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What happens to our umbilical cord internally as we age?
I was always curious as to if or how much of the internal structure or workings of the umbilical cord are still in existence as we age.  What happens to it over time?
The wikipedia article naval almost exclusively describes physical appearance and only touches on some disorders that affect physical appearance.
Then the wikipedia article on umbilical cord explains what happens briefly after birth, and only touches on it becoming part of the circulatory system.  What does it actually do in later life, as part of that system?

It's considered appropriate on biology.se that you present some of your reading/research with your question. What have you read about the human umbilicus? The site tour and the help center  provide guidance on how to use this site. Please take a few minutes to read about the kind of questions which are on topic here.

@anongoodnurse added research that was useless

The research wasn't useless. It actually had links to every last bit of what happens to the remains of the umbilical structures in the adult. Had you clicked through, you would have found them.

The umbilical cord, which contains the umbilical arteries and vein so important for nutrient, oxygen and waste transport before birth, becomes useless pretty much the moment the lungs start working (the kidneys already started before birth). As the baby breathes, the two arteries constrict to stop flow from the newborn to the placenta. The Wharton's Jelly, a structural element in the cord, causes collapse of the vein on cooling to outside temperature. Within a matter of a few minutes, the whole system is shut down.
Within the infant, the umbilical vein and ductus venosus (which carried blood to the liver and the inferior vena cava) close up and degenerate into fibrous remnants known as the round ligament of the liver and the ligamentum venosum respectively. The round ligament basically just divides the left part of the liver into medial and lateral sections. The ligamentum venosum does basically the same thing on the posterior surface of the liver. Both ligaments maintain their attachment to the umbilicus. (The small paraumbilical veins are unimportant except in the case of severe portal hypertension, where they act as a portacaval anastomosis.)
The parts of the arteries closest to the navel degenerate into what are known as the medial umbilical ligaments (which basically serve no purpose whatsoever and are buried in the abdominal wall), while the more internal remaining sections are retained as part of the circulatory system as the umbilical artery.
The umbilical artery in adults is a branch of the anterior division of the internal iliac artery. The umbilical artery is found in the pelvis, and gives rise to the superior vesical arteries (supplying the superior part of the bladder, and parts of the ureters. In males, it also gives rise to the the deferential artery which supplies the ductus deferens.

Don't forget the urachus.

I didn't. I didn't think it would missed. :-/

Just a wow answer. What is the urachus?! +1

Is this one of your my-duty-is-fair-thus-I-close-vote-but-answer-for-the-sake-of-cool-answers projects?

@ChrisStronks - yep! :-) To be fair, the OP did, at least, indicate that he tried to find out. The urachus is a channel between the bladder and the allantois in the embryology. If it remains patent, urine can drain from the umbilicus, or a cyst can form. Normally it forms the median umbilical ligament, which is just a shriveled piece of tissue.


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Can you disconnect sensation from the brain without destroying muscle functions?
I want to know if it would be possible for a surgeon to "shut off" a person's senses without causing paralysis of any kind. The person would have to maintain their ability to speak and move, but not be able to hear their own voice or sense the physical world around them at all.

It's possible to block hearing, or sight (especially sight- just block the optic nerve), but destroying feeling in the body would be neigh-on impossible without completely paralyzing someone. The nerves for muscle control and sensation are bundled very close to each other the farther up the extremity you go, and by the time you get to the spinal cord they're all mashed in there together.

I don't know about that. Leprosy often causes numbness in the extremities. When victims don't feel pain they get injuries that can lead to infections and loss of tissue. But the limbs usually still work, suffering from weakness, until they get injured. Of course that weakness could be interpreted as partial paralysis.

@user137 Leprosy is a disease primarily of the peripheral nerves, while nerves responsible for motion are often deeper. However, like I said the two tend to become bundled closer together the closer to the trunk (and spine) you get. I just don't think it would be plausible to selectively block sensory neurons without affecting motor neurons as well.

Even if it were possible to selectively destroy sensory (afferent) nerves alone, the individual would not be able to walk/move normally immediately afterwards, because there would be no environmental stimuli with which to orient one's actions.
Two of the senses people often overlook are balance (or spatial orientation) and proprioception (where the body parts are with respect to each other and the physical world). (Some people lump the two; it's easier to think of them independently, because they really do quite different things.)
Proprioception (and closely associated kinesthetics) is possible because of nerve signals from skin, tendons, muscles, and joints. This gives us the senses of limb position and movement, the sense of tension or force, the sense of effort, the sense of gravity.[1]

Patients (lacking proprioceptive feedback) made large directional and amplitude errors when performing a reaching task without visual feedback....The errors were associated with differences in velocity and acceleration for movements in different directions.[2]

Basically, without proprioception, you could not touch your little finger to your thumb without visual input, let alone actually perform more complex voluntary movements successfully.
The vestibular system exists through input from three semicircular canals located in the inner ear on each side. The canals are oriented at (basically) 90° angles to each other so that we can maintain our balance no matter which position our head is in. These are also connected to one's ocular reflexes. Together they're called the vestibolo-occular system, which functions as a reflex (VOR).

Three sensory systems, vestibular, visual, and somatosensory/proprioceptive, serve our sense of balance. Each of these systems provides unique sensory information to the brain where it is integrated (often in complex ways) to produce a final motor output to muscle groups for maintaining gaze, head and body posture, and controlling static and dynamic balance.[3]

Without these systems, we would be completely unable to get or stay in a standing position. We certainly could not walk.
So, long story short, the answer is no. Not only is it surgically impossible, but if it were, you would not have a coordinated, working body left. Eventually even speech would be affected (as proprioceptors on the mouth are involved in speaking).
[1] Proprioception From a Spinocerebellar Perspective
[2] mpairments of reaching movements in patients without proprioception. I. Spatial errors
[3] Anatomical and Physiological Considerations in Vestibular Dysfunction and Compensation

The nerves for sensation do travel intimately close to motor nerves outsides the spinal cord. In these locations only disease processes can preferentially target a particular class of nerves. Diabetic neuropathy is a common condition causing this kind of peripheral nerve injury resulting in sensory loss. In the spinal cord, afferent (incoming) and efferent (outgoing) conduction pathways are segregated into bundles so a traumatic event can cause a selective deficit of sensation or movement of a region. Further up the chain (brain) entire functions are segregated so it is possible to create not only deficits of physical regions but of specific complex motor functions or sensory processes. Interestingly most of our senses are handled by cranial nerves that are not in the spinal cord. These nerves are generally purely sensory.

While the information is welcome, this doesn't really address the OP's question: can a body without sensation work "properly"?

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What happens when you mix type A with type B blood (and v.v)
What happens when you mix different blood types that are not compatible?
For example, if a patient has type A blood and receives type B blood, or v.v.?

The blood type of people is determined by proteins which are anchored into the surface of the red blood cells. Cells can either express the A, B, A and B or no marker, resulting in the blood groups A, B, AB and 0. People which have a certain blood type (for example A) have antibodies against the other blood type (in this case B).
This will lead to the attachment of antibodies against the subtype which you don't have (so B for people with A and vice versa) on the transfused blood cells and their lysis. Depending on the amount of blood transfused, this can also lead to a very serious immune reaction. This works like in the figure below (from here):

To avoid this reaction a sample of the blood, which is about to be transfused is mixed with blood from the patient before the transfusion takes place. This looks like this:
For more information look here:

Acute hemolytic transfusion reaction
Transfusion reaction - hemolytic
Genes and Blood Type
Adverse reactions to transfusion

Whoever sownvoted the answer, what is the problem with it? I can improve it, if I know the criticism.

I didn't down vote, but I'd say it needs a few more words about the 'very serious immune reaction' you've mentioned.

What determines blood types is the type of antigens found on the surface of the blood. A blood cells have A antigens on their surface, B has B antigens, AB has both, and O has no antigens. What is an antigen?
According to Wikipedia:

an antigen (Ag), or antibody generator, is any substance which provokes an adaptive immune response. An antigen is a molecule that also induces an immune response in the body.

Now, not only is the blood important here but also the plasma. The plasma contains antibodies. Antibodies are (according to wikipedia)

a large Y-shape protein produced by plasma cells that is used by the immune system to identify and neutralize foreign objects such as bacteria and viruses. The antibody recognizes a unique part of the foreign target, called an antigen.

