In our study, we compare the three dimensional (3D) morphologic characteristics of Earth's first reef-building animals (archaeocyath sponges) with those of modern, photosynthetic corals. Within this repository are the 3D image data products for both groups of animals. The archaeocyath images were produced through serial grinding and imaging with the Grinding, Imaging, and Reconstruction Instrument at Princeton University. The images in this repository are the downsampled data products used in our study, and the full resolution (>2TB) image stacks are available upon request from the author. For the coral image data, the computed tomography (CT) images of all samples are included at full resolution. Also included in this repository are the manual and automated outline coordinates of the archaeocyath and coral branches, which can be directly used for morphological study.
Martin, James K; Sheehan, Joseph P; Bratton, Benjamin P; Moore, Gabriel M; Mateus, André; Li, Sophia Hsin-Jung; Kim, Hahn; Rabinowitz, Joshua D; Typas, Athanasios; Savitski, Mikhail M; Wilson, Maxwell Z; Gitai, Zemer
Abstract:
The rise of antibiotic resistance and declining discovery of new antibiotics have created a global health crisis. Of particular concern, no new antibiotic classes have been approved for treating Gram-negative pathogens in decades. Here, we characterize a compound, SCH-79797, that kills both Gram-negative and Gram-positive bacteria through a unique dual-targeting mechanism of action (MoA) with undetectably-low resistance frequencies. To characterize its MoA, we combined quantitative imaging, proteomic, genetic, metabolomic, and cell-based assays. This pipeline demonstrates that SCH-79797 has two independent cellular targets, folate metabolism and bacterial membrane integrity, and outperforms combination treatments in killing MRSA persisters. Building on the molecular core of SCH-79797, we developed a derivative, Irresistin-16, with increased potency and showed its efficacy against Neisseria gonorrheae in a mouse vaginal infection model. This promising antibiotic lead suggests that combining multiple MoAs onto a single chemical scaffold may be an underappreciated approach to targeting challenging bacterial pathogens.
This entry contains video files and tabular data associated with the PhD dissertation titled: The Evolution and Regulation of Morphological Complexity in the Vibrios.
Martin, Nicholas R; Blackman, Edith; Bratton, Benjamin P; Chase, Katelyn J; Bartlett, Thomas M; Gitai, Zemer
Abstract:
Bacterial species have diverse cell shapes that enable motility, colonization, and virulence. The cell wall defines bacterial shape and is primarily built by two cytoskeleton-guided synthesis machines, the elongasome and the divisome. However, the mechanisms producing complex shapes, like the curved-rod shape of Vibrio cholerae, are incompletely defined. Previous studies have reported that species-specific regulation of cytoskeleton-guided machines enables formation of complex bacterial shapes such as cell curvature and cellular appendages. In contrast, we report that CrvA and CrvB are sufficient to induce complex cell shape autonomously of the cytoskeleton in V. cholerae. The autonomy of the CrvAB module also enables it to induce curvature in the Gram-negative species Escherichia coli, Pseudomonas aeruginosa, Caulobacter crescentus, and Agrobacterium tumefaciens. Using inducible gene expression, quantitative microscopy, and biochemistry we show that CrvA and CrvB circumvent the need for patterning via cytoskeletal elements by regulating each other to form an asymmetrically-localized, periplasmic structure that directly binds to the cell wall. The assembly and disassembly of this periplasmic structure enables dynamic changes in cell shape. Bioinformatics indicate that CrvA and CrvB may have diverged from a single ancestral hybrid protein. Using fusion experiments in V. cholerae, we find that a synthetic CrvA/B hybrid protein is sufficient to induce curvature on its own, but that expression of two distinct proteins, CrvA and CrvB, promotes more rapid curvature induction. We conclude that morphological complexity can arise independently of cell shape specification by the core cytoskeleton-guided synthesis machines.
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)).
This deposit contains data related to clay formwork 3D printing for fabricating reinforced concrete beams. Two sets of data are provided: (1) point cloud deviations representing the clay formwork deformations during concrete casting, and (2) the load-displacement behavior of the resulting concrete beams during the four-point flexural tests. For more details, please see the corresponding article.
This dataset contains input files, training data and other files related to the machine learning models developed during the work by Muniz et al. In this work, we construct machine learning models based on the MB-pol many-body model. We find that the training set should include cluster configurations as well as liquid phase configurations in order to accurately represent both liquid and VLE properties. The results attest for the ability of machine learning models to accurately represent many-body potentials and provide an efficient avenue for water simulations.
Muniz, Maria Carolina; Gartner III, Thomas E.; Riera, Marc; Knight, Christopher; Yue, Shuwen; Paesani, Francesco; Panagiotopoulos, Athanassios Z.
