Predictive Ability Of Model Research Articles

Dissection of X chromosome dosage compensation for quantitative traits in sheep using different statistical models

Since males and females have different number of X chromosome, different mechanisms have evolved to equalize dosage of gene products from the X chromosome between XX females and XY males. The aim of this study was to study X chromosome dosage compensation for growth rate (GR), Kleiber ratio (KR), efficiency of growth (EF) and relative growth rate (RGR) in Zandi sheep. A two steps procedure was adopted to analysis data. In the first step, each trait was analyzed with a series of 6 animal models including different combinations of direct and maternal effects. Using Akanke’s Information Criterion (AIC) the best model (Model I) was selected for each trait. In the second step, five additional models were fitted by adding X chromosome effects to the Model I, considering 5 strategies for modeling X chromosome dosage compensation: (1) no global dosage compensation (ngdc), (2) random inactivation in the homogametic sex (hori), (3) doubling of the single shared sex chromosome in the heterogametic sex (hedo), (4) halving expression of both sex chromosomes in the homogametic sex (hoha) or (5) inactivation of the paternal sex chromosome in the homogametic sex (hopi). Predictive ability of models was measured using the mean squared error of prediction (MSE) and Pearson’s correlation coefficient between the real and predicted values of records Correlations between traits due to autosomal- and X-linked genetic effects were estimated by bi-variate analyses. For GR and KR, models including X-linked effects lead to a much better fit of data, expressed by the strong decrease in the AIC criterion. Models including X-linked effects had also better predictive ability as they provided smaller MSE and higher For GR and KR, although all strategies for modeling X chromosome dosage compensation improved general properties of the model, the model “ngdc” fitted the data significantly better than other models. Including X-linked genetic effects in the model led to 10% (GR, KR) decrease in the autosomal additive variance, and 7% (KR) to 19% (GR) decrease in the residual variance. Estimates of autosomal heritability (), were 0.15 ± 0.03, 0.13 ± 0.03, 0.9 ± 0.03 and 0.13 ± 0.03 for GR, KR, EF and RGR, respectively. X-linked heritability () was 0.08 ± 0.03 for GR and 0.04 ± 0.03 for KR, respectively. Maternal heritability () were 0.02 ± 0.01, 0.01 ± 0.01, 0.03 ± 0.02 and 0.03 ± 0.02 for GR, KR, EF and RGR, respectively. For GR and KR, the Spearman’s correlation between breeding values obtained from the best model and model I deviated from unity, indicating re-ranking of top animals across models. The X-linked additive genetic correlation and autosomal additive genetic correlation were similar in terms of sign and magnitude in a way that they were all positive and high. As considering X-linked genetic effects resulted to an improvement in the general properties of the model and possibility of re-ranking of top animals, including these effects in the model, considering dosage compensation on the X chromosome was recommended.

Essential Structural Profile of Novel Adenosine Derivatives as Antiplatelet Aggregation Inhibitors based on 3D-QSAR Analysis

Aims: In this research, 3D-QSAR evaluation on a set of fresh purinoid compounds that we produced was conducted. This analysis aims to illustrate the correlation between the structure of purine and its ability to prevent platelet aggregation. Our findings could pave the way to discovering novel antithrombotic medications. Background: The incidence of cardiovascular disease triggered by the clumping of platelets poses a significant danger to human health. Purine derivatives are important molecules with antiplatelet aggregation activity. Objective: The objectives of this research are to establish the correlation between the structure of purine and its ability to prevent platelet aggregation. Such a correlation could aid in the development of innovative antithrombotic medications. Methods: In this study, 3D-QSAR investigation on a collection of 75 new purine derivatives, which we synthesized, was conducted, utilizing Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Results: Significant correlation coefficients (CoMFA, q2= 0.843, r2= 0.930, F value= 266.755, SEE= 0.165; CoMSIA, q2= 0.869, r2= 0.918, F value= 222.571, SEE= 0.179) were obtained, and assessed the model's predictive capabilities by validating it with the test set. Conclusion: Our findings indicate that the introduction of an appropriately sized structure at position 2 of the compound yields significant benefits. Conversely, the attachment of an excessively large group is detrimental. Direct attachment of a bulky substituent at C-6 of the compound is not feasible, and its activity increases when the structure with low electron cloud density is added. Moreover, the presence of a voluminous functional group at the 5' position of the compound is advantageous, and its activity will be further increased by the presence of hydrogen bond receptors in this region. These discoveries furnish significant comprehension for the formation of innovative structures with heightened efficacy.

