THE TIME FOR TOOLS

Mischa Wanek-Libman

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THE TIME FOR TOOLS

机译：工具的时间

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摘要

New tools, as well as enhancements to existing tools, are helping increase production while providing new safety features. A handful of suppliers have jumped into the battery-powered market with others performing research into how best to incorporate the power option into their product lines.

机译：新工具以及对现有工具的增强，在提供新的安全功能的同时，有助于提高产量。少数供应商已进入电池供电市场，其他供应商则进行了有关如何最好地将电源选件纳入其产品线的研究。

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《Railway Track and Structures》 |2017年第12期|26-30|共5页
作者
Mischa Wanek-Libman;
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收录信息美国《工程索引》(EI);
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正文语种 eng
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4. Time-tunnel: visual analysis tool for time-series numerical data and its aspects as multimedia presentation tool [C] . Akaishi M., Okada Y. Information Visualisation, 2004. IV 2004. Proceedings. Eighth International Conference on . 2004

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6. Time Capture Tool (TimeCaT): Development of a Comprehensive Application to Support Data Capture for Time Motion Studies. [O] . Marcelo Lopetegui, Po-Yin Yen, Albert M. Lai, 2012

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7. Figure 4: (A) One conserved sequence, which occurs 79 times in 46,264 binding site peaks from the ChIP-seq data-set. The mutation profile of this conserved sequence is illustrated, where ’_ ’ indicates this base is unchanged; DEL indicates this base is lost; INS X indicates a new base X is inserted in front of this base. (B) Several repeated elements patterns are listed. (C) In the first column, the top five DNA motifs, mined by meme-chip tools (Machanick Bailey, 2011) are illustrated. The resemblant conserved sequences, found by the CFSP algorithm are listed in the second column. In the third column, the position-specific scoring matrices, which are transformed from mutational information are listed. The similarity between meme motif and resemblant conserved sequence with PSSM format was calculated via a stamp motif comparison tool (Mahony Benos, 2007). The E-values for the similarity of those pairs is displayed in the fourth column. (D) One motif is selected in each group clustered by gkmsvm descriptors, and the corresponding motif found by the CFSP algorithm is listed below. (E) There are additional datasets (File No: ENCFF100GRL, ENCFF616IRT, ENCFF870CER, Target: SREBF1) collected from https://www.encodeproject.org. The top two motifs are selected in each file using meme tools, and the corresponding motifs found by our algorithm are listed below. [O] . -1

机译：图4：（a）一种保守序列，其发生在芯片-SEQ数据集中的46,264个结合位点峰值中的79倍。说明了这种保守序列的突变分布，其中'_'表示该碱度不变; del表示此基础丢失; INS X表示新的基础X插入此基础前面。（b）列出了几种重复的元素模式。（c）在第一栏中，示出了由MEME芯片工具（Machanick＆Bailey，2011）开采的前五个DNA主题。由CFSP算法发现的相应保守序列列于第二列中。在第三列中，列出了从突变信息转换的特定位置的评分矩阵。 MEME主题与PSSM格式的相似性与PSSM格式之间的相似性通过邮票图章比较工具（Mahony＆Benos，2007）计算。这些对相似性的电子值显示在第四列中。（d）在由GKMSVM描述符聚集的每个组中选择了一个图案，下面列出了CFSP算法的相应主题。（e）从https://www.encodeproject.org收集的，有附加数据集（文件no：cernff100grl，cenf616irl，conf8.20cer，target：srebf1）。使用MEME工具在每个文件中选择前两个图案，并且我们的算法发现的相应主题如下所示。

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