[BOINC] [生命科学类及其他] World Community Grid

vmzy

Decade of discovery: doubling carbon-based solar cell efficiency  
22 十月 2014         

摘要
To mark our 10th anniversary, we’re looking at key scientific discoveries of the last decade. This week, we’re driving the search for organic solar cells through The Clean Energy Project in partnership with Harvard University. So far, we’ve helped researchers identify over 35,000 compounds with the potential to double carbon-based solar cell efficiency. With your help, we can explore thousands more.

Solar cells are traditionally made from silicon, which is expensive and rigid. Constructing them from carbon-based materials is a cheaper and more flexible alternative. Harvard researchers are working to discover carbon-based compounds that can efficiently generate electricity from sunlight. Dr. Alán Aspuru-Guzik of Harvard University, lead researcher of The Clean Energy Project, explains that this technology could act “as a cheap power source for more than two billion people worldwide without access to electricity.”
http://www.youtube.com/embed/VNuOcbWeL90

Dr. Asparu-Guzik explains, “Unlike their silicon-based cousins, organic solar cells are far cheaper and easier to produce – some can even be printed in a process similar to that used by inkjet printers. The flexible, lightweight cells can also be molded into virtually any shape, and then rolled up and easily transported.” They could be painted on roofs and walls, or even woven into clothing.

Since launching on World Community Grid in 2008, The Clean Energy Project has screened more than two million organic molecules, with the help of our volunteers – the most extensive investigation of quantum chemicals ever performed. These results were made available to other researchers and the public last summer as part of President Obama’s Materials Genome Initiative – a public-private collaborative effort to double the pace of high-tech materials development. The White House praised The Clean Energy Project and highlighted the crucial role it plays in advancing materials science.

So far, more than 35,000 of the compounds analyzed on World Community Grid show the ability to perform at approximately double the efficiency of most organic solar cells in production today. Before this initiative, scientists knew of just a handful of carbon-based materials that were able to convert sunlight into electricity efficiently. The Harvard team – who so far have been provided with the equivalent of 17,000 years of computing time – continues to investigate the most promising candidates for use in cheaper, more efficient and flexible solar cells.

Projects like this require a massive amount of computing power – and the more volunteers we have contributing, the faster this vital research can be completed.

Get competitive for good with our 10th anniversary challenge!

To celebrate a decade of discovery, we invite you to participate in an exciting community-wide competition to introduce new volunteers to World Community Grid. The most successful volunteer recruiters will win special limited-edition prizes!

Learn more here and get started today by inviting your friends to help power the search for affordable clean energy.

Here’s to another decade of discovery.

Related Articles:
IBM World Community Grid Powers Hunt for Organic Solar Cell Materials
Harvard publishes World Community Grid data, rating millions of compounds for use in solar cells
The Clean Energy Project - Phase 2 project overview
The Clean Energy Project - Phase 2 Frequently Asked Questions
大意：
十年探索之将碳基太阳能电池的效率提升一倍
与哈佛大学合作的清洁能源项目的目标是寻找有机太阳能电池材料，目前已经鉴定了3.5万个潜在化合物（而在此项目之前科学家仅仅找到了几种潜在化合物），在大家的努力下，我们还可以鉴定更多的物质。
传统的太阳能电池是基于硅材料的，既昂贵又坚硬。相比之下，碳基材料就要更便宜更柔软，而且可以用喷墨打印机来进行打印。它们可以印在屋顶、墙壁甚至你的衣服上。
自从08年CEP项目开始起，在志愿者们的帮助下，我们已经筛选了超过2百万个有机分子。去年夏天响应奥巴马发起的材料基因倡议，我们已经公开了研究结果。该举动得到了白宫的好评。加快了高端材料科学的发展。

超哥不郁闷

vmzy 发表于 2014-10-23 11:03
Decade of discovery: doubling carbon-based solar cell efficiency  
22 十月 2014          

好消息~
要不宣传一下？@zhouxiaobo

vmzy

A productive summer for the Clean Energy Project
By: The Clean Energy Project team
Harvard University
23 十月 2014          

摘要
The Clean Energy Project team has an end-of-summer update for all the World Community Grid volunteers. Several changes to the database and work units were put in place over the summer. The team sends a big thank-you to the volunteers who make this work possible, as well as to the lab’s summer students and the departing CEP web developer.

Hi all!

The time has come for another update on the The Clean Energy Project - Phase 2 (CEP) on World Community Grid.

Wow, it has been a busy and productive summer! Our redesign of the database is complete, and all new jobs are being created from, and their results being stored into, the new design. This will give us a much more quickly searchable database, capable of storing a wider variety of data – very exciting! The data that has been produced so far is being parsed into this structure as well, and is also being recompressed using a more efficient algorithm. We estimate that this recompression will save us a significant amount of storage space, meaning we can now store more results than ever!

