Predictive modelling of TBARS changes in the intramuscular lipid fraction of raw ground pork enriched with plant extracts.

The aim of the study was to develop and compare the predictive models of lipid oxidation in minced raw pork meat enriched with selected plant extracts (allspice, basil, bay leaf, black seed, cardamom, caraway, cloves, garlic, nutmeg, onion, oregano, rosemary and thyme) by investigation TBARS values changes during storage at different temperatures. Meat samples with extract addition were stored under various temperatures (4, 8, 12, 16, and 20°C). TBARS values changes in samples stored at 12°C were used as external validation dataset. Lipid oxidation was evaluated by the TBARS content. Lipid oxidation increased with storage time and temperature. The dependence of lipid oxidation on temperature was adequately modelled by the Arrhenius and log-logistic equation with high R2 coefficients (0.98-0.99). Kinetic models and artificial neural networks (ANNs) were used to build the predictive models. The obtained result demonstrates that both kinetic Arrhenius (R 2 = 0.83) and log-logistic (R 2 = 0.84) models as well as ANN (R 2 = 0.99) model can predict TBARS changes in raw ground pork meat during storage.

Matching IRT Models to Patient-Reported Outcomes Constructs: The Graded Response and Log-Logistic Models for Scaling Depression.

Item response theory (IRT) model applications extend well beyond cognitive ability testing, and various patient-reported outcomes (PRO) measures are among the more prominent examples. PRO (and like) constructs differ from cognitive ability constructs in many ways, and these differences have model fitting implications. With a few notable exceptions, however, most IRT applications to PRO constructs rely on traditional IRT models, such as the graded response model. We review some notable differences between cognitive and PRO constructs and how these differences can present challenges for traditional IRT model applications. We then apply two models (the traditional graded response model and an alternative log-logistic model) to depression measure data drawn from the Patient-Reported Outcomes Measurement Information System project. We do not claim that one model is "a better fit" or more "valid" than the other; rather, we show that the log-logistic model may be more consistent with the construct of depression as a unipolar phenomenon. Clearly, the graded response and log-logistic models can lead to different conclusions about the psychometrics of an instrument and the scaling of individual differences. We underscore, too, that, in general, explorations of which model may be more appropriate cannot be decided only by fit index comparisons; these decisions may require the integration of psychometrics with theory and research findings on the construct of interest.

Predictive Modeling of Thiol Changes in Raw Ground Pork as Affected by 13 Plant Extracts-Application of Arrhenius, Log-logistic and Artificial Neural Network Models.

In this study, predictive models of protein oxidation, expressed as the content of thiol groups (SH), in raw ground pork were established and their accuracy was compared. The SH changes were monitored during, maximum, 11 days of storage at five temperature levels: 4, 8, 12, 16, and 20 °C. The effect of 13 plant extracts, including spices such as allspice, black seed, cardamom, caraway, cloves, garlic, nutmeg, and onion, and herbs such as basil, bay leaf, oregano, rosemary, and thyme, on protein oxidation in pork was studied. The zero-order function was used to described SH changes with time. The effect of temperature was assessed by using Arrhenius and log-logistic equations. Artificial neural network (ANN) models were also developed. The results obtained showed very good acceptability of the models for the monitoring and prediction of protein oxidation in raw pork samples. High average R2 coefficients equal to 0.948, 0.957, and 0.944 were obtained for Arhhenius, log-logistic and ANN models, respectively. Multiple linear regression (MLR) was used to assess the influence of plant extracts on protein oxidation and showed oregano as the most potent antioxidant among the tested ones in raw ground pork.

Predictive Modeling of Changes in TBARS in the Intramuscular Lipid Fraction of Raw Ground Beef Enriched with Plant Extracts.

The aim of the study was to develop and compare the predictive models of lipid oxidation in minced raw beef meat enriched with selected plant extracts (allspice, basil, bay leaf, black seed, cardamom, caraway, cloves, garlic, nutmeg, onion, oregano, rosemary and thyme) expressed as value changes of TBARS (thiobarbituric acid reactive substances) in various time/temperature conditions. Meat samples were stored at the temperatures of 4, 8, 12, 16 and 20 °C. The value changes of TBARS in samples stored at 12 °C were used as the external validation dataset. Lipid oxidation increased significantly with storage time and temperature. The rate of this increase varied depending on the addition of the plant extract and was the most pronounced in the control sample. The dependence of lipid oxidation on temperature was adequately modeled by the Arrhenius and log-logistic equation with high average R2 coefficients (≥0.98) calculated for all extracts. Kinetic models and artificial neural networks (ANNs) were used to build the predictive models. The obtained result demonstrates that both kinetic Arrhenius (R2 = 0.972) and log-logistic (R2 = 0.938) models as well as ANN (R2 = 0.935) models can predict changes in TBARS in raw ground beef meat during storage.

