Issues with Fedora & Hydra, experiences from a research-data-driven implementation

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

The Human Communication Science Virtual Lab (HCS vLab): A repository microclimate in a rapidly evolving research-ecosystem

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

Molecular mimicry between cardiac myosin and Trypanosoma cruzi antigen B13: identification of a B13-driven human T cell clone that recognizes cardiac myosin

Previous reports from our group have demonstrated the association of molecular mimicry between cardiac myosin and the immunodominant Trypanosoma cruzi protein B13 with chronic Chagas' disease cardiomyopathy at both the antibody and heart-infiltrating T cell level. At the peripheral blood level, we observed no difference in primary proliferative responses to T. cruzi B13 protein between chronic Chagas' cardiopathy patients, asymptomatic chagasics and normal individuals. In the present study, we investigated whether T cells sensitized by T. cruzi B13 protein respond to cardiac myosin. T cell clones generated from a B13-stimulated T cell line obtained from peripheral blood of a B13-responsive normal donor were tested for proliferation against B13 protein and human cardiac myosin. The results showed that one clone responded to B13 protein alone and the clone FA46, displaying the highest stimulation index to B13 protein (SI = 25.7), also recognized cardiac myosin. These data show that B13 and cardiac myosin share epitopes at the T cell level and that sensitization of a T cell with B13 protein results in response to cardiac myosin. It can be hypothesized that this also occurs in vivo during T. cruzi infection which results in heart tissue damage in chronic Chagas' disease cardiomyopathy

A comparative analysis of preprocessing techniques of cardiac event series for the study of heart rhythm variability using simulated signals

In the present study, using noise-free simulated signals, we performed a comparative examination of several preprocessing techniques that are used to transform the cardiac event series in a regularly sampled time series, appropriate for spectral analysis of heart rhythm variability (HRV). First, a group of noise-free simulated point event series, which represents a time series of heartbeats, was generated by an integral pulse frequency modulation model. In order to evaluate the performance of the preprocessing methods, the differences between the spectra of the preprocessed simulated signals and the true spectrum (spectrum of the model input modulating signals) were surveyed by visual analysis and by contrasting merit indices. It is desired that estimated spectra match the true spectrum as close as possible, showing a minimum of harmonic components and other artifacts. The merit indices proposed to quantify these mismatches were the leakage rate, defined as a measure of leakage components (located outside some narrow windows centered at frequencies of model input modulating signals) with respect to the whole spectral components, and the numbers of leakage components with amplitudes greater than 1%, 5% and 10% of the total spectral components. Our data, obtained from a noise-free simulation, indicate that the utilization of heart rate values instead of heart period values in the derivation of signals representative of heart rhythm results in more accurate spectra. Furthermore, our data support the efficiency of the widely used preprocessing technique based on the convolution of inverse interval function values with a rectangular window, and suggest the preprocessing technique based on a cubic polynomial interpolation of inverse interval function values and succeeding spectral analysis as another efficient and fast method for the analysis of HRV signals

Nonconventional amide bond formation catalysis: programming enzyme specificity with substrate mimetics

This article reports on the design and characteristics of substrate mimetics in protease-catalyzed reactions. Firstly, the basis of protease-catalyzed peptide synthesis and the general advantages of substrate mimetics over common acyl donor components are described. The binding behavior of these artificial substrates and the mechanism of catalysis are further discussed on the basis of hydrolysis, acyl transfer, protein-ligand docking, and molecular dynamics studies on the trypsin model. The general validity of the substrate mimetic concept is illustrated by the expansion of this strategy to trypsin-like, glutamic acid-specific, and hydrophobic amino acid-specific proteases. Finally, opportunities for the combination of the substrate mimetic strategy with the chemical solid-phase peptide synthesis and the use of substrate mimetics for non-peptide organic amide synthesis are presented.

Customer-driven service product development

This thesis studies customer-driven service product development and how to manage customer involvement in the service product development process. The theory part of this thesis is a literature review of the prior studies and the thesis also includes empirical evidence in the form of a case study about ABB.

Developing an introductory object-oriented programming course

The state of the object-oriented programming course in Lappeenranta University of Technology had reached the point, where it required changes to provide better learning opportunities and thus the learning outcomes. Based on the student feedback the course was partially dated and ineffective. The components of the course were analysed and the ineffective elements were removed and new methods were introduced to improve the course. The major changes included the change from traditional teaching methods to reverse classroom method and the use of Java as the programming language. The changes were measured by the student feedback, lecturer’s observations and comparison to previous years. The feedback suggested that the changes were successful; the course received higher overall grade than before.

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.

Direct-Driven Interior Magnet Permanent Magnet Synchronous Motors for a Full Electric Sports Car

The design process of direct-driven permanent magnet synchronous machines (PMSMs) for a full electric 4 ´ 4 sports car is presented. The rotor structure of the machine consists of two permanent magnet layers embedded inside the rotor laminations thus resulting in some inverse saliency, where the q-axis inductance is larger than the d-axis one. An integer slot stator winding was selected to fully take advantage of the additional reluctance torque. The performance characteristics of the designed PMSMs were calculated by applying a twodimensional finite element method. Cross-saturation between the d- and q-axes was taken into account in the calculation of the synchronous inductances. The calculation results are validated by measurements.