Members of the A group have B antibodies in their plasma, members of the B group have A antibodies in their plasma, AB has neither, and O has both.
Now taking this all into account and to answer your question, if B blood is given to a patient of type A, the antigens on the surface of the A blood cells would react as if the incoming B blood cells are foreign, harmful substances. To from the book "Blood Groups and Red Cell Antigens"1,

If incompatible blood is given in a transfusion, the donor cells are treated as if they were foreign invaders, and the patient's immune system attacks them accordingly.
  Not only is the blood transfusion rendered useless, but a potentially massive activation of the immune system and clotting system can cause shock, kidney failure, circulatory collapse, and death.”

The same would result if A blood is given to a B blood type patient, the B blood cells would initiate an immune response to the A blood cells.
To further expand on your question, this is why O is the universal donor. There are no antigens on its surface that will illicit an immune response from other blood groups. It also has both A and B antibodies in its plasma. Similarly, AB is the universal recipient. It has both antigens on its surface meaning that neither A nor B antigens will illicit an immune response from other blood groups.

This answer is too much simplified. You should at least take the Rhesus factor into account when discussing transfusion compatibilities. You can find why here.

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Could transfusion of a different blood type cure blood-based cancers?
Different antigen detection triggers an immune system response that could perhaps stimulate mitochondria and such in killing cancer cells - something like chemo without the hair-loss?

It is the other way round. For example, post bone marrow transplant reinfusion of donor lymphocytes (termed DLI) is used as a means of triggering a graft-versus-host reaction to prevent relapse in hematogenous cancers.

@Raoul It has a special name: graft-versus-tumor in this case.

@inf3rno yes. Btw I hold an M.D on the complications of DLI ;-)

No. When a different blood type is introduced in the body, the host immune system recognizes the foreign blood as non-self and attacks it. The transfused blood becomes useless, and the potentially massive immune reaction can cause shock, which itself can be fatal.
More details in the book Blood Groups and Red Cell Antigens, which can be found at the NCBI library.


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Identify this insect - genus, species (orange, black)
Please help identify this insect - genus, species and anything interesting about it. Do the colors matter in the identification?
Note, this is posted separately due to comments in this other thread.

Any idea where the species is from (country / type of ecosystem / ...)? Did you capture / find this insect yourself? What is its approximative size? (you can consider answering to these questions to your related posts as well)

i did not capture these - they were sent from the artist. i'll try asking, but not sure if they know where the original source image was from

The colours usually matter, but there can sometimes be variability within a species.

This looks like a Weevil. Specifically it looks like a red/orange-spotted lily weevil

Here is some more information on it:
https://en.wikipedia.org/wiki/Brachycerus_ornatus
though the information is very limited.

are they always colored like that?

its a general weevil. this one is just stopped but many arent spotted


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Identify this insect - genus, species
Please help identify this insect - genus, species and anything interesting about it.
Note, this is posted separately due to comments in this other thread.

I'm

Please add the location where it was found. That helps with ID

Locations are not available :( - there's very little data associated with each one other than additional photos

This one is most probably a longhorn beetle (Cerambycidae). The long elytrae (protective forewings) and the long curved antennae are quite typical.
Yours looks a bit like this one: https://en.wikipedia.org/wiki/Rosalia_longicorn
It is probably a different species, though.

do you have further advice or methodology insight on identifying the species

@ina The way that is taught at school is to use a determination key, but in practice, identification often starts with a familiarity with the group of interest. I would advise to browse an insect identification guide, where species are grouped in families, and get an intuitive feeling of what insects in a given family look like, alongside with general description that the guide gives. Then, to get more precise ID, one has to find a determination key that covers the group of interest. Often, these documents are specific for a region. Some technical notions are often needed.


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Identify this insect
Please help identify this insect - genus, species and anything interesting about it. Also - please outline your general methodology for identifying such insects
Note, this is posted separately due to comments in this other thread.

Which previous thread? Your link doesn't point to a previous thread, it points to https://i.sstatic.net/FtdoD.png.

@arboviral https://biology.stackexchange.com/q/64042/1486

You should add the location for all species ID questions.

It is a long-horned beetle in the genus Anoplophora. Most likely A. chinensis, A. macularia or A. oshimana.

Okay, to identify to Order level you can use the key here (although it's actually not as user-friendly as I'd expect; I might suggest they revise it). The key characteristic is that the forewings are uniformly hardened. So, starting at question 1:

"Insect has wings" - yes; go to 2.
"Two pairs of wings" - yes (wing cases/elytra are the first pair); go
to 7.
"Fore wings hard or leathery" - yes; go to 8.
"Fore wings of uniform texture throughout" - yes; go to 9.
"Fore wings (elytra) hard and veinless, meeting in centre line" -
yes; go to 10.
"Abdomen without forceps: elytra commonly cover whole abdomen =
Coleoptera [beetles]".

So we know it's a beetle.
There's a key to beetle families here. Starting at question 1:

antennae are "clubbed or thickened" so go to 5.
"Elytra covering all of abdomen" yes, go to 7.
"Head not stretched forward into a beak" - go to 9.
"the last few segments [of the antennae] clearly wider than the rest" - no, so go to 24.
the elytra (wing cases aren't "soft (easily bent, folded or even
rolled up)" so go to 28.
this is a tricky one, but basically you can (just) see that all the tarsi (feet) have the same number of segments, so we go to 29.
29: "      Beetle usually elongate and parallel-sided; antennae very long often longer than head and body; most antennal segments very elongate (at lease twice as long as wide)" - sounds like our beetle, so that makes it a longhorn beetle. (Some of the other criteria - eye nothces etc - only apply to some species, but this key is a UK-specific one).

So we have a longhorn beetle (Coleoptera: Cerambycidae). In theory you shouldn't need to geographical location to get this to species level as it could be an introduced species etc; in practice most keys are specific to certain geographical regions so you might need to provide the collection location to get it to species level. You could start by Googling "longhorn beetle identification key" and give it a go though.

Well done, nice research made... +1

From overall appearance, it also looks like an Lamiinae (flat-faced longhorns).

@fileunderwater overall appearance?

would this fit the bill of Asian longhorn beetle?

@ina Flat vertical face, robust, reniform eyes etc. Still thousands of species to choose from though.

any advice on how to narrow down on the species?

Or you could just look at the picture and say "that's a longhorn beetle"

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Protein Isolation from mammalian cells
What non-SDS buffer can I use to isolate protein from mammalian cells? The protein is intended for testing electron transport chain complexes.

Short answer - you will need to use a nonionic detergent. These will dissolve lipid membranes but leave most protein complexes intact, and many proteins in active form.
However there are many of these available and finding the right one for a particular job is a black art.
The usual approach is to start off with something widely-used and not too expensive like Triton X-100. Detergents with glycoside head groups are also widely-used but tend towards the expensive.


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Clarification on BMI formula
In the BMI formula= (Weight)  X  703) / (Height^2) , what does 703 represent? What is a reliable source that I can cite for this answer?
weight= lbs    height= inches

I think it has to do with the conversion: normally the formula is used on a metric scale. the units are weight in kg and height in meters. 1 pound = 0.45 kg. 1 inch = 0.0245 meters. 0.45/(0.0245)^2 = 756. So I must say the 703 is kind of odd. But I think it's a conversion factor

It's a conversion factor.
$$ 1~lbs = 0.453592~kg \\
1~in = 0.0254000~m $$
$$ \frac{lbs}{in^2} = \frac{0.453592~kg}{(0.0254~m)^2} = \frac{703.069~kg}{m^2} $$


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Apoptosis vs necroptosis
I understand that apoptosis and necroptosis share the same upper part of the pathway, but I cannot seem to distinguish when is each one activated? From my readings, it seems that when procaspases 8 or 10 are unavailable, the cell will shift to necroptosis. What makes these procaspases unavailable and why does a cell second the necropotosis?

From my readings, it seems that when procaspases 8 or 10 are
  unavailable, the cell will shift to necroptosis. What makes these procaspases unavailable and why does a cell second the necropotosis?

This seems to be right.

Mice with a conditional deletion of caspase-8 in the intestinal
  epithelium (Casp8ΔIEC) spontaneously developed inflammatory lesions in
  the terminal ileum and were highly susceptible to colitis. Casp8ΔIEC
  mice lacked Paneth cells and showed reduced numbers of goblet cells,
  indicating dysregulated antimicrobial immune cell functions of the
  intestinal epithelium. Casp8ΔIEC mice showed increased cell death in
  the Paneth cell area of small intestinal crypts. Epithelial cell death
  was induced by tumour necrosis factor (TNF)-α, was associated with
  increased expression of receptor-interacting protein 3 (Rip3; also
  known as Ripk3) and could be inhibited on blockade of necroptosis.
Caspase-8 is a cysteine protease critically involved in regulating
  cellular apoptosis. On activation of death receptors, including
  TNF-receptor and Fas, caspase-8 is activated by limited
  autoproteolysis and the processed caspase-8 subsequently triggers the
  caspase cascade that finally leads to apoptotic cell death.
  Caspase-mediated apoptosis is important for the turnover of IECs and
  for shaping the morphology of the gastrointestinal tract 4.

