MSCS Faculty Research and Publications

Genome-Wide Associations of Signaling Pathways in Glioblastoma Multiforme

Serdar Bozdag et al. — Tue, 18 Jun 2013 07:57:07 PDT

Background: eQTL analysis is a powerful method that allows the identification of causal genomic alterations, providing an explanation of expression changes of single genes. However, genes mediate their biological roles in groups rather than in isolation, prompting us to extend the concept of eQTLs to whole gene pathways. Methods: We combined matched genomic alteration and gene expression data of glioblastoma patients and determined associations between the expression of signaling pathways and genomic copy number alterations with a non-linear machine learning approach. Results: Expectedly, over-expressed pathways were largely associated to tag-loci on chromosomes with signature alterations. Surprisingly, tag-loci that were associated to under-expressed pathways were largely placed on other chromosomes, an observation that held for composite effects between chromosomes as well. Indicating their biological relevance, identified genomic regions were highly enriched with genes having a reported driving role in gliomas. Furthermore, we found pathways that were significantly enriched with such driver genes. Conclusions: Driver genes and their associated pathways may represent a functional core that drive the tumor emergence and govern the signaling apparatus in GBMs. In addition, such associations may be indicative of drug combinations for the treatment of brain tumors that follow similar patterns of common and diverging alterations.

Age-Specific Signatures of Glioblastoma at the Genomic, Genetic, and Epigenetic Levels

Serdar Bozdag et al. — Tue, 18 Jun 2013 07:47:47 PDT

Age is a powerful predictor of survival in glioblastoma multiforme (GBM) yet the biological basis for the difference in clinical outcome is mostly unknown. Discovering genes and pathways that would explain age-specific survival difference could generate opportunities for novel therapeutics for GBM. Here we have integrated gene expression, exon expression, microRNA expression, copy number alteration, SNP, whole exome sequence, and DNA methylation data sets of a cohort of GBM patients in The Cancer Genome Atlas (TCGA) project to discover age-specific signatures at the transcriptional, genetic, and epigenetic levels and validated our findings on the REMBRANDT data set. We found major age-specific signatures at all levels including age-specific hypermethylation in polycomb group protein target genes and the upregulation of angiogenesis-related genes in older GBMs. These age-specific differences in GBM, which are independent of molecular subtypes, may in part explain the preferential effects of anti-angiogenic agents in older GBM and pave the way to a better understanding of the unique biology and clinical behavior of older versus younger GBMs.

Bayesian Analysis of Hypothesis Testing Problems for General Population: A Kullback–Leibler Alternative

Naveen Bansal et al. — Fri, 14 Jun 2013 13:28:55 PDT

We consider a hypothesis problem with directional alternatives. We approach the problem from a Bayesian decision theoretic point of view and consider a situation when one side of the alternatives is more important or more probable than the other. We develop a general Bayesian framework by specifying a mixture prior structure and a loss function related to the Kullback–Leibler divergence. This Bayesian decision method is applied to Normal and Poisson populations. Simulations are performed to compare the performance of the proposed method with that of a method based on a classical z-test and a Bayesian method based on the “0–1” loss.

A Note on Sub-Independence and S Class of Bivariate Mixtures

Gholamhossein Hamedani et al. — Fri, 14 Jun 2013 12:51:18 PDT

First, we recall a concept which is called sub-independence. This concept is stronger than that of uncorrelatedness but a lot weaker than independence. The concept of sub-independence, unlike that of uncorrelatedness, does not depend on the existence of any moments. Then, we consider a particular bivariate mixture to construct a pair (X, Y), which is sub-independent but not independent.

