Group foraging in Socotra cormorants: A biologging approach to the study of a complex behavior

Group foraging contradicts classic ecological theory because intraspecific competition normally increases with aggregation. Hence, there should be evolutionary benefits to group foraging. The study of group foraging in the field remains challenging however, because of the large number of individuals involved and the remoteness of the interactions to the observer. Biologging represents a cost‐effective solution to these methodological issues. By deploying GPS and temperature–depth loggers on individuals over a period of several consecutive days, we investigated intraspecific foraging interactions in the Socotra cormorant Phalacrocorax nigrogularis, a threatened colonial seabird endemic to the Arabian Peninsula. In particular, we examined how closely birds from the same colony associated with each other spatially when they were at sea at the same time and the distance between foraging dives at different periods of the day. Results show that the position of different birds overlapped substantially, all birds targeting the same general foraging grounds throughout the day, likely following the same school of fish. There were as many as 44,500 birds within the foraging flock at sea at any time (50% of the colony), and flocking density was high, with distance between birds ranging from 8 to 1,380 m. Birds adopted a diving strategy maximizing time spent underwater relative to surface time, resulting in up to 72% of birds underwater in potential contact with prey at all times while foraging. Our data suggest that the benefits of group foraging outweigh the costs of intense aggregation in this seabird. Prey detection and information transmission are facilitated in large groups. Once discovered, shoaling prey are concentrated under the effect of the multitude. Fish school cohesiveness is then disorganized by continuous attacks of diving birds to facilitate prey capture. Decreasing population size could pose a risk to the persistence of threatened seabirds where group size is important for foraging success.     

Advances in construction and modeling of functional neural circuits in vitro

Neurochemical Research - Over the years, techniques have been developed to culture and assemble neurons, which brought us closer to creating neuronal circuits that functionally and structurally...     

Trophic network structure emerges through antagonistic coevolution in temporally varying environments

Understanding the mechanisms underlying ecological specialization is central to our understanding of community ecology and evolution. Although theoretical work has investigated how variable environments may affect specialization in single species, little is known about how such variation impacts bipartite network structure in antagonistically coevolving systems. Here, we develop and analyse a general model of victim-enemy coevolution that explicitly includes resource and population dynamics. We investigate how temporal environmental heterogeneity affects the evolution of specialization and associated community structure. Environmental productivity influences victim investment in resistance, which will shape patterns of specialization through its regulating effect on enemy investment in infectivity. We also investigate the epidemiological consequences of environmental variability and show that enemy population density is maximized for intermediate lengths of productive seasons, which corresponds to situations where enemies can evolve higher infectivity than victims can evolve defence. We discuss our results in the light of empirical studies, and further highlight ways in which our model applies to a range of natural systems.     

STDP allows fast rate-modulated coding with Poisson-like spike trains

Spike timing-dependent plasticity (STDP) has been shown to enable single neurons to detect repeatedly presented spatiotemporal spike patterns. This holds even when such patterns are embedded in equally dense random spiking activity, that is, in the absence of external reference times such as a stimulus onset. Here we demonstrate, both analytically and numerically, that STDP can also learn repeating rate-modulated patterns, which have received more experimental evidence, for example, through post-stimulus time histograms (PSTHs). Each input spike train is generated from a rate function using a stochastic sampling mechanism, chosen to be an inhomogeneous Poisson process here. Learning is feasible provided significant covarying rate modulations occur within the typical timescale of STDP (~10-20 ms) for sufficiently many inputs (~100 among 1000 in our simulations), a condition that is met by many experimental PSTHs. Repeated pattern presentations induce spike-time correlations that are captured by STDP. Despite imprecise input spike times and even variable spike counts, a single trained neuron robustly detects the pattern just a few milliseconds after its presentation. Therefore, temporal imprecision and Poisson-like firing variability are not an obstacle to fast temporal coding. STDP provides an appealing mechanism to learn such rate patterns, which, beyond sensory processing, may also be involved in many cognitive tasks.     

New Gold Nanoparticles Adhesion Process Opening the Way of Improved and Highly Sensitive Plasmonics Technologies

Gold nanostructures have very suitable physical properties for plasmonic applications but do not stick on glass substrates. One usually uses a chromium adhesion layer that gives good mechanical adhesion but quench the plasmon. We developed a new adhesion process that permits a covalent bonding between gold and glass thanks to an MPTMS molecular layer throughout nanolithography process. We demonstrate that this new adhesion layer allows an improvement of the optical properties of the gold nanoparticles as well as an essential improvement of their surface-enhanced Raman scattering performances.     

Novel Apolar Plasmonic Nanostructures with Extended Optical Tunability for Sensing Applications

This paper outlines the design of complex nanostructures with apolar behavior which pave the way to a wider range of plasmon resonance tuning and applications requiring higher enhancement. These new nanostructure families are simply defined by symmetry considerations. An irreducible decomposition of optical response tensor demonstrates that nanoparticles which belong to C _n , with n?≥?3, symmetry point group for at least one scale have an optical response insensitive on the light polarization. This is experimentally confirmed by extinction and surface-enhanced Raman-scattering measurements.     

PASTEC: An Automatic Transposable Element Classification Tool

Summary

The classification of transposable elements (TEs) is key step towards deciphering their potential impact on the genome. However, this process is often based on manual sequence inspection by TE experts. With the wealth of genomic sequences now available, this task requires automation, making it accessible to most scientists. We propose a new tool, PASTEC, which classifies TEs by searching for structural features and similarities. This tool outperforms currently available software for TE classification. The main innovation of PASTEC is the search for HMM profiles, which is useful for inferring the classification of unknown TE on the basis of conserved functional domains of the proteins. In addition, PASTEC is the only tool providing an exhaustive spectrum of possible classifications to the order level of the Wicker hierarchical TE classification system. It can also automatically classify other repeated elements, such as SSR (Simple Sequence Repeats), rDNA or potential repeated host genes. Finally, the output of this new tool is designed to facilitate manual curation by providing to biologists with all the evidence accumulated for each TE consensus.

Availability

PASTEC is available as a REPET module or standalone software (http://urgi.versailles.inra.fr/download/repet/REPET_linux-x64-2.2.tar.gz). It requires a Unix-like system. There are two standalone versions: one of which is parallelized (requiring Sun grid Engine or Torque), and the other of which is not.     

Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains

Experimental studies have observed Long Term synaptic Potentiation (LTP) when a presynaptic neuron fires shortly before a postsynaptic neuron, and Long Term Depression (LTD) when the presynaptic neuron fires shortly after, a phenomenon known as Spike Timing Dependent Plasticity (STDP). When a neuron is presented successively with discrete volleys of input spikes STDP has been shown to learn 'early spike patterns', that is to concentrate synaptic weights on afferents that consistently fire early, with the result that the postsynaptic spike latency decreases, until it reaches a minimal and stable value. Here, we show that these results still stand in a continuous regime where afferents fire continuously with a constant population rate. As such, STDP is able to solve a very difficult computational problem: to localize a repeating spatio-temporal spike pattern embedded in equally dense 'distractor' spike trains. STDP thus enables some form of temporal coding, even in the absence of an explicit time reference. Given that the mechanism exposed here is simple and cheap it is hard to believe that the brain did not evolve to use it.