Abundance of Colistin-Resistance Genes in Retail Meats in Vietnam.

The degree of contamination of retail meat with colistin-resistant bacteria and its potential contribution to dissemination within communities remains to be determined. Thus, we aimed to elucidate the contamination status of colistin-resistance genes, indicative of colistin-resistant bacteria, in retail meats in Vietnam. In total, 46 chicken and 49 pork meats from stores in Vietnam and Japan were examined.

Virus detections among patients with severe acute respiratory illness, Northern Vietnam.

Severe acute respiratory illness (SARI) is a major cause of death and morbidity in low- and middle-income countries, however, the etiologic agents are often undetermined due to the lack of molecular diagnostics in hospitals and clinics. To examine evidence for select viral infections among patients with SARI in northern Vietnam, we studied 348 nasopharyngeal samples from military and civilian patients admitted to 4 hospitals in the greater Hanoi area from 2017-2019. Initial screening for human respiratory viral pathogens was performed in Hanoi, Vietnam at the National Institute of Hygiene and Epidemiology (NIHE) or the Military Institute of Preventative Medicine (MIPM), and an aliquot was shipped to Duke-NUS Medical School in Singapore for validation.

PDM-ENLOR for segmentation of mouse brain gene expression images.

Statistical shape models, such as Active Shape Models (ASMs), suffer from their inability to represent a large range of variations of a complex shape and to account for the large errors in detection of (point) landmarks. We propose a method, PDM-ENLOR (Point Distribution Model-based ENsemble of LOcal Regressors), that overcomes these limitations by locating each landmark individually using an ensemble of local regression models and appearance cues from selected landmarks. We first detect a set of reference landmarks which were selected based on their saliency during training.

3D dense local point descriptors for mouse brain gene expression images.

Anatomical landmarks play an important role in many biomedical image analysis applications (e.g., registration and segmentation).

Similarity-based appearance-prior for fitting a subdivision mesh in gene expression images.

Automated segmentation of multi-part anatomical objects in images is a challenging task. In this paper, we propose a similarity-based appearance-prior to fit a compartmental geometric atlas of the mouse brain in gene expression images. A subdivision mesh which is used to model the geometry is deformed using a Markov random field (MRF) framework.

Landmark/Image-based Deformable Registration of Gene Expression Data.

Analysis of gene expression patterns in brain images obtained from high-throughput in situ hybridization requires accurate and consistent annotations of anatomical regions/subregions. Such annotations are obtained by mapping an anatomical atlas onto the gene expression images through intensity- and/or landmark-based registration methods or deformable model-based segmentation methods. Due to the complex appearance of the gene expression images, these approaches require a pre-processing step to determine landmark correspondences in order to incorporate landmark-based geometric constraints.

Markov Random Field-based Fitting of a Subdivision-based Geometric Atlas.

An accurate labeling of a multi-part, complex anatomical structure (e.g., brain) is required in order to compare data across images for spatial analysis.

A supervised classification-based method for coronary calcium detection in non-contrast CT.

Accurate quantification of coronary artery calcium provides an opportunity to assess the extent of atherosclerosis disease. Coronary calcification burden has been reported to be associated with cardiovascular risk. Currently, an observer has to identify the coronary calcifications among a set of candidate regions, obtained by thresholding and connected component labeling, by clicking on them.