dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , a verb maps to a relation and a noun to a concept). In constrast, we propose an approach which can generate complex sentences from KB data; where the grammar is acquired from the data; and where no assumption is made about the mapping between semantics and NL expressions. Recent work has focused on data-driven generation from frames, lambda terms and data base entries. (DeVault et al. , 2008)  describes an approach for generating from the frames produced by a dialog system. They induce a probabilistic Tree Adjoining Grammar from a training set aligning frames and sentences using the grammar induction technique of  (Chiang, 2000)  and use a beam search that uses weighted features learned from the training data to rank alternative expansions at each step. (Lu and Ng, 2011)  focuses on generating natural language sentences from logical form (i.
e. , lambda terms) using a synchronous context-free grammar. They introduce a novel synchronous context free grammar formalism for generating from lambda terms; induce such a synchronous grammar using a generative model; and extract the best output sentence from the generated forest using a log linear model. (Wong and Mooney, 2007; Lu et al. , 2009)  focuses on generating from variable-free treestructured representations such as the CLANG formal language used in the ROBOCUP competition and the database entries collected by  (Liang et al. , 2009)  for weather forecast generation and for the air travel domain (ATIS dataset) by  (Dahl et al. , 1994). (Wong and Mooney, 2007)  uses synchronous grammars to transform a variable free tree structured meaning representation into sentences. (Lu et al.
, 2009)   Finally, more recent papers propose approaches which perform both surface realisation and content selection. (Angeli et al. , 2010)  proposes a log linear model which decomposes into a sequence of discriminative local decisions. The first classifier determines which records to mention; the second, which fields of these records to select; and the third, which words to use to verbalise the selected fields. (Kim and Mooney, 2010)  uses a generative model for content selection and verbalises the selected input using WASP - 1  , an existing generator. Finally,  (Konstas and Lapata, 2012b; Konstas and Lapata, 2012a ) develop a joint optimisation approach for content selection and surface realisation using a generic, domain independent probabilistic grammar which captures the structure of the database and the mapping from fields to strings.
Inducing a based FB-LTAG from the aligned data To extract a Feature-Based Lexicalised Tree Adjoining Grammar (FB-LTAG) from the KBGen data, we parse the sentences of the training corpus; project the entity and event variables to the syntactic projection of the strings they are aligned with; and extract the elementary trees of the resulting FB-LTAG from the parse tree using semantic information. Figure  4  shows the trees extracted from the scenario given in Figure  1. To associate each training example sentence with a syntactic parse, we use the Stanford parser. After alignment, the entity and event variables occurring in the input semantics are associated with substrings of the yield of the syntactic parse tree. We project these variables up the syntactic tree to reflect headedness. A variable aligned with a noun is projected to the NP level or to the immediately dominating PP if it occurs in the subtree dominated by the leftmost daughter of that PP.
Generation To generate with the grammar extracted from the KBGen data, we use the GenI surface realiser  (Gardent et al. , 2007). Briefly, given an input semantics and a FB-LTAG with a unification based semantics, GenI selects all grammar entries whose semantics subsumes the input semantics; combines these entries using the FB-LTAG combination operations (i. e. , adjunction and substitution); and outputs the yield of all derived trees which are syntactically complete and whose semantics is the input semantics. To rank the generator output, we train a language model on the GeniA corpus 4  , a corpus of 2000 MEDLINE asbtracts about biology containing more than 400000 words  (Kim et al. , 2003)  and use this model to rank the generated sentences by decreasing probability. Thus for instance, given the input semantics shown in Figure  1  and the grammar depicted in Figure  4 , the surface realiser will select all of these trees; combine them using FB-LTAG substitution operation; and output as generated sentence the yield of the resulting derived tree namely the sentence The function of a gated channel is to release particles from the endoplasmic reticulum.
