dataesr/scientific-paragraphs-categorization huggingface dataset:
The recommenders consisting of such intelligent capabilities is very useful for industrial applications and researchers. The rating prediction technique proposed in this paper also minimizes the data sparsity and cold start problem. Technically, the proposed system uses a Big Data tourism dataset to analyze the customers' choices. A sequential statistical analysis is performed, and the Big Data tourism data set is structured to fit the requirement of the model. The proposed model is an intelligent recommender system that consists of collaborative and content-based filtering to achieve an accurate result. Due to this, ITRS encompasses the advantages of constituent algorithms. As a result, drawbacks of the constituent algorithm are substantially minimized. Therefore, the system could be deployed, providing suggestions of various products.
Conclusions The rapid development of the web and its applications has been significant for recommender systems. Applied in different spaces, recommender frameworks were intended to create proposals, for example, for things or administrations depending on client interests. A big data tourism dataset is used for filtering the users to whom tourist industries recommend in the future. Statistical analysis is performed by a sequential pilot survey that divides the data into three different groups. The groups are formed based on users' preferences and the content of their desired items. Crow search-based hybrid recommendation techniques, combined with collaborative and content-based filtering, is deployed to achieve the desired result. Hybrid filtering techniques are implemented within the proposed model to achieve recommendation accuracy.
Data Availability Statement: Three different datasets are used in this research: Tripadvisor, Available online: http://www. tripadvisor. com (accessed on 1 November 2021), Data. world, Available online: https://data. world/datafiniti/hotel-reviewsz (accessed on 1 November 2021), Zenodo, Available online: https://zenodo. org/record/1219899#. YWaztBpBxPY (accessed on 1 November 2021).
Methods Data were collected in two experiments from two similar and overlapping participant groups. Ultrasound data (exp. 1) were obtained from eight participants (seven men and one woman, height: 179 ± 4 cm, mass: 80 ± 13 kg, age: 26 ± 4 years). After confirming the premise set for experiment 1, we then collected motor unit data (exp. 2) obtained from nine participants (six men and three women, height: 178 ± 9 cm, mass: 76 ± 14 kg, age: 33 ± 11 years), of which three (two men, one woman) were tested on two separate occasions to increase the number of motor units available for analysis. Two participants performed both experiment 1 (ultrasound) and experiment 2 (motor unit). All participants were healthy adults and provided written informed consent. The study was approved by The University of Queensland local Human Research Ethics Committee (file number: 2011001398) and the experiments were performed in accordance with the Declaration of Helsinki.
Protocol and data collection-experiment 1 Participants were positioned prone on an examination table with their trunk raised and supported by a padded wooden box to position their knee joints at 120°-internal angle (Fig. 1 ). Their preferred foot was firmly secured with strapping to a rigid plate that was connected to a torque transducer (Maywood Instruments, Basingstoke, UK). The combined foot and plate were positioned with the ankle at 90°, measured as the angle between the tibia and sole of the foot. After a brief warm-up, consisting of isometric, submaximal plantar flexion contractions, each participant was asked to perform three plantar flexion maximal voluntary contractions (MVCs) with at least 60 s rest between each MVC. The highest torque value for each participant was used as their MVC torque in further analyses.
During all experiments, the participants were instructed to remain in the position as seen in Fig. 1 , and no knee or hip flexion or extension occurred, as observed visually by the investigator. Participants then performed isometric ramp contractions up to a torque of 30% MVC at three different rates of force development: 2% MVC/s (slow), 10% MVC/s (medium) and 20% MVC/s (fast) guided by visual feedback. When the target torque was reached, participants were asked to hold the torque constant for several seconds (i. e. plateau region) before relaxing. Only the ramp data, i. e. from rest to the target torque, was used for this study, thus excluding the data from the brief plateau region. Prior to data collection, each participant practiced all three ramp rates until they were deemed to be competent by the experimenter.
A period of rest was given between each contraction to avoid any possible fatigue. All ramps were administered in random order. A schematic of the data collection setup and example signals from a representative subject are shown in Fig. 1. Torque signals were amplified 100 times (BK 1-5, Nobel Elektronik, Karlskoga, Sweden), low-pass filtered at 25 Hz and then analogue-to-digital converted at a sampling rate of 200 Hz (Micro 1401-3, Spike2 software, Bottom panel: example data from one participant for all three conditions (slow, medium, fast). Target torque (black) and the participant's torque (orange) on top, the fascicle length below. 1-s before and after each ramp is shown for all data. The vertical dashed lines mark the start and end of each ramp Cambridge Electronic Design, Cambridge, UK).
