text:
Why is ab initio protein secondary structure prediction less reliable than alternatives?
To predict secondary structure of proteins three types of Algorithms are used Ab initio, homology based and neural networks. Among these neural networks prove to be more accurate and give good results as compared to ab initio, especially when multiple sequence alignment and neural networks are combined the accuracy of results reach almost to 75%. My query is that what actually lacks in ab initio methods which reduced their sensitivity and specificity and lacks the ability to predict reliable secondary structures? (Some of ab initio based softwares that I have studied are Chau Fasman and GOR Method)

Why do you mention specifically GOR methods and Chou Fasman?

Because I studied and used them thats why I have mentioned them as an example, only.

This article may be outdated but they outline three major problems in ab initio modeling that include energy functions, conformational search engines and model selection scheme. Open access.

Preamble
In the comments @CMosychuk linked the most comprehensive text on ab initio I have seen this side of the year 2000 (Lee et al., 2009). It agrees that you've raised a good point.

It is important to acknowledge that ab initio prediction methods
  solely based on the physicochemical principles of interaction are
  currently far behind, in terms of their modelling speed and accuracy,
  compared with the methods utilizing bioinfor- matics and
  knowledge-based information.

This is a good real life example of different philosophical approaches of inductive reasoning and deductive reasoning (AKA top-down logic) behaving as one might expect. Each specific software has it's own problems and advantages and may not fit into the below categories. I'll grossly simplify each approach in crude categories and then broadly explain why errors crop up in them.
Below, we'll discuss ab initio and why it goes wrong and then the alternatives including why they're currently generally more reliable. We'll round up with how ab initio is probably going to become realistically involved in reliable protein predictions.
Ab intio.

Figure showing the broad pipeline difference between ab initio and homology modelling.Nara Institute of Science and Technology.
Ab initio often works on the premise that physical laws can be applied universally in biology. As you are most probably aware, biochemistry is far messier than chemistry and often those physical laws appear bent if not completely broken. The goal of ab initio is therefore to combine physical properties with some fudge factors to account for a complex system. Due to the complexity of the system never being fully modelled, ab initio predictions are limited to what your input is. There are also limitations pointed out in the 2009 article:

potential energy functions.
conformational search engines
model selection scheme

What you can see now is that ab initio is missing something that the others have: biological experimental empiricism. Without that inductive reasoning ab initio modelling is very vulnerable to assuming far too much and oversimplifying the system (This xkcd springs to mind).
Although perfect ab initio prediction is the "Holy Grail" of protein structure prediction, currently if there is homology which provides an experimentally known structure then that is often more reliable.
Ab initio is not completely without merit. For relatively short proteins (100 residues) the accuracy has improved over the last decade (Lee et al., 2009). From the above mentioned article:

...the physics-based atomic potentials have proven to be useful in
  refining the detailed packing of the side chain atoms and the peptide
  backbones.

Homology modelling.
This type of modelling is a safe bet provided that the MSA is available and reliable. The premise here is that if two proteins look similar enough at a sequence level, and you know the structure of one, then probably there is structural conservation as well. The problem is that obviously, those small changes in sequence, or other contextual differences like post translational modifications, localised pH differences etc. might mean that the protein folds differently.
Machine Learning.
Neural networks are a form of machine learning. Conceptually, these use the same assumptions as homology modelling, but on a larger more sophisticated scale (not necessarily an advantage). A good example of “machine learning” winning out in terms of homology clustering and secondary structure prediction is that of HHpred. An algorithm will make subsets of "if" statements. If a protein has a certain combination of "ifs" and "nots" then it's safe to assume other proteins with the same "ifs" and "nots" have similar structures.
You need to know a lot of information about the dataset that you put in, and also one needs to know which of that information is relevant. If these conditions aren't thoroughly complied with the categorisation results will just be amplified errors, arbitrary categorisations, missing categories, or a combination of those three.
It's worth pointing out at this point that most modern secondary structure predictors use a combination of homology and machine learning.
A reasonable ab initio approach.
More recently iterative optimisation based on vague experimental data has become the standard practice shifting ab initio from a top down approach to something more inductive. Kulic et al., 2012 is an interesting example of such a modern holistic approach. It involves an iterative optimisation of ab initio protein structures based on experimentally derived structures.
Another excerpt from the 2009 article.

Thus, developing composite methods using both knowledge-based and
  physics-based energy terms may represent a promising approach to the
  problem of ab initio modelling.


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Can exhaustive algorithms produce MSAs that are suitable for 3D structure modelling?
While predicting 3D structure of a protein through homology modelling, the most important step is Multiple Sequence Alignment of template sequences with the target protein whose 3D model is to be predicted. At this step according to my book for Multiple Sequence Alignment, the either T-coffee or Praline software is used; these programs are heuristic in nature and based on a progressive alignment method. There are always concerns regarding the sensitivity and specificity of heuristic  algorithms.
To avoid that problem I imagine one option is to use exhaustive algorithms. During this alignment step, which is very critical in 3D modelling, can we use exhaustive algorithms to yield good models?

Which exhaustive algorithms are you talking about? Finding the right homolog is, AKAIK, an NP-complete problem and that's why heuristic methods are used.

I am talking about Dynamic programming modeling as it is exhaustive.

@WYSIWYG can you explain your answer please, so that I can develop better understanding.

Heuristic/greedy algorithms are faster than DP. I am not an expert in computer science to explain this in detail. You can have a look at this SO post.

No.
Methods for multiple sequence alignment (MSA) like T-coffee, which you mention, and Clustal (http://www.clustal.org), which is also widely used, employ heuristics for the alignment for the very good reason that exhaustive algorithms to do this are NP-complete.
You mention the dynamic programing algorithm as exhaustive in your comment. Alignment of two sequences by dynamic programming has an order of time complexity (big O) of $n^2$ and may take a few minutes depending on sequence length etc. Alignment of three has a big O of $n^3$ and can be done if you have lots of time on your hands, but it should be obvious that that's the limit to the number of sequences that can be aligned using dynamic programming.
There's no shame in heuristics. Perhaps the most widely used bioinformatics program in the world, BLAST, employs a heuristic. (And your brain makes decisions that your life depends on every day using heuristics.)
The methods that use heuristics for MSA have been successively modified to deal with known factors that can cause problems, so often they perform very well. How can you tell? What you need to do in using MSA programs is to perform a "sanity check" on any alignment that the programs produce — does it look reasonable, has it matched up similar regions well or are there vast gaps introduced or obvious regions of homology missed. And if you are doing 3D modeling and you don't get an alignment that looks good, then your modelling isn't likely to work.
Finally you always have to consider the possibility that there aren't any proteins of known structure that are similar to the one you want to model so you'll never get a good MSA.

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sort: votes


text:
Positive control for microorganisms
I'm planning to test the antimicrobial activity of endophytic fungi (their secondary metabolites) against Gram-positive bacteria, Gram-negative bacteria, and one fungus (Candida).
I was wondering what could be a suitable positive control for them? One kind of control each for the Gram (-), Gram (+), and the fungus? If yes, what kind? Or is there one kind of substance that would work against them all?

Do you mean "I'm planning to test the anti-microbial acrivity"?

Yes. Sorry for that.

Streptomycin or penicillin/streptomycin, and potentially amphotericin B. If you need a penicillin-free alternative, gentamycin is a good option.
I think streptomycin is a good candidate with a broad enough spectrum to target Gram positive, negative and yeasts. You can buy from Thermo Fisher a 1000X penicillin/streptomycin cocktail that you simply add at 1X to your medium, and the spectrum is broader. Due to penicillin allergy, you may need to find an alternative. Gibco markets gentamycin in 10mg/mL and 50mg/mL formats, but I think with gentamycin you definitely want to add amphotericin B. Use the same cocktail across all three types of control.
You can even go as far as to test your antibiotic cocktail in advance, in triplicate on your strains of interest, to ensure you have a valid positive control.
I find these compounds to be quite common and so there are other anti-fungals you could use, but I can't attest to their efficacy.

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text:
Why do membrane proteins at lower temperatures contain more alpha helices than beta sheets?
Why do organisms found at low temperatures have membrane proteins with a higher percentage of alpha helices compare to beta sheets?

This is an interesting question that I've not really come across before. Is there a citation for this claim? I'd be interested to read more.

@James Porins are beta barrel but yeah many membrane proteins are alpha-helical, though I don't have an exact number.

@WYSIWYG I once looked at the difference between the two and TMHs are far more common (~13k proteins 90% redundant with a TMH in humans, and only around ~1k TM beta sheet containing proteins in Swissprot without selecting a specific taxonomy or redundancy ). What I have not come across is something saying that the ratio shifts in organisms that are at lower temperatures.The annotation of beta sheet TMDs is nowhere near as high quality as TMHs, so I assumed that nobody had looked into this.

The reason could be the greater motility of the alpha helix, compared with the more rigid beta sheet.
This allows the enzyme to remain functional at low temperature.
The proteins of such organisms tend also to have less hydrophobic regions and covalent bonds.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1692995/pdf/12171655.pdf
Molecular basis of cold adaptation
Salvino D’Amico, Paule Claverie, Tony Collins, Daphne´ Georlette,
Emmanuelle Gratia, Anne Hoyoux, Marie-Alice Meuwis, Georges Feller
and Charles Gerday*
This doesn't refer to alpha helix explicitly, however alpha helix are known to allow more flexibility than beta sheets, so it's reasonable to argue that the increase in alpha helix could be one of several strategies used to obtain more flexible enzymes, as well as other means cited in the paper.
This point is also made by Brock-Biology of microorganisms(2015) pag.161

Several cold-active enzymes whose structure is known show a
  greater content of α-helix and lesser content of β-sheet secondary
  structure ( Section 4.14) than do enzymes that show little
  or no activity in the cold. Because β-sheet secondary structures
  tend to be more rigid than α-helices, the greater α-helix content of cold-active enzymes allows these proteins greater flexibility
  for catalyzing their reactions at cold temperatures.

