An evolutionary approach for determining Hidden Markov Model for medical image analysis

Hidden Markov Model (HMM) is a technique highly capable of modelling the structure of an observation sequence. In this paper, HMM is used to provide the contextual information for detecting clinical signs present in diabetic retinopathy screen images. However, there is a need to determine a feature set that best represents the complexity of the data as well as determine an optimal HMM. This paper addresses these problems by automatically selecting the best feature set while evolving the structure and obtaining the parameters of a Hidden Markov Model. This novel algorithm not only selects the best feature set, but also identifies the topology of the HMM, the optimal number of states, as well as the initial transition probabilities. © 2012 IEEE

Optimal and simultaneous designs of Hermitian transforms and masks for reducing intraclass separations of feature vectors for anomaly detection of diabetic retinopathy images

This paper proposes a novel methodology for the optimal and simultaneous designs of both Hermitian transforms and masks for reducing the intraclass separations of feature vectors for anomaly detection of diabetic retinopathy images. Each class of training images associates with a Hermitian transform, a mask and a known represented feature vector. The optimal and simultaneous designs of both the Hermitian transforms and the masks are formulated as least squares optimization problems subject to the Hermitian constraints. Since the optimal mask of each class of training images is dependent on the corresponding optimal Hermitian transform, only the Hermitian transforms are required to be designed. Nevertheless, the Hermitian transform design problems are optimization problems with highly nonlinear objective functions subject to the complex valued quadratic Hermitian constraints. This kind of optimization problems is very difficult to solve. To address the difficulty, this paper proposes a singular value decomposition approach for deriving a condition on the solutions of the optimization problems as well as an iterative approach for solving the optimization problems. Since the matrices characterizing the discrete Fourier transform, discrete cosine transform and discrete fractional Fourier transform are Hermitian, the Hermitian transforms designed by our proposed approach are more general than existing transforms. After both the Hermitian transforms and the masks for all classes of training images are designed, they are applied to test images. The test images will assign to the classes where the Euclidean 2-norms of the differences between the processed feature vectors of the test images and the corresponding represented feature vectors are minimum. Computer numerical simulation results show that the proposed methodology for the optimal and simultaneous designs of both the Hermitian transforms and the masks is very efficient and effective. The proposed technique is also very efficient and effective for reducing the intraclass separations of feature vectors for anomaly detection of diabetic retinopathy images. © 2012 IEEE

A Self-Reference False Memory Effect in the DRM Paradigm: Evidence from Eastern and Western Samples

It is well established that processing information in relation to oneself (i.e., selfreferencing) leads to better memory for that information than processing that same information in relation to others (i.e., other-referencing). However, it is unknown whether self-referencing also leads to more false memories than other-referencing. In the current two experiments with European and East Asian samples, we presented participants the Deese-Roediger/McDermott (DRM) lists together with their own name or other people’s name (i.e., “Trump” in Experiment 1 and “Li Ming” in Experiment 2). We found consistent results across the two experiments; that is, in the self-reference condition, participants had higher true and false memory rates compared to those in the other-reference condition. Moreover, we found that selfreferencing did not exhibit superior mnemonic advantage in terms of net accuracy compared to other-referencing and neutral conditions. These findings are discussed in terms of theoretical frameworks such as spreading activation theories and the fuzzytrace theory. We propose that our results reflect the adaptive nature of memory in the sense that cognitive processes that increase mnemonic efficiency may also increase susceptibility to associative false memories

A network medicine approach to quantify distance between hereditary disease modules on the interactome

We introduce a MeSH-based method that accurately quantifies similarity between heritable diseases at molecular level. This method effectively brings together the existing information about diseases that is scattered across the vast corpus of biomedical literature. We prove that sets of MeSH terms provide a highly descriptive representation of heritable disease and that the structure of MeSH provides a natural way of combining individual MeSH vocabularies. We show that our measure can be used effectively in the prediction of candidate disease genes. We developed a web application to query more than 28.5 million relationships between 7,574 hereditary diseases (96% of OMIM) based on our similarity measure

