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We evaluated whether aliskiren, valsartan, or a combination of both was protective following myocardial infarction (MI) through effects on matrix metalloproteinase (MMP)-9. C57BL/6J wild type (WT, n=94) and MMP-9 null (null, n=85) mice were divided into 4 groups at 3h post-MI: saline (S), aliskiren (A; 50mg/kg/day), valsartan (V; 40mg/kg/day), or A+V and compared to no MI controls at 28days post-MI. All groups had similar infarct areas, and survival rates were higher in the null mice. The treatments influenced systolic function and hypertrophy index, as well as extracellular matrix (ECM) and inflammatory genes in the remote region, indicating that primary effects were on the viable myocardium. Saline treated WT mice showed increased end systolic and diastolic volumes and hypertrophy index, along with reduced ejection fraction. MMP-9 deletion improved LV function post-MI. Aliskiren attenuated the increase in end systolic volume and hypertrophy index, while valsartan improved end diastolic volumes and aliskiren+valsartan improved the hypertrophy index only when MMP-9 was absent. Extracellular matrix and inflammatory gene expression showed distinct patterns among the treatment groups, indicating a divergence in mechanisms of remodeling. This study shows that MMP-9 regulates aliskiren and valsartan effects in mice. These results in mice provide mechanistic insight to help translate these findings to post-MI patients.

Hotelling's T2 statistic has many applications in multivariate analysis. In particular, it can be used to measure the influence that a particular observation vector has on parameter estimation. For example, in the bivariate case, there exists a direct relationship between the ellipse generated using a T2 statistic for individual observations and the hyperbolae generated using Hampel's influence function for the corresponding correlation coefficient. In this paper, we jointly use the components of an orthogonal decomposition of the T2 statistic and some influence functions to identify outliers or influential observations. Since the conditional components in the T2 statistic are related to the possible changes in the correlation between a variable and a group of other variables, we consider the theoretical influence functions of the correlations and multiple correlation coefficients. Finite-sample versions of these influence functions are used to find the estimated influence function values.

A control procedure is presented for monitoring changes in variation for a multivariate normal process in a Phase II operation where the subgroup size, m, is less than p, the number of variates. The methodology is based on a form of Wilk' statistic, which can be expressed as a function of the ratio of the determinants of two separate estimates of the covariance matrix. One estimate is based on the historical data set from Phase I and the other is based on an augmented data set including new data obtained in Phase II. The proposed statistic is shown to be distributed as the product of independent beta distributions that can be approximated using either a chi-square or F-distribution. An ARL study of the statistic is presented for a range of conditions for the population covariance matrix. Cases are considered where a p-variate process is being monitored using a sample of m observations per subgroup and m

When a process is monitored with a T 2 control chart in a Phase II setting, the MYT decomposition is a valuable diagnostic tool for interpreting signals in terms of the process variables. The decomposition splits a signaling T 2 statistic into independent components that can be associated with either individual variables or groups of variables. Since these components are T 2 statistics with known distributions, they can be used to determine which of the process variable(s) contribute to the signal. However, this procedure cannot be applied directly to Phase I since the distributions of the individual components are unknown. In this article, we develop the MYT decomposition procedure for a Phase I operation, when monitoring a random sample of individual observations and identifying outliers. We use a relationship between the T 2 statistic in Phase I with the corresponding T 2 statistic resulting when an observation is omitted from this sample to derive the distributions of these components and demonstrate ...

The left ventricle (LV) remodels with age and in response to pressure overload. While aging and pressure overload are superimposed in the clinical context, the structural and functional consequences of the individual processes are not well-understood. Accordingly, the objective of this study was to compare the effects of both early and late chronic hypertension on extracellular matrix remodeling. The following groups of Dahl rats were studied: 1) Young Salt Resistant (control, n=6); 2) Young Salt Sensitive (early phase of chronic hypertension, n=6); 3) Middle-aged Salt Resistant (aging, n=5); and 4) Middle-aged Salt Sensitive (late phase of chronic hypertension, n=6). We measured LV mass and body weight (BW) and immunoblotted a panel of matrix metalloproteinases (MMPs), tissue inhibitors of metalloproteinases (TIMPs), and extracellular matrix (ECM) proteins. Total collagen increased, several MMPs decreased, and TIMP-1 increased in the early phase of hypertension, consistent with fibrosis. Active-MMP-8 decreased from 8010±81 units in young salt resistant to 5260±313 units in young salt sensitive (p

When the production rate is low or only one observation is available at each time that a sample is to be taken, the traditional control chart for monitoring the process variation is the moving range chart. In this paper, we propose a chart based on the sequential probability ratio test (SPRT) for detecting increases in process variability, which can have some impact on process capability and product quality. Critical values for the control chart are presented. To demonstrate an application of the SPRT chart, an example using real manufacturing data is presented.

