dataesr/scientific-paragraphs-categorization huggingface dataset:
PlantRNA (http://plantrna. ibmp. cnrs. fr/) is a comprehensive database of transfer RNA (tRNA) gene sequences retrieved from fully annotated nuclear, plastidial and mitochondrial genomes of photosynthetic organisms. In the first release (PlantRNA 1. 0), tRNA genes from 11 organisms were annotated. In this second version, the annotation was implemented to 51 photosynthetic species covering the whole phylogenetic tree of photosynthetic organisms, from the most basal group of Archeplastida, the glaucophyte Cyanophora paradoxa, to various land plants. Transfer RNA genes from lower photosynthetic organisms such as streptophyte algae or lycophytes as well as extremophile photosynthetic species such as Eutrema parvulum were incorporated in the database. As a whole, circa 37 000 tRNA genes were accurately annotated.
INTRODUCTION Beyond their major role in translation as carriers of amino acids, transfer RNAs (tRNAs) have many non-canonical functions and are involved in a multitude of biological processes, as already emphasized in PlantRNA 1. 0 database  (Cognat et al. , 2013)  or in more recent reviews (e. g. (Su et al. , 2020)   (Tosar et al. , 2021) ). For instance, the key role played by tRNAs in the production of tRNA-derived small RNAs (tDRs)  (Holmes et al. , 2022)  involved in diverse functions is expanding rapidly  (Krishna et al. , 2021). Furthermore, there is growing evidence that not only tRNAs but also tRNA genes appear to play important roles. In particular, tRNA genes can be involved in the organization of the nuclear genome and affect the chromatin structure (e. g. (Van Bortle and Corces, 2012)   (Hamdani et al.
Different tRNA gene databases including plant tRNA genes have been developed over time (Table  1 ). We can cite the Transfer RNA database (tRNAdb),  (Juhling et al. , 2009) , a restructured version of the first compilation of tRNA and tRNA gene sequences created in 1989  (Sprinzl et al. , 1989) , the tRNA Gene DataBase Curated by Experts (tRNADB-CE),  (Abe et al. , 2011) , the Genomic tRNA Database (GtRNAdb),  (Chan and Lowe, 2016) , and the T-psi-C database (T-Psi-C)  (Sajek et al. , 2020). In 2011, we developed PlantRNA, the first tRNA database dedicated to photosynthetic organisms and provided information on 11 eukaryotes of the photosynthetic lineage  (Cognat et al. , 2013). Although covering evolutionary distinct branches of the photosynthetic lineage, this database was rather limited.
During the last decade, the number of completed nuclear, plastidial and mitochondrial genome sequences has considerably increased. This recent accumulation of knowledge has now made it possible to include the tRNA gene sequences of about 40 additional species covering the phylogenetic tree of the Archeplastida lineage (PlantRNA 2. 0, http://plantrna. ibmp. cnrs. fr/). This includes species representative of terrestrialisation, vascularisation, flowers emergence, or growth in extreme environments as well as two rhodophytes whose genomes contain permuted tRNA genes or peculiar intronic sites. The genomes of a cryptophyte, a haptophyte and two heterokontophytes were also annotated. The recently optimized version of tRNAscan-SE program  (Chan et al. , 2021)  was used to predict and annotate tRNA genes as accurately as possible.
Nevertheless, manual curation was still essential at different levels, for example, to avoid false annotations due to contamination from external (e. g. bacterial) or organellar DNA during nuclear genome sequencing programs, to eliminate remaining tRNA pseudogenes that escape detection, or to identify missing plastidial tRNA genes containing group II introns. Biological information relevant to tRNA biology (e. g. intron sequences, flanking sequences controlling tRNA gene expression, tRNA mitochondrial import) is also made available. To note, two other tRNA databases dedicated to plants, PltRNAdb (https://bioinformatics. um6p. ma/PltRNAdb/index. php ),  (Mokhtar and El Allali, 2022)  and PtRNAdb (http://14. 139. 61. 8/PtRNAdb/index. php) (Singh et al. , 2022)  were published in 2022, demonstrating the need to compile plant tRNA gene sequences.
