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PubMed2002

Modeling assessment of air emission flux of mercury from soils in terrestrial landscape components: model tests and sensitivities.

The abilities of a screening-level model to predict variations in elemental mercury (Hg0) air emissions from soils in terrestrial landscapes are examined by comparing simulation results to published observational data and by performing sensitivity analyses. Despite uncertainties and simplifications, the model results obtained offer some degree of confidence in the model's joint ability to relate readily available environmental parameters to airborne emissions of Hg predicted by coupling simple atmospheric and soil parameters with Hg cycling and transport algorithms. The model reasonably predicted the observational data in the considered data sets except for one site for which significant uncertainty was associated with model input data. Predictions are consistent with many trends observed in the field studies; better predictions were obtained for nonvegetated systems (relative errors between 0.4 and 9.7%) than for shaded-soil landscapes (relative errors between 2.3 and 27%). The model reflected field data showing that daily average emission rates of Hg0, formed by the reduction of Hg(II), are primarily controlled by changes in solar radiation, soil moisture, temperature, and, to a lesser extent, wind conditions. The model may have potential use in several preliminary studies to characterize trends of airborne Hg emitted from terrestrial sources to the atmosphere.

Air Pollutants↗

PubMed2001

The simple model versus the super model: translating experimental traumatic brain injury research to the bedside.

Despite considerable investigation in rodent models of traumatic brain injury (TBI), no novel therapy has been successfully translated from bench to bedside. Although well-described limitations of clinical trails may account for these failures, several modeling factors may also contribute to the lack of therapeutic translation from the laboratory to the clinic. Specifically, models of TBI may omit one or more critical, clinically relevant pathophysiologic features. In this invited review article, the impact of the limited incorporation of several important clinical pathophysiologic factors in TBI, namely secondary insults (i.e., hypotension and/or hypoxemia), coma, and aspects of standard neurointensive care monitoring and management strategies (i.e., intracranial pressure [ICP] monitoring and ICP-directed therapies, sedation, mechanical ventilation, and cardiovascular support) are discussed. Comparative studies in rodent and large animal models of TBI (which may, in some cases, represent super models) are also presented. We conclude that therapeutic breakthroughs will likely require a multidisciplinary approach, involving investigation in a range of models, including clinically relevant modifications of established animal models, along with development and application of new innovations in clinical trial design.

Animals↗

PubMed2006

The identifiability of tree topology for phylogenetic models, including covarion and mixture models.

For a model of molecular evolution to be useful for phylogenetic inference, the topology of evolutionary trees must be identifiable. That is, from a joint distribution the model predicts, it must be possible to recover the tree parameter. We establish tree identifiability for a number of phylogenetic models, including a covarion model and a variety of mixture models with a limited number of classes. The proof is based on the introduction of a more general model, allowing more states at internal nodes of the tree than at leaves, and the study of the algebraic variety formed by the joint distributions to which it gives rise. Tree identifiability is first established for this general model through the use of certain phylogenetic invariants.

Computational Biology↗

PubMed1999

Four-component model of body composition in children: density and hydration of fat-free mass and comparison with simpler models.

BACKGROUND: Body composition in children is generally measured by 2-component (2C) models, which are subject to error arising from variation in fat-free mass (FFM) composition. The 4-component (4C) model, which divides body weight into fat, water, mineral, and protein, can overcome these limitations. OBJECTIVE: The aims of our study were to 1) describe 4C model data for children aged 8-12 y; 2) evaluate interindividual variability in the hydration, bone mineral content, and density of FFM; 3) evaluate the success with which 2C models and bedside techniques measure body composition in this age group with use of the 4C model as a reference. DESIGN: Dual-energy X-ray absorptiometry, underwater weighing, deuterium dilution, bioelectrical impedance analysis, and anthropometry were used to determine body composition in 30 children. The contribution of methodologic error to the observed variability in the hydration and density of FFM was evaluated by using propagation of error. RESULTS: Mean (+/-SD) FFM density and hydration were 1.0864+/-0.0074 kg/L and 75.3+/-2.2%, respectively, and were significantly different from adult values (P < 0.02). Relative to the 4C model, deuterium dilution and dual-energy X-ray absorptiometry showed no mean bias for fatness, whereas underwater weighing underestimated fatness (P < 0.025). Fatness determined by using skinfold-thickness and bioelectrical impedance analysis measurements along with published equations showed poor agreement with 4C model data. CONCLUSIONS: Biological variability and methodologic error contribute equally to the variability of FFM composition. Our findings have major implications for bedside prediction methods used for children, traditionally developed in relation to underwater weighing.

