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Automatically Selecting a Suitable Integration Scheme for Systems of Differential Equations in Neuron Models.

Front Neuroinform. 2018;12:50

Authors: Blundell I, Plotnikov D, Eppler JM, Morrison A

Abstract
On the level of the spiking activity, the integrate-and-fire neuron is one of the most commonly used descriptions of neural activity. A multitude of variants has been proposed to cope with the huge diversity of behaviors observed in biological nerve cells. The main appeal of this class of model is that it can be defined in terms of a hybrid model, where a set of mathematical equations describes the sub-threshold dynamics of the membrane potential and the generation of action potentials is often only added algorithmically without the shape of spikes being part of the equations. In contrast to more detailed biophysical models, this simple description of neuron models allows the routine simulation of large biological neuronal networks on standard hardware widely available in most laboratories these days. The time evolution of the relevant state variables is usually defined by a small set of ordinary differential equations (ODEs). A small number of evolution schemes for the corresponding systems of ODEs are commonly used for many neuron models, and form the basis of the neuron model implementations built into commonly used simulators like Brian, NEST and NEURON. However, an often neglected problem is that the implemented evolution schemes are only rarely selected through a structured process based on numerical criteria. This practice cannot guarantee accurate and stable solutions for the equations and the actual quality of the solution depends largely on the parametrization of the model. In this article, we give an overview of typical equations and state descriptions for the dynamics of the relevant variables in integrate-and-fire models. We then describe a formal mathematical process to automate the design or selection of a suitable evolution scheme for this large class of models. Finally, we present the reference implementation of our symbolic analysis toolbox for ODEs that can guide modelers during the implementation of custom neuron models.

PMID: 30349471 [PubMed]

Neonicotinoid pesticides can negatively affect bee colonies, but the behavioral mechanisms by which these compounds impair colony growth remain unclear. Here, we investigate imidacloprid’s effects on bumblebee worker behavior within the nest, using an automated, robotic platform for continuous, multicolony monitoring of uniquely identified workers. We find that exposure to field-realistic levels of imidacloprid impairs nursing and alters social and spatial dynamics within nests, but that these effects vary substantially with time of day. In the field, imidacloprid impairs colony thermoregulation, including the construction of an insulating wax canopy. Our results show that neonicotinoids induce widespread disruption of within-nest worker behavior that may contribute to impaired growth, highlighting the potential of automated techniques for characterizing the multifaceted, dynamic impacts of stressors on behavior in bee colonies.

Well orderings have slightly perplexed me for a long time, so every now and then I have a go at seeing if I can understand them better. The insight I’m about to explain doesn’t resolve my perplexity, it’s pretty trivial, and I’m sure it’s well known to lots of people. But it does provide a fresh perspective on well orderings, and no one ever taught me it, so I thought I’d jot it down here.

In short: the axiom of choice allows you to choose one element from each nonempty subset of any given set. A well ordering on a set is a way of making such a choice in a consistent way.

Write P′(X)P'(X) for the set of nonempty subsets of a set XX. One formulation of the axiom of choice is that for any set XX, there is a function h:P′(X)→Xh: P'(X) \to X such that h(A)∈Ah(A) \in A for all A∈P′(X)A \in P'(X).

But if we think of hh as a piece of algebraic structure on the set XX, it’s natural to ask that hh behaves in a consistent way. For example, given two nonempty subsets A,B⊆XA, B \subseteq X, how can we choose an element of A∪BA \cup B?

• We could, quite simply, take h(A∪B)∈A∪Bh(A \cup B) \in A \cup B.

• Alternatively, we could take first take h(A)∈Ah(A) \in A and h(B)∈Bh(B) \in B, then use hh to choose an element of {h(A),h(B)}\{h(A), h(B)\}. The result of this two-step process is h({h(A),h(B)})h(\{ h(A), h(B) \}).

A weak form of the “consistency” I’m talking about is that these two methods give the same outcome:

h(A∪B)=h({h(A),h(B)}) h(A \cup B) = h(\{h(A), h(B)\})

for all A,B∈P′(X)A, B \in P'(X). The strong form is similar, but with arbitrary unions instead of just binary ones:

h(⋃Ω)=h({h(A):A∈Ω}) h\Bigl( \bigcup \Omega \Bigr) = h\Bigl( \bigl\{ h(A) : A \in \Omega \bigr\} \Bigr)

for all Ω∈P′P′(X)\Omega \in P'P'(X).

Let’s say that a function h:P′(X)→Xh: P'(X) \to X satisfying the weak or strong consistency law is a weakly or strongly consistent choice function on XX.

The central point is this:

A consistent choice function on a set XX is the same thing as a well ordering on XX.

That’s true for consistent choice functions in both the weak and the strong sense — they turn out to be equivalent.

The proof is a pleasant little exercise. Given a well ordering ≤\leq on XX, define h:P′(X)→Xh: P'(X) \to X by taking h(A)h(A) to be the least element of AA. It’s easy to see that this is a consistent choice function. In the other direction, given a consistent choice function hh on XX, define ≤\leq by

x≤y⇔h({x,y})=x. x \leq y \Leftrightarrow h(\{x, y\}) = x.

