dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Prior to analysis, we checked for sensor glitches, numerical artefacts of the sensor raw signal and measurement errors caused by faulty sensors. All measured variables but sap velocity were treated as continuous, with the hypothesis that they cannot abruptly change over half an hour. For this reason, it was decided to study the evolution of their first derivative in time and consider as outliers the points whose absolute value lied outside the two standard deviations confidence interval. A graphical evaluation of the data points flagged as outliers was carried out before they were discarded.
, 2017)  packages. The parameterization of the two models can be found in Tab. C. 1. Two BRT models were built using 75% of the dataset to explain the relative importance of the potential drivers of transpiration during the dry Harmattan season. The remaining 25% was used to fit the models and evaluate them against the measured values. The two BRT models considered soil volumetric water content (VWC), photosynthetic active radiation above the canopy (PAR) and the vapour pressure deficit of the atmosphere (VPD) as environmental predictors, the leaf area index (LAI) of the two plots as indicator of the general state of the canopy and the diameter of individual trees as a proxy for their dimension. The first model (model 1) used the half-hourly daytime data (PAR > μmol*s -1 *m -2 ) to investigate the importance of each aforementioned predictors in determining the daily daytime cycle of sap velocity.
Acknowledgements This research would not have been possible without  Matthew Stolz , president of  Rockwinds , and the  National Association for Research and Technology (ANRT)  that co-financed this study. We are equally grateful to CIRAD for the fruitful working environment and for its contribution to the travel and training required by the project. Furthermore, we thank the entire staff of the  Cocoa Research Institute of Ghana (CRIG)  for the assistance with the field experiment with a special mention to  Kent Agyemang ,  Collins Akuoko Addo  and  Samuel Walker  for assistance with the data collection and maintenance of the field and instrumentation.
Plasmids from the IncC group are often found in multiple antibiotic-resistant Gram-negative bacteria belonging to several different species, indicating a broad host range. These plasmids have played a major role in the dissemination of genes encoding cephalosporinases and carbapenemases  [9]. In this regard, most of the reports describing these plasmids refer to CMY and NDM enzymes  [8]  and there is scarce information about the presence of CTX-M enzymes in IncC plasmids in publicly available databases. Regarding their characterisation, IncC plasmids have been classified into two groups, known as type 1 and type 2, primarily based on variations observed in the plasmid backbone and in the resistance islands (RIs), including the loss of areas adjacent to them  [ 7 , 10 ]. Within the RIs, some differences can be observed, such as the presence of ARI-A and/or ARI-B and even the absence of both, as is the case with certain IncC  [7].
Plasmid sequencing and analysis Plasmid DNA was obtained using a QIAGEN® Large-Construct Kit (QIAGEN) according to the manufacturer's instructions. Whole-genome sequencing (WGS) of plasmid DNA from both transconjugants was performed using an Illumina MiSeq platform (Illumina Inc. , San Diego, CA, USA) with a 2 × 250-bp or 2 × 300-bp paired-end approach. Raw reads were assembled using SPAdes 3. 1  [11]  , and gaps were closed following a PCR and amplicon sequencing approach. Gene annotation was performed using the RAST platform  [12]  and were manually revised using plasmid pR55 (GenBank accession no. JQ010984 ) as reference  [13]. Sequence alignments were performed using Mauve 2. 4 ( http://darlinglab. org/mauve/mauve. html ), and physical maps were generated using Easyfig 2. 2 ( http://mjsull.
github. io/Easyfig/ ). Plasmid multilocus sequence typing (pMLST) was performed in silico using the pMLST database ( https://pubmlst. org/plasmid/ ). Comparisons with publicly available plasmid sequences were performed with BLAST ( http://blast. ncbi. nlm. nih. gov/ ), and in silico determination of plasmid replicons and antibiotic resistance determinants were performed using PlasmidFinder ( https://cge. cbs. dtu. dk/services/PlasmidFinder/ ) and ResFinder ( https://cge. cbs. dtu. dk/ services/ResFinder/ ), respectively. Identification and characterisation of insertion sequence (IS) elements was performed using the ISfinder database ( http://www-is. biotoul. fr )  [14].
Nucleotide accession numbers Plasmid sequences of KP0923 and KP1025 and their respective RIs are available in the GenBank database under accession numbers MN207908 and MW26 60 61 (RI) for pUR-KP0923 and MN065773 and MW273770 (RI) for pUR-KP1025. Transposons numbers were designated by The Transposon Registry ( https://transposon. lstmed. ac. uk/ )  [15]  as Tn 7057 and Tn 7058 located in pUR-KP0923 and pUR-KP1025, respectively.
