T*t
2 楼
最近遇到了一件遭心事，实在不知道能够和谁说了。我有一个女朋友，在我刚刚到美国
的时候我们就在一起了，她也是中国人，我们在一个学校留学，因为各种活动，所以很
快就在一起了，到现在两个人在一起已经一年了。在我们在一起的期间，我带她认识了
我所有的好朋友，今年过年的时候，我还带她去见了我的爸爸妈妈，家里人也是挺喜欢
她的。但是她从来没有带我去见她父母，不管我怎么要求，说是希望可以见见她父母她
都不同意，现在看来应该是早有预谋的吧。
最近女朋友和我提出分手了，但是她希望我可以和她在一起3个月之后再分手，因为这
学期结束她就不在美国留学了，准备回国了。我以为她是不想异国恋，所以也提出过可
以回国陪她，可是她还是坚持三个月之后就分手。可笑的是，我居然就这样答应了她的
要求，我真的不知道自己是怎么想的，怎么就答应了呢？我没有告诉别人，可能也是害
怕别人会笑话我吧，我自己也觉得挺没有出息的，可是我真的挺喜欢她的，我甚至幻想
着可能她会回心转意也说不定。
的时候我们就在一起了，她也是中国人，我们在一个学校留学，因为各种活动，所以很
快就在一起了，到现在两个人在一起已经一年了。在我们在一起的期间，我带她认识了
我所有的好朋友，今年过年的时候，我还带她去见了我的爸爸妈妈，家里人也是挺喜欢
她的。但是她从来没有带我去见她父母，不管我怎么要求，说是希望可以见见她父母她
都不同意，现在看来应该是早有预谋的吧。
最近女朋友和我提出分手了，但是她希望我可以和她在一起3个月之后再分手，因为这
学期结束她就不在美国留学了，准备回国了。我以为她是不想异国恋，所以也提出过可
以回国陪她，可是她还是坚持三个月之后就分手。可笑的是，我居然就这样答应了她的
要求，我真的不知道自己是怎么想的，怎么就答应了呢？我没有告诉别人，可能也是害
怕别人会笑话我吧，我自己也觉得挺没有出息的，可是我真的挺喜欢她的，我甚至幻想
着可能她会回心转意也说不定。
s*g
3 楼
就是官网上那个649没有contract的，买了回国用和在国内买有什么区别？好像美国买
的在国内没有保修，除了这个之外还有什么区别，能用国内的3G,4G吗？信号会不会不
好？
的在国内没有保修，除了这个之外还有什么区别，能用国内的3G,4G吗？信号会不会不
好？
j*7
4 楼
1. T400 可以换成什么样的SSD？本身接口是SATAII么？如果按成SATAIII会如何，就是
速度有瓶颈，还是根本就不兼容？
2. 看到网上的Deal：Intel X25-M SATA Solid State Drive；这个兼容么？还有什么
好推荐？
多谢～
速度有瓶颈，还是根本就不兼容？
2. 看到网上的Deal：Intel X25-M SATA Solid State Drive；这个兼容么？还有什么
好推荐？
多谢～
l*o
5 楼
今天无聊去为软店玩了会winPhone, 包括三星,HTC和Nokia的几款感觉非常好用, 非常
流畅! 考虑今年到期把iphone4换成Nokia的winPhone.
流畅! 考虑今年到期把iphone4换成Nokia的winPhone.
D*a
6 楼
Nature忘了它有一整本杂志都是这玩意儿了？
两年前Science发了一篇GWAS方法研究人类长寿相关基因分析的文章，一年后被说有技
术问题，假阳性，撤稿了，最近刚从Plos One发出来了。两年前就瞅了一眼结果，现在
看貌似大结论没变啊？还没细看，细看也看不懂。。。
http://www.nature.com/nature/journal/v487/n7408/full/487427a.ht
Scientists and journals must work together to ensure that eye-catching
artefacts are not trumpeted as genomic insights, says Daniel MacArthur.
When a study of the genomes of centenarians reported genetic variants
strongly associated with exceptional longevity1, it received widespread
media and public interest. It also provoked an immediate sceptical response
from other geneticists. That individual genetic variants should have such
large effects on a complex human trait was totally unexpected. As it turned
out, at least some of the results from this study were surprising simply
because they were wrong. In a retraction published a year later2, the
authors admitted to “technical errors” and “an inadequate quality control
protocol”. The work was later republished in a different journal after
heavy revision3.