We were very lucky to have three brilliant students work on the CEP over the summer: Kewei, Trevor and Daniel. They were mainly focused on harnessing the power of machine learning techniques to improve how we generate molecules. Their research was very promising, and we hope to write it up into a paper or two in the near future – well done, guys! In fact, two of them (Kewei and Trevor) have agreed to continue working with us during term time, and we hope to get many more exciting projects done. We will keep you all posted on those as details emerge.

As you have probably seen in the forums, we have had a redesign of the structure of the work units. We want to thank everyone for their patience while we sorted out all the “teething” problems, but they now seem to be working well. The reason for these changes was to allow us to try and move onto slightly different families of molecules which we have identified as being particularly interesting. It is important for the CEP to be constantly updating the molecular libraries so we can really live on the cutting edge, and hopefully discover the next “blockbuster” Organic Photovoltaic molecule (the type of molecule the CEP is looking for). To do this, we have to push up against the limits of what is possible on the grid, and we really appreciate the patience of the crunchers when we occasionally push too hard!

We have also changed the way that we build the molecules for these libraries. This was done in order to prioritize molecules that are more synthesizable (i.e. easier for our experimental friends to make in a lab). This is a win-win, because we are also able to sample a more diverse area of chemical space.

Thanks to all the crunchers and our friends at IBM; without you the project literally would not happen!

We would also like to take a moment to give a big thank you to Carolina Roman-Salgado, our awesome web developer. She is moving to California at the end of September, and so will be leaving the CEP. Carolina has been absolutely fantastic in working with the CEP database and molecularspace.org (where our results are all hosted for public access), and has recently been working on an update, which we hope to release soon. Aside from her brilliant work, we will really miss having Carolina around the office – please don't wait too long before you come visit, Carolina; you will always be welcome here!

Your Harvard CEP Team
大意：
CEP项目盛夏的果实
首先我们对数据库表进行了重新设计，提升了查询速度，增加了存储字段，改进了数据压缩算法（可以节省不少空间）。
暑假来了3名实习生（Kewei, Trevor和Daniel），他们利用机器学习技术对我们生成分子的方法进行了优化。今后他们还会继续帮助我们。
论坛的朋友应该清楚，我们改变了WU的数据结构，虽然期间出了点儿问题，不过还好现在都已经解决了。新的数据结构可以让我们添加更多的分子进行筛选，有助于我们找到最佳的有机太阳能分子。
感谢大家一直以来的帮助。最后还要特别感谢Carolina Roman-Salgado。她9月底就要离开CEP项目组，搬去加利福尼亚州了。她为数据库改造、网站建设、还有我们的结果公开网站molecularspace.org做出来巨大的贡献。而且最近她还在义务帮网站进行改造升级。Carolina你永远是我们的好朋友，CEP随时欢迎你回家探亲。

vmzy

Early-stage results from the Mapping Cancer Markers team
By: The Mapping Cancer Markers research team
27 十月 2014          

摘要
Thanks to your help, the Mapping Cancer Markers team is nearly finished with benchmarking their first set of genetic markers. In this update, the team presents an in-depth review of what they've accomplished thus far, and what significance this early work will have for cancer research at their lab and elsewhere.

The Mapping Cancer Markers (MCM) team would like to extend a huge thank you to World Community Grid members everywhere. As of October 27, 2014, we have surpassed 89,000 years of computation, a goal that simply would not be possible without your help.

We are happy to report that we have begun to analyze the results using a high-throughput analytics package to assess the fitness and landscape of gene signature sizes between 5 and 25 genes. This analysis has shown that smaller signatures usually comprise different genes compared to larger signatures (i.e., you cannot "build" a larger signature from small ones), and that those genes are targeting many different signaling cascades and biological processes.

Analytics

To get a better understanding of how much data our team is receiving, we'd like to briefly introduce one of the tools that we have adopted to analyze the incoming results. From the very beginning of the project, it was clear that analyzing such a large, ongoing flow of data would be a challenge. Thus, we started to use the IBM® InfoSphere® Streams real-time analytics platform to streamline the analysis pipeline. When complete, our Streams application will run continuously, processing members' work units in real time as we receive them. We currently have the core analysis framework implemented and running on a subset of the MCM results. We will continue to add additional layers of analysis, and fine-tune our system until it is running at full capacity. For that reason, we have dedicated one of our main compute servers (IBM Power® 780) to analyzing MCM results.

