dataesr/scientific-paragraphs-categorization huggingface dataset:
References: Hinton, P. R. ,  Brownlow, C. , & McMurray, I. (2004). SPSS Explained. Routledge. COMMENT 12: 4) Lines 680-683: "We have chosen to keep the models as simple as possible because the sample sizes for each experiment are small. These experiments were designed to determine whether grackles attend to causal cues or not. If results show that they do, then we will conduct further tests to investigate the extent of these abilities. " This is circular thinking. The sample size may prevent authors from finding a causal cognition and so the decision to extend this experiment to other places and to a larger sample size is created by this small sample size. COMMENT 15: This study explores the role of causal cognition in behavioural flexibility in 8 wild great-tailed grackles temporarily kept in captivity (up to 6 months).
Software description The software for the e-RFIDuino station was coded on an Arduino IDE platform (downloadable at: https://www. arduino. cc/en/Main/Software). The program consists of a main section and seven functions, and has the following features (summarized in Fig. 3 ). -The ''Libraires and variables declaration". Different default libraries are used: SPI. h to manage the SPI-bus; SD. h for the SD card; Wire. h to manage the I2C-bus; and SoftwareSerial. h to manage the virtual serial port. Only RTClib. h for the RTC is not available by default for the Arduino IDE platform, and is instead provided by Adafruit Version 1. 12. 4. Next, all the variables are declared. -The ''Setup" section is used to configure the software serial communication (9600 bauds), RTC, input and output pins, and SD card.
-The battery cables must be cut one by one to avoid any short circuit at this stage. Connect the two black wires (GND) together with the black wire of the connector (retrieved from one of the two batteries), solder, and isolate them. Then, strip the red wires and solder them together with the red wire of the connector and insulate the whole. It is important not to create a situation where two stripped wires of opposite polarity can meet. To upload the program to the Arduino board, the host PC needs to be connected to the Mini Pro with a USB-Series FTDI GT1125 converter (Fig. 6 ). In the Arduino IDE, click on ''Tools/Port" and select the desired port. Next, in ''Tools/Port" select ''Tools/Card type: 'Arduino Pro or Pro Mini'". If not available, go into the AVR Arduino boards and Arduino Pro or Pro Mini.
-Check that the recording is correct, that the ID number corresponds, and that the time stamp is correct, by 1) first switching off the power supply (switch on/off the solar panel control board), and 2) removing the SD card. -The system can also be checked directly through the serial port by connecting the USB-Series FTDI GT1125 converter to the Arduino (Fig. 8 ). At this stage, it is important that the switching voltage of the FTDI GT1125 converter is set to 5 V (Fig. 6 ); otherwise, there is a risk of damaging this component. Data recovery and station maintenance: -Switch off the e-RFIDuino station: on/off switch on the LiPo Rider solar panel controller; -Remove the SD card and retrieve the data from the computer; -Check the data and store it; -Format the SD card and insert it correctly; -Check the RTC drift and set the RTC time if necessary (Figs.
Future developments While the e-RFIDuino station was developed to function in an off-grid manner, a potential improvement would involve data transmission over the mobile telephone network, thus reducing the field effort and time required to retrieve data, as well as providing real-time monitoring that could be used to trigger field acquisitions at the most important times. With a supplementary module to transmit data to the mobile telephone network, further optimization of the power supply and consumption may be needed, but this is likely to be feasible. Although the RFID system used in this study functioned with a proprietary communication protocol, the e-RFIDuino hardware could be customized to operate with other RFID systems, using nonproprietary communication protocol for instance. A small modification of software libraries would probably be needed to adapt the new RFID system frame format.
Acknowledgements The authors thank the electricity company  Électricité de France (EDF)  who provided us with the discharge values measured at its gauging station. Daniel Vázquez-Tarrío ,  Léonie Besson , and  Nils Dumarski  are acknowledged for their participation in the e-RFIDuino station installation and sensing field delineation during field campaigns. Alain Recking ,  Frederic Liébault , and  Maarten Bakker  are acknowledged for their constructive advice and comments. The authors also thank the two reviewers and the  HardwareX managing Editor  for their constructive comments that helped to improve this article. This work was partially funded by the  LabEx DRIIHM ,  French program ''Investissements d'Avenir"  ( ANR-11-LABX-0010 ) managed by the  ANR. It was supported by the  OHM Vallée du Rhône.
