Coupling & Sequencing
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Related Articles from SNS
Steering Fractional-Order Network Dynamics via Joint Parameter and State Control
arXiv:2605.31270v1 Announce Type: new Abstract: This paper studies the control of discrete-time linear fractional-order networks, a flexible modeling framework for systems with long-range memory such as power grids, biological networks, and neuronal circuits. In contrast to the common view that fractional exponents (time-scales) are fixed parameters, we show that they can be systematically steered, together with the network coupling matrix, by appropriately designed input sequences. We first...
Ultra-low biomass sequencing workflow (LBV-Seq) enables de novo metagenomic reconstruction of DNA and RNA viral genomes
Genome-resolved virome analysis remains inaccessible for many samples, including those with clinical relevance, because viral nucleic acid recovered after enrichment is often too scarce to support de novo genome assembly. As a result, many analyses are limited to sparse read-level detection, which cannot recover divergent viruses, resolve strains, or interpret gene-level variation. Here, we developed Low Biomass Viral Sequencing (LBV-Seq), a workflow that couples low-input viral sample...
Identification of highly immunogenic endogenous dsRNAs from cellular MDA5 filaments
ADAR1 converts adenosine to inosine in endogenous double-stranded RNAs (dsRNAs) to prevent excessive MDA5-driven interferon-stimulated gene expression. The source of endogenous immunogenic dsRNAs remains enigmatic because only a small fraction of ADAR1 substrates activate MDA5, and cellular MDA5 filaments have not been isolated. Here, we couple affinity purification of cellular MDA5 filaments with RNA sequencing to define immunogenic endogenous dsRNAs.
Using protein language models for pangenome construction
Current pangenome construction methods rely largely on nucleotide or protein sequence alignment, limiting their ability to detect remote orthologs and semantic relations. We introduce a novel method that leverages protein language model embeddings to capture functional and semantic relationships beyond sequence similarity. Our approach employs approximate nearest-neighbor search coupled with a clustering step utilizing HDBSCAN, DBSCAN, or weighted single-linkage clustering with multiple...
Vibe Coding Is Not Engineering
Vibe coding produces code. Engineering produces systems. The gap between those two things is where production failures live.
The Dark Regulome: Disentangling Predictability from Regulation in Genomic Foundation Models
Announce Type: new Abstract: High-grade gliomas integrate into neural circuits through functional synapses with neurons, raising the question of which noncoding elements shape synaptogenic gene expression in tumor cells. The regulatory program written across the dark genome, what we call the $\textit{dark regulome}$, is the natural substrate to probe, and sequence foundation models offer a zero-shot route through in-silico mutagenesis (ISM); yet likelihood-based scoring is tautologically...
Spontaneous flows and interfacial instabilities in oxygen-sensitive living active matter
Announce Type: cross Abstract: Active fluids generate motion and stress internally, but in living systems this activity is often regulated by environmental fields that the organisms consume or produce. Here we show that oxygen gradients organise and destabilise dense suspensions of the flagellated microswimmer \textit{Euglena gracilis}.
mRNAutilus: Multi-Objective-Guided Discrete Generation of mRNA with Optimized Therapeutic Properties
arXiv:2605.31296v1 Announce Type: cross Abstract: Therapeutic mRNA design requires coordinating multiple interacting sequence features across the full transcript, where codon usage, untranslated regions (UTRs), and their coupling jointly determine stability, translation efficiency, and protein expression. Here, we present mRNA generation via unrolled trajectories and informed latent updates (mRNAutilus), a framework for simultaneous codon optimization and de novo UTR design directly from...
Data-Efficient Exploration of Enzyme Function Using Family-Specific Machine Learning
Enzymes are essential biocatalysts across diverse industries, driving demand for high-performing variants. Foundation models are attractive for guiding enzyme discovery, but often lack the resolution to model subtle variations driving function within homologous families. Navigating these rugged functional landscapes to identify elite variants remains challenging and experimentally costly, even when guided by such models.
MotionDisco: Motion Discovery for Extreme Humanoid Loco-Manipulation
arXiv:2606.06139v1 Announce Type: new Abstract: We present MotionDisco, a framework that discovers contact-rich, long-horizon humanoid loco-manipulation motions from scratch, without relying on teleoperation or motion retargeting from human demonstrations. This is challenging because the space of possible contact interactions grows combinatorially with the task horizon and the number of objects in the scene. MotionDisco enables rapid discovery of novel motions by coupling a large language...