Determining an individual target torque was necessary because the torque at which a motor unit could clearly be identified for several discharges can vary substantially between individuals. Then, each participant performed isometric ramp contractions to their individual target torque at the same three rates as in 1-s before and after each ramp is shown for all data except for the motor unit action potential spikes as these depend on when the motor unit was recruited. The vertical dashed lines mark the start and end of each ramp experiment 1 (2% MVC/s (slow), 10% MVC/s (medium) and 20% MVC/s (fast)). Single motor units were recorded using a pair of fine-wire electrodes. A sterile needle (0. 6 × 32 mm; BD Technologies, NJ, USA) containing a pair of Teflon coated, stainless steel wires (140 μm, half hard; A-M Systems, WA, USA) was inserted into the MG muscle under ultrasound guidance (Sonix MDP Ultrasonix, British Columbia, Canada).
The needle was inserted to a depth of approximately 20 mm. The ends of the wires were fabricated with exposed 1 mm hooks, separated by 1 mm that allowed the wires to remain in the muscle when the needle was carefully retracted prior to data collection. During the experiment, the wires were retracted by 1 mm several times to make measurements in slightly different locations in the muscle, and thus increasing the motor unit yield. Intramuscular signals were amplified 1000 times (NL125, 126, Neurolog NL900D; Digitimer, UK) and bandpass filtered between 500 Hz and 5 kHz, before analogue-todigital conversion at 20 kHz using the same hardware and software as used for the torque and EMGs data. Bipolar surface EMG (EMGs) electrodes were placed over the MG and tibialis anterior muscle belly with an interelectrode centre-to-centre distance of 20 mm.
Data processing and analyses The filtered torque data from both experiments were exported to Matlab (R2018b, The Mathworks, MA, USA) and used to define the start and end of each ramp. An increase in torque greater than two times the standard deviation of the signal at rest was used to define the start, with the end defined as the time when the torque reached the target torque. The root mean square (RMS) error between the torque data and the target data was calculated to determine how closely the participants followed the target ramp. To quantify torque fluctuations, the linear trend, calculated as the best-fit straight line, was removed from the torque data and the standard deviation calculated. Ultrasound videos were processed offline using a semi-automatic algorithm implemented in Matlab, using UltraTrack software  (Farris and Lichtwark 2016).
Fascicle velocities are reported in absolute terms (mm/s) as well as relative to resting lengths (L R /s). Single motor unit action potentials were discriminated from the intramuscular electrodes using Spike2 software. Action potentials were classified based on shape and amplitude using a semi-automatic approach. Visual examination of each action potential confirmed that they were classified to the correct motor unit. Only motor units of which action potentials could be clearly discriminated between recruitment and the end of the ramp for all three ramp rates were used for further analyses, which significantly reduces the total number of motor units available for analysis, but was a requirement for testing our hypothesis. All action potentials were verified on two separate occasions. The recruitment threshold of each motor unit was determined in Matlab by moving a 500-ms window forward in steps of 1 ms until the inter-spike interval coefficient of variation within the window was ≤ 0.
First, post-earthquake positions may be affected by the coseismic motion that deformed the VPPS base stations network and that was not fully corrected. As the coseismic motion of distant base stations is relatively small (<1 cm, Figure  2 ), we neglect this effect. Second, part of the measured displacement is biased by interseismic loading and post-seismic motion. Although the pre-seismic displacement rates are poorly constrained in the area  (Metois et al. , 2015) , we apply the Bayesian inversion method developed by  Pagani et al. (2021)  to interpolate the available horizontal interseismic velocity fields  (Metois et al. , 2015; Zurutuza et al. , 2019). We find interseismic velocities of 2. 3 ± 0. 3 mm/yr to the East and 0. 5 ± 0. 1 mm/yr to the North, resulting in corrections lower than 1 cm for our data set.
