What's the frequency resolution of the human ear?

The frequency limen, or frequency resolution can be determined in various ways using psychophysical measures. You refer to a simultaneous method, in which two (or more) frequencies are presented at the same time. This has consequences for the test as different frequencies are perceived with different perceived loudness under constant sound pressure levels, meaning that additional cues are present besides pitch cues.

One carefully controlled study Zwicker et al (1957) in this regard defined the critical band basically as "those frequencies where no intensity summation occurs", meaning that adding those frequencies (below or above the center frequency) do not result in differences in loudness perception (as expressed in acoustic threshold of the center frequency). This method nicely prevents loudness summation cues by deploying them in the criterion. This article shows the following picture (after Zwicker et al. (1957)):

The critical band (upper graph) is dependent on frequency, and ranges from 0.1 kHz to >2 kHz.

The markedly lower difference limen of 3.6 Hz as touched upon in the comments may have been obtained using an alternative psychophysical test where the test frequency is modulated by another frequency (bottom graph). This procedure is based on adding a frequency to a certain sinusoidal stimulus, basically resulting in a single stimulus instead of two (or more). This procedure is technically not defined as a critical band and indeed results in a difference limen of ~3.5 hz and up. The other graph plotted in the figure is a masking procedure (middle plot), which basically determines the physiological overlap between frequencies in the cochlea in the intensity domain by determining the amount of masking from one frequency by another.

NB: The authors worked with headphones so no effects of direction.

Reference
Zwicker et al. JASA 1957; 29: 548-57


How did dolphins acquire their dorsal fin?

Short answer
I reckon that the dorsal fin of sea-faring mammals are a new development altogether.

Background
The presence of a dorsal fin is one of the defining characteristics of fishes like sharks. Mammals that returned to the sea generally lack a dorsal fin.

However, killer whales and dolphins, while being mammals, indeed do have dorsal fins. In contrast to the dorsal fin in fish, these structures do not have bones (Fig. 1). Instead, the dorsal fins of mammals are made up of dense tissue, somewhat like a ridge of thick, folded skin. Hence, it is not a 'revived structure' that was 'activated' from a latent state in mammals.

Hence, given that mammals returned to sea later in evolution and lacked a dorsal fin on land, I would deduce from these data that dorsal fins are an example of convergent evolution where analogous structures with a similar function (like in fishes, the dorsal fin acts as a stabilizer or a rudder in mammals (source: The National Marine Life Center) develop from unrelated bodily structures. Hence the genes involved may, and likely are, different.

Fig. 1. Dorsal fins of a killer whale (left) and in a fish (a redhorse on the right). sources: Sea World and Mosxostoma


That's the description in my book which has the same image.
The book is: Janeway's Immunology (9th Edition)
Article discussing MHC Class II expression in humans:
https://doi.org/10.4049/jimmunol.168.2.763
Excerpt from the book:

The processing and presentation of pathogen-derived antigens has two
distinct purposes: inducing the development of armed effector T cells,
and triggering the effector functions of these armed cells at sites of
infection. MHC class I molecules bind peptides that are recognized by
CD8 T cells, and MHC class II molecules bind peptides that are
recognized by CD4 T cells, a pattern of recognition determined by
specific binding of the CD8 or CD4 molecules to the respective MHC
molecules (see Section 4-18). The importance of this specificity of
recognition lies in the different distributions of MHC class I and
class II molecules on cells throughout the body. Nearly all somatic
cells (except red blood cells) express MHC class I molecules.
Consequently, the CD8 T cell is primarily responsible for pathogen
surveillance and cytolysis of somatic cells. Also called cytotoxic T
cells, their function is to kill the cells they recognize. CD8 T cells
are therefore an important mechanism in eliminating sources of new
viral particles and bacteria that live only in the cytosol, and thus
freeing the host from infection.
By contrast, MHC class II molecules
are expressed primarily only on cells of the immune system, and
particularly by dendritic cells, macrophages, and B cells. Thymic
cortical epithelial cells and activated, but not naive, T cells can
express MHC class II molecules, which can also be induced on many
cells in response to the cytokine IFN-γ. Thus, CD4 T cells can
recognize their cognate antigens during their development in the
thymus, on a limited set of ‘professional’ antigen-presenting cells,
and on other somatic cells under specific inflammatory conditions.
Effector CD4 T cells comprise several subsets with different
activities that help eliminate the pathogens. Importantly, naive CD8
and CD4 T cells can become armed effector cells only after
encountering their cognate antigen once it has been processed and
presented by activated dendritic cells.


What is the meaning of pygo and pagus in the word pygopagus?

According to Wiktionary.com:

pygo- means "rump" or "posterior" from Ancient Greek πυγή (pugḗ, “tail, rump”).

According to dictionary.com:

-pagus means "fixation" or "something fixed or solid" from Greek  págos.

So literally, pygopagus (or conjoined twins attached at the butt) means "fixed rumps".


Hearing system as in visual system: exists well also - in protecting the inner mechanical parts of both sensing systems - a similarity?

The cooperative functioning of the eardrum and ossicular chain attenuation can be regarded as in this schematic drawing of the ear as an externally activated spring-mass system. Fig. 2.

Ossicular chain and eardrum function, together operating as a continuous sound reduction mechanism for changes in the strength of the offered signal. The tympanic membrane and the ossicular chain can be seen as a spring-mass system with amplification facilities, like it is shown in this figure.

The combined action of the ossicular chain and the eardrum is hypothesized to provide for both permanent volume control of the input sound and for overload protection. The stapedius reflex, evoked by sudden strong sound signal, cannot be seen as anything else than an extreme manifestation of the very mechanism that the schematic drawing of the ear as an externally activated spring-mass system propose.


How would one calculate the availability of nucleotides to an enzyme?

I could read this question more than one way.  Are you talking about an enzyme in a cell or in a reaction in vitro?  Kinetics and mechanical explanations can overlap, but are not entirely the same and are usually dealt with separately - kinetics with watching the rate of reactions in a tube and mechanism in a crystal structure or possibly Nuclear Magnetic Resonance.

Protein structures are usually static- you only get a time lapse snapshot of the reaction in most cases. kinetics allows you to see how fast these things go and can help you understand the order of binding when more than one component is needed for the reaction, etc.

The polymerase has a binding pocket where the template strand and the growing strand are used to build a double helix together.  if the base being introduced does not form Watson Crick base pair bonds, the geometry of the reaction will not allow the base to be added to the growing strand.

Hope this helps? If I can make this clearer, can you drop me a comment and I'll revise...


Horned Mohawk looking insect. What is it?

Its definitely a True bug (Hemiptera), and based on its distinct pronotum and small head I'm guessing its a Wheel bug (Arilus cristatus). It is a common species that is also found in Indiana.

They are aggresive predators and are part of the family Reduviidae also known as Assassin bugs. Assassin bugs have a painful bite, and they inject a toxin when they stab their victim (so humans should watch out). This is not a part of the world I know well though, and there might be similar closely related species that I've missed.

Links:

Bug guide
Records in GBIF
Univerity of Florida: Entomology & Nematology

Some pictures:


Why is there no U3 snRNP in the spliceosome complex of transcription initiation?

U3 RNA with its proteins forming a complex is localized at nucleolar. The box C'/D motif in U3 snRNA is responsible for this localization. Therefore U3 snRNA does not have a chance to go to mRNA transcription active sites. Instead, U3 snRNP play important roles in ribosomal-RNA processing.
Because of necleolar localization, U3 snRNA is called U3 snoRNA alternatively.

https://en.wikipedia.org/wiki/Small_nucleolar_RNA_U3


For dandruff caused mainly from dry scalp, I imagine that there is a delicate balance between moisturizing the scalp and getting oily hair. In my uninformed opinion, this is what shampoos like Head and Shoulders attempt to do.

Taking a more general look at skin health, growing research suggests that the right compilation of microbiota dictates lots of skin health issues. Just like your bowel has an endless supply of mutualistic bacteria, so does your skin. Disruption to this ecosystem (whether genetic, by way of a pathogen, or environmental) could have various negative effects. Dandruff, I imagine, is one of them. It is unfortunately very difficult to understand what a healthy ecosystem looks like, as every person is different both in genetic and environmental factors. This is part of the reason why other dermatological conditions like acne are difficult to treat.

You may be interested in research done through the Human Microbiome Project. Additionally, this article includes some discussion of the fungi mentioned above in the comments and postulates the role of microbiota in determining their success.


How does cell detect if a RNA polymerase II is stalled during transcription and in turn deploy the proper transcription-coupled repair factors?

The beauty of RNA polymerase II is its faculty to act like a sensor towards damage to genetic data. The response of the RNA molecule is variable on the type of DNA lesion which presents itself.
DNA lesions may be a consequence of ultraviolet radiation, or through the metabolic by-products of oxidative free radicals. These specific faults within the lesion, it is hypothesised, are respectively detected by the RNA pol II molecule by interaction with its active site.

Some examples of different genetic damage to bases

In the example you provided, namely, RNA is limited fully or partially from transcription elongation, this is detected as an inhibition of the active site. This could be caused by presence of cyclobutane which distorts the shape of the DNA molecule such that it prevents further RNA synthesis.

Therefore, in the incident that RNA pol II is fully stalled, the first co-factor to commence the signalling process is Transcription factor II H, which repositions the polymerase molecule away from the site of damage. Although it is not fully known, this allows the XPG, having certain endonuclease properties  to excise the faulty bases (thymine dimer, for instance).

In order to fully repair the molecule, DNA polymerase uses translocation to reinsert the correct complementary bases. PCNA, proliferated cell nuclear antigen, may be used as a DNA clamp to increase the processivity, ability for multiples catalyses without leaving the substrate, of DNA polymerase. Then protein complexes XPD operates as a ligase.

