Unlocking Solutions: Innovative Approaches to Identifying and Mitigating the Environmental Impacts of Undocumented Orphan Wells in the United States.

In the United States, hundreds of thousands of undocumented orphan wells have been abandoned, leaving the burden of managing environmental hazards to governmental agencies or the public. These wells, a result of over a century of fossil fuel extraction without adequate regulation, lack basic information like location and depth, emit greenhouse gases, and leak toxic substances into groundwater. For most of these wells, basic information such as well location and depth is unknown or unverified. Addressing this issue necessitates innovative and interdisciplinary approaches for locating, characterizing, and mitigating their environmental impacts. Our survey of the United States revealed the need for tools to identify well locations and assess conditions, prompting the development of technologies including machine learning to automatically extract information from old records (95%+ accuracy), remote sensing technologies like aero-magnetometers to find buried wells, and cost-effective methods for estimating methane emissions. Notably, fixed-wing drones equipped with magnetometers have emerged as cost-effective and efficient for discovering unknown wells, offering advantages over helicopters and quadcopters. Efforts also involved leveraging local knowledge through outreach to state and tribal governments as well as citizen science initiatives. These initiatives aim to significantly contribute to environmental sustainability by reducing greenhouse gases and improving air and water quality.

G Protein-Coupled Receptor-Ligand Pose and Functional Class Prediction.

G protein-coupled receptor (GPCR) transmembrane protein family members play essential roles in physiology. Numerous pharmaceuticals target GPCRs, and many drug discovery programs utilize virtual screening (VS) against GPCR targets. Improvements in the accuracy of predicting new molecules that bind to and either activate or inhibit GPCR function would accelerate such drug discovery programs. This work addresses two significant research questions. First, do ligand interaction fingerprints provide a substantial advantage over automated methods of binding site selection for classical docking? Second, can the functional status of prospective screening candidates be predicted from ligand interaction fingerprints using a random forest classifier? Ligand interaction fingerprints were found to offer modest advantages in sampling accurate poses, but no substantial advantage in the final set of top-ranked poses after scoring, and, thus, were not used in the generation of the ligand-receptor complexes used to train and test the random forest classifier. A binary classifier which treated agonists, antagonists, and inverse agonists as active and all other ligands as inactive proved highly effective in ligand function prediction in an external test set of GPR31 and TAAR2 candidate ligands with a hit rate of 82.6% actual actives within the set of predicted actives.

Substituted diaryl ether compounds as retinoic acid-related orphan Receptor-γt (RORγt) agonists.

The discovery of a series of substituted diarylether compounds as retinoic acid related orphan receptor γt (RORγt) agonists is described. Compound 1 was identified from deck mining as a RORγt agonist. Hit-to-lead optimization led to the identification of lead compound 5, which possesses improved potency (10x). Extensive SAR exploration led to the identification of a potent and selective compound 22, that demonstrated an improved pharmacokinetic profile and a dose-dependent pharmacodynamic response. However, when dosed in a MC38 syngeneic tumor model, no evidence of efficacy was observed. ©2020 Elsevier Science Ltd. All rights reserved.

Substituted benzyloxytricyclic compounds as retinoic acid-related orphan receptor gamma t (RORγt) agonists.

Substituted benzyloxy aryl compound 2 was identified as an RORγt agonist. Structure based drug design efforts resulted in a potent and selective tricyclic compound 19 which, when administered orally in an MC38 mouse tumor model, demonstrated a desired pharmacokinetic profile as well as a dose-dependent pharmacodynamic response. However, no perceptible efficacy was observed in this tumor model at the doses investigated.

Concise total syntheses of amphidinolides†C and F.

The marine natural products amphidinolide C (1) and F (4) differ in their side chains but share a common macrolide core with a signature 1,4-diketone substructure. This particular motif inspired a synthesis plan predicating a late-stage formation of this non-consonant ("umpoled") pattern by a platinum-catalyzed transannular hydroalkoxylation of a cycloalkyne precursor. This key intermediate was assembled from three building blocks (29, 41 and 47 (or 65)) by Yamaguchi esterification, Stille cross-coupling and a macrocyclization by ring-closing alkyne metathesis (RCAM). This approach illustrates the exquisite alkynophilicity of the catalysts chosen for the RCAM and alkyne hydroalkoxylation steps, which activate triple bonds with remarkable ease but left up to five other π-systems in the respective substrates intact. Interestingly, the inverse chemoselectivity pattern was exploited for the preparation of the tetrahydrofuran building blocks 47 and 65 carrying the different side chains of the two target macrolides. These fragments derive from a common aldehyde precursor 46 formed by an exquisitely alkene-selective cobalt-catalyzed oxidative cyclization of the diunsaturated alcohol 44, which left an adjacent acetylene group untouched. The northern sector 29 was prepared by a two-directional Marshall propargylation strategy, whereas the highly adorned acid subunit 41 derives from D-glutamic acid by an intramolecular oxa-Michael addition and a proline-mediated hydroxyacetone aldol reaction as the key steps; the necessary Me3 Sn-group on the terminus of 41 for use in the Stille coupling was installed via enol triflate 39, which was obtained by selective deprotonation/triflation of the ketone site of the precursor 38 without competing enolization of the ester also present in this particular substrate.

