REPdenovo: Inferring De Novo Repeat Motifs from Short Sequence Reads.

Repeat elements are important components of eukaryotic genomes. One limitation in our understanding of repeat elements is that most analyses rely on reference genomes that are incomplete and often contain missing data in highly repetitive regions that are difficult to assemble. To overcome this problem we develop a new method, REPdenovo, which assembles repeat sequences directly from raw shotgun sequencing data. REPdenovo can construct various types of repeats that are highly repetitive and have low sequence divergence within copies. We show that REPdenovo is substantially better than existing methods both in terms of the number and the completeness of the repeat sequences that it recovers. The key advantage of REPdenovo is that it can reconstruct long repeats from sequence reads. We apply the method to human data and discover a number of potentially new repeats sequences that have been missed by previous repeat annotations. Many of these sequences are incorporated into various parasite genomes, possibly because the filtering process for host DNA involved in the sequencing of the parasite genomes failed to exclude the host derived repeat sequences. REPdenovo is a new powerful computational tool for annotating genomes and for addressing questions regarding the evolution of repeat families. The software tool, REPdenovo, is available for download at https://github.com/Reedwarbler/REPdenovo

Development of Novel Semisolid Powder Processing for Micromanufacturing

Semisolid powder processing (SPP) is a promising approach for near net–shape forming of features in micro/meso–scale. By combining the concept of forming in the semisolid state and conventional powder metallurgy, SPP provides a novel solution to various processing and materials engineering challenges faced in micromanufacturing. Replacing bulk materials with powdered materials adds a new dimension to the traditional semisolid technique by allowing tailoring of material properties. In this dissertation, experimental study to understand flow characteristics of metallic powders in the semisolid state is performed, and its potential application to the fabrication of a functionally graded structure (FGS) is demonstrated. The viscosity and phase segregation behavior of Al–Si powders in the semisolid state were first studied with back extrusion experiments. Effects of process parameters including shear rate, extrusion ratio, heating time and pre–compaction pressure were analyzed using the design of experiments method. The results showed that the effects of shear rate, extrusion ratio and heating time were statistically significant factors influencing the viscosity. The semisolid state powders showed a shear thinning behavior. Moreover, microstructure analysis of extruded parts indicated severe phase segregation during the forming process. As the extrusion opening became small (about 400 μm), the phase segregation increased. A two–layer FGS with one layer reinforced by SiC particles was fabricated with SPP. The results indicate that SPP is capable of fabricating graded structures with promising microstructures and mechanical properties. When the SiC particles are larger than the matrix powder, dense and strong parts were formed. Smaller SiC particles can isolate the metal powders and result in porous and weak structures. The roughness of the SiC particle surface affects interface bonding between SiC particles and Al–Si–Cu matrix phase. In summary, SPP has the potential to become a viable micromanufacturing method that can be used to make graded structures with low cost, good microstructure and promising properties

Fabrication of metal matrix composite by semi-solid powder processing

Various metal matrix composites (MMCs) are widely used in the automotive, aerospace and electrical industries due to their capability and flexibility in improving the mechanical, thermal and electrical properties of a component. However, current manufacturing technologies may suffer from insufficient process stability and reliability and inadequate economic efficiency and may not be able to satisfy the increasing demands placed on MMCs. Semi-solid powder processing (SPP), a technology that combines traditional powder metallurgy and semi-solid forming methods, has potential to produce MMCs with low cost and high efficiency. In this work, the analytical study and experimental investigation of SPP on the fabrication of MMCs were explored. An analytical model was developed to understand the deformation mechanism of the powder compact in the semi-solid state. The densification behavior of the Al6061 and SiC powder mixtures was investigated with different liquid fractions and SiC volume fractions. The limits of SPP were analyzed in terms of reinforcement phase loading and its impact on the composite microstructure. To explore adoption of new materials, carbon nanotube (CNT) was investigated as a reinforcing material in aluminum matrix using SPP. The process was successfully modeled for the mono-phase powder (Al6061) compaction and the density and density distribution were predicted. The deformation mechanism at low and high liquid fractions was discussed. In addition, the compaction behavior of the ceramic-metal powder mixture was understood, and the SiC loading limit was identified by parametric study. For the fabrication of CNT reinforced Al6061 composite, the mechanical alloying of Al6061-CNT powders was first investigated. A mathematical model was developed to predict the CNT length change during the mechanical alloying process. The effects of mechanical alloying time and processing temperature during SPP were studied on the mechanical, microstructural and compositional properties of the Al6061-CNT composites. A shear lag model was applied to predict the mechanical property (hardness) of the composite. This work demonstrated the promising potential of SPP in the fabrication of particle/fiber (nanotube) reinforced MMCs

