http://www.villainrom.co.uk/wiki/index.php?title=VillainROM_10_Series
http://www.villainrom.co.uk/releases/VillainROM10.3/download.php
http://forum.xda-developers.com/showthread.php?t=645253
http://www.youtube.com/watch?v=VxkWFsJC9lY
Wednesday, June 30, 2010
CTI Life Sciences
http://www.ctisciences.com/english/
Based in Montreal, CTI Life Sciences Fund L.P. is a limited partnership formed in 2006. The Fund make venture capital investments in high quality emerging life sciences companies at the start-up and clinical development stage primarily in Canada. This fund is the first of its kind created in Quebec since 2002.
Based in Montreal, CTI Life Sciences Fund L.P. is a limited partnership formed in 2006. The Fund make venture capital investments in high quality emerging life sciences companies at the start-up and clinical development stage primarily in Canada. This fund is the first of its kind created in Quebec since 2002.
Prediction of protease substrates using sequence and structure features
http://bioinformatics.oxfordjournals.org/cgi/content/abstract/26/14/1714?etoc
Prediction of protease substrates using sequence and structure features
David T. Barkan 1,2,3,4,{dagger}, Daniel R. Hostetter 3,4,{dagger}, Sami Mahrus 3,4, Ursula Pieper 2,3,4, James A. Wells 3,4,5, Charles S. Craik 3,4,* and Andrej Sali 2,3,4,*
Abstract
Motivation:Granzyme B (GrB) and caspases cleave specific protein substrates to induce apoptosis in virally infected and neoplastic cells. While substrates for both types of proteases have been determined experimentally, there are many more yet to be discovered in humans and other metazoans. Here, we present a bioinformatics method based on support vector machine (SVM) learning that identifies sequence and structural features important for protease recognition of substrate peptides and then uses these features to predict novel substrates. Our approach can act as a convenient hypothesis generator, guiding future experiments by high-confidence identification of peptide-protein partners.
Results:The method is benchmarked on the known substrates of both protease types, including our literature-curated GrB substrate set (GrBah). On these benchmark sets, the method outperforms a number of other methods that consider sequence only, predicting at a 0.87 true positive rate (TPR) and a 0.13 false positive rate (FPR) for caspase substrates, and a 0.79 TPR and a 0.21 FPR for GrB substrates. The method is then applied to ~25 000 proteins in the human proteome to generate a ranked list of predicted substrates of each protease type. Two of these predictions, AIF-1 and SMN1, were selected for further experimental analysis, and each was validated as a GrB substrate.
Availability: All predictions for both protease types are publically available at http://salilab.org/peptide. A web server is at the same site that allows a user to train new SVM models to make predictions for any protein that recognizes specific oligopeptide ligands.
Contact: craik@cgl.ucsf.edu; sali@salilab.org
Prediction of protease substrates using sequence and structure features
David T. Barkan 1,2,3,4,{dagger}, Daniel R. Hostetter 3,4,{dagger}, Sami Mahrus 3,4, Ursula Pieper 2,3,4, James A. Wells 3,4,5, Charles S. Craik 3,4,* and Andrej Sali 2,3,4,*
Abstract
Motivation:Granzyme B (GrB) and caspases cleave specific protein substrates to induce apoptosis in virally infected and neoplastic cells. While substrates for both types of proteases have been determined experimentally, there are many more yet to be discovered in humans and other metazoans. Here, we present a bioinformatics method based on support vector machine (SVM) learning that identifies sequence and structural features important for protease recognition of substrate peptides and then uses these features to predict novel substrates. Our approach can act as a convenient hypothesis generator, guiding future experiments by high-confidence identification of peptide-protein partners.
Results:The method is benchmarked on the known substrates of both protease types, including our literature-curated GrB substrate set (GrBah). On these benchmark sets, the method outperforms a number of other methods that consider sequence only, predicting at a 0.87 true positive rate (TPR) and a 0.13 false positive rate (FPR) for caspase substrates, and a 0.79 TPR and a 0.21 FPR for GrB substrates. The method is then applied to ~25 000 proteins in the human proteome to generate a ranked list of predicted substrates of each protease type. Two of these predictions, AIF-1 and SMN1, were selected for further experimental analysis, and each was validated as a GrB substrate.
