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Articles 1 - 7 of 7
Full-Text Articles in Life Sciences
Msis-Glenn: Natural Selection In Wolves Leads To Domesticated Dogs Predicted By Agent-Based Model Simulations, Alex Capaldi, David C. Elzinga
Msis-Glenn: Natural Selection In Wolves Leads To Domesticated Dogs Predicted By Agent-Based Model Simulations, Alex Capaldi, David C. Elzinga
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
A Cost-Effective Method To Passively Sample Communities At The Forest Canopy-Aerosphere Interface, Michael Cunningham-Minnick, H. Patrick Roberts, Brian Kane Ph.D., Joan Milam, David I. King Ph.D.
A Cost-Effective Method To Passively Sample Communities At The Forest Canopy-Aerosphere Interface, Michael Cunningham-Minnick, H. Patrick Roberts, Brian Kane Ph.D., Joan Milam, David I. King Ph.D.
Data and Datasets
HOBO logger data of hourly measurements at canopy-aerosphere interface from June to August above temperate forest on campus of University of Massachusetts. Weather station data (precipitation and wind speeds) from nearby weather station extracted from Mesowest.com and needed for manuscript figures. Code (R language) to recreate foundation of figures in manuscript.
Deciphering The Associations Between Gene Expression And Copy Number Alteration Using A Sparse Double Laplacian Shrinkage Approach, Shuangge Ma
Shuangge Ma
Both gene expression levels (GEs) and copy number alterations (CNAs) have important implications in the development of complex diseases. GEs are partly regulated by CNAs, and much effort has been devoted to understanding their relations. The expression of a gene can be regulated by multiple CNAs, and one CNA can regulate the expression of multiple genes. In addition, multiple GEs (CNAs) can be correlated with each other. The existing methods for associating GEs with CNAs have limitations in deciphering the complex data structures. In this study, we develop a sparse double Laplacian shrinkage approach. It jointly models the effects of …
A Penalized Robust Semiparametric Approach For Gene-Environment Interactions, Shuangge Ma
A Penalized Robust Semiparametric Approach For Gene-Environment Interactions, Shuangge Ma
Shuangge Ma
In genetic and genomic studies, gene-environment (G*E) interactions have important implications. Some of the existing G$\times$E interaction methods are limited by analyzing a small number of G factors at a time, by assuming linear effects of E factors, by assuming no data contamination, and by adopting ineffective selection techniques. In this study, we propose a new approach for identifying important G*E interactions. It jointly models the effects of all E and G factors and their interactions. A partially linear varying coefficient model (PLVCM) is adopted to accommodate possible nonlinear effects of E factors. A rank-based loss function is used to …
Hamamatsu Flash4.0 Scmos Exposure Time Series, George Mcnamara
Hamamatsu Flash4.0 Scmos Exposure Time Series, George Mcnamara
George McNamara
Hamamatsu FLASH4.0 scientific cMOS camera exposure time series are pairs of images of:
1 millisecond (00,001ms series)
10 millisecond (00,010ms series)
100 millisecond (00,100ms series)
1,000 millisecond (01,000ms series)
4,000 millisecond (04,000ms series)
10,000 millisecond (10,000ms series)
I also included:
* difference images (exposure 2 minus exposure 1 plus 100 intensity values).
* a series of eleven 1 second (1,000 ms) exposure time images in a multi-plane TIFF file (different images than the pair of 1,000ms images above).
* Stack Arithmetic: Median, Average, Minimum, Maximum, of the eleven plane series (Stack Arithmetic is a MetaMorph command).
These images were acquired …
Software For Assumption Weighting For Meta-Analysis Of Genomic Data, Debashis Ghosh, Yihan Li
Software For Assumption Weighting For Meta-Analysis Of Genomic Data, Debashis Ghosh, Yihan Li
Debashis Ghosh
This is the software that accompanies Li and Ghosh, "Assumption weighting for incorporating heterogeneity into meta-analysis of genomic data."
Understanding The Physical Properties That Control Protein Crystallization By Analysis Of Largescale Experimental Data, W. Nicholson Price Ii, Yang Chen, Samuel K. Handelman, Helen Neely, Philip Manor, Richard Karlin, Rajesh Nair, Jinfeng Liu, Michael Baran, John Everett, Saichiu N. Tong, Farhad Forouhar, Swarup S. Swaminathan, Thomas Acton, Rong Xiao, Joseph R. Luft, Angela Lauricella, George T. Detitta, Burkhard Rost, Gaetano T. Montelione, John T. Hunt
Understanding The Physical Properties That Control Protein Crystallization By Analysis Of Largescale Experimental Data, W. Nicholson Price Ii, Yang Chen, Samuel K. Handelman, Helen Neely, Philip Manor, Richard Karlin, Rajesh Nair, Jinfeng Liu, Michael Baran, John Everett, Saichiu N. Tong, Farhad Forouhar, Swarup S. Swaminathan, Thomas Acton, Rong Xiao, Joseph R. Luft, Angela Lauricella, George T. Detitta, Burkhard Rost, Gaetano T. Montelione, John T. Hunt
Law Faculty Scholarship
Crystallization is the most serious bottleneck in high-throughput protein-structure determination by diffraction methods. We have used data mining of the large-scale experimental results of the Northeast Structural Genomics Consortium and experimental folding studies to characterize the biophysical properties that control protein crystallization. This analysis leads to the conclusion that crystallization propensity depends primarily on the prevalence of well-ordered surface epitopes capable of mediating interprotein interactions and is not strongly influenced by overall thermodynamic stability. We identify specific sequence features that correlate with crystallization propensity and that can be used to estimate the crystallization probability of a given construct. Analyses of …