dataesr/scientific-paragraphs-categorization huggingface dataset:
Differentially expressed genes in type 2 diabetic islets In total, 4438 out of 29,529 probe sets, corresponding to 2976 unique genes, were differentially expressed in type 2 diabetic vs non-diabetic OD islets (false discovery rate [FDR] ≤0. 05). Overall, the expression of 608 probe sets, corresponding to 444 gene annotations (of which 421 were regulated only in OD and not in PPP; ESM Table  7 ) changed ≥1. 5 fold (Fig. 2a  and GEO, accession number: GSE76896). Among the top 20 regulated genes, 18 were downregulated, and two were upregulated (ESM Table  8 ). A total of 1439 of 29,612 probe sets were differentially expressed in type 2 diabetic compared with non-diabetic PPP islets (FDR ≤0. 05), corresponding to 1039 unique genes. Overall, the expression of 208 probe sets, corresponding to 136 gene annotations (of which 113 were regulated only in PPP and not in OD; ESM Table  7 ), changed ≥1.
5 fold (Fig. 2a  and GEO, accession number: GSE76896). Among the top 20 expressed genes, 12 were downregulated and eight upregulated (ESM Table  8 ). In type 2 diabetic OD islets, 69% (307/444) of the differentially regulated genes were downregulated, whereas 62% (84/136) of the differentially regulated genes were upregulated in type 2 diabetic PPP islets (Fig. 2a ). Exocrine and ductal pancreatic markers were comparably low in OD and PPP islets (see ESM Table  5 ). Furthermore, islets isolated enzymatically from OD and PPP clustered together by principal component analysis (PCA) and separately from the cluster of islets isolated by LCM from the same OD and PPP (Fig. 1b, c ), suggesting the influence of the isolation procedure (enzymatic for OD and LCM for PPP) rather than differences between OD and PPP.
These 19 genes were correlated with stimulated insulin secretion from OD islets (ESM Fig. 2 ). Notably, nine of these genes, namely ANKRD23/39, ASCL2, HHATL, NSG1, PCDH20, SCTR, CD44, FAM102B and FBXO32 have not been previously reported to be dysregulated in type 2 diabetes. Meta-analysis of a published transcriptomic dataset  [20]  revealed dysregulation (FDR ≤0. 05) of 114 probe sets, corresponding to 94 unique genes. We searched this dataset for the 19 genes dysregulated in both our islet cohorts and found that CHL1, FFAR4 and SLC2A2 were downregulated with an FDR of ≤0. 05 and a fold change of ≥1. 5, while ANKRD23/39, ARG2, HHATL, PPP1R1A and UNC5D were downregulated with an FDR of ≤0. 1 in type 2 diabetic islets, thus confirming the significant downregulation of CHL1, FFAR4 and SLC2A2 in type 2 diabetic OD islets in a different cohort.
The present results likely represent only the tip of the iceberg in terms of potential targets and biological hypotheses for beta cell alterations in type 2 diabetes. Several other groups have published findings derived from OD islets  [11] [12] [13] [14] [15] [16] [17] [18] [19] [20]. Since all these studies, including our own, are each likely to capture only part of the 'truth', a more complete picture will conceivably emerge from the integration of all available human islet transcriptomic datasets. An intriguing finding is that none of the 19 signature genes showed significant expression changes in type 3c diabetes or IGT PPP islets. While these findings must be validated in larger cohorts, one implication is that the diverse transcriptomic changes in type 2 diabetic, IGT and type 3c diabetic islets may reflect pathophysiological idiosyncrasies of these conditions and/or the duration and severity of hyperglycaemia.
Acknowledgements Open access funding provided by  Max Planck Society. We dedicate this work to  A. Ktorza  ( Servier, Paris, France ), an excellent scientist and friend who left us. We thank  W. Kramer  ( Coordinator IMIDIA, Frankfurt, Germany ),  P. Hecht  ( Alliance and Operations Manager IMIDIA, Graz, Austria ),  V. Koivisto  ( Eli Lilly, Bad Homburg, Germany ), and all the members of the  IMIDIA consortium  for their support and advice. We thank  E. Schöniger  and  N. Radish  ( PLID, Dresden, Germany ) and  M. Marcelo  ( Eli Lilly, Indianapolis, IN, USA ) for excellent technical assistance,  H. Hartmann  ( TU Dresden, Dresden, Germany ) and  B. Schroth-Diez  ( MPI-CBG, Dresden, Germany ) for their advice. We thank  F. Möller  ( PLID, Dresden, Germany ) and  N. Smith  ( AMICULUM, Auckland, New Zealand ) for his editorial support.
