dataesr/scientific-paragraphs-categorization huggingface dataset:
The daily estimated LW of each individual, used as a reference value, was first calculated after calculating ADG from the available weekly LW records obtained with the GS measurements. Then, all values falling out of the individually accepted range (i. e. , daily estimated LW based on GS ± 2 Â SD) were removed from the database. At the end of this three-step data filtering approach, the result was a cleaned database able to be further processed and interpreted. For each step of the filtering process, the concordance of the records obtained with the WoW and the GS data was evaluated using the concordance methodology proposed by Bland and Altman  (Bland and Altman, 1999). The bias coefficient of concordance evaluates the repeatability of the system. As the bias between the two methods, it is a measure of the lack of agreement and is estimated by the mean difference (d) and the variation around the bias is estimated as SD (sd).
Assuming the differences are normally distributed, the variation of the results is calculated as ± 1. 96 Â sd and referred to as limits of agreement  (Yellareddygari and Gudmestad, 2017 ). Lin's concordance correlation coefficient (CCC) was also calculated to evaluate the extent of agreement between the WoW and the GS method  (Lin, 1989). The CCC combines the measurements of accuracy and precision to define how far the WoW data deviate from perfect concordance (i. e. , CCC = 1. 0). It evaluates the reproducibility of the system. It also provides the correction bias factor to estimate how far the method is from the perfect correlation. These analyses were assessed using the BlandAltmanLeh and Desc-Tools packages, and the linear models function on R software, with the use of ggplot2 package, for plotting graphics.
LW data procedure and analysis A complex mixed model with repeated measurements (Week) was used, applying the lme4 package  (Bates et al. , 2015) , based on the fixed effects LW measurement System (n = 2; WoW vs GS), the experimental measurement Week (n = 14) and the LW subgroup (n = 3; Heavy, Medium and Light). Data were analysed considering both crossed (not nested) and nested random effects. Crossed random effects (i. e. , a given factor appearing in more than one level of the upper-level factor) were considered for the LW subgroup in the different and progressive experimental weeks, and both for the GS and WoW weighing system scales. Nested random effects occurred for each individual ewe lamb within each of the three specific LW subgroup levels. The model was fitted as follows: lmer(LW $ Group + System + Group Â Week + Group Â System + (1|System) + (1|Week) + (1|Group/RFID), data = data_analy sis).
With data_analysis using the final database for the statistical analysis (i. e. , after the filtering data process was finished) and on the weekly static measurement days, the retained statistical model was as follows: Y ijkl = l + System i + Week j + LWGroup k + LWGroup kðEweLambÞ + LWGroup ÂSystem ik + LWGroup ÂWeek kj + e ijkl where Y ijkl is the observed LW of the ewe, l is the overall (fixed) mean of the sample population, System i denotes the main fixed effect of the i th weighing system scale (static vs WoW), Week j is the fixed effect of the j th experimental week (1. 14), LWGroup k is the random associated effect of the k th experimental subgroup, according to the LW of the ewes (n = 3; Heavy, Medium and Light), LWGroup k(EweLamb) is the nested random effect of each individual ewe lamb within each of the three specific LW subgroup levels, LWGroup Â System ik and LWGroup Â Week kj are the random interaction effects associated with the k th LW subgroup and the l th weighing system and j th Week, respectively, and e ijkl is the associated residual error.
Database filtering outputs A summary of the number of records obtained in the WoW database before and after the cleaning procedure is shown in Table  2. From the total of 21 days during the adaptation phase, 6 days were excluded due to battery dysfunction issues. A total of 5 411 records were initially downloaded and 1 429 (i. e. , 26. 4%) were retained after the removal of LW spurious values (misbehaviours and outliers), which represent an average of 95 records/effective day during the adaptation phase. During the second phase of the experiment (data collection), a total of 25 172 records were collected during the 65 effective days (from the total of 74 i. e. , 9 days were lost due to battery issues). Of these data, 38. 7% were retained after the filtering process, representing on average 150 records/effective day -i.
Validation of the WoW system Validation of WoW was made after the adaptation of the animals, in the second phase of the experiment. Table  3  presents the descriptive statistics, linear regression and CCC reports. The linear regression coefficient of the LW WoW measured values explained by the GS reference estimated values is about 0. 92 with an error of 1. 181 kg (Fig. 3 ). The CCC (0. 94) shows moderate agreement between the two LW measurement methods. On the raw database, the Bland and Altman concordance coefficient is À6. 95 ± 30. 12 kg, which is considerably improved after the full cleaning process (i. e. , À0. 83 ± 2. 32 kg in final clean database after the threestep procedure; Fig. 4 ).