Results

Pictured below is a sampling (a very small fraction) of some of the ongoing work that will establish a benchmark for further experiments. Each dot in both of the graphs is a potential lung-cancer biomarker. These graphics are distilled from thousands of MCM results sent back by World Community Grid members.

图1

图2

Most of the dots have very little significance; this is expected because not everything shuts down or is activated in cancer. In other words, the graphics show differences between the disease state and the non-disease state, so we expect some things to be different, but not everything. For those reasons, most biomarkers cannot significantly differentiate cancer from non-cancer samples - this is represented by the haze of dots along the zero line. We show two graphs to illustrate the difference between shorter and longer gene signatures. Some genes that are more predictive in the shorter signature sizes do not necessarily hold their predictive power when considering more genes per signature. Most importantly, in each analysis, a few biomarkers frequently appear in high-scoring signatures. Our analysis wades through massive amounts of data to recognize those few markers that stand out.

The first half of the “benchmarking” experiment involves determining the performance of markers as the size of the signature changes. For instance, when we compare successful 5-marker signatures against 20-marker signatures, which markers are consistently useful? Which ones increase or diminish in predictive power? Is there an optimum size for signatures? And most importantly, can we identify seemingly minor players that are critical, but not yet in clinical use that can discriminate between normal and disease states?

图3

After surveying the first several billion signatures, we have identified the highest-ranking combinations and underlying single genes. After separating those genes by signature size, we can see how some genes remain important regardless of the size, and how other genes “appear” to be important but are only showing up as single events. Considering we have not yet analyzed the complete data set, we have identified the genes by their known functions rather than names, to eliminate any bias towards known markers. However, even by their functions, we can see that many important signaling cascades and biological processes are affected. The most notable of these is “Cellular Fate and Organization”, which makes sense. Sometimes, when an organism loses the ability to naturally kill defective cells, it leads to uncontrolled growth, one of the hallmarks of cancer.

Network Analysis of Major Genes:

To further analyze the nature of our top-performing genes, we can identify their inter-relations in biological networks. We currently maintain one of the largest curated protein-protein interaction databases, which enables us to determine whether our genes (when converted to proteins) are known to interact with other important biomarkers, and in turn, what biological processes may be involved. The graph below shows one such network; nodes in the graph represent genes, edges are physical protein interactions. Node color highlights biological function as described in the legend. Use of biological networks can reveal very small subtleties of how the mechanisms of disease function and elucidate how our proteins may be causing problems; thus, eventually leading to understanding how cancer starts, progresses and how can we treat it.

图4

In the above network, 20 out of 24 important proteins we have identified on World Community Grid (right hand side) can be linked through known protein interactions and 56 other proteins (left hand side). We have also conducted a short analysis of the 4 proteins not yet identified using a software prediction package and found those to have significant partners. Those interactions will be evaluated in the near future. The 20 proteins noted above, strikingly, do not interact directly, however, 4 of them show very high interactivity, and can be considered as hubs. From other analyses we know that “hub proteins” are often critical, as they affect many signaling cascades and biological processes. When such proteins malfunction, catastrophic changes often result. On the other hand, proteins with low interactivity could be useful as clinical biomarkers. If they are known to only interact with a few other proteins, then their activity may help to identify particular states of cancer, while having less background “noise”. As a whole we can see that for the most part, our genes of interest are targeting mostly “genome maintenance” and “cellular fate and organization” proteins, which make up about 70% of the interacting proteins (left hand side). This is a good indication that most of the pathways affected are in those major categories, which is consistent with how we understand lung cancer to progress.

Funding & Fundraising:

This past August, we completed our 4th successful Team Ian Ride for Cancer Informatics Research. We were able to raise over $80,000 for cancer research in the name of a former Jurisica student, Ian Van Toch.

Part of this funding is used for the best student paper award at the ISMB conference, and for supporting Cancer Informatics interns.

