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NMR data and fluorescence microscopy data
No full text<p>The provided data includes the FID files for all recorded NMR spectra and all fluorescence microscopy images as reported in <a href="https://doi.org/10.1002/cbic.202200363">10.1002/cbic.202200363</a> (freely accessible). The NMR datasets are organized as obtained directly after the measurement, allowing further processing with commonly used software. The files and folders are named after the original experiment. For the assignment of the data to the respective compound numbers as specified in <a href="https://doi.org/10.1002/cbic.202200363">10.1002/cbic.202200363</a> please see the provided PDF file ‘file_names_for_compound_numbers.pdf’. In addition, copies of the spectra are provided in the PDF file ‘NMR_spectra_and_file_names.pdf’.</p>
<p>Fluorescene microscopy images are provided as TIF files.</p>
<p>For details on the used compounds and all compound numbers see <a href="https://doi.org/10.1002/cbic.202200363">10.1002/cbic.202200363</a>.</p>
<p> </p>
<h2>Technical information</h2>
<p>- NMR: <sup>1</sup>H and <sup>13</sup>C NMR spectra were recorded on a Bruker Ascend 600 MHz spectrometer at 20 °C.</p>
<p>- Fluorescence microscopy: HT1080 cells were seeded into a 96-well plate at 3,000 cells per well and allowed to grow overnight. The medium was removed, and the cells were treated with a 200 nM solution of sulfo-cTCO-DMEDA-CA4 (compound 12) in media. <em>In situ</em> click-to-release was initiated by addition of DMT (compound 7) at a final concentration of 10 µM. As controls, cells were left untreated or incubated with either the parent drug CA4 (200 nM) or 10 µM DMT (compound 7). After an incubation time of 6 h cells were stained with SiR-tubulin (a fluorogenic, cell permeable and highly specific probe for microtubules). An 11X stock solution of the probe was directly added to the growth medium to obtain a final concentration of 1 µM and incubation was carried out for 1 h. Subsequently, the medium was removed, and cells were stained with Hoechst 33342 nuclear dye (Invitrogen, 5 µM in growth medium) for 10 minutes and washed once with PBS. Multichannel imaging of the cells was carried out in FluoroBrite DMEM medium (Gibco) on an Olympus IX82 microscope.</p>
<p> </p>
<h2>Summary of results</h2>
<p>- NMR: The obtained data confirmed the chemical structures of all synthesized compounds. Results of data analysis are provided in <a href="https://doi.org/10.1002/cbic.202200363">10.1002/cbic.202200363</a> (freely accessible).</p>
<p>- Fluorescence microscopy: Cell imaging via fluorescence microscopy (scale bars: 50 µm) upon staining with Hoechst 33342 (blue, nuclei) and SiR-tubulin (red, microtubules) shows comparable depletion of tubulin signals after 6 h treatment with CA4 (200 nM) or bioorthogonal activation of prodrug 12 (200 nM) by in situ reaction with DMT (compound 7). No significant change (compared to untreated cells) was observed after treatment with DMT (compound 7) or prodrug 12.</p>
Do You Need Instructions Again? Predicting Wayfinding Instruction Demand
No full text<h2>How to Cite?</h2>
<p>Alinaghi, N., Kwok, T. C., Kiefer, P., & Giannopoulos, I. (2023, September). Do You Need Instructions Again? Predicting Wayfinding Instruction Demand. In <em>GIScience 2023</em>.</p>
<h2>Abstract</h2>
<p>The demand for instructions during wayfinding, defined as the frequency of requesting instructions for each decision point, can be considered as an important indicator of the internal cognitive processes during wayfinding. This demand can be a consequence of the mental state of feeling lost, being uncertain, mind wandering, having difficulty following the route, etc. Therefore, it can be of great importance for theoretical cognitive studies on human perception of the environment. From an application perspective, this demand can be used as a measure of the effectiveness of the navigation assistance system. It is therefore worthwhile to be able to predict this demand and also to know what factors trigger it. This paper takes a step in this direction by reporting a successful prediction of instruction demand (accuracy of 78.4%) in a real-world wayfinding experiment with 45 participants, and interpreting the environmental, user, instructional, and gaze-related features that caused it.</p>
