Today I Learned:
1) How to make spring rolls! The crispy chinese kind. Without meat. Anyway, here's what goes in them:
* About 2/3 of a block of tofu, finely chopped
* a cup or so of finely minced vegetables (recipe recommended carrot, onion, and wood-ear mushroom; I used wood-ear mushroom and Trader Joes' cabbage/carrot slaw mix)
* A couple cloves of garlic
* A cup or so of cooked bean thread (I used rice vermicelli and it worked fine)
* Some small amount of soy sauce
* Some small amount of sugar
* Some small amount of pepper
* Some small amount of salt
These things get mixed in a bowl. Then you lay out a spring roll wrapper (get these at Asian marts, internet recipes claim American ones suck, but IDK), put a little bit* of the filling in one corner, roll it up enough that the filling is tucked in, fold the sides over so that the edges are "vertical", and wrap it the rest of the way up. Use a mix of water and corn starch to seal the last bit. Repeat this until you run out of filling or you run out of wrappers, whichever comes first.
To cook these things, just fry them in vegetable oil at about 300-350 F (yeah, I actually measured it -- it was slightly below a 5 on my stovetop). Make sure the rolls have room to move around in the pan/pot/wok. Takes about 7 minutes of cooking per roll-batch.
* The trick for apportioning is to bear in mind that the final roll is going to be a little bit bigger than the size of the stuffing (though oddly, variation in stuffing size mostly seemed to change the length of my rolls, not their girth). It doesn't take much.
2) When making sushi, don't make all of your ingredients salty. It's good to have something a like cucumber or avocado or crab meat to offset the saltiness of the rice and any pickled ingredients.
3) The civilized part of the Star Wars galaxy has about a million inhabited planets of note. Also, the New Republic never gained control of nearly as many planets as the Empire before it or the Galactic Federation of Free Alliances (the reformed government put together from the scraps of the governments that survived the first half of the Yuuzhan Vong invasion).
Saturday, October 31, 2015
Roman Taxes, Restriction Enzyme Etymology, and Collapsed Lungs
Today I Learned:
1) The Roman Republic (this was Rome before Julius Ceasar) collected taxes using private contractors. These contractors would bid on how much money they would supply the government using tax money from some province or another. I gather that this was something of a gamble, because it was hard to tell exactly how much tax would be collectable from a province until someone actually went out and did it. The winning contractor would then loan their bid to Rome, go out and collect whatever taxes they could, and return. The contractor would give whatever they bid to Rome, Rome would pay back the initial loan, plus some interest, and any tax money collected in excess of the bid would be kept by the contractor.
2) Restriction enzymes (enzymes that cut DNA at (usually) specific sequences) are called restriction enzymes because they were first discovered as enzymes that "restricted" the growth of lambda phage in E. coli. Yet another poorly-named biology term.
3) How a collapsed lung works. Er, doesn't work. Anyway, there's apparently a membrane covering the outside of a lung which attaches the alveoli in the lung to the musculature that controls breathing. If the lung detaches from that membrane, it collapses and sticks to itself like a wet plastic bag, and the body can't pull the lung apart.
1) The Roman Republic (this was Rome before Julius Ceasar) collected taxes using private contractors. These contractors would bid on how much money they would supply the government using tax money from some province or another. I gather that this was something of a gamble, because it was hard to tell exactly how much tax would be collectable from a province until someone actually went out and did it. The winning contractor would then loan their bid to Rome, go out and collect whatever taxes they could, and return. The contractor would give whatever they bid to Rome, Rome would pay back the initial loan, plus some interest, and any tax money collected in excess of the bid would be kept by the contractor.
2) Restriction enzymes (enzymes that cut DNA at (usually) specific sequences) are called restriction enzymes because they were first discovered as enzymes that "restricted" the growth of lambda phage in E. coli. Yet another poorly-named biology term.
3) How a collapsed lung works. Er, doesn't work. Anyway, there's apparently a membrane covering the outside of a lung which attaches the alveoli in the lung to the musculature that controls breathing. If the lung detaches from that membrane, it collapses and sticks to itself like a wet plastic bag, and the body can't pull the lung apart.
Friday, October 30, 2015
Chinese Dishes and Two NASA Plans
Today I Learned:
1) Many Chinese dishes are named after a story or metaphor. For example, there's a Sichuan noodle dish called "ants climbing up a tree" because it kind of looks like that.
2) NASA plans for any manned missions to Mars to run on a vegan diet. Why? Because animal products go bad too quickly, and animals are too inefficient to cultivate on-ship.
3) Speaking of NASA, NASA is planning a mission to capture an asteroid and move it to the moon. The idea is to park it in lunar orbit until around 2020, when a manned mission can go retrieve samples and bring them back. This mission will also test some key technologies for removing dangerous asteroids in the future, in case NASA or an equivalent agency ever gets the money to be able to detect city-destroying asteroids in time to launch a mission to stop it. (Right now we can probably detect extinction-event-level asteroids, but not city-busting ones)
1) Many Chinese dishes are named after a story or metaphor. For example, there's a Sichuan noodle dish called "ants climbing up a tree" because it kind of looks like that.
2) NASA plans for any manned missions to Mars to run on a vegan diet. Why? Because animal products go bad too quickly, and animals are too inefficient to cultivate on-ship.
3) Speaking of NASA, NASA is planning a mission to capture an asteroid and move it to the moon. The idea is to park it in lunar orbit until around 2020, when a manned mission can go retrieve samples and bring them back. This mission will also test some key technologies for removing dangerous asteroids in the future, in case NASA or an equivalent agency ever gets the money to be able to detect city-destroying asteroids in time to launch a mission to stop it. (Right now we can probably detect extinction-event-level asteroids, but not city-busting ones)
Thursday, October 29, 2015
Ant Pheromones, Supercoiling, and Off-Target Cas9
Today I Learned:
1) As of a couple years ago, the most thoroughly-studied ant pheremone system was that of the invasive red fire ant.
2) The supercoils introduced during transcription and replication are permanent. They're not just a physical winding that can be relaxed easily -- those supercoils are at least some of the time made permanent by helicases and gyrases.
3) It seems there have been a number of studies of off-target binding by Cas9. One of the somewhat (though not very) surprising findings is that Cas9 binding is not very predictive of Cas9 cutting. The vast majority of off-target binding sites don't get cut, and it isn't always the strongest-bound sites that do get cut. Moreover, sequence similarity (measured by simple Hamilton distance) is not a good predictor of where Cas9 will bind. Off-target binding sites do, however, pretty much universally have a correct PAM site upstream of the target.
1) As of a couple years ago, the most thoroughly-studied ant pheremone system was that of the invasive red fire ant.
2) The supercoils introduced during transcription and replication are permanent. They're not just a physical winding that can be relaxed easily -- those supercoils are at least some of the time made permanent by helicases and gyrases.
3) It seems there have been a number of studies of off-target binding by Cas9. One of the somewhat (though not very) surprising findings is that Cas9 binding is not very predictive of Cas9 cutting. The vast majority of off-target binding sites don't get cut, and it isn't always the strongest-bound sites that do get cut. Moreover, sequence similarity (measured by simple Hamilton distance) is not a good predictor of where Cas9 will bind. Off-target binding sites do, however, pretty much universally have a correct PAM site upstream of the target.
Wednesday, October 28, 2015
CCR5, CXCR4, and Klein Bottles
Today I Learned:
1) There is a human cytokine receptor called CCR5 which HIV uses to enter human immune cells. Individuals with homozygous knockout mutations in their CCR5 gene appear to be immune to HIV, and in a few rare cases, bone marrow replacement by CCR5-negative donors has cured individuals of AIDS. Today I learned that there is a non-HIV-related phenotype associated with CCR5 knockout -- CCR5-negative individuals are somewhat more succeptible to West Nile virus, and possibly other viruses. Specifically, they're just as likely to get infected by West Nile, but their clinical outcomes are worse once they're sick.
2) Speaking of CCR5, today I learned that a group of scientists from Wuhan University, China, have built a Cas9-knockout system for CXCR4, another cytokine receptor required by HIV. Their adenovirus-based system is pretty effective at removing CXCR4 from cultured cells, and seems to pretty much prevent HIV from infecting those cells.
3) How a Klein bottle is *actually* shaped. If you go buy a Klein bottle at any of the usual places, you'll get a jug where the handle passes back through the side of the jug and out the bottom, effectively putting the entrance to the bottle on its bottom. These are cool, but they're not *really* Klein bottles. A true Klein bottle doesn't intersect itself at all.
Here's how to imagine a real Klein bottle. Take a commercial Klein bottle. Consider the part where the handle intersects the body. Firstly, fill in the bit inside the handle so that the body has a complete, hole-less surface. Now grab the handle where it intersects the surface of the body and pull it out of plane in the 4th dimension a little bit -- this is similar to layout out a string so it crosses itself and pulling one of the threads out of plane in the 3rd dimension.
...and that's why nobody makes real Klein bottles.
1) There is a human cytokine receptor called CCR5 which HIV uses to enter human immune cells. Individuals with homozygous knockout mutations in their CCR5 gene appear to be immune to HIV, and in a few rare cases, bone marrow replacement by CCR5-negative donors has cured individuals of AIDS. Today I learned that there is a non-HIV-related phenotype associated with CCR5 knockout -- CCR5-negative individuals are somewhat more succeptible to West Nile virus, and possibly other viruses. Specifically, they're just as likely to get infected by West Nile, but their clinical outcomes are worse once they're sick.
2) Speaking of CCR5, today I learned that a group of scientists from Wuhan University, China, have built a Cas9-knockout system for CXCR4, another cytokine receptor required by HIV. Their adenovirus-based system is pretty effective at removing CXCR4 from cultured cells, and seems to pretty much prevent HIV from infecting those cells.
3) How a Klein bottle is *actually* shaped. If you go buy a Klein bottle at any of the usual places, you'll get a jug where the handle passes back through the side of the jug and out the bottom, effectively putting the entrance to the bottle on its bottom. These are cool, but they're not *really* Klein bottles. A true Klein bottle doesn't intersect itself at all.
Here's how to imagine a real Klein bottle. Take a commercial Klein bottle. Consider the part where the handle intersects the body. Firstly, fill in the bit inside the handle so that the body has a complete, hole-less surface. Now grab the handle where it intersects the surface of the body and pull it out of plane in the 4th dimension a little bit -- this is similar to layout out a string so it crosses itself and pulling one of the threads out of plane in the 3rd dimension.
...and that's why nobody makes real Klein bottles.
Tuesday, October 27, 2015
Colony Variation, Workers and Soldiers, and Measures of Spreading
Today I Learned:
1) There is good evidence that within-colony genetic variation is good for most social insects. There's some tension to this fact, because sociality requires very *close* genetic relatedness. There's a very narrow evolutionary window of intracolony relatedness through which a species has to pass to become eusocial.
