Linear Gaussian Process Research Articles

Machine learning-based optimization of photogrammetric JRC accuracy

To improve the accuracy of photogrammetric joint roughness coefficient (JRC) estimation, this study proposes two optimization models based on ground sample distance (GSD), point density, and the root mean square error (RMSE) of checkpoints. First, an algorithm that automatically generates spatial positions for equipment based on the convergence strategy was developed, using principles of Structure from Motion and Multi-View Stereo (SfM-MVS) and the shooting parameter selection algorithm (SPSA). Second, a portable positioning plate containing ground control points and checkpoints was designed based on optical principles, and a moving camera capture strategy guided by SPSA was proposed. Combining SPSA, portable positioning plate, and moving camera capture strategy, a photogrammetric experiment for small-scale rock samples in the field was conducted, collecting 48 datasets with different shooting parameters. Subsequently, a dataset incorporating GSD, point density, RMSE, and three JRC estimation metrics was established, revealing their correlations and sensitivities. Using seven machine learning algorithms, optimization models for photogrammetric JRC accuracy were developed, with Linear Multidimensional Regression and Gaussian Process Regression models improving JRC accuracy by an average of 85.73%. Finally, the applicability and limitations of the newly proposed method were further discussed.

Moving Target Detection in Clutter Environment Based on Track Posture Hypothesis Testing

ABSTRACTMoving target detection in a heavy clutter area is a challenging problem in the field of radar automatic target detection. Within the framework of track posture hypothesis testing, the radar‐blips‐oriented target detection algorithms named ErgSad and RanSaD are proposed for single target and formation targets detection, respectively in this paper. With the assumption that target motion is a linear Gaussian process in a short time, the algorithms use dual blips in different scans to generate the track hypothesis and test the hypothesis with residual blips in other scans to determine the existence of moving targets. To reduce time consumption and minimize the error of model estimation, the sampling rules and the supporting domain related to timestamps of track hypothesis models were designed. Simulation data experiments show that the proposed algorithms have more superior performance to detect targets than the state‐of‐the‐art algorithms in the serious clutter environment.

Gradient-enhanced deep Gaussian processes for multifidelity modeling

Multifidelity models integrate data from multiple sources to produce a single approximator for the underlying process. Dense low-fidelity samples are used to reduce interpolation error, while sparse high-fidelity samples are used to compensate for bias or noise in the low-fidelity samples. Deep Gaussian processes (GPs) are attractive for multifidelity modeling as they are non-parametric, robust to overfitting, perform well for small datasets, and, critically, can capture nonlinear and input-dependent relationships between data of different fidelities. Many datasets naturally contain gradient data, most commonly when they are generated by computational models that have adjoint solutions or are built in automatic differentiation frameworks.Principally, this work extends deep GPs to incorporate gradient data. We demonstrate this method on an analytical test problem and two realistic aerospace problems: one focusing on a hypersonic waverider with an inviscid gas dynamics truth model and another focusing on the canonical ONERA M6 wing with a viscous Reynolds-averaged Navier-Stokes truth model.In both examples, the gradient-enhanced deep GP outperforms a gradient-enhanced linear GP model and their non-gradient-enhanced counterparts.

TAMie Force Field for Alkanethiols: Multifidelity Gaussian Processes for Dealing with Scarce Experimental Data.

This study extends the transferable anisotropic Mie potential (TAMie) to alkanethiols. The force field parameters are optimized by using an analytic equation of state as a surrogate model. Given the lack of experimental density data at elevated temperatures where Monte Carlo simulations have high statistical precision, the equation of state is supplemented by a linear multifidelity Gaussian process approach to bridge the temperature gap. Force field parameters are adjusted by minimizing squared deviations of calculated vapor pressures and liquid densities from experimental data of 1-propanethiol, 1-butanethiol and 1-pentanethiol leading to small mean absolute relative deviations in liquid densities and vapor pressures. The force field is transferable to higher 1-thiols, as shown for 1-hexanethiol and 1-octanethiol. Individual parameter sets are provided for methanethiol and ethanethiol. The shear viscosity of pure substances is predicted in fair agreement with experimental data, considering that it is not included in the parametrization. Further, the phase behavior of binary mixtures of alkanethiols with alkanes is studied, and predictions of the TAMie model are found in excellent agreement with experimental data.