Availability: All predictions for both protease types are publically available at http://salilab.org/peptide. A web server is at the same site that allows a user to train new SVM models to make predictions for any protein that recognizes specific oligopeptide ligands.
Contact: craik@cgl.ucsf.edu; sali@salilab.org
Fast integration of heterogeneous data sources for predicting gene function with limited annotation Sara Mostafavi 1,2,* and Quaid Morris 1,2,*
http://bioinformatics.oxfordjournals.org/cgi/content/abstract/26/14/1759?etoc
Motivation: Many algorithms that integrate multiple functional association networks for predicting gene function construct a composite network as a weighted sum of the individual networks and then use the composite network to predict gene function. The weight assigned to an individual network represents the usefulness of that network in predicting a given gene function. However, because many categories of gene function have a small number of annotations, the process of assigning these network weights is prone to overfitting.
Results: Here, we address this problem by proposing a novel approach to combining multiple functional association networks. In particular, we present a method where network weights are simultaneously optimized on sets of related function categories. The method is simpler and faster than existing approaches. Further, we show that it produces composite networks with improved function prediction accuracy using five example species (yeast, mouse, fly, Esherichia coli and human).
Availability: Networks and code are available from: http://morrislab.med.utoronto.ca/sara/SW
Contact: smostafavi@cs.toronto.edu; quaid.morris@utoronto.ca
Motivation: Many algorithms that integrate multiple functional association networks for predicting gene function construct a composite network as a weighted sum of the individual networks and then use the composite network to predict gene function. The weight assigned to an individual network represents the usefulness of that network in predicting a given gene function. However, because many categories of gene function have a small number of annotations, the process of assigning these network weights is prone to overfitting.
Results: Here, we address this problem by proposing a novel approach to combining multiple functional association networks. In particular, we present a method where network weights are simultaneously optimized on sets of related function categories. The method is simpler and faster than existing approaches. Further, we show that it produces composite networks with improved function prediction accuracy using five example species (yeast, mouse, fly, Esherichia coli and human).
Availability: Networks and code are available from: http://morrislab.med.utoronto.ca/sara/SW
Contact: smostafavi@cs.toronto.edu; quaid.morris@utoronto.ca
Tuesday, June 29, 2010
Relying on Origami Techniques, Researchers Show Programmable Matter Folding Into a Boat Or Plane-Shape
ScienceDaily (June 29, 2010) — "More than meets the eye" may soon become more than just a tagline for a line of popular robotic toys.
http://www.sciencedaily.com/releases/2010/06/100628152641.htm?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sciencedaily+%28ScienceDaily%3A+Latest+Science+News%29
http://www.sciencedaily.com/releases/2010/06/100628152641.htm?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sciencedaily+%28ScienceDaily%3A+Latest+Science+News%29
Monday, June 28, 2010
wedding blogs O.o
Clicked on 10 Next blogs, started out with photography types, then a series of wedding blogs ...
Thursday, June 24, 2010
Language of RNA Decoded: Study Reveals New Function for Pseudogenes and Noncoding RNAs
http://www.sciencedaily.com/releases/2010/06/100623132102.htm?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sciencedaily+%28ScienceDaily%3A+Latest+Science+News%29
More microRNA
More microRNA
An 'Omics' for Databases in the Life Sciences
http://www.sciencedaily.com/releases/2010/06/100624092014.htm?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sciencedaily+%28ScienceDaily%3A+Latest+Science+News%29
Monday, June 21, 2010
Korean Movies
May 18
Also known as 화려한 휴가 / Hwaryeohan hyuga lit / Elaborate Holiday
http://www.mysoju.com/may-18/
100 Days With Mr Arrogant (Complete)
Also known as My Love Ssagajy / slave love / 내 사랑 싸가지 / Naesarang ssagaji
http://www.mysoju.com/100-days-with-mr-arrogant/
Also known as 화려한 휴가 / Hwaryeohan hyuga lit / Elaborate Holiday
http://www.mysoju.com/may-18/
100 Days With Mr Arrogant (Complete)
Also known as My Love Ssagajy / slave love / 내 사랑 싸가지 / Naesarang ssagaji
http://www.mysoju.com/100-days-with-mr-arrogant/
1000 Genomes Project Releases Data from Pilot Projects on Path to Providing Database for 2,500 Human Genomes
http://www.sciencedaily.com/releases/2010/06/100621084644.htm?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sciencedaily+%28ScienceDaily%3A+Latest+Science+News%29
Researchers can obtain the data freely through the 1000 Genomes website (www.1000genomes.org) or from the NCBI at ftp://ftp-trace.ncbi.nih.gov/1000genomes/ or the EBI at: ftp://ftp.1000genomes.ebi.ac.uk/.
