A copula-based method for synthetic microarray data generation

In this work, we propose a copula-based method to generate synthetic gene expression data that account for marginal and joint probability distributions features captured from real data. Our method allows us to implant significant genes in the synthetic dataset in a controlled manner, giving the possibility of testing new detection algorithms under more realistic environments.

Nonlinear prediction based on score function

The linear prediction coding of speech is based in the assumption that the generation model is autoregresive. In this paper we propose a structure to cope with the nonlinear effects presents in the generation of the speech signal. This structure will consist of two stages, the first one will be a classical linear prediction filter, and the second one will model the residual signal by means of two nonlinearities between a linear filter. The coefficients of this filter are computed by means of a gradient search on the score function. This is done in order to deal with the fact that the probability distribution of the residual signal still is not gaussian. This fact is taken into account when the coefficients are computed by a ML estimate. The algorithm based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics and is based on blind deconvolution of Wiener systems [1]. Improvements in the experimental results with speech signals emphasize on the interest of this approach.

Improving Pitch Tracking Performance in Hard Noise Conditions by a Preprocessing Based on Mathematical Morphology

In this paper we show how a nonlinear preprocessing of speech signal -with high noise- based on morphological filters improves the performance of robust algorithms for pitch tracking (RAPT). This result happens for a very simple morphological filter. More sophisticated ones could even improve such results. Mathematical morphology is widely used in image processing and has a great amount of applications. Almost all its formulations derived in the two-dimensional framework are easily reformulated to be adapted to one-dimensional context

Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance.

Derivation of an observer model adapted to irregular signals based on convolution channels.

Anthropomorphic model observers are mathe- matical algorithms which are applied to images with the ultimate goal of predicting human signal detection and classification accuracy across varieties of backgrounds, image acquisitions and display conditions. A limitation of current channelized model observers is their inability to handle irregularly-shaped signals, which are common in clinical images, without a high number of directional channels. Here, we derive a new linear model observer based on convolution channels which we refer to as the "Filtered Channel observer" (FCO), as an extension of the channelized Hotelling observer (CHO) and the nonprewhitening with an eye filter (NPWE) observer. In analogy to the CHO, this linear model observer can take the form of a single template with an external noise term. To compare with human observers, we tested signals with irregular and asymmetrical shapes spanning the size of lesions down to those of microcalfications in 4-AFC breast tomosynthesis detection tasks, with three different contrasts for each case. Whereas humans uniformly outperformed conventional CHOs, the FCO observer outperformed humans for every signal with only one exception. Additive internal noise in the models allowed us to degrade model performance and match human performance. We could not match all the human performances with a model with a single internal noise component for all signal shape, size and contrast conditions. This suggests that either the internal noise might vary across signals or that the model cannot entirely capture the human detection strategy. However, the FCO model offers an efficient way to apprehend human observer performance for a non-symmetric signal.