Monte Carlo inference for semiparametric Bayesian regression

Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations or nonparametric representations that are computationally inefficient and cumbersome for implementation and theoretical analysis, which limits their usability in practice. This paper introduces a simple, general, and efficient strategy for joint posterior inference of an unknown transformation and all regression model parameters. The proposed approach directly targets the posterior distribution of the transformation by linking it with the marginal distributions of the independent and dependent variables, and then deploys a Bayesian nonparametric model via the Bayesian bootstrap. Crucially, this approach delivers (1) joint posterior consistency under general conditions, including multiple model misspecifications, and (2) efficient Monte Carlo (not Markov chain Monte Carlo) inference for the transformation and all parameters for important special cases. These tools apply across a variety of data domains, including real-valued, positive, and compactly-supported data. Simulation studies and an empirical application demonstrate the effectiveness and efficiency of this strategy for semiparametric Bayesian analysis with linear models, quantile regression, and Gaussian processes. The R package SeBR is available on CRAN.

Erosive wear and particle attrition in multi-stage solar particle receivers and screw conveyors: A CFD-DEM approach with machine learning and artificial neural networks

A coupled finite volume and discrete element method (CFD-DEM) numerical model is developed to unravel the fundamental mechanisms of granular transport, surface erosive wear and particle attrition in multistage solar particle receivers (MSPR) and screw conveyors (SC). Additionally, various regression-based supervised machine learning (ML) and artificial neural networks (ANN) are trained and optimized with the goal of quickly quantifying erosion and attrition thereby overcoming (a) the inherent challenges of high computational expense of CFD-DEM simulations and (b) exorbitant time required to post-process and extract DEM results. This study is the first of its kind to harness a synergistic numerical framework (CFD-DEM, ML, ANN) to unravel the fundamental mechanisms of erosion and attrition in MSPRs, non-vibrationally induced SCs, and vibrationally-induced SCs. It is found that the packing distribution, particle curtain width, and severity of particle scattering in a MSPR exhibits a subtle variation with particle diameter. In the context of SCs, the magnitudes of erosion and attrition are contingent on the particle diameter and flow operating conditions such as screw blade velocity, vibrational frequency and vibrational amplitude. Interestingly, a vibrationally-induced SC (VI-SC) exhibits superior performance in terms of low erosion and attrition unlike non-vibrationally induced SC (NVI-SC). It is found that MSPRs with 750–1000 µm particles and SCs with 750–1000 µm coupled with vibrational frequencies and vibrational amplitudes of 5 Hz and 0.007 m, respectively, are the preferred configurations due to minimum particle scattering, low erosive wear and low particle attrition. The trained and optimized ML models such as XGBoost, CatBoost, and Multilayer Perceptron (MLP) exhibit superior performance in predicting erosive wear and particle attrition on unseen DEM data, whereas the Multiple Linear Regression (MLR) and Gaussian Process Regression (GPR) ML exhibit poor performance prediction. For the first time, new information on fundamental granular flow dynamics, erosive wear and particle attrition in MSPRs and SCs is discerned. The methodology allows scientists to quickly examine material degradation for a myriad of industrial applications such as concentrated solar thermal (CST), among others.

Microwave-based and convective drying of cabbage (Brassica oleracea L. var capitata L.): Computational intelligence modeling, thermophysical properties, quality and mid-infrared spectrometry

