Unified approach to reset processes and application to coupling between process and reset.

Processes under reset, where realizations are interrupted according to some stochastic rule and restarted from the initial state, find broad application in modeling physical, chemical, and biological phenomena and in designing search strategies. While a wealth of theoretical results has been recently obtained, current derivations tend to focus on specific processes, obscuring the general principles and preventing broad applicability. We present a unified approach to those observables of stochastic processes under reset that take the form of averages of functionals depending on the most recent renewal period.

Encoding of Predictive Associations in Human Prefrontal and Medial Temporal Neurons During Pavlovian Appetitive Conditioning.

Pavlovian conditioning is thought to involve the formation of learned associations between stimuli and values, and between stimuli and specific features of outcomes. Here, we leveraged human single neuron recordings in ventromedial prefrontal, dorsomedial frontal, hippocampus, and amygdala while patients of both sexes performed an appetitive Pavlovian conditioning task probing both stimulus-value and stimulus-stimulus associations. Ventromedial prefrontal cortex encoded predictive value along with the amygdala, and also encoded predictions about the identity of stimuli that would subsequently be presented, suggesting a role for neurons in this region in encoding predictive information beyond value.

Benthic sediment as stores and sources of bacteria and viruses in streams: A comparison of baseflow vs. stormflow longitudinal transport and residence times.

The presence of bacteria and viruses in freshwater represents a global health risk. The substantial spatial and temporal variability of microbes leads to difficulties in quantifying the risks associated with their presence in freshwater. Fine particles, including bacteria and viruses are transported and accumulated into shallow streambed (i.

Fluid-Solid Reaction in Porous Media as a Chaotic Restart Process.

Chemical and biological reactions at fluid-solid interfaces are central to a broad range of porous material applications and research. Pore-scale solute transport limitations can reduce reaction rates, with marked consequences for a wide spectrum of natural and engineered processes. Recent advances show that chaotic mixing occurs spontaneously in porous media, but its impact on surface reactions is unknown.

Neurons in human pre-supplementary motor area encode key computations for value-based choice.

Adaptive behaviour in real-world environments requires that choices integrate several variables, including the novelty of the options under consideration, their expected value and uncertainty in value estimation. Here, to probe how integration over decision variables occurs during decision-making, we recorded neurons from the human pre-supplementary motor area (preSMA), ventromedial prefrontal cortex and dorsal anterior cingulate. Unlike the other areas, preSMA neurons not only represented separate pre-decision variables for each choice option but also encoded an integrated utility signal for each choice option and, subsequently, the decision itself.

Cardiopulmonary Imaging Utilization and Findings among Hospitalized COVID-19 Patients in Latin America: (From "RIMAC: Registry IMAging Cardiopulmonary among Hospitalized COVID-19 Patients in LATAM").

Objectives: Describe the use and findings of cardiopulmonary imaging-chest X-ray (cX-ray), echocardiography (cEcho), chest CT (cCT), lung ultrasound (LUS), and/or cardiac magnetic resonance imaging (cMRI)-in COVID-19 hospitalizations in Latin America (LATAM).

Background: There is a lack of information on the images used and their findings during the SARS-CoV-2 pandemic in LATAM.

Methods: Multicenter, prospective, observational study of COVID-19 inpatients, conducted from March to December 2020, from 12 high-complexity centers, in nine LATAM countries.

Value-Related Neuronal Responses in the Human Amygdala during Observational Learning.

The amygdala plays an important role in many aspects of social cognition and reward learning. Here, we aimed to determine whether human amygdala neurons are involved in the computations necessary to implement learning through observation. We performed single-neuron recordings from the amygdalae of human neurosurgical patients (male and female) while they learned about the value of stimuli through observing the outcomes experienced by another agent interacting with those stimuli.

Kinetics of contact processes under segregation.

The kinetics of contact processes are determined by the interplay among local mass transfer mechanisms, spatial heterogeneity, and segregation. Determining the macroscopic behavior of a wide variety of phenomena across the disciplines requires linking reaction times to the statistical properties of spatially fluctuating quantities. We formulate the dynamics of advected agents interacting with segregated immobile components in terms of a chemical continuous-time random walk.

Survival and confinement under quenched disorder.

We study the survival and confinement of random walkers under quenched disorder characterized by spatially-varying waiting times and decay rates. Spatial heterogeneity and segregation lead to a dynamic coupling between transport and reaction, resulting in history-dependent dynamics exhibiting long survivals and confinement. The survival probability decays as a power law, in contrast to the classical exponential law for decay at a homogeneous rate.

Chemical Continuous Time Random Walks.

Kinetic Monte Carlo methods such as the Gillespie algorithm model chemical reactions as random walks in particle number space. The interreaction times are exponentially distributed under the assumption that the system is well mixed. We introduce an arbitrary interreaction time distribution, which may account for the impact of incomplete mixing on chemical reactions, and in general stochastic reaction delay, which may represent the impact of extrinsic noise.

A Process-Based Model for Bioturbation-Induced Mixing.

Bioturbation refers to the transport processes carried out by living organisms and their physical effects on soils and sediments. It is widely recognized as an important mixing mechanism, particularly at the sediment-water interface in many natural systems. In order to quantify its impact on mixing, we propose a process-based model based on simple assumptions about organism burrowing behavior.

Noise-Driven Return Statistics: Scaling and Truncation in Stochastic Storage Processes.

In countless systems, subjected to variable forcing, a key question arises: how much time will a state variable spend away from a given threshold? When forcing is treated as a stochastic process, this can be addressed with first return time distributions. While many studies suggest exponential, double exponential or power laws as empirical forms, we contend that truncated power laws are natural candidates. To this end, we consider a minimal stochastic mass balance model and identify a parsimonious mechanism for the emergence of truncated power law return times.

An Integrated Experimental and Modeling Approach to Predict Sediment Mixing from Benthic Burrowing Behavior.

Bioturbation is the dominant mode of sediment transport in many aquatic environments and strongly influences both sediment biogeochemistry and contaminant fate. Available bioturbation models rely on highly simplified biodiffusion formulations that inadequately capture the behavior of many benthic organisms. We present a novel experimental and modeling approach that uses time-lapse imagery to directly relate burrow formation to resulting sediment mixing.

Host immunity and pathogen diversity: A computational study.

The distinctive features of human influenza A phylogeny have inspired many mathematical and computational studies of viral infections spreading in a host population, but our understanding of the mechanisms that shape the coupled evolution of host immunity, disease incidence and viral antigenic properties is far from complete. In this paper we explore the epidemiology and the phylogeny of a rapidly mutating pathogen in a host population with a weak immune response, that allows re-infection by the same strain and provides little cross-immunity. We find that mutation generates explosive diversity and that, as diversity grows, the system is driven to a very high prevalence level.

Characterization of the endemic equilibrium and response to mutant injection in a multi-strain disease model.

We explore a model of an antigenically diverse infection whose otherwise identical strains compete through cross-immunity. We assume that individuals may produce upon infection different numbers of antibody types, each of which matches the antigenic configuration of a particular epitope, and that one matching antibody type grants total immunity against a challenging strain. In order to reduce the number of equations involved in the analytic description of the dynamics, we follow the strategy proposed by Kryazhimskiy et al.

Stochastic single-gene autoregulation.

A detailed stochastic model of single-gene autoregulation is established and its solutions are explored when mRNA dynamics is fast compared with protein dynamics and in the opposite regime. The model includes all the sources of randomness that are intrinsic to the autoregulation process and it considers both transcriptional and post-transcriptional regulation. The time-scale separation allows the derivation of analytic expressions for the equilibrium distributions of protein and mRNA.