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[WCG]Help Conquer Cancer(帮助征服癌症)项目资料

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发表于 2007-12-16 15:21:50 | 显示全部楼层 |阅读模式
注:WCG官网已有中文版本




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非专业人士业余瞎翻译,如有谬误,欢迎指正

项目FAQ(已翻译)
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Help Conquer Cancer  
帮助征服癌症   

Project Status and Findings:
项目状况与结果:
  
Information about this project is provided on the web pages below and by the project scientists on the Help Conquer Cancer website. To comment or ask questions about this project, please submit a post in the Help Conquer Cancer forum.
有关本项目的信息在网页下方;在“帮助征服癌症”网页也有项目科学家提供的信息。如需对项目发表评论或提出问题,请至“帮助征服癌症”论坛发帖。

Mission
任务

The mission of Help Conquer Cancer is to improve the results of protein X-ray crystallography, which helps researchers not only annotate unknown parts of the human proteome, but importantly improves their understanding of cancer initiation, progression and treatment.
“帮助征服癌症”项目的任务是改善蛋白质X射线结晶学的效果。这不仅能帮助研究人员注解人类蛋白的未知部分,更重要的是帮助他们提升对癌症的产生、发展及治疗的了解。

Significance
意义

In order to significantly impact the understanding of cancer and its treatment, novel therapeutic approaches capable of targeting metastatic disease (or cancers spreading to other parts of the body) must not only be discovered, but also diagnostic markers (or indicators of the disease), which can detect early stage disease, must be identified.
为了更有效的影响对癌症及其治疗的理解,不仅需要找出提议以新陈代谢疾病(或在人体内蔓延的癌症)为目标的新型疗法,而且必须能够查出查出早期疾病的症状标记(或疾病指示)。

Researchers have been able to make important discoveries when studying multiple human cancers, even when they have limited or no information at all about the involved proteins. However, to better understand and treat cancer, it is important for scientists to discover novel proteins involved in cancer, and their structure and function.
研究人员已在对多种人类癌症的研究中得到了很多重大发现,甚至在他们仅有部分或根本没有相关蛋白质的信息的情形之下。然而为了更好的了解和治疗癌症,科学家们发现与癌症相关的新型蛋白质及其结构和功能就显得十分重要。

Scientists are especially interested in proteins that may have a functional relationship with cancer. These are proteins that are either over-expressed or repressed in cancers, or proteins that have been modified or mutated in ways that result in structural changes to them.
科学家对可能与癌症有功能关系的蛋白质特别有兴趣。这包括在癌症中被过表达(?)或被抑制的蛋白质,以及那些被修改或被转变并从而导致结构性变化的蛋白质。

Improving X-ray crystallography will enable researchers to determine the structure of many cancer-related proteins faster. This will lead to improving our understanding of the function of these proteins and enable potential pharmaceutical interventions to treat this deadly disease.
改进的X射线结晶学将使研究人员更快地测定多种与癌症相关的蛋白质的结构。这将提高我们对这些蛋白质功能的了解,并有助于发现可能治愈这些致死疾病的药物。

==================================================================

About the Project  
关于“帮助征服癌症”项目   

hcc1_inset_xtal.jpg


X-ray Crystallography
X射线结晶学


One of the favored methods for protein-structure determination is X-ray crystallography. Through this method, scientists use the high-throughput crystallization pipeline to help annotate unknown parts of the human proteome, which in turn will help to improve their understanding of cancer initiation, progression and treatment.*
X射线结晶学是一种极受欢迎的蛋白质机构测定方法。通过这种方法,科学家使用高吞吐量(?)结晶化管道来帮助注解(?)人类蛋白的未知部分。反过来这也会有助于提高科学家对癌症产生、发展及治疗的了解。(*参1#末尾处)

There are two main steps involved in X-ray crystallography:
X射线结晶学有两个主要步骤:

   1. Crystallizing the protein: Although a lot more complex, this is similar to putting sugar into a cup of water and letting it sit for a while. Once the water evaporates, tiny sugar crystals appear.
   1. 使蛋白质结晶:这就好比把糖置入一杯水中并保持一段时间一样,当水蒸发之后,微小的糖晶体就会显现出来。当然,蛋白质结晶的过程要比这复杂的多。

