Iron oxide-copper–gold(IOCG)deposits encompass a diverse set of mineralization styles,leading to outstanding questions about how different alteration facies are related across a single ore-producing system and the ov...Iron oxide-copper–gold(IOCG)deposits encompass a diverse set of mineralization styles,leading to outstanding questions about how different alteration facies are related across a single ore-producing system and the overarching mechanisms of ore genesis.This study investigates the age and characteristics of mineralization at the La Farola deposit,a hematite-dominated IOCG deposit located at the southern margin of the Candelaria-Punta del Cobre IOCG district of northern Chile.Two lithologically-controlled ore bodies occur along the WSW-ENE striking,∼18°NNW-dipping contact between the Lower Cretaceous Chañarcillo Group and Punta del Cobre Formation.Syn-mineralization N-S to NNW-SSE striking sinistral strike-slip faults likely acted as fluid pathways.Distinct mineral assemblages include an early Na-Ca assemblage(albite-scapolite)overprinted by skarnoid garnet with minor pyroxene,Ca-Fe(magnetite-actinolite),and K-Fe(magnetite-k-feldspar-biotite and minor sulfides)assemblages.The main sulfide mineralization(chalcopyrite-pyrite with minor bornite)is associated with specular hematite-white mica-K-feldspar-calcite and overprints all previous assemblages.The presence of hematite as the dominant Fe-oxide phase associated with Cu mineralization is characteristic of lower-temperature IOCG deposits,and may be a result of La Farola’s stratigraphic position<700 m higher than other deposits in the district.New U-Pb ages of 115.7±1.2 Ma for garnet and Re-Os ages of∼113–114 Ma for sulfides indicate mineralization occurred within a 3-million-year timeframe.These findings confirm hematite-dominant mineralization at La Farola was coeval with IOCG mineralization across the district.This research contributes to understanding IOCG systems and their formation mechanisms,highlighting the control local geological structures and alteration processes has on the diversity of mineralization types associated with a single IOCG system.展开更多
In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to hi...In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to high volume data feeds that are common in large Tier-1 ISP networks and providing rich, timely information on observed attacks. It is a software solution that is designed to run on off-the-shelf hardware platforms and incorporates a scalable data processing architecture along with lightweight analysis algorithms that make it suitable for deployment in large networks. RTFF also makes use of state of the art machine learning algorithms to construct attack models that can be used to detect as well as predict attacks.展开更多
The development of fast ionic conductors to improve the performance of electrochemical devices relies on expensive high-throughput(HT)density functional theory(DFT)calculations of transport properties.Machine learning...The development of fast ionic conductors to improve the performance of electrochemical devices relies on expensive high-throughput(HT)density functional theory(DFT)calculations of transport properties.Machine learning(ML)can accelerate HT workflows but requires high-quality data to ensure accurate predictions from trained models.In this study,we introduce the LiTraj dataset,which comprises 13,000 percolation and 122,000 migration barriers,and 1700 migration trajectories,calculated for Li-ion in diverse crystal structures using empirical force fields and DFT,respectively.With LiTraj,we demonstrate that classicalMLmodels and graph neural networks(GNNs)for structureto-property prediction of percolation and migration barriers can distinguish between“fast”and“poor”ionic conductors.Furthermore,we evaluate the capability of GNN-based universal ML interatomic potentials(uMLIPs)to identify optimal Li-ion migration trajectories.Fine-tuned uMLIPs achieve near-DFT accuracy in predicting migration barriers,significantly accelerating HT screenings of new ionic conductors.展开更多
High-throughput search for new crystal structures is extensively assisted by data-driven solutions.Here we address their prospects for more narrowly focused applications in a data-efficient manner.To verify and experi...High-throughput search for new crystal structures is extensively assisted by data-driven solutions.Here we address their prospects for more narrowly focused applications in a data-efficient manner.To verify and experimentally validate the proposed approach,we consider the structure of higher tungsten borides,WB_(4.2),and eightmetals asWsubstituents to set a search space comprising 375k+inequivalent crystal structures for solid solutions.Their thermodynamic properties are predicted with errors of a few meV/atom using graph neural networks fine-tuned on the DFT-derived properties of ca.200 entries.Amongthe substituents considered,Ta provides thewidest range of predicted stable concentrations and leads to the most considerable changes inmechanical properties.The vacuumless arc plasmamethod is used to perform synthesis of higher tungsten borides with different concentrations of Ta.Vickers hardness of WB_(5-x)samples with different Ta contents is measured,showing increase in hardness.展开更多
