Aplicação de técnicas de aprendizado de máquina no reconhecimento de classes estruturais de proteínas

Nowadays, classifying proteins in structural classes, which concerns the inference of patterns in their 3D conformation, is one of the most important open problems in Molecular Biology. The main reason for this is that the function of a protein is intrinsically related to its spatial conformation. However, such conformations are very difficult to be obtained experimentally in laboratory. Thus, this problem has drawn the attention of many researchers in Bioinformatics. Considering the great difference between the number of protein sequences already known and the number of three-dimensional structures determined experimentally, the demand of automated techniques for structural classification of proteins is very high. In this context, computational tools, especially Machine Learning (ML) techniques, have become essential to deal with this problem. In this work, ML techniques are used in the recognition of protein structural classes: Decision Trees, k-Nearest Neighbor, Naive Bayes, Support Vector Machine and Neural Networks. These methods have been chosen because they represent different paradigms of learning and have been widely used in the Bioinfornmatics literature. Aiming to obtain an improvment in the performance of these techniques (individual classifiers), homogeneous (Bagging and Boosting) and heterogeneous (Voting, Stacking and StackingC) multiclassification systems are used. Moreover, since the protein database used in this work presents the problem of imbalanced classes, artificial techniques for class balance (Undersampling Random, Tomek Links, CNN, NCL and OSS) are used to minimize such a problem. In order to evaluate the ML methods, a cross-validation procedure is applied, where the accuracy of the classifiers is measured using the mean of classification error rate, on independent test sets. These means are compared, two by two, by the hypothesis test aiming to evaluate if there is, statistically, a significant difference between them. With respect to the results obtained with the individual classifiers, Support Vector Machine presented the best accuracy. In terms of the multi-classification systems (homogeneous and heterogeneous), they showed, in general, a superior or similar performance when compared to the one achieved by the individual classifiers used - especially Boosting with Decision Tree and the StackingC with Linear Regression as meta classifier. The Voting method, despite of its simplicity, has shown to be adequate for solving the problem presented in this work. The techniques for class balance, on the other hand, have not produced a significant improvement in the global classification error. Nevertheless, the use of such techniques did improve the classification error for the minority class. In this context, the NCL technique has shown to be more appropriated

Estratégias baseadas em aprendizado para coordenação de uma frota de robôs em tarefas cooperativas

In multi-robot systems, both control architecture and work strategy represent a challenge for researchers. It is important to have a robust architecture that can be easily adapted to requirement changes. It is also important that work strategy allows robots to complete tasks efficiently, considering that robots interact directly in environments with humans. In this context, this work explores two approaches for robot soccer team coordination for cooperative tasks development. Both approaches are based on a combination of imitation learning and reinforcement learning. Thus, in the first approach was developed a control architecture, a fuzzy inference engine for recognizing situations in robot soccer games, a software for narration of robot soccer games based on the inference engine and the implementation of learning by imitation from observation and analysis of others robotic teams. Moreover, state abstraction was efficiently implemented in reinforcement learning applied to the robot soccer standard problem. Finally, reinforcement learning was implemented in a form where actions are explored only in some states (for example, states where an specialist robot system used them) differently to the traditional form, where actions have to be tested in all states. In the second approach reinforcement learning was implemented with function approximation, for which an algorithm called RBF-Sarsa($lambda$) was created. In both approaches batch reinforcement learning algorithms were implemented and imitation learning was used as a seed for reinforcement learning. Moreover, learning from robotic teams controlled by humans was explored. The proposal in this work had revealed efficient in the robot soccer standard problem and, when implemented in other robotics systems, they will allow that these robotics systems can efficiently and effectively develop assigned tasks. These approaches will give high adaptation capabilities to requirements and environment changes.