This work was performed within the framework of the  EUR H2O ' Lyon  ( ANR-17-EURE-0018 ) of  Université de Lyon , part of the  program ''Investissements d'Avenir''  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). This work was carried out as part of the project  Greendam , which is an original partnership integrating an  interdisciplinary scientific group  ( EVS UMR 5600  and SiSyPh UMR5672 du CNRS et INRAE), a large electricity producer ( Électricité de France -EDF ), and a local  SME (GeoPeka)  as part of a partnership project with the  Région Auvergne-Rhône-Alpes.
2013). More recently, another technique has been used, based on the raw light data recorded by light-level geolocators (global location sensors, GLS) -small data devices recording light to determine twicedaily positions of birds during the non-breeding season  (Krüger et al. 2017). Given that nocturnal fisheries typically use intense light sources on deck, evidence of close en counters with these artificial light sources is re corded by geolocators attached to those individuals coming close to fishing boats at night. These devices also record conductivity (indicating contact with water) that can be used to infer bird behaviour during encounters (hereafter referred to as activity). The intensity of interactions between seabirds and fisheries varies among species and areas  (Furness 2003 , Krüger et al.
Processing of GLS data We used a dataset based on 569 GLS deployments (out of a total of 593) that provided light data for the full deployment duration. Herein, a 'track' refers to a time period of 1 yr, from one breeding season to the next. Some individuals were equipped with a geolocator for longer than 1 yr; therefore, 1 deployment could record several suc cessive annual tracks. The number of tracks per individual was on average 2. 3 ± 0. 1 SE (range 1-8 tracks ind. -  ). We focused on the non-breeding season (October to March) when birds are not central-place foragers and are therefore less constrained in their movements. Excluding the summer was also relevant to include highlatitude colonies that experience midnight sun, which prevented the detection of artificial light encounters. Loggers sampled light every 3 s, and recorded the maximum value in each 5 or 10 min interval.
Different logger models recorded light differently. BAS and Biotrack loggers recorded light values from 0 (night) to a threshold value of 64 (day), whereas Migrate Technology loggers recorded light levels in lux. Loggers also sampled conductivity every 3, 6 or 30 s, and recorded the number of wet samples in each 5 or 10 min bin between 0 (dry) and a maximum value of 200, 50 or 20 (wet). Conductivity data were standardized (from 0 to 1) to be comparable among the logger types. Positions were estimated using a threshold method, where light data were used to first characterize local times for sunrise and sunset, and from these, estimate 2 positions per 24 h based on time of apparent midnight and noon for longitudes, and length of night and day for latitudes. Unrealistic positions were removed as described by  Bråthen et al.
(2021) , using speed, angle and zone filters. In addition, the determination of latitudes (but not longitudes) is inaccurate during equinoxes when day length is similar everywhere on the globe. Therefore, positions estimated during the equinox periods (8 September to 20 October and 20 February to 3 April) were also removed  (Frederiksen et al. 2012 , Bråthen et al. 2021). Finally, many locations could not be estimated when the birds were situated within the polar night area. These successive filters led to a heavily biased dataset, owing to the non-random distributions of data gaps (both in time, during the equinoxes, and in space, in the areas affected by the polar night). To alleviate this bias, all filtered-out locations were reestimated using an algorithm that interpolates missing locations between 2 known locations.
Encounter detection To retain only periods of night in the raw light dataset, we used time of sunrise and sunset (hereafter twilight events), obtained when calculating positions. To avoid detecting potential remaining sunlight, we applied 2 corrections. Firstly, we removed light data 30 min around each twilight event. We then applied a correction during the polar night period, as some twilight can occur around the solar noon. Consequently, we removed all light data from the 60 min around the estimated time of solar noon during the polar night. Solar noon was estimated by using the solar noon times 10 d before and after each polar night period. We used a light intensity threshold method to de tect encounters of fulmars with artificial lights as described by  Krüger et al. (2017). Since the light sensitivity of different loggers varied (Fig.