2) I've always thought of ants belonging to one the four castes of queen, drone, worker, or soldier. Myrmicologists more or less also recognize this, but the worker is known as a "minor worker" and the soldier a "major soldier", and major soldiers also include storage castes in some species (as in, they store food for other ants).
3) The usual measure of "spreading" in statistical mechanics is mean squared displacement, which has units of distance^2. However! Many authors also measure in *root* mean squared displacement, which has units of distance. If you're ever reading about characteristics of diffusion, make sure you know which measure the author is using!
1) There is good evidence that within-colony genetic variation is good for most social insects. There's some tension to this fact, because sociality requires very *close* genetic relatedness. There's a very narrow evolutionary window of intracolony relatedness through which a species has to pass to become eusocial.
2) I've always thought of ants belonging to one the four castes of queen, drone, worker, or soldier. Myrmicologists more or less also recognize this, but the worker is known as a "minor worker" and the soldier a "major soldier", and major soldiers also include storage castes in some species (as in, they store food for other ants).
3) The usual measure of "spreading" in statistical mechanics is mean squared displacement, which has units of distance^2. However! Many authors also measure in *root* mean squared displacement, which has units of distance. If you're ever reading about characteristics of diffusion, make sure you know which measure the author is using!
Sunday, October 25, 2015
Circumnavigation, Octopus Shelters, and Brain Surgery
Today I Learned:
1) Ferdinand Magellan was not the first man to circumnavigate the Earth. In fact, he died along the way fighting natives to prove the value of Christianity to a local king he had just converted.... Anyway, today I learned that the first man to circumnavigate the Earth was probably one of Magellan's slaves. After three months sailing the Pacific ocean, the crew knew they were getting close to the East because this particular slave started to be able to understand the locals they were encountering. Turns out he'd been born somewhere out in the East, and was returning to somewhere near his homeland.
2) There is a species of octopus which builds underground burrows in the sand. It liquefies the sand with a spray of water, forces itself in, shoves the sand away from itself until it's got a nice burrow with a ventilation shaft, then spreads mucus on the interior to keep its shape intact.
More here: http://ift.tt/1R7PcZA Props to New Scientist for actually including a link to the relevant journal article -- this is surprisingly uncommon in popular science articles, much to my infuration.
3) Most brain surgery is done with the patient awake and conscious. Local anesthetics keep the skull-being-opened part from hurting, and poking around in the brain doesn't hurt anyway, so it's supposedly not *that* unpleasant. It's also very helpful to the surgeons to be able to ask how the patient is doing, and for the patient to be able to tell the surgeons if anything seems wrong.
1) Ferdinand Magellan was not the first man to circumnavigate the Earth. In fact, he died along the way fighting natives to prove the value of Christianity to a local king he had just converted.... Anyway, today I learned that the first man to circumnavigate the Earth was probably one of Magellan's slaves. After three months sailing the Pacific ocean, the crew knew they were getting close to the East because this particular slave started to be able to understand the locals they were encountering. Turns out he'd been born somewhere out in the East, and was returning to somewhere near his homeland.
2) There is a species of octopus which builds underground burrows in the sand. It liquefies the sand with a spray of water, forces itself in, shoves the sand away from itself until it's got a nice burrow with a ventilation shaft, then spreads mucus on the interior to keep its shape intact.
More here: http://ift.tt/1R7PcZA Props to New Scientist for actually including a link to the relevant journal article -- this is surprisingly uncommon in popular science articles, much to my infuration.
3) Most brain surgery is done with the patient awake and conscious. Local anesthetics keep the skull-being-opened part from hurting, and poking around in the brain doesn't hurt anyway, so it's supposedly not *that* unpleasant. It's also very helpful to the surgeons to be able to ask how the patient is doing, and for the patient to be able to tell the surgeons if anything seems wrong.
Saturday, October 24, 2015
Rice Cooker, Yellow Culture, and Cold Glycerol
Today I Learned:
1) Our lab has a rice cooker in the lounge. Mid-day steamed buns may have to become a thing.
2) E. coli cell extract turns vibrant yellow when bleached! I'm guessing it's not the actual cell innards that turn color, though, because actual E. coli turns colorless when bleached. It must be one of our buffers.
3) When glycerol is drizzled into liquid nitrogen, it tends to bubble and foam up a bit before crystallizing. If it forms small bubbles, it will harden into a solid foam with a similar appearance to cheetos or styrofoam. It can also form large, singular bubbles, it harden into beautiful, delicate shells. Andy Halleran -- did we try this before?
1) Our lab has a rice cooker in the lounge. Mid-day steamed buns may have to become a thing.
2) E. coli cell extract turns vibrant yellow when bleached! I'm guessing it's not the actual cell innards that turn color, though, because actual E. coli turns colorless when bleached. It must be one of our buffers.
3) When glycerol is drizzled into liquid nitrogen, it tends to bubble and foam up a bit before crystallizing. If it forms small bubbles, it will harden into a solid foam with a similar appearance to cheetos or styrofoam. It can also form large, singular bubbles, it harden into beautiful, delicate shells. Andy Halleran -- did we try this before?
Thursday, October 22, 2015
Bonus TIL: Primitive Eusociality!
One more TIL for the day, because I just learned it after posting my TIL and it's really cool: The Japanese carpenter bee Ceratina flavipes is probably at one of the very earliest stages of eusociality. Most of them are completely solitary and build solitary nests, but one in a thousand female Ceratina flavipes bees pairs with another female Ceratina flavipes bee and builds a joint nest. When they pair, they divide labor much like a eusocial insect -- one bee lays eggs and guards the nest, while the other forages for food. I'm not sure what the foraging bee gets from this relationship... perhaps this only happens in sisters? Or maybe they used to be more eusocial, and this is an occasional misfiring that brings back the old instincts.
OD, Soaked Rice, and Black Cultures
Today I Learned:
1) OD, or optical density, the measurement used by biologists to measure the density of bacterial cells in suspended culture, is actually a log scale. Specifically, an OD at a wavelength is the negative base-10 logarithm of the ratio of the amount of outgoing light at that wavelength to the amount of incoming light at that wavelength. This is... disquieting, because OD is usually assumed to vary linearly with cell density. If, however, OD is a log measure, then it should vary as the *log* of cell density, which means a linear increase in OD is actually an exponential increase in cell density. Can anyone out there clear this up?
EDIT: A reader pointed out to me that it actually makes a ton of sense for a log measure of light getting through to vary linearly with cell density. If there's one particle of bacteria in your solution, it absorbs some fraction F of of the light coming in. If you add a second, it absorbs F of the F that got through the first one, so the remaining light is F^2, etc, so the fraction of light getting through with N particles is F^N. Therefore log(F^N) ~ the number of particles.
2) Rice can be kept warm in the water it's going to be cooked in before it's cooked for a surprisingly long time apparently without affecting the end result.
3) Certain kinds of bacterial culture turn startlingly *black* when bleached. And it's not because of the phosphate buffer we used. *shrug*.
1) OD, or optical density, the measurement used by biologists to measure the density of bacterial cells in suspended culture, is actually a log scale. Specifically, an OD at a wavelength is the negative base-10 logarithm of the ratio of the amount of outgoing light at that wavelength to the amount of incoming light at that wavelength. This is... disquieting, because OD is usually assumed to vary linearly with cell density. If, however, OD is a log measure, then it should vary as the *log* of cell density, which means a linear increase in OD is actually an exponential increase in cell density. Can anyone out there clear this up?
EDIT: A reader pointed out to me that it actually makes a ton of sense for a log measure of light getting through to vary linearly with cell density. If there's one particle of bacteria in your solution, it absorbs some fraction F of of the light coming in. If you add a second, it absorbs F of the F that got through the first one, so the remaining light is F^2, etc, so the fraction of light getting through with N particles is F^N. Therefore log(F^N) ~ the number of particles.
2) Rice can be kept warm in the water it's going to be cooked in before it's cooked for a surprisingly long time apparently without affecting the end result.
3) Certain kinds of bacterial culture turn startlingly *black* when bleached. And it's not because of the phosphate buffer we used. *shrug*.
Wednesday, October 21, 2015
Natural Axles, Dissociation Constants, and Occam's Razor, Bayes Style
Today I Learned:
1) It's a well-known quirk of natural design that nature doesn't feature a lot of wheels. Animals and plants and other creatures have implemented virtually every kind of simple machine you can think of, and probably some you can't. Wheels, however, or in fact any kind of freely-rotatly axle, are incredibly rare.
There are two known molecular-scale examples of freely-rotating axles in nature, and both are relatively ubiquitous. The first is ATP synthase, which is a protein that sits in the membranes of bacteria, archaea, and mitochondria. They're essentially molecular-scale water-sheels. Hydrogen traveling down a gradient across the membrane forces the protein to spin, the motion of which is used to forcibly jam phosphates onto ADT, turning it into ATP, which is used to power processes throughout the cell (ATP synthase can sometimes also be run in reverse -- ATP spins the motor backward, forcing hydrogen back out of the cell).
The other molecular axle is the flagellar motor, which is thought to be derived from ATP synthase but acts to spin a flagella, which works like a propeller to (typically) move a cell.
Today I learned that there's also a macro-scale example of a freely-rotating axle in animals, and that is the crystalline style of certain mollusks. The crystalline style is essentially a buffer of digestive enzymes in crystal form kept in the midgut. The mollusk spins the style against its abrasive stomach lining, wrapping it in mucus and dissolving or abrading off the digestive enzymes. Go mollusks!
2) A dissociation constant is just an equillibrium constant for the special case where you have chemicals that do something like A + B <=> C like breakdown/synthesis or binding and unbinding (as it's commonly encountered in biology)
3) ...how Bayes Theorem leads naturally to Occam's Razor, at least in some cases. I'm not going to go into the full details here (though I can if there's interest), but the basic idea is that if you compare the probability of two hypotheses, each hypotheses' likelihood term gets multiplied by one over a normalization term that's related to the plausible range of each parameter involved, and that term is multiplicative in parameters, so that a hypothesis with more parameters ends up with a larger normalization constant.
1) It's a well-known quirk of natural design that nature doesn't feature a lot of wheels. Animals and plants and other creatures have implemented virtually every kind of simple machine you can think of, and probably some you can't. Wheels, however, or in fact any kind of freely-rotatly axle, are incredibly rare.
There are two known molecular-scale examples of freely-rotating axles in nature, and both are relatively ubiquitous. The first is ATP synthase, which is a protein that sits in the membranes of bacteria, archaea, and mitochondria. They're essentially molecular-scale water-sheels. Hydrogen traveling down a gradient across the membrane forces the protein to spin, the motion of which is used to forcibly jam phosphates onto ADT, turning it into ATP, which is used to power processes throughout the cell (ATP synthase can sometimes also be run in reverse -- ATP spins the motor backward, forcing hydrogen back out of the cell).
