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2,960 results for “elements”
IODP Expedition 350 ICP-AES elemental analysis (interstitial water)
<p>Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 350 ICP-AES elemental analysis (solids)
<p>Elemental contents in hard rock and sediment samples was measured by inductively coupled plasma - atomic emission spectrocopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 350 Elemental analysis (CHNS)
<p>Fundamental elemental component (total carbon, hydrogen, nitrogen, and sulfur) fluctuations help define the origin, depositional environment, and diagenetic alteration of source materials. To determine C, H, N, and S, solid samples are reacted with a catalyst, separated by chromatography, and detected by thermal conductivity on a FlashEA 1112 CHNS elemental analyzer. Organic carbon can be directly measured on the elemental analyzer by acidification of the sample to drive off carbonate as carbon dioxide before analyzing. Total organic carbon on this report is measured rather than calculated.</p>
IODP Expedition 376 Elemental analysis (CHNS)
<p>Fundamental elemental component (total carbon, hydrogen, nitrogen, and sulfur) fluctuations help define the origin, depositional environment, and diagenetic alteration of source materials. To determine C, H, N, and S, solid samples are reacted with a catalyst, separated by chromatography, and detected by thermal conductivity on a FlashEA 1112 CHNS elemental analyzer. Organic carbon can be directly measured on the elemental analyzer by acidification of the sample to drive off carbonate as carbon dioxide before analyzing. Total organic carbon on this report is measured rather than calculated.</p>
Ultraconserved elements resolve the phylogeny and corroborate patterns of molecular rate variation in herons (Aves: Ardeidae)
<p>Thoroughly sampled and well-supported phylogenetic trees are essential to taxonomy and to guide studies of evolution and ecology. Despite extensive prior inquiry, a comprehensive tree of heron relationships (Aves: Ardeidae) has not yet been published. As a result, the classification of this family remains unstable, and their evolutionary history remains poorly studied. Here, we sample genome-wide ultraconserved elements (UCEs) and mitochondrial DNA sequences (mtDNA) of >90% of extant species to estimate heron phylogeny using a combination of maximum likelihood (ML), coalescent, and Bayesian inference (BI) methods. The UCE and mtDNA trees are mostly concordant with one another, providing a topology that resolves relationships among the five heron subfamilies and indicates that the genera <em>Gorsachius</em>, <em>Botaurus</em>, <em>Ardea</em>, and <em>Ixobrychus</em> are not monophyletic. We also present the first genetic data from the Forest Bittern <em>Zonerodius</em> <em>heliosylus</em>, an enigmatic species of New Guinea; our results suggest that it is a member of the genus <em>Ardeola</em> and not the Tigrisomatinae (tiger herons), as previously thought. Lastly, we compare molecular rates between heron clades in the UCE tree with those in previously constructed mtDNA and DNA-DNA hybridization trees. We show that rate variation in the UCE tree corroborates rate patterns in the previously constructed trees, i.e., that bitterns (<em>Ixobrychus</em> and <em>Botaurus</em>) evolved comparatively faster, and some tiger herons (<em>Tigrisoma</em>) and the Boat-billed Heron (<em>Cochlearius</em>) more slowly, than other heron taxa. </p>
Datasets and Scripts associated with "Transposable elements are associated with the variable response to influenza infection"
<p>Scripts and datasets included here were used for the main analyses in the article "Transposable elements are associated with the variable response to influenza infection" (BioRxiv doi: <a href="https://doi.org/10.1101/2022.05.10.491101">https://doi.org/10.1101/2022.05.10.491101</a>).</p> <p>Study Summary:</p> <p>Influenza A virus (IAV) infections are frequent every year and result in a range of disease severity. Given that the regulation of transposable elements (TEs) contributes to the activation of innate immunity, we wanted to explore their potential role in this variability. Transcriptome profiling in monocyte-derived macrophages from 39 individuals following IAV infection revealed significant inter-individual variation in viral load post-infection. Using ATAC-seq we identified a set of TE families with either enhanced or reduced accessibility upon infection. Of the enhanced families, 15 showed high variability between individuals and had distinct epigenetic profiles. Motif analysis showed an association with known immune regulators (e.g., BATFs, FOSs/JUNs, IRFs, STATs, NFkBs, NFYs, and RELs) in stably enriched TE families and with other factors in variable families, including KRAB-ZNFs. We also observed a strong association between basal TE transcripts and viral load post infection and showed that TEs, and host factors regulating TEs, were predictive of the response. Our findings shed light on the variable transcriptional and epigenetic response to infection and the role TEs and KRAB-ZNFs may play in inter-individual variation in immunity.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Sustainable recovery of critical elements from seawater saltworks bitterns by integration of high selective sorbents and reactive precipitation and crystallisation: Developing the probe of concept with on-site produced chemicals and energy
