dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioinformatic analyses Illumina sequence reads were mapped to the A. thaliana Col-0 reference genome (TAIR10) using Bowtie v0. 12. 8  (Langmead et al. , 2009). End-to-end best hits were reported allowing a maximum of one mismatch per read and up to 50 genomic mappings (for reads with multiple best hits). Reads corresponding to 45S rRNA gene arrays, chloroplast transcripts or mitochondrial transcripts were excluded at this stage. For each RNA size class and genomic position, the number of reads mapped with coinciding 5' ends, oriented in the same direction was estimated. Read counts were then normalized by dividing by the total count of mapped reads for the sample and multiplying by the maximum total read count across all samples. For subsequent genomic analyses tRNAs, small nuclear RNAs (snRNAs) and small nucleolar RNAs (snoRNAs) were excluded from datasets based on their TAIR10 annotation coordinates.
05 U/ml of RNase ONE in 1 Â reaction buffer. The reaction was incubated at 37 ˚C for 30 min and terminated by 0. 2% SDS. seq) datasets. Unique reads are those that map to only one genomic location. Total reads include reads that can map to two or more genomic loci. DOI: 10. 7554/eLife. 09591. 019.
Acknowledgements TB and CP designed the study. VM performed RDR2 overexpression and terminal transferase assays. TB, JW, and VM performed the exogenous DCL3 dicing assay. MM conducted in vitro transcription assays. TB performed all other experiments. RP conducted the bioinformatics analyses. TB and RP generated the graphics and figures and  CSP  wrote the manuscript. HT and DR provided guidance and advice for informatics analyses. The authors thank  Jered Wendte  for providing homozygous seed of the ago4-7 mutant, and  Ross Cocklin  for performing the genetic cross to generate the rdr2 dcl2/3/4 quadruple mutant.
A strategy for the prophylaxis and therapy of tetanus has been developed by the French National Health Authorities that includes, for emergency services only, immunochromatographic testing. 2] [13]  In France, the lack of epidemiological data on the wounds treated in general practice limits the application of those tests outside the hospital setting. The current overcrowding of emergency services has led to public health policies that focus on general practitioners (GPs), as they are the primary care providers, and the strategies used in emergency services, including immunochromatographic testing, should also be applied to general practices. To optimize wound management in general practice, an epidemiological study was conducted in France, including vaccination status and taking into account vaccination schedule changes after the study period.
Any such variations in foliar cation concentrations should also be related to variations in absolute carbon content  (Poorter and Villar 1997; Patiño et al. 2012)  potentially linking this trade-off to variations in M a  (Fyllas et al. 2012 ). Here we report on variations in M a as well as foliar nitrogen, phosphorus, carbon, potassium, magnesium and calcium concentrations along a precipitation transect in West Africa for which data on soil properties and leaf photosynthetic characteristics have already been presented  (Domingues et al. 2010; Saiz et al. 2012). This data, collected within the ecotonal area of this transect where both savanna and forest exist in discrete patchesthe so called 'zone of transition' (ZOT)is also compared with a similar forest and savanna foliar trait dataset collected some 1000 km away in an independently replicated ZOT located in Cameroon.
Materials and methods This dataset derives from ten 1. 00 (100 Â 100 m) and four 0. 25 ha (50 Â 50 m) permanent sampling plots which were established as part of the Tropical Biomes in Transition (TROBIT) project in August to September 2006 (Ghana) and October-December 2007 (Cameroon). For further details on plot locations, climate and soil characteristics see Table  S1. 1 and Fig. S1. 1, available as Supplementary Material to this paper. All field campaigns were conducted towards the end of the wet season. Plot locations were selected according to the following criteria: characteristic of the potential natural vegetation of the region; as low direct anthropogenic disturbance (in particular wood felling and arable agriculture) as possible; low altitudinal variation and no obvious hydrological differences.
