Biomedical subjects

Xiaolei Huang

Publications and source records attributed to Xiaolei Huang.

3 recordsLinked to original sources

PubMed2026

Structural insights into histone mimicry by the small hepatitis delta antigen.

Hepatitis delta virus (HDV) is a satellite RNA virus that requires hepatitis B virus (HBV) for propagation but replicates its genome independently in the nucleus. The small form of the hepatitis delta antigen (S-HDAg) is essential for replication and is regulated by post-translational modifications. Acetylation at lysine 72 (K72ac) enables S-HDAg to interact with the bromodomain (BRD) of the host chromatin remodeler bromodomain adjacent to zinc finger domain protein 2B (BAZ2B) to promote viral replication. However, the structural basis for this interaction has remained elusive. Here, we provide structural and biophysical insights into this interaction through quantitative binding assays and X-ray crystallography. Isothermal titration calorimetry revealed that BRDs of BAZ2B and its close homolog BAZ2A bind to the viral peptide weakly, with BAZ2A-BRD exhibiting a modestly higher affinity. The crystal structure of BAZ2A-BRD in complex with the S-HDAg-K72ac peptide demonstrates an inverted binding orientation relative to canonical histone ligands, rationalizing the weak interaction. Mutagenesis studies confirmed the critical binding interface both in vitro and in cells. These findings elucidate the molecular mechanism by which HDV co-opts host BAZ2 bromodomains via a unique, weak-affinity interaction, providing a structural framework for understanding viral replication.

Hepatitis delta Antigens↗

PubMed2006

Shape registration in implicit spaces using information theory and free form deformations.

We present a novel, variational and statistical approach for shape registration. Shapes of interest are implicitly embedded in a higher-dimensional space of distance transforms. In this implicit embedding space, registration is formulated in a hierarchical manner: the Mutual Information criterion supports various transformation models and is optimized to perform global registration; then, a B-spline-based Incremental Free Form Deformations (IFFD) model is used to minimize a Sum-of-Squared-Differences (SSD) measure and further recover a dense local nonrigid registration field. The key advantage of such framework is twofold: (1) it naturally deals with shapes of arbitrary dimension (2D, 3D, or higher) and arbitrary topology (multiple parts, closed/open) and (2) it preserves shape topology during local deformation and produces local registration fields that are smooth, continuous, and establish one-to-one correspondences. Its invariance to initial conditions is evaluated through empirical validation, and various hard 2D/3D geometric shape registration examples are used to show its robustness to noise, severe occlusion, and missing parts. We demonstrate the power of the proposed framework using two applications: one for statistical modeling of anatomical structures, another for 3D face scan registration and expression tracking. We also compare the performance of our algorithm with that of several other well-known shape registration algorithms.

Algorithms↗

PubMed2005

Efficient learning by combining confidence-rated classifiers to incorporate unlabeled medical data.

In this paper, we propose a new dynamic learning framework that requires a small amount of labeled data in the beginning, then incrementally discovers informative unlabeled data to be hand-labeled and incorporates them into the training set to improve learning performance. This approach has great potential to reduce the training expense in many medical image analysis applications. The main contributions lie in a new strategy to combine confidence-rated classifiers learned on different feature sets and a robust way to evaluate the "informativeness" of each unlabeled example. Our framework is applied to the problem of classifying microscopic cell images. The experimental results show that 1) our strategy is more effective than simply multiplying the predicted probabilities, 2) the error rate of high-confidence predictions is much lower than the average error rate, and 3) hand-labeling informative examples with low-confidence predictions improves performance efficiently and the performance difference from hand-labeling all unlabeled data is very small.

Algorithms↗