Analysis of risk factors and establishment of prediction model for immune checkpoint inhibitor related myocarditis and major adverse cardiovascular events

Objectives: To explore the risk factors of major adverse cardiovascular events (MACEs) in immune checkpoint inhibitor (ICI) related myocarditis and establish a predictive model. Methods: This was a retrospective case-control study. Tumor patients diagnosed with ICI related myocarditis in the First Affiliated Hospital of Guangzhou Medical University from May 2019 to August 2023 were selected and divided into non-MACE group and MACE group based on whether MACE occurred. Clinical and imaging data of the two groups were collected. Univariate and multivariate logistic regression models were used to analyze the risk factors for MACE in patients with ICI related myocarditis. According to the results of multivariate logistic regression analysis, R 4.1.0 software was used to construct the MACE risk prediction model for these patients and draw a nomogram. The receiver operating characteristic curve was used to evaluate the prediction ability of the prediction model. Results: A total of 35 patients with ICI related myocarditis, aged (63.9±8.2) years, were included, including 28 males (80%). There were 18 patients in the non-MACE group and 17 patients in the MACE group. Multivariate logistic regression analysis showed that elevated neutrophil to lymphocyte ratio (OR=1.115, 95%CI 1.007-1.235, P=0.036) and ST-T segment changes (OR=24.942, 95%CI 1.239-502.194, P=0.036) were risk factors for MACE in patients with ICI related myocarditis. The receiver operating characteristic curve indicated that the area under the curve of the prediction model was 0.967 (95%CI 0.916-1.000, P<0.001), with a sensitivity of 88.2% and specificity of 100%, demonstrating good predictive ability. Conclusion: Elevated neutrophil to lymphocyte ratio and ST-T segment change are independent risk factors for MACE in patients with ICI related myocarditis. Risk prediction model based on the above two indicators can assist in the early identification and individualized intervention of ICI related myocarditis patients.

Identification and crude protein prediction of porcini mushrooms via deep learning-assisted FTIR fingerprinting

Wild edible mushrooms are natural, green, and sustainable foods, and the key to their post-harvest safety and quality control lies in species identification, geographic traceability, and quality assessment. This study analyses the validity of partial least squares discriminant analysis (PLS-DA) model with residual convolutional neural network (ResNet) model based on Fourier transform infrared (FTIR) spectroscopy and two-dimensional correlation spectroscopy (2DCOS) for identifying species and geographic origin of porcini mushrooms. Exploring the feasibility of the partial least squares regression (PLSR) model and long short-term memory network (LSTM) model for predicting the crude protein content of porcini mushrooms. The results show that the ResNet model outperforms PLS-DA with 1.00 and 0.98 prediction results and 1.00 and 0.92 prediction accuracy for new samples, respectively. The LSTM model was more effective than the PLSR model in estimating crude protein content. Among them, LSTM model with first order derivative-multiple scattering correction-Savitzky-Golay (1D-MSC-SG) preprocessing combination was optimal with a prediction set RPD = 2.7, Sig. = 0.418. Besides, the sequential projection algorithm (SPA) feature extraction improves the prediction ability of the LSTM model. Its residual prediction deviation (RPD) was 2.9, Sig. = 0.627. The study provides a new reference for quality evaluation of edible mushrooms and other food products in the market.

