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PubMed1995

Statistical response models for ozone exposure: their generality when applied to human spirometric and animal permeability functions of the lung.

Exposure of humans or animals to ozone (O3) alters spirometric and permeability functions of the lung. While these responses show clear concentration (C) dependency, the interactive role of exposure duration (T) has not been well defined. Ozone-induced alterations in forced expiratory volume in 1 s (FEV1) obtained from human studies and in levels of bronchoalveolar lavage fluid protein (BALP) obtained from studies of rats and guinea pigs were used to compare the utility of several proposed response models as functions of C and T. A large human-study database compiled for T = 2 h and a wide-ranging C and T study on animals were used to contrast each model. The models examined included the quadratic, logistic, log regression, and exponential models. This work suggests that models used for risk assessment should incorporate both T and C. Our results suggest that modified forms of many of these models perform well with both human and animal responses and can be additionally modified to include ventilation rate. As a simple biological model, the exponential model showed advantages. The absolute concentration rates-of-change in the exponential function of integrated physiological changes like BALP and FEV1 were equal for low O3 exposure.

Animals↗

PubMed1994

The identification of Class III malocclusions by discriminant analysis.

Lateral cephalometric radiographs of 210 control and 285 Class III subjects were traced, digitized, and 43 calculated variables submitted to a stepwise discriminant analysis. A 10-factor model was generated, giving 95.2 per cent correct classification of the control children and 95.1 per cent accurate identification of the Class III group. Ten control and 14 Class III children were categorized incorrectly. To test the validity of the analysis, radiographs of these 24 individuals were examined in detail. In all cases, a satisfactory reason for the misgrouping was identified. This investigation underlined the importance of rigorous standards of case selection when compiling the groups. The robustness of the 10-factor model was also examined. Subjects were arbitrarily split into two groups, the odd- and the even-numbered cases, and the discriminant analysis repeated on each. Both new models contained the same 10 variables, but with slightly different values for their accompanying coefficients. The cases erroneously identified by the whole group analysis were again misclassified, together with a few additional cases. Each new model performed equally well on the data from the opposing group as on that from which it had been derived. Thus, the model generated in this study was both valid and acceptably robust. It would therefore appear that discriminant analysis may be a viable tool in the identification and classification of groups of individuals.

Adolescent↗

PubMed1997

Development and prospective validation of a clinical index to predict survival in ambulatory patients referred for cardiac transplant evaluation.

BACKGROUND: Risk stratification of patients with end-stage congestive heart failure is a critical component of the transplant candidate selection process. Accurate identification of individuals most likely to survive without a transplant would facilitate more efficient use of scarce donor organs. METHODS AND RESULTS: Multivariable proportional hazards survival models were developed with the use of data on 80 clinical characteristics from 268 ambulatory patients with advanced heart failure (derivation sample). Invasive and noninvasive models (with and without catheterization-derived data) were constructed. A prognostic score was determined for each patient from each model. Stratum-specific likelihood ratios were used to develop three prognostic-score risk groups. The models were prospectively validated on 199 similar patients (validation sample) by calculation of the area under the receiver operating characteristic curve for 1-year event-free survival, the censored c-index for event-free survival, and comparison of event-free survival curves for prognostic-score risk strata. Outcome events were defined as urgent transplant or death without transplant. The noninvasive model performed well in both samples, and increased performance was not attained by the addition of catheterization-derived variables. Prognostic-score risk groups derived from the noninvasive model in the derivation sample effectively stratified the risk of an outcome event in both samples (1-year event-free survival for derivation and validation samples, respectively: low risk, 93% and 88%; medium risk, 72% and 60%; high risk, 43% and 35%). CONCLUSIONS: Selection of candidates for cardiac transplantation may be improved by use of this noninvasive risk-stratification model.

Cardiac Output, Low↗

PubMed2006

Validation of a tertiary model for predicting variation of Salmonella typhimurium DT104 (ATCC 700408) growth from a low initial density on ground chicken breast meat with a competitive microflora.

