Data set for "Ocean emission of microplastic by bursting bubble jet drops." Two .csv files are provided: one for the size of a jet drop carrying microplastic, and another for the amount of microplastic captured by a jet drop.
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.
This repository contains the raw photon-by-photon single-molecule FRET (smFRET) trajectories, SAXS data, and MD simulation trajectories, multi-sequence alignment, and gel images for the paper titled "Sub-Domain Dynamics Enables Chemical Chain Reactions in Nonribosomal Peptide Synthetases."
This dataset contains supplementary materials for Chapter 4 and Chapter 5 of Yiheng Tao's PhD dissertation (2022). The dissertation’s abstract is provided here:
Carbon capture, utilization, and storage (CCUS) mitigates climate change by capturing carbon dioxide (CO2) emissions from large point sources, or CO2 from the ambient air, and subsequently reusing the captured CO2 or injecting it into deep geological formations for long-term and secure storage. Almost all current decarbonization pathways include large-scale CCUS, on the order of a billion tonnes (Gt) of CO2 captured and stored each year globally starting in 2030, yet the actual deployment has lagged far behind (around 0.04 Gt CO2 was captured in 2021). In this dissertation, I contribute to several aspects of largescale deployment of CCUS by (1) developing and applying efficient numerical models to simulate geological CO2 storage and (2) identifying key policies to address the bottlenecks of overall CCUS deployment. This dissertation concerns the United States, China, and the Belt and Road Initiative (BRI) region through research projects that are consistent with each location’s current development stage of CCUS.
Chapters 2 and 3 contain computational modeling studies. In Chapter 2, I develop a new series of vertical-equilibrium (VE) models in the dual-continuum modeling framework to simulate CO2 injection and migration in fractured geological formations. Those models are shown to be effective and efficient when properties of the formation allow for the VE assumption. In Chapter 3, I apply a VE model to simulate basin-scale CO2 injection in the Junggar Basin of Northwestern China. The results show that current regional emissions of more than 100 million tonnes of CO2 per year can be stored effectively, thereby confirming the great potential of the Junggar Basin for early CCUS deployment.
Chapters 4 and 5 contain policy analyses. In Chapter 4, I propose a dynamic system consisting of new CO2 pipelines and novel Allam-cycle power plants in the Central United States, and examine how government policies, including an extended Section 45Q tax credit, may improve the economic feasibility of this system. Lastly, in Chapter 5, I investigate and quantify CO2 emissions implications of power plant projects associated with the BRI. I also propose a “greenness ratio” to measure the level of environmental sustainability of BRI in the power sector.
Taylor, Jenny A.; Bratton, Benjamin P.; Sichel, Sophie R.; Blair, Kris M.; Jacobs, Holly M.; DeMeester, Kristen E.; Kuru, Erkin; Gray, Joe; Biboy, Jacob; VanNieuwenhze, Michael S.; Vollmer, Waldemar; Grimes, Catherine L.; Shaevitz, Joshua W.; Salama, Nina R.
Abstract:
Helical cell shape is necessary for efficient stomach colonization by Helicobacter pylori, but the molecular mechanisms for generating helical shape remain unclear. We show that the helical centerline pitch and radius of wild-type H. pylori cells dictate surface curvatures of considerably higher positive and negative Gaussian curvatures than those present in straight- or curved-rod bacteria. Quantitative 3D microscopy analysis of short pulses with either N-acetylmuramic acid or D-alanine metabolic probes showed that cell wall growth is enhanced at both sidewall curvature extremes. Immunofluorescence revealed MreB is most abundant at negative Gaussian curvature, while the bactofilin CcmA is most abundant at positive Gaussian curvature. Strains expressing CcmA variants with altered polymerization properties lose helical shape and associated positive Gaussian curvatures. We thus propose a model where CcmA and MreB promote PG synthesis at positive and negative Gaussian curvatures, respectively, and that this patterning is one mechanism necessary for maintaining helical shape.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples.
Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
This dataset contains all the model output used to generate the figures and data reported in the article "Climate, soil organic layer, and nitrogen jointly drive forest development after fire in the North American boreal zone". The data was generated during spring 2015 using the a modified version of the Ecosystem Demography model version 2, provided as a supplement accompanying the article. The data was generated using the computational resources supported by the PICSciE OIT High Performance Computing Center and Visualization Laboratory at Princeton University. The dataset contains a pdf Readme file which explains in detail how the data can be used. Users are recommended to go through this file before using the data.
Vecchi, Gabriel A.; Landsea, Christopher; Zhang, Wei; Villarini, Gabriele; Knutson, Thomas
Abstract:
These are the data and scripts supporting the manuscript: Vecchi, Landsea, Zhang, Villarini and Knutson (2021): Changes in Atlantic Major Hurricane Frequency Since the Late-19th Century. Nature Communications.
