text:
Can DNA methylation induce breast cancer?
What is the role of DNA methylation in breast cancer? DNA methylation is a process by which methyl groups are added to the DNA molecule.
In the September 2015 paper The Role of Methylation in Breast Cancer Susceptibility and Treatment published in Anticancer Research, Pouliot, Labrie, Diorio and Durocher write:

DNA methylation is a critical mechanism of epigenetic modification involved in gene expression programming, that can promote the development of several cancers, including breast cancer. The methylation of CpG islands by DNA methyltransferases is reversible and has been shown to modify the transcriptional activity of key proliferation genes or transcription factors involved in suppression or promotion of cell growth. Indeed, aberrant methylation found in gene promoters is a hallmark of cancer that could be used as non-intrusive biomarker in body fluids such as blood and plasma for early detection of breast cancer.

In addition, Pouliot, Labrie, Diorio and Durocher state that:

DNA methylation is a reversible mechanism that occurs most commonly with an addition of a methyl group in the fifth position of the pyrimidine ring of cytosine on CpG sites within the genome. Human DNA methylation is introduced into the sequence of nucleotides by enzymes of the DNA cytosine methyltransferases family including DNMT1, DNMT3A, DNMT3B and DNMT3L (6).
The DNMT1 protein methylates newly-synthesized strands prior to chromatin packaging and is localized to the replication foci during the S-phase. This highly expressed enzyme is mainly responsible for maintenance of the methylation pattern during replication (7). An increased expression of DNMT1 is observed in many types of cancers and this overexpression is associated with cellular transformation, while reduced DNMT1 expression levels seem to be associated with a protective effect (8, 9).
The DNMT3A and -B proteins allow de novo DNA methylation activity in vitro, without distinction between unmethylated and hemi-methylated DNA (10). In particular, the expression of DNMT3B is elevated in several human cancer types, while its suppression results in tumor cell apoptosis (11, 12). Indeed, inactivation of the TSG Ras association domain family 1 isoform A (RASSF1A), through promoter hypermethylation triggered by up-regulation of DNMT3B, is a common event in numerous cancers or tumor types (13). Several studies suggest that DNMT3B plays a predominant role over DNMT3A and DNMT1 in breast tumorigenesis, given its overexpression in breast cancer tissues compared to DNMT1 and -3A (14). Although DNMT3L has no catalytic activity on its own, this enzyme promotes the activity of DNMT3A and -B by increasing their binding capacity for the methyl group donor, S-adenosyl-L-methionine (15).
In mammalian tissues, DNA methylation occurs in CpG dinucleotides, and clustering of these elements in the 5’ regulatory regions of approximately 60% of genes are referred to as CpG islands (16). Unusual methylation of these CpG islands may lead to silencing of certain genes involved in key proliferation and apoptosis pathways such as TSGs, DNA repair and hormone receptor genes, as well as genes that inhibit angiogenesis (17). Two different DNA methylation mechanisms lead to transcriptional gene repression: CpG dinucleotide methylation inhibits the binding of transcriptional factors to their promoter regulatory elements; and methylated cytosines enhance the recruitment of methylated binding domain proteins, which inhibit binding of transcription factors through inactive chromatin configuration state around the genes (18). On the other hand, ten-eleven translocation (TET) proteins such as TET2 can remove DNA methyl marks and trigger re-expression of silenced genes. Of interest, TET2 has been shown to be mutated and therefore inactivated in many types of cancer (19).
In addition, growing evidence suggests that methylation of regions located upstream of CpG islands, called CpG island shores, as well as intragenic sequences, are also important for regulation of gene expression and can be involved in disease development and progression (20, 21). Generally in normal cells, gene promoters are unmethylated, while gene bodies and intergenic regions are methylated.

How do you analyze DNA methylation data generated from the EPIC array from Illumina as described here,   https://www.illumina.com/products/by-type/microarray-kits/infinium-methylation-epic.html?

Analysis of DNA methylation can be assessed through different methods including bisulfite conversion. This can be performed in combination with real-time methylation-specific PCR and specific primers to amplify CpG islands of a panel of selected genes (2). A genome-wide approach with high-throughput sequencing can also be performed using bisulfite-converted DNA from different non-invasive biological sources such as whole blood, serum and plasma.

The rapidGSEA method described in https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1244-x takes five default arguments and three optional arguments rapidGSEA <- function(exprsData, labelList, geneSets, numPermutations, metricString, dumpFileName="", checkInput=TRUE, doublePrecision=FALSE) {...}
metricString denotes the local ranking measure (one of the following) and
numPermutations denotes the number of permutations in the resampling test.
If metricString denotes the local ranking measure we are requesting that rapidGSEA use when it is invoked, which metricString value listed below should I choose so that rapidGSEA properly analyzes DNA methylation data generated from the EPIC array from Illumina ?
naive_diff_of_classes
naive_ratio_of_classes
naive_log2_ratio_of_classes
stable_diff_of_classes
stable_ratio_of_classes
stable_log2_ratio_of_classes
onepass_signal2noise
onepass_t_test
twopass_signal2noise
twopass_t_test
stable_signal2noise
stable_t_test
overkill_signal2noise
overkill_t_test

I finally found the answer to the question, " If metricString denotes the local ranking measure we are requesting
that rapidGSEA use when it is invoked, which metricString value should I choose so that rapidGSEA operates
in a manner most closely resembling fgseaL?"
Please read this great article in May 12 2017 BioMed Central (BMC) Bioinformatics article titled
 with the
URL, https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1674-0
Also, please read this blog , "Diving into Genetics and Genomics: Gene Set Enrichment Analysis (GSEA) explained."
with the URL http://crazyhottommy.blogspot.com/2016/08/gene-set-enrichment-analysis-gsea.html
After reading these two articles, my choice for the best rapidGSEA local ranking measure is
Minimum Significant Difference(i.e. MSD) because it has the best overall false positive rate(i.e FPR) for larger sample sizes.
Finally, it is important to realize that fgseaL's phenotype labeling principle cannot be emulated by GSEA  or rapidGSEA with any of it's sixteen possible ranking metrics.
Thank you.

Can you please use > at the start of your quotations and link to the primary sources (not every cited papers in the quotation, but at list the source you quote)?

@Remi.b The source I quote from is the September 2015 paper "The Role of Methylation in Breast Cancer Susceptibility and Treatment" published in Anticancer Research by Pouliot, Labrie, Diorio and Durocher.

@canadianer, Thank you for the very nice edit just now.

I'm voting to close this question as off-topic because this is not a question it is a review inviting a discussion. If you cannot answer your own question after so much reading you can hardly expect a simple definitive answer here.

@David,I just found this article https://github.com/gravitino/cudaGSEA/blob/master/cudaGSEA/man/GSEA.Rd which answers my question,Thank you

Okay I'll try to put together a quick answer.
First: 'How do you analyze DNA methylation data generated from the EPIC array from Illumina'
The most common method to analyse DNA methylation is based on bisulfite conversion of DNA. This chemical converts the base cytosine (C) to uracil (U), which 'reads' the same as thymine (T), but it only works on non-methylated cytosine. When you want to know which bases in your DNA are methylated, you split your sample in two parts, do the bisulfite conversion on one and then sequence both. The C/T differences in the sequence then tell you, which C's in your sequence were methylated.
The Illumina arrays are special sequencing methods, that do not look for all sequences in a given sample, but only check specific ones (usually some genes of interest). For methylation analysis these chips allow the sequencing of the two versions (normal & bisulfite converted) of each region of interest separately.
Now: 'Can DNA methylation induce breast cancer?'
In principle yes, but it is very unlikely/almost impossible to be the only factor. A cancer forms only, if a whole range of cellular functions (esp. growth) are abnormally regulated and if the various control mechanisms intended to prevent this fail. Therefore most cancers have hundreds to thousands of mutations and some of these might also be abnormal DNA methylation patterns or regulation. Further details of how DNA methylation can affect which genes are active or repressed is also discussed in these questions.

Could you please select the rapidGSEA * gene set enrichment analysis * metric string value shown at the bottom of the original question which most properly measures the statistical significance of rejecting the null hypothesis where we are comparing a normal healthy control group of subjects with a group of subjects diagnosed with a distinct category of breast cancer? Thank you.


text:
What is effectiveness of methyl-alcohol as sporicide?
Methyl-alcohol is the weakest alcohol in terms of bactericidal properties. However, I have read that it also works as sporicide.
What is the effectiveness of methyl-alcohol in killing the spores of fungi and bacteria compared to other substances and what is the best dilution?

Are you specifically looking out for fungal spores, or do bacterial spores work too?

I would say that a little bit of information about also the bacterial spore is not useless... Question edited! @MarchHo

From page 233 of this book: Bacillus anthracis spores resist methanol completely at concentrations of 0.004% to 95% but are killed by ethanol in 48h at concentrations of 42% to 100%. Methanol can be oxidized to formaldehyde (which is a disinfectant) but this reaction cannot be performed without certain catalysts.

