dataesr/scientific-paragraphs-categorization huggingface dataset:
Additional photometric measurements were obtained by TESS  17  with the 30-min cadence during sector-9 of its primary mission and with 10-min cadence during sector-36 of its extended mission. The reduced transit light curves of WASP-193 b obtained with WASP-South, TESS, TRAPPIST-South, and SPECULOOS-South are presented in Fig. 1 , 2 and 3, respectively. In addition, we used the Euler-1. 2m/CORALIE and ESO-3. 6m/HARPS spectrographs to observe WASP-193. We obtained 12 and 11 spectroscopic measurements with CORALIE and HARPS in 2015 and 2019, respectively. We used the cross-correlation technique in order to extract the radial velocity measurements as described by ref. 18  (see Methods). The reduced radial velocity curve is presented in Fig. 4. All the follow-up data were consistent with the planetary origin of the transit signal identified by WASP.
The research leading to these results has received funding from the European Research Council (ERC) under the FP/2007-2013 ERC grant agreement no. 336480, and under the H2020 ERC grant agreement no. 679030; and from an Actions de Recherche Concertée (ARC) grant, financed by the Wallonia-Brussels Federation. The Euler Swiss telescope by the Swiss National Science Foundation (SNF). This work has been carried out in part within the framework of the NCCR PlanetS supported by the Swiss National Science Foundation. This study is based on observations collected at the European Southern Observatory under ESO.
WASP observations and data reduction The Super-WASP project consists of data obtained at telescopes located at two sites: Sutherland Station of the South African Astronomical Observatory (SAAO) and the Observatorio del Roque de los Muchachos on the island of La Palma in the Canary Islands. The field-of-view (FOV) of each instrument is 482 deg 2 with a pixel scale of 13. 7" pixel -1. These instruments are capable of obtaining photometry with a precision better than 1% for objects with V -magnitudes between 7. 0 and 11. 5 1. The hot-star WASP-193 * was observed by the WASP-South survey in 2006 and 2014. The WASP-South data calibration (bias and dark subtraction, and flat-field division) was done with STARLINK pipeline 8 developed in Fortran. Aperture photometry was performed on the final calibrated images, where the stars fluxes were measured in three apertures with radii of 2.
SPECULOOS-South photometry The SPECULOOS (Search for habitable Planets EClipsing ULtra-cOOl Stars) robotic telescope network is composed of the SPECULOOS Southern Observatory (SSO), with four 1-m telescopes at ESO Paranal Observatory in Chile  [12] [13] [14]  and two 1-m telescopes in the Northern hemisphere: the SPECULOOS Northern Observatory (SNO) at Teide Observatory in Spain and SAINT-EX (Search And characterIsatioN of Transiting EXoplanets) at San Pedro Mártir Observatory in Mexico  61. We observed a partial transit of WASP-193 b with one of the four telescopes of SSO on 08 June 2019 in the Johnson-B filter with an exposure time of 25s. Data calibration and differential photometry were performed using the PROSE pipeline  60. The reduced light curve obtained is presented in Fig. 3.
TESS photometry The TESS (Transiting Exoplanet Survey Satellite  17  ) mission was launched by NASA in April 2018. Its main goal is to detect and characterise transiting exoplanets smaller than Neptune orbiting nearby bright stars. TESS's combined FOV is 24 • × 96 • , and one pixel is ∼ 21" on the sky. WASP-193 was observed by TESS with the 30-min cadence in the ninth sector of its primary mission (from 28 February to 16 March 2019) and with 10-min cadence during its extended mission (from 07 March to 02 April 2021). We used the TESScut ‡ website to download the calibrated full-frame images (FFI). TESS's FFIs are widely used to search for transits on the stars that were not selected for the 2-min sampling  62, 63. The data calibration was done by the SPOC pipeline (TESS Science Processing Operations Center) as described by ref.
programme 0102. C-0414, PI: Nielsen. TRAPPIST-South is funded by the Belgian Fund for Scientific Research (Fond National de la Recherche Scientifique, FNRS) under the grant FRFC 2. 5. 594. 09. F, with the participation of the Swiss National Science Fundation (SNF). M. G. and E. J. are FNRS Senior Research Associates. The ULiege's contribution to SPECULOOS has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) (grant Agreement n • 336480/SPECULOOS), from the Balzan Prize Foundation, from the Belgian Scientific Research Foundation (F. R. S. -FNRS; grant n • T. 0109. 20), from the University of Liege, and from the ARC grant for Concerted Research Actions financed by the Wallonia-Brussels Federation. This work is supported by a grant from the Simons Foundation (PI Queloz, grant number 327127).
