The exploration of strong chemical bonds as synthetic handles offers new disconnection strategies for the synthesis functionalized molecules via transition metal catalysis. However, slow oxidative addition rate these covalent to a center hampers their utility. Here, we report C(sp

Research question answering requires accurate retrieval and contextual understanding of scientific literature. However, current Retrieval-Augmented Generation (RAG) methods often struggle to balance complex document relationships with precise information retrieval. In this paper, we introduce Contextualized Graph (CG-RAG), a novel framework that integrates sparse dense signals within graph structures enhance efficiency subsequently improve generation quality for research answering. First,...

We report a nickel‐catalyzed cyanation of vinyl triflates using tert‐butyl isonitrile as nucleophilic CN source. This reaction employs simple Ni(OTf)₂ the catalyst, operating without exogenous ligands or reductants. The method enables selective and efficient access to broad range cyclic acyclic alkenyl nitriles, accommodating diverse functional groups substrates, including natural products pharmaceuticals.

This article aims to address the problem of time-series forecasting. Current state-of-the-art forecasting models lack ability mine spatiotemporal dependence. How more useable features time series make accuracy predictions is still an open issue. To these challenges, from perspective fuzzy interaction between nodes, we propose a novel network constructing model called cognitive visibility graph (FCVG) for convert into pair directed weighted graphs. calculate similarity nodes in FCVG, develop...

While Retrieval-Augmented Generation (RAG) enhances the accuracy and relevance of responses by generative language models, it falls short in graph-based contexts where both textual topological information are important. Naive RAG approaches inherently neglect structural intricacies graphs, resulting a critical gap generation process. To address this challenge, we introduce $\textbf{Graph (GRAG)}$, which significantly retrieval processes emphasizing importance subgraph structures. Unlike that...

Here, we report a photoredox and nickel-catalyzed cross-electrophile coupling strategy for the asymmetric three-component 1,2-alkylarylation of vinyl boronates with (hetero)aryl bromides (2°, 3°)-alkyl redox-active esters in presence Hantzsch ester. With fluorinated pyridyl-substituted chiral biimidazoline ligand, this reaction enables straightforward access to wide variety synthetically valuable α-aryl from readily available starting materials. This features mild conditions, broad substrate...

Abstract Annulation of benzoxy‐tethered N ‐sulfonyl‐1,2,3‐triazoles and indoles has been developed in this paper, providing an efficient convenient access to valuable cyclopenta[ b ]indoles moderate good yields. α,β‐Unsaturated imine, which generated situ from denitrogenation 1,2‐OBz migration triazole, provided three carbons for the formal [3+2] cyclization reaction first time. magnified image

Polygon representation learning is essential for diverse applications, encompassing tasks such as shape coding, building pattern classification, and geographic question answering. While recent years have seen considerable advancements in this field, much of the focus has been on single polygons, overlooking intricate inner- inter-polygonal relationships inherent multipolygons. To address gap, our study introduces a comprehensive framework specifically designed representations polygonal...

Textual-edge Graphs (TEGs), characterized by rich text annotations on edges, are increasingly significant in network science due to their ability capture contextual information among entities. Existing works have proposed various edge-aware graph neural networks (GNNs) or let language models directly make predictions. However, they often fall short of fully capturing the contextualized semantics edges and topology, respectively. This inadequacy is particularly evident link prediction tasks...

Text-Attributed Graphs (TAGs) augment graph structures with natural language descriptions, facilitating detailed depictions of data and their interconnections across various real-world settings. However, existing TAG datasets predominantly feature textual information only at the nodes, edges typically represented by mere binary or categorical attributes. This lack rich edge annotations significantly limits exploration contextual relationships between entities, hindering deeper insights into...

In this work, we present HiReview, a novel framework for hierarchical taxonomy-driven automatic literature review generation. With the exponential growth of academic documents, manual reviews have become increasingly labor-intensive and time-consuming, while traditional summarization models struggle to generate comprehensive document effectively. Large language (LLMs), with their powerful text processing capabilities, offer potential solution; however, research on incorporating LLMs...

Semantic trajectories, which enrich spatial-temporal data with textual information such as trip purposes or location activities, are key for identifying outlier behaviors critical to healthcare, social security, and urban planning. Traditional detection relies on heuristic rules, requires domain knowledge limits its ability identify unseen outliers. Besides, there lacks a comprehensive approach that can jointly consider multi-modal across spatial, temporal, dimensions. Addressing the need...

We report a radical cyano-cyclization of 1,6-enynes with isonitriles via photochemically driven nickel catalysis, forging alkenyl nitrile-tethered γ-lactams under mild conditions. This reaction leverages the photoexcitation

Text-Attributed Graphs (TAGs) are graphs of connected textual documents. Graph models can efficiently learn TAGs, but their training heavily relies on human-annotated labels, which scarce or even unavailable in many applications. Large language (LLMs) have recently demonstrated remarkable capabilities few-shot and zero-shot TAG learning, they suffer from scalability, cost, privacy issues. Therefore, this work, we focus synergizing LLMs graph with complementary strengths by distilling the...

Text-Attributed Graphs (TAGs) enhance graph structures with natural language descriptions, enabling detailed representation of data and their relationships across a broad spectrum real-world scenarios. Despite the potential for deeper insights, existing TAG learning primarily relies on supervised methods, necessitating extensive labeled limiting applicability diverse contexts. This paper introduces new self-supervised framework, Text-And-Graph Multi-View Alignment (TAGA), which overcomes...

Medical wastes are posing a growing threat to the environment and human health. A good system design serves as guarantee for successful efficient practice of medical waste handling. Laws decrees have been enacted, which has put handling onto legal track.However, this era building sustainable economy calls clarifying meaning principles, core Such plays key role not only minimizing resources consumption better protecting environment, but also guidance standardization handling. Key words:...

<title>Abstract</title> Objectives This study aimed to investigate the mechanism of successful autologous pulp transplantation through semi-<italic>in situ</italic>pulp regeneration in animal experiments and three case reports protocols using concentrated growth factor (CGF)-enriched pulp. Material methods Wisdom tooth was removed, placed anterior molar canal, implanted subcranial space cranial apex SD rats establish an model hemi-<italic>in regeneration. Postoperative histological...

Large language models (LLMs) have achieved impressive performance on many natural processing tasks. However, their capabilities graph-structured data remain relatively unexplored. In this paper, we conduct a series of experiments benchmarking leading LLMs diverse graph prediction tasks spanning node, edge, and levels. We aim to assess whether can effectively process leverage topological structures enhance performance, compared specialized neural networks. Through varied prompt formatting...