<h2>Material</h2>
<p>All data is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, and all software files are licensed under the MIT License. </p>
<ul>
<li><strong>Data: </strong>A CSV file containing 75 computed features for classifying instruction demand. These include 41 Environmental, 16 Instruction-, 12 User-related, and 6 Gaze features. Detailed explanations of all features and how they are computed are provided in the accompanying paper. But here you can see a summary of these features:
<ul>
<li>Environmental Features:
<ul>
<li>unified-segment
<ul>
<li>distance from/to previous/next turn junctions</li>
<li>distance from/to previous/next non-turn junctions</li>
<li>segment-length</li>
<li>route-length</li>
<li>time passed since start</li>
</ul>
</li>
<li>landuse</li>
<li>PoI</li>
</ul>
</li>
<li>User Features:
<ul>
<li>demographics
<ul>
<li>gender (binary)</li>
<li>age (in years)</li>
<li>familiarity (binary)</li>
</ul>
</li>
<li>Big Five Personality traits</li>
<li>Spatial Strategies Questionnaire FRS</li>
</ul>
</li>
<li>Instruction Features
<ul>
<li>length-related
<ul>
<li>number of words</li>
<li>number of characters</li>
</ul>
</li>
<li>content-related
<ul>
<li>OSM PoI</li>
<li>landmark OSM type</li>
<li>contains-street-names (boolean)</li>
<li>last instruction (boolean)</li>
</ul>
</li>
</ul>
</li>
<li>Gaze Features
<ul>
<li>fixation count</li>
<li>min/max/sd fixation</li>
<li>mean fixation duration</li>
<li>fixation duration skewness</li>
</ul>
</li>
</ul>
</li>
</ul>
<ul>
<li><strong>Code: </strong>The analysis code used in the paper is available as a Jupyter Notebook.</li>
</ul>
Global Reference Frame VLBI solution VIE2023 (SX&VG)
No full text<h2>Context and methodology</h2><ul><li>Global reference frame solution from Very Long Baseline Interferometry based on IVS data at S/X-band and VGOS</li><li>Celestial Reference Frame: Source positions are in the J2000 frame, but at the 2015.0 galactic aberration epoch. Reported source position errors are inflated with scaling factors: 1.5 RA, 1.5 De, and a RA/Dec noise floor 30/ 30 micro-arc-sec is applied.</li><li>Terrestrial Reference Frame: reference epoch 2015.0 and linear velocity</li><li>Earth Orientation Parameters: reported is one offset per session</li></ul><h3>Technical details</h3><ul><li>GENERATION_TIME: 2024-03-20T21:37:10</li><li>DATA_START: 1979-08-03T00:00:00</li><li>DATA_END: 2023-12-29T00:00:00</li><li>ANALYSIS_CENTER: VIE (TU Wien, Austria)</li><li>CONTACT: VIE Analysis Center ([email protected])</li><li>SOFTWARE: VieVS v3.2</li><li>TECHNIQUE: VLBI </li><li>FREQUENCY BANDS: S/X + VGOS</li><li>DESCRIPTION: Earth orientation parameters from the VLBI global solution S/X + VGOS (session-wise reduced parameters)</li></ul>
Regionalised Emission Model (MoRE) for PFAS from H2020 Project PROMISCES - Case Study 2
No full text<h1>MoRE model for PFAS emissions into surface waters in the upper Danube basin</h1>
<p>This record contains a SQLite database driven emission model for modelling PFAS emissions into surface waters of the upper Danube basin, and a collection of flowcharts in PDF (and <a href="https://en.wikipedia.org/wiki/PDF/A">PDF/A</a>) format demonstrating the model setup processes.</p>
<h2>Description of the model</h2>
<p>The model system MoRE (Modeling of Regionalized Emissions) was initially developed by the Karlsruhe Institute of Technology (KIT) in cooperation with the German Federal Environment Agency. It is based on the MONERIS model system. MoRE was developed as a tool in an open source environment for modelling substance emissions into surface waters for a wide range of substances with relevance for water quality (Fuchs et al. 2017).</p>
<p>The modelling in MoRE is carried out as a regionalized pathway analysis. The substance emissions are modelled with temporal and spatial differentiation via various emission <strong>pathways, </strong>as indicated in the EU Guidance Document No 28 (EC 2012) for tier 3 for establishing an inventory of emissions. The temporal resolution of the model are annual time steps and the spatial resolution is 526 sub-catchments with a size of 354 ± 352 km².</p>