Wang, Rui; Guo, Xuehui; Pan, Da; Kelly, James; Bash, Jesse; Sun, Kang; Paulot, Fabien; Clarisse, Lieven; Van Damme, Martin; Whitburn, Simon; Coheur, Pierre-François; Clerbaux, Cathy; Zondlo, Mark
Abstract:
Monthly, high resolution (~2 km) ammonia (NH3) column maps from the Infrared Atmospheric Sounding Interferometer (IASI) were developed across the contiguous United States and adjacent areas. Ammonia hotspots (95th percentile of the column distribution) were highly localized with a characteristic length scale of 12 km and median area of 152 km2. Five seasonality classes were identified with k-means++ clustering. The Midwest and eastern United States had a broad, spring maximum of NH3 (67% of hotspots in this cluster). The western United States, in contrast, showed a narrower mid-summer peak (32% of hotspots). IASI spatiotemporal clustering was consistent with those from the Ammonia Monitoring Network. CMAQ and GFDL-AM3 modeled NH3 columns have some success replicating the seasonal patterns but did not capture the regional differences. The high spatial-resolution monthly NH3 maps serve as a constraint for model simulations and as a guide for the placement of future, ground-based network sites.
This distribution compiles numerous physical properties for 2,585 intrinsically disordered proteins (IDPs) obtained by coarse-grained molecular dynamics simulation. This combination comprises "Dataset A" as reported in "Featurization strategies for polymer sequence or composition design by machine learning" by Roshan A. Patel, Carlos H. Borca, and Michael A. Webb (DOI: 10.1039/D1ME00160D). The specific IDP sequences are sourced from version 9.0 of the DisProt database. The simulations were performed using the LAMMPS molecular dynamics engine. The interactions used for simulation are obtained from R. M. Regy , J. Thompson , Y. C. Kim and J. Mittal , Improved coarse-grained model for studying sequence dependent phase separation of disordered proteins, Protein Sci., 2021, 1371 —1379.
This item provides access to all configurations of single-chain nanoparticles analyzed in the manuscript "Sequence Patterning, Morphology, and Dispersity in Single-Chain Nanoparticles: Insights from Simulation and Machine Learning" by Roshan A. Patel, Sophia Colmenares, and Michael A. Webb (DOI: 10.1021/acspolymersau.3c00007). The single-chain nanoparticles derive from 320 unique precursor chains that are distinguished by the fraction of linker beads that decorate a fixed-length polymer backbone and the distribution or blockiness of those linker beads. The data is provided in the form of serialized object using the `pickle' python module. The data was compiled using Python version 3.8.8 and Clang 10.0.0. The Python object loaded from the .pkl file is a nested list, with the first dimension having 7,680 entries for the 7,680 unique single-chain nanoparticles produced in the aforementioned paper. Each of those 7,680 entries is itself a list with 20 entries, representing the 20 different simulation snapshots of the given single-chain nanoparticle. Each of the 20 entries is another list with two entries, with the first being a numpy.ndarray containing the x,y,z coordinates of all the beads comprising the single-chain nanoparticle and the second being a numpy.ndarray with a numerical encoding to indicate whether the beads are backbone (indicated as '0') or linker beads (indicated as '1'). Altogether, this provides 153,600 configurations of single-chain nanoparticles.
This distribution contains experimentally measured data for the extent of retained enzyme activity post thermal stressing for three distinct enzymes: glucose oxidase, lipase, and horseradish peroxidase. The data is used to form conclusions and develop machine learning models as reported in the publication "Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids" by Matthew Tamasi, Roshan Patel, Carlos Borca, Shashank Kosuri, Heloise Mugnier, Rahul Upadhya, N. Sanjeeva Murthy, Michael Webb*, and Adam Gormley. Details regarding the experimental protocols are reported in the aforementioned paper but are briefly discussed in the README.
Webb, Michael; Jacobs, William; An, Yaxin; Oliver, Wesley
Abstract:
This distribution compiles thermodynamic and (where available) dynamic properties of short protein sequences as obtained from coarse-grained molecular dynamics simulations. The dataset features 2114 protein sequences with sequence lengths ranging from N=20 up to N=50 amino acids. The simulation and analysis of these sequences is described in "Active learning of the thermodynamics--dynamics tradeoff in protein condensates'' by Yaxin An, Michael A. Webb*, and William M. Jacobs* (https://doi.org/10.48550/arXiv.2306.03696). Of the 2114 protein sequences, 80 are homomeric polypeptides (replicating a single amino acid for N = 20, 30, 40, and 50), 1266 are sourced from version 9.0 of the DisProt database, and the remaining 768 sequences are novel sequences generated during an active learning campaign described in the aforementioned manuscript. The simulations were performed using the LAMMPS molecular dynamics engine. The interactions used for simulation are obtained from R. M. Regy , J. Thompson , Y. C. Kim and J. Mittal , Improved coarse-grained model for studying sequence dependent phase separation of disordered proteins, Protein Sci., 2021, 1371 —1379. Properties included in this distribution include second virial coefficients, pressure-density data, expectation for phase behavior at 300 K, estimated condensed-phase densities at 300 K (if exist), and condensed-phase self-diffusion coefficients at 300 K (if exist).
Extrapolation -- the ability to make inferences that go beyond the scope of one's experiences -- is a hallmark of human intelligence. By contrast, the generalization exhibited by contemporary neural network algorithms is largely limited to interpolation between data points in their training corpora. In this paper, we consider the challenge of learning representations that support extrapolation. We introduce a novel visual analogy benchmark that allows the graded evaluation of extrapolation as a function of distance from the convex domain defined by the training data. We also introduce a simple technique, context normalization, that encourages representations that emphasize the relations between objects. We find that this technique enables a significant improvement in the ability to extrapolate, considerably outperforming a number of competitive techniques.
