Improving algorithm speed in PNL mixture separation and Wiener system inversion

This paper proposes a very simple method for increasing the algorithm speed for separating sources from PNL mixtures or invertingWiener systems. The method is based on a pertinent initialization of the inverse system, whose computational cost is very low. The nonlinear part is roughly approximated by pushing the observations to be Gaussian; this method provides a surprisingly good approximation even when the basic assumption is not fully satisfied. The linear part is initialized so that outputs are decorrelated. Experiments shows the impressive speed improvement.

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.

New Algorithm for Managing Childhood Illness Using Mobile Technology (ALMANACH): A Controlled Non-Inferiority Study on Clinical Outcome and Antibiotic Use in Tanzania.

INTRODUCTION: The decline of malaria and scale-up of rapid diagnostic tests calls for a revision of IMCI. A new algorithm (ALMANACH) running on mobile technology was developed based on the latest evidence. The objective was to ensure that ALMANACH was safe, while keeping a low rate of antibiotic prescription. METHODS: Consecutive children aged 2-59 months with acute illness were managed using ALMANACH (2 intervention facilities), or standard practice (2 control facilities) in Tanzania. Primary outcomes were proportion of children cured at day 7 and who received antibiotics on day 0. RESULTS: 130/842 (15∙4%) in ALMANACH and 241/623 (38∙7%) in control arm were diagnosed with an infection in need for antibiotic, while 3∙8% and 9∙6% had malaria. 815/838 (97∙3%;96∙1-98.4%) were cured at D7 using ALMANACH versus 573/623 (92∙0%;89∙8-94∙1%) using standard practice (p<0∙001). Of 23 children not cured at D7 using ALMANACH, 44% had skin problems, 30% pneumonia, 26% upper respiratory infection and 13% likely viral infection at D0. Secondary hospitalization occurred for one child using ALMANACH and one who eventually died using standard practice. At D0, antibiotics were prescribed to 15∙4% (12∙9-17∙9%) using ALMANACH versus 84∙3% (81∙4-87∙1%) using standard practice (p<0∙001). 2∙3% (1∙3-3.3) versus 3∙2% (1∙8-4∙6%) received an antibiotic secondarily. CONCLUSION: Management of children using ALMANACH improve clinical outcome and reduce antibiotic prescription by 80%. This was achieved through more accurate diagnoses and hence better identification of children in need of antibiotic treatment or not. The building on mobile technology allows easy access and rapid update of the decision chart. TRIAL REGISTRATION: Pan African Clinical Trials Registry PACTR201011000262218.

Managing the Sick Child in the Era of Declining Malaria Transmission: Development of ALMANACH, an Electronic Algorithm for Appropriate Use of Antimicrobials.

OBJECTIVE: To review the available knowledge on epidemiology and diagnoses of acute infections in children aged 2 to 59 months in primary care setting and develop an electronic algorithm for the Integrated Management of Childhood Illness to reach optimal clinical outcome and rational use of medicines. METHODS: A structured literature review in Medline, Embase and the Cochrane Database of Systematic Review (CDRS) looked for available estimations of diseases prevalence in outpatients aged 2-59 months, and for available evidence on i) accuracy of clinical predictors, and ii) performance of point-of-care tests for targeted diseases. A new algorithm for the management of childhood illness (ALMANACH) was designed based on evidence retrieved and results of a study on etiologies of fever in Tanzanian children outpatients. FINDINGS: The major changes in ALMANACH compared to IMCI (2008 version) are the following: i) assessment of 10 danger signs, ii) classification of non-severe children into febrile and non-febrile illness, the latter receiving no antibiotics, iii) classification of pneumonia based on a respiratory rate threshold of 50 assessed twice for febrile children 12-59 months; iv) malaria rapid diagnostic test performed for all febrile children. In the absence of identified source of fever at the end of the assessment, v) urine dipstick performed for febrile children <2 years to consider urinary tract infection, vi) classification of 'possible typhoid' for febrile children >2 years with abdominal tenderness; and lastly vii) classification of 'likely viral infection' in case of negative results. CONCLUSION: This smartphone-run algorithm based on new evidence and two point-of-care tests should improve the quality of care of <5 year children and lead to more rational use of antimicrobials.

