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Physical and biogeochemical variables from the NOAA-GFDL Earth System Model 2M experiments, and previously published observation-based datasets, used for the study 'Hydrological cycle amplification reshapes warming-driven oxygen loss in Atlantic Ocean'.
Physical and biogeochemical variables from the NOAA-GFDL Earth System Model 2M experiments (pre-processed), previously published observation-based datasets, and code to reproduce figures from these datasets, used for the study 'Hydrological cycle amplification reshapes warming-driven oxygen loss in Atlantic Ocean'.
Griffies, Stephen M; Beadling, Rebecca L; Krasting, John P; Hurlin, William J
Abstract:
This output was produced in coordination with the Southern Ocean Freshwater release model experiments Initiative (SOFIA) and is the Tier 1 experiment where freshwater is delivered in a spatially and temporally uniform pattern at the surface of the ocean at sea surface temperature in a 1-degree latitude band extending from Antarctica’s coastline. The total additional freshwater flux imposed as a monthly freshwater flux entering the ocean is 0.1 Sv. Users are referred to the methods section of Beadling et al. (2022) for additional details on the meltwater implementation in CM4 and ESM4. The datasets in this collection contain model output from the coupled global climate model, CM4, and Earth System Model, ESM4, both developed at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). The ocean_monthly_z and ocean_annual_z output are provided as z depth levels in meters as opposed to the models native hybrid vertical ocean coordinate which consists of z* (quasi-geopotential) coordinates in the upper ocean through the mixed layer, transitioning to isopycnal (referenced to 2000 dbar) in the ocean interior. Please see README for further details.
Mondal, Shanka Subhra; Webb, Taylor; Cohen, Jonathan
Abstract:
A dataset of Raven’s Progressive Matrices (RPM)-like problems using realistically rendered
3D shapes, based on source code from CLEVR (a popular visual-question-answering dataset) (Johnson, J., Hariharan, B., Van Der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., & Girshick, R. (2017). Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2901-2910)).
The materials include codes and example input / output files for Monte Carlo simulations of lattice chains in the grand canonical ensemble, for determining phase behavior, critical points, and formation of aggregates.
In this publication we provide the LAMMPS example files to reproduce simulations for the manuscript "A Deep Potential model for liquid-vapor equilibrium and cavitation rates of water"
Data set corresponding to "NAPS: Integrating pose estimation and tag-based tracking." This dataset contains the corresponding videos, tracking scripts, and SLEAP models along with SLEAP, NAPS, and ArUco tracking results.
Microscopy images are part of a paper entitled "Structured foraging of soil predators unveils functional responses to bacterial defenses" by Fernando Rossine, Gabriel Vercelli, Corina Tarnita, and Thomas Gregor. For detailed acquisition methods see the paper. Experiments were performed between 2019 and 2020 at Princeton University. Two types of images are provided, macroscopic and microscopic widefiled Images. Macroscopic images all show Petri dishes covered in fluorescent bacteria being consumed by amoebae. Images are shown for D. discoideum, P. violaceum, and A. castellanii. Images depicting drug treatments (Nystatin and Fluorouracil) were obtained using D. discoideum. Images used for the creation of a profile were all taken within 30 minutes of each other. Within each directory numbered images are independent replicates. The raw video directory contains time series for dishes under drug treatments. Each numbered folder is a sequence of photos (taken 30 minutes apart of each other) of a single dish. Microscopic images all show amoebae consuming bacteria on a petri dish. The 45 minute videos show either edge cells (located at the edge of amoebae colonies), or inner cells (located 2.5 millimeters towards the center of the colony, from the edge). Videos are confocal stacks, with bacteria showing in green and amoebae appearing as black holes within the bacterial lawn. As was for the macroscopic images, images are shown for D. discoideum, P. violaceum, and A. castellanii. Images depicting drug treatments (Nystatin and Fluorouracil) were obtained using D. discoideum.