Methanol (methyl alcohol) tends to lack sporicidal activity on its own. However, fresh mixtures of methanol and hypochlorite, with at least 15% methanol, are known to have low sporicidal activity.
With an increasing methanol concentration, right up to about 50% methanol, the sporicidal activity can be increased. The preparation, however, isn't overly stable and the sporicidal activity will start to decrease after 8 hours. It is thus recommended, should a methanol + hypchlorite mix be used to counteract spores, to only use a freshly prepared solution of 50% methanol and a sufficient amount of (sodium) hypochlorite that provides 2000 parts per million available chlorine in distilled water and allow for a contact time of at least 15 minutes.
Sources:

Russell 1990 - Bacterial Spores and Chemical Sporicidal Agents
Kelsey JC, Mackinnon IH, Maurer IM Sporicidal activity of hospital
    disinfectants Journal of Clinical Pathology 1974;27:632-638.

source: Stack Exchange

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sort: votes


text:
Cancer cure statistics questions
I have a question about cancer cure statistics. Many of the cancer literature or databases I have come across speak about 5 year or 10 year survival rates. In this case survival means that the patient with cancer is still alive 5 or 10 years after diagnosis. I am in need of references about the statistics which explain what is the percentage of people in these groups who are in progression-free survival group and disease-free survival group. That is for instance among the cancer patients which survive for some X years, who are completely cured of cancer (which means that the cancer is completely cured and has not reoccured with in this X years).
Update: USA data is preferred
Thanks

So you want to know actual numbers of survivors?

No I want to now the actual number of people among the survivors whose cancer has been completely cured. Survivors also include people who survive for some time with the cancer still in their body.

@Sina Any particular cancer or geographical location you are particularly interested in?

Thanks for the question. I would prefer USA if available. Age stratification would also be very useful.

Does this help? http://www.cancercenter.com/ctca-results/

Check the NCI database

This question seems well fitted for Skeptics.

I would caution you against using phrases such as "completely cured" or even "cured". This goes beyond our technical ability to detect the presence of tumor cells within someone's body. All we can conclude is progression-free survival (i.e. living with cancer that does not get worse) or disease-free survival (i.e.  or no signs or symptoms of the tumor). Many of these data are included in cancer trials along with overall survival rates. Some general statistics for many groups of cancers can be found here: http://www.cancer.gov/statistics/find
And a glossary of terms used in reporting cancer statistics here: http://www.cancer.gov/statistics/glossary

In cancer research, we never talk about "curing" cancer because, as others have pointed out, there is no way of being sure that all of the cancer has been eradicated. You may be interested to consider "Recurrence" statistics which describe the amount of time from when a cancer was treated to when it was detected to have returned.
If you are willing to do some statistics computations of your own, The Cancer Genome Atlas (http://cancergenome.nih.gov/) will let you download clinical datasets that include datasets like recurrence, "Overall Survival" (how long did the person live after being diagnosed with cancer), and others.
You can plug in either recurrence values or overall survival values and perform a Survival Analysis (http://en.wikipedia.org/wiki/Survival_analysis, http://lifelines.readthedocs.org/en/latest/Survival%20Analysis%20intro.html) to see how long people tend to live cancer-free or live in general after their diagnosis depending on their cancer type (or other variable you feel like comparing).

I didn't know this was publicly available now. Thanks this is really interesting.

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text:
Proteins in water vs proteins in crystal
I am not very familiar with the experimental procedure of x-ray crystallography except that it involves the very delicate matter of producing crystal that contain proteins and then diffracting rays through it to get a pattern that tells us about the shape of the protein.
I am curious though when you crystallize a protein that usually stays in the cellular fluid, does it go through any conformational or size changes. For instance isn't there some pressure applied by the water that would potentially result in protein being smaller then what it might be under less pressure. Or what about hydrophobic\philic effects that play an important role in protein folding. Of course once the protein is folded it is bonded through interactions stronger than hydrophobicity so it is not that delicate. But still a complete change of surrounding environment should count as a big change, should it not? So are there any theoretical or experimental explanations as to whether the protein changes size and\or shape during crystallization? References to both experimental and theoretical work are very welcome. Although I guess experimental evidence would make more sense in this matter since usually potentials are optimized to account for the crystal structure to be the minimum of the energy so it I can't see how theoretical works could potentially help to understand this issue. I guess one way would be to take a native protein structure determined by X-ray and run it through AB initio  molecular mechanics where potentials do not depend on parameters obtained from the native states of proteins on PDB database. I don`t know how theoretically sound that would be though.

a similar question was asked recently on chemistry.SE: http://chemistry.stackexchange.com/questions/48788/

so its expected that the volume of the protein stays the same, that it does not shrink or expand after crystallization but only side chains might be fixed in one of many confirmation?

The assumption is that it stays (at least somewhat) the same, otherwise the structure would not be biologically relevant. This assumption is not always true, there can be changes as a result of crystal formation/buffer/pH/etc. This is why crystal structures are often verified by targeted mutagenesis or another method like NMR.

This is a very good question that's not talked about enough. I often find it hard to believe that structures from incredibly extreme solutions could possibly reflect the native protein state. In some cases, at the very least we one could argue that a structure would have very different surface protein dynamics and particularly flexible regions would be forced into non-native conformations.

I would totally agree with this comment. I am also interested in the packing density and whether if crystallization has any effect on packing density. I guess at this point one could do an AB initio molecular mechanics to see if there is any length difference for the bonds. I would assume it must have been done already thousands of times and we would have known if there were significant differences?

Protein crystals are not like crystals of more commonly found substances like salt [NaCl] or diamond [carbon only.]  These materials do not include other atoms in their crystal structures.  For instance, a crystal of NaCl will contain sodium ions and chloride ions.  X-ray crystallography of that material will, after mathematical processing, show electron density peaks of two varieties, each easily distinguished by intensity as either a sodium ion or chloride ion.  Any other electron density peaks will be few and far between, as well as clearly identifiable as an interloper.
Because most proteins have hydrophilic regions on the exterior surface of the structure, crystals of proteins actually contain a considerable fraction of water molecules within the crystal itself.  These water molecules are part of the crystal because they are interacting with those hydrophilic residues on the tertiary surface of the protein, both by hydrogen bonding, in some cases, and less specific polar interactions in other cases.   This is at least one of the reasons why obtaining a crystal of any random protein is not at all a routine endeavor.  The large proportion of water in these crystals make them very fragile once they do form, as well as not necessarily likely to form in the first place.
If you go into the technical literature to look at the electron density maps that the more commonly diagrammed structure maps are derived from, you'll be able to actually trace water molecules surrounding the individual protein molecules.
In fact, in the days of development of protein crystallography, assigning correct specific atoms to the various electron density peaks was a decidedly non-trivial task.
Effectively, despite being a crystal, the microenvironment experienced by the protein within the crystal is very much like that in an aqueous environment.  The hydrophobic interactions & hydrophilic interactions will not be very different from those in solution.
That is, the crystalline state achieved is not, in fact, "a complete change of surrounding environment" to use the phrase that you've used in your post.

I think your answer generally explains well why protein crystallisation can be used for structure determination in the first place. However, in order to address the main point of the question it might good to include some caveats of the method (e.g. problems with highly unstructurerd regions & the inability to capture confirmations that are less stable than others or only occur under specific conditions).


text:
How is each region of the DNA replicated only once?
In "Molecular Biology of THE CELL" 3rd Edition, 1994, by Alberts, Et al. (Yes I know there is a newer edition)
the question is posed on page 362

How is each region of the DNA replicated only once?

Two suggestions are offered:

Inhibitor-addition model
Initiation-removal model

Since the 3rd edition of the book is now 24 years old, has the answer been discovered and if so what is it?
I am reading the 3rd edition now because that is what I have and plan to buy the newer 6th edition if a 7th edition will not be out in the next few months. This is for self-study so no need for me to rush or have the latest and greatest info for the first pass of learning as I am only reading.

Replication licensing: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2688777/#!po=24.2574

Of interest: Cell Cycle

DNA Replication ensures it only occurs once by the use of licensing factors. These factors are released and bind to the origins of replication during one distinct phase. After the factors have been released and the first phase has ended, the actual replication initiates.
Information on the licensing factors:
The licensing factors used to create the pre-replication complex is the Minichromosome Maintenance (Mcm2-7) protein, Cdc6, and Cdt1. The Cdc6 and Cdt1 are required to load the Mcm’s onto the DNA and therefore, are highly controlled during the cell cycle. The Mcm2-7 hexamer is the actual DNA helicase that is used during DNA replication.
The way that the re-replication of DNA is prevented is that when the DNA replication starts, the Mcm helicase moves away from the ORC and the newly replicated DNA, meaning it then cannot be reinitiated. Along with the physical moving of the helicase, Cdt1’s activity is suppressed by the protein Geminin, which prevents the licensing of the helicase during times of non-replication.
Image from the article on the licensing.

More information on this topic:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2688777/#!po=21.8085

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text:
How are the two mating types, a and α, for S. cerevisiae pronounced?
In reading about S. cerevisiae there are two mating types, one being the Latin letter a and the other being the Greek letter α.
How are these two types pronounced such that they can be differentiated?
In other words when I pronounce the text I don't want to say 'a' for both of them; there should be a different word or phrase for each one so that they are known to be different.
Is it simply that a is pronounced as a and α is pronounced as alpha?

Correct.
Haploid mating types in Saccharomyces cerevisiae are MATa (pronouce a as in day) and MATα (i.e. MAT-alpha).
For convenience you could also refer to the corresponding alleles as MATa or MATalpha (see also here)

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text:
Studying the fruitfly nervous system
I'm studying Neuroscience and Fruit Flies are a pretty big deal but I don't know as much as I think I should about them.  Are there and comprehensive sources that I could read?
I'd like a review of all the important parts of the nervous system, where they are, what they do, and how they connect.