Database content and web interface The current release incorporates data sources of the previous version for 7 species  (Cognat et al. , 2013) , novel data sources for 4 updated genomes, and a choice of 40 new data sources selected on the basis of the interest of the species regarding their position in the phylogenetic tree of the photosynthetic lineage. For instance, they cover 7 genomes of streptophyte algae belonging to the lineage being at the transition step from aquatic algae to land plants, the genomes of a lycophyte (Selaginella moellendorffii) the first non-seed vascular plant genome reported  (Banks et al. , 2011) , a fern (Azolla filiculoides), one of the fastest-growing plants, or Amborella trichopoda, representative of the sister lineage of all flowering plants. Plant species were also chosen because of their growth in peculiar environments such as the cave plant Primulina huajiensis  (Feng et al.
, 2020)  or the halophytic plant Eutrema parvulum (also called Schrenkiella parvula or Thellungiella parvula)  (Dassanayake et al. , 2011 , Wang et al. , 2016). The quality of the genome annotations was also taken into account whenever possible. In addition, when available, plastidial and mitochondrial genomes were also used as sources. Whole nuclear, plastidial, and mitochondrial genomes were scanned by tRNAscan-SE 2. 0  (Chan et al. , 2021)  and automatically processed by a homemade workflow (available at https://github. com/ibmp-bip/tRNAflow) and then manually curated and annotated as in  (Michaud et al. , 2011). Figure  1  shows the phylogenetic tree of the photosynthetic organisms from which the tRNA genes were retrieved. The sources of genome sequences listed in Supplemental Table  S1  are accessible via the entry point "Species" on the PlantRNA website.
This tRNA-like group comprises true tRNA pseudogenes but likely also highly divergent tRNA molecules which functions remain to be elucidated. Without experimental studies, it is difficult to discriminate between the two categories. In addition, numerous mitochondrial or plastidial tRNA gene sequences inserted into nuclear genomes, and reported so far not be expressed and functional  (Michaud et al. , 2011) , or contaminating the nuclear DNA preparations are recognized as true tRNA genes by tRNAscan-SE. They were annotated and can be downloaded in xls format. Finally, tRNA genes of bacterial origin likely due to DNA contamination were also discarded. All tRNA sequences, annotations, and biological information are stored in a database implemented in PostgreSQL version 12 (https://www. postgresql.
org/). To increase the traceability, we added the genome resources (genome assembly version and source) and the software and versions used for automatic annotation. The web application querying the underlying database is running in a Java-based Apache Tomcat 8. 5 environment (https://tomcat. apache. org/). To manage the increasing number of genomes to process and improve the data quality, some automation and systematic data checks are included in a private area of the application.
, 2021). Conversely, the high number of mitochondria-encoded tRNAs in A. trichopoda reflects the presence of numerous horizontal transfers of foreign mitochondrial DNA from green algae, mosses and angiosperms  (Rice et al. , 2013). Even with the existence of 126 mitochondria-encoded tRNA genes, the only tRNA Thr (GGT) is likely not sufficient to decode the four threonine ACN codons, thus the need to import at least one nuclear-encoded tRNA Thr is expected. Based on the lack of mitochondriaencoded tRNA species, imported tRNAs were proposed in the PlantRNA database for all sequenced mitochondrial genomes.
DISCUSSION Nuclear tRNA genes are transcribed by RNA polymerase III (pol III). The high number of available nuclear tRNA sequences now allow studying in more detail the important characteristics of pol III transcription. In particular, it will be possible to follow how conserved are the TATA-like elements and CAA motifs found upstream of tRNA gene sequences in photosynthetic organisms and whether their presence is linked to plant evolution or adaptation to the environment. Similarly, stretches of T residues normally involved in pol III transcription termination are not always present or are variable in length or number. Whether this variability reflects differences in genome organization and expression remains to be elucidated. As mentioned in the introduction, six other databases, where tRNA genes from photosynthetic organisms have been annotated, are freely available (Table  1 ).
The oldest one, tRNAdb only contains 247 annotated plant nuclear tRNA gene sequences, mostly from A. thaliana, and no full genome has been analyzed. This database has not been updated since 2009. Nonetheless, it is an essential database, as it provides not only tRNA gene sequences but also tRNA sequences with important information on nucleotide modifications, including 55 plant tRNA sequences. More recently, the T-Psi-C database has been released. It includes an updated collection of tRNA sequences obtained through high-throughput tRNA sequencing and many 3D tRNA structures. This tRNA database is particularly relevant for scientists working on human cells for instance. However, only very few plant tRNA sequences are available, mainly those found in tRNAdb. In the third tRNA database, tRNADB-CE, the identified tRNA genes were manually checked by experts and thus provide highly reliable sets of tRNA genes.