Coseismic Deformation Pattern Figure  1  shows that the coseismic offsets calculated in the mid to far-field from the epicentral area are consistent with the observed right-lateral mechanism of the main shock (see also a comparison between the presented GNSS measurements and the available InSAR data in the Figure  S1  in Supporting Information S1). No significant surface displacement is measured at distances greater than 90 km. Over the short time span covered by the continuous GNSS time-series (i. e. , in the 20 days following the earthquake), no clear post-seismic signal is observed, consistently with preliminary post-seismic InSAR maps  (Baize et al. , 2022; Ganas et al. , 2021). The extension of the post-seismic signal that has been observed in the very near-field of the fault is likely limited to localized afterslip  (Baize et al.
Data Availability Statement All data sets and materials underlying this study are published open access in the zenodo repository https://doi. org/10. 5281/zenodo. 7229324, and additional details are provided in Supporting Information S1. Topographic data are from the Japan Aerospace Exploration Agency (https://www. eorc. jaxa. jp/ALOS/en/dataset/aw3d30/ aw3d30_e. htm).
Three individual clones (biological replicates) were used for DCA, and four for TCA experiments. Culture samples were spun down during 15 s at 13,000 rpm in 2 mL tubes and pellets were used for total RNA extraction as described  83. RNA quality was evaluated on Bioanalyzer 2100 (Agilent Technologies) at the @BRIDGE platform (INRAE, Jouy en Josas, France). RNA samples were then utilized for RNA sequencing (RNA-Seq) and qRT-PCR experiments. RNA-sequencing and data processing. RNA-seq of DCA treated samples was performed by the MetaGenoPolis company (Jouy en Josas, France) on a SOLiD 5500 W technology instrument (Thermo Fisher Scientific). RNA-seq of TCA treated samples was performed by the contractor company MGX (Montpellier, France) on an Illumina platform (Hi-Seq 2500 technology instrument).
RNA-Seq reads were aligned against the genome sequence of the E. faecalis strain V583 [GenBank:AE016830. 1] using Bowtie (version 1. 2. 2)  86  for SOLiD reads and BWA-backtrack (version 0. 712-r1039)  16  for Illumina reads. Gene-level read counts were obtained with Htseq-count (version 0. 9. 1)  87  based on the gene annotation of AE016830. 1 enriched with non-annotated small RNA encoding genes (sRNAs) previously identified  51. Statistical analysis of differential expression relied on R library "DESeq2"  88  and the associated "median ratio method" normalization procedure. Count data are provided in Supplementary Tables  S1  and S4  for DCA and TCA, respectively. DESeq2 p-values were converted into q-values using R library "fdrtool"  89. Genes were called differentially expressed (DEGs) when adjusted p-values (q-values) < 0.
Programs 'Primer 3' and 'NormFinder' were used to design primers and identify genes utilized as internal control, respectively  90. Based on transcriptomic data, ef0836 and ef1093 (ebpC) mRNAs were selected as internal controls as they did not show significant changes along the experimental time course of the TCA response, and had similar expression levels to RNAs probed ef0581, ef0634 and ef3110 (Table  S5 ). The normalization was done using the 'geNorm' program  91. Primer sequences are reported Table  S6.
Acknowledgements This work was supported by grant  ANR-12-BSV6-0008  (ReadRNA) from the  Agence Nationale pour la Recherche ,  INRAE ,  UFMG, 'Science without borders' (CNPq). We thank our colleagues  Nicolas Crapart  and  Claudia Bevilacqua  at the @BRIDGE platform for guidance on qRT-PCR and use of devices ( INRAE, Jouy en Josas ),  Elise Borezée-Durant  for discussions on ABC and Opp systems, and the members of the  CPE team. We are grateful to the anonymous reviewers for their careful reading of the manuscript and their insightful comments.