We also support a special seminar series at Princess Margaret Cancer Center, and the recent presentation by Dr. Wan Lam from BC Cancer Agency discussed “Multi-dimensional Analysis of Lung Cancer Genomes”.
大意：
MCM的早期结果
在大家的齐心协力下，第一组基因标记已经快算完了。截止10月27日，我们已经完成了8.9万年的计算。
当前我们已经开始了结果的初步分析工作。图1、2就是利用一小部分结果做出的分析图，每一个点都代表一个潜在的肺癌分子标记。目前我们仍在在对结果分析程序进行优化。
大部分标记都不明显（零轴附近），而且基因标记越少，有效性越大，我们需要慢慢统计分析数据，找出最有效的标记。
上半场的海选分析工作，主要集中于：找出有效性和分子标记数量的关联性（比如：5个标记一组和20个标记一组，结果有效性差异如何？）是否存在最佳的标记数量？
在海选结束后，下半场我们会精选最佳标记。寻找基因和生理功能的联系。有些基因的异常会导致细胞增生、无法被杀灭，而这是癌细胞最大的特点。
最终我们将把基因、蛋白质、以及蛋白质间的相互关系找出来，并将他们存档入库，以便其他人使用。如图4，左手边是我们已经研究了的20个蛋白质，后面是其他与它们有关联的56个蛋白质，另外我们还分析了4个新蛋白质。前面提到的20个蛋白质并不都是直接相互作用。其中有4个起了关键作用，一旦他们出了问题，往往会导致严重的后果。但是对我们而言，我们更关注关联性较小的蛋白质，因为他们往往只存在于特定癌细胞（肺癌）的蛋白质关联作用中，是最佳的标记，很少会出现偏差（误诊）。


译注：刚翻译完MCM的长文，超累。
下面是几篇有关新项目（Uncovering Genome Mysteries）的媒体软文，内容大同小异，我就不翻译了，鸟语好的自己去看吧。
2014 十月 27 – Crowdsourced power to solve microbe mysteries        
University of New South Wales press release about the new Uncovering Genome Mysteries project on World Community Grid.


2014 十月 27 – Join in the Discovery of Nature's Hidden Superpowers        
Lead researchers, Wim Degrave and Torsen Thomas, give us an insight on the new World Community Grid project, Uncovering Genome Mysteries, in this Citizen IBM blog post.


2014 十月 27 – Sydney Scientists Are Linking Home Computers Around The World To Create A Huge Super Processor        
An article on Business Insider-Australia about the new Uncovering Genome Mysteries project on World Community Grid.         


2014 十月 27 – Community Grid Fosters Microbial Discovery        
An article published on HPC wire about the new Uncovering Genome Mysteries project on World Community Grid.  

超哥不郁闷

Decade of discovery: achieving a breakthrough in childhood cancer


摘要
To mark week three of our decade of discovery celebrations, we’re focusing our efforts on childhood cancer research. Through the Help Fight Childhood Cancer project, World Community Grid volunteers powered the screening of over three million drug candidates for neuroblastoma – a common and dangerous form of childhood cancer. Through this screening, researchers identified seven drug candidates that showed great potential for drug development – a breakthrough that could save many young lives.

Neuroblastoma is a form of childhood cancer that affects nerve tissues, often starting in the spine, neck, chest, abdomen or pelvis. As with most cancers, the cause is unknown and it affects approximately one in 8,000 children in the United States and Japan. While 80% of children diagnosed with cancer are cured, the prognosis is not nearly as good for those with neuroblastoma - only 30% of high-risk cases are cured.

The urgent need for new treatments to fight this dangerous disease inspired researchers at the Chiba Cancer Center in Japan to partner with World Community Grid to create the Help Fight Childhood Cancer project. Launched in 2009, the project aims to develop new medicines to fight neuroblastoma.

The researchers’ strategy was to identify small molecules that would activate a natural self-destruct mechanism in diseased cells - a defense that is suppressed by the cancer. Over the course of two years, World Community Grid volunteers helped the researchers screen three million molecules - a search that would have taken more than 55,000 years on a single computer. Following this testing, in February this year the researchers announced the discovery of seven promising molecules that proved effective at activating the self-destruct function and destroying neuroblastoma cancer tumors when tested on mice, without any apparent side effects on healthy tissue.

This was a significant breakthrough - particularly because over the course of the last 20 years, very little progress has been made in improving the cure rate for this deadly disease. What makes this discovery even more important is the fact that it could aid the research of many adult cancers including breast, lung, pancreatic, prostate and colon cancers.

Building on this success, in July lead researcher Dr. Nakagawara announced his plans to develop a second phase of the project on World Community Grid to cover multiple additional childhood cancers.

Speaking to the volunteers who made this crucial project possible, Dr. Nakagawara commented: “We are so grateful for the support of World Community Grid volunteers and the role you've played in powering our phase one research.”

If you’d like to help advance cancer research, please share this story with your friends and colleagues, and encourage them to support another fantastic cancer research project currently running on World Community Grid - Mapping Cancer Markers.

Here’s to another decade of discovery.

To contribute to Mapping Cancer Markers go to your My Projects page and make sure the box is checked. Mapping Cancer Markers is dedicated to improving cancer treatment by identifying cancer biomarkers, which could help doctors detect cancer earlier and customize treatment.

