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Evidence accumulated over the past decade has verified that long non-coding RNAs (lncRNAs) exert important functions in multiple cell programs. As a novel class of cellular regulatory molecules, lncRNAs interact with different molecules, such as DNA, RNA or proteins, depending on their subcellular distribution, to modulate gene transcription and kinase cascades. It has been widely clarified that lncRNAs play important roles in modulating metabolic reprogramming and reshaping the immune landscape and serve as hinges bridging tumor metabolism and anti-tumor immunity. Given these facts, lncRNAs, as putative regulators of tumor initiation and progression, have attracted extensive attention in recent years. In this review, we summarized the current research progress on the role of lncRNAs in tumor metabolic reprogramming and tumor-immune microenvironment remodeling, and conclude with our laboratory's contributions in advancing the clinical applications of lncRNAs.

Cholesterol contributes to the structural basis of biological membranes and functions as a signaling molecule, whose dysregulation has been associated with various human diseases. Here, we report that the long non-coding RNA (lncRNA) SNHG6 increases progression from non-alcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC) by modulating cholesterol-induced mTORC1 activation. Mechanistically, cholesterol binds ER-anchored FAF2 protein to promote the formation of a SNHG6-FAF2-mTOR complex. As a putative cholesterol effector, SNHG6 enhances cholesterol-dependent mTORC1 lysosomal recruitment and activation via enhancing FAF2-mTOR interaction at ER-lysosome contacts, thereby coordinating mTORC1 kinase cascade activation with cellular cholesterol biosynthesis in a self-amplified cycle to accelerate cholesterol-driven NAFLD-HCC development. Notably, loss of SNHG6 inhibits mTORC1 signaling and impairs growth of patient-derived xenograft liver cancer tumors, identifyifng SNHG6 as a potential target for liver cancer treatment. Together, our findings illustrate the crucial role of organelle-associated lncRNA in organelle communication, nutrient sensing, and kinase cascades.

The purpose of this study is to evaluate and compare the quantitative material decomposition performance of the dual-energy CT (DECT) and differential phase contrast CT (DPCT) via numerical observer studies.The electron density (The model observer results show that the DECT is superior to DPCT at 0.3 mm spatial resolution (300 mm object size); the DECT and DPCT show similar quantitative imaging performance at 0.1 mm spatial resolution (100 mm object size); and the DPCT outperforms the DECT by approximately 1.5 times for the 0.3 mm sized imaging target at 0.03 mm spatial resolution (30 mm object size).In conclusion, the DECT would be recommended to obtain

The purpose of this paper is to present an end-to-end deep convolutional neural network to improve the dual-energy CT (DECT) material decomposition performance.In this study, we proposes a unified mutual-domain (sinogram domain and CT domain) material decomposition network (DIRECT-Net) for DECT imaging. By design, the DIRECT-Net has immediate access to mutual-domain data, and utilizes stacked convolution neural network layers for noise reduction and material decomposition. The training data are numerically generated following the fundamental DECT imaging physics. Numerical simulation of the XCAT digital phantom, experiments of a biological specimen, a calcium chloride phantom and an iodine solution phantom are carried out to evaluate the performance of DIRECT-Net. Comparisons are performed with different DECT decomposition algorithms.Results demonstrate that the proposed DIRECT-Net can generate water and bone basis images with less artifacts compared to the other decomposition methods. Additionally, the quantification errors of the calcium chloride (75-375 mg/cmAn end-to-end material decomposition network is proposed for quantitative DECT imaging. The qualitative and quantitative results demonstrate that this new DIRECT-Net has promising benefits in improving the DECT image quality.

