dataesr/scientific-paragraphs-categorization huggingface dataset:
05 m/s 2 ). Data corresponding to sampled dives were archived and then processed on-board to create a "per dive" summary (see details below), which was later transmitted via the Argos satellite system while the tag was at the surface. In addition, the tag also recorded the time from the end of a sampled dive (when the wet-dry sensor first registered as dry following a dive) to the subsequent first depth measurement that exceeded 10 m to give the surface interval. The tag transmitted each dive summary a maximum of 12 times, with a minimum interval of 45 min between each uplink attempt. At least one Argos location was obtained each day from resulting transmissions.
| Dive segmentation: BSMs The start of a dive was identified on-board the DSA tag using a wetdry sensor. Dive events were identified from the archival data as periods when the tag exceeded a depth of 5 m (in place of 0 m to avoid interference from near surface measurement noise) for at least 60 s. To account for a small amount of sensor drift with time, prior to this, recursive filtering and smoothing across set quantiles was used to apply a zero offset correction to the time series of archival depth measurements (see  Luque & Fried, 2010). No depth correction was performed on-board the DSA tag. In both pre-transmission data abstraction and post-hoc archival processing, each dive was then split into five segments by identifying the four most characteristic inflection points of the profile (inclusive of the maximum depth) via BSMs  (Fedak et al.
| Depths, durations and vertical speeds Transmitted depth inflection points, phase durations and vertical speeds showed negligible signs of bias, were strongly correlated with archival estimates (marginal R 2 values greater than. 9) and had small RMSE's (Figure  5  and Table  1 ). Depth corrections applied to archival datasets varied from -9 m to -0. 5 m and averaged -4. 4 ± 2. 4 m.
| Depths, durations and vertical speeds Transmitted estimates of phase durations, vertical speeds and depth inflection points showed negligible signs of bias and were strongly correlated to corresponding archival outputs, thus validating the performance of the on-board BSM, alongside the use of the wet-dry sensor to delineate the start and end of dives. Moreover, these results suggest the effect of sensor drift was small, which is reiterated by the small size of the zero offset depth correction applied to archival datasets (averaging <3% of average inflection point depths, and never more than 6%-9 m).
| PrCA behaviours Estimates of PrCA behaviours from transmitted data included a number of false detections and misclassifications (Table  2 ), and were positively biased during bottom phases. This likely stemmed from the misidentification of erratic movements and/or signal noise as PrCA behaviours, possibly because the 5 m/s 2 detection threshold used in on-board processing algorithms was too low. To obtain similar algorithm performance to that from adult datasets, a threshold of 7 m/s 2 would be required (see Supplementary materials S. 7 vs. S. 2). Further improvements could also be made by increasing the length of the time window over which varA is calculated (see Section 2), which increased true positive rates (Supplementary material S. 8) and possibly reflects the sensitivity of the current algorithm to small spikes in acceleration during periods of relative calm.
| CONCLUSIONS In this study, we have presented a new approach for remotely obtaining detailed information from accelerometers on the sub-surface behaviour of a free-ranging marine predator in its natural environment. While outputs from these methods were comparable to those requiring device retrieval, improvements could be made, for which our study provides valuable insight. The data acquired represent some of the first quantifications of the fine-scale movements of juvenile southern elephant seals during their first at-sea foraging trip immediately following weaning. Consistencies in algorithm performance across individuals suggest these outputs can be used to address a number of exciting and novel questions regarding ontogenetic behavioural changes and suspected survival rates. In particular, changes in descent swimming speeds and ascent swimming efforts  (Biuw et al.
Red text (b:d) indicates corresponding transmitted estimates (TEs) for each dive phaseconducted separately for each of these phases (see Supplementary material S. 5 for details on how dive phases were assigned). For both datasets, all data corresponding to a particular dive phase were either averaged or summed to provide a single comparative value (see details in Supplementary material S. 6-which also outlines how archival and transmitted datasets were matched alongside preanalytical data cleaning protocols). Of note, for pitch estimates, descent and ascent values from transmitted datasets were based upon the first and last segments of a dive, whilst archival values comprised data from all segments corresponding to a dive phase.
