dataesr/scientific-paragraphs-categorization huggingface dataset:
For instance, bta-miR-100, one highly expressed miRNA in ejaculated sperm, has been proposed as one of the main factors associated with the initiation of pluripotency  [69, 70]  and bta-miR-191, the second most expressed miRNA in our dataset has been associated with fertilization rate and embryo quality in humans  [71]. Likewise, miRNA-34c is known to enhance the germinal phenotype of cells already committed to this lineage during spermatogenesis  [6] , but is also a key player in murine early embryo development  [72] [73] [74]. In addition, clusters 10-12 were found to be associated with GO terms related to embryo development such as developmental process, embryonic morphogenesis, as well as cell proliferation, differentiation, and migration. Beside miRNAs, rsRNAs, tsRNAs, and some piRNAs are also gained post-epididymis, which may impact embryo development and be involved in trans-generational epigenetic inheritance.
Expression levels were computed both at the miRNA and isomiR levels. In parallel, all unique sequences were annotated using several public databases including Ensembl release 94 (http:// www. ensem bl. org), RFAM release 14 (http:// rfam. wustl. edu), cattle ncRNA from ENA (https:// www. ebi. ac. uk/ ena), cattle tRNA from GtRNAdb (www. gtrna db. ucsc. edu), human, mouse and cattle piRNAs from ENA, as well as  [79, 80]. All workflow scripts are available on GitHub (https:// github. com/ Smart BioInf/ PAQmiR). Subgroups of tsRNA were identified as previously described  [27] : sequences aligned to the beginning of 5′ end of the mature tRNA were classified as tRF5, while those aligned to the 3′ end of mature tRNA were classified as tRF3; sequences generated by a cleavage at the anticodon-loop and producing tRNA halves were classified as 5′-tRHs and 3′-tRHs according to their localization on mature tRNA; internal fragments were classified as i-tRFs.
Bovine piRBase databases were used to annotate sperm piRNA according to their target type (LINE, SINE, LTR, or Satellite) or target location (intergenic or genic). To locate piRNA sequences in mtDNA, all sperm piRNA sequences were mapped to bovine mitochondrial genome (ENA accession V006541) using blast (options-task "blastn-short"), keeping only perfect matches along the whole piRNA sequence. Targetscan 7. 2 (release March 2018) was used to predict miRNAs targets. Functional annotations and enrichment analysis were done using Gorilla  [81]  (http:// cblgoril la. cs. techn ion. ac. il/) and WebGestalt  [82]  (http:// www. webge stalt. org/). GO results were summarized to produce scatterplots and interactive graphs of enriched terms grouped by semantic similarity using Revigo  [83]. The DESeq2 R package (R v3.
6. 0 and DESeq2 package v1. 24. 0)  [84]  was used to estimate library size normalization factors using a median-of-ratios (i. e. , relative log expression) approach and test for differential sncRNA expression between regions. Statistical significance was evaluated based on Benjamini-Hochberg adjusted p values, with a significance threshold of 5%. The proportion of each sncRNA classes was computed per region based on counts (proportion) as well as normalized expression (relative expression). A between-class analysis (BCA) was performed using the ade4 R package (dudi. bca function). K-means clustering (Lloyd algorithm, 100 random starts and 1000 iterations) was performed using the kmeans() function in R on standardized data (read counts were averaged across the 3 bulls for each region before centering and scaling values for each row).
RT-qPCR validation of differential expression among regions Expression of three miRNAs (bta-miR-100: AAC CCG TAG ATC CGA ACT TGT, bta-miR-16a: TAG CAG CAC GTA AAT ATT GGCG and bta-chr27-30001: CGC CGG GGC GGG TTC CGG AGG) as well as two bta-miR-191 isomiRs (CAA CGG AAT CCC AAA AGC and CAA CGG AAT CCC AAA AGC AG) was assessed by RT-qPCR on 3 testis parenchyma sperm and 3 ejaculated sperm samples to evaluate their differential expression. LNA primers were supplied by Qiagen. Duplicates were assayed in 10-μl/5-ng qPCR reactions following the miRCURY LNA kit protocol, using a StepOnePlus Real-Time PCR System (Applied biosystems). Amplification curves were analyzed using the StepOne software v2. 3 to compute Ct values and analyze the melting curve. Relative expression was computed with the qbase + software (Biogazelle).