A Feedfordward Adaptive Controller to Reduce the Imaging Time of Large-Sized Biological Samples with a SPM-Based Multiprobe Station

The time required to image large samples is an important limiting factor in SPM-based systems. In multiprobe setups, especially when working with biological samples, this drawback can make impossible to conduct certain experiments. In this work, we present a feedfordward controller based on bang-bang and adaptive controls. The controls are based in the difference between the maximum speeds that can be used for imaging depending on the flatness of the sample zone. Topographic images of Escherichia coli bacteria samples were acquired using the implemented controllers. Results show that to go faster in the flat zones, rather than using a constant scanning speed for the whole image, speeds up the imaging process of large samples by up to a 4x factor.

Toward on-the-fly quantum mechanical/molecular mechanical (QM/MM) docking: development and benchmark of a scoring function.

We address the challenges of treating polarization and covalent interactions in docking by developing a hybrid quantum mechanical/molecular mechanical (QM/MM) scoring function based on the semiempirical self-consistent charge density functional tight-binding (SCC-DFTB) method and the CHARMM force field. To benchmark this scoring function within the EADock DSS docking algorithm, we created a publicly available dataset of high-quality X-ray structures of zinc metalloproteins ( http://www.molecular-modelling.ch/resources.php ). For zinc-bound ligands (226 complexes), the QM/MM scoring yielded a substantially improved success rate compared to the classical scoring function (77.0% vs 61.5%), while, for allosteric ligands (55 complexes), the success rate remained constant (49.1%). The QM/MM scoring significantly improved the detection of correct zinc-binding geometries and improved the docking success rate by more than 20% for several important drug targets. The performance of both the classical and the QM/MM scoring functions compare favorably to the performance of AutoDock4, AutoDock4Zn, and AutoDock Vina.

Association between parental history and genetic risk scores for coronary heart disease prediction: The population-based CoLaus study.

BACKGROUND AND AIMS: Parental history (PH) and genetic risk scores (GRSs) are separately associated with coronary heart disease (CHD), but evidence regarding their combined effects is lacking. We aimed to evaluate the joint associations and predictive ability of PH and GRSs for incident CHD. METHODS: Data for 4283 Caucasians were obtained from the population-based CoLaus Study, over median follow-up time of 5.6 years. CHD was defined as incident myocardial infarction, angina, percutaneous coronary revascularization or bypass grafting. Single nucleotide polymorphisms for CHD identified by genome-wide association studies were used to construct unweighted and weighted versions of three GRSs, comprising of 38, 53 and 153 SNPs respectively. RESULTS: PH was associated with higher values of all weighted GRSs. After adjustment for age, sex, smoking, diabetes, systolic blood pressure, low and high density lipoprotein cholesterol, PH was significantly associated with CHD [HR 2.61, 95% CI (1.47-4.66)] and further adjustment for GRSs did not change this estimate. Similarly, one standard deviation change of the weighted 153-SNPs GRS was significantly associated with CHD [HR 1.50, 95% CI (1.26-1.80)] and remained so, after further adjustment for PH. The weighted, 153-SNPs GRS, but not PH, modestly improved discrimination [(C-index improvement, 0.016), p = 0.048] and reclassification [(NRI improvement, 8.6%), p = 0.027] beyond cardiovascular risk factors. After including both the GRS and PH, model performance improved further [(C-index improvement, 0.022), p = 0.006]. CONCLUSION: After adjustment for cardiovascular risk factors, PH and a weighted, polygenic GRS were jointly associated with CHD and provided additive information for coronary events prediction.

Efficient total variation algorithm for fetal MRI reconstruction

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy [1], Total Variation (TV)based energies [2,3] and more recently non-local means [4]. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm for fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n(2)) and O(1/root epsilon), while existing techniques are in O(1/n) and O(1/epsilon). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.

Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics.

