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PubMed1992

The in vivo orthodontic banding model for vital teeth and the in situ orthodontic banding model for hard-tissue slabs.

This paper presents the orthodontic banding model for vital teeth and the orthodontic in situ model for slabs of enamel, root surface, dentin, or other mineralized tissues such as shark enamel. The model for vital teeth is an in vivo model, since a crevice for plaque accumulation is created behind orthodontic bands on the buccal enamel surfaces of teeth in situ. Visible white-spot lesions are usually seen after a four-week banding period in the absence of fluoride. The microbiological flora developed behind the bands shows a similarity to that of natural caries. Microradiographic data show that the initial lesion is a softening of the enamel surface. Later, a subsurface lesion develops. A modification of the model has been developed for the use of slabs of mineralized tissues. In this model, slabs are mounted on a removable appliance. The slabs are covered with orthodontic banding material for plaque accumulation. Lesion development in enamel in the two model systems is almost identical. The benefit of the in vivo model is that caries development can be studied on vital teeth in young individuals. The model is independent of the patient's cooperation. No special diet is required, e.g., no sucrose rinsing. In the in situ model, slabs could be examined after one study period and then replaced for another period.

Dental Caries↗
PubMed1991

Regressive logistic models for familial diseases: a formulation assuming an underlying liability model.

Statistical models have been developed to delineate the major-gene and non-major-gene factors accounting for the familial aggregation of complex diseases. The mixed model assumes an underlying liability to the disease, to which a major gene, a multifactorial component, and random environment contribute independently. Affection is defined by a threshold on the liability scale. The regressive logistic models assume that the logarithm of the odds of being affected is a linear function of major genotype, phenotypes of antecedents and other covariates. An equivalence between these two approaches cannot be derived analytically. I propose a formulation of the regressive logistic models on the supposition of an underlying liability model of disease. Relatives are assumed to have correlated liabilities to the disease; affected persons have liabilities exceeding an estimable threshold. Under the assumption that the correlation structure of the relatives' liabilities follows a regressive model, the regression coefficients on antecedents are expressed in terms of the relevant familial correlations. A parsimonious parameterization is a consequence of the assumed liability model, and a one-to-one correspondence with the parameters of the mixed model can be established. The logits, derived under the class A regressive model and under the class D regressive model, can be extended to include a large variety of patterns of family dependence, as well as gene-environment interactions.

Environment↗
PubMed1989

Testing between the TRACE model and the fuzzy logical model of speech perception.

The TRACE model of speech perception (McClelland & Elman, 1986) is contrasted with a fuzzy logical model of perception (FLMP) (Oden & Massaro, 1978). The central question is how the models account for the influence of multiple sources of information on perceptual judgment. Although the two models can make somewhat similar predictions, the assumptions underlying the models are fundamentally different. The TRACE model is built around the concept of interactive activation, whereas the FLMP is structured in terms of the integration of independent sources of information. The models are tested against test results of an experiment involving the independent manipulation of bottom-up and top-down sources of information. Using a signal detection framework, sensitivity and bias measures of performance can be computed. The TRACE model predicts that top-down influences from the word level influence sensitivity at the phoneme level, whereas the FLMP does not. The empirical results of a study involving the influence of phonological context and segmental information on the perceptual recognition of a speech segment are best described without any assumed changes in sensitivity. To date, not only is a mechanism of interactive activation not necessary to describe speech perception, it is shown to be wrong when instantiated in the TRACE model.

Adult↗
PubMed1991

Model of kinetic behavior of deoxyglucose in heterogeneous tissues in brain: a reinterpretation of the significance of parameters fitted to homogeneous tissue models.

Effects of tissue heterogeneity on regional CMRglc (rCMRglc) calculated by use of the deoxyglucose (DG) method at 45 min following the pulse of DG were evaluated in simulation studies. A theoretical model was developed to describe the kinetics of DG uptake and metabolism in heterogeneous brain tissues. Rate constants were fitted to simulation data for mixed tissue and rCMRglc computed on the basis of this tissue heterogeneity model. The results were compared with those obtained by use of the original model of the DG method for homogeneous tissue, both without (3K model) and with (4K model) a term to describe an apparent loss of deoxyglucose-6-phosphate (DG-6-P). As a direct consequence of tissue heterogeneity, the effective rate constant for phosphorylation of DG, k3*, declined with time. To compensate for the time-changing k3*, estimates of the dephosphorylation rate constant, k4*, were artifactually high when the 4K model was used, even though no dephosphorylation of DG-6-P actually occurred. The present study demonstrates that the finding of a significant k4*, at least within 45 min following a pulse of DG, may not represent dephosphorylation at all, but rather the consequence of measuring radioactivity in a heterogeneous tissue and applying a model designed for a homogeneous tissue. Furthermore, the high estimates of k4* resulted in significant overestimation of rCMRglc. When rCMRglc was computed with the conventional single-scan or autoradiographic method at 45 min after a pulse of DG, the 3K and tissue heterogeneity models yielded values that were within 5% of the true weighted average value for the heterogeneous tissue as a whole. We conclude that the effects of tissue heterogeneity alone can give the appearance of product loss, even when none occurs, and that the use of the 4K model with the assumption of product loss in the 45-min experimental period recommended for the DG method may lead to overestimation of the rates of glucose utilization.