Abstract:
This dataset contains all data (including input files, simulation trajectories as well as other data files and analysis scripts) related to the publication "Vapor-liquid equilibrium of water with the MB-pol many-body potential" by Muniz et al. in preparation (2021). In this work, we assessed the performance of the MB-pol many-body potential with respect to water's vapor-liquid equilibrium properties. Through the use of direct coexistence molecular dynamics, we calculated properties such as coexistence densities, surface tension, vapor pressures and enthalpy of vaporization. We found that MB-pol is able to predict these properties in good agreement with experimental data. The results attest to the chemical accuracy of MB-pol and its large range of application across water's phase diagram.
This dataset contains example input files, training data sets and potential files related to the publication "First-principles-based Machine Learning Models for Phase Behavior and Transport Properties of CO2." by Mathur et al (2023). In this work, we developed machine learning models for CO2 based on different exchange-correlation DFT functionals. We assessed their performance on liquid densities, vapor-liquid equilibrium and transport properties.
Notterman, Daniel A; Schneper, Lisa M; Drake, Amanda; Piyasena, Chinthika
Abstract:
This entry contains the data used in the PLOS ONE publication entitled, "Characteristics of salivary telomere length shortening in preterm infants" by Schneper et al. The objective of the study was to examine the association between gestational age, telomere length (TL) and rate of shortening in newborns. Genomic DNA was isolated from buccal samples of 39 term infants at birth and one year and 32 preterm infants at birth, term-adjusted age (40 weeks post-conception) and age one-year corrected for gestational duration. Telomere length was measured by quantitative real-time PCR. Demographic and clinical data were collected during clinic or research visits and from hospital records. Socioeconomic status was estimated using the deprivation category (DEPCAT) scores derived from the Carstairs score of the subject's postal code.
O'Neill, Eric; Lark, Tyler; Xie, Yanhua; Basso, Bruno
Abstract:
Collection of the underlying spatially explicit data for Available Land for Cellulosic Biofuel Production: A Supply Chain Centered Comparison. Includes raw biomass yield data and soil carbon sequestration potential data for three types of marginal land for the USA midwest at the field level including field areas. Collection also includes raw land rasters for the three types of marginal land, model parameters for the MILP model used in the study, and results used to generate the figures in the paper.
Pacheco, Diego A; Thiberge, Stephan; Pnevmatikakis, Eftychios; Murthy, Mala
Abstract:
Sensory pathways are typically studied starting at receptor neurons and following postsynaptic neurons into the brain. However, this leads to a bias in analysis of activity towards the earliest layers of processing. Here, we present new methods for volumetric neural imaging with precise across-brain registration, to characterize auditory activity throughout the entire central brain of Drosophila and make comparisons across trials, individuals, and sexes. We discover that auditory activity is present in most central brain regions and in neurons responsive to other modalities. Auditory responses are temporally diverse, but the majority of activity is tuned to courtship song features. Auditory responses are stereotyped across trials and animals in early mechanosensory regions, becoming more variable at higher layers of the putative pathway, and this variability is largely independent of spontaneous movements. This study highlights the power of using an unbiased, brain-wide approach for mapping the functional organization of sensory activity.
Pan, Da; Gelfand, Ilya; Tao, Lei; Abraha, Michael; Sun, Kang; Guo, Xuehui; Chen, Jiquan; Robertson, G. Philip; Zondlo, Mark A.
Abstract:
This dataset contains spectroscopic simulations, experimental results for the 2202 cm-1 N2O absorption line, and N2O flux measurements shown in "A New Open-path Eddy Covariance Method for N2O and Other Trace Gases that Minimizes Temperature Corrections" by Da Pan, Ilya Gelfand, Lei Tao, Michael Abraha, Kang Sun, Xuehui Guo, Jiquan Chen, G. Philip Robertson, and Mark A. Zondlo. The HITRAN Application Programming Interface (HAPI) with HITRAN 2016 was used for spectroscopic simulations. Experiments were conducted to quantify H2O-broadened half-width at half maximum and validate spectroscopic simulations. N2O flux was measured with both eddy covariance and static chamber methods.
