Medical free-text to concept mapping as an information retrieval problem

Concept mapping involves determining relevant concepts from a free-text input, where concepts are defined in an external reference ontology. This is an important process that underpins many applications for clinical information reporting, derivation of phenotypic descriptions, and a number of state-of-the-art medical information retrieval methods. Concept mapping can be cast into an information retrieval (IR) problem: free-text mentions are treated as queries and concepts from a reference ontology as the documents to be indexed and retrieved. This paper presents an empirical investigation applying general-purpose IR techniques for concept mapping in the medical domain. A dataset used for evaluating medical information extraction is adapted to measure the effectiveness of the considered IR approaches. Standard IR approaches used here are contrasted with the effectiveness of two established benchmark methods specifically developed for medical concept mapping. The empirical findings show that the IR approaches are comparable with one benchmark method but well below the best benchmark.

Post-transcriptional regulation in the nucleus and cytoplasm: study of mean time to threshold (MTT) and narrow escape problem

Messenger RNAs (mRNAs) can be repressed and degraded by small non-coding RNA molecules. In this paper, we formulate a coarsegrained Markov-chain description of the post-transcriptional regulation of mRNAs by either small interfering RNAs (siRNAs) or microRNAs (miRNAs). We calculate the probability of an mRNA escaping from its domain before it is repressed by siRNAs/miRNAs via cal- culation of the mean time to threshold: when the number of bound siRNAs/miRNAs exceeds a certain threshold value, the mRNA is irreversibly repressed. In some cases,the analysis can be reduced to counting certain paths in a reduced Markov model. We obtain explicit expressions when the small RNA bind irreversibly to the mRNA and we also discuss the reversible binding case. We apply our models to the study of RNA interference in the nucleus, examining the probability of mRNAs escaping via small nuclear pores before being degraded by siRNAs. Using the same modelling framework, we further investigate the effect of small, decoy RNAs (decoys) on the process of post-transcriptional regulation, by studying regulation of the tumor suppressor gene, PTEN : decoys are able to block binding sites on PTEN mRNAs, thereby educing the number of sites available to siRNAs/miRNAs and helping to protect it from repression. We calculate the probability of a cytoplasmic PTEN mRNA translocating to the endoplasmic reticulum before being repressed by miRNAs. We support our results with stochastic simulations

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

Palliative care problem severity score: Reliability and acceptability in a national study

Background The Palliative Care Problem Severity Score is a clinician-rated tool to assess problem severity in four palliative care domains (pain, other symptoms, psychological/spiritual, family/carer problems) using a 4-point categorical scale (absent, mild, moderate, severe). Aim To test the reliability and acceptability of the Palliative Care Problem Severity Score. Design: Multi-centre, cross-sectional study involving pairs of clinicians independently rating problem severity using the tool. Setting/participants Clinicians from 10 Australian palliative care services: 9 inpatient units and 1 mixed inpatient/community-based service. Results A total of 102 clinicians participated, with almost 600 paired assessments completed for each domain, involving 420 patients. A total of 91% of paired assessments were undertaken within 2 h. Strength of agreement for three of the four domains was moderate: pain (Kappa = 0.42, 95% confidence interval = 0.36 to 0.49); psychological/spiritual (Kappa = 0.48, 95% confidence interval = 0.42 to 0.54); family/carer (Kappa = 0.45, 95% confidence interval = 0.40 to 0.52). Strength of agreement for the remaining domain (other symptoms) was fair (Kappa = 0.38, 95% confidence interval = 0.32 to 0.45). Conclusion The Palliative Care Problem Severity Score is an acceptable measure, with moderate reliability across three domains. Variability in inter-rater reliability across sites and participant feedback indicate that ongoing education is required to ensure that clinicians understand the purpose of the tool and each of its domains. Raters familiar with the patient they were assessing found it easier to assign problem severity, but this did not improve inter-rater reliability.