A huge number of genomes were analyzed (in particular, numerous metagenome analyses of microorganisms), including 2325 complete plastidial genomes but only two nuclear genomes of photosynthetic organisms were annotated and the database has not been updated since 2011. The fourth genomic tRNA database, GtRNAdb, is a very nice and valuable follow-up of the powerful tRNAscan-SE prediction program on complete or nearly complete genomes and 17 land plant genomes were analyzed. Thanks to the efficiency of the tRNAscan-SE 2. 0 tool, the data provided are very useful regarding angiosperms studies and score of confidence are easily accessible. However, no lower photosynthetic organisms are integrated in the analysis and no organellar genomes annotated. Very recently, two tRNA gene databases dedicated to photosynthetic organisms were made available: PtRNAdb and PltRNAdb.
As an example with the model plant A. thaliana, 4 plastidial tRNA genes (Gly, Val, Leu and Lys) are omitted  (Sato et al. , 1999)  and its mitochondrial genome only encodes 22 tRNA genes  (Unseld et al. , 1997)  not 35 as retrieved in PtRNAdb. Thus, although valuable and very useful, data provided in PtRNAdb have to be carefully checked. In the PltRNAdb database, nuclear and organellar tRNA gene sequences from 256 plant species were identified. This database is very extensive with plants belonging to Streptophyta, Chlorophyta, and Rhodophyta. For tRNA genes prediction, tRNAscan-SE 2. 0 was used but no other curation has been performed and relevant published literature has not been taken into account. Taking A. thaliana as a case study, as above for ptRNAdb, plastidial intron-containing tRNA genes are lacking, the number of 36 mitochondrial tRNA genes exceeds by far the true number of 22, organellar tRNA Ile (CAT) is annotated as tRNA Met gene and the G-1 of tRNA His sequences is missing.
Such types of errors exist for all plant species. As other examples, circa 130 tRNA Leu genes retrieved from the Populus trichocarpa genome are not true tRNAs Leu , the introns present in C. crispus tRNAs were not predicted, the Chlamydomonas reinhardtii tRNA Sec  (Rao et al. , 2003)  is missing and the set of tRNA genes of bacterial origin contaminating the Coffea canephora genome has not been deleted. As a whole, ptRNAdb, represents an extensive database for a rapid overview of the tRNA gene populations found in photosynthetic organisms. Here, the PlantRNA database provides tRNA gene sequences from nuclear and organellar genomes of only 51 photosynthetic organisms but these organisms were selected to reflect the whole phylogenetic tree and our stringent manual curation ensures the recovery of tRNA gene sets of high quality where relevant biological information have been considered.
Thanks to these two criteria, choice of species and high curation, the PlantRNA database is complementary to the other databases and represent an excellent resource for researchers in tRNA biology. In PlantRNA 2. 0, no emphasis has been given on related biological information such as aminoacyl-tRNA synthetases or modification enzymes and work needs to be done to fill this gap. From an informatics point of view, many steps required to implement the PlantRNA database have now been automatized. The main bottleneck in updating more regularly the database remains the essential manual curation. We now plan, with the large number of curated tRNA sequences, to implement a neural network so that any new genome is accurately annotated and the data quickly incorporated into the database. Finally, the incorporation into the database of tRNA and tDR expression profiles as well as the modified nucleotide composition of each tRNA species is biologically relevant but will first require more experimental work.
FUNDING This work was supported by the  Centre National de la Recherche Scientifique (CNRS)  and by the  ITI 2021-2028 program  of the  University of Strasbourg, CNRS  and  Inserm  supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), and  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program. 1365313x, ja, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/tpj. 15997by  Université De Strasbourg, Wiley Online Library on  [ 14/10/2022 ]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions)on  Wiley Online Library  for rules of use; OA articles are governed by the applicable Creative Commons License.
Results Functional organization of the Drosophila Femoral Chordotonal Organ (FeCO) We reconstructed the peripheral ultrastructure of the FeCO using an X-ray holographic nanotomography dataset of the Drosophila front leg  (Kuan et al. , 2020). We identified 152 total cell bodies in the FeCO, which are organized into three separate compartments called scoloparia  (Field and Matheson, 1998 ) (Fig. 1 , Movies S1, S2). We constructed split-Gal4 genetic driver lines that specifically label club, claw, and hook neurons (Fig. S1, S2 ) and compared fluorescence and X-ray images to map the peripheral location of each FeCO subtype within the femur (Fig. 1C, E-F, S1C-D ). We found that the largest and most dorsal compartment (Group 1) contains the club neurons, which encode tibia vibration and cyclic movement.