Conclusion In Tree Adjoining Grammar, the extended domain of locality principle ensures that TAG trees group together in a single structure a syntactic predicate and its arguments. Moreover, the semantic principle requires that each elementary tree captures a single semantic unit. Together these two principles ensure that TAG elementary trees capture basic semantic units and their dependencies. In this paper, we presented a grammar extraction approach which ensures that extracted grammars comply with these two basic TAG principles. Using the KBGen benchmark, we then showed that the resulting induced FB-LTAG compares favorably with competing symbolic and statistical approaches when used to generate from knowledge base data. In the current version of the generator, the output is ranked using a simple language model trained on the GENIA corpus.
When we considered cassette exons with exon inclusion level significantly changed more than 0. 1 (ΔΨ >0. 1, 1 being the total abundance of all isoforms), MISO identified 103 or 552 cassette exons in any of the EJC KDs in both replicates (intersection of MISO replicates) or any of the replicates (union of MISO replicates), respectively (Figure  1A , Additional files 5A,B and 6). DiffSplice identified 715 cassette exons that have exon inclusion level changed more than 0. 1 (ΔΨ >0. 1) in EJC KD (Additional files 5C and 7). Among those cassette exons, 61 (9%) or 237 (30%) overlap with the ones identified by MISO, if we select genes changed in both or either MISO replicates, respectively (Figure  1A ). The discrepancy between the two methods is partially due to the ability of DiffSplice to identify novel splicing events that are not within the MISO database.
EJC regulates intron retention with shorter introns and weaker splice sites In Drosophila, it has been recently shown that the core EJC components and the peripheral factor Acinus and RNPS1 can enhance the splicing of neighbouring introns  [29, 30]. If the EJC regulate splicing in a similar manner in mammalian cells, EJC downregulation would cause an increase in intron retention (IR) events. We therefore analysed the IR events predicted by MISO and DiffSplice. Overall, MISO and DiffSplice identified 134 events with increased intron retention upon EJC KD (ΔΨ <0. 1, -), and 135 events with decreased intron level upon EJC KD (ΔΨ >0. 1, +) (Additional file 21). We also identified 393 events in the MISO database that did not change in response to EJC KD. We performed the same analysis as for cassette exons.
As a control, we used two sets of exons: alternative exons from the MISO database that were not affected by eIF4A3 KD (Ctr, 991 exons) and constitutive exons from the genome (Ctr CON, 811 exons). Those exons were selected among expressed genes (read coverage more than 10). For the Ctr category, we filtered events with a splicing change less than 0. 03 and with estimates of Ψ values included in (0. 1;0. 9) as predicted by MISO. For the Ctr CON category, we selected three successive constitutive exons based on Ensembl65 annotations, excluding the first and last exon of transcripts and allowing at most one triplet per gene. Once exons with splicing changes have been identified, we look at possible correlations with different features. Computational analyses were performed using custom scripts in Python and R.
Analysis of intron retention with splicing changes We took the union of events with significant splicing change identified with MISO or DiffSplice with the threshold of |ΔΨ| >0. 1. We retrieved the genomic coordinates of the exons flanking the intron retention events and the exons further upstream or downstream to define the flanking intron. We classified splicing events into two categories based on the direction of the change: events showing an increase in exon retention in EJC KD (ΔΨ <0. 1, -, 134 exons) and events showing a decrease in exon retention in EJC KD (ΔΨ >0. 1, +, 135 exons). As a control, we used intron retention events from the MISO database that were not affected by EJC KD with criteria as above (Ctr, 393 exons). Again, computational analyses were performed using custom scripts in Python and R.
Acknowledgements We thank  Corinne Blugeon ,  Fanny Couplier  and  Stéphane Le Crom  at the sequencing platform of IBENS for mRNA-seq. We thank  Olivier Bensaude ,  Xavier Darzacq ,  Gideon Dreyfuss ,  Elisa Izaurralde ,  Melissa Moore ,  James Stévenin  and  Catherine Tomasetto , for providing antibodies and plasmids. We also thank  Yin Hu  for helps and advices on DiffSplice software. We acknowledge  Javier Cáceres ,  Nicolas Charlet-Berguerand ,  Torben Heick Jensen ,  Hugues Roest Crollius ,  Bo Gu ,  Olivier Bensaude  and other members of the lab for technical assistance and helpful advices and discussions. This work was supported in part by the  Centre National de la Recherche Scientifique  and the  Institut National de la Santé et de la Recherche Médicale , the  Agence Nationale de la Recherche  ( 2011-BLAN-BSV8-01801 ) and the  Fondation pour la Recherche Médicale.
