Local and Global Variability in Developing Human T-Cell Repertoires.

The adaptive immune response relies on T cells that combine phenotypic specialization with diversity of T-cell receptors (TCRs) to recognize a wide range of pathogens. TCRs are acquired and selected during T-cell maturation in the thymus. Characterizing TCR repertoires across individuals and T-cell maturation stages is important for better understanding adaptive immune responses and for developing new diagnostics and therapies.

Demonstration that sublinear dendrites enable linearly non-separable computations.

Theory predicts that nonlinear summation of synaptic potentials within dendrites allows neurons to perform linearly non-separable computations (LNSCs). Using Boolean analysis approaches, we predicted that both supralinear and sublinear synaptic summation could allow single neurons to implement a type of LNSC, the feature binding problem (FBP), which does not require inhibition contrary to the exclusive-or function (XOR). Notably, sublinear dendritic operations enable LNSCs when scattered synaptic activation generates increased somatic spike output.

The evolutionary consequences of interactions between the epigenome, the genome and the environment.

The epigenome is the suite of interacting chemical marks and molecules that helps to shape patterns of development, phenotypic plasticity and gene regulation, in part due to its responsiveness to environmental stimuli. There is increasing interest in understanding the functional and evolutionary importance of this sensitivity under ecologically realistic conditions. Observations that epigenetic variation abounds in natural populations have prompted speculation that it may facilitate evolutionary responses to rapid environmental perturbations, such as those occurring under climate change.

Cyclodextrin-based supramolecular nanogels decorated with mannose for short peptide encapsulation.

Nanogels are aqueous dispersions of hydrogel particles formed by physically or chemically cross-linked polymer networks of nanoscale size. Herein, we devised a straightforward technique to fabricate a novel class of physically cross-linked nanogels via a self-assembly process in water involving α-cyclodextrin and a mannose molecule that was hydrophobically modified using an alkyl chain. The alkyl chain-modified mannose was synthesized in five steps, starting with D-mannose.

A Mean Field to Capture Asynchronous Irregular Dynamics of Conductance-Based Networks of Adaptive Quadratic Integrate-and-Fire Neuron Models.

Mean-field models are a class of models used in computational neuroscience to study the behavior of large populations of neurons. These models are based on the idea of representing the activity of a large number of neurons as the average behavior of mean-field variables. This abstraction allows the study of large-scale neural dynamics in a computationally efficient and mathematically tractable manner.

Mid-ocean ridge unfaulting revealed by magmatic intrusions.

Mid-ocean ridges (MORs) are quintessential sites of tectonic extension, at which divergence between lithospheric plates shapes abyssal hills that cover about two-thirds of the Earth's surface. Here we show that tectonic extension at the ridge axis can be partially undone by tectonic shortening across the ridge flanks. This process is evidenced by recent sequences of reverse-faulting earthquakes about 15 km off-axis at the Mid-Atlantic Ridge and Carlsberg Ridge.

Universality of evolutionary trajectories under arbitrary forms of self-limitation and competition.

The assumption of constant population size is central in population genetics. It led to a large body of results that is robust to modeling choices and that has proven successful to understand evolutionary dynamics. In reality, allele frequencies and population size are both determined by the interaction between a population and the environment.

Interoceptive rhythms in the brain.

Sensing internal bodily signals, or interoception, is fundamental to maintain life. However, interoception should not be viewed as an isolated domain, as it interacts with exteroception, cognition and action to ensure the integrity of the organism. Focusing on cardiac, respiratory and gastric rhythms, we review evidence that interoception is anatomically and functionally intertwined with the processing of signals from the external environment.

Emergent statistical laws in single-cell transcriptomic data.

Large-scale data on single-cell gene expression have the potential to unravel the specific transcriptional programs of different cell types. The structure of these expression datasets suggests a similarity with several other complex systems that can be analogously described through the statistics of their basic building blocks. Transcriptomes of single cells are collections of messenger RNA abundances transcribed from a common set of genes just as books are different collections of words from a shared vocabulary, genomes of different species are specific compositions of genes belonging to evolutionary families, and ecological niches can be described by their species abundances.

DRAG in situ barcoding reveals an increased number of HSPCs contributing to myelopoiesis with age.

Ageing is associated with changes in the cellular composition of the immune system. During ageing, hematopoietic stem and progenitor cells (HSPCs) that produce immune cells are thought to decline in their regenerative capacity. However, HSPC function has been mostly assessed using transplantation assays, and it remains unclear how HSPCs age in the native bone marrow niche.

Optimizing airborne wind energy with reinforcement learning.

Airborne wind energy is a lightweight technology that allows power extraction from the wind using airborne devices such as kites and gliders, where the airfoil orientation can be dynamically controlled in order to maximize performance. The dynamical complexity of turbulent aerodynamics makes this optimization problem unapproachable by conventional methods such as classical control theory, which rely on accurate and tractable analytical models of the dynamical system at hand. Here we propose to attack this problem through reinforcement learning, a technique that-by repeated trial-and-error interactions with the environment-learns to associate observations with profitable actions without requiring prior knowledge of the system.

Parametric control of flexible timing through low-dimensional neural manifolds.

Biological brains possess an unparalleled ability to adapt behavioral responses to changing stimuli and environments. How neural processes enable this capacity is a fundamental open question. Previous works have identified two candidate mechanisms: a low-dimensional organization of neural activity and a modulation by contextual inputs.

Multistability and evolution of chimera states in a network of type II Morris-Lecar neurons with asymmetrical nonlocal inhibitory connections.

