One of the hottest topics in plant hormone biology is the crosstalk mechanisms,whereby multiple classes of phytohormones interplay with each other through signaling networks.To better understand the roles of hormonal ...One of the hottest topics in plant hormone biology is the crosstalk mechanisms,whereby multiple classes of phytohormones interplay with each other through signaling networks.To better understand the roles of hormonal crosstalks in their complex regulatory networks,it is of high significance to investigate the spatial and temporal distributions of multiple-phytohormones simultaneously from one plant tissue sample.In this study,we develop a high-sensitivity and high-throughput method for the simultaneous quantitative analysis of 44 phytohormone compounds,covering currently known 10 major classes of phytohormones(strigolactones,brassinosteroids,gibberellins,auxin,abscisic acid,jasmonic acid,salicylic acid,cytokinins,ethylene,and polypeptide hormones[e.g.,phytosulfokine])from only 100 mg of plant sample.These compounds were grouped and purified separately with a tailored solid-phase extraction procedure based on their physicochemical properties and then analyzed by LC–MS/MS.The recoveries of our method ranged from 49.6%to 99.9%and the matrix effects from 61.8%to 102.5%,indicating that the overall sample pretreatment design resulted in good purification.The limits of quantitation(LOQs)of our method ranged from 0.06 to 1.29 pg/100 mg fresh weight and its precision was less than 13.4%,indicating high sensitivity and good reproducibility of the method.Tests of our method in different plant matrices demonstrated its wide applicability.Collectively,these advantages will make our method helpful in clarifying the crosstalk networks of phytohormones.展开更多
Differential phase contrast microscopy(DPC) provides high-resolution quantitative phase distribution of thin transparent samples under multi-axis asymmetric illuminations. Typically, illumination in DPC microscopic sy...Differential phase contrast microscopy(DPC) provides high-resolution quantitative phase distribution of thin transparent samples under multi-axis asymmetric illuminations. Typically, illumination in DPC microscopic systems is designed with two-axis half-circle amplitude patterns, which, however, result in a non-isotropic phase contrast transfer function(PTF). Efforts have been made to achieve isotropic DPC by replacing the conventional half-circle illumination aperture with radially asymmetric patterns with three-axis illumination or gradient amplitude patterns with two-axis illumination. Nevertheless, the underlying theoretical mechanism of isotropic PTF has not been explored, and thus, the optimal illumination scheme cannot be determined. Furthermore, the frequency responses of the PTFs under these engineered illuminations have not been fully optimized, leading to suboptimal phase contrast and signal-to-noise ratio for phase reconstruction. In this paper, we provide a rigorous theoretical analysis about the necessary and sufficient conditions for DPC to achieve isotropic PTF. In addition,we derive the optimal illumination scheme to maximize the frequency response for both low and high frequencies(from 0 to 2 NAobj) and meanwhile achieve perfectly isotropic PTF with only two-axis intensity measurements.We present the derivation, implementation, simulation, and experimental results demonstrating the superiority of our method over existing illumination schemes in both the phase reconstruction accuracy and noise-robustness.展开更多
Quantitative phase imaging(QPI)is a label-free computational imaging technique used in various fields,including biology and medical research.Modern QPI systems typically rely on digital processing using iterative algo...Quantitative phase imaging(QPI)is a label-free computational imaging technique used in various fields,including biology and medical research.Modern QPI systems typically rely on digital processing using iterative algorithms for phase retrieval and image reconstruction.Here,we report a diffractive optical network trained to convert the phase information of input objects positioned behind random diffusers into intensity variations at the output plane,all-optically performing phase recovery and quantitative imaging of phase objects completely hidden by unknown,random phase diffusers.This QPI diffractive network is composed of successive diffractive layers,axially spanning in total~70λ,where is the illumination