Multidither coherent optical adaptive technique for deep tissue two-photon microscopy

Two-photon microscopy normally suffers from the scattering of the tissue in biological imaging.Multidither coberent optical adaptive technique(COAT)can correct the scattered wavefront in parallel.However,the determination of the corrective phases may not be completely accurate using conventional method,which undermines the performance of this technique.In this paper,we theoretically demonstrate a method that can obtain more accurate corrective phases by determining the phase values from the square root of the fuorescence signal.A numnerical simulation model is established to study the performance of adaptive optics in two-photon micros-copy by combining scalar diffraction theory with vector diffraction theory.The results show that the distortion of the wavefront can be corrected more thoroughly with our method in two-photon imaging.In our simulation,with the scattering from a 450-mn-thick mouse brain tissue,excitation focal spots with higher peak-to background ratio(PBR)and images with higher contrast can be obtained.Hence,further enhancement of the multidither COAT correction performance in two-photon imaging can be expected.

Simplifying the detection of optical distortions by machine learning

Adaptive optics has been widely used in biological science to recover high-resolution optical image deep into the tissue,where optical distortion detection with high speed and accuracy is strongly required.Here,we introduce convolutional neural networks,one of the most popular machine learning models,into Shack-Hartmann wavefront sensor(SHWS)to simplify optical distortion detection processes.Without image segmentation or centroid positioning algorithm,the trained network could estimate up to 36th Zernike mode coefficients directly from a full SHWS image within 1.227ms on a personal computer,and achieves prediction accuracy up to 97.4%.The simulation results show that the average root mean squared error in phase residuals of our method is 75.64%lower than that with the modal-based SHWS method.With the high detection accuracy and simplified detection processes,this work has the potential to be applied in wavefront sensor-based adaptive optics for in vivo deep tissue imaging.

Physiological and Biochemical Characteristics and Response Patterns of Salinity Stress Responsive Genes (SSRGs) in Wild Quinoa (Chenopodium quinoa L.)

Cultivating salt-tolerant crops is a feasible way to effectively utilize saline-alkali land and solve the problem of underutilization of saline soils.Quinoa,a protein-comprehensive cereal in the plant kingdom,is an exceptional crop in terms of salt stress tolerance level.It seems an excellent model for the exploration of salt-tolerance mechanisms and cultivation of salt-tolerant germplasms.In this study,the seeds and seedlings of the quinoa cultivar Shelly were treated with different concentrations of NaCl solution.The physiological,biochemical characteristics and agronomic traits were investigated,and the response patterns of three salt stress-responsive genes(SSRGs)in quinoa were determined by real-time PCR.The optimum level of stress tolerance of quinoa cultivar Shelly was found in the range of 250–350 mM concentration of NaCl.Salt stress significantly induced expression of superoxide dismutase(SOD),peroxidase(POD),and particularly betaine aldehyde dehydrogenase(BADH).BADH was discovered to be more sensitive to salt stress and played an important role in the salt stress tolerance of quinoa seedlings,particularly at high NaCl concentrations,as it displayed upregulation until 24 h under 100 mM salt treatment.Moreover,it showed upregulation until 12 h under 250 mM salt stress.Taken together,these results suggest that BADH played an essential role in the salt-tolerance mechanism of quinoa.Based on the expression level and prompt response induced by NaCl,we suggest that the BADH can be considered as a molecular marker for screening salt-tolerant quinoa germplasm at the early stages of crop development.Salt treatment at different plant ontogeny or at different concentrations had a significant impact on quinoa growth.Therefore,an appropriate treatment approach needs to be chosen rationally in the process of screening salt-tolerant quinoa germplasm,which is useful to the utilization of saline soils.Our study provides a fundamental information to deepen knowledge of the salt tolerance mechanism of quinoa for the development of salt-tolerant germplasm in crop breeding programs.

Deep learning based wavefront sensor for complex wavefront detection in adaptive optical microscopes

The Shack-Hartmann wavefront sensor(SHWS)is an essential tool for wavefront sensing in adaptive optical microscopes.However,the distorted spots induced by the complex wavefront challenge its detection performance.Here,we propose a deep learning based wavefront detection method which combines point spread function image based Zernike coefficient estimation and wavefront stitching.Rather than using the centroid displacements of each micro-lens,this method first estimates the Zernike coefficients of local wavefront distribution over each micro-lens and then stitches the local wavefronts for reconstruction.The proposed method can offer low root mean square wavefront errors and high accuracy for complex wavefront detection,and has potential to be applied in adaptive optical microscopes.