Vegetation formation type affiliation ( ) Based on the ordination study by  Torello-Raventos et al. (2013)  sites were first grouped into one of either the forest or savanna vegetation domains on the basis of both floristics and structure, as detailed further in work by  Veenendaal et al. (2014). Each species sampled was also classed as typically affiliated with either forest or savanna vegetation formation types using plot locality information for each species from  Torello-Raventos et al. (2013) , local expert knowledge and habitat information from local floras and the African Plant Database (http://www. ville-ge. ch/musinfo/bd/cjb/africa/recherche. php, accessed 23 July 2012).
Statistical analyses Exploratory analysis and precipitation effects The effects of vegetation formation type ( ) and precipitation on the seven measured foliar traits were first examined using a generalised additive modelling approach using the gam mgcv. function  (Wood 2006 (Wood , 2011) )  as available within the R statistical package (R-Development-Core-Team 2012). The equation individually fitted for each trait (Q) was Q ¼ m þ a þ sðP A Þ þ U S þ e; ð1Þ where m represents the dataset mean for trees located within the forest ( ) vegetation formation type, is an indicator variable taking a value of one for all trees located within savanna formations and zero otherwise, s is a non-parametric smoother, P A is the mean annual precipitation of the plot in which the tree was growing, U S is a random effect assuming a different value for each species and e is the residual error: In terms of fixed effects, the parameterisation of Eqn 1, allows an assessment of trait differences between forest and savanna stands after controlling for any precipitation effects (the trends in which could also be both visually inspected and statistically assessed).
The variance in the dataset not accounted for by or P A is partitioned into that attributable to a trees' taxonomic identity (U S ) and then to other causes (e, in this case, soils, other climatic effects, intraspecific variability and measurement error). Differences in trait characteristics between forest and savanna species and between the four leaf pre-defined habit types ( ) for sites within the forest/savanna transition zone were then further assessed applying non-parametric multivariate ANOVA  (Anderson 2001 ) and dispersion tests using similarity matrices  (Anderson 2006 ) estimated using Gower's metric  (Gower 1971)  to help define our final three vegetation/habit groups ( ) using the adonis, betadisp and vegdist functions available in the R vegan package  (Oksanen et al. 2012 ). These tests were conducted without reference to taxonomic identity.
Comparison of Eqn 2 and Eqn 3 shows that the first analysis considers the estimate of the mean Q for each to be independent of the site conditions in which the tree was found as well as species identity. On the other hand, Eqn 3 allows for systematic variations in Q to occur across plots, also probing the extent to which trait differences within the various vary systematically with genotype. For each trait this allowed each species to be assigned a characteristic value ( + U S ), with these 'species' estimates then representing the expected Q at the dataset average site for that trait (i. e. with U P = 0) with the U P for each site giving a measure of the associated plot modulating effect on the trait values observed. We thus term the estimates of Q so derived in this case as being ab intra ('from within', i.
Bivariate relationships Bivariate relationships were first assessed for each and for the dataset as a whole using Pearson's correlation coefficient (r 2 ), and standardised major axis (SMA) line fits, which were subsequently investigated where r 2 was significantly different from zero. SMA regression lines represent the first axis of a principal component analysis (of a correlation matrix) and are often used in plant allometry studies. When the slopes of these lines were not statistically different we further tested for differences in elevation and shift across the SMA lines between high and low fertility plots, with all analyses being performed using the R smatr library  (Warton et al. 2012 ). These analyses were undertaken for both the 'raw' dataset and for the species effects derived as outlined above (centred on the dataset mean) with the latter analyses also incorporating the inevitable error associated with the statistical retrieval of the ab intra 'species effects'.
15 of the total dataset variance that an examination of the random terms shows that ~0. 6 of the remaining 0. 85 was attributable to the trees taxonomic affiliation with only 0. 4 attributable to unmeasured factors such as edaphic effects and/or intra-species variability (see Table  S2. 1). In Fig. 1. 1b, data is shown for trees growing in forest plots ( = ) but with the lines shown simply being a reproduction of the savanna fit of Eqn 1 (as in Fig. 1b ). Comparing the points with the line thus illustrates a clear tendency for trees to have lower M a than their equivalents; as is also shown by the significant term of 16 AE 5 g m -2 obtained from the Eqn 1 M a model fit (Table  S2. 1). Identified on Fig. 1a , b are several species that were sampled in both forest and savanna. Although the effect is by no means ubiquitous, a tendency for the same species to have a slightly lower M a when growing in as compared with formations is suggested.
