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At least 649 records · Page 36Linked to original sources
PubMed2005

Predicting the behaviour of proteins in hydrophobic interaction chromatography. 2. Using a statistical description of their surface amino acid distribution.

This paper focuses on the prediction of the dimensionless retention time (DRT) of proteins in hydrophobic interaction chromatography (HIC) by means of mathematical models based on the statistical description of the amino acid surface distribution. Previous models characterises the protein surface as a whole. However, most of the time it is not the whole protein but some of its specific regions that interact with the environment. It seems much more natural to use local measurements of the characteristics of the surface. Therefore, the statistical characterisation of the distribution of an amino acid property on the protein surface was carried out from the systematic calculation of the local average of this property in a neighbourhood placed sequentially on each of the amino acids on the protein surface. This process allowed us to characterise the distribution of this property quantitatively using three main statistics: average, standard deviation and maximum. In particular, if the property considered is a hydrophobicity scale, these statistics allowed us to characterise the average hydrophobicity and the hydrophobic content of the most hydrophobic cluster or hotspot, as well as the heterogeneity of the hydrophobicity distribution on the protein surface. We tested the performance of the DRT predictive models based on these statistics on a set of 15 proteins. We obtained better predictive results with respect to the models previously reported. The best predictive model was a linear model based on the maximum. This statistic was calculated using an index of the mobilities of amino acids in chromatography. The predictive performance of this model (measured as the Jack Knife MSE) was 26.9% better than those obtained by the best model which does not consider the amino acid distribution and 19.5% better than the model based on the hydrophobic imbalance (HI). In addition, the best performance was obtained by a linear multivariable model based on the HI and the maximum. The difference between the experimental data and the prediction carried out by this model was smaller than those observed previously. In fact, this model obtained better predictive capacities than a previous linear multivariable model decreasing the Jack Knife MSE in 8.7%. In addition, this model allowed us to diminish the number of variables required, increasing, in this way, the degrees of freedom of the model.

Amino Acids↗
PubMed2007

The statistical performance of an MCF-7 cell culture assay evaluated using generalized linear mixed models and a score test.

Biological assays often utilize experimental designs where observations are replicated at multiple levels, and where each level represents a separate component of the assay's overall variance. Statistical analysis of such data usually ignores these design effects, whereas more sophisticated methods would improve the statistical power of assays. This report evaluates the statistical performance of an in vitro MCF-7 cell proliferation assay (E-SCREEN) by identifying the optimal generalized linear mixed model (GLMM) that accurately represents the assay's experimental design and variance components. Our statistical assessment found that 17beta-oestradiol cell culture assay data were best modelled with a GLMM configured with a reciprocal link function, a gamma error distribution, and three sources of design variation: plate-to-plate; well-to-well, and the interaction between plate-to-plate variation and dose. The gamma-distributed random error of the assay was estimated to have a coefficient of variation (COV) = 3.2 per cent, and a variance component score test described by X. Lin found that each of the three variance components were statistically significant. The optimal GLMM also confirmed the estrogenicity of five weakly oestrogenic polychlorinated biphenyls (PCBs 17, 49, 66, 74, and 128). Based on information criteria, the optimal gamma GLMM consistently out-performed equivalent naive normal and log-normal linear models, both with and without random effects terms. Because the gamma GLMM was by far the best model on conceptual and empirical grounds, and requires only trivially more effort to use, we encourage its use and suggest that naive models be avoided when possible.

Biological Assay↗
PubMed2024

Detecting inbreeding depression in structured populations.

Measuring inbreeding and its consequences on fitness is central for many areas in biology including human genetics and the conservation of endangered species. However, there is no consensus on the best method, neither for quantification of inbreeding itself nor for the model to estimate its effect on specific traits. We simulated traits based on simulated genomes from a large pedigree and empirical whole-genome sequences of human data from populations with various sizes and structures (from the 1,000 Genomes project). We compare the ability of various inbreeding coefficients ([Formula: see text]) to quantify the strength of inbreeding depression: allele-sharing, two versions of the correlation of uniting gametes which differ in the weight they attribute to each locus and two identical-by-descent segments-based estimators. We also compare two models: the standard linear model and a linear mixed model (LMM) including a genetic relatedness matrix (GRM) as random effect to account for the nonindependence of observations. We find LMMs give better results in scenarios with population or family structure. Within the LMM, we compare three different GRMs and show that in homogeneous populations, there is little difference among the different [Formula: see text] and GRM for inbreeding depression quantification. However, as soon as a strong population or family structure is present, the strength of inbreeding depression can be most efficiently estimated only if i) the phenotypes are regressed on [Formula: see text] based on a weighted version of the correlation of uniting gametes, giving more weight to common alleles and ii) with the GRM obtained from an allele-sharing relatedness estimator.

