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PubMedDate not supplied

Noninvasive determination of the location and distribution of DNAPL using advanced seismic reflection techniques.

Recent advances in seismic reflection amplitude analysis (e.g., amplitude versus offset-AVO, bright spot mapping) technology to directly detect the presence of subsurface DNAPL (e.g., CCl4) were applied to 216-Z-9 crib, 200 West Area, DOE Hanford Site, Washington. Modeling to determine what type of anomaly might be present was performed. Model results were incorporated in the interpretation of the seismic data to determine the location of any seismic amplitude anomalies associated with the presence of high concentrations of CCl4. Seismic reflection profiles were collected and analyzed for the presence of DNAPL. Structure contour maps of the contact between the Hanford fine unit and the Plio/Pleistocene unit and between the Plio/Pleistocene unit and the caliche layer were interpreted to determine potential DNAPL flow direction. Models indicate that the contact between the Plio/Pleistocene unit and the caliche should have a positive reflection coefficient. When high concentrations of CCl4 are present, the reflection coefficient of this interface displays a noticeable positive increase in the seismic amplitude (i.e., bright spot). Amplitude data contoured on the Plio/Pleistocene-caliche boundary display high values indicating the presence of DNAPL to the north and east of the crib area. The seismic data agree well with the well control in areas of high concentrations of CCl4.

Carbon Tetrachloride↗

PubMed2006

Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants.

BACKGROUND: Approximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census. METHODS: A five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN. RESULTS: CEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0-1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool. CONCLUSION: State-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide.

Birth Weight↗

PubMed2026

Machine learning-integrated multi-omics risk prediction for pulmonary fungal infection in COPD and lung cancer: a transcriptomic and immune profiling study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) and lung cancer are major risk factors for invasive pulmonary fungal infection (IPFI), carrying an attributable mortality of 30%-80%. Their coexistence further amplifies immunosuppression, while current diagnostic criteria remain inadequate for early risk identification. METHODS: Transcriptomic data from the GEO dataset GSE296912 (scRNA-seq; 12,078 cells from normal and COPD lung tissue) and The Cancer Genome Atlas (TCGA)-lung adenocarcinoma (LUAD) bulk RNA-seq cohort (539 tumor and 59 normal samples) underwent differential expression and cross-omics integration analysis. Five machine learning models were constructed: logistic regression, SVM, random forest, XGBoost, and LASSO. Candidate genes were validated by qRT-PCR in A549 cells and THP-1-derived macrophages stimulated with heat-inactivated Aspergillus fumigatus conidia, a protocol selected to ensure BSL-2 biosafety compliance and isolate PAMP-mediated innate immune signaling. Model performance was evaluated using 5-fold stratified cross-validation with AUC, calibration curves, and decision curve analysis. RESULTS: Single-cell transcriptomic analysis of 12,078 cells identified 14 distinct cell populations, with marked myeloid expansion and immune dysregulation in COPD lung tissue. Cross-omics integration with TCGA-LUAD data identified 1,145 shared genes (79 immune-related), converging on NF-κB, TLR4, and cytokine receptor signaling. The random forest model achieved excellent discriminative performance (5-fold CV AUC = 0.988), with Treg infiltration, TLR4, and MMP9 as the top predictors. qRT-PCR confirmed significant upregulation of all five candidate genes (DEFB4A, S100A8, IL-8, MMP9, and TLR4) in both A549 and THP-1 cells following fungal stimulation. CONCLUSION: This multi-omics machine learning model integrating scRNA-seq and TCGA transcriptomic data demonstrates excellent discriminative performance (AUC = 0.988), with mechanistic convergence of NF-κB, TLR4, and oncogenic signaling pathways identified across shared immune gene signatures. In vitro qRT-PCR validation confirms the biological relevance of five key antifungal immune genes, providing a transcriptomic foundation for future prospective IPFI risk stratification in patients with COPD and lung cancer.

TLR4↗

PubMed2005

[Investigating the anti-inflammatory effect of dexamethasone in an asthma mouse model].

