The Smell of Fear

Protecting gardens and crops from insects using the 'smell of fear'

Aug 2021, phys.org

https://phys.org/news/2021-08-gardens-crops-insects.html

They're using methoxypyrazines, such as isopropyl methoxypyrazine, isobutyl methoxypyrazine and sec-butyl methoxypyrazine. Methoxypyrazines smell like "green, herbaceous, vegetative, green peppers, freshly cut grass, and asparagus." 

But for aphids, methoxypyrazines smell like ladybugs. Aphids hate ladybugs. And farmers hate aphids. 

On a related note, the smell of cut grass is a defense mechanism for grass to tell other grass that it's being attacked, and to "brace yourselves." The next time you smell it, you can think of the sound of grass screaming.  

via American Chemical Society: Smell of fear: Harnessing predatory insect odor cues as a pest management tool for herbivorous insects, ACS Fall 2021.

https://www.acs.org/content/acs/en/pressroom/newsreleases/2021/august/protecting-gardens-and-crops-from-insects-using-the-smell-of-fear.html

Temporal Patterns in Olfactory Perception

Temporality in olfactory perception is understudied. But as our ability to measure spike activity over small timescales improves, so will our understanding of olfaction, and human behavior in general.

On Time

Facebook knows what's up; they're making Time Cards for computers and networks so they can perform better, and integrate information better. They understand the mismatch between the size of today's clocks vs the speed of information. 

Today's clocks go down to the picosecond (trillionth of a second), and some things are being measured at the femtoscond (quadrillionth of a second). There's even some zepto-sensitive devices out there. The smaller the time-slice, the better your pattern recognition. Even back as far as Barabási's Bursts (2010) we knew that high resolution temporal patterns could be really informative of network behavior. More people in your study is also helpful, but networks like Facebook are running out of people* (but bots are infinite!), so they have to find another way of mining user data, hence an interest in temporal activity. 

If you think the fine-grained timing of patterns isn't that big a deal, why don't you ask the New York Stock Exchange, which is actually not located on Wall Street, but a few miles away in Carteret, New Jersey, because high frequency trading algorithms compete with each other at such high speeds (the speed of light) that they need the extra light-seconds provided by placing their servers a few miles closer to the international trunk connecting the Internet to the United States (it's called a latency advantage; and that trunk actually lands in Manasquan, a town on the Jersey Shore; still, closer to Carteret than New York).

The point here is that light travels fast, and if we want to understand it, then we will need to look at smaller and smaller slivers of the clock, and add this to the growing body of knowledge about how.

On Olfactory Timing

Think about it -- the brain is a network of billions of blinking neurons. Consciousness, or any neural activity, is an intelligent organization of these blinking neurons into meaningful patterns. But when you have billions of lights blinking on and off at the same time, you need to start slicing up the "time," so it's not all happening "at once." 

In the world of olfactory perception, a new look at the temporal side of things should supplement the intense interest we're seeing in this subject (check out the books Smellosophy or Nose Dive, and stutter at the thought of a $2 million grant in olfactory artifact research).

The author of Smellosophy, Barwich, actually gets into this aspect of olfaction. For her, it's a major distinction in the way we categorize olfactory information - between the historical approach to "mapping" the receptors of the two-dimensional olfactory bulb to "measuring" the timing of receptor activity, which includes the temporal dimension.

She argues that the measurement approach can reveal a key to the coding of the olfactory network. Olfaction is not just about identifying the different molecules present and their combinatorial patterns, but about when they appear and at what concentrations, and how these concentrations change over time. After all, olfaction is about detecting change, which implies time.

She says, "Rather than molecules, your brain depicts transient information patterns" (p246)

-Smellosophy: What the Nose Tells the Mind, A.S. Barwich, 2020, Harvard University Press

http://www.smellosophy.com/

And here's a recent article on the topic:

Fast changing smells can teach mice about space

May 2021, phys.org

https://phys.org/news/2021-05-fast-mice-space.html

Mice can sense extremely fast and subtle changes in the structure of odors and use this to guide their behavior. The findings, published in Nature today, alter the current view on how odors are detected and processed in the mammalian brain.

via The Francis Crick Institute: Ackels, T., Erskine, A., Dasgupta, D. et al. Fast odour dynamics are encoded in the olfactory system and guide behaviour. Nature (2021). doi.org/10.1038/s41586-021-03514-2

Post Script:

Start with the 2014 article from Princeton (cited below) that said Facebook is losing its users like a susceptible-infected-recovered model of disease transmission, and that it would be done by 2018. Granted the article came from the unlikely Department of Mechanical and Aerospace Engineering in Princeton, and despite all the bad press, we can now fast forward to 2018 when Facebook is suspected to be more bots than people. Or is it that the people are acting more bot-like as they co-evolve with engagement algorithms in the artificial arena of natural selection. Bottom line is, social media networks, and all technologies adopted by individuals in a society, will follow a similar disease model. 