Adipose Tissue↗

PubMed2026

Causal circuit tracing reveals distinct computational architectures in single-cell foundation models: inhibitory dominance, biological coherence, and cross-model convergence.

MOTIVATION: Sparse autoencoders (SAEs) decompose foundation-model activations into interpretable features, but the model-internal causal interactions between those features (i.e. what ablating one feature does to the others, as distinct from the biological causal structure of the underlying cells)-and how those model-internal relationships relate to biological structure-are uncharacterized in single-cell foundation models. RESULTS: We introduce model-internal causal circuit tracing-zeroing one SAE feature at a source layer and measuring the resulting change in all downstream SAE features, for each of 120 source features-and apply it to Geneformer V2-316M and scGPT whole-human across four conditions (96 892 ablation-derived edges, 80 191 forward passes). On annotation-selected source features, edges share GO/KEGG/Reactome/STRING/TRRUST ontology terms at 50.9%-68.5%, a 2.9-6.2× enrichment over a configuration-preserving permutation null (P<.002); on 20 randomly sampled source features this attenuates to 21.5%-26.3%-still 2.5-3.1× above null-quantifying the annotation-selection contribution. Inhibitory dominance (fraction of ablation edges with d<0, i.e. source activation supports downstream target) is 65.5%-89.4%. scGPT produces larger raw per-edge effects (mean |d|=1.40 versus 1.05); after feature-share normalization, Geneformer is stronger (paired gene-pair ratio 0.64 on 33 301 shared pairs). Cross-model consensus yields 1142 architecture-invariant domain pairs (ordered pairs of GO biological-process categories "A→B" each connected by at least one ablation edge in both models; 10.6× enrichment over permutation null; P<.001). Circuit edge magnitude explains <1% of the variance in marginal driver-gene coexpression on the same cells (R2=0.010, n=31 176): the graph encodes structure beyond bivariate correlation. Against a matched-cell-type ENCODE ChIP-seq prior, circuit-predicted transcription factor (TF)→target pairs are enriched 2.06× (Fisher OR 5.84), markedly higher than 1.12× against TRRUST; direct ChIP-seq-supported target pairs show 10-30× larger CRISPRi sign-bias-corrected excess than indirect pairs. Gene-level CRISPRi validation on Replogle K562 and the noncancer RPE1 arm (and a true primary-T-cell control from Shifrut E, Carnevale J, Tobin V et al. Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell 2018; 175: 1958-71.e15) after sign-bias correction shows excess over baseline of +0.03 and +0.35 percentage points on K562 and RPE1, respectively (baseline already 52%-56% from sign marginals); effect-magnitude Spearman correlations ρ≈0. Bootstrap and per-cell-type stability (N∈{50,100,200}; B cell, CD4 + T, macrophage) give Pearson r≥0.97 on shared edges with 100% sign agreement; edge Jaccard grows monotonically with sample size. The circuit graph is therefore highly reproducible as an effect-size map, cell type specific in edge identity, consistent with coexpression encoding, and weakly but detectably enriched for ChIP-seq-supported direct regulatory edges. AVAILABILITY AND IMPLEMENTATION: https://github.com/Biodyn-AI/bio-sae-circuits (Python). Archival DOI: 10.5281/zenodo.19,633,166 (Zenodo).