Predicting practical ship powering and speed loss in waves using added resistance test results

ABSTRACT This paper introduces a practical technique for accurately estimating ship propulsion and speed loss in the presence of waves. Utilising the previously established self-propulsion estimation framework, our approach enhances the method, which previously solely considered bare hull resistance, propeller performance, thrust deduction factor, and wake fraction, by incorporating added resistance test results. The full-scale DTC container ship is the focus of our investigation, providing a thorough examination of its performance in different sea states. The model's predictive abilities are enhanced by incorporating test data on added ship resistance, and it addresses a crucial aspect that is generally disregarded while estimating self-propulsion. This research is considered to be a contribution to the advancement of ship hydrodynamics, as it offers a practical and reliable tool for ship designers, operators, and researchers to accurately assess powering requirements and speed loss in harsh ocean conditions.

Predicting the volatility of Chinese stock indices based on realized recurrent conditional heteroskedasticity.

The realized recurrent conditional heteroscedasticity (RealRECH) model improves volatility prediction by integrating long short-term memory (LSTM), a recurrent neural network unit, into the realized generalized autoregressive conditional heteroskedasticity (RealGARCH) model. However, at present, there is no literature on the ability of the RealRECH model to fit and predict volatility in the Chinese market. In this paper, a study is conducted to test the in-sample explainability and out-of-sample prediction ability of the RealRECH model for the SSE50, CSI300, CSI500 and CSI1000 indices in the Chinese market and to determine whether it performs better than the RealGARCH model. The results of the in-sample analysis show that the RealRECH model not only provides better in-sample interpretability for all four indices but also captures the complex dynamics of time series volatility that the RealGARCH model cannot capture, such as long-term dependence and nonlinearity. The results of out-of-sample volatility prediction show that the RealRECH model better predicts the volatility of the CSI500 and CSI1000 indices but yields worse predictions for the SSE50 and CSI300 indices. Thus, the RealRECH model can be used for CSI500 and CSI1000 prediction.

Performance of Machine Learning, Artificial Neural Network (ANN), and stacked ensemble models in predicting Water Quality Index (WQI) from surface water quality parameters, climatic and land use data

Assessing water quality is essential for managing freshwater resources, safeguarding ecosystems, and guaranteeing public health. Traditional water quality assessment methods suffer from seasonal sampling, multi-parameter requirements, and labor-intensive sampling processes, which are major constraints for the frequent monitoring of vast river basins. To overcome this issue, the study modeled the remote sensing-based climatic and land use parameters with Principal Component Analysis (PCA) to leverage Artificial Neural Networks (ANN) and machine learning (ML) algorithms to predict the Water Quality Index (WQI). The Weighted Arithmetic Water Quality Index (WAWQI) method was used to calculate the WQI of the Godavari River Basin for the available 19 stream water quality parameters (SWQPs). Further, PCA was applied to reduce the dimensionality of the parameters from 19 to 6. These results led to the development of two modeling methods to predict the WQI. In the first method, the correlation-based model was developed to predict WQI by evaluating six SWQPs. The second method, the causal-effect model, uses land use and meteorological factors to determine WQI using causality. Using advanced AutoML techniques, the initial pool of 40 ML models was meticulously evaluated and refined, culminating in the selection of the top three exemplary models such as Extreme Gradient Boosting (XGB), Extra Trees (ET), and Random Forest (RF). In both methods, XGB models show better prediction, with the coefficient of determination (R2) value of 0.95 during training and 0.83 during testing in method one. Whereas in the second method, R2 of 0.93 in training and 0.80 in testing are obtained. Further, XGB, ET, and ANN outputs were stacked with each model to enhance these results in both methods. Among these three stacked models, the stacked ANN_ML model performed better compared to stacked XGB_ML and stacked ET_ML. In the first method, the stacked ANN_ML model predicts R2 values of 0.95 and 0.91 for training and testing. In the second method, 0.95 and 0.90 for training and testing are obtained using stacked ANN_ML model. These findings emphasize the stacked model prediction ability to capture nonlinear relationships in the parameters and the novel approach of land use and climate parameters based WQI prediction, which replace the laborious, time-consuming SWQP measurements.

Noninvasive assessment of single kidney glomerular filtration rate using multiple diffusion weighted imaging models.