   2. Sending X-rays through the crystal: Depending on how they diffract, a mathematical model is used to determine and observe the protein's structure.
   2. 令X射线穿过晶体:我们使用一个数学模型根据通过它们时产生的衍射来测量和观察蛋白质结构。

Crystallizing the protein is not a straightforward procedure. There are many thousands of possible conditions that affect the process (concentration of a protein and solution, temperature, pH, chemical additives, etc.), but scientists must find the appropriate combination of these conditions for a protein to crystallize. For example, with sugar, if you change the water to another liquid, change the temperature or concentrations, you may not get a crystal. Similarly, for a given protein, the challenge is to know what conditions will lead to forming a crystal — what solution, what temperature, pH, etc.
将蛋白质结晶化并非是一个简单的过程。有数千种情形(蛋白质溶液的浓度、温度、酸碱度、化学附加物,等等)都可能会影响这一过程。但科学家必须找到能够适合蛋白质结晶的上述各个条件。拿糖来说,如果您把水换成其他液体,改变温度或浓度,就有可能无法得到结晶。同样的,对于给定的蛋白质来说,挑战就在于如何知道什么样的条件才能形成晶体——何种溶液、何种温度、酸碱度,等等。

hcc_inset_circle2.jpg


The resultant protein crystal also must be well-formed and large enough in order for x-rays to detect the protein's structure at high resolution. If the conditions are not perfect for crystallizing the protein, the process can result in either a micro-crystal, which is too small for the protein's structure to be determined; a precipitate, which shows some changes, but does not lead to crystallization event directly; or no change may have occurred at all.
为了用X射线侦测高解析度的蛋白质结构,我们需要合成出成型良好并且足够大的的蛋白质晶体。如果结晶化蛋白质的条件无法很好的满足,这一过程就可能得到一个由于过小而导致无法测定蛋白质结构的微晶体;或是能够显示某些变化,但却无法直接完成结晶化过程;或者根本不起任何变化。

Frustrating the situation is that, as yet another barrier to progress, usually the more important the protein is to cancer research, the harder that protein is to crystallize. Many proteins involved in cancer are long chains, or they require additional proteins to properly fold and cannot be crystallized by themselves.
令人沮丧的是,至今为止另一个阻碍我们前进的是,通常对于癌症研究越重要的蛋白质就越难以被结晶化。许多与癌症相关的蛋白质都是长链,或者它们需要额外的蛋白质才能正确的折叠,并且无法单独结晶化它们。

hcc1_inset_xtal2.jpg


In order to run the millions of combinations necessary to successfully crystallize a protein, scientists have used robots to perform the work. Robots are able to put in place the various crystallization conditions faster and more accurately. To further facilitate the process, result of each of the millions of crystallization experiments are photographed.
为了进行成功结晶化蛋白质所必需的数百万次合成试验,科学家使用了机器人来完成这项工作。机器人能够更快更准确的完成不同结晶化条件的试验工作。为了更加促进这一进程,这全部的数百万次结晶化试验都被用照片记录了下来。

Currently, scientists at the Hauptman-Woodward Medical Research Institute (HWI) in Buffalo have run more than 86 million crystallography experiments for more than 9,400 proteins. As a result, they have 86 million pictures of these proteins that have gone through the X-ray crystallography high-throughput screening pipeline. Each of these pictures needs to be analyzed to determine what the result of the experiment is — i.e., crystal, precipitate, phase separation, skin effect, no change.
现在,位于布法罗的豪普特曼-伍德沃德医学研究院(HWI)的科学家们已经对9400多种蛋白质进行了超过8600万次的结晶学试验。作为结果,他们通过X射线结晶学高吞吐量(?)拍摄管道得到了这些蛋白质的8600万张照片。每张照片都需要被分析测定试验的结果如何——即,结晶,沉淀,相位分离,表面效应,无改变。(?)