Few principles are more depressingly familiar to the veteran scientist: the
more surprising a result seems to be, the less likely it is to be true. We
cannot know whether, or why, this principle was overlooked in any specific
study. However, more generally, in a world in which unexpected results can
lead to high-impact publication, acclaim and headlines in The New York Times
, it is easy to understand how there might be an overwhelming temptation to
move from discovery to manuscript submission without performing the
necessary data checks.
SHUTTERSTOCK/W. FERNANDES
In fact, it has never been easier to generate high-impact false positives
than in the genomic era, in which massive, complex biological data sets are
cheap and widely available. To be clear, the majority of genome-scale
experiments yield real results, many of which would be impossible to uncover
through targeted hypothesis-driven studies. However, hunting for biological
surprises without due caution can easily yield a rich crop of biases and
experimental artefacts, and lead to high-impact papers built on nothing more
than systematic experimental 'noise'.
Flawed papers cause harm beyond their authors: they trigger futile projects,
stalling the careers of graduate students and postdocs, and they degrade
the reputation of genomic research. To minimize the damage, researchers,
reviewers and editors need to raise the standard of evidence required to
establish a finding as fact.
Two processes conspire to delude ambitious genomicists. First, the sheer
size of the genome means that highly unusual events occur by chance much
more often than we would intuitively expect. The limited grasp of statistics
that many biologists have and the irresistible appeal of biological
findings that neatly fit the facts are a recipe for spurious findings.
Second, all high-throughput genomic technologies come with error modes and
systematic biases that, to the unwary eye, can seem like interesting biology
. As a result, researchers who are inexperienced with a technology — and
some who should know better — can jump to the wrong conclusion.
Again, whether these factors play a part in any specific case is often
impossible to know, but several high-profile controversies highlight the
potential impact of chance and technical artefacts on genome-scale analyses.
For instance, rare loss-of-function mutations in a gene called SIAE were
reported to have a large effect on the risk of autoimmune diseases4. But a
later, combined analysis of more than 60,000 samples5 showed no evidence of
an association, suggesting that the finding in the original publication was
down to chance. Key results in the retracted genetic analysis of longevity
mentioned earlier1 turned out to be errors that arose as a result of
combining data from multiple genotyping platforms. And a study published
last year that reported widespread chemical modification of RNA molecules6
was heavily criticized by experts, who argued that the majority of claimed
modifications were, in fact, the product of known classes of experimental
error7, 8, 9.
Resolving such controversies after results have been published can take time
. Even after a strong consensus has emerged among experts in the field that
a particular result is spurious, it can take years for that view to reach
the broader research community, let alone the public. That provides plenty
of opportunity for damage to be done to budding careers and to public trust.
Replication and reviewing
How can the frequency with which technical errors are trumpeted as
discoveries be minimized? First, researchers starting out in genomics must
keep in mind that interesting outliers — that is, results that deviate
significantly from the sample — will inevitably contain a plethora of
experimental or analytical artefacts. Identifying these artefacts requires
quality-control procedures that minimize the contribution of each to the
final result. Finding different ways to make data visual (including simply
plotting results across the genome) can be more helpful than many
researchers appreciate. The human eye, suitably aided, can spot bugs and
biases that are difficult or impossible to see in massive data files.
Crucially, genomicists should try to replicate technology-driven findings by
repeating the study in new samples and using experimental platforms that
are not subject to the same error modes as the original technology.
Stringent quality control takes time, a scarce resource in the fast-paced
world of genomics. But researchers should weigh the risk of being scooped
against the embarrassment of public retraction.
For 'paradigm-shifting' genomics papers, journal editors must recruit
reviewers who have enough experience in the specific technologies involved
to spot subtle artefacts. Often these will be junior researchers working in
the trenches of quality control and manual data inspection. In addition to
having the necessary experience, such reviewers often have more time for
careful analysis than their supervisors.
Finally, the genomics community must take responsibility for establishing
standards for the generation, quality control and statistical analysis of
high-throughput data generated using new genomic technologies (a model that
has generally worked well, for instance, in genome-wide association studies)
and for responding rapidly to published errors. Traditionally, scientists
wrote politely outraged letters to journals. Many now voice their concerns
in online media, a more rapid and open way to ensure that the public view of
a finding is tempered with appropriate caution. Such informal avenues for
rapid post-publication discourse should be encouraged.