Growth of a multiple antibiotic-resistant strain (ATCC 700408) of Salmonella Typhimurium definitive phage type 104 (DT104) from a low initial density (10(0.6) most probable number [MPN] or CFU/g) on ground chicken breast meat with a competitive microflora was investigated and modeled as a function of time and temperature (10 to 40 degrees C). MPN and viable counts (CFU) on a selective medium with four antibiotics enumerated the pathogen. Data from five replicate challenge studies per temperature were combined and fit to a primary model to determine maximum specific growth rate (micro), maximum population density (Nmax), and the 95% prediction interval (PI). Nonlinear regression was used to obtain secondary models as a function of temperature for micro, Nmax, and PI, which ranged from 0.04 to 0.4 h(-1), 1.6 to 9.4 log MPN or CFU/g, and 1.4 to 2.4 log MPN or CFU/g, respectively. Secondary models were combined with the primary model to create a tertiary model for predicting variation (95% PI) of pathogen growth among batches of ground chicken breast meat with a competitive microflora. The criterion for acceptable model performance was that 90% of observed MPN or CFU data had to be in the 95% PI predicted by the tertiary model. For data (n=344) used in model development, 93% of observed MPN and CFU data were in the 95% PI predicted by the tertiary model, whereas for data (n=236) not used in model development but collected using the same methods, 94% of observed MPN and CFU data were in the 95% PI predicted by the tertiary model. Thus, the tertiary model was successfully verified against dependent data and validated against independent data for predicting variation of Salmonella Typhimurium DT104 growth among batches of ground chicken breast meat with a competitive microflora and from a low initial density.

Animals↗

PubMed1995

Predicting future functional status for seriously ill hospitalized adults. The SUPPORT prognostic model.

OBJECTIVE: To develop a model estimating the probability of an adult patient having severe functional limitations 2 months after being hospitalized with one of nine serious illnesses. DESIGN: Prospective cohort study. SETTING: Five teaching hospitals in the United States. PARTICIPANTS: 1746 patients (model development) who survived 2 months and completed an interview, selected from 4301 patients in the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT); independent validation sample of 2478 patients. MEASUREMENTS AND MAIN OUTCOMES: Patient function 2 months after admission categorized as absence or presence of severe functional limitations (defined as Sickness Impact Profile scores > or = 30 or as activities of daily living scores > or = 4 [levels that require near-constant personal assistance]). A logistic regression model was constructed to predict severe functional limitation. RESULTS: One third (n = 590) of patients who were interviewed at 2 months had severe functional limitations. Changes in functional status were common: Of those with no baseline dependencies (not dependent on personal assistance), 21% were severely limited at 2 months; of those with 4 or more baseline limitations, 30% had improved. The patient's ability to do activities of daily living was the most important predictor of functional status. Physiologic abnormalities, diagnosis, days in hospital, age, quality of life, and previous exercise capacity also contributed substantially. Model performance, assessed using receiver-operating characteristic curves, was 0.79 for the development sample and 0.75 for the validation sample. The model was well calibrated for the entire risk range. CONCLUSIONS: Functional outcome varied substantially after hospitalization for a serious illness. A small amount of readily available clinical information can estimate the probability of severe functional limitations.

Activities of Daily Living↗

PubMed1987

Collicular ensemble coding of saccades based on vector summation.

The superior colliculus in the monkey contains a topographically organized representation of the target in its upper layers and saccade-related activity in its deeper layers. Since collicular movement fields are quite large, a considerable region of the colliculus is active whenever a saccade is made. We have modelled the collicular role in saccade generation based on the idea, proposed earlier in the literature, that each movement cell causes a movement tendency in the direction of the external world point which it represents in the collicular map. The model is organized as follows: An anisotropic logarithmic mapping transforms retinal coordinates into collicular coordinates. A two-dimensional Gaussian function describes the spatial extent of the movement-related activity in the deeper layers. An efferent mapping function specifies how the direction and the size of the movement contribution of each colliculus neuron depends on its location and its firing rate. The total saccade is the vector sum of the individual cell contributions. This very simple model (seven fixed parameters) has been used to simulate metrical properties of saccades: in response to visual targets; in response to electrical stimulation in one colliculus, and after a colliculus lesion. Model performance appears to be remarkably realistic but cannot account for some border effects and responses to double stimulation. Suggestions on how the model can be improved and extended will be presented.