For teeth a second micro-CT acquisition was performed with a smaller reconstructed voxel size (36363 mm 3 ) using 2 000 projections and three average frames per projection (100. 0 kV, 70 mA, and exposure time of 500 ms). 3D-reconstructions were done using datos|x software and algorithm including beam hardening correction and ring artefact reduction. Anatomical landmarks were identified as remarkable points (for instance cranial sutures) that could be easily recognized on each specimen. Fig. S1  (supplementary material) illustrates this step, showing on a 3D isosurface rendering the landmarks (red points)  and their anatomical definitions. A similar approach was used for molar analysis (Fig. S2 ). Calculation involved euclidean distances between selected pairs of points that could be compared to identify statistical significant differences between WT and mutant mice.
Acknowledgments The authors are grateful to the patients and families attending the Reference centre for orodental manifestations of rare diseases,  HUS, Strasbourg, France , and to  Dr. Karen Niederreither  for proofreading of the manuscript. They also wish to thank  Cathy Herouard-Molina ,  Doulaye Dembele , and  Christelle Thibault  from the  IGBMC Microarray  and sequencing platform.
Local vegetation trends in the Sahel of Mali and Senegal from Geoland Version 1 (GEOV1) (5 km) and the third generation Global Inventory Modeling and Mapping Studies (GIMMS3g) (8 km) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) time series are studied over 29 years. For validation and interpretation of observed greenness trends, two methods are applied: (1) a qualitative approach using in-depth knowledge of the study areas and (2) a quantitative approach by time series of biomass observations and rainfall data. Significant greening trends from 1982 to 2010 are consistently observed in both GEOV1 and GIMMS3g FAPAR datasets. Annual rainfall increased significantly during the observed time period, explaining large parts of FAPAR variations at a regional scale. Locally, GEOV1 data reveals a heterogeneous pattern of vegetation change, which is confirmed by long-term ground data and site visits.
1 km from LAC (Local Area Coverage) AVHRR (Advanced Very High Resolution Radiometer) receiving stations  [14] [15] [16]. However, poor quality, difficulties in data processing and availability hampers the use in the West African Sahel. Alternatively, the Normalized Difference Vegetation Index (NDVI) Global Inventory Modeling and Mapping Studies (GIMMS) time series starting in the year 1981 with 8 km spatial resolution derived from the AVHRR global GAC (Global Area Coverage) dataset  [17]  has been widely used for long-term trends  [4, [18] [19] [20]. The latest version, termed the third generation GIMMS3g dataset has been recently produced for the period July 1981 to December 2011 with AVHRR sensor data from NOAA (National Oceanic and Atmospheric Administration) 7-18 satellites with an improved calibration.
In addition to the NDVI, Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and Leaf Area Index (LAI) products have been derived from GIMMS3g dataset to provide quantitative information of the state of earth's vegetation at 8 km resolution and 15-day intervals  [21]. The recently delivered Geoland Version 1 (GEOV1) time series of FAPAR, LAI and Fraction of Vegetation Cover (FCOVER) based on AVHRR LTDR (Long Term Data Record) dataset combined with SPOT VGT offer a spatial resolution of approximately 5 km for the period 1981-1998 and 1 km from 1999 to present  [22, 23]. Although the main problem of these long-term time series is their very coarse resolution, merging heterogeneous processes and characteristics on the ground into one single pixel, the higher spatial resolution of the GEOV1 dataset as compared to GIMMS3g may contribute to improve local trend analysis.
Compared with NDVI, the FAPAR satellite products mitigate the impact of soil background for low vegetated canopies and the saturation effects for high vegetation amount  [29]. The purpose of this study is to assess local vegetation trends in the Sahel of Mali and Senegal in the period 1982-2010 by combining long-term FAPAR satellite datasets with ground based data. The potential of GEOV1 and GIMMS3g time series for trend detection is assessed and validated with biomass observations, rainfall data and site visits. This study is designed to better understand the processes responsible for satellite derived trends and, thus, to shed more light on the re-greening debate of the Sahel area.