This study assessed and compared the impact of hot air oven drying (HAD) at 50, 60 and 70 °C and microwave drying (MWD) at 195, 307 and 521 W on the kinetics of thermophysical properties and quality of dried cabbage. The thermophysical properties were computed using established model equations. The proximate composition, bioactive compounds, antioxidant capacity (AOC), color and functional groups were analyzed using Association of official analytical chemists (AOAC) test protocol, High performance liquid chromatography (HPLC), Ultraviolet-visible (UV–Vis) spectrometry and Attenuated total reflectance coupled to Fourier transformed infrared spectroscopy (ATR-FTIR), respectively. The moisture ratio was modeled using probabilistic computational intelligence modeling approaches. The results revealed that only density, thermal diffusivity and thermal effusivity appeared to be kinetically controlled by moisture content. Stepwise linear regression (SLR) model and Gaussian process regression (GPR) model were more accurate in simulating the moisture ratio. Increasing HAD temperature preserved all of the proximate compositions except carbohydrates. Similarly, increasing MWD power preserved all the proximate compositions except crude fiber. Increasing HAD temperature from 50 to 60 °C and 60–70 °C decreased the total flavonoid content (TFC) and total phenolic content (TPC) by 21.31 % and 13.60 %, respectively but increased the AOC from 27.2 to 39.6 %. Increasing MWD power levels from 195 to 307 W and 307–521 W decreased TFC by 13.2 % and 0.7 %, respectively but increased the TPC by 9.72 % and 13.14 %, thus AOC increased from 35.2 to 45.7 %. MWD presented higher AOC relative to HAD. Drying at 50 °C and 307 W gave the highest whitening index in HAD and MWD. ATR-FTIR analysis revealed the formation of new compounds by thermal effect. SLR and GPR were more accurate in modeling the drying kinetics. HAD and MWD affected the quality parameters of cabbage differently, thus the choice of the dryer type will depend on the parameters of interest.

Propensity Weighted federated learning for treatment effect estimation in distributed imbalanced environments

Estimating treatment effects from observational data in medicine using causal inference is a very relevant task due to the abundance of observational data and the ethical and cost implications of conducting randomized experiments or experimental interventions. However, how could we estimate the effect of a treatment in a hospital that has very restricted access to treatment? In this paper, we want to address the problem of distributed causal inference, where hospitals not only have different distributions of patients, but also different treatment assignment criteria. Furthermore, it is necessary to take into account that due to privacy restrictions, personal patient data cannot be shared between hospitals. To address this problem, we propose an adaptation of the federated learning algorithm FederatedAveraging to one of the most advanced models for the prediction of treatment effects based on neural networks, TEDVAE. Our algorithm adaptation takes into account the shift in the treatment distribution between hospitals and is therefore called Propensity WeightedFederatedAveraging (PW FedAvg). As the distributions of the assignment of treatments become more unbalanced between the nodes, the estimation of causal effects becomes more challenging. The experiments show that PW FedAvg manages to reduce errors in the estimation of individual causal effects when imbalances are large, compared to VanillaFedAvg and other federated learning-based causal inference algorithms based on the application of federated learning to linear parametric models, Gaussian Processes and Random Fourier Features.

Machine learning for predicting compressive strength of sustainable cement paste incorporating copper mine tailings as supplementary cementitious materials

Copper mining produces significant amounts of copper mine tailings (CMT), necessitating appropriate waste handling and disposal practices. By substituting a portion of cement with CMT as supplementary cementitious materials (SCMs), we aim to address two environmental issues simultaneously: reducing copper mine waste in landfills and decreasing embodied carbon by using less cement. The exploration of CMT recycling as a cement replacement requires evaluation of its impact on material performance, such as compressive strength. In this paper, we address this by machine learning that features data fusion of large public data with our own small data on compressive strength of CMT-incorporated cement. We developed and critically evaluated three machine learning models: a simple linear model, Gaussian process, and random forest that predict the compressive strength of CMT-incorporated cement pastes with different mix designs (e.g., varying amounts of CMT and water-binder ratios) and curing ages. Hyperparameters in the random forest model were tuned using Bayesian optimization. Following a comprehensive evaluation of the models, we find that the random forest model can accurately estimate the compressive strength of cement paste across the mix designs. Furthermore, results from SHapley Additive exPlanation (SHAP), Individual Conditional Expectation (ICE), and Partial Dependence Plots (PDP) revealed that cement, ground-granulated blast furnace slag, superplasticizers, and curing ages positively influence compressive strength. This study contributes to acceleration of sustainable material technology to obtain the best mix design and desired compressive strength.