Brain Mapping↗

PubMed2006

Equibiaxial strain stimulates fibroblastic phenotype shift in smooth muscle cells in an engineered tissue model of the aortic wall.

Many cells in the body reside in a complex three-dimensional (3D) environment stimulated by mechanical force. In vitro bioreactor systems have greatly improved our understanding of the mechanisms behind cell mechanotransduction. Current systems to impose strain in vitro are limited either by the lack of uniform strain profile or inability to strain 3D engineered tissues. In this study, we present a system capable of generating cyclic equibiaxial strain to an engineered vascular wall model. Type I collagen hydrogels populated with rat aortic smooth muscle cells (RASMCs) were created either as a compacting disk or constrained hemisphere. Both models were adhered to silicone membranes precoated with collagen I, fibronectin, or Cell-Tak and assayed for adhesion characteristics. The best performing model was then exposed to 48 h of 10% strain at 1Hz to simulate wall strain profiles found in vascular aneurysms, with static cultures serving as controls. The finite strain profile at the level of the membrane and the free surface of the construct was quantified using microbeads. The results indicate that the hemisphere model adhered with Cell-Tak had the most stable adhesion, followed by fibronectin and collagen I. Disk models did not adhere well under any coating condition. Uniform strain propagation was possible up to a maximum area strain of 20% with this system. RASMC responded to 10% equibiaxial strain by becoming less elongated, and immunohistochemistry suggested that stretched RASMC shifted to a more synthetic phenotype in comparison to static controls. These results suggest that equibiaxial strain may induce smooth muscle cell differentiation. We conclude that this system is effective in stimulating cells with cyclic equibiaxial strain in 3D cultures, and can be applied to a variety of biomaterial and tissue engineering applications.

Animals↗

PubMed2006

A multiple chemical equilibria approach to modeling and interpreting the separation of amino acid enantiomers by chiral ligand-exchange chromatography.

A model of chiral ligand-exchange chromatography (CLEC) is presented that combines the non-ideal equilibrium-dispersion equation for solute transport with equations describing all chemical equilibria within the column. The model connects elution band profiles to the time and space resolved formation of diastereomeric complexes in both the mobile and stationary phases, thereby providing insights into the overall separation mechanism. The stoichiometries and formation constants for all equilibrium complexes formed in the mobile phase are taken from standard thermodynamic databases and independent potentiometric titration experiments. Formation constants for complexes formed with the stationary phase ligand are determined from potentiometric titration data for a water-soluble analogue of the ligand. Together this set of pure thermodynamic parameters can be used to calculate the equilibrium composition of the system at any operating condition. The model includes a temperature-dependent pure-component parameter, determined by regression to a single elution band for the pure component, that corrects for subtle effects associated with immobilizing the ligand (i.e., the chiral selector) onto the stationary phase. Model performance is assessed through comparison with chromatograms for two hydrophobic amino acid racemates loaded on the Nucleosil Chiral-1 CLEC column. The model is also applied to a restricted optimization of column operating conditions to assess its predictive power. In both cases, model predictions compare well with experiment while also providing a molecular understanding of the separation process and its dependence on column operating conditions.

Amino Acids↗

PubMed2003

Multivariate calibration standardization across instruments for the determination of glucose by Fourier transform near-infrared spectrometry.