hcc1_inset_outcomes.jpg


One of the challenges is the tremendous size of these datasets, which requires over 25 TB of storage (or equivalent to more than 9,000 DVDs). IBM's Blue Gene supercomputer has provided assistance in this phase of the work, by running a special image compression algorithm to reduce the size of these images without losing content. The other challenge is to comprehensively analyze an image to determine the crystallization outcome, a task that requires approximately 10 hours to process on a single computer. Researchers would thus require almost 100,000 years to analyze the existing pictures.
有一个挑战就是这些数据实在太多了。它们总计超过了25TB(相当于9000多张DVD)。IBM的蓝色基因超级计算机为我们这一阶段的工作提供了帮助。它通过一个专门的图像压缩计算法则来对这些图像进行无损压缩(译注:BiscuiT似乎比较喜欢无损)。还有个挑战是,需要在一台通用计算机上运算大约10个小时才能够全面分析一张图像并测定结晶化的结果。这样一来,研究人员将需要几乎100,000年来分析现有的这些照片。


World Community Grid and "Help Conquer Cancer"
WCG与“帮助征服癌症”


hcc_inset_reader1.jpg


Using the power of World Community Grid, scientists at the Ontario Cancer Institute (OCI), Princess Margaret Hospital, and the University Health Network will process the existing 86 million images of proteins that have been screened in the high-throughput crystallization pipeline at HWI. World Community Grid will run a CrystalVision program that the researchers at OCI have developed to analyze the features of individual images to determine the outcome of the crystallization screen — crystal, micro crystal, phase separation, skin, precipitate, or no change.
通过借助WCG的力量,安大略癌症研究院(OCI)、玛格丽特公主医院以及健康网络大学的科学家得以对HWI的高吞吐量结晶化管道所拍摄的8600万张蛋白质图像进行处理。WCG通过OCI的研究人员开发的CrystalVision程序来分析每幅图片的特征,测定结晶化拍摄的结果——晶体,微晶体,相位分离,表面效应,沉淀,或是无改变。(?)

If a crystal occurs, crystallographers can put the protein through the optimization process to determine the optimal conditions for the crystallization, and in turn perform a diffraction experiment to determine the structure of the protein. What's more, scientists can compare proteins that have successfully crystallized against proteins of unknown structure that have similar characteristics, based on the results from the crystallization screen. This can be the starting point for crystallization for these proteins so that their structure can be determined.
如果发生了结晶,检测仪就会让该蛋白质进行这个最优化的过程,以此来检测这一结晶化的最优化条件,并进行衍射试验来测定蛋白质的结构。此外,科学家还将在基于结晶化拍摄结果的基础上,把成功结晶的蛋白质与具有相似特性但不知结构的蛋白质进行对比。这会是对这些蛋白质结晶化的出发点,并因此能够测定它们的结构。

hcc_inset_reader2.jpg


If the crystal produced was not well-formed or large enough, scientists can still use the information to help them better determine the conditions necessary to create a well-formed crystal. For example, they may learn that Protein X and Condition A resulted in a micro crystal, and Protein A and Condition Z resulted in a micro crystal as well. Based on this information, they can then run additional experiments to deduce what conditions need to be optimized to create a larger and more well-formed crystal.
如果没能得到成型良好或足够大的晶体,科学家仍可利用这些信息来帮助他们更好的测定得到成型良好的晶体的必需条件。举例来说,他们发现蛋白质X和条件A导致了微晶体,蛋白质A和条件Z也导致了微晶体。基于这些信息,他们能够进行附加试验来推论需要优化何种条件以便于制出更大、成型更好的晶体。

Analyzing the results from this experiment will also lead to better understanding the underlying principles of protein crystallography. For the first time, a comprehensive crystallography image analysis will be done, which was impossible before due to computational complexity. In turn, CrystalVision will be improved to provide faster and more accurate image classification.
分析这一试验的结果还能够更好的了解蛋白质结晶学的根本原理。一个全面的结晶学图像分析将首次被完成,而这在以前由于计算的复杂性是不可能完成的。反过来,CrystalVision将会被改进,以便于提供更快更准确的图像分类。