Across all analyses, a random effect (intercept and/or slope) of individual was included if this lowered the Akaike information criterion (Zuur, Ieno, Walker, Saveliev, & Smith, 2009). Pseudo r-squared (R 2 ) values were used to assess correlations between abstracted and archival data, and were calculated according to Nakagawa and Schielzeth (2013)  and Johnson (2014) , using the muMIn package (Barton, 2015)  in r. Marginal R 2 values correspond to only the fixed component of a model whilst conditional R 2 values correspond to the entire model (composed of both fixed and random effects). In addition, the root mean square error (RMSE) was calculated for the fitted values of the model vs. the retrieved archival outputs, alongside the transmitted estimates vs. retrieved archival outputs (using both standardised and raw datasets).
Finally, because archival datasets included a record of second resolution detections of PrCA behaviours from the on-board processing algorithms, confusion matrices were constructed (using the SDMTools package in r; Van der Wal, Falconi, Januchowski, Shoo, & Storlie, 2014)  and used to assess how well these classifications reflected those from established archival methods (this analysis included all retrieved archival data, including dives that were not transmitted).
ACKNOWLEDGEMENTS This study was funded by a  European Research Council   Advanced Grant  as part of the program EARLYLIFE under the  European Community's Seven Framework  (grant agreement  FP7/2007-2013/ ERC-2012-ADG_20120314 ; PI  Dr Henri Weimerskirsch ), and was supported by the  IPEV program 109 (PI Dr Henri Weimerskirsch ). We thank  J. Laborie  and  Martin Delpuech  for field tag recovery, alongside  Yves Le Bras ,  Joffrey Jouma 'a,  Baptiste Picard ,  Gaétan Richard  and especially  Matthieu Authier  for analytical input. We thank one.
DATA ACCESSIBILITY Matlab and R codes and scripts used to process the transmitted and archival datasets are available at github accounts "SamLCox" and "SESman" under https://doi. org/10. 5281/zenodo. 809385, https://doi. org/10. 5281/zenodo. 809383, https://doi. org/10. 5281/zenodo. 809387 and https://doi. org/10. 5281/zenodo. 809182. Data used in the comparative analyses of the abstracted transmissions and retrieved archives is available under https://doi. org/10. 5061/dryad. vv107 (Cox et al. , 2017).
In our recent study, we showed that after nine months of training, tactile Braille reading recruits the ventral visual cortex in sighted adults  20. Here, we used voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) data from the same group of subjects to describe structural reorganization associated with learning this skill. Additionally, we used resting-state fMRI data to relate potential structural changes to the long-distance functional connectivity between visual and somatosensory-motor cortices. Based on our previous study  20  and results from blind subjects  27  , we expected to observe anatomical reorganization induced by the Braille training in the visual cortex. Furthermore, since Braille reading is a tactile-motor task, we expected that this anatomical reorganization will occur in a section of the visual cortex that has inherent functional connections to somatosensory and motor cortices.
4a ; see also Supplementary Table  S7 ). Of particular importance to our hypotheses, we also observed preferential connectivity between the peripheral early visual cortex and somatosensory-motor cortices (Fig. 4a ; Supplementary Table  S7 ). Notably, this preferential connectivity was present already before the onset of the Braille training. We did not observe a change in the functional connectivity of peripheral early visual cortex following the training. To test for generality of effects observed in the resting-state fMRI analysis in the experimental group, the same comparison between functional connectivity of the peripheral early visual cortex and functional connectivity of the central early visual cortex was performed in control subjects (see Methods). Using this independent dataset, we replicated results observed in the experimental group, particularly the preferential functional connectivity between the peripheral early visual cortex and somatosensory-motor cortices (Fig.
Data from the same subjects were reported in our previous studies  20, 33, 34. In addition, 15 individuals (12 females, 3 males; mean age = 25. 53; SD = 3. 30; range = 21-32) participated in the study as a longitudinal control group. All were right-handed, fluent in Polish, had normal or corrected-to-normal vision, no neurological or psychiatric disorders and no familiarity with tactile or visual Braille reading. The control group did not undergo training. It was matched for age, sex and education to the experimental group (all p > 0. 05). The size of the control group was similar to the size of the experimental group at the follow-up session, for unbiased ROI interaction analyses (see Statistical Analysis section). The control group was scanned twice: first at a baseline session and then in a follow-up session after 18 months.
