dataesr/scientific-paragraphs-categorization huggingface dataset:
Brassicicolin A, a fungal metabolite considered as being a mannitol derivative, represents a potent necrotic toxin produced by A. brassicicola  (Pedras et al. , 2009). We hypothesized that the weak virulence in mutants lacking mannitol may also be explained by the absence of brassicicolin A synthesis. To test this hypothesis, ethyl acetate extracts of culture filtrates from submerged cultures of A. brassicicola wild-type and abmpd-abmdh strains were analysed by HPLC-UV-MS. The resulting total ion chromatograms (TIC) of both extracts revealed a major metabolite at R t = 39 min (Figures  12A, B  of two acetyl units) and 473 (loss of one α-hydroxyisovaleryl unit together with one α-isocyanoisovaleryl unit) (data not shown). The corresponding compound, which seems to accumulate in the same amounts in both strains, was purified through preparative TLC from the wild-type extract and analyzed ( 1 H NMR, COSY and HMQC) in a capillary NMR probe (500 MHz).
ACKNOWLEDGMENTS This work was partly supported by the  French Région Pays de la Loire  ( QUALISEM research program ). We thank  Stéphanie Pochon ,  Guillaume N'guyen ,  Claire Campion  for help in the seed transmission assay. We are grateful to  Sylvain Hanteville  and  Bruno Hamon ,  Pascale Satour ,  Ingrid Freuze  for technical assistance. We also thank  David Manley  for correcting the English version of the manuscript.
(b) Remote-sensing data To quantify greenness, or photosynthetic activity, we used the enhanced vegetation index (EVI) data from the MODIS sensor. EVI was extracted from the '16-Day L3 Global 500 m product (MOD13A1 c5)' from January 2000 to December 2009. EVI is directly related to photosynthetic activity  [16, 28]  and compared with other vegetation indices such as NDVI that quickly saturates for high values of chlorophyll activity. EVI provides improved sensitivity for high biomass areas such as tropical forests  [15, 29]. To separate forest from non-forest vegetation types (mainly savannas and very open forests), we used the Surface Reflectance satellite data '8-Day L3 Global 500 m product (MOD09A1 c5)' from the MODIS sensor to calculate the shortwave infrared water stress index (SIWSI) for the same period of time.
SIWSI is related to leaf water content  [30]  and allows the discrimination of forest (with low canopy water content amplitude between dry and rainy seasons) from non-forest vegetation types (with high canopy water content amplitude between dry and rainy seasons). Both EVI 16-day and SIWSI 8-day composites are based on the minimum blue band reflectance, which reduces atmospheric biases. We reconstructed a 10 year time-series mosaic and then performed a two-step classification approach of the newly built EVI and SIWSI datasets. During remote-sensing processing, even when composite images are used to reduce atmospheric and angular artefacts, pixels contaminated by clouds can persist and lead to strong misinterpretations  [22]. To eliminate remnant clouds in the 10 year EVI dataset, we computed for each 16 day period the average value of the 10 satellite images available for each 16 day period (figure  S2  in the electronic supplementary material).
We thus obtained a mean EVI seasonal profile across a synthetic year. This process was not sufficient for the SIWSI dataset. We replaced the algorithm dedicated to EVI dataset by another one dedicated to SIWSI dataset. For each 8 day period and spectral band among the 10 year dataset, we retained the minimum pixel value that was the least likely to be affected by atmospheric artefacts. Temporal smoothing was then performed based on a simple linear interpolation designed to remove and replace contaminated pixels. We thus obtained a minimum SIWSI seasonal profile across a synthetic year. To identify vegetation types with contrasted structure and greenness, we used a two-step classification approach of the EVI and SIWSI datasets both combining (i) an unsupervised ISODATA (iterative self-organizing data analysis technique) classification and (ii) visual interpretation of the results.
The ISODATA classification is a K-mean algorithm which allows selecting clusters by splitting and merging the initial pixels datasets. The main advantage of this technique is the stabilization of the number of classes when the gravitational centre of the classes could not be split any more. We used an unsupervised classification because of the lack of training data for the whole area. This algorithm has already been successfully used for similar purpose in Madagascar  [31]. Iterative work on two parameters (maximum number of iteration and maximum range of resulting classes) was done by empirically modifying their values to optimize the results of the classification, until it fully visually matched with Mayaux's  [11]  forested and non-forested patches delineation. This classification of the area of interest was performed by using ENVI v.
