Global Quantification Research Articles

Design-space dimensionality reduction in global optimization of functional surfaces: recent developments and way forward

ABSTRACT In shape optimization of complex industrial products (such as, but not limited to, hull forms, rudder and appendages, propellers), there exists an inherent similarity between global optimization (GO) and uncertainty quantification (UQ): they rely on an extensive exploration of the design and operational spaces, respectively; often, they need local refinements to ensure accurate identification of optimal solutions or probability density regions (such as distribution tails), respectively; they both are dramatically affected by the curse of dimensionality as GO and UQ algorithms' complexity and especially computational cost rapidly increase with the problem dimension. Therefore, there exists a natural ground for transferring dimensionality reduction methods from UQ to GO. These enable the efficient exploration of large design spaces in shape optimization, which, in turn, enable global optimization (possibly in a multidisciplinary and stochastic setting). The paper reviews and discusses recent techniques for design-space dimensionality reduction in shape optimization, based on the Karhunen-Loève expansion (equivalent to proper orthogonal decomposition and, at the discrete level, principal component analysis). An example is shown and discussed for the hydrodynamic optimization of a ship hull.

Exploring global uncertainty quantification and sensitivity analysis methodologies: CO2 capture absorber model with MEA solvent as a test case

A set of global metamodeling uncertainty quantification (UQ) techniques belonging to non-intrusive and forward propagation categories; Polynomial Chaos Expansion (PCE), Kriging, Canonical Low Rank Approximation (LRA), and Polynomial Chaos Kriging (PC-Kriging) and global sensitivity analysis (SA) techniques, Sobol’ indices, Borgonovo, and Morris were compared to a benchmark methodology of direct Monte Carlo Simulation (MCS). The comparative analysis was demonstrated on a CO2 capture absorber model with MEA solvent. Our analysis concluded as follows; (1) although significant variation in the CO2 capture ratio in the prediction profile is shown, there is no apparent advantage of using a larger sample size via direct MCS except achieving a higher clarity in the output distribution, (2) while a very little effect on the convergence was observed which confirms the number of sample size, the fraction of computational time is enhanced by using Sobol sampling method, (3) the benefit of using metamodeling techniques compared to direct MCS was proven as they provide equal predictions in the output statistical measures with fewer evaluations of the original model and are therefore computationally cheaper, and (4) the global SA results showed Sobol indices is computationally more efficient while sharing similar ranking for the most influential parameter with Borgonovo and Morris.

Comparison of two corticosteroid regimens on brain volumetrics in patients with Duchenne muscular dystrophy.

Duchenne muscular dystrophy (DMD) is a neuromuscular disorder in which many patients also have neurobehavioral problems. Corticosteroids, the primary pharmacological treatment for DMD, have been shown to affect brain morphology in other conditions, but data in DMD are lacking. This study aimed to investigate the impact of two corticosteroid regimens on brain volumetrics in DMD using magnetic resonance imaging (MRI). In a cross-sectional, two-center study, T1-weighted MRI scans were obtained from three age-matched groups (9-18 years): DMD patients treated daily with deflazacort (DMDd, n = 20, scan site: Leuven), DMD patients treated intermittently with prednisone (DMDi, n = 20, scan site: Leiden), and healthy controls (n = 40, both scan sites). FSL was used to perform voxel-based morphometry analyses and to calculate intracranial, total brain, gray matter, white matter, and cerebrospinal fluid volumes. A MANCOVA was employed to compare global volumetrics between groups, with site as covariate. Both patient groups displayed regional differences in gray matter volumes compared to the control group. The DMDd group showed a wider extent of brain regions affected and a greater difference overall. This was substantiated by the global volume quantification: the DMDd group, but not the DMDi group, showed significant differences in gray matter, white matter, and cerebrospinal fluid volumes compared to the control group, after correction for intracranial volume. Volumetric differences in the brain are considered part of the DMD phenotype. This study suggests an additional impact of corticosteroid treatment showing a contrast between pronounced alterations seen in patients receiving daily corticosteroid treatment and more subtle differences in those treated intermittently.

