Bias Factor (bias + factor)

Distribution by Scientific Domains

Selected Abstracts

Response Surface Model for the Estimation of Escherichia coli O 157:H7 Growth under Different Experimental Conditions

Rose Maria García-Gimeno
ABSTRACT: In this study, a Response Surface Model (RSM) of Escherichia coli O157:H7 as affected by pH levels, sodium chloride and nitrite concentrations, temperature, and aerobic/anaerobic conditions is presented. The standard error of prediction (%SEP) obtained was acceptable for the growth rate prediction (33%SEP), although a bit high for lag time (53.01 %SEP). Mathematical validation demonstrated that the RSM predicts growth rate values on the fail-safe side in aerobic conditions and within the acceptable range (bias factor [Bf] = 0.99) with acceptable accuracy (accuracy factor [Af] = 1.15), as well as for lag time (Bf = 1.05; Af = 1.25). Temperature was found to have the greatest effect on the kinetic parameters, followed by NaCl concentration and pH. In the experimental range considered here (0 to 200 ppm), NaNO2 concentration was found to have a significant effect on growth rate but not on lag time. [source]

CODES/Neural Network Model: a Useful Tool for in Silico Prediction of Oral Absorption and Blood-Brain Barrier Permeability of Structurally Diverse Drugs

Isabel Dorronsoro
Abstract Two different neural network models able to predict both oral absorption (OA) and blood-brain barrier (BBB) permeability of structurally diverse drugs in use clinically are presented here. Using the descriptors generated by CODES, a program which codifies molecules from a topological point of view, we avoid the uncertain choice of molecular conformation and physicochemical parameters. In this work, a method called Reduction of Dimensions, designed for compressing data, is applied for the first time in order to minimize the bias factor added to a QSAR study when the selection of descriptors are performed. [source]

Where are z= 4 Lyman Break Galaxies?

Results from conditional luminosity function models of luminosity-dependent clustering
ABSTRACT Using the conditional luminosity function (CLF) , the luminosity distribution of galaxies in a dark matter halo , as a way to model galaxy statistics, we study how z= 4 Lyman Break Galaxies (LBGs) are distributed in dark matter haloes. For this purpose, we measure luminosity-dependent clustering of LBGs in the Subaru/XMM,Newton Deep Field by separating a sample of 16 920 galaxies to three magnitude bins in i, band between 24.5 and 27.5. Our model fits to data show a possible trend for more-luminous galaxies to appear as satellites in more-massive haloes; the minimum halo mass in which satellites appear is 3.9+4.1,3.5× 1012, 6.2+3.8,4.9× 1012 and 9.6+7.0,4.6× 1012 M, (1, errors) for galaxies with 26.5 < i, < 27.5, 25.5 < i, < 26.5 and 24.5 < i, < 25.5 mag, respectively. The satellite fraction of galaxies at z= 4 in these magnitude bins is 0.13,0.3, 0.09,0.22 and 0.03,0.14, respectively, where the 1, ranges account for differences coming from two different estimates of the z= 4 LF from the literature. To jointly explain the LF and the large-scale linear bias factor of z= 4 LBGs as a function of rest UV luminosity requires central galaxies to be brighter in UV at z= 4 than present-day galaxies in same dark matter mass haloes. Moreover, UV luminosity of central galaxies in haloes with total mass greater than roughly 1012 M, must decrease from z= 4 to today by an amount more than the luminosity change for galaxies in haloes below this mass. This mass-dependent luminosity evolution is preferred at more than 3, confidence level compared to a pure-luminosity evolution scenario where all galaxies decrease in luminosity by the same amount from z= 4 to today. The scenario preferred by the data is consistent with the ,downsizing' picture of galaxy evolution. [source]