<p>In the PROMISCES project the model was adapted for modelling of PFAS, which means additional emission pathways were implemented, which might be significant for PFAS and other pathways with less significance for this substance group were simplified and grouped together. Thus, the model contains now the following pathways:</p>
<p><strong>Point pathways:</strong></p>
<ul>
<li>Municipal wastewater treatment plants</li>
<li>Industrial direct dischargers</li>
</ul>
<p><strong>Diffuse pathways:</strong></p>
<ul>
<li>direct atmospheric deposition onto water surface</li>
<li>surface runoff from unsealed areas</li>
<li>soil erosion</li>
<li>groundwater with contribution from
<ul>
<li>legacy pollution from PFAS production site (in case of PROMISCES cs#2, the industrial park at Gendorf, Germany)</li>
<li>legacy pollution from aerodromes caused by fire-fighting training activities</li>
<li>legacy pollution from municipal landfills</li>
</ul>
</li>
<li>sewer systems</li>
</ul>
<p>Due to the <strong>flexible structure</strong> of MoRE, new substances and emission pathways can be integrated at any time, provided that the necessary input data are available and modelling can be carried out in a reasonable way. In addition, MoRE offers the possibility to modify existing calculation approaches and to test different input data sets by comparing them. For this purpose, different <strong>variants</strong> can be created. In the PROMISCES project three model variants for the current state were implemented to represent the uncertainty in the model input data:</p>
<ul>
<li>Base variant: Based on the median evaluation of environmental concentrations this variant should present the most likely model outcome. If more than 80% of the environmental concentrations were measured as below the analytical limit of quantitation (LOQ), or less than 3 concentrations were observed above the LOQ, half value of the LOQ was used as input data.</li>
<li>Best-Case: This variant is based on the 25<sup>th</sup> percentile of environmental concentrations and represents a best-case evaluation with rather low pollution. If more than 80% of the environmental concentrations were measured as below the LOQ, or less than 3 concentrations were observed above the LOQ, 0 was used as input data.</li>
<li>Worst-Case: This variant is based on the 75<sup>th</sup> percentile of environmental concentrations and represents a worst-case evaluation with rather high pollution. If more than 80% of the environmental concentrations were measured as below the LOQ, or less than 3 concentrations were observed above the LOQ, the value of the LOQ was used as input data.</li>
</ul>
<p>The PROMISCES modelling guidance document (D2.4) in Chapter XX provides an example of the application of the model in the Upper Danube region.</p>
<h2>References</h2>
<p><a name="_CTVL0014f85d7ecb89843a5b3b40ed5bc26bce3"></a>EC (2012), European Commission: Guidance Document No. 28. Technical guidance on the preparation of an inventory of emissions, discharges and losses of priority and priority hazardous substances, 1st edn. Common implementation strategy for the Water Framework Directive (2000/60/EC), vol 058. ISBN: 978-92-79-23823-9. European Commission, Brussels</p>
<p><a name="_CTVL0011f353f0febcc4267884c61c7d865f51d"></a>Fuchs S, Kaiser M, Kiemle L, Kittlaus S, Rothvoß S, Toshovski S, Wagner A, Wander R, Weber T, Ziegler S (2017): Modeling of Regionalized Emissions (MoRE) into Water Bodies: An Open-Source River Basin Management System. Water 9:239. https://doi.org/10.3390/w9040239</p>
<h2>Technical details</h2>
<p>The MoRE model system is based on an open source PostgreSQL or SQLite database, a generic calculation engine and the MoRE Developer user interface, which can be used to read, modify and extend the contents of the database. All computations are performed by the calculation engine, which is controlled via the user interface. The modelling results can be exported as tables via the MoRE Developer user interface and the results can be used in GIS for mapping. Users can work with MoRE in two different ways: on the basis of a multi-user access in a PostgreSQL database via the Internet or as a stand-alone application on the PC.</p>
<p>Here the SQLite based version is provided as an executable in a zip file. After extraction from the zip file the executable (.exe file) can be started on a Windows operating system (Windows 10 and 11 tested).</p>
