The Classification of Vitreous Seeds in Retinoblastoma and Response to Intravitreal Melphalan.

PURPOSE: To evaluate the clinical characteristics of the 3 classifications of vitreous seeds in retinoblastoma-dust (class 1), spheres (class 2), and clouds (class 3)-and their responses to intravitreal melphalan. DESIGN: Retrospective, bi-institutional cohort study. PARTICIPANTS: A total of 87 patient eyes received 475 intravitreal injections of melphalan (median dose, 30 μg) given weekly, a median of 5 times (range, 1-12 times). METHODS: At presentation, the vitreous seeds were classified into 3 groups: dust, spheres, and clouds. Indirect ophthalmoscopy, fundus photography, ultrasonography, and ultrasonic biomicroscopy were used to evaluate clinical response to weekly intravitreal melphalan injections and time to regression of vitreous seeds. Kaplan-Meier estimates of time to regression and ocular survival, patient survival, and event-free survival (EFS) were calculated and then compared using the Mantel-Cox test of curve. MAIN OUTCOME MEASURES: Time to regression of vitreous seeds, patient survival, ocular survival, and EFS. RESULTS: The difference in time to regression was significantly different for the 3 seed classes (P < 0.0001): the median time to regression was 0.6, 1.7, and 7.7 months for dust, spheres, and clouds, respectively. Eyes with dust received significantly fewer injections and a lower median and cumulative dose of melphalan, whereas eyes with clouds received significantly more injections and a higher median and cumulative dose of melphalan. Overall, the 2-year Kaplan-Meier estimates for ocular survival, patient survival, and EFS (related to target seeds) were 90.4% (95% confidence interval [CI], 79.7-95.6), 100%, and 98.5% (95% CI, 90-99.7), respectively. CONCLUSIONS: The regression and response of vitreous seeds to intravitreal melphalan are different for each seed classification. The vitreous seed classification can be predictive of time to regression, number, median dose, and cumulative dose of intravitreal melphalan injections required.

Genotypic classification of patients with Wolfram syndrome: insights into the natural history of the disease and correlation with phenotype

Purpose: Wolfram syndrome is a degenerative, recessive rare disease with an onset in childhood. It is caused by mutations in WFS1 or CISD2 genes. More than 200 different variations in WFS1 have been described in patients with Wolfram syndrome, which complicates the establishment of clear genotype-phenotype correlation. The purpose of this study was to elucidate the role of WFS1 mutations and update the natural history of the disease. Methods: This study analyzed clinical and genetic data of 412 patients with Wolfram syndrome published in the last 15 years. Results: (i) 15% of published patients do not fulfill the current ­inclusion criterion; (ii) genotypic prevalence differences may exist among countries; (iii) diabetes mellitus and optic atrophy might not be the first two clinical features in some patients; (iv) mutations are nonuniformly distributed in WFS1; (v) age at onset of diabetes mellitus, hearing defects, and diabetes insipidus may depend on the patient"s genotypic class; and (vi) disease progression rate might depend on genotypic class. Conclusion: New genotype-phenotype correlations were established, disease progression rate for the general population and for the genotypic classes has been calculated, and new diagnostic criteria have been proposed. The conclusions raised could be important for patient management and counseling as well as for the development of treatments for Wolfram syndrome.

Classification-Based compression of Multispectral Images

The purpose of this thesis is to present a new approach to the lossy compression of multispectral images. Proposed algorithm is based on combination of quantization and clustering. Clustering was investigated for compression of the spatial dimension and the vector quantization was applied for spectral dimension compression. Presenting algo¬rithms proposes to compress multispectral images in two stages. During the first stage we define the classes' etalons, another words to each uniform areas are located inside the image the number of class is given. And if there are the pixels are not yet assigned to some of the clusters then it doing during the second; pass and assign to the closest eta¬lons. Finally a compressed image is represented with a flat index image pointing to a codebook with etalons. The decompression stage is instant too. The proposed method described in this paper has been tested on different satellite multispectral images from different resources. The numerical results and illustrative examples of the method are represented too.