You can convince yourself that ≤\leq is a well ordering and that h(A)h(A) is the least element of AA, for any nonempty A⊆XA \subseteq X. The final task, also easy, is to show that the two constructions (of a consistent choice function from a well ordering and vice versa) are mutually inverse. And that’s that.

(For anyone following in enough detail to wonder about the difference between weak and strong: you only need to assume that hh is a weakly consistent choice function in order to prove that the resulting relation ≤\leq is a well ordering, but if you start with a well ordering ≤\leq, it’s clear that the resulting function hh is strongly consistent. So weak is equivalent to strong.)

For me, the moral of the story is as follows. As everyone who’s done some set theory knows, if we assume the axiom of choice then every set can be well ordered. Understanding well orderings as consistent choice functions, this says the following:

If we’re willing to assume that it’s possible to choose an element of each nonempty subset of a set, then in fact it’s possible to make the choice in a consistent way.

People like to joke that the axiom of choice is obviously true, and that the well orderability of every set is obviously false. (Or they used to, at least.) The theorem on well ordering is derived from the axiom of choice by an entirely uncontroversial chain of reasoning, so I’ve always taken that joke to be the equivalent of throwing one’s hands up in despair: isn’t math weird! Look how this highly plausible statement implies an implausible one!

So the joke expresses a breakdown in many people’s intuitions. And with well orderings understood in the way I’ve described, we can specify the point at which the breakdown occurs: it’s in the gap between making a choice and making a consistent choice.

We’ve discussed the prospects for adding modalities to type theory for many a year, e.g., here at the Café back at Modal Types, and frequently at the nLab. So now I’ve written up some thoughts on what philosophy might make of modal types in this preprint. My debt to the people who helped work out these ideas will be acknowledged when I publish the book.

This is to be the fourth chapter of a book which provides reasons for philosophy to embrace modal homotopy type theory. The book takes in order the components: types, dependency, homotopy, and finally modality.

The chapter ends all too briefly with mention of Mike Shulman et al.’s project, which he described in his post – What Is an n-Theory?. I’m convinced this is the way to go.

PS. I already know of the typo on line 8 of page 4.

The brain actively represents incoming information, but these representations are only useful to the extent that they flexibly reflect changes in the environment. How does the brain transform representations across changes, such as in size or viewing angle? We conducted a fMRI experiment and a magnetoencephalography experiment in humans (both sexes) in which participants viewed objects before and after affine viewpoint changes (rotation, translation, enlargement). We used a novel approach, representational transformation analysis, to derive transformation functions that linked the distributed patterns of brain activity evoked by an object before and after an affine change. Crucially, transformations derived from one object could predict a postchange representation for novel objects. These results provide evidence of general operations in the brain that are distinct from neural representations evoked by particular objects and scenes.

SIGNIFICANCE STATEMENT The dominant focus in cognitive neuroscience has been on how the brain represents information, but these representations are only useful to the extent that they flexibly reflect changes in the environment. How does the brain transform representations, such as linking two states of an object, for example, before and after an object undergoes a physical change? We used a novel method to derive transformations between the brain activity evoked by an object before and after an affine viewpoint change. We show that transformations derived from one object undergoing a change generalized to a novel object undergoing the same change. This result shows that there are general perceptual operations that transform object representations from one state to another.

Lab-grown ‘mini brains’ produce electrical patterns that resemble those of premature babies

Lab-grown ‘mini brains’ produce electrical patterns that resemble those of premature babies, Published online: 15 November 2018; doi:10.1038/d41586-018-07402-0

Structures could help researchers to study the early stages of brain development disorders, including epilepsy.

Stop exploitation of foreign postdocs in the United States

Stop exploitation of foreign postdocs in the United States , Published online: 21 November 2018; doi:10.1038/d41586-018-07479-7

A survey reveals some lab heads are using the need for visas to create unacceptable conditions for junior researchers.

The death toll from the Camp Fire in California has risen to at least 63, with 631 people reported missing. As California continues to battle the deadliest fire in the state’s history, we turn to the hidden heroes on the front lines the raging climate-fueled wildfires: prisoner firefighters. At least 1,500 of the 9,400 firefighters currently battling fires in California are incarcerated. They make just a dollar an hour battling on the front lines but are rarely eligible to get jobs as firefighters after their release. In September, the Democracy Now! team traveled to the Delta Conservation Camp about an hour north of San Francisco, a low-security prison where more than 100 men are imprisoned. We interviewed incarcerated firefighters who had just returned from a 24-hour shift fighting the Snell Fire in Napa County.

Two weeks ago, New Scientist warmed up the story about a Danish groups’ claim that the LIGO collaboration’s signal identification is flawed. This story goes back to a paper published in Summer 2017. After the publication of this paper, however, the VIRGO gravitational wave interferometer came online, and in August 2017 the both collaborations jointly detected another event. Not only was this

There are various overused terms in mathematics. “Normal” is one of them. Perhaps the four commonest uses are the following:

• A complex square matrix is normal if it commutes with its conjugate transpose. Normal matrices are precisely the ones which can be diagonalised by a unitary matrix (that is, have an orthonormal basis of eigenvectors).
• A topological space is normal if two disjoint closed sets have disjoint open neighbourhoods.
• A field extension L/K is normal if every polynomial over K which has a linear factor over L splits completely into linear factors over L.
• A subgroup H of a group G is normal in G if it is mapped to itself by conjugation by elements of G; equivalently, its left and right cosets coincide.