Drosophila by Ashburner (and others) is a standard drosophila book (found in many drosophila groups). Also check out resources like flybase, flyatlas, Bloomington drosophila stock center - they will often have some info. Not sure about neuroscience specific though

Please narrow this down a little, you could fill libraries with the sources of info on this little fly. What exactly are you after? Text books? Reviews? Original research? On what? Anatomy? Nervous system? Immune system? Sex determination?

I'd like a rundown of all the important ports of the nervous system, where they are, what they do, and how they connect.

Flybase is a generally useful source for Drosophila anatomy/physiology including the nervous system. From there I found this link which has some neuroscience-specific info.


text:
Construct Phylogeny form Edgelist
I am working on large scale biodiversity simulations and have collected a lot of data on simulated phylogenies. I have a list of child-parent edgelist species in the form:
list = [1, 1, 2, 2]

      /-- 3
1 -- 2
      \-- 4

children = [1, 2, 3, 4]
parents = [1, 1, 2, 2]

Where the index is the child and the value is the parent. 1 is it's own parent (the root).
I have such a tree for a large dataset upwards of 1000 individuals. I can build the tree programmatically in igraph or networkx easily and visualise it. However, I cannot find a way to convert the tree into a format for phylogenetic analysis. There are function to convert dendropy and ete trees from igraph/networkx into their own formats but no way to re-import the igraph/networkx trees. There also appear to be no way to write the igraph/networkx trees into phylo formats (newick).
There also seems to be no support in dendropy or ete to programmatically build trees and it would be infeasible to manually build all the trees I have.
Is there a way to build the trees so I can analyse and visualise them as phylogenies?
Tried approach
library(ape)
p <- c(NA, 1, 1, 3, 3, 4, 4)
x<-data.frame(name=as.character(c(1:7)) , id=1:7,  parent = c(NA, 1,   1, 3, 3, 4, 4))
x1<-na.omit(x)[,3:2]
n <- c(c(1, 3, 4), c(2, 5, 6, 7))
edges <- matrix(c( x1$parent, x1$id), ncol=2)

tree<-list(edge=edges, Nnode = 3, tip.label = as.character(x$name[c(2, 5, 6, 7)]), node.label = as.character(x$name[c(1, 3, 4)]))

class(tree)<-"phylo"

#plot.phylo(tree, show.node.label=TRUE)

The plot command just causes R to crash and the tree object has an invalid structure.

Perhaps https://stat.ethz.ch/pipermail/r-sig-phylo/2009-July/000405.html, let me know if this works. I'm not very skilled in R myself, unfortunately! (just for converting edge list matrix to phylo)

Thanks. I tried to get this code to work but I keep on getting errors with my edgelist. I also found this question on stack overflow convert df to dendrogram.  However I don't think their code would work for an edge-list as it seems to require all possible paths to mapped into a dataframe. I will add an example of what I tried to the question.

It seems like standard edge list format is two columns (e.g., for igraph: http://www.shizukalab.com/toolkits/sna/sna_data). Can you output your list in that format?

sure ill edit the first example. In the second example the child list is [1:7]. I believe the graph should be possible using graph.adjlist(mylist, mode = 'out')

It looks like the phylo constructor changed since the link posted above: https://stat.ethz.ch/pipermail/r-sig-phylo/2014-May/003467.html

I can't find anything that changed in how they construct the example tree in the documentation.

This will be a complete answer, but it's got code, so it won't work as a comment. Working backwards from rtree(3), here's a way to build a phylo object from component parts, which might be helpful:
children <- c(5, 1, 2, 3)
parents <- c(4, 5, 5, 4)
x <- matrix(c(parents, children), ncol = 2)
tr <- list(edge = x, tip.label = 1:3, Nnode = 2)
class(tr) <- "phylo"
plot(tr)

Doing the same with your children and parents, doesn't work, however.
children <- c(1, 2, 3, 4)
parents <- c(1, 1, 2, 2)
x <- matrix(c(parents, children), ncol = 2)
tr <- list(edge = x, tip.label = 1:3, Nnode = 2)
class(tr) <- "phylo"
tr

Gives
Error in if (tabulate(phy$edge[, 1])[length(phy$tip.label) + 1] > 2) FALSE else TRUE : missing value where TRUE/FALSE needed
I don't know hot to make the edge list conform to the expected order for a phylo.

yeah I get the same problem. Maybe R isn't the answer. I really only want to extract the newick tree so I can analyse the tree probably in python.

@kmm's code works with a few tweaks. One problem your edgelist has is that it contains a singleton node, which ape does not like to deal with. Turning it into a proper phylo object means removing the singleton 'root' node and instead making the child of this singleton node the root. Here is an example using a slightly larger tree, which demonstrates how to make a tree with both leaf nodes and internal, non-root nodes.
The trickiest part is translating the parents and children list into ape's unusual internal numbering system.
phylo_from_el <- function(el){
  i <- 0
  for(n in el[2:length(el)]){
    i <- i+1
    parents[i] <- n
    children[i] <- i+1
  }
  nnode <- sum(children %in% parents) + 1
  nleaf <- length(el)-nnode
  # Translate preorder sequence into ape sequence
  ttable <- matrix(c(1:length(el), rep(0, length(el))), ncol=2)
  ttable[unique(parents),2] <- 1:nnode + nleaf
  ttable[-unique(parents),2] <- 1:nleaf

  parents_t <- sapply(parents, FUN = function(x)ttable[x,2])
  children_t <- sapply(children, FUN = function(x)ttable[x,2])

  edge <- matrix(c(parents_t, children_t), ncol = 2)
  tr <- list(edge = edge, tip.label = 1:nleaf, Nnode = nnode)
  class(tr) <- "phylo"
  return(tr)
}
el <- c(1,1,1,3,3)

tr <- phylo_from_el(el)

plot(tr)

This code did not work for me so I wrote an R Script to generate a Newick code.
I have a edge list tree_data
 head(tree_data)
  parent node branch.length label
1    117  112      5.093319   124
2    117  116      9.090761   128
3    117  136      9.219770   153
4    117  109      9.292008   121
5    117  108      9.370598   120
6    117  118      9.552086   130

Here is the R code:
tree_data$newick = paste(tree_data$label,':',signif(tree_data$branch.length,3),sep="")
nparents = 2
while(nparents > 1){
leaf = setdiff(tree_data$node,tree_data$parent)
parent_leaf = unique(tree_data$parent[!is.na(match(tree_data$node,leaf))])
index_remove = integer(0)
for(i in 1:length(parent_leaf)){
  parent_children = tree_data$node[which(tree_data$parent == parent_leaf[i])]
  all_children_in_leaf = all(parent_children %in% leaf)
  if(all_children_in_leaf==T){
    index_remove = c(index_remove,match(parent_children,tree_data$node))
    tree_data$newick[which(tree_data$node==parent_leaf[i])] = paste("(",paste(tree_data$newick[match(parent_children,tree_data$node)],collapse=" , "),")",tree_data$newick[which(tree_data$node==parent_leaf[i])] ,sep = " ")
  }
}
tree_data = tree_data[-index_remove,]
parent_next = unique(tree_data$parent[!is.na(match(tree_data$node,leaf))])
nparents = length(parent_next)
if (nparents==1) newick_code = paste("(",paste(tree_data$newick,collapse=","),")",colnames(Adj)[parent_next],";",sep = " ")
}

tree <- ape::read.tree(text=newick_code)
p = ggtree(tree, layout="circular")

There is an issue in this code, see here for more info: https://bioinformatics.stackexchange.com/questions/20677/plot-phylogenetic-tree-from-list-of-edges


text:
Is it dangerous to eat a mutated eggplant descended from a radioactive ancestor?
What is the risk, if any, associated with eating a 100th-generation mutated eggplant derived from a radioactive ancestor?
In another word, is that possible that organic toxin will be produced by the mutated plant and is it a good idea to plant it in America? These plant seems very beautiful to me! Ha Ha
Note: The descendant eggplant is not radioactive.
Motivation: There are many mutated eggplants around Japanese power plants.

If it is not radioactive, why should it be dangerous? Mutations are occuring always and we also introduce changes in plants by selective breeding. Assuming that no toxins are produced, I would say there is no risk about these plants.

@Articuno That's why I wrote "assuming no toxins are produced"...

@Articuno - But people complaining about genetically engineered food already, i will be surprised if that anything is not much severe than the normal genetic modified plant...

@Chris - REad the above comment

@user5479 You mean about genetically engineered food? Actually all our main food plants are modified since we settled some 8000 years ago. Until very recently this was done by selecting and breeding but essentially this modified the plants, too. So I don't see a reason to panic about genetic modifications when they are tested and safe.

@Chris - I mean terrorist could modify our food possibly

@user5479 Of course this would be possible, but how likely is that? That would first need the expertise to make the changes, than they need to propagate the plants to cause widespread poisoning. And when it is detected, the counter measure is rather easy: Discard all plants which might be contaminated. Packing a car with explosives is more easy and brings more fear, I would say.

While it's always possible that a mutated eggplant will produce a new toxin, this is very unlikely. I don't know how much higher the mutation rate is near Fukushima than in general but the difference can't be huge. Even if it's, say, 5 or 10 times higher than normal (I'm making up these numbers), the risk is still tiny. Actually, plant breeders sometimes use radiation to produce more variety to work with and we don't worry about that.


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Can a brain be transplanted?
Is it possible to transplant a brain and would it keep the memory? If not, what would be the prerequisite to transplant one?

