Dissertations / Theses on the topic 'Literature based discovery – methods'

We are living within the age of information. The ever increasing flow of data and publications poses a monumental bottleneck to scientific progress as despite the amazing abilities of the human mind, it is woefully inadequate in processing such a vast quantity of multidimensional information. The small bits of flotsam and jetsam that we leverage belies the amount of useful information beneath the surface. It is imperative that automated tools exist to better search, retrieve, and summarize this content. Combinations of document indexing and search engines can quickly find you a document whose content best matches your query - if the information is all contained within a single document. But it doesn’t draw connections, make hypotheses, or find knowledge hidden across multiple documents. Literature-based discovery is an approach that can uncover hidden interrelationships between topics by extracting information from existing published scientific literature. The proposed study utilizes a semantic-based approach that builds a graph of related concepts between two user specified sets of topics using semantic predications. In addition, the study includes properties of bibliographically related documents and statistical properties of concepts to further enhance the quality of the proposed intermediate terms. Our results show an improvement in precision-recall when incorporating citations.

Climate change caused by anthropogenic activity is one of the biggest challenges of our time. Researchers are striving to understand the effects of global warming on the ecological systems of the oceans, and how these ecological systems influence the global climate, a line of research that is crucial in order to counteract or adapt to the effects of global warming. A major challenge that researchers in this area are facing, is the huge amount of potentially relevant literature, as insights from widely different fields such as biology, chemistry, climatology and oceanography can prove crucial in understanding the effects of global warming on the oceans. To alleviate some of the work load from researchers, information extraction tools can be used to extract relevant information from the scientific literature automatically, and discovery support tools can be developed to assist researchers in their efforts. This master thesis conducts fundamental research into the development of discovery support tools for oceanographic climate science, focusing primarily on the information extraction component.

The exponential growth of scientific literature is creating an increased need for systems to process and assimilate knowledge contained within text. Literature Based Discovery (LBD) is a well established field that seeks to synthesize new knowledge from existing literature, but it has remained primarily in the theoretical realm rather than in real-world application. This lack of real-world adoption is due in part to the difficulty of LBD, but also due to several solvable problems present in LBD today. Of these problems, the ones in most critical need of improvement are: (1) the over-generation of knowledge by LBD systems, (2) a lack of meaningful evaluation standards, and (3) the difficulty interpreting LBD output. We address each of these problems by: (1) developing indirect relatedness measures for ranking and filtering LBD hypotheses; (2) developing a representative evaluation dataset and applying meaningful evaluation methods to individual components of LBD; (3) developing an interactive visualization system that allows a user to explore LBD output in its entirety. In addressing these problems, we make several contributions, most importantly: (1) state of the art results for estimating direct semantic relatedness, (2) development of set association measures, (3) development of indirect association measures, (4) development of a standard LBD evaluation dataset, (5) division of LBD into discrete components with well defined evaluation methods, (6) development of automatic functional group discovery, and (7) integration of indirect relatedness measures and automatic functional group discovery into a comprehensive LBD visualization system. Our results inform future development of LBD systems, and contribute to creating more effective LBD systems.

Thesis (Ph.D.)--Indiana University, School of Library and Information Science, 2006.
"Title from dissertation home page (viewed July 10, 2007)." Source: Dissertation Abstracts International, Volume: 67-08, Section: A, page: 2796. Adviser: Javed Mostafa.

Given that data is increasing exponentially everyday, extracting and understanding the information, themes and relationships from large collections of documents is more and more important to researchers in many areas. In this paper, we present a cooperative semantic information processing system to help biomedical researchers understand and discover knowledge in large numbers of titles and abstracts from PubMed query results. Our system is based on a prevalent technique, topic modeling, which is an unsupervised machine learning approach for discovering the set of semantic themes in a large set of documents. In addition, we apply a natural language processing technique to transform the “bag-of-words” assumption of topic models to the “bag-of-important-phrases” assumption and build an interactive visualization tool using a modified, open-source, Topic Browser. In the end, we conduct two experiments to evaluate the approach. The first, evaluates whether the “bag-of-important-phrases” approach is better at identifying semantic themes than the standard “bag-of-words” approach. This is an empirical study in which human subjects evaluate the quality of the resulting topics using a standard “word intrusion test” to determine whether subjects can identify a word (or phrase) that does not belong in the topic. The second is a qualitative empirical study to evaluate how well the system helps biomedical researchers explore a set of documents to discover previously hidden semantic themes and connections. The methodology for this study has been successfully used to evaluate other knowledge-discovery tools in biomedicine.

Drug discovery is a complicated and expensive process in the billion dollar range. One way of making the drug development process more efficient is better information handling, modelling and visualisation. The majority of todays drugs are small molecules, which interact with drug targets to cause an effect. Since the 1980s large amounts of compounds have been systematically tested by robots in so called high-throughput screening. Ligand-based drug discovery is based on modelling drug molecules. In the field known as Quantitative Structure–Activity Relationship (QSAR) molecules are described by molecular descriptors which are used for building mathematical models. Based on these models molecular properties can be predicted and using the molecular descriptors molecules can be compared for, e.g., similarity. Bioclipse is a workbench for the life sciences which provides ligand-based tools through a point and click interface.  The aims of this thesis were to research, and develop new or improved ligand-based methods and open source software, and to work towards making these tools available for users through the Bioclipse workbench. To this end, a series of molecular signature studies was done and various Bioclipse plugins were developed. An introduction to the field is provided in the thesis summary which is followed by five research papers. Paper I describes the Bioclipse 2 software and the Bioclipse scripting language. In Paper II the laboratory information system Brunn for supporting work with dose-response studies on microtiter plates is described. In Paper III the creation of a molecular fingerprint based on the molecular signature descriptor is presented and the new fingerprints are evaluated for target prediction and found to perform on par with industrial standard commercial molecular fingerprints. In Paper IV the effect of different parameter choices when using the signature fingerprint together with support vector machines (SVM) using the radial basis function (RBF) kernel is explored and reasonable default values are found. In Paper V the performance of SVM based QSAR using large datasets with the molecular signature descriptor is studied, and a QSAR model based on 1.2 million substances is created and made available from the Bioclipse workbench.

With an ever-growing number of publications in the biomedical domain, it becomes likely that important implicit connections between individual concepts of biomedical knowledge are overlooked. Literature based discovery (LBD) is in practice for many years to identify plausible associations between previously unrelated concepts. In this paper, we present a new, completely automatic and interactive system that creates a graph-based knowledge base to capture multifaceted complex associations among biomedical concepts. For a given pair of input concepts, our system auto-generates a list of ranked subgraphs uncovering possible previously unnoticed associations based on context information. To rank these subgraphs, we implement a novel ranking method using the context information obtained by performing random walks on the graph. In addition, we enhance the system by training a Neural Network Classifier to output the likelihood of the two concepts being likely related, which provides better insights to the end user.

3315Thesis (Ph. D.)--University of California, San Diego, 2008.
Title from first page of PDF file (viewed August 4, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 128-139).

A drug discovery project typically starts with a pharmacological hypothesis: that the modulation of a specific molecular biological mechanism would be beneficial in the treatment of the targeted disease. In a small-molecule project, the next step is to identify hits, i.e. molecules that can effect this modulation. These hits are subsequently expanded into hit series, which are optimised with respect to pharmacodynamic and pharmacokinetic properties, through medicinal chemistry. Finally, a drug candidate is clinically developed into a new drug. This thesis concerns the use of structure-based virtual screening in the hit identification phase of drug discovery. Structure-based virtual screening involves using the known 3D structure of a target protein to predict binders, through the process of docking and scoring. Docking is the prediction of potential binding poses, and scoring is the prediction of the free energy of binding from those poses. Two new methodologies, based on post-processing of scoring results, were developed and evaluated using model systems. Both methods significantly increased the enrichment of true positives. Furthermore, correlation was observed between scores and simple molecular properties, and identified as a source of false positives in structure-based virtual screening. Two target proteins, Mycobacterium tuberculosis ribose-5-phosphate isomerase, a potential drug target in tuberculosis, and Plasmodium falciparum spermidine synthase, a potential drug target in malaria, were subjected to docking and virtual screening. Docking of substrates and products of ribose-5-phosphate isomerase led to hypotheses on the role of individual residues in the active site. Additionally, virtual screening was used to predict 48 potential inhibitors, but none was confirmed as an inhibitor or binder to the target enzyme. For spermidine synthase, structure-based virtual screening was used to predict 32 potential active-site binders. Seven of these were confirmed to bind in the active site.

My three-years-project has been focused on two main topics, Sigma Receptors (SRs) and Lithium carbenoids. Concerning SRs, we spent our efforts in identifying new anticancer agents, acting via SR modulation. In detail, we generated a QSAR model. This approach allowed to identify the structural features that guarantee a high affinity towards both SR subtypes. From this study emerged that compounds presenting a bulky aminic portion, i.e. 4-benzylpiperidine, possess pan-SR affinity. Therefore, we designed and synthetized a compound series of racemic and enantiomeric arylalkyl(alkenyl)-4-benzylpiperidine derivatives. On the bases of binding profile towards S1R and S2R and of its cytotoxic properties towards a panel of cancer cell lines, we select RC-106 as the most promising antiproliferative agent. Lastly, a biotin-pan-SR conjugate has been designed as pharmacological tool able to enhance the uptake of antiproliferative molecules in cancer cells, as well as to avoid side effects. Although several attempts have been carried out, up to now, the desired product has not been obtained. Regarding Lithium Carbenoids, their reactivity/stability has been studied toward enone substrates. The chemical profile of Carbenoids leads to consider these reagents as versatile homologating agents. The work presents herein is part of this scenario and it is a pursuance of the prior publication of Pace and co-workers, which developed a strategy to carry out a chemoselective 1,2-addition of LiCH2Cl to cyclic enones. Once the applicability of the lithium carbenoids towards these ketones has been confirmed, the reactivity/stability of these species has been investigated in depth. Therefore, we focused on the study of the parameters that may influence the behavior of monohalomethyllithium reagents, using a syringe pump instrument. Interestingly, from this work unexpected products emerged. Indeed, under particular conditions, a double homologation may occur, furnishing aldehydes. We analyzed this surprising reaction and disclosed the mechanism that took place.