Improving the protein crystallography pipeline will enable researchers to determine the structure of many cancer-related proteins faster. This will lead to improving our understanding of the function of these proteins, and enable potential pharmaceutical interventions to treat this deadly disease.
改进的蛋白质结晶学管道将使研究人员更快地测定多种与癌症相关的蛋白质的结构。这将提高我们对这些蛋白质功能的了解,并有助于发现可能治愈这些致死疾病的药物。

* There are other approaches to understanding the structure and function of proteins, including the method used in the Human Proteome Folding Project also running on World Community Grid. Given the essential nature of this work, it's important to advance every research technique to complete our understanding of the human organism and disease.
* 其他一些方法也可以了解蛋白质的结构和功能,包括同样运行于WCG的人类蛋白质折叠项目所采用的方法。基于这项工作的本质,最重要的就是提升每项研究技术来完成我们对人类组织和疾病的认识。

[ 本帖最后由 Julian_Yuen 于 2009-1-30 19:16 编辑 ]

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 楼主| 发表于 2007-12-16 15:29:42 | 显示全部楼层
Research Participants
参与研究人员


Researchers 研究人员
Igor Jurisica, Principal Investigator, Ontario Cancer Institute
主要研究者,安大略癌症研究院

Computational Scientists 计算科学家
Christian A. Cumbaa, Research Associate, Ontario Cancer Institute
助理研究员,安大略癌症研究院
                                          
Collaborators 合作人员
Dr. George DeTitta, Chief Executive Officer, Hauptman-Woodward Medical Research Institute and Chairman, Department of Structural Biology
豪普特曼-伍德沃德医学研究院CEO兼结构生物学系系主任
Joseph R. Luft, Principal Investigator, Hauptman-Woodward Medical Research Institute
主要研究者,豪普特曼-伍德沃德医学研究院
Michael Malkowski, Principal Investigator, Hauptman-Woodward Medical Research Institute
主要研究者,豪普特曼-伍德沃德医学研究院

[ 本帖最后由 Julian_Yuen 于 2007-12-16 18:00 编辑 ]
 楼主| 发表于 2007-12-16 15:43:06 | 显示全部楼层
Help Conquer Cancer  项目FAQ链接


# What are the potential benefits of the "Help Conquer Cancer" project?
"Help Conquer Cancer"项目可能得到哪些有益的成果?

# What computers can run the "Help Conquer Cancer" Project?
什么样的计算机可以运行"Help Conquer Cancer"项目?

# What will World Community Grid's calculations produce?
WCG的计算程序将算出什么来?

# What will happen with the data generated by all these calculations?
所有这些计算所得到的数据将如何被处理?

# When will this project be completed?
本项目何时结束?

# When I look at the image, what is my computer working on?
当我看到图片时,我的计算机正在做什么?

# What is the moon-crater object in the middle of the background?
图片背景正中那个如环形山一般的东西是什么?

# What are the round disks? Each disk has a different color. What does that mean?
那些圆盘状的又是什么呢?每个圆盘的颜色都不同。这又代表什么意思?

# I noticed that the right most disk is occasionally replaced by a new disk and all the other disks move to the left and the last one falls off. What is going on?
我注意到有时最右边的圆盘的位置会被一个新的圆盘所取代,期间所有其他的圆盘都向左移动,而且原先最左边的圆盘从队列中被剔除。究竟发生了什么?

[ 本帖最后由 Julian_Yuen 于 2008-6-20 15:07 编辑 ]
 楼主| 发表于 2007-12-16 15:43:53 | 显示全部楼层
What are the potential benefits of the "Help Conquer Cancer" project?
"Help Conquer Cancer"项目可能得到哪些有益的成果?


There are several direct and indirect benefits of the project. For the first time, scientists will execute a comprehensive image analysis and classification of crystallography images. This will lead to better understanding of the crystallization process, and will enable scientists to improve the accuracy and speed of CrystalVision. Improved understanding of the crystallization process and improved CrystalVision also will enable more disease proteins to be crystallized faster. Finally, more 3D structures will improve our understanding of disease and potentially its treatment, and will lead to improved in silico (performed on a computer or via computer simulation) structure prediction.

