Supplementary data are available at Bioinformatics online.

No abstract

The annotation of protein function is a vital step to elucidate the essence of life at a molecular level, and it is also meritorious in biomedical and pharmaceutical industry. Developments of sequencing technology result in constant expansion of the gap between the number of the known sequences and their functions. Therefore, it is indispensable to develop a computational method for the annotation of protein function. Herein, a novel method is proposed to identify protein function based on the weighted human protein-protein interaction network and graph theory. The network topology features with local and global information are presented to characterise proteins. The minimum redundancy maximum relevance algorithm is used to select 227 optimized feature subsets and support vector machine technique is utilized to build the prediction models. The performance of current method is assessed through 10-fold cross-validation test, and the range of accuracies is from 67.63% to 100%. Comparing with other annotation methods, the proposed way possesses a 50% improvement in the predictive accuracy. Generally, such network topology features provide insights into the relationship between protein functions and network architectures. The source code of Matlab is freely available on request from the authors.

Background Alopecia affects millions of individuals globally, with hair loss becoming more common among young people.  Various traditional Chinese medicines (TCM) have been used clinically for treating alopecia, however, the effective compounds and underlying mechanism are less known. We sought to investigate the effect of Alpinetin (AP), a compound extracted from Fabaceae and Zingiberaceae herbs, in hair regeneration. Methods Animal model for hair regeneration was mimicked by depilation in C57BL/6J mice. The mice were then topically treated with 3 mg/ml AP, minoxidil as positive control (PC), or solvent ethanol as vehicle control (VC) on the dorsal skin. Skin color changes which reflected the hair growth stages were monitored and pictured, along with H&E staining and hair shaft length measurement. RNA-seq analysis combined with immunofluorescence staining and qPCR analysis were used for mechanism study. Meanwhile, Gli1CreERT2; R26RtdTomato and Lgr5EGFP−CreERT2; R26RtdTomato transgenic mice were used to monitor the activation and proliferation of Gli1+ and Lgr5+ HFSCs after treatment. Furthermore, the toxicity of AP was tested in keratinocytes and fibroblasts from both human and mouse skin to assess the safety. Results When compared to minoxidil-treated and vehicle-treated control mice, topical application of AP promoted anagen initiation and delayed catagen entry, resulting in a longer anagen phase and hair shaft length. Mechanistically, RNA-seq analysis combined with immunofluorescence staining of Lef1 suggested that Lgr5+ HFSCs in lower bulge were activated by AP via Wnt signaling. Other HFSCs, including K15+, Lef1+, and Gli1+ cells, were also promoted into proliferating upon AP treatment. In addition, AP inhibited cleaved caspase 3-dependent apoptosis at the late anagen stage to postpone regression of hair follicles. More importantly, AP showed no cytotoxicity in keratinocytes and fibroblasts from both human and mouse skin. Conclusion This study clarified the effect of AP in promoting hair regeneration by activating HFSCs via Wnt signaling. Our findings may contribute to the development of a new generation of pilatory that is more efficient and less cytotoxic for treating alopecia.

This Letter proposes a combination of target detection and blockmatching 3D filter for despeckling SAR images. The proposed method is able to effectively preserve targets, such as the edges and dots of synthetic aperture radar (SAR) images, whilst removing noises. In the first step of our proposed method, based on despekling results of bilateral filtering and edge detection of a canny operator, some targets are detected and removed from an SAR image. The second step uses BM3D for denoising the targets-removed image. Finally, the removed targets are added to the despeckled targetsremoved image and desirable results can be achieved.Introduction: Synthetic aperture radar (SAR) images are inherently affected by speckle noise, which is due to the coherent nature of the scattering phenomena. The classical despeckling algorithms such as the Lee filtering [1], the Frost filtering [2], the gamma MAP filtering [3] and other despeckling algorithms can not perform well in both despeckling and preserving edges of SAR images simultaneously. Recently, the Block-matching 3-D algorithm (BM3D) [4] was introduced to remove white gaussian noise of images whilst effectively preserving details. In the BM3D algorithm, once a group of similar patches are collected, the whole group is denoised by means of a (3D) wavelet shrinkage process. Then, the partially cleaned image is used to estimate the parameters of a further denoising step based on Wiener filtering. But for SAR images, despeckling using BM3D shows that dots blur and edges lack brightness owing to the energy loss. To overcome these disadvantages, we propose a despeckling algorithm which uses the despeckling results of bilateral filtering (BF) [5] and edge detection of a canny operator to detect and preserve target and the detected targets are, then, removed from the SAR images. Then we extend the application of BM3D to targets-removed SAR image despeckling, and compute the noise standard deviation via the equivalent number of looks (ENL) and the edge save index (ESI) [6]. After BM3D with estimated parameters imposed on a targets-removed SAR image and then the removed targets are added to the despeckled targets-removed image, an ideal result can be achieved.