Functional Brain Imaging with Multi-Objective Multi-Modal Evolutionary Optimization

Functional brain imaging is a source of spatio-temporal data mining problems. A new framework hybridizing multi-objective and multi-modal optimization is proposed to formalize these data mining problems, and addressed through Evolutionary Computation (EC). The merits of EC for spatio-temporal data mining are demonstrated as the approach facilitates the modelling of the experts' requirements, and flexibly accommodates their changing goals

Quantifying non-Gaussianity for quantum information

We address the quantification of non-Gaussianity of states and operations in continuous-variable systems and its use in quantum information. We start by illustrating in details the properties and the relationships of two recently proposed measures of non-Gaussianity based on the Hilbert-Schmidt (HS) distance and the quantum relative entropy (QRE) between the state under examination and a reference Gaussian state. We then evaluate the non-Gaussianities of several families of non-Gaussian quantum states and show that the two measures have the same basic properties and also share the same qualitative behaviour on most of the examples taken into account. However, we also show that they introduce a different relation of order, i.e. they are not strictly monotone each other. We exploit the non-Gaussianity measures for states in order to introduce a measure of non-Gaussianity for quantum operations, to assess Gaussification and de-Gaussification protocols, and to investigate in details the role played by non-Gaussianity in entanglement distillation protocols. Besides, we exploit the QRE-based non-Gaussianity measure to provide new insight on the extremality of Gaussian states for some entropic quantities such as conditional entropy, mutual information and the Holevo bound. We also deal with parameter estimation and present a theorem connecting the QRE nonG to the quantum Fisher information. Finally, since evaluation of the QRE nonG measure requires the knowledge of the full density matrix, we derive some {\em experimentally friendly} lower bounds to nonG for some class of states and by considering the possibility to perform on the states only certain efficient or inefficient measurements.Comment: 22 pages, 13 figures, comments welcome. v2: typos corrected and references added. v3: minor corrections (more similar to published version

Donor genetic determinant of thymopoiesis, rs2204985, and stem cell transplantation outcome in a multipopulation cohort

\ua9 2024 The Author(s)Background: A genetic polymorphism, rs2204985, has been reported to be associated with the diversity of T-cell antigen receptor repertoire and TREC levels, reflecting the function of the thymus. As the thymus function can be assumed to be an important factor regulating the outcome of stem cell transplantation (SCT), it was of great interest that rs2204985 showed a genetic association to disease-free and overall survival in a German SCT donor cohort. Tools to predict the outcome of SCT more accurately would help in risk assessment and patient safety. Objective: To evaluate the general validity of the original genetic association found in the German cohort, we determined genetic associations between rs2204985 and the outcome of SCT in 1,473 SCT donors from four different populations. Study design: Genetic associations between rs2204985 genotype AA versus AG/GG and overall survival (OS) and disease-free survival (DFS) in 1,473 adult, allogeneic SCT from Finland, the United Kingdom, Spain, and Poland were performed using the Kaplan-Meier analysis and log-rank tests. We adjusted the survival models with covariates using Cox regression. Results: In unrelated SCT donors (N = 425), the OS of genotype AA versus AG/GG had a trend for a similar association (p = 0.049, log-rank test) as previously reported in the German cohort. The trend did not remain significant in the Cox regression analysis with covariates. No other associations were found. Conclusion: Weak support for the genetic association between rs2204985, previously also associated with thymus function, and the outcome of SCT could be found in a cohort from four populations

Reproducibility Assessment of Independent Component Analysis of Expression Ratios From DNA Microarrays

