dataesr/scientific-paragraphs-categorization huggingface dataset:
5- Ultrastructural examination (19-082) Pieces (1 mm 3 ) of mantle, gills and digestive gland of one oyster showing an abnormal black coloration and for which examination of imprints revealed the presence of parasites looking like parasites of the genus Haplosporidium were immediately fixed with 4% glutaraldehyde for 24 hours at 4°C. The samples were then washed in 0. 2 M cacodylate buffer (3x30 min) and post-fixed for 1 h in 1% osmium tetroxide (OsO 4 ) in 0. 2 M cacodylate buffer, cleared in propylene oxide, and embedded in epon resin. Ultrathin sections were obtained using copper grids and double-stained with 5% uranyl acetate and 5% lead citrate and then examined at 80 kV on a JEOL 1110 transmission electron microscope equipped with a Morada digital camera and iTEM imaging software (Soft Imaging System, Olympus).
2- Real Time TaqMan™ PCR development Oyster DNA Extraction DNA extraction was performed on fresh oyster tissues or archived material frozen or conserved in ethanol as described above. Primers and probe design We used the primer set targeting the 18s gene (1358F 1507R) initially designed for conventional PCR  (Ko et al. 1995). In addition, two probes were designed using Primer Express® Software v3. 0 (Applied Biosystems, Foster City, CA, USA) after comparing Haplosporidium costale sequence with closely related ones: Haplosporidium lusitanicum (AY449713. 1), H. pickfordi (AY452724. 1), H. edule (DQ458793. 1), H. raabei (HQ176468. 1), H. montforti (DQ219484. 1) and H. nelsoni (U19538. 2). The set was validated in silico using NCBI primerBLAST (Basic Local Alignment Search Tool; www. ncbi. nlm.
As few information is available on the H. costale conventional PCR, DSe estimation was based on data available for a conventional PCR targeting a closely related parasite, H. nelsoni  (Gagné et al. , 2015). So, the beta priors (152. 8758, 27. 8016) were used for DSe, reflecting 95% confidence that the DSe of the conventional PCR is above 80% with a mode set at 85%. The corresponding beta distribution was generated using the betabuster software (downloaded from http://www. epi. ucdavis. edu/diagnostictests/betabuster. html). The models were implemented in JAGS within the R statistical software environment using packages rjags. For all models, four Monte-Carlo Markov chains (MCMCs) were run with 50°000 iterations with the first 10°000 iterations discarded as a "burn-in". Convergence of the MCMC chain was assessed by confirming that the history plots ran stably and did not switch to different regions.
2-Molecular characterization Amplification at the expected size was obtained for 12 to 17 oysters from sample 19-030 and for 1 to 5 oysters for sample 19-082 depending on the primer pair (Table  2 ). Some PCR products were sequenced (Table  3 ). For a same primer pair, sequences were identical. Whatever the primer pair used, these sequences showed 99% to 100% identity with Haplosporidium costale (KC578010. 1; AF387122. 1) (Table  3 ). In order to extend sequences obtained on the 18S fragment and to amplify the internal transcribed spacer (ITS) region and actin gene, different primer pairs were tested and combined (Table  1 ). Two sequences, 1445 and 1354 bp in size, were obtained on the 18S fragment for samples 19-030 and 19-082, respectively (GenBank references: MZ666334 and MZ666335). These sequences displayed between 98,31 and 99,45 % identities with H.
costale (AF387122. 1) and Haplosporidium sp. Saccostrea glomerata (JX977120. 1)  Three 453 bp sequences were obtained for the ITS region including partial small subunit ribosomal RNA gene, the ITS 1, 5. 8S rRNA gene, ITS 2, and partial sequence of the large subunit ribosomal RNA gene. Two sequences obtained from oysters from sample 19-030 were 100% identical (GenBank reference: MZ666374) and the third one from 19-082 (GenBank reference: MZ666375) showed 2 transversions (G instead of C and A instead of C in position 267 and 272 respectively in 19-082). These sequences appeared 99,56 and 100% identical with H. costale KF790901. 1. Finally, 2 identical sequences (598nt), one from each oyster sample, were obtained for the actin gene (GenBank reference: MZ673037). These sequences were 100% identical with H.
