Evolution of schooling drives changes in neuroanatomy and motion characteristics across predation contexts in guppies.

One of the most spectacular displays of social behavior is the synchronized movements that many animal groups perform to travel, forage and escape from predators. However, elucidating the neural mechanisms underlying the evolution of collective behaviors, as well as their fitness effects, remains challenging. Here, we study collective motion patterns with and without predation threat and predator inspection behavior in guppies experimentally selected for divergence in polarization, an important ecological driver of coordinated movement in fish. We find that groups from artificially selected lines remain more polarized than control groups in the presence of a threat. Neuroanatomical measurements of polarization-selected individuals indicate changes in brain regions previously suggested to be important regulators of perception, fear and attention, and motor response. Additional visual acuity and temporal resolution tests performed in polarization-selected and control individuals indicate that observed differences in predator inspection and schooling behavior should not be attributable to changes in visual perception, but rather are more likely the result of the more efficient relay of sensory input in the brain of polarization-selected fish. Our findings highlight that brain morphology may play a fundamental role in the evolution of coordinated movement and anti-predator behavior.

Statistical genetics in and out of quasi-linkage equilibrium.

This review is about statistical genetics, an interdisciplinary topic between statistical physics and population biology. The focus is on the phase ofquasi-linkage equilibrium(QLE). Our goals here are to clarify under which conditions the QLE phase can be expected to hold in population biology and how the stability of the QLE phase is lost. The QLE state, which has many similarities to a thermal equilibrium state in statistical mechanics, was discovered by M Kimura for a two-locus two-allele model, and was extended and generalized to the global genome scale byNeher&Shraiman (2011). What we will refer to as the Kimura-Neher-Shraiman theory describes a population evolving due to the mutations, recombination, natural selection and possibly genetic drift. A QLE phase exists at sufficiently high recombination rate (r) and/or mutation ratesµwith respect to selection strength. We show how in QLE it is possible to infer the epistatic parameters of the fitness function from the knowledge of the (dynamical) distribution of genotypes in a population. We further consider the breakdown of the QLE regime for high enough selection strength. We review recent results for the selection-mutation and selection-recombination dynamics. Finally, we identify and characterize a new phase which we call the non-random coexistence where variability persists in the population without either fixating or disappearing.

Temporal epistasis inference from more than 3 500 000 SARS-CoV-2 genomic sequences.

We use direct coupling analysis (DCA) to determine epistatic interactions between loci of variability of the SARS-CoV-2 virus, segmenting genomes by month of sampling. We use full-length, high-quality genomes from the GISAID repository up to October 2021 for a total of over 3 500 000 genomes. We find that DCA terms are more stable over time than correlations but nevertheless change over time as mutations disappear from the global population or reach fixation. Correlations are enriched for phylogenetic effects, and in particularly statistical dependencies at short genomic distances, while DCA brings out links at longer genomic distance. We discuss the validity of a DCA analysis under these conditions in terms of a transient auasilinkage equilibrium state. We identify putative epistatic interaction mutations involving loci in spike.

Electrically controlled spin reversal and spin polarization of electronic transport in nanoporous graphene nanoribbons.

Based on first-principles calculations, the spin-dependent electronic transport of nanoporous graphene nanoribbons is investigated. A three-terminal configuration is proposed, which can electronically control the spin polarization of transmission, instead of magnetic methods. By modulating the gate voltage, not only could the transmission be switched between completely spin up and spin down polarized states to realize a dual-spin filter, but also the spin polarization could be finely tuned between 100% and -100%. Any ratio of spin up to spin down transport electrons can be realized, providing more possibilities for the design of nanoelectronic devices. Further analysis shows that the transmission spectra, with two distinct transmission peaks with opposite spins around EF, are the key point, which are contributed by p orbitals. And such a phenomenon is robust to the width and length of the nanoporous graphene nanoribbons, suggesting that it is an intrinsic feature of these systems. The electrical control on spin polarization is realized in pure-carbon systems, showing great application potential.

Global analysis of more than 50,000 SARS-CoV-2 genomes reveals epistasis between eight viral genes.