Not to mention, the bad press (possibly influenced by the same entity that took a full-page ad in the New York Times btw, and somewhere around 100K in case you were wondering) didn't age well.

But I'll let you be the judge of that:

"Facebook may be a massive drain on our attention that some people get sick of, but that doesn’t mean it actually operates like a virus." -TechCrunch, 2014

Epidemiological modeling of online social network dynamics, John Cannarell, Joshua A. Spechler. Department of Mechanical and Aerospace Engineering, Princeton University. arxiv:1401.4208v1 [cs.SI] 17 Jan 2014. https://arxiv.org/pdf/1401.4208v1.pdf

Post Post Script:

Zeptoseconds - New world record in short time measurement

Oct 2020, phys.org

https://phys.org/news/2020-10-zeptoseconds-world-short.html

Dark Taxa AKA Creating Taxonomies From Scratch

New norms needed to name never-seen fungi

May 2021, phys.org

https://phys.org/news/2021-05-norms-never-seen-fungi.html

There's 150,000 species of fungi known, yet a projected 2.2 to 3.8 million still waiting to be discovered (these are called dark taxa). But because of advances in DNA sequencing and microscopy, we're learning so fast that we need a new way to organize it all. 

This comes up in the context of biosecurity, where it can only work if "organisms detected can be reliably identified and have accurate names." For fungi, that's not really possible, because believe it or not, we don't have a good catalog of fungi. 

-via: Robert Lücking et al. Fungal taxonomy and sequence-based nomenclature, Nature Microbiology (2021). DOI: 10.1038/s41564-021-00888-x

We also don't have a good way to organize the words we use to describe everyday smells, and we don't have something like a "smell taxonomy." There are plenty of sub-domains that organize their relevant smells, found in subjects like coffee, wine, perfume, and culinary arts. They always seem to take the form of a wheel (not the most complex form). You can get a good start with everyday smells at the South Coast Air Quality Management District, who created a "Characterization of Odor Nuisance" odor wheel, with the help of environmental scientist Jane Curren at UCLA circa 2016. It was based on a bunch of phone calls made to the District where people were complaining about odors in their neighborhood. She took all the words they used and organized them. 

You could also look into Ann-Sophie Barwich who is a cognitive scientist who did her dissertion on olfactory categorization, and then wrote a book called Smellosophy. Probably one of the most interesting academics you will ever hear of. I mean, her master's thesis was about the relevance of  Leibniz causality on biological classification.

Image credit: Penicillin, Kew Royal Botanical Gardens for BBC

Notes:

State of the World's Fungi, by the Kew Royal Botanical Gardens (2018), is the first ever State of the World's Fungi report revealing how important fungi are to all life on Earth. [pdf]

[State of the World's Fungi]

International Commission on the Taxonomy of Fungi (ICTF)

MycoBank is the on-line repository and nomenclatural registry provided in collaboration between the International Mycological Association and the Westerdijk Fungal Biodiversity Institute. It provides a free service to the mycological and scientific society by databasing mycological nomenclatural novelties (new names and combinations) and associated data, such as descriptions, illustrations and DNA barcodes. Nomenclatural novelties are each allocated a unique MycoBank number to be cited in the publication where the nomenclatural novelty is introduced, to conform with the requirements of the International Code of Nomenclature for algae, fungi and plants.

Identification and quantification of nuisance odors at a trash transfer station. Jane Curren, et al.  PubMed, Waste Manag. 2016 Dec;58:52-61. doi: 10.1016/j.wasman.2016.09.021. Epub 2016 Sep 28.

https://pubmed.ncbi.nlm.nih.gov/27692531/

Post Script:

I'm looking at a popular science article about fungi. The first two "interesting" points, when looked at together, remind me of why I always have the feeling like fungi are from outerspace:

• Fungi are in a kingdom of their own but are closer to animals than plants
• They have chemicals in their cell walls shared with lobsters and crabs (you do know we're all becoming crabs, right?)

