Paper List
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An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for as...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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Hierarchical Molecular Language Models (HMLMs)
This paper addresses the core challenge of accurately modeling context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple ...
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Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium chan...
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Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
This paper addresses the core challenge of inferring the relative contributions of multiple, simultaneous generative mechanisms in network formation w...
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EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
This paper addresses the core challenge of jointly predicting enzyme kinetic parameters (Kcat and Km) by modeling dynamic enzyme-substrate interaction...
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Tissue stress measurements with Bayesian Inversion Stress Microscopy
This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which ...
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DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prio...
Scalable DNA Ternary Full Adder Enabled by a Competitive Blocking Circuit
Institute of Computational Science and Technology, Guangzhou University, China | School of Computer Science and Technology, Wuhan University of Science and Technology, China | School of Computer Science and Technology, Dalian University of Technology, China | School of Computing Science, Peking University, China
30秒速读
IN SHORT: This paper addresses the core bottleneck of carry information attenuation and limited computational scale in DNA binary adders by introducing a scalable ternary architecture.
核心创新
- Methodology Proposes a novel Competitive Blocking (CB) circuit that leverages differential reaction kinetics (k2 ≫ k1, k3) to dynamically select and block reaction pathways for precise carry information management.
- Methodology Introduces a ternary (base-3) adder architecture, moving beyond binary systems, which inherently reduces the frequency of carry propagation and increases single-bit information density.
- Methodology Implements a Dynamic Concentration Adjustment (CA) strategy, applying chemical equilibrium principles to optimize reactant ratios and signal transmission, enabling significant bit-width extension.
主要结论
- The CB circuit reliably performs ternary full-adder logic, with experimental validation showing successful 10-bit addition operations.
- The integrated CA strategy enables the adder to scale to 17-bit addition, representing a massive increase in computational scale.
- The architecture achieves a 'scale/strand' metric improvement of 2,405,552x compared to a recent state-of-the-art binary DNA adder capable of only 4 consecutive carries.
摘要: DNA adder circuits are programmable reaction networks that process DNA molecular inputs to compute a sum and serve as essential components for digital computation. Currently, DNA adders primarily focus on binary addition. While efforts extend the operational bit-width by minimizing the number of DNA strands and developing carry-transmission mechanisms, challenges such as the susceptibility of carrying information to attenuation and the limited expressive capacity of the binary system impose significant constraints on computational scale. This paper proposes a scalable ternary adder architecture by introducing an innovative competitive blocking (CB) circuit. The architecture employs a dual cooperative optimization strategy that significantly enhances single-bit computational capacity and incorporates a dynamic concentration adjustment (CA) to effectively broaden the computational bit-width. Consequently, a significant increase in molecular computing scale is achieved compared to previous binary adders. Biochemical experimental results indicate that the CB circuit effectively outputs the ternary full-adder bit and successfully performs 10-bit addition. Furthermore, by implementing the CA strategy, this adder can be further extended to support 17-bit addition. This research provides a novel methodological foundation for advancing DNA computing technologies and offers promising potential for scalable digital computing applications.