. Their function besides silencing of the viral gene, can be oncogenic and therefore the cause of cancer. Hence, their roles are highlighted in human diseases, which increases the interest in using them as biomarkers and drug targets. One of the major problems to overcome is recognition of miRNA. Owing to a stable hairpin structure, chain invasion by conventional Watson-Crick base-pairing is difficult. One way to enhance the hybridization is exploitation of metal-ion mediated base-pairing, i. e. oligonucleotide probes that tightly bind a metal ions and are able to form a coordinative bonds between modified and natural nucleobases. This kind of metallo basepairs containing short modified oligonucleotides can also be useful for recognition of other RNA sequences containing hairpin-like structural motives, such as the TAR sequence of HIV. In addition, metal-ion-binding oligonucleotides will undoubtedly find applications in DNA-based nanotechnology. In this study, the 3,5-dimethylpyrazol-1-yl substituted purine derivatives were successfully incorporated within oligonucleotides, into either a terminal or non-terminal position. Among all of the modified oligonucleotides studied, a 2-(3,5-dimethylpyrazol-1-yl)-6-oxopurine base containing oligonucleotide was observed to bind most efficiently to their unmodified complementary sequences in the presence of both Cu2+ or Zn2+. The oligonucleotide incorporating 2,6-bis(3,5-dimethylpyrazol-1-yl)purine base also markedly increased the stability of duplexes in the presence of Cu2+ without losing the selectivity.

Isolation of DNA sequences with homology to a degenerate FaRP oligonucleotide from the crayfish Procambarus clarkii

The neuropeptide Th1RFamide with the sequence Phe-Met-Arg-Phe-amide was originally isolated in the clam Macrocallista nimbosa (price and Greenberg, 1977). Since its discovery, a large family ofFl\1RFamide-related peptides termed FaRPs have been found to be present in all major animal phyla with functions ranging from modulation of neuronal activity to alteration of muscular contractions. However, little is known about the genetics encoding these peptides, especially in invertebrates. As FaRP-encoding genes have yet to be investigated in the invertebrate Malacostracean subphylum, the isolation and characterization ofFaRP-encoding DNA and mRNA was pursued in this project. The immediate aims of this thesis were: (1) to amplify mRNA sequences of Procambarus clarkii using a degenerate oligonucleotide primer deduced from the common amino acid sequence ofisolated Procambarus FaRPS, (2) to determine if these amplification products encode FaRP gene sequences, and (3) to create a selective cDNA library of sequences recognized by the degenerate oligonucleotide primer. The polymerase chain reaction - rapid amplification of cDNA ends (PCR-RACE) is a procedure in which a single gene-specific primer is used in conjunction with a generalized 3' or 5' primer to amplify copies ofthe region between a single point in the transcript and the 3' or 5' end of cDNA of interest (Frohman et aI., 1988). PCRRACE reactions were optimized with respect to primers used, buffer composition, cycle number, nature ofgenetic substrate to be amplified, annealing, extension and denaturation temperatures and times, and use of reamplification procedures. Amplification products were cloned into plasmid vectors and recombinant products were isolated, as were the recombinant plaques formed in the selective cDNA library. Labeled amplification products were hybridized to recombinant bacteriophage to determine ligated amplification product presence. When sequenced, the five isolated PCR-RACE amplification products were determined not to possess FaRP-encoding sequences. The 200bp, 450bp, and 1500bp sequences showed homology to the Caenorhabditis elegans cosmid K09A11, which encodes for cytochrome P450; transfer-RNA; transposase; and tRNA-Tyr, while the 500bp and 750bp sequences showed homology with the complete genome of the Vaccinia virus. Under the employed amplification conditions the degenerate oligonucleotide primer was observed to bind to and to amplify sequences with either 9 or 10bp of 17bp identity. The selective cDNA library was obselVed to be of extremely low titre. When library titre was increased, white. plaques were isolated. Amplification analysis of eight isolated Agt11 sequences from these plaques indicated an absence of an insertion sequence. The degenerate 17 base oligonucleotide primer synthesized from the common amino acid sequence ofisolated Procambarus FaRPs was thus determined to be non-specific in its binding under the conditions required for its use, and to be insufficient for the isolation and identification ofFaRP-encoding sequences. A more specific primer oflonger sequence, lower degeneracy, and higher melting temperature (TJ is recommended for further investigation into the FaRP-encoding genes of Procambarlls clarkii.

