We present an approach to recover absolute 3D human poses from multi-view images by incorporating multi-view geometric priors in our model. It consists of two separate steps: (1) estimating the 2D poses in multi-view images and (2) recovering the 3D poses from the multi-view 2D poses. First, we introduce a cross-view fusion scheme into CNN to jointly estimate 2D poses for multiple views. Consequently, the 2D pose estimation for each view already benefits from other views. Second, we present a recursive Pictorial Structure Model to recover the 3D pose from the multi-view 2D poses. It gradually improves the accuracy of 3D pose with affordable computational cost. We test our method on two public datasets H36M and Total Capture. The Mean Per Joint Position Errors on the two datasets are 26mm and 29mm, which outperforms the state-of-the-arts remarkably (26mm vs 52mm, 29mm vs 35mm). Our code is released at https:// github.com/ microsoft/ multiview-human-pose-estimation-pytorch. * This work is done when Haibo Qiu is an intern at Microsoft Research Asia.

Microrchidia2 (MORC2) is a member of the MORC protein family that is localized to both the nucleus and cytoplasm when transiently expressed in gastric cancer cells. We identified and analyzed the functional domains of MORC2, which has specific unique structural characteristics compared to the other MORC proteins. Our data showed that nuclear localization signals (NLS) of MORC2 was mainly dependent on the NLS amino acids (aa) 657-781 and cytoplasmic localization of MORC2 was attributed to the nuclear export signal (NES) aa 481-657. Moreover, the NLS appears to predominate over the NES in the localization of full-length human MORC2 indicating that MORC2 is localized mainly in the nucleus. Our results also demonstrated that the NLS (aa 657-781) and proline-rich domain within MORC2 C-terminus were required for the transcriptional repressive role in cancer cells. Anat Rec, 293:1002Rec, 293: -1009

A web tool for information retrieval and analysis of soybean miRFNs and the relevant target functional gene networks can be accessed at SoymiRNet: http://nclab.hit.edu.cn/SoymiRNet.

Predicting protein subcellular location is necessary for understanding cell function. Several machine learning methods have been developed for computational prediction of primary protein sequences because wet experiments are costly and time consuming. However, two problems still exist in state-of-the-art methods. First, several proteins appear in different subcellular structures simultaneously, whereas current methods only predict one protein sequence in one subcellular structure. Second, most software tools are trained with obsolete data and the latest new databases are missed. We proposed a novel multi-label classification algorithm to solve the first problem and integrated several latest databases to improve prediction performance. Experiments proved the effectiveness of the proposed method. The present study would facilitate research on cellular proteomics.

Motivation: The number of ion channels is increasing rapidly. As many of them are associated with diseases, they are the targets of more than 700 drugs. The discovery of new ion channels is facilitated by computational methods that predict ion channels and their types from protein sequences. Methods: We used the SVMProt and the k-skip-n-gram methods to extract the feature vectors of ion channels, and obtained 188- and 400-dimensional features, respectively. The 188- and 400-dimensional features were combined to obtain 588-dimensional features. We then employed the maximum-relevance-maximum-distance method to reduce the dimensions of the 588-dimensional features. Finally, the support vector machine and random forest methods were used to build the prediction models to evaluate the classification effect. Results: Different methods were employed to extract various feature vectors, and after effective dimensionality reduction, different classifiers were used to classify the ion channels. We extracted the ion channel data from the Universal Protein Resource (UniProt, http://www.uniprot.org/ ) and Ligand-Gated Ion Channel databases ( http://www.ebi.ac.uk/compneur-srv/LGICdb/LGICdb.php ), and then verified the performance of the classifiers after screening. The findings of this study could inform the research and development of drugs.

No abstract

The disorder distribution of protein in the compartment or organelle leads to many human diseases, including neurodegenerative diseases such as Alzheimer's disease. The prediction of protein subcellular localization play important roles in the understanding of the mechanism of protein function, pathogenes and disease therapy. This paper proposes a novel subcellular localization method by integrating the Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost), where CNN acts as a feature extractor to automatically obtain features from the original sequence information and a XGBoost classifier as a recognizer to identify the protein subcellular localization based on the output of the CNN. Experiments are implemented on three protein datasets. The results prove that the CNN-XGBoost method performs better than the general protein subcellular localization methods.

No abstract