Humans↗

PubMed2001

Statistical modeling of interlocus interactions in a complex disease: rejection of the multiplicative model of epistasis in type 1 diabetes.

In general, common diseases do not follow a Mendelian inheritance pattern. To identify disease mechanisms and etiology, their genetic dissection may be assisted by evaluation of linkage in mouse models of human disease. Statistical modeling of multiple-locus linkage data from the nonobese diabetic (NOD) mouse model of type 1 diabetes has previously provided evidence for epistasis between alleles of several Idd (insulin-dependent diabetes) loci. The construction of NOD congenic strains containing selected segments of the diabetes-resistant strain genome allows analysis of the joint effects of alleles of different loci in isolation, without the complication of other segregating Idd loci. In this article, we analyze data from congenic strains carrying two chromosome intervals (a double congenic strain) for two pairs of loci: Idd3 and Idd10 and Idd3 and Idd5. The joint action of both pairs is consistent with models of additivity on either the log odds of the penetrance, or the liability scale, rather than with the previously proposed multiplicative model of epistasis. For Idd3 and Idd5 we would also not reject a model of additivity on the penetrance scale, which might indicate a disease model mediated by more than one pathway leading to beta-cell destruction and development of diabetes. However, there has been confusion between different definitions of interaction or epistasis as used in the biological, statistical, epidemiological, and quantitative and human genetics fields. The degree to which statistical analyses can elucidate underlying biologic mechanisms may be limited and may require prior knowledge of the underlying etiology.

Animals↗

PubMed1997

Validity of methods for model selection, weighting for model uncertainty, and small sample adjustment in capture-recapture estimation.

In log-linear capture-recapture approaches to population size, the method of model selection may have a major effect upon the estimate. In addition, the estimate may also be very sensitive if certain cells are null or very sparse, even with the use of multiple sources. The authors evaluated 1) various approaches to the issue of model uncertainty and 2) a small sample correction for three or more sources recently proposed by Hook and Regal. The authors compared the estimates derived using 1) three different information criteria that included Akaike's Information Criterion (AIC) and two alternative formulations of the Bayesian Information Criterion (BIC), one proposed by Draper ("two pi") and one by Schwarz ("not two pi"); 2) two related methods of weighting estimates associated with models; 3) the independent model; and 4) the saturated model, with the known totals in 20 different populations studied by five separate groups of investigators. For each method, we also compared the estimate derived with or without the proposed small sample correction. At least in these data sets, the use of AIC appeared on balance to be preferable. The BIC formulation suggested by Draper appeared slightly preferable to that suggested by Schwarz. Adjustment for model uncertainty appears to improve results slightly. The proposed small sample correction appeared to diminish relative log bias but only when sparse cells were present. Otherwise, its use tended to increase relative log bias. Use of the saturated model (with or without the small sample correction) appears to be optimal if the associated interval is not uselessly large, and if one can plausibly exclude an all-source interaction. All other approaches led to an estimate that was too low by about one standard deviation.

Bayes Theorem↗

PubMed2006

Reliability of the ICRP'S dose coefficients for members of the public: IV. basis of the human alimentary tract model and uncertainties in model predictions.

The biokinetic and dosimetric model of the gastrointestinal (GI) tract applied in current documents of the International Commission on Radiological Protection (ICRP) was developed in the mid-1960s. The model was based on features of a reference adult male and was first used by the ICRP in Publication 30, Limits for Intakes of Radionuclides by Workers (Part 1, 1979). In the late 1990s an ICRP task group was appointed to develop a biokinetic and dosimetric model of the alimentary tract that reflects updated information and addresses current needs in radiation protection. The new age-specific and gender-specific model, called the Human Alimentary Tract Model (HATM), has been completed and will replace the GI model of Publication 30 in upcoming ICRP documents. This paper discusses the basis for the structure and parameter values of the HATM, summarises the uncertainties associated with selected features and types of predictions of the HATM and examines the sensitivity of dose estimates to these uncertainties for selected radionuclides. Emphasis is on generic biokinetic features of the HATM, particularly transit times through the lumen of the alimentary tract, but key dosimetric features of the model are outlined, and the sensitivity of tissue dose estimates to uncertainties in dosimetric as well as biokinetic features of the HATM are examined for selected radionuclides.

