Image enhancement for fluorescence microscopy based on deep learning with prior knowledge of aberration.

In this Letter, we propose a deep learning method with prior knowledge of potential aberration to enhance the fluorescence microscopy without additional hardware. The proposed method could effectively reduce noise and improve the peak signal-to-noise ratio of the acquired images at high speed. The enhancement performance and generalization of this method is demonstrated on three commercial fluorescence microscopes. This work provides a computational alternative to overcome the degradation induced by the biological specimen, and it has the potential to be further applied in biological applications.

Learning-based Shack-Hartmann wavefront sensor for high-order aberration detection: erratum.

An erratum is presented to correct the sizes of feature maps of Fig. 1(a) in [Opt. Express27, 33504 (2019)10.1364/OE.27.033504].

Deep learning assisted Shack-Hartmann wavefront sensor for direct wavefront detection.

The conventional Shack-Hartmann wavefront sensor (SHWS) requires wavefront slope measurements of every micro-lens for wavefront reconstruction. In this Letter, we applied deep learning on the SHWS to directly predict the wavefront distributions without wavefront slope measurements. The results show that our method could provide a lower root mean square wavefront error in high detection speed. The performance of the proposed method is also evaluated on challenging wavefronts, while the conventional approaches perform insufficiently. This Letter provides a new approach, to the best of our knowledge, to perform direct wavefront detection in SHWS-based applications.

Learning-based Shack-Hartmann wavefront sensor for high-order aberration detection.

We present a learning-based Shack-Hartmann wavefront sensor (SHWS) to achieve the high-order aberration detection without image segmentation or centroid positioning. Zernike coefficient amplitudes of aberrations measured from biological samples are referred and expanded to generate the training datasets. With one SHWS pattern inputted, up to 120th Zernike modes could be predicted within 10.9 ms with 95.56% model accuracy by a personal computer. The statistical experimental results show that compared with traditional modal-based SHWS, the root mean squared error in phase residuals of this method is reduced by ∼40.54% and the Strehl ratio of the point spread functions is improved by ∼27.31%. The aberration detection performance of this method is also validated on a mouse brain slice with 300 µm thickness and the median improvement of peak-to-background ratio of this method is ∼30% to 40% compared with traditional SHWS. With the high detection accuracy, simple processes, fast prediction speed and good compatibility, this work offers a potential approach to improve the wavefront sensing ability of SHWS, which could be combined with an existing adaptive optics system and be further applied in biological applications.

Machine learning based adaptive optics for doughnut-shaped beam.

The doughnut-shaped beam has been widely applied in the field of super-resolution microscopic imaging, micro-nanostructure lithography, ultra-high-density storage, and laser trapping. However, how to maintain the doughnut-shaped focus inside the scattering medium becomes a challenge, due to the wavefront aberrations. Here we demonstrate a machine learning based adaptive optics method to recover the doughnut-shaped focus with high speed. In our method, the relationship between the distorted doughnut-shaped intensity point spread function and the coefficients of the first 15 Zernike modes for phase correction is established. Experimental results show that the wavefront aberration with 101,784 optical control elements can be predicted within ~17 ms even using a personal computer, and 97.5% correction accuracy can be achieved in 200 repeated tests. Besides, we successfully apply this method in the scanning microscopy theoretically. With a large number of optical control elements and fast operation speed, our method may pave the way for many important applications in bioimaging, such as deep tissue stimulated emission depletion (STED) microscopy.

Machine learning guided rapid focusing with sensor-less aberration corrections.

Non-invasive, real-time imaging and deep focus into tissue are in high demand in biomedical research. However, the aberration that is introduced by the refractive index inhomogeneity of biological tissue hinders the way forward. A rapid focusing with sensor-less aberration corrections, based on machine learning, is demonstrated in this paper. The proposed method applies the Convolutional Neural Network (CNN), which can rapidly calculate the low-order aberrations from the point spread function images with Zernike modes after training. The results show that approximately 90 percent correction accuracy can be achieved. The average mean square error of each Zernike coefficient in 200 repetitions is 0.06. Furthermore, the aberration induced by 1-mm-thick phantom samples and 300-µm-thick mouse brain slices can be efficiently compensated through loading a compensation phase on an adaptive element placed at the back-pupil plane. The phase reconstruction requires less than 0.2 s. Therefore, this method offers great potential for in vivo real-time imaging in biological science.

Microsomal prostaglandin E synthase-1 inhibition prevents adverse cardiac remodelling after myocardial infarction in mice.

BACKGROUND AND PURPOSE: Heart failure with reduced ejection fraction (HFrEF) is a major consequence of myocardial infarction (MI). The microsomal prostaglandin E synthase-1 (mPGES-1)/PGE2 pathway has been shown to constrain reperfusion injury after acute myocardial ischaemia. However, it is unknown whether pharmacological inhibition of mPGES-1, a target with lower risk of thrombosis compared with selective inhibition of cyclooxygenase-2, affects chronic cardiac remodelling after MI. EXPERIMENTAL APPROACH: Mice were subjected to left anterior descending coronary artery ligation, followed by intraperitoneal treatment with the mPGES-1 inhibitor compound III (CIII) or 118, celecoxib (cyclooxygenase-2 inhibitor) or vehicle, once daily for 28 days. Urinary prostanoid metabolites were measured by liquid chromatography-tandem mass spectrometry. KEY RESULTS: Chronic administration of CIII improved cardiac function in mice after MI compared with vehicle or celecoxib. CIII did not affect thrombogenesis or blood pressure. In addition, CIII reduced infarct area, augmented scar thickness, decreased collagen I/III ratio, decreased the expression of fibrosis-related genes and increased capillary density in the ischaemic area. Shunting to urinary metabolites of PGI2 , not thromboxane B2 or PGD2 , after inhibition of mPGES-1 was positively correlated with cardiac function after MI. CIII administration significantly increased urinary PGI2 /PGE2 metabolite ratio compared to vehicle or celecoxib. The PGI2 /PGE2 metabolite ratio correlated positively with ejection fraction, fractional shortening and scar thickness. Treatment with 118 also improved cardiac function. CONCLUSION AND IMPLICATIONS: Inhibition of mPGES-1 prevented chronic adverse cardiac remodelling via an augmented PGI2 /PGE2 metabolite ratio and therefore represents a potential therapeutic strategy for development of HFrEF after MI.