Design and Drive-Cycle Based Analysis of Direct-Driven Permanent Magnet Synchronous Machine for a Small Urban Use Electric Vehicle

A direct-driven permanent magnet synchronous machine for a small urban use electric vehicle is presented. The measured performance of the machine at the test bench as well as the performance over the modified New European Drive Cycle will be given. The effect of optimal current components, maximizing the efficiency and taking into account the iron loss, is compared with the simple id=0 – control. The machine currents and losses during the drive cycle are calculated and compared with each other.

Statistical analysis of event-related potential elicited by verb-complement merge in Brazilian Portuguese

An interesting fact about language cognition is that stimulation involving incongruence in the merge operation between verb and complement has often been related to a negative event-related potential (ERP) of augmented amplitude and latency of ca. 400 ms - the N400. Using an automatic ERP latency and amplitude estimator to facilitate the recognition of waves with a low signal-to-noise ratio, the objective of the present study was to study the N400 statistically in 24 volunteers. Stimulation consisted of 80 experimental sentences (40 congruous and 40 incongruous), generated in Brazilian Portuguese, involving two distinct local verb-argument combinations (nominal object and pronominal object series). For each volunteer, the EEG was simultaneously acquired at 20 derivations, topographically localized according to the 10-20 International System. A computerized routine for automatic N400-peak marking (based on the ascendant zero-cross of the first waveform derivative) was applied to the estimated individual ERP waveform for congruous and incongruous sentences in both series for all ERP topographic derivations. Peak-to-peak N400 amplitude was significantly augmented (P < 0.05; one-sided Wilcoxon signed-rank test) due to incongruence in derivations F3, T3, C3, Cz, T5, P3, Pz, and P4 for nominal object series and in P3, Pz and P4 for pronominal object series. The results also indicated high inter-individual variability in ERP waveforms, suggesting that the usual procedure of grand averaging might not be considered a generally adequate approach. Hence, signal processing statistical techniques should be applied in neurolinguistic ERP studies allowing waveform analysis with low signal-to-noise ratio.

A SOA-based Framework for Multi-Devices and Multi-Platform User Interfaces

This thesis reports investigations on applying the Service Oriented Architecture (SOA) approach in the engineering of multi-platform and multi-devices user interfaces. This study has three goals: (1) analyze the present frameworks for developing multi-platform and multi-devices applications, (2) extend the principles of SOA for implementing a multi-platform and multi-devices architectural framework (SOA-MDUI), (3) applying and validating the proposed framework in the context of a specific application. One of the problems addressed in this ongoing research is the large amount of combinations for possible implementations of applications on different types of devices. Usually it is necessary to take into account the operating system (OS), user interface (UI) including the appearance, programming language (PL) and architectural style (AS). Our proposed approach extended the principles of SOA using patterns-oriented design and model-driven engineering approaches. Synthesizing the present work done in these domains, this research built and tested an engineering framework linking Model-driven Architecture (MDA) and SOA approaches to developing of UI. This study advances general understanding of engineering, deploying and managing multi-platform and multi-devices user interfaces as a service.

Highly sensitive C-reactive protein and male gender are independently related to the severity of coronary disease in patients with metabolic syndrome and an acute coronary event

Patients with metabolic syndrome are at high-risk for development of atherosclerosis and cardiovascular events. The objective of this study was to examine the major determinants of coronary disease severity, including those coronary risk factors associated with metabolic syndrome, during the early period after an acute coronary episode. We tested the hypothesis that inflammatory markers, especially highly sensitive C-reactive protein (hsCRP), are related to coronary atherosclerosis, in addition to traditional coronary risk factors. Subjects of both genders aged 30 to 75 years (N = 116) were prospectively included if they had suffered a recent acute coronary syndrome (acute myocardial infarction or unstable angina pectoris requiring hospitalization) and if they had metabolic syndrome diagnosed according to the National Cholesterol Education Program/Adult Treatment Panel III. Patients were submitted to a coronary angiography and the burden of atherosclerosis was estimated by the Gensini score. The severity of coronary disease was correlated (Spearman’s or Pearson’s coefficient) with gender (r = 0.291, P = 0.008), age (r = 0.218, P = 0.048), hsCRP (r = 0.256, P = 0.020), ApoB/ApoA ratio (r = 0.233, P = 0.041), and carotid intima-media thickness (r = 0.236, P = 0.041). After multiple linear regression, only male gender (P = 0.046) and hsCRP (P = 0.012) remained independently associated with the Gensini score. In this high-risk population, male gender and high levels of hsCRP, two variables that can be easily obtained, were associated with more extensive coronary disease, identifying patients with the highest potential of developing new coronary events.

Attentional bias modulation by reappraisal in patients with generalized anxiety disorder: an event-related potential study

Affective states influence subsequent attention allocation. We evaluated emotional negativity bias modulation by reappraisal in patients with generalized anxiety disorder (GAD) relative to normal controls. Event-related potential (ERP) recordings were obtained, and changes in P200 and P300 amplitudes in response to negative or neutral words were noted after decreasing negative emotion or establishing a neutral condition. We found that in GAD patients only, the mean P200 amplitude after negative word presentation was much higher than after the presentation of neutral words. In normal controls, after downregulation of negative emotion, the mean P300 amplitude in response to negative words was much lower than after neutral words, and this was significant in both the left and right regions. In GAD patients, the negative bias remained prominent and was not affected by reappraisal at the early stage. Reappraisal was observed to have a lateralized effect at the late stage.