Chemoproteomic capture of RNA binding activity in living cells

Proteomic methods for RNA interactome capture (RIC) rely principally on crosslinking native or labeled cellular RNA to enrich and investigate RNA-binding protein (RBP) composition and function in cells. The ability to measure RBP activity at individual binding sites by RIC, however, has been more challenging due to the heterogenous nature of peptide adducts derived from the RNA-protein crosslinked site. Here, we present an orthogonal strategy that utilizes clickable electrophilic purines to directly quantify protein-RNA interactions on proteins through photoaffinity competition with 4-thiouridine (4SU)-labeled RNA in cells. Our photo-activatable-competition and chemoproteomic enrichment (PACCE) method facilitated detection of >5500 cysteine sites across ~3000 proteins displaying RNA-sensitive alterations in probe binding. Importantly, PACCE enabled functional profiling of canonical RNA-binding domains as well as discovery of moonlighting RNA binding activity in the human proteome. Collectively, we present a chemoproteomic platform for global quantification of protein-RNA binding activity in living cells.

Vibration-based hidden damage imaging using stereo cameras with digital image correlation

This paper explores a full-field non-contact optical sensing technique using a stereo camera for imaging hidden damage based on vibration-based damage detection methodology in structural health monitoring. The technique utilizes a pair of digital cameras to capture dynamic operational deflection shapes (ODSs) over the region of interest (ROI) of a structure’s surface via digital image correlation (DIC) when subjected to vibrational excitation. This research overcomes bottlenecks in using high vibration modes for imaging the hidden damage area by (1) applying DIC to operational modal analysis with simple pick-peaking techniques to gather natural frequencies and operational mode shapes in plate structures, while (2) using wavelet analysis to reveal the image of the damage region as a means for baseline-free global damage quantification. In the feasibility study, four cases with two aluminum plates with large damage regions were investigated with a vibration shaker generating a frequency sweep up to 1 kHz. The stereo camera imaged the speckled surface of the plate with white light. Once the dynamic ODSs in the ROI were observed using DIC, the natural frequencies and associated operational mode shapes were extracted using a peak-picking technique in the frequency spectrum. Natural frequencies and operational mode shapes from finite element analysis correlated well with the experimental observations from three-dimensional DIC for all 12 vibration modes respectively. A wavelet transform mode shape curvature (WT-MSC) technique to obtain the modal shape curvature via a two-dimensional continuous wavelet transform with a Mexican Hat analyzing wavelet was then implemented on each of the first 12 vibration mode shapes. A damage image condition that incorporates all weighted wavelet coefficients is proposed to image the damage region. The hidden damage was visualized clearly with WT-MSC, as the technique is much less sensitive to noise than the use of MSC alone, and the use of high vibration modes exhibiting larger mode shape curvatures provided a greater sensitivity for imaging the damage region. Hidden damage regions were successfully visualized in all four cases.

Soil organic carbon is a key determinant of CH4 sink in global forest soils

Soil organic carbon (SOC) is a primary regulator of the forest–climate feedback. However, its indicative capability for the soil CH4 sink is poorly understood due to the incomplete knowledge of the underlying mechanisms. Therefore, SOC is not explicitly included in the current model estimation of the global forest CH4 sink. Here, using in-situ observations, global meta-analysis, and process-based modeling, we provide evidence that SOC constitutes an important variable that governs the forest CH4 sink. We find that a CH4 sink is enhanced with increasing SOC content on regional and global scales. The revised model with SOC function better reproduces the field observation and estimates a 39% larger global forest CH4 sink (24.27 Tg CH4 yr−1) than the model without considering SOC effects (17.46 Tg CH4 yr−1). This study highlights the role of SOC in the forest CH4 sink, which shall be factored into future global CH4 budget quantification.

Energy recovery in a commercial building using pico-hydropower turbines: An Australian case study

Optimising energy use in systems and buildings is crucial to reduce climate change. This paper aims to address the gap in knowledge for pico-hydropower (<5 kW) that has been identified as an area of untapped potential in the water industries. A literature review and multivariate analysis are used to find a suitable pico-hydro turbine to install into a coral reef aquarium system in a government owned facility. Key findings from the literature review are untapped potential, gaps in knowledge and global quantification of small hydropower for energy recovery, and lack of enabling data contributing to slow uptake of small hydropower. The study showed a propeller pico-hydropower turbine could be used to recover approximately 10% of the energy used for pumping water through a filtration system. At 2.3 m available head, and 90 L/s water flow, power output up to 1.124 kW was achieved. The project was economically viable with financial and non-financial benefits for the life cycle of the product. There remain sparse case studies for energy recovery using small hydropower in the scientific literature. A growing number of authors see the potential of this renewable energy technology to reduce global greenhouse gas emissions and contribute to the UN Sustainable Development Goals to provide affordable clean energy and address climate change. This study helps to shine a light on opportunities to find value from waste using a novel application of hydropower in a water industry.

