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Effects of tidal volume and methacholine on low-frequency total respiratory impedance in dogs.

The frequency dependence of respiratory impedance (Zrs) from 0.125 to 4 Hz (Hantos et al., J. Appl. Physiol. 60: 123-132, 1986) may reflect inhomogeneous parallel time constants or the inherent viscoelastic properties of the respiratory tissues. However, studies on the lung alone or chest wall alone indicate that their impedance features are also dependent on the tidal volumes (VT) of the forced oscillations. The goals of this study were 1) to identify how total Zrs at lower frequencies measured with random noise (RN) compared with that measure with larger VT, 2) to identify how Zrs measured with RN is affected by bronchoconstriction, and 3) to identify the impact of using linear models for analyzing such data. We measured Zrs in six healthy dogs by use of a RN technique from 0.125 to 4 Hz or with a ventilator from 0.125 to 0.75 Hz with VT from 50 to 250 ml. Then methacholine was administered and the RN was repeated. Two linear models were fit to each separate set of data. Both models assume uniform airways leading to viscoelastic tissues. For healthy dogs, the respiratory resistance (Rrs) decreased with frequency, with most of the decrease occurring from 0.125 to 0.375 Hz. Significant VT dependence of Rrs was seen only at these lower frequencies, with Rrs higher as VT decreased. The respiratory compliance (Crs) was dependent on VT in a similar fashion at all frequencies, with Crs decreasing as VT decreased. Both linear models fit the data well at all VT, but the viscoelastic parameters of each model were very sensitive to VT. After methacholine, the minimum Rrs increased as did the total drop with frequency. Nevertheless the same models fit the data well, and both the airways and tissue parameters were altered after methacholine. We conclude that inferences based only on low-frequency Zrs data are problematic because of the effects of VT on such data (and subsequent linear modeling of it) and the apparent inability of such data to differentiate parallel inhomogeneities from normal viscoelastic properties of the respiratory tissues.

Airway Resistance↗

Study of codes of disposal at different parities of Large White sows using a linear censored model.

To study the genetic relationship between three grouped reasons for sow removal (SR) in consecutive parities, accounting for censoring, 13,838 records from Large White sows were analyzed. Data were from seven pure-line farms having, on average, 5.9% unknown SR. Three traits were subjectively defined, each corresponding to a classification of SR (reproductive [RR], nonreproductive [RN], and others [RO]). Records for each trait could take one of five categories, according to parity at removal (0 to 4 or later). A multivariate linear censored model was implemented. The model to estimate (co)variance components and parameters included the effects of year-season, region, contemporary group, and additive genetic effects. The most common SR was related to reproduction (48.5%). Diseases of different origin and cause, old age/parity, and sow death or loss accounted for about 18, 7, and 4% of total culls, respectively. Estimates of variance components showed heterogeneity of additive genetic and residual variances for the three traits. Estimates of heritability were 0.18, 0.13, and 0.15 for RR, RN, and RO, respectively. Genetic correlations between removal codes were high (> or =0.90). Results suggest sizeable additive genetic variances exist for parity at removal and different codes of removal. Different SR reasons seem to operate similarly or as a closely related genetic trait associated with fitness. In particular, RN and RO seem to be genetically indistinguishable. Data structure, definition, and volume are major limitations in studies of sow survival. A multiple-trait censored model is preferred to evaluate reasons of sow disposal. Grouped removal causes seem to be strongly genetically correlated but with heterogeneous variances, suggesting that combining all removal causes and treating the trait as parity at disposal is an alternative approach.

Animal Husbandry↗

Efficacy of pour-on and injectable formulations of moxidectin and ivermectin in cattle naturally infected with Psoroptes ovis: parasitological, clinical and serological data.

