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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↗
PubMed1993

A predictive model for the combined effect of pH, sodium chloride and storage temperature on the growth of Brochothrix thermosphacta.

Growth of Brochothrix thermosphacta was observed under ranges of pH (5.6-6.8), NaCl (0.5-8.0% w/v) and incubation temperature (1-30 degrees C). In order to compare different approaches, two models were used to fit growth curves to viable count data, and to calculate parameters from those fitted curves. Growth responses as a function of pH, NaCl and temperature were described with a quadratic function which was then used to predict growth within the limits where growth was observed. The predictions of the model show good agreement with published observations from other laboratories.

Cell Division↗
PubMed1993

Uncertainties in pharmacokinetic modeling for perchloroethylene: II. Comparison of model predictions with data for a variety of different parameters.

In this paper we compare expectations derived from 10 different human physiologically based pharmacokinetic models for perchloroethylene with data on absorption via inhalation, and concentrations in alveolar air and venous blood. Our most interesting finding is that essentially all of the models show a time pattern of departures of predictions of air and blood levels relative to experimental data that might be corrected by more sophisticated model structures incorporating either (a) heterogeneity of the fat compartment (with respect to either perfusion or partition coefficients or both) or (b) intertissue diffusion of perchloroethylene between the fat and muscle/VRG groups. Similar types of corrections have recently been proposed to reduce analogous anomalies in the fits of pharmacokinetic models to the data for several volatile anesthetics. A second finding is that models incorporating resting values for alveolar ventilation in the region of 5.4 L/min seemed to be most compatible with the most reliable set of perchloroethylene uptake data.

Biological Transport, Active↗
PubMed1985

Importance of glucose per se to intravenous glucose tolerance. Comparison of the minimal-model prediction with direct measurements.

Glucose disappearance after an oral or intravenous challenge is a function of the effects of both endogenously secreted insulin and of glucose itself. We previously introduced the term "glucose effectiveness," or SG, defined as the ability of glucose per se to enhance its own disappearance independent of an increment in plasma insulin. The present investigation, performed in conscious dogs, was undertaken to quantify this glucose effect by minimal-model-based analysis of insulin and glucose dynamics after a frequently sampled intravenous glucose tolerance test (FSIGT). The values from the standard FSIGT were then compared with direct measurements obtained from experiments in which the dynamic insulin response to glucose was suppressed with somatostatin (SRIF). In addition, we examined SG values from the modified FSIGT protocol, which involves both glucose and tolbutamide injections. Protocol l (N = 9): FSIGTs were performed and the glucose and insulin data were analyzed by computer. KG was 2.65 +/- 0.28 min-1, S1 was 4.09 +/- 0.34 X 10(4) min-1/(microU/ml), and SG was 0.033 +/- 0.004 min-1. Protocol II (N = 6): FSIGTs were performed on animals in which SRIF was infused (0.8 micrograms/min X kg) to obliterate the dynamic insulin response to glucose injection. Before the FSIGt, insulin and glucagon were infused intraportally to reattain basal glycemia. Without dynamic insulin, KG was reduced to 0.96 +/- 0.18 min-1 (P less than 0.0001). However, SG, estimated from the exponential rate of fall of plasma glucose in the absence of dynamic insulin, was similar to the standard FSIGTs: 0.025 +/- 0.004 (P greater than 0.25). Protocol III (N = 6): modified FSIGTs were performed using glucose and tolbutamide injections for a better estimate of model parameters. Model parameters Sl and SG, and the KG were not different from standard FSIGTs (P greater than 0.3). In fact, the value of SG (0.028 +/- 0.003 min-1) was nearly identical to the direct measure from protocol II. Therefore, the effect of glucose per se on glucose decline, estimated by modeling the standard and modified FSIGTs, was confirmed by a direct measurement with the endogenous insulin response suppressed with SRIF. Also, the time course of the insulin effect to enhance net glucose disappearance from plasma [Ieff(t)] was calculated from the data of protocol II, and was the same as the time course predicted by the model. These studies demonstrate the ability of the computer modeling approach to separate insulin-dependent and glucose-dependent glucose disappearance, and represent a direct confirmation of the minimal model.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗
PubMed1993

Growth of Listeria monocytogenes, Aeromonas hydrophila and Yersinia enterocolitica in pâté and a comparison with predictive models.