Dinocyst biochronology and palynofacies : inferred systems tract character of Miocene sequences from New Jersey mid-Atlantic transect (ODP legs 174A and 174AX) /

One of the main objectives of the mid-Atlantic transect is to improve dating resolution of sequences and unconfonnity surfaces. Dinoflagellate cysts from two Ocean Drilling Program boreholes, the onshore Leg 174AX Ocean View Site and Leg 174A continental shelf Site 1071, are used to provide age estimates for sequences and unconfonnities fonned on the New Jersey continental margin during the Miocene epoch. Despite the occasional lack of dinocysts in barren and oxidized sections, dinocyst biochronology still offers greater age control than that provided by other microfossils in marginal marine environments. An early Miocene to late Miocene chronology based on ages detennined for the two study sites is presented. In addition, .palynofacies are used to unravel the systems tract character of the Miocene sequences and provide insight into the effects of taphonomy and preservation of palynomorphs in marginal marine and shelf environments under different ~ea level conditions. More precise placement of maximum flooding surfaces is possible through the identification of condensed sections and palynofacies shifts can also reveal subaerially exposed sections and surfaces not apparent in seismic or lithological analyses. The problems with the application of the pollen record in the interpretation of Miocene climate are also discussed. Palynomorphs provide evidence for a second-order lowering of sea level during the Miocene, onto which higher order sea level fluctuations are super-imposed. Correlation of sequences and unconfonnities is attempted between onshore boreholes and from the onshore Ocean View borehole to offshore Site 1071.

Isolation and characterization of genomic DNA sequences that enhance the stability of plasmid DNA in mammalian cells /

The ease of production and manipulation has made plasmid DNA a prime target for its use in gene transfer technologies such as gene therapy and DNA vaccines. The major drawback of plasmid however is its stability within mammalian cells. Plasmid DNA is usually lost by cellular mechanisms or as a result of mitosis by simple dilution. This study set out to search for mammalian genomic DNA sequences that would enhance the stability of plasmid DNA in mammalian cells.Creating a plasmid based genomic DNA library, we were able to screen the human genome by transfecting the library into Human Embryonic Kidney (HEK 293) Cells. Cells that contained plasmid DNA were selected, using G418 for 14 days. The resulting population was then screened for the presence of biologically active plasmid DNA using the process of transformation as a detector.A commercially available plasmid DNA isolation kit was modified to extract plasmid DNA from mammalian cells. The standardized protocol had a detection limit of -0.6 plasmids per cell in one million cells. This allowed for the detection of 45 plasmids that were maintained for 32 days in the HEK 293 cells. Sequencing of selected inserts revealed a significantly higher thymine content in comparison to the human genome. Sequences with high A/T content have been associated with Scaffold/Matrix Attachment Region (S/MAR) sequences in mammalian cells. Therefore, association with the nuclear matrix might be required for the stability of plasmids in mammalian cells.

An examination of the effects on learning seen in children afforded the opportunity to control the order of repetitions for three novel spatiotemporal sequences

Children were afforded the opportunity to control the order of repetitions for three novel spatiotemporal sequences. The following was predicted: a) children and adults in the self-regulated (SELF) groups would produce faster movement (MT) and reaction times (R T) and greater recall success (RS) during retention compared to the age-matched yoked (YOKE) groups; b) children would choose to switch sequences less often than adults; c) adults would produce faster MT and RT and greater RS than the children during acquisition and retention, independent of experimental group. During acquisition, no effects were seen for RS, however for MT and RT there was a main effect for age as well as block. During retention a main effect for practice condition was seen for RS and failed to reach statistical significance for MT and RT, thus partially supporting our first and second hypotheses. The third hypothesis was not supported.

Melanosomal targeting sequences from gp100 are essential for MHC class II-restricted endogenous epitope presentation and mobilization to endosomal compartments

CD4+ T lymphocytes play an important role in CD8+ T cell-mediated responses against tumors. Considering that about 20% of melanomas express major histocompatibility complex (MHC) class II, it is plausible that concomitant antigenic presentation by MHC class I and class II complexes shapes positive (helper T cells) or negative (regulatory T cells) anti-tumor responses. Interestingly, gp100, a melanoma antigen, can be presented by both MHC class I and class II when expressed endogenously, suggesting that it can reach endosomal/MHC class II compartments (MIIC). Here, we demonstrated that the gp100 putative amino-terminal signal sequence and the last 70 residues in carboxy-terminus, are essential for MIIC localization and MHC class II presentation. Confocal microscopy analyses confirmed that gp100 was localized in LAMP-1+ endosomal/MIIC. Gp100-targeting sequences were characterized by deleting different sections in the carboxy-terminus (residues 590 to 661). Transfection in 293T cells, expressing MHC class I and class II molecules, revealed that specific deletions in carboxy-terminus resulted in decreased MHC class II presentation, without effects on MHC class I presentation, suggesting a role in MIIC trafficking for these deleted sections. Then, we used these gp100-targeting sequences to mobilize the green fluorescent protein (GFP) to endosomal compartments, and to allow MHC class II and class I presentation of minimal endogenous epitopes. Thus, we concluded that these specific sequences are MIIC targeting motifs. Consequently, these sequences could be included in expression cassettes for endogenously expressed tumor or viral antigens to promote MHC class II and class I presentation and optimize in vivo T cell responses, or as an in vitro tool for characterization of new MHC class II epitopes.