Description of the plasmid backbone In silico analysis of sequences revealed that both plasmids belonged to the IncC group type 2, carrying a repA2 allele identical to that of the IncC reference plasmid pR55 (GenBank accession no. JQ010984 )  [13]  , and were classified within the ST3/ST3. 3 lineage according to the pML ST/cgPML ST typing schemes  [16]. Overall, the genetic organisation of both plasmids consisted of: (i) a backbone region of size 129,207 bp (pUR-KP0923) and 131,901 bp (pUR-KP1025), comprising core genes encoding replication, plasmid maintenance, partitioning and transfer functions; and (ii) a resistance island (RI-4) inserted in the rhs2 gene ( Fig. 1 A ). The difference in size detected in the backbone region of pUR-KP1025 was basically explained by the presence of two insertion sequences belonging to the IS 4 family: IS 10R located between nucleotides 34,586 and 35,794 and IS 186B between nucleotides 100,292 and 101,404.
IS 10R was found interrupting a thymidylate kinase gene ( A058 ) that is listed in the pMLST scheme  [16]  , how-  ever we were able to identify the corresponding A058 allele with the sequences on both sides of IS 10R. Several characteristics of IncC type 2 plasmids were also found. For instance, the occurrence of orf1847 (5544 bp) between traA and dsbC , an rhs2 gene (4263 bp) interrupted by a resistance island (RI-4) as mentioned before, and the presence of insertions i1 and i2 located upstream of dcm1 and downstream of dcm2 , respectively ( Fig. 1 A ). Nevertheless, the ARI-A and ARI-B resistance islands, present in certain IncC plasmids, were absent  [8]. Two IncC plasmids harbouring bla CTX-M-2 , pCR14 (GenBank accession no. CP015394 )  [17]  and pRCS39 (GenBank accession no. LT985244 )  [8]  were compared with both plasmids reported here ( Fig.
2  ). The antibiotic resistance genes embedded in such a platform usually confer resistance to amikacin, kanamycin and tobramycin ( aacA4 ), broad-spectrum β-lactams and β-lactamase inhibitors ( bla OXA-2 ), sulfonamides ( sul1) and oxyimino-cephalosporins ( bla CTX-M-2 ). The presence of bla CTX-M-2 in this kind of platform has not only been frequently reported in our country but also has remained unchanged for at least 25 years  [ 3 , 19 , 20 ]. The presence of IncC plasmids carrying bla CTX-M-2 in RI-4 is scarce both in the literature and databases. We compared our plasmids with the RI-4 of pCR14 and pRCS39 and they had highly similar RIs comprising the region downstream of the first part of the rhs gene to IS Kpn12 of pUR-KP1025 and to the first copy of IS Kpn13 of pUR-KP0923. The structures found downstream of such insertion sequences are missing in pCR14 and pRCS39, including the Tn 21 ::mercury operon module and the second part of rhs ( Fig.
1 B ). Other members of the CTX-M family have been found in IncC type 2 plasmids but in different positions to the one here reported. For instance, bla CTX-M-15 was found to be inserted in ARI-B (Gen-Bank accession no. MT151380 ) and bla CTX-M-25 was found downstream of the rhs gene (GenBank accession no. KF056330 ).
They estimated spots with temperature deficits in the range between 400-1200 K. Using photometric data gathered between 1994-1998, they pointed out that a high-latitude feature can be responsible for the continuous decline of the mean brightness. Fröhlich et al. (2002)  performed long-term photometry covering 35 years, using Sky-Patrol plates obtained at Sonneberg Observatory and found a secular dimming of 0. 0057 ±0. 0008 mag/yr within the observation time-range considered. Järvinen et al. (2005)  analyzed V-band photometric data of EK Dra covering 21 years using light curve inversions and found that the system shows long-lived, non-axisymmetric spot distribution with active longitudes on opposite hemispheres. König et al. (2005)  calculated the masses of the primary and secondary components as 0.
