Phase diagram of a polydisperse soft-spheres model for liquids and colloids

The phase diagram of soft spheres with size dispersion is studied by means of an optimized Monte Carlo algorithm which allows us to equilibrate below the kinetic glass transition for all size distributions. The system ubiquitously undergoes a first-order freezing transition. While for a small size dispersion the frozen phase has a crystalline structure, large density inhomogeneities appear in the highly disperse systems. Studying the interplay between the equilibrium phase diagram and the kinetic glass transition, we argue that the experimentally found terminal polydispersity of colloids is a purely kinetic phenomenon.

Expression profiling and sequence diversity of novel DREB genes from common bean (Phaseolus vulgaris L.) and their association with drought-related traits

Common bean is a major dietary component in several countries, but its productivity is negatively affected by abiotic stresses. Dissecting candidate genes involved in abiotic stress tolerance is a paramount step toward the improvement of common bean performance under such constraints. Thereby, this thesis presents a systematic analysis of the DEHYDRATION RESPONSIVE ELEMENT-BINDING (DREB) gene subfamily, which encompasses genes that regulate several processes during stress responses, but with limited information for common bean. First, a series of in silico analyses with sequences retrieved from the P. vulgaris genome on Phytozome supported the categorization of 54 putative PvDREB genes distributed within six phylogenetic subgroups (A-1 to A-6), along the 11 chromosomes. Second, we cloned four novel PvDREB genes and determined their inducibility-factors, including the dehydration-, salinity- and cold-inducible genes PvDREB1F and PvDREB5A, and the dehydration- and cold-inducible genes PvDREB2A and PvDREB6B. Afterwards, nucleotide polymorphisms were searched through Sanger sequencing along those genes, revealing a high number of single nucleotide polymorphisms within PvDREB6B by the comparison of Mesoamerican and Andean genotypes. The nomenclature of PvDREB6B is discussed in details. Furthermore, we used the BARCBean6K_3 SNP platform to identify and genotype the closest SNP to each one of the 54 PvDREB genes. We selected PvDREB6B for a broader study encompassing a collection of wild common bean accessions of Mesoamerican origin. The population structure of the wild beans was accessed using sequence polymorphisms of PvDREB6B. The genetic clusters were partially associated with variation in latitude, altitude, precipitation and temperature throughout the areas such beans are distributed. With an emphasis on drought stress, an adapted tube-screening method in greenhouse conditions enabled the phenotyping of several drought-related traits in the wild collection. Interestingly, our data revealed a correlation between root depth, plant height and biomass and the environmental data of the location of the accessions. Correlation was also observed between the population structure determined through PvDREB6B and the environmental data. An association study combining data from the SNP array and DREB polymorphisms enabled the detection of SNP associated with drought-related traits through a compressed mixed linear model (CMLM) analysis. This thesis highlighted important features of DREB genes in common bean, revealing candidates for further strategies aimed at improvement of abiotic stress tolerance, with emphasis on drought tolerance

Constraining the GRB-Magnetar Model by Means of the Galactic Pulsar Population

A large fraction of Gamma-ray bursts (GRBs) displays an X-ray plateau phase within <105 s from the prompt emission, proposed to be powered by the spin-down energy of a rapidly spinning newly born magnetar. In this work we use the properties of the Galactic neutron star population to constrain the GRB-magnetar scenario. We re-analyze the X-ray plateaus of all Swift GRBs with known redshift, between 2005 January and 2014 August. From the derived initial magnetic field distribution for the possible magnetars left behind by the GRBs, we study the evolution and properties of a simulated GRB-magnetar population using numerical simulations of magnetic field evolution, coupled with Monte Carlo simulations of Pulsar Population Synthesis in our Galaxy. We find that if the GRB X-ray plateaus are powered by the rotational energy of a newly formed magnetar, the current observational properties of the Galactic magnetar population are not compatible with being formed within the GRB scenario (regardless of the GRB type or rate at z = 0). Direct consequences would be that we should allow the existence of magnetars and "super-magnetars" having different progenitors, and that Type Ib/c SNe related to Long GRBs form systematically neutron stars with higher initial magnetic fields. We put an upper limit of ≤16 "super-magnetars" formed by a GRB in our Galaxy in the past Myr (at 99% c.l.). This limit is somewhat smaller than what is roughly expected from Long GRB rates, although the very large uncertainties do not allow us to draw strong conclusion in this respect.

