Capturing limbal epithelial stem cell population dynamics, signature, and their niche

Stem cells (SCs) are traditionally viewed as rare, slow-cycling cells that follow deterministic rules dictating their self-renewal or differentiation. It was several decades ago, when limbal epithelial SCs (LSCs) that regenerate the corneal epithelium were one of the first sporadic, quiescent SCs ever discovered. However, LSC dynamics, heterogeneity and genetic signature are largely unknown. Moreover, recent accumulating evidence strongly suggested that epithelial SCs are actually abundant, frequently dividing cells that display stochastic behavior.In this work, we performed an in-depth analysis of the murine limbal epithelium by single-cell RNA sequencing and quantitative lineage tracing. The generated data provided an atlas of cell states of the corneal epithelial lineage, and particularly, revealed the co-existence of two novel LSC populations that reside in separate and well-defined sub-compartments. In the “outer” limbus, we identified a primitive widespread population of quiescent LSCs (qLSCs) that uniformly express Krt15/Gpha2/Ifitm3/Cd63 proteins, while the “inner” limbus host prevalent active LSCs (aLSCs) co-expressing Krt15-GFP/Atf3/Mt1-2/Socs3. Analysis of LSC population dynamics suggests that while qLSCs and aLSCs possess different proliferation rates, they both follow similar stochastic rules that dictate their self-renewal and differentiation. Finally, T cells were distributed in close proximity to qLSCs. Indeed, their absence or inhibition resulted in the loss of quiescence and delayed wound healing. Taken together, we propose that divergent regenerative strategies are tailored to properly support tissue-specific physiological constraints. The present study suggests that in the case of the cornea, quiescent epithelial SCs are abundant, follow stochastic rules and neutral drift dynamics.

Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm

Environmental modelling is complex, and models often require the calibration of several parameters that are not able to be directly evaluated from a physical quantity or field measurement. Multi-objective calibration has many advantages such as adding constraints in a poorly constrained problem or finding a compromise between different objectives by defining a set of optimal parameters. The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers not just one but a family of parameter sets that are optimal with regard to a multi-objective target. The idea behind caRamel is to rely on stochastic rules while also allowing more “local” mechanisms, such as the extrapolation along vectors in the parameter space. The caRamel algorithm is a hybrid of the multi-objective evolutionary annealing simplex (MEAS) method and the non-dominated sorting genetic algorithm II (ε-NSGA-II). It was initially developed for calibrating hydrological models but can be used for any environmental model. The caRamel algorithm is well adapted to complex modelling. The comparison with other optimizers in hydrological case studies (i.e. NSGA-II and MEAS) confirms the quality of the algorithm. An R package, caRamel, has been designed to easily implement this multi-objective algorithm optimizer in the R environment.

Elements of a stochastic 3D prediction engine in larval zebrafish prey capture

The computational principles underlying predictive capabilities in animals are poorly understood. Here, we wondered whether predictive models mediating prey capture could be reduced to a simple set of sensorimotor rules performed by a primitive organism. For this task, we chose the larval zebrafish, a tractable vertebrate that pursues and captures swimming microbes. Using a novel naturalistic 3D setup, we show that the zebrafish combines position and velocity perception to construct a future positional estimate of its prey, indicating an ability to project trajectories forward in time. Importantly, the stochasticity in the fish’s sensorimotor transformations provides a considerable advantage over equivalent noise-free strategies. This surprising result coalesces with recent findings that illustrate the benefits of biological stochasticity to adaptive behavior. In sum, our study reveals that zebrafish are equipped with a recursive prey capture algorithm, built up from simple stochastic rules, that embodies an implicit predictive model of the world.

Stochastic rules for predator and prey hunting and escape behavior in a lattice-based model

Understanding of ecosystem resilience and stability requires comprehending predator–prey dynamics because ecosystems consist of dynamically interacting subsystems that include predator–prey relationships. This relationship is closely related to the hunting–escaping strategies employed by the predator and prey. Therefore, understanding the effects of hunting and escaping strategies on ecosystems will lead to a better understanding of these systems. As an approach for describing the predator–prey interaction, lattice-based models have been adopted because this approach has strong advantages for simulating various dynamical processes of individual–individual interaction. In the models, each lattice cell is either considered as an attractive/repulsive cell, or an individual cell, or else it is empty. The attractive (or repulsive cell) can be interpreted as the prey (or predator) of the individual. These states allow us to incorporate the ecological processes of local antagonistic interactions, namely the spread of disturbances (by the predator) and regrowth or recovery (by the prey). These processes are directly related to the strategic behavior of individuals, such as hunting and escaping. In this study, we suggest a simple and effective mapping formula as a stochastic rule to describe the hunting and escaping behavior. This formula could be widely used not only in the behavior but also in competitive and cooperative relationships.

On cellular automata models of traffic flow with look-ahead potential

We study the statistical properties of a cellular automata model of traffic flow with the look-ahead potential. The model defines stochastic rules for the movement of cars on a lattice. We analyze the underlying statistical assumptions needed for the derivation of the coarse-grained model and demonstrate that it is possible to relax some of them to obtain an improved coarse-grained ODE model. We also demonstrate that spatial correlations play a crucial role in the presence of the look-ahead potential and propose a simple empirical correction to account for the spatial dependence between neighboring cells.

AnaWEGE: a weather generator based on analogues of atmospheric circulation

This paper presents a stochastic weather generator based on analogues of circulation (AnaWEGE). Analogues of circulation have been a promising paradigm to analyse climate variability and its extremes. The weather generator uses precomputed analogues of sea-level pressure over the North Atlantic. The stochastic rules of the generator constrain the continuity in time of the simulations. The generator then simulates spatially coherent time series of a climate variable, drawn from meteorological observations. The weather generator is tested for European temperatures, and for winter and summer seasons. The biases in temperature quantiles and autocorrelation are rather small compared to observed variability. The ability of simulating extremely hot summers and cold winters is also assessed.

AnaWEGE: a weather generator based on analogues of atmospheric circulation

This paper presents a stochastic weather generator based on analogues of circulation (AnaWEGE). Analogues of circulation have been a promising paradigm to analyse climate variability and its extremes. The weather generator uses precomputed analogues of sea-level pressure over the North Atlantic. The stochastic rules of the generator constrain the continuity in time of the simulations. The generator then simulates spatially coherent time series of a climate variable, drawn from meteorological observations. The weather generator is tested for European temperatures, and for winter and summer seasons. The biases in temperature quantiles and autocorrelation are rather small compared to observed variability. The ability of simulating extremely hot summers and cold winters is also assessed.