Verbal learning in mild cognitive impairment and alzheimer's disease : behavioural and neural approaches

The main focus of the present thesis was at verbal episodic memory processes that are particularly vulnerable to preclinical and clinical Alzheimer’s disease (AD). Here these processes were studied by a word learning paradigm, cutting across the domains of memory and language learning studies. Moreover, the differentiation between normal aging, mild cognitive impairment (MCI) and AD was studied by the cognitive screening test CERAD. In study I, the aim was to examine how patients with amnestic MCI differ from healthy controls in the different CERAD subtests. Also, the sensitivity and specificity of the CERAD screening test to MCI and AD was examined, as previous studies on the sensitivity and specificity of the CERAD have not included MCI patients. The results indicated that MCI is characterized by an encoding deficit, as shown by the overall worse performance on the CERAD Wordlist learning test compared with controls. As a screening test, CERAD was not very sensitive to MCI. In study II, verbal learning and forgetting in amnestic MCI, AD and healthy elderly controls was investigated with an experimental word learning paradigm, where names of 40 unfamiliar objects (mainly archaic tools) were trained with or without semantic support. The object names were trained during a 4-day long period and a follow-up was conducted one week, 4 weeks and 8 weeks after the training period. Manipulation of semantic support was included in the paradigm because it was hypothesized that semantic support might have some beneficial effects in the present learning task especially for the MCI group, as semantic memory is quite well preserved in MCI in contrast to episodic memory. We found that word learning was significantly impaired in MCI and AD patients, whereas forgetting patterns were similar across groups. Semantic support showed a beneficial effect on object name retrieval in the MCI group 8 weeks after training, indicating that the MCI patients’ preserved semantic memory abilities compensated for their impaired episodic memory. The MCI group performed equally well as the controls in the tasks tapping incidental learning and recognition memory, whereas the AD group showed impairment. Both the MCI and the AD group benefited less from phonological cueing than the controls. Our findings indicate that acquisition is compromised in both MCI and AD, whereas long13 term retention is not affected to the same extent. Incidental learning and recognition memory seem to be well preserved in MCI. In studies III and IV, the neural correlates of naming newly learned objects were examined in healthy elderly subjects and in amnestic MCI patients by means of positron emission tomography (PET) right after the training period. The naming of newly learned objects by healthy elderly subjects recruited a left-lateralized network, including frontotemporal regions and the cerebellum, which was more extensive than the one related to the naming of familiar objects (study III). Semantic support showed no effects on the PET results for the healthy subjects. The observed activation increases may reflect lexicalsemantic and lexical-phonological retrieval, as well as more general associative memory mechanisms. In study IV, compared to the controls, the MCI patients showed increased anterior cingulate activation when naming newly learned objects that had been learned without semantic support. This suggests a recruitment of additional executive and attentional resources in the MCI group.

Neural correlates of language learning in adults

The human language-learning ability persists throughout life, indicating considerable flexibility at the cognitive and neural level. This ability spans from expanding the vocabulary in the mother tongue to acquisition of a new language with its lexicon and grammar. The present thesis consists of five studies that tap both of these aspects of adult language learning by using magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) during language processing and language learning tasks. The thesis shows that learning novel phonological word forms, either in the native tongue or when exposed to a foreign phonology, activates the brain in similar ways. The results also show that novel native words readily become integrated in the mental lexicon. Several studies in the thesis highlight the left temporal cortex as an important brain region in learning and accessing phonological forms. Incidental learning of foreign phonological word forms was reflected in functionally distinct temporal lobe areas that, respectively, reflected short-term memory processes and more stable learning that persisted to the next day. In a study where explicitly trained items were tracked for ten months, it was found that enhanced naming-related temporal and frontal activation one week after learning was predictive of good long-term memory. The results suggest that memory maintenance is an active process that depends on mechanisms of reconsolidation, and that these process vary considerably between individuals. The thesis put special emphasis on studying language learning in the context of language production. The neural foundation of language production has been studied considerably less than that of perceptive language, especially on the sentence level. A well-known paradigm in language production studies is picture naming, also used as a clinical tool in neuropsychology. This thesis shows that accessing the meaning and phonological form of a depicted object are subserved by different neural implementations. Moreover, a comparison between action and object naming from identical images indicated that the grammatical class of the retrieved word (verb, noun) is less important than the visual content of the image. In the present thesis, the picture naming was further modified into a novel paradigm in order to probe sentence-level speech production in a newly learned miniature language. Neural activity related to grammatical processing did not differ between the novel language and the mother tongue, but stronger neural activation for the novel language was observed during the planning of the upcoming output, likely related to more demanding lexical retrieval and short-term memory. In sum, the thesis aimed at examining language learning by combining different linguistic domains, such as phonology, semantics, and grammar, in a dynamic description of language processing in the human brain.