Description of the data We collated data from 20 studies that included 243 trees and more than 1 million hours of tree motion data at resolutions ranging from 4 to 20 Hz. These collated data are now available online  (Jackson et al. , 2021). These studies used three types of sensor: (1) strain gauges which measure the bending strains at the base of the tree  (Moore et al. , 2005) , (2) inclinometers measuring the changing inclination angle of the trunk  (Bunce et al. , 2019; Schindler et al. , 2010) , and (3) accelerometers measuring the acceleration at the top of the trunk  (van Emmerik et al. , 2017). We refer to these in general as measures of tree deflection. Five studies additionally measured branch motion, which is not used here but may provide further insight in future studies. Some data sets span multiple years, while others are confined to a short time during windy conditions.
Conclusions In this study, we collated data on tree motion in order to explore the key similarities and differences amongst trees, as well as possible trends with tree size and wind speed. We compared trees and tested trends based on features extracted from the tree motion time series. We found that trees in conifer forests exhibit similar responses to wind loading and that these were distinct from those of broadleaves, presumably as a result of their simpler branching architecture. However, we could not reliably distinguish between open-grown and forest broadleaves based on their motion in the wind despite their substantial architectural differences. Our analysis confirms previous studies showing that the fundamental frequency, which describes the speed of tree sway, is a robust feature with which to compare the motion of trees and which is strongly related to tree size.
Code availability. All code created for this analysis is available here: https://github. com/TobyDJackson/Tree_motion_data_ synthesis  (Jackson, 2021). Please contact the corresponding author if you have any questions or need additional materials. Data availability. This data set is a product of the work of many researchers, and collaborations are encouraged. Most of the data used in this study are available in a single repository at https://doi. org/10. 5281/zenodo. 4911395 (Jackson et al. , 2021)  which also stores metadata for all trees used in this study. Other data sets are available in individual repositories as follows. The Singapore data are available at https://doi. org/10. 7910/DVN/FHJBYG (Burcham, 2020). The Montmorency data are available at https://doi. org/10. 5683/SP2/WZIKSR (Duperat et al.
, 2019). The Manaus data are available at https://data. 4tu. nl/articles/ dataset/Tree_of_19_Amazon_trees/12714989/1 (van  Emmerik et al. , 2021). The Wytham Woods data are available at https://doi. org/10. 5285/533d87d3-48c1-4c6e-9f2f-fda273ab45bc (Jackson, 2018a). The Danum Valley data are available at https://doi. org/10. 5285/657f420e-f956-4c33-b7d6-98c7a18aa07a (Jackson, 2018b). The Trout Lake and Fond du Lac data are available at http://www. hydroshare. org/resource/ 38ae9d9fb88d49f9ad2eed1ee07475c0  (Ciruzzi and Loheide, 2021). For any questions regarding data availability please contact the corresponding author. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-18-4059-2021-supplement.
The network is trained using standard backpropagation techniques, so a proper loss function must be chosen; we use the negative log-likelihood function, which makes the problem equivalent to a maximum likelihood estimate  (Bishop 1994). We adapt the existing CNN described in  (Rhea et al. 2020 ) to be a single-phase mixture density model (i. e. assuming a single Gaussian distribution is sufficient to describe the output variables). Our tensorflow  (Abadi et al. 2015)  implementation can be found at https://github. com/sitelle-signals/Pamplemousse. In LUCI, the mean values, µ Θ , are used as the best estimates and the optimization problem 1  is constrained using bounds taken from the 3-σ values (i. e. 3 times the σ Θ calculated by the network).
ACKNOWLEDGMENTS The authors would like to thank the  Canada-France-Hawaii Telescope (CFHT) , which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii. The observations at the CFHT were performed with care and respect from the summit of  Maunakea , which is a significant cultural and historic site. C. L. R. acknowledges financial support from the physics department of the  Université de Montréal, IVADO, and le fonds de recherche -Nature et Technologie. J. H. -L. acknowledges support from  NSERC  via the  Discovery grant program , as well as the  Canada Research Chair program. Software: astropy (Collaboration et al. 2013), scipy (Virtanen et al.
Aims. Among the γ-ray sources discovered at high and very-high energies, a large fraction still lack a clear identification. In particular, the H. E. S. S. Galactic Plane Survey (HGPS) revealed 78 TeV sources among which 47 are not clearly associated with a known object. Multiwavelength data can help identify the origin of the very-high energy γ-ray emission, although some bright TeV sources have been detected without clear counterparts. We present a multiwavelength approach to constrain the origin of the emission from unidentified HGPS sources. Methods. We present a generic pipeline that explores a large database of multiwavelength archival data toward any region in the Galactic plane. Along with a visual inspection of the retrieved multiwavelength observations to search for faint and uncataloged counterparts, we derive a radio spectral index that helps disentangle thermal from nonthermal emission and a mean magnetic field through X-ray and TeV data in case of a leptonic scenario.
