Re: Estimate Book By Max Fajardo Pdf Free 60
Gro Bert
Methods: We recruited 15 participants between the ages of 18 and 35 y who were 1-5 y post-ACLR. For the MRI assessment, we used an iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) sequence to assess the mid-thigh. A single reader manually segmented the rectus femoris on two consecutive MRI slices using ITK-Snap to estimate the percent intramuscular fat. For the ultrasound assessment, a single investigator captured transverse panoramic ultrasound images of the mid-thigh with the participant positioned supine and the knee flexed to 30. A separate single reader used ImageJ to manually segment the rectus femoris ultrasound images. Ultrasound metrics included muscle cross-sectional area, echo intensity and subcutaneous fat thickness. A stepwise linear multiple regression was used to develop an equation to predict MRI percent intramuscular fat using the ultrasound metrics and common demographics (i.e., age, sex, height, mass). Additionally, intraclass correlation coefficients (ICC2,k) and Bland-Altman plots were used to assess the agreement between true and estimated percent intramuscular fat.
Results: Echo intensity and age significantly predicted MRI intramuscular fat percent (p = 0.003, r2 = 0.62). When using the conversion equation, there was high agreement (ICC2,k = 0.87, 95% confidence interval: 0.62-0.96) between the estimated and true percent intramuscular fat.
EPA scientists and analysts recently completed a risk assessment to understand the impact of EtO emissions from the Customed, Inc. facility. As part of this risk assessment, we used the most recent available information about how much EtO the company emits into the air and we modeled estimated cancer risks to people living nearby. The risk assessment identified elevated cancer risk in the Fajardo community. EPA is committed to working with state and local agencies, facilities, and communities to reduce this risk.
The area in blue shows estimated lifetime cancer risks of 100 in a million or greater from breathing air containing EtO emitted from the facility (or the same as 1 additional cancer case in 10,000 people). A lifetime cancer risk of 100 in a million means that, if 1 million people were exposed to this level of EtO in the air 24 hours a day for 70 years, 100 people would be expected to develop cancer from that exposure.
For this risk assessment, we looked at excess cancer risk attributable to a single chemical, EtO. This estimated risk is in addition to the risk of developing cancer from other causes. This is a worst-case scenario that assumes a person stays in the highest risk area 24 hours a day continuously for 70 years. EPA takes this approach because we want to be protective of the most exposed and most vulnerable individuals from risk associated with EtO emissions from this facility.
The Fajardo Metropolitan Statistical Area was a United States Census Bureau defined Metropolitan Statistical Area (MSA) in northeastern Puerto Rico. A July 1, 2009 Census Bureau estimate placed the population at 80,707, a 2.77% increase over the 2000 census figure of 78,533.[1]
We propose a criterion to estimate the regression function by means of a nonparametric and fuzzy set estimator of the Nadaraya-Watson type, for independent pairs of data, obtaining a reduction of the integrated mean square error of the fuzzy set estimator regarding the integrated mean square error of the classic kernel estimators. This reduction shows that the fuzzy set estimator has better performance than the kernel estimations. Also, the convergence rate of the optimal scaling factor is computed, which coincides with the convergence rate in classic kernel estimation. Finally, these theoretical findings are illustrated using a numerical example.
In this paper we estimate the regression function by means of the nonparametric and fuzzy set estimator of the Nadaraya-Watson type, for independent pairs of data, introduced by Fajardo et al. [3], obtaining a significant reduction of the integrated mean square error of the fuzzy set estimator regarding the integrated mean square error of the classic kernel estimators. This reduction is obtained by the conditions imposed on the thinning function, a function that allows to define the estimator proposed by Fajardo et al. [4], which implies that the fuzzy set estimator has better performance than the kernel estimations. The above reduction is not obtained in Fajardo et al. [3]. Also, the convergence rate of the optimal scaling factor is computed, which coincides with the convergence rate in classic kernel estimation of the regression function. Moreover, the function that minimizes the integrated mean square error of the fuzzy set estimator is obtained. Finally, these theoretical findings are illustrated using a numerical example estimating a regression function with the fuzzy set estimator and the classic kernel estimators.
