Isolation of cardiovascular precursor cells from the human fetal heart.

Weakening of cardiac function in patients with heart failure results from a loss of cardiomyocytes in the damaged heart. Cell replacement therapies as a way to induce myocardial regeneration in humans could represent attractive alternatives to classical drug-based approaches. However, a suitable source of precursor cells, which could produce a functional myocardium after transplantation, remains to be identified. In the present study, we isolated cardiovascular precursor cells from ventricles of human fetal hearts at 12 weeks of gestation. These cells expressed Nkx2.5 but not late cardiac markers such as α-actinin and troponin I. In addition, proliferating cells expressed the mesenchymal stem cell markers CD73, CD90, and CD105. Evidence for functional cardiogenic differentiation in vitro was demonstrated by the upregulation of cardiac gene expression as well as the appearance of cells with organized sarcomeric structures. Importantly, differentiated cells presented spontaneous and triggered calcium signals. Differentiation into smooth muscle cells was also detected. In contrast, precursor cells did not produce endothelial cells. The engraftment and differentiation capacity of green fluorescent protein (GFP)-labeled cardiac precursor cells were then tested in vivo after transfer into the heart of immunodeficient severe combined immunodeficient mice. Engrafted human cells were readily detected in the mouse myocardium. These cells retained their cardiac commitment and differentiated into α-actinin-positive cardiomyocytes. Expression of connexin-43 at the interface between GFP-labeled and endogenous cardiomyocytes indicated that precursor-derived cells connected to the mouse myocardium. Together, these results suggest that human ventricular nonmyocyte cells isolated from fetal hearts represent a suitable source of precursors for cell replacement therapies.

Statistical reference criteria for adaptive signal processing in digital communications

A general criterion for the design of adaptive systemsin digital communications called the statistical reference criterionis proposed. The criterion is based on imposition of the probabilitydensity function of the signal of interest at the outputof the adaptive system, with its application to the scenario ofhighly powerful interferers being the main focus of this paper.The knowledge of the pdf of the wanted signal is used as adiscriminator between signals so that interferers with differingdistributions are rejected by the algorithm. Its performance isstudied over a range of scenarios. Equations for gradient-basedcoefficient updates are derived, and the relationship with otherexisting algorithms like the minimum variance and the Wienercriterion are examined.

Selection of bee species for environmental risk assessment of GM cotton in the Brazilian Cerrado

The objective of this work was to list potential candidate bee species for environmental risk assessment (ERA) of genetically modified (GM) cotton and to identify the most suited bee species for this task, according to their abundance and geographical distribution. Field inventories of bee on cotton flowers were performed in the states of Bahia and Mato Grosso, and in Distrito Federal, Brazil. During a 344 hour sampling, 3,470 bees from 74 species were recovered, at eight sites. Apis mellifera dominated the bee assemblages at all sites. Sampling at two sites that received no insecticide application was sufficient to identify the three most common and geographically widespread wild species: Paratrigona lineata, Melissoptila cnecomola, and Trigona spinipes, which could be useful indicators of pollination services in the ERA. Indirect ordination of common wild species revealed that insecticides reduced the number of native bee species and that interannual variation in bee assemblages may be low. Accumulation curves of rare bee species did not saturate, as expected in tropical and megadiverse regions. Species-based approaches are limited to analyze negative impacts of GM cotton on pollinator biological diversity. The accumulation rate of rare bee species, however, may be useful for evaluating possible negative effects of GM cotton on bee diversity.

On the differential evolution algorithm and its appliation to training radial basis function networks

The parameter setting of a differential evolution algorithm must meet several requirements: efficiency, effectiveness, and reliability. Problems vary. The solution of a particular problem can be represented in different ways. An algorithm most efficient in dealing with a particular representation may be less efficient in dealing with other representations. The development of differential evolution-based methods contributes substantially to research on evolutionary computing and global optimization in general. The objective of this study is to investigatethe differential evolution algorithm, the intelligent adjustment of its controlparameters, and its application. In the thesis, the differential evolution algorithm is first examined using different parameter settings and test functions. Fuzzy control is then employed to make control parameters adaptive based on an optimization process and expert knowledge. The developed algorithms are applied to training radial basis function networks for function approximation with possible variables including centers, widths, and weights of basis functions and both having control parameters kept fixed and adjusted by fuzzy controller. After the influence of control variables on the performance of the differential evolution algorithm was explored, an adaptive version of the differential evolution algorithm was developed and the differential evolution-based radial basis function network training approaches were proposed. Experimental results showed that the performance of the differential evolution algorithm is sensitive to parameter setting, and the best setting was found to be problem dependent. The fuzzy adaptive differential evolution algorithm releases the user load of parameter setting and performs better than those using all fixedparameters. Differential evolution-based approaches are effective for training Gaussian radial basis function networks.