Super-resolution imaging has revolutionized our ability to visualize biological structures at subcellular scales.However,deep-tissue super-resolution imaging remains constrained by background interference,which leads ...Super-resolution imaging has revolutionized our ability to visualize biological structures at subcellular scales.However,deep-tissue super-resolution imaging remains constrained by background interference,which leads to limited depth penetration and compromised imaging fidelity.To overcome these challenges,we propose a novel imaging system,confocal^(2) spinning-disk image scanning microscopy(C^(2)SD-ISM).It integrates a spinning-disk(SD)confocal microscope,which physically eliminates out-of-focus signals,forming the first confocal level.A digital micromirror device(DMD)is employed for sparse multifocal illumination,combined with a dynamic pinhole array pixel reassignment(DPA-PR)algorithm for ISM super-resolution reconstruction,forming the second confocal level.The dual confocal configuration enhances system resolution,while effectively mitigating scattering background interference.Compared to computational out-of-focus signal removal,SD preserves the original intensity distribution as the penetration depth increases,achieving an imaging depth of up to 180μm.Additionally,the DPA-PR algorithm effectively corrects Stokes shifts,optical aberrations,and other non-ideal conditions,achieving a lateral resolution of 144 nm and an axial resolution of 351 nm,and a linear correlation of up to 92%between the original confocal and the reconstructed image,thereby enabling high-fidelity super-resolution imaging.Moreover,the system's programmable illumination via the DMD allows for seamless realization with structured illumination microscopy modality,offering excellent scalability and ease of use.Altogether,these capabilities make the C^(2)SD-ISM system a versatile tool,advancing cellular imaging and tissue-scale exploration for modern bioimaging needs.展开更多
We introduce a novel method for estimating the spatial distribution of absolute permeability in oil reservoirs,consistent with well logging and well test measurements.The primary objective is to create a permeability ...We introduce a novel method for estimating the spatial distribution of absolute permeability in oil reservoirs,consistent with well logging and well test measurements.The primary objective is to create a permeability map,incorporating the well test interpretation results and achieving hydrodynamic similarity to the actual permeability distribution around each well.This enhancement aims to improve the accuracy of reservoir modeling outcomes in reproducing real data.We utilize Nadaraya-Watson kernel regression to parameterize the two-dimensional spatial distribution of rock permeability.The kernel regression parameters are optimized by minimizing the discrepancies between actual and predicted values of permeability at well locations,the integral permeability of the reservoir domain around each well,and skin factors.This inverse optimization problem is addressed by repeatedly solving forward problems,where an artificial neural network(ANN)predicts the integral permeability of the formation surrounding a well and skin factor.The ANN is trained on a physics-based dataset generated through a synthetic well test procedure,which includes the numerical modeling of the bottomhole pressure decline curve in a reservoir simulator and its interpretation using a semi-analytical reservoir model.The proposed method is tested on the“Egg Model”,a synthetic reservoir with significant heterogeneity due to highly permeable channels.The permeability map created by our approach demonstrates hydrodynamic similarity to the original map.Numerical reservoir simulations,corresponding to the constructed and original permeability maps,yield comparable pore pressure and water saturation distributions at the end of the simulation period.Additionally,we observe a notable match in flow rates and total volumes of produced oil,water,and injected water between simulations.The developed approach outperforms kriging in terms of numerical reservoir modeling outcomes.This research advances existing geostatistical interpolation techniques by fusing well logging and well test data to build the reservoir permeability map through an optimization framework coupled with machine learning.Unlike traditional variogrambased geostatistical simulation algorithms,our method provides a permeability distribution that is hydrodynamically similar to the actual one,enhancing initial guess in the history matching process.The novel incorporation of well test interpretation results into the permeability map represents a significant improvement over existing methods,offering an innovative approach that can benefit the petroleum industry.We also provide recommendations for further development of the proposed algorithm to account for geological realism.展开更多