We performed an asthma mice model in this study and aimed to investigate the levels of mediators in bronchoalveolar lavage fluid (BALF), and lung tissue, and the pathological changes response to the steroid treatment. BALB/c mice divided into three groups. PBS was applied to group 1 (control group). Asthma model was performed by exposing to ovalbumin in group 2 and 3. DEX was injected to group 3. After the last DEX dose all of the mice were killed by cervical dislocation. The samples of BALF and lung tissue were obtained. IL-4 and IL-5 levels of all samples were measured and inflammatory cells were counted in BALF. Evident eosinophilia was determined in BALF of group 2. Eosinophil numbers were lower in group 3 when compared with group 2 and this was statistically significant (p< 0.001). Inflammatory cell infiltration, eodema and hyperemia observed around the walls of bronchus and bronchiols in group 2. The lungs of group 3 had normal histological appearance. Both two cytokin levels of lung tissue were higher in group 2 than group 1, and this was statistically significant (for IL-4 p< 0.003, and for IL-5 p< 0.002). In group 3, both two cytokin levels were statistically lower than group 2 (for IL-4 p< 0.001, and for IL-5 p< 0.026). In BALF samples both two cytokin levels were higher in group 2 than group 1, and this was statistically significant (for IL-4 p< 0.004, and for IL-5 p< 0.001). In group 3, both two cytokin levels were lower than group 2, but it was not statistically significant (p> 0.05). In conclusion, it is thought that antiinflammatory effect of glucocorticoids occur by inhibiting the formation of IL-4, IL-5 and eosinophils.

Animals↗

PubMed1998

Explorations of Cohen, Dunbar, and McClelland's (1990) connectionist model of Stroop performance.

The J. D. Cohen, K. Dunbar, and J. L. McClelland (1990) model of Stroop task performance is used to model data from a study by D. H. Spielder, D. A. Balota, and M. E. Faust (1996). The results indicate that the model fails to capture overall differences between word reading and color naming latencies when set size is increased beyond 2 response alternatives. Further empirical evidence is presented that suggests that the influence of increasing response set size in Stroop task performance is to increase the difference between overall color naming and word reading, which is in direct opposition to the decrease produced by the Cohen et al. architecture. Although the Cohen et al. model provides a useful description of meaning-level interference effects, the qualitative differences between word reading and color naming preclude a model that uses identical architectures for each process, such as that of Cohen et al., to fully capture performance in the Stroop task.

Aged↗

PubMed2002

A methodology to explain neural network classification.

Neural networks are still frustrating tools in the data mining arsenal. They exhibit excellent modelling performance, but do not give a clue about the structure of their models. We propose a methodology to explain the classification obtained by a multilayer perceptron. We introduce the concept of 'causal importance' and define a saliency measurement allowing the selection of relevant variables. Once the model is trained with the relevant variables only, we define a clustering of the data built from the hidden layer representation. Combining the saliency and the causal importance on a cluster by cluster basis allows an interpretation of the neural network classifier to be built. We illustrate the performances of this methodology on three benchmark datasets.

Classification↗

PubMed2002

Human performance cognitive-behavioral modeling: a benefit for occupational safety.

Human Performance Modeling (HPM) is a computer-aided job analysis software methodology used to generate predictions of complex human-automation integration and system flow patterns with the goal of improving operator and system safety. The use of HPM tools has recently been increasing due to reductions in computational cost, augmentations in the tools' fidelity, and usefulness in the generated output. An examination of an Air Man-machine Integration Design and Analysis System (Air MIDAS) model evaluating complex human-automation integration currently underway at NASA Ames Research Center will highlight the importance to occupational safety of considering both cognitive and physical aspects of performance when researching human error.

Computer Simulation↗

PubMed1976

Verbalization in EMR children's observational learning.

The effect of descriptive verbalization during observation of a model on mentally retarded boys' retention for what they had observed was examined. Forty 9- to 12-year-old boys in public-school EMR classes were grouped on the basis of relatively high or low IQ scores. One-half of each group observed a videotaped model perform a series of novel acts, while in addition to viewing the tape, the other half described the model's actions. Observational learning was immediately tested through a set of prompts for imitation, with prizes offered commensurate with level of performance. Regardless of IQ group, the boys who were required to verbalize the model's behavior were able to imitate it significantly better than boys who merely watched the model; high and low IQ groups did not significantly differ in observational learning. Further directions for research on mentally retarded children's observational learning were suggested.