Accuracy of temporal clinician prediction of survival (CPS) by clinician and patient age. The y-axis represents the predicted values from a multivariate logistic regression examining temporal accuracy of CPS as a function of clinician and patient age. The x-axis represents the actual patient age (in years) for each observation. Patient age was a significant predictor of clinician accuracy (p = .019). On average, a significantly more accurate temporal CPS was provided for older patients than for younger patients, regardless of which clinician provided the estimate.
To our knowledge, this is the first study to directly compare probabilistic CPS with temporal CPS. We found that the probabilistic approach was superior to the temporal approach in predicting survival. Although nurses still overestimated with the probabilistic model, they were much more accurate than with the temporal approach. The high accuracy rate can be attributed to the use of the probability question, which is easier to ask and answer, provided that the respondents were comfortable with the concept of probability. This approach, coupled with the fact that we tested the questions with a group of experienced clinicians and in a patient population with a relatively short survival time, made it possible to achieve a high degree of accuracy. Interestingly, only a minority of clinicians expressed uncertainty when using this approach, supporting the clinical utility of probabilistic CPS.
By exploiting the capability of identifying and extracting surface waves existing in a seismic signal, we can proceed to estimate the angular displacement (rotation about the horizontal axis normal to the direction of propagation of the wave; rocking) associated with Rayleigh waves as well as the angular displacement (rotation about the vertical axis; torsion) associated with Love waves. For a harmonic Rayleigh (Love) wave, rocking (torsion) would be proportional to the harmonic vertical (transverse horizontal) velocity component and inversely proportional to the phase velocity corresponding to the particular frequency of the harmonic wave (a fact that was originally exploited by Newmark (1969) [15] to estimate torsional excitation). Evidently, a reliable estimate of the phase velocity (as a function of frequency) is necessary. As pointed out by Stockwell (2007) [17], because of its absolutely referenced phase information, the S-Transform can be employed in a cross-spectrum analysis in a local manner. Following this suggestion a very reliable estimate of the phase velocity may be obtained from the recordings at two nearby stations, after the dispersed waves have been identified and extracted. Synthesis of the abovementioned harmonic components can provide a reliable estimate of the rocking (torsional) motion induced by an (extracted) Rayleigh (Love) wave. We apply the proposed angular displacement estimation procedure for two well recorded data sets: (1) the strong motion data generated by an aftershock of the 1999 Chi-Chi, Taiwan earthquake and recorded over the Western Coastal Plain (WCP) of Taiwan, and (2) the strong motion data generated by the 2010 Darfield, New Zealand earthquake and recorded over the Canterbury basin. The former data set is dominated by basin-induced Rayleigh waves while the latter contains primarily Love waves.
Of the more than 140,000 islanders estimated to have left since the storm, more than 130,000 went to Florida alone, followed by Pennsylvania, Texas, New York and New Jersey, researchers at Hunter College said. Among them are an estimated 14,000 public school students, Education Secretary Julia Keleher said.
A total of 141 Ipomoea batatas (L.) Lam. accessionsderived from botanical seed originally collected from 26 sites in 4 Provinces inPapua New Guinea, a secondary center of genetic diversity for sweetpotato, weregenetically analyzed. Two hundred Amplified Fragment Length Polymorphism (AFLP)markers were identified and utilized in the analysis. Relatedness amongaccessions was estimated by analyzing the AFLP data using the Dice coefficientof similarity and UPGMA methods. The molecular analysis revealed relativelylimited genetic diversity within and between sites. Genotypes collected in agiven region often displayed molecular marker variability similar to thatobserved over the entire sampled area. However, a subset of 14 genotypes derivedfrom seed collected from New Ireland island differed from genotypes collected onNew Guinea island. Estimates of genetic diversity-based similarity valuescalculated from the AFLP data indicated a moderate level of diversity (0.767mean coefficient of similarity) across all plant materials analyzed. Threemethods of selection were evaluated for their efficacy in capturing themolecular marker diversity within the plant materials in the form of a subset.They were random, stratified-random (geographic based), and marker-assistedselection (MAS). MAS was the most efficient. A Maximally Diverse Subset (MDS) of12 genotypes capturing 92% of the molecular marker diversity was identified.
aa06259810