下載App 希平方
攻其不背
App 開放下載中
下載App 希平方
攻其不背
App 開放下載中
IE版本不足
你的 IE 瀏覽器太舊了 更新 IE 瀏覽器或點選連結下載 Google Chrome 瀏覽器 前往下載

免費註冊
! 這組帳號已經註冊過了
Email 帳號
密碼請填入 6 位數以上密碼
已經有帳號了?
忘記密碼
! 這組帳號已經註冊過了
您的 Email
請輸入您註冊時填寫的 Email,
我們將會寄送設定新密碼的連結給您。
寄信了!請到信箱打開密碼連結信
密碼信已寄至
沒有收到信嗎? 點這裡重寄一次
如果您尚未收到信,請前往垃圾郵件查看,謝謝!

恭喜您註冊成功!

查看會員功能

註冊未完成

《HOPE English 希平方》服務條款關於個人資料收集與使用之規定

隱私權政策
上次更新日期:2014-12-30

希平方 為一英文學習平台,我們每天固定上傳優質且豐富的影片內容,讓您不但能以有趣的方式學習英文,還能增加內涵,豐富知識。我們非常注重您的隱私,以下說明為當您使用我們平台時,我們如何收集、使用、揭露、轉移及儲存你的資料。請您花一些時間熟讀我們的隱私權做法,我們歡迎您的任何疑問或意見,提供我們將產品、服務、內容、廣告做得更好。

本政策涵蓋的內容包括:希平方 如何處理蒐集或收到的個人資料。
本隱私權保護政策只適用於: 希平方 平台,不適用於非 希平方 平台所有或控制的公司,也不適用於非 希平方 僱用或管理之人。

個人資料的收集與使用
當您註冊 希平方 平台時,我們會詢問您姓名、電子郵件、出生日期、職位、行業及個人興趣等資料。在您註冊完 希平方 帳號並登入我們的服務後,我們就能辨認您的身分,讓您使用更完整的服務,或參加相關宣傳、優惠及贈獎活動。希平方 也可能從商業夥伴或其他公司處取得您的個人資料,並將這些資料與 希平方 所擁有的您的個人資料相結合。

我們所收集的個人資料, 將用於通知您有關 希平方 最新產品公告、軟體更新,以及即將發生的事件,也可用以協助改進我們的服務。

我們也可能使用個人資料為內部用途。例如:稽核、資料分析、研究等,以改進 希平方公司 產品、服務及客戶溝通。

瀏覽資料的收集與使用
希平方 自動接收並記錄您電腦和瀏覽器上的資料,包括 IP 位址、希平方 cookie 中的資料、軟體和硬體屬性以及您瀏覽的網頁紀錄。

隱私權政策修訂
我們會不定時修正與變更《隱私權政策》,不會在未經您明確同意的情況下,縮減本《隱私權政策》賦予您的權利。隱私權政策變更時一律會在本頁發佈;如果屬於重大變更,我們會提供更明顯的通知 (包括某些服務會以電子郵件通知隱私權政策的變更)。我們還會將本《隱私權政策》的舊版加以封存,方便您回顧。

服務條款
歡迎您加入看 ”希平方”
上次更新日期:2013-09-09

歡迎您加入看 ”希平方”
感謝您使用我們的產品和服務(以下簡稱「本服務」),本服務是由 希平方 所提供。
本服務條款訂立的目的,是為了保護會員以及所有使用者(以下稱會員)的權益,並構成會員與本服務提供者之間的契約,在使用者完成註冊手續前,應詳細閱讀本服務條款之全部條文,一旦您按下「註冊」按鈕,即表示您已知悉、並完全同意本服務條款的所有約定。如您是法律上之無行為能力人或限制行為能力人(如未滿二十歲之未成年人),則您在加入會員前,請將本服務條款交由您的法定代理人(如父母、輔助人或監護人)閱讀,並得到其同意,您才可註冊及使用 希平方 所提供之會員服務。當您開始使用 希平方 所提供之會員服務時,則表示您的法定代理人(如父母、輔助人或監護人)已經閱讀、了解並同意本服務條款。 我們可能會修改本條款或適用於本服務之任何額外條款,以(例如)反映法律之變更或本服務之變動。您應定期查閱本條款內容。這些條款如有修訂,我們會在本網頁發佈通知。變更不會回溯適用,並將於公布變更起十四天或更長時間後方始生效。不過,針對本服務新功能的變更,或基於法律理由而為之變更,將立即生效。如果您不同意本服務之修訂條款,則請停止使用該本服務。

