dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank  Junhui Wu  and members of the  Amsterdam Cooperation Lab  for valuable suggestions on the manuscript. We thank  Tim Alkemade ,  Margriet Bentvelzen ,  Marloes Doeswijk ,  Terence Dores Cruz ,  Maaike Homan , and  Sterre van Niekerken  for translations of study materials. C. M. acknowledges IAST funding from the  French National Research Agency (ANR)  under grant  ANR-17-EURE-0010  ( Investissements d'Avenir program ). C. M. and D. B. were supported by a  European Research Council  grant ( ERC StG-2014-635356 ). J. M. T. was also supported by the  ERC  ( ERC StG-2015-680002-HBIS ).
2009). During the duration of the experiment, fish were hand-fed with 40 mg of large food pellets (Gemma Micro 300) delivered twice a day. This quantity represented a daily ration of 5% of individual initial body mass calculated from the average weight of all fish at the beginning of the experiment. In addition, 2 mg of small food pellets (Gemma Micro 150) were added daily in each tank to feed the larvae. For each artificial population (n = 32), several demographic components were estimated as proxies for fitness (i. e. growth in length and weight of marked fish, adult survival probability, reproduction probability, recruitment estimated as the number of larvae and juveniles). Except for growth, these fitness components were quantified from count data derived from 25 visual observation events established during 20 th March to 20 th July at various intervals (at interval of 2 to 15 days, mean = 5 ± 4 SD).
2020). Hence, although arbitrary, a 50% larval survival seems to be a reasonable assumption in presence of cannibalisms from adults. The larvae-to-juvenile and juvenile-to-adult transitions lasted for more than 11 days and, at each time-step, larvae and juveniles could either change stage at a rate of g 0 and g 1 , respectively, or remain in their stage at a rate of 1-g 0 and 1-g 1 , respectively (conditional on survivals s 0 and s 1 , Fig. 1 ). This made it impossible to estimate g 0 and g 1 from count data. Instead, we relied on female somatic growth data from marked medaka. We used average standard length at the start and end of the experiment for each sex per size category in each tank (n = 80 sex-size groups) and (i) modelled average female size increment as a function of initial standard length, (ii) simulated growth trajectories and time taken to grow through the larval (4 -6 mm) and juvenile stages (7 -17 mm), and (iii) computed g x as the ratio between the projection interval in our model (11 days) and duration of each stage.
Details of the statistical models used to estimate the F, s 1 and g x parameters are provided in Table  1. In order to gain precision in parameter estimation, we used an exploratory analysis and selected the most parsimonious models by discarding all non-significant effects. The exploratory analysis differs from a hypothesis testing approach in terms of aims, data and methods (see Statistical analysis above), and should not be used to draw conclusions about the effects of experimental treatments on fitness components. Posterior distributions for F, s 1 and g x parameters were obtained using the sim() function (package arm v. 1. 11. 1; Gelman and Su 2007). The approach required first to estimate the vector of the parameters, the unscaled  estimation covariance matrix , and the residual variance by maximum likelihood using    2 the lme4 package.
Then the sim function simulated the coefficient vector and residual  standard deviation through repeating: 1. Simulating where X is a random draw from the distribution with  =  ( -)   2 n -k degrees of freedom (k = number of model parameters). 2. Given the random draw of , simulate from a multivariate normal distribution with   mean and variance matrix. 2   The number of repetitions for these two simulation steps were set to 5000. The resultant posterior parameter distributions were used to propagate the error in the model parameter estimates to the computation of λ from the A matrices (obtained using the lambda() function in the package popbio v. 2. 7;  Stubben and Milligan 2007 ). Specifically, s 1 was always fixed at its mean posterior value, and we used either the posterior distribution of F with g 0 and g 1 fixed to their mean posterior values, or the posterior distributions of g 0 and g 1 with F fixed to its mean posterior value.
Acknowledgments -We are grateful to numerous colleagues and particularly to  Clementine Renneville  and  Arnaud Le Rouzic  for initiating the medaka lines, to  David Carmignac  and  Romain Peronnet  for their assistance at the laboratory, and to  Rebecca E. Holt  for final language editing. The final manuscript was improved by the constructive comments of the Associate Editor and two anonymous reviewers. This study was funded by the  Research Council of Norway  (projects EvoSize  RCN 251307/F20  and  REEF RCN 255601/E40 ). CE and BDP were also supported by the mobility programme from the  RCN  (projects number  272354  and  268218/MO ). EE was supported by  IDEX SUPER  (project  Convergences   J14U257   MADREPOP ) and by  Rennes Métropole  ( AIS 18C0356 ). CE and EE conceived the ideas and design the methodology; CE conducted the experiment; CE and EE analysed the data; CE wrote the initial draft of the manuscript and BDP, EE and LAV contributed critically to revisions.
