Low-Light Enhancement Using a Plug-and-Play Retinex Model With Shrinkage Mapping for Illumination Estimation.

Low-light photography conditions degrade image quality. This study proposes a novel Retinex-based low-light enhancement method to correctly decompose an input image into reflectance and illumination. Subsequently, we can improve the viewing experience by adjusting the illumination using intensity and contrast enhancement. Because image decomposition is a highly ill-posed problem, constraints must be properly imposed on the optimization framework. To meet the criteria of ideal Retinex decomposition, we design a nonconvex Lp norm and apply shrinkage mapping to the illumination layer. In addition, edge-preserving filters are introduced using the plug-and-play technique to improve illumination. Pixel-wise weights based on variance and image gradients are adopted to suppress noise and preserve details in the reflectance layer. We choose the alternating direction method of multipliers (ADMM) to solve the problem efficiently. Experimental results on several challenging low-light datasets show that our proposed method can more effectively enhance image brightness as compared with state-of-the-art methods. In addition to subjective observations, the proposed method also achieved competitive performance in objective image quality assessments.

MSRCall: a multi-scale deep neural network to basecall Oxford Nanopore sequences.

MOTIVATION: MinION, a third-generation sequencer from Oxford Nanopore Technologies, is a portable device that can provide long-nucleotide read data in real-time. It primarily aims to deduce the makeup of nucleotide sequences from the ionic current signals generated when passing DNA/RNA fragments through nanopores charged with a voltage difference. To determine nucleotides from measured signals, a translation process known as basecalling is required. However, compared to NGS basecallers, the calling accuracy of MinION still needs to be improved. RESULTS: In this work, a simple but powerful neural network architecture called multi-scale recurrent caller (MSRCall) is proposed. MSRCall comprises a multi-scale structure, recurrent layers, a fusion block and a connectionist temporal classification decoder. To better identify both short-and long-range dependencies, the recurrent layer is redesigned to capture various time-scale features with a multi-scale structure. The results show that MSRCall outperforms other basecallers in terms of both read and consensus accuracies. AVAILABILITY AND IMPLEMENTATION: MSRCall is available at: https://github.com/d05943006/MSRCall. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic testing, ultrasonography and preimplantation genetic testing of men with autosomal dominant polycystic kidney disease in Hunan, China.

The present study identified novel mutations in polycystic kidney disease (PKD) genes in China, determined the prevalence of cysts in the genital tract and accessory gonad in autosomal dominant PKD (ADPKD) patients, correlated these genes with ADPKD and male infertility and investigated whether male infertility associated with ADPKD affected the clinical outcomes in a preimplantation genetic testing (PGT) cycle cohort. This study was a cross-sectional study. Twenty-four unrelated men with ADPKD recruited from the Reproductive and Genetic Hospital of CITIC-Xiangya in China were investigated between January 2019 and December 2020. A total of 24 variations were identified in 22 patients, including 23, 1 and 0 variations in PKD1, PKD2 and GANAB, respectively. Genital tract and accessory gonadal cysts were significant dependent variables for male infertility. A diagnosis was made in 87.04% (94/108) and 51.85% (56/108) of the embryos for ADPKD and PGT-A respectively. Clinical pregnancy reached 72.73% per embryo transfer and 84.21% per patient. We identified a group of novel mutations in PKD genes, which enriches the PKD mutation spectrum. Although genital tract and accessory gonadal cysts greatly influence the fertility of men with ADPKD, they have minimal clinical consequences on pregnancy by intracytoplasmic sperm injection (ICSI) and PGT.

BLASTP-ACC: Parallel Architecture and Hardware Accelerator Design for BLAST-Based Protein Sequence Alignment.

In this study, we design a hardware accelerator for a widely used sequence alignment algorithm, the basic local alignment search tool for proteins (BLASTP). The architecture of the proposed accelerator consists of five stages: a new systolic-array-based one-hit finding stage, a novel RAM-REG-based two-hit finding stage, a refined ungapped extension stage, a faster gapped extension stage, and a highly efficient parallel sorter. The system is implemented on an Altera Stratix V FPGA with a processing speed of more than 500 giga cell updates per second (GCUPS). It can receive a query sequence, compare it with the sequences in the database, and generate a list sorted in descending order of the similarity scores between the query sequence and the subject sequences. Moreover, it is capable of processing both query and subject protein sequences comprising as many as 8192 amino acid residues in a single pass. Using data from the National Center for Biotechnology Information (NCBI) database, we show that a speed-up of more than 3X can be achieved with our hardware compared to the runtime required by BLASTP software on an 8-thread Intel Xeon CPU with 144 GB DRAM.