Most of these uses seem completely unconnected. But the use of the same term for the last two is not coincidence, but comes from Galois theory. If L is a Galois extension of a base field E, and K an intermediate field, then L/K is a normal extension if and only if the Galois group of L over K is a normal subgroup of its Galois group over E. (If this happens, the Galois group of K over E is the quotient group.)

But this also hides some mystery. The most important property of normal subgroups is that they are kernels of homomorphisms (and conversely). But step outside group theory, to semigroup theory or universal algebra, and you learn that the kernel of a homomorphism is a partition, not a subalgebra: two elements are in the same part if they have the same image under the homomorphism. It just happens that, in groups, the kernel partition of a homomorphism is precisely the partition into cosets (left or right, it doesn’t matter) of the kernel subgroup.

Indeed, in German, one talks of a “normal divisor” rather than “normal subgroup”, which presumably arises from this interpretation as partition (but I am guessing, I don’t know the etymology).

You can see kernels of homomorphisms in the Galois connection. If L/K is a normal extension, with L Galois over the subfield E, then any E-automorphism of L fixes K setwise, and so induces an E automorphism of K. So we have a homomorphism from Gal(L/E) (the group of E-automorphisms of L) to Gal(K/E). The kernel of this homomorphism consists of the automorphisms which act trivially on K; these are the K-automorphisms of L, the elements of Gal(L/K). [An E-automorphism of L is a field automorphism of L fixing E elementwise.]

In group theory, the term “normal” could be, and sometimes is, replaced by “invariant”. An invariant subgroup is one mapped to itself by all conjugations; this fits in with the notion of fully invariant subgroup, mapped to itself by all endomorphisms. Indeed, for the notion that most people call “subnormal subgroup” (a term in a series of subgroups, each normal in the next, with top element the whole group) was called by Marshall Hall a “subinvariant subgroup”; he remarked in a footnote that he found the term subnormal “unnecessarily distracting”. [Footnote on p.124 of his book The Theory of Groups, published in 1959. He says “The more colorful term subnormal series has been urged on the writer by Irving Kaplansky”, suggesting that it wasn’t yet in common use in 1959.]

All well and good, if a little confusing so far; the first three uses mentioned above are so well separated that probably mathematical papers using each of them form disjoint open neighbourhoods.

But when we come to Cayley graphs, there is real confusion.

Let G be a group, and S an inverse-closed subset of G not containing the identity. The Cayley graph Cay(G,S) is the graph with vertex set G, in which two elements g and h are joined if and only if hg⁻¹∈S. The fact that S is inverse-closed makes the graph undirected, and the fact that it doesn’t contain the identity makes the graph loopless. The group G acts on itself by right multiplication; this action embeds G into the automorphism group of the Cayley graph.

Now each of the following two definitions occurs in the literature:

• The Cayley graph Cay(G,S) is normal if the set S is closed under conjugation in G; equivalently, the action of G by left multiplication is also contained in the automorphism group of the Cayley graph.
• The Cayley graph Cay(G,S) is normal if G (embedded by the right action as before) is a normal subgroup of the automorphism group of the graph.

These two definitions are quite different. Indeed, the second one restricts the symmetry of the graph (its automorphisms are all contained in the normaliser of G in the symmetric group), while the second expands it (the left, as well as the right, action of G consists of automorphisms).

The complete graph on G is a Cayley graph for any group G; it is normal in the first sense but not the second (if the order of G is greater than 4). On the other hand, the Cayley graph of S₃ with respect to two of its transpositions is a 6-cycle, and its automorphism group contains S₃ as a (normal) subgroup of index 2; so it is normal in the second sense but not the first (since the three transpositions are conjugate).

Both terms, as I said, are well-established, and it is probably too late to change the terminology now.

This was on my mind because of recent events. The argument about synchronization for groups with regular subgroups mentioned in the last-but-one post depends on a relevant graph being a normal Cayley graph (in the first sense); but I learned about the result of Cai and Zhang at the conference in Shenzhen, which also had a talk about normal Cayley graphs (in the second sense).

Philosophers of mathematics argue about whether mathematics is discovered or invented. In the book of Genesis we read that God created the animals but Adam gave them their names. I think what the examples above show is that, whether mathematics is discovered or invented, the names we give to the concepts are our own invention.

This blog was born in 2006 when a philosopher, a physicist and a mathematician found they shared an interest in categorification — and in particular, categorical groups, also known as 2-groups. So it’s great to see 2-groups showing up in theoretical condensed matter physics. From today’s arXiv papers:

• J.P. Ang and Abhishodh Prakash, Higher categorical groups and the classification of topological defects and textures.