Categorgonzola

A perennial topic on this weblog is the categorization of smells. Today I'm looking at a study from 2011 that looks at common features that group smells together. One of the common denominators is hedonics, or pleasantness vs non-pleasantness.

It always makes me pause to think about this, because it seems that people can never really agree on what makes a smell good or bad, and yet the hedonic dimension is the only one that keeps coming back as the primary distinction between odors. I guess that's just the law of large numbers at work, a law which is against natural human cognition.

If you include enough people in your study, the differences between us cancel out and you're left with a fuzzy but recognizable picture of a smell map, which is seen above.

The other common denominator (it’s not a denominator if there’s two, right?) is a dimension the researchers call natural/chemical.

This map is organized as follows: Whereas the pleasantness of an odor can be predicted on the number of carbon atoms per molecule (related to how fast it evaporates), the natural/chemical dimension is predicted by the polarity of the molecules, or how attracted they are to water.

Why? Not so sure. Mention is made to the difference in the olfactory receptors themselves - some are from when we were fish and some are from when we became land animals, so the two may have a different relationship with water (polarity).

For example, odorants are dispersed more slowly in the water. Also, smellable molecules to fish don’t have to be volatile organic compounds, because for a fish, the air itself is already a liquid. So fish detect water soluble molecules whereas humans detect airborne molecules.

Actually, now that I look at the ‘natural’ part of the map, I realize that none of those things exist underwater, right? Burnt? Nope. Moldy? Although mold is always associated with moisture, it doesn’t grow underwater. And Earthy? Kind of the opposite of water.

Natural - Burnt, Smoky, Nutty, Woody, Resinous, Musty, Earthy, Moldy, Almond, Popcorn, Peanut Butter, Oily, Fatty, Warm, Dry, Powdery

Chemical - Etherish, Anaesthetic, Chemical, Medicinal, Disinfectant, Carbolic, Sharp, Pungent, Acid, Gasoline, Solvent, Cook, Cooling, Cleaning Fluid, Paint, Camphor

Good - Fragrant, Sweet, Perfumery, Floral, Light, Aromatic, Cool, Cooling, Fruity, Citrus, Rose

Bad - Sharp, Pungent, Acid, Heavy, Musty, Earthy, Moldy, Burnt, Smoky, Oily, Fatty, Sour, Vinegar

-image source: link

Notes:

In search of the structure of human olfactory space. A. A. Koulakov, B. E. Kolterman, A. G. Enikolopov, D. Rinberg. Front. Syst. Neurosci. 5, 65 (2011).

doi: 10.3389/fnsys.2011.00065

Attempts at Olfactory Space Shaping

In bits and pieces, we are seeing the organization of olfactory precepts become a possibility. It's really only been happening in the past five years or so, and because of a couple changes in the game, namely better and newer data, and better algorithms to do the prediction. And yes, those better algorithms are powered by the new-style machine learning artificial intelligence that is blasted from every headline on the tech feed -- deep learning. 

I posted something not long ago about the 2015 DREAM Olfaction Prediction Challenge, and the new dataset that made it all possible.

Today we're looking at another group of ambitious osmologists who figured out some good rules for organizing the category-averse sensory world of smells. Their results are quite different from those of the DREAM Challenge, so I thought it was worth it to summarize their results. 

Their database comes from the Dragon set of chemoinformatics (odorous molecules and their chemical properties) and the Arctander set of olfactory descriptors (chemicals and the names they are likely to be associated with). I'm not sure why they chose the Arctander set instead of the newer Keller Vosshall set. They reviewed other sets and pretty extensively, such as the Dravnieks set, FlavorNet, and one other, but they don't mention the Keller Vosshall set, which is the newest of the bunch, so maybe they're waiting until it proves its worthiness.

The Data

Their database ends up with 1689 molecules each with 82 chemical attributes, and corresponding with 74 olfactory descriptions, which they use to generate "rules" for matching the chemical info to the olfactory info. They call it "more of an exploratory data analysis than an accurate prediction machine." But that's ok, because the science of predicting odor qualities by their chemical attributes is still in its exploratory phase.

