dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Steffen Bock ,  Frieder Mayer ,  Detlef Willborn  (MfN),  Roberto Portela Miguez ,  Louise Tomsett  (BMNH),  Géraldine Veron  (MNHN),  Emmanuel Gilissen  and  Wim Wendelen  (RMCA),  Chris Conroy  (MVZ),  Darrin Lunde ,  Paula Bohaska ,  John Ososky  (USNM), and  Eleanor Hoeger  ( AMNH ) for access to collections. We additionally want to show our gratitude to  Guillaume Billet ,  Kévin Le Verger  (MNHN), and  Allowen Evin  ( ISEM ) for fruitful discussions on modularity, and  Chris Klingenberg  and  Soledad de Esteban  for valuable insights on geometric morphometrics during a Tranmitting Science course. Lastly, we would like to thank three anonymous reviewers for their valuable comments.
net (last access: 26 July 2022) database  (Lopez-Gonzalez et al. , 2011). Our plots represent long-term fire-protected vegetation types, and a rare case of very well-preserved typical cerrado and cerradão vegetation in the Amazon and Cerrado transition (de  Oliveira et al. , 2017). Typical cerrado is a cerrado stricto sensu subtype with predominantly arboreal shrubby vegetation, 20 % to 50 % tree cover and tree heights between 3 and 6 m  (Ribeiro and Walter, 1998). The cerradão is considered an ecotonal community  (Ratter et al. , 1973) , characterized by a mostly continuous canopy and an abundance of species that indicate the transition between forests and savannas on the southern Amazonian border, such as Hirtella glandulosa and Emmotum nitens  (Marimon et al. , 2006). Within each 1 ha plot, 25 subplots were delimited, from which soil samples at 0-10 and 10-20 cm depth were collected in all four corners and at the centre of each subplot, totalling 200 samples for each vegetation type.
Soil chemistry data were analysed according to the EMBRAPA procedure (EMBRAPA, 1997) and were provided by the Forest-Plots database  (Lopez-Gonzalez et al. 2011 ; Table  S1  in the Supplement). Despite slightly sandier soils in the cerrado, the soil in both areas is very similar and characterized as dystrophic yellow oxisols, with low nutrient concentration, reduced sum of bases and low cation-exchange capacity  (Marimon-Junior and Haridasan, 2005). They are highly acidic, with alic properties, well drained and without concretions up to 2 m deep (Table  S1 ).
Nutrient sampling In each plot, we randomly selected five individuals of the seven most abundant species representing approximately 70 % of the community basal area (Table  1 ). Calculation of relative dominance was based on previous census data collected in the plots  (2009, 2012, 2014) , by dividing the species dominance (i. e. total basal area of the species) by the community total basal area, and by multiplying it by 100. The IVI was calculated as the sum of the relative frequency, the relative density and the relative dominance of each species within the community. We chose only adult trees of at least 5 cm in diameter at breast hight (DBH) in the cerrado and 10 cm DBH in the cerradão, following standardized criteria for individual inclusion in forest and savanna vegetation. For each species, five individuals were chosen, from which we collected samples from different tissues: heartwood and sapwood, inner and outer bark, branch, mature and old leaves, and fine roots.
Estimation of NPP Net primary productivity is the rate of organic material production and can be quantified at the scale of an individual plant or an ecosystem  (Malhi et al. , 2011). Here we quantified the major components of NPP, including the canopy (leaves, twigs and reproductive parts), wood (stem, coarse roots and branches), and fine roots, during the period from 2014 to 2016. Data were collected following GEM protocols  (Malhi et al. , 2021)  and methods are described in detail in  Marthews et al. (2014)  and  Malhi et al. (2021). Briefly, for the canopy NPP component estimation, litter traps sampled biweekly together with monthly canopy leaf area index were used. For wood component estimation, annual census and dendrometers measuring growth rates were converted into woody biomass production.
Conclusion By using the direct relationship between nutrient stoichiometry and NPP for different biomass components, we showed that cerrado vegetation is less efficient in fine root and wood production, requiring more nutrients per unit of NPP compared to the cerradão. Ultimately, we also report the different strategies in P use between cerrado and cerradão that may reflect savanna vegetation adaptation to the indirect effects of fire in the soil biota, which is a recurrent disturbance that increases the risk of losing P. With the increasing fire frequency associated with extreme drought events due to climate change, cerradão vegetation, which strongly relies on rapid nutrient cycling, may suffer further P limitation. Code and data availability. Dataset and code are available through figshare (https://doi.