The purpose of this dissertation is to develop explanatory theory for the learning processes facilitated by art-­‐based methods in management education (ABMs). Such theory is important because managerial educators increasingly use ABMs, and without a well-­‐developed theory it may be difficult to realise these methods’ full potential. Current research on ABMs uses theories from other fields but generally sees ABMs as methods for making important information available for reflection, e.g. information about unconscious assumptions, aesthetic experience, or non-­‐propositional or tacit knowledge. This shows that the field is grounded in a representationalist view of cognition. This view of cognition makes it difficult to explain certain themes in the research field, such as, the importance of staying with the senses without reflecting, aesthetic agency, and the process of making. I therefore asked: What insights can be gained from exploring ABMs, using theories grounded in the embodied view of cognition, in particular Conceptual Metaphor Theory (CMT) (Lakoff & Johnson, 1999) and simulation theories (Barsalou, 2008). For the empirical work, I used an experimental design with 60 managers from Danish companies. All participants selected problems from their work they perceived as important, yet unsolvable. They were randomly divided into three groups: Two groups using different ABMs to address problems and a comparison group where no ABM was used. The experiment indicated that 1) creating new metaphors for a problem based on different sensory metaphors enabled the participants to import behaviour from contexts unrelated to the problematic situation, and 2) focusing on sensory experience enabled participants to remove judgments about self or others. Furthermore, the experiment indicated that learning outcomes reflected participants’ experience of the concrete learning intervention. These findings contribute to CMT by suggesting that it is possible to formulate relationships between changes in metaphors and specific learning outcomes. They contribute to ABM by suggesting that experiences that participants have during ABMs are later used as tools for structuring other experiences – not merely as data for reflection.
ESRC (Economic and Social Research Council)

The development of high-throughput next-generation sequencing (NGS) techniques produces massive amount of data, allowing the identification of biomarkers in early disease diagnosis and driving the transformation of most disciplines in biology and medicine. A greater concentration is needed in developing novel, powerful, and efficient tools for NGS data analysis. This dissertation focuses on modeling ``omics'' data in various NGS applications with a primary goal of developing novel statistical methods to identify sequence variants, find transcription factor (TF) binding patterns, and decode the relationship between TF and gene expression levels. Accurate and reliable identification of sequence variants, including single nucleotide polymorphisms (SNPs) and insertion-deletion polymorphisms (INDELs), plays a fundamental role in NGS applications. Existing methods for calling these variants often make simplified assumption of positional independence and fail to leverage the dependence of genotypes at nearby loci induced by linkage disequilibrium. We propose vi-HMM, a hidden Markov model (HMM)-based method for calling SNPs and INDELs in mapped short read data. Simulation experiments show that, under various sequencing depths, vi-HMM outperforms existing methods in terms of sensitivity and F1 score. When applied to the human whole genome sequencing data, vi-HMM demonstrates higher accuracy in calling SNPs and INDELs. One important NGS application is chromatin immunoprecipitation followed by sequencing (ChIP-seq), which characterizes protein-DNA relations through genome-wide mapping of TF binding sites. Multiple TFs, binding to DNA sequences, often show complex binding patterns, which indicate how TFs with similar functionalities work together to regulate the expression of target genes. To help uncover the transcriptional regulation mechanism, we propose a novel nonparametric Bayesian method to detect the clustering pattern of multiple-TF bindings from ChIP-seq datasets. Simulation study demonstrates that our method performs best with regard to precision, recall, and F1 score, in comparison to traditional methods. We also apply the method on real data and observe several TF clusters that have been recognized previously in mouse embryonic stem cells. Recent advances in ChIP-seq and RNA sequencing (RNA-Seq) technologies provides more reliable and accurate characterization of TF binding sites and gene expression measurements, which serves as a basis to study the regulatory functions of TFs on gene expression. We propose a log Gaussian cox process with wavelet-based functional model to quantify the relationship between TF binding site locations and gene expression levels. Through the simulation study, we demonstrate that our method performs well, especially with large sample size and small variance. It also shows a remarkable ability to distinguish real local feature in the function estimates.
Doctor of Philosophy
The development of high-throughput next-generation sequencing (NGS) techniques produces massive amount of data and bring out innovations in biology and medicine. A greater concentration is needed in developing novel, powerful, and efficient tools for NGS data analysis. In this dissertation, we mainly focus on three problems closely related to NGS and its applications: (1) how to improve variant calling accuracy, (2) how to model transcription factor (TF) binding patterns, and (3) how to quantify of the contribution of TF binding on gene expression. We develop novel statistical methods to identify sequence variants, find TF binding patterns, and explore the relationship between TF binding and gene expressions. We expect our findings will be helpful in promoting a better understanding of disease causality and facilitating the design of personalized treatments.

Thus far, Pierre Bourdieu’s concepts of capital, field and habitus have not been discussed together with the literature a teacher chooses in the subject English at a Swedish secondary school. However, this study aims to provide more research in this area, as well as how the concepts of capital, field and habitus affect the teacher in how he or she works with literature. This essay discusses the choice of literature of four teachers, as well as how the teachers work with the literature in the classroom. Furthermore, this essay observes that these teachers’ colleagues, time and experience are important factors in their choice of literature and teaching methods. Since these specific factors were found important, the essay concludes that Bourdieu’s concepts of capital and field are more represented than his idea of habitus in the teachers’ choice of literature, as well as how they work with the literature. This conclusion has been made since the teachers used the same approaches when it came to teaching literature, even though they did not have the same education or experience. In other words, the different habitus the teachers have, in this case their education, are in the end almost irrelevant since the teachers used the same approaches as each other, no matter what education they had or when they had it.

UA Open Access Publishing Fund
Background: Internet and mobile health (mHealth) apps hold promise for expanding the reach of evidence-based health interventions. Research in this area is rapidly expanding. However, these studies may experience problems with recruitment and retention. Web-based and mHealth studies are in need of a wide-reaching and low-cost method of recruitment that will also effectively retain participants for the duration of the study. Online recruitment may be a low-cost and wide-reaching tool in comparison to traditional recruitment methods, although empirical evidence is limited. Objective: This study aims to review the literature on online recruitment for, and retention in, mHealth studies. Methods: We conducted a review of the literature of studies examining online recruitment methods as a viable means of obtaining mHealth research participants. The data sources used were PubMed, CINAHL, EbscoHost, PyscINFO, and MEDLINE. Studies reporting at least one method of online recruitment were included. A narrative approach enabled the authors to discuss the variability in recruitment results, as well as in recruitment duration and study design. Results: From 550 initial publications, 12 studies were included in this review. The studies reported multiple uses and outcomes for online recruitment methods. Web-based recruitment was the only type of recruitment used in 67% (8/12) of the studies. Online recruitment was used for studies with a variety of health domains: smoking cessation (58%; 7/12) and mental health (17%; 2/12) being the most common. Recruitment duration lasted under a year in 67% (8/12) of the studies, with an average of 5 months spent on recruiting. In those studies that spent over a year (33%; 4/12), an average of 17 months was spent on recruiting. A little less than half (42%; 5/12) of the studies found Facebook ads or newsfeed posts to be an effective method of recruitment, a quarter (25%; 3/12) of the studies found Google ads to be the most effective way to reach participants, and one study showed better outcomes with traditional (eg in-person) methods of recruitment. Only one study recorded retention rates in their results, and half (50%; 6/12) of the studies recorded survey completion rates. Conclusions: Although online methods of recruitment may be promising in experimental research, more empirical evidence is needed to make specific recommendations. Several barriers to using online recruitment were identified, including participant retention. These unique challenges of virtual interventions can affect the generalizability and validity of findings from Web-based and mHealth studies. There is a need for additional research to evaluate the effectiveness of online recruitment methods and participant retention in experimental mHealth studies.

The information retrieval (IR) field has been successful in developing techniques to address many types of information needs. However, there are cases in which traditional approaches to IR are not able to produce adequate results. Examples include: when a small set of (2-3) documents is needed as an answer rather than a single document, or when "query splitting" is required to satisfactorily explore the document space. We explore an alternative model of building and presenting retrieval results for such cases. In particular, we research effective methods for handling information needs that may: 1. Include multiple topics: A typical query is interpreted by current IR systems as a request to retrieve documents that each discusses all topics included in that query. We propose an alternative interpretation based on query splitting. It allows queries to be interpreted as requests to retrieve sets of documents rather than individual documents, with meaningful relationships among the members of each such set. 2. Be interpreted as parts in a chain of relationships: Suppose a query concerns topics t1 and tm. Is there a relation between topics t1 and tm that involves t2 and possibly other topics as in {t1, t2, â ¦ tm}? Thus, we propose an alternative interpretation of user queries and presentation of the results. Our interpretation has the potential to improve retrieval results whenever there is a mismatch between the user's understanding of the collection and the actual collection content. We define and refine a retrieval scheme that enhances retrieval through a framework that combines multiple sources of evidence. Query results in our interpretation are networks of document groups representing topics, each group relating to and connecting to other groups in the network that partially answer the user's information need. We devise new and more effective representations and techniques to visualize results, and incorporate the user as part of the retrieval process. We also evaluate the improvement of the query results based on multiple measures. In particular, we verify the validity of our approach through a study involving a collection of Operating Systems research papers that was specially built for this dissertation.
Ph. D.

