Research Advance on Prediction and Optimization for Fracture Propagation in Stimulated Unconventional Reservoirs

AbstractMultistage stimulation horizontal wells are prerequisite technologies for efficient development of unconventional reservoir. However, the induced fracture network morphology from hydraulic fracturing is very complex and affected by many factors, such as the in situ stress, rock mechanical properties, and natural fracture distribution. The large numbers of natural fractures and strong reservoir heterogeneity in unconventional reservoirs result in enhanced complexity of induced fractures from hydraulic fracturing. Accurate description of fracture network morphology and the flow capacity in different fractures form an important basis for production forecasting, evaluation (or optimization) of stimulation design, and development plan optimization. This paper focuses on hydraulic fracturing in unconventional reservoirs and discusses the current research advances from four aspects: (1) the prediction of induced fracture propagation, (2) the simulation of fluid flow in complex fracture networks, (3) the inversion of fracture parameter (fracture porosity, fracture permeability, etc.), and (4) the optimization of hydraulic fracturing in unconventional reservoirs. In addition, this paper provides comparative analysis of the characteristics and shortcomings of the current research by outlining the key technical problems in the study of flow characterization, parameter inversion, and optimization methods for stimulation in unconventional reservoirs. This work can provide a certain guiding role for further research

Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task

Resource limitations make it hard to provide all students with one of the most effective educational interventions: personalized instruction. Reinforcement learning could be a key tool to reduce the development cost and improve the effectiveness of, intelligent tutoring software that aims to provide the right support, at the right time, to a student. Here we illustrate that deep reinforcement learning can be used to provide adaptive pedagogical support to students learning about the concept of volume in a narrative storyline software. Using explainable artificial intelligence tools, we also extracted interpretable insights about the pedagogical policy learned, and we demonstrate that the resulting policy had similar performance in a different student population. Most importantly, in both studies the reinforcement-learning narrative system had the largest benefit for those students with the lowest initial pretest scores, suggesting the opportunity for AI to adapt and provide support for those most in need.Comment: 23 pages. Under revie

RNA sequencing reveals CircRNA expression profiles in chicken embryo fibroblasts infected with velogenic Newcastle disease virus

IntroductionNewcastle disease virus (NDV) is an important avian pathogen prevalent worldwide; it has an extensive host range and seriously harms the poultry industry. Velogenic NDV strains exhibit high pathogenicity and mortality in chickens. Circular RNAs (circRNAs) are among the most abundant and conserved eukaryotic transcripts. They are part of the innate immunity and antiviral response. However, the relationship between circRNAs and NDV infection is unclear.MethodsIn this study, we used circRNA transcriptome sequencing to analyze the differences in circRNA expression profiles post velogenic NDV infection in chicken embryo fibroblasts (CEFs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to reveal significant enrichment of differentially expressed (DE) circRNAs. The circRNA- miRNA-mRNA interaction networks were further predicted. Moreover, circ-EZH2 was selected to determine its effect on NDV infection in CEFs.ResultsNDV infection altered circRNA expression profiles in CEFs, and 86 significantly DE circRNAs were identified. GO and KEGG enrichment analyses revealed significant enrichment of DE circRNAs for metabolism-related pathways, such as lysine degradation, glutaminergic synapse, and alanine, aspartic-acid, and glutamic-acid metabolism. The circRNA- miRNA-mRNA interaction networks further demonstrated that CEFs might combat NDV infection by regulating metabolism through circRNA-targeted mRNAs and miRNAs. Furthermore, we verified that circ-EZH2 overexpression and knockdown inhibited and promoted NDV replication, respectively, indicating that circRNAs are involved in NDV replication.ConclusionsThese results demonstrate that CEFs exert antiviral responses by forming circRNAs, offering new insights into the mechanisms underlying NDV-host interactions

Genome-wide CRISPR/Cas9 screening for drug resistance in tumors

Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated nuclease 9 (Cas9) screening is a simple screening method for locating loci under specific conditions, and it has been utilized in tumor drug resistance research for finding potential drug resistance-associated genes. This screening strategy has significant implications for further treatment of malignancies with acquired drug resistance. In recent years, studies involving genome-wide CRISPR/Cas9 screening have gradually increased. Here we review the recent application of genome-wide CRISPR/Cas9 screening for drug resistance, involving mitogen-activated protein kinase (MAPK) pathway inhibitors, poly (ADP-ribose) polymerase inhibitors (PARPi), alkylating agents, mitotic inhibitors, antimetabolites, immune checkpoint inhibitors (ICIs), and cyclin-dependent kinase inhibitors (CDKI). We summarize drug resistance pathways such as the KEAP1/Nrf2 pathway MAPK pathway, and NF-κB pathway. Also, we analyze the limitations and conditions for the application of genome-wide CRISPR/Cas9 screening techniques

Intracranial vessel wall segmentation with deep learning using a novel tiered loss function incorporating class inclusion.

