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PubMed2026

Dissecting genetic variance structure and evaluating genomic prediction models for single-cross hybrids derived from Stiff Stalk and Non-Stiff Stalk maize heterotic groups.

The early 20th-century discovery of heterosis and the establishment of heterotic groups transformed maize (Zea mays L.) into a keystone of global agriculture. However, maize breeding faces two significant challenges: the gradual decline of general combining ability (GCA) variance within heterotic groups and the impracticality of testing all possible single crosses in the early stages of a breeding program. Here, we developed genomic best linear unbiased prediction (GBLUP)-based multikernel models, using additive and two alternative nonadditive genomic relationship matrices, to estimate the variance components associated with the general combining ability of Stiff Stalk (SS) and Non-Stiff Stalk (NSS) heterotic groups and the specific combining ability arising from their crosses. We further applied these models to predict the performance of untested single-cross combinations under varying levels of parental information. We showed that the SS and NSS groups retained significant GCA variance across traits in both early- and late-maturity groups. The SS group, in contrast, exhibited no detectable GCA variance in grain yield for the intermediate-flowering subset of hybrids, highlighting a limitation for future genetic improvement. Furthermore, our results showed that GBLUP-based multikernel models effectively identified superior hybrids when parental information was available. In the absence of this information, however, these models underperformed compared to covariance-based approaches. Both nonadditive matrices yielded similar results, indicating that they capture comparable genetic relationship patterns despite their distinct formulations. Overall, this study sheds light on the future use of US maize commercial germplasm and demonstrates how GBLUP-based multikernel models can improve the efficiency of hybrid breeding programs.

Zea mays↗
PubMed1978

A predictive model for visual recovery following retinal detachment surgery.

By multiple regression analysis we have identified 26 out of 200 observations which significantly affect the visual acuity following retinal detachment surgery. In addition, we have developed a highly significant mathematical model, which is able to predict in rather broad ranges of visual acuity to approximately 67% accuracy. There is still a large percentage of patients for whom we cannot account for the variability in final vision, a problem requiring future investigation. Potentially important factors which were not analyzed in this study include duration of macular detachment, afferent pupil defect, drainage of subretinal fluid, extent of the scleral-buckling procedure, and postoperative follow-up longer than six months. While most of the variables are fixed and cannot be alterd, such as age, senile cataract, and refractive error, improved knowledge of influential factors may allow us to manipulate some of them and provide mechanisms for improving results in recovery or maintenance of macular function after retinal detachment surgery.

Adult↗
PubMed1987

Performance of dust respirators with facial seal leaks: II. Predictive model.

A performance model for half-mask and single-use respirators is presented. It represents a possible alternative to field measurements of respirator performance. Experimental data on filter and leak performance given in Part I were used to develop a model that allows one to predict 1) the overall respirator penetration as a function of particle size for any work rate and 2) overall total mass penetration for any work rate and exposure aerosol-size distribution for a known respirator filter and facial seal leak condition. A simplified method based on general regression equations is presented that allows one to estimate these quantities based on QNFT (quantitative fit testing) measurements and a knowledge of the exposure aerosol-size distribution. Example calculations are given for a situation in which QNFT gives a fit factor of 50 for a half-mask with dust, fume and mist filter cartridges, but predicted protection factors for various use conditions range from 20 to 81 depending on exposure particle-size distribution and work rate of the wearer.

Aerosols↗
PubMed1986

Prediction modeling of physiological responses and human performance in the heat.

Over the last two decades, our laboratory has been establishing the data base and developing a series of predictive equations for deep body temperature, heart rate and sweat loss responses of clothed soldiers performing physical work at various environmental extremes. Individual predictive equations for rectal temperature, heart rate and sweat loss as a function of the physical work intensity, environmental conditions and particular clothing ensemble have been published in the open literature. In addition, important modifying factors such as energy expenditure, state of heat acclimation and solar heat load have been evaluated and appropriate predictive equations developed. Currently, we have developed a comprehensive model which is programmed on a Hewlett-Packard 41 CV hand held calculator. The primary physiological inputs are deep body (rectal) temperature and sweat loss while the predicted outputs are the expected physical work--rest cycle, the maximum single physical work time if appropriate, and the associated water requirements. This paper presents the mathematical basis employed in the development of the various individual predictive equations of our heat stress model. In addition, our current heat stress prediction model as programmed on the HP 41 CV is discussed from the standpoint of propriety in meeting the Army's needs and therefore assisting in military mission accomplishment.