Search of Method for Analyzing "Viability" of Innovative Projects

Questions of "viability" evaluation of innovation projects are considered in this article. As a method of evaluation Hidden Markov Models are used. Problem of determining model parameters, which reproduce test data with highest accuracy are solving. For training the model statistical data on the implementation of innovative projects are used. Baum-Welch algorithm is used as a training algorithm.

Modeling Depression Progression Dynamics from Electronic Health Record

Thesis (Master's)--University of Washington, 2016-06

Inherent size constraints on prokaryote gene networks due to "accelerating" growth

Networks exhibiting accelerating growth have total link numbers growing faster than linearly with network size and either reach a limit or exhibit graduated transitions from nonstationary-to-stationary statistics and from random to scale-free to regular statistics as the network size grows. However, if for any reason the network cannot tolerate such gross structural changes then accelerating networks are constrained to have sizes below some critical value. This is of interest as the regulatory gene networks of single-celled prokaryotes are characterized by an accelerating quadratic growth and are size constrained to be less than about 10,000 genes encoded in DNA sequence of less than about 10 megabases. This paper presents a probabilistic accelerating network model for prokaryotic gene regulation which closely matches observed statistics by employing two classes of network nodes (regulatory and non-regulatory) and directed links whose inbound heads are exponentially distributed over all nodes and whose outbound tails are preferentially attached to regulatory nodes and described by a scale-free distribution. This model explains the observed quadratic growth in regulator number with gene number and predicts an upper prokaryote size limit closely approximating the observed value. (c) 2005 Elsevier GmbH. All rights reserved.

hnRNP A2, a potential ssDNA/RNA molecular adapter at the telomere

The heterogeneous nuclear ribonucleoprotein (hnRNP) A2 is a multi-tasking protein that acts in the cytoplasm and nucleus. We have explored the possibility that this protein is associated with telomeres and participates in their maintenance. Rat brain hnRNP A2 was shown to have two nucleic acid binding sites. In the presence of heparin one site binds single-stranded oligodeoxyribonucleotides irrespective of sequence but not the corresponding oligoribonucleotides. Both the hnRNP A2-binding cis-acting element for the cytoplasmic RNA trafficking element, A2RE, and the ssDNA telomere repeat match a consensus sequence for binding to a second sequence-specific site identified by mutational analysis. hnRNP A2 protected the telomeric repeat sequence, but not the complementary sequence, against DNase digestion: the glycine-rich domain was found to be necessary, but not sufficient, for protection. The N-terminal RRM (RNA recognition motif) and tandem RRMs of hnRNP A2 also bind the single-stranded, template-containing segment of telomerase RNA. hnRNP A2 colocalizes with telomeric chromatin in the subset of PML bodies that are a hallmark of ALT cells, reinforcing the evidence for hnRNPs having a role in telomere maintenance. Our results support a model in which hnRNP A2 acts as a molecular adapter between single-stranded telomeric repeats, or telomerase RNA, and another segment of ssDNA.

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes. Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation) and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.

The relationship between extra-role behaviours and job burnout for primary school teachers: A preliminary model and development of an organisational citizenship behaviour scale

Recent research on teacher stress in primary schools (e.g. Leonard, Bourke & Schofield, 1999) has shown that higher levels of teacher exhaustion are associated with higher levels of student satisfaction. This paper seeks to explain this surprising finding by considering a construct discussed widely in the organisational literature known as extra-role or organisational citizenship behaviour (OCB). Teacher OCB may include extra efforts to make lessons enjoyable and interesting, organising extra-curricular activities and spending personal time talking with students. The proposed model of analysis also draws on literature relating to job burnout (Maslach, 1982), which generally suggests that the three components of chronic occupational stress - exhaustion, depersonalisation and reduced accomplishment - occur together. However, this paper proposes that although teachers who engage in more OCB experience more exhaustion, they may simultaneously increase their feelings of personal accomplishment and work identification, which may in turn help to avert burnout. It is argued that only with this particular set of job attitudes are the effects of exhaustion caused by high levels of OCB sufficiently buffered to avoid job burnout, and thus positively affect students' quality of school life. The development and piloting of an instrument to measure teachers' OCB will be discussed. The preliminary findings reported herein are part of a larger ongoing study investigating the consequences of stress and OCB in primary school teachers.