Savanna foliar traits and precipitation/rainfall seasonality It has frequently been reported that M a tends to increase with declining precipitation  (Schulze et al. 1998; Fonseca et al. 2000; Wright et al. 2002; Midgley et al. 2004; Cernusak et al. 2011 ) and this has now been suggested as representing a general trend  (Poorter et al. 2009 ). Nevertheless, no such pattern was observed in our savanna tree dataset which showed, if anything, a slight tendency for a decline in M a as P A declined (Fig. 1a ). Although this could be attributable to all our savanna species being of the deciduous habit  (Wright et al. 2005) , some of the above cited studies which report increases in M a as P A declines have also involved deciduous species  (Schulze et al. 1998; Midgley et al. 2004 ). This suggests that leaf habit is not the sole explanation for the study differences.
This is in contrast with the more commonly studied low P A , low u situation (rainfall becoming 'less predictable' in more arid regions) for which the structural resilience associated with a high M a would be expected to contribute to leaf survival during extended periods between rainfall events, themselves of an intrinsically more stochastic nature. Although we detected a slight tendency for M a to increase with declining P A , variability within a site was as large as or larger than variability across sites: the most striking examples of which were the two driest sites (HOM-01 and HOM-02). There, despite only three woody species being present (Acacia senegal, Acacia tortilis subsp. raddiana and Combretum glutinosum)all of which are considered to be of exceptional drought tolerance  (Arbonnier 2004 )a variation in M a more or less spanning the range of the entire dataset was observed.
1) for which 0. 57 of the total dataset variance not accounted for by the fixed effect terms (viz. biome or precipitation) was attributed to species differences (with the rest reflecting within-species variation and experimental error). Some systematic variations at the genus level are also apparent. For example, strongly negative random effect terms (U S ), such as those found for the two Acacia species mentioned above, were also found for all other sampled members of this genus. In contrast, for all Combretum species, positive random effect terms were found, although some other members of this genus were not characterised by an appreciably higher M a than average (e. g. C. adenogonium, also sampled at MLE-01 for which U S is only slightly positive). A second savanna genus characterised by consistently higher than average M a was Terminalia (pink coloured symbols in Figs  1 2 3 4 5 6 7 ) for which all five species sampled had a positive U S.
For [N] m an even high proportion of the random model component was explained by species identity with t 0 2 / (s 0 2 + t 0 2 ) = 0. 70 (see Table  S2. 2), and with the positive (fixed effect) slope for P A < 1. 0 m a -1 (Fig. 2 ) also indicating that the higher [N] m under low precipitation regimes have an additional climate induced component. Especially as there was proportionally a less marked decline in M a (Fig. 1a ), this increase in  [N]  m was also associated with an increase in [N] a and the high area based photosynthetic capacities as water availability declines  (Domingues et al. 2010). This is consistent with theory  (Buckley et al. 2002)  and has also been observed for transect studies in Australia  (Cernusak et al. 2011)  and China  (Wei et al. 2011 ) with a general increase in  [N]  a with decreasing precipitation also having been demonstrated with the global dataset presented by  Wright et al.
(2005). However, our dataset also showed an increase in foliar [N] for savanna species for P A above 1. 0 m a -1 suggesting that factors other than climate and species identity may have been influencing [N] m and [N] a at the moister end of the savanna spectrum. One potential candidate is the declining fire frequency associated with reductions in herbaceous cover as P A increases  (Saiz et al. 2012). Nevertheless, although rates of nutrient loss through biomass combustion may be considerable, net fire-induced nutrient losses are usually minimal at a regional scale due to the subsequent return of emitted nutrients through wet-and dry-atmospheric deposition. Thus, other factors may be involved with a general increase in savanna soil N cycling rates with higher P A having been suggested by  Aranibar et al.
