RESUMEN
The study of small RNAs is a field that is expanding quickly. Other functional short RNA molecules other than microRNAs, and gene expression regulators, have been found in animals and plants. MicroRNAs play a significant role in host-microbe interactions, and parasite microRNAs may affect the host's innate immunity. Furthermore, short RNAs are intriguing non-invasive biomarker possibilities because they can be found in physiological fluids. These trends suggest that for many researchers, quick and simple techniques for expression profiling and subsequent downstream analysis of miRNA-seq data are crucial. We selected sRNAtoolbox to make integrated sRNA research easier. Each tool can be used separately or to explore and analyze sRNAbench results in further depth. A special focus was placed on the tools' usability. We review available miRNA research tools to have an overview of the evaluation of the tools. Mainly we evaluate the tool sRNAtoolbox.
Asunto(s)
MicroARNs , Animales , MicroARNs/genética , MicroARNs/metabolismo , Programas Informáticos , Plantas/genética , Biología Computacional/métodos , Análisis de Secuencia de ARN/métodosRESUMEN
[This corrects the article DOI: 10.1155/2018/3654210.].
RESUMEN
Minimally invasive surgery (MIS) minimizes the surgical incisions that need to be made and hence reduces the physical trauma involved during the surgical process. The ultimate goal is to reduce postoperative pain and blood loss as well as to limit the scarring area and hence accelerate recovery. It is therefore of great interest to both the surgeon and the patient. However, a major problem with MIS is that the field of vision of the surgeon is very narrow. We had previously developed and tested an MIS panoramic endoscope (MISPE) that provides the surgeon with a broader field of view. However, one issue with the MISPE was its low rate of video stitching. Therefore, in this paper, we propose using the region of interest in combination with the downsizing technique to improve the image-stitching performance of the MISPE. Experimental results confirm that, by using the proposed method, the image size can be increased by more than 160%, with the image resolution also improving. For instance, we could achieve performance improvements of 10× (CPU) and 23× (GPU) as compared to that of the original method.