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Figs 132–139. Liroetis prominensis Jiang, 1988. 132–136 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 132–139. Liroetis prominensis Jiang, 1988. 132–136 – Male, paratype (9.2 mm). 132 – dorsal view; 133 – lateral view; 134 – ventral view; 135 – head and pronotum; 136 – labels. 137–139 – Male, paratype (9.0 mm). 137 – dorsal view; 138 – head and pronotum; 139 – labels.
Figs 72–76. Liroetis flavipennis Bryant, 1954. 72–74 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 72–76. Liroetis flavipennis Bryant, 1954. 72–74 – Male, syntype (10.0 mm). 72 – dorsal view; 73 – labels; 74 – apex of abdomen. 75–76 – Female, syntype (11.8 mm). 75 – dorsal view; 76 – labels.
Figs 87–94. Liroetis lonicernis Jiang, 1988. 87–90 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 87–94. Liroetis lonicernis Jiang, 1988. 87–90 – Male, paratype (9.4 mm). 87 – dorsal view; 88 – ventral view; 89 – head and pronotum; 90 – labels. 91–94 – Female, paratype (9.2 mm). 91 – dorsal view; 92 – ventral view; 93 – head and pronotum; 94 – labels.
Figs 164–171 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 164–171. Liroetis elongatus (Kimoto, 1977). 164–167 – Female, holotype (8.4 mm). 164 – dorsal view; 165 – lateral view; 166 – head and pronotum; 167 – labels. 168–171 – Male, paratype (7.8 mm). 168 – dorsal view; 169 – lateral view; 170 – head and pronotum; 171 – labels.
Figs 32–41 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 32–41. Spermatheca (Figs 32–36) and sternite VII (Figs 37–41) of Liroetis. 32, 37 – L. aeneipennis Weise, 1889; 33, 38 – L. coeruleipennis Weise, 1889; 34, 39 – L. ephippiatus (Laboissière, 1930); 35, 40 – L. leechi Jacoby, 1890; 36, 41 – L. nigricollis (Jiang, 1990). Scales 0.25 mm for Figs 32–36, 0.5 mm for Figs 37–41.
Figs 65–71 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 65–71. Details of new species of Liroetis. 65–70 – L. aurantiacus sp. nov. 65 – Aedegus in dorsal, lateral and ventral views; 66 – female pygidium; 67 – spermatheca; 68 – sternite VIII; 69 – male abdomen in ventral and lateral views. 70–71 – L. baolocanus sp. nov. 70 – Aedegus in dorsal, lateral and ventral views; 71 – male abdomen in ventral and lateral views. Scales: 0.5 mm for Figs 65–66, 68 and 70, 0.25 mm for Fig. 67; 1 mm for Figs 69, 71.
Figs 118–131 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 118–131. Spermatheca (Figs 118–124) and sternite VII (Figs 125–131) of Liroetis. 118, 125 – L. flavipennis Bryant, 1954; 119, 126 – L. humeralis Jiang, 1988; 120, 127 – L. medvedevi nom. nov.; 121, 128 – L. nepalensis Chûjô, 1966; 122, 129 – L. sichuanensis Jiang, 1988; 123, 130 – L. tibetanus Jiang, 1988; 124, 131 – L. zhongdianicus Jiang, 1988. Scales 0.25 mm for Figs 118–124, 0.5 mm for Figs 125–131.
Figs 189–192 in Redefinition of Liroetis, with descriptions of two new species and an annotated list of species (Coleoptera: Chrysomelidae: Galerucinae)
Figs 189–192. Aedeagus of Liroetis, dorsal and lateral views. 189 – L. jungchani (Lee, 2016); 190 – L. liui (Lee, 2016); 191 – L. metallipennis (Chûjô, 1962); 192 – L. modestus (Weise, 1922). Scale 0.5 mm.