Acknowledgements We gratefully acknowledge the many generous individuals who took part in the making of this work either in the field or in the UK. Fieldwork assistance was provided by  A. den Holander  and  W. Zakaria. The  Forest Research Institute of Ghana (FORIG)  provided vehicles, drivers, laboratory space and local support in  West Africa  through the diligent work of  K. Mensah. We also thank  WCS-Cameroon  and  J. Sonké  for logistical assistance in  Cameroon. Shiela Lloyd  assisted with manuscript and figure preparation. This work was funded as part of the  UK Natural Environment Research Council TROBIT Consortium project  ( Award NE/D005590/1 ) with additional support for  Simon Lewis  through a  Royal Society University Research Fellowship.
Satellite altimetric time series allow high-precision monitoring of ice-sheet mass balance. Understanding elevation changes in these regions is important because outlet glaciers along ice-sheet margins are critical in controlling flow of inland ice. Here we discuss a new airborne altimetry dataset collected as part of the ICECAP (International Collaborative Exploration of the Cryosphere by Airborne Profiling) project over East Antarctica. Using the ALAMO (Airborne Laser Altimeter with Mapping Optics) system of a scanning photon-counting lidar combined with a laser altimeter, we extend the 2003-09 surface elevation record of NASA's ICESat satellite, by determining cross-track slope and thus independently correcting for ICESat's cross-track pointing errors. In areas of high slope, cross-track errors result in measured elevation change that combines surface slope and the actual Áz=Át signal.
As part of this program, the International Collaborative Exploration of the Cryosphere by Airborne Profiling (ICECAP) consortium collected a 3 year surface-elevation dataset acquired over East Antarctica using an advanced airborne scanning photon-counting lidar (PCL), coupled to a legacy nadir-pointing laser profiler (here called the LAS). ICECAP systematically targeted high-leverage ICESat tracks for re-flight to extend the surface-elevation record, in particular for tracks where clouds had interrupted the ICESat record. We use the well-characterized LAS system to provide profile measurements of surface elevation, in tandem with the PCL (which has higher absolute range uncertainties) which provides cross-track slope information for correction of ICESat observations. Together the LAS and PCL form the Airborne Laser Altimeter with Mapping Optics (ALAMO).
The Airborne Laser Altimeter with Mapping Optics The processing flow for the ALAMO data products is outlined in Figure  2. The legacy LAS gives our best estimate of surface elevation, while the filtered PCL product allows us to efficiently estimate cross-track slopes using methods Fig. 2. Flow chart illustrating the processing flow for combining the GPS, LAS and PCL datasets. NSIDC datasets are underlined. The LAS derives its timing information from a GPS-linked time code generator (TCG) linked by a common counter on the Environment for Linked Serial Acquisition (ELSA). Gray arrows denote geolocation data paths. Uncertainties are given in Table  1  and described in the text. required for ICESat-2. These ALAMO datasets (ILSNP1B and ILSNP4), acquired during Operation IceBridge, along with the input aircraft positioning data (IPUTG1B) and LAS data (ILUPT1B and ILUTP2), are freely available through the US National Snow and Ice Data Center (NSIDC) and NASA's Reverb data server  (Blankenship and others, 2013a,b).
The LAS and the PCL share the same inertial measurement unit (IMU) system for position and orientation estimation. While the LAS is only able to obtain along-track measurements, our PCL scans over a swath (typically $200 m wide on the ground), from which it is possible to determine cross-track slope. Time-dependent scaling uncertainties in ranges produced by our PCL system (detailed below) require us to combine the PCL swath information with the stable range information provided by the LAS, for comparisons with the ICESat dataset. Both systems are mounted over a 45 cm diameter observation port in the underside of the aircraft, with the LAS mounted under the cabin floor and our PCL mounted in a rack over a hole in the cabin floor. During ICP4 and ICP5, the IMU was directly attached to our PCL's body (Fig.
Due to occasional dropped data packets from the EGI, we were unable to perform loosely coupled merging of the EGI acceleration data and GNSS position data; however, accuracies consistent with altimetry were still obtained for most flights. The PPP positions were estimated for the CG antenna. During the ICP4 and ICP5 seasons, we used a NovAtel OM-4 receiver recording at 1 Hz to obtain dual carrier phase data. An iMar FSAS IMU records raw acceleration and roll rate at 200 Hz. These data were processed after the field season using NovAtel's Inertial Explorer package, with the ITRF 2008 reference system. Combined orientation/position data were output at 50 Hz estimated at the center of the FSAS unit, which was mounted 35 cm above the front of our PCL telescope. These combined position solutions were used as input for 'loosely coupled' position/orientation solutions, where the PPP data were allowed to control drifts on the IMU.