S1  in the Supple ment), we used 3 different thresholds. Biotrack mk3006 and mk 3005, and BAS mk15, mk19, mk3, mk4 and mk7 formed the 'Biotrack-high' group. For these, we used the same threshold of 10 as in  Krüger et al. (2017) , then we adjusted the threshold for other recording modes to have comparable data in terms of number and duration of en counters. Therefore, we used a threshold of 5 for the 'Biotrack-low' group (Biotrack mk4093 and 4083, and BAS mk13, mk14 and mk18) and 20 for Migrate Techno logy loggers. To avoid overestimating the number of encounters, we assessed whether 2 light detections belonged to the same encounter by using the Bout-Ending Criterion method in the R package 'DiveMove'  (Sibly et al. 1990 , Luque & Guinet 2007). This method was originally designed for diving vertebrates  (Sato et al.
Fishery data We used open-access data available from the Global Fishing Watch website (https:// global fishing watch. org/). These data provide the cumulative number of fishing hours per pixel and per day on a 0. 01° pixel grid between 2012 and 2016. Data were merged into a raster where each cell is 200 km wide to be compared to the raster containing the density of encounters. Areas with high fishing intensity did not vary from year to year (Fig. S3 ), as mentioned in other studies  (Guiet et al. 2019). We therefore pooled all years together. We also pooled all fishing vessel types, be cause our dataset contains mainly trawlers and fixed gears (Fig. S4 ). Fishing effort was calculated as the sum of fishing hours per grid cell, and we included all fishing gear types available in the dataset.
Assessing fulmar activity from conductivity data We used conductivity data to assess fulmar activity. Conductivity measures whether the geolocator (on the bird's leg) is in saltwater ('wet') or not ('dry'). During the non-breeding season, a bird that was not in contact with saltwater was assumed to be flying; if the leg was constantly immersed, the bird was assumed to be resting on the sea surface, and if it was alternating between short wet and dry periods, the bird was assumed to be foraging  (Lecomte et al. 2010). To analyse fulmar activity budgets, we aggregated the conductivity data by individual and by en - counter with artificial lights and calculated the proportion of time spent in each type of activity. We classified standardized conductivity values (see Section 2. 2) in 3 activity types, as described by  Lecomte et al.
Statistical analyses We used R version 3. 6. 3 for all statistical analyses (R Core Team 2020). All R code used for the analyses is available at https://github. com/benjamindps/ Detections-of-seabird-fishery-interactions-using-GLS. A generalized linear mixed-effects model (GLMM) with a binomial distribution was used to test whether the probability of encountering artificial lights per night varied among wintering areas and colonies. A second GLMM with a binomial distribution was used to test for differences in encounter duration between colonies or wintering areas. More specifically, we tested the effect of the wintering areas and colonies on the probability of having short encounters (≤10 min) versus long encounters (>10 min), the median duration being 10 min. Three wintering areas with very low numbers of detections had to be removed for this model to ensure convergence (between 4 and 12 de tections for Kara Sea, Labrador Sea and Canadian Eastern Arctic, versus 90-1992 de tections for the other wintering areas).
All models were built using the 'lme4' package  (Bates et al. 2015)  in R, including individual ID as a random effect to take the non-independence in the data into account. We then investigated the changes in fulmar behaviour when encountering artificial lights using activity data. Each individual (n = 293; 3 loggers failed to record conductivity) was characterized by activity data (proportion spent flying, foraging or resting) during encounters and during a randomly selected control period (i. e. when no light was detected; see Section 2. 6 for details). No test was appropriate for comparing activity when encountering artificial lights and in control groups (e. g. distribution of the data was not symmetrical around the median as required for non-parametric Wilcoxon signed-rank tests, and/or the distribution of the differences be tween paired data was not normal as required for paired t-tests), and we therefore based our discussion on bootstrap confidence intervals (CIs).