Acknowledgements. We thank  NERC (Natural Environmental Research Council) BIO-RED  (# ACR00460 ) for funding this research. We also thank  Camila Borges Silva ,  Halina Jancoski ,  Edmar Almeida  and  Igor Araújo  for the valuable help during fieldwork. Marina Corrêa Scalon  is currently supported by a postdoctoral fellowship from  CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) ,  Brazil. Yadvinder Malhi  is supported by the  Jackson Foundation. Financial support. This research has been supported by the  Natural Environment Research Council  (grant no. ACR00460 ). Review statement. This paper was edited by  Sara Vicca  and reviewed by two anonymous referees.
The experiment observed, in detail, the evolution of a sandy mound under a variety of wave, current, and combined wave-current flow conditions. For all prescribed flow conditions, MODEX produced measurements of velocities around the mound and maps of the mound's evolution over time. The resulting robust dataset of velocity observations is used here to understand how wave-current interaction influences bed shear stress patterns across a three-dimensional bedform. Wave flume observations are used to force and validate the wave-resolving numerical model SWASH. Wave-resolving models, while computationally expensive and difficult to use in a regional context, allow for more detailed calculations of shear stress relative to wave-averaged models, as they can explicitly simulate the near-bed velocity owing to a variety of forced wave-current conditions  [38].
Model Analysis SWASH depth-averaged flow results were validated against velocity observations (Figure  3A-E ) at the five ADV locations (Figure  1A , yellow dots, and Figure  3F ) showing good agreement between the model and data at these locations. For all MODEX tests used as the basis for SWASH runs seen in Figure  3 , the model captures the velocity amplitude at all sensor locations. The phase and amplitude of the velocity is captured immediately around the mound ('center', 'side' and, 'back' locations in Figure  3A-E ). The amplitude is also comparable between model and data at the ADVs to the side of the mound and the flume, though phase shifts between model and data are also evident (see SIDE and WALL in Figure  3A-E ). It is possible that boundary effects are present in the observations which are not also captured in the model (as SWASH was ran with periodic side boundary conditions to avoid wave reflection).
For all MODEX tests used as the basis for SWASH runs seen in Figure  3 , the model captures the velocity amplitude at all sensor locations. The phase and amplitude of the velocity is captured immediately around the mound ('center', 'side' and, 'back' locations in Figure  3A-E ). The amplitude is also comparable between model and data at the ADVs to the side of the mound and the flume, though phase shifts between model and data are also evident (see SIDE and WALL in Figure  3A-E ). It is possible that boundary effects are present in the observations which are not also captured in the model (as SWASH was ran with periodic side boundary conditions to avoid wave reflection). Further, the model may not capture flow transformation owing to subtle bathymetric features apparent in the flume but not the model (edges of the mound were smoothed in the model for numerical stability, for instance, and the model considers an ideally smoothed bed).
The bed shear stress is the least skillful in the trough of a wave, but the phase and magnitude are comparable between model and data (Figure  4A-D ). In all cases modeled, the shear stress on the top of the mound is greater than the shear stress on the side (Figure  4F ). The comparison further shows agreement in how the shear stress amplification changes as the current-towave ratio increases, with the amplification decreasing from side to top in both model and data. Although the ADV profilers were only located at two points around the mound, the similarities shown here suggest that SWASH has some skill in simulating the near-bed velocities used to calculated shear stress. SWASH flow conditions were used to investigate the amplification of shear stress around a three-dimensional mound in combined wave-current flows and the role of quantifiable nonlinear wave factors in generating this amplification.
Data Availability Statement: Data can be accessed and can be found at the following repository for both model and data results: MODEX/SWASH Observations-10. 5281/zenodo. 4311679; 10. 5281/zenodo. 4311660; Model-Data Comparison-10. 5281/zenodo. 4312033; 10. 5281/zenodo. 4312011; 10. 5281/zenodo. 4311941; 10. 5281/zenodo. 4311767; 10. 5281/zenodo. 4311681; Model Boundary Conditions-10. 5281/zenodo. 4311648.