本项目有数种直接或间接的好处。首先科学家将完成全面的图像分析并对结晶学图像进行分级。这将更好地理解结晶化过程,并使科学家能够改善CrystalVision的准确度和速度。对结晶过程的进一步理解以及改良过的CrystalVision也将使更多的疾病蛋白质以更快的速度来结晶化。最后,更多的3D结构将增进我们对疾病及其可能的治疗方式的了解,并使silico(在计算机上执行或模拟)结构预测得到改善和提升。

[ 本帖最后由 Julian_Yuen 于 2007-12-16 22:34 编辑 ]
 楼主| 发表于 2007-12-16 15:45:51 | 显示全部楼层
What computers can run the "Help Conquer Cancer" Project?
什么样的计算机可以运行"Help Conquer Cancer"项目?


Due to the inherent granularity of our image analysis problem, there are very modest memory and CPU requirements for the compute nodes. However, without access to thousands of CPUs, researchers would not be able to process 80 million images in a reasonable amount of time. Multiple platforms will be able to run the project; World Community Grid is launching Linux and Windows compiled code first, with Macintosh OS to follow.
受我们所研究的图像分析问题本身固有的颗粒度所决定,参与项目的计算机节点只需适当的内存和CPU即可。然而,如果没有数千CPU的帮助,研究人员将无法在合理的时间内来处理8000万张图像。本项目可在多种平台上运行;WCG已先期完成了Linux和Windows版的程序,此外Mac版的也将被发布。

[ 本帖最后由 Julian_Yuen 于 2007-12-16 19:15 编辑 ]
 楼主| 发表于 2007-12-16 15:46:58 | 显示全部楼层
What will World Community Grid's calculations produce?
WCG的计算程序将算出什么来?


On the lowest level, CrystalVision will compute thousands of image features for each crystallography image. This data objectively measures characteristics of the image, which will enable scientists to use a system to discern image classification. In turn, this will allow them to automatically and objectively characterize results from the high-throughput crystallization screens, and then apply data mining techniques to optimize future crystallization experiments.
最少的情形下,CrystalVision将为每一幅结晶学图像计算成千上万的图像特征。这一数据客观衡量了图像的特征。这一特征使科学家能够通过一个系统来进行图像分级。反过来他们能够从高吞吐量(?)的结晶化拍摄中自动客观地描述结果的特征,并运用数据挖掘技术来优化未来的结晶化试验。

[ 本帖最后由 Julian_Yuen 于 2008-4-26 00:21 编辑 ]
 楼主| 发表于 2007-12-16 15:47:42 | 显示全部楼层
What will happen with the data generated by all these calculations?
所有这些计算所得到的数据将如何被处理?


After careful analysis, evaluation and interpretation, all results will be published in the public domain. The scientists' first goal is to improve the CrystalVision system to enable automated, accurate and fast crystallography image classification. This algorithm will then be deployed at Hauptman-Woodward Medical Research Institute to ensure that this public high-throughput crystallography screening facility will speed up crystallization of many disease-related proteins.
在经过仔细的分析、评估、解释后,所有的结果将被公开。科学家的首个目标是把CrystalVision系统改进成自动化的、准确的、快速的结晶学图像分级系统。这一运算法则将被运用到豪普特曼-伍德沃德医学研究院,以期这一公共的高吞吐量(?)结晶化拍摄设备能够加速众多与疾病有关的蛋白质的结晶化过程。

[ 本帖最后由 Julian_Yuen 于 2008-4-26 00:21 编辑 ]
 楼主| 发表于 2007-12-16 15:48:41 | 显示全部楼层
When will this project be completed?
本项目何时结束?


