New Technologies for the Information Society

The omitting presence of a baseline (background, systemic noise, mobile phase) in liquid chromatography -mass spectrometry (LC-MS) measurements impedes objective analysis. Therefore, there is a demand to remove its contribution from the signal response. Elimination of baseline contribution is justified by increasing the data mining output, both qualitatively and quantitatively. Behavior of the baseline content is not perfectly constant on the time axis, and it is often necessary to experiment with gradient changes. However, the behavior could be parametrized using a technique derived from statistical moments. In this article, we propose adaptive thresholding as an unsupervised method for baseline removal from the measurement data. Results of a real analyte measurement are discussed to illustrate its efficiency.

Mass spectrometers are sophisticated, fine instruments which are essential in a variety applications. However, the data they produce are usually interpreted in a rather primitive way, without considering the accuracy of this data and the potential errors in identifying peaks. Our new approach corrects this situation by dividing the LC-MS output into three components: (i) signature of the analyte, (ii) random noise and (iii) systemic noise. The systemic noise is related to the instrument and to the particular experiment; its characteristics change in time and depend on the analyzed substance. Working with these components allows us to quantify the probability of peak errors and, at the same time, to retrieve some peaks which get lost in the noise when using the existing methods. Our software tool, Expertomica metabolite profiling, automatically evaluates the given instrument, detects compounds and calculates the probability of individual peaks. It does not need any artificial user-defined parameters or thresholds. Availability: MATLAB scripts with a simple graphical user interface are free to download from http://sourceforge.net/projects/ expertomica-eda/. The software reads data exported by most Thermo and Agilent spectrometers, and it can also read the more general JCAMP-DX ASCII format. Other formats will be supported on request, assuming that the user can provide representative data samples. Contact: [email protected]

High performance liquid chromatography (HPLC) in tandem with mass spectrometry (MS) is widely used in chemical and biochemical analysis of the content of measured samples, especially in so-called omics science. Currently, there are methods for processing and analyzing the measurement data sets from proteomics as well as metabolomics. However, only some of the problems of processing and analyzing tasks have been compensated for, and even in these cases, they have only been considered independently. Therefore, the current state of data handling is studied. This review describes the currently available methods and techniques commonly used in HPLC-MS for processing and analysis. All the main types of processing and most popular methods are mentioned. However, any overview will fail to be exhaustive. Therefore, some additional literature that goes into further detail is recommended. The review itself reveals some of the important shortcomings of the current state of the art. A list of relevant subtopics relating to different research focuses is provided in the conclusion.

This paper is focused on applying image analysis algorithms in biological field. Nowadays, all the modern measurement equipments produce a large amount data in digitally form including microscopes. Developing of automatic or semi-automatic tools e.g. in Matlab for various tasks could be helpful. The algorithm set described in this paper analyze a large sequence of microscope images of scenedesmus growth and evaluate quantitative features. Within developing this set, a novel segmentation algorithm which is based on artificial neural networks was proposed. This algorithm highly outperform the classical image processing approaches.

Time-lapse microscopic movies are being increasingly utilized for understanding the derivation of cell states and predicting cell futures. Often, fluorescence and other types of labeling are not available or desirable, and cell state-definitions based on observable structures must be used. We present the methodology for cell behavior recognition and prediction based on the short term cell recurrent behavior analysis. This approach has theoretical justification in non-linear dynamics theory. The methodology is based on the general stochastic systems theory which allows us to define the cell states, trajectory and the system itself. We introduce the usage of a novel image content descriptor based on information contribution (gain) by each image point for the cell state characterization as the first step. The linkage between the method and the general system theory is presented as a general frame for cell behavior interpretation. We also discuss extended cell description, system theory and methodology future development. The methodology may be used for many practical purposes, ranging from advanced, medically relevant, precise cell culture diagnostics to very utilitarian cell recognition in a noisy or uneven image background. In addition, the results are theoretically justified.

The construction of state trajectory of the cell is not a simple task. It must be decided what are the state variables. Since we work with coloured images, it sounds logical to start with the trajectory in RGB colour space. Each axis is then processed via several Rényi entropies. The value of Rényi coefficient spread or collapsed the trajectory along the axis of the state space. Each combination of Rényi coefficients gives us one subtraction of the whole trajectory. The major issue is how to choose the proper combination. However, all subtractions are correct. The single cell trajectory should be divided into several clusters, once the trajectory is constructed. Each cluster of the trajectory represents an event or subset of the states of the cell. Size and position of clusters depends on the trajectory and on number of clusters. We proposed eight clusters now as a first estimation, satisfied by the results: Clusters are well separated; images in transitions between clusters show some changes. Changes are also observable in images at the borders of the clusters. If we compare content of the clusters in different trajectories some images stay in the same cluster but some images may change the cluster. This is caused by the method of acquisition of the trajectory. The different coefficient in the Rényi equation highlights different part of the image so the trajectory can be little bit different as well as size, position and content of clusters. © Selection and peer-review under responsibility of FET11 conference organizers and published by Elsevier B.V.