Aerial imagery dataset of lost oil wells.

Orphaned wells are wells for which the operator is unknown or insolvent. The location of hundreds of thousands of these wells remain unknown in the United States alone. Cost-effective techniques are essential to locate orphaned wells to address environmental problems. In this paper, we present a dataset consisting of 120,948 aerial images of recently documented orphan wells. Each of these 512 × 512 images is paired with segmentation masks that indicate the presence or absence of such well. These images, sourced from the National Agriculture Imagery Program, cover the continental United States with spatial resolutions ranging from 30 centimeters to 1 meter. Additionally, we included negative examples by selecting locations uniformly across the United States. Accompanying metadata includes the IDs and spatial resolution of the original images, which are available for free through the United States Geological Survey, and the pixel coordinates of documented orphaned wells identified in these images. This dataset is intended to support the development of deep-learning models that can help locating undocumented orphan wells from such imagery, thereby blunting the environmental damage they do.

Progressive transfer learning for advancing machine learning-based reduced-order modeling.

To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.

Addressing quantum's "fine print" with efficient state preparation and information extraction for quantum algorithms and geologic fracture networks.

Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, including those that model geologic fracture flow. However, this revolutionary gain in efficiency does not come without difficulty. Quantum algorithms require that problems satisfy not only algorithm-specific constraints, but also application-specific ones. Otherwise, the quantum advantage carefully attained through algorithmic ingenuity can be entirely negated. Previous work addressing quantum algorithms for geologic fracture flow has illustrated core algorithmic approaches while incrementally removing assumptions. This work addresses two further requirements for solving geologic fracture flow systems with quantum algorithms: efficient system state preparation and efficient information extraction. Our approach to addressing each is consistent with an overall exponential speed-up.

Discovery of Novel TLR7 Agonists as Systemic Agent for Combination With aPD1 for Use in Immuno-oncology.

We have designed and developed novel and selective TLR7 agonists that exhibited potent receptor activity in a cell-based reporter assay. In vitro, these agonists significantly induced secretion of cytokines IL-6, IL-1ß, IL-10, TNFa, IFNa, and IP-10 in human and mouse whole blood. Pharmacokinetic and pharmacodynamic studies in mice showed a significant secretion of IFNα and TNFα cytokines. When combined with aPD1 in a CT-26 tumor model, the lead compound showed strong synergistic antitumor activity with complete tumor regression in 8/10 mice dosed using the intravenous route. Structure-activity relationship studies enabled by structure-based designs of TLR7 agonists are disclosed.

Physics-embedded inverse analysis with algorithmic differentiation for the earth's subsurface.

Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented with embedded physics and a technique known as algorithmic differentiation. We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.g., permeability or P-wave velocity), that embeds physical knowledge of the subsurface properties into inverse analysis and improves its performance. We tested the application of this approach on four geologic problems: two heterogeneous hydraulic conductivity fields, a hydraulic fracture network, and a seismic inversion for P-wave velocity. This physics-embedded inverse analysis approach consistently characterizes these geological problems accurately. Furthermore, the excellent performance in matching the observational data demonstrates the reliability of the proposed method. Moreover, the application of algorithmic differentiation makes this an easy and fast approach to inverse analysis when dealing with complicated geological structures.

Quantum algorithms for geologic fracture networks.

Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglect information at small scales, an approach known as coarse-graining. For many practical applications, such as flow in porous, homogenous materials, coarse-graining offers a sufficiently-accurate approximation of the solution. Unfortunately, fractured systems cannot be accurately coarse-grained, as critical network topology exists at the smallest scales, including topology that can push the network across a percolation threshold. Therefore, new techniques are necessary to accurately model important fracture systems. Quantum algorithms for solving linear systems offer a theoretically-exponential improvement over their classical counterparts, and in this work we introduce two quantum algorithms for fractured flow. The first algorithm, designed for future quantum computers which operate without error, has enormous potential, but we demonstrate that current hardware is too noisy for adequate performance. The second algorithm, designed to be noise resilient, already performs well for problems of small to medium size (order 10-1000 nodes), which we demonstrate experimentally and explain theoretically. We expect further improvements by leveraging quantum error mitigation and preconditioning.

Identification and characterization of select oxysterols as ligands for GPR17.