List of Saturn magnetopause and bow shock crossings by the Cassini spacecraft
<p>This is an updated list of magnetopause (MP) and bow shock (BS) crossings from Cassini, based on the list presented in Jackman et al. [2019] <a href="https://doi.org/10.1029/2019JA026628">https://doi.org/10.1029/2019JA026628</a>. This uploaded list has the following changes from the list first presented in that paper (and included in that supplementary material):</p> <p> </p> <p>I = inbound</p> <p>O = outbound</p> <p>Amendments to Cassini MP/BS crossing list<br> Changes made 26/01/2021</p> <p>Total #MP crossings: 2118 (some times changed)<br> Total #BS crossings: 1247 (3 pairs added, 1 pair removed, some times changed)</p> <p>Change list below:</p> <p>Change #1:<br> Note the following crossings come from a list on MAPSView which was originally collated based on MAG and CAPS data. <br> MAG data gap spans these dates so crossings are based on other instruments at these times.<br> ELS and SNG data available 2004 181-185 and 195-196 (except 196 13-23 UT).</p> <p>2004 181 02 43 MP I <br> 2004 186 04 42 MP O <br> 2004 186 10 24 MP I <br> 2004 186 17 57 MP O <br> 2004 186 20 17 MP I <br> 2004 188 07 06 MP O <br> 2004 188 08 11 MP I <br> 2004 188 13 14 MP O <br> 2004 188 15 13 MP I <br> 2004 188 19 39 MP O <br> 2004 189 14 05 BS O <br> 2004 189 16 15 BS I <br> 2004 189 21 46 BS O <br> 2004 190 00 30 BS I <br> 2004 190 04 13 BS O <br> 2004 190 10 56 BS I <br> 2004 190 11 54 BS O <br> 2004 190 11 54 DG S_SW <br> 2004 194 05 12 DG E_SH <br> 2004 194 05 12 BS O <br> 2004 196 03 11 BS I <br> 2004 196 06 59 BS O </p> <p>Change #2:<br> Change<br> 2004 348 05 27 MP I <br> to<br> 2004 348 05 00 MP I </p> <p>Change #3:<br> Added some crossings on 2004 day 345<br> 2004 345 11 52 BS I<br> 2004 345 11 58 BS O<br> 2004 345 12 20 BS I<br> 2004 345 12 26 BS O<br> 2004 345 23 06 BS I<br> 2004 345 23 13 BS O</p> <p>Change #4: <br> Changed 2004 day 345 13 24 BS I <br> to<br> 2004 day 345 12 57 BS I</p> <p>Change #5: <br> Remove<br> 2014 322 6 8 BS I <br> 2014 326 1 23 BS O </p> <p>_________________________________________________________________________________________<br> Additional changes made through discussion with Matthew Cheng, August 13th 2021<br> Original list: Master_BS_MP_Crossing_List_04_16_incCMJcorr_published<br> 1st Revised list: Master_BS_MP_Crossing_List_04_16_incCMJcorr_revised<br> 2nd Revised list: Master_BS_MP_Crossing_List_04_16_incCMJcorr_revised_120821</p> <p>Since 1st revised list:<br> Total #MP crossings: 2122 (2 new pairs, some times changed)<br> Total #BS crossings: 1249 (1 new pair, some times changed)</p> <p>Change #6<br> Changed typo<br> 2016 068 12 19 DG E_MP <br> to<br> 2016 068 12 19 DG E_SP </p> <p><br> Change #7<br> Change <br> 2016 242 12 00 MP O <br> To:<br> 2016 242 11 15 MP O </p> <p>Change #8<br> Change <br> 2016 152 16 33 BS O<br> to <br> 2016 152 15 22 BS O</p> <p><br> Change #9<br> Change <br> 2016 153 02 24 BS O <br> to <br> 2016 153 02 08 BS O </p> <p><br> Change #10<br> Change <br> 2016 153 12 00 BS I <br> to<br> 2016 153 11 45 BS I </p> <p>Change #11<br> Add new MP pair<br> 2016 155 11 05 MP O <br> 2016 155 12 52 MP I </p> <p><br> Change #12<br> Change<br> 2011 2 03 14 MP O <br> to<br> 2011 2 01 48 MP O </p> <p><br> Change #13<br> Change:<br> 2012 145 15 11 MP O <br> To<br> 2012 145 14 36 MP O</p> <p>Change #14<br> Changed<br> 2007 33 16 25 MP I <br> to <br> 2007 33 15 25 MP I </p> <p>Change #15<br> Add new MP pair<br> 2005 45 11 55 MP O <br> 2005 45 12 36 MP I </p> <p>Change #16<br> Add new BS pair<br> 2012 277 00 15 BS I <br> 2012 277 01 36 BS O</p> <p>Change #17<br> Add new MP pair<br> 2008 85 14 31 MP I <br> 2008 85 15 38 MP O </p>
List of DNS over HTTPS resolvers on the internet
<p>The DoH Internet Servers dataset comprises a verified list of Internet servers offering DNS over HTTPS (DoH) service. The list was created by active scanning of the IPv4 address space. The scanning was done two times in April 2021 and in January 2022. Together, the list contains 1987 different IP addresses with their reverse DNS record (if available), supported DoH method, and TLS 1.3 support. </p> <p>The scanning was done in three phases:</p> <ol> <li>We scanned the IPv4 address space for opened port 443 using masscan. </li> <li>IP addresses found in the previous step were scanned for DoH support using a custom Nmap-NSE script.</li> <li>The IP addresses found in previous steps were reached by a slower python script that also validated responses, obtained domain names.</li> </ol> <p> </p> <p><strong>Scanning limitation: </strong>The main limitation of our scanning was that it could not find DoH resolvers hosted on infrastructures hosting multiple services behind a single IP address. In such cases, an SNI, or HTTP Host header, or HTTP/2 <em>:authority</em> header is needed for a successful request. Since we did not have the SNI, it was impossible to provide it. </p> <p><strong>Used Scripts:</strong></p> <p>NMap: https://github.com/cejkato2/dns-doh.nse<br> Python: https://github.com/hynekkar/DoH-Checker</p> <p> </p> <p> </p> <p> </p>
