CRISPR isn't random. It's directed by a template RNA strand (called a "guide" and abbreviated sgRNA for historical reasons) to bind sections of DNA complementary to the guide. In addition to matching the guide, the target DNA must have a protospacer adjacent motif (NGG), which limits things a bit in practice.
What happens after target DNA is recognized by the Cas9/sgRNA depends on the specific Cas9 variant and potentially the presence of other exogenous DNA introduced along with the Cas9 and sgRNA.
Gene silencing, or CRISPRi (for interference), targets inactive dCas9 ("dead" Cas9) to the transcription start site (TSS) within a gene. The dCas9 then physically occludes the TSS, preventing transcription factor binding and thus gene expression.
Gene activation, or CRISPRa, also targets the TSS. The dCas9 protein is fused to a constitutively active transcription factor, which initiates gene expression directly.
Editing using CRISPR/Cas9 is also possible. Active Cas9 will make cuts in both strands of the target DNA double-helix, leaving "blunt ends" (unlike the "sticky ends" created by restriction enzymes). The blunt ends are addressed in one of two ways: non-homologous end-joining (NHEJ) or homology-directed repair (HDR). With NHEJ, the blunt ends will either be joined directly (resulting in gene deletion), or with stochastic inclusion of another piece of DNA introduced along with the CRISPR/Cas9 system (resulting in inefficient gene insertion). In HDR, the additional DNA will contain "homology arms" complementary to the DNA flanking the target sequence. After the cuts are made, this DNA can hybridize (forming a double-helix) on those flanking arms. The new DNA finally is copied into the hole created by the Cas9, yielding specific and relatively efficient gene insertion.
Regardless of the specific application, CRISPER/Cas9 approaches are stochastic and imperfect. Off-target DNA can be cut non-specifically, resulting in random deletions or insertion into random regions. Repair may proceed without insertion of the desired sequences, etc. There is a ton of ongoing research into engineered Cas9 variants with improved specificity and efficiency.
Most concerns about CRISPR-based genetic modification relate either to off-target effects (i.e. specificity and/or efficiency), or to the relative ease in making persistent, germ-line changes compared to prior methods.
Trump isn't a Capitalist at all, maybe more of an old school Mercantilist.
I think that's exactly right. Look at his statements on trade, e.g. that a country we trade with is "up 100 billion on us" and not that it was a free exchange where they got a financial asset they preferred to their real goods, and we got their real goods which we preferred to our financial assets.
It's partly because of an incredibly simplistic, mercantilist perspective in which trade is intrinsically zero-sum. It's also partly because having never made a dime without bilking somebody, he cannot conceive of someone else agreeing to a fair exchange perceived as a good deal by both sides.
You do realize that it's kind of silly to bring up the specter of "criminal negligence" when the Tempe police cleared them of wrongdoing, right?
And if the Tempe police say it, it must be true./s
The video shows a situation in which a human would have at least tried to react. Uber's car didn't even start slowing, and it has LIDAR. Contrary to what you seem to believe, no current self-driving car technology employs computer vision using a visible light camera to guide the car. Instead, they use IR-band LIDAR, detailed mapping data, and other sensor packages common in existing assisted-navigation / crash avoidance systems (e.g. small RADARs).
Anyway, it's confirmed - Uber's program is far less safe, and less advanced, than Waymo's.
raises new questions about autonomous-vehicle technology.
No, it raises further questions about Uber's poor, perhaps criminally negligent, implementation. In the last year Uber's had more, and more serious, accidents than I think every other driverless program combined. Google/Waymo has been testing in San Francisco - not Tempe - for years with nothing comparable.
The way I wrote that makes it sound like more students enter faculty positions than not, which is incorrect. The national average (US) is ~8%, whereas at my school it's closer to ~35%.
Seems to me that the majority of PhDs should be going into the private sector.
I agree. My university has a much higher proportion of graduates entering faculty positions, yet it also helps students enter suitable industry positions, fosters industry contact, and provides several ways for students to pursue their own entrepreneurship. There's certainly demand for highly trained individuals from all STEM fields across multiple industries.
I'd also add that in my experience, which includes a Master's program at a lower-tier school and a PhD program at an elite one, students are actually pretty realistic about their prospects, and most do not really intend to enter academic careers. They are often willing to give it a shot, or know they'll seek an academic postdoc before entering industry, but the number who have principal investigator as their only desired career outcome is pretty similar to the average proportion of graduates who do attain that status.
