Hepatocellular carcinoma (HCC) is an invasive malignant tumour and the second major cause of cancer‐related deaths over the world. CRNDE and miR‐217 are non‐coding RNAs which play critical roles in cell growth, proliferation, migration. Mitogen‐activated protein kinase 1 (MAPK1) also participates in cancer cell process. Hence, this study aimed at investigating the effect of CRNDE on migration and invasion of HCC and figuring out the role of miR‐217 and MAPK1 in this process. The overexpression of CRNDE was demonstrated by a microarray‐based lncRNA profiling study. CRNDE expression in HCC was verified by qRT‐PCR. MTT assay and BrdU staining were applied to detect cell proliferation level. Transwell assay was utilized to examine cell migration and invasiveness abilities. Wound healing assay was performed for further exploration of cell migration capacity. MiR‐217 was predicted by bioinformatics. The dual luciferase reporter assay was performed to corroborate the targeting relationship between CRNDE, miR‐217 and MAPK1. MAPK1, the downstream target of miR‐217, was predicted using bioinformatics and was further confirmed by qRT‐PCR and Western blot. The interaction between CRNDE, miR‐217 and MAPK1 was studied by qRT‐PCR, Western blot, MTT, BrdU, transwell assay and wound healing assay. CRNDE was up‐regulated in HCC tissues and HCC cell lines. The high expression of CRNDE facilitated cell proliferation, migration and invasion, while the inhibited one affected on the contrary. MiR‐217, negatively correlated with CRNDE expression, was the target of CRNDE and was more lowly expressed in HCC. With the high expression of miR‐217, HCC cell proliferation, migration and invasion were suppressed. MAPK1, the possible target of miR‐217, was negatively correlated with miR‐217 but positively correlated with CRNDE and had the same effect in HCC formation process as CRNDE. Long non‐coding RNA CRNDE promotes the proliferation, migration and invasion of HCC cells through miR‐217/MAPK1 axis.

We compare three optical architectures for compressive imaging: sequential, parallel, and photon sharing. Each of these architectures is analyzed using two different types of projection: (a) principal component projections and (b) pseudo-random projections. Both linear and nonlinear reconstruction methods are studied. The performance of each architecture-projection combination is quantified in terms of reconstructed image quality as a function of measurement noise strength. Using a linear reconstruction operator we find that in all cases of (a) there is a measurement noise level above which compressive imaging is superior to conventional imaging. Normalized by the average object pixel brightness, these threshold noise standard deviations are 6.4, 4.9, and 2.1 for the sequential, parallel, and photon sharing architectures, respectively. We also find that conventional imaging outperforms compressive imaging using pseudo-random projections when linear reconstruction is employed. In all cases the photon sharing architecture is found to be more photon-efficient than the other two optical implementations and thus offers the highest performance among all compressive methods studied here. For example, with principal component projections and a linear reconstruction operator, the photon sharing architecture provides at least 17.6% less reconstruction error than either of the other two architectures for a noise strength of 1.6 times the average object pixel brightness. We also demonstrate that nonlinear reconstruction methods can offer additional performance improvements to all architectures for small values of noise.

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A major theme of computational photography is the acquisition of lightfield, which opens up new imaging capabilities, such as focusing after image capture. However, to capture the lightfield, one normally has to sacrifice significant spatial resolution as compared to normal imaging for a fixed sensor size. In this work, we present a new design for lightfield acquisition, which allows for the capture of a higher resolution lightfield by using two attenuation masks. They are positioned at the aperture stop and the optical path respectively, so that the four-dimensional (4D) lightfield spectrum is encoded and sampled by a two-dimensional (2D) camera sensor in a single snapshot. Then, during post-processing, by exploiting the coherence embedded in a lightfield, we can retrieve the desired 4D lightfield with a higher resolution using inverse imaging. The performance of our proposed method is demonstrated with simulations based on actual lightfield datasets.

Static feature-specific imaging (SFSI), where the measurement basis remains fixed/static during the data measurement process, has been shown to be superior to conventional imaging for reconstruction tasks. Here, we describe an adaptive approach that utilizes past measurements to inform the choice of measurement basis for future measurements in an FSI system, with the goal of maximizing the reconstruction fidelity while employing the fewest measurements. An algorithm to implement this adaptive approach is developed for FSI systems, and the resulting systems are referred to as adaptive FSI (AFSI) systems. A simulation study is used to analyze the performance of the AFSI system for two choices of measurement basis: principal component (PC) and Hadamard. Here, the root mean squared error (RMSE) metric is employed to quantify the reconstruction fidelity. We observe that an AFSI system achieves as much as 30% lower RMSE compared to an SFSI system. The performance improvement of the AFSI systems is verified using an experimental setup employed using a digital micromirror device (DMD) array.