2000). The BM of ciliates was estimated as 0. 19 pg C µm -3 (Putt and Stoecker 1989) while radiolarians were 0. 001 pg C um -3  (Stukel et al. 2018). Diatoms and other protists were estimated based on their BV using allometric relationships from  Mendendeur and Lessard (2000). White spheres and any unidentified particles were treated as protists for carbon content calculations. FlowCam image analyses were conducted using FlowCam's dedicated classification software  VisualSpreadsheet (v. 4. 18. 5). First, duplicate images resulting from parabolic flow within the flow cell were manually removed. The particles within the remaining images were then classified based on the quality with which VisualSpreadsheet detected their outlines. For particles where the outline appeared to provide good estimates of L and W, size was calculated for a prolate spheroid as above using Equations 1-3 with the exception that W was left as the directly measured minimum feret length.
Conversely, high SDPPSR and/or low PPSR predators like dinoflagellates, raptorial ctenophores, or ciliates led to an ecosystem state with the opposite characteristics. Here, we carefully quantified not only the mean PPSR for 7 salp species, but also the range of sizes that comprised the majority of their carbon-weighted prey. We found PPSRs that were typically higher (typical values of 1000:1 to 10000:1, with all but 4 individual salps >1000:1 and 70% of our dataset above their maximum reported range of 2236:1, Figure  4 ) than the summary of estimates for salps reported in a meta-analysis by  Fuchs and Franks (2010). These values are also substantially greater than any other taxa in their dataset. As salps are nonselective feeders, a large portion of this difference is probably due to the different prey communities present in the regions from which Fuchs and Franks (2010) acquired their data, which include the Mid-Atlantic Bight  (Vargas and Madin 2004) , Subarctic Pacific  (Madin and Purcell 1992) , Florida Current, and Gulf of California  (Madin 1974).
Their data also include several species not observed in our study such as Cyclosalpsa affinis, Cyclosalpa pinnata, Cyclosalpa bakeri, and Weelia cylindrica which are quite distinct physiologically to the species investigated here and may exhibit different feeding characteristics. The individuals in their dataset are also on the small end of our size range, with 3 out of their 5 datasets containing only salps <20 mm and none containing salps > 100 mm (our T. vagina were as large as 163 mm). This also likely plays a role in the larger values we observed, because we found little difference in prey size between large and small salps. Taken together this indicates that the PPSR of salps as a whole may range over two orders of magnitude, which is comparable to the difference between pallium feeding dinoflagellates and suspension feeding copepods.
Acknowledgements We would like to thank the captain and crew of the R/V Tangaroa for help in the deployment of equipment as well as our many collaborators in the SalpPOOP project including Scott Nodder, Sadie Mills, Florian Lüskow, Morgan Meyers, Sarah Searson, Siobhan O'Connor, Karl Safi, Adriana Lopes dos Santos, and Fenella Deans.
The MA1-B9 also presented a normal karyotype 46, XX (Figure  S3d ), revealing that the edition protocol had not induced the appearance of karyotypic abnormalities. The line was also checked to be mycoplasma-free (Figure  S3e ). Finally, a DNA fingerprinting was performed to prove the genetic identity of the isogenic control MA1-B9 from the previously created iPSC line MA1 (IISHDOi001-A; data available upon request).
of pluripotency. As shown in Figure  S3c , the edited line can generate cell types related to the three germ layers: mesoderm (positive for SMA), endoderm (AFP), and ectoderm (positive for Tuj1). The MA1-B9 also presented a normal karyotype 46, XX (Figure  S3d ), revealing that the edition protocol had not induced the appearance of karyotypic abnormalities. The line was also checked to be mycoplasma-free (Figure  S3e ). Finally, a DNA fingerprinting was performed to prove the genetic identity of the isogenic control MA1-B9 from the previously created iPSC line MA1 (IISHDOi001-A; data available upon request).
The FISH analysis was performed with specific DNA probes for centromeric regions DXZ1/DYZ1. Slides were denatured at 72 • C for 2 min and incubated at 37 • C for 16 h for hybridization. Afterwards, they were washed in SSC with 0. 1% Tween-20 and mounted using DAPI. A Nikon fluorescent microscope was used to analyse 200 interphase cells. Digital images were acquired with a monochrome CCD camera linked to Metasystem software.
