arXiv subjects

Yanjun Yang

Publications and source records attributed to Yanjun Yang.

5 recordsLinked to original sources

arXiv2026

Gel-Confined Rolling-Circle Amplification Enables Sensitive Single-Cell Proteoform Analysis

Protein abundance alone does not capture the molecular diversity generated by protein processing and post-translational modification, yet most single-cell protein assays do not resolve these proteoform states. Here, we develop RCAmp-scWB, a post-separation amplification strategy that performs rolling-circle amplification directly within the polyacrylamide gel after single-cell protein electrophoresis. Reaction-transport modeling identifies a balance between reagent access and confinement of the template-associated amplification product that supports signal amplification while retaining the electrophoretically encoded spatial readout. Using purified protein standards, RCAmp-scWB produced a 4.0-7.8-fold steeper concentration-response slope than conventional single-cell western blotting. Across six human cancer cell lines, RCAmp-scWB quantified protein abundance and resolved distinct Vimentin and PD-L1 proteoform states whose relative abundance and single-cell distributions were not captured by total protein measurements alone. Extension to individual small extracellular vesicles further revealed source-dependent shifts in Vimentin proteoform composition despite comparatively similar abundance of one major Vimentin species. By separating electrophoretic molecular discrimination from signal amplification, RCAmp-scWB enables proteoform-resolved analysis of heterogeneous cells and small extracellular vesicles.

q-bio.QM↗

arXiv2026

Views: A Hardware-Aware Recursively Labeled Graph Database Model for Knowledge Representation and Reasoning

Knowledge representation remains a central challenge for reasoning-centered artificial intelligence, particularly when semantic structures involve relations over relations, contextual annotations, and recursively nested descriptions. This paper introduces Views, a recursively labeled graph database (GDB) model designed to represent such graph-structured knowledge within a uniform graph abstraction while retaining a hardware-aware organization for associative search and traversal. The model refactors directed labeled graphs into linked-list-like chains of linknodes, supports recursive labeling of vertices and edges, and admits mappings from RDF- and LPG-style graph representations. We describe the data structure, its hardware-oriented memory mappings under the Associative Chip Architecture (ASOCA), and selected associative operations for retrieval over Views-based GDBs. We then evaluate storage footprint under stated mapping and store boundaries using three scales of the Social Network Benchmark published by the Linked Data Benchmark Council, and characterize directed K-hop traversal on an Associative Memory Chip III (ASOCA3) FPGA implementation. The storage results show that allocation and entry width materially affect the reported footprints rather than establishing an intrinsic advantage for Views, while the K-hop measurements show increasing mean returned-vertex count and mean latency with hop bound over the fixed resident graph image. Worked semantic-reasoning and Copycat-inspired examples further illustrate how retrieval operations can be composed over the model, rather than providing application-level reasoning or cognitive validation. Taken together, these results position Views as a model-architecture co-design for associative storage and selected graph traversal under the stated conditions; broader database workloads and end-to-end reasoning applications remain to be evaluated.

cs.DB↗

arXiv2023

Recommending Analogical APIs via Knowledge Graph Embedding

Library migration, which re-implements the same software behavior by using a different library instead of using the current one, has been widely observed in software evolution. One essential part of library migration is to find an analogical API that could provide the same functionality as current ones. However, given the large number of libraries/APIs, manually finding an analogical API could be very time-consuming and error-prone. Researchers have developed multiple automated analogical API recommendation techniques. Documentation-based methods have particularly attracted significant interest. Despite their potential, these methods have limitations, such as a lack of comprehensive semantic understanding in documentation and scalability challenges. In this work, we propose KGE4AR, a novel documentation-based approach that leverages knowledge graph (KG) embedding to recommend analogical APIs during library migration. Specifically, KGE4AR proposes a novel unified API KG to comprehensively and structurally represent three types of knowledge in documentation, which can better capture the high-level semantics. Moreover, KGE4AR then proposes to embed the unified API KG into vectors, enabling more effective and scalable similarity calculation. We build KGE4AR' s unified API KG for 35,773 Java libraries and assess it in two API recommendation scenarios: with and without target libraries. Our results show that KGE4AR substantially outperforms state-of-the-art documentation-based techniques in both evaluation scenarios in terms of all metrics (e.g., 47.1%-143.0% and 11.7%-80.6% MRR improvements in each scenario). Additionally, we explore KGE4AR' s scalability, confirming its effective scaling with the growing number of libraries.

cs.SE↗

arXiv2020

Fast List Decoders for Polarization-Adjusted Convolutional (PAC) Codes

A latest coding scheme named polarization-adjusted convolutional (PAC) codes is shown to approach the dispersion bound for the code (128,64) under list decoding. However, to achieve the near-bound performance, the list size of list decoding needs to be excessively large, which leads to insufferable latency. In this paper, to improve the speed of list decoding, fast list decoders for PAC codes are proposed. We define four types of constituent nodes and provide fast list decoding algorithms for each of them. Simulation results present that fast list decoding with three types of constituent nodes can yield exactly the same error-correction performance as list decoding, and reduce more than 50% time steps for the code (128,64). Moreover, fast list decoding with four types of constituent nodes can further reduce decoding latency with negligible performance degradation.

cs.IT↗

arXiv2020

Leader-cell-driven epithelial sheet fingering

Collective cell migration is crucial in many biological processes such as wound healing, tissue morphogenesis, and tumor progression. The leading front of a collective migrating epithelial cell layer often destabilizes into multicellular finger-like protrusions, each of which is guided by a leader cell at the fingertip. Here, we develop a subcellular-element-based model of this fingering instability, which incorporates leader cells and other related properties of a monolayer of epithelial cells. Our model recovers multiple aspects of the dynamics, especially the traction force patterns and velocity fields, observed in experiments on MDCK cells. Our model predicts the necessity of the leader cell and its minimal functions for the formation and maintenance of a stable finger pattern. Meanwhile, our model allows for an analysis of the role of supra-cellular actin cable on the leading front, predicting that while this observed structure helps maintain the shape of the finger, it is not required in order to form a finger. In addition, we also study the driving instability in the context of continuum active fluid model, which justifies some of our assumptions in the computational approach. In particular, we show that in our model no finger protrusions would emerge in a phenotypically homogenous active fluid and hence the role of the leader cell and its followers are often critical.

physics.bio-ph↗