Evidence of novel type of ribosome in eukaryotic intermediate flatworm

In all organisms, messenger-directed protein synthesis is catalyzed by ribonucleoprotein particles called ribosomes. A ribosome is typically composed of one small and one large subunit which contain one short (18S) and one long (28S) rRNAs, respectively. Surprisingly, in this study, three similar size rRNAs (18-21S) were revealed in the electrophoresis profile of the total RNAs of tapeworm _Spirometra erinaceiuropaei_. Northern blot analysis shows that one of the three bands belongs to 18S rRNA, and the other two bands are of 28S rRNAs, implying structurally distinct ribosomes in this intermediate animal. Furthermore, similar, but not identical profiles were observed in two other tapeworms _Diphyllobothrium hottai_ and _Diphyllobothrium Nipponkaiizeme_. Relevant to this finding, in flatworm _Paragonimus westermani_, 18S rRNAs were found much more numerous than 28S rRNAs. Moreover, consistent with this biochemical finding, transmission electron microscopy examinations show that the ribosomes isolated from _Spirometra erinaceiuropaei_ are composed of either one ball or two similar size subunits (balls), while the structure of ribosomes isolated from control liver tissue exactly match the conventional large and small subunit ribosome model. Our study provides direct biochemical and biophysical evidence of structurally distinct novel type of ribosomes in intermediate eukaryotic flatworms. These finding may be important for re-recognition of biological protein synthesis and evolutionary process of living things

Locality Preserving Projections for Grassmann manifold

Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos. However, such learning algorithms particularly on high-dimensional Grassmann manifold always involve with significantly high computational cost, which seriously limits the applicability of learning on Grassmann manifold in more wide areas. In this research, we propose an unsupervised dimensionality reduction algorithm on Grassmann manifold based on the Locality Preserving Projections (LPP) criterion. LPP is a commonly used dimensionality reduction algorithm for vector-valued data, aiming to preserve local structure of data in the dimension-reduced space. The strategy is to construct a mapping from higher dimensional Grassmann manifold into the one in a relative low-dimensional with more discriminative capability. The proposed method can be optimized as a basic eigenvalue problem. The performance of our proposed method is assessed on several classification and clustering tasks and the experimental results show its clear advantages over other Grassmann based algorithms.Comment: Accepted by IJCAI 201

Incremental Learning Method for Data with Delayed Labels

Most research on machine learning tasks relies on the availability of true labels immediately after making a prediction. However, in many cases, the ground truth labels become available with a non-negligible delay. In general, delayed labels create two problems. First, labelled data is insufficient because the label for each data chunk will be obtained multiple times. Second, there remains a problem of concept drift due to the long period of data. In this work, we propose a novel incremental ensemble learning when delayed labels occur. First, we build a sliding time window to preserve the historical data. Then we train an adaptive classifier by labelled data in the sliding time window. It is worth noting that we improve the TrAdaBoost to expand the data of the latest moment when building an adaptive classifier. It can correctly distinguish the wrong types of source domain sample classification. Finally, we integrate the various classifiers to make predictions. We apply our algorithms to synthetic and real credit scoring datasets. The experiment results indicate our algorithms have superiority in delayed labelling setting