The blocking of the membrane was performed with 5% non-fat dry milk, 0. 1 % Tween-20 (Merck, Darmstadt, Germany; #P1379) in TBS for 1 h at RT. The PYGM primary antibody (Merck, Darmstadt, Germany; #HPA056003) was diluted 1:50 in 0. 1 % Tween-20, 1 % non-fat dry milk in TBS, with an incubation period of 17 h at 4 • C with gentle agitation. GARPO secondary antibody (Molecular Probes, Eugene, OR, USA; #G21234) was diluted 1:2500 in the same solution as the primary antibody, and it was incubated at RT for 1 h. The membrane was revealed with Clarity Max™ Western ECL Blotting Substrates (Bio-Rad, Hercules, CA, USA; #1705062). α-Tubulin-HRP (Abcam, Cambridge, UK; #ab40742) was used as a loading control, diluted 1:5000. Western Blot analyses were performed by Dr García-Consuegra, from the Proteomics Service in the Research Institute of Hospital 12 de Octubre.
Immunocytochemistry The assessment of the myophosphorylase expression by immunocytochemistry was performed following the protocol described in Section 4. 4. 1, using the primary antibody rabbit anti-PYGM (Merck, Darmstadt, Germany; #HPA056003) at a 1:100 dilution. 4. 6. Gene Editing with CRISPR/Cas9 4. 6. 1. Design of the sgRNAs We designed the crRNA domain of two possible sgRNAs (named sgRNA. 1 and sgRNA. 2), targeting exon 1 of the PYGM gene. For this purpose, we took into account the proximity of the guides to the c. 148C>T; p. R50* mutation, along with the predictions of higher on-target efficiency and lower possibility of off-targets modifications provided by two different tools: the design page integrated by IDT "https://eu. idtdna. com/site/order/ designtool/index/CRISPR_CUSTOM (accessed on 30 January 2022)" and the prediction software CRISPOR  [50].
Gene Editing We started from a 10 cm culture plate of the McArdle iPSC line MA1  [28]  at approximately 70% confluence and treated with StemMACS™ Y27632 Rock inhibitor for about 2 h (Miltenyi Biotec, Bergisch Gladbach, Germany; #130-103-922). The gene editing was performed following the protocol previously described by  Bruntraeger et al. , 2019 [52]. This protocol is based on the generation of CRISPR/Cas9 RNP complexes and the use of ssODNs to drive HDR mechanisms. We used a high-fidelity Cas9 Alt-R ® S. p. HiFi Cas9 Nuclease V3 (IDT, Newark, NJ, USA; #1081060) to form the RNPs. That way a more precise editing and lower off-targets risk was ensured. The nucleofection was performed in a single-cell suspension of 1•10 6 cells using the P3 Primary Cell 4D-Nucleofector ® X Kit L (Lonza, Basel, Switzerland; #V4XP-3012) in a 4D Nucleofector System by Lonza (Program CA-137) following the manufacturer's instructions.
Funding: This work has been funded by grants from the  Fondo de Investigación Sanitaria, Instituto de Salud Carlos III (ISCIII) :  PI15/00484 ,  CP16/00046  and  PI18/00151  to MEG and  PI17/02052  to JA (co-funded by  European Regional Development Fund  "A way to make Europe");  PI21/00162  and  CPII21/00011  co-funded by the  European Union  to  MEG. MdCOC  receives grant support from the  'Ministerio de Educación, Cultura y Deporte ' ( FPU16/03895 ), ' Fundación para la Investigación Biomédica Hospital 12 de Octubre'  ( 2022/0065 ,  i+12-AY20220114-1 ) and  EMBO  Grant  8917. CL and MD were recipient of a fellowship from the  French Ministry of Education. The work in FM's laboratory was funded by " Association Française contre les Myopathies " (AFM; TRIM-RD and MoThARD) and from the  Excellence Initiative of Aix-Marseille University-A*Midex , a  French "investissement d'avenir programme "  AMX-19-IET-007  through the  Marseille Maladies Rares (MarMaRa) Institute  (phD fellowship to CL).
