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.

Calcium-based polymers for suppression of soil acidification by improving acid-buffering capacity and inhibiting nitrification

Soil acidification is a major threat to agricultural sustainability in tropical and subtropical regions.Biodegradable and environmentally friendly materials,such as calcium lignosulfonate(CaLS),calcium poly(aspartic acid)(PASP-Ca),and calcium polyγ-glutamic acid(γ-PGA-Ca),are known to effectively ameliorate soil acidity.However,their effectiveness in inhibiting soil acidification has not been studied.This study aimed to evaluate the effect of CaLS,PASP-Ca,andγ-PGA-Ca on the resistance of soil toward acidification as directly and indirectly(i.e.,via nitrification)caused by the application of HNO_(3)and urea,respectively.For comparison,Ca(OH)_(2)and lignin were used as the inorganic and organic controls,respectively.Among the materials,γ-PGA-Ca drove the substantial improvements in the pH buffering capacity(pHBC)of the soil and exhibited the greatest potential in inhibiting HNO_(3)-induced soil acidification via protonation of carboxyl,complexing with Al~(3+),and cation exchange processes.Under acidification induced by urea,CaLS was the optimal one in inhibiting acidification and increasing exchangeable acidity during incubation.Furthermore,the sharp reduction in the population sizes of ammonia-oxidizing bacteria(AOB)and ammonia-oxidizing archaea(AOA)confirmed the inhibition of nitrification via CaLS application.Therefore,compared to improving soil pHBC,CaLS may play a more important role in suppressing indirect acidification.Overall,γ-PGA-Ca was superior to PASP-Ca and CaLS in enhancing the soil pHBC and the its resistance to acidification induced by HNO_(3) addition,whereas CaLS was the best at suppressing urea-driven soil acidification by inhibiting nitrification.In conclusion,these results provide a reference for inhibiting soil re-acidification in intensive agricultural systems.

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.