, 2014; Wingate et al. , 2015; Klosterman & Richardson, 2017; Nezval et al. , 2020) , we used the PhenoCam Dataset V2. 0  (Richardson et al. , 2018; Seyednasrollah et al. , 2019a; Seyednasrollah et al. , 2019b)  to assess the relationships between satellite DLCO and the DLCO derived from GCC (green chromatic coordinate) and VCI (vegetation contrast index) observed by PhenoCam (Section 2 of the Supplementary Methods).
| DLCO from in situ phenological observations DLCO was extracted at the species level from datasets of in situ phenological observations in China provided by the Chinese Academy of Sciences (CAS). The CAS dataset uses the date of first leaf coloring as DLCO. For a given species at a given site, the date of first leaf coloring was identified as the day when the first batch (about 5%) of leaves on more than half of three to five marked individuals started to change color  (China Meteorological Administration, 1993). The in situ phenological observations were performed according to standard observation guidelines  (China Meteorological Administration, 1993)  every other day by professional observers trained well by CAS. The CAS dataset is available from National Earth System Science Data Sharing Infrastructure, National Science and Technology Infrastructure of China (http://www.
| DPDO estimated from maximum canopy photosynthetic capacity The timing of the onset of the decrease in maximum canopy photosynthetic capacity in autumn (in day of year, DPDO) is defined as the date when the capacity decreases by 10% of its annual amplitude after the data have been fitted to a generalized sigmoid function  [Equation (7)  in  Klosterman et al. (2014) ]. The capacity was calculated from half-hourly or hourly gross primary productivity (GPP_NT_CUT_MEAN) based on eddy covariance measurements in the FLUXNET2015 dataset (http://fluxnet. fluxdata. org/data/fluxnet2015-dataset/, accessed on 10 March 2018)  (Pastorello et al. , 2017). We followed the procedure of  Shen et al. (2014)  to estimate daily canopy photosynthetic capacity, except that the parameters in the rectangular hyperbolic function were estimated by using half-hourly/hourly GPP and incident shortwave radiation calculated by using 15-day moving windows throughout a year.
Here, the daily minimum temperature used to determine cold events for satellite-derived DLCO was extracted from the CRU-NCEP dataset (Version 7. 2, https://vesg. ipsl. upmc. fr, assessed on 10 January 2019), which provides 6-hourly data at a spatial resolution of 0. 5° × 0. 5° through 2016  (Viovy, 2018). The CRU-NCEP 7. 2 is a combination of two datasets: the CRU TS3. 2 0. 5° × 0. 5° monthly data covering the period 1901 to 2002 and the NCEP reanalysis 2. 5° × 2. 5° 6-hourly data covering the period 1948 to 2016. We determined daily Tmin as the minimum value of the four 6-hourly minimum temperature values for each day. The CRU-NCEP data were resampled at 0. 05° × 0. 05° by replication to match the DLCO data. Daily Tmin for in situ observations in China was extracted from the "Daily Surface Climate Variables of China" catalog (a dataset named SURF_CLI_CHN_MUL_DAY_V3.
03 dataset (http://data. ceda. ac. uk, accessed on 11 June 2019), which provided monthly data at a spatial resolution of 0. 5° × 0. 5° until 2018. It should be noted that Tmin in the dataset is an approximation of the mean of daily minimum temperature for a calendar month, which is arithmetically calculated from gridded monthly mean temperature and the diurnal temperature range  (Harris et al. , 2014)  and does not exactly reflect the interannual variations in the absolute minimum temperature  (Körner & Hiltbrunner, 2018)  experienced by plants before DLCO. The CRU data were resampled at 0. 05° × 0. 05° by replication to match the DLCO data. We also investigated the impact of Tmin and precipitation on DLCO from ground-based observations in China and on DPDO from eddy-covariance sites as complementary to satellite-derived of DLCO.
05, Mann-Kendall test; Fig. S4a  and Table  S6 ). When we redefined DLCO as the date when the rate of change of curvature of a double logistic curve  (Beck et al. , 2006)  fitted to the NDVI series reached its first local minimum in the descending period  (Zhang et al. , 2003) , we obtained similar results (Fig. S4b  and c ). Complementary to satellite-derived DLCO, we also examined the temporal changes of DLCO by using ground-based leaf coloration data from China. DLCO was not significantly delayed for 90% and 94% of the 332 time series as shown by OLSR (Fig. 3b ) and the Theil-Sen method (Table  S6 ), respectively. As with the remotely sensed data, excluding cold events from the ground-based data revealed only a few significant delays of DLCO (Table  S5 ).