Humans↗
PubMed2000

Applying the equivalent uniform dose formulation based on the linear-quadratic model to inhomogeneous tumor dose distributions: Caution for analyzing and reporting.

We apply the concept of equivalent uniform dose (EUD) to our data set of model distributions and intensity modulated radiotherapy (IMRT) treatment plans as a method for analyzing large dose inhomogeneities within the tumor volume. For large dose nonuniformities, we find that the linerar-quadratic based EUD model is sensitive to the linear-quadratic model parameters, alpha and beta, making it necessary to consider EUD as a function of these parameters. This complicates the analysis for inhomogeneous dose distributions. EUD provides a biological estimate that requires interpretation and cannot be used as a single parameter for judging an inhomogeneous plan. We present heuristic examples to demonstrate the dose volume effect associated with EUD and the correlation to statistical parameters used for describing dose distributions. From these examples and patient plans, we discuss the risk of incorrectly applying EUD to IMRT patient plans.

Humans↗
PubMed1992

Prediction in censored survival data: a comparison of the proportional hazards and linear regression models.

Although the analysis of censored survival data using the proportional hazards and linear regression models is common, there has been little work examining the ability of these estimators to predict time to failure. This is unfortunate, since a predictive plot illustrating the relationship between time to failure and a continuous covariate can be far more informative regarding the risk associated with the covariate than a Kaplan-Meier plot obtained by discretizing the variable. In this paper the predictive power of the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-202) proportional hazards estimator and the Buckley-James (1979, Biometrika 66, 429-436) censored regression estimator are compared. Using computer simulations and heuristic arguments, it is shown that the choice of method depends on the censoring proportion, strength of the regression, the form of the censoring distribution, and the form of the failure distribution. Several examples are provided to illustrate the usefulness of the methods.

Biometry↗
PubMed2004

Investigation of biomechanical factors affecting rowing performance.

It was hypothesized that a crew's rowing performance was predictable based on their total propulsive power, synchrony (a real-time comparison of rower propulsive force magnitudes) and total drag contribution (a measure of the rowers' effect on shell drag forces during the recovery), quantities calculated from individual rower's force-time profiles and recovery kinematics. A rowing pair was equipped with transducers to gather shell velocity, propulsive blade force, oar angular position and seat displacement. Eight subjects (four port, four starboard) participated in two rounds of data collection. The first round pairings were random, while the second round pairings were assigned based on Round 1 results. Regression analysis and ANCOVA were used to test the validity of assumptions inherent in the predictive model and, if applicable, explore a linear model predicting rowing performance based on total propulsive power, synchrony and total drag contribution. Total propulsive power, synchrony and total drag contribution were correlated and further were affected by pairing, violating assumptions inherent in the linear model. The original hypothesis was not supported based on these violations. Important findings include (1) performance cannot be predicted using the simple linear model proposed, (2) rowers' force-time profiles are repeatable between trials, with some but not all rowers adapting their force-time profile dependent on their pair partner, presumably in an effort to increase the level of synchrony between the two, and (3) subtle biomechanical factors may play a critical role in performance.

Adult↗
PubMed2026

Scale reliant mixed effects models enhance microbiome data analysis.

