dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , the increasing number of specific domain combinations. In the early twenty-first century, whole plant genome sequences have started to be released, and a wealth of data is now available  (100). For example, the kinomes of almost 200 genomes of plants are stored in the iTAK database  (166) , and they are classified based on the latest Shiu classification proposed in 2012  (77). Moreover, the recent availability of many new genome sequences in Charophyta and basal land plant lineages has led to a more precise description of the fusion events that have shaped RLK subfamilies in angiosperms. These data from the literature and our unpublished studies are described below and summarized in Figure  3. The presence and frequencies of RLK domains within major green lineages. The color code is the same as in Figure  1 ; a filled circle depicts the presence of the concerned structure, and its size is proportional to the number of sequences found; an open circle means that no gene has been found.
Receptor-Like Kinases in Angiosperms To supplement the data harvested in the literature, we selected 176,020 protein kinases classified as RLK/Pelle in the iTAK database  (166)  and scanned them to annotate their structural domains (Supplemental Table  1 ). On average, there are approximately 900 RLKs per genome in the 127 dicot and 53 monocot species analyzed in accordance with previous results  (123, 167). One third consist only of a kinase domain (62,642 proteins). Some of these proteins are RLCKs. In 12% of the analyzed receptor configurations, only a TM and kinase domain were detected without any other motifs in the ECD (13,591 proteins). However, in many of these proteins, the ECD was very long (several hundred AAs) (Supplemental Figure  2 ). New motifs or domains may ultimately be discovered in these receptors.
Chlorophyta Clade In the Chlorophyta clade, there have been several attempts to screen for the presence of RLKs. A search in the Ostreococcus tauri  (35)  and Chlamydomonas reinhardtii genomes revealed the presence of two RLCK genes in C. reinhardtii but none in O. tauri  (78). When using the standalone iTAK program  (166)  to assess several chlorophyte genomes, we found a few kinase domains classified as RLK/Pelle, some with various associated domains [e. g. , ankyrin-or WD40-repeats annotated by the InterProScan program (102) but no TM domain detected by the TMHMM program  (67) ]. One domain that was found repeatedly in several species from the Chlorophyceae (three copies), Ulvophyceae (one copy), or Trebouxiophyceae (one copy) was a C-terminal U-box domain associated or not with other N-terminal domains resembling the UspA kinase U-box RLCKs described above (Figure  3 ).
(13,75,111,147) the InterPro database(32,102)] or DOMAIN OF UN-KNOWN FUNCTION 26 (DUF26) (in the literature). The latest research on this receptor subfamily suggests that they play roles in defense and oxidative stress responses, programmed cell death, and development(13,75,111,147). 2. 1. 10. Pro-rich/extensin domain. In 2002, Silva & Goring (125) described and characterized a new gene from Brassica napus: PROLINE EXTENSIN-LIKE RECEPTOR KINASE1 (PERK1).
Using the crystal structure of SARS-CoV-2 papain-like protease (PL pro ) as a template, we developed a pharmacophore model of functional centers of the PL pro inhibitor-binding pocket. With this model, we conducted data mining of the conformational database of FDAapproved drugs. This search identified 147 compounds that can be potential inhibitors of SARS-CoV-2 PL pro. The conformations of these compounds underwent 3D fingerprint similarity clusterization, followed by docking of possible conformers to the binding pocket of PL pro. Docking of random compounds to the binding pocket of protease was also done for comparison. Free energies of the docking interaction for the selected compounds were lower than for random compounds. The drug list obtained includes inhibitors of HIV, Hepatitis C, and cytomegalovirus (CMV), as well as a set of drugs that have demonstrated some activity in MERS, SARS-CoV, and SARS-CoV-2 therapy.
Pharmacophore design and use Analyzing a pocket, we elucidated a majority of possible interactions between PL pro (PDB ID: 6W9C) and a potential ligand for developing a protein-based pharmacophore model with potential fictional centers that would bind to the residues in the pocket (Figure  1A ). Resolution of the protein structure used in the study is 2. 7A. From our experience such a resolution is not the best but sufficient for the pharmacophore-based modeling. Using Molecular Operating Environment (MOE; CCG, Montreal, Canada), we constructed two pharmacophore models including ten features (Pha01) and ten features with excluded volume R=1. 3 Å (Pha02): two donors, two donors or acceptors, one hydrophobic, and five hydrophobic or aromatic features (Figure  1A ). Based on developed pharmacophores to select potential drug-candidates, we conducted a pharmacophore search with both pharmacophore models on our conformational database (DB) of FDA-approved drugs, containing around 2500 drugs and 600 000 conformations.