Abstract. Sigma models effectively describe ordered phases of systems with spontaneously broken symmetries. At low energies, field configurations fall into solitonic sectors, which are homotopically distinct classes of maps. Depending on context, these solitons are known as textures or defect sectors. In this paper, we address the problem of enumerating and describing the solitonic sectors of sigma models. We approach this problem via an algebraic topological method – combinatorial homotopy, in which one models both spacetime and the target space with algebraic objects which are higher categorical generalizations of fundamental groups, and then counts the homomorphisms between them. We give a self-contained discussion with plenty of examples and a discussion on how our work fits in with the existing literature on higher groups in physics.

The fun will really start when people actually synthesize materials described by these materials! Condensed matter physicists are doing pretty well at realizing theoretically possible phenomena in the lab, so I’m optimistic. But I don’t think it’s happened yet.

My friend Chenchang Zhu, a mathematician, has also been working on these things with two physicists. The abstract only briefly mentions 2-groups, but they play a fundamental role in the paper:

• Chenchang Zhu, Tian Lan and Xiao-Gang Wen, Topological non-linear σ\sigma-model, higher gauge theory, and a realization of all (3+1)d topological orders for boson systems.

Abstract. A discrete non-linear σ\sigma-model is obtained by triangulate both the space-time M d+1M^{d+1} and the target space KK. If the path integral is given by the sum of all the complex homomorphisms ϕ:M d+1→K\phi \colon M^{d+1} \to K, with an partition function that is independent of space-time triangulation, then the corresponding non-linear σ\sigma-model will be called a topological non-linear σ\sigma-model which is exactly soluble. Those exactly soluble models suggest that phase transitions induced by fluctuations with no topological defects (i.e. fluctuations described by homomorphisms ϕ\phi) usually produce a topologically ordered state and are topological phase transitions, while phase transitions induced by fluctuations with all the topological defects give rise to trivial product states and are not topological phase transitions. If KK is a space with only non-trivial first homotopy group GG which is finite, those topological non-linear σ\sigma-models can realize all (3+1)d(3+1)d bosonic topological orders without emergent fermions, which are described by Dijkgraaf-Witten theory with gauge group π 1(K)=G\pi_1(K)=G. Here, we show that the (3+1)d(3+1)d bosonic topological orders with emergent fermions can be realized by topological non-linear σ-models with π 1(K)=\pi_1(K) = finite groups, π 2(K)=ℤ 2\pi_2(K)=\mathbb{Z}_2, and π n>2(K)=0\pi_{n > 2}(K)=0. A subset of those topological non-linear σ\sigma-models corresponds to 2-gauge theories, which realize and classify bosonic topological orders with emergent fermions that have no emergent Majorana zero modes at triple string intersections. The classification of (3+1)(3+1)d bosonic topological orders may correspond to a classification of unitary fully dualizable fully extended topological quantum field theories in 4-dimensions.

The cobordism hypothesis, too, is getting into the act in the last sentence!

Excerpts from an article published in this month's edition of the American Journal of Medicine:

Approximately 15.5 million Americans have a history of cancer, with an estimated 1,688,780 new cases and 609,640 deaths annually. With 87% of diagnoses occurring in persons ≥50 years of age, cancer remains the second leading cause of death in the United States. Cancer's financial burden is often substantial during treatment phases and often worsens with improving prognoses.

With 6.5% of direct costs among nonelderly persons alone involving out-of-pocket payments, over half of all persons with cancer experienced house repossession, bankruptcy, loss of independence, and relationship breakdowns. Additionally, 40%-85% of cancer patients stop working during initial treatment, with absences ranging up to 6 months. Deductibles and copayments for treatment, supportive care, and nonmedical or indirect costs (eg, travel, caregiver time, and lost productivity) may be financially devastating even with healthcare coverage.

At year₊₂, 42.4% depleted their entire life's assets, with higher adjusted odds associated with worsening cancer, requirement of continued treatment, demographic and socioeconomic factors (ie, female, Medicaid, uninsured, retired, increasing age, income, and household size), and clinical characteristics (ie, current smoker, worse self-reported health, hypertension, diabetes, lung disease) (P<.05); average losses were $92,098. At year₊₄, financial insolvency extended to 38.2%, with several consistent socioeconomic, cancer-related, and clinical characteristics remaining significant predictors of complete asset depletion.

The American Journal of Medicine is peer-reviewed and is one of the most highly respected medical publications in the United States.  

Data suggest that the cannabinoid and glutamatergic systems are implicated in the pathophysiology of schizophrenia (SZ). In several brain regions associated with SZ, cannabinoid receptor type 1 (CB1Rs) and glutamate N‐methyl‐D‐aspartate receptors (NMDARs) have direct and indirect interactions. CB1Rs and NMDARs act upon gamma‐aminobutyric acid (GABA) interneurons to reduce GABAergic neurotransmission, which could disrupt neural network oscillations and lead to psychotic symptoms.

