Algorithms predicting gestational stage from the maternal steroid metabolome of mares

2021 ◽  
Author(s):  
Paul R. Shorten ◽  
Erin L. Legacki ◽  
Pascale Chavatte-Palmer ◽  
Alan J Conley
Keyword(s):  
Hormone Secretion ◽  
Model Performance ◽  
Predictive Performance ◽  
Serum Samples ◽  
Gonadotropin Secretion ◽  
Paired Samples ◽  
Mixed Breed ◽  
Liquid Chromatography Tandem Mass ◽  
Gestational Stage ◽  
Improved Model

Hormone secretion by the maternal ovaries, trophoblast/placenta and fetus occurs sequentially, creating distinct steroid metabolomic “signatures” in systemic blood of pregnant mares that vary with gestational stage. Algorithms were developed to predict the gestational day (GD) from the maternal steroid metabolome [9 steroids; pregnenolone (P5), progesterone (P4), 5α-dihydroprogesterone (DHP), 17α-hydroxyprogesterone, allopregnanolone, 20α-hydroxy-DHP, 3β,20α-dihydroxy-DHP, dehydroepiandrosterone, androstenedione] determined by liquid chromatography tandem mass spectrometry (LC-MS/MS) of eight thoroughbred mares bled longitudinally throughout pregnancy. A physiologically based model was developed to infer rates of steroid secretion during chorionic gonadotropin secretion, the luteo-placental shift, and by the equine feto-placenta unit, demonstrating more variability in P5 and DHP than P4. The average of four empirical models, using 9 steroids to predict GD, was calibrated (5 mares, R2 = 0.94, RMSE = 20 days) and validated (3 mares, R2 = 0.84, RMSE = 32 days). Validation performance was improved using paired samples taken 14 or 30 days apart (RMSE = 29 and 19 days, respectively). A second validation used an independent dataset (single serum samples from 56 mixed breed mares, RMSE = 79 days) and an additional longitudinal subset from the same population sampled monthly throughout gestation (7 mares, RMSE = 42 days). Again, using paired samples improved model performance (RMSE = 32.5 days). Despite less predictive performance of the mixed breed than the thoroughbred datasets, these models demonstrate the feasibility and potential for using maternal steroid metabolomic algorithms to estimate the stage of gestation in pregnant mares and perhaps monitor fetal development. (247 words)

scholarly journals Serological Responses to Toxoplasma gondii and Matrix Antigen 1 Predict the Risk of Subsequent Toxoplasmic Encephalitis in People Living with HIV

2021 ◽  
Author(s):  
Jianchun Xiao ◽  
Fiona Bhondoekhan ◽  
Eric C Seaberg ◽  
Otto Yang ◽  
Valentina Stosor ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Toxoplasmic Encephalitis ◽  
People Living With Hiv ◽  
Serum Samples ◽  
Peptide Antigens ◽  
Unit Increase ◽  
At Risk Populations ◽  
Living With Hiv ◽  
Nested Case Control ◽  
Control Study

Abstract Background Clinically useful predictors for fatal toxoplasmosis are lacking. We investigated the value of serological assays for antibodies to whole Toxoplasma antigens and to peptide antigens of the Toxoplasma cyst protein MAG1, for predicting incident toxoplasmic encephalitis (TE) in people living with HIV (PLWH). Methods We performed a nested case control study, conducted within the Multicenter AIDS Cohort Study (MACS), using serum samples obtained 2 years prior to diagnosis of TE from 28 cases, and 37 HIV disease-matched Toxoplasma seropositive controls at matched time-points. Sera were tested for Toxoplasma antibodies using a commercial assay and for antibodies to MAG1_4.2 and MAG1_5.2 peptides in ELISA. Results Two years prior to clinical diagnosis, 68% of TE cases were MAG1_4.2 seropositive compared with 16% of controls (OR 25.0, 95% CI 3.14-199.18). Corresponding results for MAG1_5.2 seropositivity were 36% and 14% (OR 3.6, 95% CI 0.95-13.42). Higher levels of antibody to MAG1_4.2 (OR 18.5 per doubling of the OD value, 95% CI 1.41-242) and to Toxoplasma (OR 2.91 for each OD unit increase, 95% CI 1.48-5.72) were also associated with the risk of TE. When seropositivity was defined as the presence of MAG1 antibody or relatively high levels of Toxoplasma antibody, the sensitivity was 89% and specificity was 68% for subsequent TE. Conclusions Antibodies to MAG1 showed predictive value on the occurrence of TE in PLWH, and the predictive performance was further improved by adding the levels of Toxoplasma antibody. These measures could be clinically useful for predicting subsequent diseases in multiple at-risk populations.