The two initial parameters selected after our iterative work, were 20 iterations and 150-160 classes to free the positioning of the gravitational centre of classes. The classification on EVI data which best visually matches with Mayaux's map  [11]  identified 56 final classes corresponding to the stabilization of spectral and temporal variability of the data during the classification process. From this classification, we kept 11 forested classes (from numbers 38 to 49; figure  1 ) out of 56 classes in total, by comparing with Mayaux's map  [11]. The 45 other classes were merged and used as a non-forest mask to extract the EVI-SIWSI dataset. The second classification, which was run on this non-forest mask, using EVI-SIWSI dataset stabilized at 14 final non-forested classes with initial parameters of 10 iterations and 25-30 classes.
In this study, we only consider the 37 898 plots for which leaf phenology was available for more than 70% of the stems and 90% of plot basal area. I v i n d o N go ko Li ko ua la R u k i D j a 18°E 17°E 16°E 15°E 14°E 13°E 4°N 4°N 3°N 3°N 2°N 2°N 1°N 1°N J F M A M J J A S O N D 10 J F M A M J J A S O N D 48 J F M A M J J A S O J F M A M J J A S O N D 8 J F M A M J J A S O N D 9 J F M A M J J A S O N D 4 J F M A M J J A S O N D 5 J F M A M J J A S O N D 6 J F M A M J J A S O N D 13 J F M A M J J A S O N D 14 J F M A M J J A S O N D 38 J F M A M J J A S O N D 39 J F M A M J J A S O N D 41 J F M A M J J A S O N D 40 J F M A M J J A S O N D 42 J F M A M J J A S O N D 46 J F M A M J J A S O N D 49 J F M A M J J A S O N D 12 J F M A M J J A S O N D 11 J F M A M J J A S O N D 43 J F M A M J J A S O N D 44 J F M A M J J A S O Because we had access to limited inventory data in the Cameroonian area, we used the detailed vegetation map of Letouzey as a field validation dataset.
(d) Climatic data We used the products derived from the FEWSNET rainfall estimation (RFE) decadal imagery (http://earlywarning. usgs. gov/ fews/africa/index. php [33] ) at 8 km spatial resolution. Monthly rainfall estimation was computed and then a monthly average from 2000 to 2008 was calculated. A comparison with lowerresolution rainfall datasets based on rain gauge data revealed that the RFE averages were satisfactorily reproducing the local rainfall regimes (not shown). We obtained ground measurements of light intensity from three meteorological stations (figure  1.
(e) Data analysis and validation To validate the map, we examined whether the classes evidenced by the classification procedure differed in terms of structure and greenness. For this, we assigned the remotely sensed vegetation classes to each available inventory plot. We then tested for differences in plot basal area and degree of deciduousness among classes with pairwise Wilcoxon tests and Bonferroni's adjustment for multiple comparisons. We restricted this analysis to the classes characterizing forests, represented by more than 100 0. 5 ha plots (figure  2 ). In Cameroon, where we did not have access to enough inventory data, we examined the consistency of the classes with the vegetation types evidenced by Letouzey  [4]. To obtain EVI and rainfall seasonal profiles, we first averaged EVI values and rainfall for all pixels per vegetation class.
Results With the two-step classification of EVI and SIWSI datasets, we identified and mapped 22 vegetation types (we took out the three water classes merged into one). Within these 22 vegetation types, six savannas, three open forests, 11 dense forests and two swamp forests were delineated and labelled thanks to the interpretation of the EVI signal shape (figure  1 , table  1 ) and visual interpretation  [11]. We identified strong variations in stand structure and deciduousness across these types (figure  2 ). Among them, we recognized (i) six classes of savanna (MODIS classes 4-9). We distinguished the  For all vegetation classes, mean EVI profiles were highly seasonal and correlated to rainfall seasonality (figure  1 , table  1 ). Savannas of the Sudano-Guinean domain showed the greatest seasonal variations with the highest standard deviation of EVI values.
