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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.

SummaryRecently more distributed acoustic sensing (DAS) data have been collected during hydraulic fracturing in shale. Low-frequency DAS signals show patterns that are intuitively consistent with the understanding of the strain field around hydraulic fractures. This study uses a fracture simulator combined with a finite element solver to further understand the various patterns of the strain field caused by hydraulic fracturing. The results can serve as a “type-curve” template for the further interpretation of cross-well strain field plots.Incorporating detailed pump schedule and fracturing fluid/proppant properties, we use a hydraulic fracture simulator to generate fracture geometries, which are then passed to a finite element solver as boundary conditions for elastic-static calculation of the strain field. Because the finite element calculated strain is a tensor, it needs to be projected along the monitoring well trajectory to be comparable with the DAS strain, which is uniaxial. Moreover, the calculated strain field is transformed into a time domain using constant fracture propagation velocity. Strain rate is further derived from the simulated strain field using differentiation along the fracture propagation direction.Scenarios including a single planar hydraulic fracture, a single fracture with a discrete fracture network (DFN), and multiple planar hydraulic fractures in both vertical and horizontal directions were studied. The scenarios can be differentiated in the strain patterns on the basis of the finite element simulation results. In general, there is a tensile heart-shaped zone in front of the propagating fracture tip shown along the horizontal strain direction on both strain and strain rate plots. On the sides, there are compressional zones parallel to the fracture. The strain field projects beyond the depth where the hydraulic fracture is present. Patterns from strain rate can be used to distinguish whether the fracture is intersecting the fiber. Along the vertical direction, the transition zone depicts the upper boundary of the fracture. A complex fracture network with DFN shows a much more complex pattern compared with a single planar fracture. Multiple planar fractures show polarity reversals in horizontal fiber because of interactions between fractures. Data from the Hydraulic Fracturing Test Site 2 (HFTS2) experiment were used to validate the simulated results.The application of the study is to provide a template to better interpret hydraulic fracture characteristics using low-frequency DAS strain-monitoring data. To our understanding, there are no comprehensive templates for engineers to understand the strain signals from cross-well fiber monitoring. The results of this study will guide engineers toward better optimization of well spacing and fracturing design to minimize well interference and improve efficiency.

We modeled cross-well strain/strain rate responses of fiber optic sensing, including distributed strain sensing (DSS) and low-frequency distributed acoustic sensing (DAS), to hydraulic stimulation. DSS and low-frequency DAS have been used to measure strain or the strain rate to characterize hydraulic fractures. However, the current application of DSS/DAS is limited to acquisition, processing, and qualitative interpretations. The lack of geomechanical models hinders the development of the technology toward quantitative interpretation and inversion. We have developed a strategy to use the displacement discontinuity method to model the strain field around kinematically propagating fractures. For a horizontal monitoring well, modeling results were able to explain the heart-shaped extending pattern before a fracture hit, the polarity flip due to fracture interaction during stimulation, and the V-shaped pattern when a fracture does not intersect with the monitoring well. For a vertical monitoring well, modeling shows the different characters of strain rate responses when a fracture is near and far away from a vertical monitoring well. We also investigated the effects of fractures with various geometries such as elliptic and layered fractures. We compared and verified the modeling with field data from the Hydraulic Fracturing Test Site 2, a research experiment performed in the Permian Basin. Our modeling work can be used to identify patterns in field observations. The results also help to improve acquisition design and lay the groundwork for quantitative interpretation and inversion.

