We have collected peer-reviewed implementation cases from different fields that align with the principles advocated by the initiative for your reference.
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Formal Sciences
Physical Sciences
Biological Sciences
Social Sciences
Data retrieval efficiency remains one of the key challenges hindering the practical application of DNA-based data storage, being at least five orders of magnitude lower than that of compact discs introduced in the last century. Here, we introduce SPIDER-WEB, a new all-in-one coding framework that eliminates the need for time-consuming sequence clustering and alignment steps in conventional pipelines, allowing data processing to proceed concurrently with the sequencing process. Using 3,000 synthesized data coding DNA sequences for validation, SPIDER-WEB demonstrated a retrieval efficiency improvement of at least two orders of magnitude (587 to 9,083-fold) over conventional methods. Projections for megabyte and exabyte-scale retrievals show even greater gains, with efficiency enhancements of 3.0-3.2 and 24.0-29.7 orders of magnitude, respectively. By integrating nanopore sequencing, SPIDER-WEB successfully retrieved a 19,200-byte file in a non-blocking manner within just 100 seconds on a standard laptop. Overall, our results demonstrate that SPIDER-WEB enables real-time data retrieval and supports the expansion of practical application scenarios for DNA-based data storage.
As the scale of artificial neural networks continues to expand to tackle increasingly complex tasks or improve the prediction accuracy of specific tasks, the challenges associated with computational demand, hyper-parameter tuning, model interpretability, and deployment costs intensify. Addressing these challenges requires a deeper understanding of how network structures influence network performance. Here, we analyse 882,000 motifs to reveal the functional roles of incoherent and coherent three-node motifs in shaping overall network performance. Our findings reveal that incoherent loops exhibit superior representational capacity and numerical stability, whereas coherent loops show a distinct preference for high-gradient regions within the output landscape. By avoiding such gradient pursuit, incoherent loops sustain more stable adaptation and consequently greater robustness. This mechanism is evident in 97,240 fixed-network training experiments, where coherent-loop networks consistently prioritized high-gradient regions during learning, and is further supported by noise-resilience analyses – from classical reinforcement learning tasks to biological, chemical, and medical applications – which demonstrate that incoherent-loop networks maintain stronger resistance to training noise and environmental perturbations. This work shows the functional impact of structural motif differences on the performance of artificial neural networks, offering foundational insights for designing more resilient and accurate networks.
If your peer-reviewed case aligns with the principles of the initiative and you would like to share it with a broader audience, please do not hesitate to contact us.