dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary Here, we applied the  Assef et al. (2018)  criteria based on the W1 and W2 WISE bands to select IR AGNs in the XXL-North 25deg 2 survey area. The above criteria yield 4798 AGNs. Moreover, we applied the optical MIR colour criterion r-W2  (Yan et al. 2013 ) in order to select obscured AGNs. We identify 561 red AGNs, of which 135 are detected in X-rays while 47 have good photon statistics (at least 50 net counts per detector), and derive a reliable estimate of the absorbing column density. We used XSPEC to fit the X-ray spectra of the 47 sources and quantify their X-ray absorption. Of these 47 sources, 76% may show signs of absorption higher than logN H /cm -2 = 21. 5. Furthermore, we applied the CIGALE code in order to study the SEDs of our 47 red sources. The SED analysis revealed that 10/47 sources are galaxy-dominated systems as the red colours are attributed to the host galaxy rather than absorption.
Expression of Genes Encoding Neuropeptides Neuropeptides can modulate nematode behavior, including locomotion and chemo-attraction  [29]. In C. elegans, 31 genes encode flp (FMRFamide-like peptides) and 75 genes encode nlp (neuropeptides-like proteins)  [28]. The M. incognita neuropeptide complement is reduced compared to that of C. elegans with only 19 flp genes and 21 nlp genes readily identifiable  [26]. A limited number (10) of M. graminicola genes encoding putative nlp or flp were identified in our transcriptomic dataset (Table  S4 ). They shared the same expression patterns in resistant plants: they were overexpressed in pre-parasitic J2s and until 4 dpi, then their expression decreased at 8 dpi. However, as for CWDEs-encoding genes, nlp or flp genes expression lasted more in resistant plants than in susceptible plants and was significantly higher at 8 dpi (Figure  3b ).
Expression of Genes Related to Stress or Detoxification M. graminicola genes putatively encoding enzymes related to detoxification of ROS or xenobiotic compounds, such as peroxiredoxins, catalases, superoxide dismutases, glutathione-S-transferases, glutathione peroxidases, were extracted from our expression dataset (Table  S4 ). Some of these genes (Mg1860, Mg2724) were up-regulated after infection of resistant and susceptible plants (DEGs across) and only one gene (Mg705) sharing homology with a catalase, was considered a DEG-RvsS. Mg705 expression was very high in pre-J2s and remained elevated in resistant plants until 8 dpi whereas it ceased to be expressed in susceptible plants from 4 dpi. These data indicate that detoxification genes are not required by the nematode during the infection of resistant plants.
M. graminicola Genes Encoding Putative Effectors Among nematode molecules involved in host parasitism, several effector proteins others than CWDEs are secreted during infection. In the M. graminicola Mg_v2 transcriptome, 18 other transcripts encoding predicted secreted proteins (i. e. , with a signal peptide and absence of a transmembrane domain) were evidenced as "DEGs-RvsS" (Tables  S3  and S5 ). In addition, we searched for already known M. graminicola effectors and sequences homologs to Meloidogyne effectors that were lacking a detectable signal peptide in our dataset (Table  S5 ). We verified that most of these genes were present in the M. graminicola draft genome even if they were not systematically identified as coding sequences (Table  S5 ). Depending on effector gene, expression varied along time or stages, except Mg1376 and Mg366, the closest M.
Hence, we propose here to rename Mg707 as Mg-VAP1 and Mg11937 as Mg-VAP2. proteins/antigen5/pathogenesis-related1 (CAP) domain. Mg707 and Mg11937 only share ~33% aa. identity. Deduced amino-acid sequences alignment of Mg707 and Mg11937 with Mi-VAP1  [47] , Mi-VAP2  [48]  and Gr-VAP1  [46]  indicated that Mg707 is close to Mi-VAP1 and Gr-VAP1, and Mg11937 is close to Mi-VAP2 (Figure  5b, c ). Hence, we propose here to rename Mg707 as Mg-VAP1and Mg11937 as Mg-VAP2. [47] ), Mi-VAP2 (Genbank ABO38019,  [48] ) and G. rostochiensis Gr-VAP1 (Genbank AEL16453,  [46] ): the signal peptides are underlined, the conserved cysteines are highlighted in grey, the Venom Allergen conserved site is highlighted in black and the SCP domain (annotated as Smart00198 in NCBI) is delimited by black triangles. (c) Percent identity matrix of the SCP domain sequences.