Laser profiler The LAS instrument has been used and validated in previous field campaigns  (Young and others, 2008a,b) , and LAS data from ICECAP are available from the NSIDC  (Blankenship and others, 2012). The LAS is a Riegl LD90-3800-HiP-LR distance meter, with a 3. 5 mW diode laser operating at 905 nm. The LAS acquires measurements at 2000 Hz, with a range resolution of 2 mm and ground spot width of $1 m. For each block of 575 pulses, the greatest range is recorded, along with the standard deviation and maximum amplitude of the detected pulse echoes. Typical point separation on the ground was 21-23 m, as expected for a target ground speed of 90 m s À1. The maximum range of the system is 1500 m over ice. To eliminate noise due to ground fog, clouds and regions of crevassing, we filter the range data to eliminate measurements with sample-to-sample jumps >2 m, and continuous runs of <20 samples.
We search the rollbias/pitch-bias space for the minimum root-mean-square (rms) crossover difference. Without accurate a priori data for the surface, range offsets are hard to determine, and can bleed into pitch-bias determination. On Flight 23 of the ICP4 season, we conducted a gridded survey of the McMurdo Ice Shelf (Fig. 4 ). The line spacing was 2 kmÂ 10 km over an area of 20 km Â 100 km, yielding 100 line crossings. We edited out crossovers over crevassed ice; the results of the crossover minimization are detailed in Table  2. Similar calibration surveys were performed in ICP3 and ICP5. Due to the bathymetric complexity of the McMurdo Sound, we did not correct for tides; based on GPS measurements on the ice shelf, we expect 8 cm rms difference between crossovers due to tides.
Laser profiler accuracy For ICP4, we validate the accuracy of the LAS component of ALAMO against the 2003-09 ICESat record using data acquired on Flight 19 of the ICP4 season, in mid-December 2011. The target for this flight was a sparse grid south of Dome C, which is a region of low accumulation, low absolute accumulation variability and low slopes (Fig. 1, box c ). We obtain crossover differences between GLAH12. R33 data and Flight 19 LAS data, for points within 300 m of each other, and then bin the crossovers by operational period (Fig. 5 ). Using the LAS data as a benchmark, we find no evidence for a linear temporal trend in the Dome C ICESat elevation data. Over the whole ICESat interval, we find an ICESat crossover difference standard deviation of 9. 5 cm on a 300 m baseline, a LAS-ICESat standard deviation of 13.
Laser profiler elevation precision We consider precision to be determined by the instrument's crossover errors with its own data (equivalent to their relative accuracy), and accuracy to be determined by comparisons with other datasets. Note that in our usage, elevation precision includes an error contribution from any betweentransect geolocation errors, while the running average approach is mostly sensitive to range measurement errors. The ICP4 dataset contains 214 locations where two transects crossed within 3 days and adequate LAS data were recovered. Of these crossovers 49 were over grounded, uncrevassed ice. To determine the elevation precision of the LAS, we compare elevations of the closest georeferenced returns from different transects that are also within a 40 m radius on the surface.
An important constraint on the use of this dataset relates to the stability of the coarse clock. The coarse clock rate can drift, due to temperature and hardware issues, by up to 0. 01%, limiting absolute range accuracy to 80 cm without registration to the LAS. The 0. 7 ns pulse width limits the precision of start times, yielding a minimum range precision per photon of $10. 5 cm. Systematic biases between individual counting cells are symmetrically distributed with a rms deviation of 15 cm. We calibrate these biases within the resolution afforded by the pulse width. Higher precision requires stacking numerous individual shots and channels. Time stamps are generated with every shot, every prism rotation and on reception of a 1 Hz GPS generated pulse for timing calibration. The timing data from each FPGA is recorded directly to independent hard drives, typically at 4-5 MB s À1.