Global quantification of newly synthesized proteins reveals cell type- and inhibitor-specific effects on protein synthesis inhibition.

Manipulation of protein synthesis is commonly applied to uncover protein functions and cellular activities. Multiple inhibitors with distinct mechanisms have been widely investigated and employed in bio-related research, but it is extraordinarily challenging to measure and evaluate the synthesis inhibition efficiencies of individual proteins by different inhibitors at the proteome level. Newly synthesized proteins are the immediate and direct products of protein synthesis, and thus their comprehensive quantification provides a unique opportunity to study protein inhibition. Here, we systematically investigate protein inhibition and evaluate different popular inhibitors, i.e. cycloheximide, puromycin, and anisomycin, through global quantification of newly synthesized proteins in several types of human cells (A549, MCF-7, Jurkat, and THP-1 cells). The inhibition efficiencies of protein synthesis are comprehensively measured by integrating azidohomoalanine-based protein labeling, selective enrichment, a boosting approach, and multiplexed proteomics. The same inhibitor results in dramatic variation of the synthesis inhibition efficiencies for different proteins in the same cells, and each inhibitor exhibits unique preferences. Besides cell type- and inhibitor-specific effects, some universal rules are unraveled. For instance, nucleolar and ribosomal proteins have relatively higher inhibition efficiencies in every type of cells treated with each inhibitor. Moreover, proteins intrinsically resistant or sensitive to the inhibition are identified and found to have distinct functions. Systematic investigation of protein synthesis inhibition in several types of human cells by different inhibitors provides valuable information about the inhibition of protein synthesis, advancing our understanding of inhibiting protein synthesis.

Review: Overview of the transcriptomic landscape in bovine blastocysts and elongated conceptuses driving developmental competence.

In cattle, pregnancy loss due to early embryonic mortality is a major concern that significantly impacts reproductive efficiency. Given the economic importance of cattle in livestock productivity, much research has been carried out to comprehend the regulatory mechanisms underlying this early embryo loss. Thus, understanding the molecular principles behind the reciprocal communication between the maternal uterus and the developing conceptus is paramount. Measurement of mRNA expression through a variety of techniques is widely used to unravel the complex and dynamic interaction between these two players. Development of high-throughput technologies, such as microarrays and RNA sequencing, have allowed global quantification of the full range of expressed mRNA, or transcriptome, of a biological sample. Therefore, numerous investigators have applied one or the other method to study the bovine embryo transcriptome at different developmental checkpoints and under different conditions. The goal of this article was to review studies involving the use of high-throughput techniques to study the transcriptome of the bovine embryo from the blastocyst (∼day 7) to the elongating conceptus stage (∼days 13-16) in terms of developmental capacity and the impact of procedures for in vitro embryo production. Furthermore, the differentially expressed genes reported by each study and enriched pathways were compared to determine common terms. The studies described here highlight differences in the transcriptome (i) between blastocysts with divergent ability to sustain a pregnancy, (ii) between age-matched elongated conceptuses with divergent developmental fates, and (iii) between blastocysts and elongated conceptuses produced in vitro or in vivo. Comparison between these works, supported by other studies involving transcriptomic data integration presented at the end of this review, highlights the involvement of pathways related to energy metabolism in embryonic competence, which may be altered because of the procedures involved in the in vitro production of embryos.

Investigation of Cellular Response to the HSP90 Inhibition in Human Cells Through Thermal Proteome Profiling

Heat shock proteins are chaperones, and they are responsible for protein folding in cells. Heat shock protein 90 (HSP90) is one of the most important chaperones in human cells, and its inhibition is promising for cancer therapy. However, despite the development of multiple HSP90 inhibitors, none of them has been approved for disease treatment due to unexpected cellular toxicity and side effects. Hence, a more comprehensive investigation of cellular response to HSP90 inhibitors can aid in a better understanding of the molecular mechanisms of the cytotoxicity and side effects of these inhibitors. The thermal stability shifts of proteins, which represent protein structure and interaction alterations, can provide valuable information complementary to the results obtained from commonly used abundance-based proteomics analysis. Here, we systematically investigated cell response to different HSP90 inhibitors through global quantification of protein thermal stability changes using thermal proteome profiling, together with the measurement of protein abundance changes. Besides the targets and potential off-targets of the drugs, proteins with significant thermal stability changes under the HSP90 inhibition are found to be involved in cell stress responses and the translation process. Moreover, proteins with thermal stability shifts under the inhibition are upstream of those with altered expression. These findings indicate that the HSP90 inhibition perturbs cell transcription and translation processes. The current study provides a different perspective for achieving a better understanding of cellular response to chaperone inhibition.