Nothing can completely prevent the publication of incorrect results. It is
the nature of cutting-edge science that even careful researchers are
occasionally fooled. We should neither deceive ourselves that perfect
science is possible, nor focus so heavily on reducing error that we are
afraid to innovate. However, if we work together to define, apply and
enforce clear standards for genomic analysis, we can ensure that most of the
unanticipated results are surprising because they reveal unexpected biology
, rather than because they are wrong.
Sebastiani, P. et al. Science http://dx.doi.org/10.1126/science.1190532 (2010).
Sebastiani, P. et al. Science 333, 404 (2011).
Sebastiani, P. et al. PLoS ONE 7, e29848 (2012).
Surolia, I. et al. Nature 466, 243–247 (2010).
Hunt, K. A. et al. Nature Genet. 44, 3–5 (2012).
Li, M. et al. Science 333, 53–58 (2011).
Kleinman, C. L. & Majewski, J. Science 335, 1302 (2012)
Lin, W. et al. Science 335, 1302 (2012).
Pickrell, J. K., Gilad, Y. & Pritchard, J. K. Science 335, 1302 (2012).
两年前Science发了一篇GWAS方法研究人类长寿相关基因分析的文章，一年后被说有技
术问题，假阳性，撤稿了，最近刚从Plos One发出来了。两年前就瞅了一眼结果，现在
看貌似大结论没变啊？还没细看，细看也看不懂。。。
http://www.nature.com/nature/journal/v487/n7408/full/487427a.ht
Scientists and journals must work together to ensure that eye-catching
artefacts are not trumpeted as genomic insights, says Daniel MacArthur.
When a study of the genomes of centenarians reported genetic variants
strongly associated with exceptional longevity1, it received widespread
media and public interest. It also provoked an immediate sceptical response
from other geneticists. That individual genetic variants should have such
large effects on a complex human trait was totally unexpected. As it turned
out, at least some of the results from this study were surprising simply
because they were wrong. In a retraction published a year later2, the
authors admitted to “technical errors” and “an inadequate quality control
protocol”. The work was later republished in a different journal after
heavy revision3.
Few principles are more depressingly familiar to the veteran scientist: the
more surprising a result seems to be, the less likely it is to be true. We
cannot know whether, or why, this principle was overlooked in any specific
study. However, more generally, in a world in which unexpected results can
lead to high-impact publication, acclaim and headlines in The New York Times
, it is easy to understand how there might be an overwhelming temptation to
move from discovery to manuscript submission without performing the
necessary data checks.
SHUTTERSTOCK/W. FERNANDES
In fact, it has never been easier to generate high-impact false positives
than in the genomic era, in which massive, complex biological data sets are
cheap and widely available. To be clear, the majority of genome-scale
experiments yield real results, many of which would be impossible to uncover
through targeted hypothesis-driven studies. However, hunting for biological
surprises without due caution can easily yield a rich crop of biases and
experimental artefacts, and lead to high-impact papers built on nothing more
than systematic experimental 'noise'.
Flawed papers cause harm beyond their authors: they trigger futile projects,
stalling the careers of graduate students and postdocs, and they degrade
the reputation of genomic research. To minimize the damage, researchers,
reviewers and editors need to raise the standard of evidence required to
establish a finding as fact.
Two processes conspire to delude ambitious genomicists. First, the sheer
size of the genome means that highly unusual events occur by chance much
more often than we would intuitively expect. The limited grasp of statistics
that many biologists have and the irresistible appeal of biological
findings that neatly fit the facts are a recipe for spurious findings.
Second, all high-throughput genomic technologies come with error modes and
systematic biases that, to the unwary eye, can seem like interesting biology
. As a result, researchers who are inexperienced with a technology — and
some who should know better — can jump to the wrong conclusion.
Again, whether these factors play a part in any specific case is often
impossible to know, but several high-profile controversies highlight the
potential impact of chance and technical artefacts on genome-scale analyses.
For instance, rare loss-of-function mutations in a gene called SIAE were
reported to have a large effect on the risk of autoimmune diseases4. But a
later, combined analysis of more than 60,000 samples5 showed no evidence of
an association, suggesting that the finding in the original publication was
down to chance. Key results in the retracted genetic analysis of longevity
mentioned earlier1 turned out to be errors that arose as a result of
combining data from multiple genotyping platforms. And a study published
last year that reported widespread chemical modification of RNA molecules6
was heavily criticized by experts, who argued that the majority of claimed
modifications were, in fact, the product of known classes of experimental
error7, 8, 9.