第三人網站的連結 本服務或協力廠商可能會提供連結至其他網站或網路資源的連結。您可能會因此連結至其他業者經營的網站,但不表示希平方與該等業者有任何關係。其他業者經營的網站均由各該業者自行負責,不屬希平方控制及負責範圍之內。

兒童及青少年之保護 兒童及青少年上網已經成為無可避免之趨勢,使用網際網路獲取知識更可以培養子女的成熟度與競爭能力。然而網路上的確存有不適宜兒童及青少年接受的訊息,例如色情與暴力的訊息,兒童及青少年有可能因此受到心靈與肉體上的傷害。因此,為確保兒童及青少年使用網路的安全,並避免隱私權受到侵犯,家長(或監護人)應先檢閱各該網站是否有保護個人資料的「隱私權政策」,再決定是否同意提出相關的個人資料;並應持續叮嚀兒童及青少年不可洩漏自己或家人的任何資料(包括姓名、地址、電話、電子郵件信箱、照片、信用卡號等)給任何人。

為了維護 希平方 網站安全,我們需要您的協助:

您承諾絕不為任何非法目的或以任何非法方式使用本服務,並承諾遵守中華民國相關法規及一切使用網際網路之國際慣例。您若係中華民國以外之使用者,並同意遵守所屬國家或地域之法令。您同意並保證不得利用本服務從事侵害他人權益或違法之行為,包括但不限於:
A. 侵害他人名譽、隱私權、營業秘密、商標權、著作權、專利權、其他智慧財產權及其他權利;
B. 違反依法律或契約所應負之保密義務;
C. 冒用他人名義使用本服務;
D. 上載、張貼、傳輸或散佈任何含有電腦病毒或任何對電腦軟、硬體產生中斷、破壞或限制功能之程式碼之資料;
E. 干擾或中斷本服務或伺服器或連結本服務之網路,或不遵守連結至本服務之相關需求、程序、政策或規則等,包括但不限於:使用任何設備、軟體或刻意規避看 希平方 - 看 YouTube 學英文 之排除自動搜尋之標頭 (robot exclusion headers);

服務中斷或暫停
本公司將以合理之方式及技術,維護會員服務之正常運作,但有時仍會有無法預期的因素導致服務中斷或故障等現象,可能將造成您使用上的不便、資料喪失、錯誤、遭人篡改或其他經濟上損失等情形。建議您於使用本服務時宜自行採取防護措施。 希平方 對於您因使用(或無法使用)本服務而造成的損害,除故意或重大過失外,不負任何賠償責任。

版權宣告
上次更新日期:2013-09-16

希平方 內所有資料之著作權、所有權與智慧財產權,包括翻譯內容、程式與軟體均為 希平方 所有,須經希平方同意合法才得以使用。
希平方歡迎你分享網站連結、單字、片語、佳句,使用時須標明出處,並遵守下列原則:

  • 禁止用於獲取個人或團體利益,或從事未經 希平方 事前授權的商業行為
  • 禁止用於政黨或政治宣傳,或暗示有支持某位候選人
  • 禁止用於非希平方認可的產品或政策建議
  • 禁止公佈或傳送任何誹謗、侮辱、具威脅性、攻擊性、不雅、猥褻、不實、色情、暴力、違反公共秩序或善良風俗或其他不法之文字、圖片或任何形式的檔案
  • 禁止侵害或毀損希平方或他人名譽、隱私權、營業秘密、商標權、著作權、專利權、其他智慧財產權及其他權利、違反法律或契約所應付支保密義務
  • 嚴禁謊稱希平方辦公室、職員、代理人或發言人的言論背書,或作為募款的用途

網站連結
歡迎您分享 希平方 網站連結,與您的朋友一起學習英文。

抱歉傳送失敗!