Linear models, including those used for differential abundance analyses, are frequently used in microbiome research to assess how experimental conditions (e.g., disease state or age) affect microbial abundance. Linear mixed-effects models (MEMs) extend linear models to accommodate complex designs, such as longitudinal sampling or hierarchical study structures. However, when applied to microbiome data, existing MEM approaches suffer from high false positive and false negative rates because sequence counts are compositional - they reflect relative rather than absolute abundances. Current methods attempt to overcome this limitation through normalization, but these approaches rely on strong, often unrealistic assumptions about the unmeasured biological scale (e.g., total microbial load). Here we introduce scale-reliant mixed-effects models (SR-MEM), which extend our earlier scale-reliant inference framework by explicitly modeling uncertainty in the unmeasured scale via user-defined probability distributions. By treating scale as a latent variable rather than fixing it through normalization, SR-MEM enables robust inference for complex experimental designs. SR-MEM can incorporate external scale measurements (e.g., flow cytometry, qPCR) or leverage scale information from independent studies to further improve inference. Across simulations and multiple real-world case studies, SR-MEM consistently controls the false discovery rate while maintaining comparable or higher power than standard approaches relying on normalization or bias correction. In reanalyses of published datasets, SR-MEM yields results that are more reproducible across studies and more consistent with known biological and pharmacological effects. SR-MEM provides a principled and practical framework for mixed-effects modeling of microbiome sequence count data in the presence of unmeasured biological scale. By avoiding normalization-based assumptions and instead propagating scale uncertainty through inference, SR-MEM improves error control and reproducibility in longitudinal and hierarchical studies. An accessible implementation is provided in the ALDEx3 R package.

Microbiota↗
PubMed1984

Tests for the fit of the linear-quadratic model to radiation isoeffect data.

The linear-quadratic (LQ) model for cell survival is frequently extended to describe multifraction isoeffect data via the formula: ln(response) = -n(alpha d + beta d2), where d is the dose per fraction and n is the number of fractions. However, estimates of the quantity "alpha/beta" derived from such data are meaningless unless the use of the model is justified. Two methods are proposed for testing the fit of the multifraction LQ model to isoeffect data. If the use of the model cannot be rejected, each method also provides a new technique for estimating alpha/beta. The two methods are applied to published data from spleen, kidney, and colon. In each case, consistent results are obtained from the two methods concerning the quality of the fit.

Animals↗
PubMed1989

Radiobiological assessment of permanent implants using tumour repopulation factors in the linear-quadratic model.

By combining existing linear-quadratic equations relating to decaying-source therapy with an assumed tumour repopulation factor, it has been possible to devise a method for the radiobiological assessment of permanent implants. For calculation purposes there is a time after which an implant can no longer be considered effective in sterilizing tumour cells. This "effective" treatment time for a permanent implant can be approximately defined in terms of the radionuclide decay constant, the potential doubling time, the initial dose-rate and the value of alpha in the tumour alpha/beta ratio. The analytical technique has been applied to a specific intercomparison of commonly encountered implants using 125I and 198Au, and suggests that, even in the most favourable cases, the former radionuclide offers few radiobiological advantages. Although not specifically discussed here, the method can also be applied to the assessment of various forms of biologically targeted radiotherapy.

Brachytherapy↗
PubMed1994

Some approaches to the analysis of recurrent event data.

Methodological research in biostatistics has been dominated over the last twenty years by further development of Cox's regression model for life tables and of Nelder and Wedderburn's formulation of generalized linear models. In both of these areas the need to address the problems introduced by subject level heterogeneity has provided a major motivation, and the analysis of data concerning recurrent events has been widely discussed within both frameworks. This paper reviews this work, drawing together the parallel development of 'marginal' and 'conditional' approaches in survival analysis and in generalized linear models. Frailty models are shown to be a special case of a random effects generalization of generalized linear models, whereas marginal models for multivariate failure time data are more closely related to the generalized estimating equation approach to longitudinal generalized linear models. Computational methods for inference are discussed, including the Bayesian Markov chain Monte Carlo approach.

Algorithms↗
PubMed2004

Sorption of biodegradation end products of nonylphenol polyethoxylates onto activated sludge.

