Abstract: A method for modeling a subsurface property for a subterranean volume of interest includes receiving input measurement data representing a subterranean volume of interest, predicting a subsurface property based at least in part on the input measurement data using a first machine learning model, predicting a subsurface property model based at least in part on the subsurface property, the input measurement data, or both, using a second machine learning model, predicting synthetic measurement data based at least in part on the subsurface property model using a third machine learning model, a physics-based model, or both, comparing the synthetic measurement data and the input measurement data, and training the first machine learning model, the second machine learning model, or both based at least in part on the comparing.

Abstract: A method of generating a three-dimensional (3D) seismic image volume includes receiving a plurality of two-dimensional (2D) seismic images. The method also includes generating a proxy volume representative of a three-dimensional volume that corresponds to the 3D seismic image volume based on the plurality of 2D seismic images, the proxy volume including multiple approximated 2D seismic images. The method also includes generating an approximate image volume including a first plurality of seismic images along a first trajectory based on updating the plurality of approximated 2D seismic images via a first machine learning algorithm. Further, the method includes generating the 3D seismic image volume including a second plurality of seismic images based on updating the first plurality of seismic images via a second machine learning algorithm in a second trajectory different from the first trajectory.

Abstract: The disclosed methods include: generating proxy volume data for a subsurface region of interest at a resource site; pseudo labeling the proxy volume data using at least a high-resolution deterministic interpolation (HRDI) computing process thereby generating labeled volume data for the subsurface region of interest; applying a two-pass deep learning interpolation process to the labeled volume data for the subsurface region of interest along orthogonal directions or orthogonal axes associated with the labeled volume data thereby predicting 3D datapoints for the subsurface region of interest; formatting the 3D datapoints to generate a 3D subsurface image for the subsurface region of interest; and rendering the 3D subsurface image for the subsurface region of interest on a display device, the 3D subsurface image being adaptable for implementing energy development operations.

Abstract: The disclosure provides diarylacetylene compounds, their preparation methods, and applications. These compounds contain a biologically active isoindolinyl-piperazinyl urea core moiety. Further chemical modification of this core generates numerous compounds with higher biological activity, or their pharmaceutically acceptable salts, hydrates, solvates, metabolites, prodrugs, or pharmaceutical compositions. This expands the broad application potential of such compounds in biomedicine and the prospects for pharmaceutical formulation development. This class of compounds can target the STAT3 protein. At low doses (nanomolar concentrations), they significantly inhibit the proliferation of various tumor cells, including lung cancer, breast cancer, liver cancer, pancreatic cancer, and gastric cancer cells. They also effectively suppress the growth of transplanted tumors in mice.

Abstract: A method includes determining a top of salt (TOS) surface in a seismic volume based on a crossline direction of the seismic volume and an inline direction of the seismic volume. The method also includes determining a binary mask based upon the TOS surface. The method also includes sampling seismic data in the seismic volume to obtain a training seismic slice. The method also includes sampling the binary mask to obtain a mask slice. The method also includes selecting a first coordinate in the training seismic slice to produce a first tile. The method also includes selecting a second coordinate in the mask slice to produce a second tile. The method also includes generating or updating a model of the seismic volume based upon the first tile and the second tile.

Abstract: A method includes receiving baseline data representing an area corresponding to a first time. receiving first measurement data representing a first portion of the area corresponding to a second time subsequent to the first time. training a machine learning model to reduce an influence of a non-repeatability factor based on a combination of the baseline data and the first measurement data. receiving second measurement data representing a second portion of the area corresponding to the second time. the second portion of the area including a feature of interest that was not present at the first time or that has changed between the first and second times. and modifying the second measurement data using the machine learning model to remove the non-repeatability factor in the second measurement data.

Abstract: A method includes receiving a seismic survey that includes a plurality of seismic slices. The method also includes converting the seismic slices into an embedding. The embedding includes one or more vectors. Each of the vectors includes more than 3 dimensions. The method also includes generating a plot based at least partially upon the embedding.

Abstract: A method includes receiving a seismic survey that includes a plurality of seismic slices. The method also includes converting the seismic slices into an embedding. The embedding includes one or more vectors. Each of the vectors includes more than 3 dimensions. The method also includes generating a plot based at least partially upon the embedding.