Abstract

Preclinical and clinical data suggest that the cannabinoid and glutamatergic systems are implicated in the pathophysiology of schizophrenia (SZ), the prototypical psychotic disorder. This has led to distinct “cannabis” and “ketamine” models of SZ, respectively. However, these two models need not be mutually exclusive. Indeed, in several brain regions implicated in the putative neural circuitry of SZ (e.g., hippocampus, frontal cortex, cerebellum), cannabinoid receptor type 1 (CB1Rs) and glutamate N‐methyl‐D‐aspartate receptors (NMDARs) have direct and indirect interactions. CB1R agonists and NMDAR antagonists act upon gamma‐aminobutyric acid (GABA) interneurons to reduce GABAergic neurotransmission. This would be predicted to result in the unsynchronized activity of pyramidal neurons, disrupting neural network oscillations involved in information processing, thus leading to psychotomimetic effects. Hence, the overarching aim of the current review is to synthesize the known literature on cannabinoids and glutamate in the context of neural oscillations in SZ. First, discussion of SZ and the basic mechanisms of neural oscillations are discussed, including a summary of the role of theta (4–7 Hz) and gamma (30–80 Hz) oscillations in neurocognition. Next, a brief review of the role of the cannabinoid and glutamatergic systems in SZ is outlined, followed by discussion of the known synaptic interactions between these two systems. Finally, the potential role of CB1Rs and NMDARs, both independently and in combination, on neural oscillations in relation to psychotic symptoms is considered. It is hoped that this review will yield a series of testable hypotheses that may be used to further elucidate the pathophysiology of SZ.

We've been seeing articles about the downside of young children and teenagers using digital social platforms, and the family conflicts resulting from trying to restrict smartphone use among teenagers. Several recent NYTimes articles note striking class differences in screen use between rich and less affluent households - the rich are reducing screen use in their home and private schools, while public schools are promoting digital tablet use. The Silicon Valley technologists who know know how smart phones really work don't want their own children anywhere near them.

For a chilling vision of our future if we continue the current trajectory I recommend the article by Yuval Harari in the August issue of the Atlantic. He suggests that most humans run the risk of becoming similar to domesticated animals, with only a small elite training their children to maintain the expertise and competence required to run the whole show. From his concluding paragraphs:

...if we want to prevent the concentration of all wealth and power in the hands of a small elite, we must regulate the ownership of data...Unfortunately, we don’t have much experience in regulating the ownership of data, which is inherently a far more difficult task than regulating land or machines...The race to accumulate data is already on, and is currently headed by giants such as Google and Facebook and, in China, Baidu and Tencent. So far, many of these companies have acted as “attention merchants”—they capture our attention by providing us with free information, services, and entertainment, and then they resell our attention to advertisers. Yet their true business isn’t merely selling ads. Rather, by capturing our attention they manage to accumulate immense amounts of data about us, which are worth more than any advertising revenue. We aren’t their customers—we are their product.

Ordinary people will find it very difficult to resist this process. At present, many of us are happy to give away our most valuable asset—our personal data—in exchange for free email services and funny cat videos. But if, later on, ordinary people decide to try to block the flow of data, they are likely to have trouble doing so, especially as they may have come to rely on the network to help them make decisions, and even for their health and physical survival.

Nationalization of data by governments could offer one solution; it would certainly curb the power of big corporations. But history suggests that we are not necessarily better off in the hands of overmighty governments. So we had better call upon our scientists, our philosophers, our lawyers, and even our poets to turn their attention to this big question: How do you regulate the ownership of data?

Currently, humans risk becoming similar to domesticated animals. We have bred docile cows that produce enormous amounts of milk but are otherwise far inferior to their wild ancestors. They are less agile, less curious, and less resourceful. We are now creating tame humans who produce enormous amounts of data and function as efficient chips in a huge data-processing mechanism, but they hardly maximize their human potential. If we are not careful, we will end up with downgraded humans misusing upgraded computers to wreak havoc on themselves and on the world.

If you find these prospects alarming—if you dislike the idea of living in a digital dictatorship or some similarly degraded form of society—then the most important contribution you can make is to find ways to prevent too much data from being concentrated in too few hands, and also find ways to keep distributed data processing more efficient than centralized data processing. These will not be easy tasks. But achieving them may be the best safeguard of democracy.

Google unveils search engine for open data

Google unveils search engine for open data, Published online: 05 September 2018; doi:10.1038/d41586-018-06201-x

The tool, called Google Dataset Search, should help researchers to find the data they need more easily.

Time-asymmetric loop around an exceptional point over the full optical communications band

Time-asymmetric loop around an exceptional point over the full optical communications band, Published online: 17 September 2018; doi:10.1038/s41586-018-0523-2

Time-asymmetric light transmission over the entire optical communications band is achieved using a silicon photonic structure with photonic modes that dynamically encircle an exceptional point in the optical domain.

I have argued that the neural coding metaphor is highly misleading (see also similar arguments by Mark Bickhard in cognitive science). The coding metaphor is very popular in neuroscience, but there is another domain of science where it is also very popular: genetics. Is there a genetic code? Many scientists have criticized the idea of a genetic code (and of a genetic program). A detailed criticism can be found in Denis Noble’s book “The music of life” (see also Noble 2011 for a short review).

