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13,064 results for “predictions”
Range contractions and reduced body mass predicted for endemic skinks of the Cameroon volcanic line with future warming
<p><em><span>Aim </span></em></p> <p><span>The vulnerability of montane species to environmental change has been increasingly recognized over the past decade. However, most of these species are regionally endemic with restricted ranges, limiting dispersal necessary for avoiding extinction. </span><span>Threats posed for montane lizards could be further complicated in species following Bergmann's rule, where body size increases with altitude.</span><span> In this study, we applied a bioclimatic and body mass trait-based species distribution model approach to identify areas suitable for montane endemic skink species of the Cameroon Volcanic Line (CVL) under current and future climates. </span></p> <p><em><span>Location</span></em></p> <p><span>Cameroon Volcanic Line</span></p> <p><em><span>Methods</span></em></p> <p><span>We recorded occurrence records and measured body mass in the field for skink species endemic to the CVL. We additionally supplemented our occurrence data with records from an online repository. We projected habitat suitability in the region by implementing the occurrence data in the bioclimatic species distribution models. We further integrated both occurrence and body mass information in our trait-based model to project potential body mass.</span></p> <p><em><span>Results</span></em></p> <p><span>Projected currently suitable ranges were limited to higher elevation regions, which are inhabited by numerous other threatened amphibian and reptile species. We found that, for our two skink species following Bergmann clines in body mass, trait model predictions covered a slightly larger geographic range than bioclimatic estimates. Under future warming, both models predict substantial contractions in suitable areas, potentially constraining species to mountain tops. Through the trait-based approach, we further detected potential warming-induced body mass reductions in projected suitable areas.</span></p> <p><em><span>Main conclusions</span></em></p> <p><span>We thus demonstrate how combining occurrence records with species trait information in predictive modelling can reveal complementary trends for more comprehensive climate change impact assessments. Overall, these challenges towards the persistence of CVL-endemic skink species should prompt urgent responses in national conservation management and local community engagement.</span></p>
Natural products structure database LOTUS supplemented with predicted 13C NMR chemical shifts.
<p>A structure database of natural products in SDF format was created from <a href="https://zenodo.org/record/6582124">the LOTUS database version 9</a> .</p> <p>This database is intended to facilitate the <a href="https://www.mdpi.com/1420-3049/26/3/637">dereplication of natural products</a>.</p> <p>The LOTUS database was described in <a href="https://elifesciences.org/articles/70780">this publication</a> (free download).</p> <p>File 220916_frozen_metadata.csv was downloaded from <a href="https://zenodo.org/record/7085063">the LOTUS database version 9</a> and the SMILES chains of the compounds were collected.</p> <p>The SMILES chains were translated to 2D chemical structures using python scripts relying on the <a href="https://github.com/rdkit/rdkit">RDKit</a> library.</p> <p>Each compound was associated to predicted <sup>13</sup>C NMR chemical shifts by means of an already reported <a href="https://www.mdpi.com/2673-4532/2/3/6">procedure</a> (free download).</p> <p>Each compound was also supplemented with metadata from file 220916_frozen_metadata.csv .</p> <p>Archive file acd_lotusv9.sdf.zip contains acd_lotusv9.sdf with 218,478 compound descriptions inside.</p> <p>Archive file acd_lotusv9.NMRUDB.zip is a compressed version of acd_lotusv9.NMRUDB, itself created by importation of file acd_lotusv9.sdf in an ACD/Labs database file (new with version 0.0.4).</p> <p>The description of the first compound was copied in file firstmolv9.sdf and is provided for a quick inspection of the database content.</p> <p>The title line in firstmolv9.sdf is Q43656_2, meaning that more data about this compound may be found by searching in <a href="https://www.wikidata.org/">Wikidata</a> for Q43656 and that the initial data was given by line 2 in file 220916_frozen_metadata.csv .</p> <p>Files acd_lotusv9.sdf acd_lotusv9.NMRUDB contain biological taxonomy data from file 220916_frozen_metadata.csv that were not exploited in acd_lotusv7. Sub-files dealing with a particular taxon can be easily produced now.</p> <p>Chemical shift calculations for <sup>13</sup>C nuclei using the HOSE code approach are available <a href="https://zenodo.org/record/7047760">here</a> for the compounds in acd_lotusv7.</p> <p> </p>
Figure 23: Theoretical resistance prediction of Imipenem
<p><strong>Figure 23: Theoretical resistance prediction of Imipenem</strong></p>
Figure 22: Theoretical resistance prediction of Meropenem