Similarly, to H. costale, no filament has been observed on spores in our study  (Perkins 1969; Rosenfield et al. 1969 ). However, some pictures could suggest the presence of epispore cytoplasmic extensions (Figure  4g ) as it has been reported for H. armoricanum in Ostrea edulis in Galicia  (Azevedo et al. 1999). The presence of Haplosporidian parasites looking like Haplosporidium costale in transmission electron microscopy has earlier been reported in three oysters Crassostrea gigas collected from Thau lagoon, South of France  (Comps and Pichot, 1991). Interestingly, these parasitized oysters appeared grey coloured macroscopically as observed for one oyster in our study collected in June 2019. Sporocyst and spore size as well as the number of spores in sporocysts appear smaller in our study compared to the data available in  Comps and Pichot (1991)  and compared to data available for H.
(2007)  have previously reported that their molecular phylogenies are closely related. As with many non-cultivable micro eukaryotes, Haplosporidium costale genome is poorly represented in public databases. However, three fragments of the genome including part of the small subunit rRNA gene, the ITS-1, 5. 8S and ITS-2 array and part of the actin gene were successfully sequenced. The actin gene sequence showed 100% identities with the sequence obtained from Crassostrea virginica from Virginia (USA). Two long 18S sequences (>1360 bp) were obtained and were between 98. 31 and 99. 45 % similar to H. costale from Crassostrea virginica from Virginia (USA) and Haplosporidium sp. from Saccostrea glomerata from New South Wales (Australia). Interestingly, the ITS1, 5. 8S and ITS-2 sequence appeared more discriminating and revealed a higher distance between the French and the Australian isolates than with the North American ones.
Acknowledgements Authors are very grateful to  Ryan Carnegie  (VIMS, U. S. A) and  Marc Engelsma  ( WUR, The Netherlands ) for having provided reference material for Haplosporidium nelsoni and H. armoricanum, respectively. This work was funded by the  French Institute for Exploitation of the Sea (Ifremer)  and by  DGAL (French National Services of Food and Agriculture)  through the  National Reference Laboratory for Mollusc Diseases, Ifremer ,  La Tremblade.
Soil and climate data extracted from digital maps To complement data from field surveys, soil and climate information was extracted, described during the period from 1985 to 2010 for each plot, using digital maps belonging to the Digitalis database by Laboratory SILVA, Université de Lorraine-AgroParisTech-INRA (https:// silvae. agroparistech. fr/home/). Monthly solar radiation calculated using the Helios model  (Piedallu and Gégout 2007) , minimum and maximum temperatures modelled and mapped using Meteo France weather stations and potential evapotranspiration (PET) calculated with Turc formula  (Turc 1961; Piedallu et al. 2016)  were used to represent available energy. Precipitation (Prec) and climatic water balance (CWB, Prec minus PET) were used to estimate available water for plants from climatic data.
Soil water balance distribution was also estimated, combining monthly climatic variables with soil water holding capacity maps (SWHC) using the Thornthwaite formula (Thornthwaite 1955) with a Geographical Information System. This calculation provided the spatial distribution of mean annual values of actual evapotranspiration (AET), soil water content (SWC) and soil water deficit (SWD)  (Piedallu et al. 2013). The resolutions of all the digital maps was 50 m, except precipitation and SWHC, which had 1-km resolution. All climatic data were provided as monthly average of the 1985-2010 period, which were later also aggregated into seasonal and annual means.
Data analyses All data analyses and models were calibrated separately for the three species, spruce, fir and beech, using R software version 3. 6. 0 and version 4. 0. 2 (R Core Team 2017), and figures were produced using the package ggplot2  (Wickham 2009). Two different datasets were used for the analyses of densities and growth of saplings. For the density analyses, plot-level data were used, with sapling densities for each species and height class pooled at the plot scale (n = 152). For the analyses of growth of saplings, each data point consisted of information of a single individual sapling (n = 2370). The final models determining annual height increment was formulated using the subset of unbrowsed saplings (n = 2128), as the height increment of browsed saplings was assumed to be zero.