Attention↗

PubMed2004

Artificial neural networks for adolescent marijuana use and clinical features of marijuana dependence.

This article compares the performance of multiple logistic regression (MLR) with feed-forward, artificial neural network (ANN) models for the assessment of adolescent marijuana use and clinical features of dependence based on self-evaluation from recent National Household Surveys on Drug Abuse (NHSDA). The effect of training and testing the neural networks with randomly selected data was compared to data selected as a function of survey year. The technical aim of the study was to account for adolescent marijuana use and features of marijuana dependence based on experiences with alcohol and tobacco. Similarities observed in MLR and ANN model performance may indicate no major complex or nonlinear relationships in cross-sectional epidemiological data selected to model adolescent drug use and dependence in this specific application. We concluded that ANNs should be further studied in future longitudinal research, perhaps with modeling of recursive networks, allowing feedback from drug dependence to levels of marijuana use. The ANN models also have the potential to model drug use and dependence based on input parameters with no obvious direct link to drug involvement--e.g., polymorphisms associated with "openness to experience" or other personality traits hypothesized to function as distal antecedents, and could thus be implemented to identify higher risk youths using assessments indirectly related or nonlinearly associated to adolescent drug use and dependence but less sensitive to survey-related response tendencies.

Adolescent↗

PubMed2005

A support vector machine approach to classify human cytochrome P450 3A4 inhibitors.

The cytochrome P450 (CYP) enzyme superfamily plays a major role in the metabolism of commercially available drugs. Inhibition of these enzymes by a drug may result in a plasma level increase of another drug, thus leading to unwanted drug-drug interactions when two or more drugs are coadministered. Therefore, fast and reliable in silico methods predicting CYP inhibition from calculated molecular properties are an important tool which can be applied to assess both already synthesized as well as virtual compounds. We have studied the performance of support vector machines (SVMs) to classify compounds according to their potency to inhibit CYP3A4. The data set for model generation consists of more than 1300 structural diverse drug-like research molecules which were divided into training and test sets. The predictive power of SVMs crucially depends on a careful selection of parameters specifying the kernel function and the penalty for misclassifications. In this study we have investigated a procedure to identify a valid set of SVM parameters which is based on a sampling of the parameter space on a regular grid. From this set of parameters, either single SVMs or SVM committees were trained to distinguish between strong and weak inhibitors or to achieve a more realistic three-class assignment, with one class representing medium inhibitors. This workflow was studied for several kernel functions and descriptor sets. All SVM models performed significantly better than PLS-DA models which were generated from the corresponding descriptor sets. As a very promising result, simple two-dimensional (2D) descriptors yield a three-class model which correctly classifies more than 70% of the test set. Our work illustrates that SVMs used in combination with simple 2D descriptors provide a very effective and reliable tool which allows a fast assessment of CYP3A4 inhibition potency in an early in silico filtering process.

Computer-Aided Design↗

PubMed2001

Comparison of the performance of two comorbidity measures, with and without information from prior hospitalizations.

OBJECTIVES: This study compares the performance of two comorbidity risk adjustment methods (the Deyo et al adaptation of the Charlson index and the Elixhauser et al method) in five groups of California hospital patients with common reasons for hospitalization, and assesses the contribution to model performance made by information drawn from prior hospital admissions. METHODS: California hospital discharge abstract data for the calendar years 1994 through 1997 were used to create a longitudinal data set for patients in the five disease groups. Eleven logistic regression models were estimated to predict the risk of in-hospital death for patients in each group, with both comorbidity risk adjustment methods applied to patient information available from only the index hospitalization, and to information available from both the index and prior hospitalizations. RESULTS: For every comparison made, the level of statistical performance (area under the receiver operating characteristics curve) demonstrated by models using the Elixhauser et al method was superior to that of models using the Deyo et al adaptation method. Although most patients have information available from prior hospital admissions, this additional information yields only small improvements in the performance of models using either comorbidity risk adjustment method. CONCLUSIONS: Better discrimination is achieved with the Elixhauser et al method using only information from the index hospitalization than is achieved with the Deyo et al adaptation using information from all identified hospital admissions. Both comorbidity risk adjustment methods achieve their best performance when information from the index hospitalization and prior admissions is separated into independent indicators of comorbid illness.