Searches were provided using pharmacophores partial match: eight of ten features for Pha01 and seven of ten features for Pha02. Search results of Pha01 (Search 1) identified 405 compounds with 63 821 conformations while Pha02 (Search 2) identified 857 compounds with 224 609 conformations. We selected 84 and 77 compounds from Search 1 and 2 respectively based on a number of H-bonds and hydrophobic Manuscript to be reviewed interactions in the best docking pose. Because some compounds appeared in both searches, we eliminated duplicate compounds, resulting in a total of 147 unique drugs. Then we clustered the selected 147 compounds, using MOE Database Viewer with a fingerprint GpiDAPH3 and similarity-overlap parameter SO = 42% to elucidate the common structure-functional features of the groups of compound to enhance further drug development.
Docking of drug conformers using the supercomputer Comet For docking the selected compounds, we used the crystal structure of the SARS-CoV-2 PLP (PDB ID: 6W9C). A binding pocket was defined based on the known residues of the S3/S4 binding pocket site of SARS-CoV-2 PLP. Docking of the selected compounds was done using Autodock Vina. Conformers of each of the selected compounds were generated using OpenBabel. However, since Autodock Vina does not support docking compounds that include boron atoms (i. e. , bortezomib), each boron atom in the conformers of bortezomib was replaced with carbon atoms due to their similar size. The random control compounds were selected by a 79-compound, simplerandom subset of all the ZINC DB compounds; these were docked with PL pro in the same processes. Likewise, the conformers of the compound with ID: ZINC001779539170 had their silicon atom replaced with carbon due to Autodock Vina's restraints regarding supported atoms.
The Comet supercomputer at the San Diego Supercomputer Center (SDSC) was primarily used for two parts of the analyses: (1) conversion of files in the pdb format to the pdbqt format, using the Open Babel software (version 2. 4. 1), and (2) all the docking computations using the AutoDock Vina software (version 1. 1. 2). We outline the system configuration and the analyses workflow details below.
File conversion and docking workflow The first step in the computational workflow on Comet was to convert 385 193 pdb files of drug conformers into the pdbqt format. The files were contained in 27 zip files and the jobs were simultaneously run on Comet (one zip file in each job). The zip files were extracted to the local SSD based file system to reduce IO loads, converted to pdbqt files in the same location, and then the results were archived in a zip file. With the local SSD approach, all the conversion jobs were completed in less than 20 minutes. The AutoDock Vina software was used to dock a total of 490 678 drug conformers using computations on Comet. The local SSD approach was used again to mitigate IO loads on the main filesystem. The docking tasks were split up into separate jobs (that were run simultaneously) with 3000-4000 drug conformers docked in each job.
The random control compounds were selected by a 79compound, simple-random subset of the ZINC DB of drug-like compounds. For docking the selected compounds, we used the same crystal structure of the SARS-CoV-2 (Protein Data Bank entry, 6W9C) imported into MOE. A S3/S4 pocket site was defined, which included the following residues: K157, L162, G163, D164, R166, P247, P248, Y264, G266, Y268, and P299. Conformers of each of the selected compounds were generated with OpenBabel before being docked with AutoDock Vina. Figure  3  shows the values of docking free energies of the selected and random compounds. The energies of interaction with PL pro are shown in Table  2. One can see that drugs of clusters 2 and 5 are at the top of the table. Note that the binding pocket of PL pro is not very specific and contains a number of hydrophobic binding centers; that is why binding energies are not overwhelmingly better than those of random compounds (Figure  3 ).
Discussion Based on the crystal structure of SARS-CoV-2 PL pro (PDB ID: 6W9C), we developed two pharmacophore models of the binding pocket of this protein. Using these models, we browsed our conformational database of FDA-approved drugs and obtained 147 hits that were clusterized for selecting the most promising candidates and then used for multi-conformational docking to the PL pro pocket. The drug list obtained includes inhibitors of HIV, Hepatitis C, and CMV, as well as a set of drugs that demonstrated some activity in MERS, SARS-CoV, and SARS-CoV-2 therapy. We developed a pharmacophore model of the binding pocket site S3/S4 of COVID-19 PL pro then conducted multi-conformational docking of these drug compounds to this site for ranging the potential inhibitors selected by pharmacophore-based search.
