Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Guided wave based methods for damage detection : experimental study on concrete joint

Since Guided wave (GW) is sensitive to small damage and can propagate a relatively longer distance with relatively less attenuation, GW-based method has been found as an effective and efficient way to detect incipient damages. In this study, a full-scale concrete joint was constructed to further verify the effectiveness of GW-based method on real civil structures. GW tests were conducted in three stages, including baseline, serviceability and damage conditions. The waves are excited by one actuator and received by several sensors, which are made up of independent piezoelectric elements. Experimental results show that the mehod is promising for damage identification in practices.

The performance of conventional and fluorescence-based methods for occlusal caries detection An in vivo study with histologic validation

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Diagnosis of Giardia infections by PCR-based methods in children of an endemic area

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Clinical applications of non-culture based methods for the diagnosis and management of opportunistic and endemic mycoses

Nonculture based methods for the detection of infections caused by fungal pathogens are becoming more important tools in the management of infected patients. Detection of fungal antigens and DNA appear to be the most promising in this respect for both opportunistic and endemic mycoses. In this article we present an overview of the most recent developments in nonculture based methods and examine their value in clinical practice.

The performance of conventional and fluorescence-based methods for occlusal caries detection: an in vivo study with histological validation

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Constraint based methods for allocation and scheduling of periodic applications

This work presents exact algorithms for the Resource Allocation and Cyclic Scheduling Problems (RA&CSPs). Cyclic Scheduling Problems arise in a number of application areas, such as in hoist scheduling, mass production, compiler design (implementing scheduling loops on parallel architectures), software pipelining, and in embedded system design. The RA&CS problem concerns time and resource assignment to a set of activities, to be indefinitely repeated, subject to precedence and resource capacity constraints. In this work we present two constraint programming frameworks facing two different types of cyclic problems. In first instance, we consider the disjunctive RA&CSP, where the allocation problem considers unary resources. Instances are described through the Synchronous Data-flow (SDF) Model of Computation. The key problem of finding a maximum-throughput allocation and scheduling of Synchronous Data-Flow graphs onto a multi-core architecture is NP-hard and has been traditionally solved by means of heuristic (incomplete) algorithms. We propose an exact (complete) algorithm for the computation of a maximum-throughput mapping of applications specified as SDFG onto multi-core architectures. Results show that the approach can handle realistic instances in terms of size and complexity. Next, we tackle the Cyclic Resource-Constrained Scheduling Problem (i.e. CRCSP). We propose a Constraint Programming approach based on modular arithmetic: in particular, we introduce a modular precedence constraint and a global cumulative constraint along with their filtering algorithms. Many traditional approaches to cyclic scheduling operate by fixing the period value and then solving a linear problem in a generate-and-test fashion. Conversely, our technique is based on a non-linear model and tackles the problem as a whole: the period value is inferred from the scheduling decisions. The proposed approaches have been tested on a number of non-trivial synthetic instances and on a set of realistic industrial instances achieving good results on practical size problem.

Kinematics and statics of cable-driven parallel robots by interval-analysis-based methods

In the past two decades the work of a growing portion of researchers in robotics focused on a particular group of machines, belonging to the family of parallel manipulators: the cable robots. Although these robots share several theoretical elements with the better known parallel robots, they still present completely (or partly) unsolved issues. In particular, the study of their kinematic, already a difficult subject for conventional parallel manipulators, is further complicated by the non-linear nature of cables, which can exert only efforts of pure traction. The work presented in this thesis therefore focuses on the study of the kinematics of these robots and on the development of numerical techniques able to address some of the problems related to it. Most of the work is focused on the development of an interval-analysis based procedure for the solution of the direct geometric problem of a generic cable manipulator. This technique, as well as allowing for a rapid solution of the problem, also guarantees the results obtained against rounding and elimination errors and can take into account any uncertainties in the model of the problem. The developed code has been tested with the help of a small manipulator whose realization is described in this dissertation together with the auxiliary work done during its design and simulation phases.

Proteomic analysis of ocular surface components by use of HPLC based mass spectrometric strategies