Obtaining reliable permeability maps of oil reservoirs is crucial for building a robust and accurate reservoir simulation model and,therefore,designing effective recovery strategies.This problem,however,remains challe...Obtaining reliable permeability maps of oil reservoirs is crucial for building a robust and accurate reservoir simulation model and,therefore,designing effective recovery strategies.This problem,however,remains challenging,as it requires the integration of various data sources by experts from different disciplines.Moreover,there are no sources to provide direct information about the inter-well space.In this work,a new method based on the data-fusion approach is proposed for predicting two-dimensional permeability maps on the whole reservoir area.This method utilizes non-parametric regression with a custom kernel shape accounting for different data sources:well logs,well tests,and seismics.A convolutional neural network is developed to process seismic data and then incorporate it with other sources.A multi-stage data fusion procedure helps to artificially increase the training dataset for the seismic interpretation model and finally to construct an adequate permeability map.The proposed methodology of permeability map construction from different sources was tested on a real oil reservoir located in Western Siberia.The results demonstrate that the developed map perfectly corresponds to the permeability estimations in the wells,and the inter-well space permeability predictions are considerably improved through the incorporation of the seismic data.展开更多
Three-dimensional structured illumination microscopy(3DSIM)is a popular method for observing subcellular/cellular structures or animal/plant tissues with gentle phototoxicity and 3D super-resolution.However,its time-c...Three-dimensional structured illumination microscopy(3DSIM)is a popular method for observing subcellular/cellular structures or animal/plant tissues with gentle phototoxicity and 3D super-resolution.However,its time-consuming reconstruction process poses challenges for high-throughput imaging and real-time observation.Moreover,traditional 3DSIM typically requires more than six z layers for successful reconstruction and is susceptible to defocused backgrounds.This poses a great gap between single-layer 2DSIM and 6-layer 3DSIM,and limits the observation of thicker samples.To address these limitations,we developed FO-3DSIM,a novel method that integrates spatial-domain reconstruction with optical-sectioning SIM.FO-3DSIM enhances reconstruction speed by up to 855.7 times with superior performance with limited z layers and under high defocused backgrounds.It retains the high-fidelity,low-photon reconstruction capabilities of our previously proposed Open-3DSIM.Utilizing fast reconstruction and optical sectioning,we achieved large field-of-view(FOV)3D super-resolution imaging of mouse kidney actin,covering a region of 0.453 mm×0.453 mm×2.75μm within 23 min of acquisition and 13 min of reconstruction.Near real-time performance was demonstrated in live actin imaging with FO-3DSIM.Our approach reduces photodamage through limited z layer reconstruction,allowing the observation of ER tubes with just three layers.We anticipate that FO-3DSIM will pave the way for near real-time,large FOV 6D imaging,encompassing xyz super-resolution,multi-color,long-term,and polarization imaging with less photodamage,removed defocused backgrounds,and reduced reconstruction time.展开更多
Structured illumination microscopy(SIM)has emerged as a promising super-resolution fluorescence imaging technique,offering diverse configurations and computational strategies to mitigate phototoxicity during real-time...Structured illumination microscopy(SIM)has emerged as a promising super-resolution fluorescence imaging technique,offering diverse configurations and computational strategies to mitigate phototoxicity during real-time imaging of biological specimens.Traditional efforts to enhance system frame rates have concentrated on processing algorithms,like rolling reconstruction or reduced frame reconstruction,or on investments in costly sCMOS cameras with accelerated row readout rates.In this article,we introduce an approach to elevate SIM frame rates and region of interest(ROI)coverage at the hardware level,without necessitating an upsurge in camera expenses