eTune: A Power Analysis Framework for Data-Intensive Computing

Rong Ge et al. — Thu, 13 Jun 2013 14:31:40 PDT

Data-intensive workloads demand a large portion of data center resources and consume massive amounts of energy. Energy conservation for data-intensive computing requires enabling technology to provide detailed and systemic energy information and to identify the energy inefficiencies in the underlying system hardware and software. In this work, we address this need and present eTune, a fine-grained, scalable power analysis framework for data-intensive computing on large-scale distributed systems. eTune leverages the fine-grained component level power measurement and the hardware performance monitoring counters (PMCs) on modern computer components and statistically builds power-performance correlation models. Using the learned models, eTune implements a software-based power estimator that runs on computer nodes and reports power at multiple levels including node, core, memory, and disks with a high accuracy. The conducted case studies with MapReduce applications reveal detailed energy behaviors of typical execution phases and data movements and provide insights on energy optimization via algorithm designs and resource allocations.

Energy Efficient Parallel Matrix-Matrix Multiplication for DVFS-Enabled Clusters

Longxiang Chen et al. — Thu, 13 Jun 2013 13:44:29 PDT

Excessive energy consumption has become one of the major challenges in high performance computing. Reducing the energy consumption of frequently used high performance computing applications not only saves the energy cost but also reduces the greenhouse gas emissions. This paper focuses on developing energy efficient algorithms and software for the widely used matrix-matrix multiplication, so that it is able to consume less energy in a DVFS-enabled cluster with little sacrifice in performance. The state-of-the-art practical parallel matrix matrix multiplication algorithm in ScaLAPACK partitions matrices into small blocks and distributes matrices using a two dimensional block cyclic distribution approach. Experimental results demonstrate that our energy efficient matrix-matrix multiplication algorithm can save up to 26.35% of energy with about 1% performance penalty. And the modified PDGEMM of ScaLAPACK is able to save energy more than 20% with less than 2% of performance loss.

Longitudinal investigation of the curricular effect: An analysis of student learning outcomes from the LieCal Project in the United States

Jinfa Cai et al. — Thu, 09 May 2013 12:55:02 PDT

In this article, we present the results from a longitudinal examination of the impact of a Standards-based or reform mathematics curriculum (called CMP) and traditional mathematics curricula (called non-CMP) on students’ learning of algebra using various outcome measures. Findings include the following: (1) students did not sacrifice basic mathematical skills if they are taught using a Standards-based or reform mathematics curriculum like CMP; (2) African American students experienced greater gain in symbol manipulation when they used a traditional curriculum; (3) the use of either the CMP or a non-CMP curriculum improved the mathematics achievement of all students, including students of color; (4) the use of CMP contributed to significantly higher problem-solving growth for all ethnic groups; and (5) a high level of conceptual emphasis in a classroom improved the students’ ability to represent problem situations. (However, the level of conceptual emphasis bears no relation to students’ problem solving or symbol manipulation skills.)

Impact of Curriculum Reform: Evidence of Change in Classroom Instruction in the United States

John Moyer et al. — Tue, 30 Apr 2013 14:11:35 PDT

The purpose of the study reported in this article is to examine the impact of curriculum on instruction. Over a three-year period, we observed 579 algebra-related lessons in grades 6–8. Approximately half the lessons were taught in schools that had adopted a Standards-based mathematics curriculum called the Connected Mathematics Program (CMP), and the remainder of the lessons were taught in schools that used more traditional curricula (non-CMP). We found many significant differences between the CMP and non-CMP lessons. The CMP lessons, emphasized the conceptual aspects of instruction to a greater extent than the non-CMP lessons and the non-CMP lessons emphasized the procedural aspects of instruction to a greater extent than the CMP lessons. About twice as many CMP lessons as non-CMP lessons were structured to use group work as a method of instruction. During lessons, non-CMP students worked individually on homework about three times as often as CMP students. When it came to text usage, CMP teachers were more likely than non-CMP teachers to work problems from the text and to follow lessons as laid out in the text. However, non-CMP students and teachers were more likely than CMP students and teachers to review examples or find formulas in the text. Surprisingly, only small proportions of the CMP lessons utilized calculators (16%) or manipulatives (11%).