The transfer of multivariate calibration models is investigated between a primary (A) and two secondary Fourier transform near-infrared (near-IR) spectrometers (B, C). The application studied in this work is the use of bands in the near-IR combination region of 5000-4000 cm(-)(1) to determine physiological levels of glucose in a buffered aqueous matrix containing varying levels of alanine, ascorbate, lactate, triacetin, and urea. The three spectrometers are used to measure 80 samples produced through a randomized experimental design that minimizes correlations between the component concentrations and between the concentrations of glucose and water. Direct standardization (DS), piecewise direct standardization (PDS), and guided model reoptimization (GMR) are evaluated for use in transferring partial least-squares calibration models developed with the spectra of 64 samples from the primary instrument to the prediction of glucose concentrations in 16 prediction samples measured with each secondary spectrometer. The three algorithms are evaluated as a function of the number of standardization samples used in transferring the calibration models. Performance criteria for judging the success of the calibration transfer are established as the standard error of prediction (SEP) for internal calibration models built with the spectra of the 64 calibration samples collected with each secondary spectrometer. These SEP values are 1.51 and 1.14 mM for spectrometers B and C, respectively. When calibration standardization is applied, the GMR algorithm is observed to outperform DS and PDS. With spectrometer C, the calibration transfer is highly successful, producing an SEP value of 1.07 mM. However, an SEP of 2.96 mM indicates unsuccessful calibration standardization with spectrometer B. This failure is attributed to differences in the variance structure of the spectra collected with spectrometers A and B. Diagnostic procedures are presented for use with the GMR algorithm that forecasts the successful calibration transfer with spectrometer C and the unsatisfactory results with spectrometer B.

Algorithms↗

PubMed2004

Modeling topographic effects on net ecosystem productivity of boreal black spruce forests.

Current regional estimates of net primary productivity (NPP) of boreal black spruce overlook the large variation in NPP caused by small-scale topographic effects on soil water, temperature and nutrient availability. Topographic effects on black spruce NPP could likely be modeled by simulating the lateral and vertical movement of water, and its effects on soil nutrient transformation and uptake, through three-dimensional watersheds defined by aspects and slopes of their topographic positions. To examine this likelihood, the ecosystem model 'ecosys' was run for 120 years on a transect that included upper- and lower-slope positions and a basin in which a basal water table was set 0.5 m below the soil surface. For the run, we used soil properties and weather conditions recorded at the 115-year-old BOREAS Southern Old Black Spruce site. Short-term model performance was tested by comparing diurnal and annual carbon (C) transfers simulated under 1994 weather conditions during the 115th year of the model run with those measured at this site during 1994 by eddy covariance, surface chambers and allometry. After 115 years, annual spruce NPP simulated at the upper-slope positions was twice that at the basin (350 versus 170 g C m-2), whereas accumulated wood C was almost three times as large (6.8 versus 2.4 kg C m-2). In the model, increases in NPP and wood growth in upper-slope positions were caused by lower soil water contents, higher soil temperatures, and more rapid O2 uptake that accelerated heterotrophic respiration and hence nutrient mineralization and uptake. Modeled differences in wood growth with topographic position were quantitatively consistent with measurements of boreal black spruce at several research sites differing in water table depth. Modeled differences also agreed with differences in wood growth rates derived from allometric measurements at boreal black spruce sites differing in productivity indices as a result of differences in subsurface hydrology. The magnitude of these differences clearly indicates the importance of accounting for subsurface hydrology in regional estimates of boreal forest productivity.

Cell Respiration↗

PubMed2000

Higherorder neural network group models for financial simulation.

Real world financial data is often discontinuous and non-smooth. If we attempt to use neural networks to simulate such functions, then accuracy will be a problem. Neural network group models perform this function much better. Both Polynomial Higher Order Neural network Group (PHONG) and Trigonometric polynomial Higher Order Neural network Group (THONG) models are developed. These HONG models are open box, convergent models capable of approximating any kind of piecewise continuous function, to any degree of accuracy. Moreover they are capable of handling higher frequency, higher order non-linear and discontinuous data. Results obtained using a Higher Order Neural network Group financial simulator are presented, which confirm that HONG group models converge without difficulty, and are considerably more accurate than neural network models (more specifically, around twice as good for prediction, and a factor of four improvement in the case of simulation).

Computer Simulation↗

PubMed2006

Predicting outbreaks: a spatial risk assessment of West Nile virus in British Columbia.

