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150 results for “hot spots”
MCMC samples of the posterior distribution from the paper "TESS spots a mini-neptune interior to a hot saturn in the TOI-2000 system"
<p>This dataset contains the Hamiltonian Monte Carlo samples of the posterior distribution of the planetary and stellar parameters from the paper "TESS Spots a Mini-Neptune Interior to a Hot Saturn in the TOI-2000 System". The file format, NetCDF, is based on HDF5, and is meant to be read by the Python package <a href="https://python.arviz.org/en/latest/">ArviZ</a>.</p> <p>Hot jupiters (<em>P</em> < 10 d, <em>M</em> > 60 M<sub>⊕</sub>) are almost always found alone around their stars, but four out of hundreds known have inner companion planets. These rare companions allow us to constrain the hot jupiter's formation history by ruling out high-eccentricity tidal migration. Less is known about inner companions to hot Saturn-mass planets. We report here the discovery of the TOI-2000 system, which features a hot Saturn-mass planet with a smaller inner companion. The mini-neptune TOI-2000 b (2.70 ± 0.15 R<sub>⊕</sub>, 11.0 ± 2.4 M<sub>⊕</sub>) is in a 3.10-day orbit, and the hot saturn TOI-2000 c (<span class="math-tex">\(8.14^{+0.31}_{-0.30}\)</span> R<sub>⊕</sub>, <span class="math-tex">\(81.7^{+4.7}_{-4.6}\)</span> M<sub>⊕</sub>) is in a 9.13-day orbit. Both planets transit their host star TOI-2000 (TIC 371188886, <em>V</em> = 10.98, <em>TESS</em> magnitude = 10.36), a metal-rich ([Fe/H] = <span class="math-tex">\(0.439^{+0.041}_{-0.043}\)</span>) G dwarf 174 pc away. <em>TESS</em> observed the two planets in sectors 9–11 and 36–38, and we followed up with ground-based photometry, spectroscopy, and speckle imaging. Radial velocities from HARPS allowed us to confirm both planets by direct mass measurement. In addition, we demonstrate constraining planetary and stellar parameters with MIST stellar evolutionary tracks through Hamiltonian Monte Carlo under the PyMC framework, achieving higher sampling efficiency and shorter run time compared to traditional Markov chain Monte Carlo. Having the brightest host star in the <em>V</em> band among similar systems, TOI-2000 b and c are superb candidates for atmospheric characterization by the JWST, which can potentially distinguish whether they formed together or TOI-2000 c swept along material during migration to form TOI-2000 b.</p>
Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions - data
<p>Data set pertaining to the manuscript "Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions", accepted for publication in Nature Chemistry.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data') if applicable.<br> 2. As-measured data ('raw').</p> <p>Files with extension .csv are comma-separated ascii-files, designed to be opened with a spreadsheet programme.</p> <p><br> The following files are provided:</p> <p>Photoemission data pertaining to ETMD measurements:<br> alcl3-K-etmd.h5 (ETMD after Al K-shell photoionization)<br> alcl3-L23-etmd.h5 (ETMD after Al L-shell photoionization)</p> <p>Calculated energies of the ETMD final states after 1s ionization. The energies were calculated at the CAS-CI/cc-pVDZ level. The states were shifted so that the lowest-energy state corresponds to the LC-ωPBE/aug-cc-pVTZ and aug-cc-pCVTZ value obtained in a polarizable continuum:<br> Dataset_ETMD_after_1s_ionization.csv<br> Dataset_ETMD_after_2p_ionization.csv</p> <p>Geometrical coordinates of the clusters that were used for energy calculation:<br> clusters.dat<br> clusters_small.dat</p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p> <p> </p> <p>Version history:</p> <p>v3 - Al L2,3 data: Orientation of the analyser hemisphere corrected. Direction of the linear polarization vector added. All other data unchanged.<br> v2 - cluster coordinates added, all other data unchanged.<br> v1 - initial upload.</p>
GIS Protocol for Multy-Scale Emerging Hot Spot Analysis
<p>This GIS protocol is primarily intended as supplementary material to the article (Štular et al., 2022). The article contains important contextual information about its intended use. In short, this GIS protocol was developed for the purposes of archaeological regional analysis of spatial data. The data are provided elsewhere in spreadsheet format (Štular et al., 2021). Data in GIS format are included in this repository. The GIS protocol can be used with any relevant data for any purpose as long as the data format matches the format of the included data.</p> <p>Includes GIS protocol (textual description) and GIS data in *.shp format.</p>
Sign-specific stimulation "hot" and "cold" spots in Parkinson's disease validated with machine learning
