Enews world

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http://dotsub.com/view/5c4da436-b65e-48b9-b575-37d6eb2cd2e5
Eli Pariser:謹防互聯网“过滤气泡”
马克·扎克伯格 曾被一位记者问及动态消息的问题。 这位记者问他， “为什么滚动新闻如此重要？” 扎克伯格说， “此时，你前院奄奄一息的松鼠 可能与你的兴趣更加“相关”， 比起非洲那些挣扎在死亡线上的人们。” 我想谈谈 建立在这个“相关”的思路上的一个网络会是什么样子。
当我生活在缅因州、 一个非常典型农村地区的时候， 互联网对我而言，有着完全不同的意义。 它意味着与整个世界的联系， 它意味着与将所有人联系起来。 那时，我确信它对民主、 对我们的社会而言，都是件了不起的事。 但是，网上的信息流动 发生了改变， 并且这种改变是隐形的。 假如我们对此毫不留意， 它会成为一个真正的问题。 所以，我最早是在我花了很多时间的地方注意到了这个问题-- 我的脸谱页面。 政治上，我是改革派--很意外吧-- 但我常常会特意去一些保守派的页面去看看。 我喜欢听他们的想法； 我喜欢看他们有哪些链接； 我喜欢从中学到一两件新鲜事。 但有一天，我注意到， 我脸谱新闻组里的保守派全都消失了，这让我很吃惊。 结果是 脸谱会看我点击的链接， 实际上，它注意到 相比我保守党派的朋友们， 我点击了更多的自由派朋友们的链接。 在没有告知我的情况下， 脸谱就把保守派信息编辑并删除了。 保守派的朋友们在我的页面上消失了。
脸谱不是唯一 进行这样隐形的、算法的 编辑网络的地方。 谷歌也是如此。 假如我搜索某种信息，你也搜索这种信息， 甚至是现在，在同一时间， 我们得到的搜索结果可能大不相同。 一位工程师告诉我，即使你退出帐号， 还会有57种信号 可供谷歌参考-- 几乎所有的信息：从你使用的电脑型号 到你用的浏览器 到你所在的位置-- 谷歌利用这些为你定制出个性化的查询结果。 稍微想想看： 从此不再会有标准版谷歌。 你知道，有趣一点的是，对此，人们很难察觉得到。 你不会看到你的搜索结果 与别人的搜索结果有什么不同。
但几周前， 我请一群朋友用谷歌搜索“埃及”， 然后将他们搜索结果的屏幕截图发给我。 这是我朋友斯科特的截屏。 这个是我朋友丹尼尔的。 当你把它们并排放在一起， 你甚至不必查看链接， 就能看出这2个搜索页面有多大差别。 但当你查看这些链接后面的内容时， 差别真的相当相当大！ 在谷歌搜索结果的第一页，丹尼尔根本就没有 任何有关埃及抗议报道的新闻。 斯科特的搜索结果却全是这类新闻。 在当时，这可是当天的头条新闻。 搜索结果就是会如此的不同。
这不仅指谷歌，也不仅指脸谱。 这种现象正在席卷整个网络。 有一大批的公司正在做这样的个性化定制服务。 雅虎新闻，网络上最大的新闻网站， 现在也个性化服务了--不同的人们得到不同的信息。 赫芬顿邮报，华盛顿邮报，纽约时报-- 它们都以不同的方式与个性化定制搭上边。 这会将我们很快地 推向这样一个世界—— 网络给我们显示它认为我们想要看到的信息， 而未必是我们需要的信息。 正如埃里克·施密特所言， “要人们观看或消费一些 在某种意义上并非 为他们个性定制的东西，是很难的。”
所以，我认为这确实是个问题。 我认为，如果你把所有这些过滤器放在一起， 还有所有这些算法， 你会得到一个我所谓的“过滤气泡”。 你的“过滤泡沫”是你自己个人的 独一无二的信息世界—— 也就是你所生活其中的网络世界。 你的“过滤气泡”中包含了什么 取决于你是谁，也取决于你做的事情。 但问题是你不能决定什么信息可以通过“过滤气泡”。 更重要的是， 实际上，你也看不到那些被删除的信息。 所以奈飞DVD在线租赁公司（Netflix）的一些研发人员发现了 “过滤气泡”的一个问题。 他们在查看奈飞数据队列时，注意到一些有意思的事； 可能我们很多人也已经注意到了， 那就是，有些电影 脱颖而出，直接进入到千家万户。 它们进入数据队列，然后直接脱颖而出。 因此，“钢铁侠”脱颖而出， 而“等待超人” 要等待很长一段时间。
他们发现, 在奈飞数据队列中, 在未来满心抱负的我们与 今天更为冲动的我们之间 始终存在着史诗般的斗争。 大家知道，我们都想成为 看过“罗生门”的那个人， 但现在 我们想第四次看“神探飞机头”。 （笑声） 而最好的编辑能兼顾这两方面的信息。 它会为我们提供一点儿有关贾斯汀·比伯的信息， 也会提供一点儿有关阿富汗的信息。 它会为我们提供一些信息“蔬菜”， 同时也为我们提供一些信息“甜点”。 这些算法过滤器和 这些个性化定制过滤器的挑战， 在于，因为它们主要参考 你最先点击的东西， 所以，它可能最后无法实现那种（信息间的）平衡。 非但不是平衡的信息“食谱”， 大家最终得到的可能 全是信息“垃圾食品”.
这表明， 实际上，我们在讲的可能是一个网络欺骗的故事。 在广播社会里-- 最初的虚构事实就是这样进行的-- 在广播社会里， 有这些审核者，编辑， 他们控制着信息流通。 随后出现了互联网，它取而代之了过去所有的信息流通方式， 它让我们所有人都联系在一起， 这曾经妙不可言。 但今天，实际上，情况已经发生了变化。 现在的情况，更像是(信息甄选的)“火炬” 从人工审核者 传递给了计算机算法“审核者”。 但问题是，这些计算机算法 自身并没有编辑们 所具备的职业道德。 所以，假若让算法为我们去创建一个世界， 假若它们来决定我们能看到什么、不能看到什么， 那么，我们必须要确保 它们不仅仅只是围绕“相关性”而已。 我们得确保它们也会给我们展示 那些不合意的、具有挑战性的或重要的信息-- 这也是TED的所追求的-- 其他的观点。
问题是，作为社会，我们实际上以前 有过如此的经历。 在1915年，报纸并没有报道很多有关 有关公民责任的信息。 那是，人们意识到， 报纸正在做的事情非常的重要。 事实上，假如得不到充分的信息 公民不可能 实现有效的民主。 报纸很关键，因为它们起的是信息过滤器的作用， 随后，新闻职业道德应运而生。 那时，它并不完美， 但是，它带我们走过了上个世纪。 所以，现在， 我们在网络上好像又回到了1915年。 我们需要新的信息审核者 将这种道德责任 输入到他们所写的算法代码中。
我知道，这里有很多来自脸谱和谷歌的朋友-- 拉里和谢尔盖-- 有很多帮助建起现有互联网的朋友， 对此，我是表示感谢的。 但我们真的需要你们来确保 互联网中的这些算法中考虑了 公共生活和公民责任感。 我们需要你们来确保这些算法有一定的透明， 使人们能了解些那些决定什么 能够通过我们的过滤器的运行规则。 我们需要你们给我们一些管理权限， 这样，我们就能决定 什么信息可以通过，什么不能通过。 因为我认为 我们真的需要互联网成为 我们所梦想的那样。 我们需要它使我们都联系在一起。 我们需要它向我们介绍新想法、 新面孔及不同的视角。 它不可能实现这些， 假如它把我们都孤立在个性化的网络中。
谢谢。
（掌声）



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Eli Pariser: Beware online "filter bubbles"
Mark Zuckerberg, a journalist was asking him a question about the news feed. And the journalist was asking him, "Why is this so important?" And Zuckerberg said, "A squirrel dying in your front yard may be more relevant to your interests right now than people dying in Africa." And I want to talk about what a Web based on that idea of relevance might look like.

So when I was growing up in a really rural area in Maine, the Internet meant something very different to me. It meant a connection to the world. It meant something that would connect us all together. And I was sure that it was going to be great for democracy and for our society. But there's this shift in how information is flowing online, and it's invisible. And if we don't pay attention to it, it could be a real problem. So I first noticed this in a place I spend a lot of time -- my Facebook page. I'm progressive, politically -- big surprise -- but I've always gone out of my way to meet conservatives. I like hearing what they're thinking about; I like seeing what they link to; I like learning a thing or two. And so I was surprised when I noticed one day that the conservatives had disappeared from my Facebook feed. And what it turned out was going on was that Facebook was looking at which links I clicked on, and it was noticing that, actually, I was clicking more on my liberal friends' links than on my conservative friends' links. And without consulting me about it, it had edited them out. They disappeared.

So Facebook isn't the only place that's doing this kind of invisible, algorithmic editing of the Web. Google's doing it too. If I search for something, and you search for something, even right now at the very same time, we may get very different search results. Even if you're logged out, one engineer told me, there are 57 signals that Google looks at -- everything from what kind of computer you're on to what kind of browser you're using to where you're located -- that it uses to personally tailor your query results. Think about it for a second: there is no standard Google anymore. And you know, the funny thing about this is that it's hard to see. You can't see how different your search results are from anyone else's.