Acclimatization↗
PubMed1979

Prediction models in small area estimation.

Finite population estimation problems are formulated as prediction problems under superpopulation models. For linear regression models, a general theorem on optimal linear estimation is presented. The theorem is applied to simple cross-classification models to generate and analyze various statistics for estimating small area totals. These statistics include the synthetic and composite estimators, as well as some interesting alternatives.

Humans↗
PubMed1991

Classification tree prediction models for dental caries from clinical, microbiological, and interview data.

Caries prediction by Classification And Regression Tree (CART) analysis is an appropriate and powerful alternative or complement to the commonly used classification methods of logistic regression and discriminant analysis, both parametric and nonparametric. The binary classification tree method discussed in this article is designed for complex data and does not require assumptions about the predictor variables or about the presence or absence of interactions among the predictor variables. Furthermore, the results give insight into the structures and interactions in the data and are easy to interpret and apply. In preliminary applications of the CART algorithms to data from The University of North Carolina Caries Risk Assessment Study, the method produced prediction rules having sensitivities and specificities that were similar to or slightly better than those associated with logistic and discriminant analyses. The classification trees constructed tended to involve far fewer predictor variables than required for adequate logistic and discriminant models. For example, for first-grade children in Aiken, South Carolina, nine variables were used to define a prediction rule having 64% sensitivity and 86% specificity. Ten-fold cross-validation estimates for future data were 58% and 79%, respectively. For first-grade children in Portland, Maine, two variables were used to define a prediction rule having 62% sensitivity and 77% specificity. The cross-validation estimates for future data were 58% and 78%, respectively. A brief, and previously unavailable, explanation of the CART method is given for the special case of a dichotomous outcome variable.

Child↗
PubMed1986

Predictive models for human glucose-6-phosphate dehydrogenase deficiency.

The present paper has discussed available test systems for determination of the response of G-6-PD-deficient human erythrocytes to environmental agents. The limitations and advantages of each model have been examined, and the results of research using each model have been presented. The future development of suitable animal models or in vitro test systems may rely on advances in fields such as genetics and biochemistry. Genetic engineering may allow researchers to develop cells with a genetic deficiency of G-6-PD. These deficient cells could then be used to simulate human G-6-PD-deficient erythrocyte responses to various agents. Advances in biochemistry, in areas such as metabolism and enzymology, may also have an impact on future test systems. Due to the fact that present model systems are limited and their predictions often unreliable, the establishment of safe environmental health standards will depend upon advances in modern science and the converging of developments from various disciplines.

Animals↗
PubMed1987

Analysis of the clinical findings used to diagnose coliform mastitis in dairy cows, and comparison to a prediction model.

Logistic regression was used to analyze the clinical findings (attributes) which predicted coliform mastitis in 113 dairy cattle, 36 of which had coliforms cultured from milk. Weakness of the cow, swelling of the udder, decreased body temperature and watery consistency of the milk were selected for the model. An analysis was then done to find the attributes which clinicians used when predicting that a cow would have a coliform cultured. Clinicians appeared to use water consistency of the milk, shivering, firmness of the udder, pulse rate, elevated body temperature, and respiratory rate. In a final analysis the clinicians' predictions were forced into the model to determine which attributes might be used by clinicians to increase diagnostic accuracy. Inclusion of weakness of the cow, swelling of the udder, decreased temperature of the cow, and duration of mastitis of less than 24 hours increased accuracy over clinical prediction alone. Accuracy of cowside diagnosis might be increased if more attention were paid to these attributes when making a diagnosis of coliform mastitis.

Animals↗
PubMed1989

Model predicting survival in stage I melanoma based on tumor progression.