Data availability Raw microarray data have been deposited in a MIAME-compliant database (GEO, accession number: GSE76896 ). Funding The work was supported by the  Innovative Medicines Initiative Joint Undertaking  (grant no. 155005 ) for IMIDIA, which received financial contributions from the  European Union 's  Seventh Framework Program  (FP7/2007-2013); companies belonging to the  European Federation of Pharmaceutical Industries and Associations ; the  German Center for Diabetes Research  (DZD e. V. ), which is supported by the  German Ministry for Education and Research  (to MSo); the  Italian Ministry of Education and Research  (Prin 2010-2011) (to PMa); the  Wellcome Trust  ( Advanced Investigator Award   WT098424AIA ); the  Medical Research Council  ( Program MR/J0003042/1 ); and the  Royal Society (Wolfson Research Merit Award)  (to GAR).
Background: The fasciclin-like arabinogalactan proteins (FLAs) belong to the arabinogalactan protein (AGP) superfamily and are known to play different physiological roles in plants. This class of proteins was shown to participate in plant growth, development, defense against abiotic stresses and, notably, cell wall biosynthesis. Although some studies are available on the characterization of FLA genes from different species, both woody and herbaceous, no detailed information is available on the FLA family of textile hemp (Cannabis sativa L. ), an economically important fibre crop. Results: By searching the Cannabis genome and EST databases, 23 CsaFLAs have been here identified which are divided into four phylogenetic groups. A real-time qPCR analysis performed on stem tissues (isolated bast fibres and shivs sampled at three heights), hypocotyls (6-9-12-15-17-20 days-old), whole seedlings, roots, leaves and female/male flowers of the monoecious fibre variety Santhica 27, indicates that the identified FLA genes are differentially expressed.
Identification of CsaFLA genes using bioinformatics In order to identify the FLA genes in C. sativa (hereafter referred to as CsaFLAs for the genes and CsaFLAs for the corresponding proteins), different databases were searched: the Medicinal Plant Genomics Resource (http://medicinalplantgenomics. msu. edu/mpgr_external _blast. shtml) and the Cannabis sativa Genome Browser Gateway (http://genome. ccbr. utoronto. ca/cgi-bin/hgBlat? command=start&org=C. +sativa&db=finola1&hgsid=93256). CsaFLAs were identified by using orthologous FLA protein sequences of Arabidopsis thaliana  [5]  and Populus trichocarpa  [7]. These sequences were used to perform a BLAT analysis against the hemp Finola and Purple Kush database (Cannabis Genome Browser Gateway;  [28] ) and a BLASTP in the MPGR database. Several incomplete sequences were retrieved when using the MPGR database; however it was possible to deduce their full length sequences either by querying the Cannabis Genome Browser Gateway, or the EST database at NCBI (dbEST; available at http://www.
In silico and phylogenetic analyses of CsaFLA protein sequences Putative FAS domains were identified with the Motif Scan algorithm (http://myhits. isb-sib. ch/cgi-bin/motif_scan), Nterminal signal peptides were identified with SignalP (http://www. cbs. dtu. dk/services/SignalP/) and SignalBlast (http://sigpep. services. came. sbg. ac. at/signalblast. html); the subcellular localization was predicted with TargetP (http:// www. cbs. dtu. dk/services/TargetP/). The big-PI Plant Predictor program (  [29] ; available at http://mendel. imp. ac. at/gpi/plant_server. html) was used to identify the glycosylphosphatidylinositol (GPI) anchor. The 3D homology models of the hemp FLA 10 and FLA 11 were generated with iTASSER Suite (  [30]  using 4ut1 and 1o70 as targets respectively; available at http://zhanglab.
org/tools/tomtom) for a comparison against the available motifs in the JASPAR CORE plant database 2016  [34]. For the phylogenetic analysis, full-length sequences were aligned with Clustal-Omega (http://www. ebi. ac. uk/Tools/msa/clustalo) and the generated alignment submitted to PHYML (http:// www. phylogeny. fr) to obtain a maximum likelihood phylogenetic tree. The Maximum Likelihood tree was constructed using an aLRT (approximate likelihood ratio test) for non-parametric branch support, based on a Shimodaira-Hasegawa-like procedure. The tree was visualized with iTOL-Interactive Tree Of Life (http://itol. embl. de/). Intron-exon junctions were visualized with Gene Structure Display Server 2. 0 (GSDS, http:// gsds. cbi. pku. edu. cn/) [35].
