Daniel Howard

Frontiers in the Convergence of Bioscience 
                        and  Information Technology

This paper describes the application of the alternating Kernel mixture (AKM) segmentation algorithm to high resolution MRI subvolumes acquired from a 1.5T scanner (hippocampus, <svg style="vertical-align:-0.1638pt;width:44.400002px;" id="M1" height="11.125" version="1.1" viewBox="0 0 44.400002 11.125" width="44.400002" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,-0,0,-.017,.062,10.862)"><path id="x1D45B" d="M495 86q-46 -47 -87 -72.5t-63 -25.5q-43 0 -16 107l49 210q7 34 8 50.5t-3 21t-13 4.5q-35 0 -109.5 -72.5t-115.5 -140.5q-21 -75 -38 -159q-50 -10 -76 -21l-6 8l84 340q8 35 -4 35q-17 0 -67 -46l-15 26q44 44 85.5 70.5t64.5 26.5q35 0 10 -103l-24 -98h2&#xA;q42 56 97 103.5t96 71.5q46 26 74 26q9 0 16 -2.5t14 -11.5t9.5 -24.5t-1 -44t-13.5 -68.5q-30 -117 -47 -200q-4 -19 -3.5 -25t6.5 -6q21 0 70 48z" /></g><g transform="matrix(.017,-0,0,-.017,13.305,10.862)"><path id="x3D" d="M535 323h-483v50h483v-50zM535 138h-483v50h483v-50z" /></g><g transform="matrix(.017,-0,0,-.017,28.008,10.862)"><path id="x31" d="M384 0h-275v27q67 5 81.5 18.5t14.5 68.5v385q0 38 -7.5 47.5t-40.5 10.5l-48 2v24q85 15 178 52v-521q0 -55 14.5 -68.5t82.5 -18.5v-27z" /></g><g transform="matrix(.017,-0,0,-.017,36.168,10.862)"><path id="x30" d="M241 635q53 0 94 -28.5t63.5 -76t33.5 -102.5t11 -116q0 -58 -11 -112.5t-34 -103.5t-63.5 -78.5t-94.5 -29.5t-95 28t-64.5 75t-34.5 102.5t-11 118.5q0 58 11.5 112.5t34.5 103t64.5 78t95.5 29.5zM238 602q-32 0 -55.5 -25t-35.5 -68t-17.5 -91t-5.5 -105&#xA;q0 -76 10 -138.5t37 -107.5t69 -45q32 0 55.5 25t35.5 68.5t17.5 91.5t5.5 105t-5.5 105.5t-18 92t-36 68t-56.5 24.5z" /></g></svg> and prefrontal cortex, <svg style="vertical-align:-0.20474pt;width:36.237499px;" id="M2" height="11.175" version="1.1" viewBox="0 0 36.237499 11.175" width="36.237499" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,-0,0,-.017,.062,10.862)"><use xlink:href="#x1D45B" /></g><g transform="matrix(.017,-0,0,-.017,13.305,10.862)"><use xlink:href="#x3D" /></g><g transform="matrix(.017,-0,0,-.017,28.008,10.862)"><path id="x39" d="M244 635q90 0 143 -72t53 -177q0 -133 -65 -229.5t-171 -139.5q-79 -32 -140 -32l-5 30q109 18 185 91t101 186l-68 -36q-29 -16 -60 -16q-79 0 -129 51.5t-50 130.5q0 80 57 146.5t149 66.5zM228 602q-52 0 -78 -45.5t-26 -98.5q0 -69 36.5 -115.5t97.5 -46.5&#xA;q53 0 90 28q4 31 4 66q0 51 -9.5 95.5t-39 80.5t-75.5 36z" /></g></svg>) and a 3T scanner (hippocampus, <svg style="vertical-align:-0.1638pt;width:44.400002px;" id="M3" height="11.125" version="1.1" viewBox="0 0 44.400002 11.125" width="44.400002" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,-0,0,-.017,.062,10.862)"><use xlink:href="#x1D45B" /></g><g transform="matrix(.017,-0,0,-.017,13.305,10.862)"><use xlink:href="#x3D" /></g><g transform="matrix(.017,-0,0,-.017,28.008,10.862)"><use xlink:href="#x31" /></g><g transform="matrix(.017,-0,0,-.017,36.168,10.862)"><use xlink:href="#x30" /></g></svg> and occipital lobe, <svg style="vertical-align:-0.1638pt;width:44.400002px;" id="M4" height="11.125" version="1.1" viewBox="0 0 44.400002 11.125" width="44.400002" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,-0,0,-.017,.062,10.862)"><use xlink:href="#x1D45B" /></g><g transform="matrix(.017,-0,0,-.017,13.305,10.862)"><use xlink:href="#x3D" /></g><g transform="matrix(.017,-0,0,-.017,28.008,10.862)"><use xlink:href="#x31" /></g><g transform="matrix(.017,-0,0,-.017,36.168,10.862)"><use xlink:href="#x30" /></g></svg>). Segmentation of the subvolumes into cerebrospinal fluid, gray matter, and white matter tissue is validated by comparison with manual segmentation. When compared with other segmentation methods that use traditional Bayesian segmentation, AKM yields smaller errors (<svg style="vertical-align:-0.1638pt;width:58.712502px;" id="M5" height="11.375" version="1.1" viewBox="0 0 58.712502 11.375" width="58.712502" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,-0,0,-.017,.062,11.112)"><path id="x1D443" d="M619 482q0 -69 -40.5 -119t-96 -72.5t-118.5 -26.5h-44l-70 20l-31 -151q-14 -67 0.5 -83t89.5 -22l-5 -28h-287l8 28q65 7 81.5 22t29.5 83l79 398q12 56 0.5 70.5t-78.5 20.5l7 28h255q108 0 164 -43t56 -125zM524 478q0 141 -146 141q-25 0 -47 -8q-16 -6 -20.5 -13.5&#xA;t-10.5 -39.5l-43 -241q37 -13 83 -13q67 0 125.5 45t58.5 129z" /></g><g transform="matrix(.017,-0,0,-.017,15.582,11.112)"><path id="x3C" d="M512 -3l-437 233v51l437 233v-58l-378 -200v-2l378 -199v-58z" /></g><g transform="matrix(.017,-0,0,-.017,30.286,11.112)"><path id="x2E" d="M113 -12q-24 0 -39.5 16t-15.5 42q0 24 16 40.5t40 16.5t40 -16.5t16 -40.5q0 -26 -16 -42t-41 -16z" /></g><g transform="matrix(.017,-0,0,-.017,34.162,11.112)"><use xlink:href="#x30" /></g><g transform="matrix(.017,-0,0,-.017,42.321,11.112)"><use xlink:href="#x30" /></g><g transform="matrix(.017,-0,0,-.017,50.481,11.112)"><path id="x35" d="M153 550l-26 -186q79 31 111 31q90 0 141.5 -51t51.5 -119q0 -93 -89 -166q-85 -69 -173 -71q-32 0 -61.5 11.5t-41.5 23.5q-18 17 -17 34q2 16 22 33q14 9 26 -1q61 -50 124 -50q60 0 93 43.5t33 104.5q0 69 -41.5 110t-121.5 41q-53 0 -102 -20l38 305h286l6 -8&#xA;l-26 -65h-233z" /></g></svg>, exact Wilcoxon signed rank test) demonstrating the robustness and wide applicability of AKM across different structures. By generating multiple mixtures for each tissue compartment, AKM mimics the increased variation of manual segmentation in partial volumes due to the highly folded tissues. AKM's superior performance makes it useful for tissue segmentation of subcortical and cortical structures in large-scale neuroimaging studies.