Desperately Seeking to Settle Of the 36,121 M. graminicola transcripts surveyed in this study, only 367 genes were differentially expressed between resistant and susceptible plants datasets (DEGs-RvsS). Globally, we did not observe expression of specific nematode genes but rather a different dynamic of gene expression associated to nematode infection in the resistant rice genotype. For instance, whether entering resistant or susceptible host roots, the nematode (pre-J2s) produce a large set of degrading enzymes, including CWDEs, peptidases as well as a series of other proteinaceous effectors (Figure  3 ). However, expression of the enzyme-coding genes sharply decreases in the susceptible host after 2 days when M. graminicola initiate their feeding sites in the vascular cylinder. On the contrary, expression of the same genes in the resistant host lasts longer and is still sustained after 8 days when only a limited number of feeding sites are initiated.
Among the M. graminicola genes highly expressed in pre-J2s, Mg448 encodes a C-type lectin that could also be involved in overcoming the host defenses as reported for Mg01965  [14]  and for the cyst nematode, Heterodera glycines  [53]. We identified here two other DEGs-RvsS whose expression profile deserved attention: Mg44061 was specifically expressed in resistant rice plants, only. Conversely, Mg66296 was not expressed at all in resistant plants and was specifically expressed at 4 dpi in the susceptible plants. No homology could be found for Mg44061 and Mg66296 in databases and both putatively encoded proteins have a predicted signal peptide for secretion. Functional analyses are required to assess the importance of these novel M. graminicola pioneer genes for parasitism.
cDNA Libraries and RNA-Seq Data We used RNA-Seq data from 14 cDNA libraries produced in our previous works (ENA/SRA accession number ERS715982,  [10, 22] ). Two replicate libraries (J2A and J2B) were obtained from freshly hatched pre-parasitic M. graminicola J2s, and six (3 × 2 replicates) libraries were obtained from M. graminicola-infected O. sativa cv Nipponbare (susceptible) root tips and galls collected at 2 dpi (Nip2A and Nip2B), 4 dpi (Nip4A and Nip4B), or 8 dpi (Nip8A and Nip8B)  [10]. Six (3 × 2 replicates) libraries came from M. graminicola-infected O. glaberrima TOG5681 (resistant) root tips or galls, when formed, also collected at 2 dpi (Tog2A and Tog2B), 4 dpi (Tog4A and Tog4B) and 8 dpi (Tog8A and Tog8B)  [22].
Functional Annotation of Genes Using the 66,396 M. graminicola transcripts described in Petitot et al. [10] , Blastn and Blastx alignments were performed against 3 datasets: the 10,895 predicted genes and the 4304 contigs from the draft genome of M. graminicola (Bioproject PRJNA411966,  [24] ) and the 43,718 CDS of the M. incognita protein database (Bioproject PRJEB8714,  [25] ) deposited at the WormBase ParaSite website  [61]. In addition, Blastx alignments were performed on the M. hapla and C. elegans protein databases and the UniProt database, and PFAM and Interproscan domains were determined for M. graminicola predicted proteins  [10]. To retrieve putative homologs of functional genes, PFAM and Interproscan accession numbers, C. elegans gene class names, as described in WormBase  [28] , or keywords were used.
Differential Expression Analyses Reads from all the libraries were mapped to the M. graminicola transcriptome with the BWA-ALN software  [63]. BAM files were converted to count files using Samtools idxstat. Normalization and differential gene expression analyses were performed on all the count files using EdgeR v3. 24. 1  [64, 65]  installed on the Galaxy instance hosted by the IRD bioinformatics computing cluster (http://itropgalaxy. ird. fr). Differential expression of one considered transcript was analyzed only if at least two samples had a CPM value > 1. Genes were considered as induced or repressed only when their log2FC was >2 or <-2, respectively, and their False Discovery Rate (FDR) was <0. 05 and designed as DEGs. Data lists were compared with the Venny 2. 1 tool  [66]. The heatmaps were constructed with the Morpheus software (https://software.
broadinstitute. org/morpheus). 4. 6. Cloning of M. graminicola Candidate Genes and Sequences Analyses Some M. graminicola genes were already cloned in Petitot et al. [10]  (Genbank accession numbers MF166620-166634). For novel selected genes (Mg693, Mg1702, Mg2129, Mg707 and Mg44061), primers were designed from the assembled sequences (Table  S6 ) and synthesized (Eurogentec, France). cDNAs were cloned as described in Petitot et al. [10]  and Sanger sequenced (Genewiz, Germany) (Genbank accession numbers MT349880-349885). The presence of signal peptide was predicted using the SignalP 5. 0 server  [67]. The Clustal-Omega alignment tool  [68]  was used to align the VAP protein sequences and establish the percent identity matrix.
