abstract: Accurate virus detection is important for diagnosis in a timely manner to facilitate rapid interventions and treatments. RNA viruses affect an extensive amount of the world’s population, particularly in tropical countries where emerging infectious agents often arise. Current diagnostic methods have three main problems: they are time consuming, typically not field-portable, and expensive. My research goal is to develop rapid, field-portable and cost sensitive diagnostic methods for RNA viruses. Herein, two different approaches to detect RNA viruses were proposed: Conjugated gold nanoparticles for detection of viral particles or virus-specific antibodies by monitoring changes in their optical properties, and Tentacle Probes coupled with qPCR for detection and differentiation of closely-related viral strains. The first approach was divided into two projects: the study and characterization of the gold nanoparticle-antibody system for detection of virus particles using dynamic light scattering (DLS) and UV-Vis spectrophotometry, and development of a detection method for antibodies using static light scattering (SLS) and antigen-conjugated gold nanoparticles. Bovine serum albumin (BSA) conjugated gold nanoparticles could successfully detect BSA-specific antibodies in vitro, and protein E from Dengue Virus serotype 2 conjugated gold nanoparticles could detect Dengue-specific antibodies, both in vitro and in serum samples. This method is more accurate than currently used detection methods such as dot blots. The second approach uses Tentacle Probes, which are modified molecular beacons, to detect with high specificity two different strains of Lymphocytic Choriomeningitis Virus (LCMV), Armstrong and Clone-13, which differ in only one nucleotide at the target sequence. We successfully designed and use Tentacle Probes for detection of both strains of LCMV, in vitro and in serum from infected mice. Moreover, detection of as little as 10% of Clone-13 strain was possible when diluted in 90% Armstrong strain. This approach enables the detection of different strains of virus even within a mixed quasispecies and may be important for improving intervention strategies for reducing disease. The detection methods provide rapid detection of viruses, including viral strains within mixed populations, and should enhance our ability in providing early responses to emerging infectious diseases due to RNA viruses including Zika or Dengue virus. / Dissertation/Thesis / Doctoral Dissertation Biochemistry 2016