But a couple of weeks ago, I asked a bunch of friends to Google "Egypt" and to send me screen shots of what they got. So here's my friend Scott's screen shot. And here's my friend Daniel's screen shot. When you put them side-by-side, you don't even have to read the links to see how different these two pages are. But when you do read the links, it's really quite remarkable. Daniel didn't get anything about the protests in Egypt at all in his first page of Google results. Scott's results were full of them. And this was the big story of the day at that time. That's how different these results are becoming.

So it's not just Google and Facebook either. This is something that's sweeping the Web. There are a whole host of companies that are doing this kind of personalization. Yahoo News, the biggest news site on the Internet, is now personalized -- different people get different things. Huffington Post, the Washington Post, the New York Times -- all flirting with personalization in various ways. And this moves us very quickly toward a world in which the Internet is showing us what it thinks we want to see, but not necessarily what we need to see. As Eric Schmidt said, "It will be very hard for people to watch or consume something that has not in some sense been tailored for them."

So I do think this is a problem. And I think, if you take all of these filters together, you take all these algorithms, you get what I call a filter bubble. And your filter bubble is your own personal unique universe of information that you live in online. And what's in your filter bubble depends on who you are, and it depends on what you do. But the thing is that you don't decide what gets in. And more importantly, you don't actually see what gets edited out. So one of the problems with the filter bubble was discovered by some researchers at Netflix. And they were looking at the Netflix queues, and they noticed something kind of funny that a lot of us probably have noticed, which is there are some movies that just sort of zip right up and out to our houses. They enter the queue, they just zip right out. So "Iron Man" zips right out, and "Waiting for Superman" can wait for a really long time.

What they discovered was that in our Netflix queues there's this epic struggle going on between our future aspirational selves and our more impulsive present selves. You know we all want to be someone who has watched "Rashomon," but right now we want to watch "Ace Ventura" for the fourth time. (Laughter) So the best editing gives us a bit of both. It gives us a little bit of Justin Bieber and a little bit of Afghanistan. It gives us some information vegetables, it gives us some information dessert. And the challenge with these kinds of algorithmic filters, these personalized filters, is that, because they're mainly looking at what you click on first, it can throw off that balance. And instead of a balanced information diet, you can end up surrounded by information junk food.

What this suggests is actually that we may have the story about the Internet wrong. In a broadcast society -- this is how the founding mythology goes -- in a broadcast society, there were these gatekeepers, the editors, and they controlled the flows of information. And along came the Internet and it swept them out of the way, and it allowed all of us to connect together, and it was awesome. But that's not actually what's happening right now. What we're seeing is more of a passing of the torch from human gatekeepers to algorithmic ones. And the thing is that the algorithms don't yet have the kind of embedded ethics that the editors did. So if algorithms are going to curate the world for us, if they're going to decide what we get to see and what we don't get to see, then we need to make sure that they're not just keyed to relevance. We need to make sure that they also show us things that are uncomfortable or challenging or important -- this is what TED does -- other points of view.

And the thing is we've actually been here before as a society. In 1915, it's not like newspapers were sweating a lot about their civic responsibilities. Then people noticed that they were doing something really important. That, in fact, you couldn't have a functioning democracy if citizens didn't get a good flow of information. That the newspapers were critical, because they were acting as the filter, and then journalistic ethics developed. It wasn't perfect, but it got us through the last century. And so now, we're kind of back in 1915 on the Web. And we need the new gatekeepers to encode that kind of responsibility into the code that they're writing.

I know that there are a lot of people here from Facebook and from Google -- Larry and Sergey -- people who have helped build the Web as it is, and I'm grateful for that. But we really need you to make sure that these algorithms have encoded in them a sense of the public life, a sense of civic responsibility. We need you to make sure that they're transparent enough that we can see what the rules are that determine what gets through our filters. And we need you to give us some control, so that we can decide what gets through and what doesn't. Because I think we really need the Internet to be that thing that we all dreamed of it being. We need it to connect us all together. We need it to introduce us to new ideas and new people and different perspectives. And it's not going to do that if it leaves us all isolated in a Web of one.

Thank you.

(Applause)

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http://dotsub.com/view/97e72d9c-4b84-4675-a0bc-b1e0caf3c439
Elliot Krane: 慢性疼痛之谜
我是一名儿科医生和麻醉师， 我以帮孩子们入睡为生。 （笑声） 我也是一名大学教师，所以我免费让听我讲课的人打瞌睡。 （笑声） 但我主要从事的 还是提供管理疼痛的服务 在帕罗奥多市 斯坦福大学的 帕卡德儿童医院。 从经验上来讲， 根据我20或25年的执业经验 今早，我想告诉你的是， 疼痛是一种病。
大多数时候， 你会认为疼痛是一种症状。 大多数情况下，这种想法是正确的。 它的确是肿瘤和感染的症状 或者是发炎、手术中的症状。 但有约10％的情况下， 病人虽然已经从上述情况下康复， 但疼痛依然继续。 有时候持续几个月 甚至持续几年。 当这种情况发生时， 疼痛本身就是一种疾病。 在我向你讲解它是如何发生 我们可以采取何种措施之前， 我想向你展示一下，病人对此的感受。 如果可以，请你想象一下， 我用这根羽毛挠你， 就像现在我挠自己的手臂一样。 现在，我要你想象 如果我是用这个（喷火枪）“挠”你呢？ 请大家只管坐好！ （笑声） 非常迥异的感觉。 这跟慢性疼痛有什么关联呢？ 如果这两个感觉被混淆了 想象一下你的生活将会怎样 如果我是用这根羽毛去挠你， 结果你的大脑却告诉你 你现在的感觉是灼伤-- 这就是我的患者对慢性疼痛的感受。 事实上，想象下更糟糕的情况。 想象下我用这根羽毛去挠你孩子的胳膊， 结果他们的大脑却告诉他们 他们感受到的是这个灼热的喷火枪。
这就是我的患者，钱德勒的遭遇， 就是照片中的女孩。 正如你看到的，她是个漂亮的年轻姑娘。 去年当我遇到她时，她16岁， 她渴望成为一名专业的舞者。 在她的一次舞蹈排练中， 她摔倒压在了自己向外伸展的手臂上，并且扭伤了手腕。 就像当时钱德勒的想法， 你可能也认为这就是人生当中 一次普通的受伤。 用绷带缠起来 吃点布洛芬，一周或两周 事情就会结束。 但是在钱德勒这次的遭遇中，这只是故事的开始。 图中是当时她手臂的情况 当她来到我的诊所时，已经是扭伤发生约三个月后 你可以看到手臂已经变色 有点青紫 摸上去像尸体一样冷 肌肉僵硬-- 我们通常所说的瘫痪性肌张力异常。 疼痛从她的手腕蔓延到手掌， 到手指，又从手腕蔓延到手肘， 一直到她的肩膀。
但最糟糕的 不是每天24小时的自发性疼痛。 最糟糕的是她患上痛觉超敏症 这是一个医学术语 说的就是我刚才用羽毛和喷火枪所演示的那种情况 轻微的触碰她的手臂 触碰手掌， 即使是她自己穿衣服时触碰到手袖、衣物 也会造成难以忍受的烧灼痛
神经系统怎么会犯这样的错呢？ 神经系统 怎么会误解一个本来无害的触觉呢？ 就像是把手的触摸 误解成 触摸火焰的恶意感觉。 把身体里的神经系统 想象成你家里的电线电路。 在你家里，电线布满在墙壁里， 从灯的开关到天花板上的接线盒 从接线盒又到灯泡。 当你打开开关的时候，灯泡亮了 等你关闭开关的时候，灯泡熄灭。 假设神经系统就是这样。 如果你用锤子敲大拇指， 你手臂里的电线——当然，我们称为神经—— 在脊髓里传递信息到接线盒 新电线，新的神经 把信息运送到大脑 因此你能自觉意识到你的大拇指受伤了。
这个情况，当然，是发生在身体里的 实际上更复杂一些。 相反 脊髓里的接线盒 仅仅是一段神经联系另一段神经的地方 通过释放这些小棕色块 也就是被称为神经递质的化学信息 以一对一的线性方式 事实上 是神经递质发散出三维空间-- 在脊髓里横向、纵向、以及上下地-- 它们和其他邻近的细胞之间 开始发生反应。 这些细胞，称为神经胶质细胞， 它们曾经被认为 是脊髓里不重要的结构构件 仅仅是把其他重要的东西连接在一起， 比如说神经。 结果却发现 在疼痛的案例中，神经胶质细胞 在调节，扩大 和歪曲 感觉体验的作用中起着非常重要的作用。 这些胶质细胞活跃起来。 它们的脱氧核糖核酸开始合成新的蛋白质 同时发散出来 与邻近的神经发生反应。 释放出神经递质。 神经递质又被发散出来 激活了邻近的胶质细胞，然后如此循环往复， 直到 神经反射。
几乎就像是有人到你家 重新在你墙壁里布线， 结果你下次打开灯的开关， 却意外的冲了三次马桶， 或者是启动了洗碗机， 或者是电脑显示器被关闭。 乱套了！ 但事实上， 这就是患上慢性疼痛后，会发生的事情。 也是为什么疼痛本身会变成一种疾病。 神经系统有可塑性。 对刺激做出反应 它会改变，也会变异。
那我们该怎么办呢？ 针对钱德勒这样的情况我们该怎么做呢？ 我们用相对粗糙的一种方式来治疗这些患者 目前是这样的。 我们用控制症状的药物来治疗 也就是止疼药 坦白说，对于这种病 不是很有效。 我们只是让那些吵闹的神经 消停下来， 用局部麻醉来使它们进入睡眠。 最重要的是，我们所做的 只是用枯燥且常不舒服的 物理治疗和职业治疗法 来在神经系统里维持神经 使它们在日常生活的 感官活动中 做出正常的反应。 对此，我们用密集的心理治疗方案 进行全力的配合 解决那些 伴随严重慢性疼痛而来的 沮丧、绝望和抑郁。
我们很成功！ 正如你从这个视频短片中看到的钱德勒， 在我们遇见她两个月之后， 她正做着后空翻。 昨天我刚和她一起吃了午饭， 因为现在，她是在长滩学习舞蹈的一名大学生。 她现在简直是棒极了！
未来更加美好！ 未来有希望 开发新的药物 不是那些只会控制症状的药物 它们仅仅是粉饰问题。 而是，正如我们现在看到的， 去开发那种治病的药物 能真正从根本上解决问题 对付神经胶质细胞 或对付那些由神经胶质细胞产生的 有害的蛋白质 它们溢出后使中枢神经系统产生发条拧紧现象， 产生神经的可塑性， 因此就有可能 歪曲和扩大了 我们称为疼痛的感觉体验。 所以，我有一个愿望
在将来 乔治·卡林的预言会实现， 他说，“我的哲学是： 没痛就不痛。”
非常感谢
（鼓掌声）



