Find research datasets worth reusing

opencc-by-4.0May 2022View details →

Untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community"

This dataset contains the untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community". The dataset has also been submitted to MetaboLights repository with ID "MTBLS3535". Please refer to the MetaboLights repository for the most up-to-date datasets.  Publication abstract: A key challenge in microbiome research is to predict the functionality of microbial communities based on community membership and (meta)-genomic data. As central microbiota functions are determined by bacterial community networks, it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on the growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM12) synthetic bacterial community, which is increasingly used as a model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments as well as in community batch culture. Metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants provided insights into the metabolic potential and activity of the individual community members. Thereby, we could show that the OMM12 interaction network is shaped by both exploitative and interference competition in vitro in nutrient-rich culture media and demonstrate how community structure can be shifted by changing the nutritional environment. In particular, Enterococcus faecalis KB1 was identified as an important driver of community composition by affecting the abundance of several other consortium members in vitro. As a result, this study gives fundamental insight into key drivers and mechanistic basis of the OMM12 interaction network in vitro, which serves as a knowledge base for future mechanistic in vivo studies.

A B cell actomyosin arc network couples integrin co-stimulation to mechanical force-dependent immune synapse formation

B-cell activation and immune synapse (IS) formation with membrane-bound antigens are actin-dependent processes that scale positively with the strength of antigen-induced signals. Importantly, ligating the B-cell integrin, LFA-1, with ICAM-1 promotes IS formation when antigen is limiting. Whether the actin cytoskeleton plays a specific role in integrin-dependent IS formation is unknown. Here we show using super-resolution imaging of mouse primary B cells that LFA-1: ICAM-1 interactions promote the formation of an actomyosin network that dominates the B-cell IS. This network is created by the formin mDia1, organized into concentric, contractile arcs by myosin 2A, and flows inward at the same rate as B-cell receptor (BCR): antigen clusters. Consistently, individual BCR microclusters are swept inward by individual actomyosin arcs. Under conditions where integrin is required for synapse formation, inhibiting myosin impairs synapse formation, as evidenced by reduced antigen centralization, diminished BCR signaling, and defective signaling protein distribution at the synapse. Together, these results argue that a contractile actomyosin arc network plays a key role in the mechanism by which LFA-1 co-stimulation promotes B-cell activation and IS formation.

Recent reconfiguration of an ancient developmental gene regulatory network in Heliocidaris Sea Urchins

Changes in developmental gene regulatory networks (dGRNs) underlie much of the diversity of life, but the evolutionary mechanisms that operate on interactions with these networks remain poorly understood. Closely related species with extreme phenotypic divergence provide a valuable window into the genetic and molecular basis for changes in dGRNs and their relationship to adaptive changes in organismal traits. Here we analyze genomes, epigenomes, and transcriptomes during early development in two sea urchin species in the genus Heliocidaris that exhibit highly divergent life histories and in an outgroup species. Signatures of positive selection and changes in chromatin status within putative gene regulatory elements are both enriched on the branch leading to the derived life history, and particularly so near core dGRN genes; in contrast, positive selection within protein-coding regions have at most a modest enrichment in branch and function. Single-cell transcriptomes reveal a dramatic delay in cell fate specification in the derived state, which also has far fewer open chromatin regions, especially near dGRN genes with conserved roles in cell fate specification. Experimentally perturbing the function of three key transcription factors reveals profound evolutionary changes in the earliest events that pattern the embryo, disrupting regulatory interactions previously conserved for ~225 million years. Together, these results demonstrate that natural selection can rapidly reshape developmental gene expression on a broad scale when selective regimes abruptly change and that even highly conserved dGRNs and patterning mechanisms in the early embryo remain evolvable under appropriate ecological circumstances.