Data of 60 hydrocarbon species in C 2 –C 15 from previous measurements in Kaohsiung (Lai et al., 2004. Atmospheric Environment 38, 1997–2011) were analyzed by applying a chemical mass balance receptor model to determine the major source contributions to atmospheric hydrocarbons. Five major source groups were identified using factor analysis, and 11 source profiles were selected based on sensitivity test results to yield best model performance. Chemical mass balance (CMB) receptor model analyses show that motor vehicle exhaust is the primary source of ambient hydrocarbons during the morning (07–10) and evening (18–21) periods corresponding to the rush hours, but drops in the afternoon non-rush-hour (13–16) period. Furthermore, industrial processes and/or organic solvents contribute most to ambient hydrocarbons during non-rush hours. Contributions from boiler combustion and sewage treatment tanks are always minor. Wind direction analyses reveal that a relatively clean sea breeze tends to reduce the afternoon hydrocarbon concentrations below those during any other period of the day.

The events of high ozone concentrations and meteorological conditions covering the Kaohsiung metropolitan area were investigated based on data analysis and model simulation. A photochemical grid model was employed to analyze two ozone episodes in autumn (2000) and winter (2001) seasons, each covering three consecutive days (or 72 h) in the Kaohsiung City. The potential influence of the initial and boundary conditions on model performance was assessed. Model performance can be improved by separately considering the daytime and nighttime ozone concentrations on the lateral boundary conditions of the model domain. The sensitivity analyses of ozone concentrations to the emission reductions in volatile organic compounds (VOC) and nitrogen oxides (NOx) show a VOC-sensitive regime for emission reductions to lower than 30–40% VOC and 30–50% NOx and a NOx-sensitive regime for larger percentage reductions. Meteorological parameters show that warm temperature, sufficient sunlight, low wind, and high surface pressure are distinct parameters that tend to trigger ozone episodes in polluted urban areas, like Kaohsiung.

Proper diagnosis of nonrandom or systematic patterns occurring in a univariate Shewhart control chart can lead to process improvement by reducing the overall system variation. Process conditions are examined that lead to the occurrence of certain nonran..

Data from manufacturing processes may often come from nonnormal distributions. Process capability indices for nonnormal distributions have attracted much research in the last 20 years. This paper presents process capability indices for nonnormal distributions and reviews various approaches to deal with the problem of nonnormality in evaluating process capability. Keywords: nonnormality; process capability indices; quantile; yield

The usefulness of Hotelling's T2 statistic for monitoring batch process in both Phase I and Phase II operations is discussed. For Phase I operation where the focus is on detecting and removing outliers, consideration is given to batch processes wh..

The Hotelling's T2statistic has been used in constructing a multivariate control chart for individual observations. In Phase II operations, the distribution of the T2statistic is related to the F distribution provided the underlying population is multivariate normal. Thus, the upper control limit (UCL) is proportional to a percentile of the F distribution. However, if the process data show sufficient evidence of a marked departure from multivariate normality, the UCL based on the F distribution may be very inaccurate. In such situations, it will usually be helpful to determine the UCL based on the percentile of the estimated distribution for T2. In this paper, we use a kernel smoothing technique to estimate the distribution of the T2statistic as well as of the UCL of the T2chart, when the process data are taken from a multivariate non-normal distribution. Through simulations, we examine the sample size requirement and the in-control average run length of the T2control chart for sample observations taken f...

A FORTRAN program which selects the estimated best-fit Johnson transformation for a set of non-normal data is presented. The parameter estimates are computed using sample percentiles, and the quali...