The last step was to test the addition of competition (competition index, browsing index), followed by topographical factors (slope, altitude, aspect). The order of these steps was chosen based on the importance of these variables derived from literature, and considering increasing pressure of climate change. When a variable from one category was selected, other variables from the same category were systematically avoided to prevent correlated variables being present in the same model (except topographical factors). The function "nlme" from the R package "nlme"  (Pinheiro et al. 2021 ) was used to run these models. Evaluation of the models was done by comparing graphical predictions made by different models and their ecological significance, and by comparing the observed vs. predicted values, along with the Akaike Information Criteria (AICs), mean square deviations (MSD) and the distribution of residuals.
Beech saplings grew most rapidly followed by spruce, while the least height increment was recorded by fir. Beech and spruce growth was negatively affected by maximum summer temperature (Fig. 4a, c ). Silver fir growth did not directly depend on any other environmental factors that we took into account in our dataset (Fig. 4b ). Apart from that, plot-specific differences were observed in sapling height increments as well, indicating a considerable random effect. This effect was stronger for beech, followed by spruce, and least for fir. The significance of other tested climate and soil variables was less obvious for height increment of saplings of all species than for densities, based on high p-values for other variables (Table  7 ). The closest models strengthened the dependence of beech and spruce on temperature, as they included different temperature variables like minimum winter temperatures and maximum monthly temperatures.
Our model predictions point out that among spruce, fir and beech, fir saplings have the highest mortality and lowest annual height increments. Added to that, the ungulate browsing is the highest in fir. This could create an advantage for spruce and beech regeneration, thus possibly starting a change in species composition of the stands  (Dobrowolska and Bolibok 2019; Häsler and Senn 2012; Bernard et al. 2017; Heuze et al. 2005; Diaci 2002; Senn and Suter 2003; Thrippleton et al. 2017; Weisberg et al. 2005; Cretaz and Kelty 2002). However, spruce and beech are also more sensitive to climatic changes, specially limited by high temperatures and water availability. This could also reduce their chances of better survival. In spite of all this information, in our dataset, and in spite of being highly browsed, the highest observed tree recruits in the last few years (7.
Code availability The code for all analyses was written in R software version 3. 6. 0 and version 4. 0. 2 (R Core Team (2017)). The function "nlme" from the R package "nlme"  (Pinheiro et al. (2021) ) was used to run models. All figures were produced using the package ggplot2  (Wickham (2009) ). The custom code will be made available by the authors on reasonable request.
1a ). Each female was successively challenged with several colocated and separated target signals with various Signal-to-Noise Ratios (SNR) and separation angles (16 females tested, mean number of trial per female = 6. 5 ± 5; Fig. 1d , see Supplementary Fig. 1a  for raw data and Supplementary Table  1  for details on the signals played back to each female; see Supplementary Movie 1 for a video showing an experimental trial). At the beginning of each playback, the loudspeakers were at approximately 20 meters from the tested female. We rated the female's response to playback according to a 0-4 level behavioral scale (score for no reaction = 0; head or body movement not in the direction of the target loudspeaker = 1; head movement towards the target loudspeaker = 2; displacement on a distance less than 1 body length towards the target loudspeaker = 3; displacement on a distance more than 1 body length towards the target loudspeaker = 4).
3a ; when non null, the angle between the separated loudspeakers and the noise loudspeaker varied between 44 ∘ and 156 ∘ , see raw data in Supplementary Fig. 1b ). Each subject was challenged several times with an interval of at least 10 min between trials (Fig. 3c ; 7-11 trials per subject; total of 30 "colocated" and 41 "separated" trials; see Supplementary Movie 2 for an example of a playback experiment; see Supplementary Table  6  for details of the signals played back to each juvenile). For each trial, we assessed the crocodile's ability to detect the target signal against the background noise by rating its behavior according to a binary scale: no orientation towards the loudspeaker emitting the target calls = score 0; orientation towards the loudspeaker = score 1. For the purpose of analysis we further modeled this scoring using a Bayesian logistic regression (Bernoulli distribution).