<p><strong>Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become a standard therapy for Parkinson’s disease (PD). Despite extensive experience, however, the precise target of optimal stimulation and the relationship between site of stimulation and alleviation of individual signs remains unclear. We examined whether machine learning could predict the benefits in specific parkinsonian signs when informed by precise locations of stimulation.</strong></p> <p> </p> <p><strong>We studied 275 PD patients who underwent STN-DBS between 2003 and 2018. We selected pre-DBS and best available post-DBS scores from motor items of the Unified Parkinson's Disease Rating Scale (UPDRS-III) to discern sign-specific changes attributable to DBS. Volumes of tissue activated (VTAs) were computed and weighted by i) tremor, ii) rigidity, iii) bradykinesia, and iv) axial signs changes. Then, sign-specific sites of optimal (“hot spots”) and suboptimal efficacy (“cold spots”) were defined. These areas were subsequently validated using machine learning prediction of sign-specific outcomes with in-sample and out-of-sample data (n=51 STN-DBS patients from another institution).</strong></p> <p><strong> </strong></p> <p><strong>Tremor and rigidity hot spots were largely located outside and dorsolateral to STN whereas hot spots for bradykinesia and axial signs had larger overlap with STN. Using VTA overlap with sign-specific hot and cold spots, support vector machine (SVM) classified patients into quartiles of efficacy with ≥92% accuracy. The accuracy remained high (68-98%) when only considering VTA overlap with hot spots but was markedly lower (41-72%) when only using cold spots. The model also performed poorly (44-48%) when using only stimulation voltage, irrespective of stimulation location. Out-of-sample validation accuracy was ≥96% when using VTA overlap with the sign-specific hot and cold spots.</strong></p> <p><br> <strong>In two independent datasets, distinct brain areas could predict sign-specific clinical changes in PD patients with STN-DBS. With future prospective validation, these findings could individualize stimulation delivery to optimize quality of life improvement. </strong></p> <p><strong>Hot and cold spots for each sign are publicly available as binary labels in NIfTI format. </strong></p>
Figs 6–9. 6 in Deepor Beel - A Ramsar Site Of India: An Interesting Hot-Spot With Its Rich Rotifera Biodiversity
Figs 6–9. 6 = Brachionus dichotomus reductus KOSTE et SHIEL, dorsal view, 7 = Notommata spinata KOSTE et SHIEL, dorsal view, 8 = Keratella edmondsoni AHLSTROM, dorsal view, 9 = Lecane blachei BERZINS, dorsal view
Fig. 1. A in Deepor Beel - A Ramsar Site Of India: An Interesting Hot-Spot With Its Rich Rotifera Biodiversity
Fig. 1. A = map of indicating location Deepor Beel, B = map showing sampling sites (2002–2003 and 2008–2010)
Figs 2–5. 2 in Deepor Beel - A Ramsar Site Of India: An Interesting Hot-Spot With Its Rich Rotifera Biodiversity
Figs 2–5. 2 = Keratella tecta (GOSSE), dorsal view, 3 = Trichocerca bidens (LUCKS), lateral view, 4 = Trichocerca tigris (MÜLLER), lateral view, 5 = Lecane paxiana HAUER, dorsal view
A Google Earth Engine code to analyze e visualize land surface temperature and thermal hot-spot patterns: a Rome (Italy) case study
<p>Link to the <strong>Google Earth Engine </strong>(GEE) code: <strong>https://code.earthengine.google.com/cc3ea6593574e321acd7b68c975a9608</strong></p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, 30 m horizontal resolution, from Landsat-8 remote sensing data, years 2017-2022)</li> <li><strong>The surface thermal hot-spot pattern </strong>(raster data,30 m horizontal resolution) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS tool. </li> </ol> <p>Here attached the .txt file from the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
Fig. 3 in Urogenital schistosomiasis transmission on Unguja Island, Zanzibar: characterisation of persistent hot-spots
Fig. 3 Number of human-water contact sites in persistent hot-spot and low-prevalence shehias in Unguja
Fig. 2 in Urogenital schistosomiasis transmission on Unguja Island, Zanzibar: characterisation of persistent hot-spots
Fig. 2 Map of Unguja Island, Zanzibar, showing the location of selected persistent hot-spot and low-prevalence shehias
Fig. 1 in Urogenital schistosomiasis transmission on Unguja Island, Zanzibar: characterisation of persistent hot-spots
Fig. 1 Flowchart showing the inclusion procedure for persistent hot-spot and low-prevalence shehias in Unguja
Figure 4 in Modelling hot spot areas for the invasive alien plant Elodea nuttallii in the EU
Figure 4. The occurrence with data thinned by extraction from subset 1 (A) and subset 2 (B) of occurrence data. The combined output of both is also presented (C). Areas with low uncertainty and above the habitat suitability threshold (i.e. priority areas) are marked in red while areas with high uncertainty are marked in yellow. The grid size of all 3 maps is 1 km2.
