Find research datasets worth reusing

Fig. 6 in A New Species of the Eliurus majori Complex (Rodentia: Muroidea: Nesomyidae) from South-central Madagascar, with Remarks on Emergent Species Groupings in the Genus Eliurus

Fig. 6. Phenogram produced from clustering (UPGMA) of Mahalanobis' distances derived from discriminant function analysis of 13 OTUs representing four species of Eliurus (see fig. 5).

Fig. 5 in A New Species of the Eliurus majori Complex (Rodentia: Muroidea: Nesomyidae) from South-central Madagascar, with Remarks on Emergent Species Groupings in the Genus Eliurus

Fig. 5. Results of discriminant function analysis performed on 18 log-transformed craniodental variables, as measured on 130 specimens representing 13 OTUs of Eliurus (see Materials and Methods). Top, projection of individual scores onto the first and second canonical variates extracted. Bottom, projection of

Fig. 4 in A New Species of the Eliurus majori Complex (Rodentia: Muroidea: Nesomyidae) from South-central Madagascar, with Remarks on Emergent Species Groupings in the Genus Eliurus

Fig. 4. Scatter plot of length of the upper molar row (LM1-3) versus greatest length of skull (ONL) for intact specimens of E. antsingy, E. danieli, E. majori, and E. penicillatus. Note that examples of the latter three species possess longer toothrows in relation to cranial size compared with those of E. antsingy.

Fig. 3 in A New Species of the Eliurus majori Complex (Rodentia: Muroidea: Nesomyidae) from South-central Madagascar, with Remarks on Emergent Species Groupings in the Genus Eliurus

Fig. 3. Lateral view (ca. 1.75X) of adult crania of species composing the Eliurus majori species group as recognized herein (same specimens as depicted in fig. 2): top, E. danieli new species; middle, E. majori; and bottom, E. penicillatus.

Fig. 1 in A New Species of the Eliurus majori Complex (Rodentia: Muroidea: Nesomyidae) from South-central Madagascar, with Remarks on Emergent Species Groupings in the Genus Eliurus

Fig. 1. Photograph of live Eliurus danieli (FMNH 175933), a young adult male from Andranohavo (Canyon des Rats) in the PN de l'Isalo. The new species is characterized by a bright white caudal tuft that is accentuated by the concentration of intensely black hairs along the middle portion of the tail. Photograph taken by Harald Schütz.

Fig. 2 in A New Species of the Eliurus majori Complex (Rodentia: Muroidea: Nesomyidae) from South-central Madagascar, with Remarks on Emergent Species Groupings in the Genus Eliurus

Fig. 2. Dorsal (upper row) and ventral (lower row) views (ca. 1.75X) of adult crania of species composing the Eliurus majori species group as recognized herein (see Discussion): left pair, E. penicillatus Thomas (USNM 49672; Fianarantsoa Province, Ampitambe, ca. 900 m; ONL 5 35.6 mm); middle pair, E. majori Thomas (FMNH 151663; Fianarantsoa Province, PN d'Andringitra, 40 km S Ambalavao, along Volotsangana River, 1210 m; ONL 5 38.1 mm); and right pair, E. danieli new species (FMNH 175934, type specimen; Fianarantsoa Province, Parc National de l'Isalo, 28 km SE Berenty-Betsileo, along Sahanafa River near foot of Bevato Mountain, 650 m; ONL 5 40.6 mm).

Dataset for the paper "Understanding the emergence of drawing behaviour with age: a multi-metric analysis"

<p>Dataset for the paper "Understanding the emergence of drawing behaviour with age: a multi-metric analysis"</p> <p>We need specific and objective methods to analyse the temporal changes of drawing in children, especially those too young to communicate via verbalisations. We asked 134 children, ranging from three to ten years old, and 38 adults to draw on a tablet under two conditions: free drawing and self-portrait. We then used seven metrics from three categories (spatial, temporal, and colorimetric) in a principal component analysis (PCA). Three dimensions of the PCA explained 77% of the variance in the drawings. We named these dimensions as diversity, sequentiality, and efficiency, which provided a mechanism for better understanding the intentionality and representativeness behind drawing. Gender had no effect, but age influenced all three dimensions differently. This multi-metric approach is a powerful tool for investigating the ontogenetic development of drawing, and could be used to understand the evolution of this behaviour by applying it to the study of primates, or to reveal drawing characteristics in people with autism and depression or those from different cultures.</p>