Validation of DEGs by RT-qPCR Specific primers were designed from the M. graminicola cloned sequences using the Beacon Designer 7. 0 software (Premier Biosoft International, Palo Alto, CA, USA) (Table  S6 ). RT-qPCR assays were conducted on RNAs extracted from pre-parasitic J2 and from susceptible or resistant rice plants after a 2-, 4-, or 8-day challenge with M. graminicola. We used the duplicate RNAs samples from the sequenced libraries and produced a third replicate in this study. The qPCR assays were performed on cDNA samples (diluted 1: 100) as described previously  [10]. Amplifications were carried out in an Mx30005P thermal cycler (Stratagene, USA) using the Takyon Kit for SYBR assays (Eurogentec, France). The SATQPCR tool (http://satqpcr. sophia. inra. fr/cgi/home. cgi) was used to calculate the relative expression between the samples R and S at each time-point using the 2 -∆∆Ct method.
1 m) of measurement. We fitted birds with a hood before measurements to reduce stress  (Cockrem et al. 2008). We then proceeded to collect thermal images of the ventral surface of the right flipper (T flipper ) and breast (T breast ) just below the axilla. Because the flipper had to be manually opened to expose the ventral side, we only measured rewarming in one flipper to reduce handling stress. We also collected a thermal image of the dorsal area of the right foot (T foot ). This set of two images, i. e. , flipper/breast and foot, was collected every 30 s. In Year 1, internal temperature was not measured, but in Year 2, it was measured by inserting a digital thermometer with a flexible tip (Gilbert, Caen, FR) 6-8 cm through the cloaca (T cloacal ). Data in Year 1 were collected for 6. 4 min ± 0.
Measurements in the colony-Group 2 Thermal imaging of free-ranging birds (N = 24, Fig. 1 ), which had not been previously measured in the laboratory, was undertaken during Year 2, by following individuals from the point at which they returned from the sea for as long as possible before they could not be seen by the observer (Fig. 1 , Free-ranging-Group 2). Data were collected on days without rain for, on average, 14. 53 min ± 2. 05 min (range 2. 77-38. 93 min). Images were collected as frequently as possible when birds were in view and the inside of the flipper was visible. This provided, on average (± s. e. ), 22 ± 3 thermal images per bird, collected roughly every 1. 6 min. Surface temperature were extracted only from thermal images where the regions of interest where displayed as in the laboratory.
Thermal image analysis Mean T flipper and T foot were delineated by fitting polygons around each region of interest with the software Ther-maCAM™ Researcher Pro 2. 10 (Flir systems, Wilsonville, Oregon, USA). Emissivity was set to 0. 98. Mean T breast was extracted from a region just below the axilla, using a standardized square with length of side equal to the height of the distal flipper. We adjusted all images for variation in T a and measurement distance (which was always 1. 1. m in the laboratory). For field measurements, distance was not measured in meters and, therefore, we derived a distance index by counting the number of pixels along a line fitted from the top of the head to base of the foot. A greater number of pixels indicated that an individual was closer to the camera.
Statistical analysis For laboratory data, general linear mixed models (GLMMs) were used to model the initial temperature and the 1 3 temperature change (i. e. , final temperature-initial temperature; Year 2) for T cloacal , T flipper , T foot , and T breast , using thermal state (fully or partially hypothermic) and ambient temperature (T a ) as explanatory variables, and bird ID as a random factor. For these analyses, only data recorded during Year 2 were used, as birds were not measured in both states in Year 1 (above) and we were specifically interested in comparing temperature change within individuals. For data collected in the colony, we used GLMMs to model separately T flipper , T foot , and T breast recorded as a function of time on land, distance (as indexed by pixel number; see above), and T a , wind index and cloud cover.
Dual-energy X-ray absorptiometry (DXA) Mice underwent DXA measurements at baseline and at 20 weeks of age for fat-free mass, fat mass, bone mineral density, and bone mineral content using the PIXImus dual-energy X-ray densitometer (GE-Lunar, Madison, WI). The instrument was calibrated daily with a mouse phantom provided by the manufacturer. Mice were anesthetized with isoflurane and placed ventral side down with each limb and tail positioned away from the body. Full body scans were obtained, and X-ray energy absorptiometry data were gathered and processed with manufacturersupplied software (Lunar PIXImus 2, version 2. 1). The head was specifically excluded from all analyses due to concentrated mineral in skull and teeth.