Experimental signals. As in experiment 1, we broadcast masking noise and target signals. The masking noise (white noise, 2 h duration) was played continuously in a loop, starting before putting the crocodile in the pond and throughout each experimental session (frequency range [20, 20,000] Hz; 83 unweighted dB SPL at 1 meter with the same sound level meter and same settings as in experiment 1). As target signals, we used twelve different sequences of three identical Nile crocodile contact calls from our recording data bank (twelve unit calls from young Nile crocodile previously recorded in the Okavango Delta, Botswana, by T. Aubin and N. Mathevon; see spectrogram on Fig. 3b ). Contact calls are known for maintaining cohesion among juveniles by soliciting their reunification  43. Each call was previously band-passed filtered between 20 Hz and 10 kHz (filter order of 3), and its intensity was normalized by its RMS value.
For each experiment, the initial perceived SNR (SNR p ) was defined as follows: SNR p ðdBÞ ¼ L T À L M : ð1Þ For each trial, we also measured the separation angle θ formed by the crocodile, the noise loudspeaker and the target loudspeaker. This angle was constrained both by the experimental configuration (Fig. 3a ) and by the initial position of the tested juvenile, and varied between 44 and 156 ∘. As in Experiment 1, we were unable to ensure a constant angle between the crocodile's head and the masker and/or the target from trial to trial. Analysis of behavioral reaction to playback. We observed and filmed the behavior of the juveniles during all the trials (infrared cameras ABUS TVCC34010). The videos were analyzed using Kinovea software (www. kinovea. org). In order to accurately measure the positions in the field and the distances traveled by the crocodiles, we took care to correct the distortion of the camera lens and the geometric perspective error.
As in Experiments 1 and 2, we observed and filmed the behavior of the tested subjects throughout the experiments. Video analyses were performed using Kinovea software. Before video analysis, we corrected for camera lens distortion and geometric perspective error. We measured the position coordinates of the loudspeakers and the initial positions of the crocodile (using the point between the eyes) before the start of the playback. Based on these coordinates, we calculated the separation angle θ between the noise loudspeaker, the crocodile and the target loudspeaker. To assess the tested subject's ability to detect the target sound against the background noise, we used the same binary scale as in the second experiment (detection score), giving a score of 1 if the juvenile showed orientation or movement toward the target loudspeaker, and 0 if it still did not respond within the 5-min observation period following the last buzz of the target signal.
If the crocodile did not respond more than twice to one of the higher SNRs (-16 dB or -18 dB) in the same session, the entire session was excluded from the final data set, considering that the motivation to respond to the target signal was not sufficient (only one session had to be excluded). We also measured the latency to respond, i. e. the time between the animal's first response and the preceding target signal. Statistics and reproducibility. All statistical analyses were performed in R (v. 3. 6. 2) from a Bayesian perspective, which provides more flexible and considerably richer investigations than the frequentist approach. Bayesian algorithms also have the advantage to be robust for any sample size. Each of the behavioral responses was investigated using this approach: the behavioral response ([0-4]; Experiment 1) score or the signal detection (0 or 1; Experiments 2 and 3), and the latency time to react.
These variables were modeled using Bayesian mixed models with random intercepts per tested crocodiles, fit in Stan computational framework accessed with brms package  61. Behavioral scores were modeled with a cumulative link function, a powerful model that is too often underestimated and left out for ratings data (ordinal regression)  62. The most appropriate link function was chosen by selecting the most predictive models. Two independent cumulative models were constructed as follows: a first one with the SNR and the position of target source (i. e. either co-located or separated) as fixed factors, and a second one by focusing only on the data obtained in the separated condition, with the SNR and the initial angle of separation between the target and the masker as fixed factors. Detection scores were modeled using the Bernoulli distribution (logistic regression), with SNR and position of the target source (co-located or separated) as fixed factors.
Acknowledgements The authors are grateful to " Crocoparc" zoo in Agadir, Morocco  ( Luc Fougeirol ,  Patrice Brunet ,  Ariane Marinetti ,  Leila Sdigui , Philippe and  Christine Alleon  and the staff), " La ferme aux crocodiles " zoo,  Colleen Reichmuth  and  Caroline Casey , and the  EMBRAPA technical team (Luis Alberto Rondon and Denis Celin Tilcara). This research has been funded by the  National Geographic Society  (field experiments), the  Institut universitaire de France (NM) , the  Labex CeLyA  (PhD fundings to  Leo Papet  and  Julie Thévenet ,  Lyon Center  of  Acoustics   ANR-10-LABX-60 ), the  CNRS  and the  University of Saint-Etienne.