Crosscorrelation is a classical signal-processing technique that plays an important role in exploration and earthquake geophysics. Seismic velocity estimation utilizes the crosscorrelation between observed and predicted seismic records in traveltime tomography. The crosscorrelation between two stations represents the Green’s functions retrieved from ambient noises in passive seismic interferometry. It can be used to estimate the subsurface velocity and amplitude information. The calculation of crosscorrelation usually assumes that the input data are stationary; however, the real seismic data are often non-stationary, due to the presence of multiple wave-modes and background noises. The seismic crosscorrelations often have low signal-to-noise ratio and frequently fail to provide correct information for subsequent processing. To address this problem, we develop a comprehensive technique to reduce contamination and improve the quality of crosscorrelation in the wavelet domain. The new procedure includes the forward wavelet transformation of raw records, the crosscorrelation between wavelet coefficients, single-channel image object detection, multi-channel Kalman-filter object tracking, and inverse wavelet transformation to produce the new crosscorrelation gathers. We effectively remove the unwanted components associated with contaminated wave-modes as the proposed detection and tracking algorithm can accurately extract the target wave-mode. We validate the method for three datasets: a marine streamer survey, a borehole survey, and a broadband dataset from seismology stations. We demonstrate that the proposed method can significantly improve the signal-to-noise ratio of the seismic crosscorrelations, considerably enhancing the quality of the data for subsequent advanced crosscorrelation-based seismic processing.

Improving the fertility of sheep is an important goal in sheep breeding as it greatly increases the productivity. Dolang sheep is a typical representative breed of lamb in Xinjiang and is the main local sheep breed and meat source in the region. To explore the genes associated with the initiation of puberty in Dolang sheep, the hypothalamic tissues of Dolang sheep prepubertal, pubertal, and postpubertal periods were collected for RNA-seq analysis on the Illumina platform, generating 64.08 Gb clean reads. A total of 575, 166, and 648 differentially expressed genes (DEGs) were detected in prepuberty_vs._puberty, postpuberty_vs._prepuberty, and postpuberty_vs._puberty analyses, respectively. Based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, the related genes involved in the initiation of puberty in Dolang sheep were mined. Ten genes that have direct or indirect functions in the initiation of puberty in Dolang sheep were screened using the GO and KEGG results. Additionally, quantitative real-time PCR was used to verify the reliability of the RNA-Seq data. This study provided a new approach for revealing the mechanism of puberty initiation in sheep and provided a theoretical basis and candidate genes for the breeding of early-pubertal sheep by molecular techniques, and at the same time, it is also beneficial for the protection, development, and utilization of the fine genetic resources of Xinjiang local sheep.

Obtaining functional human cells through interspecies chimerism with human pluripotent stem cells (hPSCs) remains unsuccessful due to its extreamly low efficiency. Here, we show that hPSCs failed to differentiate and contribute teratoma in presence of mouse PSCs (mPSCs). While, MYCN, a pro-growth factor dramatically promotes hPSC contribution in teratoma co-formation by hPSCs/mPSCs. MYCN combined with BCL2 (M/B) greatly enhanced conventional hPSCs to integrate into preimplantation embryos of different species, such as mice, rabbits and pigs and substrantially contributed to mouse post-implantation chimera in embryonic and extra-embyonic tissues. Strikingly, M/B-hPSCs injected into preimplantation Flk-1+/- mouse embryos show further enhanced chimerism that allows obtaining live human CD34+ blood progenitor cells from chimeras through cell sorting. The chimera derived human CD34+ cells further gave rise to various subtype blood cells in typical colony forming unit(CFU) assay. Thus, we proof the concept to obtain functional human cells through enchanced interspecies chimerism with hPSCs.

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)

Arrival-time picking is an essential step in seismic processing and imaging. The explosion of seismic data volume requires automated arrival-time picking in a faster and more reliable way than existing methods. We have treated arrival-time picking as a binary image segmentation problem and used an improved pixel-wise convolutional network to pick arrival times automatically. Incorporating continuous spatial information in training enables us to preserve the arrival-time correlation between nearby traces, thus helping to reduce the risk of picking outliers that are common in a traditional trace-by-trace picking method. To train the network, we first convert seismic traces into gray-scale images. Image pixels before manually picked arrival times are labeled with zeros, and those after are tagged with ones. After training and validation, the network automatically learns representative features and generates a probability map to predict the arrival time. We apply the network to a field microseismic data set that was not used for training or validation to test the performance of the method. Then, we analyze the effects of training data volume and signal-to-noise ratio on our autopicking method. We also find the difference between 1D and 2D training data with borehole seismic data. Microseismic and borehole seismic data indicate the proposed network can improve efficiency and accuracy over traditional automated picking methods.