Multi-scale effects of nestling diet on breeding performance in a terrestrial top predator inferred from stable isotope analysis

Inter-individual diet variation within populations is likely to have important ecological and evolutionary implications. The diet-fitness relationships at the individual level and the emerging population processes are, however, poorly understood for most avian predators inhabiting complex terrestrial ecosystems. In this study, we use an isotopic approach to assess the trophic ecology of nestlings in a long-lived raptor, the Bonelli"s eagle Aquila fasciata, and investigate whether nestling dietary breath and main prey consumption can affect the species" reproductive performance at two spatial scales: territories within populations and populations over a large geographic area. At the territory level, those breeding pairs whose nestlings consumed similar diets to the overall population (i.e. moderate consumption of preferred prey, but complemented by alternative prey categories) or those disproportionally consuming preferred prey were more likely to fledge two chicks. An increase in the diet diversity, however, related negatively with productivity. The age and replacements of breeding pair members had also an influence on productivity, with more fledglings associated to adult pairs with few replacements, as expected in long-lived species. At the population level, mean productivity was higher in those population-years with lower dietary breadth and higher diet similarity among territories, which was related to an overall higher consumption of preferred prey. Thus, we revealed a correspondence in diet-fitness relationships at two spatial scales: territories and populations. We suggest that stable isotope analyses may be a powerful tool to monitor the diet of terrestrial avian predators on large spatio-temporal scales, which could serve to detect potential changes in the availability of those prey on which predators depend for breeding. We encourage ecologists and evolutionary and conservation biologists concerned with the multi-scale fitness consequences of inter-individual variation in resource use to employ similar stable isotope-based approaches, which can be successfully applied to complex ecosystems such as the Mediterranean.

Paper Surface Image Processing for Roughness Quality Measurement

Diplomityössä on käsitelty paperin pinnankarkeuden mittausta, joka on keskeisimpiä ongelmia paperimateriaalien tutkimuksessa. Paperiteollisuudessa käytettävät mittausmenetelmät sisältävät monia haittapuolia kuten esimerkiksi epätarkkuus ja yhteensopimattomuus sileiden papereiden mittauksissa, sekä suuret vaatimukset laboratorio-olosuhteille ja menetelmien hitaus. Työssä on tutkittu optiseen sirontaan perustuvia menetelmiä pinnankarkeuden määrittämisessä. Konenäköä ja kuvan-käsittelytekniikoita tutkittiin karkeilla paperipinnoilla. Tutkimuksessa käytetyt algoritmit on tehty Matlab® ohjelmalle. Saadut tulokset osoittavat mahdollisuuden pinnankarkeuden mittaamiseen kuvauksen avulla. Parhaimman tuloksen perinteisen ja kuvausmenetelmän välillä antoi fraktaaliulottuvuuteen perustuva menetelmä.

Image quality in CT: From physical measurements to model observers.

Evaluation of image quality (IQ) in Computed Tomography (CT) is important to ensure that diagnostic questions are correctly answered, whilst keeping radiation dose to the patient as low as is reasonably possible. The assessment of individual aspects of IQ is already a key component of routine quality control of medical x-ray devices. These values together with standard dose indicators can be used to give rise to 'figures of merit' (FOM) to characterise the dose efficiency of the CT scanners operating in certain modes. The demand for clinically relevant IQ characterisation has naturally increased with the development of CT technology (detectors efficiency, image reconstruction and processing), resulting in the adaptation and evolution of assessment methods. The purpose of this review is to present the spectrum of various methods that have been used to characterise image quality in CT: from objective measurements of physical parameters to clinically task-based approaches (i.e. model observer (MO) approach) including pure human observer approach. When combined together with a dose indicator, a generalised dose efficiency index can be explored in a framework of system and patient dose optimisation. We will focus on the IQ methodologies that are required for dealing with standard reconstruction, but also for iterative reconstruction algorithms. With this concept the previously used FOM will be presented with a proposal to update them in order to make them relevant and up to date with technological progress. The MO that objectively assesses IQ for clinically relevant tasks represents the most promising method in terms of radiologist sensitivity performance and therefore of most relevance in the clinical environment.