Nayoung A. Lee

Carey E. Priebe

Michael I. Miller

J. Tilak Ratnanather

Validation of Alternating Kernel Mixture Method: Application to Tissue Segmentation of Cortical and Subcortical Structures

In nationwide mammography screening, thousands of mammography examinations must be processed. Each consists of two standard views of each breast, and each mammogram must be visually examined by an experienced radiologist to assess it for any anomalies. The ability to detect an anomaly in mammographic texture is important to successful outcomes in mammography screening and, in this study, a large number of mammograms were digitized with a highly accurate scanner; and textural features were derived from the mammograms as input data to a SONNET selforganizing neural network. The paper discusses how SONNET was used to produce a taxonomic organization of the mammography archive in an unsupervised manner. This process is subject to certain choices of SONNET parameters, in these numerical experiments using the craniocaudal view, and typically produced O(10), for example, 39 mammogram classes, by analysis of features from O(<svg style="vertical-align:-0.17555pt;width:21.85px;" id="M1" height="14.2" version="1.1" viewBox="0 0 21.85 14.2" width="21.85"  xmlns="http://www.w3.org/2000/svg">
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				<tspan style="font-size: 12.50px; " x="0" y="0">1</tspan>
				<tspan style="font-size: 12.50px; " x="6.2515001" y="0">0</tspan>
			</text>

			<text transform="matrix(1,0,0,-1,-59.45,66.03)">
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</svg>) mammogram images. The mammogram taxonomy captured typical subtleties to discriminate mammograms, and it is submitted that this may be exploited to aid the detection of mammographic anomalies, for example, by acting as a preprocessing stage to simplify the task for a computational detection scheme, or by ordering mammography examinations by mammogram taxonomic class prior to screening in order to encourage more successful visual examination during screening. The resulting taxonomy may help train screening radiologists and conceivably help to settle legal cases concerning a mammography screening examination because the taxonomy can reveal the frequency of mammographic patterns in a population.