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Elliot Krane: The mystery of chronic pain
I'm a pediatrician and an anesthesiologist, so I put children to sleep for a living. (Laughter) And I'm an academic, so I put audiences to sleep for free. (Laughter) But what I actually mostly do is a manage the pain management service at the Packard Children's Hospital up at Stanford in Palo Alto. And it's from the experience from about 20 or 25 years of doing that that I want to bring to you the message this morning, that pain is a disease.

Now most of the time, you think of pain as a symptom of a disease. And that's true most of the time. It's the symptom of a tumor or an infection or an inflammation or an operation. But about 10 percent of the time, after the patient has recovered from one of those events, pain persists. It persists for months and oftentimes for years. And when that happens, it is its own disease. And before I tell you about how it is that we think that happens and what we can do about it, I want to show you how it feels for my patients. So imagine, if you will, that I'm stroking your arm with this feather, as I'm stroking my arm right now. Now, I want you to imagine that I'm stroking it with this. Please keep your seat. (Laughter) A very different feeling. Now what does it have to do with chronic pain? Imagine, if you will, these two ideas together. Imagine what your life would be like if I were to stroke it with this feather, but your brain was telling you that this is what you are feeling -- and that is the experience of my patients with chronic pain. In fact, imagine something even worse. Imagine I were to stroke your child's arm with this feather, and their brain [was] telling them that they were feeling this hot torch.

That was the experience of my patient, Chandler, whom you see in the photograph. As you can see, she's a beautiful, young woman. She was 16 years old last year when I met her, and she aspired to be a professional dancer. And during the course of one of her dance rehearsals, she fell on her outstretched arm and sprained her wrist. Now you would probably imagine, as she did, that a wrist sprain is a trivial event in a person's life. Wrap it in an ACE bandage, take some ibuprofen for a week or two, and that's the end of the story. But in Chandler's case, that was the beginning of the story. This is what her arm looked like when she came to my clinic about three months after her sprain. You can see that the arm is discolored, purplish in color. It was cadaverically cold to the touch. The muscles were frozen, paralyzed -- dystonic is how we refer to that. The pain had spread from her wrist to her hands, to her fingertips, from her wrist up to her elbow, almost all the way to her shoulder.

But the worst part was, not the spontaneous pain that was there 24 hours a day. The worst part was that she had allodynia, the medical term for the phenomenon that I just illustrated with the feather and with the torch. The lightest touch of her arm -- the touch of a hand, the touch even of a sleeve, of a garment, as she put it on -- caused excruciating, burning pain.

How can the nervous system get this so wrong? How can the nervous system misinterpret an innocent sensation like the touch of a hand and turn it into the malevolent sensation of the touch of the flame. Well you probably imagine that the nervous system in the body is hardwired like your house. In your house, wires run in the wall, from the light switch to a junction box in the ceiling and from the junction box to the light bulb. And when you turn the switch on, the light goes on. And when you turn the switch off, the light goes off. So people imagine the nervous system is just like that. If you hit your thumb with a hammer, these wires in your arm -- that, of course, we call nerves -- transmit the information into the junction box in the spinal cord where new wires, new nerves, take the information up to the brain where you become consciously aware that your thumb is now hurt.

But the situation, of course, in the human body is far more complicated than that. Instead of it being the case that that junction box in the spinal cord is just simple where one nerve connects with the next nerve by releasing these little brown packets of chemical information called neurotransmitters in a linear one-on-one fashion, in fact, what happens is the neurotransmitters spill out in three dimensions -- laterally, vertically, up and down in the spinal cord -- and they start interacting with other adjacent cells. These cells, called glial cells, were once thought to be unimportant structural elements of the spinal cord that did nothing more than hold all the important things together, like the nerves. But it turns out the glial cells have a vital role in the modulation, amplification and, in the case of pain, the distortion of sensory experiences. These glial cells become activated. Their DNA starts to synthesize new proteins, which spill out and interact with adjacent nerves. And they start releasing their neurotransmitters. And those neurotransmitters spill out and activate adjacent glial cells, and so on and so forth, until what we have is a positive feedback loop.

It's almost as if somebody came into your home and rewired your walls, so that the next time you turned on the light switch, the toilet flushed three doors down, or your dishwasher went on, or your computer monitor turned off. That's crazy, but that's, in fact, what happens with chronic pain. And that's why pain becomes its own disease. The nervous system has plasticity. It changes, and it morphs in response to stimuli.

Well, what do we do about that? What can we do in a case like Chandler's? We treat these patients in a rather crude fashion at this point in time. We treat them with symptom-modifying drugs -- pain-killers -- which are, frankly, not very effective for this kind of pain. We take nerves that are noisy and active that should be quiet, and we put them to sleep with local anesthetics. And most importantly, what we do is we use a rigorous, and often uncomfortable, process of physical therapy and occupational therapy to retrain the nerves in the nervous system to respond normally to the activities and sensory experiences that are part of everyday life. And we support all of that with an intensive psychotherapy program to address the despondency, despair and depression that always accompanies severe, chronic pain.

It's successful, as you can see from this video of Chandler, who, two months after we first met her, is now doings a back flip. And I had lunch with her yesterday, because she's a college student studying dance at Long Beach here. And she's doing absolutely fantastic.

But the future is actually even brighter. The future holds the promise that new drugs will be developed that are not symptom-modifying drugs that simply mask the problem, as we have now, but that will be disease-modifying drugs that will actually go right to the root of the problem and attack those glial cells, or those pernicious proteins that the glial cells elaborate, that spill over and cause this central nervous system wind-up, or plasticity, that so is capable of distorting and amplifying the sensory experience that we call pain. So I have hope

that in the future, the prophetic words of George Carlin will be realized, who said, "My philosophy: No pain, no pain."

Thank you very much.

(Applause)