大意：
                                                                        十年探索：儿童癌症领域里的突破
Help Fight Childhood Cancer项目的志愿者们为该项目从三百多万种药物中筛选出7种极具潜力的药物，这些药物很可能成为今后治疗成神经细胞瘤的药物，这7种药物已在小白鼠身上进行了实验并取得了很好的效果。这项成果给研究人员很大的鼓舞，因为在过去20年里对这种绝症的研究进展十分缓慢，而且该项目目前取得的成果对许多成人癌症也有较大帮助！为此日本千叶大学的Nakagawara博士非常感谢所有为该项目作出贡献的志愿者们（志愿者们为该项目贡献了超过5.5万年的计算时间），为了进一步的研究，他还会在WCG上发起第二阶段的研究，用以找到其他儿童癌症的潜在治疗药物。

超哥不郁闷

超哥不郁闷 发表于 2014-10-29 09:29
Decade of discovery: achieving a breakthrough in childhood cancer

@zhouxiaobo 宣传一下？

vmzy

Decade of discovery: New precision tools to diagnose and treat cancer
By: Dr. David J. Foran, PhD
Rutgers Cancer Institute of New Jersey
3 十一月 2014          

摘要
It's week four of our 10th anniversary celebrations, and we're following up last week's childhood cancer feature by spotlighting another cancer project that's helped researchers develop powerful new tools to diagnose cancer and tailor treatments to individual patients, using big data and analytics.


When it comes to cancer, a doctor's diagnosis affects how aggressively a patient is treated, which medications might be appropriate and what levels of risk are justified. New precision medicine techniques are enabling physicians and scientists to refine diagnoses by identifying changes and patterns in individual cancers at unprecedented levels of granularity - ultimately improving treatment outcomes for patients.

A key tool for precision medicine is tissue microarray analysis. This enables investigators to analyze large batches of tissue sample images simultaneously, so they can look for patterns and identify cancer signatures. It also provides them with a deeper understanding of cancer biology and uncovers new sub-classifications of cancer and likely patient responses - all of which influence new courses of treatment and future drug design.

Tissue microarray analysis shows great promise, but it is not without its limitations. Pathologists typically examine the specimens visually, resulting in subjective interpretations and variations in diagnoses.

We realized that if this method of analysis could be automated using digital pattern recognition algorithms, we could improve accuracy and reveal new patterns across large sets of data. This would make it possible for researchers to determine a patient's type and stage of cancer more precisely, meaning they can prescribe therapies or combinations of treatments that are most likely to be effective.

To study the feasibility of automating tissue microarray analysis, we partnered with IBM's World Community Grid in 2006 to launch the Help Defeat Cancer project. At the time, we were pioneering a new approach that nobody else was investigating, and it was met with tremendous skepticism by many of our colleagues.

However, with the support of more than 200,000 World Community Grid volunteers from around the globe who donated over 2,900 years of their computing time, we were able to study over 100,000 patient tissue samples to search for cancer signatures.

Access to this vast computing power enabled our team to rapidly conduct this research under a much wider range of environmental conditions and to perform specimen analysis at much greater degrees of sensitivity.

Thanks to World Community Grid and the Help Defeat Cancer project, we demonstrated the success of using computer-based analysis to automatically investigate and classify cancer specimens based on expression signature patterns. We were able to develop a reference library of cancer signatures that can be used to systematically analyze and compare tissue samples across large patient cohorts.

Leveraging these experimental results, our team secured competitive funding from the National Institutes of Health (NIH) to build a clinical decision support system to automatically analyze and classify cancer specimens with improved diagnostic and prognostic accuracy. We used the core reference library of expression signatures generated through the Help Defeat Cancer project to demonstrate the proof-of-concept for the system.

These decision support tools are now being tested and refined by investigators from the Rutgers Cancer Institute of New Jersey, Stony Brook University School of Medicine, University of Pittsburgh Medical Center and Emory University. They are exploring how the tools can aid clinical decision-making, plus are pursuing further investigative research. Together, our ultimate aim is to refine these tools sufficiently so they can be certified for routine clinical use in diagnosing and treating patients.

Although the Help Defeat Cancer project has completed its research on World Community Grid, we continue to investigate the findings and they have contributed to some significant new beginnings. At Rutgers Cancer Institute of New Jersey, physicians and scientists - aided by high-performance computing resources - are analyzing genomes and human tissues, and identifying cancer patterns, faster than ever before.

In collaboration with our research partners at the Rutgers Discovery Informatics Institute (RDI2) and RUCDR Infinite Biologics (the world's largest university-based biorepository, located within the Human Genetics Institute of New Jersey), the Rutgers Cancer Institute is shaping a revolution in how best to determine cancer therapy for patients - a vast improvement from the time-intensive, trial-and-error approach that doctors have faced for years. To date, only a fraction of known cancer biomarkers have been examined. The long-term goal is to create a library of biomarkers and their expression patterns so that, in the future, physicians can consult the library to help diagnose cancer patients and provide them with the most effective treatment.