RNA extraction and RT-qPCR Total RNA was extracted using a modified CTAB extraction protocol combined with an RNeasy Plant Mini Kit (Qiagen) according to  [23]. The RNA concentration and quality were measured by using a Nanodrop ND-1000 (Thermo Scientific) and a 2100 Bioanalyzer (Agilent), respectively. One microgram of RNA was retrotranscribed into cDNA using the ProtoScript II RTase (NEB) and random primers, according to the manufacturer's instructions. The cDNA was diluted to 2 ng/μL and 2 μl used for the RT-qPCR analysis in 384-wells microplates. An automated liquid handling robot (epMotion 5073) was used to prepare the 384-wells microplates (10 μl final volume). A tissue maximization design was used to prepare the microplates  [36]. The expression of each CsaFLA was normalized using 5 reference genes (tubulin, CDPK, RAN, clathrin and F-box, which geNORM PLUS identified as sufficient for appropriate data normalization) for the stem tissues, as described in  [20] , and 3 (RAN, TIP41 and Fbox) for the other tissues (leaves, seedlings, flowers and roots).
Results Identification of putative FLAs in C. sativa: Protein architecture and phylogenetic analysis BLAST/BLAT analyses of the 21 A. thaliana sequences (AtFLAs) performed against the Medicinal Plant Genomics Resource, the NCBI EST and the Cannabis Genome Browser Gateway databases led to the identification of 23 CsaFLAs (Additional file 3: Table  S3 ). It should be noted that, during the database queries, a contig, i. e. csa_locus_44222_iso_1_len_407_ver_2, which was initially called CsaFLA22 and retrieved at the Medicinal Plant Genomics Resource (MPGR), was also found. However, we believe that this partial gene was erroneously attributed to C. sativa, since we never amplified any product with different primers designed on it and the reported FPKM values at the MPGR are 0 for all the tissues examined.
Identification of conserved motifs in the promoters of some CsaFLAs A bioinformatic analysis was carried out on the available promoter regions (between 116 and 1064 bp retrieved at the Cannabis Genome Browser Gateway) of the CsaFLA genes showing a distinctive expression pattern in the bast fibres. The promoters of the genes within group I, II and V were selected (Additional File 10), in the light of their upregulation at the snap point, at the younger and older stem regions respectively (Fig. 3 ). While no conserved motifs could be found with the MEME suite tool for the genes in group I, 1 motif was found for the genes within group II and V (Table  2 ). The search carried out with the conserved sequence of group II and V FLAs in the JASPAR CORE 2016 plants database identified SOC1 (lowest p-value among the matches retrieved) and MYB3 as candidates recognizing similar motifs (Table  3 ).
1. 59e-02 The first motif in each table cell is the one found in the database, the second motif is the one found in the promoters of hemp FLAs. The function and computed p-value are indicated. with a tissue-type and a stem region. Among them, we would like to draw the reader's attention on the first group of genes, represented by CsaFLA2-6-24, because they show a different expression profile in the bast fibres and the shivs. The expression in the shivs shows a decrease from the top to the bottom of the stem, while in the bast fibres their expression peaks at the snap point. This is quite interesting if we consider that the snap point is the region marking a shift in the stem mechanical properties, as it determines the transition from cell elongation to thickening  [27]. It was shown that the young stem regions of hemp at the vegetative stage of growth are characterized by the presence of ca.
MYB4 negatively regulates phenylpropanoid biosynthesis (more specifically, in thale cress it is a negative regulator of hydroxycinnamic acid metabolism and it exerts its silencing function by displacing the activators binding to the MYB motifs present in many promoters of genes involved in the phenylpropanoid metabolism;  [44] ). It is therefore possible that the identified element is involved in the coordination of phenylpropanoid biosynthesis in bast fibres and might regulate the hypolignification observed in these cells  [45, 46]. In our recently-published transcriptomic dataset  [47] , we observed an upregulation of the SOC1 gene at the top (4-fold induction with respect to the bottom and 1. 3-fold induction with respect to the middle) and MYB4 at the bottom (1. 7-fold induction with respect to the top and 4.