Offering planning process development: Bridging the Offering Gaps and Consolidating Top Priority Needs in Product Offering

The aim of this Thesis is to study how to manage the front-end of the offering planning process. This includes actual process development and methods to gather and analyze information to achieve the best outcome in customer oriented product offering. Study is carried out in two parts: theoretical part and company related part. Theoretical framework is created introducing different types of approaches to manage product planning processes. Products are seen as platforms and they are broken down to subsystems to show different parts of the development. With the help of the matrix-based approaches product platform related information is gathered and analyzed. In this kind of analysis business/market drivers and cus-tomer/competitor information are connected with product subsystems. This gives possibilities to study product gaps/needs and possible future ideas/scenarios in different customer segments. Company related part consists of offering planning process development in real company environment. Process formation includes documents and tools that guide planning from the information gathering to the prioritization and decision making.

Sarbanes- Oxley – lain vaikutus suomalaisten yhtiöiden tilintarkastukseen

Tutkielmassa tarkastellaan Sarbanes-Oxley -lain Auditing Standard 5:n (AS 5) vaikutusta suomalaisten yhtiöiden tilintarkastukseen. Teoriaosuudessa käsitellään corporate governancea ja tilintarkastusta sen osana, COSO ERM -viitekehystä ja kontrolleja sekä Sarbanes Oxley -lakia. Tutkielman painopisteenä ovat AS 5:n sisältökokonaisuudet: mm. ylhäältä alas- ja riskiperusteinen lähestymistapa sekä turhien tarkastusprosessien poistaminen. Tutkimusaineisto kerättiin vuosina 2007–2009 erilaisista ammattilehdistä ja blogeista sekä haastattelemalla kolmea suurten tilintarkastusyhteisöjen tilintarkastajaa. AS 5 näyttää korostaneen riskiperusteisuutta tilintarkastuksessa sekä tehostaneen tilintarkastusprosessia mm. sallimalla aiempien vuosien tarkastustietojen entistä laajemmin käytön. Näyttää myös siltä, että AS 5 on alentanut tilintarkastuspalkkioita.

Targeting striatal metabotropic glutamate receptor type 5 in Parkinson's disease: bridging molecular studies and clinical trials

Metabotropic glutamate (mGlu) receptors are G protein-coupled receptors expressed primarily on neurons and glial cells modulating the effects of glutamatergic neurotransmission. The pharmacological manipulation of these receptors has been postulated to be valuable in the management of some neurological disorders. Accordingly, the targeting of mGlu5 receptors as a therapeutic approach for Parkinson's disease (PD) has been proposed, especially to manage the adverse symptoms associated to chronic treatment with classical PD drugs. Thus, the specific pharmacological blocking of mGlu5 receptors constitutes one of the most attractive non-dopaminergic-based strategies for PD management in general and for the L-DOPA-induced diskynesia (LID) in particular. Overall, we provide here an update of the current state of the art of these mGlu5 receptor-based approaches that are under clinical study as agents devoted to alleviate PD symptoms.

A systematic mapping study of web services discovery mechanisms

Context: Web services have been gaining popularity due to the success of service oriented architecture and cloud computing. Web services offer tremendous opportunity for service developers to publish their services and applications over the boundaries of the organization or company. However, to fully exploit these opportunities it is necessary to find efficient discovery mechanism thus, Web services discovering mechanism has attracted a considerable attention in Semantic Web research, however, there have been no literature surveys that systematically map the present research result thus overall impact of these research efforts and level of maturity of their results are still unclear. This thesis aims at providing an overview of the current state of research into Web services discovering mechanism using systematic mapping. The work is based on the papers published 2004 to 2013, and attempts to elaborate various aspects of the analyzed literature including classifying them in terms of the architecture, frameworks and methods used for web services discovery mechanism. Objective: The objective if this work is to summarize the current knowledge that is available as regards to Web service discovery mechanisms as well as to systematically identify and analyze the current published research works in order to identify different approaches presented. Method: A systematic mapping study has been employed to assess the various Web Services discovery approaches presented in the literature. Systematic mapping studies are useful for categorizing and summarizing the level of maturity research area. Results: The result indicates that there are numerous approaches that are consistently being researched and published in this field. In terms of where these researches are published, conferences are major contributing publishing arena as 48% of the selected papers were conference published papers illustrating the level of maturity of the research topic. Additionally selected 52 papers are categorized into two broad segments namely functional and non-functional based approaches taking into consideration architectural aspects and information retrieval approaches, semantic matching, syntactic matching, behavior based matching as well as QOS and other constraints.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Quantification of Toxin Biosynthesis Genes In Cyanobacteria and Dinoflagellates - Genetic Factors as Predictors of Toxin Production in the Environment