More importantly, our IPs also revealed several additional components, some of which harbor remote homology to other known kinetochore components (Figure  1B ). Among those, we identified a CENP-K-like protein, experimentally supporting previous homology predictions in Bombyx mori  [31]. We also identified two components (GSSPFG00019785001 and GSSPFG00001205001) with remote similarities to the coiled-coil and RWD (RING finger-containing proteins, WD-repeat-containing proteins, and yeast DEAD [DEXD]-like helicases) domains of the budding yeast CENP-O and CENP-P proteins, respectively. Furthermore, we   [30]. Background coloring of the residues is based on the ClustalX coloring scheme. Full-length insect CENP-T sequences and accession numbers, where available, are listed in Table  S1. See also Figure  S2.
Homologs of CENP-T Are Retained in Independently Derived CenH3-Deficient Insects Having characterized the function and importance of CENP-T for CenH3-independent kinetochore formation in Lepidoptera, we next profiled its conservation across other CenH3-deficient and CenH3-encoding insects. Orthologs of the lepidopteran CENP-T protein can be readily identified using BLASTP in all CenH3-deficient insects analyzed as well as several   S1. A phylogeny of a set of HFD sequences from various families, including CENP-T, is shown in Figure  S2D , based on HFD sequences listed in Data S2. Although our previous studies did not identify any holocentric insect species that encode for CenH3  [29] , searches in additional organisms revealed the presence of putative CenH3 proteins in water striders (Hemiptera).
METHOD DETAILS Alignments and phylogenetic analyses For Figure  1C  and Figure  S2C , sequences were aligned with MAFFT on the EMBL-EBI web interface  [62] , and visualized and processed with Jalview  [49]  (ClustalX coloring scheme). Secondary structures were predicted using Jpred4  [30]. For Figure  S2D , histone fold domains were aligned using MAFFT  [48, 63]. A maximum likelihood phylogeny was build using PhyML 3. 0  [50]  with automatic model selection  [64] , default parameters and 100 bootstrap permutations. The tree was visualized using iTOL vs4  [51]. Sequences used for the phylogeny are listed in Data S2. Primary sequence analyses of the B. mori and G. gallus CENP-T (GenBank: NP_001263242. 1) for Figure  1C  were performed using fLPS  [52]  with default parameters. The Arginine-rich N-terminal regions are located between amino acid 4 and 32 of the G.
Homology predictions The annotation of the S. frugiperda corn strain proteome were used for all computational analyses  [59]. In case annotations were missing or incorrect we complemented the data using annotations from the S. frugiperda ''rice strain''  [59] , EST (Transcriptome TR2012b) data  [60]  or our own analyses. Homologs of proteins identified in the kinetochore IPs were predicted in B. mori proteome  [61]. Both S. frugiperda and B. mori proteins were used for homology searches. BLASTP and PSI-BLAST searches were performed in the NCBI non-redundant protein database  [65]. Jackhmmer searches were performed on the HMMER webserver  [53]  against reference proteomes as current as September 2019. HHpred version 3. 2. 0 searches were performed against the PDB_mmCIF70_3_Aug  [54]. Coiled-Coil domains were predicted using PCOILS (window 28)  [54, 66, 67].
Iterative blastp and tblastn searches using lepidopteran CENP-T proteins against select annotated insect proteomes, genome assemblies and assembled transcriptomes revealed additional orthologs of CENP-T proteins in insects (Table  S1 ). KWMTBOMO14835 and GSSPFG00001205001 BLASTP searches using both proteins against the non-redundant protein database revealed only hits in Lepidoptera. Iterative PSI-BLAST searches and jackhmmer searches using both proteins also revealed no homologous hits in other insect orders. HHpred searches of GSSPFG00001205001 did not reveal any hits with high probability. However, HHpred searches identified similarity between the N terminus of KWMTBOMO14835 and resolved structure of the S. cerevisiae Ctf19 protein (CENP-P)  [68]  as best hit with high probability (93. 58%) while only 13% amino acid identity.