Abstract 1138: Novel SMARCA2 degrading bifunctional molecules as therapeutics in SMARCA4 mutant lung cancer

Abstract Lung cancer is the top cause of cancer mortality. Despite recent advances, the majority of patients with lung cancer still lack effective therapeutic options, underscoring the dire need for additional treatment approaches. Genomic studies have identified frequent mutations in subunits of the SWI/SNF chromatin remodeling complex including SMARCA4 and ARID1A in non-small cell lung cancer with a frequency of up to 33% in advanced stage disease, making it the most frequently mutated complex in lung cancer. Recent reports, and our own data, have identified the paralogue SMARCA2 to be synthetic lethal to SMARCA4 suggesting SMARCA2 is a valuable therapeutic target. However, the discovery of selective inhibitors of SMARCA2 has been challenging. To overcome this hurdle, we have utilized iterative structure-activity relationship (SAR) studies to develop novel, potent and selective SMARCA2 degrading small molecules based on proteolysis targeting chimera (PROTAC) technology. We demonstrated that YD23, our lead SMARCA2 PROTAC, potently and selectively induces degradation of SMARCA2. Using global proteomic analysis and quantification of more than 8000 proteins, we showed that our SMARCA2 PROTAC is highly selective for SMARCA2. Importantly, we showed that YD23 selectively inhibits growth of SMARCA4 mutant lung cancer cells in vitro. Mechanistically, we demonstrated that YD23 reduces chromatin accessibility only in SMARCA4 deficient cells. In particular, YD23 profoundly decreased chromatin accessibility at enhancers of a number of genes including cell cycle and cell growth regulatory genes. Gene expression profiling and pathway analysis indicated that various cell cycle genes were downregulated by YD23 consistent with the reduced chromatin accessibility at their regulatory regions. In conclusion, our study provides a potent chemical probe for studying the synthetic lethal interaction between SMARCA2 and SMARCA4, dissect the chromatin and epigenetic landscape alterations and lay the foundation for future preclinical and clinical development of SMARCA2 degraders as therapeutics. Citation Format: Sasi Kotagiri, Nicholas Blazanin, Yuanxin Xi, Jing Wang, Yonathan Lissanu. Novel SMARCA2 degrading bifunctional molecules as therapeutics in SMARCA4 mutant lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1138.

A Global Quantification of the Physical Processes Leading to Near‐Surface Cold Extremes

AbstractKinematic backward trajectories are used to globally quantify the contributions of temperature advection, adiabatic compression and diabatic processes to near‐surface temperature anomalies (hereafter ) during the coldest day of each year (TN1day events) based on ERA5. Diabatic cooling dominates TN1day anomalies in the climatologically coldest regions, while advection forms TN1day anomalies over most ocean regions. Over most extratropical land masses, TN1day anomalies arise from a combination of both processes. The mean age and formation distance of TN1day anomalies vary strongly in space, from one to 8 days, and 500–5,500 km, respectively. Five distinct types of TN1day events are identified from these physical and spatio‐temporal characteristics, and their geographical occurrence is investigated. Furthermore, advective, adiabatic and diabatic contributions typically cancel each other partially, but less so for the most intense TN1day events, which occur when the atmosphere's ability to dampen near‐surface temperature anomalies is limited.

Collisions of sailing vessels with cetaceans worldwide: First insights into a seemingly growing problem

Vessel-whale collisions are of growing concern worldwide, but information about collisions involving sailing vessels is especially scarce. This study represents the first global quantification of this kind. A total of 111 collisions and 57 near misses were identified, spanning from 1966 until 2010; 75% of cases was reported for the period from 2002–2010, suggesting an increasing trend. Reported collisions and near misses occurred on all oceans, often during ocean races and regattas, and were most frequent in the North Atlantic. Vessel type and speed as well as circumstances of the incident varied widely, but most often monohulls were involved, predominantly sailing at speeds between 5 and 10 knots. Most reports referred to ‘large whales’ as opposed to ‘small whales’ or ‘dolphins’. The species could be identified in 54 cases. Most recognised animals were humpback or sperm whales. Injuries to the whales varied strongly from ‘not visible’ to ‘dead after collision’, but mostly could not be determined. Sailing crew members were hurt in several cases, including collisions occurring at low speeds, and collisions often damaged vessels, including major impairment and seven cases of vessel loss. The findings presented here suggest that elevated vessel speed contributes to a higher risk of collisions. Conversely, the outcome of a collision (e.g. injury to whale or crew, damage to vessel) appears not to be a direct function of vessel speed. Several measures are discussed which potentially can contribute to mitigating the problem, including placing watchposts, an open dialogue with regatta organisers, changes in the design of regattas and ocean races and public outreach initiatives.

