Breanndán Ó Nualláin

Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting

Abstract Operational flood forecasting requires that accurate estimates of the uncertainty associated with model-generated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ensemble implementation of the Sacramento model used routinely by the National Weather Service for deterministic streamflow forecasting. The approach, the simultaneous optimization and data assimilation method (SODA), uses an ensemble Kalman filter (EnKF) for recursive state estimation allowing for treatment of streamflow data error, model structural error, and parameter uncertainty, while enabling implementation of the Sacramento model without major modification to its current structural form. Model parameters are estimated in batch using the shuffled complex evolution metropolis stochastic-ensemble optimization approach (SCEM-UA). The SODA approach was implemented using parallel computing to handle the increased computational requirements. Studies using data from the Leaf River...

Comparison of HIV-1 genotypic resistance test interpretation systems in predicting virological outcomes over time

Background Several decision support systems have been developed to interpret HIV-1 drug resistance genotyping results. This study compares the ability of the most commonly used systems (ANRS, Rega, and Stanfords HIVdb) to predict virological outcome at 12, 24, and 48 weeks. Methodology/Principal Findings Included were 3763 treatment-change episodes (TCEs) for which a HIV-1 genotype was available at the time of changing treatment with at least one follow-up viral load measurement. Genotypic susceptibility scores for the active regimens were calculated using scores defined by each interpretation system. Using logistic regression, we determined the association between the genotypic susceptibility score and proportion of TCEs having an undetectable viral load (<50 copies/ml) at 12 (8–16) weeks (2152 TCEs), 24 (16–32) weeks (2570 TCEs), and 48 (44–52) weeks (1083 TCEs). The Area under the ROC curve was calculated using a 10-fold cross-validation to compare the different interpretation systems regarding the sensitivity and specificity for predicting undetectable viral load. The mean genotypic susceptibility score of the systems was slightly smaller for HIVdb, with 1.92±1.17, compared to Rega and ANRS, with 2.22±1.09 and 2.23±1.05, respectively. However, similar odds ratios were found for the association between each-unit increase in genotypic susceptibility score and undetectable viral load at week 12; 1.6 [95% confidence interval 1.5–1.7] for HIVdb, 1.7 [1.5–1.8] for ANRS, and 1.7 [1.9–1.6] for Rega. Odds ratios increased over time, but remained comparable (odds ratios ranging between 1.9–2.1 at 24 weeks and 1.9–2.2 at 48 weeks). The Area under the curve of the ROC did not differ between the systems at all time points; p = 0.60 at week 12, p = 0.71 at week 24, and p = 0.97 at week 48. Conclusions/Significance Three commonly used HIV drug resistance interpretation systems ANRS, Rega and HIVdb predict virological response at 12, 24, and 48 weeks, after change of treatment to the same extent.

A Logical Approach to Formalizing Organizational Ecology

Theories should be consistent and coherent. Unfortunately, inconsistency, incoherence, and other defects in logic are difficult to detect when a theory is stated in natural language (e.g., English). Translation into a formal logical language makes the theorys structure more explicit, and better accessible for repair Furthermore, new hypotheses are more easily derived in a logical language. We formalize Hannan and Freemans theory of organizational inertia in first-order logic. We then examine the logical properties of the formalized theory, provide new theorems about organizational inertia, and discuss the implications of logicalformalization for sociological theorizing.

Application of parallel computing to stochastic parameter estimation in environmental models

Parameter estimation or model calibration is a common problem in many areas of process modeling, both in on-line applications such as real-time flood forecasting, and in off-line applications such as the modeling of reaction kinetics and phase equilibrium. The goal is to determine values of model parameters that provide the best fit to measured data, generally based on some type of least-squares or maximum likelihood criterion. Usually, this requires the solution of a non-linear and frequently non-convex optimization problem. In this paper we describe a user-friendly, computationally efficient parallel implementation of the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm for stochastic estimation of parameters in environmental models. Our parallel implementation takes better advantage of the computational power of a distributed computer system. Three case studies of increasing complexity demonstrate that parallel parameter estimation results in a considerable time savings when compared with traditional sequential optimization runs. The proposed method therefore provides an ideal means to solve complex optimization problems.