LOC101741561 and GSSPFG00006732001 The B. mori homolog is not part of the most recent annotations  [61] , we therefore refer to the ID of previous annotations present on NCBI Genebank in Figure  1. GSSPFG00006732001 is likely misannotated because most other lepidopteran homologous proteins including GenBank: LOC101741561 only align to the C-terminal part of the protein. We derived a new consensus sequence by aligning available TR2012b transcriptome data  [60]. The encoded ORF translates into the following protein: > S. frugiperda CENP-K MSSDRNKETRDAVKREIKEIQARCKHEWNIIDNSPLDAPNIDLEQINEKALCYIEGLGTGIQNSNTPITADDNLLTSQFLKEIRDKTGQVEE YTAFVRGSIHDIDAEINRLQTLIKITQEAKARPMLNKCEVQPEHVHRAKERFQVMKNELHSLIHSLFPNCDSLIIETMGQLMAEHLNEESN GYIPVTAETFQIIELLKDMKIVTVNPYNKLEVKLSY One of the EST that contain the full ORF is Sf2H05447-5-1 that is listed in Figure  1B.
Iterative PSI-BLAST searches using the B. mori protein revealed hits in Hymenoptera including Athalia rosae (GenBank: LOC105689000, E value 5x10 -5 ) after the 1 st iteration. The 2 nd iteration revealed hits in Hemiptera including Bemisia tabaci (GenBank: LOC109036758, E value 0. 003), the Blattodean Zootermopsis nevadensis (GenBank: LOC110834781, E value 2x10 -11 ) and the mollusk Mizuhopecten yessoensis (centromere protein K-like, E value 0. 001). The human CENP-K was identified after the 3 rd iteration. These analyses are consistent with previous predictions identifying CENP-K in B. mori  [31]. Jackhmmer searches of a N-terminal truncated version of the B. mori protein without a coiled-coil region also revealed a hit in the phthirapteran Pediculus humanus corporis after the 3 rd iteration (UniprotKB: E0VW71, E value 4.
5 mL/min with buffer A (2/98 MeCN/H2O in 0. 1% formic acid). After 3 min or 4 min of desalting, the precolumn was switched on line with the analytical C18 column (75 mm inner diameter x 50 cm; nanoViper Acclaim PepMapTM RSLC, 2 mm, 100A ˚ , Thermo Fisher Scientific Cat#164535) equilibrated in buffer A. Separation was then performed with a linear gradient of 5% to 25% or 30% buffer B (100% MeCN in 0. 1% formic acid) at a flow rate of 300 nL/min over 100 min or 91 min. MS full scans were performed in the ultrahigh-field Orbitrap mass analyzer in ranges m/z 400-1500 or m/z 375-1500 with a resolution of 120 000 at m/z 200, ions from each full scan were HCD fragmented and analyzed in the linear ion trap or orbitrap. For identification, the data were merged and searched against the S. frugiperda corn strain proteome  [59]  using Sequest HF through Proteome Discoverer (version 2.
2, Thermo Fisher Scientific) with the S. frugiperda CENP-T, CENP-I, CENP-N, Nsl1 candidate and Spc25 (which were all not correctly annotated in the S. frugiperda corn strain assembly) manually added to the proteome database. For identification of proteins in the B. mori IPs, the data were searched against the B. mori proteome. Enzyme specificity was set to trypsin and a maximum of two-missed cleavage sites were allowed. Oxidized methionine, Carbamidomethyl cysteines and N-terminal acetylation were set as variable modifications. Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0. 6 Da for MS/MS peaks. The resulting files were further processed using myProMS  [56]  v3. 6. FDR calculation used Percolator and was set to 1% at the peptide level for the whole study.
For the kinetochore IPs on full-length lepidopteran proteins identified proteins that were at least four-fold enriched over the control (the two controls were combined) with a minimum of seven derived peptides were analyzed further (Data S1). For the S. frugiperda CENP-T-HFD-FLAG IP (Figure  S1B ) proteins that were at least four-fold enriched over the control with at least 5 derived peptides were selected for further analyses (see above). For Figure  1 , known kinetochore homologs or proteins with at least seven derived peptides and that were enriched at least four-fold in three kinetochore immunoprecipitates are listed.