E. S. S. source (H. E. S. S. Collaboration 2011). Several databases have been developed to gather multiwavelength observations that can be easily retrieved. The Centre d'Analyse des Données Étendues 1  and the Space Science Data Center 2  provide interactive databases, such as the Skyview 3  tool, which enables the production of images at multiple wavelengths for any position in the sky. The interactive tool gamma sky 4  permits us to superimpose catalogs such as those of the Fermi-LAT and known SNRs. However, using multiwavelength data to identify γ-ray sources is not always straightforward. The best example is the nearby and evolved PWN Vela X, which exhibits a changing morphology at different wavelengths. The brightest part of the VHE emission is spatially coincident with the X-ray PWN, but its largest extent is consistent with that of the surrounding extended radio nebula of 2 • × 3 •  (Abramowski et al.
2017). We note that energetic pulsars are considered indirect associations since we do not expect them to produce TeV emission but rather to generate a PWN that could be seen at TeV energies. We also used the catalogs of H II regions obtained with WISE data  (Anderson et al. 2014) , of MCs in the Milky Way  (Rice et al. 2016 ), of Galactic O stars (Maíz Apellániz et al. 2013) , and information reported in the TeV source catalog 7. The details of the catalogs are given in Appendix A. To reduce potential missing associations, we automatically requested the Simbad 8 database, which provides information on the listed astronomical objects.
4 µG, but the model fails to account for the flux measured with Suzaku. We defined the ON region such as it encompasses the brightest part of the emission (located between the two MGPS-2 sources) and MGPS J142755-605038 (lying at the center of the Suzaku source), excluding the compact source MGPS J142714-604755 of unknown origin. The radio fluxes were calculated using MGPS-2, SGPS and PMN data and are reported in Table  1. We found a MGPS-2 source with flux roughly six times higher than that of MGPS J142755-605038, which was considered as the radio counterpart in the modeling of  Vorster et al. (2013). We also note that the flux upper limit derived with the PMN data is underestimated as a result of the masking of MGPS J142714-604755, which leads to a large number of pixels in the ON region that are not taken into account in the flux calculation.
For HESS J1458-608, the values correspond to those derived within the regions R ON1 and R ON2 shown in Fig. 3 (right). during the data reduction; for example, the MOST telescope does not detect structures on angular scales larger than 20-30. If Suzaku J1427-6051 and the nonthermal radio emission are both associated with HESS J1427-608, the latter is then probably affected by this instrumental limitation. The displacement of the centroid of the radio emission from that of the X-ray emission is found in other evolved PWNe such as Vela X and can be explained by different particle populations. This could explain why the brightest part of the radio emission is displaced from the Suzaku J1427-6051 centroid, even though it is coincident with the northern tip of the extended X-ray emission. It could thus be connected to the compact source MGPS J142714-604755 if the latter is assumed to be the pulsar at the origin of this multiwavelength wind nebula.
Discussion Since HESS J1427-608 is unresolved at TeV energies, a blazar or binary origin could have been considered. However, given  the existence of an extended X-ray emission spatially coincident with the H. E. S. S. source, these two scenarios are clearly disfavored. With an extended and center-filled nonthermal radio emission detected within HESS J1427-608 and a 4FGL Fermi-LAT cataloged source exhibiting a spectrum reminiscent of a pulsar, our multiwavelength data exploitation strengthens the scenario of an evolved PWN. To constrain the physical parameters in more detail, we modeled the broadband nonthermal spectrum of HESS J1427-608 using the Naima package  (Zabalza 2015)  assuming a one-zone leptonic model. The electron spectrum is in the form dN e /dE e ∝ E -p e exp(-E e /E cut ) for E e ∈ [E min , E max ], where p is the particle spectral index.