Petsev, Nikolai D.; Nikoubashman, Arash; Latinwo, Folarin
Abstract:
Source code for our genetic algorithm optimization investigation of conglomerate and racemic chiral crystals. In this work, we address challenges in determining the stable structures formed by chiral molecules by applying the framework of genetic algorithms to predict the ground state crystal lattices formed by a chiral tetramer model. Using this code, we explore the relative stability and structures of the model’s conglomerate and racemic crystals, and extract a structural phase diagram for the stable Bravais crystal types in the zero-temperature limit.
This dataset contains all data relevant to a forthcoming publication in which we used molecular simulation methods to study the phase behavior of supercooled water. The dataset contains simulation input and output files, processed data files, and image files used to create all plots in the manuscript. Python analysis scripts are also included, including instructions for how to re-generate all plots in the manuscript.
Severe acute respiratory coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, is of zoonotic origin. Evolutionary analyses assessing whether coronaviruses similar to SARS-CoV-2 infected ancestral species of modern-day animal hosts could be useful in identifying additional reservoirs of potentially dangerous coronaviruses. We reasoned that if a clade of species has been repeatedly exposed to a virus, then their proteins relevant for viral entry may exhibit adaptations that affect host susceptibility or response. We perform comparative analyses across the mammalian phylogeny of angiotensin-converting enzyme 2 (ACE2), the cellular receptor for SARS-CoV-2, in order to uncover evidence for selection acting at its binding interface with the SARS-CoV-2 spike protein. We uncover that in rodents there is evidence for adaptive amino acid substitutions at positions comprising the ACE2-spike interaction interface, whereas the variation within ACE2 proteins in primates and some other mammalian clades is not consistent with evolutionary adaptations. We also analyze aminopeptidase N (APN), the receptor for the human coronavirus 229E, a virus that causes the common cold, and find evidence for adaptation in primates. Altogether, our results suggest that the rodent and primate lineages may have had ancient exposures to viruses similar to SARS-CoV-2 and HCoV-229E, respectively. Included in this repository are the instructions and corresponding code required to build the dataset and run the analysis in the manuscript.
Gartner, Thomas III; Zhang, Linfeng; Piaggi, Pablo; Car, Roberto; Panagiotopoulos, Athanassios; Debenedetti, Pablo
Abstract:
This dataset contains all data related to the publication "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water", by Gartner et al., 2020. In this work, we used neural networks to generate a computational model for water using high-accuracy quantum chemistry calculations. Then, we used advanced molecular simulations to demonstrate evidence that suggests this model exhibits a liquid-liquid transition, a phenomenon that can explain many of water's anomalous properties. This dataset contains links to all software used, all data generated as part of this work, as well as scripts to generate and analyze all data and generate the plots reported in the publication.
This dataset is a sequence of laser-induced fluorescence images of a dye injected in a channel flow with canopy-like stainless steel rods simulating a vegetation canopy stand. The data is acquired close to the channel bottom at z/h=0.2, where z is the height referenced to the channel bed and h is the canopy height. The dataset provides spatial distribution of scalar concentration in a plane parallel to the channel bed. The data has been used (but the data itself has not been published or available to the public) in previous work. The references are: Ghannam, K., Poggi, D., Porporato, A., & Katul, G. (2015). The spatio-temporal statistical structure and ergodic behaviour of scalar turbulence within a rod canopy. Boundary-Layer Meteorology,157(3), 447–460. Ghannam, K, Poggi, D., Bou-Zeid, E., Katul, G. (2020). Inverse cascade evidenced by information entropy of passive scalars in submerged canopy flows. Geophysical Research Letters (accepted).
