Edward B. Duffy

The transcriptomic responses of the eastern oyster, Crassostrea virginica, to environmental conditions

Understanding the mechanisms by which organisms adapt to environmental conditions is a fundamental question for ecology and evolution. In this study, we evaluate changes in gene expression of a marine mollusc, the eastern oyster Crassostrea virginica, associated with the physico‐chemical conditions and the levels of metals and other contaminants in their environment. The results indicate that transcript signatures can effectively disentangle the complex interactive gene expression responses to the environment and are also capable of disentangling the complex dynamic effects of environmental factors on gene expression. In this context, the mapping of environment to gene and gene to environment is reciprocal and mutually reinforcing. In general, the response of transcripts to the environment is driven by major factors known to affect oyster physiology such as temperature, pH, salinity, and dissolved oxygen, with pollutant levels playing a relatively small role, at least within the range of concentrations found in the studied oyster habitats. Further, the two environmental factors that dominate these effects (temperature and pH) interact in a dynamic and nonlinear fashion to impact gene expression. Transcriptomic data obtained in our study provide insights into the mechanisms of physiological responses to temperature and pH in oysters that are consistent with the known effects of these factors on physiological functions of ectotherms and indicate important linkages between transcriptomics and physiological outcomes. Should these linkages hold in further studies and in other organisms, they may provide a novel integrated approach for assessing the impacts of climate change, ocean acidification and anthropogenic contaminants on aquatic organisms via relatively inexpensive microarray platforms.

Grammar Recovery from Parse Trees and Metrics-Guided Grammar Refactoring

Many software development tools that assist with tasks such as testing and maintenance are specific to a particular development language and require a parser for that language. Because a grammar is required to develop a parser, construction of these software development tools is dependent upon the availability of a grammar for the development language. However, a grammar is not always available for a language and, in these cases, acquiring a grammar is the most difficult, costly, and time-consuming phase of tool construction. In this paper, we describe a methodology for grammar recovery from a hard-coded parser. Our methodology is comprised of manual instrumentation of the parser, a technique for automatic grammar recovery from parse trees, and a semi-automatic metrics-guided approach to refactoring an iterative grammar to obtain a recursive grammar. We present the results of a case study in which we recover and refactor a grammar from version 4.0.0 of the GNU C++ parser and then refactor the recovered grammar using our metrics-guided approach. Additionally, we present an evaluation of the recovered and refactored grammar by comparing it to the ISO C++98 grammar.

JUMMP: Job Uninterrupted Maneuverable MapReduce Platform

In this paper, we present JUMMP, the Job Uninterrupted Maneuverable MapReduce Platform, an automated scheduling platform that provides a customized Hadoop environment within a batch-scheduled cluster environment. JUMMP enables an interactive pseudo-persistent MapReduce platform within the existing administrative structure of an academic high performance computing center by “jumping” between nodes with minimal administrative effort. Jumping is implemented by the synchronization of stopping and starting daemon processes on different nodes in the cluster. Our experimental evaluation shows that JUMMP can be as efficient as a persistent Hadoop cluster on dedicated computing resources, depending on the jump time. Additionally, we show that the cluster remains stable, with good performance, in the presence of jumps that occur as frequently as the average length of reduce tasks of the currently executing MapReduce job. JUMMP provides an attractive solution to academic institutions that desire to integrate Hadoop into their current computing environment within their financial, technical, and administrative constraints.

An Automated Approach to Grammar Recovery for a Dialect of the C++ Language

In this paper we present the design and implementation of a fully automated technique for reverse engineering or recovering a grammar from existing language artifacts. The technique that we describe uses only test cases and a parse tree, and we apply the technique to a dialect of the C++ language. However, given test cases and a parse tree for a language or a dialect of a language, our technique can be used to recover a grammar for the language, including languages such as Java, C, Python or Ruby.

gflow: software for modelling circuit theory‐based connectivity at any scale

Summary Increasing habitat connectivity is important for mitigating the effects of climate change, landscape fragmentation and habitat loss for biodiversity conservation. However, modelling connectivity at the relevant scales over which these threats occur has been limited by computational requirements. Here, we introduce the open-source software gflow, which massively parallelizes the computation of circuit theory-based connectivity. The software is developed for high-performance computing, but scales to consumer-grade desktop computers running modern Linux or Mac OS X operating systems. We report high computational efficiency representing a 173× speedup over existing software using high-performance computing and a 8·4× speedup using a desktop computer while drastically reducing memory requirements. gflow allows large-extent and high-resolution connectivity problems to be calculated over many iterations and at multiple scales. We envision gflow being immediately useful for large-landscape efforts, including climate-driven animal range shifts, multitaxa connectivity, and for the many developing use-cases of circuit theory-based connectivity.

