dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The results on the simulated and clinical data demonstrate the feasibility of our innovative analytical method to drastically reduce the computation time of a WASABI dataset. Using this fast post-processing we are able to obtain ΔB 0 and rB 1 maps of clinical data in a few seconds. This acceleration is of fundamental importance in the clinical routine, as there is little waiting in the post-processing of the inhomogeneity corrected CEST parametric maps. Although our method is the only one to be both analytical and rapid, other methods for advanced processing for WASABI data based on Neural Networks (NN) have recently been presented  [24]. Even if the quality of those computed maps is very encouraging, NN must be trained for any parameter setup (t p ,t rec, B 1 ,…), offset number and magnetic field strength.
Although the number of noise realizations for a given set of parameters in our simulations (Section 2. 2) was limited to 25 due to the long execution time of the original method, our variation analysis additionally includes all possible combination of the rest three parameters (apart from the parameter in question). This leads to a sufficient number of observations (2015 for ΔB 0 and 1105 for B 1 ) for a reliable statistical analysis. In this work, we have focused only on reducing the WASABI data post-processing time and not the acquisition time. In fact, despite the  (rB 1 ), FLAIR map and B 0 -B 1 -corrected-APTw/FLAIR maps overlay. After the correction of the B 0 and B 1 inhomogeneities, a more homogeneous signal intensity can be seen in the white matter and gray matter. Most importantly, the APTw signal hypo-intensity in the anterior frontal lobe area is improved after the two corrections.
APTw data were acquired using a 3D-gradient-echosnapshot sequence  [3]  as WASABI data. For APTw data a mean B 1 value of 2. 6 μT and Duty Cycle of 55% were chosen. APTw-Z-Spectra were corrected by B 0 and B 1 inhomogeneities using ΔB 0 and rB 1 maps computed from WASABI data. The Z-Spectra at mean B 1 value of 2. 6 μT were B 1 -corrected using an additional APTw data acquisition at 1. 8 μT acquired with mean B 1 value of 2. 6 μT (2-point Z-B 1 -correction  [6] ). rB 1 was obtained by diving B 1 map by WASABI sequence RF nominal B 1 value (3. 7 μT). great advantages of the WASABI method in terms of acquisition complexity and setup (same sequence, readout, and resolution as CEST data), two minutes of data acquisition may not be optimal, in the clinical practice, to map exclusively B 0 and B 1 inhomogeneities.
The relationship between the environment and marine animal small-scale behavior is not fully understood. This is largely due to the difficulty in obtaining environmental datasets with a high spatiotemporal precision. The problem is particularly pertinent in assessing the influence of environmental factors in rapid, high energy consuming behavior such as seabird take-off. To fill the gaps in the existing environmental datasets, we employed novel techniques using animal-borne sensors with motion records to estimate wind and ocean wave parameters and evaluated their influence on wandering albatross take-off patterns. Measurements revealed that wind speed and wave heights experienced by wandering albatrosses during take-off ranged from 0. 7 ~ 15. 4 m/s and 1. 6 ~ 6. 4 m, respectively. The four indices measured (flapping number, frequency, sea surface running speed, and duration) also varied with the environmental conditions (e.
Acknowledgments We thank  Yoshinari Yonehara ,  Julien Collet ,  Timothée Poupart , and all the members of the research station in the  Crozet Islands  for their support during the fieldwork. We appreciate the fruitful comments of  Kagari Aoki ,  Chihiro Kinoshita ,  Laxmi Kumar Parajuli , and  Aran Garrod. We are grateful to  Taichi Sakamoto  and  Takashi Mukai  ( ATTACCATO ) for providing customized rechargeable Li-ion batteries for the Ninja-scans. We also thank  Michihiko Suzuki  and  Koichiro Ikeda  ( Little Leonardo ) for molding the Ninja-scans. The study was financially supported by  Grants-in-Aid for Scientific Research  from  JSPS  ( 22K21355  to  K. Q. Sakamoto ). The field work was funded by  IPEV program  n°109, and financially supported by a research project entitled " Cyber Ocean: next generation navigation system on the sea " funded by the  CREST program  ( JPMJCR1685 ) of  Japan Science and Technology Agency ;  Grants-in-Aid for Scientific Research  from  JSPS  ( 17H00776  to K.
