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513 results for “prone”
Data-Error Scaling Laws in Machine Learning on Combinatorial Mutation-prone Sets: Proteins and Small Molecules
<div> </div> <h3>Data</h3> <p> This folder contains the raw data used during this work. `out_seq_total.txt` contains information on the sequences used (mutations, number of mutations, etc.). `output_energies_total.txt` contains the response variables, which include: a) unrelaxed EvoEF energies (peptides); b) relaxed EvoEF energies (peptides, `*_repaired.txt`); and c) solvation energies (molecules). 3D structures are provided in `.xyz` format in the subfolder `XYZ` (molecules).</p> <div> <div><strong>GB1 dataset</strong></div> <br> <div>The GB1 dataset was <strong>not</strong> generated by us (https://doi.org/10.48550/arXiv.2405.05167). If you use the GB1 dataset, please cite the original paper:</div> <br> <div>Wu, N. C., Dai, L., Olson, C. A., Lloyd-Smith, J. O., & Sun, R. (2016). <em>Adaptation in protein fitness landscapes is facilitated by indirect paths. </em><strong>eLife</strong>, 5:e16965. doi:10.7554/eLife.16965</div> </div> <h3>Results</h3> <p>This folder contains the results (outputs) of the ML models trained using the provided scripts (see github repository). Such results are incuded in the form of `.npy` files. To load the files please include the option `allow_pickle=True`.</p> <p> Each `.npy` file contains the following keys:<br> * `initial_parameters`: script inputs.<br> * `d_encoder`: encoder used (not always included).<br> * `ns_train`: number of training points used for the LCs (rounded, integers).<br> * `ns_train_float`: number of training points used for the LCs (not rounded, float).<br> * `ns_train_norm`: number of training points used for the LCs (normalized, float).<br> * `res`: test MAEs.<br> * `res_tot`: (train,validation,test) MAEs.<br> * `res_tot_mut`: (train,validation,test) MAEs sorted by mutation number.<br> * `l_opt`: optimal kernel length used during the test.<br> * `ls`: kernel lengths used for grid search.<br> * `idx_seeds`: indices used to reshuffle the data. If one want to rebild the initial order use `np.argsort(idx_seeds)`.<br> * `alpha_opt`: optimal regression parameters used to calculate the test error. To sort the data use `alpha_opt[i][ii][np.argsort(idx_seeds[ii,0:arg_train_max].astype(int)[:ns_train[i]]`. Where `i` is the replicate number (0-99) and ii is the idex in the LC.<br> * `valid_errs`: validation error (MAE) calculated for each point in the hyperparameter (kernel scale) optimisation.<br> * `test_errs`: test error (MAE) calculated for each point in the hyperparameter (kernel scale) optimisation.</p>
Supplementary material 1 from: Galán Díaz J, de la Riva EG, Martín-Forés I, Vilà M (2023) Which features at home make a plant prone to become invasive? NeoBiota 86: 1-20. https://doi.org/10.3897/neobiota.86.104039
Species list, references accessed during bibliographic research, phylogenetic inference used in the analyses, PCA of climatic variables, and results of linear regressions
Data from: Long term monitoring reveals the importance of large, long unburnt areas and smaller fires in moderating mammal declines in fire-prone savanna of northern Australia
<p>Biodiversity loss is often attributable to multiple interacting pressures that are moderated across environmental gradients. These processes contribute to complex responses that are challenging to interpret and understand. Well-designed and well-implemented monitoring can play a vital role in this but is rarely undertaken.</p> <p>Mounting evidence suggests that current fire regimes across savanna ecosystems have contributed to the decline of a range of biota. Hence, contemporary fire regimes are at odds with conservation goals.</p> <p>Using an extensive spatio-temporal monitoring dataset from three large National Parks in northern Australia, we applied generalised linear mixed models to examine: 1) trends in mammal richness and abundance through time and how these vary across environmental gradients such as productivity or landscape position (e.g., terrain ruggedness); and 2) how fire, a potential driver, is moderated by environmental gradients.</p> <p>Across 24-years, major declines in mammal richness and abundance were observed with greater reductions in less rugged and less mesic habitats. Patterns of decline were related to multiple aspects of fire regimes, but not changes in vegetation structure and composition, or the arrival of Cane Toads <em>Rhinella marina</em> and concomitant poisoning of predatory animals during consumption. More pronounced declines occurred in sites that were exposed to larger fires, had less long unburnt (5 years without fire) vegetation, and were more distant from large, long unburnt patches.