<p>More information on how to use MoRE can be found in the <a href="https://more.iwu.kit.edu/wiki-en-neu" target="_blank" rel="noopener">MoRE documentation wiki</a></p>
<h2><strong>Licensing</strong></h2>
<p><a name="_Hlk175561325"></a><a name="_Hlk175560686"></a>The MoRE-Developer graphical user interface is property of COS Geoinformatik GmbH & Co. KG. Redistribution is only allowed with the permission of COS Geoinformatik GmbH & Co. KG, Karlsruher Str. 10b, 76275 Ettlingen, Germany, <a href="http://www.cosgeo.de">www.cosgeo.de</a>, Phone: +49 7243 3241-11, email: <a href="mailto:[email protected]">[email protected]</a>.</p>
<p>The MoRE calculation engine (MoRE Rechenkern.dll) is licensed under a GNU Affero General Public License, Version 3 (AGPL V3.0 <a href="http://www.gnu.org/licenses/agpl.html">http://www.gnu.org/licenses/agpl.html</a>).</p>
<p>The content of the database, if not differently stated in the data itself is licensed under a Creative Commons Attribution Share Alike 4.0 International license (CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/).</p>
Supporting Human-Robot Interaction by Projected Augmented Reality and a Brain Interface
No full text<p><strong>UPDATE - Files are restricted. The last version is V3</strong></p><p>This repository stores the raw data and the videos of the manuscript titled "Supporting Human-Robot Interaction by Projected Augmented Reality and a Brain Interface".</p><p>The data.xlsx file contains the raw data collected during the user study.</p><p>The video_final.mp4 video shows the APA and NAPA approaches as well as their integration with a robotic arm.</p>
Near-surface soil electrical conductivity data of two selected sites in the Nationalpark Neusiedlersee, Austria, obtained with an experimental Electromagnetic Induction System
No full text<h2>Introduction</h2><p>Low-frequency electromagnetic induction (EMI) is a non-invasive geophysical method that is based on the induction of electromagnetic (EM) waves into the subsurface to quantify changes in electrical conductivity. The datasets included in this repository contain near-surface soil electrical conductivity data which were obtained with an experimental Electromagnetic Induction System during field measurements in the Nationalpark Neusiedlersee, Austria.</p><h3>Publication</h3><p>The datasets belong to the publication 'Design, Development and Application of a Modular Electro-magnetic Induction (EMI) Sensor for Near-Surface Geophysical Surveys'.</p><h3>Files</h3><p>The datasets are in CSV and TSV (DAT files) format and can be opened in, e.g., MS-Excel. A PDF document describing the column headers is included.<br>The figures are provided in common image formats (JPG, PNG, GIF) and can be opened with any image viewer.</p>
Tetraethylammonium Salts as Solid, Easy to Handle Ethylene Precursors and Their Application in Mizoroki–Heck Coupling
No full text<p><strong>Analytical Data and Compound Numbering (in paper numbering vs. ELN entries) for the Publication entitled:<br><em>"Tetraethylammonium Salts as Solid, Easy to Handle Ethylene Precursors and Their Application in Mizoroki−Heck Coupling"</em></strong></p>
<p>The paper was published on 2024-03-11 in The Journal Organic Chemistry</p>
<p>J. Org. Chem. 2024, 89, 5126−5133</p>
<p>DOI: <a title="DOI URL" href="https://doi.org/10.1021/acs.joc.3c02867">10.1021/acs.joc.3c02867</a></p>
<p>Authors: Eleni Papaplioura, Maëva Mercier, Michael E. Muratore, Tobias Biberger, Soufyan Jerhaoui, Michael Schnürch</p>
<p>This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie, Grant Agreement No. 860762.</p>
<p><strong>Context and methodology</strong></p>
<p>In this study, we introduce a convenient Heck vinylation protocol that eliminates the requirement for ethylene gas as a coupling partner. In contrast to traditional methodologies, quaternary ammonium salts can serve as solid olefin precursors under ambient atmosphere conditions. The practicality of this method, distinguished by its convenience and safety in a one-pot reaction, renders it appealing for applications in research and discovery context.</p>
<p>The publication and its Supporting Information can be found as open-access files on the publisher's website (see DOI above).</p>