The higher initial plume also seems associated with larger infrasound power (Figure  4 ) and smaller anisotropy (Section 3. 2) than the lower-height 08:47 injection. Contrary to WP1 and 3, tentative attribution of WP2 is not obvious. The second extended mesospheric intrusion occurs slightly West of Hunga and closely follows the first in time. CTH data also indicate a 48-km overshoot at Hunga's location at 05:17 which may better correspond. Event identification is challenging and not always meaningful given the complexity of the plume evolution and sources at play. Nevertheless, the comparison tends to suggest a significant role of processes related to plume dynamics  (Fee & Matoza, 2013; Matoza et al. , 2009; Watson et al. , 2022; Woulff & McGetchin, 1976)  in Hunga infrasound generation. It highlights the value of STR1's records which, gathered inside a waveguide, appear well-placed for source characterization.
Data Availability Statement Strateole-2 data is available at https://data. ipsl. fr/catalog/strateole2/eng/catalog. search#/search?from=1&to=30. IMS data is available upon request at https://www. ctbto. org/specials/vdec (last accessed on 2022-05-11). ECMWF data can be found at https://cds. climate. copernicus. eu/cdsapp%23%21/dataset/reanalysis%2Dera5%2Dpres-sure%2Dlevels%3Ftab%3Dform (last accessed on 2022-05-11). GOES-17 and Himawari-8 datasets are publicly accessible through Amazon Web Services (AWS). AWS Open Data description pages: https://registry. opendata. aws/noaa-goes/ and https://registry. opendata. aws/noaa-himawari/.
This suggests that this is the dominant influence on the I-V characteristics. Physics-based modeling of the quasi-static response of V 3 O 5based devices was undertaken using a 2D finite element model to solve the electrical and thermal continuity equations for a metal/oxide/metal device structure and was undertaken using the COMSOL package (see Supporting Information for details). Results from the simulations are shown in Figure  3d -f and are in excellent qualitative agreement with the corresponding experimental data. The main difference is that the calculated temperature distributions are generally broader than the corresponding experimental distributions. The above results are in contrast to those of Adda et al. ,  [13]  who showed that the filament temperature reaches the IMT temperature at the threshold point, with a sudden jump in local temperature.
The authors used Pseudo-Voigt functions to fit the data based on χ 2 minimization using the Fullprof software package. Threshold and hold voltages were determined from the mean and standard deviation of measurements performed on ten devices for each device size. The uncertainty in the absolute value of the thermal conductivity represented the standard deviation of at least 15-line measurements over a length of 8 µm using a resolution of 250 measuring points. It originated from calibration uncertainties, including the absolute value used for the thermal conductivity of the reference.
Acknowledgements The authors would like to acknowledge the access to NCRIS facilities at the  ACT  node of the  Australian National Fabrication Facility (ANFF)  and the  Heavy-Ion Accelerator Capability (HIA). The authors also acknowledge the facilities, and the scientific and technical assistance of the  Australian Microscopy & Microanalysis Research Facility at the Centre of Advanced Microscopy, ANU. A. R. and C. V. gratefully acknowledge the support from the  National Science Foundation , Award No. 2033328. This work was also partly funded by the  Lyon Institute of Nanotechnologies (INL)  in the framework of the  BQRi 2021 project  " Team BabAlbPuy ". S. K. Nath  acknowledges the support of the  Forrest Prospect Fellowship  awarded by the  Forrest Research Foundation ,  Australia. The authors would also like to thank  Dr.
(1) We assessed the distribution of ice and snow/firn from a manual delineation on Landsat satellite images of the surfaces at the end of the ablation season. We could not use a specific firn density value to quantify the annual mass balance due to the lack of density data measured in situ as well as the small proportion of the surface of the glacier covered by firn compared to that covered by ice and annual snow. For the five annual mass balances and the 2013-15 mass balance, the calculated mean density over the whole glacier ranged from 721 to 783 kg m -3 (mean 756 kg m -3 , median 0. 774 kg m -3 ). For multi-annual mass balances, we used an estimated constant density of 850 ± 60 kg m -3  (Huss, 2013). The density we used for snow for the quantification of the seasonal winter mass balances was the mean of the densities measured in situ.