Kalman filter with impulse noised outliers: a robust sequential algorithm to filter data with a large number of outliers.

Impulse noised outliers are data points that differ significantly from other observations. They are generally removed from the data set through local regression or the Kalman filter algorithm. However, these methods, or their generalizations, are not well suited when the number of outliers is of the same order as the number of low-noise data (often called nominal measurement). In this article, we propose a new model for impulsed noise outliers. It is based on a hierarchical model and a simple linear Gaussian process as with the Kalman Filter. We present a fast forward-backward algorithm to filter and smooth sequential data and which also detects these outliers. We compare the robustness and efficiency of this algorithm with classical methods. Finally, we apply this method on a real data set from a Walk Over Weighing system admitting around 60 % of outliers. For this application, we further develop an (explicit) EM algorithm to calibrate some algorithm parameters.

Prediction of jet impingement solar thermal air collector thermohydraulic performance using soft computing techniques

Solar thermal air collectors are economically efficient for the purpose of heating air in specific applications. This research examines the efficiency of a solar thermal air collector called a jet impingement solar thermal air collector (JISTAC) that is fitted with discrete multi-arc-shaped ribs (DMASRs) utilizing soft computing techniques. Linear Regression (LR), M5P, Gaussian Process (GP), and Random Forest (RF) are used to predict the Nusselt number (Nu), friction factor (f), and thermohydraulic performance (ηthp) of JISTAC. DMASRs absorber plates with different relative rib height (0.025–0.047), relative discrete width (0.32–1.72), arc angle (35°–65°), relative discrete distance (0.27–0.86), and relative rib pitch (0.58–3.1) were used. The tests yielded 245 data sets, with 173 assigned for training and 72 for soft computing testing. The GP surpasses other models in performance owing to its minimum error and maximum correlation coefficient value. The GP model has a MAE of 0.0347, a RMSE of 0.0564, a RAE of 10.88%, a RRSE of 12.77%, and a CC value of 0.9920. Furthermore, it has a model efficiency of 99%, making it the most prominent among the other models. The results indicate that the GP model is quite precise in predicting the ηthp of JISTAC.

Prediction of Three-Directional Ground Reaction Forces during Walking Using a Shoe Sole Sensor System and Machine Learning.

We developed a shoe sole sensor system with four high-capacity, compact triaxial force sensors using a nitrogen added chromium strain-sensitive thin film mounted on the sole of a shoe. Walking experiments were performed, including straight walking and turning (side-step and cross-step turning), in six healthy young male participants and two healthy young female participants wearing the sole sensor system. A regression model to predict three-directional ground reaction forces (GRFs) from force sensor outputs was created using multiple linear regression and Gaussian process regression (GPR). The predicted GRF values were compared with the GRF values measured with a force plate. In the model trained on data from the straight walking and turning trials, the percent root-mean-square error (%RMSE) for predicting the GRFs in the anteroposterior and vertical directions was less than 15%, except for the GRF in the mediolateral direction. The model trained separately for straight walking, side-step turning, and cross-step turning showed a %RMSE of less than 15% in all directions in the GPR model, which is considered accurate for practical use.

Surrogate Modelling of an Industrial Distillation Column Obtained from Statistical Techniques and Machine Learning

Purpose: To study a case of modeling an industrial distillation system, using the Aspen Plus simulator as the mathematical model and subsequently generating surrogate models using Machine Learning techniques in Matlab. Theoretical Framework: Metamodels are reduced models obtained from data generated by rigorous models, replacing them entirely or partially when the computational codes originating from them require excessively large computational effort to be used feasibly. Method/Design/Approach: The simulation of the process was performed in Aspen Plus. Subsequently, the most important variables were selected, and an experimental design was created using Latin Hypercube Sampling in Matlab, generating points to be used in sensitivity analysis in Aspen Plus. The next step was the construction of metamodels using the Statistics and Machine Learning toolbox in Matlab, employing linear regression and Gaussian process regression models. Finally, a statistical analysis was conducted. Results and Conclusion: The distillation system simulation converged, and the obtained metamodels had good regression indicators, especially the Gaussian process regression models, making them the most suitable for representing the rigorous Aspen Plus model. Research Implications: Understanding the multicomponent distillation process integrated with computational tools and data regression models, leading to a reduction in computational effort. Originality/Value: Development of a tool that enables the simulation and evaluation of a distillation process without the need to acquire software with rigorous equation databases.