PURPOSE: To develop an automated vessel wall segmentation method on T1-weighted intracranial vessel wall magnetic resonance images, with a focus on modeling the inclusion relation between the inner and outer boundaries of the vessel wall. METHODS: We propose a novel method that estimates the inner and outer vessel wall boundaries simultaneously, using a network with a single output channel resembling the level-set function height. The network is driven by a unique tiered loss that accounts for data fidelity of the lumen and vessel wall classes and a length regularization to encourage boundary smoothness. RESULTS: Implemented with a 2.5D UNet with a ResNet backbone, the proposed method achieved Dice similarity coefficients (DSC) in 2D of 0.925 ± 0.048, 0.786 ± 0.084, Hausdorff distance (HD) of 0.286 ± 0.436, 0.345 ± 0.419 mm, and mean surface distance (MSD) of 0.083 ± 0.037 and 0.103 ± 0.032 mm for the lumen and vessel wall, respectively, on a test set; compared favorably to a baseline UNet model that achieved DSC 0.924 ± 0.047, 0.794 ± 0.082, HD 0.298 ± 0.477, 0.394 ± 0.431 mm, and MSD 0.087 ± 0.056, 0.119 ± 0.059 mm. Our vessel wall segmentation method achieved substantial improvement in morphological integrity and accuracy compared to benchmark methods. CONCLUSIONS: The proposed method provides a systematic approach to model the inclusion morphology and incorporate it into an optimization infrastructure. It can be applied to any application where inclusion exists among a (sub)set of classes to be segmented. Improved feasibility in result morphology promises better support for clinical quantification and decision

VWI-APP: Vessel wall imaging-dedicated automated processing pipeline for intracranial atherosclerotic plaque quantification.

BACKGROUND: Quantitative plaque assessment based on 3D magnetic resonance (MR) vessel wall imaging (VWI) has been shown to provide valuable numerical markers of the burden and risk of intracranial atherosclerotic disease (ICAD). However, plaque quantification is currently time-consuming and observer-dependent due to the demand for heavy manual effort. A VWI-dedicated automated processing pipeline (VWI-APP) is desirable. PURPOSE: To develop and evaluate a VWI-APP for end-to-end quantitative analysis of intracranial atherosclerotic plaque. METHODS: We retrospectively enrolled 91 subjects with ICAD (80 for pipeline development, 10 for an end-to-end pipeline evaluation, and 1 for demonstrating longitudinal plaque assessment) who had undergone VWI and MR angiography. In an end-to-end evaluation, diameter stenosis (DS), normalized wall index (NWI), remodeling ratio (RR), plaque wall contrast ratio (CR), and total plaque volume (TPV) were quantified at each culprit lesion using the developed VWI-APP and a computer-aided manual approach by a neuroradiologist, respectively. The time consumed in each quantification approach was recorded. Two-sided paired t-tests and intraclass correlation coefficient (ICC) were used to determine the difference and agreement in each plaque metric between VWI-APP and manual quantification approaches. RESULTS: There was no significant difference between VWI-APP and manual quantification in each plaque metric. The ICC was 0.890, 0.813, 0.827, 0.891, and 0.991 for DS, NWI, RR, CR, and TPV, respectively, suggesting good to excellent accuracy of the pipeline method in plaque quantification. Quantitative analysis of each culprit lesion on average took 675.7 s using the manual approach but shortened to 238.3 s with the aid of VWI-APP. CONCLUSIONS: VWI-APP is an accurate and efficient approach to intracranial atherosclerotic plaque quantification. Further clinical assessment of this automated tool is warranted to establish its utility in the risk assessment of ICAD lesions

MRA-free intracranial vessel localization on MR vessel wall images.

