On the Multiway Principal Component Analysis

Multiway data are becoming more and more common. While there are many approaches to extending principal component analysis (PCA) from usual data matrices to multiway arrays, their conceptual differences from the usual PCA, and the methodological implications of such differences remain largely unknown. This work aims to specifically address these questions. In particular, we clarify the subtle difference between PCA and singular value decomposition (SVD) for multiway data, and show that multiway principal components (PCs) can be estimated reliably in absence of the eigengaps required by the usual PCA, and in general much more efficiently than the usual PCs. Furthermore, the sample multiway PCs are asymptotically independent and hence allow for separate and more accurate inferences about the population PCs. The practical merits of multiway PCA are further demonstrated through numerical, both simulated and real data, examples

Visual object discovery and understanding

Learning to recognize objects is a fundamental and essential step in human perception and understanding of the world. Accordingly, research of object discovery across diverse modalities plays a pivotal role in the context of computer vision. This field not only contributes significantly to enhancing our understanding of visual information but also offers a plethora of potential applications, like augmented reality, e-commerce, and robotics, particularly in industrial manipulation scenarios. We first address the task of discovering objects from still images regardless of any predefined categories. We introduce a novel variational relaxation approach tailored to the task. By framing it as an optimization problem for piecewise-constant segmentation, this technique enables direct training of a fully convolutional network (FCN) for predicting object labels on each pixel. Applying our approach to the instance segmentation task achieved results almost as good as mask R-CNN without depending on a two-stage framework. Note that the training of the network does not depend on the category label, enabling our approach to discover objects unbounded by predefined categories. Next, we extend our exploration to video sequences, focusing on the task of unsupervised video object segmentation. Here, we aim to discover and track objects within videos. Noticing that single-frame object proposals often fail to obtain a good proposal due to motion blur, occlusion, and other reasons, our approach involves refining key frame proposals using a Multi-proposal graph constructed from proposals initially generated in nearby frames and then propagated to the key frame. We then compute the maximal cliques within this graph, which contains proposals that represent the same object. Pixel-level voting is performed within each clique to generate the key frame proposals that could be better than any of the single-frame proposals. Then a semi-supervised VOS algorithm subsequently tracks these key frame proposals across the entire video, showcasing the potential for precise and robust object tracking in dynamic visual environments. We further explore into the domain of Vision-Language, where we seek to identify objects associated with a specific textual context. In this multifaceted context, we tackle the intricate challenge of content moderation (CM), which assesses multimodal user-generated content to detect material that is illegal, harmful, or insulting. We present a novel CM model to address the asymmetric in semantics between vision and language. Our model features an innovative asymmetric fusion architecture that not only fuses the common knowledge in both modalities but also leverages the unique information present in each modality. Additionally, we introduce a novel cross-modality contrastive loss to capture knowledge that arises exclusively in multimodal context, which is crucial for addressing harmful intent that may emerge at the intersection of these modalities

Phosphorylation of TGB1 by protein kinase CK2 promotes barley stripe mosaic virus movement in monocots and dicots.

The barley stripe mosaic virus (BSMV) triple gene block 1 (TGB1) protein is required for virus cell-to-cell movement. However, little information is available about how these activities are regulated by post-translational modifications. In this study, we showed that the BSMV Xinjiang strain TGB1 (XJTGB1) is phosphorylated in vivo and in vitro by protein kinase CK2 from barley and Nicotiana benthamiana. Liquid chromatography tandem mass spectrometry analysis and in vitro phosphorylation assays demonstrated that Thr-401 is the major phosphorylation site of the XJTGB1 protein, and suggested that a Thr-395 kinase docking site supports Thr-401 phosphorylation. Substitution of Thr-395 with alanine (T395A) only moderately impaired virus cell-to-cell movement and systemic infection. In contrast, the Thr-401 alanine (T401A) virus mutant was unable to systemically infect N. benthamiana but had only minor effects in monocot hosts. Substitution of Thr-395 or Thr-401 with aspartic acid interfered with monocot and dicot cell-to-cell movement and the plants failed to develop systemic infections. However, virus derivatives with single glutamic acid substitutions at Thr-395 and Thr-401 developed nearly normal systemic infections in the monocot hosts but were unable to infect N. benthamiana systemically, and none of the double mutants was able to infect dicot and monocot hosts. The mutant XJTGB1T395A/T401A weakened in vitro interactions between XJTGB1 and XJTGB3 proteins but had little effect on XJTGB1 RNA-binding ability. Taken together, our results support a critical role of CK2 phosphorylation in the movement of BSMV in monocots and dicots, and provide new insights into the roles of phosphorylation in TGB protein functions

A novel method for high accuracy sumoylation site prediction from protein sequences

<p>Abstract</p> <p>Background</p> <p>Protein sumoylation is an essential dynamic, reversible post translational modification that plays a role in dozens of cellular activities, especially the regulation of gene expression and the maintenance of genomic stability. Currently, the complexities of sumoylation mechanism can not be perfectly solved by experimental approaches. In this regard, computational approaches might represent a promising method to direct experimental identification of sumoylation sites and shed light on the understanding of the reaction mechanism.</p> <p>Results</p> <p>Here we presented a statistical method for sumoylation site prediction. A 5-fold cross validation test over the experimentally identified sumoylation sites yielded excellent prediction performance with correlation coefficient, specificity, sensitivity and accuracy equal to 0.6364, 97.67%, 73.96% and 96.71% respectively. Additionally, the predictor performance is maintained when high level homologs are removed.</p> <p>Conclusion</p> <p>By using a statistical method, we have developed a new SUMO site prediction method – SUMOpre, which has shown its great accuracy with correlation coefficient, specificity, sensitivity and accuracy.</p