dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection Anonymized data were collected by site investigators or clinical research staff using a standard electronic data capture (EDC) form for each patient, and uploaded to a secure study database. Data quality was maintained and checked periodically, and the EDC system incorporated an audit trail for all entries and changes. The following data were collected at the 'baseline' visit for all recruited patients: demographics, neurological impairment assessed by the NP-C disability scale, verbal fluency tests  (22)  and neurological examination. The following retrospective data were also collected based on medical chart information for all patients diagnosed with NP-C ('NP-C positive' patients): psychiatric history, history of neurological signs and symptoms, history of liver disease, childhood development, neonatal medical history, ongoing and previous medication, cognitive impairment based on the MMSE  (25)  and neuroimaging data.
Genetic and biochemical analyses Blood samples were collected from all patients for NPC1 and NPC2 gene sequencing analysis, which included characterisation of exons and exon-intron boundaries. MLPA was performed where NPC1 and NPC2 sequencing results were not conclusive. Investigators were also encouraged to refer patients with uncertain NP-C gene sequencing results to NP-C reference centres for further standard work up, including filipin staining  (3). When available, filipin staining results were collected in the study database. Carriers of two known or novel mutant NP-C alleles with a published or predicted (in the case of novel mutant NP-C alleles) functional impact were diagnosed with NP-C (i. e. 'NP-C positive' patients). Individuals with novel mutant NP-C alleles and positive filipin staining evidence of NP-C were also diagnosed as NP-C positive.
All heterozygote carriers with only one published or novel mutant NP-C allele and no filipin staining data were classified as 'NP-C uncertain' based on the possibility that they could either not have NP-C, or be NP-C patients in whom the molecular diagnosis had not been established due to a lack of testing sensitivity. Plasma levels of cholestane-3b,5a,6b-triol and selected sphingolipids [sphingosyl-phosphocholine (SPC), sphingosine-1-phosphate, sphingosine, glucosylsphingosine, dihydrosphingosine-1-phosphate, dihydrosphingosine, lysoGb3 and lactosylsphingosine] were quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) according to previously published methods in all patients with available plasma samples  (8, 9, 26, 27). Both oxysterol and sphingolipid analyses were included via a protocol amendment, and assays were performed on anonymized samples.
ACKNOWLEDGMENTS The authors wish to acknowledge  David Calle at Nuvisan CDS, Madrid, Spain  for conducting the statistical analyses, and  Dr Arndt Rolfs  and  Dr Christine Remy  for their contributions to the design and conduct of the study. The authors also wish to pay tribute to the late  Dr J. Edmond Wraith  for his many important contributions to this and numerous other studies of NP-C and related disorders over the course of his distinguished career. Medical writing assistance in the preparation of this manuscript was provided by  Matthew Reilly PhD  associated with InTouch Medical Ltd, paid for by  Actelion Pharmaceuticals Ltd. Conflict of Interest statement. P. B. , H. H. K. , J. P. , D. S. O. , M. P. and F. S. have each received travel expenses, carried out paid consultancy work and received presentation honoraria from  Actelion Pharmaceuticals Ltd.
First, the results of our studies reveal that MeJA delays the switch from the mitotic cell cycle to the endoreduplication cycle in a COI1-dependent manner as well as inhibits the mitotic cycle itself, arresting the cell cycle in G1 prior to the S transition. These data allow us to propose a novel model for MeJA-regulated COI1dependent leaf growth inhibition. Second, a novel global transcriptional profiling of Arabidopsis seedlings grown in medium containing MeJA was carried out to identify the molecular players whose expression is regulated during leaf development by this hormone via COI1. Significantly, we show that MeJA activates critical regulators of endoreduplication and affects the expression of key determinants of DNA replication. Together, our discoveries also suggest that MeJA may contribute to the maintenance of a cellular "stand-by mode" by maintaining a high level of expression of ribosomal genes.