A reference or type strain and a food derived-strain of the cold-tolerant pathogens Listeria monocytogenes, Aeromonas hydrophila and Yersinia enterocolitica were individually inoculated into samples of commercial pâté and incubated at 4 and 10 degrees C. The organisms were periodically estimated by presumptive counts, then values for the lag and generation times were calculated. Both strains of L. monocytogenes grew at both temperatures. The food strain of A. hydrophila grew only at 10 degrees C, and the type strain did not grow at either temperature. Similarly, the type strain of Y. enterocolitica did not grow at either temperature, whereas the food strain grew at both 4 and 10 degrees C. In some cases growth of non-test organisms may have inhibited the growth of these latter two species. The measured values of lag and generation times did not, in general, correlate well with those predicted by response surface models, taken from the literature and produced in this laboratory. It may be that the pâté contained an inhibitor that affected the growth of the organisms. The two strains of A. hydrophila and Y. enterocolitica showed significantly different growth characteristics, reinforcing the value of using a 'cocktail' of strains in growth experiments. Differences in predicted kinetic values from the models indicate that a model for any particular strain may not reflect the growth of naturally occurring contaminants of the same species.

Aeromonas hydrophila↗
PubMed2026

How negative sampling shapes the performance of transcription factor binding site prediction models.

MOTIVATION: Transcription factors (TFs) are key players in gene regulation and development, where they activate and repress gene expression through DNA binding. Predicting transcription factor binding sites (TFBSs) has long been an active area of research, with many deep learning methods developed to tackle this problem. These models are often trained on TF ChIP-seq data, which is generally seen as only providing positive samples. The choice of datasets and negative sampling techniques is a critical yet often overlooked aspect of this work. RESULTS: In this study, we investigate the impact of different negative sampling techniques on TFBS prediction performance. We create high-quality test datasets based on ChIP-seq and ATAC-seq data, where true negatives can be identified as positions that are accessible but not bound by the TF in question. We then train models using various negative sampling techniques, including genomic sampling, shuffling, dinucleotide shuffling, neighborhood sampling, and cell line specific sampling, simulating cases where matching ATAC-seq data is not available. Our results show that, generally, metrics calculated on training datasets give inflated performance scores. Of the tested techniques, genomic sampling of negatives based on similarity to the positives performed by far the best, although still not reaching the performance of baseline models trained on high-quality datasets. Models trained on dinucleotide shuffled negatives performed poorly, despite being a common practice in the field. Our findings highlight the importance of carefully selecting negative sampling techniques for TFBS prediction, as they can significantly impact model performance and the interpretation of results. AVAILABILITY AND IMPLEMENTATION: The code used in this study is available at https://github.com/NatanTourne/TFBS-negatives (DOI: 10.5281/zenodo.18007567).

Binding Sites↗
PubMed1982

Prediction of remission in adult acute leukemia: development and testing of predictive models.

Logistic regression methods were applied to derive a set of models relating achievement of CR to prognostic characteristics in a group of 300 adult acute leukemia patients treated with cytosine arabinoside, vincristine, and prednisone combined with adriamycin (ADOAP) or rubidazone (ROAP). These models were tested prospectively in an independent group of 107 subsequent patients treated with ADOAP or ROAP therapy, by comparing observed outcomes to predictions of response based on the models. Several models were able to identify subgroups of patients with good, intermediate, and poor prognoses. A model regarded as clinically useful and which provided a good fit to both the population from which it was derived and the test population included the pretreatment factors age, history of an antecedent hematologic disorder, temperature, blood urea nitrogen, hemoglobin, and liver size.

Acute Disease↗
PubMed1995

Validation and adjustment of the mathematical prediction model for human sweat rate responses to outdoor environmental conditions.

Under outdoor conditions this model was over estimating sweat loss response in shaded (low solar radiation) environments, and underestimating the response when solar radiation was high (open field areas). The present study was conducted in order to adjust the model to be applicable under outdoor environmental conditions. Four groups of fit acclimated subjects participated in the study. They were exposed to three climatic conditions (30 degrees, 65% rh; 31 degrees C, 40% rh; and 40 degrees C, 20% rh) and three levels of metabolic rate (100, 300 and 450 W) in shaded and sunny areas while wearing shorts, cotton fatigues (BDUs) or protective garments. The original predictive equation for sweat loss was adjusted for the outdoor conditions by evaluating separately the radiative heat exchange, short-wave absorption in the body and long-wave emission from the body to the atmosphere and integrating them in the required evaporation component (Ereq) of the model, as follows: Hr = 1.5SL0.6/I(T) (watt) H1 = 0.047Me.th/I(T) (watt), where SL is solar radiation (W.m-2), Me.th is the Stephan Boltzman constant, and I(T) is the effective clothing insulation coefficient. This adjustment revealed a high correlation between the measured and expected values of sweat loss (r = 0.99, p < 0.0001).