Pharmaceutical nanonization by green supercritical processing: Investigation of Exemestane anti-estrogenic medicine solubility using machine learning

Oral administration is known as the most prevalently-employed approach of drug delivery because of possessing different benefits like simplicity of use, affordability and negligible sterility restrictions. Despite the noteworthy interests of scientists to manufacture oral therapeutic medicines, poor solubility/bioavailability of orally-administered drugs remains a big challenge in recent years. Therefore, various scientists are trying to develop novel techniques to enhance the solubility of orally-administered therapeutic agents. One of the promising ways, which has recently attracted the global attentions is the use of supercritical fluids (SCFs). This technique could overcome the unfavorable/detrimental influences of organic solvents on the environment due to its environmentally-benign characteristics, non-inflammability and low toxicity. In this research, the solubility of Exemestane (EXE) drug is modeled based on two parameters of temperature and pressure. In order to handle the modeling process, the AdaBoost method with three core models of linear regression (LR), K-Nearest Neighbor (KNN) and Gaussian Process (GPR) are considered, which are tuned by AEO method in terms of their hyper-parameters. Based on the evaluations carried out in this research, ADA GPR, ADA KNN, and ADA LR models showed 0.996, 0.991, and 0.925 values in terms of R-square score. Also, in terms of the error rate, the three models ADA GPR, ADA KNN, and ADA LR have an RMSE error equal to 1.3459, 1.8967, and 5.8875, respectively. Based on these facts and some other analyses, the Gaussian process model optimized and boosted with AdaBoost can be accepted as the most accurate model of this research.

Predicting the Cycle Life of Lithium-Ion Batteries Using Data-Driven Machine Learning Based on Discharge Voltage Curves

Battery degradation is a complex nonlinear problem, and it is crucial to accurately predict the cycle life of lithium-ion batteries to optimize the usage of battery systems. However, diverse chemistries, designs, and degradation mechanisms, as well as dynamic cycle conditions, have remained significant challenges. We created 53 features from discharge voltage curves, 18 of which were newly developed. The maximum relevance minimum redundancy (MRMR) algorithm was used for feature selection. Robust linear regression (RLR) and Gaussian process regression (GPR) algorithms were deployed on three different datasets to estimate battery cycle life. The RLR and GPR algorithms achieved high performance, with a root-mean-square error of 6.90% and 6.33% in the worst case, respectively. This work highlights the potential of combining feature engineering and machine learning modeling based only on discharge voltage curves to estimate battery degradation and could be applied to onboard applications that require efficient estimation of battery cycle life in real time.

MISPEL: A Multi-Crop Spectral Library for Statistical Crop Trait Retrieval and Agricultural Monitoring

Spatiotemporally accurate estimates of crop traits are essential for both scientific modeling and practical decision making in sustainable agricultural management. Besides efficient and concise methods to derive these traits, site- and crop-specific reference data are needed to develop and validate retrieval methods. To address this shortcoming, this study first includes the establishment of ’MISPEL’, a comprehensive spectral library (SpecLib) containing hyperspectral measurements and reference data for six key traits of ten widely grown crops. Secondly, crop-specific statistical leaf area index (LAI) models for winter wheat are developed based on a hyperspectral (MISPELFR) and a simulated Sentinel-2 (MISPELS2) SpecLib applying four nonparametric methods. Finally, an independent Sentinel-2 model evaluation at the DEMMIN test site in Germany is conducted, including a comparison with the commonly used SNAP-LAI product. To date, MISPEL comprises a set of 1411 spectra of ten crops and more than 6800 associated reference measurements. Cross-validations of winter wheat LAI models revealed that Elastic-net generalized linear model (GLMNET) and Gaussian process (GP) regressions outperformed partial least squares (PLS) and random forest (RF) regressions, showing RSQ values up to 0.86 and a minimal NRMSE of 0.21 using MISPELFR. GLMNET and GP models based on MISPELS2 further outperformed SNAP-based LAI estimates derived for the external validation site. Thus, it is concluded that the presented SpecLib ’MISPEL’ and applied methodology have a very high potential for deriving diverse crop traits of multiple crops in view of most recent and future multi-, super-, and hyperspectral satellite missions.

