LncRNA LINC01857 drives pancreatic adenocarcinoma progression via modulating miR-19a-3p/SMOC2

Objectives: Emerging evidence has demonstrated that LINC01857 exerts a pivotal function in many cancers. However, its function in Pancreatic Ductal Adenocarcinoma (PDAC) still remains unclear. This study was designed to investigate the regulatory character of LINC01857 in PDAC. Methods: Bioinformatic tools and databases were used to seek potential miRNAs and mRNAs. Gene expression was evaluated by Reverse Transcription quantitative real-time Polymerase Chain Reaction (RT-qPCR), and western blot was used for protein level detection. A subcellular fraction assay was done to ascertain the location of LINC01857 in PANC-1 and BxPC-3 human pancreatic cancer cells. CCK-8, EdU, wound healing and Transwell assays were performed to inquire into the influence of LINC01857, and SPARC -related Modular Calcium-binding protein-2 (SMOC2) on cell viability, proliferation, migration, and invasion, respectively. The interaction between LINC01857 and its downstream genes was explored by RNA immunoprecipitation and luciferase reporter assays. Results: LINC01857 levels were significantly elevated in PDAC. Knockdown of LINC01857 significantly restrained the proliferation, migration, invasion, and Epithelial-Mesenchymal Transition (EMT) process of PDAC cells. MiR-19a-3p was a downstream target of LINC01857, and miR-19a-3p levels were significantly decreased in PDAC cells. In addition, SMOC2 expression had a negative correlation with that of miR-19a-3p, and SMOC2 was a downstream target of miR-19a-3p. Furthermore, SMOC2 upregulation partially abolished the inhibitive influence of LINC01857 downregulation on cell proliferation, migration, invasion, and the EMT process. Conclusion: LINC01857 promotes malignant phenotypes of PDAC cells via upregulation of SMOC2 by interacting with miR-19a-3p

3D GAN Inversion with Facial Symmetry Prior

Recently, a surge of high-quality 3D-aware GANs have been proposed, which leverage the generative power of neural rendering. It is natural to associate 3D GANs with GAN inversion methods to project a real image into the generator's latent space, allowing free-view consistent synthesis and editing, referred as 3D GAN inversion. Although with the facial prior preserved in pre-trained 3D GANs, reconstructing a 3D portrait with only one monocular image is still an ill-pose problem. The straightforward application of 2D GAN inversion methods focuses on texture similarity only while ignoring the correctness of 3D geometry shapes. It may raise geometry collapse effects, especially when reconstructing a side face under an extreme pose. Besides, the synthetic results in novel views are prone to be blurry. In this work, we propose a novel method to promote 3D GAN inversion by introducing facial symmetry prior. We design a pipeline and constraints to make full use of the pseudo auxiliary view obtained via image flipping, which helps obtain a robust and reasonable geometry shape during the inversion process. To enhance texture fidelity in unobserved viewpoints, pseudo labels from depth-guided 3D warping can provide extra supervision. We design constraints aimed at filtering out conflict areas for optimization in asymmetric situations. Comprehensive quantitative and qualitative evaluations on image reconstruction and editing demonstrate the superiority of our method.Comment: Project Page is at https://feiiyin.github.io/SPI

TaleCrafter: Interactive Story Visualization with Multiple Characters

Accurate Story visualization requires several necessary elements, such as identity consistency across frames, the alignment between plain text and visual content, and a reasonable layout of objects in images. Most previous works endeavor to meet these requirements by fitting a text-to-image (T2I) model on a set of videos in the same style and with the same characters, e.g., the FlintstonesSV dataset. However, the learned T2I models typically struggle to adapt to new characters, scenes, and styles, and often lack the flexibility to revise the layout of the synthesized images. This paper proposes a system for generic interactive story visualization, capable of handling multiple novel characters and supporting the editing of layout and local structure. It is developed by leveraging the prior knowledge of large language and T2I models, trained on massive corpora. The system comprises four interconnected components: story-to-prompt generation (S2P), text-to-layout generation (T2L), controllable text-to-image generation (C-T2I), and image-to-video animation (I2V). First, the S2P module converts concise story information into detailed prompts required for subsequent stages. Next, T2L generates diverse and reasonable layouts based on the prompts, offering users the ability to adjust and refine the layout to their preference. The core component, C-T2I, enables the creation of images guided by layouts, sketches, and actor-specific identifiers to maintain consistency and detail across visualizations. Finally, I2V enriches the visualization process by animating the generated images. Extensive experiments and a user study are conducted to validate the effectiveness and flexibility of interactive editing of the proposed system.Comment: Github repository: https://github.com/VideoCrafter/TaleCrafte

Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation

Generating videos for visual storytelling can be a tedious and complex process that typically requires either live-action filming or graphics animation rendering. To bypass these challenges, our key idea is to utilize the abundance of existing video clips and synthesize a coherent storytelling video by customizing their appearances. We achieve this by developing a framework comprised of two functional modules: (i) Motion Structure Retrieval, which provides video candidates with desired scene or motion context described by query texts, and (ii) Structure-Guided Text-to-Video Synthesis, which generates plot-aligned videos under the guidance of motion structure and text prompts. For the first module, we leverage an off-the-shelf video retrieval system and extract video depths as motion structure. For the second module, we propose a controllable video generation model that offers flexible controls over structure and characters. The videos are synthesized by following the structural guidance and appearance instruction. To ensure visual consistency across clips, we propose an effective concept personalization approach, which allows the specification of the desired character identities through text prompts. Extensive experiments demonstrate that our approach exhibits significant advantages over various existing baselines.Comment: Github: https://github.com/VideoCrafter/Animate-A-Story Project page: https://videocrafter.github.io/Animate-A-Stor

Demonstration of Adiabatic Variational Quantum Computing with a Superconducting Quantum Coprocessor

Adiabatic quantum computing enables the preparation of many-body ground states. This is key for applications in chemistry, materials science, and beyond. Realisation poses major experimental challenges: Direct analog implementation requires complex Hamiltonian engineering, while the digitised version needs deep quantum gate circuits. To bypass these obstacles, we suggest an adiabatic variational hybrid algorithm, which employs short quantum circuits and provides a systematic quantum adiabatic optimisation of the circuit parameters. The quantum adiabatic theorem promises not only the ground state but also that the excited eigenstates can be found. We report the first experimental demonstration that many-body eigenstates can be efficiently prepared by an adiabatic variational algorithm assisted with a multi-qubit superconducting coprocessor. We track the real-time evolution of the ground and exited states of transverse-field Ising spins with a fidelity up that can reach about 99%.Comment: 12 pages, 4 figure

Consistency of P53 immunohistochemical expression between preoperative biopsy and final surgical specimens of endometrial cancer

ObjectiveThe aim of this study is to explore the consistency of P53 immunohistochemical expression between preoperative biopsy and final pathology in endometrial cancer (EC), and to predict the prognosis of patients based on the 4-tier P53 expression and classic clinicopathological parameters.MethodsThe medical data of patients with stage I-III EC who received preoperative biopsy and initial surgical treatment in two medical centers was retrospectively collected. The consistency of P53 immunohistochemistry expression between preoperative biopsy and final pathology was compared using Cohen’s kappa coefficient and Sankey diagram, then 4-tier P53 expression was defined (P53wt/P53wt, P53abn/P53wt, P53wt/P53abn, and P53abn/P53abn). Univariate and multivariate Cox regression analysis was used to determine the correlation between 4-tier P53 expression and the prognosis of patients. On this basis, the nomogram models were established to predict the prognosis of patients by combining 4-layer P53 expression and classic clinicopathological parameters, then risk stratification was performed on patients.ResultsA total of 1186 patients were ultimately included in this study through inclusion and exclusion criteria. Overall, the consistency of P53 expression between preoperative biopsy and final pathology was 83.8%, with a kappa coefficient of 0.624. ROC curve suggested that the AUC of 4-tier P53 expression to predict the prognosis of patients was better than AUC of P53 expression in preoperative biopsy or final pathology alone. Univariate and multivariate Cox regression analysis suggested that 4-tier P53 expression was an independent influencing factor for recurrence and death. On this basis, the nomogram models based on 4-tier P53 expression and classical clinicopathological factors were successfully established. ROC curve suggested that the AUC (AUC for recurrence and death was 0.856 and 0.838, respectively) of the models was superior to the single 4-tier P53 expression or the single classical clinicopathological parameters, which could provide a better risk stratification for patients.ConclusionThe expression of P53 immunohistochemistry had relatively good consistency between preoperative biopsy and final pathology of EC. Due to the discrepancy of P53 immunohistochemistry between preoperative biopsy and final pathology, the prognosis of patients can be better evaluated based on the 4-layer P53 expression and classic clinical pathological parameters

The NTSC VLBI System and its application in UT1 measurement

In order to measure the Universal Time (UT1) in real time, National Time Service Center (NTSC) has built a VGOS-like (VLBI Global Observing System) broadband VLBI network, which includes three 13-m radio telescopes located in Jilin, Sanya and Kashi, and a data analysis center in Xi'an. Each station is equipped with a highly stable hydrogen atomic clock and a self-developed VLBI backend, and is co-located with two GPS receivers. This VGOS-like VLBI network may play an important role in improving the Chinese broadband VLBI technology and making valuable contributions to domestic VLBI measurements of UT1. In this paper, we introduce the specifications of this VLBI network, and present the UT1 measurements at C-band conducted in 2018 using the Jilin-Kashi baseline of this network. The comparisons between our UT1 estimates and those provided by IERS suggest that the NTSC VLBI network is capable to determine UT1 accurate at the level of 58.8 microseconds