GS = Gold Standard live weight measures (obtained with the static scales); WoW = Walk-over-Weighing live weight measures. 1 The ewe lambs were chosen according to their LW and then further classed into three subgroups: Light (LW average: 21. 2 ± 1. 4 kg; n = 33), Medium (average LW: 24. 4 ± 0. 8 kg; n = 33) and Heavy (LW over the average: 28. 8 ± 2. 0 kg; n = 34). Such subgroups are representative of the three contrasting LW strata typically found in the source flock. Individual level Due to the validation of the WoW data and the absence of significant differences between WoW and the GS reference scale for the flock and subgroup data, individual growth monitoring with the WoW is considered possible. Three extreme ewe lambs were chosen as examples of the individual growth monitoring i. e. , the individuals identified as number 3 537 (with the higher number of correct LW recorded), 3 601 (with the smaller number of correct LW recorded) and 3 954 (ewe lamb that died during the experiment; Fig.
7 ). The graphs show the LW monitoring with all data measured. For the lambs 3 537 and 3 954, there were more LW values obtained with the WoW than with the GS. Their growth rate curves showed similar shapes whatever the LW measurement system. The graph for ewe lamb 3 954 showed an abrupt LW decrease after March 19th using the WoW, whereas visual information of death was only recorded after March 25th with the weekly LW data recorded with the GS scale. The LW progress of ewe lamb 3 537 followed a similar shape as the growth rate of the flock, with its final LW being higher than the average LW of its subgroup. Ewe lamb 3 601 remained in a stable LW during the experiment, with similar LW values collected irrespective of the LW scale. Variations of the ADG per week for the three individuals are illustrated in Fig.
8. The first ADG obtained with WoW for ewe lamb 3 601 was in week 8, because no LW value was obtained  before the 15 first days of phase II. Its ADG per week did not vary and was low (around 0 g/day). The ADG of the ewe lamb 3 537 was around 200 g/day during the experiment. Concerning ewe lamb 3 954, the decrease in LW observed results in a loss of more than 1 000 g/d during week 10. For the three individuals, no differences were detected linked to the weighing system that estimated the individual ADG during this period. The initial raw database is available in the Data Repository https://doi. org/10. 15454/IXSHF7.
In our study, data cleaning decreased the number of records actually available but according to the Bland and Altman coefficient (À0. 83 ± 2. 32), enabled considerably increased repeatability of WoW data  (Grenier et al. , 2000). The reproducibility, evaluated by Lin's CCC (= 0. 94), also increased as the cleaning process progressed. This coefficient showed however that only was moderate agreement between the two weighing systems. This could be explained by the lack of controlled flow of animals crossing the platform  (Alawneh et al. , 2011; González-García et al. , 2021). Even with an individual level of data cleaning, some misbehaviour data were still present and seemed undetectable. Despite this moderate agreement, the values obtained show a good distribution around the perfect correlation line at 45°.
In our study, it was then possible to accurately estimate the LW of the flock. Individual LW values that were close to those mea- sured with the GS reference static weighing, and without significant difference, were obtained. Using the WoW, it is possible to observe variations over time more precisely due to the quantity of data recorded. At the beginning of phase II, the ewe lambs were in good physical condition i. e. , the LW at 127 ± 6 days corresponded to a LW slightly higher than those expected at this age for the Merino d'Arles breed  (Bénévent et al. , 1971 ). The growth rates of the animals, estimated at 123 g/d and 84 g/d respectively by the static GS weighing scale, and the WoW, are similar to the ADG reported for Merino d'Arles lambs between 130 and 160 days  (Bénévent et al. , 1971).
Tables, Images, Figures, R-Codes to manipulate datasets and carry out statistical analysis How data were acquired Using two different weighing scales i. e. static (Gold Standard) and Walk-over-Weighing (WoW) Data format Datasets are presented as Excel files. All results are presented in tables and figures Parameters for data Individual LW trajectories automatically registered by the WoW platform for ewe lambs after weaning collection under grazing conditions Description of data collection.
