Yunhe Wang

Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. However, the training data in source domain required by most of the existing methods is usually unavailable in real-world applications due to privacy preserving policies. Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data. In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation. Firstly, we produce robust pseudo-labels for target data with spherical k-means clustering, whose initial class centers are the weight vectors (anchors) learned by the classifier of pretrained model. Furthermore, we propose to estimate the class-conditioned feature distribution of source domain by exploiting target data and corresponding anchors. Finally, we sample surrogate features from the estimated distribution, which are then utilized to align two domains by minimizing a contrastive adaptation loss function. Extensive experiments show that the proposed method achieves state-of-the-art performance on multiple DA benchmarks, and even outperforms traditional DA methods which require plenty of source data.

Single-cell RNA sequencing (scRNA-seq) provides high-throughput information about the genome-wide gene expression levels at the single-cell resolution, bringing a precise understanding on the transcriptome of individual cells. Unfortunately, the rapidly growing scRNA-seq data and the prevalence of dropout events pose substantial challenges for cell type annotation. Here, we propose a single-cell model-based deep graph embedding clustering (scTAG) method, which simultaneously learns cell–cell topology representations and identifies cell clusters based on deep graph convolutional network. scTAG integrates the zero-inflated negative binomial (ZINB) model into a topology adaptive graph convolutional autoencoder to learn the low-dimensional latent representation and adopts Kullback–Leibler (KL) divergence for the clustering tasks. By simultaneously optimizing the clustering loss, ZINB loss, and the cell graph reconstruction loss, scTAG jointly optimizes cluster label assignment and feature learning with the topological structures preserved in an end-to-end manner. Extensive experiments on 16 single-cell RNA-seq datasets from diverse yet representative single-cell sequencing platforms demonstrate the superiority of scTAG over various state-of-the-art clustering methods.

In recent years, hydrophobic deep eutectic solvents as new generation of green solvents have attracted wide attention in liquid microextraction technique. In this article, four hydrophobic deep eutectic solvents composed of trioctylmethylammonium chloride and oleic acid were designed and prepared firstly. Combined with high-performance liquid chromatography, these deep eutectic solvents were used as an extraction solvent in vortex-assisted dispersive liquid-liquid microextraction for the selective enrichment and indirect determination of trace nitrite from real water and biological samples. This method is based on the diazotization-coupling reaction of nitrite with p-nitroaniline and diphenylamine in acidic water, and then the nitrite is quantified indirectly by measuring the obtained azo compounds. Some factors influencing the extraction efficiency, including the reaction and extraction conditions, were investigated. Under the optimized conditions, the method has a linear range of 1-300 μg/L with a correlation coefficient of 0.9924, limit of detection of 0.2 μg/L, limit of quantitation of 1 μg/L, intraday and interday relative standard deviations of 4.0 and 6.0%. This method was successfully applied in determination of nitrite from three environmental water and two biological samples with the recovery in the range of 90.5-115.2%. In addition, these results were well agreement with those obtained by the conventional Griess method.