The control-group scans corresponded with the baseline and follow-up sessions in the experimental group. To test for generality of effects observed in the resting-state fMRI functional connectivity analysis of the experimental group, we performed an additional resting-state fMRI analysis on a separate control group of 31 subjects (17 females, 14 males; mean age = 27. 29; SD = 4. 54; range = 19-37; due to time constraints, resting-state fMRI data were not collected from the longitudinal control group). The inclusion criteria were the same as in the longitudinal control group. The research described in this paper was approved by the Committee for Research Ethics of the Institute of Psychology of the Jagiellonian University (decision 28/06/2012). An informed consent and a consent to publish were obtained from each of the participants.
MRI and fMRI data acquisition. All data were acquired on the same Siemens MAGNETOM Tim Trio 3 T scanner with a 32-channel head coil. High-resolution T1-weighted images were acquired using a MPRAGE sequence (176 slices; FOV = 256; TR = 2530 ms; TE = 3. 32 ms; voxel size = 1 × 1 × 1 mm). Two sets of diffusion-weighted images (64 directions; b-value = 1000 s/mm 2 ; FOV = 256; TR = 8700 ms, TE = 92 ms; voxel size = 2 × 2 × 2 mm), along with two images with no diffusion gradient applied (b-value = 0), were also acquired. In addition to structural scans, resting-state fMRI data were collected in the same scanning session. A gradient-echo planar imaging sequence sensitive to blood-oxygen-level dependent contrast (282 volumes; 33 contiguous axial slices; TR = 2190 ms; TE = 30 ms; voxel size = 3 × 3 × 3.
6 mm) was used. During the resting-state fMRI scan the subjects were asked to fix their gaze on the point displayed on the screen and to relax, but not fall asleep. Data preprocessing. T1-weighted images were preprocessed with VBM8 toolbox (http://dbm. neuro. uni-jena. de/vbm/) and SPM8 software (www. fil. ion. ucl. ac. uk). We used the standard VBM8 longitudinal routine, including: 1) within-subject registration of all scanning time-points 2) intra-subject bias correction 3) segmentation of the different tissues classes 4) linear (i. e. , affine) and nonlinear (i. e. , DARTEL) registration and normalization to the MNI space. Finally, the grey matter images were smoothed with a 6 mm FWHM Gaussian kernel. Diffusion-weighted images were preprocessed with the tract-based spatial statistics approach (TBSS)  74  implemented in the FSL 5.
06 software (FMRIB Software Library, FMRIB, Oxford, UK)  75. TBSS is widely-used and reliable approach to analyse DTI data across the whole-brain  76, 77. Thanks to its automaticity, it overcomes many pitfalls of tractography-based methods (see for example:  78  ). For each subject, diffusion-weighted images were affine registered to the first b0 image to correct for subject motion and eddy current induced distortion. Brain mask was created from the b0 image using BET  79. Next, the FDT toolbox  80  was used to fit the tensor model and compute the fractional anisotropy (FA), axial diffusivity (AD) and radial diffusivity (RD) maps  81. Further processing of the maps was optimized for longitudinal analysis  82, 83. First, the maps were smoothed with a 1-voxel median filter to account for small, within-subject registration errors between time points and increase test-retest reliability.
Based on the results of the whole-brain analyses and our apriori expectation to find training-induced changes in the visual cortex, a region-of-interest (ROI) analysis (Figs  2c  and 3d ) was then conducted to describe the time-course of anatomical changes in the early visual cortex and to test whether this reorganization is specific to the experimental group. To avoid double-dipping  96, 97  , the ROIs were defined based on the after-training vs. baseline contrast in 10 Braille learners who did not participate in the follow-up session. Then, only the remaining 19 subjects from the experimental group and the participants in the longitudinal control group were tested. Thus, definition of ROIs and further analysis were performed on independent datasets. ROIs were defined as 10 mm × 10 mm cubes centred on peaks of increase in grey matter (for VBM) or FA (for DTI) in the left hemisphere, following the training.