BACKGROUND: West Nile virus (WNv) has recently emerged as a health threat to the North American population. After the initial disease outbreak in New York City in 1999, WNv has spread widely and quickly across North America to every contiguous American state and Canadian province, with the exceptions of British Columbia (BC), Prince Edward Island and Newfoundland. In this study we develop models of mosquito population dynamics for Culex tarsalis and C. pipiens, and create a spatial risk assessment of WNv prior to its arrival in BC by creating a raster-based mosquito abundance model using basic geographic and temperature data. Among the parameters included in the model are spatial factors determined from the locations of BC Centre for Disease Control mosquito traps (e.g., distance of the trap from the closest wetland or lake), while other parameters were obtained from the literature. Factors not considered in the current assessment but which could influence the results are also discussed. RESULTS: Since the model performs much better for C. tarsalis than for C. pipiens, the risk assessment is carried out using the output of C. tarsalis model. The result of the spatially-explicit mosquito abundance model indicates that the Okanagan Valley, the Thompson Region, Greater Vancouver, the Fraser Valley and southeastern Vancouver Island have the highest potential abundance of the mosquitoes. After including human population data, Greater Vancouver, due to its high population density, increases in significance relative to the other areas. CONCLUSION: Creating a raster-based mosquito abundance map enabled us to quantitatively evaluate WNv risk throughout BC and to identify the areas of greatest potential risk, prior to WNv introduction. In producing the map important gaps in our knowledge related to mosquito ecology in BC were identified, as well, it became evident that increased efforts in bird and mosquito surveillance are required if more accurate models and maps are to be produced. Access to real time climatic data is the key for developing a real time early warning system for forecasting vector borne disease outbreaks, while including social factors is important when producing a detailed assessment in urban areas.

Animals↗

PubMedDate not supplied

The pay-for-performance dilemma.

Who was it that talked about how to ruin motivation with pay? Frederick S. Hills digs hard into pay-for-performance programs in a striking excavation of the reasons why pay does not motivate performance. He examines the problematic effects of equity in pay (in terms of both internal wage structure and market surveys), the inflation factor, problems with the basic performance model, and the system of administration of merit pay systems in terms of the salaries of two hypothetical supervisors--one a high performer, the other a minimal performer. And while he raises a number of questions, he also leaves us with possible answers--which we can explore for ourselves. One result may well be significant changes in how organizations view their pay systems--and another may be the development of new approaches. One frequently recommended approach Hills espouses is the use of annual merit bonuses rather than annual merit increases. This way merit dollars can be made more effective.

Employee Incentive Plans↗

PubMed1975

Cost-performance analysis of cataloging and card production in a medical center library.

The unit cost of cataloging current English language monographs was studies and compared with the cost of purchasing catalog cards from a commercial source. Two hypotheses were proposed: (1) in-library costs for cataloging and card production are higher than those for the purchased-card method; (2) throughput time is faster for the in-library method. In addition, the data can be used to develop an analytical cost-performance model for administrative purposes. The data presented support the hypotheses. The model developed provides a mechanism for arriving at a cost for different levels of service and can be used to measure the performance of other alternative methods of cataloging. Implications for the use of CATLINE are discussed and suggestions for further studies are described.

Cataloging↗

PubMed1997

Lateralized spatial strategies in oscillating drawing movements.

Kinematic characteristics and lateral differences between two upper extremities were investigated in a unimanual graphic task involving fast and precise oscillating movements on the vertical plane. The spatial locations of sequential reversal points were used to calculate the pairs of angles, relative to the horizontal axis. The point biserial coefficient of correlation was used to analyze the difference between big and large angles and their sequence in each pair. Three main groups (A, B, and C) of performance models were distinguished in 132 tests by 33 strongly right-handed male subjects. Group A showed strong variation in vertical movement, Group B covariation in vertical and horizontal vectors, while Group C reflected independent variation of both vertical and horizontal directions. It is suggested that the movement strategies might reflect three different models of motor control involving coupling of an oscillator controlling pools of motoneurons which regulates horizontal movements with an oscillator controlling vertical movement (Groups A + B) or with nonoscillating control signal (Group B). It is argued that Group A represents the simplest strategy and only performance Type A met by the left hand.

Adult↗

PubMed2025

Opportunities for machine learning to predict cross-neutralization in FMDV serotype O.