</p> <p>Relationships between mammal persistence and fire varied among vegetation communities, with the strongest fire effects observed in lowland woodland – relatively drier with few barriers to fire spread – where the availability of long unburnt areas moderated declines more than in other vegetation communities.</p> <p><em>Synthesis and applications</em>. Current fire regimes are contributing to mammal declines in northern Australian savanna. Collectively, our results highlight: 1) the value of long term monitoring, and importance of considering landscape position when assessing faunal responses to landscape-level perturbations like fire; 2) that significant improvements in fire regimes are required to ameliorate mammal declines; and 3) the need to shift management focus from retaining small, short-lived unburnt patches towards preserving relatively large contiguous areas of long unburnt habitat, particularly in less rugged lowland and sandstone woodlands.</p>
Trial Comparing Prone and Supine Intensity-modulated Radiotherapy (IMRT) After Breast-conserving Surgery in Patients With Large Breast Volume at High Risk for Skin Toxicity and Fibrosis
ClinicalTrials.gov study NCT00887523. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Prone Position in infantS/Children With Acute Respiratory Distress Syndrome
ClinicalTrials.gov study NCT06020404. IPD Sharing: NO. Countries: 1. Publications: 15.
Preload Dependence During Prone Position In ARDS Patients
ClinicalTrials.gov study NCT01965574. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Effect of Prone Position Use Ventilator-Associated Pneumonia in Intensive Care Patients
ClinicalTrials.gov study NCT05760716. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Fluid Responsiveness in Prone Position
ClinicalTrials.gov study NCT05401526. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Artificial Pancreas With Carbohydrate Suggestion for Patients With Type 1 Diabetes Prone to Hypoglycemia
ClinicalTrials.gov study NCT05628662. IPD Sharing: NO. Countries: 1. Publications: 3.
A Trial Comparing High-flow Nasal Oxygen With Standard Management for Conscious Sedation During Endoscopic Retrograde Cholangiopancreatography in Prone Position
ClinicalTrials.gov study NCT03872674. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Early Use of Prone Position in ECMO for Severe ARDS
ClinicalTrials.gov study NCT04139733. IPD Sharing: NO. Countries: 1. Publications: 15.
Awake Prone Positioning for Non-intubated COVID-19 Patients
ClinicalTrials.gov study NCT04760561. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Duration of Prone Position in the Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19).
ClinicalTrials.gov study NCT05012267. IPD Sharing: NO. Countries: 1. Publications: 14.
The Prone Breast Radiation Therapy Trial
ClinicalTrials.gov study NCT01815476. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Prone Positioning in Pediatric Acute Lung Injury
ClinicalTrials.gov study NCT00133614. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Prone-Supine Study II: The Effect of Prone Positioning for Patients Affected by Acute Respiratory Distress Syndrome
ClinicalTrials.gov study NCT00159939. IPD Sharing: Not stated. Countries: 1. Publications: 9.
The Prone Position in Covid-19 Affected Patients
ClinicalTrials.gov study NCT04365959. IPD Sharing: Not stated. Countries: 1. Publications: 13.
Effect of Prone Position onV/Q Matching in Non-intubated Patients With COVID-19
ClinicalTrials.gov study NCT04754113. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Awake Prone Positioning for COVID-19 Acute Hypoxaemic Respiratory Failure
ClinicalTrials.gov study NCT05866289. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
Comparison of Myocardial Injury After Noncardiac Surgery (MINS) Incidence in Supine vs. Prone Positioning During Percutaneous Nephrolithotomy (PNL)
ClinicalTrials.gov study NCT06944301. IPD Sharing: NO. Countries: 0. Publications: 7.
ScienceDex guides
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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)
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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.