Oversized Materials, Box 1 1873, 1876, 1880

Oversized materials is the digitized contents of one box (OS1) that consists of correspondence and an address from Box 2, Folders 12, 13 and 17.

Mining Sequential Data – in Search of Segmental Structures

Segmentation is a data mining technique yielding simplified representations of sequences of ordered points. A sequence is divided into some number of homogeneous blocks, and all points within a segment are described by a single value. The focus in this thesis is on piecewise-constant segments, where the most likely description for each segment and the most likely segmentation into some number of blocks can be computed efficiently. Representing sequences as segmentations is useful in, e.g., storage and indexing tasks in sequence databases, and segmentation can be used as a tool in learning about the structure of a given sequence. The discussion in this thesis begins with basic questions related to segmentation analysis, such as choosing the number of segments, and evaluating the obtained segmentations. Standard model selection techniques are shown to perform well for the sequence segmentation task. Segmentation evaluation is proposed with respect to a known segmentation structure. Applying segmentation on certain features of a sequence is shown to yield segmentations that are significantly close to the known underlying structure. Two extensions to the basic segmentation framework are introduced: unimodal segmentation and basis segmentation. The former is concerned with segmentations where the segment descriptions first increase and then decrease, and the latter with the interplay between different dimensions and segments in the sequence. These problems are formally defined and algorithms for solving them are provided and analyzed. Practical applications for segmentation techniques include time series and data stream analysis, text analysis, and biological sequence analysis. In this thesis segmentation applications are demonstrated in analyzing genomic sequences.

The effectiveness of the teach-back method on adherence and self-management in health education for people with chronic disease: A systematic review

- BACKGROUND Chronic diseases are increasing worldwide and have become a significant burden to those affected by those diseases. Disease-specific education programs have demonstrated improved outcomes, although people do forget information quickly or memorize it incorrectly. The teach-back method was introduced in an attempt to reinforce education to patients. To date, the evidence regarding the effectiveness of health education employing the teach-back method in improved care has not yet been reviewed systematically. - OBJECTIVES This systematic review examined the evidence on using the teach-back method in health education programs for improving adherence and self-management of people with chronic disease. - INCLUSION CRITERIA Types of participants: Adults aged 18 years and over with one or more than one chronic disease. Types of intervention: All types of interventions which included the teach-back method in an education program for people with chronic diseases. The comparator was chronic disease education programs that did not involve the teach-back method. Types of studies: Randomized and non-randomized controlled trials, cohort studies, before-after studies and case-control studies. Types of outcomes: The outcomes of interest were adherence, self-management, disease-specific knowledge, readmission, knowledge retention, self-efficacy and quality of life. - SEARCH STRATEGY Searches were conducted in CINAHL, MEDLINE, EMBASE, Cochrane CENTRAL, Web of Science, ProQuest Nursing and Allied Health Source, and Google Scholar databases. Search terms were combined by AND or OR in search strings. Reference lists of included articles were also searched for further potential references. - METHODOLOGICAL QUALITY Two reviewers conducted quality appraisal of papers using the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument. - DATA EXTRACTION Data were extracted using the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument data extraction instruments. - DATA SYNTHESIS There was significant heterogeneity in selected studies, hence a meta-analysis was not possible and the results were presented in narrative form. - RESULTS Of the 21 articles retrieved in full, 12 on the use of the teach-back method met the inclusion criteria and were selected for analysis. Four studies confirmed improved disease-specific knowledge in intervention participants. One study showed a statistically significant improvement in adherence to medication and diet among type 2 diabetics patients in the intervention group compared to the control group (p < 0.001). Two studies found statistically significant improvements in self-efficacy (p = 0.0026 and p < 0.001) in the intervention groups. One study examined quality of life in heart failure patients but the results did not improve from the intervention (p = 0.59). Five studies found a reduction in readmission rates and hospitalization but these were not always statistically significant. Two studies showed improvement in daily weighing among heart failure participants, and in adherence to diet, exercise and foot care among those with type 2 diabetes. - CONCLUSION Overall, the teach-back method showed positive effects in a wide range of health care outcomes although these were not always statistically significant. Studies in this systematic review revealed improved outcomes in disease-specific knowledge, adherence, self-efficacy and the inhaler technique. There was a positive but inconsistent trend also seen in improved self-care and reduction of hospital readmission rates. There was limited evidence on improvement in quality of life or disease related knowledge retention.