Genome-wide epistasis analysis is a powerful tool to infer gene interactions, which can guide drug and vaccine development and lead to deeper understanding of microbial pathogenesis. We have considered all complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes deposited in the Global Initiative on Sharing All Influenza Data (GISAID) repository until four different cutoff dates, and used direct coupling analysis together with an assumption of quasi-linkage equilibrium to infer epistatic contributions to fitness from polymorphic loci. We find eight interactions, of which three are between pairs where one locus lies in gene ORF3a, both loci holding nonsynonymous mutations. We also find interactions between two loci in gene nsp13, both holding nonsynonymous mutations, and four interactions involving one locus holding a synonymous mutation. Altogether, we infer interactions between loci in viral genes ORF3a and nsp2, nsp12, and nsp6, between ORF8 and nsp4, and between loci in genes nsp2, nsp13, and nsp14. The paper opens the prospect to use prominent epistatically linked pairs as a starting point to search for combinatorial weaknesses of recombinant viral pathogens.

Electrically precise control of the spin polarization of electronic transport at the single-molecule level.

Compared with the conventional magnetic means (such as ferromagnetic contacts), controlling a spin current by electrical methods could largely reduce the energy consumption and dimensions of nano-devices, which has become a focus of research in spintronics. Inspired by recent progress in the synthesis of an iron-based metal-organic nanostructure, we investigate the spin-dependent electronic transport of the molecule of Fe3-terpyridine-phenyl-phenyl-terpyridine-Fe3 (Fe3-TPPT-Fe3) through first-principles calculations, and propose a three-terminal device without ferromagnetics. By applying a gate voltage, not only the spin polarization can be switched between 100% and -100% to achieve a dual-spin filter, but also its fine regulation can be realized, where the transmission with any ratio of spin-up to spin-down electron numbers is achievable. Analysis shows that the particular transmission spectra are the key mechanism, where two peaks reside discretely on both sides of the Fermi level with opposite spins. Such a feature is found to be robust to the number of Fe atoms and TPPT chain length, suggesting that it is an intrinsic feature of such systems and very conducive to practical applications. The electrical control (such as an electric field) of spin polarization is realized at the single-molecule level, showing great application potential.

Inferring genetic fitness from genomic data.

The genetic composition of a naturally developing population is considered as due to mutation, selection, genetic drift, and recombination. Selection is modeled as single-locus terms (additive fitness) and two-loci terms (pairwise epistatic fitness). The problem is posed to infer epistatic fitness from population-wide whole-genome data from a time series of a developing population. We generate such data in silico and show that in the quasilinkage equilibrium phase of Kimura, Neher, and Shraiman, which pertains at high enough recombination rates and low enough mutation rates, epistatic fitness can be quantitatively correctly inferred using inverse Ising-Potts methods.

Edge-modulated dual spin-filter effect in zigzag-shaped buckling Ag2S nanoribbons.

Unlike MoS2, single-layered Ag2S nanoribbons (Ag2SNRs) exhibit a nonmetal-shrouded and a zigzag-shaped buckling structure and possess two distinct edges, S- or Ag-terminated ones. By performing first principle calculations, the spin-dependent electron transport of Ag2SNRs in a ferromagnetic state has been investigated. It is found that the SS- and AgAg-terminated Ag2SNRs exhibit semi-metallic characteristics, but with opposite spin-polarized directions. And AgS-terminated ones show metallic characteristics, but with completely spin-unpolarized transmission. That is to say, all three states, i.e., spin up polarized, spin down polarized and spin unpolarized ones, could be achieved by modulating the edge geometry. Further analysis shows that, the spatial separation on edges of the energy states with different spins around EF is responsible for the switch in the three states. The system could operate as a dual spin-filter, and the direction of the spin polarization can be switched by the edge morphology. Furthermore, calculations show that such a phenomenon is robust to the width of the ribbon and strain, showing great application potential.

Edge morphology induced rectifier diode effect in C3N nanoribbon.