Molecular Understanding of the Action of MeJA on Cell Cycle Regulation during Leaf Development Using Full-Genome Transcript Profiling Publicly available microarray studies conducted on whole seedlings and single time points designed mainly to analyze the role of JAs with regard to their function during stress and defense are unsuitable to dissect the MeJA-dependent signaling underlying the physiological responses we have observed in planta. Hence, we have undertaken a novel full-genome  transcription profiling to dissect the effect of MeJA on the cell cycle and differentiation using the wellestablished Arabidopsis leaf as a model system. We present here new data about MeJA inducibility, developmental regulation, as well as COI1 and AOS dependency at the same time. Plant microarray studies have revealed that JA signaling alters gene expression and that genes induced by MeJA generally required COI1 in Arabidopsis (for review, see  Balbi and Devoto, 2008; Acosta and Farmer, 2010; Avanci et al.
Leaf Measurements First true leaves of Arabidopsis seedlings grown on horizontal plates as described above were collected at 9, 13, and 19 DAS, cleared in 100% ethanol, mounted in DL-lactic acid on microscope slides, and photographed. Leaf abaxial epidermal cells (40-100 cells) were drawn for four to 21 leaves with a DMLB microscope (Leica) using a DIC objective. Photographs of leaves and drawings were used to measure the leaf area and calculate the average cell area and number with ImageJ software (http://rsb. info. nih. gov/ij/). Leaf and cell areas were used to calculate cell numbers.
Root Measurements In both MeJA-treated and untreated plants, primary root, cell, and root hair lengths were measured in 6-d-old seedlings vertically grown (75°to the horizontal). The length of a cell was determined between two fully expanded root hairs on the same cell file via high-resolution images using DIC microscopy and ImageJ software. This was done for six to 10 cells per plant. The length of fully grown root hairs was also measured. A minimum of eight seedlings per genotype were analyzed, and three independent biological replicates were performed. Percentage inhibition in the MeJA-treated seedlings was calculated. As described by  Ishida et al. (2009) , root meristem organization was visualized using seedlings stained with 5 mg mL 21 propidium iodide solution, and root nuclei were visualized using transgenic plants harboring H2B-YFP fusion protein constructs.
Microarray Experiments and Data Analysis The first true leaves of Col gl1 and coi1-16B at 9, 13, and 19 DAS grown horizontally on Murashige and Skoog medium as described above with and without 50 mM MeJA were collected, snap frozen immediately, and stored at 280°C until further RNA extraction. aos samples were collected at 13 DAS only. Total RNA was isolated using Tri Reagent according to the manufacturer's instructions (Sigma-Aldrich). Isolated RNA was further purified using RNeasy mini columns (Qiagen) according to the manufacturer's instructions. After quality controls, copy RNA, labeling, hybridization, and scanning of the high-density oligonucleotide microarrays (Arabidopsis ATH1 genome array; Affymetrix) were performed at the Nottingham Arabidopsis Stock Centre microarray platform (http://affymetrix.
arabidopsis. info/). Data were generated from two independent biological replicates for all samples but the 19-DAS time point, for which only one replicate was available (Affymetrix; NASCARRAY-568/573). ATH1 Expression Profiling and Data Analysis RNA samples were hybridized to single Affymetrix ATH1 Genome arrays at the Nottingham Arabidopsis Stock Centre. Expression data were processed with RMA (background correction, normalization, and summarization) as implemented in BioConductor  (Irizarry et al. , 2003a (Irizarry et al. , , 2003b;; Gentleman et al. , 2004). The microarray data were checked for anomalous RNA degradation by ordering and grouping the probes for the ATH1 array according to their 59 to 39 position. In order to check the reproducibility of the biological replicates, (1) dendrograms using hierarchical clustering of the correlation between any two pairs of CEL files based on the normalized expression of all their probe sets and (2) scatterplots of normalized expression values for individual genes between replicates and samples were drawn (Supplemental Fig.
S7, A  and B ). The BioConductor package Limma, which employs a hierarchical model to estimate the t statistics, was used to identify differentially expressed genes  (Smyth, 2004). The default settings from the front-end package affylmGUI were employed  (Wettenhall et al. , 2006). The hierarchical model applied by Limma allows computing Student's t tests for samples where only one replicate was available. False discovery rate-corrected P , 0. 05 was used as a cutoff  (Benjamini and Hochberg, 1995). Finally, any genes that did not display at least a 1. 5-fold change between any two genotypes or time points or between treated versus untreated samples within a genotype were removed. The remaining list was considered a robust set of differentially expressed genes. Venn diagrams with the numbers of genes upregulated or down-regulated at each time point were used to compare the results between the three genotypes  (Oliveros, 2007).