Brain↗
PubMed2023

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results across studies. Here, we performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) and genetic data to understand which decision points may affect performance. Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from two large independent health systems and polygenic risk scores (PRS) were generated across all patients with genetic data in the corresponding biobanks. Crohn's disease was used as the model phenotype based on its substantial genetic component, established EHR-based definition, and sufficient prevalence for model training and testing. We investigated the impact of PRS integration method, as well as choices regarding training sample, model complexity, and performance metrics. Overall, our results show that including PRS resulted in higher performance by some metrics but the gain in performance was only robust when combined with demographic data alone. Improvements were inconsistent or negligible after including additional clinical information. The impact of genetic information on performance also varied by PRS integration method, with a small improvement in some cases from combining PRS with the output of a clinical model (late-fusion) compared to its inclusion an additional feature (early-fusion). The effects of other modeling decisions varied between institutions though performance increased with more compute-intensive models such as random forest. This work highlights the importance of considering methodological decision points in interpreting the impact on prediction performance when including PRS information in clinical models.

Preprint↗
PubMed1990

Learning-forgetting independence, unidimensional memory models, and feature models: comment on Bogartz (1990).

In his recent articles, Bogartz offered a definition of what it means for forgetting rate to be independent of degree of original learning. He showed that, given this definition, independence is confirmed by extant data. Bogartz also criticized Loftus's (1985b) proposed method for testing independence. In this commentary, we counter Bogartz's criticisms and then offer two observations. First, we show that Loftus's horizontal-parallelism test distinguishes between two interesting class of memory models: unidimensional models wherein the memory system's state can be specified by a single number and multidimensional models wherein at least two numbers are required to specify the memory system's state. Independence by Loftus's definition is implied by a unidimensional model. Bogartz's definition, in contrast, is consistent with either model. Second, to better understand the constraints on memory mechanisms dictated by the mathematics of the models under consideration, we develop a simple but general feature model of learning and forgetting. We demonstrate what constraints must be placed on this model to make learning and forgetting rate independent by Loftus's and by Bogartz's definitions.

Humans↗
PubMed2026

A trainable language model with potential to modulate translation rates in non-model organisms by generating upstream untranslated region sequence libraries.

Tuning protein expression in non-model organisms is often constrained by the lack of validated genetic parts and predictive design tools. Translational tuning through the modulation of upstream untranslated regions (5'-UTRs) offers a potentially organism-agnostic route, but existing methods typically rely on mechanistic assumptions, prior knowledge that may not be available in non-model contexts, or the screening of sequence libraries. Here, we present a simple generative approach for creating synthetic 5'-UTR libraries based solely on the genomic sequence statistics of any desired organism. The method uses a sliding-window n-gram language model applied to native 5'-UTR sequences to produce novel sequences that preserve organism-specific base distributions and motifs without hard-coding specific motifs or mechanistic rules into inflexible statistical templates. We have applied this approach to the model bacterium Escherichia coli and the non-model probiotic Limosilactobacillus reuteri. Libraries of approximately 1,000 sequences were generated for each organism, from which about 100 unique sequences were experimentally tested for translation of a fluorescent reporter protein. In both organisms, the synthetic libraries yielded a broad range of translation levels from this relatively small number of tested variants. Sequences derived from an organism's own genomic statistics provided a more uniformly distributed range of translation rates in that organism than sequences derived from the other species. Correlations of individual sequence performance across the two species were weak, and thermodynamic predictions of ribosome binding strength showed very little predictive power, especially in the non-model L. reuteri. The results demonstrate that simple statistical language model approaches applied to genomic data can generate functional translational regulatory sequence libraries without detailed mechanistic knowledge or explicit reference to consensus motifs. The approach requires minimal computational resources, avoids reproducing native sequences, and can be readily applied to any organism with a sequenced genome. This strategy may lower technical barriers to expression tuning in non-model organisms.

5' Untranslated Regions↗
PubMed1989

Applied muscle modelling: implementation of muscle-specific models.