Dilema da diversidade-acurácia: um estudo empírico no contexto de multiclassificadores

Multi-classifier systems, also known as ensembles, have been widely used to solve several problems, because they, often, present better performance than the individual classifiers that form these systems. But, in order to do so, it s necessary that the base classifiers to be as accurate as diverse among themselves this is also known as diversity/accuracy dilemma. Given its importance, some works have investigate the ensembles behavior in context of this dilemma. However, the majority of them address homogenous ensemble, i.e., ensembles composed only of the same type of classifiers. Thus, motivated by this limitation, this thesis, using genetic algorithms, performs a detailed study on the dilemma diversity/accuracy for heterogeneous ensembles

Teaching real-time with a scheduler simulator

In this paper we describe a scheduler simulator for real-time tasks, RTsim, that can be used as a tool to teach real-time scheduling algorithms. It simulates a variety of preprogrammed scheduling policies for single and multi-processor systems and simple algorithm variants introduced by its user. Using RTsim students can conduct experiments that will allow them to understand the effects of each policy given different load conditions and learn which policy is better for different workloads. We show how to use RTsim as a learning tool and the results achieved with its application on the Real-Time Systems course taught at the B.Sc. on Computer Science at Paulista State University - Unesp - at Rio Preto.

Considering Urban Dynamics in spatial electric load forecasting

When dealing with spatio-temporal simulations of load growth inside a service zone, one of the most important problems faced by a Distribution Utility is how to represent the different relationships among different areas. A new load in a certain part of the city could modify the load growth in other parts of the city, even outside of its radius of influence. These interactions are called Urban Dynamics. This work aims to discuss how to implement Urban Dynamics considerations into the spatial electric load forecasting simulations using multi-agent simulations. To explain the approach, three examples are introduced, including the effect of an attraction load, the effect of a repulsive load, and the effect of several attraction/repulsive loads at the same time when considering the natural load growth. © 2012 IEEE.

Prognostic factors associated with time to hCG remission in patients with low-risk postmolar gestational trophoblastic neoplasia

Objective The purpose of this study was to identify the clinical factors associated with time to hCG remission among women with low-risk postmolar GTN. Methods This study included a non-concurrent cohort of 328 patients diagnosed with low-risk postmolar GTN according to FIGO 2002 criteria. Associations of time to hCG remission with history of prior mole, molar histology, time to persistence, use of D&C at persistence, presence of metastatic disease, FIGO score, hCG values at persistence, type of first line therapy and use of multiagent chemotherapy were investigated with both univariate and multivariate analyses. Results Overall median time to remission was 46 days. Ten percent of the patients required multi-agent chemotherapy to achieve hCG remission. Multivariate analysis incorporating the variables significant on univariate analysis confirmed that complete molar histology (HR 1.45), metastatic disease (HR 1.66), use of multi-agent therapy (HR 2.00) and FIGO score (HR 1.82) were associated with longer time to remission. There was a linear relationship between FIGO score and time to hCG remission. Each 1-point increment in FIGO score was associated with an average 17-day increase in hCG remission time (95% CI: 12.5-21.6). Conclusions Complete mole histology prior to GTN, presence of metastatic disease, use of multi-agent therapy and higher FIGO score were independent factors associated with longer time to hCG remission in low-risk GTN. Identifying the prognostic factors associated with time to remission and effective counseling may help improve treatment planning and reduce anxiety in patients and their families. © 2013 Elsevier Inc. All rights reserved.

Comportamento do pedestre e ambiente térmico urbano

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Serviço de referência digital: uma análise apoiada em agentes de interface

Pós-graduação em Ciência da Informação - FFC

Influência do local de seguimento da mola hidatiforme no prognóstico e tratamento da neoplasia trofoblástica gestacional pós-molar

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Analytical challenges in the fields of Nanobioscience and Nanobiotecnology: integrated methods for separation and characterization