BACKGROUND AND PURPOSE: G-protein coupled receptor 17 (GPR17) is an orphan receptor involved in the process of myelination, due to its ability to inhibit the maturation of oligodendrocyte progenitor cells (OPCs) into myelinating oligodendrocytes. Despite multiple claims that the biological ligand has been identified, it remains an orphan receptor. EXPERIMENTAL APPROACH: Seventy-seven oxysterols were screened in a cell-free [35 S]GTPγS binding assay using membranes from cells expressing GPR17. The positive hits were characterized using adenosine 3',5' cyclic monophosphate (cAMP), inositol monophosphate (IP1) and calcium mobilization assays, with results confirmed in rat primary oligodendrocytes. Rat and pig brain extracts were separated by high-performance liquid chromatography (HPLC) and endogenous activator(s) were identified in receptor activation assays. Gene expression studies of GPR17, and CYP46A1 (cytochrome P450 family 46 subfamily A member 1) enzymes responsible for the conversion of cholesterol into specific oxysterols, were performed using quantitative real-time PCR. KEY RESULTS: Five oxysterols were able to stimulate GPR17 activity, including the brain cholesterol, 24(S)-hydroxycholesterol (24S-HC). A specific brain fraction from rat and pig extracts containing 24S-HC activates GPR17 in vitro. Expression of Gpr17 during mouse brain development correlates with the expression of Cyp46a1 and the levels of 24S-HC itself. Other active oxysterols have low brain concentrations below effective ranges. CONCLUSIONS AND IMPLICATIONS: Oxysterols, including but not limited to 24S-HC, could be physiological activators for GPR17 and thus potentially regulate OPC differentiation and myelination through activation of the receptor.

Quantum computing and preconditioners for hydrological linear systems.

Modeling hydrological fracture networks is a hallmark challenge in computational earth sciences. Accurately predicting critical features of fracture systems, e.g. percolation, can require solving large linear systems far beyond current or future high performance capabilities. Quantum computers can theoretically bypass the memory and speed constraints faced by classical approaches, however several technical issues must first be addressed. Chief amongst these difficulties is that such systems are often ill-conditioned, i.e. small changes in the system can produce large changes in the solution, which can slow down the performance of linear solving algorithms. We test several existing quantum techniques to improve the condition number, but find they are insufficient. We then introduce the inverse Laplacian preconditioner, which improves the scaling of the condition number of the system from O(N) to [Formula: see text] and admits a quantum implementation. These results are a critical first step in developing a quantum solver for fracture systems, both advancing the state of hydrological modeling and providing a novel real-world application for quantum linear systems algorithms.

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management.

Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO[Formula: see text] sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. The heterogeneity typically requires high-fidelity physics-based models to make predictions on CO[Formula: see text] fate. Furthermore, characterizing the heterogeneity accurately is fraught with parametric uncertainty. Accounting for both, heterogeneity and uncertainty, makes this a computationally-intensive problem challenging for current reservoir simulators. To tackle this, we use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization at critical reservoir locations. We use DPFEHM framework, which has trustworthy physics based on the standard two-point flux finite volume discretization and is also automatically differentiable like machine learning models. Our physics-informed machine learning framework uses convolutional neural networks to learn an appropriate extraction rate based on the permeability field. We also perform a hyperparameter search to improve the model's accuracy. Training and testing scenarios are executed to evaluate the feasibility of using physics-informed machine learning to manage reservoir pressures. We constructed and tested a sufficiently accurate simulator that is 400 000 times faster than the underlying physics-based simulator, allowing for near real-time analysis and robust uncertainty quantification.

Quantum annealing algorithms for Boolean tensor networks.

Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality heuristic solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks. Tensors offer a natural way to model high-dimensional data commonplace in many scientific fields, and representing a binary tensor as a Boolean tensor network is the task of expressing a tensor containing categorical (i.e., [Formula: see text]) values as a product of low dimensional binary tensors. A Boolean tensor network is computed by Boolean tensor decomposition, and it is usually not exact. The aim of such decomposition is to minimize the given distance measure between the high-dimensional input tensor and the product of lower-dimensional (usually three-dimensional) tensors and matrices representing the tensor network. In this paper, we introduce and analyze three general algorithms for Boolean tensor networks: Tucker, Tensor Train, and Hierarchical Tucker networks. The computation of a Boolean tensor network is reduced to a sequence of Boolean matrix factorizations, which we show can be expressed as a quadratic unconstrained binary optimization problem suitable for solving on a quantum annealer. By using a novel method we introduce called parallel quantum annealing, we demonstrate that Boolean tensor's with up to millions of elements can be decomposed efficiently using a DWave 2000Q quantum annealer.

Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning.