The main archive of life sciences literature currently contains more than 18,000,000 references, and it is virtually impossible for any human to stay up-to-date with this large number of papers, even in a specific sub-domain. Not every fact that is reported in the literature is novel and distinct. Scientists report repeat experiments, or refer to previous findings. Given the large number of publications, it is not surprising that information on certain topics is repeated over a number of publications. From consensus to contradiction, there are all shades of agreement between the claimed facts in the literature, and considering the volume of the corpus, conflicting findings are not unlikely. Finding such claims is particularly interesting for scientists, as they can present opportunities for knowledge consolidation and future investigations. In this thesis we present a method to extract and contextualise statements about molecular events as expressed in the biomedical literature, and to find those that potentially conflict each other. The approach uses a system that detects event negations and speculation, and combines those with contextual features (e.g. type of event, species, and anatomical location) to build a representational model for establishing relations between different biological events, including relations concerning conflicts. In the detection of negations and speculations, rich lexical, syntactic, and semantic features have been exploited, including the syntactic command relation. Different parts of the proposed method have been evaluated in a context of the BioNLP 09 challenge. The average F-measures for event negation and speculation detection were 63% (with precision of 88%) and 48% (with precision of 64%) respectively. An analysis of a set of 50 extracted event pairs identified as potentially conflicting revealed that 32 of them showed some degree of conflict (64%); 10 event pairs (20%) needed a more complex biological interpretation to decide whether there was a conflict. We also provide an open source integrated text mining framework for extracting events and their context on a large-scale basis using a pipeline of tools that are available or have been developed as part of this research, along with 72,314 potentially conflicting molecular event pairs that have been generated by mining the entire body of accessible biomedical literature. We conclude that, whilst automated conflict mining would need more comprehensive context extraction, it is feasible to provide a support environment for biologists to browse potential conflicting statements and facilitate data and knowledge consolidation.

Arts-based methods are increasingly used to facilitate meta-level learning in management education. Such increased use suggests that these methods are relevant and offer a unique contribution meeting a need in today’s management education. Yet, the literature is not clear on what this unique contribution may be even though it abounds with suggestions of varying quality. To explore this matter, I conduct a systematic literature review focused on arts-based methods, management education, and meta-level learning. I find that the unique contribution of arts-based methods is to foreground the process of making and expressing more refined perceptual distinctions, not to get accurate data, but as integral to our thinking/learning. This finding is important, because it imply that certain (commonly applied) ways of using arts-based methods may limit their potential. Finally, I suggest that future research regarding arts-based methods should focus on exploring the impact the process of learning to make and express more refined perceptual distinctions may have on managerial practice to further understand the relevance of these methods to managers.

Protein sequencing has produced overwhelming amount of protein sequences, especially in the last decade. Nevertheless, the majority of the proteins' functional and structural classes are still unknown, and experimental methods currently used to determine these properties are very expensive, laborious and time consuming. Therefore, automated computational methods are urgently required to accurately and reliably predict functional and structural classes of the proteins. Several bioinformatics methods have been developed to determine such properties of the proteins directly from their sequence information. Such methods that involve signal processing methods have recently become popular in the bioinformatics area and been investigated for the analysis of DNA and protein sequences and shown to be useful and generally help better characterise the sequences. However, there are various technical issues that need to be addressed in order to overcome problems associated with the signal processing methods for the analysis of the proteins sequences. Amino acid indices that are used to transform the protein sequences into signals have various applications and can represent diverse features of the protein sequences and amino acids. As the majority of indices have similar features, this project proposes a new set of computationally derived indices that better represent the original group of indices. A study is also carried out that resulted in finding a unique and universal set of best discriminating amino acid indices for the characterisation of allergenic proteins. This analysis extracts features directly from the protein sequences by using Discrete Fourier Transform (DFT) to build a classification model based on Support Vector Machines (SVM) for the allergenic proteins. The proposed predictive model yields a higher and more reliable accuracy than those of the existing methods. A new method is proposed for performing a multiple sequence alignment. For this method, DFT-based method is used to construct a new distance matrix in combination with multiple amino acid indices that were used to encode protein sequences into numerical sequences. Additionally, a new type of substitution matrix is proposed where the physicochemical similarities between any given amino acids is calculated. These similarities were calculated based on the 25 amino acids indices selected, where each one represents a unique biological protein feature. The proposed multiple sequence alignment method yields a better and more reliable alignment than the existing methods. In order to evaluate complex information that is generated as a result of DFT, Complex Informational Spectrum Analysis (CISA) is developed and presented. As the results show, when protein classes present similarities or differences according to the Common Frequency Peak (CFP) in specific amino acid indices, then it is probable that these classes are related to the protein feature that the specific amino acid represents. By using only the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient, as biologically related features can appear individually either in the real or the imaginary spectrum. This is successfully demonstrated over the analysis of influenza neuraminidase protein sequences. Upon identification of a new protein, it is important to single out amino acid responsible for the structural and functional classification of the protein, as well as the amino acids contributing to the protein's specific biological characterisation. In this work, a novel approach is presented to identify and quantify the relationship between individual amino acids and the protein. This is successfully demonstrated over the analysis of influenza neuraminidase protein sequences. Characterisation and identification problem of the Influenza A virus protein sequences is tackled through a Subgroup Discovery (SD) algorithm, which can provide ancillary knowledge to the experts. The main objective of the case study was to derive interpretable knowledge for the influenza A virus problem and to consequently better describe the relationships between subtypes of this virus. Finally, by using DFT-based sequence-driven features a Support Vector Machine (SVM)-based classification model was built and tested, that yields higher predictive accuracy than that of SD. The methods developed and presented in this study yield promising results and can be easily applied to proteomic fields.

Cancer researchers and oncologists benefit greatly from text mining major knowledge sources in biomedicine such as PubMed. Fundamentally, text mining depends on accurate text classification. In conventional natural language processing (NLP), this requires experts to annotate scientific text, which is costly and time consuming, resulting in small labelled datasets. This leads to extensive feature engineering and handcrafting in order to fully utilise small labelled datasets, which is again time consuming, and not portable between tasks and domains. In this work, we explore emerging neural network methods to reduce the burden of feature engineering while outperforming the accuracy of conventional pipeline NLP techniques. We focus specifically on the cancer domain in terms of applications, where we introduce two NLP classification tasks and datasets: the first task is that of semantic text classification according to the Hallmarks of Cancer (HoC), which enables text mining of scientific literature assisted by a taxonomy that explains the processes by which cancer starts and spreads in the body. The second task is that of the exposure routes of chemicals into the body that may lead to exposure to carcinogens. We present several novel contributions. We introduce two new semantic classification tasks (the hallmarks, and exposure routes) at both sentence and document levels along with accompanying datasets, and implement and investigate a conventional pipeline NLP classification approach for both tasks, performing both intrinsic and extrinsic evaluation. We propose a new approach to classification using multilevel embeddings and apply this approach to several tasks; we subsequently apply deep learning methods to the task of hallmark classification and evaluate its outcome. Utilising our text classification methods, we develop and two novel text mining tools targeting real-world cancer researchers. The first tool is a cancer hallmark text mining tool that identifies association between a search query and cancer hallmarks; the second tool is a new literature-based discovery (LBD) system designed for the cancer domain. We evaluate both tools with end users (cancer researchers) and find they demonstrate good accuracy and promising potential for cancer research.

Increased use of data is influencing the existing practices in the engineering domain,including that of systems engineering. Complex products and systems (CoPS), along with its predominant methodology of development, Model-based systems engineering(MBSE), is no exception to this. This thesis explores the possible integration of the emerging data driven methods and the established model-based methods in the context of CoPS development. It also explores what the implications of such an integration could be for the organizations building such systems, the system integrators. To analyse the current state of the art in CoPS development and model based methods as well as the emerging trends in data driven methods, this research employs an integrative literature review method. The literature search concluded in 71 selected articles to be reviewed. These articles where divided over three main categories, CoPS, Model-based systems engineering (MBSE) and data driven methods.The results of the analysis suggest that data driven methods and the model-based methods complement rather than compete throughout the innovation life cycle of CoPS. The findings indicate that an integration of the methods is beneficial to the architectural, systemic, and component level innovation in CoPS. MBSE and data driven methods could however have different levels of influence in these three types of innovation. The findings indicate that MBSE could have more influence in architectural innovations, while data driven methods could be more influential in systemic and component innovation. The continuous innovation in the use phase of system is also seen to be improved by this integration. The system integrators benefit from the improved project to project learning resulting from the integration which enhances their economy of repetition. An integrated method could also increase the speed of which decisions can be made while still maintaining reliability in the system. The results indicate that the number of iterations could increase due to the increased feedback of data and the learnings gained from it, which could pose some challenge to the existing project management methods. Further research is needed to find out what are the full benefits of an integrated method and identify other potential conflicts.