The other molecular axle is the flagellar motor, which is thought to be derived from ATP synthase but acts to spin a flagella, which works like a propeller to (typically) move a cell.
Today I learned that there's also a macro-scale example of a freely-rotating axle in animals, and that is the crystalline style of certain mollusks. The crystalline style is essentially a buffer of digestive enzymes in crystal form kept in the midgut. The mollusk spins the style against its abrasive stomach lining, wrapping it in mucus and dissolving or abrading off the digestive enzymes. Go mollusks!
2) A dissociation constant is just an equillibrium constant for the special case where you have chemicals that do something like A + B <=> C like breakdown/synthesis or binding and unbinding (as it's commonly encountered in biology)
3) ...how Bayes Theorem leads naturally to Occam's Razor, at least in some cases. I'm not going to go into the full details here (though I can if there's interest), but the basic idea is that if you compare the probability of two hypotheses, each hypotheses' likelihood term gets multiplied by one over a normalization term that's related to the plausible range of each parameter involved, and that term is multiplicative in parameters, so that a hypothesis with more parameters ends up with a larger normalization constant.
Sunday, October 18, 2015
Entropy, Gender Equality in the USSR, and HIV Modeling
Today I Learned:
1) ...a solid theoretical reason that information (entropy) is measured as a logarithm. It comes from four axiomatic assumptions about information, chosen to basically make it make sense.
a) Information should be a number between 0 and infinity.
b) If an event has probability 1, there is no information associated with learning that it happens, so the entropy of an event with probability 1 is 0.
c) The entropy of two independent events jointly occurring is the sum of their entropies (that is, the information you learn from two independent events is the sum of their information).
d) The entropy function should be continuous. From these axioms, the only possible functions are logarithms, though the choice of base is up to you.
2) The Soviet Union of World War II may have had the most gender-equal army of all time. Hundreds of thousands of women served in the USSR armed forces as soldiers, elite snipers, pilots, and tank crews.
3) Under certain models of viral infection, it can be very difficult to distinguish successful clearance of viruses from super-effective viruses. They both result in similar time traces of decreasing viral load, one because the viruses get killed and one because the pool of host cells dies off. This is particularly relevant for monitoring HIV.
1) ...a solid theoretical reason that information (entropy) is measured as a logarithm. It comes from four axiomatic assumptions about information, chosen to basically make it make sense.
a) Information should be a number between 0 and infinity.
b) If an event has probability 1, there is no information associated with learning that it happens, so the entropy of an event with probability 1 is 0.
c) The entropy of two independent events jointly occurring is the sum of their entropies (that is, the information you learn from two independent events is the sum of their information).
d) The entropy function should be continuous. From these axioms, the only possible functions are logarithms, though the choice of base is up to you.
2) The Soviet Union of World War II may have had the most gender-equal army of all time. Hundreds of thousands of women served in the USSR armed forces as soldiers, elite snipers, pilots, and tank crews.
3) Under certain models of viral infection, it can be very difficult to distinguish successful clearance of viruses from super-effective viruses. They both result in similar time traces of decreasing viral load, one because the viruses get killed and one because the pool of host cells dies off. This is particularly relevant for monitoring HIV.
Saturday, October 17, 2015
Bayes Theorem, Library Views, and Xbox Controller Screws
Today I Learned:
1) A great example for teaching Bayes' Theorem, courtesy of one Deniz Senyuz.
Here goes my attempt to explain Bayes' Theorem on Facebook.
Bayes' Theorem is really just a simple equation that tells you how to correctly change how much you believe something based on what you knew before and what you see. In other words, it's the math that explains how learning works (if done correctly).
A quick review of terms:
P(X) = the probability of x, which is a number between 0 and 1
P(X|Y) = the probability of X *given* that Y is true.
P(X,Y) = the probability of X and Y both being true.
Bayes' Theorem: P(X|Y) = P(Y|X) * P(X) / P(Y)
or, as it shows up in scientific contexts:
P(Hypothesis | Data) = P(Data | Hypothesis) * P(Hypothesis) / P(Data)
First, a quick derivation of Bayes' Theorem. I find that going through this proof and knowing how to reproduce it are helpful for understanding how the theorem works. It's a very simple theorem, but feel free to skip this bit to see the example, which is the novel thing I actually learned today.
<PROOF>
First, we note that P(X,Y) = P(Y,X). After all, the order you list them in really doesn't matter.
Now note that P(X,Y) = P(X|Y) * P(Y). For X and Y to be true, Y certainly has to be true, thus the P(Y) term. Once you know that Y is true, the probability of X is, by definition, P(X|Y).
Similarly, P(Y,X) = P(Y|X) * P(X).
Thus, P(X|Y) * P(Y) = P(X,Y) = P(Y,X) = P(Y|X) * P(X)
Divide both sides by P(Y), and you get P(X|Y) = P(Y|X) * P(X) / P(Y).
Next... there is no next. I just wrote Bayes' Theorem. That's the whole proof.
</PROOF>
<AN ASIDE ON THE PARTS OF BAYES'S THEOREM>
Before I actually get to Bayes' Theorem, let me mention how Bayes' Theorem describes the mathematically correct way of updating knowledge when you get some data. As I wrote before, a common use of Bayes' Theorem is the specific case of
P(Hypothesis | Data) = P(Data | Hypothesis) * P(Hypothesis) / P(Data)
The number on the left -- P(Hypothesis | Data) is the confidence you have, as a probability, that some hypothesis about the world is true, given that you just saw some evidence (the Data). This term is usually called the "posterior probability" or "posterior likelihood", that is, the probability (of the hypothesis) after seeing the data.
The first term on the right -- P(Data | Hypothesis) -- describes how probably the data were *IF* the hypothesis is correct. It's often called the "likelihood", short for "likelihood of the data". You can kind of intuit why the posterior likelihood might be proportional to the likelihood. After all, if your hypothesis strongly predicts an outcome, and you get that outcome... you're more likely to believe that hypothesis than if the hypothesis was wishy-washy on the outcome, or make a contradictory prediction.
The second term on the right -- P(Hypothesis) -- is the confiecne, as a probability, that you placed in the hypothesis *before* you saw the data. It's usually called the "prior", short for "prior probability", or the probability of the hypothesis *prior* to the data. (If it weirds you out that Bayes' Theorem relies on your prior beliefs... you're not alone. That's a bigger topic than I'm going to address here, but the single-sentence response is that any kind of learning or statistics is going to involve prior beliefs, and Bayes' Theorem makes them nice and explicit).
The last term is the probability of the data. Not under any particular hypothesis, mind you, just the probability of getting it under *any* possible hypothesis -- to calculate this, you have to sum up all of the probabilities of the data under the different hypotheses, weighted by the (prior) probability of those hypotheses. That's a hell of a pain, but for a lot of purposes you don't actually need to calculate this, for reasons I'll get to in the example. There's a name for this term, but it's terribly unhelpful, so I won't repeat it here. I also don't have a better word for it. For the purpose of this post, I'll call it the "overall likelihood of the data".
</AN ASIDE ON THE PARTS OF BAYES'S THEOREM>
Ok, now the example.
Say you just met me. You know I'm from the US, and you want to know what state I live in. I won't tell you, but I *will* tell you that my district representative is a Democrat. How likely is it that I come from Indiana?
The competing hypotheses here are statements like "Sam lives in Indiana" and "Sam lives in Virginia". The data here is (and yes I know it's "datum" in the singular but SCREW THAT PARTICULAR LINGUISTIC CONVENTION I'M AN ADULT I DO WHAT I WANT) that I live in a district with a Democratic Representative. Bayes Theorem will tell you how likely it is that I'm from a particular state.
What is the likelihood? That is, what's the likelihood that my representative is a Democrat, under the assumption that I live in Indiana? To save some math, let's assume that every representative has an equal number of constituents, so that if I'm from Indiana, I'm equally likely to have any of the Indiana representatives. The probability of my representative being a Democrat (given that I'm from Indiana), then, is the fraction of Indiana representatives that are Democrats. In this case, as of this writing, that number is 2/9. Not very likely.
What is the prior probability? That is, how likely did you think it was that I'm from Indiana *before* you learned that my representative is a Democrat? Well, that depends on what information you have coming in. You might say that since you don't have any idea what state I live in, you assign equal probabilities to my living in any state. You could also say that I'm a random person, so the probability that I'm from Indiana is the fraction of Americans who are from Indiana. Maybe you hear my accent and don't think it sounds very Indianaian, or you see my drivers' license and see that it's actually from Michigan, in which case the prior probability would be very low.
Let's make a pretty minimal assumption and use the assumption I made above, namely that every representative has the same number of constituents. Then the probability that I'm from Indiana is the same as the fraction of total representatives that are from Indiana, which is 9/435 ~ 2%.
What's the overall-likelihood-of-the-data? Well, this would be the probability of my having a Democratic representative if you have *no* idea what state I'm from. Currently, the Democrats hold 188 out of 435 seats, so the overall-likelihood-of-the-data is 188/435 ~ 43%.
Now we just plug those numbers into Bayes' Theorem and it tells you how likely I am to live in Indiana. In this case, it's (2/9) * .02 / 0.43 ~ 0.01, so it's about 1% probable that I'm from Indiana.
Incidentally, there's another question you might ask, which is "what state is Sam most likely to be from?". This kind of question gets asked a lot in science. If you want find the *most* likely state, you can calculate the posteriors for each of the 50 hypotheses involved and see which one's highest. If you want to save some calculation, you might note that the overall-likelihood-of-the-data, P(Democrat), is equally likely no matter which hypothesis you're considering. It doesn't care what state you're asking about. Since *every single* hypothesis you're considering is being divided by the same term, you could multiply all of them by that term and it wouldn't change which is most likely. That's why you often just don't bother calculating the overall-likelihood-of-the-data -- if you're comparing *relative* likelihoods of different hypotheses, it doesn't really matter and can be dropped.
What I love about this example is that there's a nice graphical, visual way to think about the terms of Bayes' Theorem in this example, which helps illustrate why it works. Draw out (or imagine drawing out, if you prefer (or go look up a picture of)) a map of the US divided up by representative districts, with the districts colored in by representative-party. In fact, here's a link to such a map: http://tinyurl.com/p6dojc6. Pretty, no?
Now you can start seeing the terms of Bayes' Theorem visually. The likelihood, for instance, is the probability that a given Indiana representative is a Democrat, so you can blot out all the states except Indiana, and the likelihood is the fraction of the districts that are left that are blue. What's the prior? It's the fraction of *all* of the districts that are in Indiana. What's the overall-likelihood-of-the-data? That's the fraction of Democratic districts that are in Indiana -- blot out all of the Republican districts, and the fraction of what's left is the overall-likelihood-of-the-data.
If you've never seen or used Bayes' Theorem, I hope this teaches you something and convinces you how awesome the theorem is! If you *have* seen Bayes' Theorem before, I hope this explanation helps!