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.

The direct conversion of CO(2) to value-added chemical commodities, thereby storing solar energy, offers a promising option for alleviating both the current energy crisis and global warming. Semiconductor-biological hybrid systems are novel approaches. However, the inherent defects of photocorrosion, photodegradation, and the toxicity of the semiconductor limit the application of these biohybrid systems. We report here that Rhodopseudomonas palustris was able to directly act as a living photosensitizer to drive CO(2) to CH(4) conversion by Methanosarcina barkeri under illumination after coculturing. Specifically, R. palustris formed a direct electric syntrophic coculture with M. barkeri. Here, R. palustris harvested solar energy, performed anoxygenic photosynthesis using sodium thiosulfate as an electron donor, and transferred electrons extracellularly to M. barkeri to drive methane generation. The methanogenesis of M. barkeri in coculture was a light-dependent process with a production rate of 4.73 ± 0.23 μM/h under light, which is slightly higher than that of typical semiconductor-biohybrid systems (approximately 4.36 μM/h). Mechanistic and transcriptomic analyses showed that electrons were transferred either directly or indirectly (via electron shuttles), subsequently driving CH(4) production. Our study suggests that R. palustris acts as a natural photosensitizer that, in coculture with M. barkeri, results in a new way to harvest solar energy that could potentially replace semiconductors in biohybrid systems.

Fecundity is an important economic trait in sheep that directly affects their economic and productive efficiency. Our study aimed to identify SNP loci associated with sheep puberty or litter size which could be used in future breeding programs to improve fertility. Genomic DNA was obtained from Hetian and Cele Black sheep breeds and used for reduced-representation genome sequencing to identify SNP loci associated with pubertal initiation and litter size. Selective signatures analysis was performed based on the fixation index and nucleotide diversity, followed by pathway analysis of the genes contained in the selected regions. The selected SNP loci in the genes associated with pubertal initiation and litter size were validated using both sheep breeds. In total, 384,718 high quality SNPs were obtained and 376 genes were selected. Functional annotation of genes and enrichment analysis identified 12 genes associated with pubertal initiation and 11 genes associated with litter size. SNP locus validation showed that two SNP on PAK1 and four on ADCY1 may be associated with pubertal initiation, and one SNP on GNAQ gene (NC_040253.1: g.62677376G > A) was associated with litter size in Cele Black sheep. Our results provide new theoretical support for sheep breeding.

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.

The present nitrogen fixation industry is usually energy-intensive and environmentally detrimental. Therefore, it is appealing to find alternatives. Here, we achieved both a synchronized biological nitrogen fixation and electric energy production by using Geobacter sulfurreducens in a microbial electrochemical system. The results showed that G. sulfurreducens was able to fix nitrogen depending on anode respiration, producing a maximum current density of 0.17 ± 0.015 mA cm−2 and a nitrogen-fixing activity of ca. 0.78 μmol C2H4 mg protein−1 h−1, thereby achieving a net total nitrogen-fixing rate of ca. 5.6 mg L−1 day−1. Specifically, nitrogen fixation did not impair coulombic efficiency. Transcriptomic and metabolic analyses demonstrated that anode respiration provided sufficient energy to drive nitrogen fixation, and in turn nitrogen fixation promoted anode respiration of the cell by increasing acetate catabolism but reducing acetate anabolism. Furthermore, we showed that G. sulfurreducens could be supplied in a bioelectrochemical system for N-deficient wastewater treatment to relieve N-deficiency stress contributing to the formation of an electroactive biofilm, thereby simultaneously achieving nitrogen fixation, current generation and dissoluble organic carbon removal. Our study revealed a synergistic effect between biological nitrogen fixation and current generation by G. sulfurreducens, providing a green nitrogen fixation alternative through shifting the nitrogen fixation field from energy consumption to energy production and having implications for N-deficient wastewater treatment.