Continent-wide recent emergence of a global pathogen in African amphibians

<p>These datasets are associated with the study entitled, "Continent-wide recent emergence of a global pathogen in African amphibians." In this study we describe the historical and recent biogeographical spread of a fungal pathogen of amphibians, <em>Batrachochytrium dendrobatidis</em> (<em>Bd</em>) and assess its risk to amphibians across the continent of Africa.</p> <p>The larger combined file, "AfricaBd_CombinedFile_LitReview_BdMaps_GhoseData.xlsx", contains <em>Bd</em> occurrence records processed by the authors of the study (N=4,623) and previously published records (N=12,297). Of the previously published records, 12,234 records came from studies reporting both <em>Bd</em>-negative and <em>Bd</em>-positive records (i.e. prevalence) that we used along with our data (N=4,623) to assess emergence of <em>Bd</em> in African amphibians.</p> <p>The file "AfricaBd_Ghosedata_Hirschfelddata_ZimkusCameroondata.xlsx" contains all georeferenced <em>Bd </em>records collected by the authors of this study, data from Hirschfeld et al. 2016, and data for Cameroon from Zimkus et al. 2020. This file includes more metadata including amphibian species tested, and includes infection intensity data detected by qPCR for <em>Bd</em>-positive records.</p> <p>Using these datasets we document a pattern of <em>Bd </em>emergence beginning largely at the turn of the century (the year 2000). From 1852–1999, we found low <em>Bd</em> prevalence (3.2% overall) and limited geographic spread, but after 2000 we documented a sharp increase in prevalence (18.7% overall), wider geographic spread, and our genotyping revealed multiple <em>Bd </em>lineages with indications of hybridization. Our habitat suitability model showed that <em>Bd</em> risk to amphibians was highest in much of eastern, central, and western Africa. Our study documents a largely overlooked yet significant increase in a fungal pathogen that could pose a threat to amphibians across an entire continent. We emphasize the need to bridge historical and contemporary datasets to better describe and predict host-pathogen dynamics over larger temporal scales.</p> <p> </p>

Figure 3 in The emerging vertebrate model species for neurophysiological studies is Danionella cerebrum, new species (Teleostei: Cyprinidae)

Figure 3. RaxmltreeforthecoxIgeneofthefivespeciesof Danionella (upperlef) fromdifferentsampling locations (data set 1) and map (upper right) showing type localities (large circles) and locations of additional samples (small circles). Note that both species, D. cerebrum and D. translucida, co-occur at each other's type locality. Roadside canal at Hmawbi (lower lef), type locality of D. cerebrum, and Daikme Chaung (lower right), type locality of D. translucida, illustrating the typical turbid streams in which these two species occur. Map created with QGIS version 3.8.3-Zanzibar (http://www.qgis.org).

Figure 1 in The emerging vertebrate model species for neurophysiological studies is Danionella cerebrum, new species (Teleostei: Cyprinidae)

Figure 1. Danionellacerebrum. (a) male (ca. 10 mmSL) and (b) female (ca. 12 mmSL) inlife, notpreserved; note yellowish chromatophores dorsally on head, melanophores scattered in rows on body in both sexes, and eggs covered by large melanophores in female; (c) MTD 39985, paratype, 10.4 mm SL, male and (d) BMNH 2021.8.30.1, holotype, 12.6 mm SL, female (below), white arrows mark position of vent, which is shifed anteriorly to the pelvic fins in males; (e) Weberian apparatus in male, MTD 39992, paratype, 11.7 mm SL and (f) female, MTD 39992, paratype, 11.8 mm SL, in lateral view; the same in male (g) and female (h) in frontal view; (e) and (g) black arrowhead marks connection between lateral process and outer arm of os suspensorium, star marks connecting flanges between inner and outer arms of os suspensorium and red arrow marks posterior extension of inner arm of os suspensorium covering swimbladder dorsally. Abbreviations: cl, claustrum; dc, drumming cartilage; ios, inner arm of os suspensorium; nc, neural complex; oos, outer arm of os suspensorium; r, rib; sc, scaphium; sw, swimbladder.

