Search for: All records

Abstract Atmospheric chemistry plays a crucial role in Earth system models (ESMs), controlling atmospheric composition and radiative balance; it is highly interactive with the physical climate, biogeochemical cycles, and human systems. However, it often imposes computational challenges in an ESM. Here we develop a full troposphere‐stratosphere interactive chemistry module for the US Department of Energy's Energy Exascale Earth System Model (E3SM). We intentionally build a streamlined module based on E3SM version 2 that interacts with other components and maintains all of major chemical and chemistry‐climate feedbacks. The module incorporates a new, highly efficient tracer advection scheme; linearization of stratospheric chemistry; and abridged tropospheric chemical mechanism with 28 reactive tracers. This new model, E3SM‐chem, can readily perform century‐long climate simulations of ozone, methane, and nitrous oxide based on emission scenarios as well as provide hourly budgets for the gas‐phase radicals that drive aerosol chemistry. We evaluate E3SM‐chem with an atmosphere‐only simulation as in the recent climate model intercomparison project (CMIP6) finding results similar to the other CMIP6 models. For the present‐day, E3SM‐chem matches the standard measurement metrics for stratospheric and tropospheric ozone, surface air quality, other key reactive gases like carbon monoxide, and the methane lifetime. Overall, E3SM‐chem maintains the climate fidelity of the baseline model while adding at most 20% to the computational cost of the atmosphere model. Hence, interactive chemistry can be a default configuration for long climate simulations at resolutions of 1° or finer, which is crucial for producing self‐consistent chemistry‐climate feedbacks that alter the climate system. 

Early computer science courses (CS1, CS2) are the cornerstone of student understanding of computer science. These courses introduce the foundational knowledge of computer science needed to understand more complex topics and to be successful in follow-on courses. It is thus important to introduce CS concepts in an engaging and easy-to-understand manner to increase student interest and retention. This paper presents a new approach to teaching the Computer Science 1 (CS1) course through our BRIDGES system. This approach aims to increase student engagement and improve learning outcomes by using audio-based assignments that they can manipulate and process audio signal information, as well as visualize and play them. We explain how to design and implement audiobased assignments and connect them to fundamental programming constructs such as variables, control flow, and simple data structures, such as arrays. These assignments encourage and engage students by using audio data they are interested in to write code, promoting problem-solving and improvements in their critical thinking skills. 

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the our substantial collective experience with EAMs. By encouraging good task-design practices and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications. 

This expert panel is the first of a two-panel series marking the 40^thanniversary of “Cognitive Systems Engineering: New Wine in New Bottles” by Hollnagel and Woods (1983) and, arguably, the beginning of Cognitive Systems Engineering (CSE). These experts were there at (or near) the beginning, devising new methods, expanding and creating new theories, and revealing a new perspective on how complex systems sustain performance and fail. They also wrestled and struggled with these new ideas to propose and implement solutions to improve performance in a number of high-consequence industries. Whether in graduate school or as early-career professionals, they saw the surprises that served as signals that the thinking that brought us to that point would not, alone, be the thinking and doing that would take us further. They will each answer the question, “What ideas and perspectives are important about Cognitive Systems Engineering, and why?” 

Abstract Biopolymers, like chromatin, are often confined in small volumes. Confinement has a great effect on polymer conformations, including polymer entanglement. Polymer chains and other filamentous structures can be represented by polygonal curves in three-space. In this manuscript, we examine the topological complexity of polygonal chains in three-space and in confinement as a function of their length. We model polygonal chains by equilateral random walks in three-space and by uniform random walks (URWs) in confinement. For the topological characterization, we use the second Vassiliev measure. This is an integer topological invariant for polygons and a continuous functions over the real numbers, as a function of the chain coordinates for open polygonal chains. For URWs in confined space, we prove that the average value of the Vassiliev measure in the space of configurations increases as O ( n 2 ) with the length of the walks or polygons. We verify this result numerically and our numerical results also show that the mean value of the second Vassiliev measure of equilateral random walks in three-space increases as O ( n ). These results reveal the rate at which knotting of open curves and not simply entanglement are affected by confinement. 