Cosmic momentum field and mass fluctuation power spectrum

Changbom Park
We introduce the cosmic momentum field as a new measure of the large-scale peculiar velocity and matter fluctuation fields. The momentum field is defined as the peculiar velocity field traced and weighted by galaxies, and is equal to the velocity field in the linear regime. We show that the radial component of the momentum field can be considered as a scalar field with the power spectrum which is practically one-third of that of the total momentum field. We present a formula for the power spectrum directly calculable from the observed radial peculiar velocity data. The momentum power spectrum is measured for the MAT sample in the Mark III catalogue of peculiar velocities of galaxies. Using the momentum power spectrum we find the amplitude of the matter power spectrum is and at the wavenumbers 0.049 and 0.074 h Mpc,1, respectively, where , is the density parameter. The 68 per cent confidence limits include the cosmic variance. The measured momentum and density power spectra together indicate that the parameter or where bO is the bias factor for optical galaxies. [source]

Closer Economic Relations with East Asia?

P.J. Lloyd
This paper examines Australia's economic links with East Asia and the policy implications of these links. The main issue is whether Australia should join the regional trading arrangements with East Asian countries that have been proposed. It examines the factors which determine the share of East Asia in Australian exports. One of these, the country bias factor, is threatened by proposed regional trading arrangements which might exclude Australia. After considering the costs of exclusion, the paper concludes that Australia should consider developing new bilateral or regional trade arrangements with countries in East Asia. [source]

Modeling the Effect of Marination and Temperature on Salmonella Inactivation during Drying of Beef Jerky

Yohan Yoon
ABSTRACT:, This study modeled the effect of drying temperature in combination with predrying marination treatments to inactivate Salmonella on beef jerky. Beef inside round slices were inoculated with Salmonella and treated with (1) nothing (C), (2) traditional marinade (M), or (3) dipped into a 5% acetic acid solution for 10 min before exposure to M (AM). After 24 h of marination at 4 °C, samples were dehydrated at 52, 57, or 63 °C. Total counts (tryptic soy agar supplemented with 0.1% sodium pyruvate, TSAP) and Salmonella (XLD agar) were enumerated after inoculation and at 0, 2, 4, 6, 8, and 10 h during drying. For calculation of death rates (DR, log CFU/cm2/h), shoulder period (h), low asymptote, and upper asymptote, cell counts from TSAP were fitted to the Baranyi model. The DRs were then further expressed as a function of storage temperature. Inactivation occurred without an initial lag phase (shoulder period), while correlation (R2) values of fitted curves were , 0.861. The DRs of C (,0.29 to ,0.62) and M (,0.36 to ,0.63) treatments were similar, while DRs of the AM treatment were higher (,1.22 to ,1.46). The DRs were then fitted to a polynomial equation as a function of temperature. After validation, good (C and M) or acceptable (AM) model performances were observed (R2= 0.954 to 0.987; bias factors: 1.03 [C], 1.01 [M], 0.71 [AM]; accuracy factors: 1.05 [C], 1.06 [M], 1.41 [AM]). The developed models may be useful in selecting drying temperatures and times in combination with predrying treatments for adequate inactivation of Salmonella in beef jerky. [source]


ABSTRACT In order to replace sensory evaluation by instrumental measurement with more accuracy for texture properties of cooked sausage, correlation analysis between sensory and instrumental texture was established by multiple regression and back propagation (BP) neural network, respectively. Effect of different fat, salt, moisture and starch addition on the texture of cooked sausage was also investigated in this paper. It indicated that the accuracy and goodness of fit of predicting sensory hardness, cohesiveness and juiciness by BP neural network were more significant than those by multiple regressions with lower root mean square error and standard error of prediction. Although both accuracy and bias factors of two models were in acceptable range, BP neural network provides an accurate and selective method for predicting sensory texture evaluation in similar meat products. PRACTICAL APPLICATIONS The effect of different fat, salt, moisture and starch addition on textural properties of cooked sausage could be valuable to the meat industry in order to select the appropriate components for improving the texture of sausage. Artificial neural network technology used in this study can be useful for the fast, on-time and convenient detection of texture measurement by instrumental instead of sensory evaluation. [source]