Figure 4. Timetreeofthefivespeciesof Danionella illustratingrelationshipsof D in The emerging vertebrate model species for neurophysiological studies is Danionella cerebrum, new species (Teleostei: Cyprinidae)

Figure 4. Timetreeofthefivespeciesof Danionella illustratingrelationshipsof D. cerebrum (lef), differencesin external appearance of preserved specimens (middle), and sexual dimorphisms in the skeleton of the Weberian apparatus (right, double column) in cleared and double stained specimens. Preserved specimens (middle) from top: Danionelladracula, BMNH 2008.1.1.1, male, holotype, BMNH.1.1.2–99, female, paratype, D. priapus, BMNH 2009.9.9.1, male, holotype, BMNH 2009.9.9.2–37, paratype, female; D. translucida NRM 32235, male and female paratypes; D. mirifica, USNM 372848, male and female paratypes; D. cerebrum, MTD 39985, male, paratype, BMNH 2021.8.30.1, female, holotype. Cleared and stained specimens (scale bar 0.1 mm), males, lef column from top: D. dracula, BMNH 2008.1.1.100–119, 16.2 mm; D. priapus, BMNH 2009.9.9.38–43, 16.5 mm; D. translucida, MTD 39992, 9.8 mm; D. mirifica, USNM 372848, 13.2 mm; D. cerebrum, MTD 39986, 11.7 mm; black arrowheads mark connection between lateral process and outer arm of os suspensorium, black stars mark connecting flanges between inner and outer arms of os suspensorium, and red arrows marks posterior extension of inner arm of os suspensorium covering swimbladder dorsally. Females, right column from top: D. dracula, BMNH 2008.1.1.100–119, 14.7 mm; D. priapus, BMNH 2009.9.9.38–43, 14.8 mm; D. translucida, MTD 39992, 11.2 mm; D. mirifica, USNM 372848, 13.2 mm, D. cerebrum, MTD 39986, 11.7 mm.

opencc-by-4.0Dec 2018View details →

Use and sharing of raw data in the Journal Citation Reports' Emergency Medicine Category: Metrics and Journals including supplementary material classification sorted by quartile of the JCR emergency medicine category.

<p>Raw data belonged to the study of use and sharing of raw research data in the Journal Citation Reports' Emergency Medicine Category.</p>

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Code and data for: Emergence of spatially structured populations by area-concentrated search

<p>The idea that populations are spatially structured has become a very powerful concept in ecology, raising interest in many research areas. However, despite dispersal being a core component of the concept, it typically does not consider the movement behavior underlying any dispersal. Using individual-based simulations in continuous space, we investigate the emergence of a spatially structured population in landscapes with spatially heterogeneous resource distribution and with organisms following simple area-concentrated search (ACS); individuals do not, however, perceive or respond to any habitat attributes per se but only to their foraging success. We investigated effects of different resource clustering patterns in landscapes (single large cluster vs. many small clusters) and different resource densities on spatial structure of populations and movement between resource clusters of individuals. As the results, we found that foraging success increased with increasing resource density and decreasing number of resource clusters. In a wide parameter space, the system exhibited attributes of a spatially structured population with individuals concentrated in areas of high resource density, searching within areas of resources, and 'dispersing' in a straight line between resource patches. 'Emigration' was more likely from patches that were small or of low quality (low resource density), but we observed an interaction effect between these two parameters. With the ACS implemented, individuals tended to move deeper into a resource cluster in scenarios with moderate resource density than in scenarios with high resource density. 'Looping' from patches was more likely if patches were large and of high quality. Our simulations demonstrate that spatial structure in populations may emerge if critical resources are heterogeneously distributed and if individuals follow simple movement rules (such as ACS). Neither the perception of habitat nor an explicit decision to emigrate from a patch on the side of acting individuals is necessary for the emergence of spatial structure.</p>