However unfortunately no reference is provided about this claim.

This is a great idea for a comment, but it's not an answer. It would be if it had sources, or calculations etc.


text:
How long does it take to form a peptide bond?
What is the time taken to form a peptide bond in vivo or in vitro? It isn't mentioned in my course on protein structures. I was just curious to find out  if any  time scale is known?
Given that prokaryotic and eukaryotic cells have difference ribosome structures, is there any chance that the time might vary between species or even within species?

Are you interested in the rate of ribosomal incorporation of amino acids into a growing polypeptide?

Canadianer : Yes . can we without any doubt say that  " the time required to link any 2 amino acids is always equal "  ???

@smileandbegin I'd imagine it's strongly dependent on the cytosol concentrations of the tRNA-bound amino acids. I'm not sure what the relation between tRNA-bound amino acids and blood/cytosol unbound amino acids is, but again, I'd imagine they're to some degree proportional. That means that the time to form a bond between any two amino acids would vary based on diet, metabolism, and possibly cell type.

Alberts et al, Molecular Biology of the Cell, say that eukaryotic ribosomes add about 2 amino acids per second, and bacterial about 20 per second (5th edition, p 275). These should be taken only as ballpark estimates: the rate certainly will vary from species to species, from cell to cell, from protein to protein. Some cells (reticulocytes, for instance) are professional protein synthesizers. Others, for instance bacterial endospores, do almost no protein synthesis at all.
These authors estimate the translation rate in mouse embryonic stem cells at around 5.5 amino acids/second. There is a lot of variation from gene to gene, even within this one cell type. They even see systematic variation depending on where you are in the gene.


text:
Where am I going wrong in a Reactive Oxygen Species Assay done in N9 cells?
I have followed the modified protocol that was given to me by my senior (who was using for his RAW 264.7 cells - Reference article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3308605/ ) for assessing ROS in N9 cells.
But, I am not able to standardize it in our lab ... every time there are variations in the results.
Protocol

Seeded 10,000 cells per well in a 96 well plate.
After 12 Hrs of growth, the spent medium is discarded and the cells were washed twice with PBS.
After the PBS wash, 50uM DCFDA was prepared in PBS and 100ul/well was added in 96 well plate and then incubated for 30 min at 37oC and 5% $CO_2$.
The DCFDA was removed from the wells, immediately and cells were washed twice with PBS.
After the wash, to standardize the protocol, the cells were treated with 10uM, 30uM, 50uM ${H}_{2}{O}_{2}$ as a positive control and 1mM and 5mM N-Acetyl Cysteine was used as negative control.
And a set of cells not treated with any compound (DCFDA/${H}_{2}{O}_{2}$/N-acetyl cysteine) are also used as control.
After treating with the compounds, the fluorescence was recorded in multi-plate reader at (excitation -485 nm and emission-530 nm) different time points with a 10 min interval - T0, T10, T20, T30, T40, T50, and T60.

We could not see in difference in different concentrations of ${H}_{2}{O}_{2}$ (newly purchased) and also with N-acetyl cysteine. We would expect to see some difference.
It will be a great help if you can suggest me where I might be going wrong with the protocol or is it anything to do with the seeding density of the cells.

Hi and welcome to Bio.SE! This is a good example of a lab-technique question as far as I can tell and I hope someone can point you to the right direction! I've made some formatting edits, but feel free to roll back.


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Intro Bio question: dihybrid punnet square
Today we had our exam. There was a punnet square for two genes. and when I multiplied , for example, Ry × rY I wrote the answer as RyrY. When I returned back home I realized that I may have made a mistake because the book used to write them as RrYy. So is my answer write or wrong? why? I also freaked out because there were 16 boxes in the punnet square so does that mean that I'll lose 16 points? I got everything else in the exam correctly. So I'm freaking out because I don't know if I'm getting a 100 or 86 which will throw all my work the entire semester. Please respond as soon as you can. I feel like I'm having a panic attack.

Hah! You can relax. There's no difference between RyrY and RrYy, as there is no fundamental ordering of genes like that. That being said, the standard convention would be to write RrYy (or at least RryY), so if your grader is a pedantic hardass they might take off a couple of points, but probably not the whole 16.

I LOVE YOU! I've been worrying so much. This literally made my day


text:
Anomalous base pairing of nucleotide tautomers
What tautomers base pair with what bases?
I know that adenine pairs with thymine and guanine pairs with cytosine normally. My question is what does the imino form of adenine base pair with? Regular cytosine? What does the enol form of guanine base pair with? Regular form of thymine?
I cannot find any books discussing this issue and Google is of no use. However, my professor wants us to know how tautomers base pair.
I tried base pairing cytosine with adenine but does the -NH2 group on cytosine really hydrogen bond with the imino -NH group on the adenine tautomer?

http://blc.arizona.edu/courses/181Lab/MoBiByMe/Tautomers.html

The tautomers are rare but they can form and it is suggested that tautomerization can lead to mutations because of non-cognate base pairing.
Khuu & Ho (2009), have inferred the presence of adenine and thymine tautomers from the crystal structure of an in-vitro assembled holliday junction. They infer that imino-Adenine base pairs with amino-cytosine (normal form) and enol-thymine base pairs with keto-guanine (normal form).

Reproduced from: Khuu & Ho (2009)

Khuu, P., & Ho, P. S. (2009). A rare nucleotide base tautomer in the structure of an asymmetric DNA junction. Biochemistry, 48(33), 7824-7832.


text:
Which DNA elements belong to the definition of a gene?
I see a lot of different DNA elements mentioned as part of a gene (talking about eukaryotes):

The length of DNA following the promoter is a gene and it contains the
  recipe for a protein. (video)

This implies that the promotor is not part of the gene.

This actually defines a gene without enhancers/silencers

 (source)

Here the gene also includes the enhancers/silencers
Question
Is there an accepted gene definition regarding the DNA elements? Thus which sequences like promotors, enhancers, silencers etc. are considered part of the gene?

Do you have a use case or that definition, something specific that requires you to have definition, i.e. "if A is gene why B is not"? Also, if I say "gene is X", how will you know if it is good or bad?

Usually a promoter is not considered a part of the gene. Distal regulatory elements qualify even lower for being considered a part of a gene because they can regulate many genes simultaneously.
Basically, a geneic region starts from the transcription start site (TSS) and ends at the terminator (or polyadenylation/cleavage site in eukaryotes). If a gene gives rise to multiple transcripts, those which have similar products (no frameshifts) but different TSS/terminators, we should consider the geneic region as a union of all the primary transcripts produced from that site.
There is a relatively old article on this topic: What is a gene, pose ENCODE? History and updated definition. It summarizes that:

There are three aspects to the definition that we will list below,
  before providing the succinct definition:

A gene is a genomic sequence (DNA or RNA) directly encoding
  functional product molecules, either RNA or protein.
In the case that there are several functional products sharing
  overlapping regions, one takes the union of all overlapping genomic
  sequences coding for them.
This union must be coherent—i.e., done separately for final protein
  and RNA products—but does not require that all products necessarily
  share a common subsequence.

This can be concisely summarized as:
The gene is a union of genomic sequences encoding a coherent set of potentially overlapping functional products.

.

My summary is that a gene, if it codes for a protein, would be a union of all primary transcripts that produce identical proteins (originating from transcripts with different TSS/terminators) or splice variants.
Ideally, all the different transcripts produced from the locus would have the same promoter.
Overall, it appears that a common promoter is imperative for considering the different products under the same gene because if two products are produced from different promoters then we consider them as products of overlapping genes.
Whether we should consider the promoter as a part of a gene is an open debate.

While your post is detailed, I'm a fifth year PhD student in genetics and I barely follow it.

@Artem  Possibly, you can ask for specific clarification instead of downvoting.

@WSIWYG. I don't think it's wrong for a textbook answer, I just think it's unclear. What about subsets of non-coding RNA? How much and what kind of non-coding overlap determines a common gene? Or if two non-coding transcripts with the same first exon and different second exons with distinct functions?

@Artem Unfortunately, even the scientific community and the genome consortia have not clearly defined these categories and hence they are unclear. How to differentiate between overlapping genes and a single gene with multiple isoforms? There is no definition out there yet. Usually splice variants are considered under the same gene. If we accept this then it appears that if many transcripts share a common promoter they are considered the product of the same gene.

@ WYIWSYG What happens if in case #2 and #3 the two genes share just a single amino acid or a single helix? The 'Coding Sequence' would be technically be in the first exon and thus it's the same case as 'A-B' 'A-C' which are the same gene.

@Artem Yes the situation is dicey. As per current norms splice variants are considered isoforms of the same gene. However, there can be situations in which independently transcribed products can also end up with the similar kind of products. In the case you pointed out classifying them as two genes because of different CDS is not consistent with A-B-C case. #1 and #2 should be considered splice isoforms. There is a need for a more rigourous definition.

Let us continue this discussion in chat.

My point is that no more rigorous definition using exon boundaries makes any sense, this is simply a convenience for us humans to discuss genic regions of the genome. A gene is first and foremost a unit of heredity.

It depends. There are two broad definitions which work.
The more common definition of a gene is the transcribed portions of DNA, a gene begins at the transcription start site and ends at the Poly-A site. This is common when discussing mRNA, lncRNA, tRNA. It's what most scientists refer to when they say the ACTB gene or IRF5 gene or something.
If you want to explicitly include all introns you say 'Gene Body', if you want to exclude introns you simply say ' ACTB Exons'.
An older and arguably better definition of the gene from Thomas Morgan is a unit of heredity, that when mutated results in a phenotype.
The second definition doesn't say anything about transcription and would be inclusive of things like promoter/enhancer mutations. From a molecular biology stand-point is more difficult to define. It's more useful for genetics because you're interested in how a phenotype is inherited and it's etiology, not how an a species of RNA functions.
98% of the time when people discuss genes they mean the former definition, but this is still an actively debated issue and as we learn more about genetics it will change.


text:
Does the radula sac in any mollusc serve a purpose other than sheathing the radula?
According to Barne Gat Shellfish:

When not feeding the radula is retracted into this chamber which protects the mouth from the sharp teeth of the radula.