All reactions took place in triplicate. Melting curves were analyzed to check primer specificity. We assessed the primer reaction efficiencies using the Comparative Quantitation Report for all genes of interest and the reference genes. All primer reaction efficiencies were comparable around 90%. Levels of each transcript as a ratio against the geometric mean  (Vandesompele et al. , 2002)  of the two internal control genes At5g55840 and At5g17510, identified using the Arabidopsis geNorm reference gene selection kit (Primerdesign) and the qBase software package  (Hellemans et al. , 2007) , were quantified using the comparative threshold cycle calculation. For each genotype, data from the 9-DAS untreated sample were used as the baseline expression value (calibrator). Primers were designed using QuantPrime (http://quantprime.
ACKNOWLEDGMENTS We thank  Amar Patel  and  Danielle Minns  for technical help and  Fred Ausubel ,  Lazslo Bögre ,  Enrique Lopez-Juez , and  Fran Robson  for critical comments. A. D. designed the research; A. D. , S. N. , B. M. , N. T. , T. I. , T. -L. T. , and V. B. performed the research; A. D. , S. N. , B. M. , N. T. , T. I. , and H. S. analyzed the data; and A. D. , S. N. , B. M. , and K. S. wrote and edited the paper.
1 m. Forty-five lines were flown to cover the 150 km 2 study area. LiDAR data were georeferenced to Lambert-RGF93/IGN-69 coordinates, using differential GPS measurements. The laser points (X-, Y-, Z-coordinates) were then classified in ground and vegetation points with TerraScan software  (Axelsson, 1999). Ground and higher vegetation points were used to derive respectively a high-accuracy Digital Terrain Model (DTM) and a Digital Surface Model (DSM), using the Nearest Neighbors (NN) interpolation method. The height image is the difference between DSM and DTM. The intensity image was calculated from mean intensity point values within grids of 0. 5 m × 0. 5 m using nearest neighbor interpolation method.
> RADAR IMAGES We used a series of five TerraSAR-X satellite images (Table  I ). TerraSAR-X is characterized by an active phased array X-Band system, with a 9. 65-GHz center frequency , a near-polar orbit around the earth and is able to register images with a spatial resolution of approximately one meter, regardless of weather conditions or absence of daylight. Data can be acquired in numerous modes (SpotLight, StripMap and ScanSAR), depending on the spatial resolution and polarization required  (Fritz et al. , 2007). TerraSAR-X appears very promising for monitoring riparian vegetation because of its specificities  (Lopez-Sanchez et al. , 2009) : shorter revisit time than previous radar sensors (11 days), a frequency band (X) suited to the monitoring of changes in vegetation phenology, a phase information preserved for dual-polarization data acquired in single look slant range complex mode, which allows the extraction and use of incomplete polarimetric decomposition and a very high spatial resolution (below 3 m), even for dual-polarization images, which appears suited to identifying and characterizing riparian vegetation.
Images were pre-processed in order to reduce speckle noise  (Lee, 1981) , and correct the topographic deformations using PolSARpro 4. 2 (http://earth. eo. esa. int/polsarpro/) and Nest Dat 4C (http://nest. array. ca/web/nest) opensource software programs. Images were then georeferenced in the Lambert-RGF93/IGN-69 system, using 55 ground control points selected from aerial photography. Polarimetric images are sensitive to object orientation and scattering properties. In synthetic aperture radar (SAR) polarimetry, the emitted and received states of polarization change during data collection (HH, HV, VH and VV for full polarimetric data), providing the phase and magnitude of the backscattered signal that is related to the material properties of the studied object (roughness, orientation, structure.
Automatic classifications regarding heterogeneous riparian corridors data can be performed at fine scales using LIDAR data. We applied a supervised oriented-object classification on the LiDAR data acquired on the Pleine-Fougères site, by considering seven land cover classes, including five vegetation classes, roads and water (Figure  5 ). To that end, water bodies and roads were first respectively identified by applying a multi-threshold segmentation on the intensity image, and extracting the road layer from the IGN R BD TOPO database. Then, vegetation strata were determined by applying a multi-threshold segmentation from the height image. Classification results were assessed using ground data collected over 215 field plots and height values estimated from LiDAR data processed via photo-interpretation.