不明原因問題造成傳送失敗,請儘速與我們聯繫!
希平方 x ICRT

「Steve Mccarroll:數據資料如何幫助我們解開大腦的謎團」- How Data Is Helping Us Unravel the Mysteries of the Brain


框選或點兩下字幕可以直接查字典喔!

Nine years ago, my sister discovered lumps in her neck and arm and was diagnosed with cancer. From that day, she started to benefit from the understanding that science has of cancer. Every time she went to the doctor, they measured specific molecules that gave them information about how she was doing and what to do next. New medical options became available every few years. Everyone recognized that she was struggling heroically with a biological illness. This spring, she received an innovative new medical treatment in a clinical trial. It dramatically knocked back her cancer. Guess who I'm going to spend this Thanksgiving with? My vivacious sister, who gets more exercise than I do, and who, like perhaps many people in this room, increasingly talks about a lethal illness in the past tense. Science can, in our lifetimes—even in a decade—transform what it means to have a specific illness.

But not for all illnesses. My friend Robert and I were classmates in graduate school. Robert was smart, but with each passing month, his thinking seemed to become more disorganized. He dropped out of school, got a job in a store...But that, too, became too complicated. Robert became fearful and withdrawn. A year and a half later, he started hearing voices and believing that people were following him. Doctors diagnosed him with schizophrenia, and they gave him the best drug they could. That drug makes the voices somewhat quieter, but it didn't restore his bright mind or his social connectedness. Robert struggled to remain connected to the worlds of school and work and friends. He drifted away, and today I don't know where to find him. If he watches this, I hope he'll find me.

Why does medicine have so much to offer my sister, and so much less to offer millions of people like Robert? The need is there. The World Health Organization estimates that brain illnesses like schizophrenia, bipolar disorder and major depression are the world's largest cause of lost years of life and work. That's in part because these illnesses often strike early in life, in many ways, in the prime of life, just as people are finishing their educations, starting careers, forming relationships and families. These illnesses can result in suicide; they often compromise one's ability to work at one's full potential; and they're the cause of so many tragedies harder to measure: lost relationships and connections, missed opportunities to pursue dreams and ideas. These illnesses limit human possibilities in ways we simply cannot measure.

We live in an era in which there's profound medical progress on so many other fronts. My sister's cancer story is a great example, and we could say the same of heart disease. Drugs like statins will prevent millions of heart attacks and strokes. When you look at these areas of profound medical progress in our lifetimes, they have a narrative in common: scientists discovered molecules that matter to an illness, they developed ways to detect and measure those molecules in the body, and they developed ways to interfere with those molecules using other molecules—medicines. It's a strategy that has worked again and again and again. But when it comes to the brain, that strategy has been limited, because today, we don't know nearly enough, yet, about how the brain works. We need to learn which of our cells matter to each illness, and which molecules in those cells matter to each illness. And that's the mission I want to tell you about today.

My lab develops technologies with which we try to turn the brain into a big-data problem. You see, before I became a biologist, I worked in computers and math, and I learned this lesson: wherever you can collect vast amounts of the right kinds of data about the functioning of a system, you can use computers in powerful new ways to make sense of that system and learn how it works. Today, big-data approaches are transforming ever-larger sectors of our economy, and they could do the same in biology and medicine, too. But you have to have the right kinds of data. You have to have data about the right things. And that often requires new technologies and ideas. And that is the mission that animates the scientists in my lab.

Today, I want to tell you two short stories from our work. One fundamental obstacle we face in trying to turn the brain into a big-data problem is that our brains are composed of and built from billions of cells. And our cells are not generalists; they're specialists. Like humans at work, they specialize into thousands of different cellular careers, or cell types.