The two-dimensional material C3N has a honeycomb structure similar to graphene, but its heterogeneity of carbon and nitrogen elements makes it multifunctional. By performing a first-principles study, we find that edge morphology induces interesting electronic transport properties in step-like heterojunction devices composed of width-variable zigzag C3N nanoribbons. As long as the right part has an edge of all-carbon morphology, negative differential resistance and rectification effects will occur. If both edges are not of all-carbon morphology due to the presence of N atoms, a forward-conducting and reverse-blocking rectifier diode behavior will appear. These phenomena originate from the peculiar electronic structure of the zigzag C3N nanoribbons. The number of energy bands crossing the Fermi level gradually decreases from 2 to 0 as the number of all-carbon edges decreases, realizing a transition from metal to semiconductor. The band gap determines the cut-off region at low bias and the presence of an interface barrier causes the cut-off state to continue under high reverse bias. Diverse edge morphologies, simple cutting methods and rich electronic transport properties make C3N materials competitive in nanodevice applications.

A progressive metal-semiconductor transition in two-faced Janus monolayer transition-metal chalcogenides.

Breaking the symmetry in the out-of-plane direction in two-dimensional materials to trigger distinctive electronic properties has long been predicted. Inspired by the recent progress in the experimental synthesis of a sandwiched S-Mo-Se structure (Janus SMoSe) at the monolayer limit [Zhang et al., ACS Nano, 2017, 11, 8192-8198], we investigate the transport and electronic structure of two-faced XMoY monolayers (X, Y = O, S, Se and Te) through first-principles calculations. It is found that all the monolayers are semiconductors except OMoTe, which is metallic. Interestingly, the "parents" of OMoTe (MoO2 and MoTe2) are both semiconductors. Further analysis shows that it is the out-of-plane asymmetry-induced strain that results in the metal-semiconductor transition between Janus OMoTe and its parents. By increasing the ratio of O atoms in one face of MoTe2, a progressive decreasing trend of the bandgap, as well as the transition to metallic, is found. In addition, a transition from the direct band gap semiconductor to the indirect one is also observed in the process. This could be used as an effective way to precisely control electronic structures, e.g., the bandgap. Different from other methods, this method uses the intrinsic features of the material, which can persist without the need of additional equipment. Moreover, such a modulating method is expected to be extended to many other transition-metal chalcogenides, showing great application potential.

Edge defect switched dual spin filter in zigzag hexagonal boron nitride nanoribbons.

Unlike graphene nanoribbons, zigzag monolayer hexagonal boron nitride nanoribbons (ZBNNRs) possess two distinct edges (B and N edges). Using first-principles calculations, we investigate the spin-dependent electronic transport of ZBNNRs with edge defects. It is found that the defects could make the system operate as a dual spin filter, where the direction of spin polarization is switched by the defect. Further analysis shows that the transmission eigenchannels for the opposite spins reside spatially separated on opposite edges. The defect on one edge could suppress the transmission for only one spin component, but preserve that for the other spin, resulting in a dual spin filter effect. This effect is found to be unaffected by the width of the ribbon and the length of the defect. Moreover, by constructing defects on both edges, the system exhibits two transmission peaks with opposite spins residing discretely on both sides of the Fermi level, suggesting that an electrically controlled dual spin filter based on ZBNNRs is also realizable. As controllable defects have been experimentally fabricated on monolayer boron nitride [T. Pham, A. L. Gibb, Z. Li, S. M. Gilbert, C. Song, S. G. Louie and A. Zettl, Nano Lett., 2016, 16, 7142-7147], our results may shed light on the development of B/N-based spintronic devices.

Brain size does not impact shoaling dynamics in unfamiliar groups of guppies (Poecilia reticulata).