Does the radula sac serve any other purpose?

Do you have any reason to believe it does?

@theforestecologist no particular reason. I just thought if the sac can be protected from the teeth, why only that and not the rest of the mouth?

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text:
What are the current theories on how baleen whales make whale song?
Wikipedia does not provide many details1 on how baleen whales produce 'whale song', saying it's still a mystery.
The linked Gizmodo article provides these paragraphs:

Humpback whales, for example, do not appear to exhale while singing, and yet their songs are believed to be produced by a phenomenon similar to how humans sing: by air flowing across internal membranes within the respiratory system. In a whale, air could be circulating back and forth between the lungs and another respiratory chamber during 20-minute dives, though this remains speculative.
The researchers argue that "ingressive" and "egressive" air movements may produce different sounds. They also posit that the shifting of the oxygen/carbon dioxide ratio (which must occur during a dive) could be changing the resonant frequency of the singing organ, by changing sound velocity in the gas in the organ.
Both of these physical constraints might contribute to the periodicity (and variability) commonly observed in whale songs. Compare this reading to a hierarchical interpretation of whale singing (which, you'll recall, involves naming the individual components of a song), in which qualities like periodicity and variability might be linked to some function of syntax, rather than physiology.
Mercado and Handel's "heterarchical" model could be empirically tested by linking subtleties of whale songs to their behavior, or their breathing patterns. For instance, this model would be well-validated if the scientists could predict the diving or breathing of a whale based on the spectrotemporal nature of its song.

But I'm not clear exactly what '"ingressive" and "egressive" air movements' these would be? Unlike for Odontocetes, in that wikipedia article, there's not cross section or diagram with any hint of explanation.

1. At least at time of access: 2021/09/23 17:40

releated, but not a dupe (I hope): https://biology.stackexchange.com/q/79858/1270

DId you have a look at reference 14 in the wikipedia article: "Discovery of a low frequency sound source in Mysticeti (baleen whales): anatomical establishment of a vocal fold homolog "

@bob1 I did not, I didn't think the sentence it was attached to was that relevant, but I can just from the paper title it would be worth a read

As you can imagine, it is difficult to study this in baleen whales. There are decades of research on odontocetes (toothed whales) in captivity, but it is much more complicated for baleen whales. The Discovery of Sound in the Sea has great general information and citations for further investigation. They provide the cross sectional image I think you are looking for--- this schematic of a humpback whale that shows the potential mechanism for sound production in a humpback whale.
 (Image credit: Dr. Joy Reidenberg. Adapted from Joy S. Reidenberg and Jeffrey T. Laitman. 2007. Discovery of a low frequency sound source in Mysticeti (baleen whales): Anatomical establishment of a vocal fold homolog. The Anatomical Record. Volume 290, Issue 6, pages 745–759.)
According to Reidenberg and Laitman, 2007: "Ingressive" vocalizations refer to those produced by airflow from the laryngeal sac to the lungs (driven by contraction of laryngeal sac muscles) and "egressive" voclizations are produced by airflow from the lungs to the laryngeal sac, driven by diaphragm contraction. Reidenberg and Laitman, 2007

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text:
Convert gene name to uniprot ID
I have a list of gene name in a file
CHRNB2
EGR2
GCK
KRT14
LMNA
FGF3
TK2
ABCC8

How can I map them to uniprot ID ?
P.S I tried Uniprot "ID mapping" (from-"GENEID" to-"UNIPROTKB AC"), but it couldn't map.
Please suggest me what to do.

I guess these are human genes?

yea your are right.

try biomart.. ..

Thats my recommendation, too. Biomart is the swiss army tool for this kind of convertion.

biomart did help...thnx a lot

apologies for the delay.I will do it asap...

for biomart goto below link
http://central.biomart.org/converter/#!/ID_converter/gene_ensembl_config_2
Also there is one more converter which i found pretty useful
http://biodbnet.abcc.ncifcrf.gov/db/db2db.php#biodb

Can you explain what these links do? Just posting links as an answer is bad practice: http://meta.stackexchange.com/questions/8231/are-answers-that-just-contain-links-elsewhere-really-good-answers

its quite self explanatory...its basically for mapping of two identifiers.Please refer question for more details.

The biomart link is broken. Why would you choose biodb.net over Uniprot? (I'm not slamming it, I've just never heard of it and am curious if it has a major advantage)

ID mapping
This is called ID mapping. It used to be a headache as programmatic access was the only real way, but it is pretty trivial these days.
As mentioned in the comments, by far the most popular and easy method is to use Uniprot's list uploader for mapping. The corresponding publication can be found here. You must convert from Gene name to Uniprot KB ID
Programmatic access
The Uniprot web user interface will work for thousands of IDs, but is like to result in errors for queries exceeding 10,000 IDs. In that case, programmatic queries are probably better. I've slightly modified this from the Uniprot docs so that the IDs are queried 1 at a time to avoid any errors in exceeding query size. The python code would be:
import urllib,urllib2

list_to_convert = ["CHRNB2", "EGR2", "GCK", "KRT14", "LMNA", "FGF3", "TK2", "ABCC8"]
url = 'http://www.uniprot.org/mapping/'

print "From To"
for i in list_to_convert:
    params = {
    'from':'GENENAME',
    'to':'ACC',
    'format':'tab',
    'query':i
    }

    data = urllib.urlencode(params)
    request = urllib2.Request(url, data)
    contact = "" # Please set your email address here.
    request.add_header('User-Agent', 'Python %s' % contact)
    response = urllib2.urlopen(request)
    page = response.read(200000)
    print page.splitlines()[1] #Ignores the header line returned by uniprot

The output from this looks like:
From    To
CHRNB2  A0A096MXS8
EGR2    A0A096P554
GCK     A0A021WXA1
KRT14   A0A024R1X6
LMNA    A0A096MQV4
FGF3    A0A096NXI3
TK2     A0A096NJ58
ABCC8   A0A088S7J5


text:
What is a membrane potential?
I know this may be silly, but I am confused to what a membrane potential actually is. I understand that at resting membrane potential is -70- -80 mV. But what does that exactly mean and how does this all tie into diffusion potentials and equilibrium potentials.

The existence of membrane potential is just that: as long as they are alive, cells try to keep their cytosol different from the outside soup, mainly by expelling sodium ions. There is a longer story, with cells pumping other ions in or out, or leaking ions due to the imbalance, but resting membrane potential really revolves around sodium. There is an imbalance in sodium across the membrane, and that makes sodium naturally try to balance out, by trying to leak back in.
I am sure you have heard about the famous sodium/potassium ATPase. It expels sodium ions, while letting in fewer positive ions. It is just the most studied way the cells are going about making sure their innards poorer in cations, that is, negatively charged when compared to their environment.
All these pumps are ATP-dependent, and you would know ATP is the main energy store. So as long as cells live and make ATP, they can maintain this difference between their innards and the environment. Once they die, ATP is gone, pumps stop, and there is no sodium disequilibrium - no membrane potential.
Diffusion potential is an artificial concept that we use in order to make sense of the membrane. We imagine a membrane that opposes sodium movement alone, and we calculate how hard will it be (how much energy must be spent) to keep the asymmetry of low sodium inside - higher sodium outside, were a hole punched in the semipermeable membrane. Luckily, we can use some physics and maths, and figure that this energy is dependent of the ratio of sodium concentrations ([Na]in divided by [Na]out). For typical cells, there are 150 mM Na outside and 15 mM Na inside a cell; physics tells us it will take 62 milli-electronvolts to hold a Na ion out once some hole is found.
If you don't provide those 62 meV and you don't close the doors for leaking, sodium ions will fly in. And that is indeed what happens in depolarization, when sodium gets a chance to move.
These electronvolts are a unit of energy, or work. We can think of a few ways of performing this work on a Na ion that tries to leak in: heat the cell until ions stop caring about direction of movement, hold the outside Na in some tight  chemical structure etc. But the simplest way is to apply an electric field. If you add to the concentration imbalance a 62 mV electrical difference in electrical potential, you stopped the flow of sodium either way. Get a bit less than 62 mV, and the Na ions will carry on their old ways and try to no avail to leak into the cell. Get a bit above 62 mV, and the flow of sodium will be force in the opposite way. Stay on 62 mV and you achieve equilibrium potential: neither direction will have the upper hand.
The picture is way more complex once you factor in the other ions, but the facts are:

resting and action potentials are voltages one can measure across membranes, quite often during cells' lives
diffusion potentials are part of an abstract model
equilibrium potential is something one could impose on a cell - not something that you'd find in natural frequently, yet not plain fiction.

To understand what a membrane potential is, you should understand the units for -80mv. The negative sign is relative to the observer, yourelf, to the inside of a cell. From your perspective, the inside has an excess of negative ions. On the outside of the cell, there is an excess of positive ions.
Unlike electricity, where charge separation is quantified by electrons, action potential is quantified by ions. Ions are matter, like Na+, K+, Cl-. When electricity flows through a wire, electrons are flowing from excess negative charge to a deficiency of negative charge.
When a cell establishes an action potential, it moves Na+ outside the cell, which moves the potential from 0mv, negative. The number -80mv, or whatever potential that may be observed, is the point at which a net flow of positive ions are not leaving the cell.
Na+ is actively pumped outside a cell, using energy. K+ is passively driven into the cell by simple diffusion to neutralize the charge imbalance. Opposing the outward flow of Na+ and inward flow of K+ is their diffusion gradients. Ions flow from a high concentration to low concentration.
At -80mv, the Na+ concentration is high outside the cell. Intracellular Na+ is being pumped out to counteract any leaking back in. The K+ concentration is high inside the cell from its passive diffusion to cancel the electrical potential.
The physics of why K+ does not neutralize the -80mv is in its concentration gradient. K+ is being pulled outside the cell by this gradient and driven inwards by an electrical potential. The net electrical potential across the cell membrane is -80mv for a given cell's capacity to maintain the concentration of its intracellular ions.

