dataesr/scientific-paragraphs-categorization huggingface dataset:
Future work will explore in more detail how Sahelian MCSs develop within high and low shear environments. The findings of this paper are both aided and limited by the novel climate model employed and some caveats to our findings are required. Whilst the CP4-A dataset allows for interrogation of storm dynamic changes due to climate change that was not previously possible, only one future climate realization exists, and any model biases associated cannot be assessed fairly using multi-model ensembles. In particular, there is no current way to assess the impact that different future sea-Accepted for publication in Journal of Climate. DOI 10. 1175/JCLI-D-19-0380. 1. surface temperature anomalies would affect our findings. We hypothesize from Fig. 12 that any increase in land/ocean temperatures in the future could lead to increased extreme precipitation rates following the processes we discuss.
Acknowledgements Accepted for publication in Journal of Climate. DOI 10. 1175/JCLI-D-19-0380. 1. Fitzpatrick, Parker, Marsham, Guichard, Taylor, Crook and Rowell were supported by the  Natural Environment Research Council/Department for International Development (NERC/DFID ) program AMMA-2050: African Monsoon Multidisciplinary Analysis for 2050 projectgrant numbers  NE/M017176/1 ,  NE/M20126/1  and numbers  NE/M020428/1. Finney,  Marsham , and  Rowell  were funded by the  NERC/DFID  funded  Integrating Hydro-Climate Science into Policy Decisions for Climate-Resilient Infrastructure and Livelihoods in East Africa (HyCRISTAL ). Crook was also supported by the  NERC  program VERA: Vegetation Effects on Rainfall in West Africagrant number  NE/M003574/1. Jackson, Tucker and Stratton were supported by  NERC/DFID  via the FCFA funded project,  Improving Model Processes for African Climate (IMPALA ,  NE/MO17176/1  and  NE/M017214/1 ).
Parker was also supported by a  Royal Society   Wolfson Research Merit Award  ( 2014-2018 ). Marsham was also supported by the  National Centre for Atmospheric Science  via the  NERC/GCRF program  ACREW: Atmospheric hazard in developing Countries: Risk assessment and  Early Warning. Vizy ,  Cook , and Fitzpatrick's contributions towards this study was also funded by the  National Science Foundation  Award number  1444505. The authors wish to extend their deepest gratitude to  Margaret LeMone , the journal editor and two anonymous reviewers for their comments, which contributed greatly to the results presented. Accepted for publication in Journal of Climate. DOI 10. 1175/JCLI-D-19-0380. 1.
In the second smaller group, only 85 DEGs displayed an opposite trend: these transcripts were down regulated in Tree in TvS, but up regulated in Tree in TvG. Similarly, we observed two groups for DETEs (Fig. 2B  and D ). 277 DETEs were up regulated in Tree in both  TvS and TvG, and 225  DETEs were down regulated in Tree in both comparisons. Only 17 DETEs displayed an opposite transcript accumulation patterns compared to the general trend. To study the main gene regulatory pathways represented in the differential transcription data we used the GDDH13 gene annotation of Malus domestica (v1. 1) combined with the MapMan software  (Lohse et al. , 2014; Fig. 3A). We also considered the TE class repartition as previously described in  Daccord et al. , (2017)  (Fig. 3B ). We observed variations in class size between TvS and TvG.
These including the classes photosynthesis, RNA biosynthesis, enzyme family, protein biosynthesis, RNA processing and cytoskeleton (Fig. 6B ). We did not observe notable shifts within the classes' repartition between TvS an TvG for DETE-DMRs (Fig. 6D ). Finally, we investigated the link between DMRs and DEGs. As previously we only considered DEG-DMR in the CHH context because of the low number of DEG-DMR we found in the CG and CHG context. We noticed that in both comparisons the majority of DMRs in DEG-DMRs were located in gene promoters (539 for TvS and 114 for TvG), followed by terminator region (284 for TvS and 58 for TvG) finally followed by those present in gene bodies (139 for TvS and 42 for TvG) (Fig. 7A, 7B. See examples of these DEG-DMRs in Supplemental fig. S2 ). Our data also indicate that, independently of the DMR position relative to a gene, hypermethylated DMRs were associated with a gene down-transcription in the Tree sample (Fig.
