Kristian Kvilekval

The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanitys projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.

Bisque: a platform for bioimage analysis and management

MOTIVATION Advances in the field of microscopy have brought about the need for better image management and analysis solutions. Novel imaging techniques have created vast stores of images and metadata that are difficult to organize, search, process and analyze. These tasks are further complicated by conflicting and proprietary image and metadata formats, that impede analyzing and sharing of images and any associated data. These obstacles have resulted in research resources being locked away in digital media and file cabinets. Current image management systems do not address the pressing needs of researchers who must quantify image data on a regular basis. RESULTS We present Bisque, a web-based platform specifically designed to provide researchers with organizational and quantitative analysis tools for 5D image data. Users can extend Bisque with both data model and analysis extensions in order to adapt the system to local needs. Bisques extensibility stems from two core concepts: flexible metadata facility and an open web-based architecture. Together these empower researchers to create, develop and share novel bioimage analyses. Several case studies using Bisque with specific applications are presented as an indication of how users can expect to extend Bisque for their own purposes.

A biosegmentation benchmark for evaluation of bioimage analysis methods

BackgroundWe present a biosegmentation benchmark that includes infrastructure, datasets with associated ground truth, and validation methods for biological image analysis. The primary motivation for creating this resource comes from the fact that it is very difficult, if not impossible, for an end-user to choose from a wide range of segmentation methods available in the literature for a particular bioimaging problem. No single algorithm is likely to be equally effective on diverse set of images and each method has its own strengths and limitations. We hope that our benchmark resource would be of considerable help to both the bioimaging researchers looking for novel image processing methods and image processing researchers exploring application of their methods to biology.ResultsOur benchmark consists of different classes of images and ground truth data, ranging in scale from subcellular, cellular to tissue level, each of which pose their own set of challenges to image analysis. The associated ground truth data can be used to evaluate the effectiveness of different methods, to improve methods and to compare results. Standard evaluation methods and some analysis tools are integrated into a database framework that is available online at http://bioimage.ucsb.edu/biosegmentation/.ConclusionThis online benchmark will facilitate integration and comparison of image analysis methods for bioimages. While the primary focus is on biological images, we believe that the dataset and infrastructure will be of interest to researchers and developers working with biological image analysis, image segmentation and object tracking in general.

Analyzing Performance of Lease-Based Schemes under Failures

Leases have proved to be an effective concurrency control technique for distributed systems that are prone to failures. However, many benefits of leases are only realized when leases are granted for approximately the time of expected use. Correct assessment of lease duration has proven difficult for all but the simplest of resource allocation problems. In this paper, we present a model that captures a number of lease styles and semantics used in practice. We consider a few performance characteristics for lease-based systems and analytically derive how they are affected by lease duration. We confirm our analytical findings by running a set of experiments with the OO7 benchmark suite using a variety of workloads and fault loads.

Increasing Concurrency in Databases Using Program Analysis

Programmers have come to expect better integration between databases and the programming languages they use. While this trend continues unabated, database concurrency scheduling has remained blind to the programs. We propose that the database client programs provide a large untapped information resource for increasing database throughput.

Scalable image informatics

Abstract Images and video play a major role in scientific discoveries. Significant new advances in imaging science over the past two decades have resulted in new devices and technologies that are able to probe the world at nanoscales to planetary scales. These instruments generate massive amounts of multimodal imaging data. In addition to the raw imaging data, these instruments capture additional critical information—the metadata—that include the imaging context. Further, the experimental conditions are often added manually to such metadata that describe processes that are not implicit in the instrumentation metadata. Despite these technological advances in imaging sciences, resources for curation, distribution, sharing, and analysis of such data at scale are still lacking. Robust image analysis workflows have the potential to transform image-based sciences such as biology, ecology, remote sensing, materials science, and medical imaging. In this context, this chapter presents BisQue, a novel eco-system where scientific image analysis methods can be discovered, tested, verified, refined, and shared among users on a shared, cloud-based infrastructure. The vision of BisQue is to enable large-scale, data-driven scientific explorations. The following sections will discuss the core requirements of such an architecture, challenges in developing and deploying the methods, and will conclude with an application to image recognition using deep learning.