Relationships Among Features of Pre-Service Teachers' Algebraic Thinking

Marta Magiera et al. — Mon, 01 Apr 2013 11:30:22 PDT

Prospective K-8 Teachers' Relational Thinking in the Context of Arithmetic and Algebra-Based Tasks

Marta Magiera et al. — Mon, 01 Apr 2013 07:26:42 PDT

Model Eliciting Activities: A Home Run

Marta Magiera — Wed, 27 Mar 2013 14:52:05 PDT

An aluminum bat activity supports goals of STEM learning by engaging students in resourceful problem solving.

Characterizations of Social-Based and Self-Based Contexts Associated With Students’ Awareness, Evaluation, and Regulation of Their Thinking During Small-Group Mathematical Modeling

Marta Magiera et al. — Wed, 27 Mar 2013 14:52:04 PDT

Characterizations of Social-Based and Self-Based Contexts Associated With Students’ Awareness, Evaluation, and Regulation of Their Thinking During Small-Group Mathematical Modeling This exploratory study focused on characterizing problem-solving situations associated with spontaneous metacognitive activity. The results came from connected case studies of a group of 3 purposefully selected 9th-grade students working collaboratively on a series of 5 modeling problems. Students’ descriptions of their own thinking during small-group mathematical modeling, elicited during video-stimulated interviews, were analyzed to identify and characterize social-based and self-based contexts associated with metacognitive activity coded as awareness, regulatory, and evaluative. Three characterizations of the social-based contexts and 3 different characterizations of the self-based contexts emerged.

K-8 Preservice Teachers’ Inductive Reasoning in the Problem-Solving Contexts

Marta Magiera — Wed, 27 Mar 2013 14:52:02 PDT

This paper reports the results from an exploratory study of K-8 pre-service teachers’ inductive reasoning. The analysis of 130 written solutions to seven tasks and 77 reflective journals completed by 20 pre-service teachers lead to descriptions of inductive reasoning processes, i.e. specializing, conjecturing, generalizing, and justifying, in the problem-solving contexts. The uncovered characterizations of the four inductive reasoning processes were further used to describe pathways of successful generalizations. The results highlight the importance of specializing and justifying in constructing powerful generalizations. Implications for teacher education are discussed.

Pre-Service Teachers' Questioning and Their Algebraic Thinking Ability

Leigh A. Van den Kieboom et al. — Wed, 27 Mar 2013 14:52:01 PDT

A Compartmentalized Approach to the Assembly of Physical Maps [Conference Proceedings]

Serdar Bozdag et al. — Tue, 05 Mar 2013 14:24:50 PST

We propose a novel compartmentalized method for the assembly of physical maps from fingerprinted clones. Our assembler exploits the presence of genetic markers at the global level to improve the accuracy of the assembly. Experimental results on the genome of rice and barley demonstrate that the compartmentalized assembler produces significantly more accurate maps, and that it can detect and isolate clones that induce chimeric contigs.

Development and Implementation of High-Throughput SNPgenotyping in Barley

Serdar Bozdag et al. — Tue, 05 Mar 2013 14:18:38 PST

Background

High density genetic maps of plants have, nearly without exception, made use of marker datasets containing missing or questionable genotype calls derived from a variety of genic and non-genic or anonymous markers, and been presented as a single linear order of genetic loci for each linkage group. The consequences of missing or erroneous data include falsely separated markers, expansion of cM distances and incorrect marker order. These imperfections are amplified in consensus maps and problematic when fine resolution is critical including comparative genome analyses and map-based cloning. Here we provide a new paradigm, a high-density consensus genetic map of barley based only on complete and error-free datasets and genic markers, represented accurately by graphs and approximately by a best-fit linear order, and supported by a readily available SNP genotyping resource.