Sato); the  Tohoku Ecosystem-Associated Marine Science ;  Sasakawa Scientific Research  Grant from  The Japan Science Society  ( 2020-4034  to L. Uesaka); European Community's  Seventh Framework Program   FP7/2007-2013  funded by a  European Research Council  ( ERC-2012-ADG_20120314  to H. Weimerskirch); European Community's  H2020 Program  funded by the  European Research Council  ( ERC-2017-PoC_780058  to  H. Weimerskirch ).
K E Y W O R D S biodiversity-ecosystem functioning, biodiversity, community-weighted mean, ecosystem functioning, functional diversity, sPlot, traits, vegetation repository of plant community data, sPlot  (Bruelheide et al. , 2019) , coupled with the plant trait database TRY  (Kattge et al. , 2011) , to quantify plant functional community structure in grassland communities around the globe. We used maximum annual NDVI as a proxy for primary productivity and modelled it using plant functional community data and climatic variables. Specifically, we were interested in the role of CWM (as a proxy for dominance) and functional diversity (as a proxy for niche partitioning) for predicting the biogeographical patterns of NDVI. In addition, we discerned the unique and shared contributions of our functional and climatic predictors using a deviance partitioning approach.
| Datasets We based this study on the integration of data compiled from three global initiatives: (1) the vegetation plot database sPlot, v. 2. 1  (Bruelheide et al. , 2019) , combined with (2) the TRY database of plant functional traits  (Kattge et al. , 2011) and  (3) MODIS satellite data providing NDVI time series  (Didan, 2015). Additionally, we used the land cover map GlobCover 2009  (Arino et al. , 2012)  and temperature and precipitation data from the CHELSA project  (Karger et al. , 2016).
| Filtering of sPlot The sPlot database, v. 2. 1, contains information on plant community composition in >1. 1 million vegetation plots contributed by >100 vegetation plot databases. In line with a long tradition of BEF research in grassland ecosystems  (Fraser et al. , 2015; Hooper et al. , 2005; Tilman et al. , 1996) , we chose grasslands as a model system for this analysis. We screened the database for vegetation plots in grassland communities. Any selected plot had to satisfy at least one of the following criteria: (1) the plot was marked as "grassland" by the vegetation survey that provided the sPlot entry; and (2) ≥90% of the plot was covered by species that were not trees or shrubs or that had a plant height >2 m (according to the TRY database). We excluded plots that were labelled as "forest", "shrubland" or "wetland", observations before the year 2000 (launch of the MODIS programme) and plots with high location uncertainty (>100 m) and imprecise GPS coordinates (less than four decimal places in decimal degrees).
This corresponds to a plant size spectrum that is commonly found in plants  (Díaz et al. , 2016). We used the two axes as composite variables characterizing the community position along the resource economics and plant size spectrum. In the subsequent analysis, we interpreted associations between the CWM axes and NDVI as evidence for dominance effects. To quantify functional diversity across all 18 traits, we calculated functional dispersion (hereafter, FD) using the R package FD  (Laliberté et al. , 2014)  and taking Gower's distance as the dissimilarity measure  (Podani, 1999). FD has the advantage of accommodating multiple traits in addition to species abundances. Furthermore, it is independent of species richness and relatively unaffected by outliers  (Laliberté & Legendre, 2010). Here, we interpret associations between FD and NDVI as evidence for niche partitioning effects.
| Data analysis We modelled annual maximum NDVI using a number of nested generalized additive models (GAMs;  Wood, 2017)  with subsets of predictors relating to traits (CWM axis 1, CWM axis 2 and FD), climate (temperature and precipitation) and space (latitude and longitude) (Table  1 ). We chose the GAM framework because it allowed us to include the spatial coordinates as smooth spherical splines, which accounts for the spatial structure of the NDVI response variable and spatial autocorrelation in the residuals. All other predictors were included as simple linear terms. As climatic variables, we included mean annual temperature (Temp) and the precipitation of the driest quarter (Prec). With the response variable NDVI ranging between zero and one, we specified the models using the beta-regression family and a logit link function, using the mgcv R package  (Wood, 2017).
e. , CWM) of vegetation assemblages remained significant, which suggests that the relationship between plant traits and productivity goes beyond the mere spatial covariation in abiotic conditions and local species pools. There is no doubt that the approach adopted in this study has some limitations. Not only does the use of trait databases lead to a very rough approximation of functional community structure (e. g. , neglecting intraspecific and site-specific trait variation) but also the vegetation plot database had vast gaps in its global coverage, which was partly attributable to the strict filtering criteria applied here. South America and Africa are particularly underrepresented in our compilation. Nevertheless, our analysis has an unprecedented spatial coverage, and spans more than nine of the ten sPlot biomes (sensu  Bruelheide et al.