A language and platform-independent approach for reverse engineering

We present an approach for reverse engineering a program to capture design and implementation artifacts such as metrics and UML class or sequence diagrams. We also describe an implementation of our approach, libthorin. However, unlike previous reverse engineering tools, libthorin can be applied to a variety of programming languages including C, C++, Java, Fortran 90 and C#. Moreover, libthorin can provide both coarse-grained and fine-grained information about the program under development to enable re-engineering of information and high-level diagrams such as metrics and class diagrams, or low-level diagrams such as sequence diagrams, control flow graphs and other program representations and analysis tools for testing, debugging and profiling an application under development.

Teaching HDFS/MapReduce Systems Concepts to Undergraduates

This paper presents the development of a Hadoop MapReduce module that has been taught in a course in distributed computing to upper undergraduate computer science students at Clemson University. The paper describes our teaching experiences and the feedback from the students over several semesters that have helped to shape the course. We provide suggested best practices for lecture materials, the computing platform, and the teaching methods. In addition, the computing platform and teaching methods can be extended to accommodate emerging technologies and modules for related courses.

FISIM: an integrated model for simulation of industrial fibre and film processes

Abstract The present study covers the structure and some applications of FISIM (FIber and film SIMulation), a versatile polymer process modelling package which is being developed by the Center for Advanced Engineering Fibers and Films. Generative programming techniques are used to overcome degradation in performance and storage which is often associated with the C++ language. The process stages are integrated using a component-based design so that configurations representing all or part of a process stage can be easily swapped in and out. The prototype code incorporates filtration, forming, external flow under quench conditions, and drawing, applied to fibre melt-spinning. Visualisation is playing a prominent role, providing a userfriendly means for initialising, monitoring, and post-processing simulation output. Process models, solution strategies, and software tools which are implemented in FISIM are presented, along with comparisons between simulation results and experimental measurements. These ingredients comprise a unique modelling environment which will substantially reduce the need for trial-anderror experiments in the development of new fibre and film processes.

Applying the decorator pattern for profiling object-oriented software

A profiler can provide valuable information to a developer to facilitate program optimization, debugging or testing. In this paper, we describe the use of the decorator pattern for non-intrusive profiling of object-oriented applications. We provide a formal specification of the decorator pattern, and show that the pattern can be used as a program transformation without altering the external, observable behavior of the system. We refer to such a transformation as a correctness preserving transformation, or CPT. As a CPT, the decorator pattern can be used to non-intrusively profile object-oriented applications and we illustrate this application with an invariant validator for enforcement of design by contract, and for profiling memory. We provide a case study to compare the cost trade-offs of validating invariants at different points in a program.

Toward real-time remote processing of laparoscopic video

Abstract. Laparoscopic surgery is a minimally invasive surgical technique where surgeons insert a small video camera into the patient’s body to visualize internal organs and use small tools to perform surgical procedures. However, the benefit of small incisions has a drawback of limited visualization of subsurface tissues, which can lead to navigational challenges in the delivering of therapy. Image-guided surgery uses the images to map subsurface structures and can reduce the limitations of laparoscopic surgery. One particular laparoscopic camera system of interest is the vision system of the daVinci-Si robotic surgical system (Intuitive Surgical, Sunnyvale, California). The video streams generate approximately 360 MB of data per second, demonstrating a trend toward increased data sizes in medicine, primarily due to higher-resolution video cameras and imaging equipment. Processing this data on a bedside PC has become challenging and a high-performance computing (HPC) environment may not always be available at the point of care. To process this data on remote HPC clusters at the typical 30 frames per second (fps) rate, it is required that each 11.9 MB video frame be processed by a server and returned within 1/30th of a second. The ability to acquire, process, and visualize data in real time is essential for the performance of complex tasks as well as minimizing risk to the patient. As a result, utilizing high-speed networks to access computing clusters will lead to real-time medical image processing and improve surgical experiences by providing real-time augmented laparoscopic data. We have performed image processing algorithms on a high-definition head phantom video (1920 × 1080 pixels) and transferred the video using a message passing interface. The total transfer time is around 53 ms or 19 fps. We will optimize and parallelize these algorithms to reduce the total time to 30 ms.