Integrating single nucleotide polymorphism (SNP) p-values from genome-wide association studies (GWAS) across genes and pathways is a strategy to improve statistical power and gain biological insight. Here, we present Pascal (Pathway scoring algorithm), a powerful tool for computing gene and pathway scores from SNP-phenotype association summary statistics. For gene score computation, we implemented analytic and efficient numerical solutions to calculate test statistics. We examined in particular the sum and the maximum of chi-squared statistics, which measure the strongest and the average association signals per gene, respectively. For pathway scoring, we use a modified Fisher method, which offers not only significant power improvement over more traditional enrichment strategies, but also eliminates the problem of arbitrary threshold selection inherent in any binary membership based pathway enrichment approach. We demonstrate the marked increase in power by analyzing summary statistics from dozens of large meta-studies for various traits. Our extensive testing indicates that our method not only excels in rigorous type I error control, but also results in more biologically meaningful discoveries.

Characterization of local and systemic immune-modulation in melanoma patients

Le mélanome cutané est un des cancers les plus agressifs et dont l'incidence augmente le plus en Suisse. Une fois métastatique, le pronostic de survie moyenne avec les thérapies actuelles est d'environ huit mois, avec moins de 5% de survie à cinq ans. Les récents progrès effectués dans la compréhension de la biologie de la cellule tumorale mais surtout dans l'importance du système immunitaire dans le contrôle de ce cancer ont permis le développement de nouveaux traitements novateurs et prometteurs. Ces thérapies, appelées immunothérapies, reposent sur la stimulation et l'augmentation de la réponse immunitaire à la tumeur. Alors que les derniers essais cliniques ont démontré l'efficacité de ces traitements chez les patients avec des stades avancés de la maladie, le contrôle de la maladie à long- terme est seulement atteint chez une minorité des patients. La suppression locale et systémique de la réponse immunitaire spécifique anti-tumorale apparaitrait comme une des raisons expliquant la persistance d'un mauvais pronostic clinique chez ces patients. Des études sur les souris ont montré que les vaisseaux lymphatiques joueraient un rôle primordial dans ce processus en induisant une tolérance immune, ce qui permettrait à la tumeur d'échapper au contrôle du système immunitaire et métastatiser plus facilement. Ces excitantes découvertes n'ont pas encore été établi et prouvé chez l'homme. Dans cette thèse, nous montrons pour la première fois que les vaisseaux lymphatiques sont directement impliqués dans la modulation de la réponse immunitaire au niveau local et systémique dans le mélanome chez l'homme. Ces récentes découvertes montrent le potentiel de combiner des thérapies visant le système lymphatique avec les immunothérapies actuellement utilisées afin d'améliorer le pronostic des patients atteint du mélanome. -- Cutaneous melanoma is one of the most invasive and metastatic human cancers and causes 75% of skin cancer mortality. Current therapies such as surgery and chemotherapy fail to control metastatic disease, and relapse occurs frequently due to microscopic residual lesions. It is, thus, essential to develop and optimize novel therapeutic strategies to improve curative responses in these patients. In recent decades, tumor immunologists have revealed the development of spontaneous adaptive immune responses in melanoma patients, leading to the accumulation of highly differentiated tumor-specific T cells at the tumor site. This remains one of the most powerful prognostic markers to date. Immunotherapies that augment the natural function of these tumor-specific T cells have since emerged as highly attractive therapeutic approaches to eliminate melanoma cells. While recent clinical trials have demonstrated great progress in the treatment of advanced stage melanoma, long-term disease control is still only achieved in a minority of patients. Local and systemic immune suppression by the tumor appears to be responsible, in part, for this poor clinical evolution. These facts underscore the need for a better analysis and characterization of immune- related pathways within the tumor microenvironment (TME), as well as at the systemic level. The overall goal of this thesis is, thus, to obtain greater insight into the complexity and heterogeneity of the TME in human melanoma, as well as to investigate immune modulation beyond the TME, which ultimately influences the immune system