Adolescent↗
PubMed1989

[Congenital malformations: a model predictive based on risk factors].

Several risk factors were studied in regard to congenital malformations. Malformed newborns (n = 1200) and controls (n = 1200) seen at the Universidad de Chile Hospital between 1969 and 1979 were examined. Their mothers were asked about possible risk factors. Parenteral age and birth order was significantly higher for malformed newborns than for controls. A family history of congenital malformations was more frequent in malformed newborns. Infertility, metrorrhagia and maternal diseases during pregnancy were more frequent in malformed newborns than in controls. A function that discriminates between controls mothers and mothers of malformed newborns was obtained by a logistic regression model. This function correctly predicted 65% of cases.

Congenital Abnormalities↗
PubMed1987

Predictive models for deposition of inhaled diesel exhaust particles in humans and laboratory species.

Mathematical and computer models of the respiratory tracts of human beings and of laboratory animals (rats, hamsters, guinea pigs) were used to estimate the deposition patterns of inhaled diesel exhaust particles from automobile emissions. The accuracy of these models was tested by comparing the calculated depositions in laboratory animals with actual laboratory data. Our goal was to be able to predict the relation between exposure to diesel exhaust particles and the deposition of these particles in the lungs of humans of various ages. Diesel exhaust particles are aggregates with a mass median aerodynamic diameter of approximately 0.2 micron. Their actual size depends on the conditions under which they are generated. Using an appropriate particle model, we derived mathematical expressions that describe the effects of diffusion, sedimentation, impaction, and interception on the deposition of these particles. Because of their small size, we found that most diesel exhaust particles deposited through diffusion, and that the role of the other mechanisms was minor. Anatomical models of the human lung from birth to adulthood, as well as models of the lungs of laboratory species were formulated mathematically using available morphometric data. We used these lung models, together with the corresponding ventilation conditions of each species, to calculate deposition of diesel exhaust particles in the lungs. Under normal breathing conditions, we calculated that 7 to 13 percent (depending on particle size) of inhaled diesel exhaust particles deposit in the alveolar region of the adult human lung. Although the breathing mode (nose or mouth breathing) did not appear to affect alveolar deposition, increasing the minute ventilation (the number of breaths per minute multiplied by the tidal volume) increased alveolar deposition significantly. The calculated deposition patterns for diesel exhaust particles in younger humans (under age 25) were similar. However, with the exception of alveolar deposition in very young children (under age two), predicted deposition was greater in the lungs of younger humans than in the lungs of humans age 25 or older. For an equal exposure, the surface minute dose (particle mass deposited per minute per unit surface area) of unciliated airways appeared to change profoundly with age. Predicted dose was maximal in the lung models of two-year-old children. At this age, the calculated dose was approximately twice as high as in the mature adult lung. Deposition predictions for laboratory species compared favorably with existing data. Distribution of deposition was found to be similar among all species studied, although surface minute dose decreased with body weight.

Adolescent↗
PubMed1992

Distinguishing between persistent and transient impaired glucose tolerance using a prediction model.

Screening for impaired glucose tolerance (IGT) and Type 2 (non-insulin dependent) diabetes was carried out in 777 people and those with high blood glucose levels completed three 2-h oral glucose tolerance tests (OGTT). Blood lipid levels, fasting and 2-h insulin levels, body mass index, and blood pressure were also measured and family history of Type 2 diabetes recorded. Fifty people were identified with IGT and of these 21 were found to have persistent IGT and 29 transient IGT. A model including the variables body mass index, fasting and 2-h insulin levels, fasting triglycerides and family history of Type 2 diabetes was developed using the Speigelhalter-Knill-Jones weighting method to predict subjects with persistent IGT. This model could be useful in identifying people with persistent IGT and therefore eliminate the need for repeat OGTTs which are time consuming and expensive.

Blood Glucose↗
PubMed1995

Expressed emotion, family dynamics and symptom severity in a predictive model of social adjustment for schizophrenic young adults.