Therefore TC-NER, transcription-coupled repair, is believed to begin at a blockage of the active site of RNA pol II!

Best regards
More information regarding this hypothesis and function may be found from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4984683/ and https://www.ncbi.nlm.nih.gov/pubmed/11245433


How does high-intensity aerobic exercise affect the brain?

In this article The Effects of Acute Exercise on Mood, Cognition, Neurophysiology, and Neurochemical Pathways: A Review (Brain Plasticity, 2017), there's a comprehensive table The Time Course of Behavioral, Functional, Physiological, and Neurochemical Effects of Acute Exercise (scroll to Fig 1 and zoom it) that shows the duration and intensity of exercise needed for the effective release of cortisol, neurotrophins (BDNF, IGF-1), neurotransmitters (dopamine, norepinephrine, serotonin, acetylcholine, GABA and gltamate) and endogenous opioids.

Examples of effects of exercise on the cognitive function and mood:

Prefrontal-dependent cognitive function (learning, memory): 50 min exercise at 85% of age-predicted mean heart rate
Enhancement of positive mood: low to high intensity exercise 7-70 min
Decrease in negative mood: 60-120 min exercise at ~50% intensity

Cortisol:

  In humans, acute exercise stimulates the HPA axis in an
  intensity-dependent fashion, with increases in cortisol occurring
  after a threshold amount of exercise that equates to a duration of 10
  minutes or more at approximately 60% of VO2 max or greater...After
  exercise cessation, peripheral cortisol levels peak around 30 minutes
  and remain elevated for up to two hours.

  ...cortisol strongly influences learning and memory...The extent
  to which cortisol influences learning and memory processes depends on
  several factors including the intensity of cortisol elevation. For
  example, moderate increases in cortisol enhance working memory...Cortisol elevation over a certain threshold, however, serves to impair memory...

  acute exercise also helps mitigate the debilitating effects of stress.

Brain derived neurotrophic factor (BDNF):

Moderate exercise for 30 minutes can release BDNF.

  ...studies have shown a positive association between acute
  exercise-induced peripheral BDNF levels and short-term memory as
  well as motor skill memory.

But, this does not work in everyone and not in all circumstances...

Dopamine:

  Twelve, healthy, regular exercisers underwent PET scans after either
  rest or 30 minutes of treadmill running at 85% of age-predicted MHR.
  Surprisingly, dopamine was not found to increase after acute exercise
  in these humans.

Serotonin:

  Acute exercise significantly increased plasma serotonin, with a
  positive linear correlation found between serotonin concentration and
  exercise intensity.

  As serotonin regulates mood, emotion, sleep, and appetite, and
  dopamine regulates motivation memory, reward, and attention, the
  central fatigue hypothesis claims that the interaction between these
  two neurotransmitters contributes to exercise-induced fatigue, with a
  high serotonin to dopamine ratio supporting a low activity, exhausted state and a low ratio supporting a high activity, activated
  state.

The effects of exercise-induced increase of norepinephrine and acetylcholine on cognitive function are not well known.

Endogenous opioids (ß-endorphins, enkephalins and dynorphins) and endocannabinoids:

  Neuromodulators are involved in a variety of processes including pain
  modulation, reward, response to stress, and autonomic control.

  In humans, acute exercise causes significant increases in peripheral
  levels of endogenous opioids; this effect is intensity-dependent,
  corresponds to acute exercise-induced changes in HPA axis hormones,
  and is linked to improvements in mood.

  Though the endogenous opioids have received much attention in terms of
  their involvement in the “runner’s high”, scientists are beginning to
  understand that endocannabinoids may be equally or perhaps more
  involved.

Other sources:

Effects of physical exercise on anxiety, depression...(Clinical Psychology Review, 2001):

  Acutely, emotional effects of exercise remain confusing, both positive
  and negative effects being reported.

...or as Alex Corb, PhD says in Boosting Your Serotonin Activity (Psychology Today):

  Interestingly, if you try to do too much exercise, or feel forced into
  doing it, it may not have the right effect. Recognizing that you are
  choosing to exercise changes its neurochemical effect. That may be a
  result of your ancient instincts — the difference between running
  because you're hunting something, and running because it's hunting
  you.

How to increase serotonin in the human brain without drugs (Journal of Psychiatry & Neuroscience, 2007):

  Exercise improves mood in subclinical populations as well as in
  patients. The most consistent effect is seen when regular exercisers
  undertake aerobic exercise at a level with which they are familiar.


How is Cas9-gRNA-tracrRNA complex lysed in mammalian cells?

There has not been much research on this part of CRISPR-Cas system, as everybody is interested in geting gene editing done, but very few think about what actually happens to the things we leave inside. So, the mechanism of its degradation is not well understood. But there has been some research on this, which leads to the conclusion that the Cas9-gRNA, being a ribonucleoprotein (RNP), degrades by the normal protein degradation pathways, and is indeed very quick process. See this article:

  By contrast, Cas9 RNPs are delivered as intact complexes, are detectable at high levels shortly after transfection, and are quickly cleared from the cell via protein degradation pathways...Second, rapid clearance of Cas9 RNPs from the cell may increase CRISPR specificity by reducing the amount of time that Cas9 is available for off-target cleavage...Cas9 RNPs are active immediately following transfection and are quickly degraded within the cell. These fast degradation kinetics enable Cas9 RNPs to modify target genes with reduced off-target effects.

For more information about RNP degradation, you can have a look at this article. An example of RNP degradation pathway is this too:


Unwanted E. coli product in plasmid product

The root cause turned out to be a lying spec. Using a different one, it was revealed that there was next to no template DNA in the initial amplification. No DNA, no good plasmids.


In the terms of the question, this can be regarded as calcium hydroxide, with the carbonate being in the form of calcium carbonate. Calcium hydroxide is a stronger base (pH of aqueous solution at 1mm = 11.27) than carbonic acid is an acid (pH of aqueous solution at 1mm = 4.68), so that 1mm calcium carbonate has a pH of 9.91. (Values from Aquion)
Calcium carbonate is, of course, the constituent of eggshell, which crystallizes out of solution when it is deposited in the uterus. Its presence within the egg white is presumably a consequence of this.
–––––––––––––
I would add that one does not generally regard there being a particular base balancing the carbonic acid: when the hydrogen ions are removed there is an increase in the free hydroxyl ions and hence an increase in pH. The way the question is formulated only really makes sense if one assumes that the egg white acquired extra carbonic acid to reduce the alkalinity resulting from the calcium carbonate/hydroxide.


Can only fully matured sperm fertilize an egg?

As far as I know, sperm cells undergo maturation in the epididymis, where they become motile. As Eliane pointed out, this is a continuous process, so the male does not have to "wait", because there is continuously a batch of sperm cells that will shortly become mature. Furthermore, maternal factors are required for the process of capacitation in which the sperm cell becomes capable of fertilization and undergoes motility increase.

An immature sperm cell is simply non-motile and even if present in the ejaculate, it will not be able to travel towards the fallopian tube to fertilize the oocyte. A sperm cell which did not go capacitation will also have no ability to fertilize an egg (that's why prior to IVF it's important to capacitate sperm).

My sources:

https://kjcscientific.wordpress.com/project-excalibur/
Gilbert's Developmental Biology (2009)


Initial population when i count backwards?

Your calculations are the following. Assuming non-overlapping generations, the number of ancestors you have in the last $t$ generation is given by:

$$\sum_{i=1}^t 2^t$$

This sounds correct. But there are some very strong assumptions:

Generations are non-overlapping. A more realistic model would need to consider $t$ as a continuous variable a give a probability of reproducing which depend on the age of the individual.
Never two related individuals mate together. Otherwise, you paternal grandfather might be your maternal grandfather. In reality we are all somehow related, we have a common ancestor (Hey Bro). For example if you consider that your parents are siblings and that their parents are siblings and so forth… then you don't need more than 2 great-great-great-great-grandparents. In order to keep track of such events (inbreeding), you need a stochastic model and you would need to know the population size $N(t)$ as a function of $t$.

Tracking the change of population size through time is part of a very big in biology which is called population biology. For example you will find in this post a model of population growth of two interacting species (prey-predator)


Has an artificial symbiotic relationship ever been created?

Here are some examples:

symbiosis between genetic modified yeast cell populations (Shou W et al. 2007)
symbiosis between green algae and embryonic chick connective tissue (Buchsbaum R et al. 1934)
symbiosis between EcoBot II and microbial fuel cells (Ieropoulos, Ioannis, et al. 2005)


This looks like a type of stinkhorn fungus.  They commonly pop up in urban settings, including mulched areas, and can come in many different appearances.  The slime on these mushrooms is used to attract insects and flies which will disperse their spores. They are found in North America including in temperate and north-temperate climates.  According to MushroomExpert.Com these mushrooms will not hurt you, your children or pets.

Specifically, the stinkhorns in your photo look like Mutinus elegans to me.

Source: http://www.projectnoah.org/spottings/635556012


How many times did terrestrial life emerge from the ocean?

I doubt we know the precise number, or even anywhere near it. But there are several well-supported theorised colonisations which might interest you and help to build up a picture of just how common it was for life to transition to land. We can also use known facts about when different evolutionary lineages diverged, along with knowledge about the earlier colonisations of land, to work some events out for ourselves. I've done it here for broad taxonomic clades at different scales - if interested you could do the same thing again for lower sub-clades.

