Synthesis of Porphyrinic Metal Organic Frameworks with High Robustness and Catalytic Activity

MOFs are ideal platforms to immobilize porphyrins and their derivatives. MOF’s high surface areas and rigid structures not only make porphyrin moieties approachable substrates but also prevent the dimerization of their active centers. These advantages effectively enhance the reactivity and optical performance of porphyrins. Extensive study has been done to synthesize porphyrinic MOFs, however, many of the previously reported porphyrinic MOFs suffer from weak chemical stabilities, which severely hinders their wide utilization. My study is focused on the design and syntheses of porphyrinic MOFs with high robustness in various chemical environments. Three porphyrinic MOFs with excellent chemical stability, namely PCN-600, PCN-601 and PCN-602, were obtained, and their performances as catalysts in different reactions were explored. PCN-600, a Fe-based mesoporous porphyrinic MOF was successfully synthesized through rational topological design and KTDA method. It exhibits high chemical stability in aqueous solutions with pH values ranging from 2 to 11. The catalytic activity of PCN-600(Fe) (with Fe³⁺ at the porphyrin center) was confirmed by its excellent performance in the co-oxidation of phenol and 4-aminoantipyrine (4-AAP) by H₂O₂. Most of the reported stable porphyrinic MOFs, including PCN-22Xs (X = 2, 3, 4, 5) and PCN-600, are constructed by high-valent metal ions and carboxylate-based porphyrinic ligands. Though these materials have very high robustness in acidic solutions, they are relatively vulnerable in aqueous solutions containing some coordinating anions, such as OH⁻, F⁻, CO₃²⁻ and PO₄³⁻. To address these problems, two iso-structural porphyrinic MOFs constructed by pyrazolate-terminated ligands were synthesized, namely PCN-601 and PCN-602. These materials exhibit extraordinary stability in the solutions of NaOH, KF, Na₂CO₃ and K₃PO₄. Their chemical stabilities were rationalized from both thermodynamic and kinetic perspectives. The catalytic performance of PCN-601(Mn) and PCN-602(Mn) (with Mn³⁺ at the coordination centers of the porphyrinic ligands) in C-H halogenation reactions was explored. Compared with PCN-601(Mn), PCN-602(Mn) has a higher porosity and displays a better activity as a recyclable hetergeneous catalyst

A Framework for E-Service Custorm Trust -An Integrated Research Model Based on multitheoritical View

As a new paradigm of internet-based transaction, e-service is thriving nowadays. However, it is distinctively different from the traditional e-commerce. Taking customer-centered philosophy as fundamental focus, e-service emphasizes customization and relationship marketing based on services selling, but not tangible goods selling, which makes transaction trust, especially customer trust become more complicated. In this paper, a framework of e-service customer trust is put forward based on multitheoretical view, which will provide theoretical support for the future research

CRISPR accelerates the cancer drug discovery

Emerging cohorts and basic studies have associated certain genetic modifications in cancer patients, such as gene mutation, amplification, or deletion, with the overall survival prognosis, underscoring patients??? genetic background may directly regulate drug sensitivity/resistance during chemotherapies. Understanding the molecular mechanism underpinning drug sensitivity/resistance and further uncovering the effective drugs have been the major ambition in the cancer drug discovery. The emergence and popularity of CRISPR/Cas9 technology have reformed the entire life science research, providing a precise and simplified genome editing tool with unlimited editing possibilities. Furthermore, it presents a powerful tool in cancer drug discovery, which hopefully facilitates us with a rapid and reliable manner in developing novel therapies and understanding the molecular mechanisms of drug sensitivity/resistance. Herein, we summarized the application of CRISPR/Cas9 in drug screening, with the focus on CRISPR/Cas9 mediated gene knockout, gene knock-in, as well as transcriptional modification. Additionally, this review provides the concerns, cautions, and ethnic considerations that need to be taken when applying CRISPR in the drug discovery.Peer reviewe

LAPTM4B-35 promotes cancer cell migration via stimulating integrin beta1 recycling and focal adhesion dynamics