wavelength;unlike existing digital image reconstruction and phase retrieval methods,it forms an all-optical processor that does not require external power beyond the illumination beam to complete its QPI reconstruction at the speed of light propagation.This all-optical diffractive processor can provide a low-power,high frame rate and compact alternative for quantitative imaging of phase objects through random,unknown diffusers and can operate at different parts of the electromagnetic spectrum for various applications in biomedical imaging and sensing.The presented QPI diffractive designs can be integrated onto the active area of standard CCD/CMOS-based image sensors to convert an existing optical microscope into a diffractive QPI microscope,performing phase recovery and image reconstruction on a chip through light diffraction within passive structured layers.展开更多
Holography provides access to the optical phase.The emerging compressive phase retrieval approach can achieve in-line holographic imaging beyond the information-theoretic limit or even from a single shot by exploring ...Holography provides access to the optical phase.The emerging compressive phase retrieval approach can achieve in-line holographic imaging beyond the information-theoretic limit or even from a single shot by exploring the signal priors.However,iterative projection methods based on physical knowledge of the wavefield suffer from poor imaging quality,whereas the regularization techniques sacrifice robustness for fidelity.In this work,we present a unified compressive phase retrieval framework for in-line holography that encapsulates the unique advantages of both physical constraints and sparsity priors.In particular,a constrained complex total variation(CCTV)regularizer is introduced that explores the well-known absorption and support constraints together with sparsity in the gradient domain,enabling practical high-quality in-line holographic imaging from a single intensity image.We developed efficient solvers based on the proximal gradient method for the non-smooth regularized inverse problem and the corresponding denoising subproblem.Theoretical analyses further guarantee the convergence of the algorithms with prespecified parameters,obviating the need for manual parameter tuning.As both simulated and optical experiments demonstrate,the proposed CCTV model can characterize complex natural scenes while utilizing physically tractable constraints for quality enhancement.This new compressive phase retrieval approach can be extended,with minor adjustments,to various imaging configurations,sparsifying operators,and physical knowledge.It may cast new light on both theoretical and empirical studies.展开更多
We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively lowcarrier frequency holograms-deep learning assisted variational Hilbert quantitative phase imaging(...We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively lowcarrier frequency holograms-deep learning assisted variational Hilbert quantitative phase imaging(DL-VHQPI).The method,incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation,reliably and robustly recovers the quantitative phase information of the test objects.It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system.Compared to the conventional end-to-end networks(without a physical model),the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization.The DL-VHQPI is quantitatively studied by numerical simulation.The live-cell experiment is designed to demonstrate the method's practicality in biological research.The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.展开更多
We present epi-diffraction phase microscopy(epi-DPM)as a non-destructive optical method for monitoring semiconductor fabrication processes in real time and with nanometer level sensitivity.The method uses a compact M...We present epi-diffraction phase microscopy(epi-DPM)as a non-destructive optical method for monitoring semiconductor fabrication processes in real time and with nanometer level sensitivity.The method uses a compact Mach–Zehnder interferometer to recover quantitative amplitude and phase maps of the field reflected by the sample.The low temporal noise of 0.6 nm per pixel at 8.93 frames per second enabled us to collect a three-dimensional movie showing the dynamics of wet etching and thereby accurately quantify non-uniformities in the etch rate both across the sample and over time.By displaying a gray-scale digital image on the sample with a computer projector,we performed photochemical etching to define arrays of microlenses while simultaneously monitoring their etch profiles with epi-DPM.展开更多