As you rightly point out, there must have been at least one colonisation event for each lineage present on land which diverged from other land-present lineages before the colonisation of land. Using the evidence and reasoning I give below, at the very least, the following 9 independent colonisations occurred:

bacteria
cyanobacteria
archaea
protists
fungi
algae
plants
nematodes
arthropods
vertebrates

Bacterial and archaean colonisation
The first evidence of life on land seems to originate from 2.6 (Watanabe et al., 2000) to 3.1 (Battistuzzi et al., 2004) billion years ago. Since molecular evidence points to bacteria and archaea diverging between 3.2-3.8 billion years ago (Feng et al.,1997 - a classic paper), and since both bacteria and archaea are found on land (e.g. Taketani & Tsai, 2010), they must have colonised land independently. I would suggest there would have been many different bacterial colonisations, too. One at least is certain - cyanobacteria must have colonised independently from some other forms, since they evolved after the first bacterial colonisation (Tomitani et al., 2006), and are now found on land, e.g. in lichens.

Protistan, fungal, algal, plant and animal colonisation
Protists are a polyphyletic group of simple eukaryotes, and since fungal divergence from them (Wang et al., 1999 - another classic) predates fungal emergence from the ocean (Taylor & Osborn, 1996), they must have emerged separately. Then, since plants and fungi diverged whilst fungi were still in the ocean (Wang et al., 1999), plants must have colonised separately. Actually, it has been explicitly discovered in various ways (e.g. molecular clock methods, Heckman et al., 2001) that plants must have left the ocean separately to fungi, but probably relied upon them to be able to do it (Brundrett, 2002 - see note at bottom about this paper). Next, simple animals... Arthropods colonised the land independently (Pisani et al, 2004), and since nematodes diverged before arthropods (Wang et al., 1999), they too must have independently found land. Then, lumbering along at the end, came the tetrapods (Long & Gordon, 2004).

Note about the Brundrett paper: it has OVER 300 REFERENCES! That guy must have been hoping for some sort of prize.

References

 Battistuzzi FU, Feijao A, Hedges SB. 2004. A genomic timescale of prokaryote evolution: insights into the origin of methanogenesis, phototrophy, and the colonization of land. BMC Evol Biol 4: 44.
 Brundrett MC. 2002. Coevolution of roots and mycorrhizas of land plants. New Phytologist 154: 275–304.
Feng D-F, Cho G, Doolittle RF. 1997. Determining divergence times with a protein clock: Update and reevaluation. Proceedings of the National Academy of Sciences 94: 13028 –13033.
 Heckman DS, Geiser DM, Eidell BR, Stauffer RL, Kardos NL, Hedges SB. 2001. Molecular Evidence for the Early Colonization of Land by Fungi and Plants. Science 293: 1129 –1133.
Long JA, Gordon MS. 2004. The Greatest Step in Vertebrate History: A Paleobiological Review of the Fish‐Tetrapod Transition. Physiological and Biochemical Zoology 77: 700–719.
 Pisani D, Poling LL, Lyons-Weiler M, Hedges SB. 2004. The colonization of land by animals: molecular phylogeny and divergence times among arthropods. BMC Biol 2: 1.
 Taketani RG, Tsai SM. 2010. The influence of different land uses on the structure of archaeal communities in Amazonian anthrosols based on 16S rRNA and amoA genes. Microb Ecol 59: 734–743.
 Taylor TN, Osborn JM. 1996. The importance of fungi in shaping the paleoecosystem. Review of Palaeobotany and Palynology 90: 249–262.
Wang DY, Kumar S, Hedges SB. 1999. Divergence time estimates for the early history of animal phyla and the origin of plants, animals and fungi. Proc Biol Sci 266: 163–171.
 Watanabe Y, Martini JEJ, Ohmoto H. 2000. Geochemical evidence for terrestrial ecosystems 2.6 billion years ago. Nature 408: 574–578.


Why don't we form immunity to some infections?

The answer is sort of hidden behind the very first paragraph of your linked question:

  Vaccines work by introducing an attenuated strain of the pathogen (or alternatively the antigens that are normally present on the pathogens surface) into the body, whereupon the body mounts an immune response.

The body mounts an immune response based upon a particular epitope (molecule/protein chain) derived from the infection itself.

If the bacteria/virus/parasite evolves so that the molecules/proteins on its surface change significantly, then the version stored in Memory Cells will no longer apply. There's some wiggle room depending on epitope length and specificity of the Immune Response (highly specific MHC proteins will invoke a very strong, very efficient response - while less specific [usually B-cell or early T-cell responses] will invoke a weaker, less effective response), but because pathogens can mutate so quickly it's not uncommon that you'll encounter a descendant of a virus you already encountered that has dramatically different characteristics.

Some pathogens are also just very "clever" at avoiding the immune system. Sometimes they can produce shells which can't be broken down to derive epitopes from, sometimes they live within host cells and simply hide (the most well known example being HIV), other times they can present the same "I'm part of the body!" MHC proteins that tells the immune system not to attack them, and they can even hide in parts of the body where the immune system rarely visits (eyes, brain, other very specialized tissues).

And at times the pathogen can be both very adaptive and very "clever", as in the case for Staphylococcus aureas - which can inactivate antibodies through surface proteins and has the ability to adapt to new antibodies very quickly.

You can think of it like a game of chess. If you play against the same person all the time you're going to learn their moves, their strategy, and be able to beat them very quickly. If you switch partners, or your regular partner learns new moves, you'll spend some time adapting to the new play style you've encountered before being able to win consistently again. Bacteria and virii are simply very, very good at coming up with new play styles.


Program (on Mac) to show 3D protein structures?

Jmol: how to install

Here are instructions for getting Jmol to run on a Mac (or with a slight variation in the runtime file you need to download and the security warnings, on a PC).

Download Jmol from
https://sourceforge.net/projects/jmol/files/latest/download?source=files
The name of the file will be something like Jmol-14.29.16-binary.zip. Double-click to unzip.
The unzipped folder has a confusing variety of files. This is because most are used for setting up web pages with Jmol embedded. However there is a double-clickable desktop application without any icon and the unpromising name of: Jmol.jar. When you double-click to launch it you will most likely get a message as shown below (1):

To get round this you must either control-click or right-click and select `Open With > Jar Launcher as shown above (2).

Click ‘Open’. (Next time it will just launch without right-clicking.)

Now, in my case the program launches because I have the Java runtime installed on my machines, but you will probably have to download and install this.
Go to the Oracle Java website at http://www.oracle.com/technetwork/java/javase/downloads/jre8-downloads-2133155.html
Download and install the appropriate Java SE Runtime Environment, e.g jre-8u172-macosx-x64.dmg
Try launching jmol.jar again. It should work this time.

Jmol: how to use

Because of their emphasis on Jmol in relation to the web, the developers of Jmol do not provide an easy entry to the desktop program. However you can find a clearly illustrated series of web tutorials at the MSOI Centre for Biomoleclar Modelling. I’d highly recommend this as a starting guide.

Although I am not going to describe how you can visualize particular amino acids as that is part of the poster’s project and can be found in the instructions. there are a couple of pointers I would make about things that are not entirely obvious.

After you have loaded a protein from the file menu, you will probably want to change its representation, not least because it may be surrounded by red balls (oxygens from water). To do this you need to right click (or click on the Jmol text on the bottom right) and go through two levels of submenus to select an appropriate representation, as shown.

In order to make the selections you require you will need to issue commands in the ‘Console’. This can be selected from the File menu:


What was the first piece of work in computational biology?

I don't believe you'll ever find the first work in bioinformatics (or computational biology, as you put it), however the field really began in the times of accumulating data about protein biochemistry. Computational biologists (before they had access to the computer) would be writing and analyzing morphologies and types of proteins with pencil and paper.

But you can go even further back than this. "Alignment" is another old technique used in bioinformatics. This is establishing the amount of similarity between two DNA sequences, or rather the degree of similarity between any two objects or data sets in computational biology.

Your question, though, asks for a scientific paper (one of the oldest) on bioinformatics. This is arguably  M. O. Dayhoff's paper: "A Computer Program to Aid Primary Protein Structure Determination." (1962)

This is the first formal paper that limits the scope of computational biology to the definition provided by this BioPlanet article and generally agreed by many:

  Bioinformatics is the application of computer technology to the management of biological information.


Question about Autosomal Recessive Alleles

If really cystic fibrose is expressed only in homozygotes, then here are some possible explanations:

Mutation

very improbable

Autofecundation

very improbable

False Negative when testing the father

Because of the test False Negative Rate. @kmm made a very interesting comment below. He said that according to this article 75% of the mutations coding for disease correspond to one pattern (3 base pair deletion). The other 25% of the mutations coding for disease might not be detected in the standard test. If so, it is very likely that a the test conducting on the father was a false negative. It would be worth further investigate the test that are usually performed to confirm this.
Because of human error. The laboratory operator might accidentally switch two samples or accidentally introduce stranger DNA before PCR (assuming the test is performed through sequencing)
I have no idea concerning the probability for such thing to occur!

trisomy

The assumes that a trisomic that have two recessive allele coding for disease and on dominant allele coding for health yield to a individual expressing the disease!
The gene for cystic fibrose is on chromosom 7. Trisomy 7 is extremely rare if I'm not mistaken.

The father is not the father!

According to different studies between 5% and 20% of the people are not fathered by the man they think is their dad.
According to this source the frequency of the disease is 1/2500 in UK. Assuming Hardy-Weinberg equilibrium the frequency of the allele coding for disease should be $p=0.02$.
The probability that the baby comes from another man and that the baby express the disease is $$a \cdot \left(\frac{2\cdot p\cdot (1-p)}{4} + \frac{p^2}{2}\right)$$, where $a$ is the probability that the baby was fathered by another man (let's say 10%) and $p$ is the frequency of the allele coding for the disease (0.02). The division by 2 and 4 are due to the probability that the (real) father and the mother transmit both the alleles coding for the disease.  This probability equals $$0.1 \cdot \left(\frac{2\cdot 0.2\cdot (1-0.2)}{4} + \frac{0.2^2}{2}\right) = 0.01$$ This seems to be from far the most likely explanation! Note: this assumes that the mother is heterozygote (does not express the disease) otherwise, the probability would be 0.02.