Elevated reactive nitrogen (Nr) deposition is a concern for alpine ecosystems, and dry NH3 deposition is a key contributor. Understanding how emission hotspots impact downwind ecosystems through dry NH3 deposition provides opportunities for effective mitigation. However, direct NH3 flux measurements with sufficient temporal resolution to quantify such events are rare. Here, we measured NH3 fluxes at Rocky Mountain National Park (RMNP) during two summers and analyzed transport events from upwind agricultural and urban sources in northeastern Colorado. We deployed open-path NH3 sensors on a mobile laboratory and an eddy covariance tower to measure NH3 concentrations and fluxes. Our spatial sampling illustrated an upslope event that transported NH3 emissions from the hotspot to RMNP. Observed NH3 deposition was significantly higher when backtrajectories passed through only the agricultural region (7.9 ng m-2 s-1) versus only the urban area (1.0 ng m-2 s-1) and both urban and agricultural areas (2.7 ng m-2 s-1). Cumulative NH3 fluxes were calculated using observed, bidirectional modeled, and gap-filled fluxes. More than 40% of the total dry NH3 deposition occurred when air masses were traced back to agricultural source regions. More generally, we identified that 10 (25) more national parks in the U.S. are within 100 (200) km of an NH3 hotspot, and more observations are needed to quantify the impacts of these hotspots on dry NH3 depositions in these regions.
Natural gas vehicles (NGVs) have been promoted in China to mitigate air pollution, yet our measurements and analyses show that NGV growth in China may have significant negative impacts on climate change. We conducted real-world vehicle emission measurements in China and found high methane emissions from heavy-duty NGVs (90% higher than current emission limits). These emissions have been ignored in previous emission estimates, leading to biased results. Applying our observations to life-cycle analyses, we found that switching to NGVs from conventional vehicles in China has led to a net increase in greenhouse gas (GHG) emissions since 2000. With scenario analyses, we also show that the next decade will be critical for China to reverse the trend with the upcoming China VI standard for heavy-duty vehicles. Implementing and enforcing the China VI standard is challenging, and the method demonstrated here can provide critical information regarding the fleet-level CH4 emissions from NGVs.
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.
China is the world's largest carbon emitter and suffers from severe air pollution. About one million deaths in China were attributable to air pollution in 2017. Alternative energy vehicles (AEVs), e.g. electric, hydrogen fuel cell, and natural gas vehicles, can help achieve both carbon emission mitigation and air quality improvement. However, climate, air quality and health co-benefit of AEVs powered by deeply decarbonized electricity generation remain poorly quantified. Here, we conduct a quantitative integrated assessment of the air quality, health, carbon emission mitigation and economic benefits of AEV deployment as the electricity grid decarbonizes in China. We find population-weighted annual PM2.5 and summer O3 concentration can decrease as large as 5.7μgm−3 and 4.9ppb. Annual avoided premature mortalities and years of life lost resulting from improved ambient air pollution can be as large as ~329,000 persons and ~1,611,000 years. We thus show that maximizing climate, air quality and health benefits of AEV deployment in China requires rapid decarbonization of the power system.
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.
Fractures in geological formations may enable migration of environmentally relevant fluids, as in leakage of CO2 through caprocks in geologic carbon sequestration. We investigated geochemically induced alterations of fracture geometry in Indiana Limestone specimens. Experiments were the first of their kind, with periodic high-resolution imaging using X-ray computed tomography (xCT) scanning while maintaining high pore pressure (100 bar). We studied two CO2-acidified brines having the same pH (3.3) and comparable thermodynamic
disequilibrium but different equilibrated pressures of CO2 (PCO2 values of 12 and 77 bar). High-PCO2 brine has a faster calcite dissolution kinetic rate because of the accelerating effect of carbonic acid. Contrary to expectations, dissolution extents were comparable in the two experiments. However, progressive xCT
images revealed extensive channelization for high PCO2, explained by strong positive feedback between ongoing flow and reaction. The pronounced channel increasingly directed flow to a small region of the fracture, which explains why the overall dissolution was lower than expected. Despite this, flow simulations revealed large increases in permeability in the high-PCO2 experiment. This study shows that the permeability evolution of dissolving fractures will be larger for faster-reacting fluids. The overall mechanism is not because more rock dissolves, as would be commonly assumed, but because of accelerated fracture channelization.