2011 - Caspase-8 regulates TNF-α-induced epithelial necroptosis and terminal ileitis

Caspase-8 has both prodeath and prosurvival roles, mediating apoptosis
  and/or preventing RIPK1-mediated necroptosis depending on cell type
  and stimulus. We found that inflammatory stimuli (LPS, lipoteichoic
  acid, or TNF-α) caused an increase in caspase-8 IETDase activity in
  primary rat microglia without inducing apoptosis. Inhibition of
  caspase-8 with either Z-VAD-fmk or IETD-fmk resulted in necrosis of
  activated microglia. Inhibition of caspases with Z-VAD-fmk did not
  kill non-activated microglia, or astrocytes and neurons in any
  condition. Necrostatin-1, a specific inhibitor of RIPK1, prevented
  microglial caspase inhibition-induced death, indicating death was by
  necroptosis.

2013 - Caspase inhibitors protect neurons by enabling selective necroptosis of inflamed microglia.

Necrosis has long been described as a consequence of extreme
  physicochemical stress, such as heat, osmotic shock, mechanical
  stress, and freeze-thawing, which kill cells quickly and directly.
  Therefore, this cell death has been described as uncontrolled and
  accidental necrosis characterized by loss of plasmamembrane integrity
  and cellular collapse, though nuclei remain largely intact during this
  process (Krysko et al., 2008a,2008b; Vanden Berghe et al., 2010). Loss
  of membrane integrity and release of intracellular content grant
  necrotic cells the ability to induce an inflammatory response. These
  immunogenic endogenous molecules fall under the umbrella term
  ‘‘damage-associated molecular patterns’’ (DAMPs) (Garg et al., 2010;
  Kryskoet al., 2011). They include, in the case of accidental necrosis,
  HMGB1, IL-1a, uric acid, DNA fragments, mitochondrial content, and ATP
  (Tables 1and2)(Eigenbrod et al., 2008; Kono et al., 2010; Krysko et
  al., 2008a; Sauter et al., 2000). Because the nomenclature of DAMPs in
  the literature is confusing, here we define DAMPs as a family of
  molecules that are intracellular in physiological condition

2013 - Necroptosis: The Release of Damage-Associated Molecular Patterns and Its Physiological Relevance

Based on the articles above, I would say that capsase-8 is a signal that apoptosis works finely, and so the cell can kill itself whenever it wants. So if that signal is lost because of an infection, mutation or extensive cell damage, the cell has no choice, but to activate the necrotic pathway.

Figure 1 - cell survival and programmed cell death - source

During tumorigenesis, significant loss or inactivation of lead members
  in the caspase family leads to impairing apoptosis induction, causing
  a dramatic imbalance in the growth dynamics, ultimately resulting in
  aberrant growth of human cancers. Recent exploitation of apoptosis
  pathways towards re-instating apoptosis induction via caspase
  re-activation has provided new molecular platforms for the development
  of therapeutic strategies effective against advanced prostate cancer
  as well as other solid tumors.

2012 - CASPASE CONTROL: PROTAGONISTS OF CANCER CELL APOPTOSIS

These findings reveal that, although apoptotic/drug resistance is a
  formidable “stronghold” of cancer against chemotherapy, necroptotic
  susceptibility is an intrinsic “weak point” of cancer.

2007 - Targeting the Weak Point of Cancer by Induction of Necroptosis

Playing a central role in cell death, caspases are targeted by
  different viruses in the aim of prolonging cell survival. For
  instance, viral IAPs (inhibitors of apoptosis) interact with and
  inhibit the processed active caspases, either by blocking the
  catalytic part of the enzymes, or through E3 ubiquitin ligase activity
  from RING domains, targeting caspases for rapid degradation via
  proteasome. Baculoviruses, asfiviruses or irridescent viruses encode
  such IAPs. Another class of caspase inhibitors frequently found among
  poxviruses include the serine proteinase inhibitors serpins:
  CrmA/SPI-2. Crma targets cysteine proteases such as host Caspase 1 and
  Caspase 8.

viralzone - Inhibition of host caspases by virus

Pathogens specifically target both the caspase 8-dependent apoptotic
  cell death pathway and the necrotic cell death pathway that is
  dependent on receptor-interacting protein 1 (RIP1; also known as
  RIPK1) and RIP3 (also known as RIPK3). The fundamental co-regulation
  of these two cell death pathways emerged when the midgestational death
  of mice deficient in FAS-associated death domain protein (FADD) or
  caspase 8 was reversed by elimination of RIP1 or RIP3, indicating a
  far more entwined relationship than previously appreciated. Thus,
  mammals require caspase 8 activity during embryogenesis to suppress
  the kinases RIP1 and RIP3 as part of the dialogue between two distinct
  cell death processes that together fulfil reinforcing roles in the
  host defence against intracellular pathogens such as herpesviruses.
The MCMV protein vICA prevents caspase 8 activation, and this
  sensitizes cells to death receptor-induced necroptosis. In addition,
  the MCMV protein vIRA blocks necroptosis and MCMV-induced programmed
  necrosis by inhibiting RHIM (RIP homotypic interaction
  motif)-dependent interactions.

2012 - Viral infection and the evolution of caspase 8-regulated apoptotic and necrotic death pathways

One potential explanation for this is that this form of death evolved
  to provide a “backup” pathway. Indeed, cowpox virus produces the
  factor CrmA, a potent apical caspase inhibitor capable of blocking
  apoptosis (Ray et al., 1992; Gagliardini et al., 1994). Several viral
  encoded genes are similarly produced to prevent apoptosis, and thus it
  is clear that inhibition of apoptosis is an important means viruses
  have exploited to avoid immune clearance. Interestingly,
  cytomegalovirus induces RIP3-mediated necroptosis via the interferon
  regulatory factor DAI (Upton et al., 2012). This virus also produces a
  protein called viRA, a protein that disrupts assembly of RIP1/RIP3
  necrosomes and consequent necroptosis. Thus, while the physiological
  function(s) of necroptosis remains to be fully elaborated, it is clear
  that this cellular process has been around for a long time during
  evolution. It is likely that necroptosis may serve as an “Achille's
  heel” in tumor cells, and thus greater understanding of the process
  may reveal novel therapies for cancer therapeutics.

2014 - Grand challenges in cell death and survival: apoptosis vs. necroptosis

It seems like there is a 3rd pathway too, it is called ferroptosis.

TNF-induced necroptosis depends on receptor-interacting protein-1
  kinase, mitochondrial complex I and cytosolic phospholipase A2
  activities, whereas H2O2-induced necrosis requires iron-dependent
  Fenton reactions.

2009 - Necroptosis, necrosis and secondary necrosis converge on similar cellular disintegration features

The transition metal iron is essential for life, yet potentially toxic
  iron-catalyzed reactive oxygen species (ROS) are unavoidable in an
  oxygen-rich environment. Iron and ROS are increasingly recognized as
  important initiators and mediators of cell death in a variety of
  organisms and pathological situations. Here, we review recent
  discoveries regarding the mechanism by which iron and ROS participate
  in cell death. We describe the different roles of iron in triggering
  cell death, targets of iron-dependent ROS that mediate cell death and
  a new form of iron-dependent cell death termed ferroptosis. Recent
  advances in understanding the role of iron and ROS in cell death offer
  unexpected surprises and suggest new therapeutic avenues to treat
  cancer, organ dam
  - age and degenerative disease

2013 - The role of iron and reactive oxygen species in cell death
2012 - Heme induces programmed necrosis on macrophages through autocrine TNF and ROS production

Not just a 3rd, there are many more pathways (pyroptosis, MPT-RN, parthanatos, ferroptosis, NETosis).

Figure 2 - Signalling pathways of regulated cell death.
2014 - Regulated cell death and inflammation: an auto-amplification loop causes organ failure


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Thermochemistry of gabapentin
So I am taking a new class at my institution called Theoretic Nuclear Thermochemical Pharmacology, (I know big name, it's an elective from a real pioneering fronteirsman in our department :P) and I am curious about the metabolism of gabapentin and how this might relate to thermochemistry?  I am curious how this drug metabolizes in the liver and how it neurochemically alters the brain such that it reduces mania in patients that are bipolar and may also have mood disorders.  Any kind of relevant literature would be appreciated, something to guide me in the right direction.  I mean I understand liver metabolism and what it does but its relation to mania and how it's thermochemical still boggles me.