A new ChEMBL dataset for FastTargetPred and target fishing for an exhaustive list of linear tetrapeptides
<p>A ChEMBL-v29 dataset was generated to be used with the ligand-based similarity search target prediction engine FastTargetPred (https://github.com/ludovicchaput/FastTargetPred). Using this new dataset, attempts to predict macromolecular targets for a published dataset of 160,000 tetrapeptides was performed.</p> <p>The dbchembl29 directory contains all the files for FastTargetPred. This command line tool compares using different types of fingerprints, a file containing small query molecules in SDF format (it can be 1 molecule or a collection) to molecules extracted from ChEMBL29. If a match is found, this suggests that your query molecule is similar to a ChEMBL compound and as the ChEMBL compound has bioactivity data against one or more macromolecular target, then this suggests that your query compound could bind to targets that interact with compounds that are similar to the query molecule. The so-called similarity principle in chemistry. Fingerprints are computed with http://www.mayachemtools.org/, a collection of Perl and Python scripts for Chemoinformatics and (Structural) Bioinformatics.</p> <p>To run FastTargetPred with the new ChEMBL 29 data, you just need to unzip the dbchembl29 directory into the FastTargetPred main directory.</p> <p>The default FastTargetPred commands (e.g., python3 FastTargetPred.py rivaroxaban.sdf, the default command uses ECFP4 fingerprints and a Tanimoto coef of 0.6, rivaroxaban here is the query compound, it is provided in the extra_data directory) will use the data present in the default db directory and thus a curated version of ChEMBL-25 release. It was the version of the ChEMBL database available when FastTargetPred was developed. Since then, many new molecules have been added and this is why we generated the ChEMBL-29 dataset (last ChEMBL version at the time of writing).</p> <p>To use the new ChEMBL-29 data, you can run the following command:</p> <p>python3 FastTargetPred.py rivaroxaban.sdf -fp MACCS -tc 0.9 -db dbchembl29/chembl29_active</p> <p>This applies a similarity search for the query compound (here rivaroxaban, you can for instance move this SDF file in the directory containing the file FastTargetPred.py) using MACCS fingerprints, a Tanimoto coefficient threshold of 0.9 and the -db option forces the system to look at the ChEMBL29 curated data and not the default ChEMBL-25 data. This 0.9 value means to focus on molecules very similar to rivaroxaban present in the ChEMBL data. If one is looking for more distantly related compounds, then a value of 0.7 can be used. Users can try different values or try consensus scoring...See FastTargetPred: a program enabling the fast prediction of putative protein targets for input chemical databases. Chaput et al., Bioinformatics. 2020 Aug 15;36(14):4225-4226</p> <p>The chembl29 directory also contains 714,780 compounds (canonical SMILES strings) extracted from ChEMBL29 that have bioactivity data. Fingerprints could not be computed for 19 molecules that have unusual chemistry. We selected the following thresholds (eg, binding assays, activity against targets less than 20 micro-molar, ChEMBL confidence_score = 6 or above, maximum = 9).</p> <p>With this new dataset, we attempted to predict potential targets with FastTargetPred for 160,000 input query peptides (4 amino acids, combination should be 20 x 20 x 20 x 20) previously reported by Dewi Prasasty and Perdana Istyastono, Data in brief 27 (2019) 104607. The peptides for which a putative target was predicted are shown in two DataWarrior files with the amino acid sequence of the query peptide, the compounds found to be similar in the ChEMBL29 dataset (fingerprints = ECFP4, Tanimoto 0.6) and thus the compound IDs, the target ChEMBL IDs, mapping to the UniProt database when available, information about disease involvements, Reactome pathway database identifiers. These two DataWarrior files are searchable, can be sorted and hyperlinks to the ChEMBL, UniProt and Reactome databases have been inserted.</p> <p> </p>