Background Mast cells are being increasingly recognized as critical components in the tumor microenvironment. Protein Kinase D (PKD) is essential for the progression of prostate cancer, but its role in prostate cancer microenvironment remains poorly understood. Methods The expression of PKD, mast cells and microvessel density were examined by IHC. The clinical significance was determined by statistical analyses. The biological function of PKD and the underlying mechanisms were investigated using in vitro and in vivo models. Results PKD2/3 contributed to MCs recruitment and tumor angiogenesis in the prostate cancer microenvironment. Clinical data showed that increased activation of PKD at Ser744/748 in prostate cancer was correlated with mast cell infiltration and microvascular density. PKD2/3 silencing of prostate cancer cells markedly decreased MCs migration and tube formation of HUVEC cells. Moreover, PKD2/3 depletion not only reduced SCF, CCL5 and CCL11 expression in prostate cancer cells but also inhibited angiogenic factors in MCs. Conversely, exogenous SCF, CCL5 and CCL11 reversed the effect on MCs migration inhibited by PKD2/3 silencing. Mechanistically, PKD2/3 interacted with Erk1/2 and activated Erk1/2 or NF-κB signaling pathway, leading to AP-1 or NF-κB binding to the promoter of scf, ccl5 and ccl11. Finally, PKD-specific inhibitor significantly reduced tumor volume and tumor growth in mice bearing RM-1 prostate cancer cells, which was attributed to attenuation of mast cell recruitment and tumor angiogenesis. Conclusions These results demonstrate a novel PKDs function that contributes to tumor angiogenesis and progression through mast cells recruitment in prostate cancer microenvironment. Electronic supplementary material The online version of this article (10.1186/s13046-019-1118-y) contains supplementary material, which is available to authorized users.

Camostat mesilate (CM) possesses potential anti-viral and anti-inflammatory activities. However, it remains unknown whether CM is involved in lipopolysaccharide (LPS)-mediated inflammatory responses and cell injury. In this project, differentially expressed proteins (DEPs, fold change ≥ 1.2 or ≤ 0.83 and Q value ≤ 0.05) in response to LPS stimulation alone or in combination with CM were identified through tandem mass tags (TMT)/mass spectrometry (MS)-based proteomics analysis in DF-1 chicken embryo fibroblasts. The mRNA expression levels of filtered genes were determined by RT-qPCR assay. The results showed that CM alleviated the detrimental effect of LPS on cell viability and inhibited LPS-induced TNF-α and IL-6 secretions in DF-1 chicken embryo fibroblasts. A total of 141 DEPs that might be involved in mediating functions of both LPS and CM were identified by proteomics analysis in DF-1 chicken embryo fibroblasts. LPS inhibited milk fat globule EGF and factor V/VIII domain containing (MFGE8) expression and induced high mobility group nucleosome binding domain 1 (HMGN1) expression, while these effects were abrogated by CM in DF-1 chicken embryo fibroblasts. MFGE8 knockdown facilitated TNF-α and IL-6 secretions , reduced cell viability, stimulated cell apoptosis in DF-1 chicken embryo fibroblasts co-treated with LPS and CM. HMGN1 loss did not influence TNF-α and IL-6 secretions, cell viability, and cell apoptosis in DF-1 chicken embryo fibroblasts co-treated with LPS and CM. In conclusion, CM exerted anti-inflammatory and pro-survival activities by regulating MFGE8 in LPS-stimulated DF-1 chicken embryo fibroblasts, deepening our understanding of the roles and molecular basis of CM in protecting against Gram-negative bacteria.

Definitive hematopoiesis generates hematopoietic stem/progenitor cells (HSPCs) that give rise to all mature blood and immune cells, but remains poorly defined in human. Here, we resolve human hematopoietic populations at the earliest hematopoiesis stage by single-cell RNA-seq. We characterize the distinct molecular profiling between early primitive and definitive hematopoiesis in both human embryonic stem cell (hESC) differentiation and early embryonic development. We identify CD44 to specifically discriminate definitive hematopoiesis and generate definitive HSPCs from hESCs. The multipotency of hESCs-derived HSPCs for various blood and immune cells is validated by single-cell clonal assay. Strikingly, these hESCs-derived HSPCs give rise to blood and lymphoid lineages in vivo. Lastly, we characterize gene-expression dynamics in definitive and primitive hematopoiesis and reveal an unreported role of ROCK-inhibition in enhancing human definitive hematopoiesis. Our study provides a prospect for understanding human early hematopoiesis and a firm basis for generating blood and immune cells for clinical purposes.