Explosive volcanic eruptions have large climate impacts, and can serve as observable tests of the climatic response to radiative forcing. Using a high resolution climate model, we contrast the climate responses to Pinatubo, with symmetric forcing, and those to Santa Maria and Agung, which had meridionally asymmetric forcing. Although Pinatubo had larger global-mean forcing, asymmetric forcing strongly shifts the latitude of tropical rainfall features, leading to larger local precipitation/TC changes. For example, North Atlantic TC activity over is enhanced/reduced by SH-forcing (Agung)/NH-forcing (Santa Maria), but changes little in response to the Pinatubo forcing. Moreover, the transient climate sensitivity estimated from the response to Santa Maria is 20% larger than that from Pinatubo or Agung. This spread in climatic impacts of volcanoes needs to be considered when evaluating the role of volcanoes in global and regional climate, and serves to contextualize the well-observed response to Pinatubo.
Pereira, Talmo D.; Aldarondo, Diego E.; Willmore, Lindsay; Kislin, Mikhail; Wang, Samuel S.-H.; Murthy, Mala; Shaevitz, Joshua W.
Abstract:
Recent work quantifying postural dynamics has attempted to define the repertoire of behaviors performed by an animal. However, a major drawback to these techniques has been their reliance on dimensionality reduction of images which destroys information about which parts of the body are used in each behavior. To address this issue, we introduce a deep learning-based method for pose estimation, LEAP (LEAP Estimates Animal Pose). LEAP automatically predicts the positions of animal body parts using a deep convolutional neural network with as little as 10 frames of labeled data for training. This framework consists of a graphical interface for interactive labeling of body parts and software for training the network and fast prediction on new data (1 hr to train, 185 Hz predictions). We validate LEAP using videos of freely behaving fruit flies (Drosophila melanogaster) and track 32 distinct points on the body to fully describe the pose of the head, body, wings, and legs with an error rate of <3% of the animal's body length. We recapitulate a number of reported findings on insect gait dynamics and show LEAP's applicability as the first step in unsupervised behavioral classification. Finally, we extend the method to more challenging imaging situations (pairs of flies moving on a mesh-like background) and movies from freely moving mice (Mus musculus) where we track the full conformation of the head, body, and limbs.
Khanna, Jaya; Medvigy, David; Fueglistaler, Stephan; Walko, Robert
Abstract:
More than 20% Amazon rainforest has been cleared in the past three decades triggering important hydroclimatic changes. Small-scale (~few kilometers) deforestation in the 1980s has caused thermally-triggered atmospheric circulations that increase regional cloudiness and precipitation frequency. However, these circulations are predicted to diminish as deforestation increases. Here we use multi-decadal satellite records and numerical model simulations to show a regime shift in the regional hydroclimate accompanying increasing deforestation in Rondônia, Brazil. Compared to the 1980s, present-day deforested areas in downwind western Rondônia are found to be wetter than upwind eastern deforested areas during the local dry season. The resultant precipitation change in the two regions is approximately ±25% of the deforested area mean. Meso-resolution simulations robustly reproduce this transition when forced with increasing deforestation alone, showing a negligible role of large-scale climate variability. Furthermore, deforestation-induced surface roughness reduction is found to play an essential role in the present-day dry season hydroclimate. Our study illustrates the strong scale-sensitivity of the climatic response to Amazonian deforestation and suggests that deforestation is sufficiently advanced to have caused a shift from a thermally- to a dynamically-driven hydroclimatic regime.
Does the default mode network (DMN) reconfigure to encode information about the changing environment? This question has proven difficult, because patterns of functional connectivity reflect a mixture of stimulus-induced neural processes, intrinsic neural processes and non-neuronal noise. Here we introduce inter-subject functional correlation (ISFC), which isolates stimulus-dependent inter-regional correlations between brains exposed to the same stimulus. During fMRI, we had subjects listen to a real-life auditory narrative and to temporally scrambled versions of the narrative. We used ISFC to isolate correlation patterns within the DMN that were locked to the processing of each narrative segment and specific to its meaning within the narrative context. The momentary configurations of DMN ISFC were highly replicable across groups. Moreover, DMN coupling strength predicted memory of narrative segments. Thus, ISFC opens new avenues for linking brain network dynamics to stimulus features and behaviour.