2) Our library has a ninth-floor lounge with a fantastic view of the mountains. Those mountains are a lot more impressive when you're nine stories up and they look just as big.
Also, downtown Pasadena has a lot more trees than I thought.
3) Xbox One controllers still use the same screws as Xbox 360 controllers, the kind that have a pin in the middle so you need a special screwdriver to unscrew them. I also learned that it's supposedly possible to unscrew those with a 2mm flathead screwdriver, though I don't have one myself and thus wasn't able to test it myself.
1) A great example for teaching Bayes' Theorem, courtesy of one Deniz Senyuz.
Here goes my attempt to explain Bayes' Theorem on Facebook.
Bayes' Theorem is really just a simple equation that tells you how to correctly change how much you believe something based on what you knew before and what you see. In other words, it's the math that explains how learning works (if done correctly).
A quick review of terms:
P(X) = the probability of x, which is a number between 0 and 1
P(X|Y) = the probability of X *given* that Y is true.
P(X,Y) = the probability of X and Y both being true.
Bayes' Theorem: P(X|Y) = P(Y|X) * P(X) / P(Y)
or, as it shows up in scientific contexts:
P(Hypothesis | Data) = P(Data | Hypothesis) * P(Hypothesis) / P(Data)
First, a quick derivation of Bayes' Theorem. I find that going through this proof and knowing how to reproduce it are helpful for understanding how the theorem works. It's a very simple theorem, but feel free to skip this bit to see the example, which is the novel thing I actually learned today.
<PROOF>
First, we note that P(X,Y) = P(Y,X). After all, the order you list them in really doesn't matter.
Now note that P(X,Y) = P(X|Y) * P(Y). For X and Y to be true, Y certainly has to be true, thus the P(Y) term. Once you know that Y is true, the probability of X is, by definition, P(X|Y).
Similarly, P(Y,X) = P(Y|X) * P(X).
Thus, P(X|Y) * P(Y) = P(X,Y) = P(Y,X) = P(Y|X) * P(X)
Divide both sides by P(Y), and you get P(X|Y) = P(Y|X) * P(X) / P(Y).
Next... there is no next. I just wrote Bayes' Theorem. That's the whole proof.
</PROOF>
<AN ASIDE ON THE PARTS OF BAYES'S THEOREM>
Before I actually get to Bayes' Theorem, let me mention how Bayes' Theorem describes the mathematically correct way of updating knowledge when you get some data. As I wrote before, a common use of Bayes' Theorem is the specific case of
P(Hypothesis | Data) = P(Data | Hypothesis) * P(Hypothesis) / P(Data)
The number on the left -- P(Hypothesis | Data) is the confidence you have, as a probability, that some hypothesis about the world is true, given that you just saw some evidence (the Data). This term is usually called the "posterior probability" or "posterior likelihood", that is, the probability (of the hypothesis) after seeing the data.
The first term on the right -- P(Data | Hypothesis) -- describes how probably the data were *IF* the hypothesis is correct. It's often called the "likelihood", short for "likelihood of the data". You can kind of intuit why the posterior likelihood might be proportional to the likelihood. After all, if your hypothesis strongly predicts an outcome, and you get that outcome... you're more likely to believe that hypothesis than if the hypothesis was wishy-washy on the outcome, or make a contradictory prediction.
The second term on the right -- P(Hypothesis) -- is the confiecne, as a probability, that you placed in the hypothesis *before* you saw the data. It's usually called the "prior", short for "prior probability", or the probability of the hypothesis *prior* to the data. (If it weirds you out that Bayes' Theorem relies on your prior beliefs... you're not alone. That's a bigger topic than I'm going to address here, but the single-sentence response is that any kind of learning or statistics is going to involve prior beliefs, and Bayes' Theorem makes them nice and explicit).
The last term is the probability of the data. Not under any particular hypothesis, mind you, just the probability of getting it under *any* possible hypothesis -- to calculate this, you have to sum up all of the probabilities of the data under the different hypotheses, weighted by the (prior) probability of those hypotheses. That's a hell of a pain, but for a lot of purposes you don't actually need to calculate this, for reasons I'll get to in the example. There's a name for this term, but it's terribly unhelpful, so I won't repeat it here. I also don't have a better word for it. For the purpose of this post, I'll call it the "overall likelihood of the data".
</AN ASIDE ON THE PARTS OF BAYES'S THEOREM>
Ok, now the example.
Say you just met me. You know I'm from the US, and you want to know what state I live in. I won't tell you, but I *will* tell you that my district representative is a Democrat. How likely is it that I come from Indiana?
The competing hypotheses here are statements like "Sam lives in Indiana" and "Sam lives in Virginia". The data here is (and yes I know it's "datum" in the singular but SCREW THAT PARTICULAR LINGUISTIC CONVENTION I'M AN ADULT I DO WHAT I WANT) that I live in a district with a Democratic Representative. Bayes Theorem will tell you how likely it is that I'm from a particular state.
What is the likelihood? That is, what's the likelihood that my representative is a Democrat, under the assumption that I live in Indiana? To save some math, let's assume that every representative has an equal number of constituents, so that if I'm from Indiana, I'm equally likely to have any of the Indiana representatives. The probability of my representative being a Democrat (given that I'm from Indiana), then, is the fraction of Indiana representatives that are Democrats. In this case, as of this writing, that number is 2/9. Not very likely.
What is the prior probability? That is, how likely did you think it was that I'm from Indiana *before* you learned that my representative is a Democrat? Well, that depends on what information you have coming in. You might say that since you don't have any idea what state I live in, you assign equal probabilities to my living in any state. You could also say that I'm a random person, so the probability that I'm from Indiana is the fraction of Americans who are from Indiana. Maybe you hear my accent and don't think it sounds very Indianaian, or you see my drivers' license and see that it's actually from Michigan, in which case the prior probability would be very low.
Let's make a pretty minimal assumption and use the assumption I made above, namely that every representative has the same number of constituents. Then the probability that I'm from Indiana is the same as the fraction of total representatives that are from Indiana, which is 9/435 ~ 2%.
What's the overall-likelihood-of-the-data? Well, this would be the probability of my having a Democratic representative if you have *no* idea what state I'm from. Currently, the Democrats hold 188 out of 435 seats, so the overall-likelihood-of-the-data is 188/435 ~ 43%.
Now we just plug those numbers into Bayes' Theorem and it tells you how likely I am to live in Indiana. In this case, it's (2/9) * .02 / 0.43 ~ 0.01, so it's about 1% probable that I'm from Indiana.
Incidentally, there's another question you might ask, which is "what state is Sam most likely to be from?". This kind of question gets asked a lot in science. If you want find the *most* likely state, you can calculate the posteriors for each of the 50 hypotheses involved and see which one's highest. If you want to save some calculation, you might note that the overall-likelihood-of-the-data, P(Democrat), is equally likely no matter which hypothesis you're considering. It doesn't care what state you're asking about. Since *every single* hypothesis you're considering is being divided by the same term, you could multiply all of them by that term and it wouldn't change which is most likely. That's why you often just don't bother calculating the overall-likelihood-of-the-data -- if you're comparing *relative* likelihoods of different hypotheses, it doesn't really matter and can be dropped.
What I love about this example is that there's a nice graphical, visual way to think about the terms of Bayes' Theorem in this example, which helps illustrate why it works. Draw out (or imagine drawing out, if you prefer (or go look up a picture of)) a map of the US divided up by representative districts, with the districts colored in by representative-party. In fact, here's a link to such a map: http://tinyurl.com/p6dojc6. Pretty, no?
Now you can start seeing the terms of Bayes' Theorem visually. The likelihood, for instance, is the probability that a given Indiana representative is a Democrat, so you can blot out all the states except Indiana, and the likelihood is the fraction of the districts that are left that are blue. What's the prior? It's the fraction of *all* of the districts that are in Indiana. What's the overall-likelihood-of-the-data? That's the fraction of Democratic districts that are in Indiana -- blot out all of the Republican districts, and the fraction of what's left is the overall-likelihood-of-the-data.
If you've never seen or used Bayes' Theorem, I hope this teaches you something and convinces you how awesome the theorem is! If you *have* seen Bayes' Theorem before, I hope this explanation helps!
2) Our library has a ninth-floor lounge with a fantastic view of the mountains. Those mountains are a lot more impressive when you're nine stories up and they look just as big.
Also, downtown Pasadena has a lot more trees than I thought.
3) Xbox One controllers still use the same screws as Xbox 360 controllers, the kind that have a pin in the middle so you need a special screwdriver to unscrew them. I also learned that it's supposedly possible to unscrew those with a 2mm flathead screwdriver, though I don't have one myself and thus wasn't able to test it myself.
Beta Function, Baked Ziti, and British Intelligence (Tangentially)
Today I Learned:
1) The beta function is a really nice function to know when working with binomial distributions. For reference, the beta function is defined as the integral from x = 0 to 1 of [x^a * (1-x)^b] with respect to x. If you look at that thing, it's a heck of a lot like the core of a binomial function, minus the constant-with-respect-to-x combinatorial term.
Conveniently, the Beta function is *also* equal to x!y!/(x + y + 1)!. Taken together, you can turn an integral over the binomial distribution into a relatively straightforward product of factorials.
2) ...a decent vegan alternative to baked ziti is rigatoni stuffed with little blocks of frozen tofu, drowned in tomato sauce with whatever vegetables you like with sauce (for me, it's onions, greek olives, thai chili, and garlic), and bake the whole thing for a while. The tofu stuffing is surprisingly ricotta-like for what it is.
3) When Nazi Germany invaded the Soviet Union during the Second World War, British intelligence estimated that the Soviet Union would be completely taken over in about ten days. US intelligence gave the Soviets a month. This gross misestimation of the Soviet armed forces was based on several factors:
a) During the *last* world war, Russian forces had consistenly underperformed relative to their German counterparts. Early in the war, for instance, a tiny German force was deployed to the Eastern front to stall for time while the actual army in the West beat France (which was supposed to take days or weeks). It ended up crushing several Russian armies and taking serious chunks out of Allied territory in the East.
b) Germany, up to that point in the war, had beaten a number of major nations in ridiculously short amounts of time. They even occupied France -- France! the nation that had ground them to a standstill for *years* just a couple decades before -- in a matter of about a month.
c) Nobody at the time thought that the Soviet Union was a stable enough government to survive a major invasion. It was the product of a revolution from just a couple decades before, had suffered *another* revolution since then, and was not well-loved by many of its member states. A lot of experts thought that the Soviet government would implode in more or less the same way the Czarist regime had near the end of the First World War.
1) The beta function is a really nice function to know when working with binomial distributions. For reference, the beta function is defined as the integral from x = 0 to 1 of [x^a * (1-x)^b] with respect to x. If you look at that thing, it's a heck of a lot like the core of a binomial function, minus the constant-with-respect-to-x combinatorial term.