Once the project starts, we will have a better idea about the time required to process the images on World Community Grid. This will be determined by the number of suitable computers and the number of projects being concurrently executed on World Community Grid. However, researchers have several interesting subsets of images, which will be analyzed first, thus enabling preliminary results to be available after a few weeks. These images comprise a set previously analyzed by an earlier version of CrystalVision, as well as by multiple human experts.
项目一旦开始运行,我们就能更好的估计在WCG上处理这些图像所需要的时间。这是由适当的计算机数量和WCG平台上同时运行的项目的数量所决定的。然而,研究人员有一些较引人注意的图像子集。这些子集将最先进行分析。因此几星期后就能得到一些初步的结果。这些图像组成了一个先前曾被早期版本的CrystalVision和多个专家分析过的图像集。

[ 本帖最后由 Julian_Yuen 于 2007-12-16 22:34 编辑 ]
 楼主| 发表于 2007-12-16 15:50:49 | 显示全部楼层
When I look at the image, what is my computer working on?
当我看到图片时,我的计算机正在做什么?


Each work unit is a photograph of a protein crystallization experiment (one out of 1,536 images per protein, photographed six times over a period of one month), a visual record of the state of a protein sample dissolved in a solution of crystallizing agents. This photograph is shown in the background of the agent window. The Grid agent performs a computer vision analysis of the image in order to interpret its contents, first determining important image features, which are then used to classify (or label) the result of the experiment. During the feature image computation, intermediate steps of this analysis are displayed in the colored circles appearing in the foreground of the agent window.
每个WU都是一个蛋白质结晶化试验的一张照片(每个蛋白质有1536张照片,一个月内照六次(?))。这是对蛋白质样本溶于结晶剂溶液的状态的直观记录。该照片显示于客户端窗口的背景。分布式网格的客户端把图像的计算分析情形显示出来,以便于解释其内容;首先测定重要的图像特征,这些特征将被用于之后对试验结果的分类(或标注)。在图像特征计算过程中,分析过程的中间步骤将以客户端窗口中前置的彩色圆来显示出来。

The analysis is a search for four large categories of features in the image: microcrystals, straight lines, discrete objects, and textural features. Intermediate steps of the texture analysis are displayed in the colored circles that appear in the foreground of the agent window. As each step is completed, the computed result appears in the agent window. Each circle is a copy of a region of the original image, transformed to highlight a different texture.
分析是对图像的四大类特征进行探究的过程:微晶体、直线、离散目标以及纹理特征。纹理分析的中间步骤将以客户端窗口中前置的彩色圆来显示出来。当所有步骤完成后,计算结果将显示在客户端窗口。每个圆都是对原始图像区域的复制,并把不同的纹理以高亮标出。

[ 本帖最后由 Julian_Yuen 于 2007-12-17 23:50 编辑 ]
 楼主| 发表于 2007-12-16 15:52:11 | 显示全部楼层
What is the moon-crater object in the middle of the background?
图片背景正中那个如环形山一般的东西是什么?


The background image is a photomicrograph of a protein crystallization experiment. The experiment takes place in a droplet of water the size of a pinhead (200 nl), suspended in an oil-filled chamber. The circular wall of the chamber, and the roughly circular droplet contained within are visible in the photo. Inside the droplet, precipitated protein or salt, or even protein crystal may be visible.
背景图像是一个蛋白质结晶化试验的显微照片。该试验是在悬浮于一个充满了油的腔室中的针头那么大(200 nl)的一小滴水中进行的。腔室的壁越圆,其中的大致越圆的小水滴更易在照片中看到。这样在小水滴中析出的蛋白质或盐,甚至蛋白质结晶都可以被看到。

[ 本帖最后由 Julian_Yuen 于 2007-12-16 22:35 编辑 ]
 楼主| 发表于 2007-12-16 15:54:00 | 显示全部楼层
What are the round disks? Each disk has a different color. What does that mean?
那些圆盘状的又是什么呢?每个圆盘的颜色都不同。这又代表什么意思?