Unipolar and Bipolar Depression Detection and Classification Based on Actigraphic Registration of Motor Activity Using Machine Learning and Uniform Manifold Approximation and Projection Methods.

Modern technology frequently uses wearable sensors to monitor many aspects of human behavior. Since continuous records of heart rate and activity levels are typically gathered, the data generated by these devices have a lot of promise beyond counting the number of daily steps or calories expended. Due to the patient's inability to obtain the necessary information to understand their conditions and detect illness, such as depression, objectively, methods for evaluating various mental disorders, such as the Montgomery-Asberg depression rating scale (MADRS) and observations, currently require a significant amount of effort on the part of specialists. In this study, a novel dataset was provided, comprising sensor data gathered from depressed patients. The dataset included 32 healthy controls and 23 unipolar and bipolar depressive patients with motor activity recordings. Along with the sensor data collected over several days of continuous measurement for each patient, some demographic information was also offered. The result of the experiment showed that less than 70 of the 100 epochs of the model's training were completed. The Cohen Kappa score did not even pass 0.1 in the validation set, due to an imbalance in the class distribution, whereas in the second experiment, the majority of scores peaked in about 20 epochs, but because training continued during each epoch, it took much longer for the loss to decline before it fell below 0.1. In the second experiment, the model soon reached an accuracy of 0.991, which is as expected given the outcome of the UMAP dimensionality reduction. In the last experiment, UMAP and neural networks worked together to produce the best outcomes. They used a variety of machine learning classification algorithms, including the nearest neighbors, linear kernel SVM, Gaussian process, and random forest. This paper used the UMAP unsupervised machine learning dimensionality reduction without the neural network and showed a slightly lower score (QDA). By considering the ratings of the patient's depressive symptoms that were completed by medical specialists, it is possible to better understand the relationship between depression and motor activity.

Forward modeling and inverse estimation for nonlinear filtering

AbstractThe highly accurate state estimation of nonlinear systems is needed in many different application fields. In many existing nonlinear filters, the uncompensated nonlinear approximation error and widely used numerical sampling methods both seriously affect the estimation accuracy and numerical stability. In this paper, based on the variational Bayesian framework, a novel iterative nonlinear filter consisting of forward modeling and inverse estimation is proposed. In the forward modeling stage, a linear Gaussian regression process with distributed variational parameters (VPs) is designed to fit the whole nonlinear measurement likelihood. The distributed VPs consider both first and second moments information, so as to compensate for approximation error from the whole likelihood perspective, instead of only the nonlinear function approximation, which can further improve the accuracy of Bayesian posterior update. In the linear Gaussian regression process, the original nonlinear measurement function (NMF) is just viewed as a prior and no longer directly participates in the modeling of the measurement likelihood. Hence, in the inverse estimation stage, when maximizing the evidence lower bound to update the posteriors of the state and VPs, we can obtain closed‐form solutions without numerical sampling methods to approximate the integral of NMF required in many existing methods, which can guarantee numerical stability. Moreover, to avoid the random setting of hyperparameters and to block the propagation of the accumulated error, we propose two rules, estimation and lower bound consistencies, to conduct the hyperparameters' initialization at the beginning of iteration at each sampling time and also derive an optimization method to update hyperparameters during iteration. Finally, the performance of our proposed method is demonstrated in the nonlinear orbital estimation problems.