Results

Approximately 22,000 SNPs were identified from barley ESTs and sequenced amplicons; 4,596 of them were tested for performance in three pilot phase Illumina GoldenGate assays. Data from three barley doubled haploid mapping populations supported the production of an initial consensus map. Over 200 germplasm selections, principally European and US breeding material, were used to estimate minor allele frequency (MAF) for each SNP. We selected 3,072 of these tested SNPs based on technical performance, map location, MAF and biological interest to fill two 1536-SNP "production" assays (BOPA1 and BOPA2), which were made available to the barley genetics community. Data were added using BOPA1 from a fourth mapping population to yield a consensus map containing 2,943 SNP loci in 975 marker bins covering a genetic distance of 1099 cM.

Conclusion

The unprecedented density of genic markers and marker bins enabled a high resolution comparison of the genomes of barley and rice. Low recombination in pericentric regions is evident from bins containing many more than the average number of markers, meaning that a large number of genes are recombinationally locked into the genetic centromeric regions of several barley chromosomes. Examination of US breeding germplasm illustrated the usefulness of BOPA1 and BOPA2 in that they provide excellent marker density and sensitivity for detection of minor alleles in this genetically narrow material.

BareMichael: A minimalistic Bare-metal Framework for the Intel SCC

Dennis Brylow et al. — Mon, 25 Feb 2013 12:03:56 PST

The many-core Intel SCC processor is one of a class of emerging, highly parallel computer architectures. Intel provides a modern Linux kernel which, running on the SCC as a separate instance per core, is able to load and launch user applications. However, there is a lack of open-source tools to facilitate development of “bare-metal” SCC applications – applications that are run directly on the chip without the support, overhead, or restrictiveness of an underlying operating system. To help fill this void, we present BareMichael, a minimalistic framework for compiling, loading, and launching mixed C and assembly code on the bare-metal Intel SCC. The framework also includes MikeTerm, a one-way pseudo-terminal for displaying output from an application as it executes on the chip. We share our solution in the hope that it will lower the barrier for others to begin development in a bare-metal environment on the SCC. Furthermore, we demonstrate the utility of BareMichael through two applications: supporting the use of the RCCE messagepassing library, and serving as the foundation for a port of the Embedded Xinu operating system.

Power-Aware High Performance Computing

Albert Y. Zomaya et al. — Tue, 05 Feb 2013 11:23:55 PST

This chapter contains sections titled:

Introduction
Background
Related Work
PowerPack: Fine-Grain Energy Profiling of HPC Applications
Power-Aware Speedup Model
Model Usages
Conclusion
References

Involvement of MicroRNA Families in Cancer

Stefan Wuchty et al. — Mon, 04 Feb 2013 13:55:17 PST

Collecting representative sets of cancer microRNAs (miRs) from the literature we show that their corresponding families are enriched in sets of highly interacting miR families. Targeting cancer genes on a statistically significant level, such cancer miR families strongly intervene with signaling pathways that harbor numerous cancer genes. Clustering miR family-specific profiles of pathway intervention, we found that different miR families share similar interaction patterns. Resembling corresponding patterns of cancer miRs families, such interaction patterns may indicate a miR family’s potential role in cancer. As we find that the number of targeted cancer genes is a naı¨ve proxy for a cancer miR family, we design a simple method to predict candidate miR families based on gene-specific interaction profiles. Assessing the impact of miR families to distinguish between (non-)cancer genes, we predict a set of 84 potential candidate families, including 75% of initially collected cancer miR families. Further confirming their relevance, predicted cancer miR families are significantly indicated in increasing, non-random numbers of tumor types.

Various Characterizations of Modified Weibull and Log Modified Distributions

Gholamhossein Hamedani — Fri, 01 Feb 2013 12:21:01 PST

Various characterizations of the well-known modifiedWeibull and log-modifiedWeibull distributions are presented. These characterizations are based on a simple relationship between two truncated moments; on the hazard function and on functions of the order statistics.