Conveniently, the Beta function is *also* equal to x!y!/(x + y + 1)!. Taken together, you can turn an integral over the binomial distribution into a relatively straightforward product of factorials.
2) ...a decent vegan alternative to baked ziti is rigatoni stuffed with little blocks of frozen tofu, drowned in tomato sauce with whatever vegetables you like with sauce (for me, it's onions, greek olives, thai chili, and garlic), and bake the whole thing for a while. The tofu stuffing is surprisingly ricotta-like for what it is.
3) When Nazi Germany invaded the Soviet Union during the Second World War, British intelligence estimated that the Soviet Union would be completely taken over in about ten days. US intelligence gave the Soviets a month. This gross misestimation of the Soviet armed forces was based on several factors:
a) During the *last* world war, Russian forces had consistenly underperformed relative to their German counterparts. Early in the war, for instance, a tiny German force was deployed to the Eastern front to stall for time while the actual army in the West beat France (which was supposed to take days or weeks). It ended up crushing several Russian armies and taking serious chunks out of Allied territory in the East.
b) Germany, up to that point in the war, had beaten a number of major nations in ridiculously short amounts of time. They even occupied France -- France! the nation that had ground them to a standstill for *years* just a couple decades before -- in a matter of about a month.
c) Nobody at the time thought that the Soviet Union was a stable enough government to survive a major invasion. It was the product of a revolution from just a couple decades before, had suffered *another* revolution since then, and was not well-loved by many of its member states. A lot of experts thought that the Soviet government would implode in more or less the same way the Czarist regime had near the end of the First World War.
Friday, October 16, 2015
Indigo Day!
Today I Learned:
Facts about dyeing jeans!
1) As of about ten years ago, about 3 BILLION jeans were sold annually, making jeans a rougly $66 billion/year industry. For scale, that's something like a fifth of the US military budget, or about nine times the budget of the NSF.
2) Indigo, the dye used to color jeans, has some very special properties. First off, it's gorgeous. But then, you already knew that*. What's really special is that it binds to cotton and other fibers without any covalent bonding. For reasons I don't really understand, this lets it dye the outside and *only* the outside of a thread, which is why it fades as the outside of the thread abrades away. The fading thing is very important.
Of course, there are plenty of dyes that don't covalently bind. What separates indigo from all the other non-covalent dyes is that it *is* persistent enough to stick to cotton even through heavy bleaching and extreme heat (i.e., the inside of a washing machine/dryer cycle). Most non-covalently binding dyes come right off in water, much less *bleach*.
* I mean, how could you not?
3) Ever seen an indigo plant? (actually there are several, but any one will do for these purposes.) The first thing you'll notice is that it isn't blue. At least, most of the time. See, the indigo plant actually stores a modified form of the molecular precursor to indigo, called indoxyl, in special vesicles in its cells. Indoxyl will spontaneously react with air to form indigo, but before it can, the cell caps its reactive group with a glycosyl group, which stabilizes it. When the indigo plant is stressed, for some reason it releases glycosylated indoxyl from its special vesicles, uncaps the glycosyl groups, and turns brilliant blue. You can induce this by spritzing an indigo plant with ethanol.
Facts about dyeing jeans!
1) As of about ten years ago, about 3 BILLION jeans were sold annually, making jeans a rougly $66 billion/year industry. For scale, that's something like a fifth of the US military budget, or about nine times the budget of the NSF.
2) Indigo, the dye used to color jeans, has some very special properties. First off, it's gorgeous. But then, you already knew that*. What's really special is that it binds to cotton and other fibers without any covalent bonding. For reasons I don't really understand, this lets it dye the outside and *only* the outside of a thread, which is why it fades as the outside of the thread abrades away. The fading thing is very important.
Of course, there are plenty of dyes that don't covalently bind. What separates indigo from all the other non-covalent dyes is that it *is* persistent enough to stick to cotton even through heavy bleaching and extreme heat (i.e., the inside of a washing machine/dryer cycle). Most non-covalently binding dyes come right off in water, much less *bleach*.
* I mean, how could you not?
3) Ever seen an indigo plant? (actually there are several, but any one will do for these purposes.) The first thing you'll notice is that it isn't blue. At least, most of the time. See, the indigo plant actually stores a modified form of the molecular precursor to indigo, called indoxyl, in special vesicles in its cells. Indoxyl will spontaneously react with air to form indigo, but before it can, the cell caps its reactive group with a glycosyl group, which stabilizes it. When the indigo plant is stressed, for some reason it releases glycosylated indoxyl from its special vesicles, uncaps the glycosyl groups, and turns brilliant blue. You can induce this by spritzing an indigo plant with ethanol.
Thursday, October 15, 2015
Point Gagues, Rheology, and The Hamilton STAR Liquid Handling Robot Programming Language
Today I Learned:
1) A point gauge is a device for measuring steady-state water height.
2) Rheology is the study of fluid flow deformation. A rheoid is a solid material that flows by shear forces at least a thousand times faster than it deforms by the same amount of force applied perpendicularly -- for example, granite, or salt under sedimentation.
3) How to program a Hamilton STAR liquid handling robot! It's really simple, conceptually, but whoever designed the programming interface... well, I have a feeling there were a bunch of decisions that were made "just for now to get things working" that ended up codified in the end. Frankly, it feels rather amateurish, especially for a company that sells several-hundred-thousand-dollar robots.
There's something C-like in the design... for instance, there's a way to loop over a sequence of positions on the robot, like a Python for-loop. The resemblances are more than skin-deep -- sequences in this language act much like generators, in that they're kind of functions that return positions in, well, a sequence. However, unlike Python generators, sequences have an explicit pointer that can be viewed and manipulated. Moreover, it *isn't set by the language* whether the pointer stays at the end of the sequence at the end of a loop or goes back to the beginning. You can do either. But you have to choose. Urgh.
1) A point gauge is a device for measuring steady-state water height.
2) Rheology is the study of fluid flow deformation. A rheoid is a solid material that flows by shear forces at least a thousand times faster than it deforms by the same amount of force applied perpendicularly -- for example, granite, or salt under sedimentation.
3) How to program a Hamilton STAR liquid handling robot! It's really simple, conceptually, but whoever designed the programming interface... well, I have a feeling there were a bunch of decisions that were made "just for now to get things working" that ended up codified in the end. Frankly, it feels rather amateurish, especially for a company that sells several-hundred-thousand-dollar robots.
There's something C-like in the design... for instance, there's a way to loop over a sequence of positions on the robot, like a Python for-loop. The resemblances are more than skin-deep -- sequences in this language act much like generators, in that they're kind of functions that return positions in, well, a sequence. However, unlike Python generators, sequences have an explicit pointer that can be viewed and manipulated. Moreover, it *isn't set by the language* whether the pointer stays at the end of the sequence at the end of a loop or goes back to the beginning. You can do either. But you have to choose. Urgh.
Tuesday, October 13, 2015
Simple Eigenvalues, 420, and Eusocial Takeovers
Today I Learned:
1) The eigenvalues of a triangular matrix are its diagonal entries! So freaking simple!
2) 420 = pot. Therefore, the appropriate time to smoke weed is 4:20. The MOST appropriate time to smoke is April 20, at 4:20.
3) Right now, eusocial insects (that is, the "truly social" insects like most ants, termites, and some bees and wasps) are arguably the dominant form of animal life on Earth by biomass (not sure if they outmass planaria or nematodes, TBH). Today I learned that was only true after about 50 million years ago, even though there have been eusocial insects since about 100 million years ago! In other words, eusocial insects (bees, specifically) evolved in the mid-Cretaceous, but were relatively rare until about 14 million years after the extinction of the dinosaurs, at which point they took off in a big way and haven't stopped since.
I always figured that insects had kind of always been the dominant animal life form. Apparently not.
(Bonus eusociality fact -- there are three species of eusocial shrimp! They form nests in coral, which they defend as a group while one female reproduces madly.)
1) The eigenvalues of a triangular matrix are its diagonal entries! So freaking simple!
2) 420 = pot. Therefore, the appropriate time to smoke weed is 4:20. The MOST appropriate time to smoke is April 20, at 4:20.
3) Right now, eusocial insects (that is, the "truly social" insects like most ants, termites, and some bees and wasps) are arguably the dominant form of animal life on Earth by biomass (not sure if they outmass planaria or nematodes, TBH). Today I learned that was only true after about 50 million years ago, even though there have been eusocial insects since about 100 million years ago! In other words, eusocial insects (bees, specifically) evolved in the mid-Cretaceous, but were relatively rare until about 14 million years after the extinction of the dinosaurs, at which point they took off in a big way and haven't stopped since.
I always figured that insects had kind of always been the dominant animal life form. Apparently not.
(Bonus eusociality fact -- there are three species of eusocial shrimp! They form nests in coral, which they defend as a group while one female reproduces madly.)
Monday, October 12, 2015
C. Elegans Reversal, Overwriting That-Which-Should-Not-Be-Overwritten, and Getting the Attention of Dark Gods
Today I Learned:
1) C. elegans fact: C. elegans, the model worm organism, has exactly four sensory neurons dedicated to detecting things-that-make-them-want-to-turn-around. Those four neurons relay their signals to two processing neurons, which integrate that information and, if they decide there's a good reason to turn around, they send a signal to the next layer of neurons, which triggers the worm to immediately reverse direction.
It is always the same four and the same two, in every worm.
2) Python fact: You can overwrite the map function in Python. It is not a good idea. You cannot, however, overwrite "for" nor "lambda".
3) Destiny fact: If you shoot Oryx in the face, he looks at you.
1) C. elegans fact: C. elegans, the model worm organism, has exactly four sensory neurons dedicated to detecting things-that-make-them-want-to-turn-around. Those four neurons relay their signals to two processing neurons, which integrate that information and, if they decide there's a good reason to turn around, they send a signal to the next layer of neurons, which triggers the worm to immediately reverse direction.
It is always the same four and the same two, in every worm.
2) Python fact: You can overwrite the map function in Python. It is not a good idea. You cannot, however, overwrite "for" nor "lambda".
3) Destiny fact: If you shoot Oryx in the face, he looks at you.
Sunday, October 11, 2015
Punic Update, Simon Stalenhag, and A Rice Recipe
Today I Learned:
1) Punic Wars update: Turns out the Fabian strategy didn't last particularly long, at least not initially. Relatively early in the war, Fabius Maximus voluntarily stepped down from Dictatorship (part of the honor of receiving the post was that it meant the government trusted you to hand back your power after some term), and pretty quickly after that one of the Consuls* in charge of the military decided to attack Hannibal with an army twice the size of Hannibal's. That battle was the Battle of Cannae, and it was a disaster. Something like 70,000 Roman soldiers were captured or killed (mostly killed). For a time after that battle, it looked very likely that Rome would fall, but Hannibal couldn't close out the war. After that, the Romans went back to the Fabian strategy, which over the course of the next decade would wear Hannibal down and lead to a Roman victory.