The kinetics of thermal and oxidative decomposition of printed circuit boards is investigated under various heating rates (5, 10, 15, 20, and 25 K/min) and oxygen concentrations (5, 10, and 15%) in the nitrogen-oxygen atmosphere by means of thermogravimetric measurements. Results show that there is only one reaction stage in the inert atmosphere, with an initial reaction temperature of 564–584 K and an activation energy at 181.59±21.80 kJ/mol. But there are two reaction stages when oxygen is present in the carrier gas. The activation energy is in the range of 170.79–184.98 kJ/mol for the first-stage reaction and 113.69–138.85 kJ/mol for the second-stage reaction. The fractional residue is reduced by 14.1–18.8% in the oxidative atmosphere. The rate equations for various oxygen concentrations can be modeled by Arrhenius-type equations, from which kinetic parameters such as activation energy, preexponential factor, and reaction orders for unreacted material and oxygen concentration are determined using an extended Friedman’s method. The cutoff conversion factor between the first and the second reactions is in the range of 0.46–0.54, depending on the oxygen concentration. The complete rate equations when two-stage reactions are involved can be obtained by summing the individual weighted rate equations; the weighting factor are determined from the cut-off conversion factor.

Hotelling's T2: is a popular charting statistic to use when it is desired to construct a multivariate control chart using individual observations. In practice, the statistic usually is computed using the sample covariance matrix, S, as an estimate of the population covariance matrix, £. This estimator has many desirable properties, but in some situations other covariance matrix estimators may be more applicable. In this paper, we consider several alternative covariance estimators and present power determinations for the corresponding T2: statistics. The T2: charting procedure based on the sample covariance estimator, S, is shown to be most effective in detecting outliers in a Phase I operation.

Truncated data is a common occurrence in many industrial processes. This is particularly true in the situation of a supplier that has several customers, each with different specifications on the same product. Each customer will typically only observe a ..

Quality characteristics analyzed in statistical process control (SPC) often are required to be normally distributed. This is true in many types of control charts and acceptance sampling plans, as well as in process capability studies. If a characteristi..

Kinetics of thermal decomposition of styrene-butadiene rubber is investigated under various heating conditions (2°, 5°, 10°, and 20°K/min) in nitrogen by means of thermogravimetric measurements. Results show that the initial reaction temperature is within 622–661°K and apparent activation energy is 50.43 kcal/mol. The initial reaction temperature decreases but reaction range and reaction rate increase when heating rate is increased. The overall rate equation can be modelled by Arrhenius-type equation from which the kinetic parameters such as the activation energy, the pre-exponential factor, and the reaction order of unreacted material are determined using the Friedman method. The results will be useful in developing pyrolysis or starved-air incineration system for rubber-related wastes. Key words: Thermal decomposition kinetics; pyrolysis kinetics; styrene-butadiene rubber

The well-known Johnson system of distributions was developed by N. L. Johnson (1949). Slifker and Shapiro (1980) presented a criterion for choosing a member from the three distributional classes (SB,SL, and Sv) in the Johnson system to fit a set of data. The criterion is based on the value of a quantile ratio which depends on a specified positive z value and the parameters of the distribution. In this paper, we present some properties of the quantile ratio for various distributions and for some selected z values. Some comments are made on using the criterion for selecting a Johnson distribution to fit empirical data.

Process capability indices Cp, CPU, and CPL have been widely used to provide measures of process performance. In this article tests on comparing the capability of two processes are presented. Critical values are given to determine which process is more ..

In the August 2011 issue of Circulation: Cardiovascular Genetics , we reported our findings on plasma biomarkers of cardiac aging phenotypes.1 We thank Dr Lee for the questions regarding the plasma collection procedure used in our study. Different anticoagulants (eg, EDTA, heparin, and citrate) are available and used in blood sampling. Every anticoagulant has its own advantages and disadvantages. Heparin has been shown to increase matrix metalloproteinase (MMP)-9 levels when added to the heparinized plasma after blood collection (postanalytical phase).2 Adding heparin in the tube …