My feeling (reinforced through peer communication at conferences, etc.) is that most students know they'll go to industry, but they may not feel safe in openly declaring this to advisers or program directors. No doubt this is a strong confounder for surveys like the one in TFA.
The 582,970 base pair M. genitalium bacterial genome
That's a huge synthesis, we routinely synthesize much smaller pieces. I think it's about a grand for a custom ~5 kbp construct. I guess it's just a matter of time until such large sequences can be synthesized with reasonable time/money investments though.
nucleus modification
The zebra fish labs are definitely injecting CRISPR into zygotes to try and create stable edited lines. It doesn't work very well, but it's because zebra fish don't do homology directed repair, and non-homologous end joining is much less efficient. I think the microinjection isn't that bad since the eggs aren't that small. I also know someone who uses a micro needle to poke mammalian cells and - without lysing them - pluck their microtubules in order to measure their viscoelastic properties as they bounce back. I think that's much more difficult.
Embryo selection though runs afoul of all the same objections as early-term abortion
True, but in practice editing will also require multiple tries, at least for a very long time. There will always be some risk of off-target effects, and it can't be perfectly efficient. People will want to give a few tries and use a validated result.
synthesize the associated DNA and implant it within a donor egg
Whole chromosomes are way, way bigger than any DNA molecules we know how to synthesize right now (by around 5 orders of magnitude). The second step is possible though, we do it with current editing technologies. One of my friends/colleagues even does it to zebra fish zygotes. We've already made it into vertebrates.
Incidentally, zebra fish will also express plasmids, which I thought was completely insane until I learned the injection volume is more than 50% that of the cell. The trick is not to bust 'em open when you do it.
editing deleterious alleles out of germline chromosomes
I don't think that will be that popular (on humans). Embyro selection - which is what they actually appear to use in Gattaca - on the other hand will probably be common place.
I do think gene editing in agricultural plants and animals will be common, but it's pretty risky and expensive compared to just sequencing a bunch of embryos and picking the good ones (in most cases).
Your first point is almost a good one, but the last three lines give away the game - you're the politician here.
Real incomes in the US have matched their 1999 levels only this year. That fact represents a radical departure from all postindustrial economic behavior, but to you if students or professors want to talk about why that is - from any perspective - they're already too "political" and should be silenced. Be serious.
It's also not generally appreciated that the quantitative part of Marx represents the foundations of econometrics even to this day, and while things have become generally more sophisticated since his time, that aspect of his work is not particularly questioned or devalued by contemporary economists with right-wing political leanings.
Students didn't demand change because the economics theories they were taught were perfect but because they were flawed.
Indeed, but interestingly large parts of textbook macroeconomics performed (and continue to perform) very well during the crisis. ISLM / ISMP style models have accurately predicted the behavior of economic actors of all different scales this past decade. Some economic programs had neglected these approaches for some time, while others had continued them, but moved them onto a more rigorous mathematical framework. (See for example Romer's treatment of ISMP).
IMHO any movement in economics towards empirical validation and towards more robust models (i.e. less overfitting) is for the better.
There is an ongoing debate in macroeconomics about the role of inequality. Does inequality impact growth, and if so negatively or positively? How do we measure inequality, and more generally distribution of resources? How do economic systems, and specific policy choices, influence the distribution of income and resources?
These are fundamental aspects of economic study.
globalization
Again...what is "globalization?" How do we measure it? How has it benefited or harmed different countries and different segments of society within countries? How do we measure that? Why have these effects been visited on these particular groups? Can we build quantitative models around these ideas? Do those models have predictive power? How can we measure their predictive power? How can we establish confidence in model outputs?
Especially given that there are conflicting viewpoints across the political spectrum, it's hard to see an inherent political aim in discussing this important contemporary economic movement (e.g. there are right-wing populists and left-wing populists who write that they oppose globalization, and right- and left-wing thinkers who favor globalization, in both cases often for different reasons).
climate change
How do different climate outlooks effect economic prospects? How does the existence of these outlooks effect economic behavior of individuals, firms, and nations? How do we measure climate risk? What are the economic effects of potential mitigation efforts? What level of risk should motivate what degree of mitigation cost, given various estimates of uncertainty? How can these ideas be placed in a quantitative framework? How can model output be assessed given the scale and ongoing nature of the topic? Are there past climatic events with known economic impacts? How do we measure those impacts? How do they compare to current data? And on and on.
It only takes a moment's reflection to note the importance of these questions, and their relevance to the study of economics. That's probably why there are economists from all over the world, and from all over the political spectrum, who spend their academic (or corporate) lives on just these ideas.
