An Introduction to Programming for Bioscientists: A Python-based Primer

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in the biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a 'variable', the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables, numerous exercises, and 19 pages of Supporting Information; currently in press at PLOS Computational Biolog

Computer usability : interactive challenges faced by less experienced computer users in South Africa

Abstract:The growing use of applications and access to the internet has increased the number of average computer users in South Africa, as people are using applications like WhatsApp, Facebook, Twitter, Instagram, and more. The goal of the study is to identify the challenges that most South African people face when they are interacting with computer applications, web applications, and mobile applications. The reason for conducting the study is that lately in South Africa we have seen an increase in South Africans who have access to computer systems, such as the use of smartphones, tablets or iPads, game consoles, and laptops. Most of the people who are using these devices or have access to them still face challenges as to how to use these devices or to use some of the applications that come with these devices. The paper will begin by introducing the topic. The paper will be followed by a literature review section, which will include four topics relating to the topic that helped the researcher to build a foundation for the research topic and to get ideas on how to do the research. The paper will then be followed by the research methodology, and the findings of, and discussions flowing from the study will then follow

Visual link retrieval and knowledge discovery in painting datasets

Visual arts have invaluable importance for the cultural, historic and economic growth of our societies. One of the building blocks of most analysis in visual arts is to find similarities among paintings of different artists and painting schools. To help art historians better understand visual arts, the present paper presents a framework for visual link retrieval and knowledge discovery in digital painting datasets. The proposed framework is based on a deep convolutional neural network to perform feature extraction and on a fully unsupervised nearest neighbor approach to retrieve visual links among digitized paintings. The fully unsupervised strategy makes attractive the proposed method especially in those cases where metadata are either scarce or unavailable or difficult to collect. In addition, the proposed framework includes a graph analysis that makes it possible to study influences among artists, thus providing historical knowledge discovery.Comment: submitted to Multimedia Tools and Application

Information Quality in Secondary Use of EHR Data : A Case Study of Quality Management in a Norwegian Hospital

The motivation for undertaking this study relates to my experiences from practice in a public hospital, where I have observed variations in reaching organizational goals of quality management informed by electronic health records (EHR) data. For example, while some departments and units have long-time traditions in meeting the quality goals that are set locally, regionally, or nationally, other departments and units struggle to meet the same quality goals. Thus, generating actionable information by reusing routinely collected EHR data does not necessary lead to action in response to the information. This process of generating information from existing EHR data, and communicating and using such information for organizational purposes, may be challenging in a highly complex environment such as health care organizations. Within this process, information quality (IQ) may influence actors’ perceptions of action possibilities the information offers, thus influencing the actual use of the information required to reach organizational goals. EHR data can be used for clinical purposes at the point-of-care (i.e., primary use) and reused for purposes that do not involve patient treatment directly (i.e., secondary use). Examples of such secondary use includes quality management, research, and policy development. Though it is widely accepted that IQ influences the use of EHR systems and the information generated by EHR systems, research on the implications of IQ on health care processes is limited: the focus of the current literature is concerned with defining and assessing IQ in primary use of EHR data, whereas the role of IQ in secondary use of EHR data remains unclear. Thus, this dissertation investigates the role of IQ in secondary use of EHR data in an organizational context. This dissertation addresses this practical and theoretical challenge by focusing on the overall research objective of understanding the role of IQ in secondary use of EHR data. To address this research objective, this dissertation explores the following research questions: RQ1. How do human actors influence in transformation of IQ while generating, communicating, and using information in secondary use of EHR data? RQ2. What are the underlying generative mechanisms through which IQ transforms in the process of secondary use of EHR data?publishedVersio

Functional models and extending strategies for ecological networks

Complex network analysis is rising as an essential tool to understand properties of ecological landscape networks, and as an aid to land management. The most common methods to build graph models of ecological networks are based on representing functional connectivity with respect to a target species. This has provided good results, but the lack of a model able to capture general properties of the network may be seen as a shortcoming when the activity involves the proposal for modifications in land use. Similarity scores, calculated between nature protection areas, may act as a building block for a graph model intended to carry a higher degree of generality. The present work compares several design choices for similarity-based graphs, in order to determine which is most suitable for use in land management