From a detailed model-atmosphere analysis of high-resolution spectra of EK Dra, they obtained  eff =5750 K, logg=4. 5, and the microturbulent velocity, , as 1. 6 km/s. They estimated an age between 30-50 Myr. Järvinen et al. (2009)  studied EK Dra, based on additional spectra obtained in 2007. They also inferred high-latitude spot concentrations. Rosén et al. (2016)  published two brightness maps, along with vector magnetic maps, using spectra obtained in 2007 and 2012. They found spots covering all longitudes and a wide latitudinal range, excluding high-latitude regions above ∼ 60 •. Waite et al. (2017)  reconstructed brightness and magnetic maps using spectropolarimetric observations covering six years of data obtained at the  CFHT and TBL observatories between 2006 and 2012. Their surface reconstructions between 2006 and 2007  show large spots located at intermediate latitudes, while the maps based on 2008 data show a dominant polar spot region.
The line list required by the LSD technique was derived from the Vienna Atomic Line Database (VALD)  (Kupka et al. 1999) , considering the effective temperature ( eff ) and the surface gravity (log ) of EK Dra. During the preparation of the line list, wavelength regions covering chromospheric lines as well as the strong telluric lines were extracted to prevent any artefacts in the LSD profiles. The LSD profiles of the template stars were also obtained, which are used for the generation of lookup tables to model the local intensity profile during DI process. The resolving power of R=62000 allows us to generate the velocity profiles with 2. 5 km/s intervals.
CHEMICAL ABUNDANCE ANALYSIS The model atmospheres were computed with ATLAS12  (Kurucz 1993; Sbordone et al. 2004 ) assuming Local Thermodynamic Equilibrium (LTE) and plane-parallel geometry. For the spectrum synthesis, the atomic data was retrieved from VALD  (Piskunov et al. 1995; Ryabchikova et al. 1997; Kupka et al. 1999 Kupka et al. , 2000) )  and the NIST database  (Kramida et al. 2015). The detailed list of the atomic lines can be found in  Kılıçoğlu et al. (2016). The oscillator strengths of 7 Li lines were adopted from  Reddy et al. (2002). The mixing length parameter were calculated to be  = 1. 53 from the calibration of  Ludwig et al. (1999). In order to avoid photospheric activity as much as possible, we selected spectra obtained by the HERMES spectrograph when the star is in a less active phase, i.
e. , the spectra have weak emission features in the Ca II H&K lines. Three out of 12 observed HERMES spectra, obtained consecutively on March 29th, 2016, fitted this condition. We merged these three spectra to increase the S/N and used the merged spectrum for chemical abundance analysis. We carefully selected 434 unblended (or barely blended) lines of Li, C, Na, Mg, Al, Si, S, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Sr, Y, Ba, Ce, and Nd in the spectrum. We then fitted these observed spectra line-by-line with synthetic profiles produced by SYNSPEC49 and its SYNPLOT interface. The code was modified to minimise the  2 between the model and the observed points, using the Levenberg-Marquardt algorithm  (Markwardt 2009 ). The rotational velocity was derived to be sin = 17. 0 ± 0. 5 km s -1 by adjusting the synthetic spectra to the unblended iron lines.
Following MEM iterations, we achieved a reduced  2 value of 1. 127. In addition to the two-dimensional grid search for the EW and  sin  parameters (Fig. 7 ), we also carried out a grid search for the axial inclination (see Appendix A for details). Although the search result gives an axial inclination value of 56 • ± 5 • , we keep the value as 63 • during DI process for a more consistent comparison of the resultant map with the one obtained by J18 (see Section 5. 2). The reconstructed spot positions and sizes do not alter significantly between inclination angles of 56 and 63 •. The best-fit models for the velocity profiles obtained using the Calar-Alto dataset are given in Fig. 8 , along with the residuals, which are within ±0. 0035. The residuals are likely related to uncertainties in the estimation of stellar parameters.
Forward modelling of Doppler images with physical models (e. g. ,  Lehmann et al. 2019 , who modelled ZDI images) is promising for a deeper understanding of the observational manifestations of stellar activity patterns. ments concerning the effect of the magnetic field on the derived abundances, and the referee, Pascal Petit, whose critical comments led to substantial improvement of the manuscript. HVŞ acknowledges the support by the Scientific and Technological Research Council of Turkey (TÜBİTAK) through the project 1001 -115F033. DM acknowledges support by the Spanish Ministry of Economy and Competitiveness (MINECO) from project AYA2016-79425-C3-1-P. This work has made use of data from the European Space Agency (ESA) mission. This work has been partially supported by the BK21 plus program through the National Research Foundation (NRF) funded by the Ministry of Education of Korea.