Comparison between satellite DLCO and DLCO from PhenoCam dataset To better match the PhenoCam images, we used the satellite MOD09A1 dataset (collection 6) which has a spatial resolution of 500 m and temporal resolution of 8 days. The dataset was downloaded from https://modis. ornl. gov/globalsubset/ on March 10, 2021. The PhenoCam dataset V2. 0 was downloaded from https://daac. ornl. gov/cgi-bin/dsviewer. pl?ds_id=1674 on August 29, 2020. From the high-frequency (typically, 30 minute) imagery collected over several years, the GCC (green chromatic coordinate) time series of a region-of-interest (ROI) that delineates an area of specific vegetation type was provided by the PhenoCam dataset. The VCI (vegetation contrast index) time series was calculated as the ratio of the green to the sum of the red and blue bands  (Zhang et al.
, 2018). For the comparison between satellite DLCO and DLCO from the PhenoCam dataset, processing steps are as follows: Step 1, the sites for agricultural lands, urban areas, or heterogeneous landscape within the area of a 500 m × 500 m pixel were excluded by visually examining the images in Google Earth. Then, daily time series were created by calculating the 90th percentile of GCC or VCI for each day. After that, 5-day medium value filtering was used to smooth the short term fluctuations and noises. Step 2, a time series was excluded if there was no data in any consecutive 30 days from annual maximum and to the end of year. Step 3, the annual time series were fitted to a generalized sigmoid function (eq 7 in Klosterman et al  (Klosterman et al. , 2014) ). Step 4, in many of the sites, there was considerable mismatch between the annual NDVI and GCC (or VCI) trajectories.
Salinity and chlorophyll a are therefore critical to understand the surface physical and biogeochemical properties of the region, as well as the air-sea fluxes of CO 2. The northwestern tropical Atlantic is a dynamically active region, with eddies several hundred kilometres in diameter and connected to the world's largest river. There are surprisingly few biogeochemical observations available for winter months during low outflow of the Amazon River. Few tropical Atlantic measurements of biogeochemical tracers are available, with one transect in winter in the Surface Ocean CO 2 Atlas (SOCAT;  Bakker et al. , 2016)  database south of 10 • N crossing the region. This scarcity is a major impediment in understanding the biological and physical processes underlying the oceanic carbon and nutrient cycles in the region.
The SST product is a 1 d average of infrared radiometer satellite data. Both datasets are sensitive to the cloud cover, but during our period of interest, they are usually without many gaps except at the end of February. The comparison between the TSG SST and the satellite SST product is detailed in RV Atalante's cruise report  (Speich and The Embarked Science Team, 2021). Daily ADT maps at a 1/4 • resolution combine data from all satellites available for the period 1993 to present. From these ADT fields, the TOEddies algorithm, developed by  Laxenaire et al. (2018) , identifies eddies and their trajectories. The eddy detection is based on the closed contours of ADT, as well as the maximum geostrophic velocity associated with the eddy. In order to compute the air-sea CO 2 fluxes, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) hourly wind speed and mean sea level pressure, P atm , are used.
ERA5 covers the period from January 1950 to present and provides hourly data on a 30 km grid. In addition, the monthly wind speed and SST fields over the period 1998-2015 are used, and a climatology over this period is computed. The wind speed in the region in winter is on average between 6 and 8 m s -1 , and its variability is low. We compare the EUREC 4 A-OA/ATOMIC observations with the observation-based CO 2 partial pressure (pCO 2 ) climatology developed by  Landschützer et al. (2020) , created using a two-step neural network method  (Landschützer et al. , 2016)  and combining open and coastal ocean datasets. The associated pCO 2 and air-sea CO 2 flux monthly field climatologies over the 1998-2015 period are computed using the ERA5 climatological wind, SST and P atm fields, as well as the atmospheric CO 2 from the Ragged Point, Barbados, station.
Reconstruction of f CO 2 from satellite maps Our approach is to derive from the large EUREC 4 A-OA dataset a relationship linking f CO 2 to SST, SSS and chl a in order to provide maps of f CO 2 based on the satellite maps of SST, SSS and chl a. Chl a was not measured on board; thus, we use satellite surface chl a co-located along the ship track. This set of parameters is used as proxy to describe the influence of ocean dynamics, chemistry and also marine biology on f CO 2. The biological carbon pump is one of the major components of the oceanic and global carbon cycles as the photosynthetic production of organic carbon by marine phytoplankton accounts for about half of the carbon fixation associated with global primary production  (Arrigo, 2007; Behrenfeld et al. , 2006; Field et al. , 1998).