opencc-zeroNov 2022View details →

opencc-by-4.0Jun 2022View details →

Metabotyping of Andean pseudocereals and characterization of emerging mycotoxins

<p>Pseudocereals are best known for three crops derived from the Andes: quinoa (<em>Chenopodium quinoa</em>, Chenopods I), canihua (<em>C. pallidicaule</em>, Chenopods I), and kiwicha (<em>Amaranthus caudatus</em>). Their grains are recognized for their nutritional benefits; however, there is a higher level of polyphenism and the chemical foundation that would rely with such polyphenism has not been thoroughly investigated. Meanwhile, the chemical food safety of pseudocereals remains poorly documented. Here we applied untargeted and targeted metabolomics approach by LC-MS to achieve both: <strong>i</strong>) a comprehensive chemical mapping of pseudocereal samples collected in the Andes to classify them according to their chemotype; <strong>ii</strong>) a quantification of their contents in emerging mycotoxins. An inventory of the fungal community was also realized with the aims to better know the filamentous fungi present in these grains and try to parallel this information with the presence of the molecules produced, especially mycotoxins. Metabotyping permitted to add new insights into the chemotaxonomy of pseudocereals, confirming the previously established phylotranscriptomic clades: Chenopods I (clusters quinoa and canihua), and Amaranthaceae s.s. (cluster kiwicha). Moreover, we report for the first time the presence of mycotoxins in pseudocereals. Sixteen samples of Peru (out of 27) and one sample from France (out of one) were contaminated with Beauvericin, an emerging mycotoxin. There were several mycotoxigenic fungi detected, including <em>Aspergillus sp.</em>, <em>Penicillium sp.</em>, and <em>Alternaria sp.</em>, but not <em>Fusaria</em>.</p>

EMERGE 2016 Autochamber Sites NMR

METHODS:<br>To follow bio-available metabolites, we used NMR on the water extracted supernatant samples. Supernatant samples (180 µL) were combined with 2,2-dimethyl-2-silapentane-5-sulfonate-d6 (DSS-d6) in D2O (20 µL, 5 mM) and thoroughly mixed prior to transfer to 3 mm NMR tubes. NMR spectra were acquired on a Bruker Neo spectrometer operating at 18.8T (1H ν0 of 800.30 MHz) equipped with a 5mm Bruker TCI/CP HCN (inverse) cryoprobe with Z-gradient.at a regulated temperature of 298.0 K. The 90° 1H pulse was calibrated prior to the measurement of each sample. The one-dimensional 1H spectra were acquired using a nuclear Overhauser effect spectroscopy (noesypr1d) pulse sequence with a spectral width of 20.1 ppm and 2048 transients. The NOESY mixing time was 100ms and the acquisition time was 4s followed by a relaxation delay of 1.5 s during which presaturation of the water signal was applied. The 1D 1H spectra were manually processed, assigned metabolite identifications and quantified using Chenomx NMR Suite 9.0. Time domain free induction decays (65536 total points) were zero filled to 131072 total points prior to Fourier transform, followed by exponential multiplication (0.3 Hz line-broadening), and semi-automatic multipoint smooth segments baseline correction. Chemical shifts were referenced to the 1H methyl signal in DSS-d6 at 0 ppm. Metabolite identification was based on matching the chemical shift, J-coupling and intensity of experimental signals to compound signals in the Chenomx, HMDB and custom in-house databases (Supplementary Data X). Quantification was based on fitted metabolite signals relative to the internal standard (DSS-d6). Signal to noise ratios (S/N) were checked using MestReNova 14.1 with the limit of quantification equal to a S/N of 10 and the limit of detection equal to a S/N of 3.<br><br><br>FUNDING:<br>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0010580 and DE-SC0016440. A portion of this research was performed under the Facilities Integrating Collaborations for User Science (FICUS) exploratory effort and used resources at the Environmental Molecular Sciences Laboratory (proposal ID 51858), which are DOE Office of Science User Facilities. A portion of the research has been performed using EMSL (grid.436923.9), a DOE Office of Science User Facility sponsored by the Office of Biological and Environmental Research.