4. 3 software ([32]; ENVI v. 4. 3 software, Research Systems Inc. ). The EVI dataset was used to identify forest classes, whereas the combined EVI-SIWSI datasets rstb. royalsocietypublishing. org Phil Trans R Soc B 368: 20120309 were used to improve the distinction among non-forested classes (including savannas, open forests and swamp forests).
): Eala in northwest Congo (0803 0 N and 18817 0 E; 320 (m). a. s. l. , from January 1957 to December 1959); Makokou in northeast Gabon (0833 0 N and 12851 0 E; 500 (m). a. s. l. , from January 1951 to December 1975) and Batouri in southeast Cameroon (4826 0 N and 14822 0 E; 630 (m). a. s. l. , from January 1983 to June 1985). Monthly averages were then computed. These data are not synchronous with the MODIS dataset.
Handgrip exercise to fatigue did not result in any change in popliteal diameter (1. 0260. 08 (SE) vs. 1. 0260. 07 cm, p = 0. 67). Given this data and prior published results, popliteal blood flow velocity was used as a reliable, withinindividual surrogate for blood flow to the muscle. However, basal blood flow to the muscle is generally proportional to the tissue volume; as a result, inter-individual differences in body size and composition may confound assessment of sympathetic neurovascular transduction, and so it is necessary to normalize blood flow to account for differences in tissue volume. Therefore, we normalized the blood flow velocity for each individual to the flow velocity during supine rest. This normalization does not impact the goodness-of-fit of neurovascular transduction estimates (described below), but allows us to accurately and reliably capture changes in blood flow while accounting for inter-subject differences in basal flow velocity.
Thus, the most accurate representation of sympathetic nerve activity is desirable for reliable characterization of regional sympathetic neurovascular transduction. After quantification of sympathetic activity, we derived beat-bybeat regional vascular resistance (the ratio of arterial pressure to flow) or conductance (the ratio of flow to arterial pressure). To account for both moment-by-moment and longer term regional sympathetic neurovascular control, we explored the relation of vascular resistance or vascular conductance to sympathetic nerve activity in two ways. First, we averaged resistance or conductance and sympathetic nerve activity over each 30 seconds of handgrip exercise for each individual, and explored each individual's relationships between averaged values. This is analogous to how this type of data has been explored previously  [12, 35, 38].
All models were estimated using beat-by-beat signals downsampled to 4 Hz and via custom-written software in Matlab (Mathworks, Natick, MA). We used Akaike's information criterion  [39]  to maximize the goodness-of-fit of the models. Note that the time-dependent relationship between resistance (or conductance) and sympathetic activity (equation 1) and Poiseuille's relation (equation 3) include comparable number of parameters. This ensures that any difference between their ability to explain the data cannot be attributed to over-parametrization. To determine the reproducibility of regional neurovascular transduction estimated via the Poiseuille relation, we used data from five young volunteers who performed two separate handgrip exercises. (Three performed handgrip exercise twice, separated by ,40 minutes within the same experimental session, and two participated in the study twice in separate sessions approximately two months apart.
Pulmonary injury and virus dissemination To assess possible other contributions of plasminogen to the pathogenesis of IAV infections, inflammation of the lungs and viral dissemination were examined after infection of mice with IAV A/ PR/8/34 or A/Netherlands/602/09. At day 3 post-infection, extensive alveolar damage and marked cellular infiltrates were observed in lungs of WT mice in contrast to those of PLG-KO mice (HE) after A/PR/8/34 virus infection (Figure  3A , left panel). This difference was also observed upon infection with A/ Netherlands/602/09 virus, at day 5 (Figure  3A , right panel) but not at day 3 post-inoculation (data not shown). For all conditions, in WT and PLG-KO mice, similar numbers of IAV-infected cells were detected by immunohistochemistry (IHC). Also, no lesions were observed in Mock-infected mice (data not shown).
The most common species is the Aleppo pine (Pinus halepensis Mill. ) occupying large areas of the western Mediterranean as well as occurring in the eastern Mediterranean  [15, 16]. The ecology and biogeography of Aleppo pine is well-researched  [17, 18]. The dendroclimatology of Aleppo pine has been investigated at individual locations within the Mediterranean basin  [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]  confirming that Aleppo pine is sensitive to climate variation. However, a comprehensive dendroclimatic analysis across the distribution range of Aleppo pine is lacking. In this paper, we investigate the variability of the climate-growth relationships of Aleppo pine using a dendroclimatic network composed of high-resolution climatic and dendrochronology databases comprising the complete climatic gradient across the Mediterranean Basin.
