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Abstract. We present a new way to extract characteristic features of the Mueller matrix images based on their frequency distributions and the central moments. We take the backscattering Mueller matrices of tissues with distinctive microstructures, and then analyze the frequency distribution histograms (FDHs) of all the matrix elements. For anisotropic skeletal muscle and isotropic liver tissues, we find that the shapes of the FDHs and their central moment parameters, i.e., variance, skewness, and kurtosis, are not sensitive to the sample orientation. Comparisons among different tissues further indicate that the frequency distributions of Mueller matrix elements and their corresponding central moments can be used as indicators for the characteristic microstructural features of tissues. A preliminary application to human cervical cancerous tissues shows that the distribution curves and central moment parameters may have the potential to give quantitative criteria for cancerous tissues detections.

A polarization microscope is a useful tool to reveal the optical anisotropic nature of a specimen and can provide abundant microstructural information about samples. We present a division of focal plane (DoFP) polarimeter-based polarization microscope capable of simultaneously measuring both the Stokes vector and the 3×4 Mueller matrix with an optimal polarization illumination scheme. The Mueller matrix images of unstained human carcinoma tissue slices show that the m24 and m34 elements can provide important information for pathological observations. The characteristic features of the m24 and m34 elements can be enhanced by polarization staining under illumination by a circularly polarized light. Hence, combined with a graphics processing unit acceleration algorithm, the DoFP polarization microscope is capable of real-time polarization imaging for potential quick clinical diagnoses of both standard and frozen slices of human carcinoma tissues.

Motor imagery-based brain-computer interface (MI-BCI) inefficiency phenomenon is one of the biggest challenges in MI-BCI research. BCI inefficiency subject is defined as the subject who cannot achieve classification accuracy higher than 70% since 70% is considered to be the minimum accuracy for communication by BCI. About 15-30% of the people are MI-BCI inefficiency according to the investigation. Most of the existing studies used common spatial patterns (CSP) to extract motor imagery feature and identify MI-BCI inefficiency subject based on the obtained classification accuracy. We think the MI-BCI performance maybe suppressed because CSP mainly extracts event-related desynchronization (ERD) feature, while the features generated by motor imagery are more than that. In this current work, we screened a total of 12 MI-BCI inefficiency subjects by CSP feature firstly, and recorded the motor imagery EEG data of them. Furthermore, we constructed a task-related brain network by calculating the coherence between EEG channels, the graph-based analysis showed that the node degree and clustering coefficient have intensity differences between left and right hand motor imagery. Finally, the two kinds of features were used to discriminate the two tasks. The results showed that both node degree and clustering coefficient features perform better than CSP, and the feature combination of brain network and CSP achieved higher accuracy than a single feature. In particular, a total of four subjects achieved accuracy higher than 70% by node degree and CSP features fusion. This work demonstrates that the accuracy of the MI-BCI inefficiency subject can be increased by using the brain network feature, but the accuracy gains are not high enough; it is worth to try other types of feature extraction algorithms for the MI-BCI inefficiency subject. INDEX TERMS Motor imagery, brain-computer interface (BCI), BCI inefficiency, network feature, feature extraction.

We present the Mueller matrix imaging system to classify morphologically similar algae based on convolutional neural networks (CNNs). The algae and cyanobacteria data set contains 10,463 Mueller matrices from eight species of algae and one species of cyanobacteria, belonging to four phyla, the shapes of which are mostly randomly oriented spheres, ovals, wheels, or rods. The CNN serves as an automatic machine with learning ability to help in extracting features from the Mueller matrix, and trains a classifier to achieve a 97% classification accuracy. We compare the performance in two ways. One way is to compare the performance of five CNNs that differ in the number of convolution layers as well as the classical principle component analysis (PCA) plus the support vector machine (SVM) method; the other way is to quantify the differences of scores between full Mueller matrix and the first matrix element m11, which does not contain polarization information under the same conditions. As the results show, deeper CNNs perform better, the best of which outperforms the conventional PCA plus SVM method by 19.66% in accuracy, and using the full Mueller matrix earns 6.56% increase of accuracy than using m11. It demonstrates that the coupling of Mueller matrix imaging and CNN may be a promising and efficient solution for the automatic classification of morphologically similar algae.

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Background: Hepatocellular carcinoma (HCC) is still a major health burden in China considering its high incidence and mortality. Long non-coding RNAs (lncRNAs) were found playing vital roles in tumor progression, suggesting a new way of diagnosis and prognosis prediction, or treatment of HCC. This study was designed to investigate the role of HIF1A-AS1 during the progression of HCC and to explore its related mechanisms. Methods: The expression of HIF1A-AS1 was detected in 50 paired carcinoma tissues and adjacent normal tissues by quantitative real-time PCR assay. HCC cell apoptosis was induced by nutrient-deficient culture medium and detected by Cell Counting Kit-8 and flow cytometer assays. HIF1A-AS1 inhibition in HCC cells was accomplished by small interfering RNA transfection. Results: HIF1A-AS1 was overexpressed in HCC tissues and was associated with tumor size, TNM stage, and lymph node metastasis. Compared with the low HIF1A-AS1 group, the high HIF1A-AS1 group had a shorter overall survival and a worse disease-free survival. HIF1A-AS1 expression was significantly higher in HCC cell lines (7721 and Huh7) than that in normal hepatocyte cell line L02 under normal culture condition. However, under nutrient-deficient condition, HIF1A-AS1 expression was significantly increased in both HCC and normal hepatocyte cell lines and was increased with the prolongation of nutrient-free culture. Inhibition of HIF1A-AS1 promoted starvation-induced HCC cell apoptosis. Furthermore, inhibition of HIF1A-AS1 could also reduce starvation-induced HCC cell autophagy. The expression of HIF-1α and phosphorylated mTOR was significantly decreased in HCC cells after HIF1A-AS1 inhibition. Conclusions: HIF1A-AS1, overexpressed in HCC and associated with HCC prognosis, could regulate starvationinduced HCC cell apoptosis by reducing HIF-1α/mTOR-mediated autophagy, promoting HCC cell progression.

In earlier studies, we developed scattering models and the corresponding CPU-based Monte Carlo simulation programs to study the behavior of polarized photons as they propagate through complex biological tissues. Studying the simulation results in high degrees of freedom that created a demand for massive simulation tasks. In this paper, we report a parallel implementation of the simulation program based on the compute unified device architecture running on a graphics processing unit (GPU). Different schemes for sphere-only simulations and sphere-cylinder mixture simulations were developed. Diverse optimizing methods were employed to achieve the best acceleration. The final-version GPU program is hundreds of times faster than the CPU version. Dependence of the performance on input parameters and precision were also studied. It is shown that using single precision in the GPU simulations results in very limited losses in accuracy. Consumer-level graphics cards, even those in laptop computers, are more cost-effective than scientific graphics cards for single-precision computation.