Researchers can obtain the data freely through the 1000 Genomes website (www.1000genomes.org) or from the NCBI at ftp://ftp-trace.ncbi.nih.gov/1000genomes/ or the EBI at: ftp://ftp.1000genomes.ebi.ac.uk/.
Friday, June 18, 2010
Tuesday, June 15, 2010
Biomarkers using miRNA (Micro RNA)
http://www.sciencedaily.com/releases/2010/06/100614161438.htm?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+sciencedaily+%28ScienceDaily%3A+Latest+Science+News%29
Aarati R. Ranade, David Cherba, Shravan Sridhar, Patrick Richardson, Craig Webb, Anoor Paripati, Brad Bowles, Glen J. Weiss. MicroRNA 92a-2*: A Biomarker Predictive for Chemoresistance and Prognostic for Survival in Patients with Small Cell Lung Cancer. Journal of Thoracic Oncology, 2010; DOI: 10.1097/JTO.0b013e3181dea6be
http://www.microbiology.ubc.ca/Faculty%20Opportunities
Dr. Michelle Krakowski
Department of Microbiology & Immunology
Life Sciences Centre
3559-2350 Health Sciences Mall
Vancouver, B.C., V6T 1Z3.
http://www.fluinfectomics.ubc.ca/
Micro RNA profiling Postdoctoral Fellowship
We are looking for an exceptional candidate possessing experience with RNA, especially Micro-RNA (miRNA) in order to investigate the hypothesis that miRNAs direct host defense during H5/H7 influenza A virus-human host interactions. Because the biological role of miRNAs in mammals are still largely unknown, the successful Postdoctoral fellow will have a novel opportunity to increase our understanding of infA-induced pathogenesis at a cellular level by identifying host-cell miRNAs in response to infA virus infection. The successful candidate will interact with an international team of researchers from laboratories and industry in order to have access to the widest range of biological samples and specialized protocols currently available. We are looking for an individual who will readily co-ordinate all aspects of the research between multiple laboratories within the timeline of the project. The individual will then interact with the entire team to help identify biological networks modulated during HP infA viral infections from data on miRNA signatures during LP and HP infA (H5/H7) viral infection in human cells.
Aarati R. Ranade, David Cherba, Shravan Sridhar, Patrick Richardson, Craig Webb, Anoor Paripati, Brad Bowles, Glen J. Weiss. MicroRNA 92a-2*: A Biomarker Predictive for Chemoresistance and Prognostic for Survival in Patients with Small Cell Lung Cancer. Journal of Thoracic Oncology, 2010; DOI: 10.1097/JTO.0b013e3181dea6be
http://www.microbiology.ubc.ca/Faculty%20Opportunities
Dr. Michelle Krakowski
Department of Microbiology & Immunology
Life Sciences Centre
3559-2350 Health Sciences Mall
Vancouver, B.C., V6T 1Z3.