In fact, each of the cell types in our body could probably give a lively TED Talk about what it does at work. But as scientists, we don't even know today how many cell types there are, and we don't know what the titles of most of those talks would be. Now, we know many important things about cell types. They can differ dramatically in size and shape. One will respond to a molecule that the other doesn't respond to, they'll make different molecules. But science has largely been reaching these insights in an ad hoc way, one cell type at a time, one molecule at a time. We wanted to make it possible to learn all of this quickly and systematically.

Now, until recently, it was the case that if you wanted to inventory all of the molecules in a part of the brain or any organ, you had to first grind it up into a kind of cellular smoothie. But that's a problem. As soon as you've ground up the cells, you can only study the contents of the average cell—not the individual cells. Imagine if you were trying to understand how a big city like New York works, but you could only do so by reviewing some statistics about the average resident of New York. Of course, you wouldn't learn very much, because everything that's interesting and important and exciting is in all the diversity and the specializations. And the same thing is true of our cells. And we wanted to make it possible to study the brain not as a cellular smoothie but as a cellular fruit salad, in which one could generate data about and learn from each individual piece of fruit.

So we developed a technology for doing that. You're about to see a movie of it. Here we're packaging tens of thousands of individual cells, each into its own tiny water droplet for its own molecular analysis. When a cell lands in a droplet, it's greeted by a tiny bead, and that bead delivers millions of DNA bar code molecules. And each bead delivers a different bar code sequence to a different cell. We incorporate the DNA bar codes into each cell's RNA molecules. Those are the molecular transcripts it's making of the specific genes that it's using to do its job. And then we sequence billions of these combined molecules and use the sequences to tell us which cell and which gene every molecule came from.

We call this approach "Drop-seq," because we use droplets to separate the cells for analysis, and we use DNA sequences to tag and inventory and keep track of everything. And now, whenever we do an experiment, we analyze tens of thousands of individual cells. And today in this area of science, the challenge is increasingly how to learn as much as we can as quickly as we can from these vast data sets.

When we were developing Drop-seq, people used to tell us, "Oh, this is going to make you guys the go-to for every major brain project." That's not how we saw it. Science is best when everyone is generating lots of exciting data. So we wrote a 25-page instruction book, with which any scientist could build their own Drop-seq system from scratch. And that instruction book has been downloaded from our lab website 50,000 times in the past two years. We wrote software that any scientist could use to analyze the data from Drop-seq experiments, and that software is also free, and it's been downloaded from our website 30,000 times in the past two years. And hundreds of labs have written us about discoveries that they've made using this approach. Today, this technology is being used to make a human cell atlas. It will be an atlas of all of the cell types in the human body and the specific genes that each cell type uses to do its job.

Now I want to tell you about a second challenge that we face in trying to turn the brain into a big data problem. And that challenge is that we'd like to learn from the brains of hundreds of thousands of living people. But our brains are not physically accessible while we're living. But how can we discover molecular factors if we can't hold the molecules? An answer comes from the fact that the most informative molecules, proteins, are encoded in our DNA, which has the recipes our cells follow to make all of our proteins. And these recipes vary from person to person to person in ways that cause the proteins to vary from person to person in their precise sequence and in how much each cell type makes of each protein. It's all encoded in our DNA, and it's all genetics, but it's not the genetics that we learned about in school.

Do you remember big B, little b? If you inherit big B, you get brown eyes? It's simple. Very few traits are that simple. Even eye color is shaped by much more than a single pigment molecule. And something as complex as the function of our brains is shaped by the interaction of thousands of genes. And each of these genes varies meaningfully from person to person to person, and each of us is a unique combination of that variation. It's a big data opportunity. And today, it's increasingly possible to make progress on a scale that was never possible before. People are contributing to genetic studies in record numbers, and scientists around the world are sharing the data with one another to speed progress.