Today, we know that ability is entirely genetic and that it follows a bell curve distribution.
Nothing could reveal a deeper lack of understanding about contemporary biology than that sentence. It simultaneously rejects our actual findings and betrays an erroneous confidence in the power of current methods, all without any apparent irony.
Under other circumstances I would feel compelled to say more, but the parent is either a troll or an ideologue and I don't want to waste the time.
Our (USA) funding system was designed under the assumption that ~30% of grant applications will receive funding. Today, that number is a little more than 5%. Science prospers when practitioners compete for excellence, rather than because there isn't enough to go around.
In this case, because they all had to read and write English. The Indian and American programmers probably grew up speaking English. The Russian and Chinese programmers who took the test were selected for preexisting proficiency in nonnative language.
Yeah, but if USA and India had the majority of contestants, the bias is in their favour
That's not how statistics work. If the samples are random, but different in size, the smaller sample will have a more biased average. So in this case, if we assume the samples are random, we just know that the Russian and Chinese averages are much less representative than the American and Indian averages.
However, we also know that the samples were biased. American and Indian programmers speak English. The testing company is based in Palo Alto, CA. Their website is totally in English. The Russian and Chinese programmers were selected not only for motivation to take an online test, but for a preexisting ability to speak a nonnative language.
I can't help but wonder if this is only a measure of publicly known hacking
Hacking? TFA is just about test scores.
On a side note, the sample size of 1.4 million doesn't matter if the sample is non-random. Many more Indian and American programmers took the test, and their average scores are most likely lower for that reason, even if there is no additional bias in the Russian and Chinese scores.
That's a big "if" though...what India and the United States have in common with the testing company is the English language. The Russian and Chinese samples are sampling Russian and Chinese people who speak English.
Slashdot Mirror
"potentially undermines the whole research publishing system."
That's the point you parasite!
CRISPR isn't random. It's directed by a template RNA strand (called a "guide" and abbreviated sgRNA for historical reasons) to bind sections of DNA complementary to the guide. In addition to matching the guide, the target DNA must have a protospacer adjacent motif (NGG), which limits things a bit in practice.
What happens after target DNA is recognized by the Cas9/sgRNA depends on the specific Cas9 variant and potentially the presence of other exogenous DNA introduced along with the Cas9 and sgRNA.
Gene silencing, or CRISPRi (for interference), targets inactive dCas9 ("dead" Cas9) to the transcription start site (TSS) within a gene. The dCas9 then physically occludes the TSS, preventing transcription factor binding and thus gene expression.
Gene activation, or CRISPRa, also targets the TSS. The dCas9 protein is fused to a constitutively active transcription factor, which initiates gene expression directly.
Editing using CRISPR/Cas9 is also possible. Active Cas9 will make cuts in both strands of the target DNA double-helix, leaving "blunt ends" (unlike the "sticky ends" created by restriction enzymes). The blunt ends are addressed in one of two ways: non-homologous end-joining (NHEJ) or homology-directed repair (HDR). With NHEJ, the blunt ends will either be joined directly (resulting in gene deletion), or with stochastic inclusion of another piece of DNA introduced along with the CRISPR/Cas9 system (resulting in inefficient gene insertion). In HDR, the additional DNA will contain "homology arms" complementary to the DNA flanking the target sequence. After the cuts are made, this DNA can hybridize (forming a double-helix) on those flanking arms. The new DNA finally is copied into the hole created by the Cas9, yielding specific and relatively efficient gene insertion.
Regardless of the specific application, CRISPER/Cas9 approaches are stochastic and imperfect. Off-target DNA can be cut non-specifically, resulting in random deletions or insertion into random regions. Repair may proceed without insertion of the desired sequences, etc. There is a ton of ongoing research into engineered Cas9 variants with improved specificity and efficiency.
Most concerns about CRISPR-based genetic modification relate either to off-target effects (i.e. specificity and/or efficiency), or to the relative ease in making persistent, germ-line changes compared to prior methods.
Trump isn't a Capitalist at all, maybe more of an old school Mercantilist.
I think that's exactly right. Look at his statements on trade, e.g. that a country we trade with is "up 100 billion on us" and not that it was a free exchange where they got a financial asset they preferred to their real goods, and we got their real goods which we preferred to our financial assets.
It's partly because of an incredibly simplistic, mercantilist perspective in which trade is intrinsically zero-sum. It's also partly because having never made a dime without bilking somebody, he cannot conceive of someone else agreeing to a fair exchange perceived as a good deal by both sides.
