Uncertainty-Aware Source-Free Domain Adaptive Semantic Segmentation.

Source-Free Domain Adaptation (SFDA) is becoming topical to address the challenge of distribution shift between training and deployment data, while also relaxing the requirement of source data availability during target domain adaptation. In this paper, we focus on SFDA for semantic segmentation, in which pseudo labeling based target domain self-training is a common solution. However, pseudo labels generated by the source models are particularly unreliable on the target domain data due to the domain shift issue.

Prediction Calibration for Generalized Few-Shot Semantic Segmentation.

Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel into either base classes with abundant training examples or novel classes with only a handful of (e. g., 1-5) training images per class.

Hyaluronic acid-coated, prodrug-based nanostructured lipid carriers for enhanced pancreatic cancer therapy.

Context: Gemcitabine (GEM) and Baicalein (BCL) are reported to have anti-tumor effects including pancreatic cancer. Hyaluronic acid (HA) can bind to over-expressed receptors in various kinds of cancer cells.

Objective: The aim of this study is to develop prodrugs containing HA, BCL and GEM, and construct nanomedicine incorporate GEM and BCL in the core and HA on the surface.

Quantification of nardosinone in rat plasma using liquid chromatography-tandem mass spectrometry and its pharmacokinetics application.

A rapid, sensitive and high-throughput liquid chromatography-tandem mass spectrometry (LC-MS-MS) method was established and validated to assay the concentration of nardosinone, a main active compound isolated from Nardostachys chinensis, in rat plasma. Plasma samples were processed by protein precipitation with acetonitrile and separated on a Venusil MP-C18 column (50 × 2.1 mm, 5 µm) at an isocratic flow rate of 0.