Recently new Distributed Acoustic Sensing (DAS) data have been collected during hydraulic fracturing in shale. Low frequency DAS signals show patterns that are intuitively consistent with the understanding of the strain field around hydraulic fractures. This study utilizes a fracture simulator combined with a finite element solver to further understand the various patterns of the strain field caused by hydraulic fracturing. The results can serve as a "type-curve" template for the further interpretation of cross well strain field plots. Incorporating detailed pump schedule and frac fluid/proppant properties, we use a hydraulic fracture simulator to generate fracture geometries, which are then passed to a finite element (FE) solver as boundary conditions for elastic-static calculation of the strain field. Since the FE calculated strain is a tensor, it needs to be projected along the monitoring well trajectory to be comparable with the fiber strain, which is uniaxial. Moreover, the calculated strain field is transformed into time domain using constant fracture propagation velocity. Strain rate is further derived from the simulated strain field using differentiation along fracture length. Scenarios including a single planar hydraulic fracture, a single fracture with a discrete fracture network (DFN), and multiple planar hydraulic fractures, in both vertical and horizontal directions were studied. The scenarios can be differentiated in the strain patterns based on the finite element simulation results. In general, there is a tensile heart shaped zone in front of the propagating fracture tip. On the sides there are compressional zones parallel to the fracture. Multiple planar fracture show polarity reversals in horizontal fiber due to interactions between fractures. Strain field/strain rate show consistent patterns with what is observed from field cross well strain data. The application of the study is to provide a template to better interpret hydraulic fracture characteristics using low frequency fiber strain monitoring. To the author's understanding, there are no comprehensive templates for engineers to understand the strain signals from cross well fiber monitoring. The results of this study will guide engineers toward better optimization of well spacing and frac design to minimize well interference and improve efficiency.

Detecting pedestrians and predicting future trajectories for them are critical tasks for numerous applications, such as autonomous driving. Previous methods either treat the detection and prediction as separate tasks or simply add a trajectory regression head on top of a detector. In this work, we present a novel end-to-end two-stage network: Spatio-Temporal-Interactive Network (STINet). In addition to 3D geometry modeling of pedestrians, we model the temporal information for each of the pedestrians. To do so, our method predicts both current and past locations in the first stage, so that each pedestrian can be linked across frames and the comprehensive spatio-temporal information can be captured in the second stage. Also, we model the interaction among objects with an interaction graph, to gather the information among the neighboring objects. Comprehensive experiments on the Lyft Dataset and the recently released large-scale Waymo Open Dataset for both object detection and future trajectory prediction validate the effectiveness of the proposed method. For the Waymo Open Dataset, we achieve a bird-eyes-view (BEV) detection AP of 80.73 and trajectory prediction average displacement error (ADE) of 33.67cm for pedestrians, which establish the state-of-the-art for both tasks.

Compositional convolutional networks are generative compositional models of neural network features, that achieve state of the art results when classifying partially occluded objects, even when they have not been exposed to occluded objects during training. In this work, we study the performance of CompositionalNets at localizing occluders in images. We show that the original model is not able to localize occluders well. We propose to overcome this limitation by modeling the feature activations as a mixture of von-Mises-Fisher distributions, which also allows for an end-to-end training of CompositionalNets. Our experimental results demonstrate that the proposed extensions increase the model's performance at localizing occluders as well as at classifying partially occluded objects., Presented at "NeurIPS 2019 workshop on Perception as generative reasoning" and "NeurIPS 2019 workshop on Context and Compositionality in Biological and Artificial Neural Systems". arXiv admin note: text overlap with arXiv:1905.11826