Gaia (https://www. cosmos. esa. int/gaia) data were processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/ dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. cating that sheared spot distributions can be well-reconstructed for up to 4 times solar DR. For comparison, we show in Fig. C1  (panel C) two Doppler images of EK Dra, reconstructed for rigid rotation and for 4 times solar DR, which is about the value corresponding to the minimum  2 in Fig. 10. In the sheared DI, there is considerably less spot coverage near the equator than in the rigid-body solution. In this sense, our FEAT models match more closely with the sheared DI of EK Dra.
Statistical analysis Analysis was performed using STATA v16. 0 statistical software (Stata Corp. , College Station, TX, USA) and GraphPad Prism v9 (GraphPad Software, LLC). Differences among the frequency of epidemiological and immunological variables were analyzed using Chi-square test for categorical variables and Mann-Whitney or Kruskal-Wallis test for numerical continuous variables to compare the median values. Spearman rank correlation coe cient test was used to evaluate the correlation between epidemiological and immunological variables. To measure differences of antibodies level at different time points between groups (related samples) per each antigen we used Wilcoxon signed-rank test, the signi cance level for all statistical analysis performed was set at p < 0. 05 or p < 0. 001.
Acknowledgments The authors are grateful to  Dr. Danett Bishop ,  Dr. Christie Joya ,  Dr. Stephen Lizewski , and  Dr. Hugo Valdivia  for their support and critical review during the development of this work at  NAMRU-6  and  Dr. Simon Draper  for his support for the use of monkey samples from RH5 studies ( Jenner Institute, University of Oxford ). Disclaimers "The views expressed in this article re ect the results of research conducted by the author and do not necessarily re ect the o cial policy or position of the  Department of the Navy, Department of Defense , nor the  United States Government ".
The concrete example also gives insights into the sensitivity of recoil lineshape on the specific location of the photoemission site in the molecule. Application of the recoil lineshape mode is summarized below: (i) The normal mode analysis was done using the GAMESS quantum chemistry package  [41] , providing the normal-mode vectors as coordinate displacements and their frequencies. (ii) The energy division factors f n were calculated for all 51 oscillators according to equation (  12 ). (iii) In an iteration over all the vibrational oscillators, the Poisson parameters S n (equation (  15 )) were calculated and the combined vibrational recoil profile's mean energy, variance and skewness obtained (equation (  19 )). Also, the total thermal kinetic energy (equation (  21 )) in the vibrational oscillators was calculated.
This method has previously been used to characterize the translation of mRNAs by immunopurifying GFP-tagged ribosomes  (41, 42). For this, we expressed either GFP alone ("mock" condition) or GFP-RPL10Ab tagged ribosomes ("TRAP" condition) specifically in SCs (Fig. 1B ). We verified the specificity of the technique by showing that ribosomes and associated mRNAs from SCs but not from main cells were efficiently enriched in the TRAP compared to the mock condition (SI Appendix, Fig. S1 ). These datasets are accessible in Dataset S1. As shown in Fig. 1C , RNAseq and TRAPseq data are highly correlated (Pearson correlation coefficient ∼0. 9), which suggests that RNAseq generally gives a relatively accurate estimation of mRNA translation for most genes. However, TRAP is highly informative for specific genes that do not behave as expected, as exemplified by Ubx and msa (Fig.
1C ). On one hand, the HOX gene Ultrabithorax (Ubx), is highly transcribed (21st most highly expressed RNA) but not translated in SCs (our TRAP data corroborate unsuccessful attempts to detect Ubx protein by immunostaining accessory glands)  (38). On the other hand, msa is annotated as a lncRNA, but is associated with ribosomes, suggesting that it may encode unknown peptides. Given the surprising finding of msa in our dataset, we sought to validate the accuracy and specificity of our screen by looking at the protein-coding genes. From the list of >7,000 genes robustly detected, we used a gating strategy to narrow the informative SC coding genes (see Fig. 1D  for data analysis and cutoffs). After Gate#2, we obtained a list of 1,415 genes. Gene ontology using this gene list is consistent with secondary cell biology, as the most highly enriched terms are related to reproduction, postmating response, vesicle-mediated transport, secretion, and endocytosis, with many proteins predicted to be located at endomembranes, in vacuoles or in the extracellular space (Fig.