We calculated the 95% CIs of the mean proportion of time spent in each activity (flying, foraging, resting) during encounters and in the control group using non-parametric bootstraps ('boot' and 'boot. ci' functions in the 'boot' package,  Davison & Hinkley 1997 , Canty & Ripley 2020). Non-overlapping CIs between the encounter and control groups were interpreted as significant differences. Results were exactly the same using Wilcoxon or t-tests, and we are therefore confident that our results and conclusions are robust. Finally, to test whether the probability of encountering artificial light was consistent within individuals and within colonies, a repeatability test was performed using the 'rpt' function of the 'rptR' package  (Stoffel et al. 2017). Repeatability was calculated as: ( 2) = + R V V V G G R where V G is the among-individual (or amongcolony) variance component, and V R is the withinindividual (or within-colony) variance component when modelling the log-transformed (to fit normal distribution) number of encounters per 6 mo nonbreeding period with individual and colony as random effects.
2020). Further investigations would be needed to study the seasonal and interannual variations in the importance of discards in fulmar diet. In the present study, we analysed only night data and indirect measurements of fulmar behaviour due to methodological constraints. We suggest that future studies should combine GLS light data with other sources of information like direct onboard observations of fulmar interactions with fisheries, which could provide information about bycatch mortality and any related biases that are crucial for conservation. Comparing day and night encounter probabilities could be useful to evaluate the role of artificial lights in attracting fulmars. Some seabird species that nest in burrows are actively attracted by artificial lights at night  (Montevecchi 2006 ), but this is less common for birds nesting on the ground, including fulmars.
Acknowledgements. We thank all fieldwork funding sources and fieldwork teams who took part in the capture and recapture of GLS-equipped fulmars. Data were mostly collected as part of the  SEATRACK  project (www. seapop. no/ en/ seatrack/). We thank  Malin Kjellstadli Johansen , coordinator of the  SEATRACK  project,  Benjamin Merkel  and the  SEATRACK  project  group , who provided critical feedback throughout the study. We thank  Stephen Votier ,  Lucas Krüger  and 2 anonymous reviewers for their very constructive comments.
2% Basic Fuchsin in ClearSee for lignin staining. Basic Fuchsin solution was removed and the seedlings were washed three times for 120 min with ClearSee with gentle shaking. Then, the seedlings were stained for 1 h in 0. 1% Calcofluor white M2R in ClearSee for CW staining. Calcofluor white M2R solution was removed and the seedlings were washed three times for 30 min with ClearSee with gentle shaking. Roots were carefully placed on a microscope slide with ClearSee and covered with a coverslip. The roots were then observed with confocal microscopes (Zeiss LSM500 and Leica SP8) using their respective software (Zeiss ZEN 2. 3 and Leica LAS X). We used an excitation at 594 nm and an emission band path of 600-650 nm for Basic Fuchsin, 405 nm and an emission band path of 425-475 nm for Calcofluor white M2R and 488 nm and an emission band path of 505-530 nm for GFP.
Samples were progressively dehydrated in ethanol (30, 50, 70, 100%  ethanol). Samples were aligned and embedded in Leica historesin using the protocol described in  60. Sections of 5 µm at 4 mm from the root tip were generated using a Leica microtome. The different samples were embedded in resin and cut at 4 mm from the root tip using a microtome with a thickness of 15 µm. Sections were mounted on superfrost glass slides. The samples were then mapped in a grid over the region of interest. The Raman imaging was performed with a Horiba LabRAM HR spectroscope equipped with a piezoelectric scan stage (Horiba Scientific, UK) using a 532 nm laser, a ×100 air objective (Nikon, NA = 0. 9) and 600 g mm -1 grating. Imaging was performed using the LabSpec 6. 4. 3 Spectroscopy Suite Software. Maps were collected for the regions of interest by setting equidistant points along the sample to ensure maximum coverage.
The main regions covered in the analysis were the endodermal cell-cell junction, the endodermal cell corners towards the endodermal-cortical junction and the xylem poles (Supplementary Fig. 1 ). The maps were acquired with two accumulations and 30 s integration time. The spectra were acquired in the range 300-3100 cm -1. The spectra were processed using MATLAB and eigenvector software. First, the spectra were trimmed (500-1800 cm -1 ), smoothed and then baseline corrected using an automatic least squares algorithm. This was followed by a percentile mean subtraction (10-20%) to remove signal from the resin. Finally, Gaussian image smoothing was performed to improve signal to noise of the Raman maps. MCR analysis was performed on the maps containing the specific ROIs and the corresponding lignin spectra were extracted.
