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

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

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

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

recombination graph

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

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)

CaSiO3 microstructure modulating the in vitro and in vivo bioactivity of poly(lactide-co-glycolide) microspheres

Poly (lactide-co-glycolide) (PLGA) microspheres have been used for regenerative medicine due to their ability for drug delivery and generally good biocompatibility, but they lack adequate bioactivity for bone repair application. CaSiO3 (CS) has been proposed as a new class of material suitable for bone tissue repair due to its excellent bioactivity. In this study, we set out to incorporate CS into PLGA microspheres to investigate how the phase structure (amorphous and crystal) of CS influences the in vitro and in vivo bioactivity of the composite microspheres, with a view to the application for bone regeneration. X-ray diffraction (XRD), N2 adsorption-desorption analysis and scanning electron microscopy (SEM) were used to analyze the phase structure, surface area/pore volume, and microstructure of amorphous CS (aCS) and crystal CS (cCS), as well as their composite microspheres. The in vitro bioactivity of aCS and cCS – PLGA microspheres was evaluated by investigating their apatite-mineralization ability in simulated body fluids (SBF) and the viability of human bone mesenchymal stem cells (BMSCs). The in vivo bioactivity was investigated by measuring their de novo bone-formation ability. The results showed that the incorporation of both aCS and cCS enhanced the in vitro and in vivo bioactivity of PLGA microspheres. cCS/PLGA microspheres improved better in vitro BMSC viability and de novo bone-formation ability in vivo, compared to aCS/PLGA microspheres. Our study indicates that controlling the phase structure of CS is a promising method to modulate the bioactivity of polymer microsphere system for potential bone tissue regeneration

Plant volatile analogues strengthen attractiveness to insect

Green leaf bug Apolygus lucorum (Meyer-Dür) is one of the major pests in agriculture. Management of A. lucorum was largely achieved by using pesticides. However, the increasing population of A. lucorum since growing Bt cotton widely and the increased awareness of ecoenvironment and agricultural product safety makes their population-control very challenging. Therefore this study was conducted to explore a novel ecological approach, synthetic plant volatile analogues, to manage the pest. Here, plant volatile analogues were first designed and synthesized by combining the bioactive components of β-ionone and benzaldehyde. The stabilities of β-ionone, benzaldehyde and analogue 3 g were tested. The electroantennogram (EAG) responses of A. lucorum adult antennae to the analogues were recorded. And the behavior assay and filed experiment were also conducted. In this study, thirteen analogues were acquired. The analogue 3 g was demonstrated to be more stable than β-ionone and benzaldehyde in the environment. Many of the analogues elicited EAG responses, and the EAG response values to 3 g remained unchanged during seven-day period. 3 g was also demonstrated to be attractive to A. lucorum adults in the laboratory behavior experiment and in the field. Its attractiveness persisted longer than β-ionone and benzaldehyde. This indicated that 3 g can strengthen attractiveness to insect and has potential as an attractant. Our results suggest that synthetic plant volatile analogues can strengthen attractiveness to insect. This is the first published study about synthetic plant volatile analogues that have the potential to be used in pest control. Our results will support a new ecological approach to pest control and it will be helpful to ecoenvironment and agricultural product safety