Nano(bio)science and nano(bio)technology play a growing and tremendous interest both on academic and industrial aspects. They are undergoing rapid developments on many fronts such as genomics, proteomics, system biology, and medical applications. However, the lack of characterization tools for nano(bio)systems is currently considered as a major limiting factor to the final establishment of nano(bio)technologies. Flow Field-Flow Fractionation (FlFFF) is a separation technique that is definitely emerging in the bioanalytical field, and the number of applications on nano(bio)analytes such as high molar-mass proteins and protein complexes, sub-cellular units, viruses, and functionalized nanoparticles is constantly increasing. This can be ascribed to the intrinsic advantages of FlFFF for the separation of nano(bio)analytes. FlFFF is ideally suited to separate particles over a broad size range (1 nm-1 μm) according to their hydrodynamic radius (rh). The fractionation is carried out in an empty channel by a flow stream of a mobile phase of any composition. For these reasons, fractionation is developed without surface interaction of the analyte with packing or gel media, and there is no stationary phase able to induce mechanical or shear stress on nanosized analytes, which are for these reasons kept in their native state. Characterization of nano(bio)analytes is made possible after fractionation by interfacing the FlFFF system with detection techniques for morphological, optical or mass characterization. For instance, FlFFF coupling with multi-angle light scattering (MALS) detection allows for absolute molecular weight and size determination, and mass spectrometry has made FlFFF enter the field of proteomics. Potentialities of FlFFF couplings with multi-detection systems are discussed in the first section of this dissertation. The second and the third sections are dedicated to new methods that have been developed for the analysis and characterization of different samples of interest in the fields of diagnostics, pharmaceutics, and nanomedicine. The second section focuses on biological samples such as protein complexes and protein aggregates. In particular it focuses on FlFFF methods developed to give new insights into: a) chemical composition and morphological features of blood serum lipoprotein classes, b) time-dependent aggregation pattern of the amyloid protein Aβ1-42, and c) aggregation state of antibody therapeutics in their formulation buffers. The third section is dedicated to the analysis and characterization of structured nanoparticles designed for nanomedicine applications. The discussed results indicate that FlFFF with on-line MALS and fluorescence detection (FD) may become the unparallel methodology for the analysis and characterization of new, structured, fluorescent nanomaterials.

Hybrid Methods for Resource Allocation and Scheduling Problems in Deterministic and Stochastic Environments

This work presents exact, hybrid algorithms for mixed resource Allocation and Scheduling problems; in general terms, those consist into assigning over time finite capacity resources to a set of precedence connected activities. The proposed methods have broad applicability, but are mainly motivated by applications in the field of Embedded System Design. In particular, high-performance embedded computing recently witnessed the shift from single CPU platforms with application-specific accelerators to programmable Multi Processor Systems-on-Chip (MPSoCs). Those allow higher flexibility, real time performance and low energy consumption, but the programmer must be able to effectively exploit the platform parallelism. This raises interest in the development of algorithmic techniques to be embedded in CAD tools; in particular, given a specific application and platform, the objective if to perform optimal allocation of hardware resources and to compute an execution schedule. On this regard, since embedded systems tend to run the same set of applications for their entire lifetime, off-line, exact optimization approaches are particularly appealing. Quite surprisingly, the use of exact algorithms has not been well investigated so far; this is in part motivated by the complexity of integrated allocation and scheduling, setting tough challenges for ``pure'' combinatorial methods. The use of hybrid CP/OR approaches presents the opportunity to exploit mutual advantages of different methods, while compensating for their weaknesses. In this work, we consider in first instance an Allocation and Scheduling problem over the Cell BE processor by Sony, IBM and Toshiba; we propose three different solution methods, leveraging decomposition, cut generation and heuristic guided search. Next, we face Allocation and Scheduling of so-called Conditional Task Graphs, explicitly accounting for branches with outcome not known at design time; we extend the CP scheduling framework to effectively deal with the introduced stochastic elements. Finally, we address Allocation and Scheduling with uncertain, bounded execution times, via conflict based tree search; we introduce a simple and flexible time model to take into account duration variability and provide an efficient conflict detection method. The proposed approaches achieve good results on practical size problem, thus demonstrating the use of exact approaches for system design is feasible. Furthermore, the developed techniques bring significant contributions to combinatorial optimization methods.

