To Avoid the Pitfall of Missing Labels in Feature Selection: A Generative Model Gives the Answer

In multi-label learning, instances have a large number of noisy and irrelevant features, and each instance is associated with a set of class labels wherein label information is generally incomplete. These missing labels possess two sides like a coin; people cannot predict whether their provided information for feature selection is favorable (relevant) or not (irrelevant) during tossing. Existing approaches either superficially consider the missing labels as negative or indiscreetly impute them with some predicted values, which may either overestimate unobserved labels or introduce new noises in selecting discriminative features. To avoid the pitfall of missing labels, a novel unified framework of selecting discriminative features and modeling incomplete label matrix is proposed from a generative point of view in this paper. Concretely, we relax Smoothness Assumption to infer the label observability, which can reveal the positions of unobserved labels, and employ the spike-and-slab prior to perform feature selection by excluding unobserved labels. Using a data-augmentation strategy leads to full local conjugacy in our model, facilitating simple and efficient Expectation Maximization (EM) algorithm for inference. Quantitative and qualitative experimental results demonstrate the superiority of the proposed approach under various evaluation metrics

Pathway Enrichment Analysis with Networks

Detecting associations between an input gene set and annotated gene sets (e.g., pathways) is an important problem in modern molecular biology. In this paper, we propose two algorithms, termed NetPEA and NetPEA’, for conducting network-based pathway enrichment analysis. Our algorithms consider not only shared genes but also gene–gene interactions. Both algorithms utilize a protein–protein interaction network and a random walk with a restart procedure to identify hidden relationships between an input gene set and pathways, but both use different randomization strategies to evaluate statistical significance and as a result emphasize different pathway properties. Compared to an over representation-based method, our algorithms can identify more statistically significant pathways. Compared to an existing network-based algorithm, EnrichNet, our algorithms have a higher sensitivity in revealing the true causal pathways while at the same time achieving a higher specificity. A literature review of selected results indicates that some of the novel pathways reported by our algorithms are biologically relevant and important. While the evaluations are performed only with KEGG pathways, we believe the algorithms can be valuable for general functional discovery from high-throughput experiments

Document Summarization with VHTM: Variational Hierarchical Topic-Aware Mechanism

Automatic text summarization focuses on distilling summary information from texts. This research field has been considerably explored over the past decades because of its significant role in many natural language processing tasks; however, two challenging issues block its further development: (1) how to yield a summarization model embedding topic inference rather than extending with a pre-trained one and (2) how to merge the latent topics into diverse granularity levels. In this study, we propose a variational hierarchical model to holistically address both issues, dubbed VHTM. Different from the previous work assisted by a pre-trained single-grained topic model, VHTM is the first attempt to jointly accomplish summarization with topic inference via variational encoder-decoder and merge topics into multi-grained levels through topic embedding and attention. Comprehensive experiments validate the superior performance of VHTM compared with the baselines, accompanying with semantically consistent topics

Evaluation of cyclooxygenase-2 fluctuation via a near-infrared fluorescent probe in idiopathic pulmonary fibrosis cell and mice models

Idiopathic pulmonary fibrosis (IPF) is a devastating and fatal interstitial lung disease due to various challenges in diagnosis and treatment. Due to its complicated pathogenesis and difficulty in early diagnosis, there is no effective cure. Cyclooxygenase-2 (COX-2) is inextricably associated with pulmonary fibrosis. The abnormal level of COX-2 leads to extremely exacerbated pulmonary fibrosis. Therefore, we reported a near-infrared fluorescent probe Cy-COX to detect the fluctuation of COX-2 levels during pulmonary fibrosis and explain its important protective effect. The probe Cy-COX showed a significant enhancement of fluorescence signal to COX-2 with excellent selectivity and sensitivity. In order to clarify the relationship between COX-2 and pulmonary fibrosis, we used the probe Cy-COX to detect COX-2 fluctuation in organisms with pulmonary fibrosis. The results showed that the COX-2 level increased in the early stage and decreased in the late stage with the aggravation of pulmonary fibrosis. Furthermore, up-regulation of COX-2 levels can effectively alleviate the severity of pulmonary fibrosis. Therefore, Cy-COX is a fast and convenient imaging tool with great potential to predict the early stage of pulmonary fibrosis and evaluate the therapeutic effects

Mitochondrial aerobic respiration is activated during hair follicle stem cell differentiation, and its dysfunction retards hair regeneration

Background. Emerging research revealed the essential role of mitochondria in regulating stem/progenitor cell differentiation of neural progenitor cells, mesenchymal stem cells and other stem cells through reactive oxygen species (ROS), Notch or other signaling pathway. Inhibition of mitochondrial protein synthesis results in hair loss upon injury. However, alteration of mitochondrial morphology and metabolic function during hair follicle stem cells (HFSCs) differentiation and how they affect hair regeneration has not been elaborated upon. Methods. We compared the difference in mitochondrial morphology and activity between telogen bulge cells and anagen matrix cells. Expression levels of mitochondrial ROS and superoxide dismutase 2 (SOD2) were measured to evaluate redox balance. In addition, the level of pyruvate dehydrogenase kinase (PDK) and pyruvate dehydrogenase (PDH) were estimated to present the change in energetic metabolism during differentiation. To explore the effect of the mitochondrial metabolism on regulating hair regeneration, hair growth was observed after application of a mitochondrial respiratory inhibitor upon hair plucking. Results. During HFSCs differentiation, mitochondria became elongated with more abundant organized cristae and showed higher activity in differentiated cells. SOD2 was enhanced for redox balance with relatively stable ROS levels in differentiated cells. PDK increased in HFSCs while differentiated cells showed enhanced PDH, indicating that respiration switched from glycolysis to oxidative phosphorylation during differentiation. Inhibiting mitochondrial respiration in differentiated hair follicle cells upon hair plucking repressed hair regeneration in vivo. Conclusions. Upon HFSCs differentiation, mitochondria are elongated with more abundant cristae and show higher activity, accompanying with activated aerobic respiration in differentiated cells for higher energy supply. Also, dysfunction of mitochondrial respiration delays hair regeneration upon injury