http://www.fluinfectomics.ubc.ca/
Micro RNA profiling Postdoctoral Fellowship
We are looking for an exceptional candidate possessing experience with RNA, especially Micro-RNA (miRNA) in order to investigate the hypothesis that miRNAs direct host defense during H5/H7 influenza A virus-human host interactions. Because the biological role of miRNAs in mammals are still largely unknown, the successful Postdoctoral fellow will have a novel opportunity to increase our understanding of infA-induced pathogenesis at a cellular level by identifying host-cell miRNAs in response to infA virus infection. The successful candidate will interact with an international team of researchers from laboratories and industry in order to have access to the widest range of biological samples and specialized protocols currently available. We are looking for an individual who will readily co-ordinate all aspects of the research between multiple laboratories within the timeline of the project. The individual will then interact with the entire team to help identify biological networks modulated during HP infA viral infections from data on miRNA signatures during LP and HP infA (H5/H7) viral infection in human cells.
Friday, June 11, 2010
Thursday, June 10, 2010
Egenvalue, PCA, SVD (Singular Value Decomposition)
http://www.gersteinlab.org/courses/545/07-spr/slides/cbb545b-spr07-bioinfo5-svd1.ppt
library(HSAUR)
data("heptathlon", package="HSAUR")
h <- heptathlon score <- which(colnames(h) == "score") h_pca <- prcomp(h[, -score], scale=T) summary(h_pca) biplot(h_pca) screeplot(h_pca) h_pca$sdev^2 / sum(h_pca$sdev^2) # variance http://cran.r-project.org/web/packages/HSAUR/vignettes/Ch_principal_components_analysis.pdf
heptathlon_pca <- prcomp(heptathlon, scale=T) plot(heptathlon_pca) # barplot of variances # get proportion of variance for each PC summary(hepathalon_pca) # linear combination of PC1 from diff. columns (events) hepathalon_pca$rotation[,1] # compute scores for each row (competitors) # score = scaled(original data) * loadings (the coefficients, rotations) # same as hepathalon_pca$x[,1] predict(hepathalon_pca)[,1] # arrows are columns, points are rows biplot(heptathlon_pca, col = c("gray", "black")) # PC1 vs PC2, A biplot allows information on both samples and variables of a data matrix to be displayed graphically, http://support.sas.com/documentation/cdl/en/imlsug/62558/HTML/default/viewer.htm#ugmultpca_sect2.htm quadrant I = NE, quadrant II = NW http://r.789695.n4.nabble.com/PCA-and-variance-explained-td866300.html
p$sdev^2 / sum(p$sdev^2)
screeplot(hepathalon_pca)
library(HSAUR)
data("heptathlon", package="HSAUR")
h <- heptathlon score <- which(colnames(h) == "score") h_pca <- prcomp(h[, -score], scale=T) summary(h_pca) biplot(h_pca) screeplot(h_pca) h_pca$sdev^2 / sum(h_pca$sdev^2) # variance http://cran.r-project.org/web/packages/HSAUR/vignettes/Ch_principal_components_analysis.pdf
heptathlon_pca <- prcomp(heptathlon, scale=T) plot(heptathlon_pca) # barplot of variances # get proportion of variance for each PC summary(hepathalon_pca) # linear combination of PC1 from diff. columns (events) hepathalon_pca$rotation[,1] # compute scores for each row (competitors) # score = scaled(original data) * loadings (the coefficients, rotations) # same as hepathalon_pca$x[,1] predict(hepathalon_pca)[,1] # arrows are columns, points are rows biplot(heptathlon_pca, col = c("gray", "black")) # PC1 vs PC2, A biplot allows information on both samples and variables of a data matrix to be displayed graphically, http://support.sas.com/documentation/cdl/en/imlsug/62558/HTML/default/viewer.htm#ugmultpca_sect2.htm quadrant I = NE, quadrant II = NW http://r.789695.n4.nabble.com/PCA-and-variance-explained-td866300.html
p$sdev^2 / sum(p$sdev^2)
screeplot(hepathalon_pca)
3D zernike descriptors for content based shape retrieval
http://portal.acm.org/citation.cfm?id=781639
Monday, June 7, 2010
When Romance Meets Destiny
When Romance Meets Destiny
http://www.mysoju.com/when-romance-meets-destiny/
http://www.mysoju.com/when-romance-meets-destiny/
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