It doesn't have to be that complicated.

The evolutionary value of the offspring to the mother is half that of herself (0.5). Then the relative value of both mother and offspring is 1 + 0.5 = 1.5. So the value of preserving both lives is about 3 times the value of preserving the offspring alone, given the simplistic assumptions from the question. Therefore, to choose to make this sacrifice, the probability of survival of the offspring alone must be at least 3 times the probability of both surviving otherwise. 3 times 25% = 75%.

Edit:

In terms of B and C as requested (though I think it's simpler to step away from this):

B is the benefit to the relative. This is not the probability of survival given the action of sacrifice, but the increased probability of survival. The baby already had a 25% chance of survival, but this action will increase that chance to some p, which we are looking for. B is the improvement in the situation:
B = p - 0.25

C is the cost to only the individual (the mother). This is not 1 (loss of survival), but the difference between the situation without making the sacrifice and the situation when she makes the sacrifice. She's only giving up a 25% chance of survival. So:

C = 0.25.

r is the relation between mother and offspring.

r = 0.5.

So the equation is:
0.5(p - 0.25) = 0.25
0.5 p - 0.125 = 0.25
0.5 p         = 0.375
p = 0.75

Someone feel free to format if you want...


DMSO can be used as a solvent for a range of experiments. In these cases it makes sense to use the solvent as the control.


Do spouses begin to share genes over time?

Microchimerism (Mc) is an interesting concept, and

  refers to the harboring of a small number of cells (or DNA) that originated in a different individual. Naturally acquired Mc derives primarily from maternal cells in her progeny, or cells of fetal origin in women.

In women, it has nothing to do with transfer during/after sex but rather is due to the presence of the developing fetus.  There is definite long-term persistence of microchimerism, although not much is yet known about its effects.  Here is a nice and free review from 2010 dealing with the subject.  Most if not all of the effects have to do with immune function, in particular autoimmunity.  It is definitely not a mechanism by which couples begin to look similar.  That is social, in particular a result of living in the same environment with similar activities and tastes.  Male contribution to cellular content in the embryo is almost nil, as sperm die and the transcript count in the successful sperm is puny compared to the egg.  Most RNA content in the embryo is maternal until it switches to embryo.


Trying to identify the tree in this picture. Shot from the Nepenthe restaurant, Big Sur, California

It's difficult to make out the leaves in the photo, but the tree in the foreground appears to be a coast live oak (Quercus agrifolia).  The tree has hairy lichen growing on its branches.


How does our body produce new antibodies?

In case of T-cells and Abs our organism first generates a great diversity of T- and B-cell receptor variants so that any new foreign antigen is likely to be recognized by some of them. When a B-cell encounters its antigen it begins proliferating and changes it phenotype. Additionally, B-cell (but not T-cell) receptor starts to mutate (so-called somatic hypermutation) generating new B-cell receptor variants that differ a bit from the original one. This process acts like evolution, with B-cell carrying a receptor with higher affinity being more likely to proliferate. So the specificity of antibodies improves indeed, this is called affinity maturation.


Autoimmunity - negative selection of T-cells - APC

Since you already know that the repertoire of T cell receptors is developed in the thymus, the missing half of the story in the context of antigen-presenting cells is that MHC complexes do not discriminate between self, foreign and mutated self peptides. The take home point is that selection in the thymus is meant to produce a repertoire of T cells which can overcome this fact.

In all healthy tissues, nucleated cells express class I MHC molecules loaded with self-peptides. A T cell probably isn't going to respond to it, however, because trafficking of T cells into tissues requires signals such as cytokines and chemokines. The T cells circulate to and from the lymph node T cell zones.

In the context of inflammation and cellular injury, self proteins may be released into the extracellular space where they're regularly cleared by macrophages via endocytosis. The extrinsic antigen presentation pathway loads peptides onto class II MHC molecules. In this very context, T cells are trafficked into distressed tissues by activated endothelium, where they interact with tissue APCs. Autoreactive clones that escaped thymic selection have the opportunity, then, to react to self antigens, ignoring regulatory or suppressor cells (ref).

Not just self-peptides, however, but also potential self-antigen mimics that take well meaning T cells directed against bacterial peptides, which happen to closely resemble a self antigen, and pit them against your own tissues (example). Unlike autoimmune T cells that have escaped selection and attack your cells, this invites T cells that have gone through the proper channels to do so as well.

See Cellular and Molecular Immunology, 8th ed. for an even better overview of T cell-based autoimmunity.


Evolution of the Redundancy of the Genetic Code

To fully comprehend the concept of wobble base-pairing we need to consider the nucleotide sequences of the anti-codons in the tRNAs that have to "read" the genetic code when the mRNA is being translated on the ribosome.  The nucleotide in the anti-codon's wobble position is, for example, often inosine.  Under the rules for wobble base-pairing an Inosine can potentially base-pair with three other nucleotides.

In real terms that means a cell can use less than 63 unique tRNA genes to decode mRNAs carrying messages made of the 63 different "words" (codons).

In an active cell the Ribosome's A-site, where the charged tRNA binds, is occupied by the incorrect tRNA most of the time (based on the law of mass action where any charged tRNA can randomly diffuse into the binding site).  With tRNAs that can recognize multiple codons (which is what the wobble hypothesis gets us), any given protein can be translated faster (assuming  that correct charged tRNAs are limiting for the polypeptide polymerization reaction).

So those are the practical ramifications of the table that you presented, but the explanation, as for most why based questions about biological evolution, is a retrofit.  Natural selection can only work with the materials at hand, and so we can infer that during the period when this genetic code was finalized that the organisms who used it were more successful than the others.  And the current code is based on whatever the previous one looked like. "Descent by modification" is the original description.

[whoops, sorry about the pedantic voice on the "how selection works" bit, I just looked at your profile and realized you can likely teach me on this subject]


What is the explanation for the smaller number of tRNA than codons?

Th reason for this is that for the third base of the tRNA non-Watson-Crick pairing is allowed. This phenomenon is called "Wobble base pairing". See the figure (from here) for illustration (from here):

If you have a look at the codon table for amino acids, than the variation in the code for one amino acid mostly happens on the third position (from here):

This allows a smaller number of tRNA than 64, as there is some flexibility. The third base can be represented like this (taken from this answer):

In the table Cricks predictions are compared to the pairings found in experiments.


What biological processes can affect the temperature of the shadow a tree casts?

The short answer is NO. A shadow is strictly due to the blocking of sunlight, so the shadow of 2 equally non-transparent (i.e., same density of leaves, both have similar trunk diameter, etc.) trees on the same hillside in the same weather conditions would not have different temperature shadows.

A plant's metabolism can and will affect the temperature surrounding the tree, but this affect is lost almost immediately as you move away from a tree. For example, in a study I did a few years ago measuring spruce tree temperatures (unpublished), we found that even within 5-10cm from the tree, the rise in ambient temperature was no longer measurable.

You're correct that the leaves of plants will absorb sunlight and as a result will increase in temperature. Walter Loomis's (1965) paper, Absorption of radiant energy by leaves (found here), suggests that leaf temperatures can raise to 30 $^\circ$C or more. However, he notes that their study found that even wind speeds of 5mph instantly cooled the leaves via conductive heat transfer. Our study with spruce trees also found this to be true. Transpiration will further help the tree rid itself of that increased temperature fairly quickly. Regardless of these facts, radiant energy from leaves in the canopy will not be felt how-ever many meters below within its shadow.

Finally, yes, transpiration can affect local climate, but this is only on the scale of whole forests. The transpiration of a single tree will likely not impact the ambient temperature around itself in any measurable way, especially at any distance greater than that I've already mentioned.


Are there public records of results with laetrile treatment against cancer?

Yes, there are several articles describing experimental data from animals and clinical trials in humans. The results clearly show that laetrile is not effective for cancer treatment, and the drive for laetrile as a cancer drug is widely considered to be a quack. See for example this NCI summary or this article.

Laetrile is a modified form of Amygdalin, a toxic plant product found in some seeds and nuts, for example bitter almonds. The compound contains a cyanide moiety and can cause cyanide poisoning. It was labeled "Vitamin B17" by Ernst Krebs in an attempt to get the compound classified as a nutritional supplement rather than a pharmaceutial (to escape FDA regulations), but this is misleading; it is not a vitamin.


Shine-Dalgarno sequence and expressing proteins

Eukaryotes have an analogous sequence called the Kozak sequence. cDNA is easily expressed in prokaryotes by substitution of the Kozak sequence for the Shine-Dalgarno sequence, using standard molecular biology techniques. A caveat is that not all eukaryotic protein will be properly expressed in bacteria because of they lack the ability to carry out post-translational modifications that would occur in a eukaryotic Golgi apparatus (e.g., glycosylation). Insulin is a special case because it is a small, unmodified peptides.


Does DNA have the equivalent of IF-statements, WHILE loops, or function calls? How about GOTO?

Biological examples similar to programming statements:

IF : Transcriptional activator; when present a gene will be transcribed. In general there is no termination of events unless the signal is gone; the program ends only with the death of the cell. So the IF statement is always a part of a loop.
WHILE : Transcriptional repressor; gene will be transcribed until repressor is not present.
There are no equivalents of function calls. All events happen is the
same space and there is always a likelihood of interference. One can
argue that organelles can act as a compartment that may have a
function like properties but they are highly complex and are
not just some kind of input-output devices.
GOTO is always dependent on a condition. This can happen in case of certain network connections such as feedforward loops and branched pathways. For example if there is a signalling pathway like this:  A → B → C and there is another connection D → C then if somehow D is activated it will directly affect C, making A and B dispensable.

Logic gates have been constructed using synthetic biological circuits. See this review for more information.