At each sampling time Seedling and Graft plants were pruned to increase vigor. Three replicates were made at three weeks intervals for Graft and Seedling materials in 2018, and one replicate was made in 2019 including Tree material (twelve leaves were sampling). Each leaf was then photographed under binocular magnifier (Olympus SZ61, Schott KL 1500 LED, Olympus DP20). Pictures were further analyzed with the ImageJ® software  (Schneider, Rasband, and Eliceiri 2012). Pictures were transformed in 8-bit grayscale and light intensity was measured on 5 areas of 0. 03cm² on each leaf. Intensity differences between samples were evaluated using the R language by Kruskal-Wallis test. We first compare biological replicates from 2018 and from 2019 (Seed and Graft). Because there were no differences between biological replicates of Seedling from 2018 and 2019 and similarly to Graft sample from 2018 and 2019, we decide to only present result of the 2019 year which include Tree sample.
Bisulfite sequencing and DMRs calling Extracted DNA was precipitated in pure ethanol (70%), water (24%) and NaAc 3M (3%). After precipitation DNA was sent to Beijing Genomics Institute (Shenzhen, Guangdong 518083, China) in pure ethanol for whole genome bisulfite sequencing. DNA methylation data can be accessed on the Gene Expression Omnibus website under accession codes GSE138377. Bisulfite sequencing reads were mapped on GDDH13_V1. 1 reference genome with Bsmap tool  (Xi and Li 2009 ) to obtain methylation calling file. Methylation averages between samples were compared by student test using R (R Core Team 2016). We called differentially methylated regions (DMRs) using a hidden Markov model (HMM)based  (Hagmann et al. 2015)  approach as in  Daccord et al. , (2017). DMRs were calculated between Tree and Seedling samples and between Tree and Graft samples with the following parameters: coverage of 3, 200bp sliding windows with 100bp overlapping.
Microarray The Malus domestica array (Agilent-085275_IRHS_Malus_domestica_v1; GPL25795; Agilent, Foster City, CA, USA) was used for microarray analysis. Complementary DNA (cDNA) were synthesized and hybridized with the Low Input Quick Amp Labeling Kit, two-color (Agilent, Foster City, CA, USA). Two biological replicates were used. Each biological replicate represents one sample for Tree and Graft materials, and a pool of two samples for Seedling material. Hybridizations were performed on a NimbleGen Hybridization System 4 (mix mode B) at 42°C overnight. Slides were then washed, dried, and scanned at 2 µm resolution. NimbleGen MS 200 v1. 2 software was used for microarray scans, and the Agilent Feature Extraction 11. 5 software was used to extract pair-data files from the scanned images. We used the dye switch approach for statistical analysis as described in  Depuydt et al.
, (2009). Analyses were performed using the R language (R Development  Core Team, 2009) ; data were normalized with the lowess method, and differential transcription analyses were performed using the lmFit function and the Bayes moderated t test using the package LIMMA  (Smyth, Michaud, and Scott 2005). Transcriptomic data are available in Gene Expression Omnibus website, with the accession GSE138491.
Genes selected to validate the microarray data were selected in DEG lists in both comparisons (TvS and TvG) with 1) a high ratio value and 2) high intensities values. Accessions and primer sequences are indicated in figureS3A. Reaction was performed with a CFX connect Real time system (Bio-Rad, Hercules, CA, USA) using the following program: 95°C, 5 min; 35 cycles comprising 95°C for 3 s, 60°C for 45 s; 65°C, 5s and 90°C for 1 min, with real-time fluorescence monitoring. Melt curves were acquired at end of each run. Data were acquired and analyzed with CFX Maestro V1. 1 (Bio-Rad, Hercules, CA, USA). Gene transcription levels were calculated using the 2 -∆∆Ct method and were corrected as recommended by  Vandesompele et al. , (2002)  (Supplemental fig. S3B ), with three reference genes: Actin (accession CV151413, MD14G1142600), Gapdh (accession CN494000, MD16G1111100), and Tubulin (accession CO065788, MD03G1004400) used for the calculation of a normalization factor.
Differentially express transcript (DET) analysis Differentially expressed transcripts were selected based on their p-value ≤ 1% (Supplemental tab. S7). For DET other than TE and miRNA a MapMan annotation (https://mapman. gabipd. org/home; version 3. 5. 0BETA), was performed, using GDDH13_1-1_mercator4 map file, in order to assign each DET to a BIN. DET not assigned to a BIN class were excluded. A representativeness percentage of each BIN class was then calculated in the comparisons TvS, TvG and in the intersection between the both comparisons. A MapMan enrichment analysis on the BIN class representativeness was performed and a BH correction was applied  (Benjamini and Hochberg 1995)  because of the high number of values. For DETEs, the TE classification  (Daccord et al. 2017)  was used in order to assign each DETE to a class.
