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We have conducted a study to investigate the sensitivity of interferometric synthetic aperture radar (InSAR) measurement to subsurface strain change and developed an inversion framework to use InSAR data for subsurface surveillance. Using the spatial domain and spatial-frequency-domain analysis, we have determined that the earth behaves as a low-pass filter in its transmission of deformation from the subsurface to the earth’s surface. As a result of this low-pass filtering effect of the earth, the horizontal resolution of InSAR images is roughly equal to the depth of the targeted activity. Therefore, the goal of InSAR data inversion is to recover high-spatial-frequency subsurface strains from InSAR measurements. Because changes in the reservoir tend to have a lower spatial-frequency InSAR signature, high-spatial-frequency surface displacement can be associated with shallow overburden activities. Based on these insights, we have developed an inversion workflow that takes into account the overburden and reservoir strain changes and applied it to an InSAR data set from the San Joaquin Valley. Our results indicate that an inversion model that only considers strain changes in the reservoir produces large spatially localized inversion residuals in locations with known shallow overburden activity. A residual analysis reveals that the high-spatial-frequency anomalies in InSAR data can be used to identify shallow activities.

Obtaining functional human cells through interspecies chimerism with human pluripotent stem cells (hPSCs) remains unsuccessful due to its extremely low efficiency. Here, we show that hPSCs failed to differentiate and contribute teratoma in the presence of mouse PSCs (mPSCs), while MYCN, a pro-growth factor, dramatically promotes hPSC contributions in teratoma co-formation by hPSCs/mPSCs. MYCN combined with BCL2 (M/B) greatly enhanced conventional hPSCs to integrate into pre-implantation embryos of different species, such as mice, rabbits, and pigs, and substantially contributed to mouse post-implantation chimera in embryonic and extra-embryonic tissues. Strikingly, M/B-hPSCs injected into pre-implantation Flk-1

Summary Interferometric synthetic aperture radar (InSAR) data provide a measurement of the Earth’s surface displacements to monitor reservoir stresses, fluid pressure, and volume changes. However, the InSAR measurements may suffer from poor sensitivity and resolution. To improve the sensitivity of the InSAR data and localize the effects of the near-surface overburden, we employ a Green’s function retrieval (GFR) approach that uses time-lapse InSAR data. In this work, we derive the equations and compute the sensitivity between InSAR displacements caused by the reservoir changes with respect to observation points (i.e., virtual sources) at the surface. We present this method with time-lapse InSAR data from an oil field in the San Joaquin Valley to demonstrate the improved resolution of the GFR-InSAR measurements for subsurface imaging and continuous reservoir monitoring with applications to development, production, and subsurface integrity.