D2) using the euclidean distance for the genotypes and the correlation dissimilarity for the genes. To define the seven clusters of cohesively expressed genes, we cut the gene dendrogram from the hierarchical clustering using the R function cutree. We visualized the expression of the 3564 DEGs and the result of the clustering approach using ggplot2. We used the compareCluster function from the clusterProfiler R package to perform gene ontology (GO) analysis for the seven clusters of cohesively expressed DEGs. We constructed individual heatmaps for the phenylpropanoid pathway, the peroxidases, the laccases and the aquaporin genes by sub setting the corresponding curated gene ids from the standardized gene matrix and procedure described above. Raw sequence data and read counts are available at the NCBI Gene Expression Omnibus accession number (GEO: GSE158809).
Additionally, the scripts created to analyze the RNA-Seq data can be found at https://github. com/isaisg/ schengenlignin with an assigned DOI (https://doi. org/10. 5281/zenodo. 4588023). Split-root experiment. Wild-type plants were grown 8 days vertically on ½ M/S square plates (12 × 12 cm) in a growth chamber under long day condition (16 h light 100 µE 22 °C/8 h dark 19 °C). The primary root was then cut below the first two lateral roots and plants were allowed to grow on the same plate for an additional 4 days. Then, plants were transferred on to a round plate (Ø: 10 cm) containing two compartments generated by a dividing partition. The root system of each plant was grouped into two halves and placed on either side of the partition. Two plants per plate were transferred. The compartments were filled with either solid ½ M/S on both compartments, solid ½ M/S supplemented with 100 nM CIF2 in both compartments or solid ½ M/S on one side and solid ½ M/S supplemented with 100 nM CIF2 on the other side.
Integration and alignment of the m/z features were performed via Progenesis QI software version 2. 1 (Waters Corporation). The raw data were imported in this software using a filter strength of 1. A reference chromatogram was manually chosen for the alignment procedure and additional vectors were added in chromatogram regions that were not well aligned. Peak picking was based on all runs with a sensitivity set on "automatic" (value = 5). The normalization was set on "external standards" and was based on the dry weight of the samples  66. In total, 13,091 m/z features were integrated and aligned across all chromatograms. Structural annotation was performed using a retention time window of 1 min, and using both precursor ion and MS/MS identity searches. The precursor ion search (10 ppm tolerance) was based on a compound database constructed via instant JChem (ChemAxon, Budapest, Hungary), whereas MS/MS identities were obtained by matching against an in-house mass spectral database (200 ppm fragment tolerance).
Root hydrostatic conductance (Kr) was calculated by the slope of the flow (Jv) to pressure relationship. The hydrostatic water conductivity (Lp r-h , ml H2O g -1 h -1 MPa -1 ) was calculated by dividing Kr by the root dry weight. Humidity, leaf surface, mortality, ionome and fitness. For the determination of the leaf surface and mortality, the seeds were stratified for 2 days at 4 °C and the plants were grown in Levington M3 compost in a growth chamber under long day condition (16 h light 100 µE 21 °C/8 h dark 19 °C). The plants were grown for 7 days with high RH (80% RH) and then half of the plants were transferred at a lower humidity (60% RH). Leaf surface was determined at 6, 9, 12 and 15 days after germination using the threshold command of the FiJi software. The plants displaying no growth after 9 days and showing necrosis in all the leave surface were considered as dead plants.
The samples were then digested in dry block heater (DigiPREP MS, SCP Science; QMX Laboratories, Essex, UK) at 115 °C for 4 h. The digested samples were diluted to 10 mL with 18. 2 MΩcm Milli-Q Direct water (Merck Millipore). Elemental analysis was performed with an ICP-MS, PerkinElmer NexION 2000 equipped with Elemental Scientific Inc. autosampler, in the collision mode (He) and Syngistix software. Twenty elements (Li, B, Na, Mg, P, S, K, Ca, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Mo and Cd) were monitored. Liquid reference material composed of pooled samples was prepared before the beginning of sample run and was used throughout the whole samples run. It was run after every ninth sample to correct for variation within ICP-MS analysis run  71. The calibration standards (with indium internal standard and blanks) were prepared from single element standards (Inorganic Ventures; Essex Scientific Laboratory Supplies Ltd, Essex, UK) solutions.