Figure S5. PKD2/3 silencing of prostate cancer cells reduced angiogenic factor expression in P815 MCs cells. (PDF 785 kb)

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%. Due to subtle texture changes of PDAC, pancreatic dual-phase imaging is recommended for better diagnosis of pancreatic disease. In this study, we aim at enhancing PDAC automatic segmentation by integrating multi-phase information (i.e., arterial phase and venous phase). To this end, we present Hyper-Pairing Network (HPN), a 3D fully convolution neural network which effectively integrates information from different phases. The proposed approach consists of a dual path network where the two parallel streams are interconnected with hyper-connections for intensive information exchange. Additionally, a pairing loss is added to encourage the commonality between high-level feature representations of different phases. Compared to prior arts which use single phase data, HPN reports a significant improvement up to 7.73% (from 56.21% to 63.94%) in terms of DSC.

Trauma is the worldwide leading cause of death and disability in those younger than 45 years, and pelvic fractures are a major source of morbidity and mortality. Automated segmentation of multiple foci of arterial bleeding from abdominopelvic trauma CT could provide rapid objective measurements of the total extent of active bleeding, potentially augmenting outcome prediction at the point of care, while improving patient triage, allocation of appropriate resources, and time to definitive intervention. In spite of the importance of active bleeding in the quick tempo of trauma care, the task is still quite challenging due to the variable contrast, intensity, location, size, shape, and multiplicity of bleeding foci. Existing work [4] presents a heuristic rule-based segmentation technique which requires multiple stages and cannot be efficiently optimized end-to-end. To this end, we present, Multi-Scale Attentional Network (MSAN), the first yet reliable end-to-end network, for automated segmentation of active hemorrhage from contrast-enhanced trauma CT scans. MSAN consists of the following components: 1) an encoder which fully integrates the global contextual information from holistic 2D slices; 2) a multi-scale strategy applied both in the training stage and the inference stage to handle the challenges induced by variation of target sizes; 3) an attentional module to further refine the deep features, leading to better segmentation quality; and 4) a multi-view mechanism to fully leverage the 3D information. Our MSAN reports a significant improvement of more than 7% compared to prior arts in terms of DSC., Comment: To appear in MICCAI-MLMI 2019

Figure S1. mRNA level of PKD1/2/3 expression from prostate cancer TCGA data. (PDF 64 kb)

Figure S3. PKD2/3 in prostate cancer cells promoted chemotactic migration of mast cells. (PDF 1025 kb)

Deep convolutional neural networks (DCNNs) are powerful models that yield impressive results at object classification. However, recent work has shown that they do not generalize well to partially occluded objects and to mask attacks. In contrast to DCNNs, compositional models are robust to partial occlusion, however, they are not as discriminative as deep models. In this work, we combine DCNNs and compositional object models to retain the best of both approaches: a discriminative model that is robust to partial occlusion and mask attacks. Our model is learned in two steps. First, a standard DCNN is trained for image classification. Subsequently, we cluster the DCNN features into dictionaries. We show that the dictionary components resemble object part detectors and learn the spatial distribution of parts for each object class. We propose mixtures of compositional models to account for large changes in the spatial activation patterns (e.g. due to changes in the 3D pose of an object). At runtime, an image is first classified by the DCNN in a feedforward manner. The prediction uncertainty is used to detect partially occluded objects, which in turn are classified by the compositional model. Our experimental results demonstrate that combining compositional models and DCNNs resolves a fundamental problem of current deep learning approaches to computer vision: The combined model recognizes occluded objects, even when it has not been exposed to occluded objects during training, while at the same time maintaining high discriminative performance for non-occluded objects., Comment: WACV 2020

Figure S8. PKD2/3 modulated Erk1/2 and NF-ÎşB activity in prostate cancer cells in response to PMA. (PDF 812 kb)

Figure S4. Effect of PKD2 and PKD3 derived prostate cancer cells on endothelial cells tube formation in vitro. (PDF 832 kb)

Figure S7. PKD2/3 did not interact with p38. (PDF 466 kb)