*The Romans had this interesting system where if two Consuls were present in the same army, they would alternate leadership day-by-day. So actually, one Consul elected to *not* attack Hannibal, and the next day the other one attacked. The first Consul died in the ensuing battle.
2) ...about the artist Simon Stalenhag. I absolutely love this art -- I would not mind living in that future. Check him out at http://tinyurl.com/ouglymn.
3) Dill weed, rice seasoning (nori komi furikake, to be exact), and rice wine vinegar is an exceptionally efficient way to make rice tasty.
1) Punic Wars update: Turns out the Fabian strategy didn't last particularly long, at least not initially. Relatively early in the war, Fabius Maximus voluntarily stepped down from Dictatorship (part of the honor of receiving the post was that it meant the government trusted you to hand back your power after some term), and pretty quickly after that one of the Consuls* in charge of the military decided to attack Hannibal with an army twice the size of Hannibal's. That battle was the Battle of Cannae, and it was a disaster. Something like 70,000 Roman soldiers were captured or killed (mostly killed). For a time after that battle, it looked very likely that Rome would fall, but Hannibal couldn't close out the war. After that, the Romans went back to the Fabian strategy, which over the course of the next decade would wear Hannibal down and lead to a Roman victory.
*The Romans had this interesting system where if two Consuls were present in the same army, they would alternate leadership day-by-day. So actually, one Consul elected to *not* attack Hannibal, and the next day the other one attacked. The first Consul died in the ensuing battle.
2) ...about the artist Simon Stalenhag. I absolutely love this art -- I would not mind living in that future. Check him out at http://tinyurl.com/ouglymn.
3) Dill weed, rice seasoning (nori komi furikake, to be exact), and rice wine vinegar is an exceptionally efficient way to make rice tasty.
Friday, October 9, 2015
Punic Wars Day!
Today I Learned:
It's Punic Wars day! Not really, but I learned some interesting things about the Punic wars, so here are your three Punic Wars facts for the day (specifically, from the second Punic Wars)
1) The famous elephants that Hannibal brought across the Alps into Rome? It seems they were pretty effective until they died from the cold at the foot of the Alps.
2) The Romans lost a lot of political leadership during the Punic Wars. Today I learned that Roman politicians of the time of the second Punic Wars were a) without exception military veterans and b) led the military, directly.
In particular, Rome of the day had a position called the Consul, which was a bit like a president except that a) they served one-year terms, b) they were truly military commanders as well as politicians, and c) there were always two of them at a time. A lot of these led armies against Hannibal, and were killed in battle. Politics may have been pretty similar then and now, but being a politician was not.
3) Speaking of Roman military leadership, one of the most famous Roman generals *possibly* of all time was Fabius Maximus, of the eponymous Fabian strategy, which I'll get to in a second. Today I learned that Fabius was elected dictator of Rome early in the Second Punic War in response to a string of early humiliating defeats by Hannibal. His strategy, the Fabian strategy, was to beat Hannibal by not fighting him. After all, Hannibal had just smashed several competent Roman armies, so Fabius figured he probably wasn't going to out-fight Hannibal. Instead, he would shadow Hannibal's army across the countryside, never engaging it directly but ensuring that it couldn't really rest and recuperate, either. Hannibal was far from home and essentially not getting any reinforcements, while Rome was marshalling new armies as quickly as she could.
It's Punic Wars day! Not really, but I learned some interesting things about the Punic wars, so here are your three Punic Wars facts for the day (specifically, from the second Punic Wars)
1) The famous elephants that Hannibal brought across the Alps into Rome? It seems they were pretty effective until they died from the cold at the foot of the Alps.
2) The Romans lost a lot of political leadership during the Punic Wars. Today I learned that Roman politicians of the time of the second Punic Wars were a) without exception military veterans and b) led the military, directly.
In particular, Rome of the day had a position called the Consul, which was a bit like a president except that a) they served one-year terms, b) they were truly military commanders as well as politicians, and c) there were always two of them at a time. A lot of these led armies against Hannibal, and were killed in battle. Politics may have been pretty similar then and now, but being a politician was not.
3) Speaking of Roman military leadership, one of the most famous Roman generals *possibly* of all time was Fabius Maximus, of the eponymous Fabian strategy, which I'll get to in a second. Today I learned that Fabius was elected dictator of Rome early in the Second Punic War in response to a string of early humiliating defeats by Hannibal. His strategy, the Fabian strategy, was to beat Hannibal by not fighting him. After all, Hannibal had just smashed several competent Roman armies, so Fabius figured he probably wasn't going to out-fight Hannibal. Instead, he would shadow Hannibal's army across the countryside, never engaging it directly but ensuring that it couldn't really rest and recuperate, either. Hannibal was far from home and essentially not getting any reinforcements, while Rome was marshalling new armies as quickly as she could.
Cas9 Specificity, Armadillo Polyembryony, and Relaxation Oscillators
Today I Learned:
1) ... a bit more about the specificity of Cas9. Turns out that cas9 can cut targets that differ from the guide by one or two nucleotides, though at reduced efficiency. More than that makes it completely non-functional. This is really important for genome editing, because you really don't want it cutting in places you don't expect. Cas9 used for regulation may be another story entirely. According to some iGEM data I found, cas9 may still be able to *bind* (but not necessarily cut) at an appreciable rate (5% of the maximum-efficiency rate) with a dozen nucleotide differences. These particular numbers seem a bit absurd to me, but I also recall seeing one paper in which the authors made tunable cas9 repressors by mutating the guide RNA a little bit off of the target. Unfortunately, I can't find that paper right now, so I'm not going to link it. This needs corroboration.
2) Armadillos use a rare form of reproduction called “polyembryony”, in which a mother has a single fertilized egg during pregnancy, but that egg splits into multiple embryos which develop into multiple genetically identical siblings. This is how you get identical twins in humans and other animals, but a few species of armadillos are the only vertebrate species known to exclusively reproduce through polyembryony.
Polyembryony is a particularly weird form of reproduction because it seems to combine the biggest disadvantages of both sexual and asexual reproduction. Unlike asexually-reproducing species, armadillos have to find mates to reproduce, and only pass half of their genes to their offspring. But unlike sexually-reproducing species, an armadillo’s offspring are not particularly diverse, and so are vulnerable to parasites, diseases, and environmental conditions that disproportionately affect certain genotypes.
Why polyembryony, then? The best proposed reason I’ve found is that it’s an adaptation to a sort of pre-existing evolutionary condition in the armadillo — the structure of the armadillo’s uterus is such that only one egg can be fertilized at a time. Why? We don’t know. Presumably it happened for some other reason, or perhaps entirely by accident, but whatever the reason, *if* you take it as a given that only one egg can be fertilized, yet the mother can provide for more than one offspring in a litter (armadillo mothers don’t care for the young particularly much or long), then polyembryony makes some sense.
Thanks to Heather Leigh for pointing me to this fascinating armadillo information!
3) A relaxation oscillator is a system that behaves periodically but not sinusoidally. Simple examples include square wave oscillators and sawtooth oscillators. A more complicated example would be a ring tone.
A typical way to think about relaxation oscillators is that there’s some timer that counts down, and when that timer hits zero, the system produces some distinct output, like a square wave or a spike or a measure from some musical piece. Then the timer resets and starts counting again. Relaxation oscillators are a common motif in electrical circuits, where the “timer” is often a capacitor. The capacitor’s charge spikes out, triggering the output signal, then slowly decays (relaxes) back to its charge value. Hence “relaxation oscillator”.
1) ... a bit more about the specificity of Cas9. Turns out that cas9 can cut targets that differ from the guide by one or two nucleotides, though at reduced efficiency. More than that makes it completely non-functional. This is really important for genome editing, because you really don't want it cutting in places you don't expect. Cas9 used for regulation may be another story entirely. According to some iGEM data I found, cas9 may still be able to *bind* (but not necessarily cut) at an appreciable rate (5% of the maximum-efficiency rate) with a dozen nucleotide differences. These particular numbers seem a bit absurd to me, but I also recall seeing one paper in which the authors made tunable cas9 repressors by mutating the guide RNA a little bit off of the target. Unfortunately, I can't find that paper right now, so I'm not going to link it. This needs corroboration.
2) Armadillos use a rare form of reproduction called “polyembryony”, in which a mother has a single fertilized egg during pregnancy, but that egg splits into multiple embryos which develop into multiple genetically identical siblings. This is how you get identical twins in humans and other animals, but a few species of armadillos are the only vertebrate species known to exclusively reproduce through polyembryony.
Polyembryony is a particularly weird form of reproduction because it seems to combine the biggest disadvantages of both sexual and asexual reproduction. Unlike asexually-reproducing species, armadillos have to find mates to reproduce, and only pass half of their genes to their offspring. But unlike sexually-reproducing species, an armadillo’s offspring are not particularly diverse, and so are vulnerable to parasites, diseases, and environmental conditions that disproportionately affect certain genotypes.
Why polyembryony, then? The best proposed reason I’ve found is that it’s an adaptation to a sort of pre-existing evolutionary condition in the armadillo — the structure of the armadillo’s uterus is such that only one egg can be fertilized at a time. Why? We don’t know. Presumably it happened for some other reason, or perhaps entirely by accident, but whatever the reason, *if* you take it as a given that only one egg can be fertilized, yet the mother can provide for more than one offspring in a litter (armadillo mothers don’t care for the young particularly much or long), then polyembryony makes some sense.
Thanks to Heather Leigh for pointing me to this fascinating armadillo information!
3) A relaxation oscillator is a system that behaves periodically but not sinusoidally. Simple examples include square wave oscillators and sawtooth oscillators. A more complicated example would be a ring tone.
A typical way to think about relaxation oscillators is that there’s some timer that counts down, and when that timer hits zero, the system produces some distinct output, like a square wave or a spike or a measure from some musical piece. Then the timer resets and starts counting again. Relaxation oscillators are a common motif in electrical circuits, where the “timer” is often a capacitor. The capacitor’s charge spikes out, triggering the output signal, then slowly decays (relaxes) back to its charge value. Hence “relaxation oscillator”.
Thursday, October 8, 2015
Light-Activated Cas9 Activation, Lobsters, and Cas9: A History
Today I Learned:
1) In March, a pair of researchers at Duke published a nifty system for activating target gene expression in the presence of light using a Cas9-based system. It’s pretty simple — they fused a light-activated transcriptional activator from Arabidopsis (the standard plant model organism) to inactivated cas9 (dCas9). Light-activated activator + targeting protein = targeted light-activation.
I guess we already had light-activation in the form of “traditional” optogenetics using channelrhodopsins, but those always struck me as pretty heavy-handed… for channelrhodopsin to work, it has to wildly change the cell’s salt balance, which seems like Not A Good Thing. This seems much more elegant.
2) Facts about lobsters!