5 3. 9 20. 3 42. 4 25 172 37. 4 15. 9 0 80 0. 062 15. 37 0. 40 0. 10 collection (Phase II) d Database 1 17 762 29. 7 3. 7 20. 3 42. 4 17 762 36. 3 8. 4 10. 4 49. 8 0. 044 8. 19 0. 49 0. 10 Database 2 9 984 30. 2 4. 0 20. 3 42. 4 9 984 31. 1 4. 3 18. 2 44. 6 0. 850 1. 66 0. 98 0. 90 Final 9 735 30. 3 4. 0 20. 4 42. 4 9 735 31. 1 4. 2 18. 8 44. 6 0. 921 1. 18 0. 98 0. 94 database Total Raw database 30 583 29. 7 4. 3 18. 4 42. 4 30 583 36. 1 16. 7 0 80 0. 077 16. 09 0. 42 0. 12 Database 1 21 327 29. 1 4. 0 18. 4 42. 4 21 327 36. 0 8. 5 10. 2 49. 8 0. 051 8. 23 0. 50 0. 11 Database 2 11 466 29. 7 4. 2 18. 4 42. 4 11 466 30. 6 4. 4 16. 6 44. 6 0. 857 1. 67 0. 98 0. 90 Final 11 164 29. 7 4. 2 18. 4 42. 4 11 164 30. 6 4. 4 17 44. 6 0. 924 1. 20 0. 98 0. 94 database Data were analysed using the following statistical model and comparisons performed with a threshold of 0.
Auditory brainstem response (ABR) Mice were anesthetized by intraperitoneal injection of Rompun 2% (3 mg/kg) and Zoletil 50 (40 mg/kg) and mouse temperature was measured with a rectal thermistor probe and maintained at 38. 5˚C ± 1 using a heated under-blanket (Homeothermic Blanket Systems, Harvard Apparatus). The acoustical stimuli consisted of 10-ms tone bursts, with an 8-ms plateau and 1-ms rise/fall time, delivered at a rate of 20. 4/s with alternate polarity by a JBL 2426H loudspeaker in a calibrated free field. Stimuli were generated and data acquired using MATLAB (MathWorks, Natick, MA, USA) and LabVIEW (National Instruments, Austin, TX, USA) software. The potential difference between vertex and mastoid intradermal needles was amplified (5000 times, VIP-20 amplifier), sampled (at a rate of 50 kHz), filtered (bandwidth of 0.
3-3 kHz) and averaged (600 times). Data were displayed using LabVIEW software and stored on a computer (Dell T7400). The ABR was recorded at decreasing intensity of sound from 100 to 10 dB SPL and ABR threshold was defined as the lowest sound intensity, which elicits a clearly distinguishable response. The ABR thresholds were measured at 6 test frequencies  5. 6, 8, 11. 3, 16, 22. 3, 32  kHz that are preferentially perceived at different levels of cochlea across the tonotopic axis, and each audiogram was stored as a vector containing these six threshold values. Audiograms obtained in control and mutant mice were classified using an agglomerative hierarchical clustering algorithm using Euclidean distance and Ward linkage method. Unless specified, Shapiro-Wilk test (p > 0. 05) and one-way ANOVA test with post-hoc Tukey's multiple comparisons test were used for statistical tests.
Acknowledgments We thank  L. Gan  for the Atoh1-Cre line and  Christopher V. E. Wright  for the Ptf1a-Cre line that we kindly received from  Huda Y. Zoghbi , as well as  A. Holz  for the Nkx2. 2-Cre line. A particular thank to  K. Moneret  and  M. Giribaldi  for animal husbandry.
This work was funded by the  "Agence Nationale Recherche"  ( ANR-15-CE15-0016-01 ) to M. S and J. L. P, and " Fondation Pour l'Audition"  ( FPA RD-2017-5 ) to M. S. Both funders fully supported this study and M. D. B. salary. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgments We thank  Robert Blanvillain ,  Michael Hodges ,  Emmanuelle Issakidis-Bourguet , and  Olivier Van Aken  for helpful discussions. SS was supported by a fellowship from the  Ministère de l'Enseignement Supeŕieur, de la Recherche et de l'Innovation (MESRI)  of the  French Government (Doctoral School of Plant Sciences (SEVE) ,  UniversitéParis-Saclay, France)  for his PhD.
Secondary PCR reactions were performed in order to add Nextera XT Illumina sequencing indices and adapters. The reaction conditions were 3 min at 98˚C, followed by 8 cycles of 30 s at 98˚C, 30 s at 55˚C and 30 s at 72˚C, and a final 10 min elongation at 72˚C. After purification, 16S amplicon libraries were pooled together at equimolar concentration and subjected to Illumina MiSeq paired-end sequencing (Illumina, USA). The 16S amplicon library of mussel samples collected in 2020 was prepared and sequenced in a different batch. All sequencing data has been submitted to the NCBI sequence read archive database (BioProject accession: PRJNA899701).