or intricate algorithms.Here,parallel acquisition-readout SIM(PAR-SIM)achieves the highest imaging speed for fluorescence imaging at currently available detector sensitivity.By using the full frame-width of the detector through synchronizing the pattern generation and image exposure-readout process,we have achieved a fundamentally stupendous information spatial-temporal flux of 132.9 MPixels·s^(−1),9.6-fold that of the latest techniques,with the lowest SNR of−2.11 dB and 100 nm resolution.PAR-SIM demonstrates its proficiency in successfully reconstructing diverse cellular organelles in dual excitations,even under conditions of low signal due to ultra-short exposure times.Notably,mitochondrial dynamic tubulation and ongoing membrane fusion processes have been captured in live COS-7 cell,recorded with PAR-SIM at an impressive 408 Hz.We posit that this novel parallel exposure-readout mode not only augments SIM pattern modulation for superior frame rates but also holds the potential to benefit other complex imaging systems with a strategic controlling approach.展开更多
Fluorescence microscopic imaging is essentially a convolution process distorted by random noise,limiting critical parameters such as imaging speed,duration,and resolution.Though algorithmic compensation has shown grea...Fluorescence microscopic imaging is essentially a convolution process distorted by random noise,limiting critical parameters such as imaging speed,duration,and resolution.Though algorithmic compensation has shown great potential to enhance these pivotal aspects,its fidelity remains questioned.Here we develop a physics-rooted computational resolution extension and denoising method with ensured fidelity.Our approach employs a multi-resolution analysis(MRA)framework to extract the two main characteristics of fluorescence images against noise:across-edge contrast,and along-edge continuity.By constraining the two features in a model-solution framework using framelet and curvelet,we develop MRA deconvolution algorithms,which improve the signal-to-noise ratio(SNR)up to 10 dB higher than spatial derivative based penalties,and can provide up to two-fold fidelity-ensured resolution improvement rather than the artifact-prone Richardson-Lucy inference.We demonstrate our methods can improve the performance of various diffraction-limited and super-resolution microscopies with ensured fidelity,enabling accomplishments of more challenging imaging tasks.展开更多
Fluorescence polarization microscopy is widely used in biology for molecular orientation properties.However,due to the limited temporal resolution of single-molecule orientation localization microscopy and the limited...Fluorescence polarization microscopy is widely used in biology for molecular orientation properties.However,due to the limited temporal resolution of single-molecule orientation localization microscopy and the limited orientation dimension of polarization modulation techniques,achieving simultaneous high temporal-spatial resolution mapping of the three-dimensional(3D)orientation of fluorescent dipoles remains an outstanding problem.Here,we present a super-resolution 3D orientation mapping(3DOM)microscope that resolves 3D orientation by extracting phase information of the six polarization modulation components in reciprocal space.3DOM achieves an azimuthal precision of 2°and a polar precision of 3°with spatial resolution of up to 128 nm in the experiments.We validate that 3DOM not only reveals the heterogeneity of the milk fat globule membrane,but also elucidates the 3D structure of biological filaments,including the 3D spatial conformation ofλ-DNA and the structural disorder of actin filaments.Furthermore,3DOM images the dipole dynamics of microtubules labeled with green fluorescent protein in live U2OS cells,reporting dynamic 3D orientation variations.Given its easy integration into existing wide-field microscopes,we expect the 3DOM microscope to provide a multi-view versatile strategy for investigating molecular structure and dynamics in biological macromolecules across multiple spatial and temporal scales.展开更多
Following publication of the original article[1],the authors reported 3 errors.1.The author name Yuzhe Fu has been corrected to Yunzhe Fu.2.In the Fig.3 legend,the sentence“e.3DOM imaging of the simulated cross-line ...Following publication of the original article[1],the authors reported 3 errors.1.The author name Yuzhe Fu has been corrected to Yunzhe Fu.2.In the Fig.3 legend,the sentence“e.3DOM imaging of the simulated cross-line sample”has been updated to“e.3DOM imaging of the simulated curve sample”.3.The term“3D-OM”on line 28 page.8(PDF)has been updated to“3DOM”.展开更多
基金National Science Foundation Grant#1822064 to J.Singleton,Fondecyt grant#1230161 to I.del Real,and a Society of Economic Geologists Student Research Grant to N.Seymour supported this work.