See also Data S1 and Figure S1. (C) Top: graphical representation of the B. mori CENP-T and G. gallus CENP-T sequence features, showing the location of the HFDs (blue box), CENP-T familyspecific extension (green box), and arginine-rich (E value 4. 7 3 10 -5 and E value 6. 0 3 10 -7 , respectively) and proline-rich regions (E value 3. 7 3 10 -4 and E value 3. 4 3 10 -8 , respectively) (orange and purple boxes, respectively). Bottom: multiple alignment of the C-terminal extension of vertebrate and insect CENP-T proteins. B. mori (top) and G. gallus (bottom) secondary structure predictions are derived from Jpred4 (a-helical regions are shown in red and b strands in yellow) [30]. Background coloring of the residues is based on the ClustalX coloring scheme. Full-length insect CENP-T sequences and accession numbers, where available, are listed in TableS1.
ACKNOWLEDGMENTS We thank  Harmit S. Malik  and  Steve Henikoff  for support during the initial steps of this project;  Alexander Schleiffer  and  Eelco Tromer  for helpful discussions regarding protein homology predictions;  Alexander Schleiffer  for sharing a collection of representative histone fold proteins;,  Takahiro Kusakabe  for the BmN4-SID1 cell line;  Tatsuo Fukagawa  for the CENP-T-FLAG DT40 cell line we used for control experiments;  Abderrahman Khila  for access to waterstrider genome sequences;  Patricia Le Baccon ,  Mickae ¨ l Garnier , and Sole ne Herve for help with microscopic analyses;  Ahmed El Marjou  and  Carlos Kikuti  for help and advice regarding protein purification and biochemistry;  Carsten Janke  for advice regarding microtubule staining; all members of the  Fachinetti and Drinnenberg labs  for helpful discussions; and  Leah Rosin  and all members of the  Drinnenberg lab  for input on the manuscript.
N. C. -S. receives salary support from  Sorbonne Universite  ( 2781/2017 ). This work was supported by '' Region Ile-de-France '' and  Fondation pour la Recherche Medicale grants  (to D. L. ). This work was supported by grants from  JSPS KAKENHI  ( 15H02482  to S. K. and T. K. ). I. A. D. and I. L. receive salary support from the  CNRS. This work was supported by  LabEx DEEP  ( ANR-11-LABX-0044 ,  ANR-10-IDEX-0001-02 ), an  ATIP-AVENIR research grant ,  Institut Curie , and the  ERC  ( CENEVO-758757 ).
Passive and Active Scanning In connectionless communication mode the BLE nodes (advertisers) periodically broadcast advertisement (ADV) packets that can be coincidentally received by scanners. Depending on nodes and scanners configuration the devices may exchange either one or three radio messages (Table  1 ). Single message mode (ADV only) is used when scanner operates in passive scanning mode or the node does not support scanning operation. Single message allows for unconfirmed, one way communication from the node to the scanners (several BLE scanners may receive the same ADV if they are within the communication range). Because the ADV message may contain up to 31 bytes of data, they can be used for application specific data transmission e. g. , in opportunistic sensing applications  [4, 5].
Three message mode requires the scanner to operate in active scanning mode an the node to support scanning procedure. In three message mode the scanner may transmit the SCAN_REQ message after receiving the ADV. Frequency of SCAN_REQ depends on the content of the ADV received as well as a backoff procedure implemented in the scanner  [1, 15]. In general the active scanner shall sent SCAN_REQ when content of the ADV changes or after reception of at most 256 ADV messages (256 is the highest value of the backoff counter). The SCAN_REQ message does not contain any data and, after it is received by the the node, it initiates transmission of a SCAN_RESP message. The structure of SCAN_RESP is similar to the structure of ADV and it may contain another 31 bytes of data. In this operation mode the radio packets are transmitted back and forth, but the data is still transmitted from the node to the scanner.
Moreover, while ADV messages can be received by all the scanners, the SCAN_RESP are received only by the scanner that has sent corresponding SCAN_REQ message. Although, the SCAN_REQ message is not transmitted for every received ADV and it cannot contain application specific data, it can be used as an acknowledgement to confirm correct reception of the ADV by the scanner. This allows to design a communication scheme that uses three message communication in order to terminate ADV transmission as soon as the transmitted data is received. This improves communication efficiency, decrease the interference in the communication channel, and reduce node's energy consumption. Figure  1  presents the difference between the connectionless communication using passive and the proposed communication scheme.