We are comparing relative performance. Uber's vehicles needed hundreds of times more human interventions over the same distance compared to Waymo's.
You do realize that it's kind of silly to bring up the specter of "criminal negligence" when the Tempe police cleared them of wrongdoing, right?
And if the Tempe police say it, it must be true. /s
The video shows a situation in which a human would have at least tried to react. Uber's car didn't even start slowing, and it has LIDAR. Contrary to what you seem to believe, no current self-driving car technology employs computer vision using a visible light camera to guide the car. Instead, they use IR-band LIDAR, detailed mapping data, and other sensor packages common in existing assisted-navigation / crash avoidance systems (e.g. small RADARs).
Anyway, it's confirmed - Uber's program is far less safe, and less advanced, than Waymo's.
raises new questions about autonomous-vehicle technology.
No, it raises further questions about Uber's poor, perhaps criminally negligent, implementation. In the last year Uber's had more, and more serious, accidents than I think every other driverless program combined. Google/Waymo has been testing in San Francisco - not Tempe - for years with nothing comparable.
The way I wrote that makes it sound like more students enter faculty positions than not, which is incorrect. The national average (US) is ~8%, whereas at my school it's closer to ~35%.
Seems to me that the majority of PhDs should be going into the private sector.
I agree. My university has a much higher proportion of graduates entering faculty positions, yet it also helps students enter suitable industry positions, fosters industry contact, and provides several ways for students to pursue their own entrepreneurship. There's certainly demand for highly trained individuals from all STEM fields across multiple industries.
I'd also add that in my experience, which includes a Master's program at a lower-tier school and a PhD program at an elite one, students are actually pretty realistic about their prospects, and most do not really intend to enter academic careers. They are often willing to give it a shot, or know they'll seek an academic postdoc before entering industry, but the number who have principal investigator as their only desired career outcome is pretty similar to the average proportion of graduates who do attain that status.
My feeling (reinforced through peer communication at conferences, etc.) is that most students know they'll go to industry, but they may not feel safe in openly declaring this to advisers or program directors. No doubt this is a strong confounder for surveys like the one in TFA.
There has to be some advantage to them to do this.
It's allowing them to claim these weren't mass lay-offs, which are separately regulated. See e.g. CA WARN law. There are probably others, as well.
"for cause," as happened here
Please. It's clear both incidents were mass lay-offs. In the Solar City division performance reviews hadn't even been carried out.
because she does not belong, gets none of it's benefits.
You and she are mistaken.
They arguably don't use it for editing, just for cutting and degradation to protect against phage. There was recently even published a viral CRISPR-like system, which functions in immunity against viruses that infect other viruses. See "MIMIVIRE is a defence system in mimivirus that confers resistance to virophage."
The 582,970 base pair M. genitalium bacterial genome
That's a huge synthesis, we routinely synthesize much smaller pieces. I think it's about a grand for a custom ~5 kbp construct. I guess it's just a matter of time until such large sequences can be synthesized with reasonable time/money investments though.
nucleus modification
The zebra fish labs are definitely injecting CRISPR into zygotes to try and create stable edited lines. It doesn't work very well, but it's because zebra fish don't do homology directed repair, and non-homologous end joining is much less efficient. I think the microinjection isn't that bad since the eggs aren't that small. I also know someone who uses a micro needle to poke mammalian cells and - without lysing them - pluck their microtubules in order to measure their viscoelastic properties as they bounce back. I think that's much more difficult.
Embryo selection though runs afoul of all the same objections as early-term abortion
True, but in practice editing will also require multiple tries, at least for a very long time. There will always be some risk of off-target effects, and it can't be perfectly efficient. People will want to give a few tries and use a validated result.
synthesize the associated DNA and implant it within a donor egg
Whole chromosomes are way, way bigger than any DNA molecules we know how to synthesize right now (by around 5 orders of magnitude). The second step is possible though, we do it with current editing technologies. One of my friends/colleagues even does it to zebra fish zygotes. We've already made it into vertebrates.
Incidentally, zebra fish will also express plasmids, which I thought was completely insane until I learned the injection volume is more than 50% that of the cell. The trick is not to bust 'em open when you do it.
editing deleterious alleles out of germline chromosomes
I don't think that will be that popular (on humans). Embyro selection - which is what they actually appear to use in Gattaca - on the other hand will probably be common place.
I do think gene editing in agricultural plants and animals will be common, but it's pretty risky and expensive compared to just sequencing a bunch of embryos and picking the good ones (in most cases).