Membrane potential AKA the membrane electrochemical potential,  may be thought of as the voltage that is experienced across the membrane, analogous to a battery.
The extracellular side is typically positive, while the cytoplasmic side is typically negative. This induces the electrochemical gradient with ions having a preference for a particular side depending on their charge.
An equilibrium of ions can be maintained by transport pumps which "force" a difference in electrochemical potential on either side of the membrane potential. If the membrane is permeable to a certain type of ion, the the diffusion processes occurs as ions diffuse to their "preferred" location dependent on ion concentrations.
Wikipedia has a nice succinct definition of the functional advantages of maintaining a membrane potential:

First, it allows a cell to function as a battery, providing power to operate a variety of "molecular devices" embedded in the membrane. Second, in electrically excitable cells such as neurons and muscle cells, it is used for transmitting signals between different parts of a cell.

This is an advanced concept, so perhaps a few video tutorials might explain better than my words. Start here perhaps.


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plausible science for why an enzyme breaks down a substrate better at 4 degrees rather than 20 degrees
An enzyme was found to have an optimal temperature of 20 degrees. The same enzyme in sheep liver extract was found to have an optimal temperature of 4 degrees. What are some of the factors that can contribute to the differences observed?

I can think of examples in living cells where the apparent optimal temperature for the activity of certain biochemical processes change in different chemical environments. But, I've never really thought about this occurring in extracts/purified enzymes. I agree with @tomd, this should not be closed.

Seems like this question does have a pretty simple answer, i.e. that chemical kinetics can vary with the solution, whether through biologically explicit mechanisms like enzyme inhibitors/activators or through more mundane mechanisms like the solution's ionic strength.  So, the performance of an enzyme'll vary with its environment, so that an enzyme was found to behave differently in two different environments is a really unsurprising result.

Then, when the question asks, "What are some of the factors that can contribute to the differences observed?", the answer's basically that stuff: inhibitors/activators, ionic strength, relative concentrations of reactants, possible denaturation during extraction, etc..  Additionally, these factors can act on not just the enzyme itself, but also the substrate(s).

tl;dr-  This question can probably be re-opened, as it has a pretty simple answer.


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After fat is burned from one part of the body, is fat moved around the body?
If you go running, for example, and burn fat from your legs, does your body send fat from other parts of the body to that area? If so, why would it do this?

Fat burning is a generalized procedure so there is no such a thing that some part of body loses fat and other part gains it.
Targeted Fat Loss still is a myth.


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Are humans not sexual dimorphic? Can genetic XY but SRY not expressed become pregnant?
My understanding was that XX/XY was how sex is determined in mammals, and so humans as well.
A science teacher posted this regarding transphobia:

"I just commented this on a transphobic post that was all like, "In a sexual species, females have two X chromosomes and males have an X and a Y, I'm not a bigot it's just science." I'm a science teacher so I responded with this.
  First of all, in a sexual species, you can have females be XX and males be X (insects), you can have females be ZW and males be ZZ (birds), you can have females be females because they developed in a warm environment and males be males because they developed in a cool environment (reptiles), you can have females be females because they lost a penis sword fighting contest (some flatworms), you can have males be males because they were born female, but changed sexes because the only male in their group died (parrotfish and clownfish), you can have males look and act like females because they are trying to get close enough to actual females to mate with them (cuttlefish, bluegills, others), or you can be one of thousands of sexes (slime mold, some mushrooms.) Oh, did you mean humans? Oh ok then. You can be male because you were born female, but you have 5-alphareductase deficiency and so you grew a penis at age 12. You can be female because you have an X and a Y chromosome but you are insensitive to androgens, and so you have a female body. You can be female because you have an X and a Y chromosome but your Y is missing the SRY gene, and so you have a female body. You can be male because you have two X chromosomes, but one of your X's HAS an SRY gene, and so you have a male body. You can be male because you have two X chromosomes- but also a Y. You can be female because you have only one X chromosome at all. And you can be male because you have two X chromosomes, but your heart and brain are male. And vice - effing - versa. Don't use science to justify your bigotry. The world is way too weird for that shit."

This question is combining different arguments: human gender based on genetics vs. based on appearances vs based on culture. This is too broad, and can likely only lead to open-ended discussion. In fact the question is unclear and this post seems to simply be seeking discussion. I'm voting to close as too broad and as being unclear what you're actually asking.

If you're asking if humans are a sexually dimorphic species, the answer is "yes".
If you're asking based on the viral response of the science teacher, then the answer is still, "yes".  What is described above are chromosomal abnormalities, or other disorders of development, but they are still describing sexual dimorphism in the phenotype. However, the answer itself is incomplete, as it's written out of political motivation.
Whilst true that a person can present, phenotypically, the opposite of their genetic sex, this isn't akin to them "being male/female" counter to their allosomes.  Of course, this in itself can prove to be a contentious issue - how is sex defined?  Under normal circumstances, phenotype is the obvious way of knowing. If someone has the external genitalia of a male, they can be assumed to be male, and vice-versa for females.  When it comes to chromosomal abnormalities, there is an "error" in devlopment, where the genotype is not properly expressed in the phenotype, and so while presenting as one sex, the "true" sex as determined by the genotype is the opposite.  It should also be noted that sexual characteristics can be ambiguous certain cases, creating abnormal sexual presentation, such as in hermaphroditism/pseudohermaphroditism, or "intersex" (the quote mentions 5-alpha-reductase deficiency, which is associated with male pseudohermaphroditism).
The problem with the quote above (at least in the way it is written) is it appears to suggest that chromosomes have little to do with phenotype, as in sex determination, and that this carries over to gender dysmorphic patients.  These are both mostly false.  Gender dysmorphic patients generally do not have any allosome abnormalities, and the reason that the given examples are deemed "errors" (eg XX male syndrome; 47,XXY) is because that is what they are, as well as being rare.  The typical effect of these problems is that the patient is infertile, (i.e. they are not sexually functioning, and have reduced biological fitness).
In short, the teacher is correct in the examples used, but overstating them with regard to the general population, and with regard to transphobia and gender dysphoria.  Humans are indeed sexually dimorphic but as with any complex biological system, errors expressing genotype can occur.


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What's the difference between morphospace expansion and packing?
I'm reading an article that talks about morphospace and niche expansion or packing.

Differences in slope above and below zero indicate dominance of
  morphospace expansion versus morphospace packing, respectively.

What do the terms mean? What's the difference?
The way I understand it is that morphospace expansion is the change in morphology from the perspective of a population, but morphospace packing is, from the perspective of the ecosystem, the process where organisms are occupying the ecosystem space entirely so that no niche are available anymore. Is that correct?
The "packing" seems to refer to the density of the different "regions" of morphospace. Or said differently, you could "pack" different morphotypes in an environment. Is this a probable explanation?
I haven't found an answer looking at the history and definitions of niche.

You're almost there--both terms concern the relationship between increases in species richness and morphological diversification. They're two opposing patterns:
Morphospace expansion indicates that a lineage is occupying new areas of morphospace with increasing species richness. (The slope is more or less relating density of morphospace occupation to speciation rate--so > 0 means new species tend to go farther away in morphospace.)
Morphospace packing indicates that a lineage is not expanding its morphospace with increasing species richness, so the existing region of morphospace that it occupies is getting more densely filled, instead of expanding.
The relationship between functional/ecological diversity and morphological diversity isn't always clear cut, though, so you have to be careful when relating the two. If you're going to use these concepts, especially if you're going to use morphological diversity as a proxy for functional diversity, it's best to state that assumption outright.
This paper includes a nice discussion of the two concepts (and is cited by the one you list): http://rspb.royalsocietypublishing.org/content/283/1822/20152013

Thanks! As a quick question, are you saying that niche expansion is the same thing as morphospace expansion and same for packing?

Not quite -- niche expanision/packing are the same base ideas, but for ecological niche instead of morphology. I'm generally pretty careful about being explicit when relating one to the other.
So, for niches:
Niche expansion = exploring/ expanding to new areas of niche (ecological diversity)-space with increasing species richness
Niche packing = filling in the existing area of occupied niche space with increasing species richness. Does that make sense?

Yes! And it would be equivalent to say that morphospace expansion is the extension of the previous (before expansion) trait distribution to new phenotypes whereas packing would be the development of of already existing phenotypes, but increasing their density. Is that ok?

Precisely! You've got it!


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What happens to a snail if we cut off its antennae?
First off, I don't know why snails have antennas. Are they eyes? And what if we cut those antennae off? If they are like eyes, could a blind snail survive somehow?

And by the way: how unethical would it be to find it by myself?

Super unethical. If you were obeying the three Rs you would realise that these animal experiments could be easily replaced by reading about snails. If you don't know what you're doing from a practical point of view and are not being careful to act scientifically (recording logs, testing against reasonable hypotheses, and following the three Rs) animal "experimentation" could be seen as nothing more than deliberately torturing animals without any real goal.

@James Thanks! Very Valuable comment, since I have no background in biology at all! I didn't even heard about 3Rs. And your answer is magnificent :)

I'm not an animal scientist so I don't have deep insight, but I know animal scientists in my institute. There is a related meta that you might find interesting. The snail answer isn't mine, it's @JordiZambrino's! I agree, it is a very good answer.

If this answer addressed your problem, please consider accepting it by clicking on the check mark/tick to the left of the answer, turning it green. This marks the question as resolved to your satisfaction, and awards reputation both to you and the person who answered.

How mean. Please don't do that. Those are fingers they use to sense the surrounding environment around them.

It really depends on which type of gastropod you are talking about, since different types of snails, have different types of eyes.