Simon C. Roberts

Conor Ryan

Adrian Brezulianu

Textural Classification of Mammographic Parenchymal Patterns 
                        with the SONNET Selforganizing Neural Network

The traditional experimental approaches used for changing the flux or the concentration of a particular metabolite of a metabolic pathway have been mostly based on the inhibition or over-expression of the presumed rate-limiting step. However, the attempts to manipulate a metabolic pathway by following such approach have proved to be unsuccessful. Metabolic Control Analysis (MCA) establishes how to determine, quantitatively, the degree of control that a given enzyme exerts on flux and on the concentration of metabolites, thus substituting the intuitive, qualitative concept of rate limiting step.  Moreover, MCA helps to understand (i) the underlying mechanisms by which a given enzyme exerts high or low control and (ii) why the control of the pathway is shared by several pathway enzymes and transporters. By applying MCA it is possible to identify the steps that should be modified to achieve a successful alteration of flux or metabolite concentration in pathways of biotechnological (e.g., large scale metabolite production) or clinical relevance (e.g., drug therapy).  The different MCA experimental approaches developed for the determination of the flux-control distribution in several pathways are described. Full understanding of the pathway properties when working under a variety of conditions can help to attain a successful manipulation of flux and metabolite concentration.

Rafael Moreno-Sánchez

Emma Saavedra

Sara Rodríguez-Enríquez

Viridiana Olín-Sandoval

Metabolic Control Analysis: A Tool for Designing Strategies to Manipulate Metabolic Pathways

A compact integrated system-on-chip (SoC) architecture solution for robust, real-time, and on-site genetic analysis has been proposed. This microsystem solution is noise-tolerable and suitable for analyzing the weak fluorescence patterns from a PCR prepared dual-labeled DNA microchip assay. In the architecture, a preceding VLSI differential logarithm microchip is designed for effectively computing the logarithm of the normalized input fluorescence signals. A posterior VLSI artificial neural network (ANN) processor chip is used for analyzing the processed signals from the differential logarithm stage. A single-channel logarithmic circuit was fabricated and characterized. A prototype ANN chip with unsupervised winner-take-all (WTA) function was designed, fabricated, and tested. An ANN learning algorithm using a novel sigmoid-logarithmic transfer function based on the supervised backpropagation (BP) algorithm is proposed for robustly recognizing low-intensity patterns. Our results show that the trained new ANN can recognize low-fluorescence patterns better than an ANN using the conventional sigmoid function.

Jaw-Chyng Lue

Wai-Chi Fang

Bio-Inspired Microsystem for Robust Genetic Assay Recognition

The equine distal forelimb is a common location of injuries related to mechanical overload. In this study, a two-dimensional model of the musculoskeletal system of the region was developed and applied to kinematic and kinetic data from walking and trotting horses. The forces in major tendons and joint reaction forces were calculated. The components of the joint reaction forces caused by wrapping of tendons around sesamoid bones were found to be of similar magnitude to the reaction forces between the long bones at each joint. This finding highlighted the importance of taking into account muscle-tendon wrapping when evaluating joint loading in the equine distal forelimb.

Jonathan S. Merritt

Helen M. S. Davies

Colin Burvill

Marcus G. Pandy

Influence of Muscle-Tendon Wrapping on Calculations 
                        of  Joint Reaction Forces in the Equine Distal Forelimb

Determining the outline or boundary contour of a two-dimensional object, or the surface of a  
three-dimensional object poses difficulties particularly when there is substantial measurement noise or uncertainty. By adapting the mathematical approach of stochastic function recovery to this task, it is possible to obtain usable estimates for these boundaries, even in the presence of large amounts of noise. The technique is applied to parametric boundary data and has potential applications in biomedical imaging. It should be considered as one of several techniques to improve the visualization of images.

Joseph Kolibal

Alternative Parametric Boundary Reconstruction  Method for Biomedical Imaging

We investigate the performance of different classification models and their ability to recognize prostate cancer in an early stage. We build ensembles of classification models in order to increase the classification performance. We measure
the performance of our models in an extensive cross-validation procedure and compare different classification
models. The datasets come from clinical examinations and some of the classification models are already in use to support
the urologists in their clinical work.

