Tumor-derived exosomes (TEXs) play instrumental roles in tumor growth, angiogenesis, immune modulation, metastasis, and drug resistance. TEX RNAs are a new class of noninvasive biomarkers for cancer. Neither current techniques, such as quantitative reverse transcription polymerase chain reaction (qRT-PCR) and next-generation sequencing, nor new ones, such as electrochemical or surface plasmon resonance-based biosensors, are able to selectively capture and separate TEXs from normal cell-derived exosomes, making TEX RNAs potentially less sensitive biomarkers. We developed an immuno-biochip that selectively captures TEXs using antibodies against tumor-associated proteins and quantifies in situ TEX RNAs using cationic lipoplexes containing molecular beacons. We used the immuno-biochip to measure the expression of miR-21 microRNA and TTF-1 mRNA in EGFR-or PD-L1-bearing exosomes from human sera and achieved absolute sensitivity and specificity in distinguishing normal controls from non-small cell lung cancer patients. Our results demonstrated that the effective separation of TEXs from other exosomes greatly improved the detection sensitivity and specificity. Compared with the traditional immunomagnetic separation−RNA isolation−qRT-PCR workflow, the immuno-biochip showed superior lung cancer diagnostic performance, consumed less samples (∼30 μL), and shortened assay time from ∼24 to 4 h.

With the abundance of high dimensional data in various disciplines, sparse regularized techniques are very popular these days. In this paper, we make use of the structure information among predictors to improve sparse regression models. Typically, such structure information can be modeled by the connectivity of an undirected graph using all predictors as nodes of the graph. Most existing methods use this undirected graph edge-by-edge to encourage the regression coefficients of corresponding connected predictors to be similar. However, such methods do not directly utilize the neighborhood information of the graph. Furthermore, if there are more edges in the predictor graph, the corresponding regularization term will be more complicate. In this paper, we incorporate the graph information node-by-node, instead of edge-by-edge as used in most existing methods. Our proposed method is very general and it includes adaptive Lasso, group Lasso, and ridge regression as special cases. Both theoretical and numerical studies demonstrate the effectiveness of the proposed method for simultaneous estimation, prediction and model selection.

Our data demonstrated a progressive amplification of Grb2 and HER2 expression in gastric carcinogenesis, suggesting the importance of Grb2 and HER2 as positive biomarkers for gastric cancer development and progression.

Robust human gait recognition is challenging because of the presence of covariate factors such as carrying condition, clothing, walking surface, etc. In this paper, we model the effect of covariates as an unknown partial feature corruption problem. Since the locations of corruptions may differ for different query gaits, relevant features may become irrelevant when walking condition changes. In this case, it is difficult to train one fixed classifier that is robust to a large number of different covariates. To tackle this problem, we propose a classifier ensemble method based on the random subspace Method (RSM) and majority voting (MV). Its theoretical basis suggests it is insensitive to locations of corrupted features, and thus can generalize well to a large number of covariates. We also extend this method by proposing two strategies, i.e, local enhancing (LE) and hybrid decision-level fusion (HDF) to suppress the ratio of false votes to true votes (before MV). The performance of our approach is competitive against the most challenging covariates like clothing, walking surface, and elapsed time. We evaluate our method on the USF dataset and OU-ISIR-B dataset, and it has much higher performance than other state-of-the-art algorithms.