Marine gastropods could only become more mobile, when their abilities of sight had improved. This happened, when the eye cup deepened and the visual opening narrowed. An effect was the result, which in historical time was used by the so-called pinhole camera: A picture of sufficient sharpness can be displayed by reducing the camera's aperture to the size of a pinhole. The smaller the aperture, the sharper the picture. But because there is only a small amount of light falling through the pinhole, the image will be quite dark. Besides, the displayed area of the image is very limited.

From left to right: Pinhole eye of an ormer, A Roman snail's simple lens eye, A limpet's cup shaped eye
Could a blind snail survive?
There are even some types of gastropods which are naturally born blind such as:

Cecilioides acicula, common name the "blind snail" or "blind awlsnail", is a species of very small, air-breathing land snail, a pulmonate gastropod mollusk in the family Ferussaciidae.

Descriptive features:

This animal is white, there are two pairs of tentacles, but eyes are lacking. 4
  The shell is long and narrow, up to a maximum of 5.5 mm and a width of 1.2 mm.5 The shell is colorless, glassy and transparent when it is fresh, a somewhat opaque milky-white when it is not fresh.

Snails which are visually impaired are not limited in their ability to move, however, and can easily navigate their habitats through their sense of touch and smell.

Regarding the ethics of your "investigation", I'd recommend to just not do it.
Sources:

https://de.wikipedia.org/wiki/Blindschnecke
http://www.animalbase.uni-goettingen.de/zooweb/servlet/AnimalBase/home/species?id=1286
https://en.wikipedia.org/wiki/Cecilioides
http://www.molluscs.at/gastropoda/index.html?/gastropoda/morphology/eyes.html


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How would one determine whether a chemical will upregulate a certain class of proteins?
I'm trying to determine whether certain organic chemicals will upregulate classes of proteins that possess deacylase activity.
How would I go about this experiment? I'm assuming I would use some sort of assay but I'm not exactly sure how I'd go about it.

One way of examining this is by looking at the transcriptome as a whole before and after the introduction of the chemical. I would use microarray or next generation sequencing RNA-seq technology to look on the expression level of all the genes after the introduction of the chemical, some inert control and no treatment control.
Then, you can calculate the relative fold change in expression of all genes, under treatment vs. control. Next, you can do a GO term enrichment of the significantly upregulated genes, and see if the term "deacylases" shows significant enrichment in genes that are upregulated under treatment vs. control.


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How does the introduction of clones would affect population genetics?
Since the ability to clone animals gives another way to preserve species, this could help for the breeding process. It has been noted that with the ability to clone animals, endangered species can have their population numbers increased in a short time. How would this impact these population from the perspective of genetics/ the gene pool?

Welcome to Biology.SE. Is this a homework question?

yes it is @Remi.b

The title should be the main question quickly phrased and should not set the field of study. Obviously saying "it is a biology question" is really not helpful as everything on this site is about biology.

Then, you should always show your effort to try to answer the question otherwise the question is off-topic. I will vote to close as you haven't shown your efforts to answer the question and will remove the vote close as soon as you added this info. You can have a look at the homerwork question policy here.

Since the ability to clone animals gives another way to persevere species, this could help for the breeding process. Cloning helps to be alternative route for the genetic line and helps with endangered species to be save and increased.

You should add this comment into your question so that others will directly see what efforts you've produced. Try to come up with as many thoughts as possible. Despite showing that you are not just asking for "someone else to do your job", it also helps to target what may be unclear to you.

@kristy I have prepared an answer for you but, as this is homework, I would like to see more effort from you, some tips for you to think about: How do you think cloning could be used? Why would cloning be useful? Where can cloning step in? Why would this impact genetic variation?

@Kristy The question is on the verge of being closed, if that happens I can't post the answer unless it gets reopened - that will only happen if you make some edits to show that you've given some thought to the reasons that cloning may or may not be useful. It's otherwise a good question and I'd like to provide the answer


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Guide to learning about Population biology / ecology / dynamics for a non-Biologist
I'm an applied mathematician who works in the field of feedback control systems but has been becoming interested in looking at population biology (e.g. Lotka-Volterra, Mathusian growth,etc.) from a mathematical feedback and systems theory standpoint. Since I know almost nothing about biology save for a poor frog I nearly threw up on while trying to dissect in High School, I wanted to ask this community if anyone could provide me with some materials that give a good overview of the current state of this field.

You will find recommendations for book on population genetics here. The recommendations in General Entry books to population genetics and the 3 first recommendations in Other books treating specific subjects within population genetics can interest you.

Can I be dumb and ask whether or not population genetics has anything to do with dynamics (e.g. number of species X in a habitat)?

Yes, it does. For example, if a population fails to adapt in an environment or if deleterious mutations accumulate in a population due to a too low population size and causes the population to get locally extinct (mutational meltdown). This kind of simple dynamic of species-interaction might be quite different when you add genetics and evolution in the model. So yes, it is very much related. I would call population genetics a subfield of population biology.

To those who voted to close: I do not think this question is "too broad" or "primarily-opinion based". The OP is just looking for recommendation of books in population biology. Look for "population biology" on amazon and you get several interesting hits. We just need one person who read these books and can formulate a critic.

Yes, as Remi.b says, I am not asking for opinions on the subject, but rather which books are commonly used. Having "standard" texts is a common thing in most fields of science and I was simply asking what those are and if people can provide general criticism to direct a newcomer.

@Remi.b I strongly disagree. Although population genetics are important to population dynamics in a broader sense, the examples the OP provides (e.g. Lotka-Volterra, Malthusian growth) have next to nothing to do with genetics but with growth and hunter-pray interactions. There must be better books to suggest here!

®RHA I never said the post that asked for recommendations on population genetics was a duplicate. I just think it is worth mentioning this post as it may interest the OP. I then argued that population dynamics also depend on its evolution (as you did as well). But I recognize the question as being valid (and am the only one who up voted it so far). I am personally not able to offer good book suggestion in this field as I haven't read them.

Remi.b's suggestion has indeed been useful to me; for one, I didn't know that genetics and dynamics were so closely related and am grateful someone took the time to explain this to me. I think I am most interested in learning about simpler models from a mathematical perspective, then diving more fully into population genetics, as undoubtedly getting to the point where I even understand the background in genetics for the latter will be an undertaking!
Consider for instance this text, which I have been reading: http://www.springer.com/us/book/9783319030258
How close is this to SoA?

I've also found this good looking book on the SIAM (professional applied math organization) website: http://epubs.siam.org/doi/book/10.1137/1.9781611970005

@Remi.b is right, population genetics is a sub field of population biology, the other being population ecology. The two fields are intimately related at their foundations, but diverged long ago to become fairly distinct. A good summary of the historical context can be found in the introduction to Singh, Rama S., and Marcy K. Uyenoyama, eds. The evolution of population biology. Cambridge University Press, 2004. They also include models that may be of interest. But for studying populations as dynamical systems, see my post below.

I'm a fan of E. O. Wilson and W. H. Bossert's "A Primer of Population Biology", which has been continuously in print since 1971, as a first introduction. (http://www.sinauer.com/a-primer-of-population-biology.html)

There are three seminal works that will give you an excellent grounding in the study of dynamical systems in population biology.
The first details the foundations of population genetics:
Crow, James F., and Motoo Kimura. An introduction to population genetics theory. (1970).
The second deals with population ecology:
Maynard-Smith, John. Models in ecology. CUP Archive, 1978.
Finally, the field of evolutionary game theory has recently embraced a dynamical systems approach to studying populations under the guise of a new theoretical framework known simply as evolutionary dynamics:
Nowak, Martin A. Evolutionary dynamics. Harvard University Press, 2006.
I would probably read these in reverse order. The introduction to Maynard Smith's book has a nice discussion about some of the interrelations between population genetics and population ecology. Most of the original work was done by Fisher, but you don't want to read his stuff - it's famously inaccessible.
It is probably worth me telling you that a lot of mathematical biologists don't actually use dynamical systems approaches in their work. Instead, they favour optimisation models. A balanced discussion about the benefits of this approach, and some of the limitations of the dynamical systems approach can be found in these papers:
Smith, J. Maynard. "Optimization theory in evolution." Annual Review of Ecology and Systematics 9 (1978): 31-56.
Parker, Geoffrey A., and J. Maynard Smith. "Optimality theory in evolutionary biology." Nature 348.6296 (1990): 27-33.
As it happens, my soon to be submitted doctoral thesis is on dynamical systems in biology. Hopefully one day I will be able to add my own book to this list :)

Most of the books you cite are really about population genetics and a more in depth review (with IMO better books) can be found here. I don't know "Models in ecology" by J. Maynard-Smith but form its title, it sounds like a good recommendation.

@Remi.b your in-depth review does not answer the OPs question, which is about population biology and dynamical systems. Only a few of the books you posted deal with this explicitly. My answer is focussed and to the point. There is only 1 book on population genetics. It is probably the most cited book on population genetics and a must read for anyone getting into the field (it is not in your list). I presume you were confusing Evolutionary Dynamics with a book about population genetics? Well, it's nothing of the sort. It is a book about modelling frequency-dependent selection in populations.

Too often in biology is mathematical modelling of the evolution of populations conflated with mathematical modelling of the dynamics of populations (from which evolution then occurs). For instance, I recently read Rousset, François. Genetic structure and selection in subdivided populations. Princeton University Press, 2013. It is an excellent book, but it was recommended to me as a book about dynamics, and it barely touches on dynamical systems at all.

All right.... Deal! +1 :)


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Is there a difference between "Alpha Helix" and "Alpha subunit"?
In my biochemistry textbook the terms alpha helix and alpha subunit are both used. Are these two terms synonymous? For instance, hemoglobin has an alpha and beta subunit, are these the same thing as alpha helix and beta pleated sheet?

These terms are completely unrelated. Alpha-subunit is an arbitrary name for a protein subunit in a multi-subunit protein complex (one having a quaternary structure like haemoglobin or G-protein etc).
Alpha-helix is a type of secondary structure.