DNA microarrays allow the measurement of transcript abundances for thousands of genes in parallel. Most commonly, a particular sample of interest is studied next to a neutral control, examining relative changes (ratios). Independent component analysis (ICA) is a promising modern method for the analysis of such experiments. The condition of ICA algorithms can, however, depend on the characteristics of the data examined, making algorithm properties such as robustness specific to the given application domain. To address the lack of studies examining the robustness of ICA applied to microarray measurements, we report on the stability of variational Bayesian ICA in this domain. Microarray data are usually preprocessed and transformed. Hence we first examined alternative transforms and data selections for the smallest modelling reconstruction errors. Log-ratio data are reconstructed better than non-transformed ratio data by our linear model with a Gaussian error term. To compare ICA results we must allow for ICA invariance under rescaling and permutation of the extracted signatures, which hold the loadings of the original variables (gene transcript ratios) on particular latent variables. We introduced a method to optimally match corresponding signatures between sets of results. The stability of signatures was then examined after (1) repetition of the same analysis run with different random number generator seeds, and (2) repetition of the analysis with partial data sets. The effects of both dropping a proportion of the gene transcript ratios and dropping measurements for several samples have been studied. In summary, signatures with a high relative data power were very likely to be retained, resulting in an overall stability of the analyses. Our analysis of 63 yeast wildtype vs. wild-type experiments, moreover, yielded 10 reliably identified signatures, demonstrating that the variance observed is not just noise

Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials

BACKGROUND: Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." RESULTS: The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. CONCLUSION: We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself

Relation of gallbladder function and Helicobacter pylori infection to gastric mucosa inflammation in patients with symptomatic cholecystolithiasis

Background. Inflammatory alterations of the gastric mucosa are commonly caused by Helicobacter pylori (Hp) infection in patients with symptomatic gallstone disease. However, the additional pathogenetic role of an impaired gallbladder function leading to an increased alkaline duodenogastric reflux is controversially discussed. Aim:To investigate the relation of gallbladder function and Hp infection to gastric mucosa inflammation in patients with symptomatic gallstones prior to cholecystectomy. Patients: Seventy-three patients with symptomatic gallstones were studied by endoscopy and Hp testing. Methods: Gastritis classification was performed according to the updated Sydney System and gallbladder function was determined by total lipid concentration of gallbladder bile collected during mainly laparoscopic cholecystectomy. Results: Fifteen patients revealed no, 39 patients mild, and 19 moderate to marked gastritis. No significant differences for bile salts, phospholipids, cholesterol, or total lipids in gallbladder bile were found between these three groups of patients. However, while only 1 out of 54 (< 2%) patients with mild or no gastritis was found histologically positive for Hp, this infection could be detected in 14 (74%) out of 19 patients with moderate to marked gastritis. Conclusion: Moderate to marked gastric mucosa inflammation in gallstone patients is mainly caused by Hp infection, whereas gallbladder function is not related to the degree of gastritis. Thus, an increased alkaline duodenogastric reflux in gallstone patients seems to be of limited pathophysiological relevance. Copyright (c) 2006 S. Karger AG, Basel

Green areas around homes reduce atopic sensitization in children

Peer reviewe

Dimensionality reduction and prediction of the protein macromolecule dissolution profile

A suitable regression model for predicting the dissolution profile of Poly (lactic-co-glycolic acid) (PLGA) micro-and nanoparticles can play a significant role in pharmaceutical/medical applications. The rate of dissolution of proteins is influenced by several factors and taking all such influencing factors into account; we have a dataset in hand with three hundred input features. Therefore, a primary approach before identifying a regression model is to reduce the dimensionality of the dataset at hand. On the one hand, we have adopted Backward Elimination Feature selection techniques for an exhaustive analysis of the predictability of each combination of features. On the other hand, several linear and non-linear feature extraction methods are used in order to extract a new set of features out of the available dataset. A comprehensive experimental analysis for the selection or extraction of features and identification of the corresponding prediction model is offered. The designed experiment and prediction models offer substantially better performance over the earlier proposed prediction models in literature for the said problem

Defect detection in textile fabric images using subband domain subspace analysis

In this work, a new model that combines the concepts of wavelet transformation and subspace analysis tools, like Independent Component Analysis, Topographic Independent Component Analysis, and Independent Subspace Analysis, is developed for the purpose of defect detection in textile images. In previous works, it has been shown that reduction of the textural components of the textile image by preprocessing has increased the performance of the system. Based on this observation, in present work, the aforementioned subspace analysis tools are aimed to be applied on the sub-band images. The feature vector of a sub-window of a test image is compared with that of the defect-free image in order to make a decision. This decision is based on a Euclidean distance classifier. The performance increase that results using wavelet transformation prior to subspace analysis has been discussed in detail. While all the subspace analysis methods has been found to lead to the same detection performances, as a further step, independent subspace analysis is used to classify the detected defects according to their directionalities