@ChrisStronks Isn't this a commonly accepted term? Anyhow, I think in some cases entropy dominates rather than heat.

Also, the way you phrased your question is too open ended, and it's hard to answer. You should rewrite it so that it has one clear answer.

@Superbest - thanks for the link. Having read this weblink, and re-reading the question I think I would have phrased the question as "I am wondering (1) what the drug metabolism is of gabapentin in the liver; (2) whether these catabolic liver reaction(s) are exothermic or endothermic; (3) how gabapentin or its metabolites reduce mania in bipolar patients (i.e., gabapentin's pharmacodynamics); and (4) how the thermochemistry of liver-catabolism of gabapentin relates to its manialytic properties" I think the question's scope is too broad, as it mixes basic toxicology and neuropharmacology.


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Why is the ebola virus so intense now?
So i'm looking into the ebola crisis and it seems the death toll is really getting crazy.  I understand that it's a cytomegalovirus and that it basically overwhelms the immune system due to it's size and virulence but why is it so bad now?  What has changed about it?
The folks at the Dallas hospital were said to have 'broken protocol' but to me it seems the virus may be more infectious this time around.  Has it changed?  Mutated?

What do you mean by " that it's a cytomegalovirus"?  CMV is a herpes virus, while Ebola is a filovirus. CMV is also a double-stranded DNA virus, while Ebola is a single-stranded RNA virus.

What did the Ebola say to Cytomegalovirus? No mono!

I think what makes this outbreak so different from previous outbreaks is that it hit large cities. Previous outbreaks hit small villages, where it couldn't spread far and burned out fairly quickly. In dense urban areas there are plenty of people to spread to and easy access to transportation.

republicans, specifically George Bush

I don't think mutations are relevant in this case. It is very similar to previous strains. The main difference that it was recognized too late.

The answer here lies in epidemiology and the pathogenic nature of the virus. Humans infected with Ebola have a range of recovery rates of 5-75%, meaning that most of those infected will not survive infection. Given the combination of preparedness factors at first recognizing a true outbreak in Ebola and viral load which had already been spread by the point healthcare workers have organized an efficient response, it is not surprising that some outbreaks are worse than others. I think attempting to correlate the virulence to the type, size with the extent of the viral spread is not pertinent, but rather, as you mentioned breaking "protocol" can have more deleterious effect than simply explaining it by a mutation.
That is not to say that this strain of Ebola is not different from those detected in other outbreaks--in fact it is likely to be quite different and accumulated several mutations.


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aspartame in relation to generating methanol
So I posted another question with respects to methanol and aspartame in metabolism with respects to toxicity.  So another question I have is "Why is aspartame used as a dietary component to generate methanol following metabolism?"  This is a topic of discussion in my thermonuclear pharmaceuticals class at my institution, and more or less it's also part of an active assignment that we're expected to cover for a weekly discussion which is no graded; otherwise I wouldn't seek free advice on this.  As much as I read in my book it does not go into detail and I am curious if anyone out there has any advice on this or perhaps you could point me towards relevant articles?

Aspartame is the methyl ester of the aspartic acid phenylalanine dipeptide. Esters are very easily hydrolyzed by esterase enzymes in the body. This will create an acid and an alcohol. In this case the alcohol is methanol, because it was generated from the methyl group. I'm not familiar with why thermonuclear pharmaceuticals would be concerned with aspartame, generally thermoncuclear refers to nuclear fusion, and even radioactive drugs don't undergo fusion.
Anyway, some people are afraid that aspartame is toxic because the methanol it generates is toxic. This fear is unrealistic because the amount of methanol it takes to cause blindness is about 10mL, and the smallest amount needed to kill someone is about 30mL 2. To generate this much pure methanol from aspartame would require you to eat an impossible amount of aspartame.


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Avoid Blood lyse color remain in DNA extraction
I tryed several times to extract DNA from freezed blood sample whit Qiagene kit but it gave very small amount of product due to good purity then I try extract manually by using CTAB PVP method and finaly gain a much more DNA but the lysate color in sample remain in DNA pellet an do not disolve in any solvent to seperation I tried to add small amount of accorbic acid but just the color get reduce to red from gray:'( . If every one have a idea which can help me please gave in a small answer.

Sounds like your DNA isn't pure.

The kit likely has a way of dealing with the RBCs, where as your CTAB PVP may not. Did you centrifuge the sample? If human, RBCs are not going to have genomic DNA, so you can extract the WBC layer and plasma and try purifying that.


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Find the probability of Filium (child) that are dark (and) heterozygote
Two mice are crossed and both are dark and heterozygote. Find the probability of their filium(children) dark (and) heterozygote in F1.
Here's what our teacher explained:

I don't understand. I thought it would be 1/2.

You are correct, the teacher must have made a typo

Well, she insisted in her answer and said that this is quite a difficult exercise, but if we do it in my way it is so easy.

What is the entire question? Is there more to it than what you posted?

No, it's not. I've posted the whole question.

Is hair color inherited in a Mendelian way?


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What does "Origin" mean in NCBI GenBank?
I'm having trouble determining what exactly is meant by the term "Origin" in this case. It is just before a 70 bp sequence that the page claims is a plasmid, but does not specify it to be what I assume is incomplete, seeing as I don't think there is such a thing as a 70 bp plasmid (correct me if I'm wrong). So do they mean the origin of replication to the plasmid?
Here is the page in question: https://www.ncbi.nlm.nih.gov/nuccore/M30888.1?report=genbank

The ORIGIN may be left blank, may appear as "Unreported," or may give a local pointer to the sequence start, usually involving an experimentally determined restriction cleavage site or the genetic locus (if available). This information is present only in older records.
The sequence data begin on the line immediately below ORIGIN.

[source]
Given that it's left blank in your file, it doesn't signify anything other than the end of the annotation and beginning of the raw sequence.


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What does pain look like in wave form?
I am interested in understanding what "pain" and "hot/cold" look like, as far as waves signatures (signal processing) go.
My general understanding of how pain (nociceptive pathways) and hot/cold (thermoreceptive pathways) works are as follows:

A sensory receptor (nociceptor for pain and thermoreceptor for temperature) responds to analog stimuli, and synapses an appropriate signal onto a first-order neuron (the neuron it directly connects to); then
The signal travels up the nerve, neuron to neuron, through the spinal cord, up into the brainstem, and finally into the somatosensory cortex; then
That cortex decodes the signal and interprets it as "pain" or "hot/cold"

Further, my understanding is that these signals are electrochemical in nature:

While passing through a neuron (dendrite -> axon) the signal is of a purely electrical form, no different than the electricity racing around the motherboard of my laptop right now; but...
To synapse from the axon of one neuron to the dendrite of the next sequential neuron, the electrical signal is transformed (somehow, this is where I'm fuzzy) into a chemical signal, and essentially hormones/messenger chemicals ferry the preserved signal to the next neuron's dendrite. At that point, the dendrite converts the chemical signal back into electricity, and on and on this goes until we reach the cortex.

So, only because I am a total neophyte here, I have to preface my actual question by asking the greater community at large: is my general understanding here correct? If not, please begin by correcting me!
The question at hand
If everything I've currently stated is more or less correct, then this implies that, while traveling inside a neuron (dendrite -> axon), neurological signals such as pain or temperature exist as waves of electricity.
I'm wondering:

What do these waves look like? Do they have special names? Are they sinusoidal in nature? Do they travel with known periods/frequencies/amplitudes? Can I represent these waves as trigonometric functions?
I'm sure this differs based on genetics and all sorts of other factors, but what are the general power ratings for the central nervous system? How much Watts/Volts/Amps travel with these signals? Are there standard ranges?

Please note: This is not homework! I am a grown man who works as a software engineer by day and I migrated here from the Stack Overflow community. I am asking this question because I have absolutely zero background in biology, and after scouring the internet for an answer to this, I could find nothing.
Please don't hold back with lofty, low-level explanations. Give me science here! Though I am not a biologist, I'm certainly no dummy either.

"Can I represent these waves as trigonometric functions?" Not really.. You can have a look at Hodgkin-Huxley model or the FitzHugh-Nagumo model.. These are frequently used to model neural impulses.. These are not actually sinusoidal. You can also have a look at what is known as relaxation oscillator

Welcome to Biology.SE! You may want to have a look at action potential.

Thanks for the welcome Remi.b, and thanks for the links @WYSIWYG (+1 to both)!