Myocardial Characterization with Extracellular Volume Mapping with a First-Generation Photon-counting Detector CT with MRI Reference.

Background Photon-counting detector (PCD) CT provides comprehensive spectral data with every acquisition, but studies evaluating myocardial extracellular volume (ECV) quantification with use of PCD CT compared with an MRI reference remain lacking. Purpose To compare ECV quantification for myocardial tissue characterization between a first-generation PCD CT system and cardiac MRI. Materials and Methods In this single-center prospective study, adults without contraindication to iodine-based contrast media underwent same-day cardiac PCD CT and MRI with native and postcontrast T1 mapping and late gadolinium enhancement for various clinical indications for cardiac MRI (the reference standard) between July 2021 and January 2022. Global and midventricular ECV were assessed with use of three methods: single-energy PCD CT, dual-energy PCD CT, and MRI T1 mapping. Quantitative comparisons among all techniques were performed. Correlation and reliability between different methods of ECV quantification were assessed with use of the Pearson correlation coefficient (r) and the intraclass correlation coefficient. Results The final sample included 29 study participants (mean age ± SD, 54 years ± 17; 15 men). There was a strong correlation of ECV between dual- and single-energy PCD CT (r = 0.91, P < .001). Radiation dose was 40% lower with dual-energy versus single-energy PCD CT (volume CT dose index, 10.1 mGy vs 16.8 mGy, respectively; P < .001). In comparison with MRI, dual-energy PCD CT showed strong correlation (r = 0.82 and 0.91, both P < .001) and good to excellent reliability (intraclass correlation coefficients, 0.81 and 0.90) for midventricular and global ECV quantification, but it overestimated ECV by approximately 2%. Single-energy PCD CT showed similar relationship with MRI but underestimated ECV by 3%. Conclusion Myocardial tissue characterization with photon-counting detector CT-based quantitative extracellular volume analysis showed a strong correlation to MRI. © RSNA, 2023 Supplemental material is available for this article.

Quantification of global orbital shape variation.

The complex anatomy of the orbit generates a complex orbital shape that can only be quantified approximatively by classic linear measurements such as maximum width and height. There is no global three-dimensional quantification of variations in orbital shape. The purpose of this study was to develop a method to quantify a global three-dimensional orbital shape variation in a healthy population and to test a series of explanatory factors. We investigated the hypotheses that orbital shape is related to gender(H1), orbital size(H2) and/or age(H3). Medical computed tomography(CT) images of 60 adult individuals were studied. The study sample consisted of 30 males and 30 females with a mean age of 25.1 years. Four anatomical landmarks and 140 semi-landmarks were measured on both positive and negative 3D reconstructed orbits and analyzed with geometric morphometrics. A principal component analysis(PCA) was computed to define a morphological space. Shape variation was visualized using vector distance maps and diagrams. The greatest variation was seen in the length of the superior orbital fissure. There was a gradient in terms of orbital shape ranging from short, wide orbits to tall, narrow orbits. The analysis did not highlight any significant age-, gender- or size-related impact in terms of orbital shape variation. Future avenues to explore include the study of other potential explanatory factors such as the different embryological origins of the orbital bones, the passage of vessels and nerves, and ethnic origins. This method can also be applied to the study of pathological orbits.

Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification.

Chemical reaction neural network (CRNN), a recently developed tool for autonomous discovery of reaction models, has been successfully demonstrated on a variety of chemical engineering and biochemical systems. It leverages the extraordinary data-fitting capacity of modern deep neural networks (DNNs) while preserving high interpretability and robustness by embedding widely applicable physical laws such as the law of mass action and the Arrhenius law. In this paper, we further developed Bayesian CRNN to not only reconstruct but also quantify the uncertainty of chemical kinetic models from data. Two methods, the Markov chain Monte Carlo algorithm and variational inference, were used to perform the Bayesian CRNN, with the latter mainly adopted for its speed. We demonstrated the capability of Bayesian CRNN in the kinetic uncertainty quantification of different types of chemical systems and discussed the importance of embedding physical laws in data-driven modeling. Finally, we discussed the adaptation of Bayesian CRNN for incomplete measurements and model mixing for global uncertainty quantification.