Background— We have previously shown that cardiac sarcopenia occurs with age in C57/BL6J mice. However, underlying mechanisms and plasma biomarkers of cardiac aging have not been identified. Accordingly, the objective of this study was to identify and evaluate plasma biomarkers that reflect cardiac aging phenotypes. Methods and Results— Plasma from adult (7.5±0.5 months old, n=27) and senescent (31.7±0.5 months old, n=25) C57/BL6J mice was collected, and levels of 69 markers were measured by multi-analyte profiling. Of these, 26 analytes were significantly increased and 3 were significantly decreased in the senescent group compared with the adult group. The majority of analytes that increased in the senescent group were inflammatory markers associated with macrophage functions, including matrix metalloproteinase-9 (MMP-9) and monocyte chemotactic protein-1 (MCP-1/CCL-2). Immunoblotting (n=12/group) showed higher MMP-9 and MCP-1 levels in the left ventricle (LV) of senescent mice ( P P Conclusions— Our results suggest that MCP-1 and MMP-9 are potential plasma markers for cardiac aging and that augmented MCP-1 and MMP-9 levels and macrophage content in the LV could provide an underlying inflammatory mechanism of cardiac aging.

Process capability indices CPU,CPL,and Cpk have been used to measure the capability of a process. Tests on determining thecapability of a process are presented.Critical values are given in order to determine whether a process is capable.For a given sample size and a given significance level,the power of each test is considered.Tables for critical values andpower values are also presented

The particular bivariate noncentral t-distribution associated with two univariate noncentral t variates having a correlation coefficient of one is considered. Some applications and properties are presented together with tables in the same form as Johnson and Welch's tables for a univariate noncentral t-distribution.

The problem of determining the minimum sample size for two-sided beta-content tolerance intervals when the population size is finite is investigated. It is assumed that a simple random sample is selected from the population. An expression for the tolera..

In sampling inspection by variables, an item is considered defective if its quality characteristic Y falls below some specification limit L0. We consider switching to a new supplier if we can be sure that the proportion of defective items for the new supplier is smaller than the proportion defective for the present supplier. Assume that Y has a normal distribution. A test for comparing these proportions is developed. A simulation study of the performance of the test is presented.

Lower confidence limits are derived for the common measures of process capability, usually indicated by Cp, CPU, CPL, and Cpk. The measures are estimated based on a random sample of observations from the process when the process is assumed to be normall..

Background:Female Dahl salt-sensitive (DS) rats fed a low-salt diet develop hypertension at 6 months of age. Ovariectomy at 2 months of age accelerates the development of hypertension, and estrogen replacement delays it. Although acute pressure overload induces structural changes in the left ventricle (LV) further effects of gradual hypertension on LV remodeling have not been examined in the DS rat model. Objective: The purpose of this study was to test the hypothesis that aging and estrogen loss in hypertensive DS rats are accompanied by changes in LV remodeling. Methods: Four groups of DS rats were examined: young intact, middle-aged (MA) intact, MA ovariectomized (MA-OVX), and MA-OVX with 17β-eestradiol (E2) supplementation (MA-OVX+E2). Myocardial matrix metalloproteinases (MMPs),tissue inhibitors of metalloproteinases (TIMPs),and extracellular matrix (ECM) proteins were assessed by immunoblotting. Results: Each of the 4 groups comprised 6 animals. Mean (SEM) LV mass was significantly greater in the MA-intact and the MA-OVX groups (1257 [31] mg and 1199 [25] mg, respectively; both, P < 0.05) compared with the young-intact group (697 [6] mg). LV mass in the MA-OVX+E2 group was significantly lower compared with the MA-intact and MA-OVX groups (both, P < 0.05), suggesting that estrogen may attenuate LV remodeling.Fibronectin and collagen III and IV concentrations increased significantly in the MA-intact and MA-OOVX groups (all, P < 0.05),indicating increased fibrosis. Multiple MMPs also increased in the MA-intact an nd MA-OVX rats, including MMP-3, -7, -99, -113, and -114, and all TIMPs. In contrast, estrogen attenuated fibrosis by increasing MMP-8 concentrations and increasing collagen III fragments. From good-fit regression modeling, MMP-13 and MMP-14 concentrations correlated positively with LV mass for the MA-intact and MA-OVX groups, respectively. Conclusions: Gradual hypertension stimulated ECM turnover by increasing both MMP/TIMP production and ECM degradation.Estrogen loss or gain resulted in a shift in MMP profiles, suggesting that MMP-13 and MMP-14 may be differentially regulated in postmenopausal hypertension.