GTM through time

The standard GTM (generative topographic mapping) algorithm assumes that the data on which it is trained consists of independent, identically distributed (iid) vectors. For time series, however, the iid assumption is a poor approximation. In this paper we show how the GTM algorithm can be extended to model time series by incorporating it as the emission density in a hidden Markov model. Since GTM has discrete hidden states we are able to find a tractable EM algorithm, based on the forward-backward algorithm, to train the model. We illustrate the performance of GTM through time using flight recorder data from a helicopter.

Delay estimation for multivariate time series

Most traditional methods for extracting the relationships between two time series are based on cross-correlation. In a non-linear non-stationary environment, these techniques are not sufficient. We show in this paper how to use hidden Markov models (HMMs) to identify the lag (or delay) between different variables for such data. We first present a method using maximum likelihood estimation and propose a simple algorithm which is capable of identifying associations between variables. We also adopt an information-theoretic approach and develop a novel procedure for training HMMs to maximise the mutual information between delayed time series. Both methods are successfully applied to real data. We model the oil drilling process with HMMs and estimate a crucial parameter, namely the lag for return.

Modelling financial time series with switching state space models

The deficiencies of stationary models applied to financial time series are well documented. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We use a dynamic switching (modelled by a hidden Markov model) combined with a linear dynamical system in a hybrid switching state space model (SSSM) and discuss the practical details of training such models with a variational EM algorithm due to [Ghahramani and Hilton,1998]. The performance of the SSSM is evaluated on several financial data sets and it is shown to improve on a number of existing benchmark methods.

Dynamical local models for segmentation and prediction of financial time series

In the analysis and prediction of many real-world time series, the assumption of stationarity is not valid. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We introduce a new model which combines a dynamic switching (controlled by a hidden Markov model) and a non-linear dynamical system. We show how to train this hybrid model in a maximum likelihood approach and evaluate its performance on both synthetic and financial data.

Approximate Bayesian techniques for inference in stochastic dynamical systems

This thesis is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variant of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here two new extended frameworks are derived and presented that are based on basis function expansions and local polynomial approximations of a recently proposed variational Bayesian algorithm. It is shown that the new extensions converge to the original variational algorithm and can be used for state estimation (smoothing). However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new methods are numerically validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein-Uhlenbeck process, for which the exact likelihood can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz '63 (3-dimensional model). The algorithms are also applied to the 40 dimensional stochastic Lorenz '96 system. In this investigation these new approaches are compared with a variety of other well known methods such as the ensemble Kalman filter / smoother, a hybrid Monte Carlo sampler, the dual unscented Kalman filter (for jointly estimating the systems states and model parameters) and full weak-constraint 4D-Var. Empirical analysis of their asymptotic behaviour as a function of observation density or length of time window increases is provided.

Estimating parameters in stochastic systems:a variational Bayesian approach

This work is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variation of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here a new extended framework is derived that is based on a local polynomial approximation of a recently proposed variational Bayesian algorithm. The paper begins by showing that the new extension of this variational algorithm can be used for state estimation (smoothing) and converges to the original algorithm. However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new approach is validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein–Uhlenbeck process, the exact likelihood of which can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz ’63 (3D model). As a special case the algorithm is also applied to the 40 dimensional stochastic Lorenz ’96 system. In our investigation we compare this new approach with a variety of other well known methods, such as the hybrid Monte Carlo, dual unscented Kalman filter, full weak-constraint 4D-Var algorithm and analyse empirically their asymptotic behaviour as a function of observation density or length of time window increases. In particular we show that we are able to estimate parameters in both the drift (deterministic) and the diffusion (stochastic) part of the model evolution equations using our new methods.