J. d. W. and MIT gratefully acknowledge financial support from the Heising-Simons Foundation, Dr. and Mrs. Colin Masson and Dr. Peter A. Gilman for Artemis, the first telescope of the SPECULOOS network situated in Tenerife, Spain. This work is supported by the Swiss National Science Foundation (PP00P2-163967, PP00P2-190080 and the National Centre for Competence in Research Plan-etS). This work has received fund from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement n • 803193/BEBOP), from the MERAC foundation, and from the Science and Technology Facilities Council (STFC; grant n • ST/S00193X/1). C. D. was supported by the Swiss National Science Foundation (SNSF) under grant PZ00P2 174028. M. L. acknowledges support of the Swiss National Science Foundation under grant number PCEFP2 194576.
This may indicate complex source mechanisms rupturing a multi-segmented and/or a non planar structure  41. We also acknowledge Figure  3. Plots comparing the synthetic (black) waveforms simulating different focal depths of the San Juan 2021 earthquake (SJ2021) and beamformed array recordings of the SJ2021 event (blue). The best fit between synthetic and observed waveforms is achieved for focal depths between 23 and 25 km. Red dashed lines denote direct P arrivals and corresponding depth phases. This figure was generated using the Python Matplotlib libraries (V3. 5. 2, https:// matpl otlib. org/). (a) was created using the GMT package (V. 6. 0. 0, https:// www. gener ic-mappi ng-tools. org/). that non-DC events can be associated with a poor coverage of seismic stations and reflect inadequacies in the velocity model used for the inversion  42.
Integrating retroarc seismicity with structural interpretations Our final, inverted SJ2021 hypocenter solution is plotted alongside historical earthquake hypocenters  13, 23, 39  , resulting from the  43  Portable Array for Numerical Data Acquisition (PANDA, 1987-1988), SIEMBRA  44  (Sierras Pampeanas Experiment using a Multicomponent Broadband Array, 2007-2009), and CHARGE  29  (Chile-Argentina Geophysical Experiment, 2000-2002; Alvarado et al. 29  ) temporary seismic experiments, as well as receiver function geophysical data  45  , to illustrate patterns of retroarc seismicity along a cross section at ∼31. 5 • S (Fig. 5 ). Seismicity beneath the Western Precordillera and Central Precordillera is concentrated between 20 and 40 km depths. With the exception of several events in the Western Precordillera, no activity is observed within the thin-skinned thrust belt (i.
e. , within the closely-spaced faults above the shallow décollement at ∼ 10 km depth  32  ). Relatively intense seismicity is documented beneath the Eastern Precordillera and Sierra de pie de Palo (SdPP) between 5 and 35 km depths, including the 1944, 1952, and 1977 historical earthquakes and the SJ2021 9,10,13,23,26-28 (Fig. 1c ). Previous interpretations of fault-like structures from these datasets include (Fig. 5 ): (1) A décollement at ∼ 20-40 km depth that may connect basement structures beneath the Precordillera and SdPP  32, 45  ; (2) An upper subhorizontal to E-dipping fault in the SdPP at ∼10-15 km depth. This fault has been interpreted as the passive roof duplex of an E-directed middle crustal wedge  6  , or the back thrust of a listric, E-directed basement thrust fault inferred beneath the central and eastern SdPP  25, 26.
Relating earthquakes to specific faults remains challenging due to persistent uncertainties in event locations and along-strike variations in the orientation of both emergent and subsurface structures, making planar faults difficult to interpret from small-magnitude seismicity data  26, 28, 29, 46. Even so, our study builds upon careful integration of historical earthquakes with the structural framework of the Precordillera and Sierras Pampeanas. For example, Alvarado and Beck  23   47  , as well as the correspondence between the La Laja surface rupture and surface displacement predicted from their best-fit shallow ( ∼11 km depth) hypocenter location (Figs. 1c, 5 ). Persistent uncertainties in the epicentral location of the 1952 earthquake have prevented association with a discrete structure, but the reverse strike-slip focal mechanism (strike N40 • E, dip 75 • SE, rake 30 • ) and best-fit focal depth range (10-13 km) are consistent with rupture along emergent NE-striking faults in the Eastern Precordillera  23.