We also conducted clusterization of the selected compounds based on their pharmacophores 3D profiles to elucidate the common features for further drug design, and compared the docking results for the selected drug compounds with the docking results of random compounds to evaluate the area of significance in the values of binding energies. We note that the pharmacophore-based selection is a very powerful tool so even the drugs with the binding energies on the same level with the random compound do not have to be completely discarded. We are aware of two other studies where docking experiments were used to predict binding of existing pharmaceuticals to the SARS-CoV-2 PL pro  [9, 18]. Both prior studies relied on homology modelling of part  [21]  or the entire SARS-CoV PL pro. Wu et al  [21]  studied 2924 compounds from ZINC Drug Database, as well as 78 known antivirals; while Arya et al.
studied 2525 FDAapproved compounds from DrugBank and the ZINC 15 database. Two compounds were identified in the present study and by Wu et al  [9] : valganciclovir and pemextred. The remaining compounds identified here are unique to our study. This may reflect the influence of using the crystal structure of SARS-CoV-2 as the starting point in the present study, and a difference in methodology in our case including preliminary pharmacophore-based search before docking computational experiments. PeerJ reviewing PDF | (2020:07:50572:1:0:CHECK 25 Aug 2020).
Manuscript to be reviewed It is interesting to note that several drugs with high docking energy were tested or are in experimental testing: nilotinib was active only for SARS-CoV  [26] ; dasatinib was confirmed to be active in cell-culture assays for MERS-CoV and SARS-CoV  [27]. Dasatinib was also shown to be active against SARS-Cov-2 in clinical cases  [30]. Terconazole and fluspirilene were shown to be active in cell-culture assays for SARS-Cov-2  [30]. Manidipine was found in the database of experimental results for broad set of antiviral drugs, DrugVirus. info [31]. Indinavir and ritonavir (HIV viral protease inhibitor), boceprevir (Hepatitis C protease inhibitor), and valganciclovir (antiviral medication for CMV) were found with energies of binding to PL pro of -6. 7 kcal/mol and better.
We note that according to the DrugVirus. info database [31], 11 of the compounds selected by the pharmacophore-based search showed activity against the set of viruses (Fig. 4 ) including amodiaquine, chloroquine, sorafenib, dasatenib, hydroxychloroquine, bortezomib, topotecan, manidipine, lovastatin, gefitinib, and ritonavir. Most experimental testing was done in cellcultures, but there is also a significant amount of animal testing and several of these drugs are in different stages of clinical trials. The prior computational studies  [10, 21]  did not identify any of these compounds as potential inhibitors of PL pro , with the exception of chloroquine  [21]. On the other hand, Wu et al. [10]  identified two antivirals that our experiments did not predict as inhibitors: ribavirin and β-thymidine.
Manuscript to be reviewed Figure Legends. Figure 12 Drugs among the predicted by pharmacophore search inhibitors of PL pro that were experimentally tested for various viruses Obtained using DrugVirus. info database  [26]  PeerJ reviewing PDF | (2020:07:50572:1:0:CHECK 25 Aug 2020) Manuscript to be reviewed Manuscript to be reviewed List of docked compounds sorted by their energies of interaction with COVID-19 papainlike protease in the docked positions PeerJ reviewing PDF | (2020:07:50572:1:0:CHECK 25 Aug 2020) Manuscript to be reviewed.
Third, to complement these two computerized tests, a paper-and-pencil Letter Cancellation task was included. Participants crossed out all the "T" and "G" on a A4 paper sheet. The total score  (Geldmacher, 1996)  combined speed and accuracy: (number of correct responses/number of targets) × (number of correct responses/total time). Higher scores indicate better performance. According to our teacher partner, this measure is more representative of what teachers conceive as "paying attention to an exercise" in the classroom, compared to computer-based tasks focused on short reaction times. Noise levels were measured with sound level meters placed on the front wall of each class. Samples of 1 min were continuously collected and sent over the Internet to an anonymized database. Overall, the choice of measures was guided by previous research (to make comparisons with other studies possible), and by teachers' suggestions (to ensure that the analyses would provide useful conclusions for them).
