A-7 Statistical Report On FIP Applications And Cases Discontinued, August 2005

Statistical Report on FIP Applicatins and Cases Discontinued August 2005

A-7 Statistical Report On FIP Applications And Cases Discontinued, September 2005

Statistical Report On FIP Applications And Cases Discontinued, September 2005

A-7 Statistical Report On FIP Applications And Cases Discontinued, October 2005

Statistical Report On FIP Applications And Cases Discontinued, October 2005

A-7 Statistical Report On FIP Applications And Cases Discontinued, November 2005

Statistical Report On FIP Applications And Cases Discontinued, November 2005

A-7 Statistical Report On FIP Applications And Cases Discontinued, December 2005

Statistical Report On FIP Applications And Cases Discontinued, December 2005

Iowa Sales and Use Tax Annual Statistical Report FY 2009

Iowa Sales and Use Tax Annual Statistical Report FY 2009

Iowa Individual Income Tax Statistical Report 2008

Iowa Individual Income Tax Statistical Report 2008

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, May 2010

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

The prediction of speed and incline in outdoor running in humans using accelerometry.

PURPOSE: To explore whether triaxial accelerometric measurements can be utilized to accurately assess speed and incline of running in free-living conditions. METHODS: Body accelerations during running were recorded at the lower back and at the heel by a portable data logger in 20 human subjects, 10 men, and 10 women. After parameterizing body accelerations, two neural networks were designed to recognize each running pattern and calculate speed and incline. Each subject ran 18 times on outdoor roads at various speeds and inclines; 12 runs were used to calibrate the neural networks whereas the 6 other runs were used to validate the model. RESULTS: A small difference between the estimated and the actual values was observed: the square root of the mean square error (RMSE) was 0.12 m x s(-1) for speed and 0.014 radiant (rad) (or 1.4% in absolute value) for incline. Multiple regression analysis allowed accurate prediction of speed (RMSE = 0.14 m x s(-1)) but not of incline (RMSE = 0.026 rad or 2.6% slope). CONCLUSION: Triaxial accelerometric measurements allows an accurate estimation of speed of running and incline of terrain (the latter with more uncertainty). This will permit the validation of the energetic results generated on the treadmill as applied to more physiological unconstrained running conditions.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, June 2010

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity.

Sarcomas are heterogeneous and aggressive mesenchymal tumors. Histological grading has so far been the best predictor for metastasis-free survival, but it has several limitations, such as moderate reproducibility and poor prognostic value for some histological types. To improve patient grading, we performed genomic and expression profiling in a training set of 183 sarcomas and established a prognostic gene expression signature, complexity index in sarcomas (CINSARC), composed of 67 genes related to mitosis and chromosome management. In a multivariate analysis, CINSARC predicts metastasis outcome in the training set and in an independent 127 sarcomas validation set. It is superior to the Fédération Francaise des Centres de Lutte Contre le Cancer grading system in determining metastatic outcome for sarcoma patients. Furthermore, it also predicts outcome for gastrointestinal stromal tumors (GISTs), breast carcinomas and lymphomas. Application of the signature will permit more selective use of adjuvant therapies for people with sarcomas, leading to decreased iatrogenic morbidity and improved outcomes for such individuals.

Novel methods improve prediction of species' distributions from occurrence data

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, July 2010

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, August 2010

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, September 2010

A-1 - Monthly Public Assistance Statistical Report Family Investment Program