, 2018). Furthermore, by explicitly including geographical coordinates in our models, we mitigated as much as The fact that we limited our analysis only to vegetation plots located within MODIS pixels representing uniform land cover classes, however, is expected to mitigate the negative consequences of this scale mismatch. Furthermore, NDVI, like other vegetation indices, is known to saturate in highly productive systems, which means that it might not correctly discern productivity differences in those areas (e. g. , the tropics). However, this mostly concerns forests with high leaf area index values and should not be a major issue for our sites in open grasslands  (Haboudane et al. , 2004). Finally, although the selected functional traits account for two of the main trait dimensions in plants (leaf economics and size), data coverage was limited for other important aspects, such as photosynthetic pathways and association with nitrogen-fixing bacteria.
C. V. was supported by the  European Research Council (ERC)  Starting Grant Project " ecophysiological and biophysical constraints on domestication in crop plants " (grant  ERC-StG-2014-639706-CONSTRAINTS ). G. E. O. was funded by  CNPq  grant  310345/2018-9. J. A. was supported by research grants  LTAUSA19137  (programme  INTER-EXCELLENCE , subprogramme  INTER-ACTION ) provided by  Czech Ministry of Education, Youth and Sports ,  20-05840Y  of the  Czech Science Foundation  and long-term research development project no. RVO 67985939  of the  Czech Academy of Sciences  (CAS). J. D. was supported by research grants  MŠMT Inter-Excellence  ( LTAUSA18007 ) and  Czech Science Foundation  ( GA 21-26883 S ). J. P. was funded by the  Spanish government  grant  PID2019-110521GB-I00 ,  Catalan  government grant  SGR-2017-1005  and  European Research Council  grant  ERC-SyG-2013-610028.
A. ,  Bradley ,  B. A. ,  Butterfield ,  B. J. ,  Tuanmu ,  M. N. ,  Sistla ,  S. ,  Wiederholt ,  R. , &  Suding , K. N. (2016). Scaling up the diversity-resilience relationship with trait databases and remote sensing data: The recovery of productivity after wildfire. Global  Change Biology , 22, 1421-1432. Suding ,  K. N. ,  Lavorel ,  S. ,  Chapin ,  F. S. ,  Cornelissen ,  J. H. C. ,  Díaz ,  S. ,  Garnier ,  E. ,  Goldberg ,  D. ,  Hooper , D. U. , Jackson, S. T. , & Navas, M. L. (2008). Scaling environmental change through the communitylevel: A trait-based response-and-effect framework for plants. Global Change Biology, 14, 1125-1140. Testolin, R. , Carmona, C. P. , Attorre, F. , Borchardt, P. , Bruelheide, H. , Dolezal, J. , Finckh, M. , Haider, S. , Hemp, A. , Jandt, U. , Korolyuk, A.
sPlot has been initiated by sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (FZT 118) and is now a platform of iDiv. We are grateful to thousands of vegetation scientists who sampled vegetation plots in the field or digitized them into regional, national or international databases. We appreciate the support of the German Research Foundation for funding sPlot as one of the iDiv research platforms (DFG FZT 118, 202548816). T. E. , D. H. and F. M. S. were funded under this scheme. We thank Ludmilla F igu eir edo and the iDiv Data and Code Unit for assistance with the curation and archiving of the dataset. The study was supported by the TRY initiative on plant traits (http://www. try-db. org).
Statistical analysis The statistical analysis of our results was carried out using standard statistical procedures operated by SigmaPlot software (Systat Software, San Jose, USA). All outliers were identified using Grubbs' test from calculator GraphPad (GraphPad Software, San Diego) or ROUT method with a Q value of 1% from GraphPad Prism 7. 01 (Motulsky et al. , 2006) (GraphPad Software, San Diego) when data with nonlinear regression. Acquired data from the behavioural characterization of 16p11. 2 rat models were analysed using one-way ANOVA followed by Student's t-test and Tukey's posthoc test whenever data presented normal distribution and equal variance. Otherwise, we used the non-parametric Kruskal-Wallis one-way analysis of variance and the Mann-Whitney U test. One sample t-test was used also to compare recognition index values to the set chance level (50%).