Recruitment and Meetings with Schools The recruitment process was different to what we experienced before (e. g. , cold calling schools). Our teacher partner convinced his local inspector about the importance of the project. Via email, this inspector contacted the schools under his jurisdiction, forwarding a document summarizing the key elements of the project (short review of the literature, description of the measures and interventions, targeted population-children in their last 2 years of elementary school). The research hypotheses were not mentioned to avoid biases (e. g. , teachers behaving differently if they expect a certain outcome). We received positive answers from six teachers, whose classes contained 126 pupils. Individual data were collected for 113 pupils. The team was gathered in a common meeting, in October 2017.
Data Collection Data collection took a maximum of 3 days per class. Behavioral tests of attention were carried out in individual sessions lasting 15 min maximum. This short duration prevented children from missing a lot of instructional time and helped adapting to unexpected events (e. g. , it was easier to pause data collection if children needed to go to a school assembly, or to a specific lesson). Children's reactions to noise and feedback about the interventions were collected via questionnaires filled during collective sessions in class, under the supervision of the teacher. For data collection to run smoothly, a great deal of anticipation was needed. Teachers were asked about their schedule and potential periods of unavailability. The lists of pupils were printed and brought on site.
It should be noted that data from only one control class could be analyzed. Measures taken from the sound level meters were directly sent to an online database, and constant interruptions in the Internet connection of the second control class considerably reduced the number of samples available. Children's reactions to noise and attentional skills were analyzed by regression analyses, each posttest value being regressed on the baseline value, age and intervention group (see Table  3 ). Children from the sound awareness group reported lower noise interference after the long-term intervention (M T3 = 1. 88, SD T3 =. 68), compared with the baseline data (M T1 = 2. 18, SD T1 =. 86), differing from both the control group (ß = -. 23; p =. 027; for which ratings were pretty Figure  4  illustrates these results.
Children in the sound awareness group also reported lower noise annoyance after the long-term intervention (M T3 = 1. 76, SD T3 =. 70), compared to the baseline data (M T1 = 2. 03, SD T1 =. 76), differing from the mindfulness group (ß = -. 25; p =. 024), for which annoyance ratings slightly increased (M T1 = 2. 18, SD T1 =. 77, M T3 = 2. 28, SD T3 =. 84), but not differing from the control group (ß = -. 10; p =. 409). We did not find any intervention effect on the behavioral measures of attention (see Table  3 ). Individual computerized tests were carried out outside of class to avoid disrupting lessons, with the assumption that the tests provided a screenshot of participants' attention skills at a given point in time (this kind of assumption is not uncommon in studies using a pre-posttest design).
Acknowledgments-This work was funded by the  Economic and Social Research Council  (grant reference:  13115739 ), by the  Centre for Brain and Cognitive Development of Birkbeck University , and by the  Rectorat de Corse (Cellule Santé et Sécurité). We would like to thank  Antoine Chélélékian ,  Julien Pons  and  Claude Mendivé , for their continuous support and confidence in the project. Their help in the funding and recruitment processes were invaluable. We would like to thank  Marie Frasseto  ( Association Pace Salute ) and  Tommy Lawson  ( Association Zone Libre ) for designing and running fascinating school interventions. Finally, we thank  Kathryn Bates  for her constructive feedback on an early draft of the paper.
1% BSA, 0. 3% Triton X-100 in PBS. Elite Standard Vectastain ABC kit and DAB kit (both Vector Labs) were finally applied according to the manufacturer's instructions. Nuclei were counterstained with 4,6-diamidino-2phenylindole (DAPI) (Dako, Glostrup, Denmark). For hematoxylin and eosin (HE)-staining, paraffin-embedded sections of 4 µm were used. Microscopic analyses were performed using an Axioskop2 (Zeiss, Oberkochen, Germany) microscope and the Axiovision software Rel4. 7 (Zeiss).
DRG cells were incubated in Ringer's solution (pH 7. 4, 140 mM NaCl, 4 mM KCl, 2 mM CaCl2, 1 mM MgCl2, 10 mM HEPES, and 4. 54 mM NaOH) with 10 µM Fura-2 AM and 0. 05% of Pluronic F-127 (Invitrogen). Coverslips were mounted on a custom-made aluminum perfusion block and viewed through an inverted microscope (Nikon TS100, Technical Instruments, Burlingame, CA). Fluorescence was excited by UV light at 340 and 380 nm alternately, and emitted light was collected via a CoolSNAP camera attached to a Lambda LS lamp and a Lambda optical filter changer (Sutter Instrument, Novato, CA). Ratiometric measurements were made using Simple PCI software (Hamamatsu, Sewickley, PA) every 3 s. Solutions were delivered by a solenoid-controlled eightchannel perfusion system (ValveLink, AutoMate Scientific, San Francisco, CA).