The discovery and dissemination of new knowledge are essential in food science. To advance our understanding of fruit chemistry, analytical methods were compared and applied. Polyphenols are secondary metabolites in fruits of particular importance in food science, as they contribute to the sensory attributes and health benefits of the products. Evaluation of common analytical methods for the quantification of polyphenols, including the Folin-Ciocalteu (F-C), Lowenthal permanganate (L-P), 4-dimethylaminocinnamaldehyde (DMAC) and the bovine serum albumin (BSA) precipitation methods, was conducted using analytical method validation procedures. The F-C method was not specific to polyphenols, and the L-P method had the widest working range but lacked accuracy. The DMAC method was the most specific to flavanols, and the BSA method was not suitable for quantification of smaller flavanols. Quantitative performance of these four methods was evaluated using a broad range of fruit-derived samples. Variation in quantitative results obtained using these four methods was explained by differences in polyphenol and matrix composition of these samples and differences in operating principles of the methods. The reactivity of individual polyphenol compounds (catechin, epicatechin, PC B2, PC pentamer, chlorogenic acid, phloretin, and quercetin) to the polyphenol and flavanol quantification results using Prussian blue (P-B), F-C, DMAC and BSA precipitation methods were also assessed and determined to differ by up to thirteen-fold, depending on the assay. Furthermore, the contribution and interactions of polyphenol compounds (catechin, PC B2, and chlorogenic acid) and potentially interfering compounds likely to be found in fruit and fruit products (ascorbic acid, glucose, and SO2) to the quantitative results of these methods were evaluated using a full factorial design. Significant interactions among polyphenol compounds, and among the interfering compounds were found. The standardized coefficient (β) for all factors and interactions of polyphenol compounds varied from 0.347 to 129, and from near 0 to -46.8 for all factors and interactions of interfering compounds. Our findings indicate that the choice of standards, polyphenol and matrix composition of the sample may cause disparity among the quantitative results of these methods. Amino acids in apple (Malus × domestica Borkh.) juice not only influence the quality of fermented cider through fermentation kinetics but also impact the flavor of the cider through yeast metabolism. Due to recent advances in analytical instrumentation, amino acids profiles in apple juice were determined much faster and more accurately than by previously applied methods. Twenty amino acids were quantified by UPLC-PDA in juices from 13 apple cultivars grown in Virginia. The relative amino acid profile was significantly different among the apple juices evaluated. The total amino acid concentration ranged from 18 mg/L in Blacktwig juice to 57 mg/L in Enterprise juice. L-Asparagine, L-aspartic acid and L-glutamine are the principal amino acids observed in most apple juices. These results will inform future research on yeast metabolism and nitrogen management during cider fermentation. To better disseminate knowledge gained through research to the next generation of food scientists, the effectiveness of new instructional technology—a cellphone-based personal response system—in food science education was evaluated. Students' academic performance was improved by the incorporation of this technology into lectures, and its use was well perceived by the students (easy to use and positively impacted their learning). This finding contributes to the scholarship of teaching and learning in food science by providing useful insight into the potential for application of such tools with improved student engagement and learning outcomes. Advances in food chemistry research will enable the development of value-added food products, and the pedagogical advancement in food science education will better convey new and existing knowledge to students, who will apply this knowledge to promote a safe and nutritious food supply that enhances human health and increases the value of specialty crops.
Doctor of Philosophy

Purpose: This article provides an integrative review of competency-based education (CBE) in medical and nursing programs and examines the effect of CBE on students’ academic performance, technical skill development, and overall satisfaction and preparedness for future practice. Background: In recent decades, CBE has increasingly been discussed in medical and nursing education programs. The impact of the CBE curriculum on learning outcomes including academic performance, technical skill development, overall satisfaction, and preparedness for future practice has not been fully elucidated. Method: A review of the literature was conducted, and multiple databases were searched for studies that analyzed the impact of CBE on learning outcomes in medical and nursing program learners. Results: The overall trends in feedback showed that CBE was well-received by students, with high satisfaction scores reported. CBE was also shown to be equally or more effective than the traditional didactic model in developing students’ competencies and improving academic and clinical performance. Conclusion: Our comprehensive review of the literature suggests that competency-based education can be an effective framework that potentially outperforms traditional educational approaches on outcome measures related to clinical knowledge, technical skill, and/or clinical judgement.

RESEARCH PROJECTS: 1 RESEARCH PROJECT ONE: A literature review: Constructivism: An alternate approach to teaching and learning. Abstract The constructivist perspectives on learning have helped enhance science educators' understanding of how students make sense of their lived experiences. Constructivism purports to be a transformation of the traditional curriculum. As such this article starts with a brief overview of behaviorism: the scientific approach to education. The main tenets underlying constructivism, how constructivism guides educators to change their classroom practice, and the implications to science teaching have been reviewed. 2 RESEARCH PROJECT TWO (Empirical study): Being Constructive: College students' learning of work and heat as aspects of the energy concept based a constructivist approach. Abstract This study is an extension of a literature review on constructivism as an alternate teaching and learning approach discussed in research project one. It is an empirical study concerning the use of a learning module based on a constructivist approach to develop pre-service student teachers' understanding of work and heat as aspects of the energy concept. The data consisted mainly of transcripts of students' interviews, written responses to questionnaires designed in the form of a worksheet, and comments from non-participant observers and students. The results seem to suggest that a carefully designed learning module based on a constructivist teaching and learning approach may be a valuable tool in developing pre-service student teachers' understanding of work and heat. 3 RESEARCH PROJECT THREE (Empirical study): A College in transition: A case study of the readiness of a college in the Eastern Cape province to implement Outcomes-Based Education in an Education Development centre. Abstract Curriculum 2005 premised on Outcomes-Based Education is the new curriculum framework for South Africa. It signifies a paradigm shift in education from the traditional 'telling-listening' relationship between the teacher and the learner to one that emphasises leamer-centred approach to the teaching process. Teachers, though recognized as crucial to the educational transformation process in the country have also being identified as ill-equipped to meet the challenges posed by Outcomes-Based Education. This study starts with a brief overview of the South African curriculum and the main tenets underlying Outcomes-Based Education. The institutional conditions and whether the lecturers at a college in the Eastern Cape province perceive the need for a change in their classroom practice were also investigated. Bearing in mind the need for further research to validate the findings of this study, positive indicators that emerged from the study suggest the readiness of the college to implement Outcomes-Based Education at the proposed Education Development Centre.

Abstract Context: This study contributes to the understanding of the current state of cross-project defect prediction (CPDP) by investigating the topic in themes, with special focus on data approaches and covering search-based training data selection, by proposing data selection methods and investigating their impact. The empirical evidence for this work is collected through a formal systematic literature review method for the review, and from experiments on open source projects. Objective: We aim to understand and summarize the manner in which various data manipulation approaches are used in CPDP and their potential impacts on performance. Further, we aim at utilizing search-based methods to produce evolving training data sets to filter irrelevant instances from other projects before training. Method: Through a series of studies following the literature review of current state of CPDP, we propose a search-based method called genetic instance selection (GIS). We validate our initial findings by conducting the next study on a large set of data sets with multiple feature sets. We refine our design decisions using an exploratory study. Finally, we investigate an existing meta-learning approach, provide insights on its design and propose an alternative iterative data selection method. Results: The literature review reveals lower performances of CPDP in comparison with within project defect prediction (WPDP) models and provides a set of primary studies to be used as the basis for future research. Our proposed data selection methods make the case for search-based approaches considering their higher effectiveness and performance. We identified potential impacting factors on the effectiveness through the exploratory study and proposed methods to create better CPDP models. Conclusions: The proposal of numerous approaches in the literature over the last decade has led to progress in the area and the acquired knowledge and tools apply to many similar domains and can act as parts of academic curricula as well. Future directions of study can include searching for better validation data, better feature selection techniques, tuning the parameters of the search-based models, tuning hyper-parameters of learners, investigating the effects of multiple sources of optimization (learner, instances and features) and the impact of the class imbalance problem
Tiivistelmä Tausta: Tämä tutkimus edistää projektienvälisten virheiden ennustamisen nykytilan ymmärtämistä (CPDP) tutkimalla aihetta teemoissa, keskittyen erityisesti tiedollisiin lähestymistapoihin ja hakuperusteisen harjoitusdatan valintaan esittelemällä datan valintamenetelmiä ja tutkimalla niiden vaikutuksia. Tämän työn empiirinen todistusaineisto on koottu muodollisella systemaattisella kirjallisuuskatsauksella ja avoimen lähdekoodin projekteissa tehdyillä kokeilla. Tavoite: Pyrimme ymmärtämään ja tiivistämään tavan, jolla erilaisia datan käsittelyn lähestymistapoja käytetään CPDP:ssa sekä niiden potentiaalisia vaikutuksia suorituskykyyn. Lisäksi, tavoitteenamme on hyödyntää hakuperusteisia menetelmiä muodostamaan kehittyviä harjoitusdata-settejä suodattamaan epäolennaisia esiintymiä muista projekteista ennen koulutusta. Menetelmä: CPDP:n nykytilan kirjallisuuskatsauksen jälkeen tehtyjen tutkimusten avulla ehdotamme hakuperusteista menetelmää, jota kutsutaan geneettisen esiintymän valinnaksi (GIS). Todistamme alustavat havaintomme suorittamalla seuraavan tutkimuksen suurella joukolla datasettejä, joilla on useita ominaisuuksia. Jalostamme suunnittelupäätöksiämme käyttäen tutkivaa tutkimusta. Lopuksi, tutkimme vallitsevaa meta-oppimisen lähestymistapaa ja tarjoamme näkemyksiä sen suunnitteluun ja ehdotamme vaihtoehtoista, toistuvaa datan valintamenetelmää. Tulokset: Kirjallisuuskatsaus paljastaa CPDP:n heikomman suorituskyvyn verrattuna projektinsisäisten virheiden ennustamisen (WPDP) malleihin ja tarjoaa joukon primaaritutkimuksia, joita voidaan käyttää perustana myöhemmälle tutkimukselle. Ehdottamamme datan valintamenetelmät puoltavat hakuperusteisten menetelmiä niiden paremman tehokkuuden ja suorituskyvyn vuoksi. Tunnistimme potentiaalisia tehokuuteen vaikuttavia tekijöitä tutkivien tutkimusten avulla ja ehdotimme metodeja parempien CPDP mallien luomiseksi. Johtopäätökset: Viime vuosikymmenten aikana kirjallisuudessa esitellyt lukuisat menetelmät ovat edistäneet alaa ja hankittu tieto ja työkalut soveltuvat monille samanlaisille alueille ja voivat toimia myös osana akateemisia opetussuunnitelmia. Tutkimuksen tulevat linjaukset voivat sisältää validointiin paremmin soveltuvan datan haun, paremmat ominaisuuksien valintatekniikat, hakuperusteisten mallien parametrien hienosäädön, oppijoiden hyper-parametrien hienosäädön, tutkimuksen useiden optimoinnin lähteiden vaikutuksista (oppija, esiintymät, ominaisuudet) ja luokan epätasapaino-ongelman vaikutuksesta