[Application of the log-Logistic Model to Dose Response Relation in Microbial Risk Assessment].

Microbial risk assessment in food safety is a valuable tool to reduce the risks of infection by pathogens. The dose-response relation is aimed to establish the relationship between the dose of a pathogen that populations are exposed to and the probability of the adverse health effect by the pathogen. Among many dose-response models ever proposed, the exponential and beta-Poisson models have been internationally applied, but the decision on which model is selected between them solely depends on the goodness of fit to specific data sets. On the other hands, the log-logistic model, one of the alternative models, has been little studied on the dose-response relation. In the present study, thus, the application of the log-logistic model to dose-response relation was studied with hypothetical and experimental data sets of infection (or death), comparing to the above two models. Here the experimental data sets were for pathogenic organisms such as pathogenic Escherichia coli, Listeria monocytogenes, and Cryptosporidium pavrum. Consequently, this model successfully fit to those data sets in comparison to the two models. These results suggested that log-logistic model would have the potential to apply to the dose-response relation, similar to the exponential and beta-Poisson models.

Perspective: common errors in dose-response analysis and how to avoid them.

Dose-response experiments are conducted to determine the toxicity of chemicals on organisms. The relationship between dose and response is described by different statistical models. The four-parameter log-logistic model is widely used in pesticide sciences to derive biologically relevant parameters such as ED50 and resistance index (RI). However, there are some common errors associated with the calculation of ED50 and RI that can lead to erroneous conclusions. Here we discuss five common errors and propose guidance to avoid them. We suggest (i) all response curves must be fitted simultaneously to allow for proper comparison of parameters across curves, (ii) in the case of nonparallel curves absolute ED50 must be used instead of relative ED50 , (iii) standard errors or confidence intervals of the parameters must be reported, (iv) the e parameter in asymmetrical models is not equal to ED50 and hence absolute ED50 must be estimated, and (v) when the four-parameter log-logistic model returns a negative value for the lower asymptote, which is biologically meaningless in most cases, the model should be reduced to its three-parameter version or other types of model should be applied. The mixed-effects model and the meta-analytic approach are suggested as appropriate to average the parameters across repeated dose-response experiments. © 2021 Society of Chemical Industry.

Statistical inferences on nonconstant relative potency with quantal response data.

Relative potency is widely used in toxicological and pharmacological studies to characterize potency of chemicals. The relative potency of a test chemical compared to a standard chemical is defined as the ratio of equally effective doses (standard divided by test). This classical concept relies on the assumption that the two chemicals are toxicologically similar-that is, they have parallel dose-response curves on log-dose scale-and thus have constant relative potency. Nevertheless, investigators are often faced with situations where the similarity assumption is deemed unreasonable, and hence the classical idea of constant relative potency fails to hold; in such cases, simply reporting a single constant value for relative potency can produce misleading conclusions. Relative potency functions, describing relative potency as a function of the mean response (or other quantities), is seen as a useful tool for handling nonconstant relative potency in the absence of similarity. Often, investigators are interested in assessing nonconstant relative potency at a finite set of some specific response levels for various regulatory concerns, rather than the entire relative potency function; this simultaneous assessment gives rise to multiplicity, which calls for efficient statistical inference procedures with multiplicity adjusted methods. In this paper, we discuss the estimation of relative potency at multiple response levels using the relative potency function, under the log-logistic dose-response model. We further propose and evaluate three approaches to calculating multiplicity-adjusted confidence limits as statistical inference procedures for assessing nonconstant relative potency. Monte Carlo simulations are conducted to evaluate the characteristics of the simultaneous limits.

Parametric survival analysis using R: Illustration with lung cancer data.