This study aimed to assess single kidney glomerular filtration rate (GFR) using various diffusion weighted imaging (DWI) models. We reviewed adult patients with kidney diseases who underwent magnetic resonance imaging (MRI) examination from February 2021 to December 2023. DWI with 13 b-values was performed using 3.0-T scanners. Diffusion parameters were calculated with multi-slice ROIs positioned in renal parenchyma using four DWI models, including monoexponential model (MEM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and intravoxel incoherent motion (IVIM). The split GFRs were measured by 99mTc-DTPA scintigraphy using Gates' method. Four different regression algorithms including the linear regression, regression tree, Gaussian regression and support vector machine (SVM) regression were employed to predict the GFR value based on different diffusion parameters. The leave-one-out cross validation was used to evaluate prediction ability of different models, and the performance of each model was quantified using the root mean square error (RMSE) and correlation coefficient. Fifteen (male/female, 10/5; age, 41.60±10.83 years) patients were included in this study. Among the four DWI models, the IVIM parameters with SVM regression model achieved the best performance with 0.184 RMSE and 0.789 correlation coefficient ( ). The parameters combining the four DWI models with SVM regression algorithm achieved the best performance in this study, with 0.171 RMSE and 0.815 correlation coefficient ( ). The DWI characteristics are able to serve as imaging biomarkers for assessing the function of single kidney. The integration of DWI into clinical practice could contribute to the advancement of non-invasive diagnostic methodologies.

A novel variable selection algorithm based on neural network for near-infrared spectral modeling

BackgroundPartial least squares (PLS) is a widely used technique for modeling spectral data. Researchers have developed numerous PLS-based variable selection algorithms to enhance model predictive ability and interpretability. In recent years, as neural network technology has advanced, these algorithms have been increasingly applied to spectral data modeling. However, current research on neural network modeling tends to prioritize network structure over variable selection. ResultsOur study introduces a neural network-based variable selection algorithm called VSNN (Variable Selection based on Neural Network). By iteratively eliminating unimportant variables using an exponentially decreasing function (EDF), the algorithm achieves the selection of variables in spectral data. VSNN can easily integrate different types of neural networks. In this study, we analyzed the impact of neural network types, activation functions, and variable importance vectors on algorithm performance. We tested the algorithm on four datasets: corn moisture, corn oil, tablets, and meat. The results indicate that VSNN significantly enhances the predictive ability of the model compared to partial least squares (PLS), neural networks (NN), and Joint Mutual Information Maximisation (JMIM). Specifically, non-linear activation functions markedly improve performance on non-linear meat datasets. Compared to PLS, the Root Mean Square Error of Prediction (RMSEP) values for the four datasets—corn moisture, corn oil, tablets, and meat—decreased from 0.0409, 0.0728, 3.97, and 3.2 to 0.002, 0.0236, 3.12, and 0.36, respectively, after applying the VSNN variable selection algorithm. SignificanceVSNN can serve as a versatile framework to enhance variable selection, modeling, and predictive performance by adapting neural network types and variable importance evaluation indicators. As machine learning technology advances, the strength of VSNN is poised to increase. This study highlights the potential of VSNN as an effective algorithmic framework for variable selection in spectroscopy applications.

Oxidative stress gene signature construction to identify subtypes and prognosis of patients with lung adenocarcinoma

BackgroundAlthough oxidative stress and malignancies are intimately connected, it is unknown how lung adenocarcinoma (LUAD) is affected by oxidative stress response-related genes (OSRGs).Our goal in this work was to create a genetic signature based on OSRGs that might both predict prognosis and hint to potential treatment options for LUAD. MethodsClinicopathological and transcriptome information on LUAD patients was obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A model for predicting risk was created using LASSO regression. The TCGA, GSE72094, and GSE41271 cohorts all demonstrated the risk model's prediction ability. Immune cell infiltration was measured using the CIBERSORT method, and the TIDE platform was implemented to evaluate the therapeutic efficacy of immune checkpoint inhibition (ICI). Chemotherapy sensitivity was predicted using drug activity data by the Genomics of Drug Sensitivity. An investigation into gene expression was conducted using qRT-PCR. CCK-8 and transwell assays were employed to look into how DKK1 affected the migration and proliferation of LUAD cells. ResultsA gene signature consisting of ANLN, FAM83A, DKK1, LOXL2, RHOV, IGFBP1, CCR2, GNG7, and C11orf16 was efficiently determined and used to calculate a patient-specific risk score, this functioned as a stand-alone biomarker for prediction. Correlations were found between risk scores and immune cell infiltration frequency, ICI therapy response rate, estimated chemotherapeutic drug susceptibility and autophagy-related genes.Furthermore, DKK1 knockdown reduced the ability of LUAD cells to multiply and migrate. ConclusionOur thorough transcriptome study of OSRGs generated a biological framework effective in forecasting outcome and responsiveness to therapy in LUAD patients.

