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Biopsy is the recommended standard for pathological diagnosis of liver carcinoma. However, this method usually requires sectioning and staining, and well-trained pathologists to interpret tissue images. Here, we utilize Raman spectroscopy to study human hepatic tissue samples, developing and validating a workflow for in vitro and intraoperative pathological diagnosis of liver cancer. We distinguish carcinoma tissues from adjacent non-tumour tissues in a rapid, non-disruptive, and label-free manner by using Raman spectroscopy combined with deep learning, which is validated by tissue metabolomics. This technique allows for detailed pathological identification of the cancer tissues, including subtype, differentiation grade, and tumour stage. 2D/3D Raman images of unprocessed human tissue slices with submicrometric resolution are also acquired based on visualization of molecular composition, which could assist in tumour boundary recognition and clinicopathologic diagnosis. Lastly, the potential for a portable handheld Raman system is illustrated during surgery for real-time intraoperative human liver cancer diagnosis.

This paper proposes a new codebook generation algorithm for image data compression using a combined scheme of principal component analysis (PCA) and genetic algorithm (GA). The combined scheme makes full use of the near global optimal searching ability of GA and the computation complexity reduction of PCA to compute the codebook. The experimental results show that our algorithm outperforms the popular LBG algorithm in terms of computational efficiency and image compression performance.

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Image semantic segmentation has been applied more and more widely in the fields of satellite remote sensing, medical treatment, intelligent transportation, and virtual reality. However, in the medical field, the study of cerebral vessel and cranial nerve segmentation based on true-color medical images is in urgent need and has good research and development prospects. We have extended the current state-of-the-art semantic-segmentation network DeepLabv3+ and used it as the basic framework. First, the feature distillation block (FDB) was introduced into the encoder structure to refine the extracted features. In addition, the atrous spatial pyramid pooling (ASPP) module was added to the decoder structure to enhance the retention of feature and boundary information. The proposed model was trained by fine tuning and optimizing the relevant parameters. Experimental results show that the encoder structure has better performance in feature refinement processing, improving target boundary segmentation precision, and retaining more feature information. Our method has a segmentation accuracy of 75.73%, which is 3% better than DeepLabv3+.

A: The Back-n white neutron source at China Spallation Neutron Source (CSNS) is designed specifically for nuclear data measurements and multidisciplinary neutron applications. The time of flight (TOF) method is deployed to obtain a high resolution of neutron energy. In the normal operation mode of CSNS there are two proton bunches with a time interval of 410 ns in each pulse which has a repetition frequency of 25 Hz. Due to the superposition of the event distributions corresponding to two bunches, the resolution of the TOF measurement at Back-n will be degraded by the double-bunch characteristics if the measured event distribution is used directly without unfolding, especially in the higher neutron energy region. To nearly recover the event distribution corresponding to a single proton bunch, the unfolding methods have been developed to obtain better time resolution and energy resolution. Two kinds of unfolding algorithms based on the iterative Bayesian method and the iterative event re-distribution method, respectively, have been developed and tested with simulated data and experimental data. The developed methods have shown nice performance in unfolding the double-bunch event distributions with high accuracy and fast convergence. With the deploying of the unfolding methods, the data of the first batch experiments conducted at Back-n in the normal double-bunch mode were analyzed and nearly the same time resolution as in the case of the single-bunch mode has been obtained. K: Analysis and statistical methods; Data processing methods

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