RE "This is not homework!..." if this is reference to a question which garnered close votes as "homework" you should note that the homework close vote is often used as closure on "poorly researched questions" not just homework (just in case you were a bit confused by this happening when it is not homework)

Thanks @GriffinEvo (+1) - I don't have enough rep to see if my questions are even garnering closevotes or not :-) I put that disclaimer in there because I asked a previous question yesterday and I quickly received comments from other users who obviously assumed I was a student looking for a "quick answer". We get this all the time on Stack Overflow, and so I wanted to combat any potential nay-sayers right up front; hence the disclaimer.

I wouldn't say that the signal in a neuron is the same as in your motherboard. Electrical current is the flow of charges (electrons) parallel to the wire. A nerve impulse is the flow of charges (cations) perpendicular to the nerve (which propagates a signal parallel).

I don't want to comment about the nature of electric signals in neurons (as I know only little about physiology and neurophysiology). But here is a short answer that may already help you.
Neuronal electric signals are called action potential. If you register the voltage at a given location on the axon of a neuron through time you will see something like this:

This picture shows one action potential. Of course when you feel pain it lasts for more than 5 milliseconds. In such case, if you record the voltage for a longer period of time you will just see a series of action potentials. You should have a look to the wiki page for more information.

Awesome answer, thanks @Remi.b (+1)! Quick followup question: say this graph above is what pain "looks" like as a wave. What about "pins and needles" (neuropathy) signals? Again, if my naive understanding of biology is correct, neuropathy occurs when a neuron ceases to function properly and sends junk/garbage signals on to the brain. The brain, in turn, can't make heads or tails as to what the signal is trying to convey, and so we experience the pins and needles sensation.

So: (1) is the pins-and-needles "wave" a specific type of wave (distinct from the "normal" one above) that the brain knows to interpret as pins and needles, or (2) does the brain just interpret anything it doesn't understand as neuropathy? Thanks again so much!

@smeeb there is no general difference in the waveform between types of perception. pain perceiving neurons are non-myelinated and are therefore slower in their conduction.

Hmm interesting @WYSIWYG (+1 and thanks) - but obviously the brain knows to interpret pain and pins/needles differently. So if these sensations don't differ by wave form, then how are the different (in other words, what is electrically-different about them that allows us to distinguish the sensations)?

They come from different neurons. Analogy in that your two USB cables both conduct electricity, but the one connected to your hard disk has different information than your charging phone.

Sorry, it is not my field of expertise, I am not able to tell much more. But the picture shows what basically any neuronal message looks like in term of voltage through time along the axon. Different tactile perception may use different neurons I think (pain, touch, warm/cold) but all of these neurons, always send message through the same kind of electric waves.

Thanks @MarchHo (+1) - what neurons do pins-and-needles come from?

http://biology.stackexchange.com/questions/24555/what-causes-feeling-of-feet-falling-asleep-to-be-delayed/25856#25856 is an answer I recently put up on the origin of pins and needles. Consider reading the question and other answers too for a general overview.

Thanks again @MarchHo (+1) - I'll dig in more tonight, and I appreciate your USB analogy. But to summarize: are you saying: (1) that "pins and needles" (parasthesia/neuropathy) travels to the brain with the same waveform as any other signal but simply carries different encoded "data" on it; or are you saying that (2) there are special neurons that specifically transmit parasthesia signals to the brain? And if, #2, then I'm curious: what are the names of these specialized neurons? Thanks again!

What is important is the sequence of action potentials. I would suggest reading about temporal coding and rate coding as a start. For instance a (very lightweight) introduction can be found on Wikipedia: http://en.wikipedia.org/wiki/Neural_coding Also, note that much of it is how we INTERPRET a certain neuronal signal; our brain is easily fooled! For instance, when eating spicy food we perceive it as "hot", even if it is not at high temperature. This is because the receptors mediating the "hot" sensation are the same for spice and temperature.

Since the action potential question has already been answered, I will attempt to answer

I'm sure this differs based on genetics and all sorts of other factors, but what are the general power ratings for the central nervous system? How much Watts/Volts/Amps travel with these signals? Are there standard ranges?

You are right in that there is a large amount of variation in the electrical variables, however neurons generally have an action potential voltage of ~70mV, give and take 30. I could not find any papers in a cursory search on the power of neurons.
To answer your "comment question" due to lack of space, (1) neurons encode data in a binary fashion due to their on or off mechanism of impulse transmission, and there have been studies done in deciphering what these pulses mean. However, it is still an active field of research with many questions. (2) However, pain signals are only transmitted on sensory neurons containing nociceptors.
You should also read up on how an action potential is generated, as that would help you understand the concept better.

Thank you so much @March Ho (+1) - last followup question (I promise), and it should get to the root of my question here: if pain signals are only transmitted on sensory neurons containing nociceptors, then what is the equivalent for "pins and needles" signals? What exact receptors are required to exist on a sensory neuron in order for that neuron to transmit pins and needles/parasthesia signals? Thanks again!

This is still an active field of research, and not being a specialist in the field I cannot say what this specific signal is. It is possible that this signal has not yet been discovered by science.

That makes sense and +1 @March Ho for the excellent help thus far. Just out of curosity, what field or specialty would be actively researching this topic? Biomedicine? Bioelectricity?


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Efficacy of the 10-20 montage for EEG
I am trying to better understand the relationship between the placement of EEG electrodes and the quality of the "brainwave" signals they produce.
There is the standard 10-20 system which has me wondering a few things:

Is the 10-20 system just a standard based on uniform distribution of the electrodes (so as to maximize brain signal collection)? Is it safe to assume one could increase the "density" of electrodes (again, in uniform distribution) and receive better quality signals? In other words, how does the placement & quantity of electrodes affect the quality of the information that can be obtain via EEG?
Is electrode gel strictly necessary? Is the gel used for adhesion, conductivity or both? I can't imagine it helps much with conductivity seeing the most of the scalp is (typically) covered with hair?

The placement in terms of location on the scalp determines what region of the brain is sampled. The electrode density determines how many locations are sampled. The spatial resolution of an EEG has been reported to be 7 mm (Im et al., 2006), meaning the variability in determined source and verified source (via fMRI in the study cited) is approximately 7 mm. Hence, increasing the density below 7 mm may not be too helpful. The 10-20 system utilizes an inter-electrode distance of 60 - 65 mm (American Clinical Neurophysiology, 2006), indicating it may make sense to use more electrodes when high resolution is needed. Note, however, that MRI and MEG have better spatial resolution than EEG and are methods of choice when you wish to do accurate source localization. EEG has excellent properties in the temporal domain, and a combination of MEG/EEG has great characteristics in both spatial and temporal domains.
Standardized EEG montages indeed promote uniformity so that different studies can be compared more efficiently.
Scrubbing with electrode paste primarily reduces impedance (increases conductivity). Hair and the skin don't conduct well and by applying a conductive paste impedances are substantially reduced. Mostly the technicians also scrape a little bit to ensure even better conductance by removing the fatty top layer of the skin. Also important is that differences in electrode impedances negatively impact recordings (American Clinical Neurophysiology, 2006). Impedance matching is hence necessary and using paste aids this purpose. So yes, conductive paste is essential.

Reference
- Im et al., J Neurosci Methods (2007); 161(1): 142–54

Thanks for the awesome answer @Christiaan (+1) - the only part of your answer that I'm not quite following is the part surrounding spatial resolution. (a) When you state "The spatial resolution of an EEG is quite poor and has been reported to be 7mm..." what exactly do you mean (what exactly is "spatial resolution", and why is 7mm a poor value for it)? (b) It sounds like increasing density (e.g. 10-5 system) would help with this poor spatial resolution? Thanks again!

@smeeb - I adapted the answer.

I don't see how it follows from the resolution of EEG being X that there is no benefit to more narrow electrode spacing than X. For example, it is possible (and likely) that electrode spacing X is not sufficient to recover sources with accuracy X. After all, resolution refers to brain sources, spacing refers to sensor density.

If the error margin is 7 mm narrowing density down below that doesn't make much sense imo.


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What would happen to organisms if stem cells didn't differentiate?
I've tried to research this online and looked through my textbooks as well. This is something that has been on my mind for a bit. Will it just mean that the organism is more simple? Or would it mean they are less efficient? I'm not sure.

What would you expect to happen if stem cells didn't change into other kinds of cells? Can you think of any organisms that don't have cell differentiation? What do they do?

The organism would be unlikely to grow any "useful" tissues, and if the appropriate growth factors were still present (with differentiation blocked) there would be a higher chance of cancer.