scholarly journals Influence of Random Forest Hyperparameterization on Short-Term Runoff Forecasting in an Andean Mountain Catchment

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 238
Author(s):  
Pablo Contreras ◽  
Johanna Orellana-Alvear ◽  
Paul Muñoz ◽  
Jörg Bendix ◽  
Rolando Célleri
Keyword(s):  
Random Forest ◽  
Model Performance ◽  
Early Warning Systems ◽  
Point Of View ◽  
Lead Times ◽  
Physical Parameters ◽  
Runoff Forecasting ◽  
Spatio Temporal ◽  
Improved Model ◽  
Search Approach

The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations of scarce spatio-temporal data and physical parameters needed for process-based hydrological models. However, the influence of RF hyperparameters is still uncertain and needs to be explored. Therefore, the aim of this study is to analyze the sensitivity of RF runoff forecasting models of varying lead time to the hyperparameters of the algorithm. For this, models were trained by using (a) default and (b) extensive hyperparameter combinations through a grid-search approach that allow reaching the optimal set. Model performances were assessed based on the R2, %Bias, and RMSE metrics. We found that: (i) The most influencing hyperparameter is the number of trees in the forest, however the combination of the depth of the tree and the number of features hyperparameters produced the highest variability-instability on the models. (ii) Hyperparameter optimization significantly improved model performance for higher lead times (12- and 24-h). For instance, the performance of the 12-h forecasting model under default RF hyperparameters improved to R2 = 0.41 after optimization (gain of 0.17). However, for short lead times (4-h) there was no significant model improvement (0.69 < R2 < 0.70). (iii) There is a range of values for each hyperparameter in which the performance of the model is not significantly affected but remains close to the optimal. Thus, a compromise between hyperparameter interactions (i.e., their values) can produce similar high model performances. Model improvements after optimization can be explained from a hydrological point of view, the generalization ability for lead times larger than the concentration time of the catchment tend to rely more on hyperparameterization than in what they can learn from the input data. This insight can help in the development of operational early warning systems.

scholarly journals Vitamin D Metabolites and Clinical Outcome in Hospitalized COVID-19 Patients

Nutrients ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 2129
Author(s):  
Sieglinde Zelzer ◽  
Florian Prüller ◽  
Pero Curcic ◽  
Zdenka Sloup ◽  
Magdalena Holter ◽  
...  
Keyword(s):  
Vitamin D ◽  
Degradation Products ◽  
University Hospital ◽  
Vitamin D Metabolites ◽  
Serum Concentrations ◽  
Serum Samples ◽  
Liquid Chromatography Tandem Mass ◽  
Relevant Role ◽  
Metabolite Ratio ◽  
The University

