8 research outputs found

    Visualizing genetic transmission patterns in plant pedigrees.

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    Ensuring food security in a world with an increasing population and demand on natural resources is becoming ever more pertinent. Plant breeders are using an increasingly diverse range of data types such as phenotypic and genotypic data to identify plant lines with desirable characteristics suitable to be taken forward in plant breeding programmes. These characteristics include a number of key morphological and physiological traits, such as disease resistance and yield that need to be maintained and improved upon if a commercial plant variety is to be successful.The ability to predict and understand the inheritance of alleles that facilitate resistance to pathogens or any other commercially important characteristic is crucially important to experimental plant genetics and commercial plant breeding programmes. However, derivation of the inheritance of such traits by traditional molecular techniques is expensive and time consuming, even with recent developments in high-throughput technologies. This is especially true in industrial settings where, due to time constraints relating to growing seasons, many thousands of plant lines may need to be screened quickly, efficiently and economically every year. Thus, computational tools that provide the ability to integrate and visualize diverse data types with an associated plant pedigree structure will enable breeders to make more informed and subsequently better decisions on the plant lines that are used in crossings. This will help meet both the demands for increased yield and production and adaptation to climate change.Traditional family tree style layouts are commonly used and simple to understand but are unsuitable for the data densities that are now commonplace in large breeding programmes. The size and complexity of plant pedigrees means that there is a cognitive limitation in conceptualising large plant pedigree structures, therefore novel techniques and tools are required by geneticists and plant breeders to improve pedigree comprehension.Taking a user-centred, iterative approach to design, a pedigree visualization system was developed for exploring a large and unique set of experimental barley (H. vulgare) data. This work progressed from the development of a static pedigree visualization to interactive prototypes and finally the Helium pedigree visualization software. At each stage of the development process, user feedback in the form of informal and more structured user evaluation from domain experts guided the development lifecycle with users’ concerns addressed and additional functionality added.Plant pedigrees are very different to those from humans and farmed animals and consequently the development of the pedigree visualizations described in this work focussed on implementing currently accepted techniques used in pedigree visualization and adapting them to meet the specific demands of plant pedigrees. Helium includes techniques to aid problems with user understanding identified through user testing; examples of these include difficulties where crosses between varieties are situated in different regions of the pedigree layout. There are good biological reasons why this happens but it has been shown, through testing, that it leads to problems with users’ comprehension of the relatedness of individuals in the pedigree. The inclusion of visual cues and the use of localised layouts have allowed complications like these to be reduced. Other examples include the use of sizing of nodes to show the frequency of usage of specific plant lines which have been shown to act as positional reference points to users, and subsequently bringing a secondary level of structure to the pedigree layout. The use of these novel techniques has allowed the classification of three main types of plant line, which have been coined: principal, flanking and terminal plant lines. This technique has also shown visually the most frequently used plant lines, which while previously known in text records, were never quantified.Helium’s main contributions are two-fold. Firstly it has applied visualization techniques used in traditional pedigrees and applied them to the domain of plant pedigrees; this has addressed problems with handling large experimental plant pedigrees. The scale, complexity and diversity of data and the number of plant lines that Helium can handle exceed other currently available plant pedigree visualization tools. These techniques (including layout, phenotypic and genotypic encoding) have been improved to deal with the differences that exist between human/mammalian pedigrees which take account of problems such as the complexity of crosses and routine inbreeding. Secondly, the verification of the effectiveness of the visualizations has been demonstrated by performing user testing on a group of 28 domain experts. The improvements have advanced both user understanding of pedigrees and allowed a much greater density and scale of data to be visualized. User testing has shown that the implementation and extensions to visualization techniques has improved user comprehension of plant pedigrees when asked to perform real-life tasks with barley datasets. Results have shown an increase in correct responses between the prototype interface and Helium. A SUS analysis has sown a high acceptance rate for Helium

    Visualizing genetic transmission patterns in plant pedigrees.