When Hotelling's T2 is used as a control statistic for a multivariate normal population with unknown parameters, the statistic is commonly expressed as a function of the sample mean vector and the sample covariance matrix. In this setting, it is well known that as the sample size increases, these sample estimators approach their true population values, as they are consistent estimators. However, it is not well established as to what sample size is needed to assure that this result is approximately true. This is due, in part, to the large number of parameters that must be estimated in the multivariate case. To solve this problem, we present three helpful procedures for determining an effective sample size when using the T2 statistic to monitor the performance of a process.

Ambient particulates of PM2.5 were sampled at three sites in Kaohsiung, Taiwan, during February and March 1999. In addition, resuspended PM2.5 collected from traffic tunnels, paved roads, fly ash of a municipal solid waste (MSW) incinerator, and seawater was obtained. All the samples were analyzed for twenty constituents, including water-soluble ions, organic carbon (OC), elemental carbon (EC), and metallic elements. In conjunction with local source profiles and the source profiles in the model library SPECIATE EPA, the receptor model based on chemical mass balance (CMB) was then applied to determine the source contributions to ambient PM2.5. The mean concentration of ambient PM2.5 was 42.69-53.68 micrograms/m3 for the sampling period. The abundant species in ambient PM2.5 in the mass fraction for three sites were OC (12.7-14.2%), SO4(2-) (12.8-15.1%), NO3- (8.1-10.3%), NH4+ (6.7-7.5%), and EC (5.3-8.5%). Results of CMB modeling show that major pollution sources for ambient PM2.5 are traffic exhaust (18-54%), secondary aerosols (30-41% from SO4(2-) and NO3-), and outdoor burning of agriculture wastes (13-17%).

Individual measurements and moving-range control charts are applied when the quality characteristic follows a normal distribution. In the semiconductor industry, certain quality characteristics are monitored where the data do not follow a normal distribution. In this paper we consider alternative design strategies for control charts where the distribution of the data is skewed. Although we use skewed data, these techniques are also applicable to other non-normally distributed data. We discuss using a 'state of the art' curve-fitting method, data transformations, and nonsymmetrical limits for the control charting of skewed data. This paper provides alternatives for the design, implementation, and maintenance of process control for non- normal or skewed data.

For samples of any given size n>-2 from a normal distribution, Gillespie (1984) has shown how to obtain the factor k so that the coverage of each tail will be at least 1-p. In order to show the effect of sample size on the coverage, this paper presents the maximum difference between the actual coverage and the required value, β, for the coverage.

Some properties are considered of two-sided 3-content tolerance intervals for a normal distribution. We are given the sample mean, x, and the sample standard deviation, s, computedfrom a random sample of size n from a N(l,az) distribution. The tolerance interval x ± ks will cover a proportion, 3, of the sampled distribution with (100)confidence. We consider the effect of sample size on the proportion of the population covered, using a criteria for measuring the precision of the coverage. We also give tables to enable the user to control this precision. 969

Representations of the doubly noncentral F-distribution are presented i n terms of the cdf of the noncentral chi-square. These are specialized t o the oncentral F and f o r an even number of degrees of freedom f o r the denominator the noncentral F is zxpressed as a weighted sum of noncentral t - probabilities When either or both of the degrees of freedom are even, this result is then specialized to give the F distribution in terms of Student's t - distribution

The effect of measurement error on the operating characteristics of one-sided (normal distribution) variables sampling plans is discussed. A table showing the effect on the operating characteristic curves for the proportion nonconforming is given in ter..

The problem of determining sample size for two-sided-β-content tolerance intervals which control both tails of the normal distribution is investigated. The tolerance limits are defined to assure that the tail proportions do not exceed specified values p 1 and p 2 . We determine the minimum sample size so that, at a given confidence level, the tail proportions will not be too small, i.e., so that the tolerance interval will not be overly conservative. Tables of the sample size n and the corresponding factors are provided.

Assume that a sample is available from a population having an exponential distribution, and that l Future sample are to be taken from the same population. This paper provides a formula for the same population. This paper provides a formula for computing a one–sided lower simulataneous prediction limit which is to be below the (ki − mi + 1) –st order statistics of a future sample of size ki for the i = 1,…,2, hased on the sample mean of a past sample. Tables for factors for one–sided lower simultaneous predicition limits are provided. Such limits are of practical importance in determining acceptance criteria and predicting system survival times.