The aberrant expression and dysfunction of long non‐coding RNAs (lncRNAs) have been identified as critical factors governing the initiation and progression of different human cancers, including diffuse large B‐cell lymphoma (DLBCL). LncRNA small nucleolar RNA host gene 16 (SNHG16) has been recognized as a tumour‐promoting factor in various types of cancer. However, the biological role of SNHG16 and its underlying mechanism are still unknown in DLBCL. Here we disclosed that SNHG16 was overexpressed in DLBCL tissues and the derived cell lines. SNHG16 knockdown significantly suppressed cell proliferation and cell cycle progression, and it induced apoptosis of DLBCL cells in vitro. Furthermore, silencing of SNHG16 markedly repressed in vivo growth of OCI‐LY7 cells. Mechanistically, SNHG16 directly interacted with miR‐497‐5p by acting as a competing endogenous RNA (ceRNA) and inversely regulated the abundance of miR‐497‐5p in DLBCL cells. Moreover, the proto‐oncogene proviral integration site for Moloney murine leukaemia virus 1 (PIM1) was identified as a novel direct target of miR‐497‐5p. SNHG16 overexpression rescued miR‐497‐5p‐induced down‐regulation of PIM1 in DLBCL cells. Importantly, restoration of PIM1 expression reversed SNHG16 knockdown‐induced inhibition of proliferation, G0/G1 phase arrest and apoptosis of OCI‐LY7 cells. Our study suggests that the SNHG16/miR‐497‐5p/PIM1 axis may provide promising therapeutic targets for DLBCL progression.
The catalytic performance in heterogeneous catalytic reactions consisting of solid reactants is strongly dependent on the nanostructure of the catalysts. Metal-oxides core-shell (MOCS) nanostructures have potential to enhance the catalytic activity for soot oxidation reactions as a result of optimizing the density of active sites located at the metal-oxide interface. Here, we report a facile strategy for fabricating nanocatalysts with self-assembled Pt@CeO-rich core-shell nanoparticles (NPs) supported on three-dimensionally ordered macroporous (3DOM) CeZrOvia the in situ colloidal crystal template (CCT) method. The nanostructure-dependent activity of the catalysts for soot oxidation were investigated by means of SEM, TEM, H-TPR, XPS, O-isothermal chemisorption, soot-TPO and so on. A CeO-rich shell on a Pt core is preferentially separated from CeZrO precursors and could self-assemble to form MOCS nanostructures. 3DOM structures can enhance the contact efficiency between catalysts and solid reactants (soot). Pt@CeO-rich core-shell nanostructures can optimize the density of oxygen vacancies (O) as active sites located at the interface of Pt-CeZrO. Remarkably, 3DOM Pt@CeO-rich/CeZrO catalysts show super catalytic performance and strongly nanostructure-dependent activity for soot oxidation in the absence of NO and NO. For example, the T of the 3DOM Pt@CeO-rich/CeZrO catalyst is lowered down to 408 °C, and the reaction rate of the 3DOM Pt@CeO-rich/CeZrO catalyst (0.12 μmol g s) at 300 °C is 4 times that of the 3DOM Pt/CeZrO catalyst (0.03 μmol g s). The structures of 3DOM CeZrO-supported Pt@CeO-rich core-shell NPs are decent systems for deep oxidation of solid reactants or macromolecules, and this facile technique for synthesizing catalysts has potential to be applied to other element compositions.
1. Cofilin 1 is overexpressed in HCC, especially in PVTT tissues. 2. Cofilin 1 is up-regulated in hypoxia condition and promotes HCC progression. 3. CFL1 binds to PLD1 and maintains PLD1 protein stability. 4. HIF-1α regulates CFL1 transcription and activates CFL1-PLD1-AKT axis in HCC.
Vehicles, pedestrians, and riders are the most important and interesting objects for the perception modules of selfdriving vehicles and video surveillance. However, the state-of-theart performance of detecting such important objects (esp. small objects) is far from satisfying the demand of practical systems. Large-scale, rich-diversity, and high-resolution datasets play an important role in developing better object detection methods to satisfy the demand. Existing public large-scale datasets such as MS COCO collected from websites do not focus on the specific scenarios. Moreover, the popular datasets (e.g., KITTI and Citypersons) collected from the specific scenarios are limited in the number of images and instances, the resolution, and the diversity. To attempt to solve the problem, we build a diverse high-resolution dataset (called TJU-DHD). The dataset contains 115,354 high-resolution images (52% images have a resolution of 1624×1200 pixels and 48% images have a resolution of at least 2,560×1,440 pixels) and 709,330 labeled objects in total with a large variance in scale and appearance. Meanwhile, the dataset has a rich diversity in season variance, illumination variance, and weather variance. In addition, a new diverse pedestrian dataset is further built. With the four different detectors (i.e., the one-stage RetinaNet, anchor-free FCOS, two-stage FPN, and Cascade R-CNN), experiments about object detection and pedestrian detection are conducted. We hope that the newly built dataset can help promote the research on object detection and pedestrian detection in these two scenes. The dataset is available at https://github.com/tjubiit/TJU-DHD.
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