, 2012). Fine resolution SAR datasets show great potential for riparian mapping and monitoring in this respect. To provide fully operational tools some progress is needed, notably in terms of data treatment to develop more automatic procedures to extract information from images and to calculate indicators (see for example  : Carbonneau et al. , 2009; Johansen et al. , 2010; Wiederkehr et al. , 2010; Aguiar et al. , 2011; Tormos et al. , 2011). Moreover, the information collected by sensors can be enhanced. For example, in the case of UAV, some developments can be expected with infra-red  (Lelong et al. , 2008; Lobo, 2009) , real-time generation of 3-D maps systems   (Stefanik et al. , 2011)  and species scale classification  (Dunford et al. , 2009; Laliberte and Rango, 2011; Fernandes et al.
Discussion First of all, our method provides reliable results, since they are very close to those obtained by other authors on the same fossil with confocal microscopy  [1] [9]. Moreover, our count of perikymata is fully automatic and very fast (a few minutes). Finally, in addition to general statements on periodicity of fossil teeth (e. g. ,  Lacruz, 2007 [9] ), we can provide a precise measures of periodicity. Hillson and Bond in 1997  [30] [11]  provided data on changes in the periodicity in extant humans. Our values obtained for KB5223 in the cervical area of the crown are very similar to those known for extant humans in homologous areas of crown height. Regarding the genus Paranthropus, the data are most often expressed in deciles, and it is therefore difficult to compare our results to these data.
Nevertheless, if we consider data graphically provided by Dean and Reid in 2001  [1] , it seems clear that the periodicity of perikymata at the cervix in Paranthropus, is hardly less than 100 microns. This Paranthropus value is much higher to the one measured on the two lower permanent incisors of KB5223. In the cervical areas of both crowns (the right central or the right lateral incisor), the periodicity was found to be 50 microns. The variation of the periodicity of perikymata is only very partially known in extant humans. Extant human values can overlap with the currently observed values in fossil hominin. Our tool then provides an objective tool to increase of knowledge using larger extant comparative samples, to compare these results with fossil hominin values, and to interpret them in terms of developmental patterns and phylogenetic relationships.
In this study, we demonstrate with observer-independent, accurate and reliable measurements, that the periodicity of perikymata decreases towards the cervix in two fossil hominin teeth from Kromdraai B : one central and one lateral permanent incisor of KB5223. It is difficult to compare our results with data on Paranthropus because they are most often expressed in deciles. Nevertheless, if we observe data graphically provided by Dean and Reid in 2001  [1] , it seems that the periodicity of perikymata at the cervix in Paranthropus is hardly less than about 100 microns; i. e. much higher than the one measured on KB5223. The measuring time of a tooth was close to ten minutes and permitted a practical and objective assessment. Given the results presented here, we can emphasize the relevance of our approach to immensely facilitating the measurements of perikymata of fossil hominin teeth and in extant samples.
The vegetation sub-model, CANOPT is an individual-centered model of pasture species growth that simulates the dynamics of a plant population made up of average individuals. Population turnover, shoot and root morphogenesis, photosynthesis, respiration, transpiration, N acquisition by uptake, allocation of assimilates between structural compartments, and reserve storage and remobilization, are simulated for each plant population within multi-species canopy layers. Four modules are assembled. First, a plant physiology and partitioning module simulates the acquisition and the balance of C and N substrates. Partitioning of growth between shoot structures, leaf photosynthetic proteins and roots is based on two assumptions: (i) functional balance between root and shoot activity  [37] , (ii) coordination of leaf photosynthesis  [38, 39].
