The telosome/shelterin complex and its functions

The telosome/shelterin protein complex bound to telomeres is essential for maintenance of telomere structure and telomere signaling functions

Regulation of cytokine-independent survival kinase (CISK) by the Phox homology domain and phosphoinositides

PKB/Akt and serum and glucocorticoid–regulated kinase (SGK) family kinases are important downstream targets of phosphatidylinositol 3 (PI-3) kinase and have been shown to mediate a variety of cellular processes, including cell growth and survival. Although regulation of Akt can be achieved through several mechanisms, including its phosphoinositide-binding Pleckstrin homology (PH) domain, how SGK kinases are targeted and regulated remains to be elucidated. Unlike Akt, cytokine-independent survival kinase (CISK)/SGK3 contains a Phox homology (PX) domain. PX domains have been implicated in several cellular events involving membrane trafficking. However, their precise function remains unknown. We demonstrate here that the PX domain of CISK interacts with phosphatidylinositol (PtdIns)(3,5)P2, PtdIns(3,4,5)P3, and to a lesser extent PtdIns(4,5)P2. The CISK PX domain is required for targeting CISK to the endosomal compartment. Mutation in the PX domain that abolished its phospholipid binding ability not only disrupted CISK localization, but also resulted in a decrease in CISK activity in vivo. These results suggest that the PX domain regulates CISK localization and function through its direct interaction with phosphoinositides. Therefore, CISK and Akt have evolved to utilize different lipid binding domains to accomplish a similar mechanism of activation in response to PI-3 kinase signaling

Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles

The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such as processed LIDAR and camera data from other vehicles. In this work, we design an integrated information sharing and safe multi-agent reinforcement learning (MARL) framework for CAVs, to take advantage of the extra information when making decisions to improve traffic efficiency and safety. We first use weight pruned convolutional neural networks (CNN) to process the raw image and point cloud LIDAR data locally at each autonomous vehicle, and share CNN-output data with neighboring CAVs. We then design a safe actor-critic algorithm that utilizes both a vehicle's local observation and the information received via V2V communication to explore an efficient behavior planning policy with safety guarantees. Using the CARLA simulator for experiments, we show that our approach improves the CAV system's efficiency in terms of average velocity and comfort under different CAV ratios and different traffic densities. We also show that our approach avoids the execution of unsafe actions and always maintains a safe distance from other vehicles. We construct an obstacle-at-corner scenario to show that the shared vision can help CAVs to observe obstacles earlier and take action to avoid traffic jams.Comment: This paper gets the Best Paper Award in the DCAA workshop of AAAI 202

Domain-dependent Function of the rasGAP-binding Protein p62Dok in Cell Signaling

p62Dok, the rasGAP-binding protein, is a common target of protein-tyrosine kinases. It is one of the major tyrosine-phosphorylated molecules in v-Src-transformed cells. Dok consists of an amino-terminal Pleckstrin homology domain, a putative phosphotyrosine binding domain, and a carboxyl-terminal tail containing multiple tyrosine phosphorylation sites. The importance and function of these sequences in Dok signaling remain largely unknown. We have demonstrated here that the expression of Dok can inhibit cellular transformation by the Src tyrosine kinase. Both the phosphotyrosine binding domain and the carboxyl-terminal tail of Dok (in particular residues 336-363) are necessary for such activity. Using a combinatorial peptide library approach, we have shown that the Dok phosphotyrosine binding domain binds phosphopeptides with the consensus motif of Y/MXXNXL-phosphotyrosine. Furthermore, Dok can homodimerize through its phosphotyrosine binding domain and Tyr146 at the amino-terminal region. Mutations of this domain or Tyr146 that block homodimerization significantly reduce the ability of Dok to inhibit Src transformation. Our results suggest that Dok oligomerization through its multiple domains plays a critical role in Dok signaling in response to tyrosine kinase activation

Action Quality Assessment with Temporal Parsing Transformer

Action Quality Assessment(AQA) is important for action understanding and resolving the task poses unique challenges due to subtle visual differences. Existing state-of-the-art methods typically rely on the holistic video representations for score regression or ranking, which limits the generalization to capture fine-grained intra-class variation. To overcome the above limitation, we propose a temporal parsing transformer to decompose the holistic feature into temporal part-level representations. Specifically, we utilize a set of learnable queries to represent the atomic temporal patterns for a specific action. Our decoding process converts the frame representations to a fixed number of temporally ordered part representations. To obtain the quality score, we adopt the state-of-the-art contrastive regression based on the part representations. Since existing AQA datasets do not provide temporal part-level labels or partitions, we propose two novel loss functions on the cross attention responses of the decoder: a ranking loss to ensure the learnable queries to satisfy the temporal order in cross attention and a sparsity loss to encourage the part representations to be more discriminative. Extensive experiments show that our proposed method outperforms prior work on three public AQA benchmarks by a considerable margin.Comment: accepted by ECCV 202