Reliability of wavefront shaping based on coherent optical adaptive technique in deep tissue focusing.

Wavefront shaping can compensate the wavefront distortions in deep tissue focusing, leading to an improved penetration depth. However, when using the backscattered signals as the feedback, unexpected compensation bias may be introduced, resulting in focusing position deviations or even no focus in the illumination focal plane. Here we investigated the reliability of wavefront shaping based on coherent optical adaptive technique in deep tissue focusing by measuring the position deviations between the foci in the illumination focal plane and the epi-detection plane. The experimental results show that when the penetration depth reaches 150 µm in mouse brain tissue (with scattering coefficient ~22.42 mm-1 ) using a 488 nm laser and an objective lens with 0.75 numerical aperture, the center of the real focus will deviate out of one radius range of the Airy disk while the optimized focus in the epi-detection plane maintained basically at the center. With the penetration depth increases, the peak to background ratio of the focus in the illumination focal plane decreases faster than that in the epi-detection plane. The results indicate that when the penetration depth reaches 150 µm, feedback based on backscattered signals will make wavefront shaping lose its reliability, which may provide a guidance for applications of non-invasive precise optogenetics or deep tissue optical stimulation using wavefront shaping methods. A, Intensity distribution in the epi-detection plane and the illumination focal plane before and after correction, corresponding to brain sections with 250 and 300 µm thickness, respectively. Scale bar is 2 µm. B, Averaged focusing deviations in the epi-detection plane (optimized) and the illumination focal plane (monitored) after compensation. The unit of the ordinate is one Airy disk diameter. Black dashed line represents one Airy disk radius. Bars represent the SE of each measurement set.

Aberration corrections of doughnut beam by adaptive optics in the turbid medium.

The doughnut beam is a spatially structured beam which has been widely used in super-resolution microscopy, laser trapping and so on. However, when it passes through thick scattering medium, aberrations will seriously affect its performance. Currently, adaptive optics (AO) has become one of the most powerful tools to compensate aberrations. However, conventional AO always suffers from limited corrected field of view (FOV). Here, we propose a method with conjugate AO system based on coherent optical adaptive technique. The results show that the corrected FOV can be improved effectively. For a wide range of the optical applications with doughnut beam, our method has potentials in correcting aberrations with high speed in turbid media.(A) Mouse brain slice, (B) the distribution of r PAO , (C) the distribution of r CAO . The vortex beam focus of the blue point in (B) and (C) among a 137.5 × 137.5 µm FOV (D1) ideally, (D2) with scattering, (D3) in pupil AO system and (D4) in conjugate AO system.

Large field of view correction by using conjugate adaptive optics with multiple guide stars.

Adaptive optics has been widely used in the optical microscopy to recover high-resolution images deep into the sample. However, the corrected field of view (FOV) with a single correction is generally limited, which seriously restricts the imaging speed. In this article, we demonstrate a high-speed wavefront correction method by using the conjugate adaptive optical correction with multiple guide stars (CAOMG) based on the coherent optical adaptive technique. The results show that the CAOMG method can greatly improve the corrected FOV. For 120-µm-thick mouse brain tissue, the corrected FOV can be improved up to ~243 times of the conventional pupil adaptive optics (PAO) without additional time consumption. Therefore, this study shows the potential of high-speed imaging through scattering medium in biological science.

Insights into the inhibitory mechanisms of NADH on the αγ heterodimer of human NAD-dependent isocitrate dehydrogenase.

Human NAD-dependent isocitrate dehydrogenase (NAD-IDH) catalyzes the oxidative decarboxylation of isocitrate in the citric acid cycle. In the α2ßγ heterotetramer of NAD-IDH, the γ subunit plays the regulatory role and the ß subunit the structural role. Previous biochemical data have shown that mammalian NAD-IDHs can be inhibited by NADH; however, the molecular mechanism is unclear. In this work, we show that the αß, αγ and α2ßγ enzymes of human NAD-IDH can be inhibited by NADH, and further determine the crystal structure of the αγ heterodimer bound with an Mg2+ and an NADH at the active site and an NADH at the allosteric site, which resembles that of the inactive αMgγ heterodimer. The NADH at the active site occupies the binding site for NAD+ and prevents the binding of the cofactor. The NADH at the allosteric site occupies the binding sites for ADP and citrate and blocks the binding of the activators. The biochemical data confirm that the NADH binding competes with the binding of NAD+ and the binding of citrate and ADP, and the two effects together contribute to the NADH inhibition on the activity. These findings provide insights into the inhibitory mechanisms of the αγ heterodimer by NADH.