We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoders (DC-AE) has been shown to capture nonlinear solution manifolds but fails to perform adequately when linear subspace approaches such as proper orthogonal decomposition (POD) would be optimal. Besides, most DL-ROM models rely on convolutional layers, which might limit its application to only a structured mesh. The proposed framework in this study relies on the combination of an autoencoder (AE) and Barlow Twins (BT) self-supervised learning, where BT maximizes the information content of the embedding with the latent space through a joint embedding architecture. Through a series of benchmark problems of natural convection in porous media, BT-AE performs better than the previous DL-ROM framework by providing comparable results to POD-based approaches for problems where the solution lies within a linear subspace as well as DL-ROM autoencoder-based techniques where the solution lies on a nonlinear manifold; consequently, bridges the gap between linear and nonlinear reduced manifolds. We illustrate that a proficient construction of the latent space is key to achieving these results, enabling us to map these latent spaces using regression models. The proposed framework achieves a relative error of 2% on average and 12% in the worst-case scenario (i.e., the training data is small, but the parameter space is large.). We also show that our framework provides a speed-up of [Formula: see text] times, in the best case, and [Formula: see text] times on average compared to a finite element solver. Furthermore, this BT-AE framework can operate on unstructured meshes, which provides flexibility in its application to standard numerical solvers, on-site measurements, experimental data, or a combination of these sources.

Enhancing high-fidelity nonlinear solver with reduced order model.

We propose the use of reduced order modeling (ROM) to reduce the computational cost and improve the convergence rate of nonlinear solvers of full order models (FOM) for solving partial differential equations. In this study, a novel ROM-assisted approach is developed to improve the computational efficiency of FOM nonlinear solvers by using ROM's prediction as an initial guess. We hypothesize that the nonlinear solver will take fewer steps to the converged solutions with an initial guess that is closer to the real solutions. To evaluate our approach, four physical problems with varying degrees of nonlinearity in flow and mechanics have been tested: Richards' equation of water flow in heterogeneous porous media, a contact problem in a hyperelastic material, two-phase flow in layered porous media, and fracture propagation in a homogeneous material. Overall, our approach maintains the FOM's accuracy while speeding up nonlinear solver by 18-73% (through suitable ROM-assisted FOMs). More importantly, the proximity of ROM's prediction to the solution space leads to the improved convergence of FOMs that would have otherwise diverged with default initial guesses. We demonstrate that the ROM's accuracy can impact the computational efficiency with more accurate ROM solutions, resulting in a better cost reduction. We also illustrate that this approach could be used in many FOM discretizations (e.g., finite volume, finite element, or a combination of those). Since our ROMs are data-driven and non-intrusive, the proposed procedure can easily lend itself to any nonlinear physics-based problem.

Enantioselective total syntheses of (-)-palau'amine, (-)-axinellamines, and (-)-massadines.

Dimeric pyrrole-imidazole alkaloids represent a rich and topologically unique class of marine natural products. This full account will follow the progression of efforts that culminated in the enantioselective total syntheses of the most structurally ornate members of this family: the axinellamines, the massadines, and palau'amine. A bio-inspired approach capitalizing on the pseudo-symmetry of the members of this class is recounted, delivering a deschloro derivative of the natural product core. Next, the enantioselective synthesis of the chlorocyclopentane core featuring a scalable, catalytic, enantioselective Diels-Alder reaction of a 1-siloxydiene is outlined in detail. Finally, the successful divergent conversion of this core to each of the aforementioned natural products, and the ensuing methodological developments, are described.

Reverse annealing for nonnegative/binary matrix factorization.

It was recently shown that quantum annealing can be used as an effective, fast subroutine in certain types of matrix factorization algorithms. The quantum annealing algorithm performed best for quick, approximate answers, but performance rapidly plateaued. In this paper, we utilize reverse annealing instead of forward annealing in the quantum annealing subroutine for nonnegative/binary matrix factorization problems. After an initial global search with forward annealing, reverse annealing performs a series of local searches that refine existing solutions. The combination of forward and reverse annealing significantly improves performance compared to forward annealing alone for all but the shortest run times.

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks.

Here we employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) for learning a forward and an inverse solution operator of partial differential equations (PDEs). We focus on steady-state solutions of coupled hydromechanical processes in heterogeneous porous media and present the parameterization of the spatially heterogeneous coefficients, which is exceedingly difficult using standard reduced-order modeling techniques. We show that our framework provides a speed-up of at least 2,000 times compared to a finite-element solver and achieves a relative root-mean-square error (r.m.s.e.) of less than 2% for forward modeling. For inverse modeling, the framework estimates the heterogeneous coefficients, given an input of pressure and/or displacement fields, with a relative r.m.s.e. of less than 7%, even for cases where the input data are incomplete and contaminated by noise. The framework also provides a speed-up of 120,000 times compared to a Gaussian prior-based inverse modeling approach while also delivering more accurate results.