"Is it possible" has a carefully conditional "yes".  Do we have the expertise or technology to do it now? No.  One of the biggest difficulties is that nerve tissues (the connections between brain and rest of body) will take time to heal... time during which the brain cannot sufficiently give commands to the rest of the body to keep autonomic functions running.
Head transplants, however, have been "successfully" completed using monkeys.  The monkey lived for sometime after the transplant, but eventually died.  China has also been known to do a similar procedure with dogs.
Caution, this may be too explicit for some; I'm not sure ethics boards would allow it nowadays:
Monkey Head Transplant

was the spinal cord reconnected in the head transplant?  I think it was not?  regenerating the nerve connections in a useful way remains a major goal in medical science.

Sergio Canavero and collaborators are controversially claiming they may be ready to perform a head transplant on a human by late 2017. @shigeta Canavero have been having some success in fusing nerve cells using polyethylene glycol.


text:
How much does a biological experiment to validate a gene function cost?
I am computer scientist working on machine learning methods applied to predicting missing Gene Ontology annotations.
In many papers, I wrote that my computational methods are very useful to suggest to the biologists  on which gene functions they should address their research, because my software need less resources (a simple laptop connected to a database) and less time (e.g. with a dataset of all the Homo sapiens Gene Ontology annotations: about 3 hours) than their biological experiments.
Now a reviewer asked me to be more precise. Truly, how much does an in vitro experiment to biologically validate a human Gene Ontology annotation run by biologists cost?
How much money, broadly?
How much time?

Wow that can vary from a couple of months and a few thousand dollars to a lifetime and millions of dollars.  I can give a couple of examples, but this is an extremely broad question.  Wet science is not cheap might be the message here.
Gene Ontology terms are meant to cover all biological roles known for genes.  Terms are added and retire regularly.  They embody thousands of person-years of work to establish the framework of what is going on in cells.  At the same time its a limited vocabulary of gene behavior.  It doesn't represent a complete catalog of all known gene behaviors (e.g. in such detail that it could only be the property of a specific gene) and it is also uneven in the level of its detail of its leaf terms.  Its an ontology that is manually assembled and edited and therefore subjective.
I just did an overview of GO 1.2.  there are 34153 unique GO terms.  That's easily 10s of thousands of separate assays, so picking a group of important ones might be useful. There are also lots of orphan GO terms, where only one gene is currently attached to the term.
For GO annotations, a simple and typical case is a metabolic gene which has a high homology to a known gene and you are only trying to verify that biological role, and you have a functioning biological laboratory.
A breakdown might be:

clone the gene ( \$200 to \$1000 and up): order primers, use PCR to insert the gene into a plasmid or get the gene synthesized if you have to, transform it into bacteria,  ideally sequence it to make sure you have it right.
purify the protein ( \$500 maybe).  Run a check culture to see if the protein is produced, then run a full culture with induction, lyse it and then purify on an affinity chromatography column.  you may need to run a second column to get the protein pure.
do a kinetics study with mass spectroscopic validation of the products (minimally about \$300).  Cost involves reagents - if you have an assay you can use a UV/Vis spectrophotometer this can be simple, if not it can be much more expensive. This part will vary with the enzyme you are looking at.

Hopefully you get all these steps right the first time.  Salaries are not included in this cost.
The more complicated the GO term, the more difficult the work will be - they are not all equal.  If the protein function can't be verified in isolation, but must be in situ (i.e. in the living organism). Then you start to get into time.  What if your gene is involved in protein routing through the golgi apparatus and modulates the function of other proteins for instance?  That's going to require a very specialized and difficult set of experiments that should involve someone with experience in that field.
Finding GO annotations which are not clear from sequence similarity - unknown functions or new genes which reflect previously unestablished GO Terms could cost even more and take a good deal longer.
Still, high throughput studies are being done to find and validate electronic GO annotations.  And with that scale, the cost per experiment will go down.  Take a look at Olga Troyanskaya's research for an example of such an effort.

Can you please escape the $ signs with \$ to prevent SX going into the math mode?

@shigeta Thanks a lot, that is what I was looking for. And for the example you give the $ prices, can you also give a broad time schedule?

Hi David, If you had a well equipped laboratory, but without high throughput scaling, It could take 2-3 weeks per enzyme in the simple case.  Also the costs of the reagents will vary - they can be expensive...

Hi @shigeta where can I get further breakdown of these costs? For instance you mention 200 USD for gene cloning. What is the breakdown of this in terms of primers, PCR machine, PCR reagents, glassware, etc? I'm trying to understand whether a startup could reduce the cost by 100x, and if not, which reagent is the expensive one preventing the cost from being reduced.

hey ghosts - new vendors pop up and prices change. in this case the $200 is for a short gene synthesized and popped into a plasmid.  of course if you do this yourself the individual cost drops a lot but you have to do hundreds of clones to justify the up front setup of building your own line. you would have to pay someone to break this down its not going to fit in a comment... its work. If you love doing cloning services you could find a good company here but you may also find the low margins cumbersome!


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In chromatin, what are the differences among states, interactions, and structures?
I am new to the study of chromatin, and I am trying to understand what people mean when they write about chromatin states, chromatin interactions, and chromatin structures.
What are the difference among these?
Is there someone available to explain them to me clearly?
Are loops interactions or structures?

Chromatin state refers to the marks (i.e. methylated DNA, histone modifications, euchromatin v. heterochromatin) found at specific loci and is often referred to as open (readily bound by DNA binding proteins, like transcription factors) or closed (inaccessible to most factors).
Chromatin interactions can include the binding of transcription factors and other DNA binding proteins, but is usually used to describe local and long distance functional contacts between two or more regions of chromatin. These interactions are generated actively through protein-dependent chromatin organization and passively via polymer physics and the nuclear environment.
Chromatin structures can refer to the actual molecular shape and composition of the DNA or to the molecular modifications found at specific regions of chromatin (e.g. heterochromatin, DNA methylation). It can also refer to the ordered compaction of mitotic/meiotic chromosomes.
Chromatin loops are structures that may act in promoting regulatory interactions. Conversely, chromatin interactions may induce looping.
Cavalli and Misteli, Functional Implications of Genome Topology, Nature Structural and Molecular Biology 2013


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What are "anchor regions" in the human genome?
I've been reading this paper entitled "A high-resolution map of the three-dimensional chromatin interactome in human cells", about interactome in 3D chromatin.

We next applied the above algorithm to the 518,032 anchor regions in
  the human genome, with each containing one or a few HindIII
  restriction fragments (fragments shorter than 2 kilobases (kb) are
  merged) (Fig. 2a), and uncovered a total of 1,116,312 chromatin
  interactions with a false discovery rate (FDR) of 0.1

Simple questions: what are these anchor regions in the human genome?
How could I group them into types/categories?
Thanks

Good question. Anchor regions are regions that connect to the nuclear matrix. More specifically, in eukaryotic organisms, chromatin is anchored to the nuclear matrix by short DNA sequences of about 100-2,000 bp called matrix attachment regions or MAR. To answer the second part of your question, there are many categories (and opinions about those categories) of anchor regions. The most comprehensive way of categorizing these regions however I believe is by whether they are constitutively (always) or transiently (sometimes) bound to the nuclear structure.

Thanks @rhill45 Where could I find the categories that you mentioned? Could you give me a list or a link to a paper?

From the reading I've done, I think they have been very well catalogued and are still being identified. Here's a 2008 review on MARS.http://genomebiology.com/content/pdf/gb-2008-9-1-201.pdf


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Why is it important to study chromatin to understand cancer?
Many labs and many projects in biology institutes and university departments have been starting to study chromatin. Chromatin states, chromatin interactions, chromatin loopings, chromatin behaviours, etc. seem to have become the new priority of cancer scientists.
I'm new to this field so there are many things that I still have to understand about chromatin and its relationship to cancer. These questions just flowed into my mind:
What is the relationship between chromatin and cancer?
In cancer research, why is it so important to study chromatin and how chromatin loopings form?
What do scientists would like to find out about chromatin when they investigate it?

This is a very very broad question. Generally speaking chromatin is condensed DNA wrapped around histones and held in place by different proteins, which control what sequences of DNA get exposed or unexposed to transcriptional enzymes. This affects what proteins get translated and what doesn't. Depending on what proteins are translated affects everything about a cell and its survival/metabolism.

Another thing, it is extremely important in the process of mitosis and meiosis in cells.

In cancer cells one of the main microscopic feature is nucleus: cytoplasm ratio. When a cancer cell is stained, it is seen in most cases that this ratio is increased as compared to a normal cell. Mitotic figures are seen much more than in normal cells. So this is another reason why chromatin are studied

I'd read through Watson's "Molecular Biology of the Gene" (7th ED) section on chromatin, nucleosomes, and it's regulation. Then, read The impact of chromatin in human cancer: linking DNA methylation to gene silencing, and Chromatin remodeling and cancer.

Most cancers involve, in addition to genetic changes, a whole suite of epigenetic changes that orchestrate changes in transcriptional profiles. Additionally, multiple epigenetic modifiers (Eg - EZH2) are associated with worse prognoses.
There is also the fact that chromatin modification machinery is very highly mutated in cancers; examples include EP300 - a histone acetyltransferase, and members of the SWI/SNF nucleosome remodelling complex. The study of epigenetic factors needed for the continued transcription of oncogenes like Myc has also led to the formulation of potential therapeutic strategies (Check out some of the literature on inhibiting Brd4 to silence Myc in certain settings).


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Do any fish have to hold their urine in deep water?
I know this is going to sound really weird! But I was confronted with this claim and the person strongly had faith in it! I said faith since he couldn't provide any evidence.