EMERGE 2016 Autochamber Sites LC-MS

METHODS:<br> Water soluble metabolites were extracted from peat by adding 7 mL of autoclaved milliQ water to 1g of peat in a sterile 15 mL Eppendorf tube. Tubes were vortexed twice for 30 seconds, and then the peat-water mixture was sonicated for 2 hours at 22˚C. Samples were then centrifuged to separate the supernatant, which served as the water extract. <br> <br> Water extracted metabolites were thawed at room temperature and centrifuged again to remove any potential particles that formed after thawing. Next, each sample was split into two 2ml glass tube vials (1 ml each), one for hydrophilic interaction liquid chromatography (HILIC) and the other for reverse-phase (RP) liquid chromatography. Samples in both vials were then dried down completely on a Vacufuge plus (Eppendorf, USA). Samples were resuspended in a solution of 50% Acetonitrile and 50% water for HILIC and a solution of 80% water and 20% HPLC grade methanol for RP.<br><br>A Thermo Scientific Vanquish Duo ultra-high performance liquid chromatography system (UHPLC) was used for the liquid chromatography step. Extracts were separated using a Waters ACQUITY HSS T3 C18 column for RP separation and a Waters ACQUITY BEH amide column for HILIC separation.<br><br>Samples were injected in a 1 μL volume on column and eluted as follows: for RP the gradient went from 99% mobile phase A (0.1% formic acid in H2O) to 95% mobile phase B (0.1% formic acid in methanol) over 16 minutes. For HILIC the gradient went from 99% mobile phase A (0.1% formic acid, 10 mM ammonium acetate, 90% acetonitrile, 10% H¬2O) to 95% mobile phase B (0.1% formic acid, 10 mM ammonium acetate, 50% acetonitrile, 50% H2O). Both columns were run at 45 °C with a flowrate of 300 μL/min.<br>A Thermo Scientific Orbitrap Exploris 480 was used for spectral data collection with a spray voltage of 3500 V for positive mode (for RP) and 2500 V for negative mode (for HILIC) using the H-ESI source. The ion transfer tube and vaporizer temperature were both 350 °C. Compounds were fragmented using data-dependent MS/MS with HCD collision energies of 20, 40, and 80.<br><br>The Compound Discoverer 3.2 software (Thermo Fisher Scientific) was used to analyze the data using the untargeted metabolomics workflow. Briefly, the spectra were first aligned followed by a peak picking step. Putative elemental compositions of unknown compounds were predicted using the exact mass, isotopic pattern, fine isotopic pattern, and MS/MS data using the built in HighChem Fragmentation Library of reference fragmentation mechanisms. Metabolite annotation was performed using spectral libraries and compound databases. First, fragmentation scans (MS2) searches in mzCloud were performed , which is a curated database of MSn spectra containing more than 9 million spectra and 20000 compounds.<br><br> Second, predicted compositions were obtained based on mass error, matched isotopes, missing number of matched fragments, spectral similarity score (calculated by matching theoretical and measured isotope pattern), matched intensity percentage of the theoretical pattern, the relevant portion of MS, and the MS/MS scan. The mass tolerance used for estimating predicted composition was 5 ppm. Finally, annotation was complemented by searching MS1 scans on different online databases with ChemSpider (using either the exact mass or the predicted formula). Based on the annotation results, metabolites were divided into three categories: 1) full match on the three methods used (mzCloud, predicted composition, and ChemSpider), 2) full match by two methods (Predicted composition and ChemSpider) and 3) annotated only by one method (ChemSpider).<br><br><br>COLUMN DEFINITIONS:<br>For both files: <br>Columns A-N : Annotation information<br>Columns O-P: KEGG pathway using molecular formula<br>Columns S-AW : Normalized peak areas per sample<br><br>FUNDING:<br>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0010580 and DE-SC0016440.