Tuotehallinnan tietoprosessin haasteet ja kehittäminen ohjelmistoyrityksessä

Diplomityössä tutustuttiin ohjelmistoyrityksen tuotteiden asiakastarpeiden ja vaatimusten käsittelyyn tuotehallinnan tietoprosessin näkökulmasta. Työssä kuvattiin yrityksen nykyinen prosessi sekä analysoitiin erityisesti sen haasteita. Teoriatiedon sekä yrityksen asiantuntijalausuntojen pohjalta laadittiin tuotehallinnan tietoprosessin kehitysehdotuksia. Kvalitatiivinen tapaustutkimus toteutettiin käytännönläheisesti esittämällä kohdeyrityksen asiantuntijoille avoimia kysymyksiä sekä heidän kanssaan keskustellen. Tutkimus tehtiin neljässä vaiheessa ja sen päätavoitteena oli selvittää, millä keinoilla voidaan tukea kohdeyrityksen tuotekehitystarpeisiin ja tuotekehitysehdotuksiin liittyvän tiedon hallintaa sekä tuotantopäätöksiä. Tutkimuksen teoreettinen viitekehys koostui organisaation tietoprosessista, organisaation päätöksentekoprosessista, ohjelmistotuotteen erityispiirteistä sekä ohjelmiston tuotehallinnasta. Kohdeyrityksen tuotehallinnan tietoprosessin kehittämisen keskeisiksi tekijöiksi nousivat tuotehallinnan tukena käytettävän tietojärjestelmän ominaisuudet, tiedon hakeminen, tiedon löytäminen ja tulkinta. Kehityskohteiksi nousivat lisäksi prosessin kulku, perustuotekehityksen ja asiakastoimituksien rinnakkainen hallinta sekä asiakasarvon tuottaminen tuotehallinnan keinoin. Tutkimuksen lopputuloksena toteutettiin kohdeyrityksen tuotehallinnan tietoprosessin tavoitetilan kuvaus. Tavoitetilan elementtejä olivat tuotteiden pidemmän tähtäimen suunnitelmat (roadmap), tuotehallinnan organisointi tiimeille sekä sensemaking-prosessin hyödyntäminen osapuolien yhteisen ymmärryksen luomiseksi kehitystarpeille. Käytössä olevan tietojärjestelmän kehitysehdotukset perustuivat näiden tuotehallinnan elementtien tukemiseen.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Detection of illegal estrogen administration through immunohistochemical markers in the bovine prostate

The immunodetection of diverse cell markers was evaluated in prostatic samples from bullocks, and bullocks showing epithelial hyperplasia-metaplasia, with oestrogen-induced changes, and in experimental samples from bullocks inoculated with dietylstilbestrol (DES). Antigen-retrieval procedures allowed the use of tissues that had been fixed in formalin for long periods. Three tissue markers were chosen for the study: cytokeratins 13 and 16, vimentin and desmin. Monoclonal antibody K8.12 (specific for cytokeratins 13 and 16) stained basal cells and hyperplastic-metaplastic epithelium; monoclonal antivimentin, and desmin, allowed the definition of fibromuscular changes.

Induction of immune response in broiler chickens immunized with recombinant FliC and challenged by Salmonella Typhimurium

The study examined (1) the immune response in broiler chickens after oral immunization with recombinant flagellin (rFliC) from Salmonella Typhimurium conjugated with sodium alginate microparticles, and the immune response enhancement in association with recombinant cholera toxin B subunit protein (rCTB) and pool of Lactobacillus spp. (PL). The immune responses were evaluated by dosage of IgY serum and IgA from intestinal fluid and immunostaining of CD8+ T lymphocytes in the cecum. The immunized animals were challenged with Salmonella Typhimurium (ST) 21 days after treatment. In all immunized groups, a significant increase (p<0.05) was observed in IgA levels (μg/mL), especially three weeks after immunization. The serum IgY levels (μg/mL) were little affected by the treatments and differed significantly among groups only in the second post-immunization week (p<0.05). After the challenge, the number of CD8+ T cells differed significantly between the treatments and negative control. Retrieval of Salmonella Typhimurium was not detected at 48 hours after the challenge in T2 (rFliC+rCTb), T3 (rFliC+PL) and T4 (rFliC+rCTB PL). The rFliC administered orally with or without rCTB and Lactobacillus spp. produces significant induction of humoral immune response, and the immunized chickens were more effective in eliminating Salmonella after challenge.

