Placental Flattening via Volumetric Parameterization

We present a volumetric mesh-based algorithm for flattening the placenta to a canonical template to enable effective visualization of local anatomy and function. Monitoring placental function in vivo promises to support pregnancy assessment and to improve care outcomes. We aim to alleviate visualization and interpretation challenges presented by the shape of the placenta when it is attached to the curved uterine wall. To do so, we flatten the volumetric mesh that captures placental shape to resemble the well-studied ex vivo shape. We formulate our method as a map from the in vivo shape to a flattened template that minimizes the symmetric Dirichlet energy to control distortion throughout the volume. Local injectivity is enforced via constrained line search during gradient descent. We evaluate the proposed method on 28 placenta shapes extracted from MRI images in a clinical study of placental function. We achieve sub-voxel accuracy in mapping the boundary of the placenta to the template while successfully controlling distortion throughout the volume. We illustrate how the resulting mapping of the placenta enhances visualization of placental anatomy and function. Our code is freely available at https://github.com/mabulnaga/placenta-flattening .Comment: MICCAI 201

Discriminative structural approaches for enzyme active-site prediction

<p>Abstract</p> <p>Background</p> <p>Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far.</p> <p>Results</p> <p>This paper introduces new machine learning algorithms that refine the similarity/deviation for comparison of local structures. The similarity/deviation is applied to two types of applications, single template analysis and multiple template analysis. In the single template analysis, a single template is used as a query to search proteins for active sites, whereas a protein structure is examined as a query to discover the possible active-sites using a set of templates in the multiple template analysis.</p> <p>Conclusions</p> <p>This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses.</p

Lost Property Detection by Template Matching using Genetic Algorithm and Random Search

In this paper, we propose an object search method which is adapted to transformation of an object to be searched to detect lost property. Object search is divided into two types; global and local searches. We used a template matching using Genetic Algorithm (GA) in the global search. Moreover we use a random search in the local search. According to experimental results, this system can detect rough position of the object to be searched. The search accuracy obtained using the present method is 83.6%, and that of a comparative experiment using only GA is 42.1%. We have verified that our proposed method is effective for lost property detection. In the future, we need to increase search accuracy to search objects more stably. In particular, we need to improve local search

Improved crystallographic models through iterated local density-guided model deformation and reciprocal-space refinement.

An approach is presented for addressing the challenge of model rebuilding after molecular replacement in cases where the placed template is very different from the structure to be determined. The approach takes advantage of the observation that a template and target structure may have local structures that can be superimposed much more closely than can their complete structures. A density-guided procedure for deformation of a properly placed template is introduced. A shift in the coordinates of each residue in the structure is calculated based on optimizing the match of model density within a 6 Å radius of the center of that residue with a prime-and-switch electron-density map. The shifts are smoothed and applied to the atoms in each residue, leading to local deformation of the template that improves the match of map and model. The model is then refined to improve the geometry and the fit of model to the structure-factor data. A new map is then calculated and the process is repeated until convergence. The procedure can extend the routine applicability of automated molecular replacement, model building and refinement to search models with over 2 Å r.m.s.d. representing 65-100% of the structure

MMBIRFinder: A Tool to Detect Microhomology-Mediated Break-Induced Replication

The introduction of next-generation sequencing technologies has radically changed the way we view structural genetic events. Microhomology-mediated break-induced replication (MMBIR) is just one of the many mechanisms that can cause genomic destabilization that may lead to cancer. Although the mechanism for MMBIR remains unclear, it has been shown that MMBIR is typically associated with template-switching events. Currently, to our knowledge, there is no existing bioinformatics tool to detect these template-switching events. We have developed MMBIRFinder, a method that detects template-switching events associated with MMBIR from whole-genome sequenced data. MMBIRFinder uses a half-read alignment approach to identify potential regions of interest. Clustering of these potential regions helps narrow the search space to regions with strong evidence. Subsequent local alignments identify the template-switching events with single-nucleotide accuracy. Using simulated data, MMBIRFinder identified 83 percent of the MMBIR regions within a five nucleotide tolerance. Using real data, MMBIRFinder identified 16 MMBIR regions on a normal breast tissue data sample and 51 MMBIR regions on a triple-negative breast cancer tumor sample resulting in detection of 37 novel template-switching events. Finally, we identified template-switching events residing in the promoter region of seven genes that have been implicated in breast cancer

An Eye-Contour Extraction Algorithm from Face Image usingDeformable Template Matching

A variety of studies on face components such as eyes, lips, noses, and teeth have been proceeding in medicine, psychology, biometrics authentication, and other areas. In this paper, we present an algorithm of extracting eye contours from a face image using the deformable template matching method. Our template for an eye contour is composed of three quadratic functions for the perimeter and one circle for the pupil. In our algorithm, a digital color face image is rst converted to a binary image of representing eyes, after the region around eyes is identied on the face image by using hues and values of the color image. Then, parameters in the template are optimized by a local search method with a tabu period and a hill-climbing, so as to t the template to the eye contour in the binary image. The accuracy of our algorithm is evaluated through sample face images of students. In addition, the application of our proposal to eye shape indices is investigated in a face image database "HOIP", where recognizable dierence exists in index distributions between males and females

Automated identification of 2612 late-k and M dwarfs in the LAMOST commissioining data using the classification template fits

We develop a template-fit method to automatically identify and classify late-type K and M dwarfs in spectra from the LAMOST. A search of the commissioning data, acquired in 2009-2010, yields the identification of 2612 late-K and M dwarfs. The template fit method also provides spectral classification to half a subtype, classifies the stars along the dwarf-subdwarf metallicity sequence, and provides improved metallicity/gravity information on a finer scale. The automated search and classification is performed using a set of cool star templates assembled from the Sloan Digital Sky Survey spectroscopic database. We show that the stars can be efficiently classified despite shortcomings in the LAMOST commissioning data which include bright sky lines in the red. In particular we find that the absolute and relative strengths of the critical TiO and CaH molecular bands around 7000A are cleanly measured, which provides accurate spectral typing from late-K to mid-M, and makes it possible to estimate metallicities in a way that is more efficient and reliable than with the use of spectral indices or spectral-index based parameters such as zeta. Most of the cool dwarfs observed by LAMOST are found to be metal-rich dwarfs. We use a calibration of spectral type to absolute magnitude and estimate spectroscopic distances for all the stars; we also recover proper motions from the SUPERBLINK and PPMXL catalogs. Our analysis of the estimated transverse motions suggests a mean velocity and standard deviation for the UVW components of velocity to be: U=-9.8 km/s; V=-22.8 km/s; W=-7.9 km/s. The resulting values are general agreement with previous reported results, which yields confidence in our spectral classification and spectroscopic distance estimates, and illustrates the potential for using LAMOST spectra of K and M dwarfs for investigating the chemo-kinematics of the local Galactic disk and halo.Comment: 18 pages,16 figures,accepted for publication A