We used the lesional steps in tumor progression and multivariable logistic regression to develop a prognostic model for primary, clinical stage I cutaneous melanoma. This model is 89% accurate in predicting survival. Using histologic criteria, we assigned melanomas to tumor progression steps by ascertaining their particular growth phase. These phases were the in situ and invasive radial growth phase and the vertical growth phase (the focal formation of a dermal tumor nodule or dermal tumor plaque within the radial growth phase or such dermal growth without an evident radial growth phase). After a minimum follow-up of 100.6 months and a median follow-up of 150.2 months, 122 invasive radial-growth-phase tumors were found to be without metastases. Eight-year survival among the 264 patients whose tumors had entered the vertical growth phase was 71.2%. Survival prediction in these patients was enhanced by the use of a multivariable logistic regression model. Twenty-three attributes were tested for entry into this model. Six had independently predictive prognostic information: (a) mitotic rate per square millimeter, (b) tumor-infiltrating lymphocytes, (c) tumor thickness, (d) anatomic site of primary melanoma, (e) sex of the patient, and (f) histologic regression. When mitotic rate per square millimeter, tumor-infiltrating lymphocytes, primary site, sex, and histologic regression are added to a logistic regression model containing tumor thickness alone, they are independent predictors of 8-year survival (P less than .0005).

Female↗
PubMed2026

Species distribution models predict genome-wide polymorphism and gene flow in an endangered amphibian.

Species distribution models (SDMs) are widely used to predict habitat suitability but their usefulness and accuracy for inferring population health is still debated. Here, we evaluate whether SDM-derived relative habitat suitability (RHS) predicts genome-wide genetic diversity and connectivity-which are key proxies for population health and the functional integrity of landscapes. We addressed this issue in the Yellow-bellied toad (Bombina variegata), an endangered amphibian species with limited dispersal. We combined hierarchical SDMs, integrating both continental-level bioclimatic data and regional-level landscape variables, with genome-wide SNP data from 404 individuals sampled across 92 sites in southeastern France. We then used a multi-scale modelling framework to test the effect of bioclimatic (BRHS) and landscape (LRHS) habitat suitability on observed heterozygosity and pairwise genetic differentiation, accounting for heterogeneous genetic drift using gravity models. Our results show that both BRHS and LRHS are significant predictors of heterozygosity, with their effects expressed at different spatial scales-11 km and 3 km for BRHS and LRHS, respectively. Connectivity patterns also widely varied depending on scale and were best explained by gravity models integrating LRHS, BRHS, and local heterozygosity, underscoring the combined role of landscape resistance and population size in shaping patterns of genetic differentiation. These findings show that SDMs, when carefully calibrated and interpreted, can provide proxies for genetic diversity and landscape resistance in species with limited dispersal.

Journal Article↗
PubMed1985

Comparison between pore model predictions and sheep lung fluid and protein transport.

The multiple pore model of T. R. Harris and R. J. Roselli (1981, J. Appl. Physiol: Respir. Environ. Exercise Physiol. 50, 1-14), was used to simulate lung lymph flow and protein transport at various levels of microvascular pressure. Response of the three-pore structure determined in that study was found to be in excellent agreement with the experimental sheep lung lymph measurements of R. E. Parker, R. J. Roselli, T. R. Harris, and K. L. Brigham (1981, Circ. Res. 49, 1164-1172). Optimal one- and two-pore model structures were also determined and their responses compared with the experimental data. The two-pore model behavior was found to be very similar to that of the three-pore model but a homoporous model which reproduced the experimental findings could not be found. All simulations required interstitial fluid pressure to increase as microvascular pressure was elevated. True filtration-independent conditions could only be simulated when lung vascular pressures were raised to physiologically unrealistic values.

Animals↗
PubMed1989

Assessment of milk transfer coefficients for use in prediction models of radioactivity transport.

The transfer coefficient (Fm) which is widely used to predict the fraction of ingested radionuclides found in milk is an important parameter for modelling. The accuracy of estimates can be improved by considering the following factors that influence the Fm; (1) the physical-chemical form of the radionuclide in the feed of cows, (2) the hay to concentrate ratio of cows diets and (3) correcting for a steady state situation for feed intake and milk concentrations. Factors such as stable element intake, soil intake, milk production rate, metabolic rate and inhalation do not appear to have significant effects upon the transfer coefficient.

