University of Dundee

About: University of Dundee is a education organization based out in Dundee, United Kingdom. It is known for research contribution in the topics: Population & Protein kinase A. The organization has 19258 authors who have published 39640 publications receiving 1919433 citations. The organization is also known as: Universitas Dundensis & Dundee University.

Disorders of attention and perception in early schizophrenia.

Abstract: This chapter traces the development of a hypothesis relating to early schizophrenia and outlines a proposed method of investigation to test the validity of the theory advanced. The basic pathological breakdown was observed to lie in an impairment of ego functions, particularly in the process of perception. A comparison of the chronic schizophrenic patient’s behaviour with that of the young child substantiated this view that the basic disturbance in schizophrenia was a cognitive one which caused the patient to operate at a perceptual level comparable to the primitive and unorganized processes characteristic of infancy and childhood. The most useful arrangement of categories was found to include the processes of attention, perception, motility, and thinking. The interview material was analysed by breaking it up into a number of separate statements, each representing the patient’s description of a symptomatic alteration in his experience.

Essential elements of questionnaire design and development.

Abstract: Aims The aims of this paper were (1) to raise awareness of the issues in questionnaire development and subsequent psychometric evaluation, and (2) to provide strategies to enable nurse researchers to design and develop their own measure and evaluate the quality of existing nursing measures. Background The number of questionnaires developed by nurses has increased in recent years. While the rigour applied to the questionnaire development process may be improving, we know that nurses are still not generally adept at the psychometric evaluation of new measures. This paper explores the process by which a reliable and valid questionnaire can be developed. Methods We critically evaluate the theoretical and methodological issues associated with questionnaire design and development and present a series of heuristic decision-making strategies at each stage of such development. The range of available scales is presented and we discuss strategies to enable item generation and development. The importance of stating a priori the number of factors expected in a prototypic measure is emphasized. Issues of reliability and validity are explored using item analysis and exploratory factor analysis and illustrated using examples from recent nursing research literature. Conclusion Questionnaire design and development must be supported by a logical, systematic and structured approach. To aid this process we present a framework that supports this and suggest strategies to demonstrate the reliability and validity of the new and developing measure. Relevance to clinical practice In developing the evidence base of nursing practice using this method of data collection, it is vital that questionnaire design incorporates preplanned methods to establish reliability and validity. Failure to develop a questionnaire sufficiently may lead to difficulty interpreting results, and this may impact upon clinical or educational practice. This paper presents a critical evaluation of the questionnaire design and development process and demonstrates good practice at each stage of this process.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Quantifying the chemical beauty of drugs

Abstract: Drug-likeness is a key consideration when selecting compounds during the early stages of drug discovery. However, evaluation of drug-likeness in absolute terms does not reflect adequately the whole spectrum of compound quality. More worryingly, widely used rules may inadvertently foster undesirable molecular property inflation as they permit the encroachment of rule-compliant compounds towards their boundaries. We propose a measure of drug-likeness based on the concept of desirability called the quantitative estimate of drug-likeness (QED). The empirical rationale of QED reflects the underlying distribution of molecular properties. QED is intuitive, transparent, straightforward to implement in many practical settings and allows compounds to be ranked by their relative merit. We extended the utility of QED by applying it to the problem of molecular target druggability assessment by prioritizing a large set of published bioactive compounds. The measure may also capture the abstract notion of aesthetics in medicinal chemistry.