Figure 2 in Modelling hot spot areas for the invasive alien plant Elodea nuttallii in the EU
Figure 2. The model performance (regularized training gain) of the MaxEnt model generated using subset 2 of the occurrence data when the variable in question is omitted or used in isolation, compared to the performance of the model when all variables are used.
Figure 6. The occurrence with data from subset 1 in Modelling hot spot areas for the invasive alien plant Elodea nuttallii in the EU
Figure 6. The occurrence with data from subset 1 (A) and subset 3 (B). The combined output of both is also presented (C). Areas with low uncertainty and above the habitat suitability threshold (i.e. priority areas) are marked in red while areas with high uncertainty are marked in yellow. The grid size of all 3 maps is 1 km2.
Figure 7 in Modelling hot spot areas for the invasive alien plant Elodea nuttallii in the EU
Figure 7. "Alert areas", i.e. areas with high probability to be invaded by Elodea nuttallii that are at least 100 km away from any known occurrence point of the species, divided by being inside or outside Natura 2000 site, generated using subset 1 and subset 3 of occurrence data.
Figure 5 in Modelling hot spot areas for the invasive alien plant Elodea nuttallii in the EU
Figure 5. "Alert areas", i.e. areas with high probability to be invaded by Elodea nuttallii that are at least 100 km away from any known occurrence point of the species, divided by being inside or outside Natura 2000 site, generated using subset 1 and subset 2 of occurrence data.
Figs 3–11. Ants from Buxa Tiger Reserve. 3–5 in THE BUXA TIGER RESERVE AS A 'HOT SPOT' OF ANT DIVERSITY IN WEST BENGAL STATE (HYMENOPTERA: FORMICIDAE)
Figs 3–11. Ants from Buxa Tiger Reserve. 3–5 – Calyptomyrmex friederikae Kutter, 1976; 6–8 – Dolichoderus brevis Santschi, 1920; 9–11 – Tetramorium curtulum Emery, 1895. (3, 6, 9 – body, dorsal view; 4, 7, 10 – body, lateral view; 5, 8, 11 – head, frontal view).
Fig. 2 in The Latitudinal Distribution Of Sphingid Species Richness In Continental Southeast Asia: What Causes The Biodiversity 'Hot Spot' In Northern Thailand?
Fig. 2. Estimated local species richness (ACE) from nine quantitative light trapping sites. Fisher's α, an alternative measure of local diversity (not shown), is lowest at the Malaysian sites (α = 7–13) and highest at a montane site in Northwestern Thailand (α = 30), whereas the Vietnam sample and other Thai sites score intermediately (α = 11–21).
Fig. 1. A in The Latitudinal Distribution Of Sphingid Species Richness In Continental Southeast Asia: What Causes The Biodiversity 'Hot Spot' In Northern Thailand?
Fig. 1. A, Estimated species richness (simplified from Beck & Kitching, 2004); B, Sampling intensity (kernels of original distribution records, smoothed; software by Hooge et al., 1999); C, altitudinal zonation (from digital elevation model, http://www.ngdc.noaa.gov/mgg/global/ seltopo.html). Elevation classes are [m]: 0–500 (white), 501–1000, 1001–1500, 1501–2000,>2001 (black); D, Landscape types (simplified from remote sensing data, http://www-gvm.jrc.it/glc2000). Agricultural and highly disturbed areas are printed in light grey, mosaic and bush in dark grey and closed forests in black.
Fig. 3 in The Latitudinal Distribution Of Sphingid Species Richness In Continental Southeast Asia: What Causes The Biodiversity 'Hot Spot' In Northern Thailand?
Fig. 3. Abundance (number of species, y-axis) and the latitudinal mean of their range in four regions. Black bars indicate the approximate latitudinal extend of the regions under investigation.
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)
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.