Red dashed lines show projected surface intercepts of Precordillera basement faults inferred from microseismicity data  27, 32. Black arrows show direction of principal compressive stress estimated from fault slip data  10. Bottom inset depicts predicted Neogene-Quaternary partitioning of the N70 • E plate convergence vector along variably oriented, middle-to upper-crustal retroarc features (modified from  10, 27, 30, 63. (b) Schematic block diagram showing proposed relationships among NE-striking, probably inherited basement faults and emergent structures in the Precordillera and western Sierras Pampeanas, in a South America fixed reference frame. Fault-like structures inferred from geophysical data are marked with red and blue numbered circles and correspond to structural features described in the text.
The Note that the SJ2021 focal mechanism has been rotated into the southern cross section plane. This figure was created with the GMT package (V. 6. 0. 0, https:// www. gener ic-mappi ng-tools. org/. Topographic information in a) comes from the Shuttle Radar Topographic Mission 90 m database (https:// srtm. csi. cgiar. org/ srtmd ata/). documented in Quaternary deposits along the N-striking, right-lateral El Tigre Fault (ETF, Fig. 1c, 5 ) expressed west of the Western Precordillera  10, 59, 60. Precursor basement faults influenced the construction and orientation of key ranges in the Sierras Pampeanas, notably including uplift of the Sierra de Valle fértil along the reactivated, N30 • W-striking Cuyania-Pampia suture zone (VFLH Fault; Figs. 1, 5, 6)  6, 61. Partitioning of the modern plate convergence vector would be expected to produce compression and left-lateral transpression along a structure of this orientation (Fig.
These basement faults notably parallel the ∼N20 • E-striking Eastern Precordillera, which exposes Paleozoic-Cenozoic sedimentary rocks (Fig. 6a ). Compressive stress in the direction of plate convergence would be expected to generate right-lateral transpression along the aforementioned NE-striking Eastern Precordillera and SdPP features. While this prediction is consistent with the right-lateral focal mechanism calculated for the deep SJ2021, it is at odds with the pure thrust solutions obtained for the shallow 1944 and double-rupture 1977 earthquakes, and with fault slip data indicative of dip-slip thrusting and N100 • E-to N120 • E-directed Neogene-Quaternary compressive stress in the Easter Precordillera and SdPP  9, 10, 23. Significantly, these discrepancies in orientation and stress conditions among Precordillera and Sierras Pampeanas structures are consistent with discrete zones and mechanisms of middle-and upper-crustal stress transmission originally hypothesized for the Sevier-Laramide flat slab segment  8  , and allow us to propose a model for retroarc deformation characterized by the following (Fig.
Conclusions We integrate seismic phase information from the ISC bulletin (ISC 13 ), waveform records from regional and teleseismic distances, and local GNSS observations to re-estimate source parameters for the January 19th, 2021 ( Mw = 6. 5 ) San Juan earthquake. Our combined approach leverages internationally available seismic datasets to resolve discrepancies in the published focal depth estimates (e. g. , Girino et al. 33  ; USGS  15  ) and confirms a mid-crustal nucleation depth (21 ± 2 km) and right-lateral transpressional mechanism for the SJ2021 event. We suggest that the SJ2021 earthquake activated a NW-dipping, mid-crustal basement fault originally defined by microseismicity data beneath the Precordillera  27  , and posit that this and other basement structures may be kinematically linked to emergent faults associated with historically devastating shallow earthquakes in the Eastern Precordillera and Sierra de Pie de Palo.
Considered alongside crustal seismicity data and geological constraints, the SJ2021 provides opportunities to explore the mechanisms and interactions controlling deformation and stress conditions along variably oriented middle-and upper-crustal structures. We propose a model in which basement-involved deformation was largely driven by a mid-crustal stress guide parallel to the plate convergent direction and the reactivation of NW-and NE-striking, probably inherited faults; development of the fold-thrust belt was controlled by a separate stress guide modulated by topography body forces in the orogenic wedge; and partitioning of deformation along linked middle-and upper-crustal structures facilitated pure compression orthogonal to NE-striking emergent faults in the Eastern Precordillera and SdPP.