Search Interfaces on the Web: Querying and Characterizing

Current-day web search engines (e.g., Google) do not crawl and index a significant portion of theWeb and, hence, web users relying on search engines only are unable to discover and access a large amount of information from the non-indexable part of the Web. Specifically, dynamic pages generated based on parameters provided by a user via web search forms (or search interfaces) are not indexed by search engines and cannot be found in searchers’ results. Such search interfaces provide web users with an online access to myriads of databases on the Web. In order to obtain some information from a web database of interest, a user issues his/her query by specifying query terms in a search form and receives the query results, a set of dynamic pages that embed required information from a database. At the same time, issuing a query via an arbitrary search interface is an extremely complex task for any kind of automatic agents including web crawlers, which, at least up to the present day, do not even attempt to pass through web forms on a large scale. In this thesis, our primary and key object of study is a huge portion of the Web (hereafter referred as the deep Web) hidden behind web search interfaces. We concentrate on three classes of problems around the deep Web: characterization of deep Web, finding and classifying deep web resources, and querying web databases. Characterizing deep Web: Though the term deep Web was coined in 2000, which is sufficiently long ago for any web-related concept/technology, we still do not know many important characteristics of the deep Web. Another matter of concern is that surveys of the deep Web existing so far are predominantly based on study of deep web sites in English. One can then expect that findings from these surveys may be biased, especially owing to a steady increase in non-English web content. In this way, surveying of national segments of the deep Web is of interest not only to national communities but to the whole web community as well. In this thesis, we propose two new methods for estimating the main parameters of deep Web. We use the suggested methods to estimate the scale of one specific national segment of the Web and report our findings. We also build and make publicly available a dataset describing more than 200 web databases from the national segment of the Web. Finding deep web resources: The deep Web has been growing at a very fast pace. It has been estimated that there are hundred thousands of deep web sites. Due to the huge volume of information in the deep Web, there has been a significant interest to approaches that allow users and computer applications to leverage this information. Most approaches assumed that search interfaces to web databases of interest are already discovered and known to query systems. However, such assumptions do not hold true mostly because of the large scale of the deep Web – indeed, for any given domain of interest there are too many web databases with relevant content. Thus, the ability to locate search interfaces to web databases becomes a key requirement for any application accessing the deep Web. In this thesis, we describe the architecture of the I-Crawler, a system for finding and classifying search interfaces. Specifically, the I-Crawler is intentionally designed to be used in deepWeb characterization studies and for constructing directories of deep web resources. Unlike almost all other approaches to the deep Web existing so far, the I-Crawler is able to recognize and analyze JavaScript-rich and non-HTML searchable forms. Querying web databases: Retrieving information by filling out web search forms is a typical task for a web user. This is all the more so as interfaces of conventional search engines are also web forms. At present, a user needs to manually provide input values to search interfaces and then extract required data from the pages with results. The manual filling out forms is not feasible and cumbersome in cases of complex queries but such kind of queries are essential for many web searches especially in the area of e-commerce. In this way, the automation of querying and retrieving data behind search interfaces is desirable and essential for such tasks as building domain-independent deep web crawlers and automated web agents, searching for domain-specific information (vertical search engines), and for extraction and integration of information from various deep web resources. We present a data model for representing search interfaces and discuss techniques for extracting field labels, client-side scripts and structured data from HTML pages. We also describe a representation of result pages and discuss how to extract and store results of form queries. Besides, we present a user-friendly and expressive form query language that allows one to retrieve information behind search interfaces and extract useful data from the result pages based on specified conditions. We implement a prototype system for querying web databases and describe its architecture and components design.

Patterns of care in recurrent glioblastoma in Switzerland: a multicentre national approach based on diagnostic nodes.

Despite moderate improvements in outcome of glioblastoma after first-line treatment with chemoradiation recent clinical trials failed to improve the prognosis of recurrent glioblastoma. In the absence of a standard of care we aimed to investigate institutional treatment strategies to identify similarities and differences in the pattern of care for recurrent glioblastoma. We investigated re-treatment criteria and therapeutic pathways for recurrent glioblastoma of eight neuro-oncology centres in Switzerland having an established multidisciplinary tumour-board conference. Decision algorithms, differences and consensus were analysed using the objective consensus methodology. A total of 16 different treatment recommendations were identified based on combinations of eight different decision criteria. The set of criteria implemented as well as the set of treatments offered was different in each centre. For specific situations, up to 6 different treatment recommendations were provided by the eight centres. The only wide-range consensus identified was to offer best supportive care to unfit patients. A majority recommendation was identified for non-operable large early recurrence with unmethylated MGMT promoter status in the fit patients: here bevacizumab was offered. In fit patients with late recurrent non-operable MGMT promoter methylated glioblastoma temozolomide was recommended by most. No other majority recommendations were present. In the absence of strong evidence we identified few consensus recommendations in the treatment of recurrent glioblastoma. This contrasts the limited availability of single drugs and treatment modalities. Clinical situations of greatest heterogeneity may be suitable to be addressed in clinical trials and second opinion referrals are likely to yield diverging recommendations.