opencc-zeroJan 2023View details →

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Data for: Emergent dynamics of adult stem cell lineages from single nucleus and single cell RNA-Seq of Drosophila testes

<p><span>Proper differentiation of sperm from germline stem cells, essential for production of the next generation, requires dramatic changes in gene expression that drive remodeling of almost all cellular components, from chromatin to organelles to cell shape itself. Here we provide a single nucleus and single cell RNA-seq resource covering all of spermatogenesis in <em>Drosophila</em> starting from in-depth analysis of adult testis single nucleus RNA-seq (snRNA-seq) data from the Fly Cell Atlas (FCA) study (Li et al., 2022). With over 44,000 nuclei and 6,000 cells analyzed, the data provide identification of rare cell types, mapping of intermediate steps in differentiation, and the potential to identify new factors impacting fertility or controlling differentiation of germline and supporting somatic cells. We justify assignment of key germline and somatic cell types using combinations of known markers, <em>in situ</em> hybridization, and analysis of extant protein traps. Comparison of single cell and single nucleus datasets proved particularly revealing of dynamic developmental transitions in germline differentiation. To complement the web-based portals for data analysis hosted by the FCA, we provide datasets compatible with commonly used software such as Seurat and Monocle. The foundation provided here will enable communities studying spermatogenesis to interrogate the datasets to identify candidate genes to test for function <em>in vivo</em>.</span></p>

Immersive haptic simulation for training nurses in emergency medical procedures - Data collected and statistical analysis

<p>Data collected during the evaluation presented in "Haptic simulation for emergency procedures in nursing training" paper.</p> <table> <caption>HR ALL</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>2.857</td> <td>29</td> <td>0.008</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-8.089</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>7.567</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-2.962</td> <td>29</td> <td>0.006</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em>  Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR FIRST MANN</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>1.665</td> <td>14</td> <td>0.118</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-7.104</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>6.498</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-1.461</td> <td>14</td> <td>0.166</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em>  Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR FIRST VR</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>2.341</td> <td>14</td> <td>0.035</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-4.612</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>4.482</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-2.688</td> <td>14</td> <td>0.018</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em>  Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR BETWEEN GROUPS</caption> <thead> <tr> <th> </th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-1.958</td> <td>28</td> <td>0.060</td> </tr> <tr> <td>Mann post HR</td> <td>-1.902</td> <td>28</td> <td>0.068</td> </tr> <tr> <td>VR pre HR</td> <td>-4.013</td> <td>28</td> <td>< .001</td> </tr> <tr> <td>VR post HR</td> <td>-2.344</td> <td>28</td> <td>0.026</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em>  Independent samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>Physiological T-Test results for the participants who started the experiment performing the procedure in the mannequin.</caption> <thead> <tr> <th>First variable</th> <th>μ</th> <th>σ</th> <th>Second variable</th> <th>μ</th> <th>σ</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>SBP pre-mannequin</td> <td>128.333</td> <td>10.715</td> <td>SBP pre-simulator</td> <td>134.533</td> <td>11.819</td> <td>-1.870</td> <td>14</td> <td>0.083</td> </tr> <tr> <td>SBP post-mannequin</td> <td>125.600</td> <td>11.648</td> <td>SBP post-simulator</td> <td>131.467</td> <td>14.643</td> <td>-2.094</td> <td>14</td> <td>0.055</td> </tr> <tr> <td>DBP pre-mannequin</td> <td>80.133</td> <td>5.527</td> <td>DBP pre-simulator</td> <td>81.533</td> <td>9.039</td> <td>-0.623</td> <td>14</td> <td>0.544</td> </tr> <tr> <td>DBP post-mannequin</td> <td>78.667</td> <td>6.956</td> <td>DBP post-simulator</td> <td>81.400</td> <td>8.475</td> <td>-2.073</td> <td>14</td> <td>0.057</td> </tr> <tr> <td>HR pre-mannequin</td> <td>92.133</td> <td>14.837</td> <td>HR pre-simulator</td> <td>75.733</td> <td>9.9625</td> <td>6.498</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>HR post-mannequin</td> <td>87.400</td> <td>9.132</td> <td>HR post-simulator</td> <td>91.400</td> <td>14.217</td> <td>-1.461</td> <td>29</td> <td>0.166</td> </tr> </tbody> </table> <p>SBP = Systolic blood pressure. DBP = Diastolic blood pressure. HR = Heart Rate.</p> <table> <caption>Physiological T-Test results for the participants who started the experiment performing the procedure in the ParaVR simulator.</caption> <thead> <tr> <th>First variable</th> <th>μ</th> <th>σ</th> <th>Second variable</th> <th>μ</th> <th>σ</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>SBP pre-mannequin</td> <td>119.067</td> <td>12.898</td> <td>SBP pre-simulator</td> <td>130.600</td> <td>12.188</td> <td>-3.799</td> <td>14</td> <td>0.002</td> </tr> <tr> <td>SBP post-mannequin</td> <td>117.533</td> <td>13.410</td> <td>SBP post-simulator</td> <td>128.200</td> <td>13.385</td> <td>-4.022</td> <td>14</td> <td>0.001</td> </tr> <tr> <td>DBP pre-mannequin</td> <td>76.533</td> <td>8.943</td> <td>DBP pre-simulator</td> <td>80.200</td> <td>6.899</td> <td>-1.815</td> <td>14</td> <td>0.091</td> </tr> <tr> <td>DBP post-mannequin</td> <td>74.333</td> <td>8.541</td> <td>DBP post-simulator</td> <td>79.133</td> <td>7.864</td> <td>-2.003</td> <td>14</td> <td>0.065</td> </tr> <tr> <td>HR pre-mannequin</td> <td>102.067</td> <td>12.876</td> <td>HR pre-simulator</td> <td>91.533</td> <td>11.825</td> <td>4.482</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>HR post-mannequin</td> <td>95.867</td> <td>14.623</td> <td>HR post-simulator</td> <td>103.667</td> <td>14.450</td> <td>-2.688</td> <td>29</td> <td>0.018</td> </tr> </tbody> </table>