In the case of DTI, the ROI was further constrained by including only voxels in which FA > 0. 2 (i. e. , excluding voxels with no data -see Data Preprocessing). In the resting-state fMRI functional connectivity analysis, we compared the functional connectivity of two seeds: the peripheral and the central part of the early visual cortex. The analysis was performed on two datasets: before-training data from the experimental group and data from the control group of 31 subjects that were not familiar with Braille reading, neither in tactile nor in visual modality (see Subjects section). Seed definitions were based on the early visual cortex mask (V1 and V2 masks combined), as implemented in the Anatomy SPM toolbox  98. In line with the principle of retinotopic organization of the early visual cortex  35  , the peripheral seed was defined as the anterior part of the mask (i.
Acknowledgements Supported by a  National Science Centre Poland  grants ( 2012/05/E/HS6/03538  and  2016/21/B/HS6/03703 ), a  Marie Curie Career Integration grant  ( 618347 ) and funds from the  Polish Ministry of Science and Higher Education  for co-financing of international projects, years 2013-2017, awarded to M. S. Ł. B. was supported by  National Science Centre Poland  ( 2014/15/N/HS6/04184  and  2017/24/T/HS6/00367 ) and  Foundation for Polish Science  ( START 7. 2017 ). K. J. and A. M. were supported by  National Science Centre Poland  ( 2014/14/M/ HS6/00918 ). K. S. -K. was supported by the  Ecole des Neurosciences de Paris Ile-de-France. The study was conducted with the aid of CePT research infrastructure purchased with funds from the  European Regional Development Fund  as part of the  Innovative Economy Operational Programme , 2007-2013.
A comparison of both results is not straightforward as the considered metric is different. Yet, our result confirms that when η N grows sub-linearly, there is little difference between the system of finite N and the mean-field. Reproducibility. The code to reproduce the paper -including simulations, figures and text -is available at https://github. com/ngast/meanFieldAccuracy. Roadmap. The paper is organized as follows. We introduce the model in Section 2. We provide the main theoretical results in Section 3: the transient regime and its extension to stationary regime when the ODE has a unique exponentially stable attractor. We illustrate the necessity of the regularity assumptions in Section 4. We apply these results to the two-choice model in Section 5. Section 6 contains the proof of the most technical lemmas.
ACKNOWLEDGMENT The author would like to thank  Romain Joly  and  Jaap Eldering  for fruitful discussions on the link between asymptotic stability and the dependence on initial conditions. The author is grateful to  Bruno Gaujal ,  Debankur Mukherjee  and  Sem Borst  whose insightful comments substantially improved this paper. This work is partially supported by the  EU  project  QUANTICOL ,  600708.
To investigate the developmental timing of the putative GRN into more detail, we measured the expression level of the 20 genes upon stress in the third leaf of wild-type plants during the proliferating (9 days after stratification [DAS]), expanding (15 DAS) and mature (22 DAS) developmental stage (Dataset EV1). With the exception of ERF8 and ERF9, which were most probably only transiently induced by mannitol, all other 18 TFs were significantly upregulated under stress conditions (Student's t-test, FDR < 0. 05) in proliferating or expanding tissue (Fig  1 ). For about half of these genes, the level of induction in proliferating and expanding tissue was similar. Three genes, ERF5, ERF6, and ERF11, were induced more highly in expanding leaf tissue, whereas six genes, ERF59, ERF98, MYB51, WRKY6, WRKY30, and WRKY40, were induced more strongly in proliferating leaf tissue.
Interestingly, none of the TFs were significantly upregulated in mature leaf tissue  (Fig 1) , suggesting that the putative stress-responsive GRN is only induced in growing leaves, because these tissues are prone to growth inhibition upon mild stress. The GRN shows the sequential activation of four TF groups Because expression analysis has previously shown the early upregulation of these genes upon mannitol treatment  (Skirycz et al, 2011) , the young developing third leaf (15 DAS) was harvested at a high temporal resolution (20 min, 40 min, 1 h, 2 h, 4 h, 8 h, 12 h, 16 h, 24 h, and 48 h) after transfer to control or 25 mM mannitolcontaining medium. RNA was extracted, and a detailed expression pattern over time for each gene of the putative GRN was generated with the nCounter Nanostring â technology (Dataset EV1).