Dose-Response Relationship, Radiation↗

PubMed1989

Human respiratory tract model for radiological protection: a revision of the ICRP Dosimetric Model for the Respiratory System.

In 1984, the International Commission on Radiological Protection (ICRP) appointed a task group of Committee 2 to review and revise, as necessary, the ICRP Dosimetric Model for the Respiratory System. The model was originally published in 1966, modified slightly in Publication No. 19, and again in Publication No. 30 (in 1979). The task group concluded that research during the past 20 y suggested certain deficiencies in the ICRP Dosimetric Model for the Respiratory System. Research has also provided sufficient information for a revision of the model. The task group's approach has been to review, in depth, morphology and physiology of the respiratory tract; deposition of inhaled particles in the respiratory tract; clearance of deposited materials; and the nature and specific sites of damage to the respiratory tract caused by inhaled radioactive substances. This review has led to a redefinition of the regions of the respiratory tract for dosimetric purposes. The redefinition has a morphologic and physiological basis and is consistent with observed deposition and clearance of particles and with resultant pathology. Regions, as revised, are the extrathoracic (E-T) region, comprising the nasal and oral regions, the pharynx, larynx, and upper part of the trachea; the fast-clearing thoracic region (T[f]), comprising the remainder of the trachea and bronchi; and the slow-clearing thoracic region (T[s]), comprising the bronchioles, alveoli, and thoracic lymph nodes. A task group report will include models for calculating radiation doses to these regions of the respiratory tract following inhalation of representative alpha-, beta-, and gamma-emitting particulate and gaseous radionuclides. The models may be implemented as a package of computer codes available to a wide range of users. This should facilitate application of the revised human respiratory tract model to worldwide radiation protection needs.

Aerosols↗

PubMed2002

Protocol of a thromboembolic stroke model in the rat: review of the experimental procedure and comparison of models.

RATIONALE AND OBJECTIVE: Different models to study the pathophysiology of cerebral ischemia and to evaluate therapeutic strategies exist. Described is the detailed procedure of a thromboembolic stroke model in the rat most closely resembling human embolic stroke and compare the model to other equivalent rodent models. MATERIALS AND METHODS: An evaluation of a new thromboembolic stroke model was performed on 35 male Wistar rats. After preparation of the carotid artery, a catheter was introduced into the external carotid artery. During injection of autologous, fibrin-rich emboli into the internal carotid artery the common carotid artery was temporarily occluded. Regional cerebral blood flow (rCBF) and lesion size were determined. RESULTS: Twelve fibrin-rich blood clots of 1.5 mm in length were necessary in order reliably to occlude the origin of the middle cerebral artery. A stable decrease of rCBF and lesion size was confirmed by autoradiography, diffusion, and perfusion MRI, TTC-staining, biochemical imaging, and histology. CONCLUSION: In this animal model, the situation of human cerebral ischemia is simulated closely. The model is suitable for investigations of the pathophysiology of stroke and facilitates studies on the effects of thrombolytic therapy.

Animals↗

PubMedDate not supplied

Modeling nursing care in health level 7 reference information model.

This article describes the attempt that was made to fit nursing knowledge into the Reference Information Model developed by Health Level 7 (HL 7 RIM). A part of nursing knowledge concerning pressure ulcer was used to show the possibilities of modeling nursing knowledge, terminology, and information in the Health Level 7 Reference Information Model. The purpose of this modeling work was to allow the integration of nursing information in electronic patient records and to ensure the interoperability and exchange of nursing information. Based on earlier reviews of pressure ulcer literature, an overview was made of relevant variables, coding, and information required for nursing care for pressure ulcer. Next, a use case was made and followed by modeling the required information in the Unified Modeling Language. It is possible to integrate nursing knowledge, terminology, and information requirements in the Health Level 7 Reference Information Model.