Resolving such controversies after results have been published can take time
. Even after a strong consensus has emerged among experts in the field that
a particular result is spurious, it can take years for that view to reach
the broader research community, let alone the public. That provides plenty
of opportunity for damage to be done to budding careers and to public trust.
Replication and reviewing
How can the frequency with which technical errors are trumpeted as
discoveries be minimized? First, researchers starting out in genomics must
keep in mind that interesting outliers — that is, results that deviate
significantly from the sample — will inevitably contain a plethora of
experimental or analytical artefacts. Identifying these artefacts requires
quality-control procedures that minimize the contribution of each to the
final result. Finding different ways to make data visual (including simply
plotting results across the genome) can be more helpful than many
researchers appreciate. The human eye, suitably aided, can spot bugs and
biases that are difficult or impossible to see in massive data files.
Crucially, genomicists should try to replicate technology-driven findings by
repeating the study in new samples and using experimental platforms that
are not subject to the same error modes as the original technology.
Stringent quality control takes time, a scarce resource in the fast-paced
world of genomics. But researchers should weigh the risk of being scooped
against the embarrassment of public retraction.
For 'paradigm-shifting' genomics papers, journal editors must recruit
reviewers who have enough experience in the specific technologies involved
to spot subtle artefacts. Often these will be junior researchers working in
the trenches of quality control and manual data inspection. In addition to
having the necessary experience, such reviewers often have more time for
careful analysis than their supervisors.
Finally, the genomics community must take responsibility for establishing
standards for the generation, quality control and statistical analysis of
high-throughput data generated using new genomic technologies (a model that
has generally worked well, for instance, in genome-wide association studies)
and for responding rapidly to published errors. Traditionally, scientists
wrote politely outraged letters to journals. Many now voice their concerns
in online media, a more rapid and open way to ensure that the public view of
a finding is tempered with appropriate caution. Such informal avenues for
rapid post-publication discourse should be encouraged.
Nothing can completely prevent the publication of incorrect results. It is
the nature of cutting-edge science that even careful researchers are
occasionally fooled. We should neither deceive ourselves that perfect
science is possible, nor focus so heavily on reducing error that we are
afraid to innovate. However, if we work together to define, apply and
enforce clear standards for genomic analysis, we can ensure that most of the
unanticipated results are surprising because they reveal unexpected biology
, rather than because they are wrong.
Sebastiani, P. et al. Science http://dx.doi.org/10.1126/science.1190532 (2010).
Sebastiani, P. et al. Science 333, 404 (2011).
Sebastiani, P. et al. PLoS ONE 7, e29848 (2012).
Surolia, I. et al. Nature 466, 243–247 (2010).
Hunt, K. A. et al. Nature Genet. 44, 3–5 (2012).
Li, M. et al. Science 333, 53–58 (2011).
Kleinman, C. L. & Majewski, J. Science 335, 1302 (2012)
Lin, W. et al. Science 335, 1302 (2012).
Pickrell, J. K., Gilad, Y. & Pritchard, J. K. Science 335, 1302 (2012).
m*s
7 楼
Bless!!!