On state and parameter estimation in chaotic systems

State-of-the-art predictions of atmospheric states rely on large-scale numerical models of chaotic systems. This dissertation studies numerical methods for state and parameter estimation in such systems. The motivation comes from weather and climate models and a methodological perspective is adopted. The dissertation comprises three sections: state estimation, parameter estimation and chemical data assimilation with real atmospheric satellite data. In the state estimation part of this dissertation, a new filtering technique based on a combination of ensemble and variational Kalman filtering approaches, is presented, experimented and discussed. This new filter is developed for large-scale Kalman filtering applications. In the parameter estimation part, three different techniques for parameter estimation in chaotic systems are considered. The methods are studied using the parameterized Lorenz 95 system, which is a benchmark model for data assimilation. In addition, a dilemma related to the uniqueness of weather and climate model closure parameters is discussed. In the data-oriented part of this dissertation, data from the Global Ozone Monitoring by Occultation of Stars (GOMOS) satellite instrument are considered and an alternative algorithm to retrieve atmospheric parameters from the measurements is presented. The validation study presents first global comparisons between two unique satellite-borne datasets of vertical profiles of nitrogen trioxide (NO3), retrieved using GOMOS and Stratospheric Aerosol and Gas Experiment III (SAGE III) satellite instruments. The GOMOS NO3 observations are also considered in a chemical state estimation study in order to retrieve stratospheric temperature profiles. The main result of this dissertation is the consideration of likelihood calculations via Kalman filtering outputs. The concept has previously been used together with stochastic differential equations and in time series analysis. In this work, the concept is applied to chaotic dynamical systems and used together with Markov chain Monte Carlo (MCMC) methods for statistical analysis. In particular, this methodology is advocated for use in numerical weather prediction (NWP) and climate model applications. In addition, the concept is shown to be useful in estimating the filter-specific parameters related, e.g., to model error covariance matrix parameters.

Local Features in Image and Video Processing - Object Class Matching and Video Shot Detection

The usage of digital content, such as video clips and images, has increased dramatically during the last decade. Local image features have been applied increasingly in various image and video retrieval applications. This thesis evaluates local features and applies them to image and video processing tasks. The results of the study show that 1) the performance of different local feature detector and descriptor methods vary significantly in object class matching, 2) local features can be applied in image alignment with superior results against the state-of-the-art, 3) the local feature based shot boundary detection method produces promising results, and 4) the local feature based hierarchical video summarization method shows promising new new research direction. In conclusion, this thesis presents the local features as a powerful tool in many applications and the imminent future work should concentrate on improving the quality of the local features.

Linear prediction in optical spectroscopy

A linear prediction procedure is one of the approved numerical methods of signal processing. In the field of optical spectroscopy it is used mainly for extrapolation known parts of an optical signal in order to obtain a longer one or deduce missing signal samples. The first is needed particularly when narrowing spectral lines for the purpose of spectral information extraction. In the present paper the coherent anti-Stokes Raman scattering (CARS) spectra were under investigation. The spectra were significantly distorted by the presence of nonlinear nonresonant background. In addition, line shapes were far from Gaussian/Lorentz profiles. To overcome these disadvantages the maximum entropy method (MEM) for phase spectrum retrieval was used. The obtained broad MEM spectra were further underwent the linear prediction analysis in order to be narrowed.

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.

Uraohjaus ja urasuunnittelu ammattikorkeakoulussa

Tutkimuksen tarkoituksena oli selvittää, millaista uraohjausta ammattikorkeakoulun tuutoriopettajat antavat ja millaista uraohjausta opiskelijat haluavat. Lisäksi tavoitteena oli selvittää, löytyykö opiskelijoiden koulutusalavalinnan perusteista yhteyttä uran suunnittelutaitoihin ja ohjauksen tarpeeseen, ja tunnistavatko tuutoriopettajat opiskelijoiden erilaiset uraohjauksen tarpeet. Tutkimuksen teoreettisissa rakenteissa hyödynnettiin kolmea postmodernia urateoriaa, jotka olivat Hodkinsonin ja Sparkesin (1997) uranvalinnan päätöksentekoteoria, Mitchellin, Lewinin ja Krumbolzin (1999) suunnitellun sattuman teoria ja Savickasin (2005) uran rakentamisteoria. Tutkimusympäristönä oli Satakunnan ammattikorkeakoulu. Tutkimus oli kaksivaiheinen. Ensimmäisessä vaiheessa kerättiin harkinnanvaraisesti valituilta tuutoriopettajilta (n=14) ja opintojensa eri vaiheissa olevilta opiskelijoilta (n=65) kirjoitettu aineisto. Kvalitatiivinen aineisto analysoitiin sisällönanalyysillä. Aineiston perusteella löydettiin kolmenlaisia urasuunnittelijoita: epävarmat, uteliaat ja tietoiset. Aineiston perusteella laadittiin kyselylomake tutkimuksen toisen vaiheen tiedonkeruuta varten. Tutkimuksen toisessa vaiheessa kerättiin opintojen eri vaiheissa olevilta opiskelijoilta kyselylomakekyselynä kvantitatiivinen aineisto (n=903), joka analysoitiin tilastollisin menetelmin. Koulutusalavalinnan perusteista elämäntilanne, alan mahdollisuudet, oma toive, kutsumus, aktiivinen tiedonhaku ja halu opiskella ammattikorkeakoulussa olivat yhteydessä opiskelijan hyvään urasuunnittelukykyyn. Näillä perusteilla koulutusalansa valinneita tietoisiksi luokiteltuja urasuunnittelijoita oli 72 % vastanneista. Alavalinnan perusteista sattuman, kavereiden, sukulaisten, lukion opinto-ohjauksen ja paikkakunnan perusteella koulutusalansa valinneet luokiteltiin epävarmoiksi urasuunnittelijoiksi, ja heitä oli 28 % vastanneista. Tulokset antavat ohjaajille tukea epävarman ja muita enemmän uraohjausta tarvitsevan opiskelijan tunnistamiseen ja heidän hops-prosessinsa tehostamiseen opintojen alusta asti. Lisäksi tulosten perusteella esitetään seuraavia suosituksia: tuutoriopettajille tulisi asettaa pätevyysvaatimukseksi ohjausalan opintojen suorittaminen; opiskelijoita tulisi ohjata tunnistamaan erilaisia satunnaisesti avautuvia mahdollisuuksia ja tietoisesti hyödyntämään niitä elämässään; uraohjaukseen tulisi kytkeä mukaan työelämäyhteistyö; ohjaajien tulisi tiivistää yhteistyötä toisen asteen ohjaajien kanssa, jotta opiskelijoiden koulutusalavalinnat onnistuisivat paremmin; uraohjausta tulisi antaa tulevaisuuden kvalifikaatioiden ennakoinnin ja elinikäisten oppimisvalmiuksien näkökulmasta.