I would like to express my gratitude to Stanley Litow, Robin Willner, and Jen Crozier from IBM and to World Community Grid's Advisory Board for supporting the Help Defeat Cancer project. I'd also like to extend my special thanks to the IBM World Community Grid team members who contributed to the success of the project - I hope to have the opportunity to work with them again in the near future.

Additionally, I would like to acknowledge the NIH, Department of Defense and IBM for supporting this research - and give credit to those individuals from my laboratory and partnering institutions who were involved in the early experiments and the initial design and development of the imaging and computational tools, which we then used throughout the project. And, of course, a very big thank you to all the World Community Grid volunteers - without their support, our accomplishments with Help Defeat Cancer would not have been possible.

The Help Defeat Cancer project has completed its analysis on World Community Grid - but another innovative project, Mapping Cancer Markers, is currently running and needs your help. Help us celebrate a decade of discovery on World Community Grid by sharing this story and encouraging your friends to donate their unused computing power to cutting-edge cancer research.

Here’s to another decade of discovery.

To contribute to Mapping Cancer Markers go to your My Projects page and make sure the box is checked. Mapping Cancer Markers is dedicated to improving cancer treatment by identifying cancer biomarkers, which could help doctors detect cancer earlier and customize treatment.

Please visit the following pages to learn more:
Mapping Cancer Markers overview
Help Defeat Cancer overview
Podcast with Dr. David Foran
'Accelerating Cancer Discovery with Precision Medicine' - Citizen IBM blog post
大意：
探索十周年：高精度的癌症诊断和治疗工具
今天我们介绍另外一个癌症项目，利用大数据分析帮助开发新的癌症诊断工具，针对不同个体对症下药的改进癌症疗法。
对于癌症来说医生的准确诊断以及准确用药，对治疗结果会产生巨大的影响。
当前一个重要的癌症诊断工具是病变组织微阵列分析法，让研究者可以同时对多个组织图像同时进行分析，由此找到癌症的特征，也可以对癌症的生理特性有更深的了解，还能对癌症进行更精确的分类。
但是组织微阵列分析法也有不足之处，因为它依赖的是病理学家的视觉主观判定，所以判定结果经常出现误差。
我们意识到如果我们利用计算机算法来进行组织分析，就能提高诊断的精确度，提高治疗效率。为此我们在WCG上启动了Help Defeat Cancer项目。在20万志愿者的帮助下，利用2900年的计算量我们对10万个病变组织进行了分析。
随后我们拿到了NIH的资助，利用HDC项目的研究数据，进行临床癌症辅助诊断。目前一些大学和癌症研究机构在优化和测试我们的辅助诊断系统。我们希望这个系统能早日完成，投入临床使用。
虽然WCG上HDC项目已经结束了，但是对于我们研究者来说这才是个开始。我们还在分析大家计算的结果，对基因和人体组织进行研究。我们正在和研究机构合作，试图建立一个病变组织生物特征数据库，这样今后医生们就可以通过访问数据库，提高诊断精度。
最后还是要感谢大家和合作机构的付出和努力。
虽然WCG上的HDC项目已经结束了，但是现在还有另一个癌症项目MCM在运行，希望大家积极参与。

译注：HDC和MCM的区别。
HDC是从病变组织（图像）视觉上来鉴别癌症特征，比较宏观。虽然精度略低但是比较实用，研究、推广周期短，化验成本低。
MCM是利用病变组织（血液）中的蛋白质分子特征来鉴别癌症特征，比较微观。虽然精度很高，误诊率极低，但是研究、推广周期很长，化验的成本较高。

超哥不郁闷

vmzy 发表于 2014-11-6 15:41
Decade of discovery: New precision tools to diagnose and treat cancer
By: Dr. David J. Foran, PhD
R ...

宣传一下~@zhouxiaobo

muclemanxb

超哥不郁闷 发表于 2014-11-6 17:49
宣传一下~@zhouxiaobo

又是新项目？omg，这个大坑～永永远远地擦亮眼～千万别跳啊

超哥不郁闷

muclemanxb 发表于 2014-11-6 21:56
又是新项目？omg，这个大坑～永永远远地擦亮眼～千万别跳啊

以前的啊

muclemanxb

超哥不郁闷 发表于 2014-11-6 23:49
以前的啊

呃，果然是，喝高了没留神，看错了，我说我怎么没有这个奖章呢～原来很早就结束了。

vmzy

Teamwork yields experimental support for FightAIDS@Home calculations
By: The FightAIDS@Home research team
6 十一月 2014          

摘要
Imaging studies have now confirmed some of the computational predictions made during FightAIDS@Home, providing important confirmation of our methodology and the value of your computational results. This work is ongoing, but promises to increase our understanding of how HIV protease can be disrupted.