Abscheidung und Charakterisierung von Plasmapolymerschichten auf Fluorkohlenstoff- und Siloxan-Basis

In dieser Arbeit wurden Fluorkohlenstoff-basierte und siliziumorganische Plasmapolymerfilme hergestellt und hinsichtlich ihrer strukturellen und funktionalen Eigenschaften untersucht. Beide untersuchten Materialsysteme sind in der Beschichtungstechnologie von großem wissenschaftlichen und anwendungstechnischen Interesse. Die Schichtabscheidung erfolgte mittels plasmachemischer Gasphasenabscheidung (PECVD) an Parallelplattenreaktoren. Bei den Untersuchungen zur Fluorkohlenstoff-Plasmapolymerisation stand die Herstellung ultra-dünner, d. h. weniger als 5 nm dicker Schichten im Vordergrund. Dies wurde durch gepulste Plasmaanregung und Verwendung eines Gasgemisches aus Trifluormethan (CHF3) und Argon realisiert. Die Bindungsstruktur der Schichten wurden in Abhängigkeit der eingespeisten Leistung, die den Fragmentationsgrad der Monomere im Plasma bestimmt, analysiert. Hierzu wurden die Röntgen-Photoelektronenspektroskopie (XPS), Rasterkraftmikroskopie (AFM), Flugzeit-Sekundärionenmassenspektrometrie (ToF-SIMS) und Röntgenreflektometrie (XRR) eingesetzt. Es zeigte sich, dass die abgeschiedenen Schichten ein homogenes Wachstumsverhalten und keine ausgeprägten Interfacebereiche zum Substrat und zur Oberfläche hin aufweisen. Die XPS-Analysen deuten darauf hin, dass Verkettungsreaktionen von CF2-Radikalen im Plasma eine wichtige Rolle für den Schichtbildungsprozess spielen. Weiterhin konnte gezeigt werden, dass der gewählte Beschichtungsprozess eine gezielte Reduzierung der Benetzbarkeit verschiedener Substrate ermöglicht. Dabei genügen Schichtdicken von weniger als 3 nm zur Erreichung eines teflonartigen Oberflächencharakters mit Oberflächenenergien um 20 mN/m. Damit erschließen sich neue Applikationsmöglichkeiten ultra-dünner Fluorkohlenstoffschichten, was anhand eines Beispiels aus dem Bereich der Nanooptik demonstriert wird. Für die siliziumorganischen Schichten unter Verwendung des Monomers Hexamethyldisiloxan (HMDSO) galt es zunächst, diejenigen Prozessparameter zu identifizieren, die ihren organischen bzw. glasartigen Charakter bestimmen. Hierzu wurde der Einfluss von Leistungseintrag und Zugabe von Sauerstoff als Reaktivgas auf die Elementzusammensetzung der Schichten untersucht. Bei niedrigen Plasmaleistungen und Sauerstoffflüssen werden vor allem kohlenstoffreiche Schichten abgeschieden, was auf eine geringere Fragmentierung der Kohlenwasserstoffgruppen zurückgeführt wurde. Es zeigte sich, dass die Variation des Sauerstoffanteils im Prozessgas eine sehr genaue Steuerbarkeit der Schichteigenschaften ermöglicht. Mittels Sekundär-Neutralteilchen-Massenspektrometrie (SNMS) konnte die prozesstechnische Realisierbarkeit und analytische Quantifizierbarkeit von Wechselschichtsystemen aus polymerartigen und glasartigen Lagen demonstriert werden. Aus dem Intensitätsverhältnis von Si:H-Molekülen zu Si-Atomen im SNMS-Spektrum ließ sich der Wasserstoffgehalt bestimmen. Weiterhin konnte gezeigt werden, dass durch Abscheidung von HMDSO-basierten Gradientenschichten eine deutliche Reduzierung von Reibung und Verschleiß bei Elastomerbauteilen erzielt werden kann.