Application of deep learning in summer climate prediction over northwestern China based on CWRF model

This study analyzes the performance of the Climate–Weather Research and Forecasting (CWRF) model in predicting the summer temperature and precipitation in northwestern China (NWC) for the 1991–2021 period. It also examines the improvements in prediction resulting from the implementation of convolutional neural network (CNN) and long short-term memory (LSTM) downscaling methods. The results indicate that the CWRF model demonstrates reasonable ability in capturing the characteristics of climatological temperature and precipitation in NWC. Both the climatological temperature and precipitation predictions consistently demonstrate a systematic underestimation, revealing evident biases in regions characterized by complex terrain. In terms of interannual variation, the temperature prediction outperforms the precipitation prediction, whereas there is no significant difference in the temperature predictions for lead Months 1–3. However, uncertainties increase as the lead time is extended in precipitation prediction. Therefore, the combination of dynamical and statistical downscaling is employed to the summer temperature and precipitation prediction over NWC. It is shown both the CNN and LSTM downscaling methods can improve the prediction ability of the CWRF model for summer climatological temperature and precipitation. The LSTM method significantly reduces the root mean square error (RMSE) of precipitation and temperature predictions, indicating an improvement in predicting the spatial structure. At the interannual scale, the CNN method is less dependent on the lead time of prediction than the LSTM method is, and the interannual correlation coefficient of precipitation and temperature is greater than 0.1 compared with that of the raw CWRF model. These results provide valuable insights into understanding the prediction capabilities of the CWRF model in NWC and highlight the necessity of applying downscaling methods to the CWRF model to increase its prediction ability.

Construction and validation of a prognostic model for esophageal cancer based on prognostic-related RNA-binding protein.

Construction of a prognostic model for esophageal cancer (ESCA) based on prognostic RNA-binding proteins (RBPs) and preliminary evaluation of RBP function. RNA-seq data of ESCA was downloaded from The Cancer Genome Atlas database and mRNA was extracted to screen differentially expressed genes using R. After screening RBPs in differentially expressed genes, R packages clusterProfiler and pathview were used to analyze the RBPs for Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway. Based on the prognosis-related RBPs, COX regression was used to establish the prognostic risk model of ESCA. Risk model predictive ability was assessed using calibration analysis, receiver operating characteristic curves, Kaplan-Meier curves, decision curve analysis, and Harrell consistency index (C-index). A nomogram was established by combining the risk model with clinicopathological features. A total of 105 RBPs were screened from ESCA. A prognostic risk model consisting of 6 prognostic RBPs (ARHGEF28, BOLL, CIRBP, DKC1, SNRPB, and TRIT1) was constructed by COX regression analysis. The prognosis was worse in the high-risk group, and the receiver operating characteristic curve showed (area under the curve = 0.90) that the model better predicted patients' 5-year survival. In addition, 6 prognostic RBPs had good diagnostic power for ESCA. In addition, a total of 39 mRNAs were identified as predicted target molecules for DKC1. ARHGEF28, BOLL, CIRBP, DKC1, SNRPB, and TRIT1, as RBPs, are associated with the prognosis of ESCA, which may provide new ideas for targeted therapy of ESCA.

Spectral Ultrasound Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastasis in Breast Cancer.