The preparation of the libraries was done by using the TruSeq Stranded Total RNA Sample Preparation Guide -PN 15031048. The molecule extracted from the biological material was polyA RNA. The Whole genome expression sequencing was performed by the platform using Illumina Hiseq 4000 and generating single-end RNA-Seq reads of 50 bps length. The raw sequenced reads were aligned by Hisat2 against the Rno6. v96. 32623 ENSEMBL Gene Ids were quantified aligning to the Rno6. v96 assembly. HTSeq-count was used to generate the raw counts. The downstream analyses were carried on with in-house bash scripts and R version 3. 6 scripts using FCROS  [31]  and DESeq2  [32]  packages to identify the DEGs. Raw reads and normalized counts have been deposited at the GEO resource database (Accession No. GSE224935 for the SD dataset and GSE for the LE dataset).
We performed the functional differential analysis  [33]  and grouped all the pathways into 25 functional categories (noted meta-pathways). Then, to assess the gene connectivity we build a minimum fully connected protein-protein interaction (PPI) network (noted MinPPINet) of genes known to be involved in all metapathways we defined as they were associated with each pathway via GO  [34]  and KEGG databases  [35]  and added regulatory information to build the final 16p11 dosage sensitive RegPPINet. We used the betweenness centrality analysis to identify hubs, and keys for maintaining the network communication flow.
Identification of central genes linked to the behavioural phenotypes To further study the genotype-phenotype relationship in those models we combined the behavioural results and the RNA-Seq data to identify central genes altered in the models linked to the observed phenotypes using the genotype-phenotype databases GO, KEGG and DisGeNET. For this, we combined the knowledge from the human disease database DIsGeNET and the GO genesets (see Supplementary information). Then we queried our RNA-Seq data for those genes to identify those found deregulated in the datasets. Table  1  summarized the results with the genes annotated with the expression level, regulation sense on each model, log2FC and standard deviation of the log2FC.
Then, we carried out the novel object location recognition test and the novel object recognition test, common assays for assessing impaired memory in rodents. For the novel object location recognition, animals were challenged to discriminate a moved object from an unmoved object. We first evaluated the performances of males and females separately, and as no significant sex differences were noted, we combined these data across both sexes. No difference was observed between genotypes in the retention session (Kruskal-Wallis one-way analysis of variance: H (3) = 5. 61; p = 0. 132; Fig. S1A ). We also compared the recognition index of the animals, i. e. the percentage of exploration time of the new object location, with the level of chance (50%). The new object position was always explored more than the object not moved for all the genotypes.
2 BP4-BP5 locus in a rat LE outbred strain and we verified the location of the specific Del interval (Fig. S2 ). Interestingly the transmission of the Del/+ allele was affected in females of the LE 16p11. 2 model (Table  S1 ) while males did not show any significant change. We also evaluated the body weight of this new model for both sexes and found that the deletion of the 16p11. 2 region caused a decrease in the body weight on the LE genetic background in both sexes (Fig. 3A ; Table  S5 ). Then, we proceeded to study the behaviour using the same battery of behavioural tests (Table  S5 ). We analysed the locomotion and exploratory activity of our second model. First, we observed a reduction in the variability between the data of each individual regarding the results observed in the SD model.
Interestingly, most of the genes, contributing to the functional alteration in the 16p11 dosage and the transcription network, were also detected and quantified by the proteomic technique (highlighted in yellow with an octagonal shape (Fig. S6A ). We found a strong correlation between the RNA level (RNA seq count) and our proteomic quantification in wt and Del/+ individuals for all expressed genes (Fig. S6B ) and DEGs (Fig. S6C ). Thus, we decided to investigate the proteomics dataset on its own and search for new insights into the 16p11 syndrome alterations. We built the proteomic-specific hippocampi 16p11 MinPPINet (Fig. S7A ). Very wellconnected 16p11 region proteins, such as ALDOA, BOLA2, CDIPT and COROA1, unravelled the existence of two main subnetworks (Fig. S7B ): the first was around MAPK3 and SRC, a proto-oncogene coding for a membranebound non-receptor tyrosine kinase, while the second was built around the ATP citrate lyase (ACLY).
