Anchorage of Plant RanGAP to the Nuclear Envelope Involves Novel Nuclear-Pore-Associated Proteins

SummaryThe Ran GTPase controls multiple cellular processes including nucleocytoplasmic transport, spindle assembly, and nuclear envelope (NE) formation [1–4]. Its roles are accomplished by the asymmetric distribution of RanGTP and RanGDP enabled by the specific locations of the Ran GTPase-activating protein RanGAP and the nucleotide exchange factor RCC1 [5–8]. Mammalian RanGAP1 targeting to the NE and kinetochores requires interaction of its sumoylated C-terminal domain with the nucleoporin Nup358/RanBP2 [9–14]. In contrast, Arabidopsis RanGAP1 is associated with the NE and cell plate, mediated by an N-terminal, plant-specific WPP domain [15–18]. In the absence of RanBP2 in plants, the mechanism for spatially sequestering plant RanGAP is unknown. Here, Arabidopsis WPP-domain interacting proteins (WIPs) that interact with RanGAP1 in vivo and colocalize with RanGAP1 at the NE and cell plate were identified. Immunogold labeling indicates that WIP1 is associated with the outer NE. In a wip1-1/wip2-1/wip3-1 triple mutant, RanGAP1 is dislocated from the NE in undifferentiated root-tip cells, whereas NE targeting in differentiated root cells and targeting to the cell plate remain intact. We propose that WIPs are novel plant nucleoporins involved in RanGAP1 NE anchoring in specific cell types. Our data support a separate evolution of RanGAP targeting mechanisms in different kingdoms

Doxorubicin@Bcl-2 siRNA core@shell nanoparticles for synergistic anticancer chemotherapy

Acquired drug resistance in malignant tumors seriously hinders effective chemotherapy against cancer. The main mechanisms of drug resistance include decreased drug influx, increased drug efflux, as well as antiapoptotic defense behavior in cancerous cells. To overcome these issues, we have designed a nanomedicine composed of pure doxorubicin (DOX) as the core and B-cell lymphoma-2 (Bcl-2) siRNA as the shell for synergistic cancer treatment. Between the core and shell, polyethylene glycol (PEG) and polyethylenimine (PEI) are employed to increase the stability of the core DOX NPs and facilitate siRNA coating, respectively. In this design, the siRNA is able to inhibit the expression of Bcl-2 protein which has a role of protecting cancer cells from apoptosis. DOX not only is for anticancer therapy but also acts as a nanocarrier for Bcl-2 siRNA delivery. Our studies show that Bcl-2 siRNA and DOX are efficiently delivered into tumor cells and tumor tissues, and such a codelivery nanosystem possesses synergistic effects on tumor inhibition, enabling significantly enhanced antitumor outcome. This work demonstrates that the codelivery of tumor-suppressive Bcl-2 siRNA and chemotherapeutic agents without using an excipient material as a drug carrier represents a promising therapy for enhanced cancer therapy

What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives

Intelligent Mesh Generation (IMG) represents a novel and promising field of research, utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG has significantly broadened the adaptability and practicality of mesh generation techniques, delivering numerous breakthroughs and unveiling potential future pathways. However, a noticeable void exists in the contemporary literature concerning comprehensive surveys of IMG methods. This paper endeavors to fill this gap by providing a systematic and thorough survey of the current IMG landscape. With a focus on 113 preliminary IMG methods, we undertake a meticulous analysis from various angles, encompassing core algorithm techniques and their application scope, agent learning objectives, data types, targeted challenges, as well as advantages and limitations. We have curated and categorized the literature, proposing three unique taxonomies based on key techniques, output mesh unit elements, and relevant input data types. This paper also underscores several promising future research directions and challenges in IMG. To augment reader accessibility, a dedicated IMG project page is available at \url{https://github.com/xzb030/IMG_Survey}

Smart surface coating of drug nanoparticles with cross- linkable polyethylene glycol for bio-responsive and highly efficient drug delivery

YesMany drug molecules can be directly used as nanomedicine without the requirement of any inorganic or organic carriers such as silica and liposome nanostructures. This new type of carrier-free drug nanoparticles (NPs) has great potential in clinical treatment because of its ultra-high drug loading capacity and biodegradability. For practical applications, it is essential for such nanomedicine to possess robust stability and minimal premature release of therapeutic molecules during circulation in the blood stream. To meet this requirement, herein, we develop GSH-responsive and crosslinkable amphiphilic polyethylene glycol (PEG) molecules to modify carrier-free drug NPs. These PEG molecules can be cross-linked on the surface of the NPs to endow them with greater stability and the cross-link is sensitive to intracellular environment for bio-responsive drug release. With this elegant design, our experimental results show that the liberation of DOX from DOX-cross-linked PEG NPs is dramatically slower than that from DOX-non-cross-linked PEG NPs, and the DOX release profile can be controlled by tuning the concentration of the reducing agent to break the cross-link between PEG molecules. More importantly, in vivo studies reveal that the DOX-cross-linked PEG NPs exhibit favorable blood circulation half-life (>4 h) and intense accumulation in tumor areas, enabling effective anti-cancer therapy. We expect this work will provide a powerful strategy for stabilizing carrier-free nanomedicines and pave the way to their successful clinical applications in the future.The National Basic Research Program of China (2013CB933500, 2012CB932400), National Natural Science Foundation of China (61422403), Natural Science Foundation of Jiangsu Province (BK20131162), QingLan Project, Collaborative Innovation Center of Suzhou Nano Science and a Project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)

Microstructure-Empowered Stock Factor Extraction and Utilization

High-frequency quantitative investment is a crucial aspect of stock investment. Notably, order flow data plays a critical role as it provides the most detailed level of information among high-frequency trading data, including comprehensive data from the order book and transaction records at the tick level. The order flow data is extremely valuable for market analysis as it equips traders with essential insights for making informed decisions. However, extracting and effectively utilizing order flow data present challenges due to the large volume of data involved and the limitations of traditional factor mining techniques, which are primarily designed for coarser-level stock data. To address these challenges, we propose a novel framework that aims to effectively extract essential factors from order flow data for diverse downstream tasks across different granularities and scenarios. Our method consists of a Context Encoder and an Factor Extractor. The Context Encoder learns an embedding for the current order flow data segment's context by considering both the expected and actual market state. In addition, the Factor Extractor uses unsupervised learning methods to select such important signals that are most distinct from the majority within the given context. The extracted factors are then utilized for downstream tasks. In empirical studies, our proposed framework efficiently handles an entire year of stock order flow data across diverse scenarios, offering a broader range of applications compared to existing tick-level approaches that are limited to only a few days of stock data. We demonstrate that our method extracts superior factors from order flow data, enabling significant improvement for stock trend prediction and order execution tasks at the second and minute level