Question Waves: A multicast query routing algorithm for social search

This paper describes Question Waves, an algorithm that can be applied to social search protocols, such as Asknext or Sixearch. In this model, the queries are propagated through the social network, with faster propagation through more trustable acquaintances. Question Waves uses local information to make decisions and obtain an answer ranking. With Question Waves, the answers that arrive first are the most likely to be relevant, and we computed the correlation of answer relevance with the order of arrival to demonstrate this result. We obtained correlations equivalent to the heuristics that use global knowledge, such as profile similarity among users or the expertise value of an agent. Because Question Waves is compatible with the social search protocol Asknext, it is possible to stop a search when enough relevant answers have been found; additionally, stopping the search early only introduces a minimal risk of not obtaining the best possible answer. Furthermore, Question Waves does not require a re-ranking algorithm because the results arrive sorted

A cast of shadow on adjuvant radiotherapy for prostate cancer: A critical review based on a methodological perspective.

OBJECTIVE: To perform a critical review focusing on the applicability in clinical daily practice of data from three randomized controlled trials (RCTs): SWOG 8794, EORTC 22911, and ARO/AUO 96-02. METHODS AND MATERIALS: An analytical framework, based on the identified population, interventions, comparators, and outcomes (PICO) was used to refine the search of the evidence from the three large randomized trials regarding the use of radiation therapy after prostatectomy as adjuvant therapy (ART). RESULTS: With regard to the inclusion criteria: (1) POPULATION: in the time since they were designed, in two among three trial (SWOG 8794 and EORTC 22911) patients had a detectable PSA at the time of randomization, thus representing de facto a substantial proportion of patients who eventually received salvage RT (SRT) at non-normalised PSA levels rather than ART. (2) INTERVENTIONS: although all the trials showed the benefit of postoperative ART compared to a wait-and-see approach, the dose herein employed would be now considered inadequate; (3) COMPARATORS: the comparison arm in all the 3 RCTs was an uncontrolled observation arm, where patients who subsequently developed biochemical failure were treated in various ways, with up to half of them receiving SRT at PSA well above 1ng/mL, a level that would be now deemed inappropriate; (4) OUTCOMES: only in one trial (SWOG 8794) ART was found to significantly improve overall survival compared to observation, with a ten-year overall survival rate of 74% vs. 66%, although this might be partly the result of imbalanced risk factors due to competing event risk stratification. CONCLUSIONS: ART has a high level of evidence due to three RCTs with at least 10-year follow-up recording a benefit in biochemical PFS, but its penetrance in present daily clinics should be reconsidered. While the benefit of ART or SRT is eagerly expected from ongoing randomized trials, a dynamic risk-stratified approach should drive the decisions making process.

Simulation-based evaluation of susceptibility distortion correction methods in diffusion MRI for connectivity analysis

Connectivity analysis on diffusion MRI data of the whole- brain suffers from distortions caused by the standard echo- planar imaging acquisition strategies. These images show characteristic geometrical deformations and signal destruction that are an important drawback limiting the success of tractography algorithms. Several retrospective correction techniques are readily available. In this work, we use a digital phantom designed for the evaluation of connectivity pipelines. We subject the phantom to a âeurooetheoretically correctâeuro and plausible deformation that resembles the artifact under investigation. We correct data back, with three standard methodologies (namely fieldmap-based, reversed encoding-based, and registration- based). Finally, we rank the methods based on their geometrical accuracy, the dropout compensation, and their impact on the resulting connectivity matrices.

Automatic Brain Extraction in Fetal MRI using Multi-Atlas-based Segmentation

In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality.

Bias in dissemination of clinical research findings: structured OPEN framework of what, who and why, based on literature review and expert consensus.