The minimum and maximum energy of the particles are set to E min = 1 GeV and E max = 1 PeV. Since the distance d is unknown, we arbitrarily set d = 8 kpc (as in  Fujinaga et al. 2013). For the IC scattering, we considered the CMB and the infrared and optical photon fields whose temperature and energy density were estimated using the data from  Porter et al. (2008)  included within the GALPROP 12  project. We obtained T IR = 25 K, U IR = 0. 82 eV cm -3 , T opt = 2005 K, and U opt = 1. 07 eV cm -3. We used the radio fluxes reported in Table 1 (excluding the upper limit derived with PMN data, see Sect. 3. 1. 2), the X-ray measurements from Suzaku  (Fujinaga et al. 2013) , the GeV data points from  Guo et al. (2017) , and the TeV SED reported in the HGPS. To simultaneously fit the radio and X-ray data, a hard particle spectral index such as p = 1.
The spectral indexes of R ON1 and R ON2 are compatible with those expected from both PWNe and shell-type SNRs. The spectral index of R ON1 is even closer to that of a SNR than a PWN, but the absence of a shell-like morphology and the location of the radio emission close to the energetic PSR J1459-6053 makes a PWN scenario more likely. The pulsar is seen in Chandra, Swift/XRT, XMM-Newton, and Suzaku data but no extended emission appears in these images. The ROSAT/PSPC data show no significant emission and give a tight constraint on the X-ray flux F X < 2. 13 × 10 -11 erg cm -2 s -1 (with N H < 1. 5 × 10 22 cm -2 , Pancrazi et al. 2012, and Γ TeV < 1. 95), giving B < 10. 1 µG at the 5σ confidence level. The pulsar PSR J1459-6053 is reported in the 2PC, 3FGL, 3FHL, and 4FGL catalogs. The Fermi-LAT catalogs also contain two sources located in the western part of HESS J1458-608 (3FGL J1456.
The electron spectrum is defined as in Sect. 3. 1. 3 and the temperature and energy density of the infrared and optical photon fields are T IR = 25 K, U IR = 0. 88 eV cm -3 , T opt = 2005 K and U opt = 1. 94 eV cm -3 (estimated using GALPROP code). We used the radio fluxes (from both regions) listed in Table  1 , the upper limit on the X-ray flux derived in Sect. 3. 2. 2 and the TeV SED from the HGPS. Owing to the lack of X-ray data points and the absence of a VHE cutoff, B and E cut cannot be accurately derived. As shown in Fig. 4  (right), the data can be reproduced with E min = 1 GeV, E cut = 60 TeV, B = 10 µG, p = 2 and W e = 7 × 10 47 × (d/8 kpc) 2 erg. For illustration, the best-fit spectrum of 4FGL J1456. 7-6050c is represented in Fig. 4  (right), keeping in mind that it was derived for a point-source morphology and that the source detection needs to be confirmed.
This algorithm, applied on the HGPS maps, has provided a list of objects that warrant further investigation. Each of the two above-mentioned excesses, which we independently identified after examining the HGPS maps, has a counterpart in the catalog of  Remy et al. (2020)  as a detected object uncataloged in the HGPS (labeled HC_147 and HC_382, respectively). Although this strengthens the interest of these two excesses, only a dedicated H. E. S. S. data analysis could confirm these as proper VHE sources. In the following, we consider them as such and investigate their origin through the existing multiwavelength data.
The south of HESS J1632-478 The unidentified source HESS J1632-478 is described in the HGPS by a Gaussian component with σ = 0. 18 • ± 0. 02 •. The HGPS significance map is shown in Fig. 6  (left) and shows a γ-ray excess with a peak significance at about 8σ in the south of HESS J1632-478 (outside of the HGPS R spec ). The pulsar PSR J1633-4805 ( Ė = 8. 5 × 10 33 erg s -1 and d = 6. 2 kpc,  Manchester et al. 2005 ) and the uncertain SNR G336. 7-0. 3  (Green 2017 ) are located near this γ-ray excess. We explored archival radio continuum data toward the south of HESS J1632-478. The MGPS-2 map is given in Fig. 6  (right) and shows a bright emission with some distorted shelllike structures. Several H II regions reported in the WISE catalog  (Anderson et al. 2014 ) and one MC  (Rice et al. 2016 ) are spatially coincident with the γ-ray excess.
Using data from the MGPS-2, SGPS and Parkes, we derived a radio spectral index of α = 0. 64 ± 0. 04 in the ON region encompassing the radio emission (white circle in Fig. 6 , right). The spectral index indicates that the radio emission is thermal in nature and could originate from the H II regions. Since these H II regions do not constitute the entire radio emission, it is possible that there is a subdominant nonthermal emission. We also found a X-ray point-source detected by ROSAT/PSPC (2RXS J163352. 2-480643) located in the western part of the ON region, which is also seen by ASCA/GIS although not cataloged. Four XMM-Newton extended sources are also reported in this region (see Fig. J163350. 4-480707 (σ = 43. 4 ). These X-ray sources have unknown origin and the Simbad database does not report any known object at these positions.