FAPAR Time Series Global GEOV1 FAPAR time series derived from AVHRR LTDR  (1981) (1982) (1983) (1984) (1985) (1986) (1987) (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000)  and from VGT (1999-present) data have been recently delivered within the Geoland2 project. Products are freely available at 10-day time intervals and approximately 5 km (1 km) for AVHRR (VGT). The GEOV1 VGT FAPAR product is based on a combination of already existing products to take advantage of their specific performances while limiting the situations where products show deficiencies. In line with the published literature on products validation, the MODIS and CYCLOPES  [28]  products are selected since they provide higher level of consistency  [33]. The selected products are combined and eventually scaled to compute the fused product that is expected to provide the best performances globally.
The trained neural network algorithm is then applied using the land-cover class, pixel-center latitude, pixel-center longitude, and NDVI3g as inputs to generate time series of FAPAR from July 1981 to December 2011 at 15-day temporal steps and 1/12 degree (about 8 km) spatial resolution over the globe. For further information, we refer to Zhu et al. [21]. For this study, monthly MVCs (Maximum Value Composites) were produced for both FAPAR datasets to derive temporally identical time series and to further eliminate contamination by clouds and atmospheric disturbances. With the assumption that exceptional low values are contaminated, the highest value of each month was selected. Figure  2  presents the accumulated GEOV1 FAPAR and GIMMS3g FAPAR over the period 1982 to 2010 for the mean rainy season (August-October) in the Bandiagara and Linguère regions.
Time Series Analysis Prior to the trend analysis, a pixel-wise Durbin-Watson test was carried out, which revealed significant autocorrelation in the monthly time series of both FAPAR datasets (values between 0. 5 and 0. 8 (p ≤ 0. 05)). Therefore, in order to correct for autocorrelation and to filter out remaining noise, we applied a Seasonal Trend decomposition based on Loess (STL) to the monthly MVCs  [35]. Here, a local regression was used to decompose a times series into seasonal, yearly and noise components. For every pixel and each year, the yearly component was extracted and further processed. Using the de-noised yearly component has the additional advantage that singular seasonal effects are not overestimated and adds focus on the evergreen woody layer  [36]. This produces trends that are more significant and detects subtle gradual changes.
A linear regression was applied for each pixel to the preprocessed datasets with the yearly components for the period 1982-2010. The slope of the yearly components was derived to quantify the direction and magnitude of the overall trend. Slope values were recalculated to FAPAR units by multiplication with the number of years to express the total change over time. Only pixels with a confidence level of 95% (p ≤ 0. 05) were further processed. For reasons of comparability and to avoid unit inconsistencies, annual anomalies in percent were calculated for rainfall, integrated FAPAR and biomass data for the period 1987 to 2010 by dividing each individual year through the long-term mean. Here, annually integrated FAPAR is used instead of decomposed components to obtain comparable results with previous studies  [37].
Quantitative Information Biomass Observations In the Bandiagara region, no long-term field data is available but the Gourma region further north has been the object of detailed studies. Dardel et al. [9]  have shown that a good correlation between GIMMS3g NDVI time series and 27 years of field observations is present in Gourma (R 2 = 0. 57). The CSE (Centre de Suivi Ecologique) collects biomass data throughout Senegal each year at the end of the rainy season at 36 sites, although only three are located in our study area (C3L5, C2L5 and C2L4). Most of the sites date back to 1987, representing areas of relatively homogeneous vegetation and soil. Trees, shrubs as well as the herbaceous layer were surveyed with a stratified sampling method based on transects and quadrats in an area of about 3 × 3 km to calculate green biomass in kg DM (Dry Matter)/ha for the woody and herbaceous layer separately.