OBJECTIVE: The aim of this study is to review highly cited articles that focus on non-publication of studies, and to develop a consistent and comprehensive approach to defining (non-) dissemination of research findings. SETTING: We performed a scoping review of definitions of the term 'publication bias' in highly cited publications. PARTICIPANTS: Ideas and experiences of a core group of authors were collected in a draft document, which was complemented by the findings from our literature search. INTERVENTIONS: The draft document including findings from the literature search was circulated to an international group of experts and revised until no additional ideas emerged and consensus was reached. PRIMARY OUTCOMES: We propose a new approach to the comprehensive conceptualisation of (non-) dissemination of research. SECONDARY OUTCOMES: Our 'What, Who and Why?' approach includes issues that need to be considered when disseminating research findings (What?), the different players who should assume responsibility during the various stages of conducting a clinical trial and disseminating clinical trial documents (Who?), and motivations that might lead the various players to disseminate findings selectively, thereby introducing bias in the dissemination process (Why?). CONCLUSIONS: Our comprehensive framework of (non-) dissemination of research findings, based on the results of a scoping literature search and expert consensus will facilitate the development of future policies and guidelines regarding the multifaceted issue of selective publication, historically referred to as 'publication bias'.

A Write-Based Solver for SAT Modulo the Theory of Arrays

The extensional theory of arrays is one of the most important ones for applications of SAT Modulo Theories (SMT) to hardware and software verification. Here we present a new T-solver for arrays in the context of the DPLL(T) approach to SMT. The main characteristics of our solver are: (i) no translation of writes into reads is needed, (ii) there is no axiom instantiation, and (iii) the T-solver interacts with the Boolean engine by asking to split on equality literals between indices. As far as we know, this is the first accurate description of an array solver integrated in a state-of-the-art SMT solver and, unlike most state-of-the-art solvers, it is not based on a lazy instantiation of the array axioms. Moreover, it is very competitive in practice, specially on problems that require heavy reasoning on array literals

Search and Discovery : OER's Open Loop

Open educational resources (OER) promise increased access, participation, quality, and relevance, in addition to cost reduction. These seemingly fantastic promises are based on the supposition that educators and learners will discover existing resources, improve them, and share the results, resulting in a virtuous cycle of improvement and re-use. By anecdotal metrics, existing web scale search is not working for OER. This situation impairs the cycle underlying the promise of OER, endangering long term growth and sustainability. While the scope of the problem is vast, targeted improvements in areas of curation, indexing, and data exchange can improve the situation, and create opportunities for further scale. I explore the way the system is currently inadequate, discuss areas for targeted improvement, and describe a prototype system built to test these ideas. I conclude with suggestions for further exploration and development.

Population-based incidence of myeloid malignancies: fifteen years of epidemiological data in the province of Girona, Spain

Myeloid malignancies (MMs) are a heterogeneous group of hematologic malignancies presenting different incidence, prognosis and survival.1–3 Changing classifications (FAB 1994, WHO 2001 and WHO 2008) and few available epidemiological data complicate incidence comparisons.4,5 Taking this into account, the aims of the present study were: a) to calculate the incidence rates and trends of MMs in the Province of Girona, northeastern Spain, between 1994 and 2008 according to the WHO 2001 classification; and b) to predict the number of MMs cases in Spain during 2013. Data were extracted from the population-based Girona Cancer Registry (GCR) located in the north-east of Catalonia, Spain, and covering a population of 731,864 inhabitants (2008 census). Cases were registered according to the rules of the European Network for Cancer Registries and the Manual for Coding and Reporting Haematological Malignancies (HAEMACARE project). To ensure the complete coverage of MMs in the GCR, and especially myeloproliferative neoplasms (MPN) and myelodysplastic syndromes (MDS), a retrospective search was performed. The ICD-O-2 (1990) codes were converted into their corresponding ICD-O-3 (2000) codes, including MDS, polycythemia vera (PV) and essential thrombocythemia (ET) as malignant diseases. Results of crude rate (CR) and European standardized incidence rate (ASRE) were expressed per 100,000 inhabitants/year