Metastasis is the main cause of cancer patients' death despite tremendous efforts invested in developing the related molecular mechanisms. During cancer cell migration, cells undergo dynamic regulation of filopodia, focal adhesion, and endosome trafficking. Cdc42 is imperative for maintaining cell morphology and filopodia, regulating cell movement. Integrin beta1 activates on the endosome, the majority of which distributes itself on the plasma membrane, indicating that endocytic trafficking is essential for this activity. In cancers, high expression of lysosome-associated protein transmembrane 4B (LAPTM4B) is associated with poor prognosis. LAPTM4B-35 has been reported as displaying plasma membrane distribution and being associated with cancer cell migration. However, the detailed mechanism of its isoform-specific distribution and whether it relates to cell migration remain unknown. Here, we first report and quantify the filopodia localization of LAPTM4B-35: mechanically, that specific interaction with Cdc42 promoted its localization to the filopodia. Furthermore, our data show that LAPTM4B-35 stabilized filopodia and regulated integrin beta1 recycling via interaction and cotrafficking on the endosome. In our zebrafish xenograft model, LAPTM4B-35 stimulated the formation and dynamics of focal adhesion, further promoting cancer cell dissemination, whereas in skin cancer patients, LAPTM4B level correlated with poor prognosis. In short, this study establishes an insight into the mechanism of LAPTM4B-35 filopodia distribution, as well as into its biological effects and its clinical significance, providing a novel target for cancer therapeutics development.Peer reviewe

AutoStory: Generating Diverse Storytelling Images with Minimal Human Effort

Story visualization aims to generate a series of images that match the story described in texts, and it requires the generated images to satisfy high quality, alignment with the text description, and consistency in character identities. Given the complexity of story visualization, existing methods drastically simplify the problem by considering only a few specific characters and scenarios, or requiring the users to provide per-image control conditions such as sketches. However, these simplifications render these methods incompetent for real applications. To this end, we propose an automated story visualization system that can effectively generate diverse, high-quality, and consistent sets of story images, with minimal human interactions. Specifically, we utilize the comprehension and planning capabilities of large language models for layout planning, and then leverage large-scale text-to-image models to generate sophisticated story images based on the layout. We empirically find that sparse control conditions, such as bounding boxes, are suitable for layout planning, while dense control conditions, e.g., sketches and keypoints, are suitable for generating high-quality image content. To obtain the best of both worlds, we devise a dense condition generation module to transform simple bounding box layouts into sketch or keypoint control conditions for final image generation, which not only improves the image quality but also allows easy and intuitive user interactions. In addition, we propose a simple yet effective method to generate multi-view consistent character images, eliminating the reliance on human labor to collect or draw character images.Comment: 19 page

GenDeF: Learning Generative Deformation Field for Video Generation

We offer a new perspective on approaching the task of video generation. Instead of directly synthesizing a sequence of frames, we propose to render a video by warping one static image with a generative deformation field (GenDeF). Such a pipeline enjoys three appealing advantages. First, we can sufficiently reuse a well-trained image generator to synthesize the static image (also called canonical image), alleviating the difficulty in producing a video and thereby resulting in better visual quality. Second, we can easily convert a deformation field to optical flows, making it possible to apply explicit structural regularizations for motion modeling, leading to temporally consistent results. Third, the disentanglement between content and motion allows users to process a synthesized video through processing its corresponding static image without any tuning, facilitating many applications like video editing, keypoint tracking, and video segmentation. Both qualitative and quantitative results on three common video generation benchmarks demonstrate the superiority of our GenDeF method.Comment: Project page: https://aim-uofa.github.io/GenDeF

CoDeF: Content Deformation Fields for Temporally Consistent Video Processing

We present the content deformation field CoDeF as a new type of video representation, which consists of a canonical content field aggregating the static contents in the entire video and a temporal deformation field recording the transformations from the canonical image (i.e., rendered from the canonical content field) to each individual frame along the time axis.Given a target video, these two fields are jointly optimized to reconstruct it through a carefully tailored rendering pipeline.We advisedly introduce some regularizations into the optimization process, urging the canonical content field to inherit semantics (e.g., the object shape) from the video.With such a design, CoDeF naturally supports lifting image algorithms for video processing, in the sense that one can apply an image algorithm to the canonical image and effortlessly propagate the outcomes to the entire video with the aid of the temporal deformation field.We experimentally show that CoDeF is able to lift image-to-image translation to video-to-video translation and lift keypoint detection to keypoint tracking without any training.More importantly, thanks to our lifting strategy that deploys the algorithms on only one image, we achieve superior cross-frame consistency in processed videos compared to existing video-to-video translation approaches, and even manage to track non-rigid objects like water and smog.Project page can be found at https://qiuyu96.github.io/CoDeF/.Comment: Project Webpage: https://qiuyu96.github.io/CoDeF/, Code: https://github.com/qiuyu96/CoDe