Locations and climatic data The distribution map for Aleppo pine (Fig. 1 ) based on an earlier map  [30]  was obtained from the European Forest Genetic Resources Programme (EUFORGEN http://www. euforgen. org/ distribution_maps. html). Climatic time series data for 1,068 spatial grid points within the distribution range for the period 1901-2000 were obtained from the Climatic Research Unit (CRU) of the University of East Anglia. For the western part (Spain, Algeria, France, Italy, Slovenia, Greece, and Turkey), monthly, seasonal, and annual mean temperatures and total precipitation were provided by the CRU TS 1. 2 dataset (10-minute resolution)  [31]. For the portion of the distribution located east of the coverage of CRU TS 1. 2, we used the CRU TS 2. 1 dataset (0. 5u grid resolution)  [32].
PCA is a data reduction technique that transforms a large group of variables into a new smaller set of variables called principal components (PC), which are linear combinations of the original variables. These PC were calculated on the correlation matrix of mean monthly temperatures and mean total monthly precipitation for the period 1901-2000. The components were rotated (Varimax) to redistribute the explained variance and to obtain more stable and robust spatial patterns  [34]. Selected PC, guided by Kaiser's Rule (eigenvalues. 1; [35] ), described climatic gradients across the Mediterranean basin were used in subsequent analysis.
Tree-ring chronology construction The tree-ring width series were derived from a total of 1634 increment cores from 818 trees (Table  1 ). Tree selection, core collection, processing, and ring-width measurement were conducted using standard techniques  [36, 37]. Ring-width index chronologies for each site in the network were constructed using ARSTAN software version 6. 05P  [38]  to retain the high-frequency variation that would be most sensitive to the climate variables to be tested. First, a negative exponential or linear regression function was fitted to each ring-width series. The residuals from this first detrending were then fitted with a cubic smoothing spline function (50% frequency cut-off of 30 years). The residuals from the second detrending were then fitted with an autoregressive model to reduce the autocorrelation in the model residuals which were then averaged to construct the ring-width residual index chronology (chronology RES in ARSTAN) for each site using the biweight robust mean function.
Implications Historical records of biological responses to climate in situ are perhaps the most dependable data we have to reconstruct past climate conditions, to predict future climate change impacts, and to develop realistic mitigation strategies. The tree-ring record is the most important and widely used source of long-term proxy data as they are available over a wide range of temporal and spatial scales. However, the use of tree-rings to reconstruct past climate patterns and to forecast of future forest growth within the context of global warming is based on the uniformitarian principle (UP) that assumes that the climate-limiting factors that controlled treering characteristics in the past continue the present and will extend into the future  [82]. Under UP, the modeled dendroclimatic relationship is viewed as stationary and consistent through time and the range of climatic variability.
Our results are related to a limited geographical area and a specific dataset and may have limited application to other species or regions. However, such patterns may occur elsewhere and if so, it may have serious implications by affecting reliability of tree-ring based climate reconstructions. In addition, climate model validation  [91] , species distribution models  [7, 9]  and forecasts of future forest growth within the context of global warming  [1, 22, 92, 93, 94]  whether or not based on dendrochronology also assumes a uniform response of species to climate variability and change without the competitive advantage conferred by genetic and/or phenotypic plasticity  [95]. These assumptions could lead to exaggerate or underestimate species at risk under future climate change. Accurate prediction of growth of Aleppo pine and other species in response to future climate variability requires an understanding of the plasticity of the response of growth to climate variability and change.