Methods for Answer Extraction in Textual Question Answering

In this thesis we present and evaluate two pattern matching based methods for answer extraction in textual question answering systems. A textual question answering system is a system that seeks answers to natural language questions from unstructured text. Textual question answering systems are an important research problem because as the amount of natural language text in digital format grows all the time, the need for novel methods for pinpointing important knowledge from the vast textual databases becomes more and more urgent. We concentrate on developing methods for the automatic creation of answer extraction patterns. A new type of extraction pattern is developed also. The pattern matching based approach chosen is interesting because of its language and application independence. The answer extraction methods are developed in the framework of our own question answering system. Publicly available datasets in English are used as training and evaluation data for the methods. The techniques developed are based on the well known methods of sequence alignment and hierarchical clustering. The similarity metric used is based on edit distance. The main conclusions of the research are that answer extraction patterns consisting of the most important words of the question and of the following information extracted from the answer context: plain words, part-of-speech tags, punctuation marks and capitalization patterns, can be used in the answer extraction module of a question answering system. This type of patterns and the two new methods for generating answer extraction patterns provide average results when compared to those produced by other systems using the same dataset. However, most answer extraction methods in the question answering systems tested with the same dataset are both hand crafted and based on a system-specific and fine-grained question classification. The the new methods developed in this thesis require no manual creation of answer extraction patterns. As a source of knowledge, they require a dataset of sample questions and answers, as well as a set of text documents that contain answers to most of the questions. The question classification used in the training data is a standard one and provided already in the publicly available data.

A data fusion approach of multiple maintenance data sources for real-world reliability modelling

A central tenet in the theory of reliability modelling is the quantification of the probability of asset failure. In general, reliability depends on asset age and the maintenance policy applied. Usually, failure and maintenance times are the primary inputs to reliability models. However, for many organisations, different aspects of these data are often recorded in different databases (e.g. work order notifications, event logs, condition monitoring data, and process control data). These recorded data cannot be interpreted individually, since they typically do not have all the information necessary to ascertain failure and preventive maintenance times. This paper presents a methodology for the extraction of failure and preventive maintenance times using commonly-available, real-world data sources. A text-mining approach is employed to extract keywords indicative of the source of the maintenance event. Using these keywords, a Naïve Bayes classifier is then applied to attribute each machine stoppage to one of two classes: failure or preventive. The accuracy of the algorithm is assessed and the classified failure time data are then presented. The applicability of the methodology is demonstrated on a maintenance data set from an Australian electricity company.

SorGSD: a sorghum genome SNP database

Sorghum (Sorghum bicolor) is one of the most important cereal crops globally and a potential energy plant for biofuel production. In order to explore genetic gain for a range of important quantitative traits, such as drought and heat tolerance, grain yield, stem sugar accumulation, and biomass production, via the use of molecular breeding and genomic selection strategies, knowledge of the available genetic variation and the underlying sequence polymorphisms, is required.

Computational Identification of Recessive Mutations in Cancers using High Throughput SNP-arrays

This thesis presents a highly sensitive genome wide search method for recessive mutations. The method is suitable for distantly related samples that are divided into phenotype positives and negatives. High throughput genotype arrays are used to identify and compare homozygous regions between the cohorts. The method is demonstrated by comparing colorectal cancer patients against unaffected references. The objective is to find homozygous regions and alleles that are more common in cancer patients. We have designed and implemented software tools to automate the data analysis from genotypes to lists of candidate genes and to their properties. The programs have been designed in respect to a pipeline architecture that allows their integration to other programs such as biological databases and copy number analysis tools. The integration of the tools is crucial as the genome wide analysis of the cohort differences produces many candidate regions not related to the studied phenotype. CohortComparator is a genotype comparison tool that detects homozygous regions and compares their loci and allele constitutions between two sets of samples. The data is visualised in chromosome specific graphs illustrating the homozygous regions and alleles of each sample. The genomic regions that may harbour recessive mutations are emphasised with different colours and a scoring scheme is given for these regions. The detection of homozygous regions, cohort comparisons and result annotations are all subjected to presumptions many of which have been parameterized in our programs. The effect of these parameters and the suitable scope of the methods have been evaluated. Samples with different resolutions can be balanced with the genotype estimates of their haplotypes and they can be used within the same study.

