Pre-service teacher development : a model to develop critical media literacy through computer game play

The primary objective of this study was to investigate the use of game-mediate learning with pre-service teachers, with the view to evaluating the use of a socially mediated knowledge construction to develop appropriate classroom pedagogical practices. Two instrumental case studies are presented in order to explore how pre-service teachers understand the use of computer games in teaching and learning. These cases are part of a collective case study to advance the theory of the use of video games in learning and teaching. Different groups of pre-service teachers participated in the study. The first group included third-year undergraduate education students who played a computer game on the biology of diseases. The second group of participants, postgraduate students reading for their teaching qualification, played computer games designed to address misconceptions related to genetics. The introduction of game puzzles into a learning activity acted as an explicit mediator of learning, and discussions between players implicitly mediated their understanding. Therefore, in a learning context it is argued that computer games as part of a lesson should never be the object of the activity, but should function as a tool that mediates learning outcomes. This approach can be used with any contemporary media that form part of a classroom lesson, to develop critical media literacy

Bioinformatics tools and database resources for systems genetics analysis in mice—a short review and an evaluation of future needs

During a meeting of the SYSGENET working group ‘Bioinformatics’, currently available software tools and databases for systems genetics in mice were reviewed and the needs for future developments discussed. The group evaluated interoperability and performed initial feasibility studies. To aid future compatibility of software and exchange of already developed software modules, a strong recommendation was made by the group to integrate HAPPY and R/qtl analysis toolboxes, GeneNetwork and XGAP database platforms, and TIQS and xQTL processing platforms. R should be used as the principal computer language for QTL data analysis in all platforms and a ‘cloud’ should be used for software dissemination to the community. Furthermore, the working group recommended that all data models and software source code should be made visible in public repositories to allow a coordinated effort on the use of common data structures and file formats

Virtual Laboratories as Preparation to a Practical Laboratory Course at the Example of Genetics

A virtual laboratory is an abstraction of a real laboratory and allows for executing experiments in a computer-based simulation. Goal of virtual laboratories is to train the student’s procedural knowledge that is needed for conducting experiments in a real laboratory environment. Students can train themselves comfortably in a secure environment using the computer and without wasting precious resources such as substances and devices. Different aspects of virtual laboratories in the field of genetics have been evaluated in the past. However, to the best of our knowledge there is so far no evaluation carried out that is investigating the impact of training with a virtual laboratory to the realworld laboratory course. In order to address this gap, we have conducted a comparative study using the photorealistic virtual laboratory GenLab for genetics and genetic engineering. While one group of students (n=18) did receive a training using GenLab prior to real-world laboratory experimentation, the others did not (n=14). We recorded the students’ own assessment of the experiments complexity and comprehensibility. For two experiments, we recorded more detailed information as they were trained using GenLab in the treatment group. In addition, we measured the time needed by the students for conducting experiments in a real laboratory course. The results show that there are some significant differences for the more complex experiment tasks, while this was not observed for the less complex ones. The differences might be explained by the amount of repetitive and rather simpler tasks versus some other tasks that are also repetitive but require higher concentration in order to avoid mistakes. Furthermore, the more complex experiment was reproduced more closely in the virtual lab. This indicates that procedural knowledge is best acquired when the experiment can be reenacted virtually step by step. Overall, working with the virtual lab was perceived positively by the students. Hence, its integration within the curriculum of genetics is considered to be beneficial for the students’ motivation and their preparedness for the real-world lab

Structured Sparse Methods for Imaging Genetics

abstract: Imaging genetics is an emerging and promising technique that investigates how genetic variations affect brain development, structure, and function. By exploiting disorder-related neuroimaging phenotypes, this class of studies provides a novel direction to reveal and understand the complex genetic mechanisms. Oftentimes, imaging genetics studies are challenging due to the relatively small number of subjects but extremely high-dimensionality of both imaging data and genomic data. In this dissertation, I carry on my research on imaging genetics with particular focuses on two tasks---building predictive models between neuroimaging data and genomic data, and identifying disorder-related genetic risk factors through image-based biomarkers. To this end, I consider a suite of structured sparse methods---that can produce interpretable models and are robust to overfitting---for imaging genetics. With carefully-designed sparse-inducing regularizers, different biological priors are incorporated into learning models. More specifically, in the Allen brain image--gene expression study, I adopt an advanced sparse coding approach for image feature extraction and employ a multi-task learning approach for multi-class annotation. Moreover, I propose a label structured-based two-stage learning framework, which utilizes the hierarchical structure among labels, for multi-label annotation. In the Alzheimer's disease neuroimaging initiative (ADNI) imaging genetics study, I employ Lasso together with EDPP (enhanced dual polytope projections) screening rules to fast identify Alzheimer's disease risk SNPs. I also adopt the tree-structured group Lasso with MLFre (multi-layer feature reduction) screening rules to incorporate linkage disequilibrium information into modeling. Moreover, I propose a novel absolute fused Lasso model for ADNI imaging genetics. This method utilizes SNP spatial structure and is robust to the choice of reference alleles of genotype coding. In addition, I propose a two-level structured sparse model that incorporates gene-level networks through a graph penalty into SNP-level model construction. Lastly, I explore a convolutional neural network approach for accurate predicting Alzheimer's disease related imaging phenotypes. Experimental results on real-world imaging genetics applications demonstrate the efficiency and effectiveness of the proposed structured sparse methods.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Computer programmes for some problems in Biometrical Genetics. II. Use of canonical variates in deriving group constellations

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Sequence analysis of the cis-regulatory regions of the bithorax complex of Drosophila

The bithorax complex (BX-C) of Drosophila, one of two complexes that act as master regulators of the body plan of the fly, has now been entirely sequenced and comprises approximate to 315,000 bp, only 1.4% of which codes for protein. Analysis of this sequence reveals significantly overrepresented DNA motifs of unknown, as well as known, functions in the nonprotein-coding portion of the sequence. The following types of motifs in that portion are analyzed: (i) concatamers of mono-, di-, and trinucleotides; (ii) tightly clustered hexanucleotides (spaced less than or equal to 5 bases apart); (iii) direct and reverse repeats longer than 20 bp; and (iv) a number of motifs known from biochemical studies to play a role in the regulation of the BX-C. The hexanucleotide AGATAC is remarkably overrepresented and is surmised to play a role in chromosome pairing. The positions of sites of highly overrepresented motifs are plotted for those that occur at more than five sites in the sequence, when <0.5 case is expected. Expected values are based on a third-order Markov chain, which is the optimal order for representing the BXCALL sequence

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

Perspectives on Case-based Multimedia Web Projects in Science

This article discusses the merits of case-based learning in an interactive online environment. Researchers used both qualitative and quantitative research over a 2-year period to examine the learning that occurred in a high school context when students were engaged in a case-based multimedia project. Part of the Case It! project, students played both the role of laboratory technician performing and presenting research as well as professionals using the information in their practice. Students were required to use three types of simulation software developed exclusively for the Case It! project. Results were measured using both pre- and post-tests, artifacts students created such as Web posters, records of Internet conferences, and interviews from both the students and the teacher involved in this project. Researches found the online format of the lesson fostered a higher level of questioning and problem solving skills, as well as extended explanations and discussions of ethics in science. Educational levels: Graduate or professional