BACKGROUND: Cox regression is the most widely used survival model in oncology. Parametric survival models are an alternative of Cox regression model. In this study, we have illustrated the application of semiparametric model and various parametric (Weibull, exponential, log-normal, and log-logistic) models in lung cancer data by using R software. AIMS: The aim of the study is to illustrate responsible factors in lung cancer and compared with Cox regression and parametric models. METHODS: A total of 66 lung cancer patients of African Americans (AAs) (data available online at http://clincancerres.aacrjournals.org) was used. To identify predictors of overall survival, stage of patient, sex, age, smoking, and tumor grade were taken into account. Both parametric and semiparametric models were fitted. Performance of parametric models was compared by Akaike information criterion (AIC). "Survival" package in R software was used to perform the analysis. Posterior density was obtained for different parameters through Bayesian approach using WinBUGS. RESULTS: The illustration about model fitting problem was documented. Parametric models were fitted only for stage after controlling for age. AIC value was minimum (462.4087) for log-logistic model as compared with other parametric models. Log-logistic model was the best fit for AAs lung cancer data under study. CONCLUSION: Exploring parametric survival models in daily practice of cancer research is challenging. It may be due to many reasons including popularity of Cox regression and lack of knowledge about how to perform it. This paper provides the application of parametric survival models by using freely available R software with illustration. It is expected that this present work can be useful to apply parametric survival models.

Model-based estimation of lowest observed effect concentration from replicate experiments to identify potential biomarkers of in vitro neurotoxicity.

A paradigm shift is occurring in toxicology following the report of the National Research Council of the USA National Academies entitled "Toxicity testing in the 21st Century: a vision and strategy". This new vision encourages the use of in vitro and in silico models for toxicity testing. In the goal to identify new reliable markers of toxicity, the responsiveness of different genes to various drugs (amiodarone: 0.312-2.5 [Formula: see text]; cyclosporine A: 0.25-2 [Formula: see text]; chlorpromazine: 0.625-10 [Formula: see text]; diazepam: 1-8 [Formula: see text]; carbamazepine: 6.25-50 [Formula: see text]) is studied in 3D aggregate brain cell cultures. Genes' responsiveness is quantified and ranked according to the Lowest Observed Effect Concentration (LOEC), which is estimated by reverse regression under a log-logistic model assumption. In contrast to approaches where LOEC is identified by the first observed concentration level at which the response is significantly different from a control, the model-based approach allows a principled estimation of the LOEC and of its uncertainty. The Box-Cox transform both sides approach is adopted to deal with heteroscedastic and/or non-normal residuals, while estimates from repeated experiments are summarized by a meta-analytic approach. Different inferential procedures to estimate the Box-Cox coefficient, and to obtain confidence intervals for the log-logistic curve parameters and the LOEC, are explored. A simulation study is performed to compare coverage properties and estimation errors for each approach. Application to the toxicological data identifies the genes Cort, Bdnf, and Nov as good candidates for in vitro biomarkers of toxicity.

Assessment of potential environmental risks of transgene flow in smallholder farming systems in Asia: Brassica napus as a case study in Korea.

The cultivation of genetically modified (GM) crops has raised many questions regarding their environmental risks, particularly about their ecological impact on non-target organisms, such as their closely-related relative species. Although evaluations of transgene flow from GM crops to their conventional crops has been conducted under large-scale farming system worldwide, in particular in North America and Australia, few studies have been conducted under smallholder farming systems in Asia with diverse crops in co-existence. A two-year field study was conducted to assess the potential environmental risks of gene flow from glufosinate-ammonium resistant (GR) Brassica napus to its conventional relatives, B. napus, B. juncea, and Raphanus sativus under simulated smallholder field conditions in Korea. Herbicide resistance and simple sequence repeat (SSR) markers were used to identify the hybrids. Hybridization frequency of B. napus × GR B. napus was 2.33% at a 2 m distance, which decreased to 0.007% at 75 m. For B. juncea, it was 0.076% at 2 m and decreased to 0.025% at 16 m. No gene flow was observed to R. sativus. The log-logistic model described hybridization frequency with increasing distance from GR B. napus to B. napus and B. juncea and predicted that the effective isolation distances for 0.01% gene flow from GR B. napus to B. napus and B. juncea were 122.5 and 23.7 m, respectively. Results suggest that long-distance gene flow from GR B. napus to B. napus and B. juncea is unlikely, but gene flow can potentially occur between adjacent fields where the smallholder farming systems exist.

Multiplicity-Adjusted Confidence Limits in Risk Assessment with Quantal Response Data.