As cianobactérias pertencem a um grupo de organismos que surgiram no planeta há cerca de 3,5 bilhões de anos. Durante 270 milhões de anos elas dominaram, em um período que ficou conhecido como o Período das Cyanophyceae (Pré-Cambriano). Águas eutrofizadas oferecem os requisitos para haver floração aumentando as chances estatísticas de aparecimento de cianobactérias, inclusive as tóxicas. Há alteração substancial das características das comunidades e do ecossistema e, por seu potencial tóxico, esses organismos causam conseqüências danosas à integridade ambiental e à saúde pública. Essas florações são resultado da interação de fatores físicos, químicos e bióticos, evidenciados por crescimento explosivo - florações - de poucas espécies, freqüentemente as menos exigentes a gás carbônico. Entre as cianobactérias formadoras de florações destacam-se os gêneros: Aphanizomenon sp., Oscillatoria sp., Anabaena sp., Microcystis sp., Gloeotrichia sp., Cylindrospermospsis sp. e Nodularia sp. Entre as cianobactérias produtoras de toxinas destacam-se as espécies: Anabaena flos-aquae, Microcysits aeruginosa, Aphanizomenon flos-aquae, Oscillatoria agardhii, Cylindrospermopsis raciborskii e Nodularia spumigena. O problema para a saúde pública e ambiental relaciona-se à liberação das toxinas que sempre ocorre após a lise celular, devido ao uso de algicidas, ao estresse celular ou à senescência. As cianotoxinas - neurotoxinas, hepatotoxinas, endotoxinas (lipopolissacarídeos) - não causam os mesmos efeitos nos diferentes organismos que as ingerem. Para análises quantitativas e qualitativas das amostras fitoplanctônicas são necessárias rotinas laboratoriais, que têm início desde a coleta, estocagem, sedimentação e identificação das espécies ao microscópio. Em câmaras de sedimentação apropriadas é possível contar e identificar os organismos em questão. O volume celular é estimado por meio de formas geométricas, correspondentes às dimensões celulares dos organismos. O carbono orgânico e os pigmentos fotossintetizantes, principalmente a clorofila a, fornecem indicação da biomassa fitoplanctônica. A extração desta pode ser feita utilizando-se acetona, etanol ou metanol. Os métodos mais usados para determinação da concentração de clorofila a são: fluorimetria, espectrofotômetria e HPLC, este último o método mais preciso. No Brasil têm sido registradas ocorrências de florações de cianobactérias e a preocupação tem aumentado significamente após o caso de Caruaru (PE). Como é difícil prever as florações de cianobactérias potencialmente tóxicas, alguns métodos laboratoriais, como bioensaios, HPLC, CG, ELISA, Inibição de Fosfatase e Inibição de Acetilcolinesterase têm sido de grande ajuda na determinação das cianotoxinas, o que possibilita que as empresas abastecedoras de água tomem as providências cabíveis, quanto ao abastecimento da população e cumprimento da Portaria 518, de 24 de março de 2004. No entanto a Portaria nº 518 do Ministério da Saúde mostra-se ineficaz pela falta de artigos sobre o gerenciamento dos corpos hídricos eutrofizados, que mostram alterações em suas características e que podem ser associados a problemas de saúde pública, e também pela falta de penalidades e outras sanções (exemplo: fiscalização) que auxiliem no cumprimento da legislação. Devido a isso, procedimentos e profissionais devem ser encontrados e treinados para o entendimento de como, onde e por que, as florações acontecem. / Cyanobacteria are part of a group of organisms that have had a long evolutionary history extending to at least 3,5 billion years ago. During 270 million years they had been dominant, in a period that was known as the \"Age of Cyanophyceae\" (Early Cambrian Period or Proterozoic Era). Eutrophic waters offer the requirements for the blooms increasing statistically the chances of cyanobacteria emergence, including the toxic ones. There is substantial disturbance of characteristics of the communities and ecosystem and, because of their toxic potential, those organisms cause harmful consequences to the environmental integrity and public health. Those blooms are a result of an interaction of physical, chemical and biotic factors, evidenced by the proliferation of few species, frequently the least demanding ones to carbonic gas. Globally among the most frequently found freshwater cyanobacteria forming water-blooms it must be emphasized the following strains: Aphanizomenon sp., Oscillatoria sp., Anabaena sp., Microcystis sp., Gloeotrichia sp., Cylindrospermospsis sp. and Nodularia sp. Among the cyanobacteria producing toxins it must be emphasized the following species: Anabaena flos-aquae, Microcysits aeruginosa, Aphanizomenon flos-aquae, Oscillatoria agardhii, Cylindrospermopsis raciborskii and Nodularia spumigena. Environmental and public health problems are largely associated with the release of toxins occurring after cellular lisys, caused by the use of algicides, cell stress or senescence. Cyanotoxins - neurotoxins, hepatotoxins, endotoxins (lipopolysaccharides) - cause distinct effects in the different organisms ingesting them. For quantitative and qualitative analyses of phytoplanktonic samples they are necessary laboratory routines as collection, stocking, sedimentation and identification of the species performed by microscopic examination. It is possible to count and identify such organisms in appropriate sedimentation chambers. Cell volumes are calculated for each species from formulae for solid geometric shapes that most closely match the cell shape based on cell dimensions. Organic carbon and photosynthetic pigments primarily chlorophyll-a are used to estimate the biomass of the phytoplankton. Chlorophyll-a extraction can be made by using acetone, ethanol or methanol. The major methods used for determination of chlorophyll-a concentrations are fluorometry, spectrophotometry and HPLC. In particular, HPLC has been considered the most accurate analytical method. Occurrences of cyanobacteria blooms in Brazil have been recorded, and a concern has becoming increasingly significant after the Caruaru (PE) episode. It is difficult to foresee the potentially toxic cyanobacteria blooms, so some laboratory methods as Bioassays, HPLC, CG, ELISA, Inhibition of Phosphatase and Inhibition of Acetylcholinesterase have been of great help in the determination of the cyanotoxins, what permits that the health authorities take reasonable providences related to drinking water supplies and fulfill the Guideline 518, of March 24, 2004. Nevertheless, national legislation becomes inefficient by the lack of ordinances about the management of eutrophic water bodies, which may notably alter the characteristics of aquatic ecosystems and cause public health problems. Penalties and other sanctions to assist in enforcement of the legislation are also needed. Procedures must be taken and operators must be trained in order to clarify how, where and why blooms happen.