Is there any kind of fish that is forced to hold his urine when in deep water ?
If yes, is there any relationship between the case with fish's scale ?
Does that make the fish's meat not proper (poisonous) to eat ?

I don't know what depth would be proper to define here but I guess the matter to be rare and not so important to define.

Is there any rational for which a fish would be forced to hold its urine in deep water?

In a salty environment there is the risk of loosing water by osmosis.
There are different strategies to prevent this.
One of them is to hold the salts of the urine (not the urine itself) by reuptaking them from the kidney.
The resulting high internal salt concentration prevents the water loss.
This strategy is used by Sharks and rays/ skates, they take up urea and TMAO from the kidneys. Those metabolites are highly concentrated in their blood, which makes them unhealthy to eat, when fresh. However, after fermenting they are edible: Hakarl is an icelandic dish of fermented shark. The linked Wikipedia-Article gives more details about the topic.
I guess your friend was referring to this phenomenon, but his memory was a bit imprecise.

Fishes have urinary bladder too. It would be hard to hide from predators with an urine trail pointing towards you. I don't think this is size dependent.

 - ref
Urine is usually not that dangerous and we remove inner organs, like urinary bladder by the preparation of the meat, so this is not a problem.


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Why analyse transcriptome instead of proteome?
Analysing the transcriptome (RNA-Seq, microarrays, qPCR etc) is probably the most widely used technology to assess gene expression and dynamic cellular processes. The results are then extrapolated (functional analyses, GO etc) to infer biological cellular consequences. RNAs are really only the middle-man. Presence of an mRNA doesn't necessarily mean that it will become a protein. It could be degraded by microRNAs etc. before translation. It is eventually the protein that are actually causal to a biological change.
So, my question is why not just go directly to the proteins. Why not just extract the proteins and sequence them, quantify them, analyse and draw conclusions? What are the compelling arguments to study the transcriptome to understand biological processes?

Another thing that is not directly related to your cellular process premise, but is nonetheless a positive of RNA-Seq: it also gives you coding and protein sequences at a cost far less than proteomics or genome sequencing, so it's useful for phylogenetics/phylogenomics

@NatWH, A good point. We also need sequence databases to search mass spectrometry-based proteomics data. In the absence of genomic data many groups use RNA-seq data to build this sequence database so they can then use MS.

Q: Why not just extract the proteins…

A: It’s not just a question of extracting the proteins, you would need to separate them and then isolate each of them. There is currently no practical way to do this for say, 10,000 proteins, which in any case may have multiple forms. The beauty of the RNAseq method is that it does not require physical separation or isolation of transcripts.
An approach more likely to be useful is to identify and quantify the proteins in situ, but this is still some way off having the comprehensiveness and sensitivity required.

Q: …sequence them…

A: Protein sequencing is slow and difficult. It cannot be applied on a mass scale, unlike sequencing of DNA copies of RNA transcripts. Other methods of identification based on physical behaviour (migration or elution position) are more promising, and the direction that is likely to be followed.

Q: …quantify them…

A: Even that is difficult, when you are eluting proteins from gels, for example. In contast, RNAseq methodology just counts transcripts. (But the likely direction this will go is quantitation by peak height, without separation or isolation, as with metabolomics, or by the intensity of some signal from the protein immobilized in the gel, staining of something more sophisticated.)
So, in general, you do protein analysis on particular individual proteins of interest to you, whereas you can do RNAseq analysis on all the transcripts in a biological sample, and then go fishing (among the results, and afterwards on the lake if that’s your thing).

There are no methods that will separate, say, 10,000 proteins  gel electrophoresis can easily separate this number of proteins. Liquid chromatography can too. Detecting intact proteins is currently low-throughput but LC-MS on peptides can provide quantitative data for 1000's of proteins, the ability to detect PTMs is a feature not a bug.

@Michael_A — technically you may be right (or partly so), but in the context of this question from a naive list member I think your comment is more likely to confuse than enlighten. I'll rewrite my answer in a way that deals with your point without diluting the explanation. But it will have to wait another day.

@Michael_A  — I have modified my answer to address the points you made. At some time I may modify it further with a consideration of how technological breakthroughs influence science, and the references to the current state of proteomics.

Why not just extract the proteins and sequence them, quantify them,
  analyse and draw conclusions?

Because proteomics is really hard. To provide one example, there is no protein equivalent of PCR. That means there is no simple way to select and amplify a particular protein from a complex mixture. The invention of PCR was central to the rapid advances in genomics over the last 40 years.
Mass spectrometry is the primary tool for high throughput proteomics and mass spec analyses are still very difficult to run, and even more difficult to interpret.

While you are correct that 'the end output' is always protein and that functional analysis on the mRNA level can ignore things translational control, there good reasons to analyse the transcriptome:
1) Like you said a lot can happen with mRNA: it can be degraded, translated or also be repressed for a time until it's activated again. Most of these control processes have pretty strong effect on the 'final protein output' but are much easier to study on the mRNA/transcriptome level (since that is where they happen).
2) Analysing the transcriptome on a genome wide level is technically relatively easy: Sequencing technology is pretty advanced by now and there are many different protocols established to look not only at expression levels of individual mRNAs but also other things like stability, processing (i.e. splicing or polyadenylation) and also translation of mRNA. All of this can also be done with relatively low material input.
Protein analysis on the other requires the use of mass-spectrometry which is not necessarily as powerful or sensitive as sequencing.

I think it's useful to bear in mind that the transcriptome does consist of non-protein coding transcripts such as miRNA, and that while protein is largely regulated, the end product is not always protein. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177046/

What are the compelling arguments to study the transcriptome to understand biological processes?
There are compelling arguments to study the transcriptome but they depend on the research question. Research that focuses on the activation of transcription factors should use transcriptome data as should research into RNA processing events (splicing, pre-mRNA processing, stability). You also want transcript data when studying RNA transcripts that don't encode proteins.
I don't think there are compelling biological arguments for studying the transcriptome when you want to know about the expression of protein-coding genes in tissues or cells. You should focus on proteins as they are the final step in the expression of protein-coding genes. The correspondence between the levels of protein-coding transcripts and their protein can be excellent but may also be very poor. Transcript data can't segregate these groups. In global estimates, transcript levels only explain around half of the variation in protein levels. I think that level of inaccuracy unacceptable when there's a viable alternative.
Why not just extract the proteins and quantify them?
There are physical and historic limitations to global quantification of proteins. I've compiled a couple limitations that I consider to be most important. Mass spectrometry (MS) is the current method of choice for high-throughput protein quantification. Methods such as Edman sequencing or immunological detection have much lower through-put.
There are physical limitations that currently stop MS from performing quantitative global measurements using intact proteins. That's very unfortunate. Global measurements are currently performed by quantifying peptides from digested proteins using a liquid chromatograpy system to feed the MS with peptides (LC-MS). The peptides are identified and then assigned to a protein or group of proteins. The end results are reliable and most will resolve to individual protein identifications. Some peptides will only resolve to identify a group of proteins. This is a minor limitation in most instances.
Making global measurements of protein quantities has only become possible in the last decade while global measurements have only become routine with the advent of ultra-high performance LC paired to the newest mass spectrometers. Many academic researchers will have access to this technology via their local mass spectrometry facility but most won't realise it. This newness is a big part of the reason it's less common.
Summary
I think LC-MS quantification of proteins will be used in place of transcript data more often over the next couple of years. This is due to the ability of  as current LC-MS instruments provide better answers in biological systems that require protein-level information. Reviewers will start to demand it. I think within the next decade high-performance LC-MS could become cheap enough to even replace lower throughput immunological techniques such as immunoblotting and immunofluorescence.

There is one aspect that has not been detailed: a transcriptome is way cheaper than a proteome. Analyse one single protein in LC-MS/MS can cost as much as a transcriptome. And if you belong to those small underfunded groups that work on non-model organisms which are really interesting but not really useful for curing cancer, then you have no choice.


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making antibodies rather than buying them
I need large amounts of anti-CD20 and anti-pan-cytokeratin antibodies. At DAKO, they go for >$1,500/mg, and I need >100mg, hence a purchase is out of question.
Instead, I plan to make a homebrew antibody. I have been producing monoclonals from hybridomas and from HEK cells for a decade, and my lab avails of all necessary equipment (Äkta, cell culture, etc.) and expertise.
What I have never done, however, is to start with a published sequence and create (and express in suitable mammalian cells) an artificial antibody H+L gene. Specifically, I was thinking of pulling a sequence from a patent filing, as these tend to be quite precise for legal reasons, making a synthetic gene, and expressing it in HEK.
A nice precedent for that would be for example http://www.ncbi.nlm.nih.gov/pubmed/25451031. Here, the author of the original publication refused to provide (or even sell at any price) the ICSM18 antibody, for reasons that he did not disclose (but which we can all imagine, given that the primary paper was published in Nature and a refutation would be seen as a dishonor). As a result, Colby lifted the sequence from the patent, and made the antibody in their kitchen! (I must say that I love democracy in science!!!)
My question is:

might you be able to direct me to good, high-affinity antibodies to
CD20 and cytokeratins whose sequences are retrievable? The latter is
true only for a minute minority of all available antibodies

This question needs to be updated with further details. Is there a patent on the antibodies in question? Are the antibodies polyclonal or monoclonal?

I am not so sure if you will get functional antibodies this way.

Apologies for the question being vague. I will remedy.

