Pathological examination is the gold standard for the diagnosis of cancer. Common pathological examinations include hematoxylin-eosin (H&E) staining and immunohistochemistry (IHC). In some cases, it is hard to make accurate diagnoses of cancer by referring only to H&E staining images. Whereas, the IHC examination can further provide enough evidence for the diagnosis process. Hence, the generation of virtual IHC images from H&E-stained images will be a good solution for current IHC examination hard accessibility issue, especially for some low-resource regions. However, existing approaches have limitations in microscopic structural preservation and the consistency of pathology properties. In addition, pixel-level paired data is hard available. In our work, we propose a novel adversarial learning method for effective Ki-67-stained image generation from corresponding H&E-stained image. Our method takes fully advantage of structural similarity constraint and skip connection to improve structural details preservation; and pathology consistency constraint and pathological representation network are first proposed to enforce the generated and source images hold the same pathological properties in different staining domains. We empirically demonstrate the effectiveness of our approach on two different unpaired histopathological datasets. Extensive experiments indicate the superior performance of our method that surpasses the state-of-the-art approaches by a significant margin. In addition, our approach also achieves a stable and good performance on unbalanced datasets, which shows our method has strong robustness. We believe that our method has significant potential in clinical virtual staining and advance the progress of computer-aided multi-staining histology image analysis.

Traditional digital holographic imaging algorithms need multiple iterations to obtain focused reconstructed image, which is time-consuming. In terms of phase retrieval, there is also the problem of phase compensation in addition to focusing task. Here, a new method is proposed for fast digital focus, where we use U-type convolutional neural network (U-net) to recover the original phase of microscopic samples. Generated data sets are used to simulate different degrees of defocused image, and verify that the U-net can restore the original phase to a great extent and realize phase compensation at the same time. We apply this method in the construction of real-time off-axis digital holographic microscope and obtain great breakthroughs in imaging speed.

Understanding spatial distribution difference and reaction kinetics of the electrode is vital for enhancing the electrochemical reaction efficiency. Here, we report a total internal reflection imaging sensor without background current interference to map local current distribution of the electrode in a vanadium redox flow battery during cyclic voltammetry (CV), enabling mapping of the activity and reversibility distribution with the spatial resolution of a single fiber. Three graphite felts with different activity are compared to verify its feasibility. In long-term cyclic voltammetry, the oxygen evolution reaction is proved to enhance activity distribution, and homogeneity of the electrode and its bubble kinetics with periodic fluctuation is consistent with the cyclic voltammetry curve, enabling the onset oxygen evolution/reduction potential determination. Higher activity and irreversibility distribution of the electrode is found in favor of the oxygen evolution reaction. This sensor has potential to detect in situ, among other processes, electrochemical reactions in flow batteries, water splitting, electrocatalysis and electrochemical corrosion.

A reflection-type phase-sensitive weak measurement for biosensing and chemical label-free sensing is presented. The phase difference between $p$ and $s$ polarizations in total internal reflection caused by biomolecular recognition is measured by weak value amplification. The system with $p$ and $s$ polarizations in a common path is stable and robust. The sensing process occurring on the silicon dioxide surface is achieved with a resolution of $3.6\times {10}^{-6}$ refractive index units. The applicability is demonstrated by real-time monitoring biomolecular interaction of IgG and protein A.

Objective: To obtain molecular information in slides directly from H&E staining slides, which apparently display morphological information, to show that some differences in molecular level have already encoded in morphology. Methods: In this paper, we selected Ki-67-expression as the representative of molecular information. We proposed a method that can predict Ki-67 positive cells directly from H&E stained slides by a deep convolutional network model. To train this model, we constructed a dataset containing Ki-67 negative or positive cell images and background images. These images were all extracted from H&E stained WSIs and the Ki-67 expression was acquired from the corresponding IHC stained WSIs. The trained model was evaluated both on classification performance and the ability to quantify Ki-67 expression in H&E stained images. Results: The model achieved an average accuracy of 0.9371 in discrimination of Ki-67 negative cell images, positive cell images and background images. As for evaluation of quantification performance, the correlation coefficient between the quantification results of H&E stained images predicted by our model and that of IHC stained images obtained by color channel filtering is 0.80. Conclusion and Significance: Our study indicates that the deep learning model has a good performance both on prediction of Ki-67 positive cells and quantification of Ki-67 expression in cancer samples stained by H&E. More generally, this study shows that deep learning is a powerful tool in exploring the relationship between morphological information and molecular information. Availability and Implementation: The main program is available at https://github.com/liuyiqing2018/predict_Ki-67_from_HE

A transmission optical rotation detection scheme based on a weak measurement was proposed for the chirality detection of enantiomers. In this transmission weak measurement system in the frequency domain, the optical activity of the chiral liquid sample was estimated with the central wavelength shift, by modifying the preselected polarization state with the optical rotation (OR). The central wavelength shift of output spectra is sensitive to the OR angle but immune to the interference of the refractive index change caused by measuring circumstances. Two isomers of chiral amino acid acquired opposite responses with this system, and a resolution of 2.17 × 10⁻⁹mol/ml for Proline detection could be obtained. Such a resolution is about 2 orders of magnitude higher than that of common methods, which shows a high sensitivity. This proposed weak measurement scenario suggested an approach to polarimetry and provided a way to accurately assess molecular chirality.