Micro-computed tomography (µCT) A high-resolution desktop micro-tomographic imaging system (µCT40, Scanco Medical AG, Bruttisellen, Switzerland) was used to assess the microarchitecture of the fifth lumbar (L5) vertebral body and the femur in accordance with published guidelines  (35). All analyses were performed using the Scanco Evaluation software. In the L5 vertebra, scans were acquired using a resolution of 10 μm 3 , 70 kVp peak x-ray tube potential, 114 mA x-ray tube current, 200 ms integration time, and were subjected to Gaussian filtration and segmentation. The endocortical region of the vertebral body was manually selected beginning 100 µm inferior to the cranial growth plate and extending to 100 µm superior to the caudal growth plate. Trabecular bone was segmented from soft-tissue using a threshold of 460 mgHA/cm 3.
Histology and Histomorphometry Tibiae were fixed in 10% neutral-buffered formalin and transferred to 70% ethanol after 24 hours. Samples were decalcified, paraffin-embedded, sectioned, and stained with hematoxylin and eosin. Adipocyte size and number in the marrow in the region of the secondary spongiosa were analyzed with BIOQUANT OSTEO software (BIOQUANT Image Analysis Corporation, Nashville, TN). Total adipocyte area was divided by total area (marrow plus trabecular bone) in the secondary spongiosa and multiplied by 100 to calculate adipocyte volume / total volume (AV/TV). Adipocyte number was normalized to the total area measured. Vertebrae were fixed in 10% neutral-buffered formalin and transferred to 70% ethanol after 24 hours. The fixed lumbar vertebrae (L2-L5) were dehydrated with acetone and embedded in methylmethacrylate.
Undecalcified 4-µm-thick sections were obtained by microtome and stained with Von Kossa method for showing the mineralized bone. The consecutive second section was left unstained for the analysis of fluorescence labeling and the third section was stained with 2% Toluidine Blue (pH 3. 7) for the analysis of osteoblasts, osteoid, and osteoclasts. The bone histomorphometric analysis was performed in the lumbar vertebra under 200X magnification in a 1. 35 mm high x 1. 3 mm wide region where was located 400 μm away from the upper and lower growth plate using OsteoMeasure analyzing software (Osteometrics Inc. , Decatur, GA), in accordance with published guidelines  (36). The structural parameters (BV/TV, Tb. Th, Tb. N, and Tb. Sp) were obtained by taking an average from two different analysis of consecutive sections.
Then, fluorescence was measured on a FlexStation 3 Multi-Mode Microplate Reader using SoftMax Pro 7 software (Molecular Devices) in kinetic mode in response to additions of a range of concentrations of PTH. Change in relative fluorescence units (maximum -minimum) is indicative of intracellular calcium (iCa 2+ ) levels. Concentrationdependent change in max-min value was plotted on a logarithmic scale and fit with a 4parameter logistic curve to calculate the EC 50. In some experiments, calcium dye incubation was concurrent with pretreatment with propranolol. Additional 24-well plates were treated simultaneously with PTH and propranolol for 1 hour, without the Calcium 6 reagents, and were saved for mRNA analyses.
Sample size estimation and statistics Sample size for mouse experiments was estimated based on previous experiments in our laboratory using trabecular BV/TV as a primary outcome. Secondary outcomes of interest included serum and histomorphometric indices of bone remodeling. All statistics were performed with Prism 7 statistical software (GraphPad Software, Inc. , La Jolla, CA). Normally distributed data were analyzed for statistically significant differences using either a two-sided Student's ttest or a two-way ANOVA followed by Holm-Sidak multiple comparison post hoc test, where appropriate. Statistical significance was set at p < 0. 05. All data are expressed as the mean ± standard error of the mean (SEM). Outliers were handled as follows: first, any scientific/biological reason for the outlier was investigated (i.
Acknowledgements The authors thank  Adriana Lelis Carvalho  and  Ryan Neilson  for critical evaluation of the work. This work was supported by the  National Institute of Arthritis And Musculoskeletal And Skin Diseases (NIAMS)  and the  National Institute of General Medical Sciences (NIGMS)  of the  National Institutes of Health (NIH)  under award numbers  K01AR067858  and  P20GM121301  to KJM. This work utilized services of the  Maine Medical Center Research Institute (MMCRI) Molecular Phenotyping Core , which is supported by  NIH/NIGMS   P30GM106391 , the  Physiology Core , which is supported by  NIH/NIGMS   P30GM106391  and  P20GM121301 , the Histopathology and Histomorphometry Core, which is supported by  NIH/NIGMS   P30GM106391 ,  P20GM121301 , and  P30GM103392 , and the  Mouse Transgenic  and In  Vivo Imaging  Core which is supported by  NIH/NIGMS   P30GM103392.