The Proposed Communication Scheme In the proposed three message communication scheme (Figure  2 ) the SCAN_REQ provides feedback to the node on the correct reception of the ADV and the data contained in the payload. Therefore, when the node has data to be transmitted it may split it into chunks of at most 31 bytes. Each chunk is then broadcasted periodically inside the payload of ADV messages. After reception of these messages the scanner will eventually send a SCAN_REQ message. When SCAN_REQ is received by the node, it is interpreted as a confirmation that at least one scanner (the one that sent the SCAN_REQ) has correctly received the data. The node may then disable further ADV transmission until new chunk of data is available. We call this operation an intermittent mode as nodes stop and restart ADV transmission when SCAN_REQ is received and new data is available.
Because the scanners run a backoff procedure before sending the SCAN_REQ message (cf. [1, 16] ), therefore, not every received ADV initiates the exchange of SCAN_REQ and SCAN_RESP messages. The transmission of SCAN_REQ can also be disturbed by the interference in the radio channel. Consequently, the node may need to transmit several ADV messages before correct reception of the data is confirmed with SCAN_REQ. According to the BLE specification  [1] , the node that receives SCAN_REQ has to respond with SCAN_RESP message that may also contain application-specific data. However, when operating in the intermittent mode the node sends SCAN_RESP only once and terminates ADV transmission making it impossible for the scanners to sent another SCAN_REQ. Therefore, the data contained in the SCAN_RESP is transmitted only once, without confirmation.
20 m 2 ) and sent messages with a transmission power of 0 dBm. The data were received by a scanner running in continuous scanning mode (i. e. , scan window and scan interval parameters were set equal) in either active or passive mode, depending on the scenario. Because our goal was to imitate real-life sensing application we decided to use RaspberryPi (RPi) as scanners. These are low-cost and very popular devices with WiFi connectivity, integrated BLE radios (RPi Zero W, and RPi 4) or supporting BLE dongles (RPi 3 and earlier). Because RPis are quite limited in computational and communication performance, the results of our experiments can be considered as a lower bound of what can be expected if more efficient scanners are used. The scanners were storing measurements in random-access memory to minimize the impact of the operating system and SD card underperformance on the results of the experiments.
Parameters The efficiency of the proposed scheme is assessed with Packet Loss Ratio (PLR) and Data Reception Ratio (DRR). The PLR is a measure that takes into account both the collisions in the communication channel as well as the underperformance of real-life scanners that may not be able to correctly receive all the packets, even if there is no collision in the radio channel. The PLR is calculated as a ratio of the number of ADV packets received by the scanner to the number transmitted by the advertising node. Because the scanner concurrently listens on a single advertisement channel, the PLR takes into account only ADV messages that are transmitted on the same channel. Therefore, the number of packets transmitted by a node (N TxPacket ) equals the number of advertisement events, which can be estimated from the value of advertisement interval (T AI ) and the time node was transmitting the ADV messages (T active ): N TxPacket = T active T AI.
Experimental Scenarios Table  3  lists sniffer settings for the scenarios verified experimentally. The first scenario is used to assess performance of typical opportunistic sensing BLE applications when the nodes continuously transmit advertisements and scanners passively listen for those messages. In the second scenario the S1 sniffer is switched to active mode but the devices still broadcast the advertisements continuously. The devices are receiving the SCAN_REQ messages from S1 but do not suspend periodic transmission of ADV. The third scenario is similar to the previous one-the devices continuously broadcast advertisements but two sniffers (S1, S2) are scanning actively and can transmit SCAN_REQ messages. The fourth scenario is also similar to the second but when SCAN_REQ is received the nodes suspend ADV transmission until new data (new sequence number) is to be transmitted (i.
Simulations It is infeasible to estimate all parameters of interest in real-life experiments for different number of nodes operating in intermittent mode, and various settings of BLE parameters (e. g. , advertisement and data intervals). Therefore, we have developed a simulator (https://mariuszslabicki. github. io/pytooth/) to model behaviour of the BLE devices and analyse how intermittent mode affects communication efficiency compared to continuous transmission of advertisements. The model of the node and its operation is derived from the BLE specification  [1]  and power profiles presented by the manufacturer of the nodes (namely nRF 52832 by Nordic Semiconductor). The model assumes that ADV messages are transmitted on all three advertising channels (37, 38 and 39) and takes into account delays and randomness that are typical to real nodes.