Recent work in musculoskeletal modelling has seen the use of models which represent individual muscles in the human system. This paper presents a model of forearm supination in which models generate specific muscular forces to produce external supinator torque. The model output is compared to measured external torque for isometric and dynamic loading conditions. These data are used to construct isometric torque-angle and torque-angular velocity graphs for both model and experimental output. The discussion focuses on specific topics regarding implementation of muscle models in applied situations. These topics are demonstrated by observing the effect of parameter alteration on model output.

Computer Simulation↗
PubMed1988

X-ray diffraction studies of 14-filament models of deoxygenated sickle cell hemoglobin fibers. II. Models based on the deoxygenated sickle hemoglobin crystal structure.

The calculated transforms of a number of crystal-based models of the deoxygenated sickle cell hemoglobin fiber have been compared with X-ray diffraction data of 15 A (1 A = 0.1 nm) resolution. The fiber models consist of 14 single strands of sickle cell hemoglobin (HbS) molecules, which associate into seven protofilaments arranged similarly to those present in the crystal structure. Six of the protofilaments are arranged in three crystallographic until cells extending in the c-axis direction with the seventh protofilament positioned so as to provide an elliptical cross-section when the assemblage is viewed down the fiber axis. Models were generated by systematically and independently translating each of the model's three subcells in steps of 3.5 A along the fiber axis. The seventh protofilament was kept fixed as a point of reference. Each translation of a subcell corresponded to a different fiber model whose transform was then compared with observed data. In all, over 46,000 transforms were computed; of these, three models with minimal residuals were identified. The free energy of packing for all crystal-based models was evaluated to find configurations of protofilaments possessing minimal free energies. The results of the calculations support the subcell configurations of two of the three models with minimal residuals.

Hemoglobin, Sickle↗
PubMed2025

Refining sequence-to-activity models by increasing model resolution.

Decoding the cis-regulatory syntax that controls gene expression is essential for improving our understanding of cell differentiation and disease. To identify regulatory motifs and their regulatory syntax, deep learning based sequence-to-activity (S2A) models learn transcription factor binding motifs and their combinations from DNA sequence by modeling measured chromatin accessibility. Previously, we developed AI-TAC, a S2A model that predicts chromatin accessibility across various immune cell types in multi-task fashion, effectively decoding the regulatory syntax underlying immune cell differentiation. While ATAC-seq is commonly used to measure regional accessibility, it also provides high-resolution profiles, the distribution of Tn5 insertion sites, that offer additional insights into the precise location and strength of TF binding sites. Here we demonstrate that modeling ATAC-seq profiles alongside accessibility consistently improves predictions of differential chromatin accessibility across cell types. Moreover, we also find that multi-task learning across related immune cell types consistently outperforms single-task models. To understand what additional information bpAITAC learns from ATAC-seq profiles, we systematically compare sequence attributions from models trained with and without ATAC-seq profiles. We identify novel motifs with strong effect sizes that emerge only when profile data is included. Our findings suggest that modeling ATAC-seq at base-pair resolution enables the model to learn a more nuanced and sensitive representation of the cis-regulatory syntax driving immune cell-specific chromatin landscapes.

ATAC-seq↗
PubMed1990

Energetics of the time-varying elastance model, a visco-elastic model, matches Mommaerts' unifying concept of the Fenn effect of muscle.

It is generally believed that the Fenn effect contradicts all visco-elastic models of muscle, including the new elastic body and the time-varying elastance models. Although it is clear that the new elastic body model can be discarded, the Fenn effect does not preclude the time-varying elastance model. Although no visco-elastic models can simulate the extra energy utilization for work above the level of the energy utilized for the maximal isometric contraction, the extra energy observed by Fenn is not generally observed, even in skeletal muscles. However, work-related extra energy utilization, above the isometric energy utilization at equivalent force (Mommaerts' unifying concept of the Fenn effect), is generally observed in both skeletal and cardiac muscles. This unifying concept of the Fenn effect in cardiac muscle can be simulated by a simple time-varying elastance model. This study demonstrates the essential difference in energetics between the new elastic body model and the time-varying elastance model.

Elasticity↗
PubMed1988

A dynamic life table model of Psorophora columbiae in the southern Louisiana rice agroecosystem with supporting hydrologic submodel. Part 1. Analysis of literature and model development.