A new optical microscopy technique,termed high spatial and temporal resolution synthetic aperture phase microscopy(HISTR-SAPM),is proposed to improve the lateral resolution of wide-field coherent imaging.Under plane w...A new optical microscopy technique,termed high spatial and temporal resolution synthetic aperture phase microscopy(HISTR-SAPM),is proposed to improve the lateral resolution of wide-field coherent imaging.Under plane wave illumination,the resolution is increased by twofold to around 260 nm,while achieving millisecond-level temporal resolution.In HISTR-SAPM,digital micromirror devices are used to actively change the sample illumination beam angle at high speed with high stability.An off-axis interferometer is used to measure the sample scattered complex fields,which are then processed to reconstruct high-resolution phase images.Using HISTR-SAPM,we are able to map the height profiles of subwavelength photonic structures and resolve the period structures that have 198 nm linewidth and 132 nm gap(i.e.,a full pitch of 330 nm).As the reconstruction averages out laser speckle noise while maintaining high temporal resolution,HISTR-SAPM further enables imaging and quantification of nanoscale dynamics of live cells,such as red blood cell membrane fluctuations and subcellular structure dynamics within nucleated cells.We envision that HISTR-SAPM will broadly benefit research in material science and biology.展开更多
We present a deep learning approach for living cells mitosis classification based on label-free quantitative phase imaging with transport of intensity equation methods.In the approach,we applied a pretrained deep conv...We present a deep learning approach for living cells mitosis classification based on label-free quantitative phase imaging with transport of intensity equation methods.In the approach,we applied a pretrained deep convolutional neural network using transfer learning for binary classification of mitosis and non-mitosis.As a validation,we demonstrated the performances of the network trained by phase images and intensity images,respectively.The convolutional neural network trained by phase images achieved an average accuracy of 98.9%on the validation data,which outperforms the average accuracy 89.6%obtained by the network trained by intensity images.We believe that the quantitative phase microscopy in combination with deep learning enables researchers to predict the mitotic status of living cells noninvasively and efficiently.展开更多
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(grant no.XDA24040202)the National Natural Science Foundation of China(grant no.31770398,31470433,31500299)+1 种基金the Key Research Program of the Chinese Academy of Sciences(grant no.KFZD-SW-112)the CAS Key Technology Talent Program(2017).
文摘One of the hottest topics in plant hormone biology is the crosstalk mechanisms,whereby multiple classes of phytohormones interplay with each other through signaling networks.To better understand the roles of hormonal crosstalks in their complex regulatory networks,it is of high significance to investigate the spatial and temporal distributions of multiple-phytohormones simultaneously from one plant tissue sample.In this study,we develop a high-sensitivity and high-throughput method for the simultaneous quantitative analysis of 44 phytohormone compounds,covering currently known 10 major classes of phytohormones(strigolactones,brassinosteroids,gibberellins,auxin,abscisic acid,jasmonic acid,salicylic acid,cytokinins,ethylene,and polypeptide hormones[e.g.,phytosulfokine])from only 100 mg of plant sample.These compounds were grouped and purified separately with a tailored solid-phase extraction procedure based on their physicochemical properties and then analyzed by LC–MS/MS.The recoveries of our method ranged from 49.6%to 99.9%and the matrix effects from 61.8%to 102.5%,indicating that the overall sample pretreatment design resulted in good purification.The limits of quantitation(LOQs)of our method ranged from 0.06 to 1.29 pg/100 mg fresh weight and its precision was less than 13.4%,indicating high sensitivity and good reproducibility of the method.Tests of our method in different plant matrices demonstrated its wide applicability.Collectively,these advantages will make our method helpful in clarifying the crosstalk networks of phytohormones.