s*e
8 楼
国内买的有九宫格输入法
【在 s*******g 的大作中提到】
: 就是官网上那个649没有contract的，买了回国用和在国内买有什么区别？好像美国买
: 的在国内没有保修，除了这个之外还有什么区别，能用国内的3G,4G吗？信号会不会不
: 好？
【在 s*******g 的大作中提到】
: 就是官网上那个649没有contract的，买了回国用和在国内买有什么区别？好像美国买
: 的在国内没有保修，除了这个之外还有什么区别，能用国内的3G,4G吗？信号会不会不
: 好？
i*z
9 楼
可以用x25。
【在 j*******7 的大作中提到】
: 1. T400 可以换成什么样的SSD？本身接口是SATAII么？如果按成SATAIII会如何，就是
: 速度有瓶颈，还是根本就不兼容？
: 2. 看到网上的Deal：Intel X25-M SATA Solid State Drive；这个兼容么？还有什么
: 好推荐？
: 多谢～
【在 j*******7 的大作中提到】
: 1. T400 可以换成什么样的SSD？本身接口是SATAII么？如果按成SATAIII会如何，就是
: 速度有瓶颈，还是根本就不兼容？
: 2. 看到网上的Deal：Intel X25-M SATA Solid State Drive；这个兼容么？还有什么
: 好推荐？
: 多谢～
e*a
10 楼
bless
X*y
11 楼
没听说过有信号不好这么一说，但你要保证有信号才行……先看看你在美国买的iPhone
的model number是多少，不懂在这个网页查：http://www.apple.com/iphone/LTE/
然后查查国内移动联通什么的网络频率多少，看看能不能用
【在 s*******g 的大作中提到】
: 就是官网上那个649没有contract的，买了回国用和在国内买有什么区别？好像美国买
: 的在国内没有保修，除了这个之外还有什么区别，能用国内的3G,4G吗？信号会不会不
: 好？
的model number是多少，不懂在这个网页查：http://www.apple.com/iphone/LTE/
然后查查国内移动联通什么的网络频率多少，看看能不能用
【在 s*******g 的大作中提到】
: 就是官网上那个649没有contract的，买了回国用和在国内买有什么区别？好像美国买
: 的在国内没有保修，除了这个之外还有什么区别，能用国内的3G,4G吗？信号会不会不
: 好？
j*7
12 楼
知道可以用，具体怎么样啊？这个SSD稳定好用么？还有别的选择么？
Tiger Direct的rebate可信度高么？看过有人抱怨的~
Tiger Direct的rebate可信度高么？看过有人抱怨的~
f*d
13 楼
bless
h*z
14 楼
老虎直接上面卖的x25-m，是7mm太薄了，放进去T400试过，有缝填不满，反正我觉得不
安稳，你要是不怕晃荡就买。
安稳，你要是不怕晃荡就买。
b*a
15 楼
bless
j*7
16 楼
难道T系列都用9mm，x系列用7mm，长见识了，多谢！
感觉选个SSD，真够复杂的，一堆要考虑的！~
感觉选个SSD，真够复杂的，一堆要考虑的！~
l*l
17 楼
bless
j*7
18 楼
这个SAMSUNG 840 Series MZ-7TD250KW 2.5 " 250GB SATA III Internal Solid State
Drive 怎么样啊？
Drive 怎么样啊？
y*c
19 楼
bless
d*e
20 楼
bless
c*h
21 楼
bless
l*r
22 楼
bless
G*0
23 楼
Bless~
【在 c********v 的大作中提到】
: 听说贵版的bless很灵
: 求bless
: 也祝各位兄弟姐妹都好运，拿到offer
: 谢谢
【在 c********v 的大作中提到】
: 听说贵版的bless很灵
: 求bless
: 也祝各位兄弟姐妹都好运，拿到offer
: 谢谢
x*s
24 楼
very very deep bless
T*a
25 楼
Bless!
c*o
26 楼
best wishes
a*r
27 楼
bless！
g*e
28 楼
Big bless!
【在 c********v 的大作中提到】
: 听说贵版的bless很灵
: 求bless
: 也祝各位兄弟姐妹都好运，拿到offer
: 谢谢
【在 c********v 的大作中提到】
: 听说贵版的bless很灵
: 求bless
: 也祝各位兄弟姐妹都好运，拿到offer
: 谢谢
a*9
29 楼
bless~!
d*i
30 楼
bless!
【在 c********v 的大作中提到】
: 听说贵版的bless很灵
: 求bless
: 也祝各位兄弟姐妹都好运，拿到offer
: 谢谢
【在 c********v 的大作中提到】
: 听说贵版的bless很灵
: 求bless
: 也祝各位兄弟姐妹都好运，拿到offer
: 谢谢
l*z
31 楼
祝福！！！
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Quit了 (请转Faculty版) (转载)美国人工肾购买求生物同行帮个忙学生物的不反转，是什么心态没绿卡的生物博后有机会去公司工作吗？求审稿（发育，遗传，gene regulation，NGS）多谢！ (转载)发一个招聘广告，生物发育生物学方向， 青岛 中国海洋大学有下个月到aacr开会的么？兼职生物医学编辑招募指定测某些基因的表达水平temporary RNA position available续房租回国面试工作的话，大家用英文还是中文呢？我就不理解NYU那个间谍罪教书求教~急！【招聘】生物&制药方向-- Business Development VP/ Director/ Manager质粒中提问题Marker for neuronal polarityexcel多列数据如何连成一列？WTF Monsanto?