Accurate cell segmentation remains challenging due to morphological variations, diverse imaging modalities, and unclear cellular boundaries. Existing deep learning (DL) methods struggle to extract features adaptively across heterogeneous cellular environments, thereby limiting generalization capacity. To address these challenges, we propose FlexiCell, a novel adaptive segmentation framework that integrates a learnable adaptive filter with dual attention mechanisms. The core innovation lies in the FlexiFilter approach, which combines standard convolution with adaptive residual learning through learnable mixing parameters. These parameters dynamically balance input preservation and feature enhancement. FlexiCell employs multi-scale FlexiFilter blocks with varying kernel sizes, channel and spatial attention networks, and a dedicated boundary extractor for precise edge detection. Extensive experiments demonstrate superior performance compared to benchmark models, achieving 3.8% improvement in detection accuracy and 5.5 % in segmentation quality on our newly developed induced pluripotent stem (iPS) cell datasets. Further evaluation on standardized Cell Tracking Challenge (CTC) benchmarks confirms state-of-the-art performance on mesenchymal stem cells and glioblastoma datasets, outperforming established CTC methods. The framework demonstrates robust generalization across fluorescence, phase contrast, and differential interference contrast microscopy, without requiring dataset-specific optimization. Codes are available at https://github.com/jovialniyo93/FlexiCell.

Neuroblastoma is a prevalent pediatric tumor with a low 5-year survival rate among high-risk patients, and the prognosis remains poor despite available therapeutic interventions. Therefore, identifying novel and effective therapeutic targets is critical for improving outcomes in these patients. In this study, we performed an integrative analysis of two neuroblastoma circRNA sequencing datasets to identify potential drug targets. By comparing circRNA expression levels between neuroblastoma tissues and adjacent normal tissues, we identified differentially expressed circRNAs and subsequently predicted 30 hub circRNAs through Weighted Gene Co-expression Network Analysis. To elucidate the functional roles of these circRNAs, we investigated their interactions with RNA-binding proteins. The results suggest that hsa_circ_0051680 and hsa_circ_0006107 may influence neuroblastoma progression through interactions with FUS and IGF2BP1, respectively. Furthermore, we analyzed the translational potential of the hub circRNAs, revealing that six circRNAs encode proteins with complex secondary structures. Molecular docking analysis identified five high-affinity complexes between circRNA-encoded proteins (hsa_circ_0000786, hsa_circ_0005087, hsa_circ_0006867) and their corresponding ligands. These circRNA-derived proteins present promising novel drug targets for both the diagnosis and treatment of neuroblastoma.

Recent studies have shed light on the potential of circular RNA (circRNA) as a biomarker for disease diagnosis and as a nucleic acid vaccine. The exploration of these functionalities requires correct circRNA full-length sequences; however, existing assembly tools can only correctly assemble some circRNAs, and their performance can be further improved. Here, we introduce a novel feature known as the junction contig (JC), which is an extension of the back-splice junction (BSJ). Leveraging the strengths of both BSJ and JC, we present a novel method called JCcirc (https://github.com/cbbzhang/JCcirc). It enables efficient reconstruction of all types of circRNA full-length sequences and their alternative isoforms using splice graphs and fragment coverage. Our findings demonstrate the superiority of JCcirc over existing methods on human simulation datasets, and its average F1 score surpasses CircAST by 0.40 and outperforms both CIRI-full and circRNAfull by 0.13. For circRNAs below 400 bp, 400-800 bp, 800 bp-1200 bp and above 1200 bp, the correct assembly rates are 0.13, 0.09, 0.04 and 0.03 higher, respectively, than those achieved by existing methods. Moreover, JCcirc also outperforms existing assembly tools on other five model species datasets and real sequencing datasets. These results show that JCcirc is a robust tool for accurately assembling circRNA full-length sequences, laying the foundation for the functional analysis of circRNAs.

Sequence-based residue contact prediction plays a crucial role in protein structure reconstruction. In recent years, the combination of evolutionary coupling analysis (ECA) and deep learning (DL) techniques has made tremendous progress for residue contact prediction, thus a comprehensive assessment of current methods based on a large-scale benchmark data set is very needed. In this study, we evaluate 18 contact predictors on 610 non-redundant proteins and 32 CASP13 targets according to a wide range of perspectives. The results show that different methods have different application scenarios: (1) DL methods based on multi-categories of inputs and large training sets are the best choices for low-contact-density proteins such as the intrinsically disordered ones and proteins with shallow multi-sequence alignments (MSAs). (2) With at least 5L (L is sequence length) effective sequences in the MSA, all the methods show the best performance, and methods that rely only on MSA as input can reach comparable achievements as methods that adopt multi-source inputs. (3) For top L/5 and L/2 predictions, DL methods can predict more hydrophobic interactions while ECA methods predict more salt bridges and disulfide bonds. (4) ECA methods can detect more secondary structure interactions, while DL methods can accurately excavate more contact patterns and prune isolated false positives. In general, multi-input DL methods with large training sets dominate current approaches with the best overall performance. Despite the great success of current DL methods must be stated the fact that there is still much room left for further improvement: (1) With shallow MSAs, the performance will be greatly affected. (2) Current methods show lower precisions for inter-domain compared with intra-domain contact predictions, as well as very high imbalances in precisions between intra-domains. (3) Strong prediction similarities between DL methods indicating more feature types and diversified models need to be developed. (4) The runtime of most methods can be further optimized.