文摘Iron oxide-copper–gold(IOCG)deposits encompass a diverse set of mineralization styles,leading to outstanding questions about how different alteration facies are related across a single ore-producing system and the overarching mechanisms of ore genesis.This study investigates the age and characteristics of mineralization at the La Farola deposit,a hematite-dominated IOCG deposit located at the southern margin of the Candelaria-Punta del Cobre IOCG district of northern Chile.Two lithologically-controlled ore bodies occur along the WSW-ENE striking,∼18°NNW-dipping contact between the Lower Cretaceous Chañarcillo Group and Punta del Cobre Formation.Syn-mineralization N-S to NNW-SSE striking sinistral strike-slip faults likely acted as fluid pathways.Distinct mineral assemblages include an early Na-Ca assemblage(albite-scapolite)overprinted by skarnoid garnet with minor pyroxene,Ca-Fe(magnetite-actinolite),and K-Fe(magnetite-k-feldspar-biotite and minor sulfides)assemblages.The main sulfide mineralization(chalcopyrite-pyrite with minor bornite)is associated with specular hematite-white mica-K-feldspar-calcite and overprints all previous assemblages.The presence of hematite as the dominant Fe-oxide phase associated with Cu mineralization is characteristic of lower-temperature IOCG deposits,and may be a result of La Farola’s stratigraphic position<700 m higher than other deposits in the district.New U-Pb ages of 115.7±1.2 Ma for garnet and Re-Os ages of∼113–114 Ma for sulfides indicate mineralization occurred within a 3-million-year timeframe.These findings confirm hematite-dominant mineralization at La Farola was coeval with IOCG mineralization across the district.This research contributes to understanding IOCG systems and their formation mechanisms,highlighting the control local geological structures and alteration processes has on the diversity of mineralization types associated with a single IOCG system.
文摘In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to high volume data feeds that are common in large Tier-1 ISP networks and providing rich, timely information on observed attacks. It is a software solution that is designed to run on off-the-shelf hardware platforms and incorporates a scalable data processing architecture along with lightweight analysis algorithms that make it suitable for deployment in large networks. RTFF also makes use of state of the art machine learning algorithms to construct attack models that can be used to detect as well as predict attacks.
基金the financial support of Russian Science Foundation project No.24-73-10204.
文摘The development of fast ionic conductors to improve the performance of electrochemical devices relies on expensive high-throughput(HT)density functional theory(DFT)calculations of transport properties.Machine learning(ML)can accelerate HT workflows but requires high-quality data to ensure accurate predictions from trained models.In this study,we introduce the LiTraj dataset,which comprises 13,000 percolation and 122,000 migration barriers,and 1700 migration trajectories,calculated for Li-ion in diverse crystal structures using empirical force fields and DFT,respectively.With LiTraj,we demonstrate that classicalMLmodels and graph neural networks(GNNs)for structureto-property prediction of percolation and migration barriers can distinguish between“fast”and“poor”ionic conductors.Furthermore,we evaluate the capability of GNN-based universal ML interatomic potentials(uMLIPs)to identify optimal Li-ion migration trajectories.Fine-tuned uMLIPs achieve near-DFT accuracy in predicting migration barriers,significantly accelerating HT screenings of new ionic conductors.
基金platform(Sber,Moscow,Russia)used for calculations with GNN models.DFT calculations were carried out using Skoltech supercomputer Zhores.Experiments on the vacuumless synthesis of higher tungsten boride were carried out with support from the Ministry of Science,Higher Education of the Russian Federation in part of the Science program(Project FSWW-2025-0003).
