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Yihan Fang

Publications and source records attributed to Yihan Fang.

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Y-Trim: Evidence-gated Adaptase tail trimming for single-stranded bisulfite sequencing

Background: Single-stranded whole-genome bisulfite sequencing (ssWGBS) enables DNA methylation profiling in low-input and highly fragmented material, including cell-free DNA. In widely used post-bisulfite protocols, Adaptase-mediated tailing adds stochastic, template-free end sequence. Unlike adapter-defined junctions, these tails lack a fixed sequence template, so trimming must be decided from FASTQ-stage observables under intrinsic uncertainty. Results: We show that bisulfite-induced compositional degeneracy implies a strictly positive error floor for any fixed per-read boundary rule under a finite nucleotide alphabet. Guided by this limit, we introduce Y-Trim, an evidence-gated framework that separates admission (should we trim) from inference (where to trim). For Read 2, Y-Trim performs per-read adaptive cut placement via a fixed, chemistry-typed matrix-linear texture scoring scheme; for Read 1, it uses automated sample-level anchoring when read-level localization is feasibility-limited. Across modules, Y-Trim is an explicit, chemistry-specific decision rule with interpretable operating points. On a curated 34-run public cohort (CCGB-34) and simulator stress tests with known latent boundaries, Y-Trim exhibits stable Read 2 operating behavior and Read 1 feasibility-limited behavior consistent with conditional read-through. Conclusions: Template-free Adaptase tail trimming is best viewed as an evidence-limited FASTQ-stage decision rather than a generic preprocessing knob. By making admissibility and abstention explicit and exposing interpretable genomic-retention versus residual-carryover trade-offs, Y-Trim provides a practical uncertainty-aware preprocessing strategy for ssWGBS.

q-bio.GN

BYOCL: Build Your Own Consistent Latent with Hierarchical Representative Latent Clustering

To address the semantic inconsistency issue with SAM or other single-image segmentation models handling image sequences, we introduce BYOCL. This novel model outperforms SAM in extensive experiments, showcasing its Hierarchical prototype capabilities across CLIP and other representations. BYOCL significantly reduces time and space consumption by dividing inputs into smaller batches, achieving exponential time reduction compared to previous methods. Our approach leverages the SAM image encoder for feature extraction, followed by Intra-Batch and Inter-Batch clustering algorithms. Extensive experiments demonstrate that BYOCL far exceeds the previous state-of-the-art single image segmentation model. Our work is the first to apply consistent segmentation using foundation models without requiring training, utilizing plug-and-play modules for any latent space, making our method highly efficientModels are available at \href{https://github.com/cyt1202/BYOCL.git

cs.CV