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http://dotsub.com/view/abd891c5-7f07-4516-a644-e4e350ba7322
Susan Lim: 移植細胞，而不是器官
我在两位伟大的外科先驱者 授权下训练移植手术： 他们是托马斯·斯塔齐尔， 世界上第一个成功完成肝脏移植手术的人 在1967年， 以及罗伊·卡恩爵士， 在英国第一个完成肝脏移植手术的人 在之后的那年（1978）。 我回到新加坡 然后，在1990年 完成了亚洲首例 尸肝移植手术， 尽管它极其的困难。 现在，当我回想起， 这次移植其实是最简单的一部分。 下一步，凑钱来资助手术。 但是可能最有挑战行的部分 是去说服那些立法者-- 一件在议会辩论过的事宜-- 关于一个年轻的女性外科医生 被给予机会 成为她的国家的先驱。 20年过去了， 我的病人，萨伦德， 是亚洲尸肝移植的 最长存活记录。 （掌声） 也许更重要的， 我有幸成为她 14岁儿子的教母。
（掌声）
但是不是所有在移植等待列表上的病人 都是这么幸运的。 现实是， 捐献的器官还是不够 周转。 当捐献器官的需求 持续的增加， 很多一部分是因为人口老龄化问题， 然而供应还是相对的保持稳定。 仅仅美国， 10万人，男人，女人还有小孩 在等待捐助的器官， 而且每天有十几人死亡 就因为缺少捐献的器官。 移植团体 活跃于器官捐献的活动中。 而且器官（捐赠范围） 已经被扩大化 从脑死亡的捐献者 到活体的亲属捐献者-- 亲属可能捐献整个器官 或者一部分器官， 比如劈裂式肝移植， 给亲属或者是所爱的人。
但是捐献器官还是十分短缺， 接着器官（捐赠范围）再一次扩大 从活体亲属捐献者 现在到了活体的非亲属捐献者。 然后这就引发了 未遇见和料想到的 道德纷争。 怎么才能辨别 捐献者是自愿和无私的 而不是被迫的或强制的 从，比如， 忠诚的伴侣，媳婿， 仆人，奴隶， 雇员？ 我们什么时候如何才能加以界限？ 在我的世界里， 很多人生活在贫困线之下。 而且有些地方， 用活体的非亲属的器官捐赠 来换取金钱上的回报 已经是一个兴旺 的行业。
在我完成第一例肝移植后不久， 我接到我下一个人物， 是步入监狱中 从死刑犯人身上 获取器官。 我当时怀孕了。 在任何女性的生命中 怀孕意味着 喜悦以及使命感。 但是我的这段喜悦的时间 却被严肃的病态的想法所摧毁-- 关于经过 最高安保的死刑囚犯的囚房， 就像这是唯一的道路 带我去那临时手术室的想法。 每当这时， 我就能感到那 跟随我的从死刑犯人眼中的冰冷的目光。 之后的2年， 我挣扎在这窘境中 早上4点30醒 每周五凌晨， 开车到监狱， 着手准备，戴上手套，消过毒， 准备接手 死刑犯人的尸体， 移出器官 然后到运送这些器官 到接受者的医院 然后当天下午 给接受者移植这些 毫无疑问的，我是被告知， 是获得了准许（捐赠器官）。
但是我一生， 现在真正感到冲突-- 冲突的范围 从清晨的极度悲伤和迷惑 到黄昏的庆祝 移植器官成功的喜悦。 我的团队中， 一两个我的同事的生活 也被这样的经历所困扰。 我们中一些已经看开了， 但是其实我们没有人还是原来的自己。 我很疑惑 从死刑囚犯取下器官 至少道德上是有争议的 就像从人类胚胎中 获取肝细胞。 并且我认为， 作为一个外科先锋人物 它让我想起 是否有更好的方法-- 一种规避死亡 同时获得器官 这样可能会影响到 全世界成千上万的病人。
现在就是时候， 外科手术的实行 正从大到小， 从大的开放性的切口 到孔洞式的 微开口。 而且移植手术的概念已经传遍 从完整器官到细胞。 1988年，在明尼苏达大学， 我参与了一系列 关于完整胰脏移植手术。 我见证此科技的困难性。 同时这激发了我的思考 一个转变从移植真个器官 也许到移植细胞。 我寻思着， 为什么不能把个体细胞 移出胰脏-- 那种分泌出胰岛素治疗糖尿病的细胞-- 并且移植这些细胞呢？-- 技术的角度讲这是更简单的过程 相比于克服移植 整个细胞的复杂程度。
那是， 肝细胞研究 已经有些许进展， 世界上首次分离出 人类胚胎肝细胞 在90年代。 观察表明肝细胞，也叫肥大细胞， 能发育成 所有种类的细胞-- 心肌细胞，肝细胞， 胰岛细胞-- 引起了医学界的关注 和公众的想象力。 我也被这种全新的 突破性的科技所吸引， 同时这激发了我思维上的转变， 从移植整个器官 到移植细胞。 我把我的研究集中在 可能成为 细胞移植的肝细胞中。
今天我们意识到 有很多种不同的肝细胞。 胚胎单细胞 已经占据了中心位置， 主要是因为他们的多能性-- 就是他们能轻松的分化成 不同种类的细胞。 但是道德上的争论 围绕着胚胎肝细胞-- 事实是这些细胞来源于 5天大的人类胚胎-- 激励研究 其他类型的肝细胞。 我激励我的实验组 集中研究我认为 是最没有争议的干细胞源 脂肪组织，或叫肥肉，对肥肉 现如今已经有着充足的可利用来源-- 你和我一样，我想，很乐意摆脱它。 脂肪源干细胞 是成体肝细胞。 成体干细胞 你和我身上都能找到-- 从我们血液中，从我们骨髓中 从我们脂肪，皮肤和其他器官中。 同时这也证明， 脂肪是一种最优来源的 成体干细胞。 但是成体干细胞 不是胚胎干细胞。 这有局限性： 成体干细胞是成熟的细胞， 就像成年的人类， 这些细胞局思维更加局限 行为更加局限 同时也不能像胚胎干细胞一样 分化成很多种 特定的细胞。
但是在2007年， 两位杰出的人， 日本的新古中山聪 和美国的杰米·汤普逊， 有了一项令人震惊的发现。 他们发现 成体细胞，从你我身上获取的， 可以重新的编辑 变回到类胚胎干细胞， 他们起名为IPS细胞，
因此猜测，在世界各地的科学家在实验室都在竞相转换成人细胞老化 - 成年细胞衰老从你和我 - 他们都在竞相重新编程回到更有用iPS细胞，这些细胞。而在我们实验室，我们致力于走脂肪细胞重新编程为青春喷泉的脂肪丘 - 细胞，那么我们可能会使用其他的形式，更专业，细胞，这一天可作为细胞移植使用。如果这项研究成功的话，那么可以减少需要研究和牺牲人类胚胎。
事实上，有很多的炒作，同时也希望干细胞的承诺，将一天的条件提供一整套的治疗方法。心脏病，中风，糖尿病，脊髓损伤，肌肉萎缩症，视网膜疾病 - 是这些条件的相关亲自告诉你们啊？
2006年5月，可怕的事情发生在我身上。我正要开始一个机器人操作，但加强对进入手术室的灯光明亮，耀眼的电梯时，我意识到我的左视场快速进入黑暗崩溃。本周早些时候，我采取了在晚春滑雪比较硬敲 - 是的，我摔倒。我开始看到漂浮物和星星，我随便过多高空晒太阳驳回。我怎么可能是灾难性的，如果不是事实，我在访问达到了良好的手术。而我有我的视力恢复，但在此之前长期的疗养 - 在头向下的位置 - 三个月。这次经历教会了我更多的同情，我的病人，特别是与视网膜疾病的。
全世界有3700万人失明，127多万患有视觉障碍。干细胞衍生的视网膜移植手术目前处于研究阶段，可能一天恢复视力，或与世界各地的数百万患者视网膜病变的部分视力。事实上，我们生活在充满挑战和激动人心的时刻。随着世界人口老龄化，科学家们正在争先恐后地发现新的方法来增强身体的力量，通过干细胞治疗本身。
这是一个事实，即当我们的器官或组织受伤，我们的骨髓干细胞释放到我们的循环细胞。而这些干细胞然后浮到受损的器官释放生长因子，修复受损组织的血液和家庭。干细胞可作为构建块来修复损坏我们的身体内支架，或提供新的肝细胞修复受损的肝脏。在我们发言，有117个左右的干细胞研究的肝脏疾病防治的临床试验。
是什么样的未来？心脏疾病是全球主要的死亡原因。 110万美国人患有心脏病的生产能力。 480万患有心力衰竭。干细胞可被用于运载生长因子，修复受损的心脏肌肉或肌肉细胞分化成心脏，恢复心脏功能。有170个调查干细胞在心脏疾病中的作用进行临床试验。虽然仍然在研究阶段，干细胞可能有一天预示着在心脏病学领域的飞跃。
干细胞为新的开端的希望 - 小，渐进的步骤，而不是器官的细胞修复，而不是替代。干细胞疗法可能有一天会减少对捐赠器官的需要。强大的新技术总是存在的谜团。在我们发言的时候，世界上第一个人类胚胎干细胞对脊髓损伤的研究已在进行继美国FDA的批准。而在英国，神经干细胞治疗中风正在试验中的第一阶段调查。
这项研究的成功，我们今天庆祝已经成为可能，好奇心和贡献，个别科学家和医疗先锋承诺。每个人都有自己的故事。我的故事一直对我的旅程从器官到细胞 - 通过争议的旅程中，希望启发 - 希望，随着年龄的增长，你和我可能一天庆祝提高生活质量的长寿。

谢谢。




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Susan Lim: Transplant cells, not organs
So I was privileged to train in transplantation under two great surgical pioneers: Thomas Starzl, who performed the world's first successful liver transplant in 1967, and Sir Roy Calne, who performed the first liver transplant in the U.K. in the following year. I returned to Singapore and, in 1990, performed Asia's first successful cadaveric liver transplant procedure, but against all odds. Now when I look back, the transplant was actually the easiest part. Next, raising the money to fund the procedure. But perhaps the most challenging part was to convince the regulators -- a matter which was debated in the parliament -- that a young female surgeon be allowed the opportunity to pioneer for her country. But 20 years on, my patient, Surinder, is Asia's longest surviving cadaveric liver transplant to date. (Applause) And perhaps more important, I am the proud godmother to her 14 year-old son.

(Applause)

But not all patients on the transplant wait list are so fortunate. The truth is, there are just simply not enough donor organs to go around. As the demand for donor organs continues to rise, in large part due to the aging population, the supply has remained relatively constant. In the United States alone, 100,000 men, women and children are on the waiting list for donor organs, and more than a dozen die each day because of a lack of donor organs. The transplant community has actively campaigned in organ donation. And the gift of life has been extended from brain-dead donors to living, related donors -- relatives who might donate an organ or a part of an organ, like a split liver graft, to a relative or loved one.

But as there was still a dire shortage of donor organs, the gift of life was then extended from living, related donors to now living, unrelated donors. And this then has given rise to unprecedented and unexpected moral controversy. How can one distinguish a donation that is voluntary and altruistic from one that is forced or coerced from, for example, a submissive spouse, an in-law, a servant, a slave, an employee? Where and how can we draw the line? In my part of the world, too many people live below the poverty line. And in some areas, the commercial gifting of an organ in exchange for monetary reward has led to a flourishing trade in living, unrelated donors.