Collective movement is achieved when individuals adopt local rules to interact with their neighbours. How the brain processes information about neighbours' positions and movements may affect how individuals interact in groups. As brain size can determine such information processing it should impact collective animal movement. Here we investigate whether brain size affects the structure and organisation of newly forming fish shoals by quantifying the collective movement of guppies (Poecilia reticulata) from large- and small-brained selection lines, with known differences in learning and memory. We used automated tracking software to determine shoaling behaviour of single-sex groups of eight or two fish and found no evidence that brain size affected the speed, group size, or spatial and directional organisation of fish shoals. Our results suggest that brain size does not play an important role in how fish interact with each other in these types of moving groups of unfamiliar individuals. Based on these results, we propose that shoal dynamics are likely to be governed by relatively basic cognitive processes that do not differ in these brain size selected lines of guppies.

Evolution of brain region volumes during artificial selection for relative brain size.

The vertebrate brain shows an extremely conserved layout across taxa. Still, the relative sizes of separate brain regions vary markedly between species. One interesting pattern is that larger brains seem associated with increased relative sizes only of certain brain regions, for instance telencephalon and cerebellum. Till now, the evolutionary association between separate brain regions and overall brain size is based on comparative evidence and remains experimentally untested. Here, we test the evolutionary response of brain regions to directional selection on brain size in guppies (Poecilia reticulata) selected for large and small relative brain size. In these animals, artificial selection led to a fast response in relative brain size, while body size remained unchanged. We use microcomputer tomography to investigate how the volumes of 11 main brain regions respond to selection for larger versus smaller brains. We found no differences in relative brain region volumes between large- and small-brained animals and only minor sex-specific variation. Also, selection did not change allometric scaling between brain and brain region sizes. Our results suggest that brain regions respond similarly to strong directional selection on relative brain size, which indicates that brain anatomy variation in contemporary species most likely stem from direct selection on key regions.

Hydrogenated carbon nanotube-based spin caloritronics.

Spin caloritronics has drawn much attention as it combines thermoelectrics and spintronics together. Carbon-based structures, such as graphene, have been found to exhibit different kinds of spin caloritronic features. However, a study of spin caloritronics in carbon nanotubes (CNTs) is still lacking. Using first-principles calculations, we investigate the spin-Seebeck effect (SSE) in partially hydrogenated CNTs. It is found that linear hydrogenation could make CNTs acquire magnetism and exhibit the spin-Seebeck effect. Moreover, an odd-even effect of the SSE is observed, where the even cases could be used as spin-Seebeck diodes. Further analysis shows that, it is induced by the difference of band structures, where the band structure of a tube is a combination of that of graphene-nanoribbon parts "divided" by hydrogenation. This mechanism could be extended to nanotubes with different diameters, showing great application potential. We believe that our results are very useful for the development of nanotube-based spin caloritronic devices.

Maximum likelihood reconstruction for Ising models with asynchronous updates.

We describe how the couplings in an asynchronous kinetic Ising model can be inferred. We consider two cases: one in which we know both the spin history and the update times and one in which we know only the spin history. For the first case, we show that one can average over all possible choices of update times to obtain a learning rule that depends only on spin correlations and can also be derived from the equations of motion for the correlations. For the second case, the same rule can be derived within a further decoupling approximation. We study all methods numerically for fully asymmetric Sherrington-Kirkpatrick models, varying the data length, system size, temperature, and external field. Good convergence is observed in accordance with the theoretical expectations.

Network inference using asynchronously updated kinetic Ising model.

Network structures are reconstructed from dynamical data by respectively naive mean field (nMF) and Thouless-Anderson-Palmer (TAP) approximations. TAP approximation adds simple corrections to the nMF approximation, taking into account the effect of the focused spin on itself via its influence on other neighboring spins. For TAP approximation, we use two methods to reconstruct the network: (a) iterative method; (b) casting the inference formula to a set of cubic equations and solving it directly. We investigate inference of the asymmetric Sherrington-Kirkpatrick (aS-K) model using asynchronous update. The solutions of the set of cubic equations depend on temperature T in the aS-K model, and a critical temperature T(c)≈2.1 is found. The two methods for TAP approximation produce the same results when the iterative method is convergent. Compared to nMF, TAP is somewhat better at low temperatures, but approaches the same performance as temperature increases. Both nMF and TAP approximation reconstruct better for longer data length L, but for the degree of improvement, TAP performs better than nMF.