A systematic review of instruments for assessment of capacity in activities of daily living in children with developmental co-ordination disorder

Children with developmental co-ordination disorder (DCD) face evident motor difficulties in activities of daily living (ADL). Assessment of their capacity in ADL is essential for diagnosis and intervention, in order to limit the daily consequences of the disorder. The aim of this study is to systematically review potential instruments for standardized and objective assessment of children's capacity in ADL, suited for children with DCD. As a first step, databases of MEDLINE, EMBASE, CINAHL and PsycINFO were searched to identify studies that described instruments with potential for assessment of capacity in ADL. Second, instruments were included for review when two independent reviewers agreed that the instruments: (1) are standardized and objective; (2) assess at activity level and comprise items that reflect ADL, and; (3) are applicable to school-aged children that can move independently. Out of 1507 publications, 66 publications were selected, describing 39 instruments. Seven of these instruments were found to fulfil the criteria and were included for review: the Bruininks-Oseretsky Test of Motor Performance-2 (BOT2); the Do-Eat (Do-Eat); the Movement Assessment Battery for Children-2 (MABC2); the school-Assessment of Motor and Process Skills (schoolAMPS); the Tuffts Assessment of Motor Performance (TAMP); the Test of Gross Motor Development (TGMD); and the Functional Independence Measure for Children (WeeFIM). As a third step, for the included instruments, suitability for children with DCD was discussed based on the ADL comprised, ecological validity and other psychometric properties. We concluded that current instruments do not provide comprehensive and ecologically valid assessment of capacity in ADL as required for children with DCD.

Family-based interventions in preventing children and adolescents from using tobacco: A systematic review and meta-analysis

- Background Tobacco is the main preventable cause of death and disease worldwide. Adolescent smoking is increasing in many countries with poorer countries following the earlier experiences of affluent countries. Preventing adolescents starting smoking is crucial to decreasing tobacco-related illness. - Objective To assess effectiveness of family-based interventions alone and combined with school-based interventions to prevent children and adolescents from initiating tobacco use. - Data Sources 14 bibliographic databases and the Internet, journals hand-searched, experts consulted. - Study Eligibility Criteria, Participants, and Interventions Randomised controlled trials (RCTs) with children or adolescents and families, interventions to prevent starting tobacco use, follow-up ≥ 6 months. - Study Appraisal/Synthesis methods Abstracts/titles independently assessed and data independently entered by two authors. Risk-of-bias assessed with the Cochrane Risk-of-Bias tool. - Results Twenty-seven RCTs were included. Nine trials of never-smokers compared to a control provided data for meta-analysis. Family intervention trials had significantly fewer students who started smoking. Meta-analysis of twoRCTs of combined family and school interventions compared to school only, showed additional significant benefit. The common feature of effective high intensity interventions was encouraging authoritative parenting. - Limitations Only 14 RCTs provided data for meta-analysis (about 1/3 of participants). Of the 13 RCTs which did not provide data for meta-analysis eight compared a family intervention to no intervention and one found significant effects, and five compared a family + school intervention to a school intervention and none found additional significant effects. - Conclusions and Implications of Key Findings There is moderate quality evidence that family-based interventions prevent children and adolescents starting to smoke.