<div> <div> <div> <p>The use of graph-structured data in applications is increasing day by day. In order to infer useful information from such data, fast analytics and software tools are required. One of the graph analytics techniques used is community detection. Community detection is the technique of finding structural communities within a graph. Such communities are defined as groups which have highly connected nodes and have similarities with each other. </p> <p>This research proposes a parallel heuristic for faster community detection using the parallel version of the Louvain algorithm: Grappolo. The Louvain algorithm is a hierarchical algorithm that focuses on modularity optimization. It gained popularity because of its ability to detect high-quality communities faster than the other existing community detection algorithms. However, the Louvain algorithm is a sequential algorithm. To reduce the execution time of the Louvain algorithm, a parallel version named Grappolo exists in the literature. This algorithm proposes parallel heuristics that address the challenges that occur due to parallelizing the sequential Louvain algorithm. </p> <p>In this study, the researcher is investigating if Grappolo can be further parallelized to further reduce the execution time maintaining the quality of communities detected. To evaluate the proposed heuristic, it was tested on an OpenMP multithreaded environment. It was implemented on source codes of Android malware applications. However, as compared to Grapplolo, the proposed modified version resulted in higher execution times for the inputs tested. The modularity of the communities detected was similar to the Grappolo implementation. </p> </div> </div> </div>

This diploma is focused on analyse existing method usable for detection surface defects of grindling surface. Also suggests and practically testing new method for detection surface defects of grindling surface

The theoretical findings about methods of earth faults indication are elaborated in this thesis into form, which is applicable to design contactless indicator of failure states on MV lines. This design contains electromagnetic field sensors, evaluation device and software support. The higher effectiveness of clearing of fault and better health and asset protection is supposed by using of these indicators.

Authorities of municipal separate storm sewer systems (MS4s) are required to address illicit discharges as part of the National Pollutant Discharge Elimination System (NPDES) stormwater program. Field reconnaissance is an effective measure to detect and identify illicit discharges, but requires substantial staff and financial resources to conduct. While risk analysis techniques and guidelines have been developed to facilitate MS4 prioritization of field operations, neither a standard set of indicators nor a standard operating procedure has been adopted. This study investigates the relationships among indicators of illicit discharge potential (IDP) and the locations of illicit discharges in two Virginia MS4s. Results of the study indicate that certain risk factors are statistically more effective at predicting IDP, suggesting that a core set of factors can be used to map illicit discharge risk. The results also show that risk mapping tools are significantly impacted by uncertainty in model inputs. Recommendations are provided for MS4s interested in pursuing IDP risk mapping as a tool to improve cost-effectiveness and guide illicit discharge program implementation. / Master of Science

This thesis is dealing with problems of creative accounting, with the access to its identification and utilization from the management position. The theoretical part defines the term of creative accounting. Moreover, it introduces possible readers into the fraudulent accounting and techniques of creative accounting. Furthermore, this thesis deals with circumstances that lead to the origin creativity. To add to this, this work addresses the psychological and behavioural point of view on the creativity. The main aim of the practical part is to answer the question of following hypothesis: The management of chosen accounting unit obligingly reacts on motivation which minimizes corporate income tax.