Note

Molecular biological processes cannot be directly compared to a computer code. It is the underlying logic that is important and not the statement construct itself and these examples should not be taken as absolute analogies. It is also to be noted that DNA is just a set of instructions and not really a fully functional entity (it is functional to some extent). However, even being just a code it is comparable to a HLL code that has to be compiled to execute its functions. See this post too.

It is also important to note that the cell, like many other physical systems, is analog in nature. Therefore, in most situations there is no 0/1 (binary) value of variables. Consider gene expression. If a transcriptional activator is present, the gene will be transcribed. However, if you keep increasing the concentration of the activator, the expression of that gene will increase until it reaches a saturation point. So there is no digital logic here. Having said that, I would add that switching behaviour is possible in biological systems (including gene expression) and is also used in many cases. Certain kinds of regulatory network structures can give rise to such dynamics. Co-operativity with or without positive feedback is one of the mechanisms that can implement switching behaviour. For more details read about ultrasensitivity. Also check out "Can molecular genetics make a boolean variable from a continuous variable?"


Absence of cytoplasmic Intermediate Filaments in Arthropods

It is stated that out of all eumetazoans in Kingdom Animalia, only arthropods do not contain such structures.

I found this paper(1), eliciting an exception (within an exception)- Isotomurus maculatus:

Here, we report the first evidence for the expression of a cytoplasmic IF protein in an arthropod - the basal hexapod Isotomurus maculatus. This new protein, we named it isomin, is a component of the intestinal terminal web and shares with IFs typical biochemical properties, molecular features and reassembly capability.

I would suggest you to read the paper, for more information.

How is this possible? Are there any replacements to the conventional intermediate filaments present in cells?

Both electron microscopy and molecular cloning studies have been unable to detect any cytoplasmic IF protein in these organisms, although they do express a nuclear IF system made of authentic lamins. Other cytoskeletal components have been proposed to have assumed, in arthropods, the mechanical functions that are usually played by cytoplasmic IFs in other organisms. For example, wing epithelial cells are stabilized in insects by a cytoskeletal array consisting of parallel bundles of 15-protofilament microtubules and actin filaments.

Do arthropods have special cellular behaviors or signaling networks to correspond with this absence?

I am not sure if this is what you might be looking for, but the Delta/Notch signalling, which may be involved in posterior elongation in arthropods, does not require IF's.(2)

The Notch cascade consists of Notch and Notch ligands, as well as
intracellular proteins transmitting the notch signal to the cell's
nucleus. The Notch/Lin-12/Glp-1 receptor family was found to be
involved in the specification of cell fates during development in
Drosophila and C. elegans.

1- https://bmcbiol.biomedcentral.com/articles/10.1186/1741-7007-9-17
2-https://en.wikipedia.org/wiki/Notch_signaling_pathway#Mechanism_of_action


Are animals that live in areas where avalanches happen equipped to deal with that?

Most animals are not well adapted to deal with avalanches. For example, more mountain goats are killed by rock slides than by predators. http://www.nationalgeographic.com/lewisandclark/record_species_150_11_2.html

Meanwhile, a mountain at risk of snow avalanche must be deep in snow, right? At that point, there is very little food to be found; it's all buried. So there won't be many animals on the surface. Most of them would leave for more hospitable locations, or hibernate.


What is the principle behind Microagglutination test (MAT)?

Microagglutination tests for the presence of antibodies, which declare themselves by sticking their antigens together into clumps.

Leptospirosis serodiagnosis by the microscopic agglutination test.

  The microscopic agglutination test (MAT) is the gold standard for
  sero‐diagnosis of leptospirosis because of its unsurpassed diagnostic
  specificity. It uses panels of live leptospires, ideally recent
  isolates, representing the circulating serovars from the area where
  the patient became infected. A dilution series of the patient's serum
  is mixed with a suspension of live leptospires in microtiter plates.
  After incubating for about 2 hr at 30°C, results are read under the
  dark‐field microscope. The titer is the last dilution in which ≥50% of
  the leptospires have remained agglutinated

You know antibodies work by sticking to things.  If I have antibodies to a thing it is because I have been exposed to that thing, here leptospirosis.  This version of the test uses live leptospires.  If the antibodies stick to them and they clump up, the plate gets clearer.

The good thing about this is that you don't need to know anything about the antigen except it is on the germs, or anything about the antibody except it sticks to the antigen.  Also the germs are big enough to make clumps.  If you have antigens for something small that won't make noticeable clumps (for example a virus, or an individual antigen of interest) you can stick it to microscopic latex beads.  Then just as the leptospires clump up when the antibody sticks to them, the beads will clump up if there is antibody specific to the antigen you have stuck onto them.

Historically the clumping was interpreted by a human with a microscope.  You can do it with an optical density meter too.

This approach is used for lots of different diagnostic tests.  You can find them with google and "agglutination" but those links do not all explain how the test works.


How do proteins and genes participate in learning?

The storage of memories in cells is rarely thought of on the protein level of the cell.  Cells are usually given a developmental state, but no memory.  A cell may become a liver cell, cancerous, or diabetic, but this is not memory, but a physiological change in the cell which is usually not reversible to a previous state.

For example cancer treatments are entirely focused on identifying the cancerous cells and killing them.   Internally the genomes of cancer cells often have deletions and duplications.  They are cancerous, they have not learned to be cancerous. Though not as dramatic, it is now thought that cellular differentiation which creates different types of cells is heavily influenced by epigenetic modification of the genome; the DNA is marked by methyl groups which dictates the state of the cell by modifying the gene.  This is mediated by proteins for sure, but is quite complex and not well understood at this time. Epigenetic markers can even change gene behavior between generations of offspring as well, though that is not usually called memory.

How is information stored in the brain?  This is thought to be reflected in the organization of the neurons in the brain.  There are many kinds of neurons.  They can be distinguished by the sorts of axons and dendrites that emanate from the cell body.  They can also be distinguished by the chemical variety of neurotransmitter they use (there are a score of different molecules).  So to a great extent the type of cell and the specific proteins it chooses to use to mediate information is very important.

That being said, information is currently thought to highly related to the placement of the axons and dendrites connecting the neuron to sometimes scores of other cells, sometimes touching the cell body, other times other dendrites or neurons.  As neural activity ensues, the cells will reconfigure their connections by physically moving them.

More recently, investigators have tried to understand the genes which internally modulate the neural signals within the cells.  This nobel prize lecture discusses how the CREB/MAPK pathway can modulate Long Term Potentiation - the shape of the neuron response to a signal over time (days or hours).

Taken as a whole, you can see that memory is likely to be stored on several levels at once - the kinds of cells (dictated by differentiation) involved, structural arrangement of the neurons (axons and dendrites connecting to various cells and places on cells), as well as internal signaling circuitry that generates and modulate the electrical and chemical activity within the cell.

"Marker protein" only refers to a protein that you can follow to see some sort of activity in the cell. A typical example is Green Fluorescent Protein, which is colored, fused with a protein of interest.  It has no specific meaning regarding learning I think.


Protoporphyrinogen (IX) formation in heme synthesis

Seemingly the enzyme uses molecular oxygen in the reaction and one of the end products is water.

This reaction scheme is more detailed and presumably accurate.

http://www.hmdb.ca/reactions/3063

In addition there is also a set of papers detailing coproporphyrinogen oxidase which was also on the website, hopefully one goes into more detail on the reaction.

http://www.hmdb.ca/proteins/HMDBP00251


How can I evaluate the hydrophobicity and/or surface charge of a protein?

Since you have the structures the best option, in my opinion, is Pymol.

Hydrophobicity

Open Pymol. Load up your protein of choice. Download color_h.py which is a script from the University of Osaka that colours the residues according to Eisenberg's scale of hydrophobicity. Load this into Pymol by File->Run->PATH/TO/color_h.py. Then in Pymol run the following commands

show surface
color_h

Note that the script can be edited to use whatever scales you like. @mimat points you toward protscale and I would suggest the same. The Eisenberg scale is a rather old consensus scale. More up to date scales might be more appropriate in your case.

To start quantifying this, Pymol has tools for calculating solvent accessibility for example.

Additionally PDBSum and PDBe-PISA can estimate solvent accesible surface area.

Surface charge

Vacuum electrostatics

The quickest and easiest way to generate electrostatics is with the inbuilt vacuum electrostatics tools. In Pymol, select the action button (A)->generate->vacuum electrostatics->protein contact potential.

ABPS

Since the Pymol inbuilt electrostatics make a lot of assumptions, for publication it is best to get a much more accurately quantified surface map. However, ABPS is a bit more involved. Read the wiki if this sounds more like what you need.


No, on cannot say it this way, since further expose always carries a risk of getting infected.
However, one could argue that after certain time of exposure one is nearly certainly infected, so being exposed longer doesn't really change the result. This statement could be formulated more rigorously in terms of compartamental epidemiological models. If we take the figure for the SIR model in the linked article, we see that the number of the susceptible individuals (S) quickly drops about time 0.2-0.4, after which it is almost zero, i.e., there are almost no uninfected individuals in the population.

The numbers of susceptible (S), infected (I), and recovered (R) individuals can be normalized by the total number of the individuals in the population ($N=S+I+R$), and then interpreted as the probabilities of a person being uninfected ($p_S=S/N$), infected ($p_I=I/N$) or recovered ($p_R=R/N$). These probabilities will behave in time in teh same way as shon in the figure (but with different y scale), i.e., the probability of being ininfected would first be nearly constant, and then quickly decrease to nearly zero.


How was it discovered that flouride helps stop tooth decay?

There is an interesting historical story of fluoride use in prevention of dental caries.
Interestingly the journey started with a "mysterious disorder-fluorosis."