Association between DMR and DEG or DETE DMRs and transcription level data (DEG or DETE) results were connected thanks to gene identifier. DMRs without associated DETs were removed. There is some redundancy of the gene or TE identification because many DMRs could be close. To avoid biases in our analysis we only kept DMR with the highest methylation variation to each gene or TE (Supplemental tab. S8). Accession numbers: GSE138492: global depository accession number comprising methylome and transcriptome data GSE138377: bisulfite sequence data and methylation calling files GSE138491: microarray data Large datasets: Supplemental table  S6 : DMRs list of TvS and TvG comparisons, including Gene-and TE-DMRs. Supplemental table  S7 : DETs list of TvS and TvG comparison Supplemental table  S8 : DEG-and DETE-DMRs list of TvS and TvG comparisons Acknowledgements: M.
In addition, we use OI (ovipositor index) = ratio of ovipositor length to length of metatibia. A subset of 19 characters (characters marked with an asterisk in Table  1  and in Supplementary Table  1 ) was used for the multivariate ratio analysis (MRA). In particular, we calculated a shape principle component analysis (shape PCA) and looked for the best separating ratios using the LDA ratio extractor (for details, see  Baur and Leuenberger 2011). All multivariate statistical analyses were done with R (version 3. 3. 3, R Core Team 2017) using slightly modified R scripts provided by  Baur et al. (2014). Terminology of morphological structures in this study mostly follows  Gibson et al. (1997) , terms for surface sculpture follow  Steinmann and Zombori (1985).
All outgroups (Podagrion bouceki Delvare, P. pachymerum Walker, Propachytomoides sp1) included in the analysis were selected based on the most recent phylogeny of Torymidae  (Janšta et al. 2018). The molecular tree Table  1. Definition of characters used for diagnoses, descriptions and morphometric part of study (characters marked with an asterisk were used in a multivariate ratio analysis (MRA) including a shape principle component analysis (shape PCA) and a PCA ratio spectrum). was reconstructed using the Maximum Likelihood method (ML) in RAxML 8. 2. 12 using GTRCAT approximation with 1000 bootstrap replicates  (Stamatakis 2006 ) and conducted on the CIPRES Science Gateway  (Miller et al. 2010). Bootstrap percentages (BP) ≥70% were considered as strong support. All COI sequences are deposited at NCBI under the accession numbers: MF956194, MF956202, MF956323, MF956381, MN822803 -MN822824.
Shellac is soluble in ethanol, which allowed us, due to the careful Ovipositor length Length of ovipositor, measured as the part of the ovipositor sheaths that extend beyond the tip of the last gastral tergite  (Janšta et al. 2016)  fixing with a minimal amount of glue to easily detach and card-mount the specimens after completion of the µCT scans. All scans were performed with a Skyscan 1272 (Bruker Corporation, Billerica, USA) with the following basic settings (for a detailed list of settings see Supplementary file 1): 30 kv, 200 µA, 1400 ms of exposure time, rotation steps of 0. 25° over 360°, frame averaging: 5, random movement 10, spatial resolution: 1. 300910 µm. Segmentation of the projections was performed with NRecon (Bruker Software Solutions). The resulting. tif files were imported into Amira 5.
3 (Thermofisher, Waltham, USA) where the needles and dirt were virtually removed from the data set using the segmentation and arithmetic functions. Subsequently, volume rendering was performed in VG Studiomax 3. 2 (Volume Graphics, Heidelberg, Germany). Final images were adjusted, assembled and labelled in Adobe Photoshop CS6 and Illustrator CS6. The µCT scans are available at morphobank (https://morphobank. org) under project number P3746.