If we consider a mammalian organism, it would eventually die, and one can presume that it would not take too long. In mammals, a lot of tissues are primarily constituted by specialized cells that cannot divide. Some of these cells have a tremendous lifetime (e.g. neurons), but others have a much shorter lifetime (e.g. erythrocytes). In tissues that are composed of the latter type of cells, certain stem cells are actively differentiating in order to maintain an adequate number of such cells in the organism. If such differentiation suddenly stopped, the concentration of such cells would progressively drop as time goes by, until the functions carried out by such cells are not executed. At this point, without these functions (e.g. oxygen transport carried out by erythrocytes), where some are essential, the organism would quickly die.

Then you'd have lumps of unspecialized stem cells. Differentiation means that most of the genome has been turned off and only a subset of the genes is active. Makes IMHO sense in the light that different cells make use of the same signalling pathways downstream of different receptors to elicit cell type specific responses.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


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Profile of a human foot
I am not sure if this is the right place to ask this question, so please let me know in case it is not. My question is just out of curiosity.
What is the reason for curved human foot profile?
First a bit of clarification in layman terms. When looking at a human foot sideways on the inside, you see an arch along the foot length, however there is no such arch when looking from the other side (imagine a footprint and how it has a curve on the inside and is flat on other side).
I looked on Google to find the answer. Pardon my lack in knowledge of biology vernacular/terminologies. Reading through wiki article on Arches of the foot, they do describe the two arches - longitudinal arch and transverse arch. In the laymen explanation I gave earlier, if I understand the anatomy right, the transverse arch is what leads to the footprint shaped that way. As I understand, they explain how the arches are responsible for the stiffness of the foot, akin to how arches of a building help support more weight will lower deflection compared to other configurations. Also the space under the dome enable the elastic soft tissue to spread and provide enough contact surface for the body weight to be effectively distributed. On transverse arch, they say it creates a half dome and the two foot together form the complete dome. However, structurally, they will not fulfill the same role as a full dome, far from it in my humble opinion. Can someone explain the structural benefit of the half dome of the transverse arch?


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Why do arteries have a small lumen?
My biology textbook says that arteries have a small lumen relative to the thickness of their walls. I understand why they need thick walls, to withstand high pressure and stretch etc. But when explaining the reason for the "small" lumen, it says that it is to "maintain the high pressure". I'm not quite sure what this means.
What would happen if the lumen was larger? Wouldn't a small lumen create resistance to blood flow? And what exactly is blood pressure? Also, it says that veins have a "large" lumen. Why is this so? I think my confusion is a lack of understanding of what blood pressure really means. Why does velocity not seem to make a difference?

Physics. Given the same volume that must pass through a tube per minute, do you think the pressure would be as high in a 3 inch pipe or a steel straw?


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Do pinhole glasses help reduce eye strain?
I've noticed that I can read about 10x longer at screens and books without my eyes tiring using the "pinhole trick" - where you curl your fingers and look through the hole (great explanation here https://www.youtube.com/watch?v=OydqR_7_DjI)
Pinhole glasses (shown below) have been advertised as being healthy for your eyes, although there seems to be no strong supporting evidence.
If not for long term affects, would these glasses use the same pinhole principle to reduce eye strain and allow you to stare at close objects for longer?

The pinhole glasses improved visual acuity, DOF [depth of focus], and accommodative amplitude; however, they resulted in decreased visual quality including general reduction of VF [visual field] sensitivity, CS [contrast sensitivity], and stereopsis.

https://iovs.arvojournals.org/article.aspx?articleid=2166618


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It is correct to construct tree species-based rarefaction curves for birds?
If I register all the species of birds that uses sp1 tree, then, all the species of birds that use sp2 tree, and so on. With this dataset, I can made a rarefaction curve using "number of tree species" on X axis of the rarefaction graph, instead of using "number of samples"
Is this incorrect? Does anyone knows a paper that made something similar?


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What's the proper term for "brain waves" read from an EEG?
An EEG records the electrical activity of the brain. An EEG shows voltage differences as a function of time and shows what have become known as "brain waves".
Is there a scientific/technical word/phrase for one of these measured "brain waves"?

fyi: galvanic skin responses are not the same as EEG - I edited the question therefore to prevent confusion

The terminology around EEGs is variable, dependent on the context;

Brain wave is fine, but has a bit of a popular undertone;
Oscillation, as per the deleted answer,  implicitly refers to a longer-term behavior, i.e., something periodic (EEG);
Waveform is often used to describe a single event with a recognizable shape (ERP, compound action potentials, etc.);
EEG signal/recording/trace is more than fine too, it doesn't carry any implicit meaning about its nature, it does carry relevant information on the method used;
EEG is fine too, EEG means electroencephalogram, the 'gram' suffix comes from Greek, where it means “something written,” or a “drawing”, so literally it means something like "graph of the electrical brain". So simply speaking of 'the EEG' is OK as well.

This list may not be exhaustive, merely illustrative


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What signal processors comprise an Event-Related Potential system for EEG?
So my 30,000 ft. understanding of the EEG signal processing data flow is:

Capture raw EEG data ("raw waveforms")
Run these raw waveforms through a Signal Processing Framework that consists of 1+ "nodes"/processors, where each processor is doing some kind of transform on the raw waveform. In doing so, new information is unlocked from the raw waveform that was previously hidden
Perform individual analyses on this unlocked data based on what suits your research/application

So first off, if the above understanding is misled in any way, please begin by correcting me!
Assuming I'm more or less correct, my specific application at hand is that I want to correlate certain raw waveforms with events (such as "thinking of a turtle", "moving head up and down", etc.). I believe Event-Related Potentials are what I'm looking for, but...
What I'm struggling with is: what does my "Signal Processing Network" need to look like in order to implement ERP? From an architectural perspective, I'm looking for this "network" to take raw waveforms in as input, and to output events, like the few I mentioned above.
Is FFT a player here? Some kinds of filters? What processors comprise an ERP system, and what does their respective "network" (data flow pipeline) look like?

Thanks @daniel - when you say "of course the data will be transformed from the time to frequency domain", why do you say "of course"? What is there to be gained by viewing the data in the frequency domain over viewing it in the time domain?

In particular the goal is often to get an idea of the distinct frequencies that comprise a complex signal, and if the signal is really complex it may be impossible get that information by observing the time signal.

Thanks @daniel - let's stick with your heart rate example. You stated "You are typically looking for the average behavior of a signal over time." Can you provide 1 concrete example of one such "behavior" about the human heart that can be observed by running heart rate data (observed in the time domain) through FFT and transforming it into the frequency domain?

Sure, so can you provide any concrete example where decomposing an original waveform into component frequency-based waveforms yields more actionable information than on the original waveform? Any example at all, but a concrete example?

Thanks again @daniel, but I'm just not seeing the value of this (still). Let's use your violin example. If we already know what the violin sounds like, why break it down into its major frequency components? To reconstruct the sound of the violin?!? We had the sound in the first place! To me, this is like saying: let's tear a car engine apart so we can have all its individual parts...so we can then put the car engine back together...and have ourselves a car engine! No, you already had the car engine in the first place!

And: https://en.wikipedia.org/wiki/Quantitative_electroencephalography

Note that the terminology is unconventional; filtering is typically a serial process, especially in EEG. Secondly, a processor is mainly associated with hardware, e.g. a cpu.

@smeeb Can you please expand your step (2)? It is too unspecific. Please, give concrete examples what you are trying to do.