Humans↗

PubMed1994

Modelling the impact of mass vaccination against hepatitis B. I. Model formulation and parameter estimation.

A new model of the transmission dynamics of hepatitis B virus in countries with high transmission rates is presented. The model is age and sex stratified, and includes details of host demography. Details of hepatitis B natural history, such as the existence of infectious and non-infectious carriers, are included. The biological assumptions of the model are discussed in full and related to the model's equations. Hepatitis B epidemiological data is reviewed with special emphasis on the estimation of model parameter values from field data. A full set of model parameter values are derived. Possible uses of the model in the assessment of strategies of mass vaccination are discussed.

Adolescent↗

PubMed2001

An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures.

This paper presents a deformable model for automatically segmenting brain structures from volumetric magnetic resonance (MR) images and obtaining point correspondences, using geometric and statistical information in a hierarchical scheme. Geometric information is embedded into the model via a set of affine-invariant attribute vectors, each of which characterizes the geometric structure around a point of the model from a local to a global scale. The attribute vectors, in conjunction with the deformation mechanism of the model, warranty that the model not only deforms to nearby edges, as is customary in most deformable surface models, but also that it determines point correspondences based on geometric similarity at different scales. The proposed model is adaptive in that it initially focuses on the most reliable structures of interest, and gradually shifts focus to other structures as those become closer to their respective targets and, therefore, more reliable. The proposed techniques have been used to segment boundaries of the ventricles, the caudate nucleus, and the lenticular nucleus from volumetric MR images.

Brain↗

PubMed2006

Modelling and evaluation of surgical performance using hidden Markov models.

Minimally invasive surgery has become very widespread in the last ten years. Since surgeons experience difficulties in learning and mastering minimally invasive techniques, the development of training methods is of great importance. While the introduction of virtual reality-based simulators has introduced a new paradigm in surgical training, skill evaluation methods are far from being objective. This paper proposes a method for defining a model of surgical expertise and an objective metric to evaluate performance in laparoscopic surgery. Our approach is based on the processing of kinematic data describing movements of surgical instruments. We use hidden Markov model theory to define an expert model that describes expert surgical gesture. The model is trained on kinematic data related to exercises performed on a surgical simulator by experienced surgeons. Subsequently, we use this expert model as a reference model in the definition of an objective metric to evaluate performance of surgeons with different abilities. Preliminary results show that, using different topologies for the expert model, the method can be efficiently used both for the discrimination between experienced and novice surgeons, and for the quantitative assessment of surgical ability.

Computer Simulation↗

PubMed2006

A comparative study of available software for high-accuracy homology modeling: from sequence alignments to structural models.

An open question in protein homology modeling is, how well do current modeling packages satisfy the dual criteria of quality of results and practical ease of use? To address this question objectively, we examined homology-built models of a variety of therapeutically relevant proteins. The sequence identities across these proteins range from 19% to 76%. A novel metric, the difference alignment index (DAI), is developed to aid in quantifying the quality of local sequence alignments. The DAI is also used to construct the relative sequence alignment (RSA), a new representation of global sequence alignment that facilitates comparison of sequence alignments from different methods. Comparisons of the sequence alignments in terms of the RSA and alignment methodologies are made to better understand the advantages and caveats of each method. All sequence alignments and corresponding 3D models are compared to their respective structure-based alignments and crystal structures. A variety of protein modeling software was used. We find that at sequence identities >40%, all packages give similar (and satisfactory) results; at lower sequence identities (<25%), the sequence alignments generated by Profit and Prime, which incorporate structural information in their sequence alignment, stand out from the rest. Moreover, the model generated by Prime in this low sequence identity region is noted to be superior to the rest. Additionally, we note that DSModeler and MOE, which generate reasonable models for sequence identities >25%, are significantly more functional and easier to use when compared with the other structure-building software.