In risk assessment, it is often desired to make inferences on the risk at certain low doses or on the dose(s) at which a specific benchmark risk (BMR) is attained. At times, [Formula: see text] dose levels or BMRs are of interest, and some form of multiplicity adjustment is necessary to ensure a valid [Formula: see text] simultaneous inference. Bonferroni correction is often employed in practice for such purposes. Though relative simple to implement, the Bonferroni strategy can suffer from extreme conservatism (Nitcheva et al., 2005; Al-Saidy et al., 2003). Recently, Kerns (2017) proposed the use of simultaneous hyperbolic and three-segment bands to perform multiple inferences in risk assessment under Abbott-adjusted log-logistic model with the dose level constrained to a given interval. In this paper, we present and compare methods for deriving multiplicity-adjusted upper limits on extra risk and lower bounds on the benchmark dose under Abbott-adjusted log-logistic model. Monte Carlo simulations evaluate the characteristics of the simultaneous limits. An example is given to illustrate the use of the methods.

Simultaneous confidence bands for log-logistic regression with applications in risk assessment.

In risk assessment, it is often desired to make inferences on the low dose levels at which a specific benchmark risk is attained. Applications of simultaneous hyperbolic confidence bands for low-dose risk estimation with quantal data under different dose-response models (multistage, Abbott-adjusted Weibull, and Abbott-adjusted log-logistic models) have appeared in the literature. The use of simultaneous three-segment bands under the multistage model has also been proposed recently. In this article, we present explicit formulas for constructing asymptotic one-sided simultaneous hyperbolic and three-segment bands for the simple log-logistic regression model. We use the simultaneous construction to estimate upper hyperbolic and three-segment confidence bands on extra risk and to obtain lower limits on the benchmark dose by inverting the upper bands on risk under the Abbott-adjusted log-logistic model. Monte Carlo simulations evaluate the characteristics of the simultaneous limits. An example is given to illustrate the use of the proposed methods and to compare the two types of simultaneous limits at very low dose levels.

Item response theory and the measurement of psychiatric constructs: some empirical and conceptual issues and challenges.

Item response theory (IRT) measurement models are now commonly used in educational, psychological, and health-outcomes measurement, but their impact in the evaluation of measures of psychiatric constructs remains limited. Herein we present two, somewhat contradictory, theses. The first is that, when skillfully applied, IRT has much to offer psychiatric measurement in terms of scale development, psychometric analysis, and scoring. The second argument, however, is that psychiatric measurement presents some unique challenges to the application of IRT - challenges that may not be easily addressed by application of conventional IRT models and methods. These challenges include, but are not limited to, the modeling of conceptually narrow constructs and their associated limited item pools, and unipolar constructs where the expected latent trait distribution is highly skewed.

Statistical strategies for averaging EC50 from multiple dose-response experiments.

In most dose-response studies, repeated experiments are conducted to determine the EC50 value for a chemical, requiring averaging EC50 estimates from a series of experiments. Two statistical strategies, the mixed-effect modeling and the meta-analysis approach, can be applied to estimate average behavior of EC50 values over all experiments by considering the variabilities within and among experiments. We investigated these two strategies in two common cases of multiple dose-response experiments in (a) complete and explicit dose-response relationships are observed in all experiments and in (b) only in a subset of experiments. In case (a), the meta-analysis strategy is a simple and robust method to average EC50 estimates. In case (b), all experimental data sets can be first screened using the dose-response screening plot, which allows visualization and comparison of multiple dose-response experimental results. As long as more than three experiments provide information about complete dose-response relationships, the experiments that cover incomplete relationships can be excluded from the meta-analysis strategy of averaging EC50 estimates. If there are only two experiments containing complete dose-response information, the mixed-effects model approach is suggested. We subsequently provided a web application for non-statisticians to implement the proposed meta-analysis strategy of averaging EC50 estimates from multiple dose-response experiments.

Exploring V1 by modeling the perceptual quality of images.

We propose an image quality model based on phase and amplitude differences between a reference and a distorted image. The proposed model is motivated by the fact that polar representations can separate visual information in a more independent and efficient manner than Cartesian representations in the primary visual cortex (V1). We subsequently estimate the model parameters from a large subjective data set using maximum likelihood methods. By comparing the various model hypotheses on the functional form about the phase and amplitude, we find that: (a) discrimination of visual orientation is important for quality assessment and yet a coarse level of such discrimination seems sufficient; and (b) a product-based amplitude-phase combination before pooling is effective, suggesting an interesting viewpoint about the functional structure of the simple cells and complex cells in V1.