During the past decade, the rice agroecosystem and its associated mosquitoes have been the subject of an extensive research effort directed toward the development and implementation of integrated pest management (IPM) strategies. The objective of this work was to synthesize the literature and unpublished data on the rice agroecosystem into a comprehensive simulation model of the key elements of the system known to influence the population dynamics of Psorophora columbiae. Subsequent companion papers will present a validation of these models, provide an in-depth analysis of the population dynamics of Ps. columbiae, and evaluate current and proposed IPM strategies for this mosquito. This paper describes the development of 2 models: WaterMod: Because spatial and temporal distributions of surface water and soil moisture play a decisive role in the dynamics of Ps. columbiae, an essentially hydrological simulator was developed. Its purpose is to provide environmental inputs for a second model (PcSim) which simulates the population dynamics of Ps. columbiae. WaterMod utilizes data on weather, agricultural practices, and soil characteristics for a particular region to generate a data set containing daily estimates of soil moisture and depth of water table for 12 representative areas comprising the rice agroecosystem. This model could be used to provide hydrologic inputs for additional simulation models of other riceland mosquito species. PcSim: This model simulates the population dynamics of Ps. columbiae by using the computer to maintain a daily accounting of the absolute number of mosquitoes within each daily age class for each life stage. The model creates estimates of the number of eggs, larvae, pupae, and adults for a representative l-ha area of a rice agroecosystem.

Actuarial Analysis↗
PubMed1989

Modelling sibship environment in the regressive logistic model for familial disease.

Recently analytical models for pedigree disease data have been developed that combine genetic and epidemiological modelling techniques. The regressive logistic model [Bonney, Biometrics 42: 611-625; 1986] relies on decomposing the likelihood of a pedigree into the product of conditional probabilities, one for each individual, by imposing a (natural) order on pedigree members. In addition to modelling measured epidemiological variables, vertical transmission, transmission of unmeasured ousiotypes (a special case being genotypes), and some modelling of sibship dependencies have been proposed. In this paper the model is extended to include an unmeasured sibship environment factor using a log-linear model for binary pedigree traits [Hopper et al., Genet Epidemiol 1: 183-188; 1984], which breaks the pedigree into conditionally independent groups. Statistical issues, such as designs for which these factors will be discernible and tests of fit, are discussed.

Environment↗
PubMed1992

Building population pharmacokinetic--pharmacodynamic models. I. Models for covariate effects.

One major task in clinical pharmacology is to determine the pharmacokinetic-pharmacodynamic (PK-PD) parameters of a drug in a patient population. NONMEM is a program commonly used to build population PK-PD models, that is, models that characterize the relationship between a patient's PK-PD parameters and other patient specific covariates such as the patient's (patho) physiological condition, concomitant drug therapy, etc. This paper extends a previously described approach to efficiently find the relationships between the PK-PD parameters and covariates. In a first step, individual estimates of the PK-PD parameters are obtained as empirical Bayes estimates, based on a prior NONMEN fit using no covariates. In a second step, the individual PK-PD parameter estimates are regressed on the covariates using a generalized additive model. In a third and final step, NONMEM is used to optimize and finalize the population model. Four real-data examples are used to demonstrate the effectiveness of the approach. The examples show that the generalized additive model for the individual parameter estimates is a good initial guess for the NONMEM population model. In all four examples, the approach successfully selects the most important covariates and their functional representation. The great advantage of this approach is speed. The time required to derive a population model is markedly reduced because the number of necessary NONMEM runs is reduced. Furthermore, the approach provides a nice graphical representation of the relationships between the PK-PD parameters and covariates.

Adult↗
PubMed1991

Domain of validity of classical models of leucine metabolism assessed by compartmental modeling.

Whole-body modeling of in vivo leucine (an essential amino acid) metabolism is fundamentally difficult due to the complexity of the system. This has favored the use of two simple kinetic models, the so-called primary and reciprocal pool models, to interpret tracer data, but their domain of validity is uncertain. We define here the error of these two approaches by using comprehensive compartmental models of leucine metabolism as true representations of the leucine system. Of particular interest is the comparison of the two simple models with an 11-compartment model characterized by a rich intracellular compartmentation that has recently been proposed as a sound physiological description of the system. Formulas are derived that define in structural terms the error of the primary and reciprocal pool models.

Humans↗
PubMed1992

Sexual mixing models: a comparison of analogue deterministic and stochastic models.

Models for sexual partner choice are discussed for the case of highly variable sexual activity in the population. It is demonstrated that the variances in the number of infected persons may be extremely large. For the random mixing model, higher order cumulants are also evaluated. On the basis of these results the applicability of deterministic models and models for expectations only are questioned. A general model is proposed for handling nonrandom, or correlated, mixing. The problem of inconsistency is overcome by considering the couples having sex as the natural unit in the model. In the case of s discrete homogeneous groups it is shown that only (s2) parameters defining the interaction between the groups can be chosen freely. Finally, the effect of correlation in partner choice is demonstrated by a bivariate lognormal model for partner choice.

Female↗