Shortly after I performed the first liver transplant, I received my next assignment, and that was to go to the prisons to harvest organs from executed prisoners. I was also pregnant at the time. Pregnancies are meant to be happy and fulfilling moments in any woman's life. But my joyful period was marred by solemn and morbid thoughts -- thoughts of walking through the prison's high-security death row, as this was the only route to take me to the makeshift operating room. And at each time, I would feel the chilling stares of condemned prisoners' eyes follow me. And for two years, I struggled with the dilemma of waking up at 4:30 am on a Friday morning, driving to the prison, getting down, gloved and scrubbed, ready to receive the body of an executed prisoner, remove the organs and then transport these organs to the recipient hospital and then graft the gift of life to a recipient the same afternoon. No doubt, I was informed, the consent had been obtained.

But in my life, the one fulfilling skill that I had was now invoking feelings of conflict -- conflict ranging from extreme sorrow and doubt at dawn to celebratory joy at engrafting the gift of life at dusk. In my team, the lives of one or two of my colleagues were tainted by this experience. Some of us may have been sublimated, but really none of us remained the same. I was troubled that the retrieval of organs from executed prisoners was at least as morally controversial as the harvesting of stem cells from human embryos. And in my mind, I realized as a surgical pioneer that the purpose of my position of influence was surely to speak up for those who have no influence. It made me wonder if there could be a better way -- a way to circumvent death and yet deliver the gift of life that might exponentially impact millions of patients worldwide.

Now just about that time, the practice of surgery evolved from big to small, from wide open incisions to keyhole procedures, tiny incisions. And in transplantation, concepts shifted from whole organs to cells. In 1988, at the University of Minnesota, I participated in a small series of whole organ pancreas transplants. I witnessed the technical difficulty. And this inspired in my mind a shift from transplanting whole organs to perhaps transplanting cells. I thought to myself, why not take the individual cells out of the pancreas -- the cells that secrete insulin to cure diabetes -- and transplant these cells? -- technically a much simpler procedure than having to grapple with the complexities of transplanting a whole organ.

And at that time, stem cell research had gained momentum, following the isolation of the world's first human embryonic stem cells in the 1990's. The observation that stem cells, as master cells, could give rise to a whole variety of different cell types -- heart cells, liver cells, pancreatic islet cells -- captured the attention of the media and the imagination of the public. I too was fascinated by this new and disruptive cell technology, and this inspired a shift in my mindset, from transplanting whole organs to transplanting cells. And I focused my research on stem cells as a possible source for cell transplants.

Today we realize that there are many different types of stem cells. Embryonic stem cells have occupied center stage, chiefly because of their pluripotency -- that is their ease in differentiating into a variety of different cell types. But the moral controversy surrounding embryonic stem cells -- the fact that these cells are derived from five-day old human embryos -- has encouraged research into other types of stem cells.

Now to the ridicule of my colleagues, I inspired my lab to focus on what I thought was the most non-controversial source of stem cells, adipose tissue, or fat, yes fat -- nowadays available in abundant supply -- you and I, I think, would be very happy to get rid of anyway. Fat-derived stem cells are adult stem cells. And adult stem cells are found in you and me -- in our blood, in our bone marrow, in our fat, our skin and other organs. And as it turns out, fat is one of the best sources of adult stem cells. But adult stem cells are not embryonic stem cells. And here is the limitation: adult stem cells are mature cells, and, like mature human beings, these cells are more restricted in their thought and more restricted in their behavior and are unable to give rise to the wide variety of specialized cell types, as embryonic stem cells.

But in 2007, two remarkable individuals, Shinya Yamanaka of Japan and Jamie Thompson of the United States, made an astounding discovery. They discovered that adult cells, taken from you and me, could be reprogrammed back into embryonic-like cells, which they termed IPS cells, or induced pluripotent stem cells. And so guess what, scientists around the world and in the labs are racing to convert aging adult cells -- aging adult cells from you and me -- they are racing to reprogram these cells back into more useful IPS cells. And in our lab, we are focused on taking fat and reprogramming mounds of fat into fountains of youthful cells -- cells that we may use to then form other, more specialized, cells, which one day may be used as cell transplants. If this research is successful, it may then reduce the need to research and sacrifice human embryos.

Indeed, there is a lot of hype, but also hope that the promise of stem cells will one day provide cures for a whole range of conditions. Heart disease, stroke, diabetes, spinal cord injury, muscular dystrophy, retinal eye diseases -- are any of these conditions relevant personally to you?

In May 2006, something horrible happened to me. I was about to start a robotic operation, but stepping out of the elevator into the bright and glaring lights of the operating room, I realized that my left visual field was fast collapsing into darkness. Earlier that week, I had taken a rather hard knock during late spring skiing -- yes, I fell. And I started to see floaters and stars, which I casually dismissed as too much high-altitude sun exposure. What happened to me might have been catastrophic, if not for the fact that I was in reach of good surgical access. And I had my vision restored, but not before a prolonged period of convalescence -- three months -- in a head down position. This experience taught me to empathize more with my patients, and especially those with retinal diseases.

37 million people worldwide are blind, and 127 million more suffer from impaired vision. Stem cell-derived retinal transplants, now in a research phase, may one day restore vision, or part vision, to millions of patients with retinal diseases worldwide. Indeed, we live in both challenging as well as exciting times. As the world population ages, scientists are racing to discover new ways to enhance the power of the body to heal itself through stem cells.

It is a fact that when our organs or tissues are injured, our bone marrow releases stem cells into our circulation. And these stem cells then float in the bloodstream and home in to damaged organs to release growth factors to repair the damaged tissue. Stem cells may be used as building blocks to repair damaged scaffolds within our body, or to provide new liver cells to repair damaged liver. As we speak, there are 117 or so clinical trials researching the use of stem cells for liver diseases.

What lies ahead? Heart disease is the leading cause of death worldwide. 1.1 million Americans suffer heart attacks yearly. 4.8 million suffer cardiac failure. Stem cells may be used to deliver growth factors to repair damaged heart muscle or be differentiated into heart muscle cells to restore heart function. There are 170 clinical trials investigating the role of stem cells in heart disease. While still in a research phase, stem cells may one day herald a quantum leap in the field of cardiology.

Stem cells provide hope for new beginnings -- small, incremental steps, cells rather than organs, repair rather than replacement. Stem cell therapies may one day reduce the need for donor organs. Powerful new technologies always present enigmas. As we speak, the world's first human embryonic stem cell trial for spinal cord injury is currently underway following the USFDA approval. And in the U.K., neural stem cells to treat stroke are being investigated in a phase one trial.

The research success that we celebrate today has been made possible by the curiosity and contribution and commitment of individual scientists and medical pioneers. Each one has his story. My story has been about my journey from organs to cells -- a journey through controversy, inspired by hope -- hope that, as we age, you and I may one day celebrate longevity with an improved quality of life.

Thank you.