<p><strong>Figure 22: Theoretical resistance prediction of Meropenem </strong></p>
Figure 21: Theoretical resistance prediction of Ceftriaxone
<p><strong>Figure 21: Theoretical resistance prediction of Ceftriaxone</strong></p>
Figure 17: Theoretical Prediction of Imipenem Resistance in Pseudomonas aeruginosa (2020-2030)
<p><strong>Figure 17: Theoretical Prediction of Imipenem Resistance in Pseudomonas aeruginosa (2020-2030)</strong></p>
Figure 20: Theoretical resistance prediction of Cefepime
<p><strong>Figure 20: Theoretical resistance prediction of Cefepime </strong></p>
Figure 15: Theoretical prediction of Meropenem resistance in Acinetobacter baumanii
<p><strong>Figure 15: Theoretical prediction of Meropenem resistance in Acinetobacter baumanii</strong></p> <p> </p>
Figure 14: Theoretical Prediction of Imipenem Resistance in Acinetobacter Baumannii(2020-2030)
<p><strong>Figure 14: Theoretical Prediction of Imipenem Resistance in Acinetobacter Baumannii(2020-2030)</strong></p>
Figure 12: Theoretical Prediction of Imipenem Resistance in Klebsiella Pneumoniae (2020-2030)
<p><strong>Figure 12: Theoretical Prediction of Imipenem Resistance in Klebsiella Pneumoniae (2020-2030)</strong></p>
Figure 11: Theoretical Prediction of Meropenem Resistance in Klebsiella Pneumoniae 2020-2030
<p><strong>Figure 11: Theoretical Prediction of Meropenem Resistance in Klebsiella Pneumoniae 2020-2030</strong></p>
Figure 5: Theoretical Prediction of Vancomycin Resistance in Enterococcus faecium(2020-2030)
<p><strong>Figure 5: Theoretical Prediction of Vancomycin Resistance in Enterococcus faecium(2020-2030)</strong></p> <p> </p>
Predicting brain activity from word embeddings during natural language comprehension: Example Data
<p>Example data for the "Predicting brain activity from word embeddings during natural language comprehension" tutorial.</p>
Structure-based self-supervised learning enables ultrafast prediction of stability changes upon mutation at the protein universe scale
<p>Pythia computed all single mutations of <em>E.coli</em> proteome, high quality high quality of Swiss-Prot structures and thermophilic proteins used in analysis.</p>
Density Functional Theory and Machine Learning for Electrochemical Square-Scheme Prediction: An Application to Quinone-type Molecules Relevant to Redox Flow Batteries
<p>The uploaded data contains (i) "<strong>01_Data</strong>" optimized molecular structure in XYZ format and the primary attributes and SMILES, (ii) "<strong>02_Datasets</strong>" datasets used in the publication, and (iv) "<strong>03_pynb_script</strong>" a Jupyter-Notebook. The <strong>01_Data </strong>directory contains more than 8000 subdirectories. Each is for a molecule that undergoes a two-proton two-electron transfer reaction. In each subdirectory, one finds the following files:</p> <p>(1) directories named corresponding to the ones in Figure 1 of the paper. Inside each, there are geometries and properties in XYZ and CSV format, respectively.</p> <p>(2)<strong> "freeEnergy.dat" </strong>contains the free energy of different states.</p> <p>(3) <strong>"schemesquare.dat" </strong>has the parameters of the electrochemical scheme of square representation.</p> <p>├── A<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── A1-<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── A2-<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH1+<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH1-<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH2<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH21+<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH22+<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── <strong>freeEnergy.dat</strong><br> └── <strong>schemesquare.dat</strong><br> ******************************************************<br> The new version (v1.1) contains some updates around:<br> (i) The DFT calculations workflow in a folder called "<strong>04_workflow_of_DFT</strong>"</p> <p> The Gaussian input files have been explained in the "README" file.</p> <p>(ii) The Python scripts for data extraction have been added and can be found in "<strong>05_how_to_extracted_data</strong>"</p> <p>(iii) We explained how to compute the Purbaix diagram in great detail "<strong>06_how_to_compute_Pourbaix_diagram</strong>/"</p> <p>All these changes/improvements were applied/made following the Referee of Digital Discovery Journal. Here, we would like to thank him/her.</p>
DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
<p>Database and models for deepARG: </p> <p>see this link for details: <a href="https://bitbucket.org/gusphdproj/deeparg-ss/src/fbe063e24cf79d83a88499353aa15a85b58a300e/?at=master">gusphdproj / deeparg-ss — Bitbucket</a></p>
SAMC Model Inputs from: Predicting dispersal and conflict risk for wolf recolonization in Colorado
