谈谈高中生诗文读写

诗歌是文学中的文学，要学会写诗，写好文章就要多读诗，从诗中学习技法。借鉴得失。

Machine-learning-aided precise prediction of deletions with next-generation sequencing

When detecting deletions in complex human genomes,split-read approaches using short reads generated with next-generation sequencing still face the challenge that either false discovery rate is high,or sensitivity is low.To address the problem,an integrated strategy is proposed.It organically combines the fundamental theories of the three mainstream methods(read-pair approaches,split-read technologies and read-depth analysis) with modern machine learning algorithms,using the recipe of feature extraction as a bridge.Compared with the state-of-art split-read methods for deletion detection in both low and high sequence coverage,the machine-learning-aided strategy shows great ability in intelligently balancing sensitivity and false discovery rate and getting a both more sensitive and more precise call set at single-base-pair resolution.Thus,users do not need to rely on former experience to make an unnecessary trade-off beforehand and adjust parameters over and over again any more.It should be noted that modern machine learning models can play an important role in the field of structural variation prediction.

Automatic segmentation of Colon Cancer Cells Based on Active Contour Method： A New Approach

Automatic interpretation of the images of colon cell biopsies requires automatic segmentation of these cells in the image obtained. The active contour method for image segmentation is a well known method for automatic detection of the cell contour. However, the application of this method on colon cell images was not effective. In this paper, the authors have proposed a new technique to reduce the analysis time needed to detect cells in a given image. This technique is based on the active contour method but now using a progressive division of the dimensions of the image to achieve convergence. The model proposed succeeded in detecting cells whose boundaries are not necessarily defined by a gradient. The initial curve can be anywhere in the image, and interior contours can be automatically detected. The developed algorithm was successfully applied on textured multispectral images of three types of cells, including benign hyperplasia （BH）, intraepithelial neoplasia （IN）, and carcinoma （Ca） cells.

TEST METHOD EFFECT ON WRITING PERFORMANCE

The main objective of this paper was to investigate the test method effect of two writing tests on the performances of sixty sophomore English majors from China University of Mining and Technology, who were divided randomly into two experiment groups and took a reading-integrated writing test and a timed impromptu essay test respectively. Their essays were rated anonymously by two independent raters using the same rating scale with categories that measured content, organization, accuracy and vocabulary. Besides examination of the reliability, Many-Faceted Rasch analysis was applied to probe into the influence of domain difficulty and rater severity. Results of the comparison revealed four important findings. First, both two tests were reliable to be fair measures for assessing writing abilities. Second, significant difference was found between two groups in content, organization, and vocabulary, but no difference was observed in accuracy. Third, the reading tasks facilitated participants in generating ideas, organizing essays and using vocabularies. Finally, compared with the participants in the reading-integrated writing test, participants in the timed impromptu essay test met difficulties in using their vocabularies in the writing process. Findings implicated that 1) a reading-integrated writing test could be an alternative to a timed impromptu essay test in academic contexts, and 2) much more investigation was still needed to probe into the writing process and read-to-write process.