Using the analysis of both sexes in the LE 16p11. 2 Del model, we identified more DEGs in the male mutant hippocampi compared to female; DEGs that were also found in the SD genetic background. Of the 248 pathways altered in the Del/+ males and 100 identified in mutant females, 49 pathways were found deregulated in both sexes. More effects were observed in males than in females in various pathways, including the synapse, the Behaviour, and mitochondria. By introducing the known gene/phenotype associations, described in the DisGeNet, GO and Kegg databases, we identified Prrt2 as a candidate gene involved in "aggressively and stereotyped behaviour" that was found upregulated in the Dup/+ model and downregulated in Del/+. We also found 4 other genes from the 16p11. 2 region involved in autistic behaviour (defined by increased time in isolation) downregulated in Del and upregulated in Dup/+: Taok2, Kctd13, Sez6l2, Mapk3.
Outside a doughnut plot representing in the centre the number of total altered pathways found by gage analysis one each dataset and the percentage of pathways altered included on each meta-pathway is represented on the coronal area under each meta-pathway. The meta-pathways are defined in the accompanying legend. FIGURE LEGENDS Figure  6 : Protein-protein interaction networks altered due to the gene dosage effect based on the transcriptome analysis of the 16p11 SD rat models. (A) Left panel, a full rat protein-protein interaction network (RegPPINet) built using as seeds all the genes identified by gage as altered in the Del or Dup SD models visualized using the edge weighed spring embedded layout by betweenness index in Cytoscape. On the right panel, highlighting the main central nodes of the rat dosage susceptible RegPPINet network.
Funding This work was supported by a grant from the  Simons Foundation  ( SFARI 548888  to YH) and by the  National Centre for Scientific Research (CNRS) , the  French National Institute of Health and Medical Research (INSERM) , the  University of Strasbourg (Unistra) ,  French government  funds through the " Agence Nationale de la Recherche " in the framework of the  Investissements d'Avenir program  by  IdEx Unistra  ( ANR-10-IDEX-0002 ), a  SFRI-STRAT'US  project ( ANR 20-SFRI-0012 ), '' TEFOR ''  «Investissements d'Avenir» (ANRIIINSB -0014 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) and  INBS PHENOMIN  ( ANR-10-IDEX-0002-02 ) and also provided to the GenomEast platform, a member of the ' France Génomique ' consortium for the  RNASeq processing  ( ANR-10-INBS-0009 ), and to the Proteomic platform of IGBMC, that was supported by an  ARC   foundation grant  (Orbitrap) and a  Canceropole Grand Est foundation grant.
Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. difference between the animals and its variability is explained in the first component « PC1 ». (B) Homogeneity plot showing the gene dosage effect of left) the 28 genes in the 16p11. 2 region and how the samples cluster by Euclidian distance, and right) all DEGs identified in both male and female datasets. (C) Venn diagram showing in the upper panel, the DEGs were found common between the male and female LE datasets. The shared DEGs correspond to 16. 2% and 20 % of the total DEGs identified respectively for the female and males. In the bottom panel, Venn diagram showing the pathways in common between the male and female LE datasets.
(D) Distribution and correlation diagram showing the DEGs in common between Del and Dup models and the model-specific DEGs. The shared DEGs correspond to 19. 7% and 28. 1 % of the total DEGs identified respectively for the Dup and Del models. (E) Venn diagram showing in the left panel, the DEGs in common between the SD Del and Dup models and SD male transcriptomics datasets and the background-specific DEGs. Highlighted inside the common genes those upregulated in both models, 120, downregulated in both 100, and regulated in opposing regulatory sense between Dup and Del models (39 upregulated in Dup and 8 downregulated in Dup).
From 3D X-ray computed tomographic image analysis to 3D structure generation The 3D composite structures were generated using the cascading (3D) reconstruction procedure of a composite material from microCT data described in Methods X. Structural descriptors of cellulose particles were extracted from 3D tomographic image. Cellulose particles were assimilated to either spheres and ellipsoids whose shape descriptors were measured using MorphoLibJ plugin in ImageJ software  [19]. Ellipsoids were characterized by the three axes, a 1 , largest axis, a 2 , medium axis and, a 3 , smallest axis and three orientation angles, named Tait-Bryan angles with x-y-z extrinsic rotation convention: ψ, roll angle, θ, elevation angle and, φ, azimuth angle. Spheres were characterized by the diameter d. The experimental distribution of these descriptors was fitted by Kernel distribution laws.