The work of this thesis was focused on the development of high-performance algorithms for a new generation of molecular descriptors, with many advantages with respect to its predecessors, suitable for diverse applications in the field of drug design, as well as its implementation in commercial grade scientific software (Pentacle). As a first step, we developed a new algorithm (AMANDA) for discretizing molecular interaction fields which allows extracting from them the most interesting regions in an efficient way. This algorithm was incorporated into a new generation of alignmentindependent molecular descriptors, named GRIND-2. The computing speed and efficiency of the new algorithm allow the application of these descriptors in virtual screening. In addition, we developed a new alignment-independent encoding algorithm (CLACC) producing quantitative structure-activity relationship models which have better predictive ability and are easier to interpret than those obtained with other methods.
El trabajo que se presenta en esta tesis se ha centrado en el desarrollo de algoritmos de altas prestaciones para la obtención de una nueva generación de descriptores moleculares, con numerosas ventajas con respecto a sus predecesores, adecuados para diversas aplicaciones en el área del diseño de fármacos, y en su implementación en un programa científico de calidad comercial (Pentacle). Inicialmente se desarrolló un nuevo algoritmo de discretización de campos de interacción molecular (AMANDA) que permite extraer eficientemente las regiones de máximo interés. Este algoritmo fue incorporado en una nueva generación de descriptores moleculares independientes del alineamiento, denominados GRIND-2. La rapidez y eficiencia del nuevo algoritmo permitieron aplicar estos descriptores en cribados virtuales. Por último, se puso a punto un nuevo algoritmo de codificación independiente de alineamiento (CLACC) que permite obtener modelos cuantitativos de relación estructura-actividad con mejor capacidad predictiva y mucho más fáciles de interpretar que los obtenidos con otros métodos.

Telemetry system, Optimisation, Sensoric networks, Smart Grid, Internet of Things, Sensors, Information security, Cryptography, Cryptography algorithms, Cryptosystem, Confidentiality, Integrity, Authentication, Data freshness, Non-Repudiation.

碩士
國立臺灣大學
資訊管理學研究所
100
Scientific literature has growth rapidly in the past century, and a great deal of knowledge can support medical researchers to keep up with up-to-date information. This large volume of data is difficult to discover hidden relationships. To overcome this problem, Swanson proposed a method called literature-based discovery in 1986 to support researchers an effective way to uncovering new, potentially meaningful relationships. After Swanson proposed this method, other researchers also try to improve the result from literature-base discovery or develop new method to improve. Researchers could employ literature-based discovery to support them reduce the time of discovering hidden relationships. But literature-based discovery method could not provide more information such as fish oil and blood viscosity is a suppressing relationship because fish oil can decrease blood viscosity. The kind of relationship we defined as impact relationship. Therefore, this study proposed a LBD (Impact) technique which is based on the concept of literature-based discovery and this technique can extract impact relationship to support researchers easier to analyze large volume of data. First, we apply literature-based discovery to retrieve related medical concepts. Subsequently, we use our proposed technique to extract impact relationship then order medical concepts in an appropriate way. We construct two scenarios to evaluate our proposed LBD (Impact) technique, disease-chemicals and drugs scenario and drug-chemicals and drugs scenario. In disease-chemicals and drugs scenario, researchers usually focus on which drug can cure disease. And our proposed technique can rank drugs that can cure disease at higher rank. In the other scenario, drug-chemicals and drugs scenario, although the experiment result is not better than disease-chemicals and drugs scenario, we still can provide a better result to researchers. For researchers, they usually pay more attention on top 100 or 300. In this study, our proposed technique can provide a better result for researchers.

碩士
國立臺灣大學
資訊管理學研究所
105
Drug development is costly and time-consuming. According to United States Food and Drug Administration (FDA), drug development consists of five stages, including drug discovery, clinical test, FDA review, etc. However, once one of the stages fails, the investment on candidate drug seldom returns. As a result, to overcome the challenges of drug development, researchers start to explore alternative methods for drug development. Drug repurposing discovery, finding new indications for existing drugs, has been proposed to help reduce cost and time needed for drug development. Swanson (1986) originally proposed a drug repurposing approach that analyzes biomedical literatures to uncover implicit relationships. Previous studies following Swanson’s ABC model encountered several limitations. Therefore, in this research, we propose a path-importance-based approach, which constructs a concept network based on semantic predication, trains a classification model to determine the importance of paths that connecting a focal drug and a candidate disease, and finally ranks candidate diseases according to the importance of paths identified by the path importance classification model. In our systematic evaluation experiments, we prove that our path importance classification model achieves a satisfactory effectiveness, and that adopting the concept of path importance into the ranking of candidate drugs for drug repurposing outperforms the traditional method.

碩士
國立高雄應用科技大學
電子工程系
99
The identification of transcription factor binding sites (TFBSs) is important for understanding the genetic regulatory system, but weak conservation of TFBSs poses a challenge in computational biology. In this thesis, a new method based on Ant Colony Optimization (ACO) and Expectation Maximization (EM) algorithm is proposed to discover DNA motifs (collections of TFBSs) in a set of bio-sequences. ACO is a well-received swarm intelligence algorithm, which is used to build candidate motifs in this method to search for putative binding sites amongst the given sequences. The EM algorithm is then applied to maximize the likelihood of a motif model being constructed from the corresponding binding sites. In ACO, each artificial ant mimics the foraging behavior of social insects to construct a possible motif by sensing the pheromones laid on each nucleotide. Due to stability issues with metaheuristic approaches, we incorporate the EM algorithm in our method to improve the reliability of binding site predictions. In a final step, a statistically-based procedure is applied to refine the predictions for compliance with real biological conditions. Experiments conducted on real test datasets indicate that the proposed method identifies binding sites with higher accuracy and reliability than two other motif discovery tools, namely GAME and GALF.

碩士
國立臺灣大學
資訊管理學研究所
107
Drug development is extremely costly and risky, so researchers are looking for alternative approaches. There’s a new approach: Drug Repurposing, using existing drugs approved by FDA to find new indications. Compared with the development of new drugs, existing drugs have more complete clinical drug information and human safety data. It not only can shorten development time but also reduce the risks on R&D. In 1986, Swanson proposed an approach using information retrieval and text mining techniques to construct a biomedical network composed of links between biomedical concepts from biomedical literatures. Through analyzing this network, drug repurposing can be achieved. Many researchers have followed this approach. Previous studies (Lee, 2017) proposed a path-importance-based approach. We follow this approach. In our research, we use machine learning techniques to convert biomedical entities and relations into representative biomedical vectors, discriminate whether a path is important or not and decide the candidate diseases given a focal drug. The empirical results show that the representative biomedical vectors can significantly improve the path-importance-based classification, which in turn can support effective drug repurposing.