Sequence processing Sequences were analysed using Mothur software v. 1. 44. 3  [17]  following the standard operating procedure (http://www. mothur. org/wiki/MiSeq_SOP). Sequences were quality filtered, dereplicated to unique sequences and aligned against the SILVA database  [18]. Suspected chimeras were removed using Uchime software  [19]. Resulting sequences (median length of 460 bp) were clustered into Operational Taxonomical Unit (OTU) using 97% similarity threshold. For taxonomic affiliation, OTU sequences were searched against the latest available SILVA database (Release 138. 1) using the Wang approach  [20]  as implemented in Mothur. OTUs corresponding to chloroplasts and mitochondria were removed from the data set.
Bioinformatics and statistical analyses Alpha diversity estimators (observed OTUs, Shannon, Simpson and Berger Parker) were calculated using rarefied data (by random picking, based on the sample having the lowest read number) with Past 3. 26 software  [21]  for each sample. All other statistical analyses were performed in R  [22]. Differences in alpha diversity patterns between sampling points and seasons were analysed using ANOVA and Tukey's honest significance test of ANOVA (pairwise comparisons) for normally distributed metrics and Kruskal-Wallis and Wilcoxon rank sum test (pairwise comparisons) for non-parametric metrics ("stats"package). Normality and homoscedasticity of each metric were tested using Shapiro-Wilks and Fligner-Killeen tests ("stats"package) respectively. To assess beta diversity, sample-specific read abundances of OTU were subjected to Hellinger-transformation using the function 'decostand' (method = 'hellinger', which consist of dividing the total reads for each OTU by the total number of reads in the corresponding sample and taking the square root of the quotient).
For taxon distribution analysis, relative abundances were calculated at each taxonomic rank for all datasets. Individuals were grouped by location and season, and the top 20 taxa of each subset visualized using the R "ggplot2" package. Based on the relative abundance, taxa significantly different between harvesting regions was determined by Kruskal-Wallis and Wilcoxon rank sum test (pairwise comparisons) with Benjamini-Hochberg FDR correction of the p-value. Finally, to evaluate the stability and the potential of this approach to trace the origin of new samples, a taxonomy based hierarchical NMDS analysis was performed combining 2019 and 2020 bacterial profiles.
The present study also allowed to recognize key genera that were potentially relevant to discriminate the geographic origin of the mussels being surveyed, such as, low proportions of Rubritalea, Roseibacillus and Rubripirellula in Catalonian region and higher levels of Persicirhabdus in Galicia. Still, our proposal is to rely on the overall community structure recorded for each production area, using the whole bacterial fingerprint instead of a few markers. As previously suggested by Liu, Teixeira  [8] , for this approach to be used as a traceability tool, the development of a database with the reference bacterial fingerprint for each location of interest is necessary. In this way, the bacterial profile of new samples can be compared to this database to verify the origin claimed by the producer or trader.
It is also important to highlight that the database must be periodically verified and updated, to enable a successful classification over time. Based on the results obtained in this preliminary study, the time window during which the bacterial fingerprint is likely to remain stable is at least a year, which enables the possibility to rely on previously recorded fingerprints without needing to constantly update the database. A well-designed strategy to maintain the database updated and facilitate the origin classification, could also provide useful information regarding the microbiological safety of the seafood products, allowing to detect potential viral and bacterial pathogens and thus, preventing contaminated products from entering markets. The microbiota profiling through 16S rRNA amplicon sequencing has shown to have the potential to be used as bioindicator of origin for traceability.
Our study was carried out thanks to the financial support of the  SEA-TRACES  ( EAPA_87/ 2016 )  INTERREG Atlantic Area EU  funded project and the  SEAFOOD-ID Project  ( PID2020-118012RB-C22 ) funded by  MCIN/AEI/10. 13039/ 501100011033  and by the  European Union. Ane del Rio Lavı  ´n is the recipient of a PhD grant from the  Education Department of the Basque Government.