Biomechanical coupling of the FeCO and tibia We next asked whether there exist biomechanical differences between proprioceptor subtypes. To understand how FeCO neurons are mechanically coupled to the tibia, we used the X-ray microscopy dataset to reconstruct their distal attachment structures (Fig. 1E ). The ciliated dendrites of each pair of FeCO neurons are ensheathed in a scolopale cell that attaches to a sensory tendon via an actin-rich cap cell (Fig. 1B ). Tracing the termination point of each cap cell allowed us to determine the tendon attachment of each FeCO neuron in the X-ray and fluorescence images (Fig. 1E-F ). We found that the three different FeCO compartments attach to different tendons (Fig. 2A ). The tendons connected to Groups 2 and 3 (claw and hook) merge and fuse ~100 µm distal to the FeCO, while the tendon connected to Group 1 (club) remained separate.
How is the tonotopic map in the dendrites of the club neurons (Fig. 5 ) generated? One possibility we considered is that the difference in the length of the tendon connected to each club cell could produce a different resonant frequency for each cell. However, a simple mechanical model based on measurements of the tendon fibrils from the X-ray microscopy data did not support this hypothesis: neither the longitudinal nor transverse vibrational modes would be sufficiently different to produce large enough changes in resonance at the club neuron dendrites. Another possibility is that systematic differences in the stiffness of each club neuron dendrite or in the mass of the cap cell could underlie their frequency tuning, similar to the situation in katydid hearing organs  (Hummel et al. , 2017).
Finally, we washed the VNC in PBST three times, once in PBS, and then mounted it on a slide with Vectashield (Vector Laboratories). We acquired z-stacks of each VNC on a confocal microscope (Zeiss 510). For confocal imaging of ChaT co-labeled legs, we crossed flies carrying the Gal4 driver to flies carrying UAS-RedStinger; LexAopnlsGFP/CyO; ChAT-LexA/TM6B. We selected non-balancer female adults, and dissected legs while flies were anesthetized with CO2. We immediately fixed the legs in 4% formaldehyde in PBS with 0. 2% Triton-X for 20 min and rinsed them in PBS three times over 30 minutes. We mounted the legs in VectaShield and acquired z-stacks on a confocal microscope (Zeiss 510). For in silico overlay of the expression patterns of Gal4 lines (Fig. S1 ), we used confocal stacks of each Gal4 line with neuropil counterstaining (from the Janelia FlyLight database  (Jenett et al.
Segmentation of X-ray microscopy data We used a previously published X-ray holographic nanotomography dataset of an adult fly's front leg  (Kuan et al. , 2020). From this dataset, we manually reconstructed FeCO structures using CATMAID  (Saalfeld et al. , 2009; Schneider-Mizell et al. , 2016) ; the reconstruction can be accessed at https://radagast. hms. harvard. edu/catmaidvnc/61/links/Mamiya2022. Annotated structures, including FeCO cell bodies and cap cells, tendons, and the arculum, were then exported to Blender with the CATMAID-to-Blender plugin  (Schlegel et al. , 2016). Unlike a previous study  (Shanbhag et al. , 1992) , we did not observe any connections between FeCO sensory structures and surrounding muscles in the femur.
Blender model of FeCO We combined X-ray microscopy data mentioned above and confocal images of the foreleg exoskeleton to generate a blender model of FeCO. For modeling the arculum, we extracted the 3D model/mesh of the arculum from the confocal stack of the foreleg exoskeleton imaged with a 40x, NA 1. 3 objective using Fiji's 3D viewer plugin (http://fiji. sc/). After importing the CATMAID files for the X-ray microscopy data into Blender (Community, B. O. , 2018. Blender -a 3D modelling and rendering package, Stichting Blender Foundation, Amsterdam: http://www. blender. org) as described above, we fitted the arculum mesh manually to replace the original wireframe. For the neuronal cell bodies and cap cells, we slightly modified the CATMAID primitives (spheres and cylinders) to resemble the morphology of these cells in confocal microscopy images.