Formation of synchronous activity patterns is an essential property of neuronal networks that has been of central interest to synchronization theory. Chimera states, where both synchronous and asynchronous activities of neurons co-exist in a single network, are particularly poignant examples of such patterns, whose dynamics and multistability may underlie brain function, such as cognitive tasks. However, dynamical mechanisms of coherent state formation in spiking neuronal networks as well as ways to control these states remain unclear.

Signatures of irreversibility in microscopic models of flocking.

Flocking in d=2 is a genuine nonequilibrium phenomenon for which irreversibility is an essential ingredient. We study a class of minimal flocking models whose only source of irreversibility is self-propulsion and use the entropy production rate (EPR) to quantify the departure from equilibrium across their phase diagrams. The EPR is maximal in the vicinity of the order-disorder transition, where reshuffling of the interaction network is fast.

The impact of sparsity in low-rank recurrent neural networks.

Neural population dynamics are often highly coordinated, allowing task-related computations to be understood as neural trajectories through low-dimensional subspaces. How the network connectivity and input structure give rise to such activity can be investigated with the aid of low-rank recurrent neural networks, a recently-developed class of computational models which offer a rich theoretical framework linking the underlying connectivity structure to emergent low-dimensional dynamics. This framework has so far relied on the assumption of all-to-all connectivity, yet cortical networks are known to be highly sparse.

A framework for macroscopic phase-resetting curves for generalised spiking neural networks.

Brain rhythms emerge from synchronization among interconnected spiking neurons. Key properties of such rhythms can be gleaned from the phase-resetting curve (PRC). Inferring the PRC and developing a systematic phase reduction theory for large-scale brain rhythms remains an outstanding challenge.

Constraining the Range and Variation of Lithospheric Net Rotation Using Geodynamic Modeling.

Lithospheric net rotation (LNR) is the movement of the lithosphere as a solid body with respect to the mantle. Separating the signal of LNR from plate tectonic motion is therefore an important factor in producing absolute plate motion models. Net rotation is difficult to constrain because of uncertainties in geological data and outstanding questions about the stability of the mantle plumes used as a reference frame.

Heat flux in chains of nonlocally coupled harmonic oscillators: Mean-field limit.

We consider one-dimensional systems of all-to-all harmonically coupled particles with arbitrary masses, subject to two Langevin thermal baths. The couplings correspond to the mean-field limit of long-range interactions. Additionally, the particles can be subject to a harmonic on-site potential to break momentum conservation.

Renormalization group approach to connect discrete- and continuous-time descriptions of Gaussian processes.

Discretization of continuous stochastic processes is needed to numerically simulate them or to infer models from experimental time series. However, depending on the nature of the process, the same discretization scheme may perform very differently for the two tasks, if it is not accurate enough. Exact discretizations, which work equally well at any scale, are characterized by the property of invariance under coarse-graining.

Consciousness matters: phenomenal experience has functional value.

'Why would we do anything at all if the doing was not doing something to us?' In other words: What is consciousness good for? Here, reversing classical views, according to many of which subjective experience is a mere epiphenomenon that affords no functional advantage, we propose that subject-level experience-'What it feels like'-is endowed with intrinsic value, and it is precisely the value agents associate with their experiences that explains why they do certain things and avoid others. Because experiences have value and guide behaviour, consciousness has a function. Under this hypothesis of 'phenomenal worthiness', we argue that it is only in virtue of the fact that conscious agents 'experience' things and 'care' about those experiences that they are 'motivated' to act in certain ways and that they 'prefer' some states of affairs vs.

Preverbal infants' sensitivity to grammatical dependencies.

During their first months of life, infants can already distinguish function words (e.g., pronouns and determiners) from content words (e.

Evidence that Pacific tuna mercury levels are driven by marine methylmercury production and anthropogenic inputs.

Pacific Ocean tuna is among the most-consumed seafood products but contains relatively high levels of the neurotoxin methylmercury. Limited observations suggest tuna mercury levels vary in space and time, yet the drivers are not well understood. Here, we map mercury concentrations in skipjack tuna across the Pacific Ocean and build generalized additive models to quantify the anthropogenic, ecological, and biogeochemical drivers.

Phase response approaches to neural activity models with distributed delay.

In weakly coupled neural oscillator networks describing brain dynamics, the coupling delay is often distributed. We present a theoretical framework to calculate the phase response curve of distributed-delay induced limit cycles with infinite-dimensional phase space. Extending previous works, in which non-delayed or discrete-delay systems were investigated, we develop analytical results for phase response curves of oscillatory systems with distributed delay using Gaussian and log-normal delay distributions.

Shaping Dynamics With Multiple Populations in Low-Rank Recurrent Networks.

An emerging paradigm proposes that neural computations can be understood at the level of dynamic systems that govern low-dimensional trajectories of collective neural activity. How the connectivity structure of a network determines the emergent dynamical system, however, remains to be clarified. Here we consider a novel class of models, gaussian-mixture, low-rank recurrent networks in which the rank of the connectivity matrix and the number of statistically defined populations are independent hyperparameters.

The Acquisition of Noun and Verb Categories by Bootstrapping From a Few Known Words: A Computational Model.

While many studies have shown that toddlers are able to detect syntactic regularities in speech, the learning mechanism allowing them to do this is still largely unclear. In this article, we use computational modeling to assess the plausibility of a context-based learning mechanism for the acquisition of nouns and verbs. We hypothesize that infants can assign basic semantic features, such as "is-an-object" and/or "is-an-action," to the very first words they learn, then use these words, the , to ground proto-categories of nouns and verbs.