Summarizing EC50 estimates from multiple dose-response experiments: a comparison of a meta-analysis strategy to a mixed-effects model approach.

Dose-response studies are performed to investigate the potency of a compound. EC50 is the concentration of the compound that gives half-maximal response. Dose-response data are typically evaluated by using a log-logistic model that includes EC50 as one of the model parameters. Often, more than one experiment is carried out to determine the EC50 value for a compound, requiring summarization of EC50 estimates from a series of experiments. In this context, mixed-effects models are designed to estimate the average behavior of EC50 values over all experiments by considering the variabilities within and among experiments simultaneously. However, fitting nonlinear mixed-effects models is more complicated than in a linear situation, and convergence problems are often encountered. An alternative strategy is the application of a meta-analysis approach, which combines EC50 estimates obtained from separate log-logistic model fitting. These two proposed strategies to summarize EC50 estimates from multiple experiments are compared in a simulation study and real data example. We conclude that the meta-analysis strategy is a simple and robust method to summarize EC50 estimates from multiple experiments, especially suited in the case of a small number of experiments.

Estimating a Hazard Function for Each of Four Items of Adverse Event Induced by the Anti-cancer Drug TS-1 / 薬剤疫学

Background : The safety of newly approved drugs must be assessed using postmarketing surveillance data. One of the difficulties in assessing the hazard rates of adverse events induced by the anti-cancer drug TS-1 was that the time to event was not exactly identified due to the interval censoring. Most patients were outpatients who underwent clinical laboratory tests almost periodically at 1- or 2-week intervals and therefore, the occurrence of an adverse event was confirmed at the time of testing days after the event occurrence.<BR>Objective : The purpose of this study was to propose a new model of hazard functions for each of 4 items of adverse event induced by TS-1 using post-marketing surveillance data considering the interval censoring.<BR>Methods : The data obtained from 3, 294 patients with gastric cancer who received an initial 4-week course of therapy with TS-1 administered orally twice a day, followed by a 4-week second course with a 2-week no-treatment period after the initial course, were used to estimate hazard functions. Four items of adverse event--hemoglobin level (HB), white blood cell (WBC), neutrophil (NEUT) and platelet counts (PLT) --were graded, respectively, using the criteria established by the Japan Society of Clinical Oncology. Slip-mixed log-logistic and slip-mixed Weibull models were proposed as candidate models for estimating hazard functions. The goodness of fit of the two candidate models was evaluated by applying them to the above-mentioned data. The hazard functions for each of 4 items were assessed using the model with the better fit.<BR>Results : The initial occurrence of adverse event was shown to follow the slip-mixed log-logistic model for each of 4 items. Although most events occurred early on in the initial course of therapy, a small peak in HB was also observed in the second course, while no such peak appeared for the other items.

Dose-response-a challenge for allelopathy?

The response of an organism to a chemical depends, among other things, on the dose. Nonlinear dose-response relationships occur across a broad range of research fields, and are a well established tool to describe the basic mechanisms of phytotoxicity. The responses of plants to allelochemicals as biosynthesized phytotoxins, relate as well to nonlinearity and, thus, allelopathic effects can be adequately quantified by nonlinear mathematical modeling. The current paper applies the concept of nonlinearity to assorted aspects of allelopathy within several bioassays and reveals their analysis by nonlinear regression models. Procedures for a valid comparison of effective doses between different allelopathic interactions are presented for both, inhibitory and stimulatory effects. The dose-response applications measure and compare the responses produced by pure allelochemicals [scopoletin (7-hydroxy-6-methoxy-2H-1-benzopyran-2-one); DIBOA (2,4-dihydroxy-2H-1,4-benzoxaxin-3(4H)-one); BOA (benzoxazolin-2(3H)-one); MBOA (6-methoxy-benzoxazolin-2(3H)-one)], involved in allelopathy of grain crops, to demonstrate how some general principles of dose responses also relate to allelopathy. Hereupon, dose-response applications with living donor plants demonstrate the validity of these principles for density-dependent phytotoxicity of allelochemicals produced and released by living plants (Avena sativa L., Secale cereale L., Triticum L. spp.), and reveal the use of such experiments for initial considerations about basic principles of allelopathy. Results confirm that nonlinearity applies to allelopathy, and the study of allelopathic effects in dose-response experiments allows for new and challenging insights into allelopathic interactions.