An Approximate Solution Of One-Dimensional Piston Problem

A new theory of shock dynamics has been developed in the form of a finite number of compatibility conditions along shock rays. It has been used to study the growth or decay of shock strength for accelerating or decelerating piston starting with a nonzero piston velocity. The results show good agreement with those obtained by Harten's high resolution TVD scheme.

A Quasi-Newton Adaptive Algorithm for Generalized Symmetric Eigenvalue Problem

In this paper, we first recast the generalized symmetric eigenvalue problem, where the underlying matrix pencil consists of symmetric positive definite matrices, into an unconstrained minimization problem by constructing an appropriate cost function, We then extend it to the case of multiple eigenvectors using an inflation technique, Based on this asymptotic formulation, we derive a quasi-Newton-based adaptive algorithm for estimating the required generalized eigenvectors in the data case. The resulting algorithm is modular and parallel, and it is globally convergent with probability one, We also analyze the effect of inexact inflation on the convergence of this algorithm and that of inexact knowledge of one of the matrices (in the pencil) on the resulting eigenstructure. Simulation results demonstrate that the performance of this algorithm is almost identical to that of the rank-one updating algorithm of Karasalo. Further, the performance of the proposed algorithm has been found to remain stable even over 1 million updates without suffering from any error accumulation problems.

Integral Formulation of the Problem of Current Distribution in Compulsator Wires of Electromagnetic Launchers and Railguns

Compulsators are power sources of choice for use in electromagnetic launchers and railguns. These devices hold the promise of reducing unit costs of payload to orbit. In an earlier work, the author had calculated the current distribution in compulsator wires by considering the wire to be split into a finite number of separate wires. The present work develops an integral formulation of the problem of current distribution in compulsator wires which leads to an integrodifferential equation. Analytical solutions, including those for the integration constants, are obtained in closed form. The analytical solutions present a much clearer picture of the effect of various input parameters on the cross-sectional current distribution and point to ways in which the desired current density distribution can be achieved. Results are graphically presented and discussed, with particular reference to a 50-kJ compulsator in Bangalore. Finite-element analysis supports the results.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

The Implication Problem for Functional and Multivalued Dependencies

Computation of the dependency basis is the fundamental step in solving the implication problem for MVDs in relational database theory. We examine this problem from an algebraic perspective. We introduce the notion of the inference basis of a set M of MVDs and show that it contains the maximum information about the logical consequences of M. We propose the notion of an MVD-lattice and develop an algebraic characterization of the inference basis using simple notions from lattice theory. We also establish several properties of MVD-lattices related to the implication problem. Founded on our characterization, we synthesize efficient algorithms for (a) computing the inference basis of a given set M of MVDs; (b) computing the dependency basis of a given attribute set w.r.t. M; and (c) solving the implication problem for MVDs. Finally, we show that our results naturally extend to incorporate FDs also in a way that enables the solution of the implication problem for both FDs and MVDs put together.

Quantum first-passage problem

Formulation of quantum first passage problem is attempted in terms of a restricted Feynman path integral that simulates an absorbing barrier as in the corresponding classical case. The positivity of the resulting probability density, however, remains to be demonstrated.

A Literary Turn : Rethinking the Roles of Generalization and Theory in Anglo-American Moral Philosophy