基金National Natural Science Foundation of China(NSFC)(61722506,11574152)Final Assembly “13th FiveYear Plan” Advanced Research Project of China(30102070102)+6 种基金Equipment Advanced Research Fund of China(61404150202)National Defense Science and Technology Foundation of China(0106173)Outstanding Youth Foundation of Jiangsu Province of China(BK20170034)Key Research and Development Program of Jiangsu Province(BE2017162)“333 Engineering”Research Project of Jiangsu Province(BRA2016407)Fundamental Research Funds for the Central Universities(30917011204)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(3091801410411)
文摘Differential phase contrast microscopy(DPC) provides high-resolution quantitative phase distribution of thin transparent samples under multi-axis asymmetric illuminations. Typically, illumination in DPC microscopic systems is designed with two-axis half-circle amplitude patterns, which, however, result in a non-isotropic phase contrast transfer function(PTF). Efforts have been made to achieve isotropic DPC by replacing the conventional half-circle illumination aperture with radially asymmetric patterns with three-axis illumination or gradient amplitude patterns with two-axis illumination. Nevertheless, the underlying theoretical mechanism of isotropic PTF has not been explored, and thus, the optimal illumination scheme cannot be determined. Furthermore, the frequency responses of the PTFs under these engineered illuminations have not been fully optimized, leading to suboptimal phase contrast and signal-to-noise ratio for phase reconstruction. In this paper, we provide a rigorous theoretical analysis about the necessary and sufficient conditions for DPC to achieve isotropic PTF. In addition,we derive the optimal illumination scheme to maximize the frequency response for both low and high frequencies(from 0 to 2 NAobj) and meanwhile achieve perfectly isotropic PTF with only two-axis intensity measurements.We present the derivation, implementation, simulation, and experimental results demonstrating the superiority of our method over existing illumination schemes in both the phase reconstruction accuracy and noise-robustness.
文摘Quantitative phase imaging(QPI)is a label-free computational imaging technique used in various fields,including biology and medical research.Modern QPI systems typically rely on digital processing using iterative algorithms for phase retrieval and image reconstruction.Here,we report a diffractive optical network trained to convert the phase information of input objects positioned behind random diffusers into intensity variations at the output plane,all-optically performing phase recovery and quantitative imaging of phase objects completely hidden by unknown,random phase diffusers.This QPI diffractive network is composed of successive diffractive layers,axially spanning in total~70λ,where is the illumination wavelength;unlike existing digital image reconstruction and phase retrieval methods,it forms an all-optical processor that does not require external power beyond the illumination beam to complete its QPI reconstruction at the speed of light propagation.This all-optical diffractive processor can provide a low-power,high frame rate and compact alternative for quantitative imaging of phase objects through random,unknown diffusers and can operate at different parts of the electromagnetic spectrum for various applications in biomedical imaging and sensing.The presented QPI diffractive designs can be integrated onto the active area of standard CCD/CMOS-based image sensors to convert an existing optical microscope into a diffractive QPI microscope,performing phase recovery and image reconstruction on a chip through light diffraction within passive structured layers.
基金the National Natural Science Foundation of China(Grant No.61827825)for financial support.
文摘Holography provides access to the optical phase.The emerging compressive phase retrieval approach can achieve in-line holographic imaging beyond the information-theoretic limit or even from a single shot by exploring the signal priors.However,iterative projection methods based on physical knowledge of the wavefield suffer from poor imaging quality,whereas the regularization techniques sacrifice robustness for fidelity.In this work,we present a unified compressive phase retrieval framework for in-line holography that encapsulates the unique advantages of both physical constraints and sparsity priors.In particular,a constrained complex total variation(CCTV)regularizer is introduced that explores the well-known absorption and support constraints together with sparsity in the gradient domain,enabling practical high-quality in-line holographic imaging from a single intensity image.We developed efficient solvers based on the proximal gradient method for the non-smooth regularized inverse problem and the corresponding denoising subproblem.Theoretical analyses further guarantee the convergence of the algorithms with prespecified parameters,obviating the need for manual parameter tuning.As both simulated and optical experiments demonstrate,the proposed CCTV model can characterize complex natural scenes while utilizing physically tractable constraints for quality enhancement.This new compressive phase retrieval approach can be extended,with minor adjustments,to various imaging configurations,sparsifying operators,and physical knowledge.It may cast new light on both theoretical and empirical studies.
基金We are grateful for financial supports from the National Natural Science Foundation of China(61905115,62105151,62175109,U21B2033,62227818)Leading Technology of Jiangsu Basic Research Plan(BK20192003)+5 种基金Youth Foundation of Jiangsu Province(BK20190445,BK20210338)Biomedical Competition Foundation of Jiangsu Province(BE2022847)Key National Industrial Technology Cooperation Foundation of Jiangsu Province(BZ2022039)Fundamental Research Funds for the Central Universities(30920032101)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(JSGP202105,JSGP202201)National Science Center,Poland(2020/37/B/ST7/03629).The authors thank F.Sun for her contribution to this paper in terms of language expression and grammatical correction.
