Exploring Numerical Priors for Low-Rank Tensor Completion with Generalized CP Decomposition

Tensor completion is important to many areas such as computer vision, data analysis, and signal processing. Enforcing low-rank structures on completed tensors, a category of methods known as low-rank tensor completion has recently been studied extensively. While such methods attained great success, none considered exploiting numerical priors of tensor elements. Ignoring numerical priors causes loss of important information regarding the data, and therefore prevents the algorithms from reaching optimal accuracy. This work attempts to construct a new methodological framework called GCDTC (Generalized CP Decomposition Tensor Completion) for leveraging numerical priors and achieving higher accuracy in tensor completion. In this newly introduced framework, a generalized form of CP Decomposition is applied to low-rank tensor completion. This paper also proposes an algorithm known as SPTC (Smooth Poisson Tensor Completion) for nonnegative integer tensor completion as an instantiation of the GCDTC framework. A series of experiments on real-world data indicated that SPTC could produce results superior in completion accuracy to current state-of-the-arts.Comment: 11 pages, 4 figures, 3 pseudocode algorithms, and 1 tabl

A Single Molecule Scaffold for the Maize Genome

About 85% of the maize genome consists of highly repetitive sequences that are interspersed by low-copy, gene-coding sequences. The maize community has dealt with this genomic complexity by the construction of an integrated genetic and physical map (iMap), but this resource alone was not sufficient for ensuring the quality of the current sequence build. For this purpose, we constructed a genome-wide, high-resolution optical map of the maize inbred line B73 genome containing >91,000 restriction sites (averaging 1 site/∼23 kb) accrued from mapping genomic DNA molecules. Our optical map comprises 66 contigs, averaging 31.88 Mb in size and spanning 91.5% (2,103.93 Mb/∼2,300 Mb) of the maize genome. A new algorithm was created that considered both optical map and unfinished BAC sequence data for placing 60/66 (2,032.42 Mb) optical map contigs onto the maize iMap. The alignment of optical maps against numerous data sources yielded comprehensive results that proved revealing and productive. For example, gaps were uncovered and characterized within the iMap, the FPC (fingerprinted contigs) map, and the chromosome-wide pseudomolecules. Such alignments also suggested amended placements of FPC contigs on the maize genetic map and proactively guided the assembly of chromosome-wide pseudomolecules, especially within complex genomic regions. Lastly, we think that the full integration of B73 optical maps with the maize iMap would greatly facilitate maize sequence finishing efforts that would make it a valuable reference for comparative studies among cereals, or other maize inbred lines and cultivars

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The Comparison of Fusion Methods for HSRRSI Considering the Effectiveness of Land Cover (Features) Object Recognition Based on Deep Learning

The efficient and accurate application of deep learning in the remote sensing field largely depends on the pre-processing technology of remote sensing images. Particularly, image fusion is the essential way to achieve the complementarity of the panchromatic band and multispectral bands in high spatial resolution remote sensing images. In this paper, we not only pay attention to the visual effect of fused images, but also focus on the subsequent application effectiveness of information extraction and feature recognition based on fused images. Based on the WorldView-3 images of Tongzhou District of Beijing, we apply the fusion results to conduct the experiments of object recognition of typical urban features based on deep learning. Furthermore, we perform a quantitative analysis for the existing pixel-based mainstream fusion methods of IHS (Intensity-Hue Saturation), PCS (Principal Component Substitution), GS (Gram Schmidt), ELS (Ehlers), HPF (High-Pass Filtering), and HCS (Hyper spherical Color Space) from the perspectives of spectrum, geometric features, and recognition accuracy. The results show that there are apparent differences in visual effect and quantitative index among different fusion methods, and the PCS fusion method has the most satisfying comprehensive effectiveness in the object recognition of land cover (features) based on deep learning

Effects of the (Pro)renin Receptor on Cardiac Remodeling and Function in a Rat Alcoholic Cardiomyopathy Model via the PRR-ERK1/2-NOX4 Pathway

Alcoholic cardiomyopathy (ACM) caused by alcohol consumption manifests mainly as by maladaptive myocardial function, which eventually leads to heart failure and causes serious public health problems. The (pro)renin receptor (PRR) is an important member of the local tissue renin-angiotensin system and plays a vital role in many cardiovascular diseases. However, the mechanism responsible for the effects of PRR on ACM remains unclear. The purpose of this study was to determine the role of PRR in myocardial fibrosis and the deterioration of cardiac function in alcoholic cardiomyopathy. Wistar rats were fed a liquid diet containing 9% v/v alcohol to establish an alcoholic cardiomyopathy model. Eight weeks later, rats were injected with 1×109v.g./100 μl of recombinant adenovirus containing EGFP (scramble-shRNA), PRR, and PRR-shRNA via the tail vein. Cardiac function was assessed by echocardiography. Cardiac histopathology was measured by Masson’s trichrome staining, immunohistochemical staining, and dihydroethidium staining. In addition, cardiac fibroblasts (CFs) were cultured to evaluate the effects of alcohol stimulation on the production of the extracellular matrix and their underlying mechanisms. Our results indicated that overexpression of PRR in rats with alcoholic cardiomyopathy exacerbates myocardial oxidative stress and myocardial fibrosis. Silencing of PRR expression with short hairpin RNA (shRNA) technology reversed the myocardial damage mediated by PRR. Additionally, PRR activated phosphorylation of ERK1/2 and increased NOX4-derived reactive oxygen species and collagen expression in CFs with alcohol stimulation. Administration of the ERK kinase inhibitor (PD98059) significantly reduced NOX4 protein expression and collagen production, which indicated that PRR increases collagen production primarily through the PRR-ERK1/2-NOX4 pathway in CFs. In conclusion, our study demonstrated that PRR induces myocardial fibrosis and deteriorates cardiac function through ROS from the PRR-ERK1/2-NOX4 pathway during ACM development