The adaptor CARD9 functions downstream of C-type lectin receptors (CLRs) for the sensing of microbial infection, which leads to responses by the T1 and T17 subsets of helper T cells. The single-nucleotide polymorphism rs4077515 at CARD9 in the human genome, which results in the substitution S12N (CARD9), is associated with several autoimmune diseases. However, the function of CARD9 has remained unknown. Here we generated CARD9 knock-in mice and found that CARD9 facilitated the induction of type 2 immune responses after engagement of CLRs. Mechanistically, CARD9 mediated CLR-induced activation of the non-canonical transcription factor NF-κB subunit RelB, which initiated production of the cytokine IL-5 in alveolar macrophages for the recruitment of eosinophils to drive T2 cell-mediated allergic responses. We identified the homozygous CARD9 mutation encoding S12N in patients with allergic bronchopulmonary aspergillosis and revealed activation of RelB and production of IL-5 in peripheral blood mononuclear cells from these patients. Our study provides genetic and functional evidence demonstrating that CARD9 can turn alveolar macrophages into IL-5-producing cells and facilitates T2 cell-mediated pathologic responses.
Gait and static body measurement are important biometric technologies for passive human recognition. Many previous works argue that recognition performance based completely on the gait feature is limited. The reason for this limited performance remains unclear. This study focuses on human recognition with gait feature obtained by Kinect and shows that gait feature can effectively distinguish from different human beings through a novel representation --relative distance-based gait features.Experimental results show that the recognition accuracy with relative distance features reaches up to 85%, which is comparable with that of anthropometric features. The combination of relative distance features and anthropometric features can provide an accuracy of more than 95%. Results indicate that the relative distance feature is quite effective and worthy of further study in more general scenarios (e.g., without Kinect).
Land use and land cover (LULC) mapping in urban areas is one of the core applications in remote sensing, and it plays an important role in modern urban planning and management. Deep learning is springing up in the field of machine learning recently. By mimicking the hierarchical structure of the human brain, deep learning can gradually extract features from lower level to higher level. The Deep Belief Networks (DBN) model is a widely investigated and deployed deep learning architecture. It combines the advantages of unsupervised and supervised learning and can archive good classification performance. This study proposes a classification approach based on the DBN model for detailed urban mapping using polarimetric synthetic aperture radar (PolSAR) data. Through the DBN model, effective contextual mapping features can be automatically extracted from the PolSAR data to improve the classification performance. Two-date high-resolution RADARSAT-2 PolSAR data over the Great Toronto Area were used for evaluation. Comparisons with the support vector machine (SVM), conventional neural networks (NN), and stochastic Expectation-Maximization (SEM) were conducted to assess the potential of the DBN-based classification approach. Experimental results show that the DBN-based method outperforms three other approaches and produces homogenous mapping results with preserved shape details.
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