Nonylphenol(NP), nonylphenoxy acetic acid (NP1EC), nonylphenol monoethoxy acetic acid (NP2EC), nonylphenol monoethoxylate (NP1EO) and nonylphenol diethoxylate (NP2EO) are biodegradation end products (BEPs) of nonionic surfactant nonylphenolpolyethoxylates (NPnEO). In this research, sorption of these compounds onto model activated sludge was characterized. Sorption equilibrium experiments showed that NP, NP1EO and NP2EO reached equilibrium in about 12 h, while equilibrium of NP1EC and NP2EC were reached earlier, in about 4 h. In sorption isotherm experiments, obtained equilibrium data at 28 degrees C fitted well to Freundlich sorption model for all investigated compounds. For NP1EC, in addition to Freundlich, equilibrium data also fitted well to Langmuir model. Linear sorption model was also tried, and equilibrium data of all NP, NP1EO, NP2EO and NP2EC except NP1EC fitted well to this model. Calculated Freundlich coefficient (K(F)) and linear sorption coefficient (K(D)) showed that sorption capacity of the investigated compounds were in order NP > NP2EO > NP1EO > NP1EC approximately NP2EC. For NP, NP1EO and NP2EO, high values of calculated K(F) and K(D) indicated an easy uptake of these compounds from aqueous phase onto activated sludge. Whereas, NP1EC and NP2EC with low values of K(F) and K(D) absorbed weakly to activated sludge and tended to preferably remain in aqueous phase.

Adsorption↗
PubMed2004

Preventive and curative effects of acupuncture on the common cold: a multicentre randomized controlled trial in Japan.

OBJECTIVE: To determine the preventive and curative effects of manual acupuncture on the symptoms of the common cold. METHOD: Students and staff in five Japanese acupuncture schools (n=326) were randomly allocated to acupuncture and no-treatment control groups. A specific needling point (Y point) on the neck was used bilaterally. Fine acupuncture needles were gently manipulated for 15 s, evoking de qi sensation. Acupuncture treatments were performed four times during the 2-week experimental period with a 2-week follow-up period. A common cold diary was scored daily for 4 weeks, and a common cold questionnaire was scored before each acupuncture treatment and twice at weekly intervals. A reliability test for the questionnaire was performed on the last day of recording. RESULTS: Five of the 326 subjects who were recruited dropped out. The diary score in the acupuncture group tended to decrease after treatment, but the difference between groups was not significant (Kaplan-Meier survival analysis, log rank test P=0.53, Cox regression analysis, P>0.05). Statistically significantly fewer symptoms were reported in the questionnaire by the acupuncture group than control group (P=0.024, general linear model, repeated measure). Significant inter-centre (P<0.001, general linear model) and sex (P=0.027, general linear model) differences were also detected. Reliability tests indicated that the questionnaire with 15 items was sufficiently reliable. No severe adverse event was reported. CONCLUSION: This is the first report of a multi-centre randomized controlled trial of acupuncture for symptoms of the common cold. A significantly positive effect of acupuncture was demonstrated in the summed questionnaire data, although a highly significant inter-centre difference was observed. Needling on the neck using the Japanese fine needle manipulating technique was shown to be effective and safe. The use of acupuncture for symptoms of the common cold symptoms should be considered, although further evidence from placebo controlled RCTs is required.

Acupuncture Points↗
PubMed1999

Application of a non-linear dispersion model to analysis of the renal handling of p-aminohippurate in isolated perfused rat kidney.

To analyze the renal handling of therapeutic compounds observed as a non-linear process with a diffusion phenomenon, we employed a non-linear dispersion model described with a partial differential equation and solved it by using the finite difference method with an implicit scheme in a model dependent analysis. In this study, the renal handling of p-aminohippurate (PAH) was investigated in isolated perfused rat kidney, in which a 50 microl bolus of injection solution containing [3H]PAH and [14C]inulin was administered rapidly into the renal artery. The venous outflows were then collected for 15 min with a fraction collector. The renal extraction ratio of PAH was decreased from 65% to 18% as the PAH concentration in the injection solution was increased from 2 microM to 10 mM. The PAH outflow profile changed as the extraction process became saturated. With the non-linear dispersion model, the Michaelis-Menten parameters for the PAH extraction process were estimated by a model fitting calculation. The calculated values were 0.83 micromol/min/kidney for Vmax and 89 microM for Km. It was demonstrated that the model dependent analysis with a non-linear dispersion model is a useful approach to characterize renal drug handling, and is probably applicable for examining other non-linear processes which involve a diffusion phenomenon.