Adult↗

PubMed2001

An evaluation of the predictive performance of distributional models for flora and fauna in north-east New South Wales.

To use models of species distributions effectively in conservation planning, it is important to determine the predictive accuracy of such models. Extensive modelling of the distribution of vascular plant and vertebrate fauna species within north-east New South Wales has been undertaken by linking field survey data to environmental and geographical predictors using logistic regression. These models have been used in the development of a comprehensive and adequate reserve system within the region. We evaluate the predictive accuracy of models for 153 small reptile, arboreal marsupial, diurnal bird and vascular plant species for which independent evaluation data were available. The predictive performance of each model was evaluated using the relative operating characteristic curve to measure discrimination capacity. Good discrimination ability implies that a model's predictions provide an acceptable index of species occurrence. The discrimination capacity of 89% of the models was significantly better than random, with 70% of the models providing high levels of discrimination. Predictions generated by this type of modelling therefore provide a reasonably sound basis for regional conservation planning. The discrimination ability of models was highest for the less mobile biological groups, particularly the vascular plants and small reptiles. In the case of diurnal birds, poor performing models tended to be for species which occur mainly within specific habitats not well sampled by either the model development or evaluation data, highly mobile species, species that are locally nomadic or those that display very broad habitat requirements. Particular care needs to be exercised when employing models for these types of species in conservation planning.

Animal Population Groups↗

PubMed2006

A comparative analysis of three self-balancing wheelchair balancing mechanisms.

In the last 20 years, three different basic, dynamic balancing designs have been proposed for a self-balancing wheelchair (SBW) that allows the wheelchair user to transition from driving on all four wheels to driving while balanced on the two large rear wheels. The dynamic performance of these three SBW designs, the hanging pendulum counterweight (HPC), the single inverted pendulum (SIP), and the double inverted pendulum (DIP), are compared when controlled by a common state space controller. The four dynamic performance considerations of stability, driver dynamic stress, maneuverability and technical requirements were used to compare these designs while performing the following five tests: 1) transition from four-wheel to two-wheel, balancing mode; 2) stationary, self-balancing stability when subjected to an impact disturbance; 3) movement initiation, and stopping while balancing; 4) response to impact disturbances while moving; and 5) stability on low traction surfaces. In addition, the movement initiation and stopping test was repeated with increased chair mass and inertia to investigate the sensitivity of model performance to changes in model parameters. After comparing the three models it was determined that the HPC mechanism is the best choice for further development based on the criteria of stability, driver dynamic stress, maneuverability, and technical requirements. The HPC ranked equal or better compared to the SIP and DIP on 15 of 29 stability and performance factors. It was also the only design that was stable for all normally expected driving conditions.

Acceleration↗

PubMed2000

Prognostic models based on literature and individual patient data in logistic regression analysis.

Prognostic models can be developed with multiple regression analysis of a data set containing individual patient data. Often this data set is relatively small, while previously published studies present results for larger numbers of patients. We describe a method to combine univariable regression results from the medical literature with univariable and multivariable results from the data set containing individual patient data. This 'adaptation method' exploits the generally strong correlation between univariable and multivariable regression coefficients. The method is illustrated with several logistic regression models to predict 30-day mortality in patients with acute myocardial infarction. The regression coefficients showed considerably less variability when estimated with the adaptation method, compared to standard maximum likelihood estimates. Also, model performance, as distinguished in calibration and discrimination, improved clearly when compared to models including shrunk or penalized estimates. We conclude that prognostic models may benefit substantially from explicit incorporation of literature data.

Age Factors↗

PubMed2004

Hybrid process models for process optimisation, monitoring and control.

Hybrid models aim to describe different components of a process in different ways. This makes sense when the corresponding knowledge to be represented is different as well. In this way, the most efficient representations can be chosen and, thus, the model performance can be increased significantly. From the various possible variants of hybrid model, three are selected which were applied for important biotechnical processes, two of them from existing production processes. The examples show that hybrid models are powerful tools for process optimisation, monitoring and control.