Not directly related to the question, but IIRC it's against Nature's publishing policies to deny supplies necessary to replication to other scientists within reason.

@March Ho: indeed. I complained to Nature, and they told me that they cannot police that and can only rely on the honor of their authors. Then I complained to Professor John Savill, the CEO of the Medical Research Council, who wrote a fiery letter to the scientist in question - and he still refused to comply. But then eventually Doolan & Colby came to the rescue. Molecular biology against bullies: isn't that great? :)

Your first question is off-topic here because it is asking for legal advice. I would strongly suggest you consult with your institution's legal department.

Sure, I removed the first question (although I respectfully disagree that it is off-topic).

@aag You can ask the patent law question on Law SE , but for proper advice you should consult your institute's IP lawyer.


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Where does female ejaculate actually originate from?
It's common knowledge that it's released via the urethra, but where does it originate? If it doesn't come from a part of the clitoris, then why is the clitoral glans called the clitoral glans? How does it travel from the originating spot to the urethra?

According to this article female ejaculate is produced by the Skene glands.

That article actually states that the ejaculate is mostly urine...

@Marius No it says there are "small amounts of urine" in some samples. And being female, I can tell you ejaculate and urine are vastly different. Nearly every sense a healthy human has can differentiate between ejaculate and urine.

@Metamorph I don't understand your comment. Your link does say that in 2 out of 7 women, the ejaculate was identical to the urine sample taken beforehand, and for the other five, it was only slightly different: "they either release plain old urine, or urine that's been diluted by fluids from the female prostate glands." they found small amounts of PSA in addition to urine. Where are you getting "small amounts of urine" from?

@YviDe The question asked where it comes from, and a quick google gave me this. Discussing it's composition isn't relevant. I've given an answer where no one else has, so critiquing mine with something I haven't mentioned doesn't make sense.

I wasn't at all critiquing your answer, I was asking why you answered @Marius 's comment about the article you linked with "No, it says X" when the link you answered with seems to be saying the exact opposite.

The question asked where it comes from, and a quick google gave me this. Discussing it's composition isn't relevant. I've given an answer where no one else has, so critiquing mine with something I haven't mentioned doesn't make sense.

@Metamorph Thank-you for your answer. Can you elaborate on the rest of my question? Because, as you mentioned yours is the only answer I've gotten.

I had written up an answer to a now-deleted post about 3 years ago addressing this very question, so I thought I'd copy my answer from that deleted post here:

The answer is dependent on which fluid (vaginal lubricant or female ejaculate) you are referring to. Since you mention both, I will address both.
Vaginal lubricant
Quoting from wikipedia > vaginal lubrification

Vaginal lubrication is a naturally produced fluid that lubricates a woman's vagina. Vaginal lubrication is always present, but production increases significantly near ovulation and during sexual arousal in anticipation of sexual intercourse. Vaginal dryness is the condition in which this lubrication is insufficient, and sometimes artificial lubricants are used to augment it. Without sufficient lubrication, sexual intercourse can be painful to women. The vaginal lining has no glands, and therefore the vagina must rely on other methods of lubrication. Plasma seepage from vaginal walls due to vascular engorgement is considered to be the chief lubrication source, and the Bartholin's glands, located slightly below and to the left and right of the introitus (vaginal opening), also secrete mucus to augment vaginal-wall secretions. Near ovulation, cervical mucous provides additional lubrication.

Female ejaculate
Salama et al. (2015) from the Journal of Sexual Medicine feel fairly confident that they have good evidence that female ejaculate is actually urine from the bladder.
However, this topic has been under scrutiny for some time. I have seen numerous suggestions (though little hard evidence) that the Skene's glands (which are homologous to the male prostate) are responsible. This belief originated from the early work of Ernst Gräfenberg (yeah, the namesake of the G-spot). However, Salanas et al. (2015) did confirm the presence of at least marginal levels of prostatic secretions (e.g., PSA) in female ejaculate from their study. This at least partially corroborates the involvement of the Skeen's glands in the ejaculatory process.  Bartholin glands (homologous to male Bulbourethral glands) have also been assumed to play a role. Further, Salanas et al. are not the first to propose that the ejaculate originates from the urethra. You can find a relatively simple outline and list of citations on the matter on Wikipedia.
We'll have to see how the scientific community responds to the work of Salama et al. ...


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Can you find malaria parasites in saliva?
Can you find plasmodium parasites (malaria) in saliva under microscope from someone who's infected? Or it's only in the blood?


text:
Is there any research paper state the pros and cons for human to be taller?
Is there any research paper state the physical health pros and physical health cons for humans to be taller?
For example, shorter people seems to more likely to get involved in heart diseases and taller people to be more likely to be involved in cancer

People with Shorter height may outlive Taller ones.
It is a statistically established fact that they are more successful in terms of money,
as the study published Journal of Applied Psychology, (Vol 89(3), Jun 2004, 428-441).
But, still in the health, the counterpart succeed over the former.

The Effect of Physical Height on Workplace Success and Income: Preliminary Test of a Theoretical Model.

Author: Judge, Timothy A.; Cable, Daniel M.

In this article, the authors propose a theoretical model of the
  relationship between physical height and career success. We then test
  several linkages in the model based on a meta-analysis of the
  literature, with results indicating that physical height is
  significantly related to measures of social esteem (p = .41), leader
  emergence (p = .24), and performance (p = .18). Height was somewhat
  more strongly related to success for men (p = .29) than for women (p =
  .21), although this difference was not significant. Finally, given
  that almost no research has examined the relationship between
  individuals' physical height and their incomes, we present four
  large-sample studies (total N = 8,590) showing that height is
  positively related to income (β = .26) after controlling for sex, age,
  and weight. Overall, this article presents the most comprehensive
  analysis of the relationship of height to workplace success to date,
  and the results suggest that tall individuals have advantages in
  several important aspects of their careers and organizational lives.
  (PsycINFO Database Record (c) 2012 APA, all rights reserved)
  Source: http://psycnet.apa.org/journals/apl/89/3/428/

Long back in 1975, the relationship between breast cancer and height had been nearly established.

Breast Cancer Incidence and Nutritional Status with Particular Reference to Body Weight and Height
Author: F. de Waard
Cancer Res 1975;35:3351-3356.
Summary The epidemiological literature on breast cancer is reviewed with particular emphasis on the possible etiological role of
  nutritional status as reflected by weight and height. The results of a
  prospective study and preliminary results of a population-based
  case-control study seem to indicate that body size or body mass rather
  than overweight (obesity) is a risk factor. A considerable proportion
  of the differences in incidence between the Netherlands and Japan can
  be explained by differences in body mass. The biological mechanisms
  mediating nutritional status and breast cancer are believed to be of
  an endocrinological- metabolic nature.

Primarily the investigators started with overweight problem but they have found a better correlation with height. They have concluded:

A further lead to biological mechanisms might be derived
  from the fact that several endocrine and metabolic functions
  (e.g., basal metabolic rate and cortisol production rate)
  seem to correlate better with body surface area than with
  body weight. The rationale for using surface area is often
  lacking except for the fact that it is estimated from both
  weight and height.

Height and cancer risk are fairly clearly linked, with a 16 per cent higher relative risk for every extra 4 inches of height.
The answer may lies in the metabolic rate:

For one, taller people tend to weigh more than shorter people, even if
  their BMI isn’t any higher. For another, poor nutrition and stress can
  stunt height growth, and higher calorie diets have been associated
  with increased height. And that doesn’t even begin to take into
  account the psychosocial variables like increased income, education,
  and socioeconomic status.

Other Diseases
On the contrary, in a study examining cardiovascular disease risk and height in men found that men who were taller than 6′ 1″ (185 cm) had a 35% lower risk of having a heart attack than men who were shorter than 5′ 7″ (170 cm). Every inch a man gained in height resulted in a 2-3% decrease in heart attack risk.
In a study in among, Israeli civil servants, shows height was also associated with lower stroke risk. Taller people are more likely,around 76%,  to suffer from recurrent blood clots.
LINKS:

http://blogs.discovermagazine.com/crux/2013/11/08/is-being-tall-hazardous-to-your-health/#.U79p3vmSzQJ
http://scienceblog.cancerresearchuk.org/2011/07/21/height-and-cancer-risk-%E2%80%93-the-long-and-short-of-it/

Link-only answers can become obsolete if the content changes or the page disappears. Instead, please summarize the information found in the link in the body of your answer, and provide the link for reference. The answer should be able to stand on its own without the external resource.


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What is some computer science technology that can help the development of biology?
What is some computer science technology that can help the development of biology(eg. artificial intelligence, resolution technology) and  briefly talks about their potentials?

This is an incredibly broad question. Computers are used for all sorts of things in modern biology that don't have anything to do with genetic engineering.

The question is very broad and it is impossible to correctly answer to it. I think you may not realize how much computation is everywhere.
Here is a bunch of things for which I personally use modern computation power

Population genetics simulations
Simulations of developments (Gillespie algorithm and variants)
Statistical analyses, incl. some analyses that are a bit demanding such as permutations test, bootstrap or maximum likelihood
Numerical approximations to mathematical equations that can't be solved analytically.

Many people make good use of computation in biology for treating DNA sequence data (annotating sequences, aligning sequences, comparing sequences, building phylogenetic trees). Typically this kind of task can be extremely demanding. I know a lab who uses Blue Gene in order to perform statistical analysis on sequence data. Some people simulate brain activities. Some people simulate the physics of molecules, some people simulate blood flow through the body while others simulate how a new substance injected in the body spread. and it goes on and on... This list is probably biased toward my field of expertise.