Dottorato di Ricerca in Medicina Traslazionale. Ciclo XXXI
Fin dalla scoperta della penicillina da parte di Alexander Fleming nel 1928, i prodotti naturali microbici hanno rappresentato una risorsa essenziale per lo sviluppo di nuovi agenti farmacologici. All’interno dell’immenso panorama di microorganismi che popolano gli ecosistemi terrestri e marini, i batteri appartenenti al phylum Actinobacteria rappresentano la principale fonte di molecole naturali bioattive. Il metabolismo secondario di questi mircroorganismi, infatti, è complesso e molto variabile, ed è responsabile della produzione di molecole molto diverse dal punto di vista chimico, biosintetico e dell’attività biologica. Una classe di metaboliti secondari relativamente recente ma in rapida espansione, è rappresentata dai peptidi sintetizzati a livello ribosomiale e modificati a livello post-traduzionale (RiPPs). Questi prodotti naturali peptidici sono dotati di diverse attività biologiche e un enorme potenziale farmacologico, con uno spettro di attività che include, tra le tante, quella antibatterica, antitumorale, ipolipidemizzante e immunomodulatrice. L’evidenza del potenziale biologico di queste molecole ha, pertanto, spinto l’interesse della ricerca biotecnologica e farmaceutica a concentrarsi su questa classe di metaboliti secondari. L’attenzione è rivolta, in particolare, all’identificazione di nuovi ceppi batterici produttori di RiPPs bioattivi e alla caratterizzazione dei pathway biosintetici, allo scopo di comprendere meglio gli aspetti biochimici alla base della loro biosintesi. In questo scenario si colloca la thioviridamide, RiPP biosintetizzato da Streptomyces olivoviridis NA005001 e caratterizzato da una potente attività antiproliferativa e pro-apoptotica nei confronti di diverse linee cellulari tumorali. Questo composto peptidico, unico nel suo genere, presenta un gruppo 2-idrossi-2-metil-4-oxopentanoile all’estremità N-terminale, un residuo di β-idrossi-N1,N3-dimetilistidinio (hdmHis), un residuo di S-(2-aminovinil)-cisteina (AviCys) che fa parte di un macro-ciclo e cinque gruppi tioammidici che sostituiscono i gruppi ammidici nello scheletro peptidico. Recentemente, il cluster genico responsabile della biosintesi della thioviridamide è stato identificato, dimostrando l'origine ribosomiale di questa molecola, ma i processi biosintetici alla base della sua produzione non sono ancora completamente noti. La promettente attività antitumorale della thioviridamide, così come la sua peculiare struttura chimica e l'interessante pathway biosintetico, rendono questo composto estremamente interessante agli occhi della ricerca. La prima parte di questo lavoro di tesi ha avuto l’obbiettivo di identificare, mediante un approccio genomico, nuovi prodotti naturali, analoghi della thioviridamide, biosintetizzati da Actinobacteria, e di effettuarne la caratterizzazione chimico-funzionale, allo scopo di valutarne il potenziale antitumorale. Studi bioinformatici, basati sull’utilizzo di tools per l’analisi di omologie di sequenza, hanno permesso di individuare, all’interno del vasto panorama di batteri dal genoma noto, microorganismi contenenti nel proprio genoma cluster genici simili a quello responsabile della biosintesi della thioviridamide. I cluster genici identificati in questi microorganismi sono risultati essere leggermente difformi da quello presente in S. olivoviridis, con differenze sia a livello del peptide precursore e sia a livello dei sistemi enzimatici di maturazione che lo convertono in un RiPP maturo. Tali microorganismi, pertanto, appaiono essere potenziali produttori di molecole strutturalmente analoghe alla thioviridamide, con caratteristiche chimiche e biologiche sconosciute alla comunità scientifica. Tre dei diversi ceppi batterici identificati si sono rivelati capaci di produrre, in determinate condizioni sperimentali, molecole analoghe alla thioviridamide: - Thiostreptamide S4, prodotto da Streptomyces sp. NRRL S-4 - Thiostreptamide S87, prodotto da Streptomyces sp. NRRL S-87 - Thioalbamide, prodotto da Amycolatopsis Alba DSM 44262 La correlazione tra i cluster genici e i nuovi prodotti naturali identificati è stata confermata mediante due diversi approcci biologico-molecolari: - la delezione del cluster genico nel ceppo batterico produttore, che ha portato alla generazione di mutanti incapaci di produrre i composti identificati. - l’espressione eterologa del cluster genico, che ha portato alla produzione dei composti precedentemente identificati in un microorganismo ospite, Streptomyces coelicolor M1146. I risultati ottenuti hanno permesso di stabilire che la thioviridamide non è una molecola unica nel suo genere, ma fa parte di una famiglia di composti, identificati in questo studio, a cui è stato dato il nome di thioviridamide-like molecules (TLMs). Uno scale-up dei processi fermentativi ha permesso di purificare i tre nuovi prodotti naturali in quantità sufficenti per la loro caratterizzazione chimica, effettuata mediante spettrometria di massa e di risonanza magnetica nucleare (NMR). Questo studi hanno confermato la diversità. chimica dei TLMs, dal punto di vista della sequenza aminoacidica, sebbene sono risultate essere conservate alcune caratteristiche peculiari della thioviridamide, quali la presenza di un macrociclo, una carica elettrica positiva conferita da un residuo di dimetil-istidinio e la presenza di legami tioammidici nello scheletro peptidico. Inoltre, dai risultati ottenuti è emerso che i TLMs sono caratterizzati dalla presenza all’estremità N-terminale di un gruppo piruvile o lattile, e il gruppo 2-idrossi-2-metil-4-oxopentanoile, caratterizzante l’estremità N-terminale della thioviridamide, è risultato essere un artefatto, generato da una reazione di condensazione aldolica tra il gruppo piruvile della molecola naturale e l’acetone utilizzato come solvente nel processo di purificazione. La thioalbamide, il prodotto naturale purificato in maggiore quantità, è stato quindi oggetto di indagini biologiche al fine di valutarne l’attività antiproliferativa e il potenziale antitumorale. I risultati ottenuti hanno evidenziato un’intensa attività antiproliferativa nei confronti di una vasta gamma di linee cellulari tumorali. Questi effetti sono risultati essere altamente selettivi per le cellule tumorali, in quanto il composto ha mostrato scarsa attività in un modello cellulare non tumorale. La seconda parte di questo lavoro di tesi ha avuto l’obbiettivo di investigare a fondo sui meccanismi molecolari alla base dell’attività antitumorale della thioalbamide in diversi modelli in vitro di carcinoma mammario, il tumore maggiormente diagnosticato tra le donne nel mondo. In questa parte del lavoro è stato utilizzato un approccio biochimico-metabolico, per valutare per la prima volta, gli effetti cellulari indotti dalla thioalbamide in linee cellulari tumorali che riflettono la diversità biologica dei diversi sottotipi di carcinoma mammario. Nei diversi modelli utilizzati, la molecola non ha mostrato significative differenze di attività antiproliferativa, dimostrando che il suo potenziale antitumorale è indipendente dal profilo recettoriale tumorale. In particolare, la thioalbamide ha dimostrato possedere abilità di indurre cambiamenti morfologici nelle cellule trattate, blocco del ciclo cellulare a livello del checkpoint G1/S e morte cellulare mediata da meccanismi apoptotici. L’apoptosi è stata confermata con diversi approcci sperimentali atti a monitorare diversi eventi chiave del processo di morte programmata, quali la frammentazione del DNA, la perdita del potenziale di membrana mitocondriale e l’esposizione della fosfatidilserina sul foglietto esterno della membrana cellulare. In aggiunta, gli eventi di morte cellulare sono risultati essere il frutto dell’innesco dei pathway apoptotici estrinseco ed intrinseco, mediati rispettivamente dal attivazione proteolitica delle caspasi 8 e 9. Lo studio delle alterazioni biochimiche indotte dalla thioalbamide è proseguito, facendo emergere la capacità del composto di derterminare nella cellula un aumento nella produzione di specie reattive dell’ossigeno (ROS), che si sono rivelate il fenomeno scatenante la morte apoptotica indotta dalla thioalbamide. L’eccessivo aumento dei livelli intracellulari di ROS indotto dal trattamento, è risultato interessare particolarmente il compartimento mitocondriale della cellula. Questa evidenza è emersa dal momento che la cellula tumorale risponde allo stress ossidativo, indotto dal composto, con un aumento selettivo dell’isoforma mitocondriale della superossido dismutasi (SOD2), enzima deputato alla neutralizzazione dell’anione superossido, principale subprodotto della respirazione cellulare. Essendo i ROS generati dal metabolismo cellulare, il loro accumulo e il conseguente stress ossidativo sono spesso associati ad alterazioni dei pathway metabolici. La riprogrammazione metabolica è una delle caratteristiche del cancro, e i tumori richiedono cataboliti per produrre ATP, mantenere un equilibrio redox e generare biomassa. A seconda della disponibilità di nutrienti, alcune cellule all’interno del tumore sono prevalentemente glicolitiche, mentre altre hanno un fenotipo dipendente dalla fosforilazione ossidativa. Pertanto, in questo lavoro, è stato valutato anche il profilo energetico delle cellule trattate con thioalbamide, e i risultati ottenuti hanno evidenziato la capacità di questo prodotto naturale di inibire la glicolisi e la fosforilazione ossidativa, i due principali pathway energetici cellulari. Il metabolismo della cellula tumorale rappresenta un potenziale target per la terapia oncologica. Infatti, è noto che le cancer stem cells (CSCs), la sottopopolazione di cellule tumorali responsabile dell’insorgenza di fenomeni di recidiva e metastatizzazione, sono caratterizzate da una elevata flessibilità metabolica. La thioalbamide, spegnendo il metabolismo energetico tumorale, si è rivelata in grado di inibire la crescita e propagazione delle CSCs, riducendo l’efficienza di formazione di mammospheres (MFE). Nel complesso, questo lavoro di dottorato ha portato alla luce nuove conoscenze sui metaboliti secondari microbici, identificando nuovi membri della classe dei RiPPs che da oggi costituiscono la famiglia delle thioviridamide-like molecules (TLMs). Inoltre, per la prima volta, sono stati studiati i meccanismi molecolari indotti da questi nuovi prodotti naturali e, dai risultati ottenuti, è emerso che l’elevato potenziale antitumorale della thioalbamide è dovuto alla sua capacità di spegnere il metabolismo energetico della cellula maligna.
Università della Calabria