Harmful algal blooms (HABs) are events caused by the massive proliferation of microscopic, often photosynthetic organisms that inhabit both fresh and marine waters. Although HABs are essentially a natural phenomenon, they now cause worldwide concern. Recent anthropogenic effects, such as climate change and eutrophication via nutrient runoff, can be seen in their increased prevalence and severity. Cyanobacteria and dinoflagellates are often the causative organisms of HABs. In addition to adverse effects caused by the sheer biomass, certain species produce highly potent toxic compounds: hepatotoxic microcystins are produced exclusively by cyanobacteria and neurotoxic saxitoxins, also known as paralytic shellfish toxins (PSTs), by both cyanobacteria and dinoflagellates. Specific biosynthetic genes in the cyanobacterial genomes direct the production of microcystin and paralytic shellfish toxins. Recently also the first paralytic shellfish toxin gene sequences from dinoflagellate genomes have been elucidated. The public health risks presented by HABs are evident, but the monitoring and prediction of toxic events is challenging. Characterization of the genetic background of toxin biosynthesis, including that of microcystins and paralytic shellfish toxins, has made it possible to develop highly sensitive molecular tools which have shown promise in the monitoring and study of potentially toxic microalgae. In this doctoral work, toxin-specific genes were targeted in the developed PCR and qPCR assays for the detection and quantification of potentially toxic cyanobacteria and dinoflagellates in the environment. The correlation between the copy numbers of the toxin biosynthesis genes and toxin production were investigated to assess whether the developed methods could be used to predict toxin concentrations. The nature of the correlation between gene copy numbers and amount of toxin produced varied depending on the targeted gene and the producing organism. The combined mcyB copy numbers of three potentially microcystin-producing cyanobacterial genera showed significant positive correlation to the observed total toxin production. However, the presence of PST-specific sxtA, sxtG, and sxtB genes of cyanobacterial origin was found to be a poor predictor of toxin production in the studied area. Conversely, the dinoflagellate sxtA4 was a good qualitative indicator of a neurotoxic bloom both in the laboratory and in the field, and population densities reflected well the observed toxin concentrations. In conclusion, although the specificity of each potential targeted toxin biosynthesis gene must be assessed individually during method development, the results obtained in this doctoral study support the use of quantitative PCR -based approaches in the monitoring of toxic cyanobacteria and dinoflagellates.

An exploratory study for the discovery of non-invasive hepatocellular carcinoma biomarkers among high-risk hepatitis C virus infected patients