文摘High-throughput search for new crystal structures is extensively assisted by data-driven solutions.Here we address their prospects for more narrowly focused applications in a data-efficient manner.To verify and experimentally validate the proposed approach,we consider the structure of higher tungsten borides,WB_(4.2),and eightmetals asWsubstituents to set a search space comprising 375k+inequivalent crystal structures for solid solutions.Their thermodynamic properties are predicted with errors of a few meV/atom using graph neural networks fine-tuned on the DFT-derived properties of ca.200 entries.Amongthe substituents considered,Ta provides thewidest range of predicted stable concentrations and leads to the most considerable changes inmechanical properties.The vacuumless arc plasmamethod is used to perform synthesis of higher tungsten borides with different concentrations of Ta.Vickers hardness of WB_(5-x)samples with different Ta contents is measured,showing increase in hardness.
基金supported by the National Key R&D Program of China(2022YFC3401100)the National Natural Science Foundation of China(62025501,92150301,62335008,62405010,and 62305004)the Postdoctoral Fellowship Program of CPSF(GZB20250669)。
文摘Super-resolution imaging has revolutionized our ability to visualize biological structures at subcellular scales.However,deep-tissue super-resolution imaging remains constrained by background interference,which leads to limited depth penetration and compromised imaging fidelity.To overcome these challenges,we propose a novel imaging system,confocal^(2) spinning-disk image scanning microscopy(C^(2)SD-ISM).It integrates a spinning-disk(SD)confocal microscope,which physically eliminates out-of-focus signals,forming the first confocal level.A digital micromirror device(DMD)is employed for sparse multifocal illumination,combined with a dynamic pinhole array pixel reassignment(DPA-PR)algorithm for ISM super-resolution reconstruction,forming the second confocal level.The dual confocal configuration enhances system resolution,while effectively mitigating scattering background interference.Compared to computational out-of-focus signal removal,SD preserves the original intensity distribution as the penetration depth increases,achieving an imaging depth of up to 180μm.Additionally,the DPA-PR algorithm effectively corrects Stokes shifts,optical aberrations,and other non-ideal conditions,achieving a lateral resolution of 144 nm and an axial resolution of 351 nm,and a linear correlation of up to 92%between the original confocal and the reconstructed image,thereby enabling high-fidelity super-resolution imaging.Moreover,the system's programmable illumination via the DMD allows for seamless realization with structured illumination microscopy modality,offering excellent scalability and ease of use.Altogether,these capabilities make the C^(2)SD-ISM system a versatile tool,advancing cellular imaging and tissue-scale exploration for modern bioimaging needs.
基金supported by the Analytical center under the RF Government (subsidy agreement 000000D730321P5Q0002,Grant No.70-2021-00145 02.11.2021)
文摘We introduce a novel method for estimating the spatial distribution of absolute permeability in oil reservoirs,consistent with well logging and well test measurements.The primary objective is to create a permeability map,incorporating the well test interpretation results and achieving hydrodynamic similarity to the actual permeability distribution around each well.This enhancement aims to improve the accuracy of reservoir modeling outcomes in reproducing real data.We utilize Nadaraya-Watson kernel regression to parameterize the two-dimensional spatial distribution of rock permeability.The kernel regression parameters are optimized by minimizing the discrepancies between actual and predicted values of permeability at well locations,the integral permeability of the reservoir domain around each well,and skin factors.This inverse optimization problem is addressed by repeatedly solving forward problems,where an artificial neural network(ANN)predicts the integral permeability of the formation surrounding a well and skin factor.The ANN is trained on a physics-based dataset generated through a synthetic well test procedure,which includes the numerical modeling of the bottomhole pressure decline curve in a reservoir simulator and its interpretation using a semi-analytical reservoir model.The proposed method is tested on the“Egg Model”,a synthetic reservoir with significant heterogeneity due to highly permeable channels.The permeability map created by our approach demonstrates hydrodynamic similarity to the original map.Numerical reservoir simulations,corresponding to the constructed and original permeability maps,yield comparable pore pressure and water saturation distributions at the end of the simulation period.Additionally,we observe a notable match in flow rates and total volumes of produced oil,water,and injected water between simulations.The developed approach outperforms kriging in terms of numerical reservoir modeling outcomes.This research advances existing geostatistical interpolation techniques by fusing well logging and well test data to build the reservoir permeability map through an optimization framework coupled with machine learning.Unlike traditional variogrambased geostatistical simulation algorithms,our method provides a permeability distribution that is hydrodynamically similar to the actual one,enhancing initial guess in the history matching process.The novel incorporation of well test interpretation results into the permeability map represents a significant improvement over existing methods,offering an innovative approach that can benefit the petroleum industry.We also provide recommendations for further development of the proposed algorithm to account for geological realism.