List of 552 Mycotoxins and associated Smiles and JOELib descriptors
<p>Mycotoxin list containing 552 compounds was created from a literature search. From the compound names, simplified molecular-input line-entry system (Smiles) data were automatically retrieved using the Python module PubChemPy (https://pypi.org/project/PubChemPy/https://pubchempy.readthedocs.io/en/latest/).The Smiles list obtained was used to identify physicochemical indicators for each mycotoxin by the JOELib tools included in the ChemMine platform. These descriptors focus on molecule composition (number of : atoms, halogen atoms, B atoms, Br atoms, Cl atoms, I atoms, F atoms, N atoms, O atoms, P atoms, S atoms, acidic groups, aliphatic OH groups, basic groups, bonds, heavy bonds, heterocycles, hydrophobic groups, NO<sub>2</sub> groups, SO groups, OSO groups, SO<sub>2</sub> groups), molecular structure (molecular weight, geometrical diameter, geometrical radius, geometrical shape coefficient, the calculated Kier Shape for paths with length one, the calculated Kier Shape for paths with length two, the calculated Zagreb Group Index 1, the Zagreb Group Index 2, the fraction of rotatable bonds) and interaction properties (the calculate Polar Surface Area, Octanol/Water partition coefficient (logP), molar refractivity, the number of Hydrogen Bond Acceptors 1, Hydrogen Bond Acceptors 2 , Hydrogen Bond Donors 1 , Hydrogen Bond Donors 2 ).</p> <p>This research was supported by the European Union : Interreg Agritox project EAPA-998-2018.</p>
Potential energy surfaces and rovibrational line lists for beryllium dihydride
<p>Molpro restart files of the potential energy and property surfaces for water and beryllium dihydride and its deuterated isotopologue. Rovibrational line lists containing infrared and Raman intensities as reported in "Efficient and Automated Quantum Chemical Calculation of Rovibrational Nonresonant Raman Spectra" ( <a href="https://doi.org/10.1063/5.0087359">https://doi.org/10.1063/5.0087359 )</a></p>
Location List for IPCC AR6 Sea Level Projections
<p>This data set contains the location list file for the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. It can be used to cross-reference location IDs with names of the locations.</p> <p>Column 1 – Location name (string with spaces having been replaced with underscores)<br> Column 2 – Location ID (integer value)<br> Column 3 – Latitude (-90 to 90 degrees)<br> Column 4 – Longitude (-180 to 180 degrees)</p> <p>See <a href="https://zenodo.org/communities/ipcc-ar6-sea-level-projections">https://zenodo.org/communities/ipcc-ar6-sea-level-projections</a> for additional related data sets.</p>
A list of body and object concepts
<p>The list includes 784 human body parts and object concepts. The concepts are based on the available concept sets in <a href="https://concepticon.clld.org/">Concepticon</a> (List et al. <a href="https://aclanthology.org/L16-1379/">2016</a>, <a href="https://doi.org/10.5281/zenodo.596412">2021</a>) and were tagged as <em>human body part,</em> <em>animal</em>, <em>clothing</em>, <em>food</em>, <em>household</em> <em>items</em>, <em>instrument</em>, <em>landscape</em>, <em>plant</em>, <em>spatial</em> <em>relation</em>, <em>tool</em>, and <em>vehicle</em>.</p>
APPENDIX A - SEARCH PROTOCOLE AND PAPERS LIST - Cognitive Neuroscience's Transposition in STEAM Classroom: A Systematic Literature Review
<p>An integrative literature review was performed corresponding to PRISMA’ protocol in order to integrate issues over an innovative curriculum through cognitive sciences didactic transposition in STEAM classrooms. The search action was handled using various international databases. Each paper was selected alongside the PICO strategy and a defined selection criterion. Analysis of the literature between 2000 and 2021 is presented.</p>
IO500 SC21 lists
<p>IO500 sc21 lists</p>
IO500 ISC20 Lists
<p>IO500 lists</p>
IO500 ISC21 Lists
<p>IO500 lists from ISC21</p>
IO500 SC19 Lists
<p>The historical, Full, 10 node challenge, and student cluster competition IO500 lists</p>
ScienceDex guides
Understand access before you commit
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.