More intriguingly, the spines contacted by MSBs belonged to different dendrites rather than to neighboring spines on the same dendrite (Figure  1B ). From the general topology of dendritic arbors, we predicted that these dendrites likely belonged to different neurons, suggesting that MSBs were transmitting signals across different cells. To confirm this, we reconstructed neurons sparsely labeled with fluorescent proteins and mapped the 3D spatial arrangement of their dendrites (Figure  S1 ). We found that dendrites tended to fan out from the soma along the SO (Figures  S1A-S1H ), rarely coming into close contact with each other. A measure of potential contacts with dendrites from the same cell decreased with distance from the soma, becoming negligible beyond $50 mm (Figure  S1I ). Since the most proximal EM dataset was obtained at a distance of $50 mm (Figure  1A ), dendrites from the same cell were unlikely to contribute more than a single spine to an MSB.
Report METHOD DETAILS Confocal imaging and dendrite tracing Confocal imaging was performed using a Nikon A1 confocal microscope, equipped with a 40X water immersion objective (Olympus, Apo 403 WI ls DIC N3), and using NIS -Elements software. Images from 6 CA1 pyramidal cells sparsely labeled with Green Fluorescent Protein were obtained using high sampling resolution (pixel size 0. 1 mm in X-Y and 0. 3 mm or 0. 8 mm in Z) and covering a large area of the basal dendritic tree (average area XY = 45551 mm2, average span Z = 45 mm). Tracing was done using the Fiji Plugin Simple Neurite Tracer  41  and traces were smoothed and re-sampled at constant node interdistance of 0. 5 mm using MATLAB TREES Toolbox. Distances to the soma for each node were calculated using the Pvec_tree function of MATLAB TREES.
The specimen was placed inside a Jeol field emission scanning electron microscope (JSM-7800F) equipped with a 3View 2XP system (Gatan). Section thickness in this case was set at 50 nm (Z resolution) and sampled to achieve a final pixel size of 5. 005 nm. This dataset was used in Figure  S1J-S. Electron microscope images were registered and manually segmented using the ImageJ plugin TrakEM2. 38 Extracted 3D structures were exported to the Blender software with the Neuromorph toolset,  39  which was used to compute surface, volume and length measurements and render 3D reconstructions.
Reconstruction of neurites Stacks of SBFSEM images were concatenated and aligned using TrackEM plugin within the ImageJ environment. 43 Portions of dendrites from proximal and distal regions of the stratum oriens were manually traced also using the TrackEM plugin. Only dendrites that could be followed for more than 5 mm through the stack were traced. When a dendritic spine was formed onto a bouton, that bouton was traced, as well as any other dendritic spines that synapsed upon the same bouton. Extracted 3D structures were exported to the Blender software using the Neuromorph toolset to render 3D reconstructions 39 where surface area, volume, and length measurements were extracted. The P22 dataset includes a subset of synapses included in a previous publication,  18  which was expanded for this study.
For tracing of dendrites on the larger area dataset (used for Figures  S1J-S1S ), five somas were identified (4 neurons and one putative astrocyte) by their nucleus. All dendrites that could be followed to the neuronal somas were traced throughout the imaged area. Multisynaptic boutons synapsing onto these dendrites were traced, as well as any other dendrites that could be followed for over 30 mm.
Dilution STORM Buffer containing 100 mg/ mL Glucose (Sigma-Aldrich) and 10% Glycerol (Fisher Scientific) in deionized water, was stored at 4 C. Multicolor dSTORM imaging was performed using a spectral-demixing SAFeRedSTORM module (Abbelight, France) mounted on an Olympus IX3 equipped with an oil-immersion objective (100 3 1. 5NA oil immersion, Olympus). The two fluorophores, AF647 and CF680 were excited with a single wavelength using a fibber-coupled 642nm laser (450mW Errol, France). Because the two fluorophores have a small shift in their emission spectrum, a long-pass dichroic beam splitter (700nm; Chroma Technology) was used to split the emission light on two cameras (ORCA-fusion sCMOS camera (Hamamatsu)). A photon ratio was calculated for each detection to assign the detection to one of the fluorophores.
44, 45 3D-dSTORM imaging was performed by using cylindrical lenses placed before each camera. Image acquisition and control of microscope hardware were driven by Abbelight's NEO software. Region of interest (ROI) of 512 x 512 pixels (pixel size = 97nm) within the stratum oriens were identified. Depth of imaging was limited to the first ten micrometres of the slice due to the working distance of the objective and to allow a proper focus stabilization using the Olympus's ZDC system. Image stacks contained 60,000 frames. Cross-correlation was used to correct for lateral drifts. N 3D calibration was performed using fluorescent beads (tetraspeck beads, invitrogen) to measure the x,y deformation obtained with the 2 cylindrical lenses on both cameras. With this information we calculated the z position of individual localizations with a precision of $1mm.