A decade of liver organoids: Advances in disease modeling.

Liver organoids are three-dimensional cellular tissue models in which cells interact to form unique structures in culture. During the past 10 years, liver organoids with various cellular compositions, structural features, and functional properties have been described. Methods to create these advanced human cell models range from simple tissue culture techniques to complex bioengineering approaches. Liver organoid culture platforms have been used in various research fields, from modeling liver diseases to regenerative therapy. This review discusses how liver organoids are used to model disease, including hereditary liver diseases, primary liver cancer, viral hepatitis, and nonalcoholic fatty liver disease. Specifically, we focus on studies that used either of two widely adopted approaches: differentiation from pluripotent stem cells or epithelial organoids cultured from patient tissues. These approaches have enabled the generation of advanced human liver models and, more importantly, the establishment of patient-tailored models for evaluating disease phenotypes and therapeutic responses at the individual level.

Locating secret messages based on quantitative steganalysis.

Steganography poses a serious challenge to forensics because investigators cannot identify even traces of secret messages embedded using a steganographer. Contrarily, the objective of locating steganalysis is to locate the embedded message, which should help extract the secret message. In this paper, a methodology of locating steganalysis using quantitative steganalysis is presented for multiple stego images with embedded messages along the same embedding path. Three typical quantitative steganalysis methods are applied to the methodology to locate the messages embedded using LSB re-placement. Experimental results show that the presented methods can reliably estimate the embedding positions, which verifies the validity of the presented methodology. The presented methodology points out a new use of quantitative steganalysis, and further demonstrates that it is necessary to design more precise quantitative steganalysis methods.

Image steganalysis feature selection based on the improved Fisher criterion.

In order to improve the detection accuracy of hidden message in images, steganalysis features are selected as inputs for steganalysers. However, the existing Fisher criterion ignores the contribution of steganalysis feature components in dispersion to classification, which causes the useful feature components to be deleted, and decreases the detection accuracy of steganalysis features. By analyzing the separability of steganalysis feature components, we introduce the sigmoid function into Fisher's criterion and propose an improved Fisher criterion (I-Fisher criterion), which can make up for the traditional Fisher criterion in separability measurement of steganalysis feature components. To optimize the steganalysis feature and reduce its dimension, we employ the improved Fisher criterion as the heuristic function of the decision rough set α-positive region reduction, and propose the feature selection method based on the improved Fisher. Experimental results show that the proposed method can reduce the dimension and memory of the GFR high-dimensional feature and the CC-PEV lowdimensional feature while maintaining or improving the detection accuracy.