Accidents↗
PubMed1985

Clinical validation of a predictive modeling equation for sodium.

Changes in plasma sodium (Na) concentration during hemodialysis were predicted by changes in Na concentration of the dialysate at equilibrium with the plasma, according to the formula C't = CD - (CD - C'0) [(V0 - QFt)/V0]A/QF, where C'0 and C't are the Na concentration of the dialysate at equilibrium with the plasma at times 0 and t, respectively; QF is the ultrafiltration flow rate; V0 is the initial total body water; and CD is the Na dialysate concentration. This modeling involves only one parameter, A, which is the effective sodium dialysance and depends on the dialyzer, the QF, the plasma water flow rate, and the actual Donnan coefficient. Parameter A was evaluated after 1 h of dialysis. Seven routine 4-h dialysis sessions were performed in which the Na concentration of dialysate at equilibrium with the plasma was measured at varying times. The mean (+/- SEM) difference between predicted and measured values was delta C = 0.5 +/- 0.2 mmol/L. These data support the validity of the model that allows the monitoring of Na dialysate concentration to obtain a prescribed Na plasma concentration at the end of a dialysis session.

Humans↗
PubMed1986

Cat lung hemodynamics: comparison of experimental results and model predictions.

Commonly, attempts have been made to learn about the structure and function of the pulmonary vascular bed from measurements of arterial and venous pressures and blood flow rate under steady-state conditions (e.g., from pressure vs. flow data) or dynamic conditions (e.g., from vascular occlusion data). Zhuang et al. (J. Appl. Physiol. 55: 1341-1348, 1983) have presented a detailed model of steady-state cat lung hemodynamics based on direct measurements of anatomical and elasticity data. This model provides an opportunity to better understand the information content of the hemodynamic data. Therefore, in the present study we carried out a series of steady-state and dynamic experiments on isolated cat lungs. We then compared the results with those predicted by the model. We found that the model provided a good fit to the steady-state data. However, to fit the dynamic data, some modifications were necessary to account for the viscous behavior of the vessel walls and to move the first moment of the distribution of vascular resistance toward the arterial end of the vascular bed relative to that of the distribution of vascular compliance. Due to the sensitivity of the vascular resistance to small changes in vessel diameters and branching ratio, the modifications in morphometry represent small changes in morphometric data and are probably within the range of uncertainty in such data. The modifications had little effect on the steady-state model simulations but substantially improved the dynamic model simulations, suggesting that the dynamic data are quite sensitive to small changes in the relative distributions of vessel diameters and elasticity.

Animals↗
PubMed1989

A predictive model for combined temperature and water activity on microbial growth during the growth phase.

An empirical and generalized model is presented, based on a modified Arrhenius equation, for predicting the combined effect of temperature and water activity on the growth rate of bacteria. When it was applied to seven separate sets of wide ranging published results, spanning some 50 years and including a spore-former and a silage micro-organism, predictions explained between 92.9 and 99.0% of the variation in the results with an overall mean of 96.6%. Advantages over existing models are that it is relatively easy to fit to data using least squares regression and requires only five coefficients. These, together with its simplicity and demonstrated wide application, will facilitate its practical use.

Bacteria↗
PubMed1986

Grading scores and survivorship functions in liver cirrhosis: a comparative statistical analysis of various predictive models.

In a group of followed-up liver cirrhotics we evaluated the reliability of prognostic estimates predicted on the basis of a previously described multivariate statistical model (MSM). In the same subjects we also compared theoretical survival estimates obtained by fitting some other liver cirrhosis grading scores (Child-Turcotte's, McCormick's and Orrego's) to prognostic purposes. No statistical difference between actual and MSM-estimated survivorship functions was found (employing a life-table method with Logrank test), thus confirming the prognostic reliability of this multivariate classification model. Such a global and prognosis-correlated index may be recommendable both for comparing different groups of patients, and for assessing treatment effectiveness. Or results also substantially confirm the other investigated classificative methods such as reliable liver cirrhosis severity indexes, although their use for prognostic purposes seems to be less suitable.

Actuarial Analysis↗