The "exo-site" discovered in HIV protease (shown here in green), showing the original bound 4d9 fragment (shown here as red and orange sticks) and the volume (shown as the orange mesh) that is being targeted by FightAIDS@Home. (image credit: Stefano Forli, TSRI)

Our lab at the Scripps Research Institute, La Jolla, is part of the HIV Interaction and Viral Evolution (HIVE) Center - a group of investigators with expertise in HIV crystallography, virology, molecular biology, biochemistry, synthetic chemistry and computational biology. This means that we have world-class resources available to verify and build upon our computational work, including the nuclear magnetic resonance (NMR) facility at the Scripps Research Institute, Florida. NMR is a technique for determining the molecular structure of a chemical sample, and therefore is very useful for validating some of the predictions made during the computational phase of FightAIDS@Home.

We’re excited to announce that our collaborators at Scripps Florida have now optimized their NMR experiments and have been able to characterize the binding of promising ligands with the prospective allosteric sites on the HIV protease. These sites represent new footholds in the search for therapies that defeat viral drug resistance. The NMR experiment allows us to detect the location of the interactions between the candidate inhibitors and the protein, but unlike X-ray crystallography experiments, these interactions are measured in solution, which better represents the biological environment.

In fact, the first results from the NMR experiments validated the exo site we so thoroughly investigated in FightAIDS@Home. As a result, we now have experimental evidence that a small molecule binds to the exo site in solution with structural effects that seem to perturb the dynamic behavior of protease, even with a known inhibitor in the active site.

There are many more NMR experiments still to run, but another advantage of NMR over crystallography is that it does not require the lengthy step of growing diffraction-quality crystals. This allows higher experimental throughput, so we look forward to experimental confirmation of many more compounds in much shorter time. So far we have shipped 15 compounds to test and another batch is going to be sent this week. The new compounds will help to validate another potential interaction site on one of HIV protease’s two movable “flaps”.

Once the validation is completed, we will proceed to test a number of compounds that we identified in different FightAIDS@Home experiments for all of the target protease allosteric sites.

As always, thank you for your support! This research would not be possible without your valuable computing time.

The Scripps research team needs your help to continue making progress on developing new treatments for AIDS! Take part in our decade of discovery competition by encouraging your friends to sign up to World Community Grid today to start donating their computer or mobile device's computing power to FightAIDS@Home. There’s just over a week left and some great prizes are up for grabs - get started today!

Here’s to another decade of discovery.
大意：
Scripps实验室通过优化的核磁共振成像技术（NMR）观察到了候选配体和候选HIV蛋白酶变构点的结合。
NMR可以观察溶液中的晶体（更像生物体内的环境），而X射线成像技术只能观察固态晶体。而且NMR成像速度非常快。目前我们已经把15个候选化合物送检了，接下来我们还会送出更多的候选化合物，以确定另一个候选变构结合点的有效性。检验结束后，我们会陆续把我们在计算中找到的所有候选变构点送测。

vmzy

Decade of Discovery: A new drug lead to combat dengue fever
By: Dr. Stan Watowich, PhD
University of Texas Medical Branch (UTMB) in Galveston, Texas
10 十一月 2014          

摘要
For week five of our decade of discovery celebrations we’re looking back at the Discovering Dengue Drugs - Together project, which helped researchers at the University of Texas Medical Branch at Galveston search for drugs to help combat dengue - a debilitating tropical disease that threatens 40% of the world’s population. Thanks to World Community Grid volunteers, researchers have identified a drug lead that has the potential to stop the virus in its tracks.


Dengue fever, also known as “breakbone fever”, causes excruciating joint and muscle pain, high fever and headaches. Severe dengue, known as “dengue hemorrhagic fever”, has become a leading cause of hospitalization and death among children in many Asian and Latin American countries. According to the World Health Organization (WHO), over 40% of the world’s population is at risk from dengue; another study estimated there were 390 million cases in 2010 alone.

The disease is a mosquito-borne infection found in tropical and sub-tropical regions - primarily in the developing world. It belongs to the flavivirus family of viruses, together with Hepatitis C, West Nile and Yellow Fever.

Despite the fact dengue represents a critical global health concern, it has received limited attention from affluent countries until recently and is widely considered to be a neglected tropical disease. Currently, no approved vaccines or treatments exist for the disease. We launched Discovering Dengue Drugs - Together on World Community Grid in 2007 to search for drugs to treat dengue infections using a computer-based discovery approach.