Extraction Method In order to retrieve automatically all components of noun+CT similes in the corpus, we used a variation of the simile detection method described in  Mpouli and Ganascia (2015). Basically, it consists of three main stages : segmenting the text into sentences, extracting sentences containing a noun+CT adjective in a sentence, and identifying the word in the sentence that adjective modifies. In this respect, each text was pre-processed with TreeTagger 4  (Schmid, 1994) , a freely available multilingual tool which performs a wide range of natural language processing tasks : tokenisation, lemmatisation, part-of-speech tagging and syntactic chunking. 18 Sentence segmentation was done using the sentence boundaries defined by TreeTagger, which were eventually corrected by a set of manual rules for sentences that are disrupted by an ellipsis, a question or an exclamation mark.
Its productive efficiency could have been high since, today, under similar conditions of hand cultivation on slopes in this same area (fig. S6 ), we have measured quinoa yields in peasants' fields ranging from 1000 to 1500 kg ha -1 (section S2). A prolonged dry and cold climate event Paleoclimatic data of the Central Andes concentrate in northern Bolivia and southern Peru. Before our period of study, between ~500 and ~1100 CE, the Tiwanaku civilization benefited from wet climatic conditions to develop intensive and specialized agricultural practices around Lake Titicaca (42) (Fig. 5A ). A dry period started after 1200 CE and lasted until 1400 CE as evidenced by lower levels at Lake Titicaca  (43). Pollen analyses associated to archeological observations in Marcacocha attest to sustained aridity between 880 and 1400 CE  (44) , and the presence of a dust layer in the Quelccaya ice core suggests a drier event during the 14th century  (45).
Archaeological survey and excavations From 2007 to 2016, we conducted eight field campaigns consisting of site prospections and surveys of residential, storage, and cropland areas over a total land area of ca. 1800 km 2. In 14 dwelling sites (Fig. 1 , sites 1, 2, 3, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, and 32), systematic surveys were carried out analyzing data from high-resolution satellites, geomorphology, topographic maps, toponymy, and interviews with current local inhabitants.
Geographic and remote sensing data processing The geographic data used in this study consisted of the following: (i) the land surface topography extracted from the Shuttle Radar Topography Mission (SRTM) v2 Digital Elevation Model (DEM) with 1-arc sec resolution (~30 m) downloaded from the Reverb|ECHO website (http:// reverb. echo. nasa. gov/), (ii) high-resolution satellite imagery available on Google Maps [source: GeoEye, DigitalGlobe, CNES (Centre National d'Études Spatiales)/Astrium, and CNES/Airbus]. Archaeological cropland areas were digitized by photo interpretation of satellite images with QGIS editing tools (www. qgis. org/). The detailed data processing for studying the distribution of topographic variables (elevation, slope, and aspect) over cropland areas is described in the next paragraph.
A metric coordinate system is required for raster operation such as slope computation. Consequently, to prepare DEM for analysis, the data were warped into the WGS84/UTM19S coordinate system, with bilinear interpolation in a 30-m resolution. To have suitable values for slope computation, we used the "Denoise" software (79) with parameters T = 0. 95 and n = 20 (the threshold T controls the sharpness of the features to be preserved, and the number of iterations n controls how much the data are changed). In the aspect file derived from DEM, we set low-slope regions (<2°) as "flat" for further analysis. All the processing on the SRTM v2 DEM was made with QGIS (www. qgis. org/) and GDAL utilities (www. gdal. org/gdal_utilities. html). The identified cropland areas were turned into raster files with the same resolution as the DEM, using QGIS software.
Climate model simulations We used last millennium simulations from the PMIP3 database  (85)  to evaluate the response from four state-of-the-art climate models (table  S4 ) to external forcing for austral summer (DJF) precipitation (Fig. 5B ) and temperature (fig. S7 ) in South America. The use of four models allows accounting for model uncertainty in response to external forcing and to better isolate the forced response, filtering out the potentially large internal variability from each simulation. Note that we excluded, from the PMIP3 database, simulations showing a very large drift, indicative of some issues of energy conservation.
Study sites and data collection We studied 115 dryland ecosystems from 13 countries (Fig. 1 ), which are a subset of the 236 sites from  Ochoa-Hueso et al. (2017). Annual mean temperature and rainfall ranged from 2. 6 to 25. 7 ºC and from 67 to 801 mm, respectively. Elevation, latitude and longitude varied from 76 to 4524 m. a. s. l. , from -41° to 40°, and from -115° to 142°, respectively. The database includes three habitat types (grasslands, shrublands and open woodlands/savanna); however, we did not use open woodlands/savanna because there were very few sites suitable for our analyses (see details below). Grassland and shrubland sites covered a wide range in species richness (2 to 39 perennial species) and total plant cover (from 4. 5 to 82. 8 %). See  Maestre et al. (2012) , for full details.