An analytical upper bound on the minimum number of recombinations in the history of SNP sequences in populations

recombination graph

Variable selection for the multicategory SVM via adaptive sup-norm regularization

The Support Vector Machine (SVM) is a popular classification paradigm in machine learning and has achieved great success in real applications. However, the standard SVM can not select variables automatically and therefore its solution typically utilizes all the input variables without discrimination. This makes it difficult to identify important predictor variables, which is often one of the primary goals in data analysis. In this paper, we propose two novel types of regularization in the context of the multicategory SVM (MSVM) for simultaneous classification and variable selection. The MSVM generally requires estimation of multiple discriminating functions and applies the argmax rule for prediction. For each individual variable, we propose to characterize its importance by the supnorm of its coefficient vector associated with different functions, and then minimize the MSVM hinge loss function subject to a penalty on the sum of supnorms. To further improve the supnorm penalty, we propose the adaptive regularization, which allows different weights imposed on different variables according to their relative importance. Both types of regularization automate variable selection in the process of building classifiers, and lead to sparse multi-classifiers with enhanced interpretability and improved accuracy, especially for high dimensional low sample size data. One big advantage of the supnorm penalty is its easy implementation via standard linear programming. Several simulated examples and one real gene data analysis demonstrate the outstanding performance of the adaptive supnorm penalty in various data settings.Comment: Published in at http://dx.doi.org/10.1214/08-EJS122 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Variable selection in quantile regression

Abstract: After its inception in Koenker and Bassett (1978), quantile regression has become an important and widely used technique to study the whole conditional distribution of a response variable and grown into an important tool of applied statistics over the last three decades. In this work, we focus on the variable selection aspect of penalized quantile regression. Under some mild conditions, we demonstrate the oracle properties of the SCAD and adaptive-LASSO penalized quantile regressions. For the SCAD penalty, despite its good asymptotic properties, the corresponding optimization problem is non-convex and, as a result, much harder to solve. In this work, we take advantage of the decomposition of the SCAD penalty function as the difference of two convex functions and propose to solve the corresponding optimization using the Difference Convex Algorithm (DCA)

A comparative study of mesoporous glass/silk and non-mesoporous glass/silk scaffolds: Physiochemistry and in vivo osteogenesis

Mesoporous bioactive glass (MBG) is a new class of biomaterials with a well-ordered nanochannel structure, whose in vitro bioactivity is far superior than that of non-mesoporous bioactive glass (BG); the material's in vivo osteogenic properties are, however, yet to be assessed. Porous silk scaffolds have been used for bone tissue engineering, but this material's osteoconductivity is far from optimal. The aims of this study were to incorporate MBG into silk scaffolds in order to improve their osteoconductivity and then to compare the effect of MBG and BG on the in vivo osteogenesis of silk scaffolds. MBG/silk and BG/silk scaffolds with a highly porous structure were prepared by a freeze-drying method. The mechanical strength, in vitro apatite mineralization, silicon ion release and pH stability of the composite scaffolds were assessed. The scaffolds were implanted into calvarial defects in SCID mice and the degree of in vivo osteogenesis was evaluated by microcomputed tomography (μCT), hematoxylin and eosin (H&E) and immunohistochemistry (type I collagen) analyses. The results showed that MBG/silk scaffolds have better physiochemical properties (mechanical strength, in vitro apatite mineralization, Si ion release and pH stability) compared to BG/silk scaffolds. MBG and BG both improved the in vivo osteogenesis of silk scaffolds. μCT and H&E analyses showed that MBG/silk scaffolds induced a slightly higher rate of new bone formation in the defects than did BG/silk scaffolds and immunohistochemical analysis showed greater synthesis of type I collagen in MBG/silk scaffolds compared to BG/silk scaffolds

Rethinking Trends in Instructional Objectives: Exploring the Alignment of Objectives with Activities and Assessment in Higher Education – A Case Study

This study explored higher education level syllabi to identify trends in educational objectives. Bloom’s Taxonomy and various strategic models were used to classify 714 objectives from 114 sections of courses administered through a Midwest teacher education institution in the United States. 1229 verbs and verb phrases were classified through the Taxonomy and differentiated between higher and lower ordered verbs as well as measureable and non-measureable learning outcomes. The results indicated that though learning outcomes the objectives are suggestive of higher ordered skills although the syllabi do not adequately provide information on the expected outcomes of the course