To explore the clinical value of the nomogram based on spectral Doppler ultrasound combined with clinical pathological parameter in predicting axillary lymph node metastasis in breast cancer. We prospectively gathered clinicopathologic and ultrasonic data from 240 patients confirmed breast cancer. The risk factors of axillary lymph node metastasis were analyzed by univariate and multivariate logistic regression, and the prediction model was established. The model calibration, predictive ability, and diagnostic efficiency in the training set and the testing set were analyzed by receiver operating characteristic curve and calibration curve analysis, respectively. Univariate analysis showed that lymph node metastasis was related with tumor size, Ki-67, axillary ultrasound, ultrasound spectral quantitative parameter, internal echo, and calcification (P < .05). Multivariate logistic regression analysis showed that the Ki-67, axillary ultrasound, quantitative parameter (the mean of the mid-band fit in tumor and posterior tumor) were independent risk factors of axillary lymph node metastasis (P < .05). The models developed using Ki-67, axillary ultrasound, and quantitative parameters for predicting axillary lymph node metastasis demonstrated an area under the receiver operating characteristic curve of 0.83. Additionally, the prediction model exhibited outstanding predictability for axillary lymph node metastasis, as evidenced by a Harrell C-index of 0.83 (95% confidence interval 0.73-0.93). Axillary ultrasound combined with Ki-67 and spectral ultrasound parameters has the potential to predict axillary lymph node metastasis in breast cancer, which is superior to axillary ultrasound alone.

Assessing the Capabilities of UV-NIR Spectroscopy for Predicting Macronutrients in Hydroponic Solutions with Single-Task and Multi-Task Learning

Macronutrients, including nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S), are the most basic nutrient elements in the solution for the hydroponic system. However, the current management of hydroponic nutrient solutions usually depends on EC and pH sensors due to the lack of accurate specific macronutrient sensing equipment, which easily leads to nutritional imbalance for the cultivated plant. In this study, the UV-NIR absorption spectroscopy (200–1100 nm) was used to predict six macronutrients in hydroponic solutions; two kinds of single-task learning algorithms, including partial least squares (PLS) and least absolute shrinkage and selection operator (LASSO), and two kinds of multi-task learning algorithms, including dirty multi-task learning (DMTL) and robust multi-task learning (RMTL), were investigated to develop prediction models and assess capabilities of UV-NIR. The results showed that N and Ca could be quantitatively predicted by UV-NIR with the ratio of performance to deviation (RPD) more than 2, K could be qualitatively predicted (1.4 &lt; RPD &lt; 2), and P, Mg, and S could not be successfully predicted (RPD &lt; 1.4); the RMTL algorithm outperformed others for predicting K and Ca benefit from the underlying task relationships with N; and predicting P, Mg, and S were identified as irrelevant (outlier) tasks. Our study provides a potential approach for predicting several macronutrients in hydroponic solutions with UV-NIR, especially using RMTL to improve model prediction ability.

Design optimization method of pipeline parameter based on improved artificial neural network

The rationality of pipeline design is directly related to its energy efficiency, reliability, and safety. Pipeline vibration may lead to negative effects such as mechanical loss and fatigue damage. Therefore, this study utilizes pipeline optimization design to mitigate these effects. Recently, neural networks have been widely used in structure design optimization. In the study, a backpropagation neural network (BP) combined with a variant slime mould algorithm (SMA) is utilized to solve the pipeline structure design optimization problem. Pipeline transport plays a crucial role in the efficient movement of various commodities, including but not limited to gas, oil, water, and other liquid substances. The interaction between liquid and pipeline can cause pipeline vibration and even damage. Therefore, based on the simulation model considering FSI (fluid-structure interaction), machine learning methods such as BP can predict vibration characteristics of fluid-conveying pipelines. However, existing research has shown that BP has insufficient parsing ability in structure mechanics problems, especially in solving the overall characteristics of complex structures (such as maximum structural strain). This study proposes an Arithmetic-based slime mould algorithm (ACSMA) with an adaptive decision strategy and a chaotic mapping strategy. A hybrid algorithm named ACSMA-BP is presented to promote the model's prediction ability. At last, to verify the effectiveness of the proposed pipeline structure design optimization approach, the ACSMA-BP model is utilized to complete a structure design optimization case for a simulated pipeline. The numerical results indicate that compared with AOA, CWOA, ESSAWOA, NGS_WOA, and RSA, the ACSMA has the best optimization ability.

