div class="center"div class="image"a href="http://pixhost.ws/avaxhome/big_show.php?/avaxhome/93/fc/0009fc93.jpeg" target="_blank"img src="http://pixhost.ws/avaxhome/93/fc/0009fc93_medium.jpeg" id="external_img_654483"//a/divbr/ b3Delight v8.01 and 3Delight for Maya v4.0 (x32/x64) | 15.6MB/b/divbr/ This release includes ray-tracing optimizations, multi-threading optimizations, improved point-based global illumination, improved photon-mapping, new displacement features and extensions to the shading language. 3Delight For Maya has now a hierarchical system for RenderMan attributes, better HyperShade support, better image based lighting capabilities, background rendering mode and many adjustments for better usability.3Delight For Maya is our plug-in for Maya users wishing to create high quality work that is sometimes difficult to achieve using Maya's internal renderer. The plug-in was designed to be easy to use for Maya artists as well as provide the necessary power and flexibility for the more demanding production environments.

Motivation: Tissue microarrays (TMAs) quantify tissue-specific protein expression of cancer biomarkers via high-density immuno-histochemical staining assays. Standard analysis approach estimates a sample mean expression in the tumor, ignoring the complex tissue-specific staining patterns observed on tissue arrays.

Methods: In this article, a cell mixture model (CMM) is proposed to reconstruct tumor expression patterns in TMA experiments. The concept is to assemble the whole-tumor expression pattern by aggregating over the subpopulation of tissue specimens sampled by needle biopsies. The expression pattern in each individual tissue element is assumed to be a zero-augmented Gamma distribution to assimilate the non-staining areas and the staining areas. A hierarchical Bayes model is imposed to borrow strength across tissue specimens and across tumors. A joint model is presented to link the CMM expression model with a survival model for censored failure time observations. The implementation involves imputation steps within each Markov chain Monte Carlo iteration and Monte Carlo integration technique.

Results: The model-based approach provides estimates for various tumor expression characteristics including the percentage of staining, mean intensity of staining and a composite meanstaining to associate with patient survival outcome.

Availability: R package to fit CMM model is available at http://www.mskcc.org/mskcc/html/85130.cfm

Contact: shenr@mskcc.org

Supplementary information: Supplementary data are available at Bioinformatics online.

You may remember Sanjiv Jivan as the GWT-Ext developer that changed over the debacle. He then went on to start work on a GWT version of SmartClient libraries and has now released SmartGWT 1.0.

It is a tour de force of not only a huge widget library, but he explains how it is much more:

So what’s so smart about SmartGWT? Let me try to explain. SmartGWT is not just another Widget library. While most Ajax frameworks focus primarily on presentation and displaying mostly read-only data, either local or via XML / JSON, SmartClient was built with server side integration in mind. Most enterprise applications are not just about about data presentation, but about being able to propagate data changes made by the user to the backend. So while its tempting to use library X that has a cool tree widget, it’s not until you actually try to use it your application that you encounter the real world hard problems. For example, how do you take data from your business objects on the server to not only display in a tree widget, but also be able to update your data model when, say, the user reorders the tree nodes or make edits / deletes in the UI. How do you display hierarchical data where nodes may have hundreds of children? And so on..

Most Ajax frameworks stop short and leave it up to the user to manage state on the client side and propagating the changes to the server. This is not a trivial problem! Over the past few years UI widget libraries have improved significantly and there are now various options available to users. Simply put, having good looking widgets is not the hard problem today. In addition to having good looking widgets, having end-to-end integration of the UI components with the backend is the hard problem. It’s this 20% of the functionality that takes 80% of the time in building most enterprise applications.

The showcase will give you a good feel for what is out there, and it contains some interesting items including:

• Miller Columns: The ColumnTree provides an alternate navigation paradigm for Tree data, sometimes called “Miller Columns” and seen in iTunes
• Nested Grid: A convenient way to display 1-n relationships
• Printable Views: SmartGWT supports printable views of its components.
• Advanced Filter Builder: a databound filter builder.
• Tile View: Representation of data as “tiles”. Again, TileGrid supports data binding so sort / filter and edits can be easily performed.
• Calendars : A Google Calendar like widget that supports databinding so edits and drag drop changes can easily be propgated to the server for persisting.
• TreeGrid : Supports multiple columns, editing, column locking, lazy loading and more.
• Live Grid / Tree: Grid / Tree virtual scrolling or live grid

Really amazing work. Sanjiv recently had a Q&A session with InfoQ where he talks about the approach taken in developing SmartGWT and the his thoughts on the differences between SmartClient and Ext.

iGenomics (24 July 2008)/ibr /br /We are studying variable selection in multiple regression models in which molecular markers and/or gene-expression measurements as well as intensity measurements from protein spectra serve as predictors for the outcome variable (i.e., trait or disease state). Finding genetic biomarkers and searching genetic-epidemiological factors can be formulated as a statistical problem of variable selection, in which, from a large set of candidates, a small number of trait-associated predictors are identified. We illustrate our approach by analyzing the data available for chronic fatigue syndrome (CFS). CFS is a complex disease from several aspects, e.g., it is difficult to diagnose and difficult to quantify. To identify biomarkers we used microarray data and SELDI-TOF-based proteomics data. We also analyzed genetic marker information for a large number of SNPs for an overlapping set of individuals. The objectives of the analyses were to identify markers specific to fatigue that are also possibly exclusive to CFS. The use of such models can be motivated, for example, by the search for new biomarkers for the diagnosis and prognosis of cancer and measures of response to therapy. Generally, for this we use Bayesian hierarchical modeling and Markov Chain Monte Carlo computation.br /iM Bhattacharjee, C H Botting, M J Sillanpää/i