Ratcave: A 3D graphics python package for cognitive psychology experiments

We present here a free, open source Python 3D graphics library called Ratcave that extends existing Python psychology stimulus software by allowing scientists to load, display, and transform 3D stimuli created in 3D modeling software. This library makes 3D programming intuitive to new users by providing 3D graphics engine concepts (Mesh, Scene, Light, and Camera classes) that can be manipulated using an interface similar to existing 2D stimulus libraries. In addition, the use of modern OpenGL constructs by Ratcave helps scientists create fast, hardware-accelerated dynamic stimuli using the same intuitive high-level, lightweight interface. Because Ratcave supplements, rather than replaces, existing Python stimulus libraries, scientists can continue to use their preferred libraries by simply adding Ratcave graphics to their existing experiments. We hope this tool will be useful both as a stimulus library and as an example of how tightly-focused libraries can add quality to the existing scientific open-source software ecosystem. Cognitive psychology and neuroscience experiments use software that presents stimuli to a subject, detects subject responses, and logs events for future analysis, all with high temporal accuracy. An ever-expanding list of other features included in this software are compatibility with third-party hardware devices (e.g. button boxes, amplifiers, eye tracking systems), support for custom experimental designs, and online analysis for adaptive stimulus sequences; these tools are available both as self-enclosed software solutions (e.g. Neurobs Presentation, BCI2000, SuperLab, E-Prime) and open-source libraries (e.g. Psychtoolbox by Brainard, 1997; PsychoPy by Peirce, 2007; VisionEgg by Straw, 2008; Expyriment by Krause & Lindemann, 2013; for a review of psychophysics libraries, see Kötter, 2009). However, these popular libraries are missing 3D graphics support, needed for a wide range visual psychophysics experiments, such as 3D mental rotation or object recognition, virtual reality in spatial navigation research, to name a few. While 3D graphics libraries do exist in Python (e.g. Panda3D, PyOgre, Vizard) and other languages (e.g. Unity3D, Unreal Engine), the stimuli, logging, and hardware support of all of these libraries are designed to work with the windows and event loops they supply, making it difficult to integrate 3D graphics functionality into different psychophysics libraries without (sometimes-extensive) modification (e.g. to mix PsychoPy’s DotStim and Expyriment’s video support). In practice, this means that each software suite is relatively self-contained; researchers who require 3D stimuli, for example, have to, thereby, resort to use or develop different experiment control software when employing 3D visual stimuli (essentially, building interface to 3D game engines), losing out on the rich features that exist in the psychophysics software ecosystem developed for the 2D graphics. Extension libraries help reduce these feature-tradeoff decisions; for example, OpenSesame, a Python-powered GUI (Mathôt & Theeuwes, 2012), uses PsychoPy, Expyriment, and PyGame as “backends” to its experiment-building graphical interface, thereby supporting all researchers who rely on those libraries. A similar extension approach could be used for 3D stimuli--not to compete with the existing 3D frameworks on a feature-by-feature basis, but to simply add simple-to-use 3D stimulus presentation and manipulation support to the feature list of existing 2D stimulus libraries in Python. In this paper, we present an open-source, cross-platform Python library called Ratcave that adds 3D stimulus support to all OpenGL-based 2D Python stimulus libraries, including VisionEgg, Psychopy, Pyglet, and PyGame. We review the core features of Ratcave (https://github.com/ratcave/ratcave) and highlight key connections of its interface to underlying graphics programming strategies (a thorough manual, complete with API guide and tutorials for first-time users can be found at https://ratcave.readthedocs.org). This library, which derives its name from our high-speed RatcaveVR experimental setup (Del Grosso, Graboski, Chen, Hernández, & Sirota, 2017), is designed to increase accessibility of 3D graphics programming to the existing ecosystem of psychology software for Python

Fermion soliton stars

A real scalar field coupled to a fermion via a Yukawa term can evade no-go theorems preventing solitonic solutions. For the first time, we study this model within General Relativity without approximations, finding static and spherically symmetric solutions that describe fermion soliton stars. The Yukawa coupling provides an effective mass for the fermion, which is key to the existence of self-gravitating relativistic solutions. We systematically study this novel family of solutions and present their mass-radius diagram and maximum compactness, which is close to (but smaller than) that of the corresponding Schwarzschild photon sphere. Finally, we discuss the ranges of the parameters of the fundamental theory in which the latter might have interesting astrophysical implications, including compact (sub)solar and supermassive fermion soliton stars for a standard gas of degenerate neutrons and electrons, respectively.Comment: 16 pages, 5 figure

Virtual Reality system for freely-moving rodents

Spatial navigation, active sensing, and most cognitive functions rely on a tight link between motor output and sensory input. Virtual reality (VR) systems simulate the sensorimotor loop, allowing flexible manipulation of enriched sensory input. Conventional rodent VR systems provide 3D visual cues linked to restrained locomotion on a treadmill, leading to a mismatch between visual and most other sensory inputs, sensory-motor conflicts, as well as restricted naturalistic behavior. To rectify these limitations, we developed a VR system (ratCAVE) that provides realistic and low-latency visual feedback directly to head movements of completely unrestrained rodents. Immersed in this VR system, rats displayed naturalistic behavior by spontaneously interacting with and hugging virtual walls, exploring virtual objects, and avoiding virtual cliffs. We further illustrate the effect of ratCAVE-VR manipulation on hippocampal place fields. The newly-developed methodology enables a wide range of experiments involving flexible manipulation of visual feedback in freely-moving behaving animals