Addition by David:
For those who may be unfamiliar with this, alpha, beta, gamma etc. are Greek letters of the alphabet (equivalent to English a, b, c) — also written α, β, γ. They are sometimes used in science to enumerate or differentiate between a series of related things — proteins, small molecules etc.

For those who may be unfamiliar with this, alpha, beta, gamma etc. are Greek letters of the alphabet (equivalent to English a, b, c) — also written α, β, γ. They are sometimes used in science to enumerate or differentiate between a series of related things — proteins, small molecules etc.

@David Much appreciated!

@Atticus283blink If this answer addressed your problem, please consider accepting it by clicking on the check mark/tick to the left of the answer, turning it green. This marks the question as resolved to your satisfaction, and awards reputation both to you and the person who answered.


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Is there a rule for the annotation of the basepair or gene locus origin in bacterial genomes?
Since all bacteria have one circular chromosome, there is no designated origin to start the systematic gene locus numbering. Therefore, what is the rule to start the numbering at a particular point (i.e. why is in the E.coli K12 strain the thrL gene given the ordered locus number b0001? If the answer is: It is the first Gene downstream of basepair-1, then why is basepair-1 at this particular position)
Why the question? Since bacteria show frequently functional synteny between genes, should I be surprised to find a gene frequently associated with the (arbitrary?) annotation origin? If the annotation origin is i.e. the origin of replication that would be an interesting case of synteny, but if no such rule exists, then such a case would most likely just reflect the historic sequence of Genome Sequencing

I don't know this from experience, but since every bacterial chromosome has an origin of replication, it'd make sense to start from there

Even I think it is the ori. But let me find a reference

Trying to find a reference has me googling ridiculous things like "numbering base pair convention circular chromosome bacteria"

I guess it is not ori.. From E.coli genome GTF: NC_007779.1 RefSeq region 3710706 3710937 . - . ID=id233;Dbxref=GeneID:12934266;Note=ECK3735:JWS0001:b4489~origin of replication;gbkey=rep_origin;gene=oriC

I suspect it's based on some kind of linkage map generated by interrupted mating experiments a long time ago. It certainly makes sense that ThrL would be one of the first genes since it used in threonine biosynthesis, making it a good marker. Why they chose that marker could be arbitrary, possibly it happened to be downstream of the F factor integration site in whatever strain the original researchers where using. If I find some kind of literature about it, I'll post an answer.

We already discussed a similar question here

@har-wradim I am not sure if people use the same terminology these days (with genomics so popular). It would make sense to set ori at 0.

@canadianer: thrL is the first gene is the Ecoli reference sequence (pos 190) but I don't understand why it would make sense to keep it first in the order for all bacteria.

@WYSIWYG: In E.coli the old "point 0 min" corresponds to the currently accepted boundary between the last and the first bp's: see here a superposition of the two systems and these articles: "old" and  "new". I have the feeling, that for new bacteria point "0" is normally defined by ori.

[I'm sure we have a skilled microbiologist somewhere here, so he would correct my answer.]
The necessity to designate gene locations in classical bacterial model species predated the genomic era, so traditional maps were normally neither bound to origin of replication, nor were they necessarily expressed in base-pairs. In E. coli, for example the traditional map was created based on the F-pili-mediated DNA transfer experiments.
When whole genome sequences became available it was only natural to continue the traditionally defined numeration in terms of placing the "base-pair one". Thus, in E. coli "the traditional and physical maps ... linkage map are closely correlated".
My understanding of the current situation with bacteria without genetic prehistory is that their genomes are numerated either respective to classic closely-related species (if any) or according to the presumed location of ori. Just to give an example of how the dilemma was solved for the Buchnera genome: it was "assumed the DnaA box upstream of the gidA gene is the replication origin" and the authors decided to designate "the start of the DnaA box as base pair one".
As you see, still, even if the authors try to define "base-pair one" based on the position of ori, the problem is that this point depends on the ori-searching tool one uses and on the contemporary understanding of how ori-sites look like (direct determination of origins of replications are in many cases not easy). See e.g. this table with a revision of ori locations in a set of genomes: while most of the genomes received their numeration irrespective of ori, some of them were indeed bound to it, but the new analysis showed that these locations were inaccurate.


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Can we harvest energy from plants?
This is a cross-cutting question but I think its core is about biology. Our society's need for energy is dramatically growing and we are messing up with our environment to answer them. Maybe another way to proceed would be to use the primary energy source that is the sun in the same way as it has been used throughout the ages: photosynthesis.
I know the energy effiency is not as good as a solar panel's but it could clearly be compensated by volume. I found surprisingly little information about harvesting energy from photosynthesis which is why I began to wonder where we are at today.
Thanks!
Edit
I meant transforming the chemical energy generated by photosynthesis into electrical energy. For instance, the first algae powered building was unveiled at the International Building Exhibition hosted in Hamburg. This is a whole different approach.
The most basic example of what I would like to talk about seems to be the algae powered lamp that has (apparently) been developped. In other words, it seems that some sort of plant solar panels are under development and I don't understand how it's done.

Yes, planting trees and burning them afterwards is a way to harvest energy from plants that's with us since millenia.

We can't currently use that energy directly, and if your question is "how can we indirectly extract the energy captured by plants" the answer is "see almost all of human history" (e.g. agriculture, burning wood, even fossil fuels)!

Do you have a link to this house?

@jonrsharpe Apparently, it is possible (cf the algae powered building, or algae powered lamps), which is why I find it surprising that there is so little information about it

@Chris If you Google algae powered building, you'll find some links (I'm not sure which link would be the most relevant to share)

Hmm, they claim that the growth of the algae produces heat - something I actually doubt. Similar systems (albeit without algae) are used as solar heating systems. They work pretty nicely.

@Chris I might have misunderstood how this building works, but what about a more simple example? Could you look up the algae powered lamp and tell me what you think about it?

If you want to use plants as glorified solar panels it's not a good idea, the best photosynthesizers are around 10% efficient at converting solar energy to chemical energy, modern solar panels can reach about 20% efficiency pretty easily. If you consider the energy lost when converting plant matter to electricity your efficiency gets even worse.

@user137 Does it actually matter that we don't get the best efficiency if we get energy that 1. didn't require digging up scarce resources to build and 2. has been proven sustainable over time in exchange?

@Thibault Solar panels are mostly silicon, which is abundant. Furthermore, old solar panels can be mostly recycled. And anytime you use plants for energy you get into the food vs fuel debate. We only have so much farmable land, and population is expected to peak around 2050 with over 9 billion people. How do you feed all those people AND their stuff? You need to get your electricity from other sources, such as solar, wind, or nuclear, not plants. Keep in mind climate change could reduce farmable land and crop yields by that time too.

@user137 I was pretty sure that rare earth elements were need for solar cells but I find one thing and its opposite on the internet right now... I need to check that out in depth. About the food vs fuel debate, I'm not talking about replacing farms with plants to produce energy, I'm talking about putting "plant solar panels" where there were none before (roofs, walls, roads, ...). Hence, I would get my electricity from the sun with the extra advantage of taking the whole ecosystem into account by using plants to do so.

@Thibault I was talking about solar panel which heat water - like the bioreactors with the algae do, not about those which produce electricity. They claim that they put the algae in thin layers of glass on the outside of the building where the sun reaches them. They then say that they circulate the medium with the algae to the inside and it gets heated by the growth of the algae to about 40°C. And this is the point that I doubt - my interpretation from similar heating systems is that the heating up is done by the sun and the algae are a nice addition. They want to dry them and use further.

Some friends of mine are working in a lab that hopes to genetically engineer plants to produce more efficient biofuels. I don't fully understand the work they're doing but they're apparently at the "proof-of-concept" stage just trying to show that they can produce specific high-energy compounds in high-enough volumes to warrant further research

The most basic example of what I would like to talk about seems to be
  the algae powered lamp that has (apparently) been developped.

I think you misunderstood the idea. That lamp uses bioluminescence and not electric power. Normally living cells don't like to give you energy. The trick we use is anaerob fermentation. Without the presence of oxygen (good electron acceptor) they cannot extract more energy from compounds like ethanol, etc... so they get rid of them. After that we can "burn" these compounds with oxygen and get a lot of energy. So currently there is no solution which uses sunlight and microbes to produce electricity directly, however it might be possible.
There are many ways to use photosynthesis in order to produce energy.

The simplest way to burn the plant itself when it has grown enough. You can burn wood, energy plants (e.g. energy grass), etc... and use a generator.
A more sophisticated approach to ferment biomass and produce methane, ethanol, etc... which you can burn. This works very well by starch (e.g. corn bioethanol), and there is active research about cellulose conversion.
There is active research about artificial photosynthesis as well.

You can feed microbes with electric power coming from a photovoltaic system (solar panel), so they can produce ethanol, methane, etc... This might be better than storing energy in batteries.
You can use electric power coming from solar panels to split water. After that microbes can use the hydrogen as electron donor to fix $CO_2$, so they can create ethanol, etc...
You can use light to split $CO_2$ into $CO$ and $1/2O_2$. You can use $CO$ in biological systems to create ethanol, etc... You can use $CO$ in shift reaction to create $H_2$. It is a new technology to use copper nanoparticles to convert $CO$ into ethanol in a completely artificial system.