(1) Background: Vitamin D, a well-established regulator of calcium and phosphate metabolism, also has immune-modulatory functions. An uncontrolled immune response and cytokine storm are tightly linked to fatal courses of COVID-19. The present retrospective study aimed to inves-tigate vitamin D status markers and vitamin D degradation products in a mixed cohort of 148 hospitalized COVID-19 patients with various clinical courses of COVID-19. (2) Methods: The serum concentrations of 25(OH)D3, 25(OH)D2, 24,25(OH)2D3, and 25,26(OH)2D3 were determined by a validated liquid-chromatography tandem mass-spectrometry method in leftover serum samples from 148 COVID-19 patients that were admitted to the University Hospital of the Medical Uni-versity of Graz between April and November 2020. Anthropometric and clinical data, as well as outcomes were obtained from the laboratory and hospital information systems. (3) Results: From the 148 patients, 34 (23%) died within 30 days after admission. The frequency of fatal outcomes did not differ between males and females. Non-survivors were significantly older than survivors, had higher peak concentrations of IL-6 and CRP, and required mechanical ventilation more frequently. The serum concentrations of all vitamin D metabolites and the vitamin D metabolite ratio (VMR) did not differ significantly between survivors and non-survivors. Additionally, the need for res-piratory support was unrelated to the serum concentrations of 25(OH)D vitamin D and the two vitamin D catabolites, as well as the VMR. (4) Conclusion: The present results do not support a relevant role of vitamin D for the course and outcome of COVID-19.

scholarly journals Incremental value of QT interval for the prediction of obstructive coronary artery disease in patients with chest pain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dong-Hyuk Cho ◽  
Jimi Choi ◽  
Mi-Na Kim ◽  
Hee-Dong Kim ◽  
Soon Jun Hong ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Chest Pain ◽  
Coronary Artery ◽  
Qt Interval ◽  
Obstructive Coronary Artery Disease ◽  
Model Performance ◽  
Clinical Score ◽  
Qtc Prolongation ◽  
Artery Disease ◽  
Improved Model

AbstractIdentification of obstructive coronary artery disease (OCAD) in patients with chest pain is a clinical challenge. The value of corrected QT interval (QTc) for the prediction of OCAD has yet to be established. We consecutively enrolled 1741 patients with suspected angina. The presence of obstructive OCAD was defined as ≥ 50% diameter stenosis by coronary angiography. The pre-test probability was evaluated by combining QTc prolongation with the CAD Consortium clinical score (CAD2) and the updated Diamond-Forrester (UDF) score. OCAD was detected in 661 patients (38.0%). QTc was longer in patients with OCAD compared with those without OCAD (444 ± 34 vs. 429 ± 28 ms, p < 0.001). QTc was increased by the severity of OCAD (P < 0.001). QTc prolongation was associated with OCAD (odds ratio (OR), 2.27; 95% confidence interval (CI), 1.81–2.85). With QTc, the C-statistics increased significantly from 0.68 (95% CI 0.66–0.71) to 0.76 (95% CI 0.74–0.78) in the CAD2 and from 0.64 (95% CI 0.62–0.67) to 0.74 (95% CI 0.72–0.77) in the UDF score, respectively. QT prolongation predicted the presence of OCAD and the QTc improved model performance to predict OCAD compared with CAD2 or UDF scores in patients with suspected angina.

scholarly journals Incremental model breakdown to assess the multi-hypotheses problem

2018 ◽  
Vol 22 (8) ◽  
pp. 4565-4581 ◽  
Author(s):  
Florian U. Jehn ◽  
Lutz Breuer ◽  
Tobias Houska ◽  
Konrad Bestian ◽  
Philipp Kraft
Keyword(s):  
Hydrological Modeling ◽  
Model Performance ◽  
Complex Model ◽  
Model Parameters ◽  
Objective Functions ◽  
Final Model ◽  
Incremental Model ◽  
Model Efficiency ◽  
Improved Model ◽  
Model Structures