A third environment module computes the radiative and N balances among soil and canopy layers. Finally, a management module runs discrete events creating disturbance (by cutting and/or grazing) and supplying nutrients (N fertilizer supply). GEMINI allows one to investigate the details of physiological and morphological processes that drive species responses to trait variations (see Figure  S1 ), such as: light interception; net photosynthesis; inorganic N uptake capacity; specific root area; partitioning coefficients of C and N substrates between shoot structures and roots (P) and between shoot structures and leaf proteins (Q); and the C:N ratio of labile substrate pools. In GEMINI, the C and N substrate pools correspond to labile carbohydrates and to NO 3 -, NH 4 + and reduced soluble N, respectively, and their mass balance (W C, W N ) results from the dynamics of the following plant processes (see  33   As such, the total amount of substrates (W C + W N ) and the C:N ratio of plant substrates are in-planta markers of coordination between ecophysiological processes determining plant performance.
In contrast, TD variations are not constrained by a potential TD value. A detailed list of all 132 equations, as well as the variables and the default parameter values is available at www1. clermont. inra. fr/urep/modeles/gemini. htm. The four studied traits refer specifically to the morphogenesis module. They all have an indirect impact on C and N internal fluxes within the plant through the coordination between the physiology and the morphogenesis GEMINI modules. A brief review of their implication in model equations is given in Text S1.
For each species, an allometric relationship was derived, considering a constant leaf thickness and constant ratio between sheath and leaf lengths (Text S1). Variations in plant height H were achieved by changing the potential length of mature leaves L 0 (cm). For each species, plant height was calculated considering a dynamic leaf shape, according to the plant population density. Variations in LLS were achieved by changing the phyllochron (thermal time, in degree days, between the appearance of two successive leaves, Ph). These two variables were closely correlated in nature  [43]  and within the 12 grass species over the year (data not shown). Finally, the initial tiller number (TD 0 , tillers m -2 ) is simply the initial value of the state variable. A fully crossed sensitivity analysis was conducted to explore the simulated dynamics of plant vegetative growth in monoculture in response to variation in each of the four traits, each trait being a factor in the experimental design (4D trait space).
For each species the model parameters reflecting the four traits were varied in ten equidistant steps (Table  S1 ). The step values for each species were determined between minimum and maximum boundaries, which were selected to obtain for each trait a ±30% variation around the species' trait mean value observed in the field. In addition to the ten predefined steps, simulations with the measured values of each trait in the field monocultures were run. This design was applied at two N availability levels corresponding to the fertilization treatments in the field experiment. Climatic data (radiation, temperature, precipitation and air moisture), recorded during the field experiment in 2003-2004, were used to run the model. Management data recorded during the experiment (cutting dates and timing and amounts of N fertilizer supply) were used for model simulations.
Each simulation ran over ten years (repeating the 2003-2004 climate data five times), a necessary running time to check for the stability of the model response. In addition, simulations started from a quasiequilibrium state which was obtained by spin-up model runs. The two simulation campaigns (N+ and N-), corresponding to more than 350 000 simulations, took 30 days on a Symmetric multiprocessor with 8 AMD 64 bits dual core, 256 Gb. of RAM under the Centos 4 operating system. The GEMINI software proved to be extremely reliable since: (i) more than 99. 99% of all simulations were executed without error; (ii) plant growth showed high stability over the 10 simulated years (data not shown).
Data analyses In one simulation run, i. e. for each trait variation step, the annual biomass (below-and aboveground) was recorded for each simulated year and then averaged over the ten years simulation period for each species. For each species and for each N level, we generated an adaptive landscape in which the dynamic of species performance could be explored through the independent variation of the four traits. Within this landscape, we were able to record: • In a 4D-trait space, the single combination of trait values (trait max ) that maximized the vegetative growth (adaptive peak, Table  1 ), • In the various 2D-trait spaces and under high-N conditions, the slopes α that each described a set of equallyoptimal trait combinations ('adaptive ridges'). These ridges can also be thought of describing the degree to which a trait can vary independently from the others with only a limited impact on plant performance (see dashed lines in Figure  1A  as an example for A.
Acknowledgments We thank  Romain Lardy  for their precious help on data analysis;  Hendrick Davi ,  Michel Lafarge ,  Sandra Lavorel ,  Isabelle Litrico ,  Robyn Butters ,  Alexandra Weigelt  and five anonymous reviewers for their constructive and fruitful comments on a previous version of the paper. C. Wirth acknowledges the support by the  German Science Foundation  within the  research unit 456 (Jena Experiment ).