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http://dotsub.com/view/805361cf-4156-4c4a-884d-6f3327276f71
Danny Hillis: 了解癌症透過蛋白質組學
我得承认我有点紧张， 因为我将要谈谈一个很是激进的观点 关于我们应该怎么从新的角度看癌症这个东西 尤其是你们中很多人 都是癌症专家，比我懂得多了。 但我也得承认我的弦绷得还不够紧， 因为我挺自信我是对的。 （笑声） 我相信，事实上，（我的观点） 将会是未来我们治疗癌症的途经。 要谈癌症前， 我其实得—— 让我展示这张图。 首先，我得让你们从另一个角度看基因组学。 我希望把基因组学放在大环境中来看 在不断变化的大环境中—— 然后我会谈谈蛋白质组学，你们可能没怎么听过。 讲了这两个之后， 大家就好接受我的关于， 怎么治疗癌症的新观点了。
现在让我从基因组学开始。 这可是热门科学。 从中我们学到到最多， 可谓科学前沿。 但是它也有美中不足之处。 特别是你们可能听过的一个比喻， 基因组学就像是你身体的蓝图， 如果真是这样就太好了。 可惜不然。 基因组学好比你身体中的零件列表， 但并没有说明每件之间是怎么连接的。 什么是因，什么是果，等等。 允许我也打个比方， 就好比你想比较 好吃又健康的餐馆 和差的餐馆， 但你手里只有它们的佐料清单， 它们贮藏室里有什么。 就好像你去一个法国餐厅， 你查一个遍最后发现 它们只用人造黄油，不用天然黄油， 你可能会说：“嗯，我知道问题在哪里了， 我能让它变成健康的餐馆。” 恐怕有时确实是这种情况。 你可能很容易说出 中国餐馆和法国餐馆之间的区别， 就凭它们贮藏室里有什么。 所以说佐料单确实提供一些信息， 有时它能告诉你问题出在哪里。 好比它们有很多食盐， 你恐怕能猜出他们用盐太多之类的。 但是只是一些信息。 因为要确定一个餐馆是不是健康， 你得尝尝它们的食物，你得了解厨房里是怎么运作的， 你需要这些佐料的最终产品。
所以如果你看一个人（是不是健康）， 如果我只看这个人的基因组，就像（只看餐馆的佐料单）一样。 我们能够从基因组看出来的， 也只是“佐料”列表而已。 所以事实上， 有的时候我们能够看出 什么“佐料”不好。 囊性纤维化就是这类病的一个例子， 只要一个“佐料”坏了就能发病， 这里我们真能在基因和疾病间 建立直接的联系。 但是大多数情况下，你真得知道厨房里是怎么回事， 因为绝大部分的病人都曾是健康的—— 这些人的基因没有变。 所以基因组真正告诉我们的， 不过是易感性而已。 光看“佐料单”你能够作出的结论 只不过是这个人是亚洲人， 那个人是欧洲人而已。 大多数的时候，病人和健康人之间的区别 从基因组学是看不出来的—— 除非很特别的情况下。
那为什么遗传学研究 这么重要呢？ 首先， 这是我们能够掌握的信息，很不容易的。 遗传信息在某些情况下特别有用。 在生物学中，遗传学 是生物理论研究上的巨大成功。 它是个生物学上唯一的 所谓理论，能经得起推敲的。 它是达尔文学说的基础， 也是孟德尔学说和后续理论的基础。 他们预测出了这个理论构架。 孟德尔认为基因 是抽象的。 达尔文把自己的整个学说 建立在基因是实体的基础上。 接下来沃森和克瑞特 观察发现了基因的存在。 这样的逻辑在物理学中常见。 人们预测了黑洞的存在， 之后用望远镜找，发现了之前预测的黑洞。 但是这个逻辑在生物学中很少见。 这就是为什么这个成功如此伟大——它如此伟大—— 几乎称得上是生物学中的 神迹 而其中达尔文的进化论 称得上是理论核心。
遗传学这么广为接受的另一个原因， 就是我们能够测量它，它是数字化的。 事实上， 感谢凯瑞莫里斯（发明了聚合酶链反应PCR的生物学家） 你能事实上测量你的基因组，就在你自己的厨房里 就靠几种材料。 举个例子，就靠着测量基因， 我们已经深入理解了我们是怎么和其他动物同源的， 就靠看我们和他们基因组间的相似性， 或者我们人类是怎么相联系的——家谱之类的， 或者是生物进化树。 就靠着比较基因的相似性， 遗传学就能提供很多的信息。 当然了，在医学应用方面， 这也是很有用的， 因为这是和医生从你的家族病史中 得到的信息是类似的—— 只不过家族病史只是一个随机的子信息， 你的基因其实能解释很多的你的病史，比你能解释的还要多。 所以通过解读基因， 我们能够比你还了解你的家庭。 我们还能发现新的信息 那些你应该早知道的 凭着观察你的亲戚们， 但这些事情可能还是会出乎你的意料。 我做了类似的一个测试， 很惊讶的发现我不但过胖还秃头。 （笑声） 但有时你能得到很多有用的信息。
多数情况下 发现疾病的必需的信息 并不是你的易感性， 而是现时你身体的发生了什么。 为了发现疾病，你真需要做的， 你真需要观察的， 是你的基因的产物， 是基因组学之后的一个层次。 这正是蛋白质组学所研究的。 就像是基因组学研究所有的基因， 蛋白质组学研究所有的蛋白质。 这些蛋白质是你体内的小小物质 它们在每个细胞间传递信息—— 它们是真正操纵你身体的迷你机器。 它们是行动者。 基本上，人体 是个 在细胞里和细胞间的持续对话， 细胞们告诉对方该长大还是该消失。 当你生病时， 这种对话就出错了。 这里的微妙之处在于—— 不幸的是，我们没有像测试基因一样容易的方法， 来测试这些蛋白质。
问题在于测试方法—— 如果你试图一起测试所有的蛋白质，这是个非常复杂的过程。 需要上百个步骤， 需要很长的时间。 蛋白质的含量也很有关系。 十分之一的蛋白质的量变就很要命了， 所以这并不是像基因一样是数码制的（分离系统）。 基本上我们的问题是，如果有人在测试蛋白质， 在长时间的操作中， 暂停了一下下， 把蛋白质留在蛋白酶中，就多一秒， 突然间所有的测量，从这一刻开始， 就不再准确了。 所以大家不断得到特别不一致的结果 因为他们是这么测量的。 大家做了很多努力来测量蛋白质， 我自己也做了几次实验 试着克服这个问题，最后我放弃了。
后来我开始不断接到从大卫 艾格斯， 一个癌症学家的电话。 我们公司“Applied Minds”总是需求不断的， 不断有人要求我们帮忙， 我以为这个电话是不会再来的。 所以我迟迟没有回他的电话。 直到有一天， 我同一天内接到约翰 德尔，比尔 伯克曼， 和埃尔 高尔的电话 让我给大卫 艾格斯回电话。 （笑声） 所以我想：“打就打，至少这个人聪明到会用关系网。” （笑声） 这样我们开始对话， 他说：“我迫切需要更好的技术来测量蛋白质。” 我说：“我试了，也失败了。 不是容易做的。” 他说：“我了，但是我是真需要。 病人天天死在我眼前 就因为我们不知道身体里面发生了什么。 我们一定要找到办法看透他们的身体。” 他还给我举了些例子， 有些病人是如何需要这个技术， 我才意识到，哇，如果我们能测量蛋白质的话， 真的能改变命运。 于是我说：“好吧让我试试。”
我们公司有些积蓄， 是用来作初级测试的， 不需要客户出钱或者授权。 于是我们就开始研发这个技术。 我们做的时候，意识到这里有个根源性的问题—— 非常基本——（这句不知道怎么翻译） 就是不该是靠人工来做这件事。 我们真正需要的 是让机器做，就像是在流水线上一样， 做出机器人 来替我们测试蛋白质。 于是我们就这样做了。 和大卫合作， 我们成立了一个小小的公司，定名为“蛋白组学应用公司”， 专门做这些能够稳定测量蛋白质的 机器人。 接下来我要介绍这个测量技术是什么样的。
基本上，我们所做的是 从病人身上 取一滴血， 然后检测这滴血里的 所有的蛋白质 根据蛋白质的不同质量， 和蛋白质的不同粘性。 我们给它们画个图， 就能从这一滴血中 同时看到 成百上千个不同的信息。 第二天我们还可以再检测一次， 你能看到第二天你的蛋白质组群是不同的—— 你吃东西或者睡觉都会改变它们。 它们是你身体里的实况报告。 这就是一个图， 看起来像是一大片污迹， 正是让我觉得我们走对了路， 让我觉得无比震撼的。 如果我放大某个部分， 你就能看到我指的是什么。 我们把蛋白质都分开了——从左到右， 是不同的蛋白片断的质量， 从上到下是它们的粘性。 我们放大图的这块，让你能看清很小的一点点。 这几条线里的每一条， 都代表了这片蛋白的不同信息。 你能看到它们是怎么分布的， 都是一小组一小组的， 这是因为我们测量质量的方法精细到—— 能看到碳原子的不同同位素， 如果这个碳原子多一个少一个中子， 我们都能测得出来，把它们分开。 所以我们其实测量得到每个同位素。
这告诉我们 这个技术有多灵敏。 我们看这张图片 就像是伽利略 看星星 第一次从望远镜中看到 你会感叹：“喔，这比我想象的复杂多了。” 但我们能够看到这些区别， 看到里面的信息。 这是个特例，我们能够通过它得到一个模式， 方法是 比如，我们可以比较两个病人 一个对药物有阳性反应，另一个药物不起作用。 然后问： “他们身体内有什么不同？” 通过精确的测量技术， 我们可以比较来看两个人的蛋白质有什么不同。
像这里爱丽丝的是绿色的， 鲍勃的是红的， 让我们比较两个结果，这是真的病人的结果。 你能看到，绝大部分是一样的，显示黄色， 但有些蛋白是爱丽丝专有的， 有的是鲍勃专有的。 如果我们能发现在对药物有阳性反应的 病人的共性， 我们从血液中能发现， 他们都有共同性 让药物能对他们起作用， 我们可能不知道起作用的蛋白质的名字， 但我们能用它作为一个标志物， 来标明对于疾病的反应。 所以这个已经是，我认为， 在医药学上极其有用的。 但我认为这其实只是 一个开始， 将来我们要用它来治疗癌症。 让我来谈谈癌症。
癌症—— 当我开始研究它， 我什么都不知道， 但是通过和大卫 艾格斯工作， 我开始观察癌症是怎样被治疗的。 我还观察了手术，癌组织是怎么被取走的。 当我研究癌症时， 对我来说，我们治疗癌症的方法 并不正确。 为了理解这个途径， 我得学习这些现今的治疗方法是怎么确定的。 我们治疗癌症，好像癌症是传染病一样， 我们治疗癌症像是癌症侵入了我们体内 我们得消灭敌人。 这是为什么取走癌组织被认为是很好的模式。 另一种情况， 这里生物的理论模式真的起作用了—— 就是疾病是细菌的理论。 医生都被训练 来诊断疾病—— 就是把你放进一个类别里去—— 给你用来治疗这个类别的人 通常起作用的那个治疗方法。 这通常对传染病是起作用的。 如果我们把你放在这个类别中， 就好像你得了梅毒，我们就给你青霉素。 我们知道青霉素能治好你。 就好像如果你得了疟疾，我们给你奎宁， 或者相似的药物。 因为这是通常医生被训练去做的。 对于传染病， 这个非常管用—— 就像是一个奇迹。 如果医生们不这样做， 我们中的很多人恐怕活不到今天。
但是当我们把类似的治疗方法用于 像癌症那样的系统性疾病， 就有问题了。对于癌症， 没有别的， 就是你出了问题。 是你，你有地方坏了， 这是因为你体内的对话出了问题， 开始各处错误对话。 我们怎样解读这样的错误对话呢？ 我们现在在做的是把癌症按照身体部分分类—— 你知道，按照癌症在什么地方发生—— 把你放进不同的类别里， 那个身体部分的类别。 接下来我们做医疗实验， 比如对肺癌用一个药， 对前列腺癌用一个药，对乳癌用一个药， 我们治疗这些癌症，把它们当作是完全不同的病。 这样对癌症的分类方法 是真的按照出了什么问题来的。 当然了，其实这和到底什么出了问题 并没有直接的关系。 因为癌症是一个系统失灵。 事实上，我认为像这样把癌症当成是一件事来谈， 都是错误的。 我认为这是个大错误。 我看癌症，不是一个事物。 我们应该用的词是“得癌”。 是我们得的，不是我们有的。 那些癌组织， 只是“得癌”的一个症状。 你的身体随时都在得癌， 但是绝大多数情况下，你的身体机构 能够不让它们发展。
这里我给你一个概念， 算是我的定义的一个比方， 想象癌症是一个过程， 只要想象我们对下水管道一无所知， 我们通常会这样描述： 我们回家，发现厨房有漏水， 我们就说：“我们的房子有水。” 我们能够粗粗分类——管道工会问：“哪里有水？” “厨房里。”“那就是厨房水了。” 这就是我们现在对癌症的认识。 “厨房水”， 首先，我们要去厨房，把水拖干净， 之后我们知道，在厨房里喷上 管道清洁剂能起作用。 如果是客厅水， “屋顶防潮剂能起作用。” 这听起来可笑， 但是这是我们现在用的对策。 我不是说得了癌症后你不该除掉“厨房水”， 我只是说那并不是问题的症结； 那只是问题的症状。
我们真的该解决的， 是正在发生的症结。 而且这个解决方案应该发生在 蛋白质组互相作用的层次上， 发生在为什么你的身体不能自行治愈， 像它通常能做的？ 通常你的身体每天都在解决这些问题。 所以说你的“房子”其实一直有漏水的问题， 但是它在自己解决，自己排出漏水等等。 我们需要的 是发展出一个症结模型 来模拟问题是怎么发生的。 蛋白质组学能够提供给我们 建立起这样的模型的能力。
大卫请我去国家癌症研究院 做个讲座， 安娜 巴克也在那里。 我做了讲座， 然后问他们：“为什么你们不按照这个思路做？” 安娜说： “因为癌症学界没有人 能从这个角度看事情。 但是我们想要做的，是成立一个计划署， 让不在癌症学界工作的人们 来和真正对付癌症的 医生们合作， 发展出一个不同的研究方案。” 这样大卫和我就向这个计划署申请 在USC（南加州大学）成立了 一个集团， 在那里我们有世界顶级的癌症学家， 还有从Cold Spring Harbor（冷泉港）， Stanford（斯坦福），Austin（奥斯汀）等多处的 一些世界级的生物学家—— 我都列不出全部这些合作者们—— 来做这个研究项目。 在未来的五年， 我们将为癌症做一个症结模型。 我们正首先在小鼠身上做这个模型。 在这个过程中， 我们需要使用很多小鼠， 但至少它们死得其所。 之后我们会到达一个阶段， 是我们能有个预测出的模型， 在这个模型里我们是真的明白 癌症是什么时候产生的， 里面是怎么回事， 什么样的治疗方案能够奏效。
这里让我稍稍描绘一下远景，来结束这个演讲 谈谈我认为未来的癌症治疗方案是怎么一回事。 我认为，总有一天， 当我们给每个病人都树立了正确的模型， 总有一天—— 光靠我们的研究队伍是不够的—— 但是最终我们会得到很好的计算模型—— 像是一个全球气候模型。 这个模型包含了很多信息 描述蛋白质组间的对话， 从不同的精确度。 这样我们就可以模拟 为你身上的那种癌症 做出一个疾病模型来—— 我们也可以为ALS（肌肉萎縮性側索硬化症） 或者任何一种系统性的神经退化疾病做（这样的模型） 这类的疾病—— 我们会特别为你 模拟一个治疗方案， 不为其他任何人， 而是根据你身体真的在发生什么，
在这个程序里，我们能 为你特别设计 一系列的治疗方案 这些可以是非常轻微的治疗，非常微量的药量 好比是，这天先别吃东西， 或者给一点点化疗， 一点点放射性治疗， 当然了，有时手术是不可避免的。 但是我们能够为你量身定做治疗方案， 帮助你的身体， 领着它逐渐恢复健康—— 领着你的身体恢复健康。 因为你的身体会尽量自己恢复， 只要我们在它走错路的时候扶一把， 只要我们能够提供支持 你的身体有很多的潜力， 自己治疗癌症。 我们只需要关键时刻帮一把， 帮它回到正路上来。
我相信这将会是 未来治疗癌症的途径。 这将需要我们不断的努力， 很多很多的科研。 需要其他的研究队伍，像我们队伍这样的 一起进行这个研究。 但我相信终有一天， 我们能为每个人 量身定做治疗癌症的方案。
谢谢大家。
（掌声）