On TFBS motif discovery, three novel GA based algorithms are developed, namely GALF-P with focus on optimization, GALF-G for modeling, and GASMEN for spaced motifs. Novel memetic operators are introduced, namely local filtering and probabilistic refinement, to significantly improve effectiveness (e.g. 73% better than MEME) and efficiency (e.g. 4.49 times speedup) in search. The GA based algorithms have been extensively tested on comprehensive synthetic, real and benchmark datasets, and shown outstanding performances compared with state-of-the-art approaches. Our algorithms also "evolve" to handle more and more relaxed cases, namely from fixed motif widths to most flexible widths, from single motifs to multiple motifs with overlapping control, from stringent motif instance assumption to very relaxed ones, and from contiguous motifs to generic spaced motifs with arbitrary spacers.
TF-TFBS associated sequence pattern (rule) discovery is further investigated for better deciphering protein-DNA interactions in regulation. We for the first time generalize previous exact TF-TFBS rules to approximate ones using a progressive approach. A customized algorithm is developed, outperforming MEME by over 73%. The approximate TF-TFBS rules, compared with the exact ones, have significantly more verified rules and better verification ratios. Detailed analysis on PDB cases and conservation verification on NCBI protein records illustrate that the approximate rules reveal the flexible and specific protein-DNA interactions with much greater generalized capability.
The comprehensive pattern discovery algorithms developed will be further verified, improved and extended to further deciphering transcriptionial regulation, such as inferring whole gene regulatory networks by applying TFBS and TF-TFBS patterns discovered and incorporating expression data.
Transcription Factor (TF) and Transcription Factor Binding Site (TFBS) bindings are fundamental protein-DNA interactions in transcriptional regulation. TFs and TFBSs are conserved to form patterns (motifs) due to their important roles for controlling gene expressions and finally affecting functions and appearances. Pattern discovery is thus important for deciphering gene regulation, which has tremendous impacts on the understanding of life, bio-engineering and therapeutic applications. This thesis contributes to pattern discovery involving TFBS motifs and TF-TFBS associated sequence patterns based on Evolutionary Computation (EC), especially Genetic Algorithms (GAs), which are promising for bioinformatics problems with huge and noisy search space.
Chan, Tak Ming.
Advisers: Kwong-Sak Leung; Kin-Hong Lee.
Source: Dissertation Abstracts International, Volume: 73-03, Section: B, page: .
Thesis (Ph.D.)--Chinese University of Hong Kong, 2010.
Includes bibliographical references (leaves 147-153).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.

The advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to an enormous progress in life sciences. Among the most important innovations is the microarray technology. It allows to quantify the expression of thousands of genes simultaneously by measuring the hybridization from a tissue of interest to probes on a small glass or plastic slide. Before launching into microarray research it is important to recall that the characteristics of this data include a fair amount of noise and an atypical dimensionality (which makes difficult the use of classic statistics tools – experimental samples in the order of dozens and measured parameters in thousands or tens of thousands). Therefore, the main goal of this thesis is the development of adequate computational methods and algorithms, capable of extracting valuable biological knowledge from this type of data. Applications of microarray technology as a tool for gene expression analysis range from the assignment of functional categories for genes of unknown biological function (based on the analysis of genes with already established biological role), to precise and early diagnosis of different tumor malignancies. However, the main goal of computational analysis of gene expression data is the extraction of regulatory knowledge at genetic level that may be used to provide a broader understanding on the functioning of complex cellular systems. In this direction, revealing the structures of regulatory networks based of gene expression data becomes a pivotal task. The thesis contributes with a framework for the discovery of biological functional category of genes based on the synergy of ICA and a dynamic SOM-based clustering algorithm, that accurately finds groups of co-regulated genes, while identifying interesting regulatory signals within the data with the help of ICA decomposition. We also pursue the task of molecular characterization of different tumor types using gene expression profiling, by providing a novel method for tissue samples classification, based on an ensemble of classifiers sequentially trained on reweighted versions of the data. The algorithm, known as boosting, is adapted to peculiarities of gene expression data and employed in conjunction with SVMs. Additionally, the novel concept of finding predictive genes whose signatures are significant for phenotype discrimination is treated. Finally, the thesis presents a method developed for reverse-engineering gene regulatory networks based on recurrent neuro-fuzzy networks, which exploits the advantages of fuzzy-based models, in terms of results interpretability, and those of neural systems, in terms of computational power and time series prediction capabilities.
H έλευση ικανών υπολογιστικών εργαλείων για την μελέτη της γενομικής ακολουθίας και της ερευνητικής βιοτεχνολογίας υψηλής ανάλυσης, οδήγησε σε μια τεράστια πρόοδο στις επιστήμες ζωής. Μεταξύ των πιο σημαντικών καινοτομιών είναι η τεχνολογία μικροσυστοιχιών. H τεχνολογία αυτή επιτρέπει την ποσοτικοποίηση της έκφρασης χιλιάδων γονιδίων ταυτόχρονα, μετρώντας τον υβριδισμό από έναν ιστό ενδιαφέροντος έως σε δείγματα σε μικρό γυαλί η σε πλαστικά τσιπ. Πριν ξεκινήσουμε την έρευνα πάνω στις μικροσυστοιχίες είναι σημαντικό να θυμόμαστε ότι τα χαρακτηριστικά των δεδομένων αυτής περιλαμβάνουν αρκετό ποσό θορύβου και ένα μη τυπικό αριθμό διαστάσεων (το οποίο καθιστά δύσκολη την χρήση κλασσικών στατιστικών μεθόδων – μέγεθος δείγματος σε δωδεκάδες και μέγεθος χαρακτηριστικών σε χιλιάδες η δεκάδες η εκατοντάδες). Επομένως, ο κύριος στόχος αυτής της διδακτορικής εργασίας είναι η ανάπτυξη ικανών υπολογιστικών μεθόδων και αλγόριθμων έτσι ώστε να εξάγουν πολύτιμη βιολογική γνώση από τον συγκεκριμένο τύπο δεδομένων. Εφαρμογές της τεχνολογίας μικροσυστοιχιών σαν ένα εργαλείο για την ανάλυση έκφρασης γονιδίων ξεκινούν από την εύρεση και απόδοση λειτουργικών κατηγοριών για γονίδια άγνωστης βιολογικής λειτουργικότητας (βασισμένη στην ανάλυση των γονιδίων ήδη εδραιωμένου βιολογικού ρόλου) έως την ακριβή και πρώιμη διάγνωση διαφορετικών κακοήθων όγκων. Όμως ο κύριος στόχος της υπολογιστικής ανάλυσης της έκφρασης γονιδίων είναι η εξαγωγή ρυθμιζόμενης γνώσης στο γενετικό επίπεδο το οποίο μπορεί να χρησιμοποιηθεί ώστε να παρέχει μία ευρύτερη κατανόηση της λειτουργίας πολύπλοκων κυτταρικών συστημάτων. Σε αυτή την κατεύθυνση, το να αναδεικνύεις τις δομές ρυθμιστικών δικτύων βασισμένων στην έκφραση γονιδίων γίνεται καίριο έργο. Η διδακτορική διατριβή συνεισφέρει στο πλαίσιο για την ανακάλυψη βιολογικά λειτουργικών κατηγοριών γονιδίων βασισμένη στην συνεργία της ΙCA και της δυναμικού βασισμένου στη SOM ομαδοποίηση αλγορίθμου η οποία με ακρίβεια βρίσκει ομάδες γονιδίων που συν-ρυθμίζονται ενώ παράλληλα αναγνωρίζει ενδιαφέροντα ρυθμιστικά σήματα μέσα στα δεδομένα με τη βοήθεια της ΙCA αποδόμησης. Eπίσης, προσανατολιζόμαστε στην εύρεση του μοριακού χαρακτηρισμού διαφορετικών τύπων όγκων χρησιμοποιώντας το προφίλ της γονιδιακής έκφρασης, βασισμένο σε ένα σύνολο κατηγοριοποιητών οι οποίοι εκπαιδεύτηκαν σειριακά σε επανασταθμισμένες παραλλαγές των δεδομένων. Ο αλγόριθμος, γνωστός και σαν boosting, έχει προσαρμοστεί στις ιδιαιτερότητες των δεδομένων έκφρασης γονιδίου και εφαρμόζεται σε συνδυασμό με τα SVMs. Επιπλέον, εξετάζεται η πρωτοποριακή τεχνική της εύρεσης προβλέψιμων τιμών των οποίων οι υπογραφές είναι σημαντικές για τον χαρακτηρισμό φαινότυπου. Τελικά, η παρούσα διδακτορική διατριβή παρουσιάζει μια μέθοδο που αναπτύχθηκε για αντίστροφα μηχανικά ελεγχόμενα από γονίδια νευρωνικά δίκτυα βασισμένα σε αναδρομικά νευρωνικά δίκτυα τύπου fuzzy, τα οποία αξιοποιούν τα πλεονεκτήματα των μοντέλων τύπου fuzzy σε βάση επεξηγηματικότητας αποτελεσμάτων, και αυτών των νευρωνικών δικτύων σε βάση υπολογιστικής δύναμης και ικανότητας πρόβλεψης χρονοσειρών.