Many of the arguments I have made in my essay on neural coding readily apply to the “genetic code”. Let us start with the technical use of the metaphor. The genome is a sequence of DNA base triplets called “codons” (ACG, TGA, etc). Each codon specifies a particular amino-acid, and proteins are made of amino-acids. So there is a correspondence between DNA and amino-acids. This seems an appropriate use of the term “code”. But even it in this limited sense, it should be used with caution. The fact that a base triplet encodes an amino-acid is conditional on this triplet being effectively translated into an amino-acid (note that there are two stages, transcription into RNA, then translation into a protein). But in fact only a small fraction of a genome is actually translated, about 10% (depending on species); the rest is called “non-coding DNA”. So the same triplets can result in the production of an amino-acid, or they can influence the translation-transcription system in various ways, for example by interacting with various molecules involved in the production of RNA and proteins, thereby regulating transcription and translation (and this is just one example).

Even when DNA does encode amino-acids, it does not follow that a gene encodes a protein. What might be said is that a gene encodes the primary structure of proteins, that is, the sequence of amino-acids; but it does not specify by itself the shape that the protein will take (which determines its chemical properties), the various modifications that occur after translation, the position that the protein will take in the cellular system. All of those crucial properties depend on the interaction of the product of transcription with the cellular system. In fact, even the primary structure of proteins is not fully determined by the gene, because of splicing.

Thus, the genome is not just a book, as suggested by the coding metaphor (some have called the genome the “book of life”); it is a chemically active substance that interacts with its chemical environment, a part of a larger cellular system.

At the other end of the genetic code metaphor, genes encode phenotypes, traits of the organism. For example, the gene for blue eyes. A concept that often appears in the media is the idea of genes responsible for diseases. One hope behind the human genome project was that by scrutinizing the human genome, we might be able to identify the genes responsible for every disease (at least for every genetic disease). Some diseases are monogenic, i.e., due to a single gene defect, but the most common diseases are polygenic, i.e., are due to a combination of genetic factors (and generally environmental factors).

But even the idea of monogenic traits is misleading. There is no single gene that encodes a given trait. What has been demonstrated in some cases is that mutations in a single gene can impact a given trait. But this does not mean that the gene is responsible by itself for that trait (surprisingly, this fallacy is quite common in the scientific literature, as pointed out by Yoshihara & Yoshihara 2018). A gene by itself does nothing. It needs to be embedded into a system, namely a cell, in order to produce any phenotype. Consequently, the expressed phenotype depends on the system in which the gene is embedded, in particular the rest of the genome. There cannot be a gene for blue eyes if there are no eyes. So no gene can encode the color of eyes; this encoding is at best contextual (in the same way as “neural codes” are always contextual, as discussed in my neural coding essay).

So the concept of a “genetic code” can only be correct in a trivial sense: that the genome, as a whole, specifies the organism. This clearly limits the usefulness of the concept, however. Unfortunately, even this trivial claim is also incorrect. An obvious objection is that the genome specifies the organism only in conjunction with the environment. The deeper objection is that the immediate environment of the genome is the cell itself. No entity smaller than the cell can live or reproduce. The genome is not a viable system, and as such it cannot produce an organism, nor can it reproduce. An interesting experiment is the following: the nucleus (and thus the DNA) from an animal cell is transferred to the egg of an animal of another species (where the nucleus has been removed) (Sun et al., 2005). The “genetic code” theory would predict that the egg would develop into an animal of the donor species. What actually happens (this was done in related fish species) is that the egg develops into some kind of hybrid, with the development process closer to that of the recipient species. Thus, even in the most trivial sense, the genome does not encode the organism. Finally, since no entity smaller than the cell can reproduce, it follows that the genome is not the unique basis of heritability – the entire cell is (see Fields & Levin, 2018).

In summary, the genome does not encode much except for amino-acids (for about 10% of it). It should be conceptualized as a component that interacts with the cellular system, not as a “book” that would be read by some cellular machinery.

Author(s): Vedika Khemani, David A. Huse, and Adam Nahum

It has been a long-standing challenge to expand the notion of exponential sensitivity to small perturbations in the initial conditions from the realm of classical chaos to that of many-body quantum systems. Recently, exponential growth in a measure known as the out-of-time-order commutator (OTOC) has been proposed as a diagnostic of chaos in the setting of many-body quantum systems. The authors examine the behavior of the OTOC along rays of different velocities for a variety of spatially local extended many-body quantum systems, both integrable and nonintegrable. They find that the velocity-dependent Lyapunov exponents are negative for velocities greater than a characteristic “butterfly speed”, which defines the light cone for the spreading of operators. It is demonstrated that a regime with well-defined positive Lyapunov exponents inside the light-cone may only exist for classical, semiclassical, weakly interacting, or large-N systems, but not for fully quantum systems with strong short-range interactions and local Hilbert space dimensions of order one.