M. Ing. / Fault detection tools have gained popularity in recent years due to the increasing need for reliable and predictable equipments. Transformer bushings account for the majority of transformer faults. Hence, to uphold the integrity of the power transmission and dis- tribution system, a tool to detect and identify faults in their developing stage is necessary in transformer bushings. Among the numerous tools for bushings monitoring, dissolved gas analysis (DGA) is the most commonly used. The advances in DGA and data storage capabilities have resulted in large amount of data and ultimately, the data analysis crisis. Consequent to that, computational intelligence methods have advanced to deal with this data analysis problem and help in the decision-making process. Numerous computational intelligence approaches have been proposed for bushing fault detection. Most of these approaches focus on the accuracy of prediction and not much research has been allocated to investigate the interpretability of the decisions derived from these systems. This work proposes a rough set theory (RST) model for bushing fault detection based on DGA data analyzed using the IEEEc57.104 and the IEC 60599 standards. RST is a rule-based technique suitable for analyzing vague, uncertain and imprecise data. RST extracts rules from the data to model the system. These rules are used for prediction and interpreting the decision process. The lesser the number of rules, the easier it is to interpret the model. The performance of the RST is dependent on the discretization technique employed. An equal frequency bin (EFB), Boolean reasoning (BR) and entropy partition (EP) are used to develop an RST model. The model trained using EFB data performs better than the models trained using BR and EP. The accuracy achieved is 96.4%, 96.0% and 91.3% for EFB, BR and EP respectively. This work also pro poses an ant colony optimization (ACO) for discretization. A model created using ACO discretized achieved an accuracy of 96.1%, which is compatible with the three methods above. When considering the overall performance, the ACO is a better discretization tool since it produces an accurate model with the least number of rules. The rough set tool proposed in this work is benchmarked against a multi-layer perceptron (MLP) and radial basis function (RBF) neural networks. Results prove that RST modeling for bushing is equally as capable as the MLP and better than RBF. The RST, MLP and RBF are used in an ensemble of classifiers. The ensemble performs better than the standalone models.

ESBL-producing Enterobacteriaceae are emerging worldwide and they are frequently multi-drug resistant, thus limiting treatment options for infections caused by these pathogens. The overall aim of the thesis was to investigate ESBL-producing Enterobacteriaceae in a Swedish county. First, we developed a molecular method, a multiplex PCR assay for identification of SHV, TEM and CTX-M genes in clinical isolates of Enterobacteriaceae with an ESBL phenotype. From 2002 until the end of 2007 all isolates of ESBL-producing Enterobacteriaceae in Östergötland, Sweden were further investigated. The prevalence of ESBL-producing Enterobacteriaceae was low, &lt;1%, but increasing,while the antibiotic consumption remained unchanged. CTX-M enzymes, particularly CTX-M group 1, dominate in our region as well as in the rest of Europe. Furthermore, we have investigated antimicrobial susceptibility by performing MIC-testing in a large, well-characterized population of CTX-M-producing E. coli. Only three oral antimicrobial agents (fosfomycin, nitrofurantoin and mecillinam) demonstrated susceptibility above 90%. High susceptibility, &gt;90%, was also demonstrated for carbapenems, colistin, tigecycline and amikacin. Sixty-eight per cent of ESBL-producing E. coli was multi-resistant, and the most common multi-resistance pattern was the ESBL phenotype with decreased susceptibility to trimethoprim, trimethoprim-sulfamethoxazole, ciprofloxacin, gentamicin and tobramycin. Isolates belonging to CTX-M group 9 are generally more susceptible to antibiotics than the CTX-M group 1-producing E. coli. Finally, a prospective multicentre case-control study examined the prevalence of ESBL-producing Enterobacteriaceae in faecal samples before and after travel abroad and the risk factors of acquisition. Sixty-eight of 226 travellers (30%) had ESBL-producing Enterobacteriaceae in the faecal flora. The geographical area visited had the highest impact on acquisition, with highest the risk for travellers visiting the Indian subcontinent, followed by Asia and Africa north of the equator. Also, acquisition of ESBL-producing Enterobacteriaceae during travel is associated with abdominal symptoms such as diarrhoea. Age also seemed to affect the risk of acquiring ESBL-producing Enterobacteriaceae, the highest risks were found among travellers ≥ 65 years. This thesis has contributed to increased understanding of the epidemiology of ESBL-producing Enterobacteriaceae and their susceptibility to both beta-lactam and non-beta-lactam agents.