On the basis of Psoroptes ovis counts performed on day -7, 32 animals were randomly allocated to a control group of five animals or to four groups comprising six or seven animals which were treated, respectively, with pour-on ivermectin (IPO), injectable ivermectin (II), pour-on moxidectin (MPO) and injectable moxidectin (IM). Living mites were counted in skin scrapings on days 0, 7, 14, 28, 42 and 56 post-treatment (PT). Lesions were recorded on a standardized map on days 0 and 56 PT. Antibody kinetics were studied using ELISA on serially diluted sera. The antibody titres were expressed as the dilution giving the positive/negative cut-off. Until their treatment on day 28, the control animals remained parasitologically positive and their antibody titres increased. In treated groups, all living mite counts were negative on days 28 and 42 PT but some animals were still infected on days 7 and 14 PT. On day 56, living P. ovis were found in one animal of the IPO group. An equation of regression describing the antibody decrease was calculated with each individual data set. In most of the treated animals, the coefficient of determination R2, which describes the closeness of fit to the linear model, was above 0.9. The linear model could not be applied (low R2) to the antibody kinetics of four animals: the day 56 positive animal and its two neighbours in the IPO group and one animal from the MPO group. In the treated groups, the differences between the numbers of infected animals, the mean daily weight gains or the mean antibody titres were not statistically significant. Mean daily weight gains of the treated groups were higher than in control animals.

Animals↗

Genome-wide association identifies and validates genomic region controlling grain yield and agronomic traits in extra-early orange maize inbred lines under drought.

In order to meet the expected maize yield by 2050, breeders must work to improve breeding program efficiency by intensifying the implementation of new and improved technologies such as marker-assisted selection (MAS). Dissecting the genomic regions associated with drought tolerance is the first step forward in MAS program deployment for maize improvement under drought stress. Genome-wide association studies (GWAS) were used to investigate and identify quantitative trait loci (QTLs) associated with six traits under drought stress. One hundred and eighty-seven extra-early orange maize inbred lines were evaluated under managed drought stress at Ikenne, in Nigeria, during the 2022 and 2023 dry seasons. The materials were also genotyped using 9355 DArTseq SNP markers and analyzed using the enriched compressed mixed linear model (ECMLM). Enriched compressed mixed linear model was used for association-trait analysis. The ECMLM-based GWAS identified 45 candidate genomic loci associated with the six traits, including five for grain yield, with R2 ranging from 8.79 to 25.3%. Independent validation using the multi-locus 3VmrMLM approach confirmed seven high-confidence genomic loci consistently detected by both methods across grain yield, anthesis-silking interval, ear aspect, and ears per plant, providing additional statistical support for these genomic regions. Candidate gene annotation identified biologically relevant genes underlying the validated loci, including Zm00001eb238250 (protein-serine/threonine phosphatase), Zm00001eb040940 (trehalose-phosphatase), Zm00001eb117820 (homeobox protein knotted-1-like 4), Zm00001eb145560 (zinc ion-binding protein), and Zm00001eb294180 (WRKY DNA-binding domain protein), suggesting their potential roles in drought adaptation and grain productivity. These findings improve our understanding of the genetic architecture of drought tolerance in extra-early orange maize and provide valuable genomic resources for accelerating drought-resilient maize breeding.

Zea mays↗

PEDA: a microcomputer program for parameter estimation and dosage adjustment in clinical practice.

PEDA, an integrated program in BASIC for implementation on microcomputers, has been developed for use in clinical practice to assist dosage adjustment for individual patients. A parameter optimization for individual patients is based on the principle of Bayes' theory and Maximum Likelihood Estimation, and utilizes a prior information on the distribution of population pharmacokinetic parameters, means and variances, as well as serum drug concentrations. The program can accommodate a one-compartment open linear model and a non-linear model at steady state (Michaelis-Menten model) and handle both uniform and non-uniform multiple dosage regimens mostly arising from clinical settings. Clinical examples which demonstrate the ability and the flexibility of the program are provided. The program may also be used as an aid for instruction in clinical pharmacokinetics.

Adult↗

Genetic and environmental relationships among somatic cell count, bacterial infection, and clinical mastitis.

Incidence of bacterial infection in 9784 lactations of 7763 cows in 31 herds, SCC in 32,448 lactations of 19,764 cows from 54 herds, and incidence of first parity mastitis recorded in the first lactations of 148,143 cows in 828 herds were analyzed. Bacterial infection was analyzed dichotomously by both threshold and linear models. The effects of parity, season, stage of lactation, and parity by stage of lactation interaction on SCC were estimated. Heritability of mean lactation log SCC--corrected for the effects of parity, season, and stage of lactation--varied from .13 to .27 for all parities in different data sets. Heritability of bacterial infection was .04 for the threshold model and .02 for the linear models. Heritability of field-recorded mastitis was .01. The genetic correlation between bacterial infection and SCC was near unity, but the genetic correlation between SCC and mastitis was .3. Selection for lowered SCC should reduce incidence of bacterial infection by 2% per unit of selection intensity.