The goal of background reconstruction is to recover the background image of a scene from a sequence of frames showing this scene cluttered by various moving objects. This task is fundamental in image analysis, and is generally the first step before more advanced processing, but difficult because there is no formal definition of what should be considered as background or foreground and the results may be severely impacted by various challenges such as illumination changes, intermittent object motions, highly cluttered scenes, etc. We propose in this paper a new iterative algorithm for background reconstruction, where the current estimate of the background is used to guess which image pixels are background pixels and a new background estimation is performed using those pixels only. We then show that the proposed algorithm, which uses stochastic gradient descent for improved regularization, is more accurate than the state of the art on the challenging SBMnet dataset, especially for short videos with low frame rates, and is also fast, reaching an average of 52 fps on this dataset when parameterized for maximal accuracy using acceleration with a graphics processing unit (GPU) and a Python implementation.
In this paper, we consider the problem of static background reconstruction. This problem is a difficult because there is no formal definition of what should be considered as background or foreground. Moving trees, fountains and moving shadows are examples of instances that are usually considered as belonging to the background although they show moving features. Other challenges such as illumination changes or the presence of objects staying still for a short time (a problem called intermittent motion) may severely impact the quality of a background reconstruction model. One should distinguish between online methods, where the length of the dataset is unknown and the background reconstruction algorithm has to update the background model in real-time and batch methods, where the algorithm is provided with a fixed dataset.
e. , where all the image patches associated with this block form only one cluster, and then iteratively extended to the whole image as a puzzle game by enforcing consistency between candidate background blocks and the partial background model. Other iterative block completion models have been proposed in  [16] [17] [18] [19] [20] [21]. Another approach which has been investigated for background reconstruction is to consider the sequence X = X 1 ,. , X n as a 3D tensor or a spatiotemporal matrix and to decompose it as the sum of a low-rank part, which is assumed to be representative of the background, and a sparse part, which should be representative of the foreground objects. For example, the Motion-assisted spatiotemporal clustering of low-rank algorithm (MSCL)  [22] , which is a dynamic background reconstruction model using robust principal component analysis (RPCA)  [23] , is able to obtain better results than state of the art fixed background reconstruction models on the scene background modeling (SBMnet) dataset using this method, although it is not directly comparable to those models because it requires some human supervision to select the final frame X from the various predicted backgrounds X1 ,.
Abnormal Image Weights If the number of images in the dataset is large, we can afford to give a low weight to images which appear to be abnormal, for example if the illumination conditions are different on these images compared to the predicted background, or if there are too many pixel errors on the image. We then first compute the average L 1 error ln of the image X n as ln = 1 hw ∑ i,j l n,i,j (9) and define a global weight associated with each image X n as w global n = e -γ ln , ( 10 ) where γ is another positive hyperparameter. As a consequence, this weight w global n will be close to zero if the image X n is globally very different from the current estimate X of the background. We use this global weight if the size of the dataset is greater than 10. It should be noted that this weight is not pixel-specific, as opposed to the bootstrap weights and optical flow weights, but is assigned to a complete frame.
Statement of the Optimization Problem Finally, the loss function is adapted using these weights and becomes the following: L W ( X, (X n ) 1≤n≤N ) = 1 N hw N ,h,w ∑ n=1,i=1,j=1 w global n w bootstrap n,i,j w OF n,i,j 3 ∑ c=1 | xc,i,j -x n,c,i,j | (11) We are then interested to solve the following optimization problem: Considering the dataset (X n ) 1≤n≤N , find an image X so that, when the weights w global n and w bootstrap n,i,j are considered as constants, the loss function L W ( X, (X n ) 1≤n≤N ) is minimal with respect to X. We can find a solution to this problem by performing an iterative computation of the weighted median of the images using the various weights defined in the previous paragraph followed by an update of the weights. We observe, however, that the images produced using this method are not smooth and that additional regularization is necessary.
Implementation Details A desktop computer with an Intel Core i7 7700K@4. 2GHz CPU and a Nvidia RTX 2080 TI GPU is used for this experiment. The model is implemented in Python using the Pytorch framework and is publicly available on the Github platform. We use the Adam optimizer  [43] , with learning rate 0. 03 and batch size 64, reduced by a factor of 10 when 3/4 of the epochs have been computed. The number of epochs depends on the size of the dataset and is adjusted so that the total number of iterations is close to 3000, with a minimum of two epochs. In order to accelerate computations, each frame sequence is fully loaded in the GPU video RAM during the optimization process. A manual hyperparameter search has been performed using the video sequences of the SBI and SBM datasets for which a ground truth is available.