I've read that unlike humans, octopuses have eyes "designed" the "right way", i.e. with the nerve fibers behind the retina, thus getting rid of the blind spot we humans have as well as theoretically improving eyesight.
Have there been tests to compare octopus sight with that of humans, and do they indeed have better vision than us?
Also, this question is going to be slightly gross, just a warning, but I promise it comes to a point.
I have read that the adenoids have a texture to them but, unlike say the palatine tonsils, they don't have crypts.
Last year my palatine tonsils became fairly badly infected and had to be removed over the summer. Before the surgery, I discovered I could reach back to the tonsils with my tongue, enabling me to remove the tonsil stones that built up. Gross, I know, but I believe it actually did make the infection noticeably better. After the surgery, and after the area had healed, I resumed discovering the back of my throat and, eventually, up the nasal cavity, all the way to the back of the nasal septum, which means I can reach the Eustachian tubes, adenoids, and posterior nares. If that sounds impossible, I assure you, it is perfectly achievable, and only mildly uncomfortable. With my tongue I can feel the texture of the adenoids but, more toward the posterior nares, this texture changes and resembles folds or crypts like I remember the texture of the palatine tonsils. Three times, always in the morning after waking up, I have discovered debris in this region strongly resembling tonsil stones but green instead of off white. The mucus, however, is always clear.
My question has several parts:
- What are these crypts? Are they even crypts at all? Are they part of the adenoids?
- Why can I not find mention to these structures in any diagrams or medical articles?
- Why can I not find mention to tonsil stone like debris except in the palatine tonsils? I am quite convinced that, if these are not tonsil stones of a sort, they are closely related.
The proportions of diagrams and cross sections of the nasal cavity all seem wildly different. Some of them are just blatantly wrong, depicting, for example, the Eustachian tubes coming from the roof of the nasal cavity instead of the sides. It has been very difficult to find good information on any of this. I am not even sure if I am referring to the region correctly. By nasal cavity, I mean everything between the back of the throat and the posterior nares, although I am aware the nasal cavity includes the region all the way up to the anterior nares as well.
This is the only picture I can find that shows the nasal septum.
This is a better diagram of the rest of the structures. The pharyngeal tonsils are the adenoids.
In a 1987 article "Nonenteric Infections Acquired through Contact with Water", the author mentioned that "Infection of the ears, throat, respiratory tract, and cornea are also encountered" by virulent strains of Acanthamoeba and Naegleria fowleri. The only reference that implicates Naegleria fowleri is this one, but it doesn't mention modes of infection.
The long and the short of it is that Acanthamoeba infections can be acquired through contact with the cornea, but it's not clear whether Naegleria infections can also be acquired in this way.
n.b. The first link takes you to the JSTOR page. In the event you do not have access, here is the PubMed page.
Well, there are a number of problems. First, the discovery of noncoding RNAs is relatively new and they are difficult to detect, so their number is unknown. In addition, it is unknown how many of them are functional. This has also made it very difficult to define what a gene is and thus made it difficult to count how many genes there are.
Protein coding genes, however, are much easier to deal with, and if you like you can enter any public protein database (at NCBI, for example) and easily query the number of distinct proteins.
Finally, the last problem is that with an organism like human, experimental methods are generally limited by sensitivity (might not detect proteins/RNA at very low concentrations) and the fact that you can't measure all possible cell types/conditions (many gene products will be cell type/condition specific).
Who said life is easy? Somehow things are always complicated in biology...
There are so many techniques/methodologies in the life sciences that we can use to interrogate interesting questions. The thing is, most of us are completely unaware of the available methods we can employ. Rather, we go with the techniques we are familiar with or that are popular in our subdomains at the time. But that's pretty limiting.
So I'm wondering... we have databases for everything else... is there one for life sciences techniques/methods? Something like this could be immensely helpful in experimental planning. In particular, I think a comprehensive database would help scientists break outside of their spheres of familiarity and to employ less known (but potentially illuminating) methods to their questions.
I know there are journals that publish protocols and methods, but they are fragmented and don't encompass everything.
Does what I'm looking for exist? If not, how might one go about creating such a tool?
There seems to be a lot of redundancy in PDB files. These files can of course be compressed with general-purpose compression programs like gzip, but I can't help but imagine that these tools are overlooking a significant amount of redundancy in PDB files. Are there compressors that specifically target PDB files? If not, what are some aspects of PDB files that are ripe for compression?