基金supported by the grant for research centers in the field of AI provided by the Ministry of Economic Development of the Russian Federation in accordance with the agreement000000C313925P4F0002 and the agreement with Skoltech N◦139-10-2025-033
文摘Obtaining reliable permeability maps of oil reservoirs is crucial for building a robust and accurate reservoir simulation model and,therefore,designing effective recovery strategies.This problem,however,remains challenging,as it requires the integration of various data sources by experts from different disciplines.Moreover,there are no sources to provide direct information about the inter-well space.In this work,a new method based on the data-fusion approach is proposed for predicting two-dimensional permeability maps on the whole reservoir area.This method utilizes non-parametric regression with a custom kernel shape accounting for different data sources:well logs,well tests,and seismics.A convolutional neural network is developed to process seismic data and then incorporate it with other sources.A multi-stage data fusion procedure helps to artificially increase the training dataset for the seismic interpretation model and finally to construct an adequate permeability map.The proposed methodology of permeability map construction from different sources was tested on a real oil reservoir located in Western Siberia.The results demonstrate that the developed map perfectly corresponds to the permeability estimations in the wells,and the inter-well space permeability predictions are considerably improved through the incorporation of the seismic data.
基金supported by the National Key R&D Program of China(2022YFC3401100)the National Natural Science Foundation of China(624B2009,62405010,62335008,62025501,92150301,and 62411540238).
文摘Three-dimensional structured illumination microscopy(3DSIM)is a popular method for observing subcellular/cellular structures or animal/plant tissues with gentle phototoxicity and 3D super-resolution.However,its time-consuming reconstruction process poses challenges for high-throughput imaging and real-time observation.Moreover,traditional 3DSIM typically requires more than six z layers for successful reconstruction and is susceptible to defocused backgrounds.This poses a great gap between single-layer 2DSIM and 6-layer 3DSIM,and limits the observation of thicker samples.To address these limitations,we developed FO-3DSIM,a novel method that integrates spatial-domain reconstruction with optical-sectioning SIM.FO-3DSIM enhances reconstruction speed by up to 855.7 times with superior performance with limited z layers and under high defocused backgrounds.It retains the high-fidelity,low-photon reconstruction capabilities of our previously proposed Open-3DSIM.Utilizing fast reconstruction and optical sectioning,we achieved large field-of-view(FOV)3D super-resolution imaging of mouse kidney actin,covering a region of 0.453 mm×0.453 mm×2.75μm within 23 min of acquisition and 13 min of reconstruction.Near real-time performance was demonstrated in live actin imaging with FO-3DSIM.Our approach reduces photodamage through limited z layer reconstruction,allowing the observation of ER tubes with just three layers.We anticipate that FO-3DSIM will pave the way for near real-time,large FOV 6D imaging,encompassing xyz super-resolution,multi-color,long-term,and polarization imaging with less photodamage,removed defocused backgrounds,and reduced reconstruction time.
基金supported by the National Key R&D Program of China (2022YFC3401100)the National Natural Science Foundation of China (62025501,92150301,62335008).
