A-1 - Monthly Public Assistance Statistical Report Family Investment Program, September 2015

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

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, November 2015

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

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, November 2015

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

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, December 2015

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

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, January 2016

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

Statistical significance of quantitative PCR.

BACKGROUND: PCR has the potential to detect and precisely quantify specific DNA sequences, but it is not yet often used as a fully quantitative method. A number of data collection and processing strategies have been described for the implementation of quantitative PCR. However, they can be experimentally cumbersome, their relative performances have not been evaluated systematically, and they often remain poorly validated statistically and/or experimentally. In this study, we evaluated the performance of known methods, and compared them with newly developed data processing strategies in terms of resolution, precision and robustness. RESULTS: Our results indicate that simple methods that do not rely on the estimation of the efficiency of the PCR amplification may provide reproducible and sensitive data, but that they do not quantify DNA with precision. Other evaluated methods based on sigmoidal or exponential curve fitting were generally of both poor resolution and precision. A statistical analysis of the parameters that influence efficiency indicated that it depends mostly on the selected amplicon and to a lesser extent on the particular biological sample analyzed. Thus, we devised various strategies based on individual or averaged efficiency values, which were used to assess the regulated expression of several genes in response to a growth factor. CONCLUSION: Overall, qPCR data analysis methods differ significantly in their performance, and this analysis identifies methods that provide DNA quantification estimates of high precision, robustness and reliability. These methods allow reliable estimations of relative expression ratio of two-fold or higher, and our analysis provides an estimation of the number of biological samples that have to be analyzed to achieve a given precision.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, February 2016

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

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, March 2016

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

Statistical Summaries of Selected Iowa Streamflow Data Through September 2013, TR-669

This report is the final product of a two-year study that began October 1, 2013. In addition to the funding provided for this study by the Iowa Highway Research Board and the Iowa Department of Transportation (TR-669), the project was also funded by the U.S. Army Corps of Engineers and the U.S. Geological Survey. The report was published as an online report on January 4, 2016. The report is available online at http://dx.doi.org/10.3133/ofr20151214 . The main body of the report provides a description of the statistics presented for the streamgages and an explanation of the streamgage summaries, also included is a discussion of the USGS streamgage network in Iowa. Individual streamgage summaries are available as links listed in table 1, or all 184 streamgage summaries are available in a zipped file named “Streamgage Summaries.”

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, April 2016

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

Statistical methods for river runoff prediction

Methods used to analyze one type of nonstationary stochastic processes?the periodically correlated process?are considered. Two methods of one-step-forward prediction of periodically correlated time series are examined. One-step-forward predictions made in accordance with an autoregression model and a model of an artificial neural network with one latent neuron layer and with an adaptation mechanism of network parameters in a moving time window were compared in terms of efficiency. The comparison showed that, in the case of prediction for one time step for time series of mean monthly water discharge, the simpler autoregression model is more efficient.

Influence of the world's most challenging mountain ultra-marathon on energy cost and running mechanics

PURPOSE: To examine the effects of the world's most challenging mountain ultra-marathon (Tor des Géants(®) 2012) on the energy cost of three types of locomotion (cycling, level and uphill running) and running kinematics. METHODS: Before (pre-) and immediately after (post-) the competition, a group of ten male experienced ultra-marathon runners performed in random order three submaximal 4-min exercise trials: cycling at a power of 1.5 W kg(-1) body mass; level running at 9 km h(-1) and uphill running at 6 km h(-1) at an inclination of +15 % on a motorized treadmill. Two video cameras recorded running mechanics at different sampling rates. RESULTS: Between pre- and post-, the uphill-running energy cost decreased by 13.8 % (P = 0.004); no change was noted in the energy cost of level running or cycling (NS). There was an increase in contact time (+10.3 %, P = 0.019) and duty factor (+8.1 %, P = 0.001) and a decrease in swing time (-6.4 %, P = 0.008) in the uphill-running condition. CONCLUSION: After this extreme mountain ultra-marathon, the subjects modified only their uphill-running patterns for a more economical step mechanics.

Bootstrapping a statistical speech translator from a rule-based one

We describe a series of experiments in which we start with English to French and English to Japanese versions of an Open Source rule-based speech translation system for a medical domain, and bootstrap correspondign statistical systems. Comparative evaluation reveals that the rule-based systems are still significantly better than the statistical ones, despite the fact that considerable effort has been invested in tuning both the recognition and translation components; also, a hybrid system only marginally improved recall at the cost of a los in precision. The result suggests that rule-based architectures may still be preferable to statistical ones for safety-critical speech translation tasks.

Statistical reference criteria for adaptive signal processing in digital communications

A general criterion for the design of adaptive systemsin digital communications called the statistical reference criterionis proposed. The criterion is based on imposition of the probabilitydensity function of the signal of interest at the outputof the adaptive system, with its application to the scenario ofhighly powerful interferers being the main focus of this paper.The knowledge of the pdf of the wanted signal is used as adiscriminator between signals so that interferers with differingdistributions are rejected by the algorithm. Its performance isstudied over a range of scenarios. Equations for gradient-basedcoefficient updates are derived, and the relationship with otherexisting algorithms like the minimum variance and the Wienercriterion are examined.