Analysis of vessel morphology is important in assessing intracranial atherosclerosis disease (ICAD). Recently, magnetic resonance (MR) vessel wall imaging (VWI) has been introduced to image ICAD and characterize morphology for atherosclerotic lesions. In order to automatically perform quantitative analysis on VWI data, MR angiography (MRA) acquired in the same imaging session is typically used to localize the vessel segments of interest. However, MRA may be unavailable caused by the lack or failure of the sequence in a VWI protocol. This study aims to investigate the feasibility to infer the vessel location directly from VWI. We propose to synergize an atlas-based method to preserve general vessel structure topology with a deep learning network in the motion field domain to correct the residual geometric error. Performance is quantified by examining the agreement between the extracted vessel structures from the pair-acquired and alignment-corrected angiogram, and the estimated output using a cross-validation scheme. Our proposed pipeline yields clinically feasible performance in localizing intracranial vessels, demonstrating the promise of performing vessel morphology analysis using VWI alone

Fully automated multiorgan segmentation in abdominal magnetic resonance imaging with deep neural networks

PurposeSegmentation of multiple organs-at-risk (OARs) is essential for magnetic resonance (MR)-only radiation therapy treatment planning and MR-guided adaptive radiotherapy of abdominal cancers. Current practice requires manual delineation that is labor-intensive, time-consuming, and prone to intra- and interobserver variations. We developed a deep learning (DL) technique for fully automated segmentation of multiple OARs on clinical abdominal MR images with high accuracy, reliability, and efficiency.MethodsWe developed Automated deep Learning-based abdominal multiorgan segmentation (ALAMO) technique based on two-dimensional U-net and a densely connected network structure with tailored design in data augmentation and training procedures such as deep connection, auxiliary supervision, and multiview. The model takes in multislice MR images and generates the output of segmentation results. 3.0-Tesla T1 VIBE (Volumetric Interpolated Breath-hold Examination) images of 102 subjects were used in our study and split into 66 for training, 16 for validation, and 20 for testing. Ten OARs were studied, including the liver, spleen, pancreas, left/right kidneys, stomach, duodenum, small intestine, spinal cord, and vertebral bodies. An experienced radiologist manually labeled each OAR, followed by reediting, if necessary, by a senior radiologist, to create the ground-truth. The performance was measured using volume overlapping and surface distance.ResultsThe ALAMO technique generated segmentation labels in good agreement with the manual results. Specifically, among the ten OARs, nine achieved high dice similarity coefficients (DSCs) in the range of 0.87-0.96, except for the duodenum with a DSC of 0.80. The inference completed within 1 min for a three-dimensional volume of 320 × 288 × 180. Overall, the ALAMO model matched the state-of-the-art techniques in performance.ConclusionThe proposed ALAMO technique allows for fully automated abdominal MR segmentation with high accuracy and practical memory and computation time demands

Proteins secreted by brain arteriolar smooth muscle cells are instructive for neural development

Abstract Intercellular communication between vascular and nerve cells mediated by diffusible proteins has recently emerged as a critical intrinsic program for neural development. However, whether the vascular smooth muscle cell (VSMC) secretome regulates the connectivity of neural circuits remains unknown. Here, we show that conditioned medium from brain VSMC cultures enhances multiple neuronal functions, such as neuritogenesis, neuronal maturation, and survival, thereby improving circuit connectivity. However, protein denaturation by heating compromised these effects. Combined omics analyses of donor VSMC secretomes and recipient neuron transcriptomes revealed that overlapping pathways of extracellular matrix receptor signaling and adhesion molecule integrin binding mediate VSMC-dependent neuronal development. Furthermore, we found that human arterial VSMCs promote neuronal development in multiple ways, including expanding the time window for nascent neurite initiation, increasing neuronal density, and promoting synchronized firing, whereas human umbilical vein VSMCs lack this capability. These in vitro data indicate that brain arteriolar VSMCs may carry direct instructive information for neural development through intercellular communication in vivo

Cobalt-Doping Induced Formation of Five-Coordinated Nickel Selenide for Enhanced Ethanol Assisted Overall Water Splitting

To overcome the low efficiency of overall water splitting, highly effective and stable catalysts are in urgent need, especially for the anode oxygen evolution reaction (OER). In this case, nickel selenides appear as good candidates to catalyze OER and other substitutable anodic reactions due to their high electronic conductivity and easily tunable electronic structure to meet the optimized adsorption ability. Herein, an interesting phase transition from the hexagonal phase of NiSe (H-NiSe) to the rhombohedral phase of NiSe (R-NiSe) induced by the doping of cobalt atoms is reported. The five-coordinated R-NiSe is found to grow adjacent to the six-coordinated H-NiSe, resulting in the formation of the H-NiSe/R-NiSe heterostructure. Further characterizations and calculations prove the reduced splitting energy for R-NiSe and thus the less occupancy in the t2g orbits, which can facilitate the electron transfer process. As a result, the Co2-NiSe/NF shows a satisfying catalytic performance toward OER, hydrogen evolution reaction, and (hybrid) overall water splitting. This work proves that trace amounts of Co doping can induce the phase transition from H-NiSe to R-NiSe. The formation of less-coordinated species can reduce the t2g occupancy and thus enhance the catalytic performance, which might guide rational material design.補正完畢DE