Extracting causal relations on HIV drug resistance from literature

BackgroundIn HIV treatment it is critical to have up-to-date resistance data of applicable drugs since HIV has a very high rate of mutation. These data are made available through scientific publications and must be extracted manually by experts in order to be used by virologists and medical doctors. Therefore there is an urgent need for a tool that partially automates this process and is able to retrieve relations between drugs and virus mutations from literature.ResultsIn this work we present a novel method to extract and combine relationships between HIV drugs and mutations in viral genomes. Our extraction method is based on natural language processing (NLP) which produces grammatical relations and applies a set of rules to these relations. We applied our method to a relevant set of PubMed abstracts and obtained 2,434 extracted relations with an estimated performance of 84% for F-score. We then combined the extracted relations using logistic regression to generate resistance values for each pair. The results of this relation combination show more than 85% agreement with the Stanford HIVDB for the ten most frequently occurring mutations. The system is used in 5 hospitals from the Virolab project http://www.virolab.org to preselect the most relevant novel resistance data from literature and present those to virologists and medical doctors for further evaluation.ConclusionsThe proposed relation extraction and combination method has a good performance on extracting HIV drug resistance data. It can be used in large-scale relation extraction experiments. The developed methods can also be applied to extract other type of relations such as gene-protein, gene-disease, and disease-mutation.

A Niche Width Model of Optimal Specialization

Niche width theory, a part of organizational ecology, predicts whether “specialist” or “generalist” forms of organizations have higher “fitness,” in a continually changing environment. To this end, niche width theory uses a mathematical model borrowed from biology. In this paper, we first loosen the specialist-generalist dichotomy, so that we can predict the optimal degree of specialization. Second, we generalize the model to a larger class of environmental conditions, on the basis of the models underlying assumptions. Third, we criticize the way the biological model is treated in sociological theory. Two of the models dimensions seem to be confused, i.e., that of trait and environment; the predicted optimal specialization is a property of individual organizations, not of populations; and, the distinction between “fine” and “coarse grained” environments is superfluous.

Online suffix trees with counts

This paper extend Ukkonens online suffix tree construction algorithm to support substring frequency queries, by adding count fields to the internal nodes of the tree. This has applications in the field of sequential data compression. One major problem is that Ukkonens online construction algorithm does not maintain explicit end of string markers in the tree. The major part of our work concerns quickly determining where the end markers for a particular edge would be, so that frequencies can be correctly obtained. So a complete characterization of all end markers on leaf edges is given. Furthermore we found that edges between two internal nodes can contain at most one end marker. Using these results, the algorithms are given to update the count fields and do frequency queries correctly. All algorithms have been implemented and tested correct in practice.

Ensemble-based prediction of SAT search behaviour

Abstract Abstract Before attempting to solve an instance of the satisfiability problem, what can we ascertain about the instance at hand and how can we put that information to use when selecting and tuning a SAT algorithm to solve the instance? We argue for an ensemble-based approach and describe an illustrative example of how such a methodology can be applied to determine optimal restart cutoff points for systematic, backtracking search procedures for SAT. We discuss the methodology and indicate how it can be applied to evaluate such strategies as restarts, algorithm comparison, randomization and portfolios of algorithms.

A collaborative environment allowing clinical investigations on integrated biomedical databases.

In order to perform clinical investigations on integrated biomedical data sets and to predict virological and epidemiological outcome, medical experts require an IT-based collaborative environment that provides them a user-friendly space for building and executing their complex studies and workflows on largely available and high-quality data repositories. In this paper, the authors introduce such a novel collaborative working environment a so-called virtual laboratory for clinicians and medical researchers, which allows users to interactively access and browse several biomedical research databases and re-use relevant data sets within own designed experiments. Firstly, technical details on the integration of relevant data resources into the virtual laboratory infrastructure and specifically developed user interfaces are briefly explained. The second part describes research possibilities for medical scientists including potential application fields and benefits as using the virtual laboratory functionalities for a particular exemplary study.

Discovering Motifs in Real-World Social Networks

We built a framework for analyzing the contents of large social networks, based on the approximate counting technique developed by Gonen and Shavitt. Our toolbox was used on data from a large forum—boards.ie—the most prominent community website in Ireland. For the purpose of this experiment, we were granted access to 10 years of forum data. This is the first time the approximate counting technique is tested on real-world, social network data.