Abstract Many very deep neural networks are proposed to obtain accurate super‐resolution reconstruction of remote sensing images. However, the deeper the network for image SR is, the more difficult it is to train. The low‐resolution inputs and features contain abundant low‐frequency information and noise, which are treated equally as the high‐frequency information to across the network. To solve these problems, a novel single‐image super‐resolution algorithm named pre‐training of gated convolution neural network (PGCNN) is proposed for remote sensing images. The proposed PGCNN consists of several residual blocks with long skip connections. Each residual block contains an additional well‐designed gated convolution unit, which provides different weights to high‐frequency information and low‐frequency information to control the transmission of information, making the main network focus on learning high‐frequency information. Compared with several state‐of‐the‐art methods, experimental results on the remote sensing datasets (SIRI‐WHU, NWPU‐RESISC45, RSSCN7 and UC‐Merced‐Land‐Use) show that the proposed PGCNN has the accuracy and visual improvements.

SUMMARY: The simplicity and precision of CRISPR/Cas9 system has brought in a new era of gene editing. Screening for desired clones with CRISPR-mediated genomic edits in a large number of samples is made possible by next generation sequencing (NGS) due to its multiplexing. Here we present CRISPR-DAV (CRISPR Data Analysis and Visualization) pipeline to analyze the CRISPR NGS data in a high throughput manner. In the pipeline, Burrows-Wheeler Aligner and Assembly Based ReAlignment are used for small and large indel detection, and results are presented in a comprehensive set of charts and interactive alignment view. AVAILABILITY AND IMPLEMENTATION: CRISPR-DAV is available at GitHub and Docker Hub repositories: https://github.com/pinetree1/crispr-dav.git and https://hub.docker.com/r/pinetree1/crispr-dav/. CONTACT: [email protected].

MOTIVATION: The Serial Analysis of Gene Expression (SAGE) technology determines the expression level of a gene by measuring the frequency of a sequence tag derived from the corresponding mRNA transcript. Several statistical tests have been developed to detect significant differences in tag frequency between two samples. However, which one of these tests has the greatest power to detect real changes remains undetermined. RESULTS: This paper compares three statistical tests for detecting significant changes of gene expression in SAGE experiments. The comparison makes use of Monte Carlo simulation that, in essence, generates "virtual" SAGE experiments. Our analysis shows that the Chi-square test has the best power and robustness. Since the POWER_ SAGE program can easily run "virtual" SAGE studies with different combinations of sample size and tag frequency and determine the power for each combination, it can serve as a useful tool for planning SAGE experiments. AVAILABILITY: The POWER_ SAGE software is available upon request from the authors. CONTACT: [email protected]