Fragmenta Membranea Project 2009 – 2012

Reports and Studies 1 / 2014

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.

Operatiivisen tason suorituskyvyn mallimittariston kehittäminen logistiikkapalveluyritykselle

Tutkimuksen tavoitteena oli kehittää mallimittaristo logistiikkapalvelualalla toimivan yrityksen operatiivisen tason suorituskyvyn seurantaan ja toiminnanohjausta varten, päivittäisen johtamisen tueksi. Tutkimus suoritettiin pääosin toiminta-analyyttisena, yhden yrityksen empiirisenä tapaustutkimuksena. Tutkimuksen kohdeyrityksen toiminnanmittaus perustuu tällä hetkellä pääasiassa taloudellisiin mittareihin ja muutamaan kyselyyn. Toiminnanohjauksen ja – kehittämisen, päätöksenteon tueksi tarvitaan, taloudellisten mittareiden lisäksi, mittareita, joilla pystytään seuraamaan suorituskyvyn taustalla vaikuttavien tekijöiden kehittymistä. Tutkimuksen kohdeyrityksen operatiivisen tason suorituskyvyn mallimittariston suunnittelussa haluttiin varmistaa, että jatkossa mittaamisella vaikutettaisiin seuraustekijöiden lisäksi myös syytekijöihin, selkiyttää liiketoiminnan tavoitteet, operatiivisen tason näkökulmasta, ja mittaamisen tavoite. Tutkimuksessa esitelty mallimittaristo on suunniteltu, tasapainotetun mittariston viitekehyksen avulla. Mittariston näkökulmiksi valittiin: talous, sidosryhmä (asiakas), prosessi ja henkilöstö. Mittariston tuottaman tiedon tavoitteena on toiminnanohjauksen, -kehittämisen ja päätöksenteon tukeminen, kun mittaustulokset ja trendi ovat yhdessä paikassa, on tiedonhaku ja - hyödyntäminen helpompaa. Mallimittaristoa ei testattu eikä käyttöönotettu tutkimuksessa.

Pup age and aggressive behavior in lactating rats

High levels of aggressive behaviors against intruders in the nest area are displayed by female rats during the first 10 days after delivery, declining thereafter to very low levels, even though lactation continues. Cross-fostering experiments were undertaken to test the hypothesis that pup age may affect aggression in lactating rats. The behavior of females on the 8th day after delivery when raising fostered 8-day-old pups was compared to that of females on the 8th postpartum day raising older pups (18 days old) for the last 5 days, and females on the 18th day after delivery raising fostered 18-day-old pups were compared to females in the same postpartum period nursing younger pups (8 days of age at the time of the maternal aggression test) for 5 days. Pup retrieval activity and plasma prolactin level were also analyzed. Females on the 8th postpartum day nursing 18-day-old pups were less aggressive than females in the same postpartum period, but with 8-day-old pups. Likewise, females on the 18th postpartum day nursing younger pups were more aggressive and presented higher levels of prolactin than females nursing older pups. Thus, pup development can alter the natural decline of maternal aggressive behavior.