An original form of photonic force microscope has been developed.Operating with a trapped lanthanide-doped crystal of nanometric dimensions,a minimum detected force of the order of 110 aN and a force sensitivity down ...An original form of photonic force microscope has been developed.Operating with a trapped lanthanide-doped crystal of nanometric dimensions,a minimum detected force of the order of 110 aN and a force sensitivity down to 1.8 fN/ffiffiffiffiffi Hz p have been realised.This opens up new prospects for force sensing in the physical sciences.展开更多
Deconvolution is a challenging inverse problem,particularly in techniques that employ complex engineered pointspread functions,such as microscopy with propagation-invariant beams.Here,we present a deep-learning method...Deconvolution is a challenging inverse problem,particularly in techniques that employ complex engineered pointspread functions,such as microscopy with propagation-invariant beams.Here,we present a deep-learning method for deconvolution that,in lieu of end-to-end training with ground truths,is trained using known physics of the imaging system.Specifically,we train a generative adversarial network with images generated with the known point-spread function of the system,and combine this with unpaired experimental data that preserve perceptual content.Our method rapidly and robustly deconvolves and super-resolves microscopy images,demonstrating a two-fold improvement in image contrast to conventional deconvolution methods.In contrast to common end-to-end networks that often require 1000-10,000s paired images,our method is experimentally unsupervised and can be trained solely on a few hundred regions of interest.We demonstrate its performance on light-sheet microscopy with propagation-invariant Airy beams in oocytes,preimplantation embryos and excised brain tissue,as well as illustrate its utility for Bessel-beam LSM.This method aims to democratise learned methods for deconvolution,as it does not require data acquisition outwith the conventional imaging protocol.展开更多
We show that superoscillating light fields,created using the method of optical eigenmodes,enable more efficient multiphoton-mediated cell transfection.Chinese hamster ovary cells are transfected with a plasmid and exh...We show that superoscillating light fields,created using the method of optical eigenmodes,enable more efficient multiphoton-mediated cell transfection.Chinese hamster ovary cells are transfected with a plasmid and exhibit expression of DsRed-Mito in the mitochondria.We demonstrate an efficiency improvement of 35%compared to the diffraction-limited spot.This opens up new vistas for nanoscale localized cell transfection.展开更多
基金supported by the Australian Research Council Centre of Excellence in Optical Microcombs for Breakthrough Science(project number CE230100006)an Australian Research Council Laureate Fellowship(FL210100099).
文摘An original form of photonic force microscope has been developed.Operating with a trapped lanthanide-doped crystal of nanometric dimensions,a minimum detected force of the order of 110 aN and a force sensitivity down to 1.8 fN/ffiffiffiffiffi Hz p have been realised.This opens up new prospects for force sensing in the physical sciences.
基金the UK Engineering and Physical Sciences Research Council(grants EP/P030017/1 and EP/R004854/1)has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement(EC-GA 871212)and H2020 FETOPEN project"Dynamic"(EC-GA 863203)+1 种基金P.W.was supported by the 1851 Research Fellowship from the Royal Commission.KRD was supported by a Mid-Career Fellowship from the Hospital Research Foundation(C-MCF 582019)K.D.acknowledges support from the Australian Research Council through a Laureate Fellowship.S.S.was funded by BBSRC(BB/M00905X/1).
文摘Deconvolution is a challenging inverse problem,particularly in techniques that employ complex engineered pointspread functions,such as microscopy with propagation-invariant beams.Here,we present a deep-learning method for deconvolution that,in lieu of end-to-end training with ground truths,is trained using known physics of the imaging system.Specifically,we train a generative adversarial network with images generated with the known point-spread function of the system,and combine this with unpaired experimental data that preserve perceptual content.Our method rapidly and robustly deconvolves and super-resolves microscopy images,demonstrating a two-fold improvement in image contrast to conventional deconvolution methods.In contrast to common end-to-end networks that often require 1000-10,000s paired images,our method is experimentally unsupervised and can be trained solely on a few hundred regions of interest.We demonstrate its performance on light-sheet microscopy with propagation-invariant Airy beams in oocytes,preimplantation embryos and excised brain tissue,as well as illustrate its utility for Bessel-beam LSM.This method aims to democratise learned methods for deconvolution,as it does not require data acquisition outwith the conventional imaging protocol.
文摘We show that superoscillating light fields,created using the method of optical eigenmodes,enable more efficient multiphoton-mediated cell transfection.Chinese hamster ovary cells are transfected with a plasmid and exhibit expression of DsRed-Mito in the mitochondria.We demonstrate an efficiency improvement of 35%compared to the diffraction-limited spot.This opens up new vistas for nanoscale localized cell transfection.