Continuous change and development are essential aspects of evolving environments and applications, including, but not limited to, smart cities, military, medicine, nuclear reactors, self-driving cars, aviation, and aerospace. That is, the fundamental characteristics of such environments may evolve, and so cause dangerous consequences, e.g., putting people lives at stake, if no reaction is adopted. Therefore, learning systems need to apply intelligent algorithms to monitor evolvement in their environments and update themselves effectively. Further, we may experience fluctuations regarding the performance of learning algorithms due to the nature of incoming data as it continuously evolves. That is, the current efficient learning approach may become deprecated after a change in data or environment. Hence, the question 'how to have an efficient learning algorithm over time against evolving data?' has to be addressed. In this thesis, we have made two contributions to settle the challenges described above. In the machine learning literature, the phenomenon of (distributional) change in data is known as concept drift. Concept drift may shift decision boundaries, and cause a decline in accuracy. Learning algorithms, indeed, have to detect concept drift in evolving data streams and replace their predictive models accordingly. To address this challenge, adaptive learners have been devised which may utilize drift detection methods to locate the drift points in dynamic and changing data streams. A drift detection method able to discover the drift points quickly, with the lowest false positive and false negative rates, is preferred. False positive refers to incorrectly alarming for concept drift, and false negative refers to not alarming for concept drift. In this thesis, we introduce three algorithms, called as the Fast Hoeffding Drift Detection Method (FHDDM), the Stacking Fast Hoeffding Drift Detection Method (FHDDMS), and the McDiarmid Drift Detection Methods (MDDMs), for detecting drift points with the minimum delay, false positive, and false negative rates. FHDDM is a sliding window-based algorithm and applies Hoeffding’s inequality (Hoeffding, 1963) to detect concept drift. FHDDM slides its window over the prediction results, which are either 1 (for a correct prediction) or 0 (for a wrong prediction). Meanwhile, it compares the mean of elements inside the window with the maximum mean observed so far; subsequently, a significant difference between the two means, upper-bounded by the Hoeffding inequality, indicates the occurrence of concept drift. The FHDDMS extends the FHDDM algorithm by sliding multiple windows over its entries for a better drift detection regarding the detection delay and false negative rate. In contrast to FHDDM/S, the MDDM variants assign weights to their entries, i.e., higher weights are associated with the most recent entries in the sliding window, for faster detection of concept drift. The rationale is that recent examples reflect the ongoing situation adequately. Then, by putting higher weights on the latest entries, we may detect concept drift quickly. An MDDM algorithm bounds the difference between the weighted mean of elements in the sliding window and the maximum weighted mean seen so far, using McDiarmid’s inequality (McDiarmid, 1989). Eventually, it alarms for concept drift once a significant difference is experienced. We experimentally show that FHDDM/S and MDDMs outperform the state-of-the-art by representing promising results in terms of the adaptation and classification measures. Due to the evolving nature of data streams, the performance of an adaptive learner, which is defined by the classification, adaptation, and resource consumption measures, may fluctuate over time. In fact, a learning algorithm, in the form of a (classifier, detector) pair, may present a significant performance before a concept drift point, but not after. We define this problem by the question 'how can we ensure that an efficient classifier-detector pair is present at any time in an evolving environment?' To answer this, we have developed the Tornado framework which runs various kinds of learning algorithms simultaneously against evolving data streams. Each algorithm incrementally and independently trains a predictive model and updates the statistics of its drift detector. Meanwhile, our framework monitors the (classifier, detector) pairs, and recommends the efficient one, concerning the classification, adaptation, and resource consumption performance, to the user. We further define the holistic CAR measure that integrates the classification, adaptation, and resource consumption measures for evaluating the performance of adaptive learning algorithms. Our experiments confirm that the most efficient algorithm may differ over time because of the developing and evolving nature of data streams.