Abstract: A method for seismic surveying includes receiving a baseline dataset and a plurality of sparse monitoring datasets, generating a decimated baseline dataset by removing one or more sources, receivers, or both from the baseline dataset, generating a reconstructed baseline dataset by inputting the decimated baseline dataset into a machine learning model, generating reconstructed monitoring datasets by inputting the plurality of sparse monitoring datasets to the machine learning model, the machine learning model having been trained based on a comparison of the reconstructed baseline dataset to the baseline seismic dataset, determining accuracies for the plurality of sparse monitoring datasets by comparing the reconstructed monitoring datasets to the baseline dataset, and selecting one or more survey geometries for arranging physical sources and physical receivers in a seismic survey based at least in part on the accuracies of the plurality of sparse monitoring datasets.

Abstract: An integrated workflow is presented including a suite of data-driven technologies that aims to substantially reduce the cost of monitoring data acquisition, improve the robustness and efficiency of time-lapse data processing procedures to shorten the turnaround time of projects utilizing seismic data for monitoring sub-surface fluid reservoirs. In particular, plumes of subsurface CO2 may be monitored, including CO2 deliberately injected into the sub-surface as a sequestration technique. The workflow may include two parts: (1) cost-effective data acquisition schemes and (2) efficient data processing algorithms. The technology components in the workflow may include deep learning sparse monitoring data reconstruction and optimal acquisition survey design, deep learning deblending of simultaneous source monitoring data, time-lapse data repeatability enforcement through deep learning, and rapid CO2 plume body and property estimation directly from pre-migration monitoring data.

Abstract: A method for modeling a subsurface property for a subterranean volume of interest includes receiving input measurement data representing a subterranean volume of interest, predicting a subsurface property based at least in part on the input measurement data using a first machine learning model, predicting a subsurface property model based at least in part on the subsurface property, the input measurement data, or both, using a second machine learning model, predicting synthetic measurement data based at least in part on the subsurface property model using a third machine learning model, a physics-based model, or both, comparing the synthetic measurement data and the input measurement data, and training the first machine learning model, the second machine learning model, or both based at least in part on the comparing.

Abstract: A computer-implemented method includes receiving a test seismic dataset associated with a known truth interpretation, receiving one or more hard constraints, training a machine learning system based on the test seismic dataset, the known truth interpretation, and the one or more hard constraints, determining an error value based on the training the machine learning system, adjusting the error value based on one or more soft constraints, updating the training of the machine learning system based on the adjusted error value, receiving a second seismic dataset after the updating the training; applying the second seismic dataset to the machine learning system to generate an interpretation of the second seismic dataset, generating a seismic image representing a subterranean domain based on the interpretation of the second seismic dataset, and outputting the seismic image.

Abstract: The present disclosure provides a kit and method for constructing a long-acting depression animal model. The present disclosure adopts Bacille Calmette-Guerin (BCG) (or Mycobacterium bovis (M bovis)) and low-dose pertussis toxin (PTX) for combined induction, which greatly reduces modeling cost. A mouse model constructed by the method of the present disclosure can clearly distinguish a time interval of pathological behaviors from a time interval of depression-like behaviors and has prominent face validity and predictive validity. The method of the present disclosure is simple and convenient and does not require a large number of tedious daily operations as the depression model construction by chronic unpredictable stress, chronic restraint stress, social defeat, mother-child separation, or the like. The mouse model of the present disclosure can provide a valuable long-term intervention window for research on antidepressant therapy.

Abstract: The present disclosure provides a kit and method for constructing a long-acting depression animal model. The present disclosure adopts Bacille Calmette-Guerin (BCG) (or Mycobacterium bovis (M. bovis)) and low-dose pertussis toxin (PTX) for combined induction, which greatly reduces modeling cost. A mouse model constructed by the method of the present disclosure can clearly distinguish a time interval of pathological behaviors from a time interval of depression-like behaviors and has prominent face validity and predictive validity. The method of the present disclosure is simple and convenient and does not require a large number of tedious daily operations as the depression model construction by chronic unpredictable stress, chronic restraint stress, social defeat, mother-child separation, or the like. The mouse model of the present disclosure can provide a valuable long-term intervention window for research on antidepressant therapy.