Animals↗

Assessment of the spatial occurrence of childhood leukaemia mortality using standardized rate ratios with a simple linear Poisson model.

Reports of a suspected cluster of childhood leukaemia cases in West Central Phoenix have led to a number of epidemiological studies in the geographical area. We report here on a death certificate-based mortality study, which indicated an elevated rate ratio of 1.95 during 1966-1986, using the remainder of the Phoenix standard metropolitan statistical area (SMSA) as a comparison region. In the process of analysing the data from this study, a methodology for dealing with denominator variability in a standardized mortality ratio was developed using a simple linear Poisson model. This new approach is seen as being of general use in the analysis of standardized rate ratios (SRR), as well as being particularly appropriate for cluster investigations.

Adolescent↗

Estimating correlation by using a general linear mixed model: evaluation of the relationship between the concentration of HIV-1 RNA in blood and semen.

Estimating the correlation coefficient between two outcome variables is one of the most important aspects of epidemiological and clinical research. A simple Pearson's correlation coefficient method is usually employed when there are complete independent data points for both outcome variables. However, researchers often deal with correlated observations in a longitudinal setting with missing values where a simple Pearson's correlation coefficient method cannot be used. General linear mixed models (GLMM) techniques were used to estimate correlation coefficients in a longitudinal data set with missing values. A random regression mixed model with unstructured covariance matrix was employed to estimate correlation coefficients between concentrations of HIV-1 RNA in blood and seminal plasma. The effects of CD4 count and antiretroviral therapy were also examined. We used data sets from three different centres (650 samples from 238 patients) where blood and seminal plasma HIV-1 RNA concentrations were collected from patients; 137 samples from 90 different patients without antiviral therapy and 513 samples from 148 patients receiving therapy were considered for analysis. We found no significant correlation between blood and semen HIV-1 RNA concentration in the absence of antiviral therapy. However, a moderate correlation between blood and semen HIV-1 RNA was observed among subjects with lower CD4 counts receiving therapy. Our findings confirm and extend the idea that the concentrations of HIV-1 in semen often differ from the HIV-1 concentration in blood. Antiretroviral therapy administered to subjects with low CD4 counts result in sufficient concomitant reduction of HIV-1 in blood and semen so as to improve the correlation between these compartments. These results have important implications for studies related to the sexual transmission of HIV, and development of HIV prevention strategies.

Anti-HIV Agents↗

The bell should toll for the linear no-threshold model.

The linear no-threshold (LNT) model has been a convenient tool in the practice of radiation protection but it is not supported by scientific data at doses less than about 100 mSv or at chronic dose rates up to at least 200 mSv yr(-1). Radiation protection practices based on the LNT model yield no demonstrable benefits to health when applied at lower annual doses. The assumption that such exposures are harmful may not even be conservative and has helped to foster an unwarranted fear of low-level radiation. For its new recommendations, to be issued probably in 2005, the ICRP has said that it expects to continue the application of the LNT model 'above a few millisieverts per year'. National societies for radiation protection may wish to consider the need to lobby the ICRP, through the auspices of IRPA, to further relax adherence to the LNT assumption-up to 'a few tens of millisieverts per year'.

Body Burden↗

Some statistical issues related to multiple linear regression modeling of beach bacteria concentrations.

As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those issues include the value and use of interaction terms, the serial correlation, the criteria for model selection, and model assessment. The present work shows that serial correlations, as often present in sequentially observed data records, deserve full attention from the modeler. The testing and adjustment for the time-series effect should be implemented in a statistically rigorous framework. The R(2) and Cp-statistic as joint criteria are recommended for the model selection process, while using the t-statistics associated with the full model is erroneous. During model selection, using interaction terms can often help to decrease the bias in reduced models, although the resulting improvement in the numerical performance may be limited. For the assessment of the model predictive capacity, which is different from testing the goodness of fit, a comprehensive set of statistics are advocated to allow for an objective evaluation of different models. Results obtained from the data at Huntington Beach, OH, show that erroneous conclusions could be drawn if only the model R(2) and the count of type I and type II errors are considered. In this sense, several previous works deserve further investigation.