Event-related potentials are an issue in many aspects of physiology, not just in EEG analysis, so this answer is more general.
The main problem is that electrical or other signals that are associated with some "event" are typically much lower in magnitude than the background noise in the system being examined. Electrocardiograms are the exception in this regard. The trick then is to get the event-related signal out of the noise.
This is done by multiple repetitions of the "event" combined with signal averaging. You mark the electrical record with the time of each event, line up portions of the record by the event markers, and average the lined-up signals around the multiple events. This can be the full waveform for sensory evoked potentials, or if you are examining action potentials in a nerve, the histogram of post-event occurrence times of action potentials.
The particular signal-processing mechanisms used have evolved with technology. I'm old enough to have known people who 60 years ago recorded electric neural activity on magnetic tape and processed the data, after analog-to-digital conversion, on what were then state-of-the art computers with 65,000 words of memory. Even then, there was discussion about the best technologies to use for specific purposes, as this 1959 MIT monograph indicates.
The main problem, the low level of event-related signals to the noise from other electrical activity, still remains 60 years later. The noise is biologic rather than technical, so more recent improvements have more to do with ease and speed of processing rather than the fundamental signal-to-noise problem. For event-related potentials, use whatever technology allows you to collect the electrical data along with linked notations of the event times, then to average the signals synchronized on the events to build up the signal out of the noise. In a standard clinical application of brainstem auditory evoked potentials to evaluate hearing, you may need to average over 500 or more stimulus presentations.
This signal-in-noise problem is also seen in functional magnetic resonance imaging (fMRI) of brain function, which must consider not only the signal over time but also differences among brain regions within individuals and differences among individuals. The Wikipedia page on fMRI goes into some detail on methods for getting specific signals out of the background noise. You should pay particular attention to the issue of "Block versus event-related design" on that page. My understanding is that for "events" of the type that you consider, like "thinking about a turtle," the block design works best in fMRI. The paper by Thierry et al, provided in the helpful answer by @Christiaan, shows how to apply this type of blocking approach to EEG measurements during presentation of visual stimuli.
The methods in that paper by Thierry et al also indicate the type of practical care that is used in professional analyses of this sort. The authors go into detail about the nature and placement of the electrodes, choice of which electrodes to use for analysis, signal-sampling rates, and so on. For example:

Scalp activity was digitized at a 1-kHz sampling rate from 64 Ag/AgCl electrodes distributed throughout the scalp according to the 10–20 convention using Cz as a reference. Impedances were kept below 7 kOhms. The electroencephalogram was filtered on-line between 0.01 and 200 Hz and off-line low pass at 35 Hz using a zero phase-shift digital filter. Eye blink artifacts were mathematically corrected, and signals exceeding $\pm$ 75 $\mu$V in any given epoch were automatically discarded.

Reading papers like this are probably the best way to learn about what's involved and to find people who can teach you how to do it.

This seems like a reasonable answer to me. Does filtering play a role in this?

@daniel, filtering is inherent in all this signal processing as any real recording system has finite bandwidth, and that bandwidth must include the signal of interest. In this type of application the electrical recording is typically AC-coupled so that low-frequency signals (e.g., drift) are omitted. If the particular waveform of the response is known then filtering to emphasize its frequency components could help, but the main technique is multiple repetitions of the event and averaging the multiple signals after they have been lined up by event times.

Thanks for the great answer @EdM (I'd upvote it if I had the rep to do so)! So it sounds like the two main problems with "raw" EEG data are: (1) signal-to-noise ratio (which is a long-standing problem seeing that the majority of the noise is coming from the subject's own body) and (2) using tricks like FFT (as well as others) to unlock more information than what was present in the original waveform. Can you please confirm these statements and correct me if I am misunderstanding or overlooking anything? Thanks again!

FFTs don't "unlock more information than what was present in the original waveform," they just help to bring a signal out of the noise in some circumstances. That's really an important distinction; the information is there but you have to find it. Yes, signal-to-noise is the main issue. For your application, signal averaging in the time domain may be more important than frequency-domain analysis with FFT, unless you happen to know the frequency characteristics of the signal you are looking for.

Thanks for the clarification @EdM - so then just to confirm, for once and for all, that ERPs are typically done by averaging the "raw" (unprocessed) EEG waveforms? And that there are no other "tricks of the trade" (filters, perhaps other signal processing transforms, etc.) exacted upon this raw data? Thanks again!

There really is no such thing as an "unprocessed" electric waveform in practice, as any real-world recording device always selects a certain range of frequencies. There are certainly some "tricks of the trade" in general for how to collect high-quality data, and there may be some specific choices of extra filtering for specific applications, but this isn't my professional area of expertise. If you want to do something like this, work with someone who has practical experience.

@EdM Can you give any code-project/site which does event detection by averaging some shape or studying histograms? I would like to understand better how they manage the signal detection. I want include whole signal in time (not just intervals). Gerstein 1960 + Arieli 2006 + Urner 2013 show that the noise includes also physiologically significant details. I would really like to understand how to avoid filtering out physiological data i.e. some elements of noise. Then, understand how the situation is with Cardiac signals. How much of noise is physiological in ECG signals?

@Masi : ECG signals are typically very large with minimal noise when electrodes are properly applied and high-quality electronics are used; don't know that there's much "physiological" noise to find within them. The more general case is beyond my current professional expertise, as I haven't had to think about signal processing in many years. I recall that wavelet analysis can be useful for finding signals of a specified shape in a noisy signal, in a way that's superior to Fourier analysis, but I have no direct experience with that.

@EdM Assume you see a signal in your Wavelet analysis/Fourier analysis. How do you confirm that it is really a signal than artifacts? Results of wavelet analysis are very noisy itself.

@Masi :sorry, this is beyond my expertise. Try the Stack Exchange Signal Processing site for better-informed answers.

ERPs are typically analyzed in terms of amplitude and latency. FFT is not really an option; it doesn't make sense. There may be some applications where it may be useful, but these are quite specific. For example, if repeated stimuli are presented and you are not averaging them, but you are collection an EEG, then FFT may help you to deduce whether stimulus frequency (e.g. 1/s) matches the response frequency and what the phase difference is between them. Note that FFT yields amplitude and phase data. But generally, again, ERPs are analyzed simply by taking amplitude and latency (Fig. 1)

Fig. 1. ERPs are characterized by taking the peaks, here the positive peak (P) at 100 ms (P1) and the negative peak (N) at 170 ms (N170). The amplitude of the N170 peak was analyzed and plotted. source: Thierry et al., (2007)
Reference
- Thierry et al., Nature Neuroscience (2007); 10: 505-11

As always, thanks again @Christiaan! Please see my last comment/followup question underneath EdM's answer...I have the same question for you! Thank you immensely!

@smeeb - Filtering is only necessary when it's necessary. If it's not, it's not :)

...Fast Fourier transform (FFT) of EEG data, is ...a reformatting of data into component frequencies. This transformation is useful because many pathological EEG patterns produce spectral patterns that can be easily recognized without the extensive training required to interpret routine EEG tracings... From the abstract of this paper. Clearly FFT does make sense and is done in some cases.

QEEG [FFT/wavelet analysis] has been accepted for clinical application in some areas, such as cerebro-vascular disorders and epilepsy, though it remains yet to be accepted in other clinical areas, such as diagnosing mild traumatic brain injury or psychiatric disorders. https://en.wikipedia.org/wiki/Quantitative_electroencephalography.

@daniel - that's EEG,  not ERP; question & answer are on ERP

Yes I see that now. I was focused on EEG and I see that FFT doesn't have much bearing in the ERP context--thanks.


text:
Why is Fast Fourier Transform applied to raw EEG data?
I am trying to understand why Fast Fourier Transform (FFT) is used in the analysis of raw EEG channel data.
My understanding (at the 30,000 ft view) is that FFT decomposes linear differential equations with non-sinusoidal source terms (which are fairly difficult to solve) and breaks them down into component equations (with sinusoidal source terms) that are easy to solve. It then combines each of these component/partial solutions to solve the original equation. In laymen's terms, it's like taking a smoothie, and breaking it down into its recipe of ingredients. Doing so would allow us to study and analyze each ingredient as it relates to the final product (the smoothie).
But how does this relate to raw (non-FFT-decomposed) EEG data? My understanding of a single channel of EEG data is that it is essentially a measurement of voltage over time.  What is to be gained by using FFT to break this voltage/time signal down into its constituent frequencies (alpha/beta/gamma/delta/mu waves), etc.? What additional information can each frequency tell us about the raw data?

Update:
Here is my updated understanding of how FFT relates to raw EEG data per @Mark's description below:

Somewhat simplistic answer so I'll post it as a comment, but Electrical Engineers Fourier Transform EVERYTHING. "Huh that's an interesting signal, what happens when I Fourier Transform it?" My guess is in the case of EEGs case some one did that and it showed patterns so they kept doing it.
FYI, "it is essentially a measurement of voltage over time." Can describe literally everything a computer can do. Camera? voltage over time, Force sensor? voltage over time, Current? voltage over time (through a very precice inductor or resistor), Rotation? voltage over time

Thanks @Sam I think I get what you're saying! However, when you say "My guess is in the case of EEGs case some one did that and it showed patterns so they kept doing it", can you elaborate a bit more? What patterns might "they" have found? Perhaps reading a concrete example of what patterns/information emerge when EEG data is ran through FFT might help clue me in to its underlying value proposition. Thanks again!