In the first phase of the project, we aimed to identify compounds that could be used to develop dengue drugs. Thanks to the computing power donated by World Community Grid volunteers, my fellow researchers and I at the University of Texas Medical Branch in Galveston, Texas, screened around three million chemical compounds to determine which ones would bind to the dengue virus and disable it.

By 2009 we had found several thousand promising compounds to take to the next stage of testing. We began identifying the strongest compounds from the thousands of potentials, with the goal of turning these into molecules that could be suitable for human clinical trials.

We have recently made an exciting discovery using insights from Discovering Dengue Drugs - Together to guide additional calculations on our web portal for advanced computer-based drug discovery, DrugDiscovery@TACC. A molecule has demonstrated success in binding to and disabling a key dengue enzyme that is necessary for the virus to replicate.

Furthermore, it also shows signs of being able to effectively disable related flaviviruses, such as the West Nile virus. Importantly, our newly discovered drug lead also demonstrates no negative side effects such as adverse toxicity, carcinogenicity or mutagenicity risks, making it a promising antiviral drug candidate for dengue and potentially other flavivirues. We are working with medicinal chemists to synthesize variants of this exciting candidate molecule with the goal of improving its activity for planned pre-clinical and clinical trials.

I’d like to express my gratitude for the dedication of World Community Grid volunteers. The advances we are making, and our improved understanding of drug discovery software and its current limitations, would not have been possible without your donated computing power.

If you’d like to help researchers make more ground-breaking discoveries like this - and have the chance of winning some fantastic prizes - take part in our decade of discovery competition by encouraging your friends to sign up to World Community Grid today. There’s a week left and the field is wide open - get started today!

Here’s to another decade of discovery.
大意：
十年探索：一种潜在的治疗登革热药物
登革热可以引起关节、肌肉痛，高烧和头疼。严重的还会导致出血热。在亚洲和拉美许多地区，发病率很高。据世卫组织统计，全球超过40%的人口属于易感人群。仅2010年就有约3.9亿人感染登革热。它是一种通过蚊子传播的（亚）热带疾病，隶属于黄病毒家族（此家族还包括：丙肝、西尼罗河病和黄热病）。
尽管登革热的危害很大，但是却被大家忽视了。到目前为止仍没有针对该病毒的疫苗及专用疗法。为此2007年我们在WCG上启动了DDDT项目寻找抗登革热药物。
在一阶段，我们主要关注于从3百万个化合物分子中筛选可能与登革热病毒绑定的候选分子。到2009年，我们找到了几千个候选分子，进行进一步测试。目前我们已经找到了一个分子可以阻止病毒复制。
更重要的是，它对整个黄病毒家族的成员都有效。同时经过实验室测试，还发现这种药物没有毒副作用，非常安全。目前我们正在和药物合成专家改进这种分子，试图尽量提高药效，希望能尽早开始临床测试。
最后感谢大家的无私奉献。

zhouxiaobo

超哥不郁闷 发表于 2014-11-6 17:49
宣传一下~@zhouxiaobo

今天才看到。。看来艾特功能已经残废，以后用私信吧！

vmzy

From simulation to prediction: advances in understanding proteins
26 十一月 2014          

摘要
The Help Cure Muscular Dystrophy project finished its grid calculations last year. Team leader Dr. Alessandra Carbone has posted an update explaining her colleagues’ ongoing work on understanding and modeling the complex protein-protein interactions that lie behind diseases such as muscular dystrophy.

Dr. Carbone’s recent Help Cure Muscular Dystrophy project update explains several methods that she and her colleagues are exploring with the aim of improving their ability to both simulate and predict the interactions of proteins. This kind of improved modeling of proteins would make future research faster and open up entirely new research options, such as predicting the sites of protein interactions based solely on the genomic data that encodes that particular protein. Eventually, a better understanding of protein interactions could help researchers develop more effective treatments for neuromuscular diseases such as muscular dystrophy.
Dr. Carbone’s post gives another great example of how the work done on World Community Grid continues to have value for researchers long after the grid phase of the project is complete.

For further details, read Dr. Carbone’s update on her project website.
大意：
从模拟到预测：蛋白质研究的进化
去年HCMD项目结束了，Carbone的团队对数据进行了分析，对造成肌营养不良症的蛋白质间作用进行了建模和研究。提高了对蛋白质间作用的模拟和预测能力。今后不仅可以模拟蛋白质，还可以仅仅凭借基因数据对蛋白质的潜在药物靶点进行预测。