In 1909 Dr. McKay (r) persuaded the Colorado State Dental Association to invite Dr. Green Vardiman Black (l), one of the nation's most eminent dental researchers, to attend 1909 convention where McKay's findings were to be presented. The two men began joint research and discovered other areas of the country where brown staining of teeth occurred.

Fluoride research had its beginnings in 1901, when a young dental school graduate named Frederick McKay left the East Coast to open a dental practice in Colorado Springs, Colorado

McKay collected the samples. And, within months, he had the answer and denouement to his 30-year quest: high levels of water-borne fluoride indeed caused the discoloration of tooth enamel.

Dr Dean recalled from reading McKay's and Black's studies on fluorosis that mottled tooth enamel is unusually resistant to decay. Dean wondered whether adding fluoride to drinking water at physically and cosmetically safe levels would help fight tooth decay.

This finding, considering the thousands of participants in the study, amounted to a giant scientific breakthrough that promised to revolutionize dental care, making tooth decay for the first time in history a preventable disease for most people.

These are excerpts from the article
Another reference
In 1802, Sir James Chrichton Browne suggested the probable cause of dental caries as a change in bread, which did not have bran or husky part of wheat containing fluorine.
Now your next question on fluoride stopping the tooth decay.
So yes apart from preventing tooth decay, research has found that fluorides like silver diamine fluoride can be treatment of choice for dental caries.

The silver component provides an anti-bacterial effect while the fluoride promotes remineralization of tooth structure.

I hope I have answered your question :)


Oral allergy syndrom, continuation of continuous consumption of allergen

When you ingest an allergen for the first time, your body produces many of an antibody called IgE with specificity toward that allergen. These IgE antibodies bind to mast cells, and basophils. When you encounter more allergen, they bind to these IgE on the surface of the mast cells and basophils, causing them to release a host of chemicals like histamine. Massive amount of histamine are the cause of anaphylaxis, so when more allergen interacts with more and more IgE, the situation worsens. The following figure is a detailed mechanism,

Source

We can also see, though, that histamine released is a function of total allergen, and eventually reaches a maximum regardless of allergen present,

Source
Here they're showing histamine release vs allergen concentration while investigating allergic rhinitis.


Why do women have less synapses / neurons / brain mass than men despite having the same average IQ?

In brief, so far as we can tell size matters far less than brain architecture.
Consider that humans have only about half as many cortical neurons as a number of whales, yet are clearly far more intelligent.
While we are still far from a complete understanding of the details of how intelligence actually works, neuron count is only part of the story.
For humans in particular, there is also a selection argument that would lead us to expect that relatively small differences in neuron count (e.g., between male and female average) are not particularly significant. General intelligence (not "IQ") appears to be a critical aspect of human selection, and thus under significant evolutionary pressure for both sexes. If a small adjustment in neuron count made a significant different in intelligence, cranial and cortical scale is a relatively simple morphogenetic adjustment, and we would expect both sexes to scale until they hit a more similar scaling boundary. Since that has not happened, it is reasonable to conclude that neuron count is at least not a major factor in intelligence.


What material is permeable for plant roots, but "impermeable" for fungi & bacteria?

I might have found a solution! Kuijken et al. (2015) first germinate seeds in a sterile eppendorf filled with agar and nutrients. They then cut off the bottom of the eppendorf and stick it through the lid of a sterile plastic jar and pad it with sterile cotton. So I guess they are using the agar as a material that is permeable to the roots and "impermeable" to bacteria and fungi.
Kuijken, R. C., Snel, J. F., Heddes, M. M., Bouwmeester, H. J., & Marcelis, L. F. (2015). The importance of a sterile rhizosphere when phenotyping for root exudation. Plant and Soil, 387(1-2), 131-142.


Using viruses to treat altered or misconfigured DNA

Consider how a Retrovirus can modify existing cell DNA to 'execute instructions' on its behalf.

Not only retroviruses do that. Actually, pretty much all viruses use the machinery of the host cell on its behalf.

  Why can we not utilize lab-generated viruses to infect sick patients with a 'healthy' virus that would rewrite bad segments of DNA with something either correct or non-malus?

We actually do that. We use viruses (incl. retrovirus but not only) to insert DNA segments into eukaryote genome. See vectors in gene therapy > viruses

  Retrovirus to reverse another retrovirus

Note that I don't really understand your title so, I ignored it a bit focusing on the content of the post.


Why heart beats increase when we are in a state of tension even though it is a disadvantage?

The heart rate increases due to any acute stress, it is part of the flight or fight response. This is part of the responses that readies the body for rapid extreme effort.

The body is not selective about it, any acute stress causes it. One idea why is that a hormone response is slower than a direct nervous response so the preparing the body needs to happen as fast as possible since it takes longer to complete, compared to an actual identification, decision and movement.

Also remember that only for a small number of snake bites is this counter-productive, for others it does no additional harm but helps get you away from the snake. Snake venoms vary greatly in their effects.


What fraction of sites are expected to be polymorphic?

The fraction of polymorphic sites that exist in a population is dependent on the biology of the organism. For instance, you would expect to find different rates of polymorphism in related plants that have different breeding systems, e.g. in Silene [1]. Past bottlenecks are also expected to decrease polymorphisms [2]. So, the answer to your question would depend on the exact species and population that you are looking at.


We can devide algorithms for protein-coding genes in eukaryotes in two main categories:

Extrinsic, these algorithms rely on the comparison with external data sources,  such as comparing genomes
Intrinsic, I suggest you are more interested in this one when you acctualy want to predict gene coding regions on your own (If I'm wrong I will further explain extrinsic methods). The intristic methods are based on ab into predictions.

I will continue on Intrinsic methods, so what can we use to identify coding genes in the sequence. Ofcourse we need to know the charasticts of those coding regions:

Most of the time we see CpG Islands(regions of a higher than expected occurrence of CpG dinucleotides over a patricular region).

  About 70% of human promoters have a high CpG content (Wiki)

Those CpG islands are important for the regulation of gene expresion.

the basics of a coding sequence are the promotor, introns and exons.
you are probably familiar with the fact that a mRNA has to be spliced in order to cut the introns (non coding regions) out, this is done by the splicesome wich recognizes special sites where it can cut, So you can search for these sites in your DNA sequence.  I will summarize some points about the coding regions here:

CpG Island near the promotor
splice sites for splicesomes
Start and stop codons (I hope you are familiar with these)

There are more things to consider but I would to suggest to look at the algorithms of these programs: GENSCAN, FgeneSH, GeneMark, Xpound and to read this atricle which is about identifying the protein-coding regions


I think that the information you are looking for could be covered on this website:https://www.addgene.org/protocols/

Not only has addgene a protocol section, it also has an education section for understanding the concepts of genetic/molecular cloning: https://www.addgene.org/mol-bio-reference/

The material is for biologist, but should be relatively easy to understand for you are starting.

They also have a nice ebook which should cover some of the topics you asked: https://info.addgene.org/download-addgenes-ebook-plasmids-101-3rd-edition

Hope that helps!


My hand was stabbed with a pencil tip 10 years ago - why is the graphite still visible?

I had the same thing happen to my leg. I think it eventually went away, but it was definitely many years.

I am going to go with the Tattoo answer. The way tattoos work is that Macrophages in the skin phagocytose the heavy metals in the inks. Because the heavy metals do not interact with Toll-like receptors, or any of the other pathogen receptors that macrophages have on their cell surface, they do not induce a transcriptional signal into the nucleus of the macrophage to induce it to chemotax out of the dermal tissue and into the lymphatic system, so they just sit there. The YouTube channel Smarter Every Day actually has two interesting videos on tattoos Tattooing Close Up and How Laser Tattoo Removal Works.

Macrophages are long lived, so unless they receive transcriptional signals to get them to move out of the tissue they are in, they will just sit there, and if they happened to have engulfed the heavy metals in tattoo ink, or the graphite from a pencil jab, then they sit there with a vesicle filled with that compound. As graphite is pure carbon, I wouldn't worry that much about it, but it is also not not in a form that the body can use, so it will persist. If eventually the macrophages are induced to move, they will likely take their contents with them, which is why tattoos fade over time.

It also is likely that the graphite was taken up by several macrophages in the region, in particles large enough that they cannot be cleared and remain visible, but not so large that the macrophages could not phagocytose them.


Could be an union/synergy between Math, Computer Science and Marine Biology?

This advice is America-centric; graduate programs in other countries may be different, especially in regards to assigned course work, etc. But this should more-or-less be solid. Also, I'm assuming you're interested in a PhD.

I don't know if what you're looking for is a proper field, but that doesn't matter.  Ideally, grad school is where you do research on your own terms, and what lab you join is the single most important consideration. What you want is a competent program that:

gives you the flexibility to create your own subfield
has supportive and knowledgeable faculty that you can work under
has excellent computer science AND marine biology programs (or analogs)

First, find faculty who are doing research you are interested in, and look at the programs available at their schools. Since they're the most important consideration, everything else will fall into place after that.

Next, email 5 of those professors with an introduction of what you're interested in, particularly about their research.  Ask them what programs are most appropriate at their university, and how flexible you can be in taking appropriate coursework.

At some point, you'll probably want to decide if you want to be more of a marine biologist or a computer scientist. Your diploma at the end of the decade is only going to say one of those things.  That shouldn't make a huge difference as to your job outlook, but is worth thinking about.  Practically, the question is whether you want to get your hands dirty, go out on a boat, and collect your own data.  Alternatively, if you join a CS program, you are more likely to be analyzing large datasets collected by other scientists.

At most universities, you'll likely only have the option to join a biology program. But there are certainly excellent CS programs with biology oriented faculty.  Even then, the CS faculty is more likely to be doing genomics and such.

For practical recommendations, the University of Washington in Seattle would probably have something appropriate to your interests. While I was there, my lab was adjacent to an Oceanography lab that had a video wall for visualizing ocean currents and such. Not precisely marine biology, but UW has a very collaborative faculty and I'm sure the two departments work together.