Algorithms↗

PubMed2003

Benchmark dose risk assessment for formaldehyde using airflow modeling and a single-compartment, DNA-protein cross-link dosimetry model to estimate human equivalent doses.

Formaldehyde induced squamous-cell carcinomas in the nasal passages of F344 rats in two inhalation bioassays at exposure levels of 6 ppm and above. Increases in rates of cell proliferation were measured by T. M. Monticello and colleagues at exposure levels of 0.7 ppm and above in the same tissues from which tumors arose. A risk assessment for formaldehyde was conducted at the CIIT Centers for Health Research, in collaboration with investigators from Toxicological Excellence in Risk Assessment (TERA) and the U.S. Environmental Protection Agency (U.S. EPA) in 1999. Two methods for dose-response assessment were used: a full biologically based modeling approach and a statistically oriented analysis by benchmark dose (BMD) method. This article presents the later approach, the purpose of which is to combine BMD and pharmacokinetic modeling to estimate human cancer risks from formaldehyde exposure. BMD analysis was used to identify points of departure (exposure levels) for low-dose extrapolation in rats for both tumor and the cell proliferation endpoints. The benchmark concentrations for induced cell proliferation were lower than for tumors. These concentrations were extrapolated to humans using two mechanistic models. One model used computational fluid dynamics (CFD) alone to determine rates of delivery of inhaled formaldehyde to the nasal lining. The second model combined the CFD method with a pharmacokinetic model to predict tissue dose with formaldehyde-induced DNA-protein cross-links (DPX) as a dose metric. Both extrapolation methods gave similar results, and the predicted cancer risk in humans at low exposure levels was found to be similar to that from a risk assessment conducted by the U.S. EPA in 1991. Use of the mechanistically based extrapolation models lends greater certainty to these risk estimates than previous approaches and also identifies the uncertainty in the measured dose-response relationship for cell proliferation at low exposure levels, the dose-response relationship for DPX in monkeys, and the choice between linear and nonlinear methods of extrapolation as key remaining sources of uncertainty.

Administration, Inhalation↗

PubMed1999

Modeling random effects for censored data by a multivariate normal regression model.

A normal distribution regression model with a frailty-like factor to account for statistical dependence between the observed survival times is introduced. This model, as opposed to standard hazard-based frailty models, has survival times that, conditional on the shared random effect, have an accelerated failure time representation. The dependence properties of this model are discussed and maximum likelihood estimation of the model's parameters is considered. A number of examples are considered to illustrate the approach. The estimated degree of dependence is comparable to other models, but the present approach has the advantage that the interpretation of the random effect is simpler than in the frailty model.

Animals↗

PubMed2002

Modeling multivariate survival data by a semiparametric random effects proportional odds model.

In this article, the focus is on the analysis of multivariate survival time data with various types of dependence structures. Examples of multivariate survival data include clustered data and repeated measurements from the same subject, such as the interrecurrence times of cancer tumors. A random effect semiparametric proportional odds model is proposed as an alternative to the proportional hazards model. The distribution of the random effects is assumed to be multivariate normal and the random effect is assumed to act additively to the baseline log-odds function. This class of models, which includes the usual shared random effects model, the additive variance components model, and the dynamic random effects model as special cases, is highly flexible and is capable of modeling a wide range of multivariate survival data. A unified estimation procedure is proposed to estimate the regression and dependence parameters simultaneously by means of a marginal-likelihood approach. Unlike the fully parametric case, the regression parameter estimate is not sensitive to the choice of correlation structure of the random effects. The marginal likelihood is approximated by the Monte Carlo method. Simulation studies are carried out to investigate the performance of the proposed method. The proposed method is applied to two well-known data sets, including clustered data and recurrent event times data.

Animals↗