New regularization method for EXAFS analysis: Application to uranium and plutonium sorption onto kaolinite

Die Röntgenabsorptionsspektroskopie (Extended X-ray absorption fine structure (EXAFS) spectroscopy) ist eine wichtige Methode zur Speziation von Schwermetallen in einem weiten Bereich von umweltrelevanten Systemen. Um Strukturparameter wie Koordinationszahl, Atomabstand und Debye-Waller Faktoren für die nächsten Nachbarn eines absorbierenden Atoms zu bestimmen, ist es für experimentelle EXAFS-Spektren üblich, unter Verwendung von Modellstrukturen einen „Least-Squares-Fit“ durchzuführen. Oft können verschiedene Modellstrukturen mit völlig unterschiedlicher chemischer Bedeutung die experimentellen EXAFS-Daten gleich gut beschreiben. Als gute Alternative zum konventionellen Kurven-Fit bietet sich das modifizierte Tikhonov-Regularisationsverfahren an. Ergänzend zur Tikhonov-Standardvariationsmethode enthält der in dieser Arbeit vorgestellte Algorithmus zwei weitere Schritte, nämlich die Anwendung des „Method of Separating Functionals“ und ein Iterationsverfahren mit Filtration im realen Raum. Um das modifizierte Tikhonov-Regularisationsverfahren zu testen und zu bestätigen wurden sowohl simulierte als auch experimentell gemessene EXAFS-Spektren einer kristallinen U(VI)-Verbindung mit bekannter Struktur, nämlich Soddyit (UO2)2SiO4 x 2H2O, untersucht. Die Leistungsfähigkeit dieser neuen Methode zur Auswertung von EXAFS-Spektren wird durch ihre Anwendung auf die Analyse von Proben mit unbekannter Struktur gezeigt, wie sie bei der Sorption von U(VI) bzw. von Pu(III)/Pu(IV) an Kaolinit auftreten. Ziel der Dissertation war es, die immer noch nicht voll ausgeschöpften Möglichkeiten des modifizierten Tikhonov-Regularisationsverfahrens für die Auswertung von EXAFS-Spektren aufzuzeigen. Die Ergebnisse lassen sich in zwei Kategorien einteilen. Die erste beinhaltet die Entwicklung des Tikhonov-Regularisationsverfahrens für die Analyse von EXAFS-Spektren von Mehrkomponentensystemen, insbesondere die Wahl bestimmter Regularisationsparameter und den Einfluss von Mehrfachstreuung, experimentell bedingtem Rauschen, etc. auf die Strukturparameter. Der zweite Teil beinhaltet die Speziation von sorbiertem U(VI) und Pu(III)/Pu(IV) an Kaolinit, basierend auf experimentellen EXAFS-Spektren, die mit Hilfe des modifizierten Tikhonov-Regularisationsverfahren ausgewertet und mit Hilfe konventioneller EXAFS-Analyse durch „Least-Squares-Fit“ bestätigt wurden.

