Paper List
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Discovery of a Hematopoietic Manifold in scGPT Yields a Method for Extracting Performant Algorithms from Biological Foundation Model Internals
This work addresses the core challenge of extracting reusable, interpretable, and high-performance biological algorithms from the opaque internal repr...
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MS2MetGAN: Latent-space adversarial training for metabolite–spectrum matching in MS/MS database search
This paper addresses the critical bottleneck in metabolite identification: the generation of high-quality negative training samples that are structura...
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Toward Robust, Reproducible, and Widely Accessible Intracranial Language Brain-Computer Interfaces: A Comprehensive Review of Neural Mechanisms, Hardware, Algorithms, Evaluation, Clinical Pathways and Future Directions
This review addresses the core challenge of fragmented and heterogeneous evidence that hinders the clinical translation of intracranial language BCIs,...
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Less Is More in Chemotherapy of Breast Cancer
通过纳入细胞周期时滞和竞争项,解决了现有肿瘤-免疫模型的过度简化问题,以定量比较化疗方案。
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Fold-CP: A Context Parallelism Framework for Biomolecular Modeling
This paper addresses the critical bottleneck of GPU memory limitations that restrict AlphaFold 3-like models to processing only a few thousand residue...
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Open Biomedical Knowledge Graphs at Scale: Construction, Federation, and AI Agent Access with Samyama Graph Database
This paper addresses the core pain point of fragmented biomedical data by constructing and federating large-scale, open knowledge graphs to enable sea...
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Predictive Analytics for Foot Ulcers Using Time-Series Temperature and Pressure Data
This paper addresses the critical need for continuous, real-time monitoring of diabetic foot health by developing an unsupervised anomaly detection fr...
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Hypothesis-Based Particle Detection for Accurate Nanoparticle Counting and Digital Diagnostics
This paper addresses the core challenge of achieving accurate, interpretable, and training-free nanoparticle counting in digital diagnostic assays, wh...
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.