In der vorliegenden Arbeit wurde eine Top Down (TD) und zwei Bottom Up (BU) MALDI/ESI Massenspektrometrie/HPLC-Methoden entwickelt mit dem Ziel Augenoberfächenkomponenten, d.h. Tränenfilm und Konjunktivalzellen zu analysieren. Dabei wurde ein detaillierter Einblick in die Entwicklungsschritte gegeben und die Ansätze auf Eignung und methodische Grenzen untersucht. Während der TD Ansatz vorwiegend Eignung zur Analyse von rohen, weitgehend unbearbeiteten Zellproben fand, konnten mittels des BU Ansatzes bearbeitete konjunktivale Zellen, aber auch Tränenfilm mit hoher Sensitivität und Genauigkeit proteomisch analysiert werden. Dabei konnten mittels LC MALDI BU-Methode mehr als 200 Tränenproteine und mittels der LC ESI Methode mehr als 1000 Tränen- sowie konjunktivale Zellproteine gelistet werden. Dabei unterschieden sich ESI- and MALDI- Methoden deutlich bezüglich der Quantität und Qualität der Ergebnisse, weshalb differente proteomische Anwendungsgebiete der beiden Methoden vorgeschlagen wurden. Weiterhin konnten mittels der entwickelten LC MALDI/ESI BU Plattform, basierend auf den Vorteilen gegenüber dem TD Ansatz, therapeutische Einflüsse auf die Augenoberfläche mit Fokus auf die topische Anwendung von Taurin sowie Taflotan® sine, untersucht werden. Für Taurin konnten entzündungshemmende Effekte, belegt durch dynamische Veränderungen des Tränenfilms, dokumentiert werden. Außerdem konnten vorteilhafte, konzentrationsabhängige Wirkweisen auch in Studien an konjunktival Zellen gezeigt werden. Für die Anwendung von konservierungsmittelfreien Taflotan® sine, konnte mittels LC ESI BU Analyse eine Regenerierung der Augenoberfläche in Patienten mit Primärem Offenwinkel Glaukom (POWG), welche unter einem “Trockenem Auge“ litten nach einem therapeutischen Wechsel von Xalatan® basierend auf dynamischen Tränenproteomveränderungen gezeigt werden. Die Ergebnisse konnten mittels Microarray (MA) Analysen bestätigt werden. Sowohl in den Taurin Studien, als auch in der Taflotan® sine Studie, konnten charakteristische Proteine der Augenoberfläche dokumentiert werden, welche eine objektive Bewertung des Gesundheitszustandes der Augenoberfläche ermöglichen. Eine Kombination von Taflotan® sine und Taurin wurde als mögliche Strategie zur Therapie des Trockenen Auges bei POWG Patienten vorgeschlagen und diskutiert.

New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.

An inter-comparison between a VIS/IR and a MW satellite-based methods for cloud detection and classification

This thesis is aimed to assess similarities and mismatches between the outputs from two independent methods for the cloud cover quantification and classification based on quite different physical basis. One of them is the SAFNWC software package designed to process radiance data acquired by the SEVIRI sensor in the VIS/IR. The other is the MWCC algorithm, which uses the brightness temperatures acquired by the AMSU-B and MHS sensors in their channels centered in the MW water vapour absorption band. At a first stage their cloud detection capability has been tested, by comparing the Cloud Masks they produced. These showed a good agreement between two methods, although some critical situations stand out. The MWCC, in effect, fails to reveal clouds which according to SAFNWC are fractional, cirrus, very low and high opaque clouds. In the second stage of the inter-comparison the pixels classified as cloudy according to both softwares have been. The overall observed tendency of the MWCC method, is an overestimation of the lower cloud classes. Viceversa, the more the cloud top height grows up, the more the MWCC not reveal a certain cloud portion, rather detected by means of the SAFNWC tool. This is what also emerges from a series of tests carried out by using the cloud top height information in order to evaluate the height ranges in which each MWCC category is defined. Therefore, although the involved methods intend to provide the same kind of information, in reality they return quite different details on the same atmospheric column. The SAFNWC retrieval being very sensitive to the top temperature of a cloud, brings the actual level reached by this. The MWCC, by exploiting the capability of the microwaves, is able to give an information about the levels that are located more deeply within the atmospheric column.

A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context

Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.

UPLC-MS-based urine metabolomics reveals indole-3-lactic acid and phenyllactic acid as conserved biomarkers for alcohol-induced liver disease in the Ppara-null mouse model

Since the development and prognosis of alcohol-induced liver disease (ALD) vary significantly with genetic background, identification of a genetic background-independent noninvasive ALD biomarker would significantly improve screening and diagnosis. This study explored the effect of genetic background on the ALD-associated urinary metabolome using the Ppara-null mouse model on two different backgrounds, C57BL/6 (B6) and 129/SvJ (129S), along with their wild-type counterparts. Reversed-phase gradient UPLC-ESI-QTOF-MS analysis revealed that urinary excretion of a number of metabolites, such as ethylsulfate, 4-hydroxyphenylacetic acid, 4-hydroxyphenylacetic acid sulfate, adipic acid, pimelic acid, xanthurenic acid, and taurine, were background-dependent. Elevation of ethyl-β-d-glucuronide and N-acetylglycine was found to be a common signature of the metabolomic response to alcohol exposure in wild-type as well as in Ppara-null mice of both strains. However, increased excretion of indole-3-lactic acid and phenyllactic acid was found to be a conserved feature exclusively associated with the alcohol-treated Ppara-null mouse on both backgrounds that develop liver pathologies similar to the early stages of human ALD. These markers reflected the biochemical events associated with early stages of ALD pathogenesis. The results suggest that indole-3-lactic acid and phenyllactic acid are potential candidates for conserved and pathology-specific high-throughput noninvasive biomarkers for early stages of ALD.