[Phys. Rev. B 98, 144304] Published Tue Oct 16, 2018

The inner workings of a biological cell or a chemical reaction can be rationalized by the network of reactions, whose structure reveals the most important functional mechanisms. For complex systems, these reaction networks are not known a priori and cannot be efficiently computed with ab initio methods, therefore an important approach goal is to estimate effective reaction networks from observations, such as time series of the main species. Reaction networks estimated with standard machine learning techniques such as least-squares regression may fit the observations, but will typically contain spurious reactions. Here we extend the sparse identification of nonlinear dynamics (SINDy) method to vector-valued ansatz functions, each describing a particular reaction process. The resulting sparse tensor regression method "reactive SINDy" is able to estimate a parsimonious reaction network. We illustrate that a gene regulation network can be correctly estimated from observed time series.

Probably the most famous of Grothendieck's contributions to Banach space theory is the result that he himself described as "the fundamental theorem in the metric theory of tensor products". That is now commonly referred to as "Grothendieck's theorem" (GT in short), or sometimes as "Grothendieck's inequality". This had a major impact first in Banach space theory (roughly after 1968), then, later on, in C∗-algebra theory, (roughly after 1978). More recently, in this millennium, a new version of GT has been successfully developed in the framework of "operator spaces" or non-commutative Banach spaces. In addition, GT independently surfaced in several quite unrelated fields:\ in connection with Bell's inequality in quantum mechanics, in graph theory where the Grothendieck constant of a graph has been introduced and in computer science where the Grothendieck inequality is invoked to replace certain NP hard problems by others that can be treated by "semidefinite programming" and hence solved in polynomial time. In this expository paper, we present a review of all these topics, starting from the original GT. We concentrate on the more recent developments and merely outline those of the first Banach space period since detailed accounts of that are already available, for instance the author's 1986 CBMS notes.

by Charles Mudede
According to the New York Times, the US government is surreptiously relocating migrant children to a "tent city" in Tornillo, Texas, which is near El Paso. This location is new and contains "rows of sand-colored tents." When it opened in June, it was meant to hold only 400 migrants, but it was expanded in September to hold nearly 4,000. Children staying at standard shelters around the US are being bussed there in the middle of the night. Immigration officials rudely wake the boys and girls up and force them into the dark unknown.

From the New York Times:

Several shelter workers, who spoke on condition of anonymity for fear of being fired, described what they said has become standard practice for moving the children: In order to avoid escape attempts, the moves are carried out late at night because children will be less likely to try to run away. For the same reason, children are generally given little advance warning that they will be moved.

As Americans sleep and dream, terrified children are being transported to a concentration camp.

But the mass transfers are raising the alarm among immigrant advocates, who were already concerned about the lengthy periods of time migrant children are spending in federal custody.

The roughly 100 shelters that have, until now, been the main location for housing detained migrant children are licensed and monitored by state child welfare authorities, who impose requirements on safety and education as well as staff hiring and training.

The tent city in Tornillo, on the other hand, is unregulated, except for guidelines created by the Department of Health and Human Services. For example, schooling is not required there, as it is in regular migrant children shelters.

The camp in Tornillo operates like a small, pop-up city, about 35 miles southeast of El Paso on the Mexico border, complete with portable toilets. Air-conditioned tents that vary in size are used for housing, recreation, and medical care. Originally opened in June for 30 days with a capacity of 400, it expanded in September to be able to house 3,800, and is now expected to remain open at least through the end of the year.

Those who think that the association with concentration camps is nothing but alarmist liberal nonsense, please read this sentence carefully: "The children [wear] belts etched in pen with phone numbers for their emergency contacts." Can you feel that? Trump's America is not fucking around. The camp, unlike the shelters, also offers few professional services or support. We are basically dumping children into a social black hole during the most informative years of their lives.

The New York Times:

The tent city in Tornillo... is unregulated, except for guidelines created by the Department of Health and Human Services. For example, schooling is not required there, as it is in regular migrant children shelters

None of this will end well for the children and the soul of this very rich nation. A process instituted by monsters will likely produce monsters.

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Author(s): Matthew T. Eiles, Zhengjia Tong, and Chris H. Greene

A series of electric and magnetic pulses applied to an atom could cause one of its electrons to behave as if “bonded” to an empty point in space.

[Phys. Rev. Lett. 121, 113203] Published Wed Sep 12, 2018

Deep learning has been transforming our ability to execute advanced inference tasks using computers. Here we introduce a physical mechanism to perform machine learning by demonstrating an all-optical diffractive deep neural network (D²NN) architecture that can implement various functions following the deep learning–based design of passive diffractive layers that work collectively. We created 3D-printed D²NNs that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum. Our all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can execute; will find applications in all-optical image analysis, feature detection, and object classification; and will also enable new camera designs and optical components that perform distinctive tasks using D²NNs.

The self-driving cars were Uber's hail mary pass. If they get rid of that... then? Their path to profitability always required establishing monopoly - and then jacking up rates - somehow, but their path to monopoly was never clear. I don't even think self-driving cars were the path. Just another shiny object to flash to investors. Now the (some) investors are done.

Some investors have told Uber officials that it may be wise to divest the self-driving car unit, said a person familiar with the issue. Uber has invested least $2 billion in the unit over the past three years. Yet the company hasn’t yet come up with a clear path to commercializing the technology it has developed.