Application of inverse theory for high spatial resolution reconstructions of thermospheric vector wind fields from Doppler shifts measured by a ground-based network of all-sky Fabry-Perot interferometers

Several types of all-sky viewing Fabry-Perot Interferometers (FPI) have been developed since the 1990s for ground-based remote sensing of thermospheric winds. The Scanning Doppler Imager (SDI) is one such instrument, which provides temporally simultaneous line-of-sight observations from hundreds of independent look directions per instrument exposure. A geographically distributed network of such instruments increases spatial coverage and, at many locations, also provides overlapping observations along multiple independent lines-of-sight. Together, these characteristics significantly increase the density and fidelity that is possible for reconstructed thermospheric vector wind fields, compared to a traditional narrow-field FPI, but at the cost of complexity and difficulty. Presently, we describe an application of inverse theory to reconstruct three-component vector thermospheric neutral wind fields using data from multiple SDI instruments. The salient features of the method used here are the ability to reconstruct three-component winds on a dense grid that is sampled regularly in latitude, longitude, and time, without assuming any a-priori underlying structure of the winds. This requires solving an inverse problem that does not in general yield a unique solution unless additional constraints are enforced. We describe this step, also known also as regularization, along with the strategy used to maximize the spatial resolution of the derived wind fields by automatically determining the minimum level of regularization that can produce stable inversions. We present example results obtained from applying this technique to one night of data from a network of SDIs in Alaska, and discuss the implications of these results for current understanding of thermospheric dynamics.

opencc-by-4.0May 2022View details →

Data for "Predicting aggregate morphology of sequence-defined macromolecules with Recurrent Neural Networks"

These are the data associated with the paper, "Predicting aggregate morphology of sequence-defined macromolecules with Recurrent Neural Networks" (DOI 10.1039/D2SM00452F). Three of the directories contains subdirectories with `GSD` files dumped from HOOMD. The other contains pretrained RNN models as TorchScript binaries exported from PyTorch.

Integrating nocturnal and diurnal interactions in a Neotropical pollination network

Plants establish pollination interactions with different groups of animals, including nocturnal ones that establish interactions with economically valuable and culturally important crops, as well as wild plants of conservation concern. Despite the considerable number of studies addressing the structure and dynamic of pollination networks, nocturnal interactions have been relatively overlooked. Using a multilayer network approach and considering diurnal and nocturnal interactions, we aimed to understand how interactions at different periods of the day are integrated and contribute to the network structural pattern. We also aimed to highlight how multilayer networks may give a more nuanced assessment of species importance across layers. We assembled a pollination network of an intensively studied Neotropical area by standardizing interaction data from 16 previous studies into a presence/absence (binary) network. Then we used a multilayer network approach to evaluate the network modularity and plant species' roles in these different temporal layers. Plants were classified as nocturnal or diurnal according to the onset of floral opening and pollinators were classified according to their foraging period. The network consisted of 178 pollinator species and 158 plant species, with 870 links. Among plant species, 135 species have diurnal floral opening while 23 species are nocturnal. The multilayer network was significantly modular, and these modules differed in the composition of pollinator groups (e.g., hawk moths, bats, bees, hummingbirds), as well as of diurnal and nocturnal plants. We show that diurnal and nocturnal interactions are organized into interconnected modules in the multilayer network. Nocturnal plants had higher values of versatility and multidegree than diurnal plants, due to their role in connecting the two temporal layers. Synthesis. Our study highlights the importance of integrating different pollination systems to understand the importance of distinct components that structure pollination networks. We also illustrate the value of tapping into existing information, particularly species interaction data, from well studied biodiversity hotspot areas, to gain a better understanding of how communities are structured. Finally, despite the relative scarcity of nocturnal pollination network studies, we showed nocturnal plants, which often make complementary use of diurnal pollinators, to be important in connecting the temporal layers.

opencc-by-4.0May 2022View details →

CNN-based Network Application for Petrophysical Parameter Inversion: Sensitivity Analysis of Input-output Parameters and Network Architecture

The uploaded file includes four groups of data. They have original elastic and reservoir parameters data, the k=5 and k=25 (k means the sampling interval in inline and crossline.), and testing dataset.