Accurately estimating cross-neutralization between serotype O foot-and-mouth disease viruses (FMDVs) is critical for guiding vaccine selection and disease management. In this study, we developed a machine learning approach to estimate r1 values-an established measure of antigenic similarity-using VP1 sequence data and published virus neutralization titer (VNT) results. Our dataset comprised 108 serum-virus pairs representing 73 distinct FMDV strains. We applied Boruta feature selection and random forest classifiers, optimizing model performance through tenfold cross-validation and sub-sampling to address class imbalance. Predictors included pairwise amino acid distances, site-specific polymorphisms, and differences in potential N-glycosylation sites. Using a 0.3 r1 threshold to define cross-neutralization, the final model achieved high accuracy (0.96), sensitivity (0.93), and specificity (0.96) in training, and performed robustly on independent test sets - accuracy was 0.75 (95% CI 0.60 and 0.90), F1 score 0.86% and PPV 0.77. Importantly, key VP1 residues-positions 48, 100, 135, 150, and 151-emerged as strong predictors of antigenic relationships. Our results demonstrate the utility of integrating routinely generated genomic data with machine learning to inform vaccine candidate selection and anticipate immune interactions among circulating FMDV strains. This approach offers a practical tool for accelerating vaccine decision-making and can be adapted to other FMDV serotypes. The latest version of the r1 predictive model is available for access via a Shiny dashboard (https://dmakau.shinyapps.io/PredImmune-FMD/).

Foot-and-Mouth Disease Virus↗

PubMed1998

Time series forecasts of ambulance run volume.

To test the hypothesis that time series analysis can provide accurate predictions of future ambulance service run volume, a prospective stochastic time series modeling study was conducted at a community-based regional ambulance service. For all requests for ambulance transport during two sequential years, the time and date, total run time, and acuity code of the run were recorded in a computer database. Time series variables were formed for ambulance service runs per hour, total run time, and acuity. Prediction models were developed from one complete year's data (1994) and included four model types: raw observations, moving average, means with moving average smoothing, and autoregressive integrated moving average. Forecasts from each model were tested against observations from the first 24 weeks of the subsequent year (1995). Each model's adequacy was tested on residuals by autocorrelation functions, integrated periodograms, linear regression, and differences among the variances. A total of 68,433 patients were seen in 1994 and 32,783 in the first 24 weeks of 1995. Large periodic variations in run volume with time of day were found (P < .001). A model based on arithmetic means of each hour of the week with 3-point moving average smoothing yielded the most accurate forecasts and explained 54.3% of the variation observed in the 1995 test series (P < .001). Time series analysis can provide powerful, accurate short-range forecasts of future ambulance service run volume. Simpler, less expensive models performed best in this study.

Ambulances↗

PubMed2026

Benchmarking large language models for extracting biobank-derived insights into health and disease.

Biobank-scale datasets such as the UK Biobank have become foundational resources for advancing biomedical discovery. Yet the complexity and heterogeneity of these resources, spanning genomics, imaging, clinical records, and metadata, pose substantial barriers to access and interpretation. Large Language Models (LLMs) offer a promising avenue for making such datasets more navigable through natural language interfaces. However, the extent to which current general-purpose LLMs can retrieve and synthesize biobank-specific insights has not yet been systematically evaluated. In this study, we present a reproducible, multi-metric evaluation framework to benchmark the capabilities of leading LLMs. We evaluated six leading large language models: Gemini 3 Pro, Claude Opus 4.5, Claude Sonnet 4.5, GPT-5.2, Mistral Large 2, and DeepSeek V3, on four benchmark tasks designed to assess biobank-related knowledge retrieval. We evaluate model performance across six dimensions (semantic accuracy, factual correctness, domain knowledge, reasoning quality, response depth, and biobank specificity) and assessed output consistency using curated UK Biobank references and a robust random baseline. All models outperformed the baseline by 2× to 3× , with strong statistical separation (p < 0.001), confirming meaningful biobank-specific knowledge retrieval. Gemini 3 Pro achieved the highest overall accuracy across tasks such as keyword synthesis, institution recognition, and topic inference, while Claude Sonnet 4.5 demonstrated the most uniform performance across evaluation dimensions. Our benchmark provides a rigorous framework for evaluating LLMs in biomedical settings. Using the UK Biobank as a real-world testbed, we highlight both the capabilities and limitations of current models, measuring their capacity to recall structured biomedical knowledge consistent with authoritative biobank metadata.

Large Language Models↗