Abstract: A method for interpreting seismic data includes receiving seismic data that represents a subterranean volume, and generating inline probability values and crossline probability values using a first machine learning technique. The first machine learning technique is trained to identify one or more vertical fault lines in a seismic volume based on the seismic data. The method includes generating a merged data set by combining the inline probability values and the crossline probability values, training a second machine learning technique based on a subset of labeled horizontal planes from the merged data set, the second machine learning technique trained to identify horizontal fault lines from the seismic volume, and generating a representation of the seismic volume based on the second machine learning technique, the representation including an indication of a three-dimensional fault structure within the seismic volume.

Abstract: A computer-implemented method includes receiving a test seismic dataset associated with a known truth interpretation, receiving one or more hard constraints, training a machine learning system based on the test seismic dataset, the known truth interpretation, and the one or more hard constraints, determining an error value based on the training the machine learning system, adjusting the error value based on one or more soft constraints, updating the training of the machine learning system based on the adjusted error value, receiving a second seismic dataset after the updating the training; applying the second seismic dataset to the machine learning system to generate an interpretation of the second seismic dataset, generating a seismic image representing a subterranean domain based on the interpretation of the second seismic dataset, and outputting the seismic image.

Abstract: The present disclosure discloses a visualized screening method for an Aspergillus recombinant strain with multigene editing and belongs to the technical field of gene engineering. CRISPR-Cas9 is used in the disclosure to cleave spore color change-related genes and a target gene in Aspergillus at the same time, such that editing of the target gene is visualized and an Aspergillus niger strain with multigene editing can be rapidly and efficiently screened out through spore phenotypes. Through different combinations of visualized genes and non-phenotypic change genes, rapid screening of the strain with multigene editing and simultaneous screening of multiple visualized genes are realized, and use of resistance genes in industrial strains is reduced.

Abstract: The disclosure provides an application of an anthocyanin extract in preparing a pharmaceutical composition for preventing and treating cardiac toxicity induced by Anthracyclines and the pharmaceutical composition, wherein the pharmaceutical composition comprises an effective dose of the anthocyanin extract; and the anthocyanin extract refers to an extract obtained from mature fruits of a zygophyllaceous nitraria plant and having a total anthocyanin content greater than 700 mg CGE/g and a total antioxidant capacity greater than 200 mg TE/g. Cytobiology and molecular biology studies prove that the anthocyanin extract has obvious protection effect to cardiomyocyte toxicity damage induced by anthracyclines and may be used for preventing and alleviating cardiotoxicity of an antharcyclines anticancer drug, thereby expanding a clinical application of the antharcyclines anticancer drug and relieving toxic and side effects brought by the antharcyclines anticancer drug to a patient and pain of a chemotherapy patient.

Abstract: A method includes determining a top of salt (TOS) surface in a seismic volume based on a crossline direction of the seismic volume and an inline direction of the seismic volume. The method also includes determining a binary mask based upon the TOS surface. The method also includes sampling seismic data in the seismic volume to obtain a training seismic slice. The method also includes sampling the binary mask to obtain a mask slice. The method also includes selecting a first coordinate in the training seismic slice to produce a first tile. The method also includes selecting a second coordinate in the mask slice to produce a second tile. The method also includes generating or updating a model of the seismic volume based upon the first tile and the second tile.

Abstract: The disclosure provides an application of an anthocyanin extract in preparing a pharmaceutical composition for preventing and treating cardiac toxicity induced by Anthracyclines and the pharmaceutical composition, wherein the pharmaceutical composition comprises an effective dose of the anthocyanin extract; and the anthocyanin extract refers to an extract obtained from mature fruits of a zygophyllaceous nitraria plant and having a total anthocyanin content greater than 700 mg CGE/g and a total antioxidant capacity greater than 200 mg TE/g. Cytobiology and molecular biology studies prove that the anthocyanin extract has obvious protection effect to cardiomyocyte toxicity damage induced by anthracyclines and may be used for preventing and alleviating cardiotoxicity of an antharcyclines anticancer drug, thereby expanding a clinical application of the antharcyclines anticancer drug and relieving toxic and side effects brought by the antharcyclines anticancer drug to a patient and pain of a chemotherapy patient.