Various diseases, including cancer, require surgery as a prime treatment method.^{1 – 7} One key factor for surgeons to operate accurately is a clear visualization of the anatomical structures.⁸ However, this has never been easy. On the one hand, some anatomical structures are very small, varying from millimeters to microns,⁹ and they might have close proximity to other organs or tissue.¹⁰ A clear view of these structures requires a resolution well beyond that of human eyes.¹¹ On the other hand, the lack of illumination in narrow cavities and deep channels, which are very common in neurosurgery, ear-nose-throat (ENT) surgery, and endodontics, results in a dim visualization with shadows.^10,12,13 Poor visualization may lead to inappropriate operation on anatomical structures or a nearby organ, which will affect the surgical outcome, reduce organ preservation, or even cause life-threatening consequences.¹⁴ Therefore, sufficient magnification and proper illumination are vital for the success of surgery.

Rotating blade vibration measurements are very important for any turbomachinery research and development program. The blade tip timing (BTT) technique uses the time of arrival (ToA) of the blade tip passing the casing mounted probes to give the blade vibration. As a non-contact technique, BTT is necessary for rotating blade vibration measurements. The higher accuracy of amplitude and vibration frequency identification has been pursued since the development of BTT. An improved circumferential Fourier fit (ICFF) method is proposed. In this method, the ToA is not only dependent on the rotating speed and monitoring position, but also on blade vibration. Compared with the traditional circumferential Fourier fit (TCFF) method, this improvement is more consistent with reality. A 12-blade assembly simulator and experimental data were used to evaluate the ICFF performance. The simulated results showed that the ICFF performance is comparable to TCFF in terms of EO identification, except the lower PSR or more number probes that have a more negative effect on ICFF. Besides, the accuracy of amplitude identification is higher for ICFF than TCFF on all test conditions. Meanwhile, the higher accuracy of the reconstruction of ICFF was further verified in all measurement resonance analysis.

Head and neck cancer (HNC) is the sixth most common cancer worldwide. Squamous cell carcinoma (SCC), which is a major type of cancer at the original sites of the upper aerodigestive tract, takes about 90% of HNC cases.^{1 – 3} It can occur in multiple organs including the nasopharynx, oral cavity, oropharynx, nasal cavity, paranasal sinuses, hypopharynx, larynx, and trachea.^{4 – 6} And histologic slides of SCC tissues are important for its histopathological analysis and diagnosis. Computer-aided pathology has been emerging in recent years. It is intended to provide a fast, reproducible, and quantitative diagnosis. Many studies have been carried out to investigate automatic cancer detection in digitalized histologic images using deep learning approaches.^{7 – 10} In these studies, image patches cropped from the whole-slide images were used to train a deep and rather complex neural network architecture, which learned to recognize the sophisticated histological structures all over the slides, as shown in Fig. 1. Due to the anatomical diversity of the histologic slides, these works usually use a large image patch size to ensure that enough morphological information is included in the patches. In addition, such deep learning methods always require a large training dataset in order to achieve effective classification. Despite the overall good classification results that can be obtained in the whole slide, some regions can still be misclassified, especially those near the tumor-normal margin and those with very few nuclei.⁷

The narrow base of genetic diversity of modern rice varieties is mainly attributed to the overuse of the common backbone parents that leads to the lack of varied favorable alleles in the process of breeding new varieties. Introgression lines (ILs) developed by a backcross strategy combined with marker-assisted selection (MAS) are powerful prebreeding tools for broadening the genetic base of existing cultivars. They have high power for mapping quantitative trait loci (QTLs) either with major or minor effects, and are used for precisely evaluating the genetic effects of QTLs and detecting the gene-by-gene or gene-by-environment interactions due to their low genetic background noise. ILs developed from multiple donors in a fixed background can be used as an IL platform to identify the best alleles or allele combinations for breeding by design. In the present paper, we reviewed the recent achievements from ILs in rice functional genomics research and breeding, including the genetic dissection of complex traits, identification of elite alleles and background-independent and epistatic QTLs, analysis of genetic interaction, and genetic improvement of single and multiple target traits. We also discussed how to develop ILs for further identification of new elite alleles, and how to utilize IL platforms for rice genetic improvement.

The purpose of this study is to develop hyperspectral imaging (HSI) for automatic detection of head and neck cancer cells on histologic slides. A compact hyperspectral microscopic system is developed in this study. Histologic slides from 15 patients with squamous cell carcinoma (SCC) of the larynx and hypopharynx are imaged with the system. The proposed nuclei segmentation method based on Principle Component Analysis (PCA) can extract most nuclei in the hyperspectral image without extracting other sub-cellular components. Both spectra-based support vector machine (SVM) and patchbased convolutional neural network (CNN) are used for nuclei classification. CNNs were trained with both hyperspectral images and pseudo RGB images of extracted nuclei, in order to evaluate the usefulness of extra information provided by hyperspectral imaging. The average accuracy of spectra-based SVM classification is 68%. The average AUC and average accuracy of the HSI patch-based CNN classification is 93.7 and 82.4%, respectively. The hyperspectral microscopic imaging and classification methods provide an automatic tool to aid pathologists in detecting SCC on histologic slides.

The aim of this study is to incorporate polarized hyperspectral imaging (PHSI) with machine learning for automatic detection of head and neck cancer on H&E stained tissue slides. A polarized hyperspectral imaging microscope had been developed in our group. The preliminary results showed that the spectral curves of the Stokes vector parameters (S0, S1, S2, S3) on the H&E stained tissue slides with squamous cell carcinoma (SCC) from the larynx are different from normal tissue in certain wavelength range. In this paper, we imaged 20 H&E stained tissue slides from 10 patients with SCC of the larynx by the PHSI microscope. Several machine learning algorithms (support vector machine, random forest, Gaussian naive Bayes, logistic regression) were applied to the collected image data for the automatic detection of SCC on the H&E stained tissue slides. The performance of these methods was compared among the collected polarized hyperspectral imaging dataset of tissue slides, the pseudo RGB images generated from the polarized hyperspectral imaging dataset, and the principle component analysis (PCA) transformation of the polarized hyperspectral imaging dataset. The results suggest that SVM is a superior classifier for the classification task based on polarized hyperspectral data cubes compared to the other three types of classifiers. Furthermore, the incorporation of the four Stokes vector parameters improved the classification accuracy. Finally, the PCA transformed image data did not improve the accuracy as it might lose some important information from the original polarized hyperspectral data cube. Polarized hyperspectral imaging can have many potential applications in digital pathology.