While numerous studies have concluded that high expressed emotion (EE) in relatives predicts relapse in schizophrenia, other aspects of patient outcome have not been well studied. Our purpose was to determine the extent to which family dynamics and expressed emotion may predict variance in patient social adjustment when controlling for symptom severity. Sixty-nine schizophrenic outpatients and 108 of their relatives participated. Relatives' EE was assessed, and they were administered the FACES III for perceptions of family cohesion and adaptability. Patients were interviewed at about the same time as their relatives and again 9 months later with the Social Adjustment Scale (SAS-II) and the BPRS. Between 9% and 58% of variance in each of the five SAS scales was explained by selected EE and family dynamics scales, while symptom severity was held constant. Among the results, it was found that better social adjustment in patients was associated with less family adaptability, and with greater emotional overinvolvement in relatives. Adjustment of patients in the work role was associated with more Critical Comments from mothers. In conclusion, some aspects of high EE are associated with better social adjustment in schizophrenic patients.

Adolescent↗
PubMed2026

Machine learning-based clinical prediction model and multi-omics integration for assessing pancreatic cancer risk in new-onset diabetes.

BACKGROUND: Given that pancreatic cancer (PC) is typically diagnosed at an advanced stage but is often preceded by new-onset diabetes mellitus (NODM), providing a window for early detection, we sought to develop and validate an interpretable machine-learning model integrated with multi-omics profiling to identify early biomarkers of NODM-associated PC. METHODS: In a population-based cohort, individuals with NODM-associated PC and NODM without PC were identified and randomly divided (70:30) into training and validation sets after feature selection. Eight machine learning (ML) classifiers were compared using fivefold cross-validation, and model performance was evaluated in terms of discrimination, calibration, and decision curve&#x2013;based clinical utility. We evaluated interpretability using the Shapley additive explanations (SHAP) analyses. Mechanistically, Olink proteomic profiling and metabolomics were analyzed through clinical classifications and model-defined risk strata. RESULTS: Categorical boosting achieved the best performance in the independent validation set (AUROC&#x2009;=&#x2009;0.844). The NODM cohort was stratified into high- (n&#x2009;=&#x2009;2,362) and low-risk (n&#x2009;=&#x2009;5,030) groups, and internal validation together with SHAP analyses demonstrated consistent model performance and identified clinically interpretable predictors. Proteomic and metabolomic analyses under clinical and risk-based grouping identified 39 overlapping differentially expressed proteins and 145 overlapping metabolites with enriched across 11 shared KEGG pathways. Cross-platform validation highlighted PLTP, CRTAC1, and ITGAV as serum biomarkers with a strong potential for early NODM-PC detection. CONCLUSIONS: We developed an interpretable ML framework centered on NODM enables practical risk stratification for early PC detection by multi-omics and provides a pathway of ML-based triage followed by biomarker confirmation for earlier detection and diagnosis.

Humans↗
PubMed2026

CLASPP: A unified model for predicting post-translational modifications.

Post-Translational Modifications (PTMs) are a fundamental mechanism for regulating cellular pathways and increasing the functional diversity of the proteome. Accurately predicting the PTM types that are likely to occur at a given site in the primary sequence is a key challenge in functional proteomics. Existing PTM prediction models predominantly focus on either single PTM types or employ ensemble methods that combine multiple models to predict different PTM types. This fragmentation is largely driven by the vast imbalance in data availability across PTM types, making it difficult to predict multiple PTM types with a single model. To address this limitation, we present the Contrastively Learned Attention-based Stratified PTM Predictor (CLASPP), a unified PTM prediction model. CLASPP addresses imbalance challenges by leveraging unsupervised clustering-based undersampling and a novel contrastive learning framework tailored to PTM data. Additionally, our hierarchical data organization and curation are shown to improve CLASPP's performance by balancing the representation of individual PTM types and provides a standardized dataset to train and validate future model designs. Drawing inspiration from advancements in image and natural language processing, the CLASPP model employs a multi-stage training strategy and a high-quality, curated training dataset to improve PTM prediction performance. To uncover what is learned during the contrastive learning stage, the CLASPP model is shown to distinguish known protein kinase substrate specificity profiles as a form of explainability. Finally, we evaluate the application of CLASPP in predicting PTMs in different model organisms and experimentally validated ubiquitination sites in the understudied DCLK3 kinase. Overall, CLASPP represents a unified model for PTM prediction that addresses key bottlenecks in data imbalance and offers new strategies for biological data curation, thereby improving PTM-type prediction performance across diverse organisms.

Protein Processing, Post-Translational↗