    Get PDF
    Ensuring food security in a world with an increasing population and demand on natural resources is becoming ever more pertinent. Plant breeders are using an increasingly diverse range of data types such as phenotypic and genotypic data to identify plant lines with desirable characteristics suitable to be taken forward in plant breeding programmes. These characteristics include a number of key morphological and physiological traits, such as disease resistance and yield that need to be maintained and improved upon if a commercial plant variety is to be successful.The ability to predict and understand the inheritance of alleles that facilitate resistance to pathogens or any other commercially important characteristic is crucially important to experimental plant genetics and commercial plant breeding programmes. However, derivation of the inheritance of such traits by traditional molecular techniques is expensive and time consuming, even with recent developments in high-throughput technologies. This is especially true in industrial settings where, due to time constraints relating to growing seasons, many thousands of plant lines may need to be screened quickly, efficiently and economically every year. Thus, computational tools that provide the ability to integrate and visualize diverse data types with an associated plant pedigree structure will enable breeders to make more informed and subsequently better decisions on the plant lines that are used in crossings. This will help meet both the demands for increased yield and production and adaptation to climate change.Traditional family tree style layouts are commonly used and simple to understand but are unsuitable for the data densities that are now commonplace in large breeding programmes. The size and complexity of plant pedigrees means that there is a cognitive limitation in conceptualising large plant pedigree structures, therefore novel techniques and tools are required by geneticists and plant breeders to improve pedigree comprehension.Taking a user-centred, iterative approach to design, a pedigree visualization system was developed for exploring a large and unique set of experimental barley (H. vulgare) data. This work progressed from the development of a static pedigree visualization to interactive prototypes and finally the Helium pedigree visualization software. At each stage of the development process, user feedback in the form of informal and more structured user evaluation from domain experts guided the development lifecycle with users’ concerns addressed and additional functionality added.Plant pedigrees are very different to those from humans and farmed animals and consequently the development of the pedigree visualizations described in this work focussed on implementing currently accepted techniques used in pedigree visualization and adapting them to meet the specific demands of plant pedigrees. Helium includes techniques to aid problems with user understanding identified through user testing; examples of these include difficulties where crosses between varieties are situated in different regions of the pedigree layout. There are good biological reasons why this happens but it has been shown, through testing, that it leads to problems with users’ comprehension of the relatedness of individuals in the pedigree. The inclusion of visual cues and the use of localised layouts have allowed complications like these to be reduced. Other examples include the use of sizing of nodes to show the frequency of usage of specific plant lines which have been shown to act as positional reference points to users, and subsequently bringing a secondary level of structure to the pedigree layout. The use of these novel techniques has allowed the classification of three main types of plant line, which have been coined: principal, flanking and terminal plant lines. This technique has also shown visually the most frequently used plant lines, which while previously known in text records, were never quantified.Helium’s main contributions are two-fold. Firstly it has applied visualization techniques used in traditional pedigrees and applied them to the domain of plant pedigrees; this has addressed problems with handling large experimental plant pedigrees. The scale, complexity and diversity of data and the number of plant lines that Helium can handle exceed other currently available plant pedigree visualization tools. These techniques (including layout, phenotypic and genotypic encoding) have been improved to deal with the differences that exist between human/mammalian pedigrees which take account of problems such as the complexity of crosses and routine inbreeding. Secondly, the verification of the effectiveness of the visualizations has been demonstrated by performing user testing on a group of 28 domain experts. The improvements have advanced both user understanding of pedigrees and allowed a much greater density and scale of data to be visualized. User testing has shown that the implementation and extensions to visualization techniques has improved user comprehension of plant pedigrees when asked to perform real-life tasks with barley datasets. Results have shown an increase in correct responses between the prototype interface and Helium. A SUS analysis has sown a high acceptance rate for Helium

    Steps to an Ecology of Networked Knowledge and Innovation: Enabling new forms of collaboration among sciences, engineering, arts, and design

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    SEAD network White Papers ReportThe final White Papers (posted at http://seadnetwork.wordpress.com/white-paper- abstracts/final-white-papers/) represent a spectrum of interests in advocating for transdisciplinarity among arts, sciences, and technologies. All authors submitted plans of action and identified stakeholders they perceived as instrumental in carrying out such plans. The individual efforts led to an international scope. One of the important characteristics of this collection is that the papers do not represent a collective aim toward an explicit initiative. Rather, they offer a broad array of views on barriers faced and prospective solutions. In summary, the collected White Papers and associated Meta- analyses began as an effort to take the pulse of the SEAD community as broadly as possible. The ideas they generated provide a fruitful basis for gauging trends and challenges in facilitating the growth of the network and implementing future SEAD initiatives.National Science Foundation Grant No.1142510. Additional funding was provided by the ATEC program at the University of Texas at Dallas and the Institute for Applied Creativity at Texas A&M University

    TRANSLATING VISUALIZATION INTERACTION INTO NATURAL LANGUAGE

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    Richly interactive visualization tools are increasingly popular for data exploration and analysis in a wide variety of domains. Recent advancements in data collection and storage call for more complex analytical tasks to make sense of readily available datasets. More complicated and sophisticated tools are needed to complete those tasks. However, as these visualization tools get more complicated, it becomes increasingly difficult to learn interaction sequences, recall past queries asked from a visualization, and correctly interpret visual states to forage the data. Moreover, the high interactivity of such tools increases the challenge of connecting low-level acquired information to higher-level analytical questions and hypotheses to support, reason, and eventually present insights. This makes studying the usability of complex interactive visualizations, both in the process of foraging and making sense of data, an essential part of visual analytic research. This research can be approached in at least two major ways. One can focus on studying new techniques and guidelines for designing interactive complex visualizations that are easy to use and understand. One can also focus on keeping the capabilities of existing complex visualizations, yet provide supporting capabilities that increases their usability. The latter is an emerging area of research in visual analytics, and is the focus of this dissertation. This dissertation describes six contributions to the field of visual analytics. The first contribution is an architecture of a query-to-question supporting system that automatically records user interactions and presents them contextually using natural written language. The architecture takes into account the domain knowledge of experts/designers and uses natural language generation (NLG) techniques to translate and transcribe a progression of interactive visualization states into a log of text that can be visualized. The second contribution is query-to-question (Q2Q), an implemented system that translates low-level user interactions into high-level analytical questions and presents them as a log of styled text that complements and effectively extends the functionality of visualization tools. The third contribution is a demonstration of the beneficial effects of accompanying a visualization with a textual translation of user interaction on the usability of visualizations. The presence of the translation interface produces considerable improvements in learnability, efficiency, and memorability of visualization in terms of speed and the length of interaction sequences that users perform, along with a modest decrease in error ratio. The fourth contribution is a set of design guidelines for translating user interactions into natural language, taking into account variation in user knowledge and roles, the types of data being visualized, and the types of interaction supported. The fifth contribution is a history organizer interface that enables users to organize their analytical process. The structured textual translations output from Q2Q are input into a history organizer tool (HOT) that imposes reordering, sequencing, and grouping of the translated interactions. HOT provides a reasoning framework for users to organize and present hypotheses and insight acquired from a visualization. The sixth contribution is a demonstration of the efficiency of a suite of arrangement options for organizing questions asked in a visualization. Integration of query translation and history organization improves users' speed, error ratio, and number of reordering actions performed during organization of translated interactions. Overall, this dissertation contributes to the analysis and discovery of user storytelling patterns and behaviours, thereby paving the way to the creation of more intelligent, effective, and user-oriented visual analysis presentation tools
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