Abstract. The ambiguous representation of hydrological processes has led to the formulation of the multiple hypotheses approach in hydrological modeling, which requires new ways of model construction. However, most recent studies focus only on the comparison of predefined model structures or building a model step by step. This study tackles the problem the other way around: we start with one complex model structure, which includes all processes deemed to be important for the catchment. Next, we create 13 additional simplified models, where some of the processes from the starting structure are disabled. The performance of those models is evaluated using three objective functions (logarithmic Nash–Sutcliffe; percentage bias, PBIAS; and the ratio between the root mean square error and the standard deviation of the measured data). Through this incremental breakdown, we identify the most important processes and detect the restraining ones. This procedure allows constructing a more streamlined, subsequent 15th model with improved model performance, less uncertainty and higher model efficiency. We benchmark the original Model 1 and the final Model 15 with HBV Light. The final model is not able to outperform HBV Light, but we find that the incremental model breakdown leads to a structure with good model performance, fewer but more relevant processes and fewer model parameters.

Cardiovascular risk model performance in women with and without hypertensive disorders of pregnancy

Heart ◽  
2018 ◽  
Vol 105 (4) ◽  
pp. 330-336 ◽  
Author(s):  
Veerle Dam ◽  
N Charlotte Onland-Moret ◽  
W M Monique Verschuren ◽  
Jolanda M A Boer ◽  
Laura Benschop ◽  
...  
Keyword(s):  
Risk Model ◽  
Model Performance ◽  
Predictive Performance ◽  
Baseline Risk ◽  
Hypertensive Disorders Of Pregnancy ◽  
Cvd Risk ◽  
Hypertensive Disorders ◽  
Framingham Risk ◽  
History Of ◽  
Score Model

ObjectivesCompare the predictive performance of Framingham Risk Score (FRS), Pooled Cohort Equations (PCEs) and Systematic COronary Risk Evaluation (SCORE) model between women with and without a history of hypertensive disorders of pregnancy (hHDP) and determine the effects of recalibration and refitting on predictive performance.MethodsWe included 29 751 women, 6302 with hHDP and 17 369 without. We assessed whether models accurately predicted observed 10-year cardiovascular disease (CVD) risk (calibration) and whether they accurately distinguished between women developing CVD during follow-up and not (discrimination), separately for women with and without hHDP. We also recalibrated (updating intercept and slope) and refitted (recalculating coefficients) the models.ResultsOriginal FRS and PCEs overpredicted 10-year CVD risks, with expected:observed (E:O) ratios ranging from 1.51 (for FRS in women with hHDP) to 2.29 (for PCEs in women without hHDP), while E:O ratios were close to 1 for SCORE. Overprediction attenuated slightly after recalibration for FRS and PCEs in both hHDP groups. Discrimination was reasonable for all models, with C-statistics ranging from 0.70-0.81 (women with hHDP) and 0.72–0.74 (women without hHDP). C-statistics improved slightly after refitting 0.71–0.83 (with hHDP) and 0.73–0.80 (without hHDP). The E:O ratio of the original PCE model was statistically significantly better in women with hHDP compared with women without hHDP.ConclusionsSCORE performed best in terms of both calibration and discrimination, while FRS and PCEs overpredicted risk in women with and without hHDP, but improved after recalibrating and refitting the models. No separate model for women with hHDP seems necessary, despite their higher baseline risk.

scholarly journals The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

2017 ◽  
Vol 10 (4) ◽  
pp. 1679-1701 ◽  
Author(s):  
Silvia Caldararu ◽  
Drew W. Purves ◽  
Matthew J. Smith
Keyword(s):  
Environmental Changes ◽  
Crop Yields ◽  
Vegetation Indices ◽  
Model Fitting ◽  
Model Performance ◽  
Predictive Performance ◽  
Crop Model ◽  
Model Fit ◽  
General Process ◽  
Fitting Algorithm

Abstract. Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process-based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In this paper, we present a generic process-based crop model (PeakN-crop v1.0) which we parametrise using a Bayesian model-fitting algorithm to three different sources: data–space-based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters, can largely capture the observed behaviour but the data-constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improve on the prior model fit, the satellite-based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection to improve our predictions of crop yields and crop responses to environmental changes.