Material and methods (a) Yield anomalies and climate data Crop yield time series were collected from yield data reported at the regional level in 17 European countries (i. e. at Nomenclature of Territorial Units for Statistics from EUROSTAT (NUTS) 2 and 3-electronic supplementary material, figure  S1 ). Across the 17 countries, 1435 geographical units (hereafter called districts) were included. The length of the time series differed among countries, with the earliest time series starting in 1901 for France (electronic supplementary material, figure  S1 ). Nine crops were considered: six cereals including winter and spring types, two tuber crops and one oilseed crop (electronic supplementary material, figure  S1 ). For maize, only grain maize was included in the study. Irrigated and rainfed yields were not systematically distinguished in the official data.
e. normal, Cauchy, logistic) to each time series, with the R package fitdist  [18]. The distribution with the lowest Akaike information criterion (AIC) was chosen to calculate the percentiles corresponding to each value in the time series of normalized anomalies, including that corresponding to the year 2018. Percentiles of 2018 yield anomalies were then mapped at the NUTS3 scale using the Eurostat R package  [19]. When only NUTS2 data were available, the data were processed at this spatial scale. Extreme low (high) yields were defined as yields lower (higher) than the 10% (90%) percentiles. Cumulative areas with extreme low and high yields were computed year by year for each crop at the European scale, and for four regions being Northern, Southern, Eastern and Western Europe (see electronic supplementary material, figure  S1 ).
All available districts were used to train the models in both cases. We also studied the ability of the RF models trained with all years to predict specifically year 2018. During the training procedure, the tuning parameters of RF were optimized using a cross-validation with a validation set including 25% of the data (electronic supplementary material, table  S2 ). The criterion maximized during the training procedure was the proportion of the normalized yield variance explained (R 2 ). RF models were implemented using the ranger R package  [24]. For each RF, we ranked the individual climate input drivers according to their relative importance for predicting normalized yield anomalies. Variable importance values were calculated based on a metric that captures the increase in mean squared error (MSE), calculated from out-of-sample predictions, after randomly permuting the values of the respective predictors.
Variable importance values in the RF were computed using the R vip library  [25]. The functional relationships between input climate drivers and yield anomalies were analyzed by plotting one-and twodimensional partial dependence graphs. A partial dependence graph shows the marginal effect of one or two exogenous features royalsocietypublishing. org/journal/rstb Phil. Trans. R. Soc. B 375: 20190510 (here, one or two climate inputs) on the outcome predicted by the RF, compared to averages over the values taken by the other inputs. These graphs allow visualizing the effect of the variables considered on the predicted normalized yield anomalies. Partial dependence plots were derived using the pdp R package  [26]. Here, we drew a partial dependence plot for the two most important climate drivers identified for each crop and each region.
The partial dependence plots were used to show how combinations of those two most important climatic drivers associate with yield anomalies in the RF models over all considered years, and to highlight their influence in the year 2018. We tested the robustness of our results by analysing the impacts of three detrending methods of the yield-time series (locally weighted scatterplot smoothing, polynomial linear regression and cubic splines  [27] ) on the outputs of our RF models. We also assessed the effects of alternative values of the tuning parameters of RF (electronic supplementary material, figure  S2 ), and of using a gradient boosting algorithm (  [28] ; implemented in R package xboost  [29] ) instead of RF. Finally, we compared the responses provided by the partial dependence plots to those provided by accumulated local effects (ALE; implemented in ALEPlot package  [30] ), i.
Our results showed that in 2018, yields in Southern Europe benefited on average from wet spring conditions (P_MAM) with exceptional cumulated precipitation for all the countries in Southern Europe (figure  7 ). Despite the fact that 2018 was characterized by extreme yields losses and gains, when investigating whether the frequency of extreme yield values (i. e. yield volatility) changed or not in recent decades, we found no clear upward or downward trend for any of the considered crops between 1990 and 2018 (figure  5 ). The years 2003, 2006, 2007 but also 1992, 1994 and 2000 showed a high proportion of cultivated area with extreme yield losses for various crops species (figure  5 ; electronic supplementary material, figure  S10 ). Note that in 2010 there was extreme drought in summer over West Russia but our dataset do not cover this region.