Mbow et al. [42]  discovered that species composition has a significant effect on the relationship between NDVI and biomass at a test site near Dahra, west of Linguère. The spatial unit size differences between ground sample areas and AVHRR pixels constitutes another source of error. The scope of this study is not a 1:1 comparison but to find investigate if (1) the direction; (2) the magnitude and (3) spatial discrepancies of long-term FAPAR trends correspond with ground data. Rainfall Data In the Sahel, annual vegetation fluctuations are largely controlled by precipitation  [43]. To better interpret the detected trends in FAPAR, rainfall data from local meteorological stations and from the Global Precipitation Climatology Centre (GPCC) dataset was included. The data is available for the Linguère weather station since 1933  [44]  and since 1901 at a 0.
5°grid from the GPCC precipitation database v6. The 0. 5°grids are created by interpolating station data over approximately 90. 000 weather stations globally  [45]. GPCC data is consistent with local station data (see Figure  3b ) and thus suited for further analysis. The two considered regions around Bandiagara in Mali (Figure  3a ) and Linguère in Senegal (Figure  3b ) have considerable intra-and inter-seasonal rainfall variability with almost the entire annual rainfall falling between June and October. After a wet period in the 1950s and 1960s, rainfall dropped far below average in both study areas from the 1970s to the 1990s. Since then, annual precipitation is increasing again, in Bandiagara more rapidly than in Linguère. Recently, mean annual rainfall lies around 430 mm in Linguère and 550 mm in Bandiagara.
On the other hand, a positive change does not necessarily mean an increase in forest-cover but in biomass, which crops and grass can also cause. In 12 of 14 countries, forest-cover is decreasing, whereas most of them have a positive trend within the same time frame  [46, 47]. For a better understanding of the processes behind changes, it is inevitable to break down the scale to a local level. Corresponding to increasing rainfall (Figure  3a ), the Malian study area has a general positive greenness trend with major spatial discrepancies seen in Figure  5a, b. The magnitude and spatial distribution of changes differ between the two FAPAR data products. However, both GIMMS3g and GEOV1 FAPAR agree that (1) stronger positive areas are present on the Dogon Plateau around Bandiagara (Figure  5a, b ); and (2) neutral or slightly positive trends indicating less productive areas are located in a large area south of Fiko (marked with MALdeg in Figure  5a, b ).
At the sandy test sites C2L5 and C2L4, the negative trends in herbaceous biomass are mainly caused by extraordinary high values at the end of the 1980s (Figure  7b, c ). Those herbaceous biomass peaks often occur after drought years and are caused by (1) the absence of livestock after severe drought years and the subsequent accumulation of biomass; (2) fertilization by dead-wood and (3) accumulation of seeds. Moreover, a change in the herbaceous species composition could be responsible for inter-annual biomass fluctuations  [42]. (c) and integrated GEOV1 FAPAR from 1987 to 2010. Note that biomass data is not available for each year. The plots give evidence of important conclusions: (1) large inter-annual discrepancies between biomass and FAPAR are not an exception; (2) the leaf biomass shows a strongly positive trend at all three sites and thus supports the FAPAR trend.
Although the two data products show good spatial consistency at an annual and regional scale (see Figure  2 ), the local pattern and magnitude of trends strongly differs. Degrading GEOV1 to 8 km resolution (results not shown for brevity) reduces the details but keeps the spatial pattern with apparent differences to GIMMS3g trend maps. Both datasets are created by sampled 1. 1 km AVHRR data which are resampled to a 8 km (5 km) grid cell by selecting subsets, while omitting other subsets  [13]. The whole processing line of the FAPAR data causes significant variations and the choice of the dataset may have significant effects on the results  [33, [55] [56] [57]. In GEOV1 FAPAR data, the values of the VGT period are sometimes higher in densely vegetated areas than the AVHRR period, causing trends to be overestimated.
On the contrary, trends in GIMMS3g FAPAR are too weak and significantly underestimated. The base levels of GIMMS3g FAPAR are much higher than those of GEOV1 FAPAR. Our comparison with ground data showed that the reality lies in between the two products, but closer to GEOV1. These differences in the processing line may influence the magnitude of trend analysis and bias the significance test, but the spatial pattern of GEOV1 trends shows agreement with ground observations (Table  3 ).