Our analysis is based on the assumption that the metabolic and signal pathways substantiate non-linear dynamic processes which are responsible for asymptotic stability of biological systems. Observed events, not pathways themselves, are the elementary asymptotically stable objects to be studied. This assumption is theoretically supported by observed structures in a relatively simple pseudo-chemical agentbased model. The state space is given by coefficients of Rényi information entropy. Each combination of Rényi coefficients gives us one subtraction of the whole trajectory. The single cell trajectory should be divided into several clusters, once the trajectory is constructed. Each cluster of the trajectory represents an event or subset of the states of the cell. Size and position of clusters depends on the trajectory and on number of clusters. We propose a recipe, using the rules of the generalized stochastic system theory, how to extract individual trajectories of the multifractal system -the biological stochastic attractor. This set of trajectories is then utilized for thermodynamic representation of the biological chaotic attractor. Our analysis is strictly based on experiment with all its practical limits. We present implementation of analysis of development of cell monolayer observed by microphotography

This paper describes the effort with building speaker-clustered acoustic models as a part of the real-time LVCSR system that is used more than one year by the Czech TV for automatic subtitling of parliament meetings broadcasted on the channelČT24. Speaker-clustered acoustic models are more acoustically homogeneous and therefore give better recognition performance than single gender-independent model or even gender-dependent models. Frequent changes of speakers and a direct connection of the LVCSR system to the audio channel require an automatic switching/fusion of models as quickly as possible. An important part of the solution is real time likelihood evaluations of all clustered acoustic models, taking advantage of a fast GPU(Graphic Processing Unit). The proposed method achieved a WER reduction to the baseline gender-independent model over 2.34% relatively with more than 2M Gaussian mixtures evaluated in real-time.

An estimation of parameters of a multivariate Gaussian Mixture Model is usually based on a criterion (e.g. Maximum Likelihood) that is focused mostly on training data. Therefore, testing data, which were not seen during the training procedure, may cause problems. Moreover, numerical instabilities can occur (e.g. for low-occupied Gaussians especially when working with full-covariance matrices in high-dimensional spaces). Another question concerns the number of Gaussians to be trained for a specific data set. The approach proposed in this paper can handle all these issues. It is based on an assumption that the training and testing data were generated from the same source distribution. The key part of the approach is to use a criterion based on the source distribution rather than using the training data itself. It is shown how to modify an estimation procedure in order to fit the source distribution better (despite the fact that it is unknown), and subsequently new estimation algorithm for diagonal-as well as full-covariance matrices is derived and tested.

The main goal of this paper is to explore the methods of genderdependent acoustic modeling that would take the possibly of imperfect function of a gender detector into consideration. Such methods will be beneficial in realtime recognition tasks (eg. real-time subtitling of meetings) when the automatic gender detection is delayed or incorrect. The goal is to minimize an impact to the correct function of the recognizer. The paper also describes a technique of unsupervised splitting of training data, which can improve gender-dependent acoustic models trained on the basis of manual markers (male/female). The idea of this approach is grounded on the fact that a significant amount of "masculine" female and "feminine" male voices occurring in training corpora and also on frequent errors in manual markers.

Gaussian mixture models (GMMs) are often used in various data processing and classification tasks to model a continuous probability density in a multi-dimensional space. In cases, where the dimension of the feature space is relatively high (e.g. in the automatic speech recognition (ASR)), GMM with a higher number of Gaussians with diagonal covariances (DC) instead of full covariances (FC) is used from the two reasons. The first reason is a problem how to estimate robust FC matrices with a limited training data set. The second reason is a much higher computational cost during the GMM evaluation. The first reason was addressed in many recent publications. In contrast, this paper describes an efficient implementation on Graphic Processing Unit (GPU) of the FC-GMM evaluation, which addresses the second reason. The performance was tested on acoustic models for ASR, and it is shown that even a lowend laptop GPU is capable to evaluate a large acoustic model in a fraction of the real speech time. Three variants of the algorithm were implemented and compared on various GPUs: NVIDIA CUDA, NVIDIA OpenCL, and ATI/AMD OpenCL.

Gender-dependent (male/female) acoustic models are more acoustically homogeneous and therefore give better recognition performance than single gender-independent model. This paper deals with a problem how to use these gender-based acoustic models in a real-time LVCSR (Large Vocabulary Continuous Speech Recognition) system that is for more than one year used by the Czech TV for automatic subtitling of Parliament meetings that are broadcasted on the channelČT24. Frequent changes of speakers and the direct connection of the LVCSR system to the TV audio stream require switching/fusion of models automatically and as soon as possible. The paper presents various techniques based on using the output probabilities for quick selection of a better model or their combinations. The best proposed method achieved over 11% relative WER reduction in comparision with the GI model.

This paper presents a method for image preprocessing based on Shannon's definition of information entropy. The enhancement algorithm analyzes the information contribution made by individual pixels to the whole image. This method was developed specifically for use on microscopy images captured in phase-contrast mode. However, the approach is generally applcable to any image. The basic concept is that the background remains informatively poor, whereas the objects carry relevant information. This method preserves the details, highlights edges, and decreases random noise; all of this is done in one calculation. Two different ways of using the information entropy in image analysis are adopted and compared in our approach. Additionally, the performance of individual methods is illustrated and discussed. Finally, an optimization of the algorithm, as well as an implementation on a graphics processing unit (GPU), is described to overcome the high computational burden.