Geochemical and geomechanical perturbations of the subsurface caused by the injection of fluids present the risk of leakage and seismicity. This study investigated how flow of acidic fluids affects hydraulic and frictional properties of fractures using experiments with 3.8 cm-long specimens of Eagle Ford shale, a laminated shale with carbonate-rich strata. In low-pressure flow cells, one set of samples was exposed to an acidic brine and another set was exposed to a neutral brine. X-ray computed tomography and x-ray fluorescence analysis revealed that samples exposed to the acidic brine were calcite-depleted and had developed a porous altered layer, while the other set showed little evidence of alteration. After reaction, samples were compacted and sheared in a triaxial cell that supplied normal stress and differential pore pressure at prescribed sliding velocities, independently measuring friction and permeability. During the initial compaction, the porous altered layer collapsed into fine particles that filled the fracture aperture. This effectively impeded flow and sealed the fracture, resulting in a decrease in fracture permeability by 1 to 2 orders of magnitude relative to the compressed unaltered fractures. During shear, the collapsed layer of fine-grained particles prevented the formation of interlocking micro-asperities resulting in lower frictional strength. With regard to subsurface risks, this study showcases how coupled geochemical and geomechanical processes could favorably seal fractures to inhibit leakage, but also could increase the likelihood of induced seismicity. These findings have important implications for geological carbon sequestration, pressurized fluid energy storage, geothermal energy, and other subsurface technologies.
This dataset contains input and output files to reproduce the results of the manuscript "Homogeneous ice nucleation in an ab initio machine learning model" by Pablo M. Piaggi, Jack Weis, Athanassios Z. Panagiotopoulos, Pablo G. Debenedetti, and Roberto Car (arXiv preprint https://arxiv.org/abs/2203.01376). In this work, we studied the homogeneous nucleation of ice from supercooled liquid water using a machine learning model trained on ab initio energies and forces. Since nucleation takes place over times much longer than the simulation times that can be afforded using molecular dynamics simulations, we make use of the seeding technique that is based on simulating an ice cluster embedded in liquid water. The key quantity provided by the seeding technique is the size of the critical cluster (i.e., a size such that the cluster has equal probabilities of growing or shrinking at the given supersaturation). Using data from the seeding simulations and the equations of classical nucleation theory we compute nucleation rates that can be compared with experiments.
Piaggi, Pablo M; Gartner, Thomas E; Car, Roberto; Debenedetti, Pablo G
Abstract:
The possible existence of a liquid-liquid critical point in deeply supercooled water has been a subject of debate in part due to the challenges associated with providing definitive experimental evidence. Pioneering work by Mishima and Stanley [Nature 392, 164 (1998) and Phys.~Rev.~Lett. 85, 334 (2000)] sought to shed light on this problem by studying the melting curves of different ice polymorphs and their metastable continuation in the vicinity of the expected location of the liquid-liquid transition and its associated critical point. Based on the continuous or discontinuous changes in slope of the melting curves, Mishima suggested that the liquid-liquid critical point lies between the melting curves of ice III and ice V. Here, we explore this conjecture using molecular dynamics simulations with a purely-predictive machine learning model based on ab initio quantum-mechanical calculations. We study the melting curves of ices III, IV, V, VI, and XIII using this model and find that the melting lines of all the studied ice polymorphs are supercritical and do not intersect the liquid-liquid transition locus. We also find a pronounced, yet continuous, change in slope of the melting lines upon crossing of the locus of maximum compressibility of the liquid. Finally, we analyze critically the literature in light of our findings, and conclude that the scenario in which melting curves are supercritical is favored by the most recent computational and experimental evidence. Thus, although the preponderance of experimental and computational evidence is consistent with the existence of a second critical point in water, the behavior of the melting lines of ice polymorphs does not provide strong evidence in support of this viewpoint, according to our calculations.
This dataset is affiliated with the publication https://doi.org/10.1007/s00348-022-03455-0. All of the data provided is necessary to reproduce the results with the aforementioned publication. The data in this repository is for the wake of a wind turbine at high Reynolds numbers. The data is mainly used for reproducing the statistics (deficit and variance profiles) and the phase averaged results.
Hepatitis B virus (HBV) infection remains a major public health problem and, in associated co-infection with hepatitis delta virus (HDV), causes the most severe viral hepatitis and accelerated liver disease progression. As a defective satellite RNA virus, HDV can only propagate in the presence of HBV infection, which makes HBV DNA and HDV RNA the standard biomarkers for monitoring the virological response upon antiviral therapy, in co-infected patients. Although assays have been described to quantify these viral nucleic acids in circulation independently, a method for monitoring both viruses simultaneously is not available, thus hampering characterization of their complex dynamic interactions. Here, we describe the development of a dual fluorescence channel detection system for pan-genotypic, simultaneous quantification of HBV DNA and HDV RNA through a one-step quantitative PCR. The sensitivity for both HBV and HDV is about 10 copies per microliter without significant interference between these two detection targets. This assay provides reliable detection for HBV and HDV basic research in vitro and in human liver chimeric mice. Preclinical validation of this system on serum samples from patient on or off antiviral therapy also illustrates a promising application that is rapid and cost-effective in monitoring HBV and HDV viral loads simultaneously.
