An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches

This paper overviews DNA microarray grid alignment and foreground separation approaches. Microarray grid alignment and foreground separation are the basic processing steps of DNA microarray images that affect the quality of gene expression information, and hence impact our confidence in any data-derived biological conclusions. Thus, understanding microarray data processing steps becomes critical for performing optimal microarray data analysis. In the past, the grid alignment and foreground separation steps have not been covered extensively in the survey literature. We present several classifications of existing algorithms, and describe the fundamental principles of these algorithms. Challenges related to automation and reliability of processed image data are outlined at the end of this overview paper.</p

Digging into Image Data to Answer Authorship Related Questions

An international, multi-institutional and multi-disciplinary team of researchers from the University of Sheffield (UoS), UK; Michigan State University (MSU), MI, USA; and University of Illinois at Urbana-Champaign (UIUC), IL, USA jointly propose the exploration of authorship across three distinct but in some respects complementary digital dataset collections: 15th-century manuscripts, 17th- and 18th-century maps and 19th- and 20th-century quilts. The datasets, freely available to the investigators, represent very large and diverse collections of digitized scans or photographs in standard image file formats. The US team will consist of members from UIUC (applying to NSF) and MSU (applying to NEH). The UIUC team led by Peter Bajcsy (as US NSF project director), the MSU team led by Dean Rehberger (as US NEH project director), and the UK team led by Peter Ainsworth (as UK JISC project director). The topic of authorship serves as a common question at the intersection of humanities, arts and social sciences research that unites the proposed exploration of image analyses

Integrating Sensory Data for Object Recognition Tasks

Object recognition is a difficult task for single sensor systems (e.g. machine vision) in unconstrained environments. A useful approach is to combine sensory data from more than one source to overcome these problems. However, using multiple sensors poses new problems with respect to coordination of the sensors, strategies for their use and integration of their data. In this paper, these problems are explored and solutions posed for the task of object recognition using passive stereo vision and active tactile sensing

Prediction of weekly nitrate-N fluctuations in a small agricultural watershed in Illinois

Agricultural nonpoint source pollution has been identified as one of the leading causes of surface water quality impairment in the United States. Such an impact is important, particularly in predominantly agricultural areas, where application of agricultural fertilizers often results in excessive nitrate levels in streams and rivers. When nitrate concentration in a public water supply reaches or exceeds drinking water standards, costly measures such as well closure or water treatment have to be considered. Thus, having accurate nitrate-N predictions is critical in making correct and timely management decisions. This study applied a set of data mining tools to predict weekly nitrate-N concentrations at a gauging station on the Sangamon River near Decatur, Illinois. The data mining tools used in this study included artificial neural networks, evolutionary polynomial regression and the naive Bayes model. The results were compared using seven forecast measures. In general, all models performed reasonably well, but not all achieved best scores in each of the measures, suggesting that a multi-tool approach is needed. In addition to improving forecast accuracy compared with previous studies, the tools described in this study demonstrated potential for application in error analysis, input selection and ranking of explanatory variables, thereby designing cost-effective monitoring networks

Communitybased Metadata Integration for Environmental Research

ABSTRACT The ability to aggregate information about environmental data and analysis processes across tools and services and across projects provides a powerful capability for discovering resources and coordinating projects and a means to convey the rich, community-scale context of data. In this paper, we summarize the science and engineering use cases motivating the metadata and provenance infrastructure of the Environmental Cyberinfrastructure Demonstrator (ECID) Cyberenvironment project at the National Center for Supercomputing Applications (NCSA) and discuss the requirements driving our system design. The user-level metadata and provenance capabilities being developed within ECID are described and we summarize the team&apos;s experiences in building them, and show how our experience can inform the continuing development and refinement of collaborative environmental science environments

Uniform cross phase modulation for nonclassical radiation pulses

We propose a scheme to achieve a uniform cross phase modulation (XPM) for two nonclassical light pulses and study its application for quantum non-demolition measurements of the photon number in a pulse and for controlled phase gates in quantum information. We analyze the scheme by quantizing a common phenomenological model for classical XPM. Our analysis first treats the ideal case of equal cross-phase modulation and pure unitary dynamics. This establishes the groundwork for more complicated studies of non-unitary dynamics and difference in phase shifts between the two pulses where decohering effects severely affect the performance of the scheme.Comment: 11 pages, 4 figures. To appear in J. Opt. Soc. Am.