Evidence accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behaviour. EAMs have generated significant theoretical advances in psychology, behavioural economics, and cognitive neuroscience, and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues, and on inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, for relating experimental manipulations to EAM parameters, for planning appropriate sample sizes, and for preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the authors’ substantial collective experience with EAMs. By encouraging good task design practices, and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications. 

During infections with the malaria parasitesPlasmodium vivax, patients exhibit rhythmic fevers every 48 h. These fever cycles correspond with the time the parasites take to traverse the intraerythrocytic cycle (IEC). In otherPlasmodiumspecies that infect either humans or mice, the IEC is likely guided by a parasite-intrinsic clock [Rijo-Ferreiraet al.,Science368, 746–753 (2020); Smithet al.,Science368, 754–759 (2020)], suggesting that intrinsic clock mechanisms may be a fundamental feature of malaria parasites. Moreover, becausePlasmodiumcycle times are multiples of 24 h, the IECs may be coordinated with the host circadian clock(s). Such coordination could explain the synchronization of the parasite population in the host and enable alignment of IEC and circadian cycle phases. We utilized an ex vivo culture of whole blood from patients infected withP. vivaxto examine the dynamics of the host circadian transcriptome and the parasite IEC transcriptome. Transcriptome dynamics revealed that the phases of the host circadian cycle and the parasite IEC are correlated across multiple patients, showing that the cycles are phase coupled. In mouse model systems, host–parasite cycle coupling appears to provide a selective advantage for the parasite. Thus, understanding how host and parasite cycles are coupled in humans could enable antimalarial therapies that disrupt this coupling. 

Locomotion generates adventitious sounds which enable detection and localization of predators and prey. Such sounds contain brisk changes or transients in amplitude. We investigated the hypothesis that ill-understood temporal specializations in binaural circuits subserve lateralization of such sound transients, based on different time of arrival at the ears (interaural time differences, ITDs). We find that Lateral Superior Olive (LSO) neurons show exquisite ITD-sensitivity, reflecting extreme precision and reliability of excitatory and inhibitory postsynaptic potentials, in contrast to Medial Superior Olive neurons, traditionally viewed as the ultimate ITD-detectors. In vivo, inhibition blocks LSO excitation over an extremely short window, which, in vitro, required synaptically evoked inhibition. Light and electron microscopy revealed inhibitory synapses on the axon initial segment as the structural basis of this observation. These results reveal a neural vetoing mechanism with extreme temporal and spatial precision and establish the LSO as the primary nucleus for binaural processing of sound transients. 

Abstract This paper describes the atmospheric component of the US Department of Energy's Energy Exascale Earth System Model (E3SM) version 3. Significant updates have been made to the atmospheric physics compared to earlier versions. Specifically, interactive gas chemistry has been implemented, along with improved representations of aerosols and dust emissions. A new stratiform cloud microphysics scheme more physically treats ice processes and aerosol‐cloud interactions. The deep convection parameterization has been largely improved with sophisticated microphysics for convective clouds, making model convection sensitive to large‐scale dynamics, and incorporating the dynamical and physical effects of organized mesoscale convection. Improvements in aerosol wet removal processes and parameter re‐tuning of key aerosol and cloud processes have improved model aerosol radiative forcing. The model's vertical resolution has increased from 72 to 80 layers with the extra eight layers added in the lower stratosphere to better simulate the Quasi‐Biennial Oscillation. These improvements have enhanced E3SM's capability to couple aerosol, chemistry, and biogeochemistry and reduced some long‐standing biases in simulating tropical variability. Compared to its predecessors, the model shows a much stronger signal for the Madden‐Julian Oscillation, Kelvin waves, mixed Rossby‐gravity waves, and eastward inertia‐gravity waves. Aerosol radiative forcing has been considerably reduced and is now better aligned with community best estimates, leading to significantly improved skill in simulating historical temperature records. Its simulated mean‐state climate is largely comparable to E3SMv2, but with some notable degradation in shortwave cloud radiative effect, precipitable water, and surface wind stress, which will be addressed in future updates.