Measurement of patch-size distributions From the 236 sites available, we used for this study 115 sites from which we were able to quantify their PSDs using high-resolution remote sensing data (see  Berdugo et al. , 2017b)  for details). In brief, we downloaded VirtualEarth (http://www. bing. com/maps) and Google Earth (https://earth. google. com) images with resolution ≤ 30cm/pixel. To obtain a sufficient number of patches as to fit power law functions to their PSDs, we took three adjacent subplots of 50m x 50m per site. The first one was placed to fit that surveyed in the field. We classified the images by using authomatic luminance threshold detection, and contrasted their results with those from other classification methods based on expert knowledge (see Appendix 1 for details). Finally, remote-sensing cover estimates from the first subplot were correlated with field measurements to ensure that the classification procedure reproduced what was observed on the field.
The image classification analyses used fitted reasonably well observed field data (see Appendix, Fig. S1 ). For each site, we extracted all the patches and their sizes resulting from the image classification analyses of the three subplots. We pooled them and fitted a power law to their distributions. We used the approach of  Clauset et al. (2009)  to get the two main parameters of power law distributions: equation (1) where x represents the patch size and p(x) describes the frequency of patches of a certain size. This equation represents the probability density function of a power decay. The parameters of the distribution are x min , the minimum patch size from which the fit to a power law starts (below that point data are discarded from the fitting procedure) and α, the rate of decay of frequency with patch sizes (see details in  Berdugo et al.
The height of the dominant species was obtained from previous studies, local floras and global databases  (Kattge et al. , 2011; see Soliveres et al. , 2014 , Le Bagousse-Pinguet et al. 2017 , for more details on trait data acquisition). We introduced habitat type (grassland and shrubland) to control for differences in the growth form of the dominant species. Habitat types were identified both types depending on the dominant plant form inhabiting the sites. We measured plant-plant interactions in two ways to disentangle their potential effects on PSDs mediated either by their frequency or the strength of soil amelioration. First, as a measure of the frequency of positive plant-plant interactions, we quantified the proportion of species more associated with a given nurse than expected by chance.
Abiotic factors drive the shape of patch-size distributions across habitats Although aridity was not a significant driver of the change in PSDs, it strongly modulated the effects of biotic attributes on PSDs in shrublands, but not in grasslands. Grasslands in our dataset were particularly prevalent under moderate aridity conditions (Appendix, Fig. S6 ), so the lack of effect of aridity interactions might be a consequence of a lower aridity range in the grasslands surveyed. Our findings indicate that, in shrublands, aridity prevents biotic attributes (traits of the dominant species, plant-plant interactions) to form large patches under the most arid conditions. This provides empirical support to the often hypothesized facilitation collapse as the mechanism underlying shifts from power law to curved PSDs under extreme environments  (Kéfi et al.
S2 ). Since total cover does not differ between habitat types (F 1,68 = 1. 14, P = 0. 29, in the dataset with facilitation [N=71]; F 1,109 < 0. 01, P = 0. 95, in the full dataset), this result suggests either that: i) cover is not enhanced in shrublands by spatial pattern formation or ii) clumping of vegetation into large patches due to space constraints is more likely in grasslands than in shrublands. In this last case, mechanisms such as the way in which the different plant types compete (and thus repel each other) might be playing a determinant role. In shrublands, individuals are larger, and also structure forming strata that might increase competition for light that is less likely to occur between grasses. The latter is supported by the significant effect of height on PLR found in shrublands, but not in grasslands, thus indicating that the size of dominant individuals is important to define PSDs.
FUNDING This work was funded by the  European Research Council (ERC)  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013)/ERC Grant agreement 242658  (BIOCOM). M. B. was supported by a  FPU fellowship  from the  Spanish Ministry of Education, Culture and Sports (Ref. AP2010-0759 ). FTM acknowledges support from the  ERC (ERC  Grant Agreement 647038 [BIODESERT]) and from a  Research Award  from the  Alexander Von Humboldt Foundation. The research of SK has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 283068  (CASCADE).