Animals↗
PubMed1993

Use of the linear quadratic model in order to accommodate a small reduction in the number of fractions of a standard radiotherapy treatment regime.

Use of the linear quadratic model is considered for reduction, by one or two fractions, of the number of fractions in a daily fractionated reference schedule while maintaining a continuous regime. The cases of maintaining late or early tumour reacting tissue are considered with the inclusion of time effects. The reduction of biologically effective dose (BED) to early-tumour type tissue is shown to be overestimated for both cases if time effects are not taken into account. A third option is outlined, which equates the magnitude of the fractional reduction of BED for early-tumour-reacting tissue to the fractional increase in BED for late-reacting tissue without accounting for time effects. Using this option, the resulting variations in BED for late- and early-tumour tissue are compared with the accepted tolerances in physical dose delivery and some examples presented. The overestimated prediction of the variation in BED for early-tumour tissue still applies in this option, suggesting that this is the way the linear quadratic model should be applied to such a schedule change.

Humans↗
PubMed1991

Application of the linear-quadratic model with incomplete repair to radionuclide directed therapy.

The linear-quadratic (LQ) model for fractionated external beam therapy has been modified by previous authors to include the effects due to an exponentially decaying dose rate. However, the LQ model has now been extended to include a general time varying dose rate profile, and the equations can be readily evaluated if an exponential radiation damage repair process is assumed. These equations are applicable to radionuclide directed therapy, including brachytherapy. Kinetic uptake data obtained during radionuclide directed therapy may therefore be used to determine the radiobiological dosimetry of the target and non-target tissues. Also, preliminary tracer studies may be used to pre-plan the radionuclide directed therapy, provided that tracer and therapeutic amounts of the radionuclide carrier are identically processed by the tissues. It is also shown that continuous radionuclide therapy will induce less damage in late-responding tissues than 2 Gy/fraction external beam therapy if the ratio of the maximum dose rate and the sublethal damage repair half-life in the tissue is less than 1.0 Gy. Similar inequalities may be derived for beta-particle radionuclide directed therapy. For example, it can be shown that radionuclide directed therapy will induce less damage to slowly repopulating tissue than 2 Gy/fraction external beam therapy for the same total dose if the maximum percentage initial uptake in tissue is less than 0.046%/g or 0.23%/g for an injected activity of 50 mCi of 90Y or 131I, respectively.

Brachytherapy↗
PubMedDate not supplied

Simulation of linear compartment models with application to nuclear medicine kinetic modeling.

Several techniques are evaluated for solving the linear ordinary differential equations arising from compartment models. The methods involve approximating the matrix exponential of the state matrix (i.e. the transition matrix). The computational efficiencies of these techniques, together with that of a general purpose differential equation solver, are compared for several models arising from radiopharmacokinetic studies. The matrix exponential calculations are performed using both Ward's Padé approximation method and an eigenvalue-eigenvector decomposition (QR factorization) of the matrix A. These two algorithms have been incorporated as simulation options into the programs of the ADAPT package. ADAPT consists of a set of high-level programs for simulation, parameter estimation and experiment design, developed primarily for basic and clinical research modeling and data analysis applications involving pharmacokinetic and pharmacodynamic processes. The advantages and disadvantages of these simulation strategies for solving linear kinetic models within a parameter estimation setting are illustrated and discussed.

Algorithms↗
PubMed2002

Simple polynomial multiplication algorithms for exact conditional tests of linearity in a logistic model.

The linear logistic model is often employed in the analysis of binary response data. The well-known asymptotic chi-square and likelihood ratio tests are usually used to detect the assumption of linearity in such a model. For small, sparse, or skewed data, the asymptotic theory is however dubious and exact conditional chi-square and likelihood ratio tests may provide reliable alternatives. In this article, we propose efficient polynomial multiplication algorithms to compute exact significance levels as well as exact powers of these tests. Two options, namely the cell- and stage-wise approaches, in implementing these algorithms will be discussed. When sample sizes are large, we propose an efficient Monte Carlo method for estimating the exact significance levels and exact powers. Real data are used to demonstrate the performance with an application of the proposed algorithms.

Algorithms↗