Joerg D. Wichard

Henning Cammann

Carsten Stephan

Thomas Tolxdorff

Classification Models for Early Detection of Prostate Cancer

Elliptic curve cryptographic algorithms convert input
data to unrecognizable encryption and the unrecognizable data back
again into its original decrypted form. The security of this form of
encryption hinges on the enormous difficulty that is required to
solve the elliptic curve discrete logarithm problem (ECDLP),
especially over <svg style="vertical-align:-2.21957pt;width:45.537498px;" id="M2" height="14.175" version="1.1" viewBox="0 0 45.537498 14.175" width="45.537498" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(1.25,0,0,-1.25,0,14.175)"><g transform="translate(72,-60.66)"><text transform="matrix(1,0,0,-1,-71.95,62.92)"><tspan style="font-size: 12.50px; " x="0" y="0">𝐺</tspan><tspan style="font-size: 12.50px; " x="9.1772022" y="0">𝐹</tspan><tspan style="font-size: 12.50px; " x="16.904057" y="0">(</tspan><tspan style="font-size: 12.50px; " x="21.067554" y="0">2</tspan></text><text transform="matrix(1,0,0,-1,-44.63,68.09)"><tspan style="font-size: 8.75px; " x="0" y="0">𝑛</tspan></text><text transform="matrix(1,0,0,-1,-39.78,62.92)"><tspan style="font-size: 12.50px; " x="0" y="0">)</tspan></text></g></g></svg>,  <svg style="vertical-align:-0.33858pt;width:48.5625px;" id="M3" height="12.7875" version="1.1" viewBox="0 0 48.5625 12.7875" width="48.5625" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(1.25,0,0,-1.25,0,12.7875)"><g transform="translate(72,-61.77)"><text transform="matrix(1,0,0,-1,-71.95,62.16)"><tspan style="font-size: 12.50px; " x="0" y="0">𝑛</tspan><tspan style="font-size: 12.50px; " x="9.6773224" y="0">∈</tspan><tspan style="font-size: 12.50px; " x="21.71771" y="0">𝑍</tspan></text><text transform="matrix(1,0,0,-1,-39.7,67.16)"><tspan style="font-size: 8.75px; " x="0" y="0">+</tspan></text></g></g></svg>. This paper
describes an effective method to find solutions to the ECDLP by
means of a molecular computer. We propose that this research
accomplishment would represent a breakthrough for applied biological
computation and this paper demonstrates that in principle this is
possible. Three DNA-based algorithms: a parallel adder, a parallel
multiplier, and a parallel inverse over <svg style="vertical-align:-2.21957pt;width:45.537498px;" id="M4" height="14.175" version="1.1" viewBox="0 0 45.537498 14.175" width="45.537498" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(1.25,0,0,-1.25,0,14.175)"><g transform="translate(72,-60.66)"><text transform="matrix(1,0,0,-1,-71.95,62.92)"><tspan style="font-size: 12.50px; " x="0" y="0">𝐺</tspan><tspan style="font-size: 12.50px; " x="9.1772022" y="0">𝐹</tspan><tspan style="font-size: 12.50px; " x="16.904057" y="0">(</tspan><tspan style="font-size: 12.50px; " x="21.067554" y="0">2</tspan></text><text transform="matrix(1,0,0,-1,-44.63,68.09)"><tspan style="font-size: 8.75px; " x="0" y="0">𝑛</tspan></text><text transform="matrix(1,0,0,-1,-39.78,62.92)"><tspan style="font-size: 12.50px; " x="0" y="0">)</tspan></text></g></g></svg> are described.
The biological operation time of all of these algorithms is
polynomial with respect to <svg style="vertical-align:-0.10033pt;width:7.8874998px;" id="M5" height="7.1374998" version="1.1" viewBox="0 0 7.8874998 7.1374998" width="7.8874998" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(1.25,0,0,-1.25,0,7.1375)"><g transform="translate(72,-66.29)"><text transform="matrix(1,0,0,-1,-71.95,66.44)"><tspan style="font-size: 12.50px; " x="0" y="0">𝑛</tspan></text></g></g></svg>. Considering this analysis,
cryptography using a public key might be less secure. In this
respect, a principal contribution of this paper is to provide
enhanced evidence of the potential of molecular computing to tackle
such ambitious computations.