Different functional networks underlying human walking with pulling force fields acting in forward or backward directions

Walking with pulling force fields acting at the body center of mass (in the forward or backward directions) is compatible with inclined walking and is used in clinical practice for gait training. From the perspective of known differences in the motor strategies that underlie walking with the respective force fields, the present study elucidated whether the adaptation acquired by walking on a split-belt treadmill with either one of the force fields affects subsequent walking in other directions. Walking with the force field induced an adaptive and de-adaptive behavior of the subjects, with the aspect evident in the anterior breaking and posterior propulsive impulses of the ground reaction force as parameters. In the parameters, the adaptation acquired during walking with a force field acting in one direction was transferred to that in the opposite direction only partially. Furthermore, the adaptation that occurred while walking in a force field in one direction was rarely washed out by subsequent walking in a force field in the opposite direction and thus was maintained independently of the other. These results demonstrated possible independence in the neural functional networks capable of controlling walking in each movement task with an opposing force field.

opencc-by-4.0Jul 2021View details →

Development of a Multi-Level Dynamic Model to Measure the Resilience Level of Transportation Infrastructure Networks

The recent increase in disasters is making the largest critical infrastructure system namely the transportation infrastructure system susceptible to unexpected damage. Discontinuation of services provided by transportation infrastructures will create significant societal, economic, and collateral damages. Therefore, this study aims to identify dimensions to measure the resilience of the transportation infrastructures. This study also aims to develop a model to measure the resilience of the transportation infrastructures resilience. To fulfill the aims of this study, a questionnaire was developed which was supported by a comprehensive literature review. 92 valid responses were received and analyzed qualitatively and quantitatively. Statistically significant variables were used to develop a resilience measurement tool. The developed tool will provide relative resilience measures for multiple projects which will help in identifying the most vulnerable segment of the transportation infrastructure network. Exploratory factor analysis (EFA) was performed to identify the constructs and structural equation modeling (SEM) was used to develop the model. Without previous experience in reconstruction works, handling integrated assets becomes very critical. Also, such inexperience makes it difficult to handle emergency resources properly. However, such issues regarding integrated assets can be resolved by investing in locating integrated assets away from the roadways, so if a break in a railroad crossing or utility line occurs or emergency repairs are needed, the impact on the roadway operations can be minimized. To avoid issues related to access to previous disaster data for the roadway this study suggess investing in preparing an interactive online platform for recording and reviewing data related to disasters as well as previous resilience enhancing activities for the roadway with easy access credentials. The findings of this study will support practitioners and decision-makers in investing in the appropriate resilience enhancement activity project for funding and investment.

Data supporting: Drivers of individual-based, antagonistic interaction networks during plant range expansion

1. Range expansion in plant populations, especially at the colonization front, can be either limited by disproportionately large effects of antagonistic interactions or facilitated by their release. How the strength of antagonistic interactions changes along successional gradients during range expansion is still poorly documented, especially when diverse assemblages of plant antagonists (rodents, invertebrates, and birds) combine within interaction networks. 2. We study the changes in individual-based, predispersal seed-pulp predator networks along a colonization gradient in a rapidly-expanding Juniperus phoenicea population in Doñana National Park (SW Spain). Additionally, we analysed the role of individual plant traits and neighbourhood attributes in network configuration by using Exponential Random Graph Models. 3. Seven seed-pulp consumer animal species varied significantly in their frequency of interaction and prevalence. While invertebrate species were well established in old and intermediately mature stands, greenfinch (Chloris chloris) was dominant at the colonization front. Variable species roles and spread of interactions among individual plants generated changes in the configuration of interactions during plant expansion. 4. Individual plant traits strongly determined the topology of these networks, although with differences between stands. Increasing individual crop size and seeds per cone increased the interaction odds of individual plants, while seed viability showed the opposite effect. The network topology at the colonization front appeared less driven by individual traits, possibly because of the short interaction history of this recently established area. The disproportionately large effect of C. chloris in these recently established stands, potentially resulted in large seed losses during range expansion. 5. Synthesis. Turnover of antagonistic interactions, characterized the colonization front, resulting in more heterogeneous interaction strengths among individual plants. We found no evidence for a complete or sizeable antagonistic release of J. phoenicea at the colonization front promoting this rapid expansion. It becomes necessary to explore interactions with seed dispersers to understand how antagonistic and mutualistic plant-animal interactions balance during range expansion. Our study highlights the importance of an individual-based approach in understanding how interactions are structured and driven in natural changing landscapes.

Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks

Benchmark data sets of CDPred as described in Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks Zhiye Guo1, Jian Liu1, Jeffrey Skolnick2, Jianlin Cheng1* 1 Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211 2 School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332-2000 *Corresponding author (chengji@missouri.edu) There is four test dataset in this package, each test dataset contains four different folders and one list file. The afpred_pdb includes all the corresponding monomer structures predicted by alphafold. The cdpred_output includes the prediction results of our tool CDPred for each dataset. The pre_gen_a3m includes the multiple sequence alignments file used by CDPred to generate prediction results. And the true_pdb includes the fasta file for the test dataset and its heavy atom distance map (h_dist) and carbon alpha distance map (real_dist) that extract from the native structure. HomoTest1: The homodimer test dataset contains 28 targets collect from CASP_CAPRI 10-13 HomoTest2: The homodimer test dataset contains 23 targets collect from CASP_CAPRI 13-14 HeteroTest1: The heterodimer test dataset contains 9 targets collect from CASP_CAPRI13-14 HeteroTest2: The heterodimer test dataset contains 55 targets collect from PDB bank 09-2021 to 11-2021

Functional composition of plant communities mediates biomass effects on ecosystem service recovery across an experimental dryland restoration network

Land degradation can result in a loss of critical ecosystem services that we often seek to restore through re-establishment of desired plant communities. Trait-based approaches have the potential to target specific ecosystem services based on associations between the functional composition of plant communities and ecosystem properties that serve as indicators of those services. The effect of functional composition on ecosystem recovery may depend on the amount of restored plant biomass, itself a supporting service frequently targeted in restoration efforts. Yet, interactions between functional composition and biomass are not formally integrated into trait-based analytical frameworks. We tested the hypothesis that functional composition of plant communities both drives, and interacts with, biomass production to influence indicators of soil functioning and weed suppression across a network of degraded dryland restoration experiments. This networked approach allowed us to identify generalized effects of functional composition on ecosystem recovery across a range of dryland climate conditions. Climate had a substantial effect on ecosystem indicators, with weed cover and soil surface stability increasing in more arid climates, water infiltration increasing with precipitation, and aggregate structure increasing with less freezing. After accounting for climate effects across study sites, we found significant effects of community-weighted mean (CWM) trait values on biomass, particularly a positive effect of leaf carbon-to-nitrogen ratio, and of CWM-biomass interactions on other ecosystem indicators. Cover of exotic species was reduced in restored communities with a combination of low leaf dry matter content and high biomass, soil water infiltration increased with lower specific root length and high biomass, and soil aggregate stability increased with higher root dry matter content and high biomass, among other effects. Functional diversity had no significant effects on any ecosystem indicators. Synthesis: The influence of community functional composition on ecosystem properties increases with community biomass, particularly in disturbed or low productivity systems. This suggests that active management should not only focus on trait values that optimize individual ecosystem indicators, but also how those functional strategies are complementary or counter to those that increase biomass.

Predicting compound-protein interaction using hierarchical graph convolutional networks

This repository contains the datasets which are used in the article "Predicting Compound-Protein Interaction using Hierarchical Graph Convolutional Networks".

opencc-by-4.0Dec 2021View details →

Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES)

Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES). Detailed description of the files in README.txt.