<p>All detailed files containing the analytical raw data, for all compounds given in the Supporting Information of the manuscript are uploaded. An additional PDF file named J. Org. Chem. 2024 compound number list.docx is uploaded, that should clearly link the compound number given in the paper to the respective entry in the ELN (jotempl) and the respective analytical data files. </p>
<p><strong>Technical details</strong></p>
<p>The files uploaded contain the FIDs of NMR spectra recorded by an in-house Bruker Spectrometer. A software to display NMR-spectra is needed, such as <a href="https://mestrelab.com/download/mnova/">MestreNova</a> or <a href="https://www.bruker.com/en/products-and-solutions/mr/nmr-software/topspin.html">Topspin</a>).</p>
<p>HRMS data is uploaded too and has to be processed via <a href="https://www.agilent.com/en/promotions/masshunter-mass-spec">MassHunter</a> software.</p>
Rhino3D surfaces to Riscan Pro planes and RealityCapture rcBox script
No full text<p>This grasshopper for Rhino3D script plus bat-script for windows can be used to transform section planes defined in Rhino3D as surfaces into planes for terrestrial laserscan processing software from Riegl LMS called RiScan PRO, and boxes the photogrammetric structure-from-motion processing software Reality Capture</p><p>NOTE: THE WHOLE PROCESS IS OPTIMIZED FOR GLCS = EPSG:4978 in RealityCaputre and RiScan</p><p>Using the script provides text-based definitions for said planes and boxes transformed into global coordinate system GLCS (assuming Rhino3D is working in project coordinate system PRCS) - for synchronizing PRCS and GLCS between RiScan Pro and RealityCapture check <a href="doi.org/10.48436/zq6qs-br095">doi.org/10.48436/zq6qs-br095</a></p>
ViPErLEED Supporting Information – LEED-I(V) Fe₂O₃(1-102)-(1×1)
No full text<p>This dataset contains two archives that serve as supporting information to the ViPErLEED paper titled "ViPErLEED package I: Calculation of <em>I</em>(<em>V</em>) curves and structural optimization". The archives contain quantitative low-energy electron diffraction — LEED-<em>I</em>(<em>V</em>) — measurements and calculations for the Fe₂O₃(1-102)-(1×1) surface.</p>
<p>For up-to-date information on ViPErLEED see our homepage at <a href="https://www.viperleed.org" target="_blank" rel="noopener">https://www.viperleed.org</a>. The ViPErLEED source code is hosted on GitHub under <a href="https://github.com/viperleed">https://github.com/viperleed</a>.</p>
<p>Below is a list of the contents of the two archives. For details, see the respective README files.</p>
<p><code>LEED-IV_data_Fe2O3-1x1/</code><br><code>├── edit_Fe2O3(1-102)-1x1.csv <-- Curve-Editor Edit file</code><br><code>├── EXPBEAMS_Fe2O3(1-102)-1x1.csv <-- Final extracted I(V) curves</code><br><code>├── IV_videos/ <-- Raw LEED-I(V) videos</code><br><code>│ └── ...</code><br><code>├── pattern_file_Fe2O3(1-102)-(1x1).csv <-- Pattern file used by the Spot Tracker</code><br><code>├── raw_image_E=261.5.png <-- Representative snapshot LEED pattern</code><br><code>├── README.txt <-- This README file</code><br><code>├── Spot_Tracker-extraction/ <-- Spot-Tracker logs and results</code><br><code>│ └── ...</code><br><code>└── Spot_Tracker-mask_1x1.tif <-- Mask used by the Spot Tracker</code></p>
<p><code>LEED-IV_calculation_Fe2O3-1x1/</code><br><code>├── errors_summary/ <-- Uncertainties for fitted structure</code><br><code>│ └── ...</code><br><code>├── history.info <-- List of calculations steps and notes</code><br><code>├── history/ <-- Intermediate inputs and outputs</code><br><code>│ └── ...</code><br><code>├── Fe2O3_inputs/ <-- Initial inputs</code><br><code>│ ├── POSCAR</code><br><code>│ ├── PARAMETERS</code><br><code>│ └── EXPBEAMS.csv</code><br><code>├── Fe2O3_converged/ <-- Final structure#</code><br><code>│ ├── CONTCAR_relaxed</code><br><code>│ ├── LEED_to_DFT_atom_numbers.txt</code><br><code>│ ├── POSCAR_bestfit</code><br><code>│ └── VIBROCC_bestfit</code><br><code>└── README.txt <-- This README file</code></p>
<p> </p>
Consider the Head Movements! Saccade Computation in Mobile Eye-Tracking
No full text<h2>How to Cite?</h2>