Will be interesting if the two major taxi app companies (Uber, Lyft) fold.

For most of us, anthrax evokes fearful memories of white powder in envelopes. The disease, however, is an ancient one. God’s fifth plague upon the Egyptians — ‘‘Behold, the hand of the Lord is upon thy cattle which is in the field, upon the horses, upon the asses, upon the camels, upon the oxen, and upon the sheep: there shall be a very grievous murrain,” Moses told Pharaoh — may well have been an anthrax outbreak. The same goes for Apollo’s bane upon the Greeks at the beginning of the Iliad. (Homer dubbed the disease “the burning wind of plague.”) Perhaps the most striking description from antiquity of what we now know as Bacillus anthracis comes from Virgil’s Georgics:

Nor was the manner of dying a simple matter: 
After the thirsty slake-seeking fever had gone 
All through the veins and withered the pitiful limbs, 
Then a fluid welled up in the suffering body, and 
Piece by piece absorbed the melting bones. 

B. anthracis is a cruel organism. In their passive form, the bacteria live as hard, oval-shaped spores with thick, nearly indestructible walls that allow them to survive for decades. When the spores colonize a victim’s bloodstream, they enter a vegetative state, dissolving their walls and gathering into neat chains that Robert Koch, the nineteenth-century German scientist whose pioneering work helped identify the disease, described as “graceful, artificially ordered strings of pearls.” In order to survive, the bacteria must kill the host and reproduce inside it before escaping back into the world and returning to a resting state.

Anthrax bacteria produce two lethal toxins in tandem, akin to those that cause tetanus and cholera. The process tends to be swift, and the chances of fatality high. The early symptoms resemble those of the common flu: your head begins to ache; your temperature rises; a general sense of weakness envelops your body; your stomach starts rumbling; you begin to cough incessantly. Then things get serious: you may go into seizures; your organs begin failing; boils break out across your skin, swelling red pustules with a trademark black center. In the fifth century bc, Hippocrates dubbed the disease anthrakes, from the ancient Greek for “charcoal.” 

The disease has triumphed once the blood begins spilling from your orifices. When the medical examiners or the veterinarians cut you open, they will find that your blood has gone black, and that certain organs, particularly the spleen, have turned into masses of melting flesh.

Now the melting of the Siberian permafrost is unleashing anthrax bacilli that have been frozen there for centuries.  Vast herds of reindeer are being decimated and a way of life destroyed for native subarctic peoples.  Details in a longread at Harper's Magazine.

Just need to keep getting gullible local officials to throw money at you for a "study." No good way to get to Dodger stadium so...how about low capacity cars on sleds! They're good for everything!

The Boring Company is proposing to build Dugout Loop, a zero-emissions, high-speed, underground public transportation system from the Los Feliz, East Hollywood, or Rampart Village neighborhoods ("western terminus") to Dodger Stadium in the City of Los Angeles.

Let's take a look. The route is 3.6 miles.

Loop is a zero-emissions, high-speed underground public transportation system in which passengers are transported on autonomous electric skates traveling at 125-150 miles per hour. Electric skates will carry between 8 and 16 passengers.

I highly doubt they'll travel this fast, but the real point is that it doesn't matter. Boarding is the real bottleneck for things like this. Picture the taxi line at the airport, or the line for the rollercoaster. That's what you get when you can only board a dozen people at a time. The line's gonna be long, Brant.

Oh, sorry, line? No there will be an app for that which will totally solve this problem (hahahaahahaha).

Initially, riders will be able to reserve times and purchase Dugout Loop tickets in advance similar to booking seats at a movie theater via a mobile app, over the phone, or in person (e.g. 5:45pm PT Dugout Loop ticket).

Remember this is primarily a baseball game transportation device. What time would you like to go to the baseball game? And sure, arrivals can be staggered a bit, but everybody wants to leave at the same time...enjoy the line!

Initially, Dugout Loop will be limited to approximately 1,400 people (approximately 2.5% of Stadium capacity) per event.

I love how it doesn't even say "per hour" but "per event" which probably includes at least a 2 hour window (guessing!). One real subway train can easily carry 1000, board them all quickly, and you can run one ever 2 minutes. One attraction to these "sleds" is the weird idea that if you have lower capacity you can run them more often, but headways aren't really a technical constraint of subway systems. Any modern subway system can run 24 trains per hour easy, and plenty do 32. At that point it's the boarding time that makes running them more often be impractical. Even our pretty antiquated trolley system in Philly runs through the tunnel with <3 minute headways at peak, and they carry about 70 people per train.

Electric skates are zero-emission vehicles, and thus do not output hazardous gases like internal combustion cars do.

Wow electric powered underground vehicles. What will Elon think of next?

The fares are not finalized but will cost around $1.

Their own projections put it at 250,000 riders per year. Let's say each does roundtrip, so 500,000 total. Time for some math. All that grad school must have been good for something. Let's see if I remember how to do this.

Oh yes. 500,000×$1= $500,000. Sure most transit systems are subsidized, but, uh...