A Magnetic Bead-Based Sensor for the Quantification of Multiple Prostate Cancer Biomarkers.

Novel biomarker assays and upgraded analytical tools are urgently needed to accurately discriminate benign prostatic hypertrophy (BPH) from prostate cancer (CaP). To address this unmet clinical need, we report a piezeoelectric/magnetic bead-based assay to quantitate prostate specific antigen (PSA; free and total), prostatic acid phosphatase, carbonic anhydrase 1 (CA1), osteonectin, IL-6 soluble receptor (IL-6sr), and spondin-2. We used the sensor to measure these seven proteins in serum samples from 120 benign prostate hypertrophy patients and 100 Gleason score 6 and 7 CaP using serum samples previously collected and banked. The results were analyzed with receiver operator characteristic curve analysis. There were significant differences between BPH and CaP patients in the PSA, CA1, and spondin-2 assays. The highest AUC discrimination was achieved with a spondin-2 OR free/total PSA operation--the area under the curve was 0.84 with a p value below 10(-6). Some of these data seem to contradict previous reports and highlight the importance of sample selection and proper assay building in the development of biomarker measurement schemes. This bead-based system offers important advantages in assay building including low cost, high throughput, and rapid identification of an optimal matched antibody pair

Co-skeletons:Consistent curve skeletons for shape families

We present co-skeletons, a new method that computes consistent curve skeletons for 3D shapes from a given family. We compute co-skeletons in terms of sampling density and semantic relevance, while preserving the desired characteristics of traditional, per-shape curve skeletonization approaches. We take the curve skeletons extracted by traditional approaches for all shapes from a family as input, and compute semantic correlation information of individual skeleton branches to guide an edge-pruning process via skeleton-based descriptors, clustering, and a voting algorithm. Our approach achieves more concise and family-consistent skeletons when compared to traditional per-shape methods. We show the utility of our method by using co-skeletons for shape segmentation and shape blending on real-world data

Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization

Domain Generalization (DG) is a fundamental challenge for machine learning models, which aims to improve model generalization on various domains. Previous methods focus on generating domain invariant features from various source domains. However, we argue that the domain variantions also contain useful information, ie, classification-aware information, for downstream tasks, which has been largely ignored. Different from learning domain invariant features from source domains, we decouple the input images into Domain Expert Features and noise. The proposed domain expert features lie in a learned latent space where the images in each domain can be classified independently, enabling the implicit use of classification-aware domain variations. Based on the analysis, we proposed a novel paradigm called Domain Disentanglement Network (DDN) to disentangle the domain expert features from the source domain images and aggregate the source domain expert features for representing the target test domain. We also propound a new contrastive learning method to guide the domain expert features to form a more balanced and separable feature space. Experiments on the widely-used benchmarks of PACS, VLCS, OfficeHome, DomainNet, and TerraIncognita demonstrate the competitive performance of our method compared to the recently proposed alternatives