The Rules

They correlate their chemicals to odor-names finding the combinations of chemical conditions that produce subsets of odor categories, and came up with 473 physiochemical rules.

The Results

First, we have some typical odor categories and the chemical features they were found to associate with:

Floral - either aromatic and strongly hydrophobic molecules or non-aromatic and moderately hydrophobic odorants.

Camphor - molecules are rather small in size, moderately hydrophobic, and eventually cyclic.

Earthy - moderately hydrophobic molecules with unsaturations.

Spicy - rigid molecules, eventually aromatic.

Woody - hydrophobic molecules, rather not cyclic nor aromatic.

Fatty - larger carbon-chain skeleton which is highly hydropobic with aldehyde or acid functions.

Fruity - moderate hydrophobicity and being medium to large in size.

***

And here is their list of subsets that do a good job of organizing all the 1600 molecules:

Sulfuraceous - encompass molecules with one or two sulfur atoms and are moderately heavy, with a maximum of six carbon atoms.

Phenolic - moderate size, with few unsaturations and low hydrophilicity (and high lipophilicity). It can be regarded as a cyclic molecule.

Vanillin - mostly cyclic molecule (like the prototypical molecule vanillin), with 3 Hydrogen bond acceptors branched on saturated carbons atoms on an aromatic cycle.

Musk - heavy and hydrophobic compounds. This is reflected by a rather large logP, surface area or molecular weight.

Sandalwood - (A diverse set; minor modifications within their structure can abolish the sandalwood note. The rules which are mined here correspond to models which are very simple and hardly capture the subtlety of this odorant family.)

Almond - at least one oxygen and/or other hydrogen bond-accepting atom but also bearing an aromatic cycle. This means that the structure bears several unsaturations. These chemicals are thus relatively small. [benzaldehyde is representative]

Orange-blossom - diverse structures ranging from very small to medium or large compounds. As a general rule, one can note the presence of unsaturations, consistent with a terpenic structure, associated with a quite hydrophobic feature.

Jasmine - (i) molecules composed mainly of carbons and oxygen atoms, (ii) molecules with an aromatic core and embranchments conferring a large flexibility, and (iii) compounds with an optimal chain length around five carbon atoms. [jasmonate is representative]

Hay - hydrophobic molecules composed of aromatic cycles, being either heterocyclic or linked to a heteroatom outside of the cycle. These atoms confer to the molecule the possibility to accept Hydrogen bonds.

Tarry - (not easy to establish specific characteristics of the molecules of this group, but overall these molecules are flexible, presenting heteroatoms while having low hydrophilicity due to the presence of double bonds.)

Smoky - (a robust rule is hard to establish because the physicochemical descriptors refer either to aromatic compounds with a hydroxyl group or flexible molecules with rotatable bonds.)

In Conclusion

This explains why I wrote a book about the language of smell:

"The more one moves towards the area of perceptual space of odors that is characterized by its heterogeneity between individuals, the higher the predictability threshold (i.e. bad prediction) becomes. This variability characterizes what could be called "the glass ceiling of olfactory diversity".

image source link

Notes:

Chemical features mining provides new descriptive structure-odor relationships

Carmen C. Licon, Guillaume Bosc, Mohammed Sabri, Marylou Mantel, Arnaud Fournel, Caroline Bushdid, Jerome Golebiowski, Celine Robardet, Marc Plantevit, Mehdi Kaytoue, Moustafa Bensafi. PLoS Comput Biol. 2019 Apr; 15(4): e1006945. Published online 2019 Apr 25.

doi: 10.1371/journal.pcbi.1006945

Post Script:

Pleasantness and trigeminal sensations as salient dimensions in organizing the semantic and physiological spaces of odors. C. C. Licon, C. Manesse, M. Dantec, A. Fournel, and M. Bensafi. Sci Rep. 2018; 8: 8444. doi: 10.1038/s41598-018-26510-5

Here is another attempt to categorize smells. I add this because they bring up a good point:

1. odor space is hierarchical, and

2. We first must separate smells into good/bad, and only then separate further into the dimensions of odor space.

I might alter that slightly and suggest that the first separation can be either good/bad or familiar/unfamiliar; the latter might be even more important in categorizing smells.

(Note that another important part of this study in particular is about how trigeminal sensations influence the way we organize smells in the brain, and that this is one of the many things that make it such a messy task.)