UW also has a strong computational biology program that you can go into either through CS or through one of the many biology programs. You should also check out the Institute for Systems Biology at UW. I saw a presentation there specifically about DNA exchange between marine microorganisms.  I'm not saying UW is your best option, I just know that school because I did my PhD there.

A final word of advice. Once you're in a program, it's very likely your interests will change as you learn about other research going on. And also, it's quite possible you won't get into the lab you want simply because of money or logistics. With both of these in mind, be flexible about where you're willing to go. What you do during your PhD isn't really that important anyway.  If you're going to be research scientist, it's what you do during your Postdoc that will establish your career. Just take the opportunity to learn everything you can and see what's out there.

Hope this helps.  Good luck!


How did a plant evolve to know there were animals alive that would eat it's fruit when it decided to produce this fruit?

The plant does not "know" there are animals. It is just that plants, that accidentally evolved (due to mutations) a fruit that attracts animals (which can spread its seeds etc.) had an advantage over plants that did not have them. The disadvantaged plants just became extinct after a while. And you must be aware that the first fruits were rather "pre-fruits", but already a selective advantage to their bearers.


Identification of species found in fly lab

I'd guess it's a larva of one of the carpet beetles (Dermestidae), some of which are synanthropic. They probably would be interested not in live flies, but in their food and cadavers.

Its appearance seems to be very characteristic - someone would be able to identify it to generic or even specific level.


Problem on finding sequence of Taq polymerase

According to the paper here "Taq polymerase" is the DNA polymerase I from Thermus aquaticus. (Incidentally the paper has the complete sequence of the gene in question.)

You can find a protein sequence entry at NCBI here. This shows the sequence of amino acids in the protein, not the DNA sequence of the gene. Several lines from the top you will see:

  DBSOURCE    locus TTHDNAP accession D32013.1

with the last word highlighted as a link. Follow that link to the DNA sequence. That entry includes the line:

  CDS             1..2499

which tells you that the initiator ATG of the gene is right at the start of the sequence shown, but that the 3026 bp sequence includes a lot of downstream DNA. If you click on CDS on that line it will highlight the coding sequence in the DNA.

A very cursory comparison of this sequence with the one in the paper that I linked to suggests that this is the same gene.


What's the role of SNP (Single Nucleotide Polymorphism) in a disease when there is no gene associated to that SNP

SNPs do not signify anything about the functionality of a particular piece of DNA. You can think of a SNP as just a sign that says "hey, something in my neighborhood might be interesting!!" In this respect many GWAS SNPs are so-called "tag SNPs", that have been selected specifically to give the minimal number of SNPs that can be linked to all of the genome (e.g. every 1 centimorgan there is a SNP, or whatever).
So: SNPs do not say anything about biology, or about genes. The only thing that they signify is linkage to something that is possibly interesting.
Your issue seems to be that you are coming up with GWAS SNPs that do not show apparent linkage or statistical associations (e.g. eQTLs) to anything that you think should be interesting.
There are a lot of ways in which this could be the case:

There actually is a gene in that part of the sequence, and we just haven't annotated it properly.

The reference genome is wrong in that SNP's location, or is missing an interesting piece of sequence (i.e. a gene) that exists in your population.

That SNP has a population structure signal (i.e. it is more common in one population than another) that is associated with your trait of interest, which you have not properly corrected for.

That SNP is spuriously linked (for whatever reason) to a more distant part of the genome.

That SNP was mis-mapped and is actually in the wrong part of the genome somehow.

There is technical interference due to high similarity to some other part of the genome, which your procedure has not corrected for.

Sometimes frequentist p-values are not actually informative and a thing that looks interesting according to p-values is simply not interesting.

Though there are no genes, there is a long-range enhancer in the region of the SNP that affects a distant gene.

Though there are no genes, there is repetitive DNA linked to the SNP that leads to a complicated regulatory feedback that affects something about your character of interest.

Something about microRNAs?

Your SNP doesn't have any genes nearby, but it is linked to a TAD boundary that affects overall chromatin architecture.

There is a long non-coding RNA there.

Your eQTL significance threshold is too stringent.

Your eQTL association analysis missed an important isoform or transcript.

... and many other hypotheses! Many of these are very hand-wavey or unlikely, but sometimes biology is just weird.
However, in general I would default to an explanation in which there is some kind of technical or statistical artifact. Even when nothing is obviously wrong, there is usually some kind of technical problem in genomic datasets.


How does Natural Selection shape Genetic Variation?

First of all, here is a program which simulates the evolution of the G-matrix over multiple generations, it's a few years old (they seem to have stopped developing it) and I've only played with it briefly. This could solve how to model the evolution of the G-matrix.

Fisher's fundamental theorem is a great place to start off with the theory of this:

  The rate of increase in fitness of any organism at any time is equal to its genetic variance in fitness at that time.

What this means (as I am sure you realise but I will put in so the answer can help others too) is that evolution by selection depends not only on the strength and form of selection, but the genetic variation underlying the selected trait. This is captured in the breeders equation $\Delta \bar z = G \beta$  where $\Delta \bar z$ is the response (in a multivariate space), $G$ is a matrix of the genetic variance within traits covariance between traits (different traits, the same traits in either sex, or the same traits in multiple environments) and $\beta$ is the vector of selection gradients on all of those contexts. It seems reasonable to expect that given enough time selection will erode variation because standing genetic variance is a finite resource and selection removes polymorphism while not adding any new variants (that's for mutation & migration).

This paper discusses the effects of both selection and drift on the G-matrix, they also deal with some modelling to support their results. More specific to simulation, this article by Arnold et al (a number of the big players in the G-matrix circle). It reviews "empirical, analytical, and simulation studies of the G-matrix with a focus on its stability and evolution." It would be a really good read for you on this subject.

This paragraph captures the essence of your question:

  Focusing on a longer time scale, we find that the G-matrix evolves in expected ways to the AL [adaptive landscape] and the pattern of mutation. In the absence of correlational selection (rω = 0) and mutational correlation (rμ = 0), the average G-ellipse is nearly circular, although the ellipse fluctuates wildly about this average (first row in Fig. 6). At the opposite extreme, when the leading eigenvectors of the AL and the M-matrix [mutation matrix] are both inclined at an angel of 45°, the leading eigenvector of G is pitched at the same angle (last row in Fig. 6). Between these two extremes, G tends to evolve to a shape and orientation that represents an intermediate compromise between the AL and M. In other words, the simulation results confirm our intuition and theoretical expectations (Lande 1980b) that G should evolve toward alignment with the AL and M.

Simulation studies of the G-matrix:

Bürger R, Wagner GP, Stettinger J. How much heritable variation can be maintained in finite populations by mutation-selection balance. Evolution. 1989;43:1748–1766.
Multivariate mutation-selection balance with constrained pleiotropic effects.
Wagner GP
Genetics. 1989 May; 122(1):223-34.
On the distribution of the mean and variance of a quantitative trait under mutation-selection-drift balance.
Bürger R, Lande R
Genetics. 1994 Nov; 138(3):901-12.
Predicting long-term response to selection.
Reeve JP
Genet Res. 2000 Feb; 75(1):83-94.

The last article there by Reeve is probably the best paper for your question because it describes in some detail the simulation model they use and how it is all set up. Briefly they simulate a population of 4000 diploid individuals with three genetically correlated traits with separate but identical sexes, random mating, and discrete generations. 20000 generations are simulated to allow mutation-selection-drift equilibrium (almost like a burn-in time on an MCMC chain). They then shift the optimum for one trait by 10 standard deviations and simulate 1500 generations in five replicates. There are 100 unlinked loci underlying the traits, with 50 loci affecting each trait (randomly assigned) meaning there is likely some degree of genetic correlation though it is not perfectly correlated (for some reading on genetic correlations and the evolution of differences you can read Bonduriansky & Rowe 2005, Poissant et al 2010, and Griffin et al 2013). The model then assigns phenotypic values to individuals, and fitness is derived. Figure two shows how the mean, skew, and kurtosis of the variance distributions changed for those 1500 generations.

Addition

Roff 2012:

  "While the mean trait values change under selection, so also will the
  G matrix, its orientation tending to shift in the direction of
  selection. ... Genetic drift may also play a role in changing the G
  matrix, but in this case the change will be random though on average
  producing a proportional change in the constituent variances and
  covariances."


Will "circus peanuts" biodegrade, and how?

Time answered this question. I didn't do anything to the candies, but moisture eventually worked its way in. After a week or so, they were showing definite signs of breaking down. After two weeks, I couldn't see a trace of them!


Are redundant codons used in translation?

The textbook is asserting that translation could function without redundant codons, not that it does. In reality, all possible codons are used.

See this answer on the interchangeability of codons.


Conversion rate of topical Retinol to Retinoic Acid (Tretinoin)?

My guess is that the effect you describe is due to compartmentalisation: both retinol and retinoate are practically lipophils and so will localize in membranes and other lipid "pools", while both human retinal dehydrogenase enzymes are cytosolic. Probably the only occasion they have to get at their substrate is when it's bound to RBP. This however introduces another factor complicating the simple model.


This depends on what kind of cartilage you are examining and where it is located. Cartilage is not all the same depending on where it is located and it's intended purpose. Cartilage also generally exists as a continuum with the bone that it is attached to where you will see transitional changes from bone tissue to cartilage without specific delineation. Depending on the cartilage location and its function it may of may not be vascular. Hyaline cartilage existing closest to synovial joint spaces is pretty much entirely avascular. This has some advantages and disadvantages. Damage to cartilage is very difficult for you body to repair for myriad reasons one being avascularity, but also the avascularity allows for you to transplant live cartilage to any person regardless of what tissue type the donor or recipient is.