39  , an averaged P-wave crustal velocities of 5. 73 km/s and the two-way travel time of ∼ 8s correspond to focal depths of ∼23 km. The use of beamforming methods for teleseismic arrays can further enhance the signal quality by a factor of 4  68. Hereby, the obtained waveforms from the individual sensors of the array are constructively stacked along the theoretical slowness vector of the arriving teleseismic wavefield. In this study, five different teleseismic arrays were used at epicentral distances between 69 • and 120 • (Fig. 3 ): Texas array (TXAR), Pinedale array (PDAR), Nevada Mina array (NVAR), Indian Mountain array (IMAR), and Alice Springs array (Alice). The beamforming parameters were estimated with the ray-tracer cake implemented in the Pyrocko package  69. The beamformed waveforms of each array are then visually compared to a set of synthetic waveforms calculated for different source depths.
S4 ) and restricted to the availability of meta information necessary for the automatic restitution. An automated data quality check and manual revision removed several stations that were unusable for the inversion due to: (1) clipped recordings in the vicinity of the epicenter (e. g. , station WA. ZON); (2) failed restitution; (3) alteration of the waveforms by local site effects (i. e. , all stations located in or at the limit of the Santiago basin, Chile); or (4) insufficient signal to noise ratios to resolve the phases of interest at greater epicentral distances. The latter case was even applied to only one component for two stations (radial component of C1. BI02 and C1. MT04). Ultimately, waveform data from 35 stations was integrated into the moment tensor inversion (Fig. 2a ). Preparation of the GNSS data required more sophisticated processing to derive the static displacement and associated uncertainties related to the SJ2021 event and is described in the following section.
The underlying velocity model for constructing the Green's functions database is based on a combination of the local Crust1. 0 model  64  and the global AK135 model  71. This integration allows for a spatial resolution of 1 km for the region from 1 km down to 30 km depth and epicentral distance ranging from 10 to 1000 km. The same velocity model was used to derive the Green's function database for static displacements modeling with a spatial resolution of 500 m. We first used the velocity model by Ammirati et al. 39  , completed by the AK135 model  71  , but the results were not fully satisfactory, which may stem from how this model is calibrated for the retroarc region of the Chilean-Argentine flat-slab segment and thus, does not account for the Chilean forearc velocity structure, where most of the stations used in the inversion are located.
GNSS time series analysis. To assess crustal displacements around the SJ2021 epicenter, we analyzed the north, east, and up-position time series data from 4 GNSS stations within a ∼ 50 km range of the earthquake. These time series were computed and distributed by the Nevada Geodetic Laboratory (NGL) at the University of Nevada Reno 79 thanks to the dissemination of GNSS station observation files by the Universidad Nacional de San Juan, the Complejo Astronómico El Leoncito, and the Instituto Geográfico Nacional. Each position time series was processed by the NGL using the GipsyX software  80  , with a single-station precise point positioning with carrier phase ambiguity resolution strategy based on Global Positioning System (GPS) observations only  81. The products used in this study are the daily position estimates based on the final orbit and clock products provided by the Jet Propulsion Laboratory.
We refer to Kreemer et al. 82  for further information about GNSS data processing. To estimate the co-seismic displacements caused by the SJ2021 with realistic uncertainties, we adjusted two complementary statistical models on each series: a trajectory model and a stochastic model. The trajectory model (Fig. S10 ) describes the deterministic effects identified in the position time series  83  , namely a linear trend, periodic oscillations, step discontinuities, outliers, and non-linear post-seismic deformations. The most frequently observed periodic oscillations, that is, the annual, biannual, and terannual signals, the 8 main GPS draconitic harmonics, and the 3 fortnightly signals, are considered  84, 85. For step discontinuities, we accounted for all instrumental changes or potential co-seismic displacements identified in the NGL's master step file database (http:// geode sy.
(c) Regional map of the study area showing main retroarc structures and associated crustal seismicity above the Chilean-Argentinian flat-slab subduction segment13, 18. Focal mechanism solutions are shown for regionally significant earthquakes at middle-crustal (blue) or upper-crustal (black) focal depths. This map was created using the GMT package (V. 6. 0. 0, https:// www. gener ic-mappi ng-tools. org/). Topographic information corresponds to the Shuttle Radar Topographic Mission 90m database (https:// srtm. csi. cgiar. org/ srtmd ata/) and the ETOPO1 Global Relief Model (https:// www. ngdc. noaa. gov/ mgg/ global/).