Bathing Beaches↗

Optimal design of the chronic animal bioassay.

Optimal experimental designs for carcinogenicity bioassays conducted for the assessment of risks associated with exposure to environmental chemicals are derived. For our purposes, an optimal experimental design is a design that minimizes the mean-squared error of the maximum likelihood estimate of the virtually safe dose from the Armitage-Doll multistage model and maintains a high power for the detection of increased carcinogenic response. Three- and four-dose designs (including control as one of the doses) are discussed for a variety of dose response patterns. Monte Carlo simulation techniques are used to estimate the power and mean-squared error for small samples sizes. Two forms of the multistage model are used to estimate the virtually safe dose: the linear model and the linear-quadratic model. The optimal designs for fitting the linear model used a control group and a group administered the maximum tolerated dose, with about 50% of the animals at each dose. The three- and four-dose optimal designs when fitting the linear-quadratic model were found to be equivalent. However, after considering several biological issues, including overt toxicity, the optimal four-dose designs would use between 150 and 300 animals, with 50 to 60 animals in the control group, and 40 to 60 animals in the group administered the maximum tolerated dose. One-third of the remaining animals would be administered a dose between 10 and 30% of the maximum tolerated dose, and two-thirds of the remaining animals would be administered 50% of the maximum tolerated dose.

Animals↗

[Genetic regulatory pathway of gene related breast cancer metastasis: primary study by linear differential model and k-means clustering].

OBJECTIVE: To investigate the potential genetic regulatory pathway of gene related breast cancer metastasis. METHODS: Microarray technique was used to identify the gene expression profile and to screen the differential expression genes in breast cancer with special emphasis on the metastasis factors. A gene chip was available, obtained from the surgical samples, including breast cancer primary tissues and metastasis tissues, of 30 female patients with breast cancer at different clinical stages. Then potential genetic regulatory pathway of gene related breast cancer metastasis was analyzed with a linear differential model and k-means clustering. RESULTS: Twenty-seven differential expression genes were identified. It was suggested that the potential regulatory pathway of gene related to breast cancer metastasis is made up of GRP, BPAG1, and SFRP2 genes. CONCLUSION: The metastasis of breast cancer is related to the cancerization caused by the abnormal expression of multiple genes. It is reliable to analyze the Genetic regulatory pathway of gene related breast cancer metastasis by using multiple bio-informatic measures.

Breast Neoplasms↗

A linear lattice model for polyglutamine in CAG-expansion diseases.

Huntington's disease and several other neurological diseases are caused by expanded polyglutamine [poly(Gln)] tracts in different proteins. Mechanisms for expanded (>36 Gln residues) poly(Gln) toxicity include the formation of aggregates that recruit and sequester essential cellular proteins [Preisinger, E., Jordan, B. M., Kazantsev, A. & Housman, D. (1999) Phil. Trans. R. Soc. London B 354, 1029-1034; Chen, S., Berthelier, V., Yang, W. & Wetzel, R. (2001) J. Mol. Biol. 311, 173-182] and functional alterations, such as improper interactions with other proteins [Cummings, C. J. & Zoghbi, H. Y. (2000) Hum. Mol. Genet. 9, 909-916]. Expansion above the "pathologic threshold" ( approximately 36 Gln) has been proposed to induce a conformational transition in poly(Gln) tracts, which has been suggested as a target for therapeutic intervention. Here we show that structural analyses of soluble huntingtin exon 1 fusion proteins with 16 to 46 glutamine residues reveal extended structures with random coil characteristics and no evidence for a global conformational change above 36 glutamines. An antibody (MW1) Fab fragment, which recognizes full-length huntingtin in mouse brain sections, binds specifically to exon 1 constructs containing normal and expanded poly(Gln) tracts, with affinity and stoichiometry that increase with poly(Gln) length. These data support a "linear lattice" model for poly(Gln), in which expanded poly(Gln) tracts have an increased number of ligand-binding sites as compared with normal poly(Gln). The linear lattice model provides a rationale for pathogenicity of expanded poly(Gln) tracts and a structural framework for drug design.

Amino Acid Sequence↗

General pharmacokinetic equations for linear mammillary models with drug absorption into peripheral compartments.