A survey of transposable element classification systems--a call for a fundamental update to meet the challenge of their diversity and complexity.

The increase of publicly available sequencing data has allowed for rapid progress in our understanding of genome composition. As new information becomes available we should constantly be updating and reanalyzing existing and newly acquired data. In this report we focus on transposable elements (TEs) which make up a significant portion of nearly all sequenced genomes. Our ability to accurately identify and classify these sequences is critical to understanding their impact on host genomes. At the same time, as we demonstrate in this report, problems with existing classification schemes have led to significant misunderstandings of the evolution of both TE sequences and their host genomes. In a pioneering publication Finnegan (1989) proposed classifying all TE sequences into two classes based on transposition mechanisms and structural features: the retrotransposons (class I) and the DNA transposons (class II). We have retraced how ideas regarding TE classification and annotation in both prokaryotic and eukaryotic scientific communities have changed over time. This has led us to observe that: (1) a number of TEs have convergent structural features and/or transposition mechanisms that have led to misleading conclusions regarding their classification, (2) the evolution of TEs is similar to that of viruses by having several unrelated origins, (3) there might be at least 8 classes and 12 orders of TEs including 10 novel orders. In an effort to address these classification issues we propose: (1) the outline of a universal TE classification, (2) a set of methods and classification rules that could be used by all scientific communities involved in the study of TEs, and (3) a 5-year schedule for the establishment of an International Committee for Taxonomy of Transposable Elements (ICTTE).

Taxogenomics of the order Chlamydiales.

Bacterial classification is a long-standing problem for taxonomists and species definition itself is constantly debated among specialists. The classification of strict intracellular bacteria such as members of the order Chlamydiales mainly relies on DNA- or protein-based phylogenetic reconstructions because these organisms exhibit few phenotypic differences and are difficult to culture. The availability of full genome sequences allows the comparison of the performance of conserved protein sequences to reconstruct Chlamydiales phylogeny. This approach permits the identification of markers that maximize the phylogenetic signal and the robustness of the inferred tree. In this study, a set of 424 core proteins was identified and concatenated to reconstruct a reference species tree. Although individual protein trees present variable topologies, we detected only few cases of incongruence with the reference species tree, which were due to horizontal gene transfers. Detailed analysis of the phylogenetic information of individual protein sequences (i) showed that phylogenies based on single randomly chosen core proteins are not reliable and (ii) led to the identification of twenty taxonomically highly reliable proteins, allowing the reconstruction of a robust tree close to the reference species tree. We recommend using these protein sequences to precisely classify newly discovered isolates at the family, genus and species levels.

Text Classification and Indexing in IP Networks

Internet on elektronisen postin perusrakenne ja ollut tärkeä tiedonlähde akateemisille käyttäjille jo pitkään. Siitä on tullut merkittävä tietolähde kaupallisille yrityksille niiden pyrkiessä pitämään yhteyttä asiakkaisiinsa ja seuraamaan kilpailijoitansa. WWW:n kasvu sekä määrällisesti että sen moninaisuus on luonut kasvavan kysynnän kehittyneille tiedonhallintapalveluille. Tällaisia palveluja ovet ryhmittely ja luokittelu, tiedon löytäminen ja suodattaminen sekä lähteiden käytön personointi ja seuranta. Vaikka WWW:stä saatavan tieteellisen ja kaupallisesti arvokkaan tiedon määrä on huomattavasti kasvanut viime vuosina sen etsiminen ja löytyminen on edelleen tavanomaisen Internet hakukoneen varassa. Tietojen hakuun kohdistuvien kasvavien ja muuttuvien tarpeiden tyydyttämisestä on tullut monimutkainen tehtävä Internet hakukoneille. Luokittelu ja indeksointi ovat merkittävä osa luotettavan ja täsmällisen tiedon etsimisessä ja löytämisessä. Tämä diplomityö esittelee luokittelussa ja indeksoinnissa käytettävät yleisimmät menetelmät ja niitä käyttäviä sovelluksia ja projekteja, joissa tiedon hakuun liittyvät ongelmat on pyritty ratkaisemaan.