Learning Analytics plays an increasing role in the analysis of virtual learning activities. This article addresses the gap between educational needs and technical supply. By means of a Systematic Literature Review of the LAK conferences the authors extracted observations, methods and tools which represent potential solutions for a given eCollaboration scenario. Based on three prioritised examples of an observation sheet, methods are derived and recommendations for the use of Learning Analytics tools are given. The result is a catalogue that enable users to select suitable methods and tools for an implementation. The (semi-) automation can increase the efficiency of Community Managers in monitoring the participants and hence make real-time intervention feasible.

Multiple Sequenzalignments (MSAs) von homologen Proteinen sind nützliche Werkzeuge, um kompensatorische Mutationen zwischen nicht-konservierten Residuen zu charakterisieren. Die Identifizierung dieser Residuen in MSAs ist eine wichtige Aufgabe um die strukturellen Grundlagen und molekularen Mechanismen von Proteinfunktionen besser zu verstehen. Trotz der vielen Anzahl an Literatur über kompensatorische Mutationen sowie über die Sequenzkonservierungsanalyse für die Erkennung von wichtigen Residuen, haben vorherige Methoden meistens die biochemischen Eigenschaften von Aminosäuren nicht mit in Betracht gezogen, welche allerdings entscheidend für die Erkennung von kompensatorischen Mutationssignalen sein können. Jedoch werden kompensatorische Mutationssignale in MSAs oft durch das Rauschen verfälscht. Aus diesem Grund besteht ein weiteres Problem der Bioinformatik in der Trennung signifikanter Signale vom phylogenetischen Rauschen und beziehungslosen Paarsignalen. Das Ziel dieser Arbeit besteht darin Methoden zu entwickeln, welche biochemische Eigenschaften wie Ähnlichkeiten und Unähnlichkeiten von Aminosäuren in der Identifizierung von kompensatorischen Mutationen integriert und sich mit dem Rauschen auseinandersetzt. Deshalb entwickeln wir unterschiedliche Methoden basierend auf klassischer- und quantum Informationstheorie sowie multiple Testverfahren. Unsere erste Methode basiert auf der klassischen Informationstheorie. Diese Methode betrachtet hauptsächlich BLOSUM62-unähnliche Paare von Aminosäuren als ein Modell von kompensatorischen Mutationen und integriert sie in die Identifizierung von wichtigen Residuen. Um diese Methode zu ergänzen, entwickeln wir unsere zweite Methode unter Verwendung der Grundlagen von quantum Informationstheorie. Diese neue Methode unterscheidet sich von der ersten Methode durch gleichzeitige Modellierung ähnlicher und unähnlicher Signale in der kompensatorischen Mutationsanalyse. Des Weiteren, um signifikante Signale vom Rauschen zu trennen, entwickeln wir ein MSA-spezifisch statistisches Modell in Bezug auf multiple Testverfahren. Wir wenden unsere Methode für zwei menschliche Proteine an, nämlich epidermal growth factor receptor (EGFR) und glucokinase (GCK). Die Ergebnisse zeigen, dass das MSA-spezifisch statistische Modell die signifikanten Signale vom phylogenetischen Rauschen und von beziehungslosen Paarsignalen trennen kann. Nur unter Berücksichtigung BLOSUM62-unähnlicher Paare von Aminosäuren identifiziert die erste Methode erfolgreich die krankheits-assoziierten wichtigen Residuen der beiden Proteine. Im Gegensatz dazu, durch die gleichzeitige Modellierung ähnlicher und unähnlicher Signale von Aminosäurepaare ist die zweite Methode sensibler für die Identifizierung von katalytischen und allosterischen Residuen.

abstract: This thesis paper, Something about Self: Moving the Creative Flow Within, explores the progression of the author's abilities as a facilitator in a creative context through her project presentation SELF(ish): grow(tru)thOUGHT. Along with the subjective assessment of creative facilitation, the underpinnings of the author's creative process and artistic vision are exposed through relevant literature, significant inspirations, personal insight, process comparisons, and imaginative metaphors. The author/artist offers a unique perspective on personal interests collected over the course of her graduate studies. Waugh expounds upon pertinent content such as intuition in creativity, the emotional link to the mind-body connection, dance movement therapy and its effects on states of being, self-realization and self-transcendence. Each of these contextual elements contributed to the creation of exercises for movement generation used in a performative dance work. Ultimately, this paper elucidates a transparent, versatile creative practice and the evolution of a unique, passionate artistry that is based on a balance between structure and flow.
Dissertation/Thesis
M.F.A. Dance 2012

Summaries in Afrikaans and English
In die studie is daar gepoog om aan te toon waarom die ondervindingsmod~l vir taalleer die aangewese model vir effektiewe tweedetaalleer is. Die kommunikatiewe onderrigbenaderingswyse, onderhandeling in die klaskamer en die belangrikheid van die prosessillabus in tweedetaalverwerwing is bespreek. Die taalonderwyser se rot as fasiliteerder van leer in kommunikatiewe FAfrikaanstaalonderrig in die interaktiewe klaskamer met klem op leerdergesentreerde onderrig is uiteengesit. Daar is verder aangetoon dat daar ten opsigte van die rot van die onderwyser 'n paradigmatiese verskuiwing moet plaasvind, veral noudat beginsels van uitkomsgebaseerde onderrig wat deel van kurrikulum 2005 vorm, in 1998/1999 in aile Suid-Afrikaanse skole ingestel is. Die onderwyser is nou 'n fasiliteerder van kennis, nie 'n oordraer daarvan nie. Belangrike aspekte van leer wat leerders se tweedetaalleer be'invloed, is bespreek, byvoorbeeld klaskamerkommunikasie, fasilitering, suggestopedia, faktore wat begrip van leerstof be'invloed, onderwyser - en leerdergedragswyses, positiewe /eeratmosfeer, behandeling van leerderfoute, Jeerderpersepsies, kommunikatiewe strategiee en evalueringsmetodes. 'n Verskeidenheid taallesse wat op T2-Afrikaans en die T2-taalklaskamer betrekking het, en wat verskillende onderrigteoriee, uitkomsgebaseerde onderrig en die ses taalvaardighede integreer, word in hoofstuk 5 ge'illustreer.
In this study it is shown why the discovery model of language learning is the appropriate model for effective language learning. The communicative teaching approach, classroom-negotiation and the importance of the process syllabus in second language acquisition is discussed. The language teacher's role as facilitator of learning, in communicative L2 - Afrikaans language teaching in the interactive classroom with a learner-centered focus is explained. It is further shown that the role of the teacher must undergo a paradigm shift especially now that principles of outcomes based education which forms part of curriculum 2005 has been introduced into all schools in South Africa in 1998/1999. The teacher is now a facilitator of knowledge and not a transmitter thereof. Important aspects of learning that influence learners' second language learning are discussed, for example classroom communication, facilitation, suggestopedia, factors that influence the understanding of subject matter, teacher and learner behaviours, positive learning atmosphere, treatment of Ieamer errors, learner perceptions, communicative strategies and methods of evaluation. A variety of language lessons which integrate various teaching theories, outcomes based education and the six language learning skills which are related to L 2-Afrikaans and the L 2-classroom are illustrated in Chapter 5.
Afrikaans and Theory of Literature
M.A. (Afrikaans)

The diploma thesis deals with problems of depression in older people. Mainly the work is focused on identifying and analyzing the role of nurses in screening for depression in older people in primary care, acute care, long-term care and home care. This thesis was focused on theoretical direction and was used the method of design and demonstration. In this thesis was set one main goals with five research questions. The main goal was to identify and analyze the role of nurses in screening for depression in the elderly. RQ 1: What is the role of the nurse in screening for depression in the elderly? RQ 2: What is the role of the nurse in the primary care in screening for depression in the elderly? RQ 3: What is the role of the nurse in screening for depression in hospitalized patients in acute care? RQ 4: What is the role of the nurse in screening for depression in seniors in long-term and home care? RQ 5: What rating scales and methods are used in screening for depression in the elderly? The thesis introduce the concept of depression. The following are specified the causes of and the important factors that affect depression in the elderly. It also deals the differences in the clinical symptomatology of depression in old age. It explains possibilities and various barriers in the diagnosis of depression. Another chapter introduces complete geriatric examination, diagnostic classification systems, possible screening methods and scales for detection of depression in the elderly population. It also deals methods of pharmacological and non-pharmacological treatment and its possible complications associated with older age. By reason of increased suicide rate caused by depressive disorder the issue of suicidal behavior in the elderly is introduced. The next chapter deals with the nursing process, which is used by nurses in practice. It consists of the evaluation of the patient's health condition, making nursing diagnosis, creating nursing plan and subsequent implementation and evaluation. The nursing process is also needy for providing quality care. The nursing process in the stage of nursing diagnosis, introduces possible nursing diagnosis for a patient suffering from depression, which are based on the latest classification. Finally is described the role of nurses in screening for depression in the elderly in different health facilities and their contribution to the timely evaluation of depression in the elderly. This chapter introduces the role of nurses, nursing screening and collaboration with a physician. The role of nurses in screening for depression in different medical facilities is based on the first phase of the nursing process of assessment. On the basis of objective and subjective information, the nurse will assess the overall health and mental condition of the patient. Primarily, it was investigated what is the role of the nurse in screening for depression. On the basis of content analysis and synthesis it was necessary to used and processed domestic and foreign literature. A number of relevant sources are the results of various studies and Meta-analyzes, mostly from abroad, but also from the Czech Republic. The thesis can serve as a basis for nurses. The result of this thesis is to create e-learning material available for students in the Faculty of Health and Social Sciences of South Bohemia in Ceske Budejovice in the tutorial called Moodle.