2a) Lobster molting is pretty cool! The lobster actually sheds some of its digestive system during molting. As part of that process, the lobster dissolves a tooth-like thing at the back of its middle gut (lobsters have three guts) that normally helps break down food. The redissolved minerals are employed in the quick regrowing of the lobster’s shell.
2b) Background reading: http://www.smbc-comics.com/?id=3169
Now, serious question: where else might evolution put excretory organs? Set a five minute timer and list out all the better places to urinate from than where we do. I highly recommend taking the time to do this before proceeding — it’s a fun little exercise.
Ok? Done?
Did you write down “at the base of the antennae”? Because that’s where lobsters pee from.
2c) Lobsters can growl… but we have no idea why. They don’t seem to growl during social interactions, and as far as I know nobody’s come up with a better hypothesis.
2d) Lobsters can live a really long time — more than 50 years! — yet show none of the skeletal dysfunctions of aging that mammals show. Of course, it’s hard to directly compare the aging of species as dissimilar as humans and lobsters, but to anybody who argues that aging is “an inevitable breakdown of physiological processes”… “lobsters”, I reply. Also “tortoises”, but that’s a TIL for a different day.
Thanks to Lady Jade Beacham for pointing me to http://www.lobsters.org/tlcbio/biology.html, from which I got the content of this TIL.
3) So, speaking of Cas9 from #1, today I learned that a bunch of people figured out how it works independently. The one most biologists know about is Jennifer Doudna, who definitely figured out Cas9. Working off of CRISPR research from as early as 2007, Doudna and her lab worked out how Cas9 does its thing, then immediately realized that it was going to be huge and took it to Science, who fast-tracked it and published within the month.
The other relatively famous discoverer of cas9 is Feng Zhang, an MIT scientist who claims to have independently discovered Cas9’s function. He published definitively after Doudna, but he *patented* the enzyme first. That patent has been under dispute ever since.
Then there’s University of Lithuania researcher Virginijus Siksnys. He *also* jumped off of 2007 research and in 2012, figured out how Cas9 works. Unlike Doudna, he submitted his findings to PNAS, and unlike Science, PNAS did not fast-track the paper. As a result, Doudna’s paper came out first. Between Doudna’s precedency and her prior fame as an RNA biologist, Doudna got all the press and Siksnys remains largely unknown. (to be fair, Doudna did more in her paper — she not only elucidated the mechanism of cas9 and suggested its use as a tool for molecular biologists, but her lab also developed a modified version with a fused tracrRNA/guide RNA, which makes the thing much more convenient to work with).
1) In March, a pair of researchers at Duke published a nifty system for activating target gene expression in the presence of light using a Cas9-based system. It’s pretty simple — they fused a light-activated transcriptional activator from Arabidopsis (the standard plant model organism) to inactivated cas9 (dCas9). Light-activated activator + targeting protein = targeted light-activation.
I guess we already had light-activation in the form of “traditional” optogenetics using channelrhodopsins, but those always struck me as pretty heavy-handed… for channelrhodopsin to work, it has to wildly change the cell’s salt balance, which seems like Not A Good Thing. This seems much more elegant.
2) Facts about lobsters!
2a) Lobster molting is pretty cool! The lobster actually sheds some of its digestive system during molting. As part of that process, the lobster dissolves a tooth-like thing at the back of its middle gut (lobsters have three guts) that normally helps break down food. The redissolved minerals are employed in the quick regrowing of the lobster’s shell.
2b) Background reading: http://www.smbc-comics.com/?id=3169
Now, serious question: where else might evolution put excretory organs? Set a five minute timer and list out all the better places to urinate from than where we do. I highly recommend taking the time to do this before proceeding — it’s a fun little exercise.
Ok? Done?
Did you write down “at the base of the antennae”? Because that’s where lobsters pee from.
2c) Lobsters can growl… but we have no idea why. They don’t seem to growl during social interactions, and as far as I know nobody’s come up with a better hypothesis.
2d) Lobsters can live a really long time — more than 50 years! — yet show none of the skeletal dysfunctions of aging that mammals show. Of course, it’s hard to directly compare the aging of species as dissimilar as humans and lobsters, but to anybody who argues that aging is “an inevitable breakdown of physiological processes”… “lobsters”, I reply. Also “tortoises”, but that’s a TIL for a different day.
Thanks to Lady Jade Beacham for pointing me to http://www.lobsters.org/tlcbio/biology.html, from which I got the content of this TIL.
3) So, speaking of Cas9 from #1, today I learned that a bunch of people figured out how it works independently. The one most biologists know about is Jennifer Doudna, who definitely figured out Cas9. Working off of CRISPR research from as early as 2007, Doudna and her lab worked out how Cas9 does its thing, then immediately realized that it was going to be huge and took it to Science, who fast-tracked it and published within the month.
The other relatively famous discoverer of cas9 is Feng Zhang, an MIT scientist who claims to have independently discovered Cas9’s function. He published definitively after Doudna, but he *patented* the enzyme first. That patent has been under dispute ever since.
Then there’s University of Lithuania researcher Virginijus Siksnys. He *also* jumped off of 2007 research and in 2012, figured out how Cas9 works. Unlike Doudna, he submitted his findings to PNAS, and unlike Science, PNAS did not fast-track the paper. As a result, Doudna’s paper came out first. Between Doudna’s precedency and her prior fame as an RNA biologist, Doudna got all the press and Siksnys remains largely unknown. (to be fair, Doudna did more in her paper — she not only elucidated the mechanism of cas9 and suggested its use as a tool for molecular biologists, but her lab also developed a modified version with a fused tracrRNA/guide RNA, which makes the thing much more convenient to work with).
Monday, October 5, 2015
Drugged Leaders, Rat Melatonin, and Zebrafish Sleep
Today I Learned:
1) JFK was physically very challenged, and spent much of his presidency on medication. Some of that was pretty damned strong medication. In fact, it's very possible that he guided the nation through the Cuban missile crisis while on opiates. Relatedly, Hitler apparently was pretty heavily medicated during his reign as well? Also Napoleon?! Rumor has it he was in terrible pain the night before Waterloo and couldn't sleep, so he took some opiates to ease the pain -- perfectly by-the-book medicine for the time, but maybe not the best thing to do the night before one of the most important battles of his career. These need confirmation.
2) Even though mice and rats are noctornal, they have virtually identical patterns of blood melatonin to humans, a diurnal species. So either they have the opposite response to melatonin from humans, or they don't use melatonin to regulate sleep.
3) Zebrafish sleep, though not particularly soundly -- they'll still dart around once or twice a minute, but they're considerably less active, and harder to stimulate, at night (or in the dark (controlled for temperature))
1) JFK was physically very challenged, and spent much of his presidency on medication. Some of that was pretty damned strong medication. In fact, it's very possible that he guided the nation through the Cuban missile crisis while on opiates. Relatedly, Hitler apparently was pretty heavily medicated during his reign as well? Also Napoleon?! Rumor has it he was in terrible pain the night before Waterloo and couldn't sleep, so he took some opiates to ease the pain -- perfectly by-the-book medicine for the time, but maybe not the best thing to do the night before one of the most important battles of his career. These need confirmation.
2) Even though mice and rats are noctornal, they have virtually identical patterns of blood melatonin to humans, a diurnal species. So either they have the opposite response to melatonin from humans, or they don't use melatonin to regulate sleep.
3) Zebrafish sleep, though not particularly soundly -- they'll still dart around once or twice a minute, but they're considerably less active, and harder to stimulate, at night (or in the dark (controlled for temperature))
Sunday, October 4, 2015
Mine-Clearing Rats, Cumulative Distribution Plots, and Gun Safety
Today I Learned:
1) There are rats who are trained to smell out land mines! They’re light enough that they don’t set off the mines, so they’re not put in any danger. They sniff out TNT, scratch at the surface, and alert their handlers to the mine. The mines can then be safely dismantled and the rat gets a treat.
2) Cumulative distribution plots are pretty much always better than cumulative histograms. They give you all the same distributional information, but without any binning choices! (pro tip: all binning choices are bad binning choices. Some are just less bad than others)
3) The CDC is specifically disallowed from researching gun safety. Thanks to Bear Bear Bear for alerting me to this one.
1) There are rats who are trained to smell out land mines! They’re light enough that they don’t set off the mines, so they’re not put in any danger. They sniff out TNT, scratch at the surface, and alert their handlers to the mine. The mines can then be safely dismantled and the rat gets a treat.
2) Cumulative distribution plots are pretty much always better than cumulative histograms. They give you all the same distributional information, but without any binning choices! (pro tip: all binning choices are bad binning choices. Some are just less bad than others)
3) The CDC is specifically disallowed from researching gun safety. Thanks to Bear Bear Bear for alerting me to this one.
Saturday, October 3, 2015
Molecular Biology Laboratory Practice Trivia Day!
Today I Learned:
1) Warning: detailed molecular biology laboratory practice trivia inbound.
One thing that comes up a lot while cloning is checking PCRed parts. Today I learned a way to speed up gel loading when there are a lot of parts, using a multichannel pipette. You make a stock of loading dye in an 8-tube PCR strip, then use the multichannel pipette to load a small amount of loading dye into your samples super quickly (about 5 uL will work in any reasonably-sized PCR). You’ll still have to manually load the individual samples, but it’s still well worth it to avoid having to individually mix all the dyes. Make sure to do this step before running your samples through PCR cleanup — that way the dye will come out in the cleanup step.
2) Also molecular biology trivia — today I learned how to use a repeater pipette. Also super-useful (if quite expensive). I hear they’re not as accurate as normal pipettes… but they’re plenty good enough to set up PCR. They do tend to be a bit wasteful, though, as they have a habit of picking up excess liquids.
3) And to round out a day of molecular biology — Phusion 2x master mix (for PCR) is stable on the bench for hours. Possibly days. Not months, though. (also, most antibiotics will apparently break down in incubator conditions over 2-3 days? Can anyone confirm or deny?)
1) Warning: detailed molecular biology laboratory practice trivia inbound.
One thing that comes up a lot while cloning is checking PCRed parts. Today I learned a way to speed up gel loading when there are a lot of parts, using a multichannel pipette. You make a stock of loading dye in an 8-tube PCR strip, then use the multichannel pipette to load a small amount of loading dye into your samples super quickly (about 5 uL will work in any reasonably-sized PCR). You’ll still have to manually load the individual samples, but it’s still well worth it to avoid having to individually mix all the dyes. Make sure to do this step before running your samples through PCR cleanup — that way the dye will come out in the cleanup step.
2) Also molecular biology trivia — today I learned how to use a repeater pipette. Also super-useful (if quite expensive). I hear they’re not as accurate as normal pipettes… but they’re plenty good enough to set up PCR. They do tend to be a bit wasteful, though, as they have a habit of picking up excess liquids.
3) And to round out a day of molecular biology — Phusion 2x master mix (for PCR) is stable on the bench for hours. Possibly days. Not months, though. (also, most antibiotics will apparently break down in incubator conditions over 2-3 days? Can anyone confirm or deny?)