You can use a photovoltaic system instead of photosynthesis if you need electric power instead of chemical compounds.
Related articles:

2011 - Light Absorption Enhancement in Thin-Film Solar Cells Using Whispering Gallery Modes in Dielectric Nanospheres
2010 - Powering microbes with electricity: direct electron transfer from electrodes to microbes
2008 - The microbe electric: conversion of organic matter to electricity
2011 - Electrosynthesis of Organic Compounds from Carbon Dioxide Is Catalyzed by a Diversity of Acetogenic Microorganisms?
2011 - Metal centers in the anaerobic microbial metabolism of CO and CO2
2014 - From Ionizing Radiation to Photosynthesis
2012 - Biological conversion of carbon monoxide to ethanol: effect of pH, gas pressure, reducing agent and yeast extract.
2014 - Electroreduction of carbon monoxide to liquid fuel on oxide-derived nanocrystalline copper
2009 - Molecular Approaches to the Photocatalytic Reduction of Carbon Dioxide for Solar Fuels
2014 - Comparison of CO2 Photoreduction Systems: A Review
2013 - Leaf-architectured 3D Hierarchical Artificial Photosynthetic System of Perovskite Titanates Towards CO2 Photoreduction Into Hydrocarbon Fuels
2009 - Water-Gas Shift Reaction Catalyzed by Redox Enzymes on Conducting Graphite Platelets

It is not possible to do this directly. Indirectly, it is possible, this is actually done by harvesting fruits - they contain the energy of the sunlight conserved in chemical compounds like sugars or starch and their cellular structures. The basic process for this is photosynthesis.
The products from the fields are used technically to produce gas by fermentation, which then can be burned to produce electricity. Read reference 1 for more details.
What is also done is the use of sugar cane (done widely in Brazil) or corn to produce ethanol which is then used in the fuel of cars. See references 2-4.
Besides these technical processes, there is of course still the possibility to simply burn whole plants or the wood of trees, which is also the result of the fixation of sun energy.
References:

Biogas Production from Maize Grains and Maize Silage
Ethanol Production Using Corn, Switchgrass, and Wood; Biodiesel
Production Using Soybean and Sunflower
Ethanol fuel in Brazil
Corn ethanol

Then I don't quite understand what has been done here http://blog.ted.com/2013/10/02/a-streetlamp-powered-by-algae/ I might be daydreaming but if this technology was enhanced, and scaled, it would be marvellous

@Thibault I'll have a look at it.

Another alternative is using plants to generate biomasse using photosynthesis and letting bacteria convert that into electricity.
There is research being done about this method and you can read more about it on the following websites:

http://www.plantpower.eu/
http://plant-e.com/technology.html

I'm totally not an expert on this but read a news report on this only yesterday.  I wouldn't mind and even appreciate if someone would integrate this into a bigger and more complete answer.

That sounds more like what I expected. As @Zombaya said, could elaborate on this and integrate it in a more complete answer?

I can highly recommend Prof David MacKay's online book http://www.withouthotair.com/
where puts things into perspective. You can find him on YouTube and TED too.
e.g. http://www.withouthotair.com/c18/page_103.shtml "Can we live on renewables"
POWER PER UNIT LAND OR WATER AREA
Wind            2 W/m2
Offshore wind   3 W/m2
Tidal pools     3 W/m2
Tidal stream    6 W/m2
Solar PV panels 5-20 W/m2
Plants          0.5 W/m2
Rain-water
  (highlands)   0.24 W/m2
Hydroelectric
  facility      11 W/m2
Geothermal      0.017 W/m2

Table 18.10. Renewable facilities have to be country-sized because all renewables are so diffuse.
So it doesn't look good for plants  as direct energy source (except for food) (in UK at least)...(or most renewable things for that matter).
Essential reading for anyone interested in energy.

this is more of a comment than an answer.

i didn't have enough points to comment. Then I got 100 more so maybe i do. Nor can i format comments

Ok - It's kind of annoying that it needs more than 1 point to comment...

I'll definitely read that, thanks. It does put things in perspective.

Everyone's looking at photosynthesis for direct energy conversion, but it's not the only approach. Another function of most plants is to pump water up to altitude, to serve its own needs.
Tap into that - picking plants where the sap is watery, not inconveniently sticky like maple syrup, resinous, or likely to congeal into rubber, and you have at least hypothetically, the potential for micro hydro power...
I'm making no claims for energy density or efficiency.


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Do free computer programs exist that can simulate drug permeability in bacterial membranes?
I am currently trying to make derivatives of a molecule in hopes of making it more permeable to bacterial membranes. I wanted to know whether there are any bacterial membrane simulating programs where I can dock my molecule and see its predicted permeability. Preferably, I would like to use a free program that is known to be trustable/accurate.

I think it would be helpful if you could specify what type of molecules you are dealing with and what kind of derivatives you are thinking about. There is a nice answer below for peptides, but e.g. for hydrophobic molecules, the considerations will be completely different.

Also, gram positive, or gram negative bacteria?

I currently work alongside many biologists that are working on simulations of anti-microbial peptides in the membrane. These seem to have a similar mechanism to what you're aiming for — permeating the lipid bilayer.
Presumably you have already come to the conclusion that molecular dynamic simulations are probably a good shot for exploring what may happen at an atomistic and energetic level. Software does exist, but an accurate answer from a project like this may take months of work and time on an HPC cluster.
The only software recommended by PDB (trustworthy) explicitly for membrane systems is GROMACS (and it's free!). This has documentation on membrane simulation which I have used in the past. However you will need to be very careful in both designing the simulation and analyzing the output, and an expert guiding hand may be required.

Gromacs is very good. But it has a steep learning curve, which is to be expected from the complexity of the simulations.

@becko This is a good point, but I'd say one would struggle to find any aspect of scientific interrogation that doesn't have a steep learning curve!


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How hard it is to determine a 3d structure of a protein?
I seeing tens of thousands of PDB files on the internet. I really want to determine a 3D structure of my protein of interest. I've heard that 3D structure determination is a complex, expensive, and specialized procedure that can take months or years and is hard to perform routinely or even purchase commercially.
Could you please explain why and how hard it is? What are the requirements and how big of a budget is typically needed to perform this kind of experiment?

It really depends on the protein. Which protein are you interested in? Or if you want to keep that secret at least tell us what type of protein it is... is it an integral membrane protein, or is it membrane associated? If yes then your experiment will be hard, but not necesarily impossible. If you want to discuss it of thread, send me a mail at (jan@mbg.au.dk) and I will try to help you. I am a trained structural biologist with significant experience in protein crystallography.

From personal experience, if your protein is not membrane associated and is generally stable in solution, it will usually take ~5-10mg to find possible crystallization conditions. I recommend starting with commercial screens and optimizing from them. Be aware that you may observe crystals that are not protein crystals in certain conditions. If you want help send me an email at the email in my first comment.

@JeppeNielsen Hi Jeppe ! Thank you for your enthusiastic and kind response. In near future, I'm planning to design a protein (currently non-membrane protein) and just want to see the final 3d structure result of it, I will contact you later if i need help from you. Thank you very much!

One warning before going heads in is that crystallography is of course only possible if the protein is capable of crystalizing. If you want to know more about biomolecular crystallography, read the book with the title biomolecular crystallography by Bernhard Rupp, it is more or less the goto source for all protein crystallographers.

@JeppeNielsen Thank you very muchhh! I will read that book. You're so kind!

I can attest from experience, the answer is "very"!

Experimental protein structure determination is hard: the most common method is X-ray crystallography, which can be done in a few months if you are lucky and can take years if you're not. The problem with X-ray crystallography is that you need good protein crystals, and in most cases, proteins don't crystallize very well, so it takes a lot of time (and a lot of purified protein) to get the right crystal. (In the comments section Cody added some good links with more in-depth abut X-ray crystallography for structure determination relating to ATP synthase).
The cost is mainly based on the materials & manpower you need for this, and you need to test a lot of different solutions to crystallize your protein in, which aren't very cheap. If you don't have access to specialised equipment for making the crystals (mostly pipetting robots) or the measurement (X-ray beamline), this is going to be super expensive.
Crystallography requires protein crystals, which are sometimes too troublesome to obtain. Other methods that do not require crystals, such as NMR and cryoEM, are even more difficult to perform though less luck dependent. These techniques rely on very expensive equipment and often cannot resolve the structure as precisely as protein crystallography. So unless you can find one of the few people who have experience with these methods, you'll run into a lot of additional problems.
There is also computational structure prediction, which only needs a powerful computer. However, unless your protein is very similar to one with a known structure it will probably not work reliably. As mentioned in the comments there are web server's for the established methods and progress is constantly made on new(er) algorithms, so depending on your needs it's definitely worth a shot to try computational structure prediction.

For what it's worth, there are many web servers for homology modelling, so all you need for that is a protein sequence and a browser.

Note that computational predictability recently made a giant leap forward and seems to work quite well (requiring primarily DNA sequences of many species rather than similarity to known protein stucture or computational folding simulations) http://science.sciencemag.org/content/355/6322/294

Two interesting & relevant papers showing how X-ray crystallography is used to determine protein structure are: Structure of ATP synthase from Paracoccus denitrificans determined by X-ray crystallography
at 4.0 Å resolution, and a review, The ATP synthase: the understood, the uncertain and the unknown. What's particularly nice about these is they deal with a protein complex virtually everyone is familiar with, yet has been very challenging to characterize using our standard techniques.

In fact, computer approach is so hard (this is a combinatorial problem where exploring all the possibilities is not feasible), that some scientists have designed Fold It!, a game where players must find the best protein folding. There is a field of work where scientists look at best player's strategies to design their own heuristics.