Improving complex agronomic and domestication traits in the perennial grain crop intermediate wheatgrass with genetic mapping and genomic prediction.

The perennial grass Thinopyrum intermedium (intermediate wheatgrass [IWG]) is being domesticated as a food crop. With a deep root system and high biomass, IWG can help reduce soil and water erosion and limit nutrient runoff. As a novel grain crop undergoing domestication, IWG lags in yield, seed size, and other agronomic traits compared to annual grains. Better characterization of trait variation and identification of genetic markers associated with loci controlling the traits could help in further improving this crop. The University of Minnesota's Cycle 5 IWG breeding population of 595 spaced plants was evaluated at two locations in 2021 and 2022 for agronomic traits plant height, grain yield, and spike weight, and domestication traits shatter resistance, free grain threshing, and seed size. Pairwise trait correlations were weak to moderate with the highest correlation observed between seed size and height (0.41). Broad-sense trait heritabilities were high (0.68-0.77) except for spike weight (0.49) and yield (0.44). Association mapping using 24,284 genome-wide single nucleotide polymorphism markers identified 30 main quantitative trait loci (QTLs) across all environments and 32 QTL-by-environment interactions (QTE) at each environment. The genomic prediction model significantly improved predictions when parents were used in the training set and significant QTLs and QTEs used as covariates. Seed size was the best predicted trait with model predictive ability (r) of 0.72; yield was predicted moderately well (r=0.45). We expect this discovery of significant genomic loci and mostly high trait predictions from genomic prediction models to help improve future IWG breeding populations.

Development of laminar burning velocity prediction model and correlation of iso-octane air mixtures using artificial neural network

Surrogate fuels provide an economical alternative for forecasting the combustion properties of transportation fuels like diesel, gasoline, and kerosene. Iso-octane (2,2,4-trimethylpentane), a primary reference fuel for gasoline, is extensively used as a surrogate component. This study utilizes a Feed-Forward Artificial Neural Network (FFANN) with back-propagation (BP) to predict the laminar burning velocity (LBV) of iso-octane/air mixtures. The ANN model was developed using a dataset of 6339 data points, including 3788 experimental data points from literature since 2004 and 2551 simulation data points from reduced kinetic reaction mechanism (RKM) CHEMKIN simulations. The grid search cross-validation (CV) method was employed to optimize the ANN hyperparameters. Implemented in Python using Keras, the ANN model addresses research gaps by forecasting LBV for hydrocarbons beyond n-heptane and modelling LBV under conditions reflective of actual engine operations. Unlike prior studies, we optimized various ML models, their complexity, size, and hyperparameters to achieve high prediction accuracy. We also integrated a hybrid model combining Particle Swarm Optimization (PSO) with Genetic Algorithms (GA) for hyperparameter optimization, ensuring efficient configuration of the ANN. The constructed ANN model was compared to other ML models, including generalized linear regression (GLR), support vector machine (SVM), random forest (RF), and XGBoost regression. For the testing set (20 % of the total dataset), the ANN model outperformed all other ML models and empirical correlations, achieving a coefficient of determination (R2) of 0.9903, root mean square error (RMSE) of 1.911, and mean absolute error (MAE) of 1.206. Optimizing the ANN model with PSO further enhanced prediction accuracy, achieving a correlation coefficient of 0.9973 and a reduced mean absolute error of 0.753. To evaluate the ANN model's predictive ability, a reduced mechanism from the Lawrence Livermore National Laboratory (LLNL) was used. Compared to the LLNL RKM, the ANN predicted values showed lower percentage deviation from experimental data. Additionally, computation cost comparisons revealed that while RKM simulations in CHEMKIN took 244.2 s per case, the PSO-optimized ANN model required only 900 s for 150 cases. A 16-term correlation for laminar burning velocity (LBV) was derived from ANN predictions, serving as a practical tool to predict LBV based on pressure, temperature, and equivalence ratio. Unlike existing literature correlations, this newly developed correlation is valid across a wide range of operating conditions, offering a fundamental database for real-time combustion applications.