SCaFoS: a tool for Selection, Concatenation and Fusion of Sequences for phylogenomics

Affiliation: Département de Biochimie, Université de Montréal

Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks

Cette thèse étudie des modèles de séquences de haute dimension basés sur des réseaux de neurones récurrents (RNN) et leur application à la musique et à la parole. Bien qu'en principe les RNN puissent représenter les dépendances à long terme et la dynamique temporelle complexe propres aux séquences d'intérêt comme la vidéo, l'audio et la langue naturelle, ceux-ci n'ont pas été utilisés à leur plein potentiel depuis leur introduction par Rumelhart et al. (1986a) en raison de la difficulté de les entraîner efficacement par descente de gradient. Récemment, l'application fructueuse de l'optimisation Hessian-free et d'autres techniques d'entraînement avancées ont entraîné la recrudescence de leur utilisation dans plusieurs systèmes de l'état de l'art. Le travail de cette thèse prend part à ce développement. L'idée centrale consiste à exploiter la flexibilité des RNN pour apprendre une description probabiliste de séquences de symboles, c'est-à-dire une information de haut niveau associée aux signaux observés, qui en retour pourra servir d'à priori pour améliorer la précision de la recherche d'information. Par exemple, en modélisant l'évolution de groupes de notes dans la musique polyphonique, d'accords dans une progression harmonique, de phonèmes dans un énoncé oral ou encore de sources individuelles dans un mélange audio, nous pouvons améliorer significativement les méthodes de transcription polyphonique, de reconnaissance d'accords, de reconnaissance de la parole et de séparation de sources audio respectivement. L'application pratique de nos modèles à ces tâches est détaillée dans les quatre derniers articles présentés dans cette thèse. Dans le premier article, nous remplaçons la couche de sortie d'un RNN par des machines de Boltzmann restreintes conditionnelles pour décrire des distributions de sortie multimodales beaucoup plus riches. Dans le deuxième article, nous évaluons et proposons des méthodes avancées pour entraîner les RNN. Dans les quatre derniers articles, nous examinons différentes façons de combiner nos modèles symboliques à des réseaux profonds et à la factorisation matricielle non-négative, notamment par des produits d'experts, des architectures entrée/sortie et des cadres génératifs généralisant les modèles de Markov cachés. Nous proposons et analysons également des méthodes d'inférence efficaces pour ces modèles, telles la recherche vorace chronologique, la recherche en faisceau à haute dimension, la recherche en faisceau élagué et la descente de gradient. Finalement, nous abordons les questions de l'étiquette biaisée, du maître imposant, du lissage temporel, de la régularisation et du pré-entraînement.

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

Analysis of Stochastic Volatility Sequences Generated by Product Autoregressive Models

The classical methods of analysing time series by Box-Jenkins approach assume that the observed series uctuates around changing levels with constant variance. That is, the time series is assumed to be of homoscedastic nature. However, the nancial time series exhibits the presence of heteroscedasticity in the sense that, it possesses non-constant conditional variance given the past observations. So, the analysis of nancial time series, requires the modelling of such variances, which may depend on some time dependent factors or its own past values. This lead to introduction of several classes of models to study the behaviour of nancial time series. See Taylor (1986), Tsay (2005), Rachev et al. (2007). The class of models, used to describe the evolution of conditional variances is referred to as stochastic volatility modelsThe stochastic models available to analyse the conditional variances, are based on either normal or log-normal distributions. One of the objectives of the present study is to explore the possibility of employing some non-Gaussian distributions to model the volatility sequences and then study the behaviour of the resulting return series. This lead us to work on the related problem of statistical inference, which is the main contribution of the thesis

A generic polynomial solution for the differential equation of hypergeometric type and six sequences of orthogonal polynomials related to it

In this work, we present a generic formula for the polynomial solution families of the well-known differential equation of hypergeometric type s(x)y"n(x) + t(x)y'n(x) - lnyn(x) = 0 and show that all the three classical orthogonal polynomial families as well as three finite orthogonal polynomial families, extracted from this equation, can be identified as special cases of this derived polynomial sequence. Some general properties of this sequence are also given.