<p>The availability of raw mineral resources containing elements included in the Critical Raw Materials (CRMs) list is a growing concern for the European Union. Sea mining has been identified as a promising secondary source. In particular, brines obtained in solar saltworks (bitterns) contain relevant amounts of valuable CRMs such as Mg(II), B(III), other alkaline/alkaline earth metals (Rb(I), Cs(I), Sr(II)) and transition/post-transition elements (Co(II), Ga(III), Ge(IV)). However, the low concentration of some of these elements (µg/L) requires an effort to develop recovery routes that are sustainable and economically feasible where the required chemicals and energy are produced on-site from the saltworks bitterns (i.e. HCl and NaOH). Even the conventional recovery processes such as ion exchange, sorption and precipitation, which have proved to be competitive for metals recovery, are challenged in the case of Trace Elements (TEs). This work studies the recovery of TEs included in the CRMs list from saltworks bitterns after ion exchange processes. First, batch crystallisation and reactive precipitation were tested for some target elements in single-component solutions: Sr(II), Co(II), Ga(III), Ge(IV) and B(III). Then, the experiments were carried out with multi-component synthetic solutions assuming different scenarios of bittern streams coming out a selective extraction stage using sorption and ion exchange processes. The targeted elements were recovered except for Ge(IV), where alternative routes need to be evaluated, as its precipitation involves the use of tannic acid or sulphide solutions that could not be produced from the bitterns. However, a further concentration step would be necessary to achieve element concentrations closer to the mineral phases saturation. Moreover, model simulations were performed using the PHREEQC program, which provided a good prediction of the experimental trends obtained in most cases.</p>
Data for: Intra- and interspecific variation in trace element concentrations in feathers of North European Trans-African migrants
<p>The knowledge of migratory connectivity is important for understanding the potential drivers of populations and it is thus important for conservation implications. Migratory connectivity of species can be studied using exogenous, such as rings and transmitters, or endogenous markers, such as stable isotopes and trace elements. The use of trace elements has been much less frequently studied compared to stable isotopes. Trace elements can be studied from the feathers of birds and this does not necessarily require trapping of individuals. Here we studied the variation of 18 different trace elements in feathers of two long-distance trans-African migrants, willow warblers Phylloscopus trochilus and barn swallows Hirundo rustica, using body feathers of museum specimens of birds from Finnish breeding grounds. The trace elements were measured using Laser-Ablation Inductively-Coupled-Plasma Mass-Spectrometry. We show that trace element concentrations were relatively stable along the rachis within the same feather except in Ni and S, which showed a quadratic pattern. In general, variation within feathers of the same individuals was smaller than in feathers between individuals for most elements. Furthermore, concentrations of 11 trace elements showed significantly higher concentrations in willow warbler feathers collected in spring than in autumn, moulted in African wintering grounds and European breeding grounds, respectively. Last, concentrations of seven trace elements were significantly higher in the spring feathers of willow warblers compared to barn swallows. This suggests that trace elements could be used to separate moulting grounds of the birds on the larger scale within the same species, but also sampling design should be carefully considered.</p>
IODP Expedition 385 Elemental analysis (CHNS)
<p>Fundamental elemental component (total carbon, hydrogen, nitrogen, and sulfur) fluctuations help define the origin, depositional environment, and diagenetic alteration of source materials. To determine C, H, N, and S, solid samples are reacted with a catalyst, separated by chromatography, and detected by thermal conductivity on a FlashEA 1112 CHNS elemental analyzer. Organic carbon can be directly measured on the elemental analyzer by acidification of the sample to drive off carbonate as carbon dioxide before analyzing. Total organic carbon on this report is measured rather than calculated.</p>
IODP Expedition 385 ICP-AES elemental analysis (interstitial water)
<p>Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 385 ICP-AES elemental analysis (solids)
<p>Elemental contents in hard rock and sediment samples was measured by inductively coupled plasma - atomic emission spectrocopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
Fig. 4. A. Dorsal, B in Ultraconserved elements-based phylogenomic systematics of the snake superfamily Elapoidea, with the description of a new Afro-Asian family
Fig. 4. A. Dorsal, B, lateral and C. ventral view of the skull of Micrelaps muelleri (SMNH.R 17777). D. Ectopterygoid of the same specimen in ventral view. E. Ventral and F. lateral views of the palatomaxillary arch.
Fig. 1. A. ASTRAL and B. wASTRAL-hybrid species trees for the superfamily Elapoidea from the 50 in Ultraconserved elements-based phylogenomic systematics of the snake superfamily Elapoidea, with the description of a new Afro-Asian family
Fig. 1. A. ASTRAL and B. wASTRAL-hybrid species trees for the superfamily Elapoidea from the 50 % complete dataset, consisting of 4561 loci. Circles on the branch represent a local posterior probability support of 0.95 to 1.0. Abbreviations – AT – Atractaspidinae, CL – Cyclocoridae, EL – Elapidae, LM – Lamprophiinae, MC – Micrelapidae fam. nov., OG – outgroup, PD – Pseudaspidinae, PR – Prosymninae, PS – Psammophiinae, PX – Pseudoxyrhophiinae.