Genomic prediction for rust resistance in pea.

Genomic selection (GS) has become an indispensable tool in modern plant breeding, particularly for complex traits. This study aimed to assess the efficacy of GS in predicting rust (Uromyces pisi) resistance in pea (Pisum sativum), using a panel of 320 pea accessions and a set of 26,045 Silico-Diversity Arrays Technology (Silico-DArT) markers. We compared the prediction abilities of different GS models and explored the impact of incorporating marker × environment (M×E) interaction as a covariate in the GBLUP (genomic best linear unbiased prediction) model. The analysis included phenotyping data from both field and controlled conditions. We assessed the predictive accuracies of different cross-validation strategies and compared the efficiency of using single traits versus a multi-trait index, based on factor analysis and ideotype-design (FAI-BLUP), which combines traits from controlled conditions. The GBLUP model, particularly when modified to include M×E interactions, consistently outperformed other models, demonstrating its suitability for traits affected by complex genotype-environment interactions (GEI). The best predictive ability (0.635) was achieved using the FAI-BLUP approach within the Bayesian Lasso (BL) model. The inclusion of M×E interactions significantly enhanced prediction accuracy across diverse environments in GBLUP models, although it did not markedly improve predictions for non-phenotyped lines. These findings underscore the variability of predictive abilities due to GEI and the effectiveness of multi-trait approaches in addressing complex traits. Overall, our study illustrates the potential of GS, especially when employing a multi-trait index like FAI-BLUP and accounting for M×E interactions, in pea breeding programs focused on rust resistance.

Can nutritional scores improve the WHO calibrated non-laboratory risk prediction model for cardiovascular disease? Golestan Cohort Study.

Evaluation of the added value of Dietary Approaches to Stop Hypertension (DASH) and Mediterranean diet scores on the prediction model of the World Health Organization (WHO) to predict 10-year cardiovascular disease (CVD) mortality using the Golestan Cohort Study data. A total of 44,648 participants (25,268 women and 18,531 men) were included in the final analysis. To assess the external validity of the non-laboratory risk model of WHO, the Area Under the Curve (AUC) and calibration plot methods were used. The multivariate Cox proportional hazards regression analysis was used to evaluate the association of 10-year CVD mortality risk with DASH and Mediterranean scores and their components. The added value of each significant variables was evaluated by the concordance C-statistic and integrated discrimination improvement (IDI). Statistical significance was defined as p-value < 0.05. DASH and Mediterranean diet scores were not significant predictors of 10-year CVD mortality in both genders (p > 0.05). However, sodium and total vegetable in both genders and added sugar in women were significant predictors for 10-year stroke mortality (p < 0.05). Sodium intake in women and monounsaturated fatty acid (MUFA) to saturated fatty acid (SFA) ratio in men had significant associations with 10-year mortality of myocardial infarction/coronary heart disease (MI/CHD). Calculation of IDI showed that none of the evaluated nutritional indices/variables could significantly improve the WHO model performance and predictive ability. Inclusion of DASH and Mediterranean diet scores and their components did not improve WHO risk prediction model performance and predictive ability to predict 10-year CVD mortality. The online version contains supplementary material available at 10.1007/s40200-024-01463-x.

Consideration of geomechanic effects during hydrodynamic model of weakly-cemented reservoir setting

The article offers an approach to tuning of heavy oil deposition model from weakly-cemented reservoir with geomechanic effects. This effects appear as changing permeability at the downhole zone near the injection well. Researchers think that the nature of low-density zones occurrence related to increasing of the resistance factor which is influenced with increasing of filtration velocity. High filtration velocity is caused by polymer flooding increasing. This work was made with basis on hydrodynamic model of the pilot area where are the experiments of displacement of heavy oil during polymer flooding. There are not only well history-matching but assessment of hydrodynamic model prediction ability. Keeping of geomechanic effects allows to achieve the adequate reproduction dynamic of fact bottom-hole pressure during polymer solution injection. Application of the given approach leads to uncertainty reduction and improved prediction reliability.