This work investigates the role of narrative literature in late-20th century and contemporary Anglo-American moral philosophy. It aims to show the trend of reading narrative literature for purposes of moral philosophy from the 1970 s and early 80 s to the present day as a part of a larger movement in Anglo-American moral philosophy, and to present a view of its significance for moral philosophy overall. Chapter 1 provides some preliminaries concerning the view of narrative literature which my discussion builds on. In chapter 2 I give an outline of how narrative literature is considered in contemporary Anglo-American moral philosophy, and connect this use to the broad trend of neo-Aristotelian ethics in this context. In chapter 3 I connect the use of literature to the idea of the non-generalizability of moral perception and judgment, which is central to the neo-Aristotelian trend, as well as to a range of moral particularisms and anti-theoretical positions of late 20th century and contemporary ethics. The joint task of chapters 2 and 3 is to situate the trend of reading narrative literature for the purposes of moral philosophy in the present context of moral philosophy. In the following two chapters, 4 and 5, I move on from the particularizing power of narrative literature, which is emphasized by neo-Aristotelians and particularists alike, to a broader under-standing of the intellectual potential of narrative literature. In chapter 4 I argue that narrative literature has its own forms of generalization which are enriching for our understanding of the workings of ethical generalizations in philosophy. In chapter 5 I discuss Iris Murdoch s and Martha Nussbaum s respective ways of combining ethical generality and particularity in a philosophical framework where both systematic moral theory and narrative literature are taken seriously. In chapter 6 I analyse the controversy between contemporary anti-theoretical conceptions of ethics and Nussbaum s refutation of these. I present my suggestion for how the significance of the ethics/literature discussion for moral philosophy can be understood if one wants to overcome the limitations of both Nussbaum s theory-centred, equilibrium-seeking perspective, and the anti-theorists repudiation of theory. I call my position the inclusive approach .

A simplified approach to a three-part Wiener-Hopf problem arising in diffraction theory

A direct and simple approach, utilizing Watson's lemma, is presented for obtaining an approximate solution of a three-part Wiener-Hopf problem associated with the problem of diffraction of a plane wave by a soft strip.

Non-reductive Physicalism, Irreducibility of the Mental and the Problem of Mental Causation : A study of Donald Davidson's and Georg Henrik von Wright's positions in the philosophy of mind

In this study I consider what kind of perspective on the mind body problem is taken and can be taken by a philosophical position called non-reductive physicalism. Many positions fall under this label. The form of non-reductive physicalism which I discuss is in essential respects the position taken by Donald Davidson (1917-2003) and Georg Henrik von Wright (1916-2003). I defend their positions and discuss the unrecognized similarities between their views. Non-reductive physicalism combines two theses: (a) Everything that exists is physical; (b) Mental phenomena cannot be reduced to the states of the brain. This means that according to non-reductive physicalism the mental aspect of humans (be it a soul, mind, or spirit) is an irreducible part of the human condition. Also Davidson and von Wright claim that, in some important sense, the mental aspect of a human being does not reduce to the physical aspect, that there is a gap between these aspects that cannot be closed. I claim that their arguments for this conclusion are convincing. I also argue that whereas von Wright and Davidson give interesting arguments for the irreducibility of the mental, their physicalism is unwarranted. These philosophers do not give good reasons for believing that reality is thoroughly physical. Notwithstanding the materialistic consensus in the contemporary philosophy of mind the ontology of mind is still an uncharted territory where real breakthroughs are not to be expected until a radically new ontological position is developed. The third main claim of this work is that the problem of mental causation cannot be solved from the Davidsonian - von Wrightian perspective. The problem of mental causation is the problem of how mental phenomena like beliefs can cause physical movements of the body. As I see it, the essential point of non-reductive physicalism - the irreducibility of the mental - and the problem of mental causation are closely related. If mental phenomena do not reduce to causally effective states of the brain, then what justifies the belief that mental phenomena have causal powers? If mental causes do not reduce to physical causes, then how to tell when - or whether - the mental causes in terms of which human actions are explained are actually effective? I argue that this - how to decide when mental causes really are effective - is the real problem of mental causation. The motivation to explore and defend a non-reductive position stems from the belief that reductive physicalism leads to serious ethical problems. My claim is that Davidson's and von Wright's ultimate reason to defend a non-reductive view comes back to their belief that a reductive understanding of human nature would be a narrow and possibly harmful perspective. The final conclusion of my thesis is that von Wright's and Davidson's positions provide a starting point from which the current scientistic philosophy of mind can be critically further explored in the future.

On a Two-Dimensional Problem in the End-Block Design of Post-Tensioned Prestressed Concrete Beams

Abstract is not available.