Friday, October 2, 2015
Lightsabers, Assyrians, and Robust Cloning
Today I Learned:
1) ...some history of the lightsaber. The precursor of the lightsaber was a weapon called the force saber, developed by the Rakata (more on them later). It ran off of pure force energy channeled from the user, and was effectively a "frozen blaster". The technology behind the force saber was lost along with most other Rakatan technology when their Infinite Empire fell.
The first lightsabers were developed by the Jedi (or possibly the Sith) probably in immitation of the Rakatan force saber. They required immense amounts of energy, which could only be supplied by a small belt-mounted nuclear reactor, and even that could only power the devices for seconds or minutes at a time. As such, they were mostly ceremonial, and were really only used as handheld seige weapons in combat.
As an aside, during this time and for a long time afterwards, the role of the modern (if such a word can be used in a fictional universe that takes place a long time ago....) lightsaber was filled by swords, sometimes enhanced by the force to be extremely good at cutting things. Swords were considered a more elegant weapon, and, like lightsabers later, the skilled use of a sword signified a certain mastery of the force.
It was the Sith that refined the lightsaber into something closely resembling the modern version. The Sith developed a technique for reflecting a blaster-like beam back to the hilt, a technology somewhat distinct from designs based on earlier Rakata weapons. These lightsabers had vastly reduced power requriements and could run without a tethered power supply. The Sith developed a kind of synthetic crystal to focus the device (I believe they lacked access to the natural crystal caves of Ilum, Adega, and Dantooine), which gave their lightsabers their traditional red color. The use of synthetic crystals actually fell out of favor for a time, and during this time the Sith used lightsabers of the same color as the Jedi. Red lightsabers were reinstated in the Sith Empire around the time of Darth Revan. The Imperial Knights circa ~50-137 ABY would later use a different form of synthetic crystal, which produced a silver color.
It took a long time for lightsabers to become standard weapons of force users. Probably out of tradition, swords dominated for quite a while after the modern lightsaber's introduction. This was especially true among the Sith, who were quite good at alchemically modifying their swords to resist lightsabers, or at simply building their swords out of cortosis or other lightsaber-resistant ores.
A friend of mine claimed today that the lightsaber was really developed as a weapon that couldn't be disrupted by the force, in order to break the kinds of stalemates that tended to happen in battles between adept force users. I have not been able to corroborate this claim.
2) The Assyrians were some seriously important people. They were a major power in the Middle East (which was a HUGE chunk of the Western world at the time) from the 23th century BC to the 6th century BC. Hold on a second. Stop. 23rd century to 6th century -- SEVENTEEN HUNDRED YEARS. And they survived as a somewhat-independent region until the 7th century AD, so that's 2.3 THOUSAND years of civilization. I mean, I knew the Assyrians were important enough to merit a mention in any history book covering the period, but I had no idea they went that far back. Oh, and they also ruled over just about all of the peoples of the Middle East for three hundred of those years.
I also learned a little bit about Assyrian culture. Most of it was militaristic, for the Assyrians were a pretty militaristic culture. They pretty much invented the pre-modern system of military division, with cavalry, missile infantry, and modern-ish infantry. Their economy, it seems, was run largely off of invading neighboring empires and looting them, then annexing them and demanding regular tithes. This worked shockingly well for a long time -- there was one king in particular, whose name I forget, who took his army to the field almost every year of his 30 year reign.
These were a bloody people, by their own accounts. They used an Imperial form of terrorism to discourage rebellions -- they had a habit of wiping out entire cities, killing the population down to the animals and reserving particularly brutal and torturous executions for the leaders of those cities, often with the Assyrian king watching. This didn't keep rebellions from popping up all the time, which goes to show how much the Assyrians' vassal states disliked them. Internal politics were no less blody, with the deaths of Assyrian kings typically followed by bloody civil wars between the kings' relatives.
When the Assyrian empire was finally toppled more or less for good (by a confluence of a particularly nasty war of succession, a very expensive war in Egypt, and a coalition rebelling Babylonian/invading Medes/invading Persians), their capital city of Assur was sacked in more or less the same fashion employed by the Assyrians. The ruins left behind was described in awe a couple hundred years later by a Greek army moving through the area -- its fortifications, devestated though they were, still far outstripped anything the Greeks had seen. When the Greeks asked the peoples of the area who had built those walls, nobody knew.
I can't help but be reminded of the Rakata of the Star Wars universe. The Rakata were a brutal Empire-building species who ruled over most of the galaxy for over 10,000 years. They developed the first hyperdrive technology, and left behind some truly devestating structures and weapons, most notably the Star Forge. And, much like the Assyrians, they were very nearly completely erased from history by their conquerers, many of whom had numbered among their slave species. (Unlike the Assyrians, the Rakata were felled by a combination of a deadly plague and a mysterious loss of their connection to the force, possibly brought on by their dark-side corruption, which itself was fueled by centuries of powering their most potent technologies from dark-side-sources.) Admittedly, the theme of a massive, highly-advanced, now-extinct civilization is a pretty common theme in fiction in general and in sci-fi in particular, but I can't help but wonder if any of the Star Wars writers responsible for the Rakata were inspired by the story of the Assyrians.
3) Ok, let's make this short. Apparently at least some kinds of molecular cloning are ridiculously robust to DNA molarities. A couple days ago, I and another student accidentally ran a bunch of golden braid cloning reactions with essentially random amounts of DNA. The result... they basically all worked. The ones with 10-fold too much of one part might have failed... or we might have just not amplified them up properly afterwards. We'll know tomorrow.
1) ...some history of the lightsaber. The precursor of the lightsaber was a weapon called the force saber, developed by the Rakata (more on them later). It ran off of pure force energy channeled from the user, and was effectively a "frozen blaster". The technology behind the force saber was lost along with most other Rakatan technology when their Infinite Empire fell.
The first lightsabers were developed by the Jedi (or possibly the Sith) probably in immitation of the Rakatan force saber. They required immense amounts of energy, which could only be supplied by a small belt-mounted nuclear reactor, and even that could only power the devices for seconds or minutes at a time. As such, they were mostly ceremonial, and were really only used as handheld seige weapons in combat.
As an aside, during this time and for a long time afterwards, the role of the modern (if such a word can be used in a fictional universe that takes place a long time ago....) lightsaber was filled by swords, sometimes enhanced by the force to be extremely good at cutting things. Swords were considered a more elegant weapon, and, like lightsabers later, the skilled use of a sword signified a certain mastery of the force.
It was the Sith that refined the lightsaber into something closely resembling the modern version. The Sith developed a technique for reflecting a blaster-like beam back to the hilt, a technology somewhat distinct from designs based on earlier Rakata weapons. These lightsabers had vastly reduced power requriements and could run without a tethered power supply. The Sith developed a kind of synthetic crystal to focus the device (I believe they lacked access to the natural crystal caves of Ilum, Adega, and Dantooine), which gave their lightsabers their traditional red color. The use of synthetic crystals actually fell out of favor for a time, and during this time the Sith used lightsabers of the same color as the Jedi. Red lightsabers were reinstated in the Sith Empire around the time of Darth Revan. The Imperial Knights circa ~50-137 ABY would later use a different form of synthetic crystal, which produced a silver color.
It took a long time for lightsabers to become standard weapons of force users. Probably out of tradition, swords dominated for quite a while after the modern lightsaber's introduction. This was especially true among the Sith, who were quite good at alchemically modifying their swords to resist lightsabers, or at simply building their swords out of cortosis or other lightsaber-resistant ores.
A friend of mine claimed today that the lightsaber was really developed as a weapon that couldn't be disrupted by the force, in order to break the kinds of stalemates that tended to happen in battles between adept force users. I have not been able to corroborate this claim.
2) The Assyrians were some seriously important people. They were a major power in the Middle East (which was a HUGE chunk of the Western world at the time) from the 23th century BC to the 6th century BC. Hold on a second. Stop. 23rd century to 6th century -- SEVENTEEN HUNDRED YEARS. And they survived as a somewhat-independent region until the 7th century AD, so that's 2.3 THOUSAND years of civilization. I mean, I knew the Assyrians were important enough to merit a mention in any history book covering the period, but I had no idea they went that far back. Oh, and they also ruled over just about all of the peoples of the Middle East for three hundred of those years.
I also learned a little bit about Assyrian culture. Most of it was militaristic, for the Assyrians were a pretty militaristic culture. They pretty much invented the pre-modern system of military division, with cavalry, missile infantry, and modern-ish infantry. Their economy, it seems, was run largely off of invading neighboring empires and looting them, then annexing them and demanding regular tithes. This worked shockingly well for a long time -- there was one king in particular, whose name I forget, who took his army to the field almost every year of his 30 year reign.
These were a bloody people, by their own accounts. They used an Imperial form of terrorism to discourage rebellions -- they had a habit of wiping out entire cities, killing the population down to the animals and reserving particularly brutal and torturous executions for the leaders of those cities, often with the Assyrian king watching. This didn't keep rebellions from popping up all the time, which goes to show how much the Assyrians' vassal states disliked them. Internal politics were no less blody, with the deaths of Assyrian kings typically followed by bloody civil wars between the kings' relatives.
When the Assyrian empire was finally toppled more or less for good (by a confluence of a particularly nasty war of succession, a very expensive war in Egypt, and a coalition rebelling Babylonian/invading Medes/invading Persians), their capital city of Assur was sacked in more or less the same fashion employed by the Assyrians. The ruins left behind was described in awe a couple hundred years later by a Greek army moving through the area -- its fortifications, devestated though they were, still far outstripped anything the Greeks had seen. When the Greeks asked the peoples of the area who had built those walls, nobody knew.
I can't help but be reminded of the Rakata of the Star Wars universe. The Rakata were a brutal Empire-building species who ruled over most of the galaxy for over 10,000 years. They developed the first hyperdrive technology, and left behind some truly devestating structures and weapons, most notably the Star Forge. And, much like the Assyrians, they were very nearly completely erased from history by their conquerers, many of whom had numbered among their slave species. (Unlike the Assyrians, the Rakata were felled by a combination of a deadly plague and a mysterious loss of their connection to the force, possibly brought on by their dark-side corruption, which itself was fueled by centuries of powering their most potent technologies from dark-side-sources.) Admittedly, the theme of a massive, highly-advanced, now-extinct civilization is a pretty common theme in fiction in general and in sci-fi in particular, but I can't help but wonder if any of the Star Wars writers responsible for the Rakata were inspired by the story of the Assyrians.
3) Ok, let's make this short. Apparently at least some kinds of molecular cloning are ridiculously robust to DNA molarities. A couple days ago, I and another student accidentally ran a bunch of golden braid cloning reactions with essentially random amounts of DNA. The result... they basically all worked. The ones with 10-fold too much of one part might have failed... or we might have just not amplified them up properly afterwards. We'll know tomorrow.
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