Age-Related Mushroom Body Expansion in Male Sweat Bees and Bumble Bees

A well-documented phenomenon among social insects is that brain changes occur prior to or at the onset of certain experiences, potentially serving to prime the brain for specific tasks. This insight comes almost exclusively from studies considering developmental maturation in females. As a result, it is unclear whether age-related brain plasticity is consistent across sexes, and to what extent developmental patterns differ. Using confocal microscopy and volumetric analyses, we investigated age-related brain changes coinciding with sexual maturation in the males of the facultatively eusocial sweat bee, Megalopta genalis, and the obligately eusocial bumble bee, Bombus impatiens. We compared volumetric measurements between newly eclosed and reproductively mature males kept isolated in the lab. We found expansion of the mushroom bodies—brain regions associated with learning and memory—with maturation, which were consistent across both species. This age-related plasticity may, therefore, play a functionally-relevant role in preparing male bees for mating, and suggests that developmentally-driven neural restructuring can occur in males, even in species where it is absent in females

Chapter 18. DAYCENT Simulated Effects of Land Use and Climate on County Level N Loss Vectors in the USA

We describe the nitrogen (N) gas (NH3, NOx, N2O, N2) emission and NO3 leaching submodels used in the DAYCENT ecosystem model and demonstrate the ability of DAYCENT to simulate observed N2O emission and NO3 leaching rates for various sites representing different climate regimes, soil types, and land uses. DAYCENT simulated seven major crops, grazing lands, and potential native vegetation at the county level for the United States. At the national scale, NO3 leaching was the major loss vector, accounting for 86%, 66%, and 56% of total N losses for cropped soils, grazed lands, and native vegetation, respectively. NH3 volatilization + NOx emissions made up the majority of national N gas losses, accounting for 58%, 89%, and 86% of N gas losses from cropped soils, grazed lands, and native vegetation, respectively. However, there was considerable spatial variability in the N loss vectors, with leaching accounting for less than 20% of total N losses and NOx + NH3 emissions accounting for less than 50% of N gas losses in some counties. Land use area weighted mean annual N losses were 43.9 (SD = 26.8) and 12.3 (SD = 22.2)kg N/ha for cropped/grazed and native systems, respectively. Area weighted mean annual N gas losses were 11.8 (SD = 4.8) and 5.4 (SD = 2.1)kg N/ha for cropped/grazed and native systems, respectively. Total N losses and NO3 leaching tended to increase as N inputs and precipitation increased, and as soils became coarser textured. Total N gas losses also increased with N inputs and as soils became coarser textured, but N2O and N2 made up a larger portion of N gas losses as soils became finer textured and as precipitation increased

Status of low-energy accelerator-based BNCT worldwide and in Argentina

Existing and active low-energy Accelerator-Based BNCT programs worldwide will be reviewed and compared. In particular, the program in Argentina will be discussed which consists of the development of an Electro-Static-Quadrupole (ESQ) Accelerator-Based treatment facility. The facility is conceived to operate with the deuteron-induced reactions 9Be(d,n)10B and 13C(d,n)14N at 1.45 MeV deuteron energy, as neutron sources. Neutron production target development status is specified. The present status of the construction of the new accelerator development laboratory and future BNCT centre is shown.Fil: Cartelli, Daniel Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; ArgentinaFil: Capoulat, Maria Eugenia. Comisión Nacional de Energía Atómica; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Baldo, M.. Comisión Nacional de Energía Atómica; ArgentinaFil: Suárez Sandín, J. C.. Comisión Nacional de Energía Atómica; ArgentinaFil: Igarzabal, M.. Comisión Nacional de Energía Atómica; ArgentinaFil: del Grosso, Mariela Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; ArgentinaFil: Valda, A. A.. Comisión Nacional de Energía Atómica; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; ArgentinaFil: Canepa, N.. Comisión Nacional de Energía Atómica; ArgentinaFil: Gun, M.. Comisión Nacional de Energía Atómica; ArgentinaFil: Minsky, Daniel Mauricio. Comisión Nacional de Energía Atómica; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Conti, G.. Comisión Nacional de Energía Atómica; ArgentinaFil: Erhardt, J.. Comisión Nacional de Energía Atómica; ArgentinaFil: Somacal, Héctor Rubén. Comisión Nacional de Energía Atómica; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; ArgentinaFil: Bertolo, A. A.. Comisión Nacional de Energía Atómica; ArgentinaFil: Bergueiro, J.. Comisión Nacional de Energía Atómica; ArgentinaFil: Gaviola, P. A.. Comisión Nacional de Energía Atómica; ArgentinaFil: Kreiner, Andres Juan. Comisión Nacional de Energía Atómica; Argentina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

The landscape model: a model for exploring trade-offs between agricultural production and the environment

We describe a model framework that simulates spatial and temporal interactions in agricultural landscapes and that can be used to explore trade-offs between production and environment so helping to determine solutions to the problems of sustainable food production. Here we focus on models of agricultural production, water movement and nutrient flow in a landscape. We validate these models against data from two long-term experiments, (the first a continuous wheat experiment and the other a permanent grass-land experiment) and an experiment where water and nutrient flow are measured from isolated catchments. The model simulated wheat yield (RMSE 20.3–28.6%), grain N (RMSE 21.3–42.5%) and P (RMSE 20.2–29% excluding the nil N plots), and total soil organic carbon particularly well (RMSE 3.1 − 13.8 %), the simulations of water flow were also reasonable (RMSE 180.36 and 226.02%). We illustrate the use of our model framework to explore trade-offs between production and nutrient losses