Holliday junctions, four-way DNA crossovers central to DNA origami and key intermediates of homologous recombination, must be accurately modeled for rational DNA-nanodevice design, yet their solution conformational ensemble remains incompletely characterized. We characterize this ensemble for the experimental junction d(TCGGTACCGA)₄ by all-atom molecular dynamics, comparing the AMBER force fields parmbsc1, OL21, and OL24 in OPC water with matched ionic conditions. In every force field, the junction explores a broad, nearly flat inter-arm-angle landscape, with <1 kcal/mol variation across inter-arm angles of 2–90°: the open state is favored by 0.45–0.63 kcal/mol, the crystallographic-like inter-arm angle occurs in 2–5% of frames, and the crossover remains coaxially stacked whether open or compact. A Mg²⁺ ion occupies the crossover pocket in 92–94% of frames, specifically bridging the A- and C-strand phosphates (Li–Merz CM Mg²⁺ set). Stacking-isomer exchange between the A/C-continuous and crystallographic B/D isomers proceeds at ~0.5 ns⁻¹ in this minimal short-arm construct, independent of force field, with A/C-continuous favored ~2:1. parmbsc1 yields a more open junction than OL21 and OL24, which reproduce the crystallographic inter-arm angle of 39–43°; the sugar-pucker shift targeted in OL24 is also visible at the junction. These results establish a force-field-robust baseline for simulation-guided design of crossover-containing nanostructures.

Accurate segmentation of the intercondylar notch of the femur is of great significance for the diagnosis of knee joint diseases, surgical planning, and anterior cruciate ligament (ACL) reconstruction. Among them, the obvious anatomical heterogeneity, the interference of structurally similar tissues, and the blurred boundaries in MRI images make the segmentation of the intercondylar notch challenging. The segmentation of the intercondylar notch is often regarded as a standard semantic segmentation problem, but doing so leaves the inherent high-order internal variation and low-contrast features of its anatomical structure unresolved. We proposed a new Wavelet Multi-scale Contextual Attention Network (WMCA-Net). We have coordinated the Shallow High-frequency Feature Dense Extraction Block (SHFDEB) and Wavelet Split and Fusion Block (WSFB) modules with each other. The SHFDEB intensively extracts high-frequency detailed features at the shallowest layer of the network, while the WSFB effectively splits and fuses features at various resolutions, suppressing noise while better preserving the high-frequency detailed structural information we need. The Multi-scale Depth-wise Convolution Block (MDCB) captures cross-scale features from the narrow intercondylar notch (5–8 mm wide) to the surrounding femoral structure (approximately 50 mm diameter), dynamically adapting to different morphologies, including pathological changes caused by osteophyte formation. The Contextual-Weighted Attention Module (CWAM) establishes long-term semantic associations between fuzzy regions and clear anatomical landmarks by precisely locating uncertain regions through foreground and background decomposition. The Dice Similarity Coefficient of WMCA-Net on the intercondylar notch dataset is 93.16%, and the 95% Hausdorff Distance is 1.42 mm, demonstrating its advanced segmentation performance and good anatomical adaptability.

Pseudo-healthy image synthesis aims to generate subject-specific, pathology-free images from pathological scans. Such images can be helpful in certain tasks, such as anomaly detection and understanding changes induced by pathology and disease. A participant cannot be “healthy” and “unhealthy” at the same time, and thus, directly obtaining pathological and healthy paired images of the same individual to train and evaluate supervised learning algorithms is infeasible. In addition, simultaneously meeting the requirements of subjects’ “identity” preservation and pathology restoration performance is frequently difficult for existing unsupervised learning methods, especially for large or information-free pathological regions, such as postoperative cavities. In this study, we propose a novel pseudo-healthy synthesis framework that combines low-resolution residual decoupling with an edge-prior-guided super-resolution reconstruction module. We named this framework LRD-ESR-Net. In particular, by using a coarse-to-fine synthesis pipeline, the residual decoupling network first decouples information-rich tumor tissues or information-free resection cavities from healthy brain tissues in low-resolution pathological magnetic resonance images. Then, a residual-shifting diffusion network with Canny edge maps is employed to reconstruct low-resolution pseudo-healthy images to their original resolutions. We evaluate the proposed framework on one in-house brain dataset, two public brain datasets, and one public liver dataset, and validate its effectiveness on low-contrast lesion segmentation and pre-/postoperative brain tumor MRI registration. Results show that LRD-ESR-Net consistently outperforms state-of-the-art methods in pseudo-healthy image quality, anatomical preservation, and downstream task performance, demonstrating strong robustness and generalization across organs, modalities, and lesion types.