Fig. 3 in Ultraconserved elements-based phylogenomic systematics of the snake superfamily Elapoidea, with the description of a new Afro-Asian family
Fig. 3. Time calibrated phylogeny (50 % complete dataset) of elapoid snakes, estimated with the Maximum Likelihood implementation of the RelTime method (with lognormal node calibration densities). Values on the branches indicate the estimated divergence times. The blue bar represents the 95 % confidence intervals around the estimated divergence times.
Fig. 2. Maximum Likelihood species tree from the concatenated 50 in Ultraconserved elements-based phylogenomic systematics of the snake superfamily Elapoidea, with the description of a new Afro-Asian family
Fig. 2. Maximum Likelihood species tree from the concatenated 50 % complete dataset consisting of 4561 loci. Values on the branch indicate Shimodaira Hasegawalike approximate likelihood ratio test and ultrafast bootstrap. Abbreviations as in Fig. 1.
Using neural networks to model Main Belt Asteroid albedos as a function of their proper orbital elements
<p>This repository contains a copy of the following repository https://github.com/r-zachary-murray/Asteroid-Albedos. It contains weights for an ensemble of neural nets trained on the Asteroid Family Portal proper elements and NEOWISE albedos. These weights can be used to predict albedos of asteroids based of their proper elements. Example.ipynb contains an ipython notebook that shows how these predictions can be made.</p>
IODP Expedition 396 ICP-AES elemental analysis (interstitial water)
<p>Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 396 ICP-AES elemental analysis (solids)
<p>Elemental contents in hard rock and sediment samples was measured by inductively coupled plasma - atomic emission spectrocopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 396 Elemental analysis (CHNS)
<p>Fundamental elemental component (total carbon, hydrogen, nitrogen, and sulfur) fluctuations help define the origin, depositional environment, and diagenetic alteration of source materials. To determine C, H, N, and S, solid samples are reacted with a catalyst, separated by chromatography, and detected by thermal conductivity on a FlashEA 1112 CHNS elemental analyzer. Organic carbon can be directly measured on the elemental analyzer by acidification of the sample to drive off carbonate as carbon dioxide before analyzing. Total organic carbon on this report is measured rather than calculated.</p>
SAGC-A68: A space access graph dataset for the classification of spaces and space elements in apartment buildings
<p><strong>SAGC-A68</strong>: "a <strong>S</strong>pace <strong>A</strong>ccess <strong>G</strong>raph dataset for the <strong>C</strong>lassification of spaces and space elements in <strong>A</strong>partment buildings"</p> <p><strong>Authors:</strong> "Amir Ziaee, Georg Suter, Laura Keiblinger"</p> <p><strong>Copyright:</strong> "Design Computing Group TU Wien, 2023"</p> <p><strong>Credits:</strong> "Design Computing Group TU Wien"</p> <p><strong>License:</strong> "GNU GENERAL PUBLIC LICENSE Version 3"</p> <p><strong>Version: </strong>"1.0.1"</p> <p><strong>Maintainer:</strong> "Amir Ziaee"</p> <p><strong>Email:</strong> "amir.ziaee@tuwien.ac.at"</p> <p><strong>Url:</strong> <a href="https://github.com/A2Amir/SAGC-A68">https://github.com/A2Amir/SAGC-A68</a></p> <p><strong>Acknowledgments:</strong> "The authors gratefully acknowledge support by Grant Austrian Science Fund (FWF): I 5171-N, Laura Keiblinger, and participants in the course '259.428-2021S Architectural Morphology' at TU Wien for data collection. "</p> <p><strong>Description:</strong> "The analysis of building models for usable area, building safety, and energy efficiency requires accurate classification data of spaces and space elements. To reduce input model preparation effort and errors, automated classification of spaces and space elements is desirable. Although existing space function classifiers use space adjacency or connectivity graphs as input, the application of Graph Deep Learning (GDL) to space layout element classification has not been extensively researched due to the lack of suitable datasets. To bridge this gap, we introduce a dataset named SAGC-A68, which comprises access graphs automatically generated from 68 digital 3D models of space layouts of apartment buildings designed or built between 1952 and 2019 in 13 countries. Each access graph contains nodes representing spaces and space elements and edges representing the connection between them. Nodes are uniquely identified and characterized by 16 features including “Position X”, “Position Y”, “Position Z”, “Width”, “Height”, “Depth”, “Area”, “Volume”, “Is_internal”, “Door_opening_quantity”, “Window_quantity”, “Max_door_width”,” Encloses_ws”, “Is_contained_in_ws”, ”bounding_box”, and “Label” (28 identified lables are shown in bold type in Table 1). Edges are identified by a unique ID and characterized by three features, including “Z_angle”, “Delta_z”, and “Length”. In total, the dataset comprises 4871 nodes and 4566 edges, including disconnected nodes representing shafts. It is suitable for developing GDL models for space element and space function classification in Building information modeling (BIM) authoring systems. "</p> <p> </p> <p>Table 1: Space and space element class hierarchies and instance count in the SAGC-A668 dataset.</p> <table> <tbody> <tr> <td> </td> <td> </td> </tr> <tr> <td><strong>Space function classes</strong></td> <td> </td> <td><strong>Space element classes</strong></td> <td> </td> </tr> <tr> <td><strong>Name</strong></td> <td><strong>Count</strong></td> <td><strong>Name</strong></td> <td><strong>Count</strong></td> </tr> <tr> <td>Space</td> <td> </td> <td>SpaceElement</td> <td> </td> </tr> <tr> <td> ResidentialSpace</td> <td> </td> <td> SpaceEnclosingElement</td> <td> </td> </tr> <tr> <td> CommunalSpace</td> <td> </td> <td> <strong>Opening</strong></td> <td><strong>140</strong></td> </tr> <tr> <td> <strong>DiningRoom</strong></td> <td><strong>3</strong></td> <td> Door </td> <td> </td> </tr> <tr> <td> <strong>FamilyRoom</strong></td> <td><strong>6</strong></td> <td> <strong>InternalDoor</strong></td> <td><strong>1428</strong></td> </tr> <tr> <td> <strong>LivingRoom</strong></td> <td><strong>275</strong></td> <td> <strong>UnitDoor</strong></td> <td><strong>291</strong></td> </tr> <tr> <td> PrivateSpace</td> <td> </td> <td> <strong>SideEntranceDoor</strong></td> <td><strong>84</strong></td> </tr> <tr> <td> <strong>Bedroom</strong></td> <td><strong>495</strong></td> <td> <strong>ElevatorDoor</strong></td> <td><strong>492</strong></td> </tr> <tr> <td> <strong>MasterBedroom</strong></td> <td><strong>23</strong></td> <td> <strong>BalconyDoor</strong></td> <td><strong>10</strong></td> </tr> <tr> <td> <strong>BoxRoom</strong></td> <td><strong>2</strong></td> <td> </td> <td> </td> </tr> <tr> <td> <strong>HomeOffice</strong></td> <td><strong>8</strong></td> <td> </td> <td> </td> </tr> <tr> <td> ServiceSpace</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> <strong>Shaft</strong></td> <td><strong>403</strong></td> <td> </td> <td> </td> </tr> <tr> <td> <strong>StorageRoom</strong></td> <td><strong>84</strong></td> <td> </td> <td> </td> </tr> <tr> <td> <strong>WalkInCloset</strong></td> <td><strong>2</strong></td> <td> </td> <td> </td> </tr> <tr> <td> SanitarySpace</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> <strong>Bathroom</strong></td> <td><strong>274</strong></td> <td> </td> <td> </td> </tr> <tr> <td> <strong>Toilet</strong></td> <td><strong>145</strong></td> <td> </td> <td> </td> </tr> <tr> <td> <strong>Kitchen </strong></td> <td><strong>117</strong></td> <td> </td> <td> </td> </tr> <tr> <td> <strong>LaundryRoom</strong></td> <td><strong>57</strong></td> <td> </td> <td> </td> </tr> <tr> <td> CirculationSpace</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> VerticalCirculationSpace</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> <strong>Elevator</strong></td> <td><strong>86</strong></td> <td> </td> <td> </td> </tr> <tr> <td> <strong>Stairway</strong></td> <td><strong>70</strong></td> <td> </td> <td> </td> </tr> <tr> <td> HorizontalCirculationSpace</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> <strong>Entrance </strong></td> <td><strong>67</strong></td> <td> </td> <td> </td> </tr> <tr> <td> <strong>Hallway </strong></td> <td><strong>12</strong></td> <td> </td> <td> </td> </tr> <tr> <td> <strong>MainHallway </strong></td> <td><strong>18</strong></td> <td> </td> <td> </td> </tr> <tr> <td> <strong>InternalHallway</strong></td> <td><strong>152 </strong></td> <td> </td> <td> </td> </tr> <tr> <td> External <strong> </strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> <strong>AccessBalcony </strong></td> <td><strong>19</strong></td> <td> </td> <td> </td> </tr> <tr> <td> <strong>Loggia</strong></td> <td><strong>108 </strong></td> <td> </td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
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