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If a fully functional lab grown pancreas can exist, can it be used as a cure for type 2 diabetes?
Is it approximately equivalent to say that if a fully functional lab grown pancreas exists, then a way to cure Type 2 diabetes could soon exist?
Recently I've already checked out that only the remission exists with some gastric surgery.
reference: 1)http://care.diabetesjournals.org/content/34/Supplement_2/S361
2) http://www.medscape.com/viewarticle/733691(full access require membership)

Your question is not clear. I understand English is not your first language, but please try to rephrase your question to avoid being closed. I tried updating your title to capture what I think you are asking, but please address further...

@theforestecologist - is it looks good right now?

it is better, but still needs improvement. Please cite the source from which you "checked out" that remission exists. I'm also concerned that your question is too hypothetical (and therefore opinion based) and will likely be closed. Perhaps try to approach the question from a less hypothetical viewpoint.

You need to define what "remission" means. Millions of people live with well-controlled diabetes by monitoring their diet and using medications, obviously along with insulin supplementation. Also, please cite things that don't require "memberships" to read them.

Since the consensus is that the underlying initial problem in t2dm is the development of insulin insensitivity in the presence of a normally functioning pancreas, then replacing that pancreas at a more advanced stage of the disease will not cause normalisation of glucose levels.
http://journal.diabetes.org/clinicaldiabetes/v18n22000/pg80.htm


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How deep does a 0.5mm radius drill need to drill before it start to hurt the brain assuming the drill bit will be pull off without wiggle?
How deep does a 0.5mm radius drill into the brain need to drill before it start to hurt the brain permanently assuming the drill bit will be pull off without wiggle?
I am assessing how fragile of a brain is.
What are three types of damage that are most likely to happen for 3mm deep, 6mm deep or even deeper

Don't you have a drill? Just test on yourself!

@mdperry I want to do this but not after you show me how much the damage is :)

I think we need more information. Are you drilling through the bone first, because that's where the pain receptors will be. The brain itself, not so much.

@kmm It is the brain as I want to know how sophisticated a brain surgeries need to be conduct as i wanting to be a brain surgeon in the future


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maximum # of generations possible in a population of cannibalistic rats
Assume a sufficiently large population of rats (to allow genetic variation and facilitate inbreeding) in a place with abundant water, sunlight, shelter but no food. For how many generations can this population persist solely by cannibalizing?

Here's a back of the envelope answer: one generation if you have a LOT of rats.
Numbers according to the Great State if Indiana.
Assuming also that we start with adult breeding rats.  50% females / 50% males.
The gestation period is 21 days.
The adult rat can eat 1/3 of its body weight each day. Assuming that the rat is eating about 80% of the 'victim' rat body weight, the colony reduces in size about 40% each day.
Lets say the rats are going to be a bit hungry and the colony loses 30% of its size each day. And they don't eat their own feces, which I think they can do, but also assuming that the malnutrition from eating only rat for food will not cause problems.
Over the period of gestation, 21 days the colony will have a final size of 0.3^21 = 0.000559 times its original size.  So in order to have a surviving gestating female you'd have to start with 1791 rats.  Also assuming that the last rat is a very pregnant female who can compete with the male cannibals who are not pregnant.
Then the average litter is not 1790. So you'd need something like 1790^N rats for N generations.  and they'd have to be pretty lucky rats to boot.
BTW similar numbers would be true for any animal including human beings. This is one reason that cannibalism is usually only a social ritual, not a means of acquiring nutrition except in extreme cases.

It may be possible that only a few male survive (having cannibalized all the others) and persist by eating females, and impregnating all of them at the same time. Also they CAN eat 1/3 of their body weight each day but maybe can survive on significantly less than that, and as they grow weaker and lose body tissue they become even less active and less cannibalistic, requiring fewer calories to survive. Ultimately I guess it's a question of how efficiently the rat transforms solar energy and water into body tissue. Perhaps a mammal with lower metabolic rate can achieve cannibalistic equilibrium.

Animals are way to dependent upon plants overall and photo synthesis - the poo just keeps piling up until its all you have.  This question might demonstrate why you need some photosynthesis to maintain a stable ecosystem. http://biology.stackexchange.com/questions/7400/plants-without-bacteria-is-it-theoretically-possible/7409#7409


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Why do some macaques have redder faces than others?
I'm watching Monkey Kingdom, and I noticed some of the macaques have redder faces than others.
Specifically the females, and as the social ladder increases, so does the redness.
An alpha female:

Does the redness-hierarchy correlation have something to do with attractiveness or aggressiveness? (These seem to be leading factors in defining hierarchy otherwise.)

Aggressiveness, seems more likely, on first thought. Heightened Aggression, leading to stress, causing High blood pressure or an increase in vascularity, maybe? Inferring the reason for the colour? A good start would be to find out why, their faces are red in the first place, and then hypothesise from there onwards.

@user19679 That's part of my question. What causes the redness, and does what cause the redness also increase a certain kind neuro connection or hormonal release, etc., that's also correlated with aggressiveness or some other dominant trait.

@user19679 increased vascularity may be part of it, yes. But from seeing the monkeys at different angles, it looked like more a pigmentation. I was hoping there'd be a wildlife vet (or something along those lines) who could speak to it.


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How is the role of ethylene in ripening demonstrated in biology (or agriculture) education?
I want to know what are some typical experiments performed in the course of biology education (or possibly in applied biology education e.g. agricutlure education) to prove to the students (or have them prove to themselves) the autocatalytic role that ethylene plays in ripening. I'm interested in both how ethylene production by plants might be demonstrated/measured as well as the demonstration of the role of ethylene on ripening.
I'm aware of some historical experiments e.g. that of Gane (1934). Is it commonly repeated in biology labs in an educational context today? Or is something else used?
I found a lab manual aimed at Indian "class XII" (I guess that's the terminal year in high-school over there); the experiments aren't well detailed but they suggest keeping a sample in open and one packed in polyethylene, or treating one with ethylene. These aren't exactly quantitative though: no suggestions for measuring anything but by eyeballing/tasting. I guess that's the best on can do with limited resources. I'd like to know about college-level experiments, if there are any.
I also know how ethylene is measured in industrial/pro-lab settings:

Ethylene
  can be measured successfully with a portable gas chromatograph fitted with a
  photo ionisation detector capable of measuring ethylene to a concentration below
  0.01 uL/L. EASI-1 uses a proprietary ‘nanoporous gold sensor technology’ for
  “accurate real-time measurement of ethylene gas concentrations”. This is licensed
  from Fluid Analytics in USA, which offers a claimed sensitivity to ethylene in
  the air at levels as low as 10 ppb.

There also exist industrial scrubbers for ethylene so the air concentration can be reduced. I don't know if either of these are commonly available on college campuses though, even in biotech or agriculture departments.
(N.B.: I don't care about proving the effect in the kitchen, which is the topic of a question on Skeptics.)

One way (I don't know how easy it is to get hold of the compounds in your locality) would be to have three sealed containers containing apples, one in standard conditions, one in elevated ethylene, and one with 1-methylcyclopropene which competitively binds the ethylene receptor, and observe the differences in ripening rate.

@JimYoung: I want to know how it's done (if it is) in existing education, not for radom ideas how it could be done. And it's not just that a "professional teacher could answer". Lab manuals etc. anyone can read.

This is a Biology list, not an education list. So it's OK to ask how it was shown that ethylene has a role in ripening — although we'd expect you to show that you'd tried to find the answer yourself — but I think it's off-topic to ask how to do it in the constrained circumstances of a teaching course.

@David: really? Let me ask on meta here. Actually, it was asked already, and you seem to be wrong: https://biology.meta.stackexchange.com/questions/3162/questions-about-teaching-biology-off-limits "questions that ask for ways and methods to teach specific biological concepts or techniques (lab or field) should be on-topic." (that's the only answer) Also "All questions asking for this type of advice should be narrowly defined, so that the scope of possible answers isn't too wide." Not asking about hypothetical but only about actually used methods seems to satisfy that well.

A question was asked on meta and one person replied that he thought they "can" be on-topic and seven people agreed with him. That does not define the scope of SE Biology or mean that I am wrong. The scope of SE Biology is laid out in the Help and my reading of it is that it does not include questions of your type. I suggest you do some research on the subject and state what you think are the problems of repeating the experiment in the classroom. Then I and others will consider whether it is or is not off-topic.

@David: I already know of some historical experiments in general terms; see my answer on Skeptics. (The details only appear in obscure publications from the 1930s that are hard to get--not online, etc.) If I take the time to read some lab manuals myself, that would make my question here moot, unless I want to waste my time typing an answer to my own question (which seldom gets any appreciation anywhere on SE). I don't see what's wrong with my questions as-is.

I second the idea that asking a forum of biologists "which typical experiments are performed in the course of biology education" is missing the area of expertise that you can expect here. During the course of my education, there have been no such demonstrations, which probably answers your question on whether such demonstrations are routine in biology classes in schools around the world. I also guess most research biologists today have had no such demonstrations in school. @NatWH already has suggested an experiment, if you are interested in a specific way to demonstrate this in a classroom


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Does melanin protect humans from gamma radiation?
It is known that radiotrophic fungi use melanin to make use of gamma radiation. It is also known that melanin protects human skin from UV radiation.
Thus I wonder whether melanin protects humans from gamma radiation as well?
Are the black people and tanned white people less vulnerable to gamma radiation?
http://web.archive.org/web/20080424001002/http://www.sciencenews.org/articles/20070526/fob5.asp

Please provide  a reference for the claim in the first sentence. Also just because a simple organism may have developed a novel use for a compound doesn't mean that can be extended to humans. And as gamma radiation penetrates deeper than UV, its effects go beyond the layer of skin that melatonin serves to protect.

http://gtcceis.anl.gov/guide/rad/index.cfm

@AMR the fungi use gamma-ray energy as energy source for growth. The growth in fungi is not because of mutations but because it is such a biological mechanism to utilize energy from environment.