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Danny Hillis: Understanding cancer through proteomics
I admit that I'm a little bit nervous here because I'm going to say some radical things about how we should think about cancer differently to an audience that contains a lot of people who know a lot more about cancer than I do. But I will also contest that I'm not as nervous as I should be because I'm pretty sure I'm right about this. (Laughter) And that this, in fact, will be the way that we treat cancer in the future. In order to talk about cancer, I'm going to actually have to -- let me get the big slide here. First, I'm going to try to give you a different perspective of genomics. I want to put it in perspective of the bigger picture of all the other things that are going on -- and then talk about something you haven't heard so much about, which is proteomics. Having explained those, that will set up for what I think will be a different idea about how to go about treating cancer.

So let me start with genomics. It is the hot topic. It is the place where we're learning the most. This is the great frontier. But it has its limitations. And in particular, you've probably all heard the analogy that the genome is like the blueprint of your body. And if that were only true, it would be great, but it's not. It's like the parts list of your body. It doesn't say how things are connected, what causes what and so on. So if I can make an analogy, let's say that you were trying to tell the difference between a good restaurant, a healthy restaurant, and a sick restaurant, and all you had was the list of ingredients that they had in their larder. So it might be that, if you went to a French restaurant and you looked through it and you found they only had margarine and they didn't have butter, you could say, "Ah, I see what's wrong with them. I can make them healthy." And there probably are special cases of that. You could certainly tell the difference between a Chinese restaurant and a French restaurant by what they had in a larder. So the list of ingredients does tell you something, and sometimes it tells you something that's wrong. If they have tons of salt, you might guess they're using too much salt, or something like that. But it's limited, because really to know if it's a healthy restaurant, you need to taste the food, you need to know what goes on in the kitchen, you need the product of all of those ingredients.

So if I look at a person and I look at a person's genome, it's the same thing. The part of the genome that we can read is the list of ingredients. And so indeed, there are times when we can find ingredients that [are] bad. Cystic fibrosis is an example of a disease where you just have a bad ingredient and you have a disease, and we can actually make a direct correspondence between the ingredient and the disease. But most things, you really have to know what's going on in the kitchen, because, mostly, sick people used to be healthy people -- they have the same genome. So the genome really tells you much more about predisposition. So what you can tell is you can tell the difference between an Asian person and a European person by looking at their ingredients list. But you really for the most part can't tell the difference between a healthy person and a sick person -- except in some of these special cases.

So why all the big deal about genetics? Well first of all, it's because we can read it, which is fantastic. It is very useful in certain circumstances. It's also the great theoretical triumph of biology. It's the one theory that the biologists ever really got right. It's fundamental to Darwin and Mendel and so on. And so it's the one thing where they predicted a theoretical construct. So Mendel had this idea of a gene as an abstract thing. And Darwin built a whole theory that depended on them existing. And then Watson and Crick actually looked and found one. So this happens in physics all the time. You predict a blackhole, and you look out the telescope and there it is, just like you said. But it rarely happens in biology. So this great triumph -- it's so good -- there's almost a religious experience in biology. And Darwinian evolution is really the core theory.

So the other reason it's been very popular is because we can measure it, it's digital. And in fact, thanks to Kary Mullis, you can basically measure your genome in your kitchen with a few extra ingredients. So for instance, by measuring the genome, we've learned a lot about how we're related to other kinds of animals by the closeness of our genome, or how we're related to each other -- the family tree, or the tree of life. There's a huge amount of information about the genetics just by comparing the genetic similarity. Now of course, in medical application, that is very useful because it's the same kind of information that the doctor gets from your family medical history -- except probably, your genome knows much more about your medical history than you do. And so by reading the genome, we can find out much more about your family than you probably know. And so we can discover things that probably you could have found by looking at enough of your relatives, but they may be surprising. I did the 23andMe thing and was very surprised to discover that I am fat and bald. (Laughter) But sometimes you can learn much more useful things about that.