I'll address NMR for structure determination. It is the less common method, only ~10% of protein structures are determined this way, though it has e.g. advantages for nucleic acids and more than a third of those was solved by NMR. Take any numbers here as very rough estimates, there are a lot of factors that influence the difficulty and cost.
For NMR, the single most important factor is size. A stable, well-behaved protein of 10-20 kD is pretty much routine. Large proteins are either very difficult to measure or outright impossible by NMR.
You need large quantities of protein for X-ray and NMR, but in the case of NMR you also need it isotope-labeled. Uniformly labelling proteins 15N/13C isn't all that expensive if you can express your protein in E. coli in minimal medium (around $100 per liter medium or so, mostly in 13C glucose), but it can get really expensive very fast if you need full medium or anything fancy.
Your protein needs to be stable in solution for a few days at least, a single 3D experiment can take multiple days to measure. If your protein is barely stable enough, you also have to produce much more protein because you need to use many samples to perform all necessary experiments. A complication here is that you can't add a lot of salt to an NMR sample without drastically reducing the sensitivity of the NMR experiments. But some proteins are hard to get stable in a low-salt environment.
You need something like a few weeks of measurement time on a high-field NMR spectrometer, though this depends a lot on the size of the protein and the concentration you can achieve in your sample and the general difficulty of assigning the protein resonances. This kind of spectrometer can cost a few millions, and you need to supply liquid nitrogen and liquid helium to keep them working. Something like a $1000 per day measurement time is a number I've heard about the cost, but there are a lot of factors that go into this and I can't say how accurate this number is.
Then you need to assign the resonances in your protein, which takes a single person several months. This again varies a lot depending on the difficulty.
Then you need to calculate the structure, and typically this requires several rounds of refining the assignment and analysis and redoing the calculations. It's not really expensive in terms of computer power, but it takes quite some work by the person running the calculations and checking them.

a 2007 article says "The largest monomeric protein structure solved by NMR to date is that of malate synthase G (MSG), an 81.4 kDa enzyme " https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2596980/

@DavePhD The fine print is important here, they solved the global fold of the protein, that's not a full structure. And even for that they had to pull out quite a few tricks with specific labeling and deuteration. If you're not Lewis Kay, trying to solve the structure of such large proteins by NMR is probably not a good idea.


text:
How are cardiac cells stimulated by an action potential?
Why, and how, does the action potential of one cell in the heart stimulate an action potential in the next cell? I'm interested in an answer with citation.

If you go to probably any physiology textbook and look at the chapter on cardiac action potentials, you'll find a number of diagrams of the different ion permeabilities and channels in the heart (e.g. fast sodium channels, calcium channels, etc) that lead to the depolarization of the cardiac myocyte. As you alluded to in your question, there is one cell, or group of cells, that is part of the sinoatrial node (or SA node) that is referred to as the "Pacemaker of the Heart". It is these cells in the SA node that have the leakiest ion channels and thus most frequent spontaneous depolarizations that set the rate of contraction for the heart.
For the cells in the atrium, the depolarization of the sinoatrial node spreads almost instantaneously across the atria because of gap junctions (see that link for more info...Michela et al. Role of connexin 43 in cardiovascular diseases. European Journal of Pharmacology, 2015) between the cardiac myocytes. Gap junctions are small communicating junctions that link all the cells of the atrium and "electrically-couple" the cells to contract in synchrony. There are typically no gap junctions between the atria and the ventricles, which is why the atria contracts before the ventricles. However, there is the bundle of His that slows the electrical conduction just enough before it rapidly spreads through the purkinje fibers and then to the remaining ventricular myocytes through gap junctions in those cells as well.
**For more information on gap junctions go to any basic cell biology text or Wiki. The picture below is a simplified diagram of gap junctions. For this question all you really need to appreciate is that the electrical depolarization (i.e. the positive charge) spreads quickly across cells connected by gap junctions because they are 'sharing' a common cytoplasm due to these channels. When ions flux during depolarization that current flows through the gap junctions and nearly instantaneously depolarizes the next cell... and so on, and so forth.


text:
What do "e" "-" "C" and "E" mean in this output?
I have given an input of this protein sequence:
MEPVDPRLEPWKHPGSQPKTACTTCYCKKCCFHCQVCFTTKALGISYGRKKRRQRRRPPQGSQTHQVSLSKQPTSQPRGDPTGPKE

from this website  along with the option of COBEpro. Now the output sent by this site to my email is as follows:
Name: temp_prot

Amino Acids:
MEPVDPRLEPWKHPGSQPKTACTTCYCKKCCFHCQVCFTTKALGISYGRKKRRQRRRPPQGSQTHQVSLSKQPTSQPRGDPTGPKE

Predicted Continuous B-cell Epitopes:
MOST LIKELY EPITOPES:
0.82848577 39 TKALGIS CCCCCEE eee-e-e
0.82036375 39 TKALGI CCCCCE eee-e-
0.76503265 38 TTKALGI ECCCCCE eeee-e-
0.73178638 73 TSQPRGDP CCCCCCCC -eeeeeee
…

and a few more. Now can you tell me, if I consider the very first result, I was able to find TKALGIS in the parent sequence but could not find CCCCCEE. What is this CCCCCEE?
And what does this eee-e-e mean?

have you tried other software to verify?

CCCEEE etc. are the secondary structural elements. In this case, the C refers to non-strand and non-helix regions i.e loop regions rather than a coiled region. The C or E usually refers to whether the residue is coiled (C) or part of a strand (E). H would be used to denote a helix, however, in the question, it appears that there are no helices. These letters are often different in different software: it's merely a point of semantics between different software.
e or - refers to if the amino acid at that position is exposed or not (- = buried).
For the method, here is the COBEpro paper, although it does seem very technical. The help page has more easily accessible information on the output.

And how is the predicted antigenic propensity score plotted?

I'm not sure what you mean. Perhaps open a new question.


text:
What is a good source that will help me classify protists?
I am a 15 year old that enjoys looking at the micro world. The only issue that I have is that I can't find a good online source that will help me classify the name of the micro organism that I'm looking at. Does anyone know a place that will help me do this? Thanks in advance.

You can have a look at Cavalier-Smith's classification of protists.

@WYSIWYG: it's pretty old

@har-wradim Any free sources?

If you need an overview of higher taxa than this scheme from Wikipedia seems be a good starting point (see also the papers they cite).

@har-wradim: could you turn your comment into an answer? It seems to be helpful.

While not comprehensive, the Portal to Protistology has many resources for exploring the diversity of protozoa. I especially recommend their videos that are really fascinating, focus on distinguishing features of different protists, and may be helpful for identification to some degree. But given your interest you should just explore all the pages on the site, check out the books they recommend (e.g. the Illustrated Guide to Protozoa), and see what is useful for you!

Since no one else decide to post anything that was put in the comments as an answer I decided to.
There is an excellent article from Wikipedia on the subject, found Here. It was also recommended in the comments that you should read the sources provided.
I also found this PDF to be quite helpful, Nebraska Med.
I hope this helps!

There is a CBOL working group that categorizes protists based on genomic sequencing and barcoding. Here is a link to their publication: CBOL Protist Working Group: Barcoding Eukaryotic Richness beyond the Animal, Plant, and Fungal Kingdoms

No offense, but I don't think a casual teenage biologist has access to high-throughout sequencing technology


text:
Pattern in parental care behaviour from lower to higher animals?
I was studying about parental care behavior. There seems a pattern present in this behavior in case of vertebrates. The more evolutionary advanced class the animal belongs to, the greater effort an animal gives to care its off spring?
Is it a real pattern?
My current thinking is that if the pattern is indeed real, it is because the more complex an animal is, it takes more time to get fit with the environment, so it takes more effort for parents to get them ready for the environment. Am I close?

What do you mean by evolutionary complexity/advanced-ness?

What would be reasons for this particular pattern (assuming it is generally applicable to animals of "increasing complexity", which you need to define) to exist? Traits don't exist in a vacuum, they are intimately connected to one another. What's different about the development of a snake vs. a mouse vs. a deer vs. a gorilla vs. a human?


text:
How do viroids propagate to other hosts?
As viroids are simply non-encapsidated non-coding RNA molecules, it is difficult to imagine a mechanism for their continual infection, other than horizontal transfer which doesn't seem to be the case.
Is there a working theory on how viroids propagate and continually infect hosts?

Viroids are commonly thought of as remnants from the "RNA world" since they are composed of only RNA and have ribozyme activity.
Viroids can be considered as plant parasites of the transcriptional machinery of the organelles (nucleus or chloroplast), since they are non-coding and they replicate by RNA–RNA transcription (in a rolling cycle mode).
Viroids infect the epidermis of their hosts after mechanical damage of the plant cell wall. After entry to the first cell layers, viroids initiate replication in the nucleus or in the chloroplast and then are transported to neighbouring cells, reaching the vascular tissues and virtually all plant tissues.

The transmission of viroids is both "natural" and human-driven. Viroids are mostly transmitted via mechanical methods by contaminated machinery and tools, pruning, and other mechanic argricultural techniques. Transmission also occurs through infected seed and pollen, they don't have important natural vectors from the animal kingdom. Also contact between adjacent plants can be another (inefficient) way to transmit viriods.
I have found a reference here for further informations. It isn't open access but it is possible to watch an abundant Google preview.


text:
Gene Sequencing and Plasmapper
Is there anything similar to this in Java (especially the circular map sequencing along with hover effect)?
For information I would like to convey that I am using Plasmapper and BioJava for achieving this, but, as the output is in image format, so I am not able to achieve the above said requirement.
Thanks...

Check these out. They provide the functionality that you want:

ApE
SnapGene viewer

Thanks, but I need a web version of this so that I could be able to implement it with in the code (Java Code).

@AbhijeetKumar. You can try using plasmapper then. It outputs svg. You can perhaps employ some libraries from d3.js for what you want.


text:
What is this insect from India called?

It was found on a roof in Jaipur, Rajasthan, North-Western India.

Can you add some more details, like its size?

It appears like a pellucid hawkmoth (Cephonodes hylas; also called coffee bee hawkmoth). This belongs to the same family as the hummingbird hawk moths.

  From wikipedia

Many photographers have misidentified this one as the hummingbird hawkmoth (See the picture and the link below. So if you google hummingbird moth india, you'll get pictures of the pellucid hawkmoth too).

  Courtesy: Nitin Prabhudesai


text:
Can NADP be converted to NADPH using d glucose?
For one of our reaction, we require NADPH enzyme. We have NADP in our stock. Is it possible to convert NADP to NADPH using D-glucose anhydrous and if so please provide me how to go about it?

Reduction potential of NADP⁺ is -0.32 and that of glucuronic acid to glucose is 0.05. So, I guess that glucose oxidation to glucuronic acid cannot reduce NADP⁺.

@tomd you would also need the enzyme. I assumed that OP wants to use the property of reducing sugars to reduce NADP.