Co-distribution and co-infection of chikungunya and dengue viruses

BACKGROUND Chikungunya and dengue infections are spatio-temporally related. The current review aims to determine the geographic limits of chikungunya, dengue and the principal mosquito vectors for both viruses and to synthesise current epidemiological understanding of their co-distribution. METHODS Three biomedical databases (PubMed, Scopus and Web of Science) were searched from their inception until May 2015 for studies that reported concurrent detection of chikungunya and dengue viruses in the same patient. Additionally, data from WHO, CDC and Healthmap alerts were extracted to create up-to-date global distribution maps for both dengue and chikungunya. RESULTS Evidence for chikungunya-dengue co-infection has been found in Angola, Gabon, India, Madagascar, Malaysia, Myanmar, Nigeria, Saint Martin, Singapore, Sri Lanka, Tanzania, Thailand and Yemen; these constitute only 13 out of the 98 countries/territories where both chikungunya and dengue epidemic/endemic transmission have been reported. CONCLUSIONS Understanding the true extent of chikungunya-dengue co-infection is hampered by current diagnosis largely based on their similar symptoms. Heightened awareness of chikungunya among the public and public health practitioners in the advent of the ongoing outbreak in the Americas can be expected to improve diagnostic rigour. Maps generated from the newly compiled lists of the geographic distribution of both pathogens and vectors represent the current geographical limits of chikungunya and dengue, as well as the countries/territories at risk of future incursion by both viruses. These describe regions of co-endemicity in which lab-based diagnosis of suspected cases is of higher priority.

Computational methods for the prediction of the structure and interactions of coiled-coil peptides

The past several years have seen significant advances in the development of computational methods for the prediction of the structure and interactions of coiled-coil peptides. These methods are generally based on pairwise correlations of amino acids, helical propensity, thermal melts and the energetics of sidechain interactions, as well as statistical patterns based on Hidden Markov Model (HMM) and Support Vector Machine (SVM) techniques. These methods are complemented by a number of public databases that contain sequences, motifs, domains and other details of coiled-coil structures identified by various algorithms. Some of these computational methods have been developed to make predictions of coiled-coil structure on the basis of sequence information; however, structural predictions of the oligomerisation state of these peptides still remains largely an open question due to the dynamic behaviour of these molecules. This review focuses on existing in silico methods for the prediction of coiled-coil peptides of functional importance using sequence and/or three-dimensional structural data.

Aerobic carbon-cycle related microbial communities in boreal peatlands: responses to water-level drawdown

Boreal peatlands represent a considerable portion of the global carbon (C) pool. Water-level drawdown (WLD) causes peatland drying and induces a vegetation change, which affects the decomposition of soil organic matter and the release of greenhouse gases (CO2 and CH4). The objective of this thesis was to study the microbial communities related to the C cycle and their response to WLD in two boreal peatlands. Both sampling depth and site type had a strong impact on all microbial communities. In general, bacteria dominated the deeper layers of the nutrient-rich fen and the wettest surfaces of the nutrient-poor bog sites, whereas fungi seemed more abundant in the drier surfaces of the bog. WLD clearly affected the microbial communities but the effect was dependent on site type. The fungal and methane-oxidizing bacteria (MOB) community composition changed at all sites but the actinobacterial community response was apparent only in the fen after WLD. Microbial communities became more similar among sites after long-term WLD. Litter quality had a large impact on community composition, whereas the effects of site type and WLD were relatively minor. The decomposition rate of fresh organic matter was influenced slightly by actinobacteria, but not at all by fungi. Field respiration measurements in the northern fen indicated that WLD accelerates the decomposition of soil organic matter. In addition, a correlation between activity and certain fungal sequences indicated that community composition affects the decomposition of older organic matter in deeper peat layers. WLD had a negative impact on CH4 oxidation, especially in the oligotrophic fen. Fungal sequences were matched to taxa capable of utilizing a broad range of substrates. Most of the actinobacterial sequences could not be matched to characterized taxa in reference databases. This thesis represents the first investigation of microbial communities and their response to WLD among a variety of boreal peatland habitats. The results indicate that microbial community responses to WLD are complex but dependent on peatland type, litter quality, depth, and variable among microbes.