Doubts regarding definition of upstream/downstream genes and cognate protein

Upstream means towards 5' direction from the reference point (conversely for downstream). Reference point can be a single position such as transcription start site (TSS) or a bigger segment such as a gene. When we say upstream of a gene, it means some region of DNA that is towards 5' direction from the TSS of the reference gene. Downstream of a gene refers to the region 3' to the transcription termination site of the reference gene.

Similar terminology is applicable to RNA but obviously, in this case your reference cannot be a gene (it can be an exon, for instance). I have not seen this terminology used in case of proteins.

This paper is about identification of the proteins that aid in secretion (TEC) of certain proteins (effectors). They identified both the proteins that help in secretion and the proteins that are secreted

  Here, we report a class of T6SS effector chaperone (TEC) proteins that
  are required for effector delivery through binding to VgrG and
  effector proteins. The TEC proteins share a highly conserved domain
  (DUF4123) and are genetically encoded upstream of their cognate
  effector genes.  [....]
  We validated this approach by verifying a predicted effector TseC in
  Aeromonas hydrophila. We show that TseC is a T6SS-secreted antibacterial effector and that the downstream gene tsiC encodes the
  cognate immunity protein. Further, we demonstrate that TseC secretion
  requires its cognate TEC protein and an associated VgrG protein.

Cognate refers to the correct binding partner which will result in a functional complex. It basically refers to biomolecular partners with a known and validated relationship. This relationship need not be synergistic. For example:

  RNA interference (RNAi) is an evolutionarily conserved process through
  which double-stranded RNA (dsRNA) induces the silencing of cognate
  genes. (Source)


Why is sarcoma more prevalent and common in right heart?

Ewing's sarcoma is a bone cancer. As such, it does not arise as a primary tumor in the heart.

Ewing's sarcoma does metastasize. Like any metastatic cancer, it seeds along it's venous return to the heart, "taking root" in suitable tissue.

Cardiac metastases of Ewing's sarcoma are exceedingly rare, with only a few reported cases. Since all blood returns to the right side of the heart first, then the lungs, this is where distant metastases of Ewing's sarcoma are found. The side doesn't matter; it's never good.

Primary heart tumors do occur, about 1/20th as often as metastatic tumors. Of these, myxomas are the most common. Of sarcomatous types, which are very rare, they can occur anywhere.

Ewing's Sarcoma
Metastatic Ewing's Sarcoma to the Right Ventricle
Primary Malignant Sarcomas of the Heart and Great Vessels in Adult Patients — A Single-Center Experience
Echocardiographic and pathologic characteristics of primary cardiac tumors: a study of 149 cases
Primary cardiac sarcomas: an immunohistochemical and grading study with long-term follow-up of 24 cases


What are the limitations of commercial-grade DNA genotyping compared to full sequencing?

23andme briefly describes the technology they use here. They are testing the genotype of your DNA at roughly 1 million locations. The technology they use to do this is known as a microarray.

The limitations of using a microarray, as compared to sequencing, is that you will only find what you are looking for -- people often describe the disadvantages of microarrays as compared to sequencing with the streetlight effect/metaphor.

Arrays can only measure regions of the genome that they were designed to probe. Technically, if they probe 1,000,000 million locations, and the human genome is roughly 3.4 billion bases ... you can do the math.

In practice, SNPs tell you a bit more about just the nucleotide being interrogated, due to linkage disequilibrium (cf. tag/proxy SNPs), so the array might tell you more than you expect.

Of course, modulo sequencing errors, whole genome sequencing will tell you "everything" as far as recovering the NTs that make up your genome, but how much information that will provide you is another thing altogether (for now, that is).


Can my eyes adjust instantly to my friend's glasses?

The phenomenon you described as "my eyes seem to adjust" is a particular case of accommodation - adjustment of the dioptric power of the crystalline lens to the object's distance.

When you place a lens before your eye, any object seen through this lens will initially appear at a different distance (and of different size of course) and will be blurred at different degrees (i.e out of focus). Thus, to overcome this discrepancy the eye must accommodate.

Now, the amplitude of accommodation is the maximal power which can be added to the current power of the crystalline lens. The younger the subject, the higher amplitude of accommodation he/she has:

Thus, if one young emmetropic person (person 1) wears some other person's myopic glasses (person 2) with a concave lens with minus power to lessen the actual "overpower" of the optic system, and this power does not have the refractive error of person 1's has (less myopia if they have any "hidden" myopia), the eye must accommodate, i.e. by increasing the convexity of the crystalline lens. If the amplitude of accommodation is much higher than the glasses in question, person 1 will easily adjust to these glasses by elimination of dioptric discrepancy.

Thus, Sherlock Holmes could deduce the following:

You are a young person (under the age of 30) with a high amplitude of accommodation.

You have a lot of "four-eyed" friends suffering from myopia.

From these facts the fiction writer could assume even your nationality (developed country) and level of close work (reading books, using computers) your friends are doing but - let's leave those assumptions to writers! :)


What is a microbe?

A microbe (or microorganism) is a microscopic organism. Anything that is considered alive and that is small enough is called a microbe.

Note that this definition has two issues.

There is no universally accepted definition of life.
There is no universally accepted size threshold for being called a microbe (to my knowledge). I would go with a threshold of about $10^{-5}$ meters.

Is a virus a microbe?

A virus IS and IS NOT alive depending on the definition. Note btw, that the definition of what is alive is not a matter of Biology but a matter of Philosophy. Most of the time, viruses are considered as not being alive. It is important to understand that the definition of life has absolutely no impact on biology and is nothing but a question of nomenclature.

If you want to call a virus a living thing, then yes, viruses are microbes. As stated on the wiki article:

  Some microbiologists also classify viruses (and viroids) as microorganisms, but others consider these as nonliving.

You can find a discussion of why viruses are generally considered as not being alive here.

Unit of life

The concept of "unit of life" has not much meaning in biology and, to my experience, is most often used as a nice image for teaching young students what a cell is. Most of the time that I heard of "unit of life" (mostly when I was in secondary school and eventually high school) was used to describe a single cell making up a multicellular individual such as a cell of your blood for example and not a unicellular individual.


Short answer
Although outside my direct area of expertise, there is quite a lot of literature on the topic. The trick often is to use the correct keywords. Key in "recording + stimulating + ensemble + neurons", or "Utah + neuron + implant".

Background
There is quite some literature on this topic. For instance, a much used device in this arena is the Utah implant (Fig. 1), which can be used to record and also stimulate neurons (e.g., Nordhausen et al. (1994)). It has 100 penetrating electrodes and is even used in humans. There are also other approaches, e.g. by implanting a bunch of microwires (e.g., Nocolelis et al (1997)). In scientific jargon, the recording of multiple neurons simultaneously is often referred to as 'ensemble recording' (Nicolelis, 2008).

Fig. 1. Utah Implant. Source: WebVision

References
- Fernandez & Normann, Introduction to Visual Prostheses. In: WebVision, online book
- Nicolelis, Methods for Neural Ensemble Recordings, 2nd ed., Front Neurosci, CRC Press/Taylor & Francis; 2008
- Nicolelis et al. Neuron (1997); 18(4): 529-37
- Nordhausen et al., Brain Res (1994); 637: 27-36


Why do lychee seeds come in different shapes?

The shrivelled seeds are aborted seeds, known as 'chicken tongues'. Some cultivars of lychees mainly produce fruits containing chicken tongues. These cultivars are usually considered more desirable, because they have more flesh.

Source: https://www.crfg.org/pubs/ff/lychee.html


Why does cell differentiation start with the morula?

(Partial response) You're actually correct that inner vs. outer position is a differentiator, but there's still controversy whether position is a cause or whether differentiation actually happens sooner.

For a long time it has been believed that cells are identical within this ball at least until they take up distinct inside and outside positions, but is this true? A number of studies [2, 3, 6, 7, 8, 9] over the last 10 years involving a variety of markers such as oil-drops, dyes, fluorescent beads and proteins, together has challenged this view. These studies indicated that cells start to develop their identity earlier than expected, before taking inside or outside positions and, importantly, that these differences between cells are significant, as they bias cell fate [2, 3, 6, 7, 8, 9]. This view has not been universally held, however, because differences between the cells were not detected by other groups and so the view that cell fate choice is random was maintained [10, 11, 12]. It was also proposed that the bias in cell fate might simply result from the shape of the zona pellucida surrounding the embryo until implantation [13], an interpretation that has itself been questioned [14]. The random (stochastic) view of development became popular as it seemed to fit more comfortably with the experiments showing that early embryo cells are plastic and can adopt different fates upon transplantation.
The new contribution from Kevin Eggan’s group [1] now further challenges the view that early mouse development is stochastic.

And from a slightly more recent paper:

Drosophila has been found to have a pre-pattern in oocytes and zygotes. Several molecules that act as maternal determinants of the future body axis (for example, bicoid mRNA) are thus localized asymmetrically in both oocytes and fertilized embryos of Drosophila [6]. No such molecule has been identified in the mouse oocyte or embryo, although certain molecular and structural asymmetries have been described in unfertilized mouse oocytes. For example, Par4 protein is asymmetrically localized in mouse oocytes [7], but whether this protein is unevenly distributed among blastomeres after fertilization remains unknown. In addition, transcriptome asymmetry in terms of mRNA regionalization within mouse oocytes and zygotes has been identified, but no such asymmetry was detected between blastomeres within 2- or 3-cell-stage embryos [8]. However, several recent studies suggest molecular differences exist between blastomeres of the mouse embryo as early as the 4-cell stage. These molecular heterogeneities include the level of H3R17me2 and H3R26me2 (dimethylation of histone H3 at arginines 17 and 26, respectively) [9] and the level of Cdx2 expression [10].

Wikipedia also mentions this fact about Drosophila, so for insects at least it seems uncontroversial.