But mostly what you need to know to find out if you're sick is not your predispositions, but it's actually what's going on in your body right now. So to do that, what you really need to do, you need to look at the things that the genes are producing and what's happening after the genetics. And that's what proteomics is about. Just like genome mixes the study of all the genes, proteomics is the study of all the proteins. And the proteins are all of the little things in your body that are signaling between the cells -- actually the machines that are operating. That's where the action is. Basically, a human body is a conversation going on, both within the cells and between the cells, and they're telling each other to grow and to die. And when you're sick, something's gone wrong with that conversation. And so the trick is -- unfortunately, we don't have an easy way to measure these like we can measure the genome.

So the problem is that measuring -- if you try to measure all the proteins, it's a very elaborate process. It requires hundreds of steps, and it takes a long, long time. And it matters how much of the protein it is. It could be very significant that a protein changed by 10 percent, so it's not a nice digital thing like DNA. And basically our problem is somebody's in the middle of this very long stage, they pause for just a moment, and they leave something in an enzyme for a second, and all of a sudden all the measurements from then on don't work. And so then people get very inconsistent results when they do it this way. People have tried very hard to do this. I tried this a couple of times and looked at this problem and gave up on it.

I kept getting this call from this oncologist named David Agus. And Applied Minds gets a lot of calls from people who want help with their problems, and I didn't think this was a very likely one to call back, so I kept on giving him to the delay list. And then one day, I get a call from John Doerr, Bill Berkman and Al Gore on the same day saying return David Agus's phone call. (Laughter) So I was like, "Okay. This guy's at least resourceful." (Laughter) So we started talking, and he said, "I really need a better way to measure proteins." I'm like, "Looked at that. Been there. Not going to be easy." He's like, "No, no. I really need it. I mean, I see patients dying every day because we don't know what's going on inside of them. We have to have a window into this." And he took me through specific examples of when he really needed it. And I realized, wow, this would really make a big difference, if we could do it. And so I said, "Well, let's look at it."

Applied Minds has enough play money that we can go and just work on something without getting anybody's funding or permission or anything. So we started playing around with this. And as we did it, we realized this was the basic problem -- that taking the sip of coffee -- that there were humans doing this complicated process and that, what really needed to be done, was to automate this process like an assembly line and build robots that would measure proteomics. And so we did that. And working with David, we made a little company called Applied Proteomics eventually, which makes this robotic assembly line, which, in a very consistent way, measures the protein. And I'll show you what that protein measurement looks like.

Basically, what we do is we take a drop of blood out of a patient, and we sort out the proteins in the drop of blood according to how much they weigh, how slippery they are, and we arrange them in an image. And so we can look at literally hundreds of thousands of features at once out of that drop of blood. And we can take a different one tomorrow, and you will see your proteins tomorrow will be different -- they'll be different after you eat or after you sleep. They really tell us what's going on there. And so this picture, which looks like a big smudge to you, is actually the thing that got me really thrilled about this and made me feel like we were on the right track. So if I zoom into that picture, I can just show you what it means. We sort out the proteins -- from left to right is the weight of the fragments that we're getting. And from top to bottom is how slippery they are. So we're zooming in here just to show you a little bit of it. And so each of these lines represents some signal that we're getting out of a piece of a protein. And you can see how the lines occur in these little groups of bump, bump, bump, bump, bump. And that's because we're measuring the weight so precisely that -- carbon comes in different isotopes, so if it has an extra neutron on it, we actually measure it as a different chemical. So we're actually measuring each isotope as a different one.

And so that gives you an idea of how exquisitely sensitive this is. So seeing this picture is sort of like getting to be Galileo and looking at the stars and looking through the telescope for the first time, and suddenly you say, "Wow, it's way more complicated than we thought it was." But we can see that stuff out there and actually see features of it. So this is the signature out of which we're trying to get patterns. So what we do with this is, for example, we can look at two patients, one that responded to a drug and one that didn't respond to a drug, and ask, "What's going on differently inside of them?" And so we can make these measurements precisely enough that we can overlay two patients and look at the differences.

So here we have Alice in green and Bob in red. We overlay them. This is actual data. And you can see, mostly it overlaps and it's yellow, but there's some things that just Alice has and some things that just Bob has. And if we find a pattern of things of the responders to the drug, we see that in the blood, they have the condition that allows them to respond to this drug. We might not even know what this protein is, but we can see it's a marker for the response to the disease. So this already, I think, is tremendously useful in all kinds of medicine. But I think this is actually just the beginning of how we're going to treat cancer. So let me move to cancer.

The thing about cancer -- when I got into this, I really knew nothing about it, but working with David Agus, I started watching how cancer was actually being treated and went to operations where it was being cut out. And as I looked at it, to me it didn't make sense how we were approaching cancer. And in order to make sense of it, I had to learn where did this come from. We're treating cancer almost like it's an infectious disease. We're treating it as something that got inside of you that we have to kill. So this is the great paradigm. This is another case where a theoretical paradigm in biology really worked -- was the germ theory of disease. So what doctors are mostly trained to do is diagnose -- that is put you into a category -- and apply a scientifically proven treatment for that diagnosis. And that works great for infectious diseases. So if we put you in the category of you've got syphilis, we can give you penicillin. We know that that works. If you've got malaria, we give you quinine, or some derivative of it. And so that's the basic thing doctors are trained to do. And it's miraculous in the case of infectious disease -- how well it works. And many people in this audience probably wouldn't be alive if doctors didn't do this.

But now let's apply that to systems diseases like cancer. The problem is that, in cancer, there isn't something else that's inside of you. It's you, you're broken. That conversation inside of you got mixed up in some way. So how do we diagnose that conversation? Well right now what we do is we divide it by part of the body -- you know, where did it appear -- and we put you in different categories according to the part of the body. And then we do a clinical trial for a drug for lung cancer and one for prostate cancer and one for breast cancer, and we treat these as if they're separate diseases and that this way of dividing them had something to do with what actually went wrong. And of course, it really doesn't have that much to do with what went wrong. Because cancer is a failure of the system. And in fact, I think we're even wrong when we talk about cancer as a thing. I think this is the big mistake. I think cancer should not be a noun. We should talk about cancering as something we do, not something we have. And so those tumors, those are symptoms of cancer. And so your body is probably cancering all the time. But there are lots of systems in your body that keep it under control.

And so to give you an idea of an analogy of what I mean by thinking of cancering as a verb, imagine we didn't know anything about plumbing, and the way that we talked about it, we'd come home and we'd find a leak in our kitchen and we'd say, "Oh, my house has water." We might divide it -- the plumber would say, "Well, where's the water?" "Well, it's in the kitchen." "Oh, you must have kitchen water." That's kind of the level at which it is. "Kitchen water? Well, first of all, we'll go in there and we'll mop out a lot of it. And then we know that if we sprinkle Draino around the kitchen, that helps. Whereas living room water, it's better to do tar on the roof." And it sounds silly, but that's basically what we do. And I'm not saying you shouldn't mop up your water if you have cancer. But I'm saying that it's not really the problem; that's the symptom of the problem.

What we really need to get at is the process that's going on, and that's happening at the level of the proteonomic actions, happening at the level of why is your body not healing itself in the way that it normally does? Because normally your body is dealing with this problem all the time. So your house is dealing with leaks all the time. But it's fixing them. It's draining them out and so on. So what we need is to have a causative model of what's actually going on. And proteomics actually gives us the ability to build a model like that.

David got me invited to give a talk at National Cancer Institute and Anna Barker was there. And so I gave this talk and said, "Why don't you guys do this?" And Anna said, "Because nobody within cancer would look at it this way. But what we're going to do, is we're going to create a program for people outside the field of cancer to get together with doctors who really know about cancer and work out different programs of research." So David and I applied to this program and created a consortium at USC where we've got some of the best oncologists in the world and some of the best biologists in the world, from Cold Spring Harbor, Stanford, Austin -- I won't even go through and name all the places -- to have a research project that will last for five years where we're really going to try to build a model of cancer like this. We're doing it in mice first. And we will kill a lot of mice in the process of doing this, but they will die for a good cause. And we will actually try to get to the point where we have a predictive model where we can understand, when cancer happens, what's actually happening in there and which treatment will treat that cancer.

So let me just end with giving you a little picture of what I think cancer treatment will be like in the future. So I think eventually, once we have one of these models for people, which we'll get eventually -- I mean, our group won't get all the way there -- but eventually we'll have a very good computer model -- sort of like a global climate model for weather. It has lots of different information about what's the process going on in this proteomic conversation on many different scales. And so we will simulate in that model for your particular cancer -- and this also will be for ALS, or any kind of system neurodegenerative diseases, things like that -- we will simulate specifically you, not just a generic person, but what's actually going on inside you.

And in that simulation, what we could do is design for you specifically a sequence of treatments, and it might be very gentle treatments, very small amounts of drugs. It might be things like, don't eat that day, or give them a little chemo therapy, maybe a little radiation. Of course, we'll do surgery sometimes and so on. But design a program of treatments specifically for you and help your body guide back to health -- guide your body back to health. Because your body will do most of the work of fixing it if we just sort of prop it up in the ways that are wrong. We put it in the equivalent of splints. And so your body basically has lots and lots of mechanisms for fixing cancer, and we just have to prop those up in the right way and get them to do the job.

And so I believe that this will be the way that cancer will be treated in the future. It's going to require a lot of work, a lot of research. There will be many teams like our team that work on this. But I think eventually, we will design for everybody a custom treatment for cancer.

So thank you very much.

(Applause)