Nanocrystalline Silicon Based Films for Renewable Energy Applications

The present thesis is focused on the study of innovative Si-based materials for third generation photovoltaics. In particular, silicon oxi-nitride (SiOxNy) thin films and multilayer of Silicon Rich Carbide (SRC)/Si have been characterized in view of their application in photovoltaics. SiOxNy is a promising material for applications in thin-film solar cells as well as for wafer based silicon solar cells, like silicon heterojunction solar cells. However, many issues relevant to the material properties have not been studied yet, such as the role of the deposition condition and precursor gas concentrations on the optical and electronic properties of the films, the composition and structure of the nanocrystals. The results presented in the thesis aim to clarify the effects of annealing and oxygen incorporation within nc-SiOxNy films on its properties in view of the photovoltaic applications. Silicon nano-crystals (Si NCs) embedded in a dielectric matrix were proposed as absorbers in all-Si multi-junction solar cells due to the quantum confinement capability of Si NCs, that allows a better match to the solar spectrum thanks to the size induced tunability of the band gap. Despite the efficient solar radiation absorption capability of this structure, its charge collection and transport properties has still to be fully demonstrated. The results presented in the thesis aim to the understanding of the transport mechanisms at macroscopic and microscopic scale. Experimental results on SiOxNy thin films and SRC/Si multilayers have been obtained at macroscopical and microscopical level using different characterizations techniques, such as Atomic Force Microscopy, Reflection and Transmission measurements, High Resolution Transmission Electron Microscopy, Energy-Dispersive X-ray spectroscopy and Fourier Transform Infrared Spectroscopy. The deep knowledge and improved understanding of the basic physical properties of these quite complex, multi-phase and multi-component systems, made by nanocrystals and amorphous phases, will contribute to improve the efficiency of Si based solar cells.

Charge-transport simulations in organic semiconductors

In this thesis we have extended the methods for microscopic charge-transport simulations for organic semiconductors. In these materials the weak intermolecular interactions lead to spatially localized charge carriers, and the charge transport occurs as an activated hopping process between diabatic states. In addition to weak electronic couplings between these states, different electrostatic environments in the organic material lead to a broadening of the density of states for the charge energies which limits carrier mobilities.rnThe contributions to the method development includern(i) the derivation of a bimolecular charge-transfer rate,rn(ii) the efficient evaluation of intermolecular (outer-sphere) reorganization energies,rn(iii) the investigation of effects of conformational disorder on intramolecular reorganization energies or internal site energiesrnand (iv) the inclusion of self-consistent polarization interactions for calculation of charge energies.These methods were applied to study charge transport in amorphous phases of small molecules used in the emission layer of organic light emitting diodes (OLED).rnWhen bulky substituents are attached to an aromatic core in order to adjust energy levels or prevent crystallization, a small amount of delocalization of the frontier orbital to the substituents can increase electronic couplings between neighboring molecules. This leads to improved charge-transfer rates and, hence, larger charge-mobility. We therefore suggest using the mesomeric effect (as opposed to the inductive effect) when attaching substituents to aromatic cores, which is necessary for example in deep blue OLEDs, where the energy levels of a host molecule have to be adjusted to those of the emitter.rnFurthermore, the energy landscape for charges in an amorphous phase cannot be predicted by mesoscopic models because they approximate the realistic morphology by a lattice and represent molecular charge distributions in a multipole expansion. The microscopic approach shows that a polarization-induced stabilization of a molecule in its charged and neutral states can lead to large shifts, broadening, and traps in the distribution of charge energies. These results are especially important for multi-component systems (the emission layer of an OLED or the donor-acceptor interface of an organic solar cell), if the change in polarizability upon charging (or excitation in case of energy transport) is different for the components. Thus, the polarizability change upon charging or excitation should be added to the set of molecular parameters essential for understanding charge and energy transport in organic semiconductors.rnWe also studied charge transport in self-assembled systems, where intermolecular packing motives induced by side chains can increase electronic couplings between molecules. This leads to larger charge mobility, which is essential to improve devices such as organic field effect transistors, where low carrier mobilities limit the switching frequency.rnHowever, it is not sufficient to match the average local molecular order induced by the sidernchains (such as the pitch angle between consecutive molecules in a discotic mesophase) with maxima of the electronic couplings.rnIt is also important to make the corresponding distributions as narrow as possible compared to the window determined by the closest minima of thernelectronic couplings. This is especially important in one-dimensional systems, where charge transport is limited by the smallest electronic couplings.rnThe immediate implication for compound design is that the side chains should assist the self-assemblingrnprocess not only via soft entropic interactions, but also via stronger specific interactions, such as hydrogen bonding.rnrnrnrn