ACKNOWLEDGMENTS We thank  Jutta Schulz ,  Ronald Manlapaz , and  Wendy Cedron  for excellent technical assistance. Parts of this work have been presented at a national and a European conference on skin research. These poster abstracts were published as Buhl et al. , J Invest Dermatol 2014,  134 ,  S1 , and Buhl et al. , Exp Dermatol 2015,  24 ,  E1.
Materials and Methods collection site. Magdalena Bay is a coastal lagoon on the Pacific coast of the Baja California Peninsula, Mexico, between 25°10′10″N-112°11′24″W and 24°26′24″N-113°33′00″W. Thermal profile data of the sea surface temperature (SST) of the lagoon (Aqua MODIS, NPP, 4 km, daytime, 11 microns, 2003-present, monthly composite) and chlorophyll-a data (mg L) (Aqua MODIS, NPP, L3SMI, global, 4 km, science quality, 2003-present, monthly composite) were obtained for a decade (2006-2016) from the NOAA (National Oceanic and Atmospheric Administration, US). Additionally, in situ SST records were obtained from the culture site (<2 m depth) every 30 min using a HOBO pendant data logger (Onset UA-002-64, US) during April-May (spring) and June-July (summer) 2016 (Supplementary Fig. 1 ).
During transport (2-3 h), the temperature was monitored using a HOBO data logger (Onset UA-002-64, US) and maintained at 22 ± 1 °C by adding precooled seawater to standardise the transport and acclimatisation temperature on both sampling dates. Upon arrival, the animals were placed in a 150 L water tank and acclimated for eight days at 22 ± 1 °C, under a controlled photoperiod (12 L: 12 D). The water temperature was continuously monitored with a HOBO data logger (Onset UA-002-64, US), while the water salinity (35-36 ppm; Extech Instruments, Waltham, MA, US) and pO 2 (90% air saturation; Microx TX2, Presens, Germany) were measured daily. During acclimation, the animals were fed a microalgae diet consisting of Isochrysis galbana and Chaetoceros calcitrans (90,000 cells mL -1 , 1:1). The food consumption was monitored daily using a particle counter (multisizer, Beckman, US).
The scallops were placed in the chamber at the acclimation temperature (22 °C) and fasted for 12 h before the thermal challenges were started. Water temperature was then gradually increased (1 °C h -1 ) from the acclimation temperature to one of the two higher target temperatures (26 or 30 °C), after which the temperature was held constant for 24 h. The temperature was continuously monitored using data loggers (Onset UA-002-64, US). Water dissolved oxygen levels were monitored using oxygen microsensors (Microx TX2, Presens, Germany) and were kept above 70% air saturation in all experiments. Once the target temperature was reached, individual oxygen consumption was measured at 0, 6, 12, 18 and 24 h. The dissolved oxygen levels (% air saturation) were measured using microsensors (Microx TX2, Presens, Germany) connected to the outflow of each chamber (60 mL min -1 ).
Acknowledgements We thank  Gilberto Gonzalez Soriano  and  Jose Luis Ramírez Arce  from  CIBNOR  for their support during the experiments. The present work was supported by the project  ECOS-ANUIES-CONACYT 262983 ,  SEP-CONACYT 286252  and  SIP-IPN 20195375. Marcial Arellano  received grants from  COFAA , EDI, and  SNI-CONACyT. This work was also supported by two " Mission Longue Durée " grants to  Edouard Kraffe  in CIBNOR by the  Institut de Recherche pour le Développement (IRD).
In contrast, sessile drops exhibit clear cone formation under microgravity conditions with an electric field (see Fig. 5d , 5c and Fig. 6 ). Along with the influence on the interface (see Fig. 5 ), which is in agreement with other experiments  [12, 10, 18, 22, 24] , the de-pinning process is also associated with the gravitational and electrical forces individually or in combination. Based on these comparisons, we can see the correlation between the body and surface force conditions and the volumes (see Fig. 3 ) and contact angles (contact angles were measured by using the ImageJ R software plugin known as DropSnake, which is based on B-spline snakes (active contours)) during de-pinning irrespective of the shape of the sessile drop interface shape. The fact that de-pinning is anticipated in the presence of an electric field can be attributed to the fact that the radial electric force is directed inwards, causing striction of the interface  [12].