S100A4 downregulates filopodia formation through increased dynamic instability

Cell migration requires the initial formation of cell protrusions, lamellipodia and/or filopodia, the attachment of the leading lamella to extracellular cues and the formation and efficient recycling of focal contacts at the leading edge. The small calcium binding EF-hand protein S100A4 has been shown to promote cell motility but the direct molecular mechanisms responsible remain to be elucidated. In this work, we provide new evidences indicating that elevated levels of S100A4 affect the stability of filopodia and prevent the maturation of focal complexes. Increasing the levels of S100A4 in a rat mammary benign tumor derived cell line results in acquired cellular migration on the wound healing scratch assay. At the cellular levels, we found that high levels of S100A4 induce the formation of many nascent filopodia, but that only a very small and limited number of those can stably adhere and mature, as opposed to control cells, which generate fewer protrusions but are able to maintain these into more mature projections. This observation was paralleled by the fact that S100A4 overexpressing cells were unable to establish stable focal adhesions. Using different truncated forms of the S100A4 proteins that are unable to bind to myosin IIA, our data suggests that this newly identified functions of S100A4 is myosin-dependent, providing new understanding on the regulatory functions of S100A4 on cellular migration

SUSY Splits, But Then Returns

We study the phenomenon of accidental or "emergent" supersymmetry within gauge theory and connect it to the scenarios of Split Supersymmetry and Higgs compositeness. Combining these elements leads to a significant refinement and extension of the proposal of Partial Supersymmetry, in which supersymmetry is broken at very high energies but with a remnant surviving to the weak scale. The Hierarchy Problem is then solved by a non-trivial partnership between supersymmetry and compositeness, giving a promising approach for reconciling Higgs naturalness with the wealth of precision experimental data. We discuss aspects of this scenario from the AdS/CFT dual viewpoint of higher-dimensional warped compactification. It is argued that string theory constructions with high scale supersymmetry breaking which realize warped/composite solutions to the Hierarchy Problem may well be accompanied by some or all of the features described. The central phenomenological considerations and expectations are discussed, with more detailed modelling within warped effective field theory reserved for future work.Comment: 29 pages. Flavor and CP constraints on left-right symmetric structure briefly discussed. References adde

Distributionally robust L1-estimation in multiple linear regression

Linear regression is one of the most important and widely used techniques in data analysis, for which a key step is the estimation of the unknown parameters. However, it is often carried out under the assumption that the full information of the error distribution is available. This is clearly unrealistic in practice. In this paper, we propose a distributionally robust formulation of L1-estimation (or the least absolute value estimation) problem, where the only knowledge on the error distribution is that it belongs to a well-defined ambiguity set. We then reformulate the estimation problem as a computationally tractable conic optimization problem by using duality theory. Finally, a numerical example is solved as a conic optimization problem to demonstrate the effectiveness of the proposed approach

The use of Bayesian latent class cluster models to classify patterns of cognitive performance in healthy ageing

The main focus of this study is to illustrate the applicability of latent class analysis in the assessment of cognitive performance profiles during ageing. Principal component analysis (PCA) was used to detect main cognitive dimensions (based on the neurocognitive test variables) and Bayesian latent class analysis (LCA) models (without constraints) were used to explore patterns of cognitive performance among community-dwelling older individuals. Gender, age and number of school years were explored as variables. Three cognitive dimensions were identified: general cognition (MMSE), memory (MEM) and executive (EXEC) function. Based on these, three latent classes of cognitive performance profiles (LC1 to LC3) were identified among the older adults. These classes corresponded to stronger to weaker performance patterns (LC1>LC2>LC3) across all dimensions; each latent class denoted the same hierarchy in the proportion of males, age and number of school years. Bayesian LCA provided a powerful tool to explore cognitive typologies among healthy cognitive agers.The study is integrated in the "Maintaining health in old age through homeostasis (SWITCHBOX)" collaborative project funded by the European Commission FP7 initiative (grant HEALTH-F2-2010-259772). NS and JAP are main team members of the European consortium SWITCHBOX (http://www.switchbox-online.eu/). NCS is supported by a SwitchBox post-doctoral fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A new class of pluripotent stem cell cytotoxic small molecules

10.1371/journal.pone.0085039PLoS ONE93-POLN