Green J.M.,Affymax Inc. |
Narayanan A.,Strand Life science
PLoS ONE | Year: 2012
Certain concepts concerning EPO/EPOR action modes have been challenged by in vivo studies: Bcl-x levels are elevated in maturing erythroblasts, but not in their progenitors; truncated EPOR alleles that lack a major p85/PI3K recruitment site nonetheless promote polycythemia; and Erk1 disruption unexpectedly bolsters erythropoiesis. To discover novel EPO/EPOR action routes, global transcriptome analyses presently are applied to interrogate EPO/EPOR effects on primary bone marrow-derived CFUe-like progenitors. Overall, 160 EPO/EPOR target transcripts were significantly modulated 2-to 21.8-fold. A unique set of EPO-regulated survival factors included Lyl1, Gas5, Pim3, Pim1, Bim, Trib3 and Serpina 3g. EPO/EPOR-modulated cell cycle mediators included Cdc25a, Btg3, Cyclin-d2, p27-kip1, Cyclin-g2 and CyclinB1-IP-1. EPO regulation of signal transduction factors was also interestingly complex. For example, not only Socs3 plus Socs2 but also Spred2, Spred1 and Eaf1 were EPO-induced as negative-feedback components. Socs2, plus five additional targets, further proved to comprise new EPOR/Jak2/Stat5 response genes (which are important for erythropoiesis during anemia). Among receptors, an atypical TNF-receptor Tnfr-sf13c was up-modulated >5-fold by EPO. Functionally, Tnfr-sf13c ligation proved to both promote proerythroblast survival, and substantially enhance erythroblast formation. The EPOR therefore engages a sophisticated set of transcriptome response circuits, with Tnfr-sf13c deployed as one novel positive regulator of proerythroblast formation. © 2012 Singh et al.
Fung W.S.,University of Waterloo |
Hariharan R.,Strand Life science |
Harvey N.J.A.,University of Waterloo |
Panigrahi D.,Massachusetts Institute of Technology
Proceedings of the Annual ACM Symposium on Theory of Computing | Year: 2011
We present a general framework for constructing cut sparsifiers in undirected graphs - weighted subgraphs for which every cut has the same weight as the original graph, up to a multiplicative factor of (1 ± ε). Using this framework, we simplify, unify and improve upon previous sparsification results. As simple instantiations of this framework, we show that sparsifiers can be constructed by sampling edges according to their strength (a result of Benczur and Karger), effective resistance (a result of Spielman and Srivastava), edge connectivity, or by sampling random spanning trees. Sampling according to edge connectivity is the most aggressive method, and the most challenging to analyze. Our proof that this method produces sparsifiers resolves an open question of Benczur and Karger. While the above results are interesting from a combinatorial standpoint, we also prove new algorithmic results. In particular, we develop techniques that give the first (optimal) O(m)-time sparsification algorithm for unweighted graphs. Our algorithm has a running time of O(m) + Õ(n/ε2) for weighted graphs, which is also linear unless the input graph is very sparse itself. In both cases, this improves upon the previous best running times (due to Benczur and Karger) of O(m log2 n) (for the unweighted case) and O(m log3 n) (for the weighted case) respectively. Our algorithm constructs sparsifiers that contain O(n log n/ε2) edges in expectation; the only known construction of sparsifiers with fewer edges is by a substantially slower algorithm running in O(n3 m / ε2) time. A key ingredient of our proofs is a natural generalization of Karger's bound on the number of small cuts in an undirected graph. Given the numerous applications of Karger's bound, we suspect that our generalization will also be of independent interest. © 2011 ACM.
Willett C.,Humane Society of the United States |
Rae J.C.,DuPont Company |
Goyak K.O.,ExxonMobil |
Minsavage G.,ExxonMobil |
And 21 more authors.
Altex | Year: 2014
A workshop sponsored by the Human Toxicology Project Consortium (HTPC), 'Building Shared Experience to Advance Practical Application of Pathway-Based Toxicology: Liver Toxicity Mode-of-Action' brought together experts from a wide range of perspectives to inform the process of pathway development and to advance two prototype pathways initially developed by the European Commission Joint Research Center (JRC): liver-specific fibrosis and steatosis. The first half of the workshop focused on the theory and practice of pathway development; the second on liver disease and the two prototype pathways. Participants agreed pathway development is extremely useful for organizing information and found that focusing the theoretical discussion on a specific AOP is helpful. It is important to include several perspectives during pathway development, including information specialists, pathologists, human health and environmental risk assessors, and chemical and product manufacturers, to ensure the biology is well captured and end use is considered.
Mestdagh P.,Ghent University |
Hartmann N.,Novartis |
Baeriswyl L.,Novartis |
Andreasen D.,Exiqon |
And 32 more authors.
Nature Methods | Year: 2014
micrornAs are important negative regulators of protein-coding gene expression and have been studied intensively over the past years. several measurement platforms have been developed to determine relative mirnA abundance in biological samples using different technologies such as small rnA sequencing, reverse transcription- quantitative PCr (rt-qPCr) and (microarray) hybridization. in this study, we systematically compared 12 commercially available platforms for analysis of micrornA expression. We measured an identical set of 20 standardized positive and negative control samples, including human universal reference rnA, human brain rnA and titrations thereof, human serum samples and synthetic spikes from micrornA family members with varying homology. We developed robust quality metrics to objectively assess platform performance in terms of reproducibility, sensitivity, accuracy, specifcity and concordance of differential expression. the results indicate that each method has its strengths and weaknesses, which help to guide informed selection of a quantitative micrornA gene expression platform for particular study goals. © 2014 Nature America, Inc. All rights reserved.
Gantayat N.,Strand Life science |
Das R.,Dell |
Cherukuri S.C.,Strand Life science
International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2014 | Year: 2014
Customer targeted markets are inundated with similar products from multiple vendors and selecting a product of choice is a challenging task. All varieties of a product have pros and cons in plenty, and the task of identifying a suitable product is daunting and cumbersome. To address this, we propose a methodology to identify preferred products in an automated manner. The end result is achieved by analyzing the history of multiple factors involved with the product of study and utilizing a supervised learning algorithm to predict the worthiness of the product with respect to the user. This algorithm is designed by combining and customizing sentiment analysis and automatic ontology construction algorithms. Dependency parsing for ontology construction, HMM/CRF for decision making, and a new personalized algorithm for sentiment analysis were utilized to customize the prediction method. For a product under consideration, the algorithm takes into account all the user specified features and predicts an outcome of it being good (positive) or bad (negative) to the interested user. This outcome is achieved by analyzing the past history of the features specified by the user. Using this algorithm we studied a set of 20 movies released during the period of January - March 2013 and achieved 70% accuracy in predicting their box office outcome. Our results indicate that there is a correlation between the selected features past performance and the overall success of a new product with the same features. Given a wide array of available choices, this algorithm can predict an ideal product for a customer. © 2014 IEEE.