Thermal Study of the Emergency Draining Tank of Molten Salt Reactor"

<p>Data set and results associated  with the ICAPP 2023 conference paper "Thermal Study of the Emergency Draining Tank of Molten Salt Reactor".</p>

Metagenome-Assembled Genome DRAM Annotations (EMERGE 97% dereplicated MAGs)

<p>This is the combined DRAM annotation outputs for the 1,864 97% dereplicated metagenome-assembled genomes from Stordalen Mire, Sweden. </p> <ul> <li>1864_97percentmags_annotations_combined.tsv.gz</li> <li>1864_97percentmags_metabolism_summary.xlsx</li> <li>product_0.html</li> <li>product_1.html</li> </ul> <p>METHODS:</p> <p>MAGs were annotated and distilled using DRAM (v1.4.0).</p> <p>FUNDING:<br> This research is a contribution of the EMERGE Biology Integration Institute ((https://emerge-bii.github.io/), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br> We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br> This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440.<br> A portion of this research was performed under the Facilities Integrating Collaborations for User Science (FICUS) program (proposal: 10.46936/fics.proj.2017.49950/60006215 and 10.46936/10.25585/60001148) and used resources at the DOE Joint Genome Institute (<a href="https://www.google.com/url?q=https://ror.org/04xm1d337&sa=D&source=docs&ust=1674859614742521&usg=AOvVaw2XgXYw9eI4JIXRMKn3S9Se">https://ror.org/04xm1d337</a>) and the Environmental Molecular Sciences Laboratory (<a href="https://www.google.com/url?q=https://ror.org/04rc0xn13&sa=D&source=docs&ust=1674859614742655&usg=AOvVaw3UXdoHIFmVjc-mXUhDXYQt">https://ror.org/04rc0xn13</a>), which are DOE Office of Science User Facilities operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL).</p>