Dataset for the Manuscript: Demonstration of optically-driven plasmonic nanomotors designed by deep learning networks

This repository contains the data corresponding to the manuscript "Demonstration of the optically-driven plasmonic nanomotor designed by deep learning networks." It consists of 5 parts: Machine learning, Numerical analysis, Rotation measurement, Scattering measurement, and Supplementary information. The code for the machine learning algorithm is available at "https://github.com/mintaechung/Nanomotor_Predictor_Generator."   <ul> <li>'Machine_learning.zip': Correlation between optical torques calculated by SIE and predicted by trained CNN, Objective loss functions at the 1st iteration, and the torque distribution of the initial randomset and the output of the nanorotor generator after the 3rd iteration.</li> <li>'Numerical_analysis.zip': MATLAB codes to retrieve 'Moments', 'Field intensity distribution', 'Poynting vectors', and 'Torques'. </li> <li>'Rotation_measurement.zip': Raw videos, Intensity profiles of ROI, Rotation measurement results.</li> <li>'Scattering_measurement.zip': Scattering intensity measurement with reference light.</li> <li>'Supplementary_Info.zip': Random geometry generation, Optical torques of 6 blades, Expanded structure, Shrinkage, Polarization independence, Angular momentum, and Machine learning progress.</li> </ul>

Pre-generated network files for "Intersecting near-optimal spaces: European power systems with more resilience to weather variability"

These are network files that can be used to investigate the impacts of weather variability on the European power system using PyPSA-Eur as in <a href="https://github.com/aleks-g/intersecting-near-opt-spaces/tree/v1.0">https://github.com/aleks-g/intersecting-near-opt-spaces/tree/v1.0</a>. Find more information about the approach in the README of that repository. These network files are a shortcut to reproduce the results and use a fixed configuration ("v1.0"). For other configurations, it may be necessary to download ERA5 reanalysis cutouts (more on this in the git repository). Instructions can be found in the git repository.

Virtual Axle Detector based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

We recorded the measurement data used in the present study on a single-span steel trough railway bridge located on a long-distance traffic line in Germany. The bridge is 18.4 m long in total with a free span of 16.4 m. A total of  10 seismic uniaxial accelerometers of the type PCB-39B04 (PCB Synotech) with a sensitivity of 1000 mV/g (±10\%), a broadband resolution of 0.000003 gRMS, a measurement range of ±5 gpk and a frequency range of 0.06 to 450 Hz (±5\%) were installed.  The measurements are triggered via the rising slope of the wheel load measuring point G1, the measurements from the ring buffer are stored from ten seconds before the trigger together with the 50 seconds long measurement after the triggering. The recorded signals thus all have a length of 60 seconds. All sensor signals were recorded with a sampling frequency of fs = 600 Hz using the catmanAP software and the CX22 data recorder connected to an MX1601B universal amplifier and an MX1616B strain gauge amplifier (all products are from HBK).

opencc-by-4.0Jul 2022View details →

Water quality of the Jaguari and Atibaia rivers and relationships among parameters: a study based on Complex Networks

<pre>The data are part of the doctoral work entitled: Water quality of the Jaguari and Atibaia rivers and relationships among parameters: a study based on Complex Networks.</pre>

opencc-by-4.0Jul 2022View details →

Supplementary table of PRIDE datasets analyzed for "FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data"

Our proteomics dataset comes from The PRoteomics IDEntifications (PRIDE) database, the world’s largest data repository of mass spectrometry-based proteomics data. Specifically, we used 633 human proteomics project experiments with a total of 32,546 runs and reanalyzed them using ionbot with an FDR threshold of 0.01 [16], resulting in a total of 154,885,151 peptide spectrum matches for 18,846 proteins. Here is the full list of projects, runs, and general statistics.