Your first point is almost a good one, but the last three lines give away the game - you're the politician here.
Real incomes in the US have matched their 1999 levels only this year. That fact represents a radical departure from all postindustrial economic behavior, but to you if students or professors want to talk about why that is - from any perspective - they're already too "political" and should be silenced. Be serious.
It's also not generally appreciated that the quantitative part of Marx represents the foundations of econometrics even to this day, and while things have become generally more sophisticated since his time, that aspect of his work is not particularly questioned or devalued by contemporary economists with right-wing political leanings.
Students didn't demand change because the economics theories they were taught were perfect but because they were flawed.
Indeed, but interestingly large parts of textbook macroeconomics performed (and continue to perform) very well during the crisis. ISLM / ISMP style models have accurately predicted the behavior of economic actors of all different scales this past decade. Some economic programs had neglected these approaches for some time, while others had continued them, but moved them onto a more rigorous mathematical framework. (See for example Romer's treatment of ISMP).
IMHO any movement in economics towards empirical validation and towards more robust models (i.e. less overfitting) is for the better.
inequality
There is an ongoing debate in macroeconomics about the role of inequality. Does inequality impact growth, and if so negatively or positively? How do we measure inequality, and more generally distribution of resources? How do economic systems, and specific policy choices, influence the distribution of income and resources?
These are fundamental aspects of economic study.
globalization
Again...what is "globalization?" How do we measure it? How has it benefited or harmed different countries and different segments of society within countries? How do we measure that? Why have these effects been visited on these particular groups? Can we build quantitative models around these ideas? Do those models have predictive power? How can we measure their predictive power? How can we establish confidence in model outputs?
Especially given that there are conflicting viewpoints across the political spectrum, it's hard to see an inherent political aim in discussing this important contemporary economic movement (e.g. there are right-wing populists and left-wing populists who write that they oppose globalization, and right- and left-wing thinkers who favor globalization, in both cases often for different reasons).
climate change
How do different climate outlooks effect economic prospects? How does the existence of these outlooks effect economic behavior of individuals, firms, and nations? How do we measure climate risk? What are the economic effects of potential mitigation efforts? What level of risk should motivate what degree of mitigation cost, given various estimates of uncertainty? How can these ideas be placed in a quantitative framework? How can model output be assessed given the scale and ongoing nature of the topic? Are there past climatic events with known economic impacts? How do we measure those impacts? How do they compare to current data? And on and on.
It only takes a moment's reflection to note the importance of these questions, and their relevance to the study of economics. That's probably why there are economists from all over the world, and from all over the political spectrum, who spend their academic (or corporate) lives on just these ideas.
Today, we know that ability is entirely genetic and that it follows a bell curve distribution.
Nothing could reveal a deeper lack of understanding about contemporary biology than that sentence. It simultaneously rejects our actual findings and betrays an erroneous confidence in the power of current methods, all without any apparent irony.
Under other circumstances I would feel compelled to say more, but the parent is either a troll or an ideologue and I don't want to waste the time.
Our (USA) funding system was designed under the assumption that ~30% of grant applications will receive funding. Today, that number is a little more than 5%. Science prospers when practitioners compete for excellence, rather than because there isn't enough to go around.
Why?
In this case, because they all had to read and write English. The Indian and American programmers probably grew up speaking English. The Russian and Chinese programmers who took the test were selected for preexisting proficiency in nonnative language.
Yeah, but if USA and India had the majority of contestants, the bias is in their favour
That's not how statistics work. If the samples are random, but different in size, the smaller sample will have a more biased average. So in this case, if we assume the samples are random, we just know that the Russian and Chinese averages are much less representative than the American and Indian averages.
However, we also know that the samples were biased. American and Indian programmers speak English. The testing company is based in Palo Alto, CA. Their website is totally in English. The Russian and Chinese programmers were selected not only for motivation to take an online test, but for a preexisting ability to speak a nonnative language.
The old tired argument that there is a lack of qualified workers in this country is pretty much not in line with reality.
He's right. Show us the wage spike and we'll believe in your worker shortage.
I can't help but wonder if this is only a measure of publicly known hacking
Hacking? TFA is just about test scores.
On a side note, the sample size of 1.4 million doesn't matter if the sample is non-random. Many more Indian and American programmers took the test, and their average scores are most likely lower for that reason, even if there is no additional bias in the Russian and Chinese scores.
That's a big "if" though...what India and the United States have in common with the testing company is the English language. The Russian and Chinese samples are sampling Russian and Chinese people who speak English.