The oscillation of some transcripts was also visible at the network level: most interactions were formed after 1 h of induction and decreased after 2 h or 4 h, but increased again after 8 h or 24 h  (Fig 3A, Source Data for Fig 3). The oscillations further strengthen the hypothesis that multiple TFs regulate the expression of the same target gene, leading to multiple indirect effects. The highly fluctuating regulations also emphasize the need for short-term analysis because the steady state of the network masks these connections. To analyze the transactivation capacities of the TFs on their target genes, the edges based on the transcriptome data at 1 h, 2 h, and 4 h (in total 81 edges) were further verified with transient expression assays (TEAs). Luciferase reporter genes were used to perform TEAs in tobacco (Nicotiana tabacum) Bright Yellow-2 (BY2) protoplasts  (Vanden Bossche et al, 2013).
Remarkably, in expanding leaf tissue, the upregulation by mannitol reached higher levels (log 2 (FC) = 4. 71 compared to log 2 (FC) = 0. 90 in proliferating tissue, Dataset EV1) and in addition, the timing of the upregulation was altered. Already 2 h after mannitol treatment, GA2-OX6 expression levels reached significant values (log 2 (FC) = 5. 21, FDR = 0. 014, Student's t-test) in expanding tissues, whereas the induction was only significant after 24 h in proliferating tissue  (Skirycz et al, 2011; Claeys et al, 2012 ) (Dataset EV1). In accordance with previous observations  (Skirycz et al, 2011; Dubois et al, 2013) , none of the other tested GA oxidases showed a significant upregulation over the analyzed time course. However, after 24 h, a significant downregulation of the ratelimiting GA biosynthesis gene, GA20-OX1 (log 2 (FC) = À1.
The relative luminescence was calculated relative to the control, 35S::GUS (n = 4 biological repeats). A At 22 DAS, leaf series were made of the LOF lines (n = 10 plants) grown under control conditions or mild osmotic stress conditions, and the reduction under mild osmotic stress was calculated. The rosette area reduction is presented relative to the corresponding wild type. B Leaf series of stz and erf11 mutants grown on control and mild osmotic stress conditions. C The rosette area of two or three independent GOF lines (n = 10 plants) germinated on DEX-containing control or mild osmotic stress medium was measured at 22 DAS, and the reduction by mild osmotic stress was calculated relative to the control line. "Independent line 1" is the line with the highest overexpression level. Data information: In (A, C), data are presented as mean AE SEM, n = 3 independent experiments, *P < 0.
In this way, the activation pathway induces the output node and, after a delay, the inhibitory pathway brings the output node back to its original state. In this GRN, an incoherent feed-forward loop could be found, which could be at the base of the plant's adaptation to the stress signal. Our expression data have shown that, under standard control conditions, ERF-1, ERF2, ERF8, WRKY6, WRKY15 and WRKY33 are expressed in expanding leaf tissue (Dataset EV1). We hypothesize that ERF8, a transcriptional repressor that is part of the core network and capable of repressing most network members, suppresses the activation of the network until a stress signal is perceived  (Fig 9A). Upon stress perception, the first transcriptional changes occur after 40 min. We found that the expression of the TFs was induced in a sequential manner upon stress, enabling the identification of four different induction groups.
The mannitol-induced GRN might be part of a central hub in a range of stress responses To assess the broader function of the 20 TFs in different stress responses, we explored their transcriptional induction in four previously published datasets, evaluating in total eight different stresses (Table  EV3 ). Almost all TFs (18/20) were significantly upregulated by Botrytis cinerea in leaves  (Windram et al, 2012). In another study, 18, 15, 14, and 12 of the 18 TFs tested by microarray were significantly up-or downregulated in plants 2 h after high salinity, dehydration, abscisic acid, and cold treatment, respectively  (Matsui et al, 2008). Furthermore, exposure of soilgrown plants to mild drought significantly altered the expression of 17 TFs in growing leaf tissue  (Dubois et al, 2017). In a fourth study, expression analysis on whole plants exposed to methyljasmonate or to the pathogen Alternaria brassicicola revealed differential expression of 12 and 14 of the 17 TFs, respectively  (McGrath et al, 2005).