You do not want human cells to get a growth advantage, and no it is not even remotely protective against gamma rays to have extra melanin. You cannot compare the adaptations of a single celled organism to the processes of a complex multicellular organism in that respect.

@AMR it protects from UV, any hint that it would not work against gamma?

Because when you get an X-Ray they cover the unimagined areas with lead shields. Because the firemen of Chernobyl died quick and horrific deaths due to Gamma radiation exposure from Gamma radiation from Cesium-137. Because we use gamma rays to kill cancer cells.

@AMR the Chernobyl radiation during the fire was orders of magnitude greater than what the fungi can utilize (which is some 500 times the norm).

Organisms on Earth DID NOT evolve with a higher than background level of gamma radiation. UV is a far more prevalent form of radiation which life on Earth evolved and adapted to. That it can still cause damage over prolonged exposure just means that modern advances in medicine, sanitation, and nutrition are keeping us around long enough to see the effects of its damage. We have no such evolutionary experience with Gamma radiation. You have a novel, single celled organism that has adapted, that happens all the time, just look at antibiotic resistance. As I said before you cannot draw a parallel

Since it is clear from the source in your question that melanin can absorb energy from gamma rays, yes higher concentrations of it would reduce the energy of gamma rays getting through the skin.  However this would not provide any real relative protection, the gamma rays are still going to go right through your skin and start damaging DNA.
Gamma rays are three orders of magnitude higher frequency and therefore carry much more energy than UV.  Of course there are energy transfer inefficiencies but since the fungus are not growing at even an order of magnitude faster, it's fair to say that melanin is not absorbing all of the energy in a gamma ray.
Since the gamma rays are not fully absorbed by melanin, they will still go through the skin and will still be able to do significant DNA damage.
So yes, but not enough to really matter.
Frequency Differences
http://www.davidterr.com/science-articles/electromagnetic_spectrum.html

Not so fast with this answer! When gamma radiation is absorbed by melanin, the energy is converted into an electrical response via the photoelectric effect. At this stage, the gamma radiation is no more. Provided that the intensity (number of) photons is not sufficient to destroy the melanin molecule, and also assuming a one-to-one mapping between photon interaxction and melanin molecule, for every absorbed photon there will be one or more electrons liberated. In theory, a melanin endowed human being exposed to gamma radiation could use it as an additional source of energy.


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If dog's DNA and human's DNA are almost same then can it be said that dogs evolved from humans?
https://www.youtube.com/watch?v=IbY122CSC5w
In this video it is said our DNA matches almost the dog's then it can be also said that dogs evolved from humans. Is it?

What!!! this is just too absurd. Have you even tried to read about how evolution really happens?

@WYSIWYG... Wow, I completely agree with you... that may be a first.

I agree the question is very introductory and I might agree with the idea that the question might deserve being closed. However, I disagree with the tone used by @WYSIWYG. It never helps to receive "it is absurd" when you have interrogations.

I agree with @Remi.b - especially as a moderator you should be respectful. And the homework close reason is misplaced too.

@Remi.b and AliceD, while I tend to try and give people the benefit of the doubt, the question is absurd. If the OP had watched the posted video, they understood absolutely nothing from it, as it went over, quickly, the idea of splitting from a common ancestor and also that the percent homology figures are contextual and do not necessarily imply phenotypic or even genotypic sameness. You cannot draw the conclusion that is implied by the question if you watch it. Minute Videos are geared towards non experts. It is perfectly clear. There are times where you just have to call it like it is.

@AliceD and Remi.b If there was a question from that video it would be how are the researchers able to make a claim that the genomes are ~99% homologous between Chimpanzee and Human if they excluded nearly a third of the genomes from the tally? At least there is a question that implies that the person actually watched and thought about the video and its content. They could also have asked if any other researchers have looked at the parts of the genomes that were excluded and determined how similar those are. At least those are thinking about experimental design and trying to look beyond No.s

@AMR- admittedly, I didn't watch the video. Perhaps WYSIWYG did as well and that could explain his reaction. Nonetheless, even then a little less harsh tone would have sufficed to convey the message.

No, it means dog and human shares common ancestor. Read here for more detailed answer to such questions. See following phylogenetic tree which shows time of divergence

Image: Wikipedia

It might be important to highlight that the tree you show is not representing the whole sister group of the platypus. It focuses on humans and represents mostly species that are closely related to humans. It would also be good to include the reference as there were debates about the exact relationship of some groups. Tolweb referring to a paper of 1995 (obviously outdated today) is showing a big polytomy.

That's true however I used diagram and reference assuming person asking question has no (or little) idea about evolution (as it is obvious from his/her question). This is what I can find which is easy to understand + is scientific enough.

I was mainly scared that the OP could bare in mind the wrong feeling that in the goal of evolution by looking at a tree that display man (the common chimpanzee actually) at it's last tip.

Species A evolving from species B makes no sense for extant species
It makes no sense to say from two extant (living) species that one evolved from the other one. Two extant species share a common ancestor though. The most recent common ancestor (MRCA) might have existed a very long time ago (as it is the case between an oak tree and a Ostrich for example) or be relatively recent (as it is the case between a reindeer and a blue whale for example).
Humans and dogs - phylogenetic relationships
Humans and dogs are both mammals. Their MRCA is about 100 millions years old. They are therefore relatively closely related and therefore share a lot of DNA in common. In other words, dogs and humans were the same species during 3.5 billions years. About 100 millions years ago, their lineage split (speciation) into two. One lineage became dogs, bears, bats and other Carnivorans (through succesive speciation events). The other lineage became elephants, orang-outans, humans, pigs, horses, rhinoceros, sea cows and many other things according to (oneZoom.org who is often nicely updated).
You can explore the tree of life by yourself. Either go on tolweb.org (Here are the Eutheria) or on oneZoom.org. I think that oneZoom is more up-to-date than tolweb.
Introductory course to evolutionary biology
You probably want to follow an introductory course of evolutionary biology. The Understanding Evolution project at Berkeley would be a great introduction

"It makes no sense to say from two extant (living) species that one evolved from the other one" in the specific example of this question the last shared ancestor was very definitely neither a dog nor a human, howsoever, Galapagos finches, your line there ^ taken in isolation (but not your answer as a whole) represents reality as little as does the question itself, quite obviously, given the right circumstances, the parent species can on occasion exist in the same timeframe as it's offspring species.


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Do artificial light sources cause enough fatalities in light-attracted insect species to cause eventual extinction?
There are various theories about how insects are drawn to light bulbs because of navigation mechanisms based on the sun and moon's position in the sky. It makes sense for that to work when the only major light source on Earth was high in the sky at a very slowly changing angle, but now that we have artificial lights it seems like that would severely interfere with the regular life cycle of insects. Being stuck buzzing around a light bulb for almost half of every day seems like it could be damaging to the life expectancy of each bug, and therefore damaging to the species as a whole.
But the reality is, most of the "livable" surface on Earth is not covered in cities, so most of the bugs aren't affected by these artificial lights.
So that leaves me wondering, if we expanded our cities to cover most of the earth livable surface, would we potentially kill off a large portion of the bugs on earth that are drawn to light or rely on bugs drawn to light as a food source?
That's sort of a hard question to answer though, because of so many factors, however I think the root of the question is a simpler one:
Do artificial light sources cause enough fatalities in light-attracted species to prevent them from successfully reproducing at the necessary rate for survival? (assuming the whole species were subjected to such light sources)

Short answer:
Yes, it is highly likely that artificial light sources may cause such high mortality that insects become (locally) extinct.
Long answer:
As you will understand, it is almost impossible to perform an experiment where lights cause extinction of a species. However, we have a lot of 'circumstantial evidence'; cases where it is clear that lights have dramatically decreased species richness (which means local extinction). If lights can cause local extinction, they can also cause global extinction if, as in your question, their entire population would be exposed to light.
Percentage of insects attracted/killed by light
Why insects are attracted to light remains unanswered, but that they do is clear. Also, it is known that insects that are attracted suffer an increased mortality, due to the light itself and due to predators actively hunting the blinded and inactive insects. The reported estimates of the number of insects that are attracted and killed by a light source differ and depend on the light source (color mainly), the background lighting (moon!) and other factors. A fair estimate is that about a third of the insects that are attracted by a light source are killed
A third is a very high mortality, that is off course added onto the other (natural and man-made) mortality insects suffer from. It is very likely that this would cause insect populations to become extinct, especially those already at risk.
Based on observations
Eisenbeis&Hänel give a number of examples that show that insects populations dramatically decline when exposed to lights:
- the number of insects drawn to the lights of newly build fuel stations is high in the first years, than rapidly declines.
- the butterfly fauna in remote Austrian valleys declines after introduction of lighting (for tourism).
- The number of caddisflies drawn to lights exceeds that of the number found in a stream.

Nice answer. Note that it's a third of the insects attracted to a particular bright light source that are killed estimated from that paper, not a third of all insects.

@Bryan. Good observation. I've edited that in.