Algorithms↗

PubMed2004

Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes.

PURPOSE: To compare three liver models (well-stirred, parallel tube, and dispersion) for the prediction of in vivo intrinsic clearance (CL(int)), hepatic clearance (CLh). and hepatic availability (Fh) of a wide range of drugs in the rat using in vitro data from two in vitro sources. METHODS: In vitro CL(int) was obtained from studies using isolated rat hepatocytes (35 drugs) or rat liver microsomes (52 drugs) and used to predict in vivo CL(int) using reported scaling factors, and subsequently CLh and Fh were predicted based on the three liver models. In addition, in vivo CL(int) values were calculated from the reported values of CLh based on each of the three models. RESULTS: For all of the parameters, predictions from hepatocyte data were consistently more accurate than those from microsomal data. Comparison of in vitro and in vivo CL(int) values demonstrated that the dispersion model and the parallel tube model were comparable and more accurate (less bias, more precise) than the well-stirred model. For CLh and Fh prediction, the three models performed similarly. CONCLUSIONS: Considering the statistics of the predictions for three liver models, the use of parallel tube model is recommended for the evaluation of in vitro CL(int) values both from microsomes and hepatocytes. However, for the prediction of the in vivo drug (hepatic) clearance from in vitro data, as there are minimal differences between the models, the use of the well-stirred liver model is recommended.

Algorithms↗

PubMed1993

Modelling the growth kinetics of Phanerochaete chrysosporium in submerged static culture.

The potential commercial application of Phanerochaete chrysosporium requires methods for quantitatively predicting growth and substrate utilization. The growth kinetics of P. chrysosporium INA-12 (CNCM I-398) were investigated and modelled under nonlimiting nitrogen and carbon conditions in submerged static culture. This strain, unlike other strains, does not require nutrient limitation for induction of lignin peroxidase. Maximum levels of lignin peroxidase activity were reached 7 days after culture initiation, when almost 80% of the initial glycerol and 70% of the initial nitrogen were still present. Lignin peroxidase levels then decreased, while biomass levels increased until about day 14. The ratio of cell dry weight to wet weight was constant until the maximum biomass concentration was achieved, after which there was a decrease in the water content. The change in this ratio reflects cell lysis as it correlated with increased concentrations of nitrogen in the media, arising from cell leakage. The suitability of four growth models to predict growth, and in some cases glycerol consumption, was evaluated. A simple linear model and the Emerson model performed poorly for the early stages of growth, while a modified Williams model and the Monod model predicted substrate and biomass concentrations equally well. All models will predict biomass concentrations during the active growth phase, but they should not be used to predict biomass concentrations after the stationary growth phase, when cell lysis becomes significant.

Basidiomycota↗

PubMed2004

Predicting functional outcome in acute stroke: comparison of a simple six variable model with other predictive systems and informal clinical prediction.

BACKGROUND: Statistical models that predict functional outcome after stroke using six simple variables (SSV) have recently been developed and validated. OBJECTIVE: To compare the accuracy of these models with other simple ways of predicting outcome soon after stroke. METHODS: The SSV model for being alive and independent (modified Rankin score < or =2) six months or one year after stroke was compared with predictions based on a model that included only age and Oxford community stroke project classification, with predictions based on conscious level and urinary continence, and with informal clinical predictions made by clinicians interested in stroke. Predictions were compared in an independent hospital based cohort of stroke patients using receiver operator characteristic (ROC) curves. RESULTS: The SSV model at six months had a significantly greater area under the curve (0.84) than the model with only age and stroke classification (0.75). Predictions based on conscious level and urinary continence were no better than those of the SSV model and were unable to predict subjects with a high probability of good outcome. The sensitivity and specificity for informal clinical predictions at one year lay on or below the SSV model curve, implying that the SSV model was at least as good as clinical predictions. CONCLUSIONS: The SSV models performed as well as or better than other simple predictive systems. These models will be useful in epidemiological studies but should not be used to guide clinical management until their impact on patient care and outcome has been evaluated.

Activities of Daily Living↗