Each disk is a visualization of a different texture measure applied to the background image. Thus, when two disks are differently colored, it means only that different textures are more or less prominent in different regions of the image. Twenty-six measures of texture are visualized in the Grid agent.
每个圆盘是将不同的纹理测量应用到背景图像后得出的视觉显示。因而,当两个圆盘的颜色不同时,就表明图像不同地方的纹理的不同被或多或少的凸显出来。客户端中可显示出26种纹理测量。

Each measure is related to frequencies of the grey-scale values of pairs of pixels found in the image, and summarizes these frequencies according to pixel-pixel contrast, correlation, variance, or entropy. Each of 13 categories of statistics is measured multiple times by changing the distance and relative orientation of the pixel-pairs.
每次测量与图像中的像素对的灰阶值的出现次数相关,并把这些出现次数根据像素-像素的比对,相关性,差异或一致性来进行总结。13类统计表的每一类都在改变像素对的距离及相对方位的情形下进行多次测量。

Each disk visualizes the results of a search for a particular texture in the original image. The texture search is done in three steps. The first step records fine-grained changes in the grey-tones of the image, the second step records medium-grained changes, and the third step records coarse-grained changes. The three steps are visualized together by using red (step 1), green (step 2), and blue (step 3) colour channels to create a full-colour image representing the whole process. A blue region of the disk would then indicate a region of the original image where the texture is most apparent in coarse-grained grey-tone changes.
每个圆盘显示了对原始图像中的特定纹理的搜寻的结果。第一步先记录图像在灰色调下的细密纹理的转变,第二步记录中等纹理的转变,第三步则记录粗糙纹理的转变。通过使用红(步骤1)、绿(步骤2)、蓝(步骤3)三个彩色通道来创建一个描绘整个过程的全彩图像,我们可以把这三个步骤一起显示出来。圆盘上的蓝色区域将可指出原始图像的哪个区域在粗糙纹理的灰色调转变中最为明显。


hcc_faq_1.jpg


[ 本帖最后由 Julian_Yuen 于 2007-12-17 23:52 编辑 ]
 楼主| 发表于 2007-12-16 15:55:53 | 显示全部楼层
I noticed that the right most disk is occasionally replaced by a new disk and all the other disks move to the left and the last one falls off. What is going on?
我注意到有时最右边的圆盘的位置会被一个新的圆盘所取代,期间所有其他的圆盘都向左移动,而且原先最左边的圆盘从队列中被剔除。究竟发生了什么?


The Grid agent will only display the results of the last 10 image analysis steps. As the next step is completed, its result is displayed, and the oldest is removed.

网格客户端仅显示最近的10个图像分析步骤的结果。当下一步完成时,这最新的一步的结果将被显示,而最早的那一个会被移除。

[ 本帖最后由 Julian_Yuen 于 2007-12-16 19:55 编辑 ]
 楼主| 发表于 2007-12-16 15:57:49 | 显示全部楼层

[转]一些资料

http://science.solidot.org/article.pl?sid=08/06/24/0010253

第一张单个病毒的X射线衍射图像                                                

matrix  发表于 2008年6月24日 08时12分 星期二

自1940年代和50年代起,生物学家就开始利用X射线结晶学,去揭开复杂生物分子如血红蛋白、DNA、胰岛素的3D结构。但这项技术有一个局限,它只对结晶状的分子有效,而结晶分子只占组成生命的蛋白质种类的极小部分。科学家花了很多年时间去寻找一种能拍摄单分子3D图像的方法,现在搜寻可能结束了,加州大学的John Miao宣布,他和他的研究小组利用X射线衍射显微镜法拍摄到单个非结晶病毒的图像。他所使用的技巧是首先拍摄病毒的衍射图,然后去除周围环绕物的衍射图。尽管图像的分辨率很低,但比以前方法提高了3个数量级。如果这项技术被证实可行,将是一次重大的突破,将为科学家理清众多生物学家至今仍不很了解的蛋白质3D结构铺平了道路。预印本

[ 本帖最后由 Julian_Yuen 于 2008-6-24 12:59 编辑 ]
发表于 2007-12-17 11:24:36 | 显示全部楼层
你还是挺牛的,想到把它翻译出来,我只是看看就走了,呵呵
毕竟要翻译好还是有难度的,但自己看懂就没那么难了
 楼主| 发表于 2007-12-17 20:33:32 | 显示全部楼层

回复 #14 gongmao1_2000 的帖子

随便翻翻,我又不是学这个的,纯当闲余时间发光发热了.....
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