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Leaky Scanning02:28

Leaky Scanning

5.2K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.2K
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.6K
2.6K
Source Transformation01:15

Source Transformation

6.6K
Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
It is essential to note that when...
6.6K
Translation01:31

Translation

15.0K
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Proteins are...
15.0K
Initiation of Translation02:33

Initiation of Translation

34.1K
Initiating translation is complex because it involves multiple molecules. Initiator tRNA, ribosomal subunits, and eukaryotic initiation factors (eIFs) are all required to assemble on the initiation codon of mRNA. This process consists of several steps that are mediated by different eIFs.
First, the initiator tRNA must be selected from the pool of elongator tRNAs by eukaryotic initiation factor 2 (eIF2). The initiator tRNA (Met-tRNAi) has conserved sequence elements including modified bases at...
34.1K
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

1.7K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
1.7K

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相关实验视频

Updated: Jul 20, 2025

Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems
06:18

Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems

Published on: April 26, 2019

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使用翻译模型检测恶意源代码.

Chen Tsfaty1, Michael Fire1

  • 1Department of Software and Information Systems Engineering, Ben-Gurion University, Beer-Sheva 8410501, Israel.

Patterns (New York, N.Y.)
|July 31, 2023
PubMed
概括
此摘要是机器生成的。

一个新的深度学习算法,恶意源代码检测使用翻译模型 (MSDT),有效地识别开源软件中的恶意代码注入. 这种方法通过检测共享代码库中的隐藏威胁来提高软件供应链的安全性.

关键词:
这就是PyPiPiPyPi.深度学习是一种深度学习.恶意软件分析 恶意软件分析这是开源的,开源的.软件供应链攻击 软件供应链攻击静态分析 静态分析

更多相关视频

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
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De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

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Analysis of Translation in the Developing Mouse Brain using Polysome Profiling
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Analysis of Translation in the Developing Mouse Brain using Polysome Profiling

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相关实验视频

Last Updated: Jul 20, 2025

Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems
06:18

Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems

Published on: April 26, 2019

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De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
08:23

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Published on: February 18, 2022

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Analysis of Translation in the Developing Mouse Brain using Polysome Profiling
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Analysis of Translation in the Developing Mouse Brain using Polysome Profiling

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科学领域:

  • 计算机科学 计算机科学
  • 软件工程 软件工程 软件工程
  • 网络安全 网络安全

背景情况:

  • 开源软件开发促进了代码的重复使用,但引入了供应链漏洞.
  • 越来越复杂的"供应链攻击"利用开源实践危害了许多产品.
  • 在共享软件包中检测恶意代码注入是一个关键的网络安全挑战.

研究的目的:

  • 引入一种基于深度学习的新算法,用于检测源代码包中的恶意代码注入.
  • 开发一种有效的方法来识别受损害的开源软件组件.
  • 提高软件供应链对恶意代码的安全性.

主要方法:

  • 使用翻译模型 (MSDT) 算法开发了恶意源代码检测.
  • 利用深度学习进行源代码分析和异常检测.
  • 采用了超过60万个函数的数据集,嵌入它们并应用聚类来识别异常 (恶意) 函数.

主要成果:

  • MSDT在检测真实世界的代码注入方面表现出了很高的有效性.
  • 该算法在实验评估中实现了高达0.909的精度@k值.
  • 通过嵌入载体集群的异常检测成功识别了恶意函数.

结论:

  • MSDT是一个强大的工具,用于识别开源软件中的恶意代码.
  • 深度学习方法为保护软件供应链提供了一个有前途的解决方案.
  • 进一步开发和应用MSDT可以显著提高对供应链攻击的软件安全性.