In this work, a general theoretical framework is presented to explain the formation of the phase signal in an X-ray microscope integrated with a grating interferometer, which simultaneously enables the high spatial resolution imaging and the improved image contrast. Using this theory, several key parameters of phase contrast imaging can be predicted, for instance, the fringe visibility and period, the conversion condition from the differential phase imaging (DPI) to the phase difference imaging (PDI). Additionally, numerical simulations are performed with certain X-ray optical components and imaging geometry. Results demonstrate the accuracy of this developed quantitative analysis method of X-ray phase-sensitive microscope imaging., 8 pages, 3 figures

Perovskite materials in different dimensions show great potential in direct X‐ray detection, but each with limitations stemming from its own intrinsic properties. Particularly, the sensitivity of two‐dimensional (2D) perovskites is limited by poor carrier transport while ion migration in three‐dimensional (3D) perovskites causes the baseline drifting problem. To circumvent these limitations, herein a double‐layer perovskite film is developed with properly aligned energy level, where 2D (PEA)2MA3Pb4I13 (PEA=2‐phenylethylammonium, MA=methylammonium) is cascaded with vertically crystallized 3D MAPbI3. In this new design paradigm, the 3D layer ensures fast carrier transport while the 2D layer mitigates ion migration, thus offering a high sensitivity and a greatly stabilized baseline. Besides, the 2D layer increases the film resistivity and enlarges the energy barrier for hole injection without compromising carrier extraction. Consequently, the double‐layer perovskite detector delivers a high sensitivity (1.95 × 104 μC Gyair −1 cm−2) and a low detection limit (480 nGyair s−1). Also demonstrated is the X‐ray imaging capacity using a circuit board as the object. This work opens up a new avenue for enhancing X‐ray detection performance via cascade assembly of various perovskites with complementary properties., By integrating a layered 2D perovskite with a vertically crystallized MAPbI3, a double‐layer perovskite is constructed for direct X‐ray detection, showing stable baseline, a high sensitivity of 19 503 μC Gyair −1 cm−2, and a low detection limit of 480 nGyair s−1. This work provides a strategy to unlock the performance limitations stemming from the intrinsic properties of the perovskite.

Objective. In this work, a dedicated end-to-end deep convolutional neural network, named as Triple-CBCT, is proposed to demonstrate the feasibility of reconstructing three different material distribution volumes from the dual-energy CBCT projection data. Approach. In Triple-CBCT, the features of the sinogram and the CT image are independently extracted and cascaded via a customized domain transform network module. This Triple-CBCT network was trained by numerically synthesized dual-energy CBCT data, and was tested with experimental dual-energy CBCT data of the Iodine-CaCl2 solution and pig leg specimen scanned on an in-house benchtop system. Main results. Results show that the information stored in both the sinogram and CT image domains can be used together to improve the decomposition quality of multiple materials (water, iodine, CaCl2 or bone) from the dual-energy projections. In addition, both the numerical and experimental results demonstrate that the Triple-CBCT is able to generate high-fidelity dual-energy CBCT basis images. Significance. An innovative end-to-end network that joints the sinogram and CT image domain information is developed to facilitate high quality automatic decomposition from the dual-energy CBCT scans.

SpecMAT is a magnetic active target (AT) for transfer reaction studies with low-intensity radioactive ion beams at the ISOLDE facility (CERN). SpecMAT combines two types of detectors: a gas-filled time projection chamber (TPC), the core detector for tracking charged particles emitted in nuclear reactions, and an array of auxiliary scintillation detectors for in-flight γ -ray spectroscopy . All active target components are optimised for operation in a strong background magnetic field. The TPC has a cylindrical shape. Its electron detector is a bulk Micromegas segmented into 2916 triangular pads for sampling charged particle tracks . The scintillation array is composed of 45 detectors with cubic 48 × 48 × 48 mm 3 CeBr3 crystals. The General Electronics for TPC (GET) is used in SpecMAT to read out both detector types allowing correlation of the detected γ -rays with the TPC events. This article describes the SpecMAT active target and its characterisation with the α -decay of 241Am. A γ -ray spectrum obtained in correlation with 3D tracks left by charged particles in an active target is reported for the first time for such detectors.