Role of histone methyltransferase SETDB1 in regulation of tumourigenesis and immune response

Epigenetic alterations are implicated in tumour immune evasion and immune checkpoint blockade (ICB) resistance. SET domain bifurcated histone methyltransferase 1 (SETDB1) is a histone lysine methyltransferase that catalyses histone H3K9 di- and tri-methylation on euchromatin, and growing evidence indicates that SETDB1 amplification and abnormal activation are significantly correlated with the unfavourable prognosis of multiple malignant tumours and contribute to tumourigenesis and progression, immune evasion and ICB resistance. The main underlying mechanism is H3K9me3 deposition by SETDB1 on tumour-suppressive genes, retrotransposons, and immune genes. SETDB1 targeting is a promising approach to cancer therapy, particularly immunotherapy, because of its regulatory effects on endogenous retroviruses. However, SETDB1-targeted therapy remains challenging due to potential side effects and the lack of antagonists with high selectivity and potency. Here, we review the role of SETDB1 in tumourigenesis and immune regulation and present the current challenges and future perspectives of SETDB1 targeted therapy

Aurora:Activating Chinese chat capability for Mixtral-8x7B sparse Mixture-of-Experts through Instruction-Tuning

Existing research has demonstrated that refining large language models (LLMs) through the utilization of machine-generated instruction-following data empowers these models to exhibit impressive zero-shot capabilities for novel tasks, without requiring human-authored instructions. In this paper, we systematically investigate, preprocess, and integrate three Chinese instruction-following datasets with the aim of enhancing the Chinese conversational capabilities of Mixtral-8x7B sparse Mixture-of-Experts model. Through instruction fine-tuning on this carefully processed dataset, we successfully construct the Mixtral-8x7B sparse Mixture-of-Experts model named "Aurora." To assess the performance of Aurora, we utilize three widely recognized benchmark tests: C-Eval, MMLU, and CMMLU. Empirical studies validate the effectiveness of instruction fine-tuning applied to Mixtral-8x7B sparse Mixture-of-Experts model. This work is pioneering in the execution of instruction fine-tuning on a sparse expert-mixed model, marking a significant breakthrough in enhancing the capabilities of this model architecture. Our code, data and model are publicly available at https://github.com/WangRongsheng/AuroraComment: 10 pages, 2 figure

The ∗-Ricci Operator on Hopf Real Hypersurfaces in the Complex Quadric

We study the ∗-Ricci operator on Hopf real hypersurfaces in the complex quadric. We prove that for Hopf real hypersurfaces in the complex quadric, the ∗-Ricci tensor is symmetric if and only if the unit normal vector field is singular. In the following, we obtain that if the ∗-Ricci tensor of Hopf real hypersurfaces in the complex quadric is symmetric, then the ∗-Ricci operator is both Reeb-flow-invariant and Reeb-parallel. As the correspondence to the semi-symmetric Ricci tensor, we give a classification of real hypersurfaces in the complex quadric with the semi-symmetric ∗-Ricci tensor

Some Curvature Properties on Lorentzian Generalized Sasakian-Space-Forms

In this paper, we investigate the Lorentzian generalized Sasakian-space-form. We give the necessary and sufficient conditions for the Lorentzian generalized Sasakian-space-form to be projectively flat, conformally flat, conharmonically flat, and Ricci semisymmetric and their relationship between each other. As the application of our theorems, we study the Ricci almost soliton on conformally flat Lorentzian generalized Sasakian-space-form

The ∗-Ricci Operator on Hopf Real Hypersurfaces in the Complex Quadric

We study the ∗-Ricci operator on Hopf real hypersurfaces in the complex quadric. We prove that for Hopf real hypersurfaces in the complex quadric, the ∗-Ricci tensor is symmetric if and only if the unit normal vector field is singular. In the following, we obtain that if the ∗-Ricci tensor of Hopf real hypersurfaces in the complex quadric is symmetric, then the ∗-Ricci operator is both Reeb-flow-invariant and Reeb-parallel. As the correspondence to the semi-symmetric Ricci tensor, we give a classification of real hypersurfaces in the complex quadric with the semi-symmetric ∗-Ricci tensor

Process of Fragment-Based Lead Discovery—A Perspective from NMR

Fragment-based lead discovery (FBLD) has proven fruitful during the past two decades for a variety of targets, even challenging protein–protein interaction (PPI) systems. Nuclear magnetic resonance (NMR) spectroscopy plays a vital role, from initial fragment-based screening to lead generation, because of its power to probe the intrinsically weak interactions between targets and low-molecular-weight fragments. Here, we review the NMR FBLD process from initial library construction to lead generation. We describe technical aspects regarding fragment library design, ligand- and protein-observed screening, and protein–ligand structure model generation. For weak binders, the initial hit-to-lead evolution can be guided by structural information retrieved from NMR spectroscopy, including chemical shift perturbation, transferred pseudocontact shifts, and paramagnetic relaxation enhancement. This perspective examines structure-guided optimization from weak fragment screening hits to potent leads for challenging PPI targets

Optimal Representation of Large-Scale Graph Data Based on Grid Clustering and K2-Tree

The application of appropriate graph data compression technology to store and manipulate graph data with tens of thousands of nodes and edges is a prerequisite for analyzing large-scale graph data. The traditional K2-tree representation scheme mechanically partitions the adjacency matrix, which causes the dense interval to be split, resulting in additional storage overhead. As the size of the graph data increases, the query time of K2-tree continues to increase. In view of the above problems, we propose a compact representation scheme for graph data based on grid clustering and K2-tree. Firstly, we divide the adjacency matrix into several grids of the same size. Then, we continuously filter and merge these grids until grid density satisfies the given density threshold. Finally, for each large grid that meets the density, K2-tree compact representation is performed. On this basis, we further give the relevant node neighbor query algorithm. The experimental results show that compared with the current best K2-BDC algorithm, our scheme can achieve better time/space tradeoff