human biology - What causes fingerprints to form and why is the pattern formed unique?

I would say genetic diversity is the primary reason which results in other reasons that you are looking for. At the lowest level, random crossing over at prophase I, random separation of homologous chromosomes at anaphase I, random separation of sister chromatids at anaphase II, and random fertilization: one sperm fertilizes one egg randomly.

The skin is developed from ectoderm so need to look at the formation of embryonic disc and specifically to the genesis of germ layers: ectoderm.

However, I would stick to the primary reasons, since it is extremely difficult to visualize the given formation - actually we do not have resources for it at the moment.

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Very good question the last part. I have an intuition that skin develops randomly because of the above reasons. You would also need a lot of memory to make identical skins for twins! It has not been useful to have identical fingerprints between two people so evolution has not resulted into it.

Feeling surfaces and gripping are movements - not much space taken things, in contrast to the memory needed in storing the exact surfaces of skin from one generation to another. - Learning is a way to save resources here and it is a lot more efficient and than storing static information to species from one generation to another.

molecular biology - Intrinsically disordered proteins as potential drug targets

IDPs are indeed attractive drug targets and there are ongoing efforts to develop drug molecules that block interactions between a disordered and a structured protein. According to this relatively recent paper, however, these efforts have not brought a drug on the market, yet.

A few promising studies have shown drug-like molecules that inhibit protein-protein interactions based on intrinsic disorder of one of the partners and target:

• oncogenic fusion protein EWS-FLI1 and RNA helicase A. A small molecule has been found that targets the disordered region of EWS-FLI1, blocks the interaction with the helicase and inhibits growth of Ewing's sarcoma.




• p53 tumor supressor and its interactor Mdm2. Mdm2, by binding to an intrinsically disordered region of p53, targets p53 for ubiquitination and also causes it to be transported out of the nucleus. Promising small molecules have been found that associate with Mdm2 and thereby block its interaction with p53.




• c-Myc oncoprotein and the interaction with its partner Max protein. This study demonstrates two small molecules that bind to c-Myc and stabilize its disordered conformation, which inhibits its interaction with the Max protein.

The challenge in targeting protein-protein interactions for therapies stems largely from the fact, that the protein-protein contact surfaces are much larger than those involved in protein–small-molecule interactions (1,500–3,000 Ų and (300–1,000 Ų, respectively) [2]. They are often flat and have no defined binding pocket. Also, IDPs often don't bind natural small ligands, that could act as starting points in developing drugs.

You may find this paper helpful:

Metallo SJ, Intrinsically disordered proteins are potential drug targets, Curr Opin Chem Biol. 2010 14(4): 481–488.

BTW: for a comprehensive, manually curated list of disordered proteins and regions, please check the Database of Protein Disorder.

molecular biology - The effect of the start codon GTG on translation in E. coli

The NCBI translation table translates all alternative start sites as methionines. To my understanding, all translation is initiated by the fMet-tRNA. I don't know if there are any exceptions to this rule.

Regarding translation efficiency, I only found a 1985 paper in PNAS (Reddy et al, PNAS 82:5656-60), in which they compared the translation efficiency of adenilate cyclase's own UUG start vs GUG or AUG, obtaining a translation ratio 1:2:6 UUG:GUG:AUG, suggesting that AUG is the most efficient one, followed by GUG. Also, Romero and Garcia, FEMS microbiology letters 84:325-330 (1991) compared the efficiency of AUG vs AUC, AUA and AUU, showing a much lower efficiency for those codons, but they did not compare it to GUG.

Why is DNA replication performed in the 5' to 3' direction?

DNA replications needs a source of energy to proceed, this energy is gained by cleaving the 5'-triphosphate of the nucleotide that is added to the existing DNA chain. Any alternative polymerase mechanism needs to account for the source of the energy required for adding a nucleotide.

The simplest way one can imagine to perform reverse 3'-5' polymerization would be to use nucleotide-3'-triphosphate instead of the nucleotide-5'-triphosphate every existing polymerase uses. This would allow for a practically identical mechanism as existing polymerases, just with different nucleotides as substrates. The problem with this model is that ribonucleotide-3'-triphosphates are less stable under acidic conditions due to the neighbouring 2'-OH (though this obviously only applies for RNA, not for DNA).