Kenli Li

Shuting Zou

Jin Xv

Fast Parallel Molecular Algorithms for DNA-Based Computation: Solving the Elliptic Curve  Discrete Logarithm Problem over <svg style="vertical-align:-2.3205pt" height="16.5" version="1.1" viewBox="0 0 49.525002 16.5" width="49.525002" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,-0,0,-.017,.062,13.55)"><path id="x1D43A" d="M713 296l-5 -25q-47 -7 -59 -20t-23 -72l-15 -79q-9 -48 -3 -74q-15 -3 -55 -13t-63 -15t-59.5 -10t-70.5 -5q-149 0 -243 80t-94 220q0 169 127.5 276.5t336.5 107.5q91 0 206 -36l-10 -165l-29 -1q1 85 -47.5 126t-146.5 41q-153 0 -243.5 -97t-90.5 -242q0 -122 68.5 -198.5t188.5 -76.5q121 0 139 75l20 86q13 58 -1.5 70.5t-99.5 20.5l5 26h267z"></path></g><g transform="matrix(.017,-0,0,-.017,12.37,13.55)"><path id="x1D439" d="M599 650l-18 -160l-30 -2q-2 70 -11 94q-5 16 -26 24t-75 8h-94q-27 0 -33.5 -6.5t-12.5 -32.5l-43 -226h108q52 0 72.5 5t31 18t28.5 52h28l-40 -200h-28q-1 42 -6 56.5t-24.5 21t-70.5 6.5h-108l-32 -177q-13 -65 1 -80.5t88 -22.5l-8 -28h-279l6 28q61 5 77.5 20.5t29.5 81.5l72 387l7.5 40.5t1 26.5t-4 18t-15.5 10t-24 6t-37 4l8 28h461z"></path></g><g transform="matrix(.017,-0,0,-.017,22.943,13.55)"><path id="x28" d="M300 -147l-18 -23q-106 71 -159 185.5t-53 254.5v1q0 139 53 252.5t159 186.5l18 -24q-74 -62 -115.5 -173.5t-41.5 -242.5q0 -130 41.5 -242.5t115.5 -174.5z"></path></g><g transform="matrix(.017,-0,0,-.017,28.824,13.55)"><path id="x32" d="M412 140l28 -9q0 -2 -35 -131h-373v23q112 112 161 170q59 70 92 127t33 115q0 63 -31 98t-86 35q-75 0 -137 -93l-22 20l57 81q55 59 135 59q69 0 118.5 -46.5t49.5 -122.5q0 -62 -29.5 -114t-102.5 -130l-141 -149h186q42 0 58.5 10.5t38.5 56.5z"></path></g><g transform="matrix(.012,-0,0,-.012,36.988,5.387)"><path id="x1D45B" d="M495 86q-46 -47 -87 -72.5t-63 -25.5q-43 0 -16 107l49 210q7 34 8 50.5t-3 21t-13 4.5q-35 0 -109.5 -72.5t-115.5 -140.5q-21 -75 -38 -159q-50 -10 -76 -21l-6 8l84 340q8 35 -4 35q-17 0 -67 -46l-15 26q44 44 85.5 70.5t64.5 26.5q35 0 10 -103l-24 -98h2q42 56 97 103.5t96 71.5q46 26 74 26q9 0 16 -2.5t14 -11.5t9.5 -24.5t-1 -44t-13.5 -68.5q-30 -117 -47 -200q-4 -19 -3.5 -25t6.5 -6q21 0 70 48z"></path></g><g transform="matrix(.017,-0,0,-.017,43.575,13.55)"><path id="x29" d="M275 270q0 -296 -211 -440l-19 23q75 62 116.5 174t41.5 243t-42 243t-116 173l19 24q211 -144 211 -440z"></path></g></svg>

We have developed a Windows-based program, <i>ConPath</i>, 
		as a scaffold analyzer. <i>ConPath</i> constructs scaffolds by ordering and orienting separate sequence contigs by exploiting the mate-pair information between
		 contig-pairs. Our algorithm builds directed graphs from link information and traverses them to find the longest acyclic graphs. Using end read pairs of fixed-sized mate-pair libraries, 
<i>ConPath</i> determines relative orientations of all contigs, 
estimates the gap size of each adjacent contig pair, and reports wrong assembly 
information by validating orientations and gap sizes. 
We have utilized ConPath in more than 10 microbial genome projects, including 
<i>Mannheimia succiniciproducens</i> and <i>Vibro vulnificus</i>, 
where we verified contig assembly and identified several erroneous contigs using the four types of error defined in <i>ConPath</i>.  Also, <i>ConPath</i>  supports some convenient features and viewers that permit investigation of each contig in detail; these include contig viewer, scaffold viewer, edge information list, mate-pair list, and the printing of complex scaffold structures.

Pan-Gyu Kim

Hwan-Gue Cho

Kiejung Park

A Scaffold Analysis Tool Using Mate-Pair Information in Genome Sequencing

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