文摘Structured illumination microscopy(SIM)has emerged as a promising super-resolution fluorescence imaging technique,offering diverse configurations and computational strategies to mitigate phototoxicity during real-time imaging of biological specimens.Traditional efforts to enhance system frame rates have concentrated on processing algorithms,like rolling reconstruction or reduced frame reconstruction,or on investments in costly sCMOS cameras with accelerated row readout rates.In this article,we introduce an approach to elevate SIM frame rates and region of interest(ROI)coverage at the hardware level,without necessitating an upsurge in camera expenses or intricate algorithms.Here,parallel acquisition-readout SIM(PAR-SIM)achieves the highest imaging speed for fluorescence imaging at currently available detector sensitivity.By using the full frame-width of the detector through synchronizing the pattern generation and image exposure-readout process,we have achieved a fundamentally stupendous information spatial-temporal flux of 132.9 MPixels·s^(−1),9.6-fold that of the latest techniques,with the lowest SNR of−2.11 dB and 100 nm resolution.PAR-SIM demonstrates its proficiency in successfully reconstructing diverse cellular organelles in dual excitations,even under conditions of low signal due to ultra-short exposure times.Notably,mitochondrial dynamic tubulation and ongoing membrane fusion processes have been captured in live COS-7 cell,recorded with PAR-SIM at an impressive 408 Hz.We posit that this novel parallel exposure-readout mode not only augments SIM pattern modulation for superior frame rates but also holds the potential to benefit other complex imaging systems with a strategic controlling approach.
基金supported by the National Key R&D Program of China(2022YFC3401100)the National Natural Science Foundation of China(62025501,31971376,92150301,62335008).
文摘Fluorescence microscopic imaging is essentially a convolution process distorted by random noise,limiting critical parameters such as imaging speed,duration,and resolution.Though algorithmic compensation has shown great potential to enhance these pivotal aspects,its fidelity remains questioned.Here we develop a physics-rooted computational resolution extension and denoising method with ensured fidelity.Our approach employs a multi-resolution analysis(MRA)framework to extract the two main characteristics of fluorescence images against noise:across-edge contrast,and along-edge continuity.By constraining the two features in a model-solution framework using framelet and curvelet,we develop MRA deconvolution algorithms,which improve the signal-to-noise ratio(SNR)up to 10 dB higher than spatial derivative based penalties,and can provide up to two-fold fidelity-ensured resolution improvement rather than the artifact-prone Richardson-Lucy inference.We demonstrate our methods can improve the performance of various diffraction-limited and super-resolution microscopies with ensured fidelity,enabling accomplishments of more challenging imaging tasks.
基金supported by the National Key R&D Program of China(2022YFC3401100)the National Natural Science Foundation of China(62335008,62025501,31971376,92150301)。
文摘Fluorescence polarization microscopy is widely used in biology for molecular orientation properties.However,due to the limited temporal resolution of single-molecule orientation localization microscopy and the limited orientation dimension of polarization modulation techniques,achieving simultaneous high temporal-spatial resolution mapping of the three-dimensional(3D)orientation of fluorescent dipoles remains an outstanding problem.Here,we present a super-resolution 3D orientation mapping(3DOM)microscope that resolves 3D orientation by extracting phase information of the six polarization modulation components in reciprocal space.3DOM achieves an azimuthal precision of 2°and a polar precision of 3°with spatial resolution of up to 128 nm in the experiments.We validate that 3DOM not only reveals the heterogeneity of the milk fat globule membrane,but also elucidates the 3D structure of biological filaments,including the 3D spatial conformation ofλ-DNA and the structural disorder of actin filaments.Furthermore,3DOM images the dipole dynamics of microtubules labeled with green fluorescent protein in live U2OS cells,reporting dynamic 3D orientation variations.Given its easy integration into existing wide-field microscopes,we expect the 3DOM microscope to provide a multi-view versatile strategy for investigating molecular structure and dynamics in biological macromolecules across multiple spatial and temporal scales.
文摘Following publication of the original article[1],the authors reported 3 errors.1.The author name Yuzhe Fu has been corrected to Yunzhe Fu.2.In the Fig.3 legend,the sentence“e.3DOM imaging of the simulated cross-line sample”has been updated to“e.3DOM imaging of the simulated curve sample”.3.The term“3D-OM”on line 28 page.8(PDF)has been updated to“3DOM”.