The downhole monitoring of strain using Fiber Optics (FO) can reveal unique information about the propagation and geometry of hydraulic fractures between nearby wells during stimulation and production. This work aims at creating a catalogue of commonly observed strain-rate signals captured in a not yet stimulated nearby observation well equipped with either a permanently or temporarily installed FO cable. This catalogue is the result of an informal collaboration between experience FO users from academia, service providers, consulting companies, and operators.In the creation of this first edition of a strain-rate catalogue, we considered two main types of stimulation categories (single and multi-entry) as well as the angle between the hydraulic fractures and the segment of the well where the strain-rate signals are observed (horizontal vs. vertical segments). In the catalogue we show a series of representative examples of two main types of far-field strain Fracture Driven Interactions (s-FDI) commonly encountered in frac diagnostics: 1. Vertical hydraulic fractures being monitored in a lateral portion of a horizontal well and 2. Vertical fractures being monitored in a vertical observation well. The catalogue is organized around commonly observed s-FDI motifs. Because interpretation of observed strain-rate signals can be subjective, when possible, we included observed examples with a brief description of our interpretation, as well as synthetic signals from geomechanical models of similar motifs. The strain-rate motifs were modeled based on first physical principles for rock deformation. These models serve to support the proposed interpretation of the observed signals.FO strain rate monitoring is changing our understanding about the hydraulics fracturing process. The information from FO strain is not available by other commonly used fracture diagnostic techniques. Strain- rate fractures driven interactions between wells occur in predictable patterns (Frac Domain and Stage Domain Corridors – FDC & SDC respectively) which are typically in line with the cluster spacing and stage length in the borehole being stimulated. Using FO strain monitoring, we now know that hydraulic fractures are larger than first anticipated, both in length and height. Many examples indicated that there is a direct correspondence between the near-field and far-field stimulation geometries. The lack of isolation due to cement quality and or plug failure manifests in the far-field geometries observed via FO strain-rate in nearby wells. The use of FO strain monitoring has also revealed that reopening of hydraulic fractures is common not only between prior and infill wells but also between wells from the same stimulation vintage. All these observations and conditions must be considered when interpreting new strain-rate datasets and more importantly when designing new hydraulic fracturing operations and considering different stimulation order (zipper schedule), as well as when making decisions about the vertical and lateral spacing of adjacent wells.The purpose of this industry-first edition strain-rate catalogue is to aid, new and experienced FO users, on the interpretation of strain-rate datasets. Ultimately, the accurate interpretation of FO strain data will not only help calibrate geomechanical and reservoir models but also directly influence where and how we complete unconventional wells. Nowadays, many s-FDI examples exist in scattered publications with formats that aren’t easily comparable for new users of the technology. In this project, we expand upon those publications to create an encompassing analysis with up-to-date interpretations where we have formalized the formatting of figures for better readability (color scheme, scales, etc.). What has resulted from this collaborative effort is a novel catalogue not available before in the FO published literature.

Summary The objective of this study is to understand how microseismic events are generated during hydraulic fracturing, as well as the role of geomechanical conditions (i.e., stress and mechanical stratigraphy) in this process. In the industry, microseismic event clouds have been generally used as an “outer boundary” of the “stimulated reservoir volume” (SRV). However, by comparing with other surveillance data (low-frequency distributed acoustic sensing, or LF-DAS, strain) in the Hydraulic Fracturing Test Site (HFTS) 2 experiment, we show that this assumption is fundamentally flawed. The HFTS 2 data have three unique observations that have not been commonly observed in other datasets: (1) Due to the influence of offset pad depletion, microseismic data show that hydraulic fractures from the child well can propagate over 3,000 ft into the depleted low-stress zone. (2) By comparing microseismic and horizontal fiber LF-DAS strain data, we observe that the microseismic event cloud does not necessarily reflect the created hydraulic fracture volume. Particularly, the extent of microseismic event clouds near heel stages is much shorter than what is shown with LF-DAS strain data. (3) Microseismic event magnitudes are larger in the depleted regions. Through geomechanical analysis, we demonstrate that the “bedding-plane-slip” model is likely the mechanism for microseismic generation during hydraulic fracturing. This model successfully explains the above field observations from the HFTS 2 experiment. We also provide a quantitative relationship connecting the microseismic event magnitude with fracture width increment and layer mechanical property contrast.

Protein kinase C epsilon (PKCε) belongs to a family of serine/threonine kinases that control cell proliferation, differentiation and survival. Aberrant PKCε activation and overexpression is a frequent feature of numerous cancers. However, its role in regulation of lipid metabolism in cancer cells remains elusive. Here we report a novel function of PKCε in regulating of prostate cancer cell proliferation by modulation of PKM2-mediated de novo lipogenesis. We show that PKCε promotes de novo lipogenesis and tumor cell proliferation via upregulation of lipogenic enzymes and lipid contents in prostate cancer cells. Mechanistically, PKCε interacts with NABD (1-388) domain of C-terminal deletion on pyruvate kinase isoform M2 (PKM2) and enhances the Tyr105 phosphorylation of PKM2, leading to its nuclear localization. Moreover, forced expression of mutant Tyr105 (Y105F) or PKM2 inhibition suppressed de novo lipogenesis and cell proliferation induced by overexpression of PKCε in prostate cancer cells. In a murine tumor model, inhibitor of PKM2 antagonizes lipogenic enzymes expression and prostate cancer growth induced by overexpression of PKCε in vivo. These data indicate that PKCε is a critical regulator of de novo lipogenesis, which may represent a potential therapeutic target for the treatment of prostate cancer.

