如何强调科研工作的意义
作者：互联网
Our study (pp. E275–E284) highlights the importance of directly assaying transcription to understand gene regulation.
我们的研究(pp. E275-E284)强调了直接检测转录对理解基因调控的重要性。
We believe that quantum Fourier analysis, now in its infancy, will have significant future impact.
我们相信，量子傅立叶分析，现在还处于婴儿期，将有重大的未来影响。
这些结果揭示了控制运动联想记忆的重要机制。
[Nice]
Our work helps address the widespread challenge of quantifying agent-based patterns and opens up possibilities for large-scale analysis of biological data and mathematical models.
我们的工作有助于解决量化基于主体模式的广泛挑战，并为大规模分析生物数据和数学模型开辟了可能性。
A major problem in data science is representation of data so that the variables driving key functions can be uncovered and explored. Correlation analysis is widely used to simplify networks of feature variables by reducing redundancies, but makes limited use of the network topology, relying on comparison of direct neighbor variables. The proposed method incorporates relational or functional profiles of neighboring variables along multiple common neighbors, which are fitted with Gaussian mixture models and compared using a data metric based on a version of optimal mass transport tailored to Gaussian mixtures. Hierarchical interactive visualization of the result leads to effective unbiased hypothesis generation. In a cancer gene expression study, this method uncovered an unanticipated immunosuppressive mechanism resembling maternal–fetal immune tolerance.
数据科学的一个主要问题是数据的表示，以便发现和探索驱动关键功能的变量。相关性分析被广泛用于简化特征变量网络，减少冗余，但依赖于直接近邻变量的比较，对网络拓扑结构的利用有限。该方法结合了多个公共邻域相邻变量的关系或功能剖面，用高斯混合模型进行拟合，并使用基于为高斯混合量身定制的最优质量传输版本的数据度量进行比较。结果的分层交互可视化导致有效的无偏假设生成。在一项癌症基因表达研究中，该方法揭示了一种类似于母胎免疫耐受的未预料到的免疫抑制机制。
Making accurate forecast or prediction is a challenging task in the big data era, in particular for those datasets involving high-dimensional variables but short-term time series points, and these datasets are omnipresent in many fields. In this work, a model-free framework, named as “randomly distributed embedding” (RDE), is proposed to accurately predict future dynamics based on such short-term but high-dimensional data. The RDE framework creates the distribution information from the interactions among high-dimensional variables to compensate for the lack of time points in real applications. Instead of roughly predicting a single trial of future values, this framework achieves the accurate prediction by using the distribution information. (See pp. E9994–E10002.)
在大数据时代，做出准确的预测或预测是一项具有挑战性的任务，特别是对于那些涉及高维变量但短期时间序列点的数据集，而这些数据集在许多领域中无处不在。本文提出了一种基于短期高维数据准确预测未来动态的无模型框架“随机分布嵌入”(RDE)。RDE框架通过高维变量之间的交互创建分布信息，以弥补实际应用中时间点的不足。该框架利用分布信息实现了准确的预测，而不是对未来值的一次粗略预测。(见E9994-E10002页。)
We suggest that seismogenic and hydraulically active faults are geologically rare and that the injection of fluid directly into them is even rarer, potentially explaining the small percentage of HF wells that cause induced earthquakes. (See pp. E10003–E10012.)
我们认为，发震断层和水力活动断层在地质学上是罕见的，而直接注入到这些断层中的流体更是罕见，这可能解释了诱发地震的HF井所占比例很小的原因。
Our findings therefore inform understanding of the possible communicative role of facial expressions of pain and orgasm, and how culture could shape their representation.
因此，我们的发现有助于理解痛苦和高潮的面部表情可能起到的交流作用，以及文化如何塑造它们的表现。
Our results illustrate the sequential steps leading to the activation of NAAA at lipid membranes, and reveal how current inhibitors block this enzyme. (See pp. E10032–E10040.)
我们的研究结果阐明了脂质膜上NAAA活化的顺序步骤，并揭示了目前的抑制剂是如何阻断这种酶的。(见E10032-E10040页。)
标签：pp,变量,意义,科研工作,variables,强调,RDE,Our,data 来源： https://blog.csdn.net/Hodors/article/details/111387877