Looking at a typical PDB file, some redundancies are immediately apparent. Other redundancies are less obvious. Consider this excerpt of two residues from 1MOB (myoglobin):
ATOM 332 N LYS A 42 16.481 27.122 -10.033 1.00 11.15 N
ATOM 333 CA LYS A 42 15.926 28.134 -9.159 1.00 8.64 C
ATOM 334 C LYS A 42 16.970 29.081 -8.512 1.00 16.74 C
ATOM 335 O LYS A 42 16.687 30.075 -7.799 1.00 11.84 O
ATOM 336 CB LYS A 42 15.093 27.489 -8.043 1.00 18.03 C
ATOM 337 CG LYS A 42 13.731 26.888 -8.502 1.00 19.65 C
ATOM 338 CD LYS A 42 12.679 27.912 -8.953 1.00 17.94 C
ATOM 339 CE LYS A 42 11.438 27.406 -9.703 1.00 24.82 C
ATOM 340 NZ LYS A 42 10.474 28.567 -9.803 1.00 19.81 N
ATOM 341 N PHE A 43 18.218 28.599 -8.544 1.00 12.28 N
ATOM 342 CA PHE A 43 19.311 29.318 -7.919 1.00 11.81 C
ATOM 343 C PHE A 43 20.223 30.024 -8.949 1.00 10.95 C
ATOM 344 O PHE A 43 21.201 29.462 -9.450 1.00 10.08 O
ATOM 345 CB PHE A 43 20.138 28.301 -7.137 1.00 9.30 C
ATOM 346 CG PHE A 43 19.494 27.689 -5.877 1.00 9.53 C
ATOM 347 CD1 PHE A 43 19.572 28.376 -4.679 1.00 12.01 C
ATOM 348 CD2 PHE A 43 18.837 26.465 -5.923 1.00 10.54 C
ATOM 349 CE1 PHE A 43 18.993 27.861 -3.536 1.00 9.59 C
ATOM 350 CE2 PHE A 43 18.261 25.959 -4.775 1.00 8.62 C
ATOM 351 CZ PHE A 43 18.341 26.666 -3.597 1.00 7.89 C
These two residues occupy 1,638 bytes as plain text; when compressed with gzip, they occupy 467 bytes. For reference, the format of ATOM records in PDB files is defined at wwpdb.org/documentation/format33/sect9.html#ATOM.
Almost all of the data in the above excerpt seems redundant. The first field (ATOM), second field (atom index, e.g. 332 in the first row), sixth field (residue index, e.g. 42), tenth field (occupancy, e.g. 1.00) and last field (element name, e.g. N) seem clearly extraneous. The fourth field (residue name) could be shortened from three characters to 1 character, or simply an integer. I'm not a data compression expert, but I imagine gzip picks up most of this redundancy.
Slightly less obviously, the atom names for each residue also seem unnecessary. To my understanding, the atomic composition of all residues' backbones will always be the same, and represented in PDB files as "N", "CA", "C", "O". The same for the atomic composition of the residues' respective sidechains: a lysine sidechain will always be "CB", "CG", "CD", "CE", "NZ" and a phenylalanine sidechain will always be "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ".
A subtler redundancy, but one that might increase compressibility a lot, seems like it could be in the atomic coordinates themselves. For example, in the backbone, would it be possible to deduce each residue atom's X, Y and Z coordinates (12 data points: 4 atoms * 3 coordinates) given only their phi, psi and omega dihedral angles (3 data points)? Could applying dihedral angles to atoms within sidechains similarly remove the need to explicitly list the 3D coordinates there?
Could "temperature factor" (the second to last field in the excerpt) be losslessly removed, or compressed in some non-obvious way? What are some other possible optimizations that could be used to more efficiently compress PDB files? Are there any obvious grave performance implications of these various compression techniques on the speed of a hypothetical decompressor to convert back to the official PDB format? Have these questions been answered in the literature or an existing PDB-specific compression program?
Thanks in advance for any answers or feedback.
Edit:
Given that no PDB-specific file compressors seem to be available, I suppose my specific goal is to develop one. One potential application I see for this is in significantly decreasing fresh times-to-render in certain use cases of browser-based molecular visualization programs, e.g. Jmol, ChemDoodle Web Components or GLmol. Another application could be decreasing the time and size of data needed to download archives of PDB files like those described here.
This would of course require a way to efficiently decompress the packed PDB files, but this trade-off between decompression time and download time seems like it could be useful in at least some niche applications.
Edit 2:
In a comment, nico asks "How would compressing the file decrease render time?". Decreasing gzipped PDB file size (e.g. by half or more) and thus decreasing time needed to download the file would decrease the time between when the PDB file was requested from a remote server and when the structure was rendered by a molecular visualization program running on a client machine. Apologies if that use of "fresh time-to-render" in that context was unclear.
A lossless compression could also involve encoding the PDB file to an object (e.g. JSON) that is faster to parse for the visualization program, and decrease render times that way. Looking around further, if the application only required displaying the 3D structure and not also retaining data about specific atoms and residues, then using a binary mesh compression (e.g. webgl-loader) seems like it would probably decrease time-to-render even more.
I want to ask what is the reason that T315I type CML leukemia is currently untreatable. I have read quite a few papers in this subject. Why the current genetic oriented engineering drugs failed to stop the leukemia stem cell's expansion but can only slow the secondary leukemia stem cell's expansion? I found it to be utterly hopeless for a patient to be in such a position, as chemical therapy does not work well and radiation therapy can only target specific area. There are experimental drugs in the developmental stage but from what I know they are mostly unreliable.