1E ). This fits the current view of SCs as vacuole-filled secretory cells that produce, secrete, import, and modify proteins involved in the PMR. We then narrowed down the list of genes to the 151 transcripts that displayed the strongest transcription and ribosome association in secondary cells (Gate#3 genes, shown in red in Fig. 1C ). These 151 genes include five of the six previously known SC markers msa (38), Rab19  (27) , Dve (39), CG17575, and Lectin-46Ca  (27, 35) , validating both procedures and data analysis (Dataset S2), and many encode secreted proteins that are associated with the PMR (regulation of female receptivity) and sperm competition (Fig. 1E ). Strikingly, a small number of RNAs make up the majority of transcripts (in terms of number of reads): the top seven genes contribute to >70% of all the transcriptome reads (Figs.
1C  and 2A ). Notably, these seven genes encode proteins known to be transferred to females during mating (43) (CG9029, Acp32CD, the CRISP CG17575, Lectin-46Cb, Lectin-46Ca, CG13965, and midline fasciclin/mfas), including two previously known to be expressed in SCs. Three of these genes [CG17575  (38) , Lectin-46Cb, and Lectin-46Ca)  (36, 37, 43, 44) ] have already been characterized as regulators of the long-term PMR through transient binding to sperm  (36, 37, 45) , thus further validating the dataset as revealing important players in the SC's reproductive role. To extend this validation, we selected five uncharacterized genes among the most highly expressed SC transcripts (Fig. 2A ) and assayed their function using standard PMR assays. We tested the ability of knockdown males to induce the decrease in female receptivity typically observed for many days after initial mating.
The msa "lncRNA" Encodes SC-Specific Micropeptides. msa is a SCspecific isoform of the iab-8 lncRNA that spans the intergenic region between the HOX genes Abd-B and abd-A in the Bithorax complex (Fig. 3A ). Previously, we showed that msa is critical for SC function  (38)  and that much of its function can be attributed to a miRNA, located within its fourth intron. As our data suggested that this transcript is also peptide-encoding, we verified our TRAP results using RT-qPCR on the ribosome immunopurified RNAs. Indeed, RT-qPCR confirms a strong enrichment of this transcript associated with SC ribosomes (SI Appendix, Fig. S1 ). This RNA contains a block of sequence located within its last exon that we previously identified based on its conservation in several Drosophila species  (49). This block starts with an ATG and finishes with two, in-frame, putative stop codons and could encode a 9-amino acid peptide.
As this analysis was based on a limited number of species, we extended the prior evolutionary analysis to more Drosophila species (Fig. 3B  and Dataset S3 ). This revealed that the block of sequence conservation extends to another upstream ATG. We also note sharp drops in conservation both upstream of the first ATG (M) and downstream of the stop codons (*), as evidenced by the "amino-acid conservation" plot and DNA sequence consensus line in Fig. 3B. Thus, the conserved region consists of a putative smORF with up to three in-frame ATGs that could code for overlapping small peptides of 20, 9, or 3 amino acids, respectively. Although we could not find any convincing homologs outside of the Drosophila genus, we find this sequence in Drosophilidae from multiple subgenera (Fig. 3B  and Dataset S3 ).
However, protein structure prediction ) (B) Our strategy to identify SC coding genes. Using a SC-specific Gal4 driver (SC>), we expressed either >GFP alone or a GFP-tagged ribosomal subunit (GFP-RPL10Ab). GFPexpressing secondary cells were sorted (fluorescence-activated cell sorting, FACS) and their RNA were sequenced to establish SC transcriptome. Affinity purification of GFP was performed and associated RNAs were sequenced to establish SC TRAPseq and mock control. (C) Scatterplot showing gene counts in transcriptome and TRAP data. For each gene, the mean normalized counts from three replicates is plotted on a logarithmic scale. PCC, Pearson correlation coefficient. P value <0. 0001. Gate#3 genes are shown in red (see D). (D) Gating strategy to find SC signature-coding genes from our normalized sequencing data (Dataset S1).
Immarigeon et al. PNAS | 3 of 11 Identification of a micropeptide and multiple secondary cell genes that modulate Drosophila male reproductive success https://doi. org/10. 1073/pnas. 2001897118 GENETICS software (PEP-FOLD 3. 5) suggests a helical conformation for the MSAmiP9 portion, which is retained in the analysis of the MSAmiP20 sequences. This predicted structural conservation suggests that selective pressure may have occurred at the protein level and could indicate that the MSAmiP-encoded peptides are functional. To investigate the translation potential and function of this smORF in vivo, we generated genetic tools to modify its endogenous sequence within the Bithorax complex (Fig. 3A ). Using CRISPR-mediated cassette exchange, we replaced the exon of msa containing the putative smORF with a site-specific integration platform (attP)  (51).