MP29-19 PRE-OPERATIVE PLASMA METABOLITES CAN PREDICT THE RISK OF BONE METASTASIS IN PROSTATE CANCER

You have accessJournal of UrologyCME1 Apr 2023MP29-19 PRE-OPERATIVE PLASMA METABOLITES CAN PREDICT THE RISK OF BONE METASTASIS IN PROSTATE CANCER Saurav Subedi, Federico La Manna, Katja Ovchinnikova, Eugenio Zoni, George N. Thalmann, and Marianna Kruithof-de Julio Saurav SubediSaurav Subedi More articles by this author , Federico La MannaFederico La Manna More articles by this author , Katja OvchinnikovaKatja Ovchinnikova More articles by this author , Eugenio ZoniEugenio Zoni More articles by this author , George N. ThalmannGeorge N. Thalmann More articles by this author , and Marianna Kruithof-de JulioMarianna Kruithof-de Julio More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003257.19AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Early identification of prostate cancer (PCa) patients that are most likely to develop a lethal disease is still a major hurdle. While for most patients, a radical treatment will result in the eradication of the disease, about 70% of patients with advanced PCa are diagnosed with bone metastasis, a lethal feature with limited treatment options. There is growing evidence suggesting that lipid metabolism cooperates with the genetic landscape of prostate cancer. Currently, several studies have shown that the metabolome of PCa could be used as non-invasive diagnostic and prognostic tool. In this study, we analyzed the plasma of 73 prostate cancer patients to identify metabolites with diagnostic or prognostic value. METHODS: The plasma was collected during fasting and shortly before the patient received radical prostatectomy. The patients were then followed up for up to 10 years, to monitor the development of bone metastases. Targeted metabolomics was run on the pre-operative plasma. 10 different machine learning models were tested on the available metabolomics data-set. All patients provided informed consent for participation in this study. RESULTS: By using machine learning algorithms, we identified a signature of 41 metabolites either positively or negatively associated with the development of bone metastatic PCa. Neural network and support vector machine yielded the best results for classification of metastasis based on the metabolites. Furthermore, linear support vector machine and Gaussian process generated the best regression models for determination of time-to-bone metastasis using absolute Spearman`s R correlation. Determination of both the classification of bone metastasis and time-to-metastasis were identified based on 41 metabolites in the Neural network and support vector machine algorithms. The top-four metabolites associated with bone metastatic progression were lysophosphatidylethanolamine(LPE 22:5/0:0)b, butyrylcarnitine (C4), pyruvic acid and dodecanoylcarnitine (C12), whereas monoacylglycerol (MAG 14:0/0:0), MAG (0:0/22:5), acetylornithine and MAG (0:0/14:0) were the bottom-four metabolites. CONCLUSIONS: In this study, we evaluated metabolites in the plasma of prostate cancer patients to determine if we could identify a predictive signature of metastatic progression. Our studies show that metabolic phenotypes have substantial clinical value. Given the context of precision medicine, we show the feasibility of a simple metabolomics test on the plasma of patients that could help stratify patient`s risk of lethal disease based on the presence/absence of signature metabolites. Further studies in a larger cohort will be required in order to test the signature metabolites found in our study to test the reliability for patient stratification. Source of Funding: Swiss Cancer Research Foundation (KFS-5510-02-2022-R) © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e389 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Saurav Subedi More articles by this author Federico La Manna More articles by this author Katja Ovchinnikova More articles by this author Eugenio Zoni More articles by this author George N. Thalmann More articles by this author Marianna Kruithof-de Julio More articles by this author Expand All Advertisement PDF downloadLoading ...

A machine learning approach to study randomness in enhanced tensile properties of KOH-treated jute fabric

The probabilistic characterization of tensile strength of potassium hydroxide (KOH) treated jute fiber is conducted in the light of machine learning methods. The concentration of KOH, soaking periodand drying temperature are considered as the input features. The experimental results after surface treatment with KOHare utilized to determine the machine learning models. The predictive capabilities of linear regression (LR) and Gaussian process regression (GPR) based modelsare compared and it has been found that the latter is more accurate than the former. Further, the data-driven sensitivity analysis and uncertainty quantification arecarried out with the help of GPRmodel. The overall probabilistic results reported in this articlewould provide the complete insight regarding tensile strength of potassium hydroxide (KOH) treated jute under the uncertain variations in the process parameters.Thestudy also provides the total design domain of treated jute under the practically relevant uncertainty associated with the discussed process parameters.