Some New Classes of Orthogonal Polynomials and Special Functions

In dieser Dissertation präsentieren wir zunächst eine Verallgemeinerung der üblichen Sturm-Liouville-Probleme mit symmetrischen Lösungen und erklären eine umfassendere Klasse. Dann führen wir einige neue Klassen orthogonaler Polynome und spezieller Funktionen ein, welche sich aus dieser symmetrischen Verallgemeinerung ableiten lassen. Als eine spezielle Konsequenz dieser Verallgemeinerung führen wir ein Polynomsystem mit vier freien Parametern ein und zeigen, dass in diesem System fast alle klassischen symmetrischen orthogonalen Polynome wie die Legendrepolynome, die Chebyshevpolynome erster und zweiter Art, die Gegenbauerpolynome, die verallgemeinerten Gegenbauerpolynome, die Hermitepolynome, die verallgemeinerten Hermitepolynome und zwei weitere neue endliche Systeme orthogonaler Polynome enthalten sind. All diese Polynome können direkt durch das neu eingeführte System ausgedrückt werden. Ferner bestimmen wir alle Standardeigenschaften des neuen Systems, insbesondere eine explizite Darstellung, eine Differentialgleichung zweiter Ordnung, eine generische Orthogonalitätsbeziehung sowie eine generische Dreitermrekursion. Außerdem benutzen wir diese Erweiterung, um die assoziierten Legendrefunktionen, welche viele Anwendungen in Physik und Ingenieurwissenschaften haben, zu verallgemeinern, und wir zeigen, dass diese Verallgemeinerung Orthogonalitätseigenschaft und -intervall erhält. In einem weiteren Kapitel der Dissertation studieren wir detailliert die Standardeigenschaften endlicher orthogonaler Polynomsysteme, welche sich aus der üblichen Sturm-Liouville-Theorie ergeben und wir zeigen, dass sie orthogonal bezüglich der Fisherschen F-Verteilung, der inversen Gammaverteilung und der verallgemeinerten t-Verteilung sind. Im nächsten Abschnitt der Dissertation betrachten wir eine vierparametrige Verallgemeinerung der Studentschen t-Verteilung. Wir zeigen, dass diese Verteilung gegen die Normalverteilung konvergiert, wenn die Anzahl der Stichprobe gegen Unendlich strebt. Eine ähnliche Verallgemeinerung der Fisherschen F-Verteilung konvergiert gegen die chi-Quadrat-Verteilung. Ferner führen wir im letzten Abschnitt der Dissertation einige neue Folgen spezieller Funktionen ein, welche Anwendungen bei der Lösung in Kugelkoordinaten der klassischen Potentialgleichung, der Wärmeleitungsgleichung und der Wellengleichung haben. Schließlich erklären wir zwei neue Klassen rationaler orthogonaler hypergeometrischer Funktionen, und wir zeigen unter Benutzung der Fouriertransformation und der Parsevalschen Gleichung, dass es sich um endliche Orthogonalsysteme mit Gewichtsfunktionen vom Gammatyp handelt.

Online Learning of Non-stationary Sequences

We consider an online learning scenario in which the learner can make predictions on the basis of a fixed set of experts. The performance of each expert may change over time in a manner unknown to the learner. We formulate a class of universal learning algorithms for this problem by expressing them as simple Bayesian algorithms operating on models analogous to Hidden Markov Models (HMMs). We derive a new performance bound for such algorithms which is considerably simpler than existing bounds. The bound provides the basis for learning the rate at which the identity of the optimal expert switches over time. We find an analytic expression for the a priori resolution at which we need to learn the rate parameter. We extend our scalar switching-rate result to models of the switching-rate that are governed by a matrix of parameters, i.e. arbitrary homogeneous HMMs. We apply and examine our algorithm in the context of the problem of energy management in wireless networks. We analyze the new results in the framework of Information Theory.

People Recognition in Image Sequences by Supervised Learning

We describe a system that learns from examples to recognize people in images taken indoors. Images of people are represented by color-based and shape-based features. Recognition is carried out through combinations of Support Vector Machine classifiers (SVMs). Different types of multiclass strategies based on SVMs are explored and compared to k-Nearest Neighbors classifiers (kNNs). The system works in real time and shows high performance rates for people recognition throughout one day.