So any 3'-5' polymerase would likely need to use the same nucleotide-5'-triphosphates as the 5'-3' polymerase. This would mean that the triphosphate providing the energy for addition of a new nucleotide would be on the DNA strand that is extended, and not on the newly added nucleotide.

One disadvantage of this approach is that nucleotide triphosphates spontaneously hydrolyze under aqeuous conditions. This is no significant problem for the 5'-3' polymerase, as the triphosphate is on the new nucleotide and the polymerase just has to find a new nucleotide. For the 3'-5' polymerase spontaneous hydrolysis is a problem because the triphosphate is on the growing chain. If that one gets hydrolyzed, the whole polymerization needs to be either aborted or the triphosphate need to be readded by some mechanism.

You can take a look at the article "A Model for the Evolution of Nucleotide Polymerase Directionality" by Joshua Ballanco and, Marc L. Mansfield for more information about this. They created a model on early polymerase evolution, though they don't reach any final conclusion.

marine biology - What would be the best dredging/trawling tools to collect macrofaunal priapulids?

My first question would be is there any external indication of them such as air holes or anything else that you can assess from the surface? I would think that if there is, you could use transects and a quadrat. If you need to dive for them you can obviously make a weighted quadrat by filling pvc pipe with sand and gluing it together. Then take an area and lay out transects and and randomly choose different lengths along each transect to put the quadrat down and that will give you an estimate of density. If you actually need to extract them from the mud, I imagine that varies by species and you could make due with a grab sampler of the right size, something on the small end I would imagine. That way you aren't dredging up tons and tons of mud to sort through. If they are even rarer than I imagine, you could just lay out transects and swim them until you see something and do away with the quadrat. Hope this helps.

genetics - How to create a collection of anonymous sequences for teaching and testing?

Here is the approach I ended up using, in part thanks to all the contributions here.

The associated R script is below or can be downloaded from:

BOLDS SEQUENCE RECOVERY

This creates 999 unique sequence files in plain text, with each sequence being identified to species level and few species being found across more than one sequence.

It also creates the matching answer key.

You can start at a random location to so that files change every year/group.

I used R to query the BOLDS database (Barcode of Life), to download a file and to split this huge file into separate sequences.

Here is the R script

rm(list=ls())

complete<-"http://services.boldsystems.org/eFetch.php?record_type=full&id_type=sampleid&ids=(*)&return_type=text"
write(complete, file="your location on disk")

rm(list=ls())

sequences.id<-data.frame("file.name", "recordID", "genus_name", "species_name")
write.table(x=sequences.id, file="sequences_id.csv", append=F, sep = ",", row.names=F, col.names=F)



set.seed(10)
start<-sample(1:1000, size=1)

i<-start
k<-1

while(k < 1000){

  sequences<-read.delim(file=complete, skip=i, nrows=1, header=F)
  sequence.compare<-read.csv(file="sequences_id.csv", skip=k-1, nrows=1, header=F)

  if(! is.na(sequences$V24)){
    if(as.character(sequences$V24)!=as.character(sequence.compare$V4)){
      writeLines(text=as.character(sequences$V55), con=paste(k, ".txt", sep=""))
      sequences.id<-c(k, sequences[,c("V3","V22", "V24")])
      write.table(x=sequences.id, file="sequences_id.csv", append=T, sep = ",", row.names=F, col.names=F)
      print("kept")
      k<-k+1
    }
  }
  i<-i+1
  print(paste(k,"/", i))
}

neuroscience - Is the squid giant axon the fastest conducting unmyelinated axon known?

The conductance velocity in the unmyelinated axon has been calculated and measured to be proportional to the square root of the axon diameter (see for example: Rushton, 1951). Since the giant axon is, well, giant, it conducts much faster than others. AFAIK, all other large neurons studied are myelinated. Maybe try to find bigger squids! ;)