General equations are derived for the disposition functions of any compartment in a linear mammillary model, when the system input occurs into a peripheral compartment. Laplace transforms and matrix algebra are used to derive these equations. Equations describing the time-course of a drug in any compartment are readily obtainable using disposition and input functions.

Intestinal Absorption↗

Using segmented linear regression models with unknown change points to analyze strategy shifts in cognitive tasks.

Some years ago, Beem (1993, 1995) described a program for fitting two regression lines with an unknown change point (Segcurve). He suggested that such models are useful for the analysis of a variety of phenomena and gave an example of an application to the study of strategy shifts in a mental rotation task. This technique has also proven to be very fruitful for investigating strategy use and strategy shifts in other cognitive tasks. Recently, Beem (1999) developed SegcurvN, which fits n regression lines with (n - 1) unknown change points. In the present article we present this new technique and demonstrate the usefulness of a three-phase segmented linear regression model for the identification of strategies and strategy shifts in cognitive tasks by applying it to data from a numerosity judgment experiment. The advantages and shortcomings of this technique are evaluated.

Attention↗

Heritability, reliability of genetic evaluations and response to selection in proportional hazard models.

The purposes of this study were 1) to investigate the heritability, reliability, and selection response for survival traits following a Weibull frailty proportional hazard model; and 2) to examine the relationship between genetic parameters from a Weibull model, a discrete proportional hazard model, and a binary data analysis using a linear model. Both analytical methods and Monte Carlo simulations were used to achieve these aims. Data were simulated using the Weibull frailty model with two different shapes of the Weibull distribution. Breeding values of 100 unrelated sires with 50 to 100 progeny (with different levels of censoring) were generated from a normal distribution and two different sire variances. For analysis of longevity data on the discrete scale, simulated data were transformed to a discrete scale using arbitrary ends of discrete intervals of 400, 800, or 1200 d. For binary data analysis, an individual's longevity was either 0 (when longevity was less than the end of interval) or 1 (when longevity was equal or greater than the end of interval). Three different statistical models were investigated in this study: a Weibull model, a discrete-time model (a proportional hazard model assuming that the survival data are measured on a discrete scale with few classes), and a linear model based upon binary data. An alternative derivation using basic expressions of reliabilities in sire models suggests a simple equation for the heritability on the original scale (effective heritability) that is not dependent on the Weibull parameters. The predictions of reliabilities using the proposed formulae in this study are in very good agreement with reliabilities observed from simulations. In general, the estimates of reliability from either the discrete model or the binary data analysis were close to estimates from the Weibull model for a given number of uncensored records in this simplified case of a balanced design. Although selection response from the binary data analysis depends on the end of interval point, there is a relatively good agreement between selection responses in the Weibull model and the binary data analysis. In general, when the underlying survival data is from a Weibull distribution, it appears that the method of analyzing data does not greatly affect the results in terms of sire ranking or response to selection, at least for the simplified context considered in this study.

Animals↗

Longitudinal data analysis for linear Gaussian models with random disturbed-highest-derivative-polynomial subject effects.

For linear regression analysis of longitudinal data with Gaussian response, I propose a new model to generalize the traditional class of random effects models in which the random effects are deterministic polynomials with coefficients randomly distributed over subjects with mean zero. The generalization is accomplished by adding zero mean Gaussian 'disturbances' to the highest derivative of each random coefficient subject polynomial, independently at each observation time. The resulting random effects, which have mean zero at each observation time, are called disturbed highest derivative polynomials (DHDPs). The disturbances induce serial correlation and also allow the subject-specific DHDP time trends to be non-linear. I do not estimate the subject-specific DHDP time trends. Analysis is based on the marginal model, that is, the fixed effects or population model obtained by integrating the random polynomial coefficients and all disturbances out of the joint distribution of themselves and the response vector. This allows a 'population averaged' interpretation. One can select the DHDP order by an information criterion. When the population time trend is not correctly modelled, the optimal DHDP order will be larger than when it is correctly modelled. One can make the covariance matrix of the regression coefficients robust to errors in modelling the within-subject dependence. I describe the relationship of a DHDP to a smoothing polynomial spline, and show how to replace the DHDP model with a smoothing polynomial spline model for the within-subject dependence in the marginal model.

Bias↗