Applications of gene sequence polymorphisms in evolutionary genetic studies of Atlantic salmon (Salmo salar) and other teleost fish species

Evolutionary genetics incorporates traditional population genetics and studies of the origins of genetic variation by mutation and recombination, and the molecular evolution of genomes. Among the primary forces that have potential to affect the genetic variation within and among populations, including those that may lead to adaptation and speciation, are genetic drift, gene flow, mutations and natural selection. The main challenges in knowing the genetic basis of evolutionary changes is to distinguish the adaptive selection forces that cause existent DNA sequence variants and also to identify the nucleotide differences responsible for the observed phenotypic variation. To understand the effects of various forces, interpretation of gene sequence variation has been the principal basis of many evolutionary genetic studies. The main aim of this thesis was to assess different forms of teleost gene sequence polymorphisms in evolutionary genetic studies of Atlantic salmon (Salmo salar) and other species. Firstly, the level of Darwinian adaptive evolution affected coding regions of the growth hormone (GH) gene during the teleost evolution was investigated based on the sequence data existing in public databases. Secondly, a target gene approach was used to identify within population variation in the growth hormone 1 (GH1) gene in salmon. Then, a new strategy for single nucleotide polymorphisms (SNPs) discovery in salmonid fishes was introduced, and, finally, the usefulness of a limited number of SNP markers as molecular tools in several applications of population genetics in Atlantic salmon was assessed. This thesis showed that the gene sequences in databases can be utilized to perform comparative studies of molecular evolution, and some putative evidence of the existence of Darwinian selection during the teleost GH evolution was presented. In addition, existent sequence data was exploited to investigate GH1 gene variation within Atlantic salmon populations throughout its range. Purifying selection is suggested to be the predominant evolutionary force controlling the genetic variation of this gene in salmon, and some support for gene flow between continents was also observed. The novel approach to SNP discovery in species with duplicated genome fragments introduced here proved to be an effective method, and this may have several applications in evolutionary genetics with different species - e.g. when developing gene-targeted markers to investigate quantitative genetic variation. The thesis also demonstrated that only a few SNPs performed highly similar signals in some of the population genetic analyses when compared with the microsatellite markers. This may have useful applications when estimating genetic diversity in genes having a potential role in ecological and conservation issues, or when using hard biological samples in genetic studies as SNPs can be applied with relatively highly degraded DNA.

An algorithm to find similar internal sequence repeats

In recent years, identification of sequence patterns has been given immense importance to understand better their significance with respect to genomic organization and evolutionary processes. To this end, an algorithm has been derived to identify all similar sequence repeats present in a protein sequence. The proposed algorithm is useful to correlate the three-dimensional structure of various similar sequence repeats available in the Protein Data Bank against the same sequence repeats present in other databases like SWISS-PROT, PIR and Genome databases.

Elevated circulating sclerostin concentrations in individuals with high bone mass, with and without LRP5 mutations

CONTEXT: The role and importance of circulating sclerostin is poorly understood. High bone mass (HBM) caused by activating LRP5 mutations has been reported to be associated with increased plasma sclerostin concentrations; whether the same applies to HBM due to other causes is unknown. OBJECTIVE: Our objective was to determine circulating sclerostin concentrations in HBM. DESIGN AND PARTICIPANTS: In this case-control study, 406 HBM index cases were identified by screening dual-energy x-ray absorptiometry (DXA) databases from 4 United Kingdom centers (n = 219 088), excluding significant osteoarthritis/artifact. Controls comprised unaffected relatives and spouses. MAIN MEASURES: Plasma sclerostin; lumbar spine L1, total hip, and total body DXA; and radial and tibial peripheral quantitative computed tomography (subgroup only) were evaluated. RESULTS: Sclerostin concentrations were significantly higher in both LRP5 HBM and non-LRP5 HBM cases compared with controls: mean (SD) 130.1 (61.7) and 88.0 (39.3) vs 66.4 (32.3) pmol/L (both P < .001, which persisted after adjustment for a priori confounders). In combined adjusted analyses of cases and controls, sclerostin concentrations were positively related to all bone parameters found to be increased in HBM cases (ie, L1, total hip, and total body DXA bone mineral density and radial/tibial cortical area, cortical bone mineral density, and trabecular density). Although these relationships were broadly equivalent in HBM cases and controls, there was some evidence that associations between sclerostin and trabecular phenotypes were stronger in HBM cases, particularly for radial trabecular density (interaction P < .01). CONCLUSIONS: Circulating plasma sclerostin concentrations are increased in both LRP5 and non-LRP5 HBM compared with controls. In addition to the general positive relationship between sclerostin and DXA/peripheral quantitative computed tomography parameters, genetic factors predisposing to HBM may contribute to increased sclerostin levels.