scholarly journals A Thermodynamic Model of the Formation, Growth, and Decay of First-Year Sea Ice

1987 ◽  
Vol 33 (113) ◽  
pp. 105-119 ◽  
Author(s):  
R. Gabison
Keyword(s):  
Sea Ice ◽  
Thermodynamic Model ◽  
Model Performance ◽  
Melting Process ◽  
The Arctic ◽  
First Year ◽  
Climatological Data ◽  
Oceanic Tides ◽  
Waves And Tides ◽  
Improved Model

AbstractThe formulation and application of a onedimensional sea-ice thermodynamic model is presented in this paper. The model’s sensitivity to changes in oceanic and atmospheric parameters is analyzed and compared with previous studies. The model is next applied to three locations in the Arctic: Cambridge Bay, Frobisher Bay, and Alert Inlet to study the model’s ability to simulate the annual cycle of first-year ice. The model’s results are compared with available climatological data and discussed in terms of the main thermodynamic processes, the combined effects of oceanic tides, and of sea-ice deterioration by melting on the break-up of sea ice.It is shown that the model is effective in simulating the climatology of the first-year ice thickness at the three Arctic locations. The study also suggests that improved model performance can be expected from additional research and application of flexural forcing of the ice by waves and tides, and of deterioration of ice strength during the melting process.

scholarly journals Mind the gap: preventing circularity in missense variant prediction

2020 ◽  
Author(s):  
Stephan Heijl ◽  
Bas Vroling ◽  
Tom van den Bergh ◽  
Henk-Jan Joosten
Keyword(s):  
Real World ◽  
Performance Metrics ◽  
Model Performance ◽  
Predictive Performance ◽  
Missense Variant ◽  
Training Methods ◽  
The Real ◽  
Evaluation Scores ◽  
Evaluation Strategies ◽  
Generic Strategy

AbstractDespite advances in the field of missense variant effect prediction, the real clinical utility of current computational approaches remains rather limited. There is a large difference in performance metrics reported by developers and those observed in the real world. Most currently available predictors suffer from one or more types of circularity in their training and evaluation strategies that lead to overestimation of predictive performance. We present a generic strategy that is independent of dataset properties and algorithms used, to deal with circularity in the training phase. This results in more robust predictors and evaluation scores that accurately reflect the real-world performance of predictive models. Additionally, we show that commonly used training methods can have an adverse impact on model performance and lead to gross overestimation of true predictive performance.

scholarly journals Predictive Performance of Hargreaves Model in Estimating Solar Radiation in Yola, Adamawa State Nigeria Using Minimum Climatological Data

2018 ◽  
pp. 1-8
Author(s):  
Ojo Samuel ◽  
Alimi Taofeek Ayodele ◽  
Amos Anna Solomon
Keyword(s):  
Solar Radiation ◽  
Model Performance ◽  
Predictive Performance ◽  
Global Solar Radiation ◽  
Climatic Data ◽  
Climatological Data ◽  
University Of Technology ◽  
Hargreaves Model ◽  
The Many

Mathematical models have been very useful in reducing challenges encountered by researchers due to the inability of having solar radiation data or lack of instrumental sites at every point on the Earth.  This work aimed at investigating the prediction performance of Hargreaves-Samani’s model in estimating global solar radiation (GSR) out of the many other empirical models so far formulated for this purpose. This model basically uses maximum and minimum temperature data and basically used in mid-latitudes. The paper attempts to assess the predictive performance of Hargreaves-Samani’s model in the Savanna region using Yola as a case study. Estimated values of GSR from one month data adopted from the Meteorological station of the Department of Geography, Federal University of Technology, Yola, Nigeria was used for this purpose. Using this model shows a 95% index of agreement (IA) with the observed values; which suggests a good model performance and can also be used in estimating global solar radiation in the Savanna region particularly in areas with little or no such climatic data.

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