You may have success approaching this differently.  Rather than trying to learn the specifics about how a FFT might be used in a given scenario analyzing EEG data, you might benefit from learning what a FFT does (to any signal), what the results look like, and why we do them, and only then worry about how it lines up with EEGs.  FFT is actually only one of many transforms we use, each has its own flavor.  FFT happens to be an easy one that shows a lot of data that is hidden in the time-domain solution, but wavelts are another approach with different tradeoffs.

Thanks @CortAmmon (+1) - I've already (sorta) done that. But the fact that no one here (even Christiaan) has been able to yield a clear-as-day explanation as to what the benefits of FFT are (that is, specifically what new information is exposed via FFT) tells me that Sam is on to something above. It tells me that people just use FFT because it presents some interesting new ways of looking at the same data.

For injstance, I would have expected a possible answer to this question to be something like "Raw waveforms just give us electrical activity that occurs at the surface of the brain. But we use FFT to decompose that general waveform into signals that represent things happening deeper in the brain." Or something like that. But so far, no one has been able to give me anything like that. Christiaan came close with his Theta wave example in sedated vs. awake subjects, but then was unable to explain how a researcher might actually be able to put that information to use.

@smeeb, first of all, the "Fast" part of FFT is just a technique to make the computation easier, you are not interested in the computational aspects of the Fourier transform so you really should just be asking about the Fourier transform ignoring the "fast" part.
Christiaans answer kind of shows you in right side of figure two, it looks to me like low and high frequency signals are effected by propofol more than the middle bands. That would lead me to ask the question "what do the high and low bands do? how are they different from the middle"  you could than start playing around with that.

So, as @sam points out, Fourier transforms don't create any new data, they merely present the data in a way which exposes the information people cared about better.  In the case of EEG, many signals that people are interested in are repeating oscillating signals.  Why?  Because it has been found that the repeating oscillating signals carry useful information for us.  Science can be circular that way.  On the other hand, if you're trying to observe the P300 waves, which are in the time domain 300ms after a novel event, a Fourier transform would obscure them rather than accentuate them.

Fast-Fourier Transform (FFT) transforms a signal from the time domain into the frequency domain. Basically, any time-dependent signal can be broken down in a collection of sinusoids. In this way, lengthy and noisy EEG recordings can be conveniently plotted in a frequency power-spectrum. By doing so, hidden features can become apparent. By adding all the sinusoids up after FFT, the original signal can be restored, so no information is lost.
A notable application of FFT in EEG is shown in Fig. 1, which shows an EEG in an awake person (top blue trace) and an EEG in a propofol-sedated person (bottom red trace). The traces are different, but exactly how different? Scientists like to quantify stuff.
Now look in Fig. 2, which shows the same data but filtered in the delta band (low-pass filtered EEG with a cut-off frequency of 1.5 Hz, left panel). Here it already becomes more apparent what's going on, but what exactly is the difference between the two traces? That difference becomes readily apparent in the frequency domain by using FFT (Fig. 2, right panel); The frequency spectrum has a peak at 0.2 Hz in both traces, but that peak is about twice as big in the anesthetized state than in the normal state. In other words, the anesthetized brain reveals more low-frequency activity.

Fig. 1. Raw EEG of an awake person (blue) and propofol-anesthetized person (red). source: Wang et al (2014).

Fig. 2.  Filtered EEGs (<1.5 Hz) of an awake person (blue) and propofol-anesthetized person (red) (left panel) and corresponding FFT spectra (right). source: Wang et al (2014).
This is reminiscent of the drowsiness encountered in slow-wave sleep; which is yet another example of why FFT is useful; various stages of sleep are markedly different in their EEG. For example, early stages of sleep are characterized by slow-wave EEG, while REM sleep is characterized by high-frequency EEG activity. By using FFT, these differences in frequency content can be captured in simple, quantifiable data.
Another widely applied FFT-based application is filtering in the frequency domain. Look at the sleep EEG in Fig. 3. By splitting the raw signal up in frequency bands, the noise can be removed (high-frequency components), but even better, the k-complex (a characteristic hallmark of normal sleep) can be beautifully isolated from a messy EEG signal (bottom trace, 12-15 Hz).

Fig. 3. EEG FFT-based filtering. source: Neurology
Reference
- Wang et al., Front Syst Neurosci (2014); 00215

A few followup questions if you don't mind: (1) my understanding of the processing flow was: Capture Raw EEG Data -> Perform FFT -> Analyze Component Frequencies (Alpha, Delta, etc.). But it sounds like you are saying that the typical "flow" is: Capture Raw EEG Data -> Filter by a Particular Frequency -> Perform FFT on that Frequency -> Analyze the Frequency. Can your confirm this or clarify it for me?

And then, (2) OK great so now we see that in the sedated patient, the Theta Frequency wave is - on average - twice as big as the awake/conscious patient. So it sounds to me like the whole point of using FFT on raw EEG data is to just give a researcher more information than what is contained in the original waveform. Can you confirm this for me as well? Thanks again for such an excellent answer!

comment 1: It can be either way - option 2 is basically FFT-based filtering followed by frequency analysis. useful when there is noise in your data and you wish to clean it up first. There is no typical flow, whatever milks your Jersey.

comment 2: I agree with that statement. Often, raw data needs signal processing (tag added) to be able to deduce useful information from it.

Any periodic waveform can be produced by adding up a series of sin waves of the appropriate frequency and amplitude.  The FFT looks at a complex waveform and calculates those frequencies and amplitudes.  The result is a new curve which plots amplitude vs frequency.  Thus, it transforms the signal from the time domain into the frequency domain.
I don't have any knowledge of EEG signals, but have worked with FFTs.  If you wanted to know what frequencies make the largest contribution to the original waveform, the FFT will provide that information.
Here is a paper I found which uses FFT analysis of EEG waveforms to look at the relative contributions of different frequencies.  It includes the following table which indicates that "Delta" waves are in the 0.5 to 4 Hz range, Theta waves are in the 4 - 8 Hz range, etc.  The FFT analysis quickly breaks the overall waveform down into its constituent frequencies to identify which of these ranges contribute the most or least.

Thanks for the insightful answer @Mark (+1) - however a few followup questions for you, if you don't mind: (1) once the FFT is performed on the original waveform, is each component "rhythm" (alpha, beta, gamma, etc.) now represented by it's own graph of amplitude over frequency? Meaning, the original waveform was a single graph of voltage/time: do we now have five graphs of amplitude/frequency, 1 for each rhythm? And (2) this still seems unnecessary to me! Why would I care if a beta rhythm contributed more or less to a subject thinking about birds? In other words: why do this?!?

Obviously, that second question above is quasi-rhetorical, as I suspect that there is a very good reason for decomposing brain waves with FFT, I'm still just not seeing it! Thanks again!

@smeeb - (1) The FFT creates a single graph of amplitude vs frequency.  To actually generate the original time series from the FFT result, you would have to create a sin wave of the right amplitude for each frequency in the answer, and then add them all together.  But the FFT result is just a single graph.

@smeeb -  (2) Sorry, I can't help there as far as the biology goes.  FFTs are used in a lot of applications where the particular frequencies are important, including mass spectrometry where the specific frequencies allow gas components to be identified; machine analysis where the individual frequencies can be used to identify defects in couplings or bearings; and audio processing where it can be used to remove noise, or analyze the harmonics of a violin.  Often, there is a great deal of information within the frequency spectrum

Thanks again @Mark, please see my updated question. I have provided an illustration of my current understanding, given your excellent answers here (thanks!). First off: please let me know if something about my illustration seems wrong to you! But if my understanding/illustration seems to be correct, then my last question for you is: can we then assume (given your data table) that the x-axis (frequency) will always be the same for any given FFT-transformed EEG data - with ticks at the 4Hz, 8Hz, 13Hz, 30Hz units...

...and that the only variable/difference when comparing different original waveforms would be the amplitude (y-axis) values?

The x-axis is definitely not always the same, but is dependent on the window (determines longest wavelength detectable) and the sampling rate (determines maximum frequency and frequency resolution)

EEG is simply measuring voltage fluctuations over the scalp caused by electric dipole(s) within the brain tissue. Communication between neurons can happen at different spatial and temporal scales. At each scale, this communication results in a distinct pattern of current flow. Because EEG measures voltage fluctuations over a relatively large patch of the brain, it reflects current flow created by communication at various scales between millions of neurons (each scale presumably associated with a different frequency of oscillations).
Current research is attempting to understand what neural processes in the brain tissue result in the EEG. There is still not a complete understanding of what each frequency band represents, but studying similar types of oscillations within brain tissue (called local field potentials) have provided invaluable insights (for instance, small range communications are associated with higher frequency bands). How the oscillations recorded inside the brain tissue are related to the EEG is an active area of research.