文摘We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively lowcarrier frequency holograms-deep learning assisted variational Hilbert quantitative phase imaging(DL-VHQPI).The method,incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation,reliably and robustly recovers the quantitative phase information of the test objects.It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system.Compared to the conventional end-to-end networks(without a physical model),the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization.The DL-VHQPI is quantitatively studied by numerical simulation.The live-cell experiment is designed to demonstrate the method's practicality in biological research.The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.
基金The authors thank Hoa Pham for assistance with the power spectral density calculations and Brian Cunningham,Xiuling Li,Logan Liu and Daniel Wasserman for helpful discussions. This work is supported by NSF CBET-1040462 MRI award.
文摘We present epi-diffraction phase microscopy(epi-DPM)as a non-destructive optical method for monitoring semiconductor fabrication processes in real time and with nanometer level sensitivity.The method uses a compact Mach–Zehnder interferometer to recover quantitative amplitude and phase maps of the field reflected by the sample.The low temporal noise of 0.6 nm per pixel at 8.93 frames per second enabled us to collect a three-dimensional movie showing the dynamics of wet etching and thereby accurately quantify non-uniformities in the etch rate both across the sample and over time.By displaying a gray-scale digital image on the sample with a computer projector,we performed photochemical etching to define arrays of microlenses while simultaneously monitoring their etch profiles with epi-DPM.
基金We acknowledge financial support from Hong Kong Innovation and Technology Fund(Nos.ITS/394/17 and ITS/098/18FP)Shun Hing Institute of Advanced Engineering(No.BME-p3-18)Croucher Innovation Awards 2019,and the U.S.National Institutes of Health(No.5P41EB015871-33).
文摘A new optical microscopy technique,termed high spatial and temporal resolution synthetic aperture phase microscopy(HISTR-SAPM),is proposed to improve the lateral resolution of wide-field coherent imaging.Under plane wave illumination,the resolution is increased by twofold to around 260 nm,while achieving millisecond-level temporal resolution.In HISTR-SAPM,digital micromirror devices are used to actively change the sample illumination beam angle at high speed with high stability.An off-axis interferometer is used to measure the sample scattered complex fields,which are then processed to reconstruct high-resolution phase images.Using HISTR-SAPM,we are able to map the height profiles of subwavelength photonic structures and resolve the period structures that have 198 nm linewidth and 132 nm gap(i.e.,a full pitch of 330 nm).As the reconstruction averages out laser speckle noise while maintaining high temporal resolution,HISTR-SAPM further enables imaging and quantification of nanoscale dynamics of live cells,such as red blood cell membrane fluctuations and subcellular structure dynamics within nucleated cells.We envision that HISTR-SAPM will broadly benefit research in material science and biology.
基金the National Natural Science Foundation of China(NSFC)(No.61927810)the Joint Fund of National Natural Science Foundation ofChina and China Academy of Engineering Physics(NSAF)(No.U1730137)the Fundamental Research Funds for the Central Universities(No.3102019ghxm018)。
文摘We present a deep learning approach for living cells mitosis classification based on label-free quantitative phase imaging with transport of intensity equation methods.In the approach,we applied a pretrained deep convolutional neural network using transfer learning for binary classification of mitosis and non-mitosis.As a validation,we demonstrated the performances of the network trained by phase images and intensity images,respectively.The convolutional neural network trained by phase images achieved an average accuracy of 98.9%on the validation data,which outperforms the average accuracy 89.6%obtained by the network trained by intensity images.We believe that the quantitative phase microscopy in combination with deep learning enables researchers to predict the mitotic status of living cells noninvasively and efficiently.