<p>The colonization of suitable yet unoccupied habitat due to natural dispersal or human introduction can benefit recovery of threatened species. Predicting habitat suitability and conflict potential of colonization areas can facilitate conservation planning.</p> <p>Planning for reintroduction of gray wolves (Canis lupus) to the U.S. state of Colorado is underway. Assessing which occupancy sites minimize the likelihood of human-wolf conflict during dispersal events and seasonal movements is critical to the success of this initiative.</p> <p>We used a spatial absorbing Markov chain (SAMC) framework, which extends random walk theory and probabilistically accounts for both movement behavior and mortality risk, to compare the viability of potential occupancy sites (public lands >500 km2 to minimally meet wolf pack range area). The SAMC framework produced spatially explicit predictions of wolf dispersal, philopatry, and conflict risk ahead of recolonization prior to reintroduction efforts. Our SAMC model included: 1) movement resistance based on terrain, roads, and housing density; 2) mortality risk and potential conflict (absorption) based on livestock presence, social tolerance, land ownership, and state boundaries; and 3) site fidelity based on habitat quality. Using this model, we compared 21 public land units by deriving predictions of: A) relative survival time outside each site, B) intensity of use and retention time within each site, and C) the probability of use on adjacent public lands. We also predicted and mapped potential conflict hotspots associated with each site.</p> <p>Among the units assessed, a complex of USFS Wilderness areas near Aspen, chiefly the Hunter-Fryingpan and Collegiate Peaks Wilderness areas, had the best overall rankings when comparing predictions of each metric. The area balances high-quality, well-connected habitat with relatively low livestock density and high social tolerance. </p> <p>Synthesis and applications: Our findings highlight the utility of the SAMC framework for assessing colonization areas and the capacity to identify locations for effective proactive management, especially of conflict-prone species. The flexibility of the SAMC framework enables predicting likely areas of philopatry and human-wildlife conflict using spatially-explicit metrics which can improve the success of conservation translocations and management of species with changing geographic extents.</p>
Microstructure-based prediction of hydrodynamic forces in stationary particle assemblies
<p>#Data linked to the manuscript: <br> Microstructure-based prediction of hydrodynamic forces in stationary particle assemblies</p> <p>#Corresponding author:<br> Berend.van.Wachem@gmail.com</p> <p>#Acknowleddgements</p> <p>This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 422037413 - TRR 287. This funding is gratefully acknowledged.</p> <p>#Files<br> - Minkowski tensors and Lagrangian forces computed for all simulations stored in the .csv files<br> - Python script to read Minkowski tensors and Lagrangian forces stored in the .csv files<br> - One flow configuration results with hdf fields files at mean flow Reynolds number 100 and solid volume fraction 0.5.</p> <p>#SimulationResult</p> <p>Note to open the simulation results:</p> <p>The Eulerian fields and Lagrangian particles information are stored in HDF5 files.<br> The relevant fileds are:<br> #Eulerian:<br> - Velocity vector<br> - Pressure<br> - AMR Level<br> - Other information relevant for the coupling between the IBM particles and<br> the fluid<br> <br> #Lagrangian:<br> - Particles coordinates<br> - Particles diameter<br> - Particles hydrodynamic force components<br> - Particles torque components<br> - Particles velocity components<br> - Particles rotational velocity components<br> <br> A .xmf file is provided to read the simulation results in paraview.<br> Only the initial state and the converged state of the simulation are provided due to storage limits.</p>
Data set used in article: Model Predictive Control for Wake Redirection in Wind Farms: a Koopman Dynamic Mode Decomposition Approach
<p>Step-wise yaw deflection in 2 wind turbines in SOWFA. More information in the article.</p>
Personalized Audio Quality Preference Prediction Dataset
<p>Dataset for the following paper. Please cite this paper if our dataset is used in your research.</p> <p>Chung-Che Wang, Yu-Chun Lin, Yu-Teng Hsu, and Jyh-Shing Roger Jang, "Personalized Audio Quality Preference Prediction", APSIPA ASC 2023.</p> <p>Here is a brief description of our dataset. For more details, please see our paper.</p> <p>This dataset is designed for personalized audio quality preference prediction. It includes recordings from 5 different mobile phones playing 7 distinct song segments at 2 volume settings. The played audio is recorded by using a binaural microphone and a computer interface. For each volume type, 70 pairs of recorded audio files are formed, where each of the two audio files in one pair corresponds to same song segment played by different mobile phones. For each volume type, each subject is asked to compare at least 14 of the 70 pairs. Subject information, which includes age, gender, and headphone/earphone specifications such as impedance, frequency response range, and sensitivity, are also collected.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.