Using music as a signal for biofeedback

Studies on the potential benefits of conveying biofeedback stimulus using a musical signal have appeared in recent years with the intent of harnessing the strong effects that music listening may have on subjects. While results are encouraging, the fundamental question has yet to be addressed, of how combined music and biofeedback compares to the already established use of either of these elements separately. This experiment, involving young adults (N = 24), compared the effectiveness at modulating participants' states of physiological arousal of each of the following conditions: A) listening to pre-recorded music, B) sonification biofeedback of the heart rate, and C) an algorithmically modulated musical feedback signal conveying the subject's heart rate. Our hypothesis was that each of the conditions (A), (B) and (C) would differ from the other two in the extent to which it enables participants to increase and decrease their state of physiological arousal, with (C) being more effective than (B), and both more than (A). Several physiological measures and qualitative responses were recorded and analyzed. Results show that using musical biofeedback allowed participants to modulate their state of physiological arousal at least equally well as sonification biofeedback, and much better than just listening to music, as reflected in their heart rate measurements, controlling for respiration-rate. Our findings indicate that the known effects of music in modulating arousal can therefore be beneficially harnessed when designing a biofeedback protocol.

Single cell tuning of Myc expression by antigen receptor signal strength and interleukin-2 in T lymphocytes.

Myc controls the metabolic reprogramming that supports effector T cell differentiation. The expression of Myc is regulated by the T cell antigen receptor (TCR) and pro-inflammatory cytokines such as interleukin-2 (IL-2). We now show that the TCR is a digital switch for Myc mRNA and protein expression that allows the strength of the antigen stimulus to determine the frequency of T cells that express Myc. IL-2 signalling strength also directs Myc expression but in an analogue process that fine-tunes Myc quantity in individual cells via post-transcriptional control of Myc protein. Fine-tuning Myc matters and is possible as Myc protein has a very short half-life in T cells due to its constant phosphorylation by glycogen synthase kinase 3 (GSK3) and subsequent proteasomal degradation. We show that Myc only accumulates in T cells exhibiting high levels of amino acid uptake allowing T cells to match Myc expression to biosynthetic demands. The combination of digital and analogue processes allows tight control of Myc expression at the population and single cell level during immune responses.

ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection

To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20 sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts (eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.), (iii) Source of abnormally high amplitude ( �100 �V). We then evaluated the outcome of this cleaning by means of the classification of patients using multilayer perceptron neural networks. Results are very satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning procedure.

ICA as a preprocessing technique for classification

In this paper we propose the use of the independent component analysis (ICA) [1] technique for improving the classification rate of decision trees and multilayer perceptrons [2], [3]. The use of an ICA for the preprocessing stage, makes the structure of both classifiers simpler, and therefore improves the generalization properties. The hypothesis behind the proposed preprocessing is that an ICA analysis will transform the feature space into a space where the components are independent, and aligned to the axes and therefore will be more adapted to the way that a decision tree is constructed. Also the inference of the weights of a multilayer perceptron will be much easier because the gradient search in the weight space will follow independent trajectories. The result is that classifiers are less complex and on some databases the error rate is lower. This idea is also applicable to regression

Feature Selection, Ranking of Each Feature and Classification for the Diagnosis of Community Acquired Legionella Pneumonia

Diagnosis of community acquired legionella pneumonia (CALP) is currently performed by means of laboratory techniques which may delay diagnosis several hours. To determine whether ANN can categorize CALP and non-legionella community-acquired pneumonia (NLCAP) and be standard for use by clinicians, we prospectively studied 203 patients with community-acquired pneumonia (CAP) diagnosed by laboratory tests. Twenty one clinical and analytical variables were recorded to train a neural net with two classes (LCAP or NLCAP class). In this paper we deal with the problem of diagnosis, feature selection, and ranking of the features as a function of their classification importance, and the design of a classifier the criteria of maximizing the ROC (Receiving operating characteristics) area, which gives a good trade-off between true positives and false negatives. In order to guarantee the validity of the statistics; the train-validation-test databases were rotated by the jackknife technique, and a multistarting procedure was done in order to make the system insensitive to local maxima.