Podagrion flabellatum Girault, 1929 Podagrion flabellatum  Girault, 1929 [431] : 342. TAS: Launceston. Bouček (1988)  Podagrion hyalinum  (Girault, 1913)  Podagrionella hyalina  Girault, 1913 [175] : 80. QLD: Longreach. Transferred to Podagrion by  Girault, 1915 [243] : 292. Bouček (1988) : 141. Material examined: 1 ♀ holotype (card and slide), SAMA; card labelled: Podagrionella hyalina Girault, 'TYPE' ♀, Longreach, Q. ; A. M. Lea, SAMA Database No. 32-035303; slide labelled: Podagrionella hyalina Girault, TYPE, ♀. Remarks. The specimen is card-mounted, fragmented and partially destroyed. It lacks the head, but an apical part of one antenna is present. One hind leg is present, the other legs are missing or are covered by glue. A hind leg, a complete antenna, two fore wings and one hind wing are present on a separate slide.
dolichurum  Cockerell, 1930 and P. grotii Girault, 1915  which in combination show all diagnostic characters. Our assignment of the new material to the name L. koebelei therefore highly depends on the assumption that the previous synonymy  (Bouček 1988 ) was correct, and on the basis of formerly complete type material. The condition of the L. washingtoni type is even worse, and there are no synonyms to try a reliable assignment of new material to the type. As a result, we are facing a significant uncertainty that we will never be able to solve that the new material we have at hand actually might not belong to the described species. All data for the redescription are collected from non-type material that cannot be undoubtedly associated to the original type specimens. In this case, all new taxonomic work on this species breaks the taxonomic rule of unambiguity.
: 141. Material examined: 1 ♀ lectotype (card; designated herein), SAMA; labelled: Podagrion flabellatus Girault, Launceston T. , FM Littler. , 'TYPE ♀', SAMA Database No. 32-035305. 1 ♂ paralectotype (card), SAMA; labelled: Podagrion flabellatus Girault, Tasmania, Launceston T. , FM Littler. , 'TYPE ♀', SAMA Database No. 32-035304. ♂ ♀ types" (slide), SAMA; labelled 'Podagrion flabellatus Girault ♂ ♀ types'. Remarks. Both card-mounted specimens are accompanied by their host mantid eggcases; both are in good condition. Three antennae are present on a separate slide labelled: TYPE, Ent. Div. Dep. Ag. & Stk. , Qld. , Podagrion flabellatus Girault ♂ ♀ types, S. Aus. Mus. P. flabellatum clearly belongs to the genus Podagrion.
Acknowledgements We are indebted to curators who provided material and information for our study, namely  Susan Wright  and  Geoff Thompson  (QMB),  Natalie Dale-Skey (NHMUK) ,  Christine LeBeau  (AMNH),  Viola Richter  (ZMHU),  Simon Hinkley  (NMVM),  Peter Hudson  (SAMA),  Michael Gates  (USMN), and  James Hogan (HDOU). We thank  Claudia Etzbauer  and  Bernhard Misof  ( ZFMK ) for help with generating COI sequences, and  Hajo Krammer ,  Phil Erkeling ,  Ximo Mengual  (ZFMK), and  Kristýna Bubeníková  ( CUPC ) for assistance with imaging of specimens. Alex Donath und  Lars Podsiadlowski  (ZFMK) cleaned and submitted the included transcriptome. Peter Rühr ,  Claudia Koch , and  Juliane Vehof  (ZFMK) assisted in generation of µCT data. Maximilian Weber made most of the morphometric measurements.
Methods This exploratory study was conducted as an offspring of the CLoterreS research project. For this project, with the guidance of international guidelines, the CLoterreS team has developed a database of local health contracts. For each contract, the database includes information, for example, on the main health issues to be addressed on the territory, on the affiliations of the signatories of the contract, on the themes addressed in each action plan, be these related to the social determinants of health, health promotion activities, disease prevention activities and health care services. An observational documentary analysis on the signed contracts was conducted between January 2018 and June 2018. This study is based on a stratified sample of 17 local health contracts (one per French region) randomly selected from the population of 165 contracts signed between January 2015 and March 2018 inventoried through the CLoterreS research project (Table  1 ).
Although the first local health contract dates back from 2011, the CLoterreS team selected this period to reflect current practices and better inform decisions. To be eligible, the full contract (terms and action plan) had to be available in the CLoterreS database in November 2018. Considering the variety of regional policies and territories across the country, this strategy proved instrumental in providing a diversity of cases (Table  2 ) rather than a representative sample of all local health contracts.
Dataset and analysis A dataset in Excel format was created, and the coding was carried out at the action plan level for each of the 17 contracts. From the 17 contracts, we retrieved 440 action plans. Insufficient information on 12 of them brought the number of plans down to 428. In order to analyse whether and how frequently primary care professionals were involved in the local health contracts, we first created a nominal variable to identify the presence of primary care professional's involvement. Two researchers (CK and YB) coded the actions as follows and reached a consensus when disagreeing: the action (1) includes the involvement of a primary care professional or organisation; (0) does not include any primary care professional or organisation; (99) includes a health care professional but primary care professional is not mentioned specifically.