<p><a href="https://doi.org/10.1145/3517031.3529624">Negar Alinaghi and Ioannis Giannopoulos. 2022. Consider the Head Movements! Saccade Computation in Mobile Eye-Tracking. In 2022 Symposium on Eye Tracking Research and Applications (ETRA '22). Association for Computing Machinery, New York, NY, USA, Article 2, 1–7. https://doi.org/10.1145/3517031.3529624</a></p>
<h2>Abstract</h2>
<p>Saccadic eye movements are known to serve as a suitable proxy for tasks prediction. In mobile eye-tracking, saccadic events are strongly influenced by head movements. Common attempts to compensate for head-movement effects either neglect saccadic events altogether or fuse gaze and head-movement signals measured by IMUs in order to simulate the gaze signal at head-level. Using image processing techniques, we propose a solution for computing saccades based on frames of the scene-camera video. In this method, fixations are first detected based on gaze positions specified in the coordinate system of each frame, and then respective frames are merged. Lastly, pairs of consecutive fixations –forming a saccade- are projected into the coordinate system of the stitched image using the homography matrices computed by the stitching algorithm. The results show a significant difference in length between projected and original saccades, and approximately 37% of error introduced by employing saccades without head-movement consideration. </p>
<p> </p>
<h2>Code and Data</h2>
<p>The data folder contains one sample gaze recording file (named gaze_positions_4bwCZ9awAx_unfamiliar.csv) and the corresponding computed fixations (named fixation_4bwCZ9awAx_unfamiliar.csv).</p>
<p><strong>Note:</strong> For data and privacy protection reasons, the corresponding video recording cannot be shared publicly. This vide is therefore published separately on a per-request basis. Check <a href="4schr-e5g95">this link</a> for requesting access.</p>
<p>The gaze positions file contains these information:</p>
<ul>
<li>'gaze_timestamp': the timestamp of the gaze position, starting at 0 (start of recording).</li>
<li>'world_index': number of the frame on the scene camera video</li>
<li>'confidence': a quality measure not yet (June 2022) implemented by PupilLabs and therefore contains integers equal to 0. If you don't have this column, create a column with this header and set all values equal to 0.</li>
<li>'norm_pos_x': normalized x-position of the gaze</li>
<li>'norm_pos_y': normalized y-position of the gaze </li>
</ul>
<p>The fixations file contains these information:</p>
<ul>
<li>'id': incrementing id starting at 0</li>
<li>'time': the duration of the fixation</li>
<li>'world_index': frame index on the scene camera video related to this fixation</li>
<li>'x_mean': normalized x-position of the fixation</li>
<li>'y_mean': normalized y-position of the fixation</li>
<li>'start_frame': the first frame that contains the fixation point</li>
<li>'end_frame': the last frame that contains the fixation point</li>
<li>'dispersion': the computed dispersion of the fixation</li>
</ul>
<p>The idt.py is the python implementation of the IDT algorithm we used for this paper to compute the fixations from the gaze positions. If you want to use your pre-computed fixations (not using our IDT implementation), just make sure that your fixation file contains the columns mentioned above. In this case just run the main.py using the video and the fixation csv file.</p>
<p>The fixation file and the video file are the two inputs for the main.py which is the algorithm we proposed for the saccadic corrections. <br>main.py creates two outputs:</p>
<ul>
<li>a csv file containing the fixations with two added columns: transformed_x,transformed_y which show the projected x and y coordinate of the fixation.</li>
<li>a csv file containing the computed saccade length and azimuth based on these newly projected coordinates. </li>
</ul>
<h3>An Ethical Note</h3>
<p>The data collected for this study was reviewed by the Pilot Research Ethics Committee at TU Wien. The participants gave written consent for their data to be used for research purposes. We also maintained the transparency of the video recordings in public spaces by wearing a sign indicating that a video recording was in progress.</p>
<h3>License</h3>
<p>All data is published under the CC-BY 4.0 license. The code is under the MIT license.</p>