(c) Cross section view of the information presented in (b) projected onto section line A-B. Note that the SJ2021 focal mechanism solution has been rotated into the cross section plane. (d) Seismic waveforms corresponding to 4 example stations (see Fig. 2a for location) with observed surface waves (gray) after restitution, filtering, tapering and rotation into ZRT coordinate system, and corresponding synthetic waveforms ensemble (colored from blue to red with decreasing misfit). (a)-(c) were generated using the GMT package (V6. 0. 0, https:// www. gener icmappi ng-tools. org/) The topographic information in (c) corresponds to the Shuttle Radar Topographic Mission 90m database (https:// srtm. csi. cgiar. org/ srtmd ata/). (d) was generated using the Python Matplotlib libraries (V3. 5. 2, https:// matpl otlib.
Acknowledgements This research was supported by the  Chilean ANID-FONDECYT program  (Grant N • 3200633 to J. -B. A. ) and  US National Science Foundation  (Grant  EAR-1952791  to C. M. -L. ). The authors are grateful to  Sotiris Valkaniotis  for the processing of the inSAR data mentioned in the present work. The authors also thank the editor,  Christopher Scholz  for handling this manuscript.
Data availability The waveforms used in this study can be downloaded from the IRIS Data management center (IRISDMC) at http:// servi ce. iris. edu/ fdsnws/ datas elect/1/ or the GEOFON EIDA node at http:// eida. gfz-potsd am. de/ webdc3/. Network codes are C, C1, G, IU and WA. GNSS time series data are available from the Nevada Geodetic Laboratory at http:// geode sy. unr. edu/ NGLSt ation Pages/ Globa lStat ionLi st. Station codes are CSJ1, CSLO, OAFA and USNJ. All websites were last accessed in October 2022.
Characterisation of miRNAs Accumulating in the Immature Oil Palm Inflorescence Small RNA-seq data, obtained from each of the three male and three female immature inflorescence samples (developmental stages indicated in Figures  S1  and 1 ), were processed and mapped to the oil palm reference genome EG5 in order to detect and measure the abundance of known and novel MIRNA genes expressed in the tissues of interest. A total of 134 miRNA-encoding genes were identified in the present dataset (Figure  2 )  [21] , representing 30 recognized MIRNA gene families. Database comparisons revealed that 109 of the 134 genes had been previously identified in oil palm (http://www. plantsrnas. org accessed on 5 January 2022), whereas the remaining 25 genes were previously unreported in this species. Compared with database sequences, only one gene in the present dataset (encoding miR398) belonged to a recognized family that was previously unreported in oil palm.
Key Regulatory Genes Displaying Sex-Dependent Expression Are miRNA-Regulated In order to evaluate the importance of miRNA-mediated regulation of gene expression in the early sexual differentiation of male and female inflorescences in oil palm, we performed database searches using the psRNATarget server (https://www. zhaolab. org/psRNATarget/ accessed on 5 January 2022)  [22]  to identify potential mRNA targets of each characterised miRNA that were also represented in either the M_up or F_up DEG sets. A summary of the information obtained is shown in Table  1 , in which only miRNAs displaying a male/female differential accumulation profile greater than 2 or less than 0. 5 are listed. Since plant miRNAs generally target specific mRNAs for post-transcriptional degradation, a reverse accumulation profile between miRNA and mRNA can provide a useful means to assess the likelihood of an in vivo interaction.
Although miR5179 is shown in the databases as also occurring in cereal and eudicot species, no experimentally validated targets appear to have been described outside monocots to date, raising the question that this miRNA/mRNA module might be monocot specific. In addition to the aforementioned cases, another regulatory module of likely significance to inflorescence development that we observed was that of AUXIN RESPONSE FACTOR (ARF) gene transcripts (LOC105034684, LOC105055363) targeted by miR167a and miR167b. The involvement of auxin in pathways regulating inflorescence branching and development has been well established for many years  [28] , with ARF proteins playing an important role through their binding to the promoters of auxin-responsive genes  [29]. Other miRNAs listed in Table  1  target additional gene families of interest to inflorescence development (LACCASE and NAC and families); however, it should be noted that in the latter cases, accumulation patterns of miRNAs were relatively similar between male and female inflorescences, and sometimes they were not reversed compared with the putative mRNA targets, thus implying that their involvement in post-transcriptional regulation was not a significant contributor to developmental regulation.