At the end of this process, we created our final nominal variables as (1) coordination of care, (2) health protection including environmental occupational, food safety and others, (3) health promotion including actions to address the social determinants of health and health inequity, (4) disease prevention including early detection of illness, (5) patient education programs, (6) access to care. When needed, double coding was applied. Following the thematic analysis, a descriptive analysis was done at the action plan level (n = 428) and at the contract level (n = 17) by using Microsoft Excel version 16. 02 and SPSS 15. 0 software.
Strengths and limitations This is the first study investigating the involvement of primary care professionals in local health contracts. Thus, it is also the first attempt to identify the typology of actions that involve primary care in the local health contracts in the context of France. Yet, the study has a number of limitations. First of all, we had a small sample size of local health contracts due to time constraints. However, based on a clustered random sampling method, our sample is diverse, that is, each local health contract was retrieved from a different French region thus providing an overview of the national landscape. Another limitation was the identification of primary care actions in order to create the study dataset. As the contracts were from different French regions, the glossary of terms was not stabilised.
Financial support. The authors Candan Kendir and Yann Bourgueil received funding by Mission RESPIRE. Mission RESPIRE was funded by French School of Public Health, French National Health Insurance Fund for Employees and French Institute for Research and Information in Health Economics (EHESP-CNAMTS-IRDES -EA MOS 7348 EHESP). CLoterreS was funded through the 2016 general grant cycle of the French Public Health Institute (IRePS: BRETON-AAP16-PREV-16.
Methods Materials, data, and code. Methods and materials for the study were preregistered and are available on the Open Science Framework (OSF; registration DOI [https://doi. org/10. 17605/OSF. IO/FDZXT]). The data and syntax that are relevant to the analyses described herein are also publicly available  53  , DOI [https:// doi. org/10. 17605/OSF. IO/DU7MP]. Sample and data collection. We used two Dutch panel agencies (Flycatcher and Link2Trials) to recruit 257 participants for a study with three parts: (a) an intake that took place in the laboratory of the VU Amsterdam; (b) a daily assessment phase, during which participants received daily surveys on their mobile phones for a 2-week period; and (c) a follow-up phase, in which participants were asked additional questions about events reported in the daily assessments, 7-14 days after they occurred.
Specifically, we measured the type of relationship they had with the victim and we asked for the victim's gender. We also assessed the emotional closeness of participants' relationship with the victim (i. e. , "At this moment, I feel close to the victim"), their WTR own toward the victim and their perceptions of the victim's WTR other toward them. Finally, we measured participants' perceived interdependence with the victim, using items from the Situational Interdependence Scale  58, 59. To measure emotions, we used arrays of facial emotional expressions from the Radboud Faces Database  31, 60  and asked participants to indicate whether these faces matched their feelings toward the offender (1 = completely disagree, 5 = completely agree). In this way, we measured five emotions (anger, disgust, fear, sadness, and happiness).
e. , direct confrontation, gossip, and social avoidance) with the same three items used in the daily assessment phase. We again included an open-ended question where participants could describe in detail how they behaved on the days after the violation. Statistical analyses. Data manipulation and data analyses were performed in R and in SPSS. Data exclusion procedures. We did not exclude any participants from the analyses, but we only analyzed data from completed daily assessments and follow-up surveys. In some cases, due to technical reasons, participants were able to complete the same daily assessment or follow-up survey more than once. When they did so, we retained the first response. This was either the most complete response or, in few cases, one of multiple incomplete responses. Data analyses procedures.
All reported statistical tests are two-sided. In all analyses, we used models that account for the hierarchical structure of our data (i. e. , reports of violations nested within days, nested within subjects). When predicting punishment motivations (continuous DVs, rated on 5-point Likert scales), we used linear mixed models (run via MIXED in SPSS) with random intercepts and slopes for days and subjects. When predicting punishment behaviors (binary DVs), we used binary logistic regression models (run via Generalized Estimating Equations in SPSS), again nesting observations within days and subjects. In both types of models-MIXED and GEEs-we specified an autocorrelation matrix, to account for the fact that measures taken closer in time can be more correlated than measures taken further in time.