This work was partially supported by  Spanish Ministry of Education and Science co -funded by  FEDER program  ( CGL2012-31668 ), the  European Union  and the  National Ministry of Education and Religion of Greece (EPEAEK-Environment -Archimedes) , the  Slovenian Research Agency  (program  P4-0015 ), and the  USDA Forest Service. The cooperation among international partners was supported by the  COST Action FP1106, STREeSS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The key objective of the Superconducting High Field Magnet work package of the European Project EuCARD, and specifically of the High Field Model task, is to design and fabricate the Nb3Sn dipole magnet FRESCA2. With an aperture of 100 mm and a target bore field of 13 T, the magnet is aimed at upgrading the FRESCA cable test facility at CERN. The design features four 1. 5 m long double-layer coils wound with a 21 mm wide cable. The windings are contained in a support structure based on a 65 mm thick aluminum shell pre-tensioned with bladders. In order to qualify the assembly and loading procedure and to validate the finite element stress computations, the structure will be assembled around aluminum blocks, which replace the superconducting coils, and instrumented with strain gauges. In this paper, we report on the status of the assembly and we update on the progress on design and fabrication of tooling and coils.
I. INTRODUCTION HE European Coordination for Accelerator R&D (EuCARD) Project was initiated in 2009 with the goal of carrying out research on new concepts and technologies for future upgrades of the European accelerators  [1]. Among its activities, the Work Package 7 is dedicated to superconducting high field magnets for higher luminosities and energies. This work package has as key objective the fabrication and test of FRESCA2, a 100 mm aperture dipole generating a bore field of 13 T  [2] ,  [3]. The magnet, which will rely on Nb 3 Sn superconductors, is aimed at upgrading the CERN cable test facility FRESCA  [4] , which uses Nb-Ti superconductors, bringing the bore field from 10 T to 13 T. In addition, it will provide the background field for an HTS insert  [5]. This paper summarizes the status of the FRESCA2 magnet development, whose design was described in detail in  [6].
II. CONDUCTOR AND CABLE PARAMETERS The coils of FRESCA2 will be wound with a Rutherford cable composed of 40 strands with a diameter of 1 mm. Both a magnet unit length of Bruker Powder-in-tube (PIT) conductor, featuring 192 filaments, and a magnet unit length of OST Restacked-rod-process (RRP) conductor, with a 132/169 stack, have been ordered and are being delivered. Both conductors will be used to fabricate FRESCA2 coils. Parameters and properties provided in this paper refer to the PIT strand, whose qualification and characterization have been completed (Table  I ). Critical current measurements performed on virgin (round) strands and strands extracted from cables are shown in Fig. 1 , where 4. 2 K experimental data have been fitted by parameterization curves, extrapolated to 1. 9 K, and compared to the magnet load-line.
METHODS SEARCH STRATEGY We conducted in April 2013 a systematic review on the neuroimaging studies exploring GxE interactions associated to SZ or BD since 1990. The publications were obtained from the PubMed electronic database. The literature search was performed using the Mesh heading: ("schizophrenia" OR "bipolar disorder") AND ("neuroimaging" OR "imagery" OR "MRI" OR "fMRI") AND ("interaction"OR"GxE"OR"gene environment"OR"environment interaction"). The search found 202 results. Three supplementary original papers were identified through reviews.
STUDY SELECTION We first removed duplicates. Then two authors (Pierre Alexis Geoffroy and Josselin Houenou) reviewed the title and/or abstracts of publications identified through databases to identify eligible studies. The two authors independently and then jointly selected studies for detailed extraction based on the full text. Studies were eligible if (1) they used neuroimaging exploration whatever was the imaging technique, (2) they were original studies without overlapping samples, and (3) reported a SZ or BD diagnosis confirmation. Studies were excluded if (1) did not assessed GxE interactions in SZ or BD, (2) were review papers without new data, (3) were not written in English and/or published in peer-review journals. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, referred as the PRISMA Statement  (34).
RESULTS Eight studies using structural MRI (sMRI) have explored the imaging GxE interactions in the physiopathology of SZ and schizoaffective disorder. No other imaging methods were found. Two main GxE interactions on brain volumes have arisen: 1. an interaction between genetic liability to SZ or schizoaffective disorder and obstetric complications (OCs). 2. an interaction between genetic liability to SZ or schizoaffective disorder and cannabis use. The literature search was performed from the PubMed electronic database and using the Mesh heading: ("schizophrenia" OR "Bipolar disorder") AND ("neuroimaging" OR "imagery" OR "MRI" OR "fMRI") AND ("interaction" OR "GxE" OR "Gene environment" OR "Environment interaction").