Global Sensitivity Analysis and Uncertainty Quantification for Simulated Atrial Electrocardiograms

The numerical modeling of cardiac electrophysiology has reached a mature and advanced state that allows for quantitative modeling of many clinically relevant processes. As a result, complex computational tasks such as the creation of a variety of electrocardiograms (ECGs) from virtual cohorts of models representing biological variation are within reach. This requires a correct representation of the variability of a population by suitable distributions of a number of input parameters. Hence, the assessment of the dependence and variation of model outputs by sensitivity analysis and uncertainty quantification become crucial. Since the standard metrological approach of using Monte–Carlo simulations is computationally prohibitive, we use a nonintrusive polynomial chaos-based approximation of the forward model used for obtaining the atrial contribution to a realistic electrocardiogram. The surrogate increases the speed of computations for varying parameters by orders of magnitude and thereby greatly enhances the versatility of uncertainty quantification. It further allows for the quantification of parameter influences via Sobol indices for the time series of 12 lead ECGs and provides bounds for the accuracy of the obtained sensitivities derived from an estimation of the surrogate approximation error. Thus, it is capable of supporting and improving the creation of synthetic databases of ECGs from a virtual cohort mapping a representative sample of the human population based on physiologically and anatomically realistic three-dimensional models.

The politics of good enough data. Developments, dilemmas and deadlocks in the production of global learning metrics

The indicator framework established for the monitoring of SDG4 is unambiguous on the need to advance in the production of global learning data. However, the production of SDG4 learning metrics has been riddled with technical and political difficulties. Drawing on a combination of documentary analysis, interviews and non-participant observation, this paper reconstructs the process of negotiation of the data suppliers, statistical routines and reporting standards necessary for the production of SDG4 learning metrics. The paper thus offers insight into the mechanics of global quantification, and on the transformative impact of such processes on the agendas and relationships of partaking organizations.

Global genomic methylation related to the degree of parasitism in cattle

The objective of the present study was to characterize a herd of 72 ½ Angus × ½ Nellore heifers, identify the resistant, resilient and susceptible animals to parasites, relate the overall DNA methylation of these animals with the degree of parasitism, evaluated by the egg count per gram of feces (EPG), Haematobia irritans count (horn fly) and Rhipicephalus microplus count (bovine tick). The experiment was carried out in a completely randomized design, containing 72 treatments, with each animal considered a treatment, and 11 repetitions, with each collection within a year considered a repetition. The data obtained from the counts of the evaluated parasites were subjected to statistical analysis using the SISVAR program, to classify heifers according to the degree of parasitism in low (resistant), intermediary (resilient) and high (susceptible) parasite load for infection by nematodes, infestation by ticks and flies. Addition the animals in these three groups, by hierarchical grouping using the GENES program, heifers were classified as to the degree of parasitism by the three parasites along with the DNA methylation content of the animals in each group. A negative relationship was observed between resistance and methylated DNA content in both classifications, with the resistant, resilient, and susceptible animals showing the highest, intermediate, and lowest methylated DNA quantifications, respectively. Thus, the methodologies used herein enabled the classification of 72 heifers according to the degree of collective infection by gastrointestinal nematodes and infestation by ticks and horn flies, thereby establishing a link between the degree of parasitic resistance in cattle and the global methylated DNA quantification.

3D multi-physics uncertainty quantification using physics-based machine learning

Quantitative predictions of the physical state of the Earth’s subsurface are routinely based on numerical solutions of complex coupled partial differential equations together with estimates of the uncertainties in the material parameters. The resulting high-dimensional problems are computationally prohibitive even for state-of-the-art solver solutions. In this study, we introduce a hybrid physics-based machine learning technique, the non-intrusive reduced basis method, to construct reliable, scalable, and interpretable surrogate models. Our approach, to combine physical process models with data-driven machine learning techniques, allows us to overcome limitations specific to each individual component, and it enables us to carry out probabilistic analyses, such as global sensitivity studies and uncertainty quantification for real-case non-linearly coupled physical problems. It additionally provides orders of magnitude computational gain, while maintaining an accuracy higher than measurement errors. Although in this study we use a thermo-hydro-mechanical reservoir application to illustrate these features, all the theory described is equally valid and applicable to a wider range of geoscientific applications.