I want to tell you a short story about a discovery we recently made about the genetics of schizophrenia. It was made possible by 50,000 people from 30 countries, who contributed their DNA to genetic research on schizophrenia. It had been known for several years that the human genome's largest influence on risk of schizophrenia comes from a part of the genome that encodes many of the molecules in our immune system. But it wasn't clear which gene was responsible. A scientist in my lab developed a new way to analyze DNA with computers, and he discovered something very surprising. He found that a gene called "complement component 4"—it's called "C4" for short—comes in dozens of different forms in different people's genomes, and these different forms make different amounts of C4 protein in our brains. And he found that the more C4 protein our genes make, the greater our risk for schizophrenia.

Now, C4 is still just one risk factor in a complex system. This isn't big B, but it's an insight about a molecule that matters. Complement proteins like C4 were known for a long time for their roles in the immune system, where they act as a kind of molecular Post-it note that says, "Eat me." And that Post-it note gets put on lots of debris and dead cells in our bodies and invites immune cells to eliminate them. But two colleagues of mine found that the C4 Post-it note also gets put on synapses in the brain and prompts their elimination. Now, the creation and elimination of synapses is a normal part of human development and learning. Our brains create and eliminate synapses all the time. But our genetic results suggest that in schizophrenia, the elimination process may go into overdrive.

Scientists at many drug companies tell me they're excited about this discovery, because they've been working on complement proteins for years in the immune system, and they've learned a lot about how they work. They've even developed molecules that interfere with complement proteins, and they're starting to test them in the brain as well as the immune system. It's potentially a path toward a drug that might address a root cause rather than an individual symptom, and we hope very much that this work by many scientists over many years will be successful.

But C4 is just one example of the potential for data-driven scientific approaches to open new fronts on medical problems that are centuries old. There are hundreds of places in our genomes that shape risk for brain illnesses, and any one of them could lead us to the next molecular insight about a molecule that matters. And there are hundreds of cell types that use these genes in different combinations. As we and other scientists work to generate the rest of the data that's needed and to learn all that we can from that data, we hope to open many more new fronts. Genetics and single-cell analysis are just two ways of trying to turn the brain into a big data problem.

There is so much more we can do. Scientists in my lab are creating a technology for quickly mapping the synaptic connections in the brain to tell which neurons are talking to which other neurons and how that conversation changes throughout life and during illness. And we're developing a way to test in a single tube how cells with hundreds of different people's genomes respond differently to the same stimulus. These projects bring together people with diverse backgrounds and training and interests—biology, computers, chemistry, math, statistics, engineering. But the scientific possibilities rally people with diverse interests into working intensely together.

What's the future that we could hope to create? Consider cancer. We've moved from an era of ignorance about what causes cancer, in which cancer was commonly ascribed to personal psychological characteristics, to a modern molecular understanding of the true biological causes of cancer. That understanding today leads to innovative medicine after innovative medicine, and although there's still so much work to do, we're already surrounded by people who have been cured of cancers that were considered untreatable a generation ago. And millions of cancer survivors like my sister find themselves with years of life that they didn't take for granted and new opportunities for work and joy and human connection. That is the future that we are determined to create around mental illness—one of real understanding and empathy and limitless possibility.

Thank you.

播放本句

登入使用學習功能

使用Email登入

HOPE English 播放器使用小提示

  • 功能簡介

    單句重覆、上一句、下一句:顧名思義,以句子為單位重覆播放,單句重覆鍵顯示橘色時為重覆播放狀態;顯示灰色時為正常播放狀態。按上一句鍵、下一句鍵時就會自動重覆播放該句。
    收錄佳句:點擊可增減想收藏的句子。

    中、英文字幕開關:中、英文字幕按鍵為綠色為開啟,灰色為關閉。鼓勵大家搞懂每一句的內容以後,關上字幕聽聽看,會發現自己好像在聽中文說故事一樣,會很有成就感喔!
    收錄單字:用滑鼠框選英文單字可以收藏不會的單字。
  • 分享
    如果您覺得本篇短片很有趣或很喜歡,在短片結束時有分享連結,可以分享給朋友一同欣賞,一起看YouTube學英文!

    或是您有收錄很優秀的句子時,也可以分享佳句給大家,一同看佳句學英文!