Chronic hepatitis B (CHB), caused by hepatitis B virus (HBV), remains a major medical problem. HBV has a high propensity for progressing to chronicity and can result in severe liver disease, including fibrosis, cirrhosis and hepatocellular carcinoma. CHB patients frequently present with viral coinfection, including HIV and hepatitis delta virus. About 10% of chronic HIV carriers are also persistently infected with HBV which can result in more exacerbated liver disease. Mechanistic studies of HBV-induced immune responses and pathogenesis, which could be significantly influenced by HIV infection, have been hampered by the scarcity of immunocompetent animal models. Here, we demonstrate that humanized mice dually engrafted with components of a human immune system and a human liver supported HBV infection, which was partially controlled by human immune cells, as evidenced by lower levels of serum viremia and HBV replication intermediates in the liver. HBV infection resulted in priming and expansion of human HLA-restricted CD8+ T cells, which acquired an activated phenotype. Notably, our dually humanized mice support persistent coinfections with HBV and HIV which opens opportunities for analyzing immune dysregulation during HBV and HIV coinfection and preclinical testing of novel immunotherapeutics.
Since 1850 the concentration of atmospheric methane (CH4), a potent greenhouse gas, has more than doubled. Recent studies suggest that emission inventories may be missing sources and underestimating emissions. To investigate whether offshore oil and gas platforms leak CH4 during normal operation, we measured CH4 mole fractions around eight oil and gas production platforms in the North Sea which were neither flaring gas nor off-loading oil. We use the measurements from summer 2017, along with meteorological data, in a Gaussian plume model to estimate CH4 emissions from each platform. We find CH4 mole fractions of between 11 and 370 ppb above background concentrations downwind of the platforms measured, corresponding to a median CH4 emission of 6.8 g CH4 s-1 for each platform, with a range of 2.9 to 22.3 g CH4 s-1. When matched to production records, during our measurements individual platforms lost between 0.04% and 1.4% of gas produced with a median loss of 0.23%. When the measured platforms are considered collectively, (i.e. the sum of platforms’ emission fluxes weighted by the sum of the platforms’ production), we estimate the CH4 loss to be 0.19% of gas production. These estimates are substantially higher than the emissions most recently reported to the National Atmospheric Emission Inventory (NAEI) for total CH4 loss from United Kingdom platforms in the North Sea. The NAEI reports CH4 losses from the offshore oil and gas platforms we measured to be 0.13% of gas production, with most of their emissions coming from gas flaring and offshore oil loading, neither of which were taking place at the time of our measurements. All oil and gas platforms we observed were found to leak CH4 during normal operation and much of this leakage has not been included in UK emission inventories. Further research is required to accurately determine total CH4 leakage from all offshore oil and gas operations and to properly include the leakage in national and international emission inventories.
The item included here is a collection of wave profiles collected and presented in the accompanying paper: Rucks, M. J., Winey, J. M., Toyoda, T., Gupta, Y. M., & Duffy, T. S. (in review). "Shock compression of fluorapatite to 120 GPa" Submitted to Journal of Geophysical Research: Planets.
This dataset includes individual CIF files with the refined structure of fluorapatite under compression to 61 GPa. The structures have been discussed in detail in the accompanying manuscript "Single-crystal X-ray diffraction of fluorapatite to 61 GPa"
Numerical data is tabulated for all plots (Figures 2, 3a-b, 4-89, S1, S4a-b,d, S5a-b,d, S6-S156) and included as separate spreadsheets categorized by figure in a .zip file in the Supplementary Material. Error bars in Figure 4 show the spread of data observed for 4 and 5 trials on independent samples for MIL-101 and MOF-235, respectively. Figure 6a shows the average of triplicate filtrate test conversions with error propagated based on this spread. Figures 6b and S165 error bars on rate constants are determined based on propagated conversion uncertainty for independent trials and extracted standard deviations of pseudo-first order rate constants from linearized plots. Error bars on other plots represent propagation of experimental uncertainty on single trials.
Sharma, A. Y.; Cole, M. D. J.; Görler, T.; Chen, Y.; Hatch, D. R.; Guttenfelder, W.; Hager, R.; Sturdevant, B. J.; Ku, S.; Mishchenko, A.; Chang, C. S.
The dataset is a compilation of real time ground observations of criteria pollutants monitored at the Central Pollution Control Board (CPCB) continuous stations in India, from 2015-2019. Pollutants included are PM2.5, PM10, SO2, NO2 and O3 and are archived at every hour for all stations across India.
Data set for "Film drop production over a wide range of liquid conditions." One .csv file is provided that contains data about the number of film drops produced by bursting bubbles of multiple sizes in various liquid conditions.
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.