Using data from the TGSS, NVSS, and PMN surveys, we investigated the radio spectrum of G28. 6-0. 1 and we derived a spectral index of α = -0. 44 ± 0. 01, fully consistent with that usually measured for shell-type SNRs. The brightest parts of the shell are reported as sources in the TGSS catalog (at 150 MHz). By summing up their respective fluxes we obtained ∼8 Jy, which is broadly consistent with our estimate of 9. 01 ± 0. 17 Jy obtained A68, page 10 of 18 for the entire SNR. At X-ray energies, the inset in Fig. 7  (right) depicts the Chandra image, which shows a faint shelllike morphology, previously revealed by  Ueno et al. (2003). No Fermi-LAT cataloged sources are reported close to the SNR. We also explored the archival multiwavelength data toward the pulsar PSR J1844-0346 and we did not find any radio or Xray counterpart that could indicate a possible PWN.
Radio data from MAGPIS and infrared data from Spitzer show a bright extended emission southeast of PSR J1844-0346. The Simbad database indicates the presence of the star-forming region N49 containing H II regions, whose distance is estimated to be 5. 1 kpc or 9. 7 kpc; the former is the most likely distance (see  Anderson & Bania 2009; Dirienzo et al. 2012). With an angular distance between N49 and PSR J1844-0346 of θ = 181. 5 , the proper velocity of the pulsar is µ = 15. 65 t -1 11. 6 mas yr -1 , assuming t ≈ τ c = 11. 6 kyr. Taking the near distance to N49 (d = 5. 1 kpc), the transverse velocity would be v t ∼ 378 d 5. 1 t -1 11. 6 km s -1 , making the association of PSR J1844-0346 with N49 more likely than with G28. 6-0. 1. We shed light on a significant TeV excess that is spatially coincident with the synchrotron-emitting shell-type SNR G28.
6-0. 1. We investigated its radio spectrum and we derived a spectral index of α = -0. 44 ± 0. 01. Multiwavelength data do not show any emission from a potential PWN originating from the nearby and energetic pulsar PSR J1844-0346. While the association between PSR J1844-0346 and G28. 6-0. 1 was previously considered, we found that the pulsar more likely originates from the star-forming region N49. The radio and X-ray synchrotron-emitting SNR G28. 6-0. 1 is a promising source to be studied at HE and VHE since it should normally be able to accelerate particles up to γ-ray emitting energies in a leptonic scenario. More generally, the unidentified source HESS J1843-033 is an interesting source to investigate at VHE, given the numerous overlapping SNR candidates. Among these, we note that there is another excess (at ∼4-5σ in the HGPS maps) lying on the eastern part of the shell of G28.
Finally, the Fermi-LAT cataloged source spectra are used to search for a smooth connection between GeV and TeV energies. This algorithm is well suited for isolated sources but faces some limitations for large and complex regions, although it gives an overall insight into the possible multiple components as the origin of the TeV emission. We applied this pipeline on two unidentified sources reported in the HGPS: HESS J1427-608 and HESS J1458-608. We found extended nonthermal radio emission toward both sources and a mean magnetic field value consistent with that expected for evolved PWNe. Fermi-LAT data revealed a pulsar-like spectrum and a PWN-like spectrum in the vicinity of HESS J1427-608 and HESS J1458-608, respectively. We modeled the broadband nonthermal emission of both sources in a leptonic scenario with B 10 µG, which is consistent with that obtained from ancient PWNe.
For ROSAT/PSPC and XMM-Newton, we create background-subtracted and exposure-corrected maps, while the count maps of Swift/XRT, ASCA and Suzaku are only corrected from the exposure because the corresponding background maps are not available. Except for large field-of-view instruments (such as ROSAT/PSPC, Integral/IBIS-ISGRI, and Swift/BAT), we create a mosaic of these images in case of multiple observations. For Chandra data, we directly retrieve stacked and subtracted-background images (combining multiple observations), which are also corrected from the exposure and the vignetting. For Integral/IBIS-ISGRI and Swift/BAT, we extract a subregion of the flux, error flux, and significance maps. Archival X-ray data are retrieved through the following links: -Chandra:   6, 4. 5, 5. 8, 8, 21, 24, 870, and 1100  µm (available on the MAGPIS website https://third.