LawBench: Benchmarking Legal Knowledge of Large Language Models

Large language models (LLMs) have demonstrated strong capabilities in various aspects. However, when applying them to the highly specialized, safe-critical legal domain, it is unclear how much legal knowledge they possess and whether they can reliably perform legal-related tasks. To address this gap, we propose a comprehensive evaluation benchmark LawBench. LawBench has been meticulously crafted to have precise assessment of the LLMs' legal capabilities from three cognitive levels: (1) Legal knowledge memorization: whether LLMs can memorize needed legal concepts, articles and facts; (2) Legal knowledge understanding: whether LLMs can comprehend entities, events and relationships within legal text; (3) Legal knowledge applying: whether LLMs can properly utilize their legal knowledge and make necessary reasoning steps to solve realistic legal tasks. LawBench contains 20 diverse tasks covering 5 task types: single-label classification (SLC), multi-label classification (MLC), regression, extraction and generation. We perform extensive evaluations of 51 LLMs on LawBench, including 20 multilingual LLMs, 22 Chinese-oriented LLMs and 9 legal specific LLMs. The results show that GPT-4 remains the best-performing LLM in the legal domain, surpassing the others by a significant margin. While fine-tuning LLMs on legal specific text brings certain improvements, we are still a long way from obtaining usable and reliable LLMs in legal tasks. All data, model predictions and evaluation code are released in https://github.com/open-compass/LawBench/. We hope this benchmark provides in-depth understanding of the LLMs' domain-specified capabilities and speed up the development of LLMs in the legal domain

Three-dimensional and single-cell sequencing of liver cancer reveals comprehensive host-virus interactions in HBV infection

BackgroundsHepatitis B virus (HBV) infection is a major risk factor for chronic liver diseases and liver cancer (mainly hepatocellular carcinoma, HCC), while the underlying mechanisms and host-virus interactions are still largely elusive.MethodsWe applied HiC sequencing to HepG2 (HBV-) and HepG2-2.2.15 (HBV+) cell lines and combined them with public HCC single-cell RNA-seq data, HCC bulk RNA-seq data, and both genomic and epigenomic ChIP-seq data to reveal potential disease mechanisms of HBV infection and host-virus interactions reflected by 3D genome organization.ResultsWe found that HBV enhanced overall proximal chromatin interactions (CIs) of liver cells and primarily affected regional CIs on chromosomes 13, 14, 17, and 22. Interestingly, HBV altered the boundaries of many topologically associating domains (TADs), and genes nearby these boundaries showed functional enrichment in cell adhesion which may promote cancer metastasis. Moreover, A/B compartment analysis revealed dramatic changes on chromosomes 9, 13 and 21, with more B compartments (inactive or closed) shifting to A compartments (active or open). The A-to-B regions (closing) harbored enhancers enriched in the regulation of inflammatory responses, whereas B-to-A regions (opening) were enriched for transposable elements (TE). Furthermore, we identified large HBV-induced structural variations (SVs) that disrupted tumor suppressors, NLGN4Y and PROS1. Finally, we examined differentially expressed genes and TEs in single hepatocytes with or without HBV infection, by using single-cell RNA-seq data. Consistent with our HiC sequencing findings, two upregulated genes that promote HBV replication, HNF4A and NR5A2, were located in regions with HBV-enhanced CIs, and five TEs were located in HBV-activated regions. Therefore, HBV may promote liver diseases by affecting the human 3D genome structure.ConclusionOur work promotes mechanistic understanding of HBV infection and host-virus interactions related to liver diseases that affect billions of people worldwide. Our findings may also have implications for novel immunotherapeutic strategies targeting HBV infection

ARA-net: an attention-aware retinal atrophy segmentation network coping with fundus images

BackgroundAccurately detecting and segmenting areas of retinal atrophy are paramount for early medical intervention in pathological myopia (PM). However, segmenting retinal atrophic areas based on a two-dimensional (2D) fundus image poses several challenges, such as blurred boundaries, irregular shapes, and size variation. To overcome these challenges, we have proposed an attention-aware retinal atrophy segmentation network (ARA-Net) to segment retinal atrophy areas from the 2D fundus image.MethodsIn particular, the ARA-Net adopts a similar strategy as UNet to perform the area segmentation. Skip self-attention connection (SSA) block, comprising a shortcut and a parallel polarized self-attention (PPSA) block, has been proposed to deal with the challenges of blurred boundaries and irregular shapes of the retinal atrophic region. Further, we have proposed a multi-scale feature flow (MSFF) to challenge the size variation. We have added the flow between the SSA connection blocks, allowing for capturing considerable semantic information to detect retinal atrophy in various area sizes.ResultsThe proposed method has been validated on the Pathological Myopia (PALM) dataset. Experimental results demonstrate that our method yields a high dice coefficient (DICE) of 84.26%, Jaccard index (JAC) of 72.80%, and F1-score of 84.57%, which outperforms other methods significantly.ConclusionOur results have demonstrated that ARA-Net is an effective and efficient approach for retinal atrophic area segmentation in PM