Interferometric synthetic aperture radar (InSAR) data provides a measurement of the Earth's surface displacements to monitor reservoir stresses, fluid pressure and volume changes. However, the InSAR measurements may suffer from poor sensitivity and resolution. To improve the sensitivity of the InSAR data and localize the effects of the near-surface overburden, we employ a Green's function retrieval (GFR) approach that uses time-lapse InSAR data. In this work, we derive the equations and compute the sensitivity between InSAR displacements caused by the reservoir changes with respect to observation points (i.e., virtual sources) at the surface. We present this method with time-lapse InSAR data from an oil field in the San-Joaquin Valley to demonstrate improved resolution of the GFR-InSAR measurements for subsurface imaging and continuous reservoir monitoring with applications to development, production, and subsurface integrity.

Part segmentations provide a rich and detailed part-level description of objects. However, their annotation requires an enormous amount of work, which makes it difficult to apply standard deep learning methods. In this paper, we propose the idea of learning part segmentation through unsupervised domain adaptation (UDA) from synthetic data. We first introduce UDA-Part, a comprehensive part segmentation dataset for vehicles that can serve as an adequate benchmark for UDA (https://qliu24.github.io/udapart). In UDA-Part, we label parts on 3D CAD models which enables us to generate a large set of annotated synthetic images. We also annotate parts on a number of real images to provide a real test set. Secondly, to advance the adaptation of part models trained from the synthetic data to the real images, we introduce a new UDA algorithm that leverages the object's spatial structure to guide the adaptation process. Our experimental results on two real test datasets confirm the superiority of our approach over existing works, and demonstrate the promise of learning part segmentation for general objects from synthetic data. We believe our dataset provides a rich testbed to study UDA for part segmentation and will help to significantly push forward research in this area., Comment: CVPR 2022 (Oral)

In this paper, the commissioning method of the current-controlled oscillator (ICO) is researched, making the ICO work under the required amplitude and frequency and exploring the best phase noise performance of ICO under the constraints of power dissipation and area. Through the theoretical analysis and simulation experiments, the commissioning process of ICO is clarified, which can meet the basic indexes of ICO, especially the amplitude and frequency, and optimize the phase noise performance of ICO.

Disseminated prostate cancer (PCa) is known to have a strong propensity for bone marrow. These disseminated tumor cells (DTCs) can survive in bone marrow for years without obvious proliferation, while maintaining the ability to develop into metastatic lesions. However, how DTCs kept dormant and recur is still uncertain. Here, we focus on the role of osteoblastic protein kinase D1 (PKD1) in PCa (PC-3 and DU145) dormancy using co-culture experiments. Using flow cytometry, western blotting, and immunofluorescence, we observed that in co-cultures osteoblasts could induce a dormant state in PCa cells, which is manifested by a fewer cell divisions, a decrease Ki-67-positive populations and a lower ERK/p38 ratio. In contrast, silencing of PKD1 gene in osteoblasts impedes co-cultured prostate cancer cell's dormancy ability. Mechanismly, protein kinase D1 (PKD1) in osteoblasts induces PCa dormancy via activating CREB1, which promoting the expression and secretion of growth arrest specific 6 (GAS6). Furthermore, GAS6-induced dormancy signaling significantly increased the expression of core circadian clock molecules in PCa cells, and a negative correlation of circadian clock proteins (BMAL1, CLOCK and DEC2) with recurrence-free survival is observed in metastatic prostate cancer patients. Interestingly, the expression of cell cycle factors (p21, p27, CDK1 and PCNA) which regulated by circadian clock also upregulated in response to GAS6 stimulation. Taken together, we provide evidence that osteoblastic PKD1/CREB1/GAS6 signaling regulates cellular dormancy of PCa cells, and highlights the importance of circadian clock in PCa cells dormancy.