Hepatocellular Carcinoma (HCC) is a major healthcare problem, representing the third most common cause of cancer-related mortality worldwide. Chronic infections with Hepatitis B virus (HBV) and/or Hepatitis C virus (HCV) are the major risk factors for the development of HCC. The incidence of HBV -associated HCC is in decline as a result of an effective HBV vaccine; however, since an equally effective HCV vaccine has not yet been developed, there are 130 million HCV infected patients worldwide who are at a high-risk for developing HCC. Because reliable parameters and/or tools for the early detection of HCC among high-risk individuals are severely lacking, HCC patients are always diagnosed at a late stage where surgical solutions or effective treatment are not possible. Using urine as a non-invasive sample source, two different approaches (proteomic-based and genomic-based approaches) were pursued with the common goal of discovering potential biomarker candidates for the early detection of HCC among high-risk chronic HCV infected patients. Urine was collected from 106 HCV infected Egyptian patients, 32 of whom had already developed HCC and 74 patients who were diagnosed as HCC-free at the time of initial sample collection. In addition to these patients, urine samples were also collected from 12 healthy control individuals. Total urinary proteins, Trans-renal nucleic acid (Tr-NA) and microRNA (miRNA) were isolated from urine using novel methodologies and silicon carbide-loaded spin columns. In the first, "proteomic-based", approach, liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) was used to identify potential candidates from pooled urine samples. This was followed by validating relative expression levels of proteins present in urine among all the patients using quantitative real time-PCR (qRT-PCR). This approach revealed that significant over-expression of three proteins: DJ-1, Chromatin Assembly Factor-1 (CAF-1) and 11 Moemen Abdalla HCC Biomarkers Heat Shock Protein 60 (HSP60), were characteristic events among HCC-post HCV infected patients. As a single-based HCC biomarker, CAF-1 over-expression identified HCC among HCV infected patients with a specificity of 90%, sensitivity of 66% and with an overall diagnostic accuracy of 78%. Moreover, the CAF-lIHSP60 tandem identified HCC among HCV infected patients with a specificity of 92%, sensitivity of 61 % and with an overall diagnostic accuracy of 77%. In the second genomic-based approach, two different approaches were processed. The first approach was the miRNA-based approach. The expression levels of miRNAs isolated from urine were studied using the Illumina MicroRNA Expression Profiling Assay. This was followed by qRT-PCR-based validation of deregulated expression of identified miRNA candidates among all the patients. This approach shed the light on the deregulated expression of a number of miRNAs, which may have a role in either the development of HCC among HCV infected patients (i.e. miR-640, miR-765, miR-200a, miR-521 and miR-520) or may allow for a better understanding of the viral-host interaction (miR-152, miR-486, miR-219, miR452, miR-425, miR-154 and miR-31). Moreover, the deregulated expression of both miR-618 and miR-650 appeared to be a common event among HCC-post HCV infected patients. The results of the search for putative targets of these two miRNA suggested that miR-618 may be a potent oncogene, as it targets the tumor-suppressor gene Low density lipoprotein-related protein 12 (LPR12), while miR-650 may be a potent tumor-suppressor gene, as it is supposed to downregulate the TNF receptor-associated factor-4 (TRAF4) oncogene. The specificity of miR-618 and miR-650 deregulated expression patterns for the early detection of HCC among HCV infected patients was 68% and 58%, respectively, whereas the sensitivity was 64% and 72%, respectively. When the deregulated expression of both miRNAs was combined as a tandem biomarker, the specificity and the sensitivity were 75% and 58% respectively. 111 Moemen Abdalla HCC Biomarkers In the second, "Trans-renal nucleic acid-based", approach, the urinary apoptotic nucleic acid (uaNA) levels of 70ng/mL or more were found to be a good predictor of HCC among chronic HCV infected patients. The specificity and the sensitivity of this diagnostic approach were 76% and 86%, respectively, with an overall diagnostic value of 81 %. The uaNA levels positively correlated to HCC disease progression as monitored by epigenetic changes of a panel of eight tumor-suppressor genes (TSGs) using methylation-sensitive PCR. Moreover, the pairing of high uaNA levels (:::: 70 ng/mL) and CAF-1 over-expreSSIOn produced a highly specific (l 00%) multiple-based HCC biomarker with an acceptable sensitivity of 64%, and with a diagnostic accuracy of 82%. In comparison to the previous pairing, the uaNA levels (:::: 70 ng/mL) in tandem with HSP60 over-expression was less specific (89%) but highly sensitive (72%), resulting in a diagnostic accuracy of 64%. The specificities of miR-650 deregulated expression in combination with either high uaNA content or HSP 60 over-expression were 82% and 79%, respectively, whereas, the sensitivities of these combinations were 64% and 58%, respectively. The potential biomarkers identified in this study compare favorably with the diagnostic accuracy of the a-fetoprotein levels test, which has a specificity of 75%, sensitivity of 68% and an overall diagnostic accuracy of 70%. Here we present an intriguing study which shows the significance of using urine as a noninvasive sample source for the identification of promising HCC biomarkers. We have also introduced new techniques for the isolation of different urinary macromolecules, especially miRNA, from urine. Furthermore, we strongly recommend the potential biomarkers indentified in this study as focal points of any future research on HCC diagnosis. A larger testing pool will determine if their use is practical for mass population screening. This explorative study identified potential targets that merit further investigation for the development of diagnostically accurate biomarkers isolated from 1-2 mL urine samples that were acquired in a non-invasive manner.