throughout the whole body. To achieve this, we established two main objectives: to precisely characterize local and systemic immune modulation (i) in untreated melanoma patients and (ii) in patients undergoing peptide vaccination or checkpoint blockade therapy with anti-cytotoxic T- lymphocyte-asisctaed protein-4 (CTLA-4) antibody. In the first and main part of this thesis, we analyzed lymphatic vessels in relation to anti-tumor immune responses in tissues from vaccinated patients using a combination of immunohistochemistry (IHC) techniques, whole slide scanning/analysis, and an automatic quantification system. Strikingly, we found that increased lymphatic vessel density was associated with high expression of immune suppressive molecules, low functionality of tumor-infiltrating CD8+ T cells and decreased cytokine production by tumor-antigen specific CD8+ T cells in the blood. These data revealed a previously unappreciated local and systemic role of lymphangiogenesis in modulating T cell responses in human cancer and support the use of therapies that target lymphatic vessels combined with existing and future T cell based therapies. In the second objective, we describe a metastatic melanoma patient who developed pulmonary sarcoid-like granulomatosis following repetitive vaccination with peptides and CpG. We demonstrated that the onset of this pulmonary autoimmune adverse event was related to the development of a strong and long-lasting tumor-specific CD8+ T cell response. This constitutes the first demonstration that a new generation tumor vaccine can induce the development of autoimmune adverse events. In the third objective, we assessed the use of Fourier Transform Infrared (FTIR) imaging to identify melanoma cells and lymphocyte subpopulations in lymph node (LN) metastasis tissues, thanks to a fruitful collaboration with researchers in Brussels. We demonstrated that the different cell types in metastatic LNs have different infrared spectral features allowing automated identification of these cells. This technic is therefore capable of distinguishing known and novel biological features in human tissues and has, therefore, significant potential as a tool for histopathological diagnosis and biomarker assessment. Finally, in the fourth objective, we investigated the role of colony- stimulating factor-1 (CSF-1) in modulating the anti-tumor response in ipilimumab-treated patients using IHC and in vitro co-cultures, revealing that melanoma cells produce CSF-1 via CTL-derived cytokines when attacked by cytotoxic T lymphocytes (CTLs), resulting in the recruitment of immunosuppressive monocytes. These findings support the combined use of CSF-1R blockade with T cell based immunotherapy for melanoma patients. Taken together, our results reveal the existence of novel mechanisms of immune modulation and thus promote the optimization of combination immunotherapies against melanoma. -- Le mélanome cutané est un des cancers humains les plus invasifs et métastatiques et est responsable de 75% de la mortalité liée aux cancers de la peau. Les thérapies comme la chirurgie et la chimiothérapie ont échoué à contrôler le mélanome métastatique, par ailleurs les rechutes sous ces traitements ont été montrées fréquentes. Il est donc essentiel de développer et d'optimiser de nouvelles stratégies thérapeutiques pour améliorer les réponses thérapeutiques de ces patients. Durant les dernières décennies, les immunologistes spécialisés dans les tumeurs ont démontré qu'un patient atteint du mélanome pouvait développer spontanément une réponse immune adaptative à sa tumeur et que l'accumulation de cellules T spécifiques tumorales au sein même de la tumeur était un des plus puissants facteurs pronostiques. Les immunothérapies qui ont pour but d'augmenter les fonctions naturelles de ces cellules T spécifiques tumorales ont donc émergé comme des approches thérapeutiques très attractives pour éliminer les cellules du mélanome. Alors que les derniers essais cliniques ont démontré un progrès important dans le traitement des formes avancées du mélanome, le contrôle de la maladie à long-terme est seulement atteint chez une minorité des patients. La suppression immune locale et systémique apparaitrait comme une des raisons expliquant la persistance d'un mauvais pronostic clinique chez ces patients. Ces considérations soulignent la nécessité de mieux analyser et caractériser les voies immunitaires non seulement au niveau local dans le microenvironement tumoral mais aussi au niveau systémique dans le sang des patients. Le but de cette thèse est d'obtenir une plus grande connaissance de la complexité et de l'hétérogénéité du microenvironement tumoral dans les mélanomes mais