8 NA, 3. 5 mm wd; Nikon Instruments) below ~25 mW during the experiment. For autofluorescence imaging of the arculum in the leg, we either used the twophoton Movable Objective Microscope mentioned above, or the three-photon excitation microscope. For three-photon excitation microscope, we generated excitation laser pulses with a commercial non-collinear OPA (Pera-F, Coherent) operating at 1300 nm pumped by a 40W fiber laser (Monaco, Coherent) operating at 1040 nm. We controlled the laser scanning mirrors and image acquisition of all microscopes with ScanImage software (version 5. 7)  (Pologruto et al. , 2003)  running in MATLAB (MathWorks). To detect GCaMP7f fluorescence and the autofluorescence from the arculum, we used an ET510/80M (Chroma Technology Corporation) emission filter (VNC imaging) or a FF03-525/50-30 (Semrock) emission filter (cell body imaging / arculum imaging).
We placed the motor on a micromanipulator (MP-285, Sutter Instruments) and adjusted its position so that the magnet moved in a circular trajectory centered at the femur-tibia joint, with the top edge of the magnet at approximately the same height as the tibia/pin. The inner edge of the magnet was ~1. 5 mm from the center of the femur-tibia joint and the distance between the pin and the magnet was ~ 300 µm. We controlled the speed and the position of the servo motor with a custom script written in QuickControl software (QuickSilver Controls). For each trial, we confirmed the movement and the position of the tibia-femur joint using a high-speed video camera and machine vision algorithms described below. Because it is difficult to fully flex the femur-tibia joint without the magnet colliding with the abdomen or other legs, we only flexed the joint up to ~ 18°.
Vibrating the tibia using a piezoelectric crystal To test the responses of the axons and dendrites of FeCO neurons to high frequency vibrations of the tibia, we attached the magnet to the tibia/pin and moved it using a piezoelectric crystal (PA3JEW, Max displacement 1. 8 µm; ThorLabs). For controlling the movement of the piezoelectric crystal, we generated command waveforms in MATLAB and sent them to the piezo through a single channel open-loop piezo controller (Thorlabs). Because previous calibration of this piezoelectric crystal's movement in response to sine waves of different frequencies  (Mamiya et al. , 2018)  showed that the power of oscillation at the target frequency drops greatly when the frequency of the command sine wave exceeds 2000 Hz, we used vibration in the range of 100 -1600 Hz (sampling frequency 10 kHz).
Processing and analysis of in vivo imaging data We performed image processing and analyses using scripts written in MATLAB (MathWorks) and Python. For all recordings, we first applied a Gaussian filter (kernel size 5x5 pixel, σ = 3) to the acquired images to reduce noise. For the recordings of FeCO axon terminals in the VNC, we aligned the filtered images (registered to ¼ pixel) to a mean image of the trial using a sub-pixel image registration algorithm  (Guizar-Sicairos et al. , 2008). For this alignment, we used the tdTomato fluorescence, which does not change as a function of intracellular calcium concentration. tdTomato fluorescence was stable over the course of the experiment, indicating that movement artifacts were absent or small for the VNC recordings. Thus, for these experiments we used the change in GCaMP7f fluorescence relative to the baseline (ΔF/F) as the indicator of the calcium activity.
Finite Element Model of the arculum We developed a finite element model (FEM) to study how the arculum moved in response to the forces applied to it via the femur-tibia joint. FEM was carried out using COMSOL Multiphysics (ver. 5. 5, Massachusetts, USA). Models were developed in the structural mechanics module and all components were modeled as simple linear elastic materials with linear behavior that follows Hooke's law. We based arculum geometry on the 3D Blender model of the arculum and tendons reconstructed from confocal images and X-ray microscopy data (Fig. 2 ). This mesh was then imported into Meshmixer. After removing the joint and tendon attachments, we generated an STL file and imported the model into COMSOL. The 3D model was meshed using tetrahedral elements with triangular surface faces.
Models to investigate joint angle selectivity in claw cells We used a series of finite element models to study the selectivity of claw cells to different femur-tibia joint angles. We found that the claw cells, the medial tendon, and the fibrils emanating from the medial tendon and attaching to the claw cell dendrites were all symmetric around a single plane through the leg (Fig. S7A ). Thus, we assumed that a simplified twodimensional model could capture the mechanics of the system without significant loss of fidelity (Fig. 3D ). We developed these two-dimensional models using the truss and solid mechanics interfaces within structural mechanics module in COMSOL Multiphysics (ver. 5. 5, Massachusetts, USA). We modelled the medial tendon, the fibrils, and the claw cell dendrites as truss elements, i.