aussi d'investiguer la modulation immunitaire au delà du microenvironement tumoral au niveau systémique. Afin d'atteindre ce but, nous avons établi deux objectifs principaux : caractériser précisément la modulation locale et systémique du système immunitaire (i) chez les patients atteints du mélanome qui n'ont pas reçu de traitement et (ii) chez les patients qui ont été traités soit par des vaccins soit par des thérapies qui bloquent les points de contrôles. Dans la première et majeure partie de cette thèse, nous avons analysé les vaisseaux lymphatiques en relation avec la réponse immunitaire anti-tumorale dans les tissus des patients vaccinés grâce à des techniques d'immunohistochimie et de quantification informatisé et automatique des marquages. Nous avons trouvé qu'une densité élevée de vaisseaux lymphatiques dans la tumeur était associée à une plus grande expression de molécules immunosuppressives ainsi qu'à une diminution de la fonctionnalité des cellules T spécifiques tumoral dans la tumeur et dans le sang des patients. Ces résultats révèlent un rôle jusqu'à là inconnu des vaisseaux lymphatiques dans la modulation directe du système immunitaire au niveau local et systémique dans les cancers de l'homme. Cette recherche apporte finalement des preuves du potentiel de combiner des thérapies visant le système lymphatique avec des autres immunothérapies déjà utilisées en clinique. Dans le second objectif, nous rapportons le cas d'un patient atteint d'un mélanome avec de multiples métastases qui a développé à la suite de plusieurs vaccinations répétées et consécutives avec des peptides et du CpG, un évènement indésirable sous la forme d'une granulomatose pulmonaire sarcoid-like. Nous avons démontré que l'apparition de cet évènement était intimement liée au développement d'une réponse immunitaire durable et spécifique contre les antigènes de la tumeur. Par là- même, nous prouvons pour la première fois que la nouvelle génération de vaccins est aussi capable d'induire des effets indésirables auto-immuns. Pour le troisième objectif, nous avons voulu savoir si l'utilisation de la spectroscopie infrarouge à transformée de Fourier (IRTF) était capable d'identifier les cellules du mélanome ainsi que les différents sous-types cellulaires dans les ganglions métastatiques. Grâce à nos collaborateurs de Bruxelles, nous avons pu établir que les diverses composantes cellulaires des ganglions atteints par des métastases du mélanome présentaient des spectres infrarouges différents et qu'elles pouvaient être identifiées d'une façon automatique. Cette nouvelle technique permettrait donc de distinguer des caractéristiques biologiques connues ou nouvelles dans les tissus humains qui auraient des retombées pratiques importantes dans le diagnostic histopathologique et dans l'évaluation des biomarqueurs. Finalement dans le dernier objectif, nous avons investigué le rôle du facteur de stimulation des colonies (CSF-1) dans la modulation de la réponse immunitaire anti-tumorale chez les patients qui ont été traités par l'Ipilimumab. Nos expériences in vivo au niveau des tissus tumoraux et nos co-cultures in vitro nous ont permis de démontrer que les cytokines secrétées par les cellules T spécifiques anti-tumorales induisaient la sécrétion de CSF-1 dans les cellules du mélanome ce qui résultait en un recrutement de monocytes immunosuppresseurs. Dans son ensemble, cette thèse révèle donc l'existence de nouveaux mécanismes de modulation de la réponse immunitaire anti-tumorale et propose de nouvelles optimisations de combinaison d'immunothérapies contre le mélanome.

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.

Characterization of Mechanically Alloyed Nanocrystalline Fe(Al): Crystallite Size and Dislocation Density

A nanostructured disordered Fe(Al) solid solution was obtained from elemental powders of Fe and Al using a high-energy ball mill. The transformations occurring in the material during milling were studied with the use of X-ray diffraction. In addition lattice microstrain, average crystallite size, dislocation density, and the lattice parameter were determined. Scanning electron microscopy (SEM) was employed to examine the morphology of the samples as a function of milling times. Thermal behaviour of the milled powders was examined by differential scanning calorimetry (DSC). The results, as well as dissimilarity between calorimetric curves of the powders after 2 and 20 h of milling, indicated the formation of a nanostructured Fe(Al) solid solution

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

Active regions for colour texture segmentation integrating region and boundary information

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal