dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We are highly grateful to  Christian Rebuf  and  Marcel Thaon  for help in insects rearing and to  Severine Lemauf  for technical help. We also thank  Pr. M. T. Kimura  ( Hokkaido University, Japan ),  Dr. R. Allemand  ( Laboratoire de Biometrie et Biologie E ´volutive, UniversiteLyon 1 ) and the  BIPE laboratory (UniversitePicardie-Jules Verne, Amiens)  for providing biological material.
A. Iacovone was funded by the  French Provence Alpes Cote d'Azur region  through the  Suzukill program  and the  Department of Plant Health (SPE)  (http://www. spe. inra. fr)from the  National Institute for Agronomic Research (INRA). This work was supported by the  European Union 's  Seventh Framework Program  for research, technological development and demonstration, under grant agreement N• 613678 (DROPSA) (http://dropsaproject. eu)and performed in the context of the " Investments for the Future" LABEX SIGNALIFE: program  reference  ANR-11-LABX-0028  (http://signalife. unice. fr). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Climate Two automatic weather stations were installed in the FS and AFS plots. Sensors installed at 2. 50 m height were connected to dataloggers (CR1000, Campbell Scientific Inc. ). Data were collected every 30 minutes from February 2012 to May 2014. Both weather stations measured relative humidity and temperature (HMP50, Campbell Scientific Inc. ) and the FS plot weather station additionally measured solar radiation (CS300, Campbell Scientific Inc. ), wind speed (03101, Campbell Scientific Inc. ) and rainfall (TE525MM/TE525M, Campbell Scientific Inc. ). Reference evapotranspiration was calculated based on the FAO Penman-Monteith equation  (Allen et al. , 1998)  using data from the automatic weather station installed in the FS plot.
At 1. 60-2. 0 m depth, there is a dark granular compact layer, without organic content, where neither roots  (Padovan et al. , 2015)  nor fractures were observed, so for the purposes of this paper, extraction of water by roots was assumed to have occurred in the 0 -2. 0 m horizon. Four to six TDR probes per pit were inserted in a total of nine pits (three pits in the FS plot and six in the AFS plot). Data were scanned every minute and stored every 30 minutes. To determine volumetric soil water content from the TDR signal (travel time on the probe rods), calibration equations were derived from extracted monoliths for each soil layer, following a protocol adapted from  Udawatta et al. (2011). The volumetric soil water contents of the layers in which each TDR probe was inserted were then multiplied by the thickness of each layer to calculate the SWR at each time step.
). Leaf area of each shoot was then calculated by multiplying the number of leaves by the mean leaf area. LAI of the coffee plots were estimated by multiplying the mean leaf area of the shoots by coffee population density and by the mean number of shoots per coffee plant. Tree leaf area was determined by using the hemispherical photograph technique on four trees of each species four times per year (February, April, July and November) in 2012 and 2013. Hemispherical photographs of the tree canopy were taken by using an upwards pointing Nikon Coolpix 4500 digital camera with a fisheye lens. Images were analyzed using the Gap Light Analyzer software  (Frazer et al. , 1999). In order to correct for the effect of branch traces in the images, hemispherical photographs of leafless Tabebuia rosea canopy in the dry season were taken and the area subtracted from photographs of canopies in leaf.
Coffee stems were protected against external heat and water ingress by thermal shields and upper waterproof protection. The heat source was turned off at night in order to protect the stems from overheating. Data were collected every 15 minutes. Leaf specific transpiration for each shoot was calculated by dividing the water flow (L d -1 ) per shoot leaf area. Coffee transpiration was scaled up to plot level (mm d -1 ) by using leaf area index (LAI). Tree sap flow rates were continuously measured over 2012 and 2013 by using the thermal dissipation technique  (Granier, 1985; Granier, 1987)  in four trees of each species. Trees were selected taking into account the average stem diameters in the plot, which were 0. 258 m and 0. 235 m for Tabebuia rosea and Simarouba glauca respectively. The set of probes (one continuously heated by a constant electrical source and the other as a non-heated reference probe) were inserted horizontally into tree stems (22 mm deep at 2.
69) and Simarouba glauca (R 2 =0. 89)  (Vertessy et al. , 1995). Probes recorded the mean sap flow rate over the conducting cross section. Calibration of thermal dissipation probes was undertaken by measuring the sap flow of the same trunks using the stem heat balance method (Dynagage/Dynamax, Inc. ) over eight days in different periods in 2012 and 2013. Although this is a direct measurement technique, this method was applied only over restricted periods in order to avoid tree stems damaged by overheating. Gauges (SGA 150) were connected to a datalogger CR 800 (Campbell Scientific Inc. ) and data were recorded every 15 minutes. For each species the coefficient α for the Granier equation was adjusted by optimization to reduce the sum of squares of the differences between the thermal dissipation and the stem heat balance measurements from different periods.
Data analysis Variance analysis was performed to compare the influence of the systems, seasons and tree species on soil water content in the treatments by using a general linear mixed-effects model (R, lme4 package,  Bates et al. (2015) ). The same model was utilized to assess and compare coffee and tree transpiration as a function of years, systems and seasons as well as the effect of the interactions of variables. The model was also applied to compare statistical differences in LWP. Soil evaporation was also analyzed as a function of LAI, lysimeter locations, systems and seasons. Analysis were carried out by using InfoStat software  (Di Rienzo et al. , 2014).
Total evapotranspiration Mean evapotranspiration rate was not significantly different between systems (p=0. 270) with 3. 48 mm d -1 (S. E. =0. 53) and 2. 61 mm d -1 (S. E. =0. 53) in AFS and in FS, respectively (Table  4 ). With respect to seasonal effects, evapotranspiration was estimated for both systems in February (middle of dry season), April (end of the dry season), July (middle of wet season) and November (end of the wet season) in 2012 and 2013. Due to missing data on coffee transpiration in November 2012 (equipment malfunction), we decided to exclude the total evapotranspiration estimation for that period (Table  4 ). Table  4. Evapotranspiration calculated from transpiration plus soil evaporation in a plot basis (with the standard error of the means in brackets) and as percentage of evapotranspiration in FS and AFS in February, April and July in 2012 and in February, April, July and November in 2013.
Acknowledgements: This research was funded by  INCAPER and FONTAGRO Caf ' Adapt  Project. CIRAD provided additional logistic and financial support. We gratefully thank  Carolina Vega-Jarquin ,  Rodolfo Munguía  and  Leonardo Garcia Centeno  that provided research facilities at  UNA. Victor Jirón Cortez ,  Ledis Navarrete  and  Elvin Navarrete ,  Mauricio Xavier Martinz ,  Dimas Nicolas Aguilar ,  Alejandro Aguirre Rosales  did part of the field work. This study is part of the  PCP ' ' Agroforestry Systems with Perennial Crops in Meso America '', a  Scientific Partnership Platform between CIRAD, CATIE, Bioversity, Promecafe´ ,  CABI ,  INCAE  and the  World Agroforestry Centre.
Unfortunately, the small amounts of the analyzed material did not allow carrying on their precise characterization. Saponification products of known wax-ester mycolates, alongside dicarboxymycolates, are secondary alcohols 2-octadecanol and 2-eicosanol  (29, 34, 35). To find these fragments, the lipids extracted from the control and EphD-overproducing strains were saponified, methylated, and analyzed by GC/MS (data not shown). C16 and C18 fatty acid methyl esters (FAMEs) were present in all of the strains and interestingly, also ␣-methyl branched FAMEs were characterized in EphD overproducers on the basis of their retention time and the base peak at m/z 88 characteristic for 2-methyl FAMEs, such as methyl-2 stearic acid and methyl 2-eicosanoic acid. In addition, minor amounts of compounds such as methyl ketone or small secondary alcohols (base peak m/z 57) were identified in the EphD overproducers, but 2-octadecanol or 2-eicosanol were not clearly detected.
Metabolism of epoxymycolic acids in mycobacteria Structural analysis MALDI-TOF/TOF-MS and MS/MS analyses were conducted in the positive ionization and reflection mode by accumulating 10 spectra of 250 laser shots, using the 5800 MALDI TOF/TOF analyzer (Applied Biosystems/AB SCIEX) equipped with a Nd:YAG laser (349-nm wavelength). For MS and MS/MS data acquisitions, uniform, continuous, and random stage motion was selected at a fixed laser intensity of 4000 (instrument-specific units) and 400 Hz pulse rate and 6000 (instrument-specific units) and 1000 Hz, respectively. For MS/MS data acquisition, the fragmentation of selected precursor ions was performed at collision energy of 1 kV. Lipid samples were dissolved in chloroform and were directly spotted onto the target plate as 0. 5-l droplets, followed by the addition of 0.
Acknowledgments-We are grateful to  Dr. W. R. Jacobs ,  Jr. ( Albert Einstein College of Medicine, New York, New York ) for the kind gift of M. bovis BCG ⌬hma, to  Dr. M. James  ( University of Alberta, Edmonton, Canada ) for the kind gift of pDEST17-ephD construct, to  Dr. Gundi  ( Colorado State University, Fort Collins, Colorado ) for help with the construction of the ephD knock-out mutant of M. tuberculosis H37Rv, to  M. Kopál  ( Comenius University, Bratislava, Slovakia ) for help with preparing the constructs, to  Dr. K. Mikus ˇová  ( Comenius University, Bratislava, Slovakia ) for critically reading the manuscript, and to  Dr. M. Nebohác ˇová  ( Comenius University, Bratislava, Slovakia ) and  Dr. Jacques Prandi  ( Institut de Pharmacologie et de Biologie Structurale, Toulouse, France ) for fruitful discussions.
Edited by Chris Whitfield This work was supported by  Slovak Research and Development Agency  Grant  APVV-0441-10  (to J. K. ),  Slovak Grant Agency VEGA  Grant  1/0284/15  (to J. K. ), the project supported by the  Research and Development Operational Programe  funded by  European Regional Development Fund  Contract  ITMS 26240120027  (to J. K. ), and  NIAID, National Institutes of Health  Grant  AI063054  (to M. J. ). The authors declare that they have no conflicts of interest with the contents of this article. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This article contains Tables  S1-S3 , Figs. S1-S7 , and references.
RNA-Seq and bioinformatics analyses RNA-Seq was performed according to the manufacturer's guidelines and previous protocols (C-10365, Life Technologies 47 ). RNA deep-sequencing analyses were performed at Epigenetics Key Laboratory of Institutes of Biomedical Sciences (IBS) of Fudan University (Shanghai, China). For bioinformatics analyses, the sequence reads were mapped to human hg19 reference genome transcripts and genome databases using TopHat software allowing 2-bp mismatches per 125-bp seed. Transcript structure and abundance were estimated using Cufflinks software, and differential expression analysis was performed using Cuffdiff software  48. The cutoff value of differential expression gene was: | log2 (fold change) | > 1, p-value <0. 05. GO enrichment analysis was performed using DAVID ver.
Real-time PCR (RT-PCR) validation RT-PCR primer sequences were designed using Primer3 web software (version 4. 0. 0). The primer sequences used are provided in Supplementary Table  S2. The GAPDH gene was used to calculate the relative fold differences based on comparative cycle threshold (2 -ΔΔCt ) values. The RT-PCR procedure was as follows: 1 μL of cDNA in H 2 O was added to 5 μL of 2 × SYBR Green buffer, 0. 1 μM each primer, and H 2 O to a final volume of 10 μL. Differences between the two samples were calculated using Student's t-test at a significance level of 0. 05 in Graphpad Prism 6. 0 software. All expression analysis was performed for three biological repeats and the average values of three repeats values were shown in the figures.
Acknowledgements This study was supported by the  National Natural Science Foundation of China  (grant numbers  81730026 ,  81425006 , and  81470640 ), the  Foundation of Shanghai Jiaotong University School of Medicine for Translational Medicine Innovation project  (grant number  15ZH4005 ), the  Foundation of Shanghai Engineering Technology Research Center for Construction project  (grant number  16dz2251500 ), the  Foundation of the Shanghai Science and Technology Committee  (grant number  16140900800 ). Work in the JY/YL laboratories was supported by the  National Natural Science Foundation of China  (grant numbers  81171846 ,  81270433 ,  81372099 ,  81471400 ,  81522017 , and  81671900 ), the  Shanghai Foundation for Basic Research of Science and Technology, China  (grant number  13JC1404001 ), the  Foundation of the Shanghai Science and Technology Committee  (grant number  11ZR1422100 ), and  Shanghai Municipal Education Commission --Gaofeng Clinical Medicine Grant  (Grant number  20161408 ).
In your research practices, work on green chemistry has changed signifi cantly your working practices? Or these changes are in line with your previous work? 16. In your team, working on green chemistry involves collaborations with other disciplines, with which you were not used to work before? 17. Do you work with toxicologists? With ecotoxicologists? With ecologists? With agronomists? With researchers in social sciences? 18. How do you know if the substances you use are toxic or not? Do you use specifi c databases, your own knowledge, or other sources? 19. Are there many of your team / lab colleagues working in green chemistry? 20. Is there an Environment department or an Agriculture department in your university / research center? Is it big? Do you work with?.
In your research practices, work on green chemistry has changed signifi cantly your working practices? Or these changes are in line with your previous work? 16. In your team, working on green chemistry involves collaborations with other disciplines, with which you were not used to work before? 17. Do you work with toxicologists? With ecotoxicologists? With ecologists? With agronomists? With researchers in social sciences? 18. How do you know if the substances you use are toxic or not? Do you use specifi c databases, your own knowledge, or other sources? 19. Are there many of your team / lab colleagues working in green chemistry? 20. Is there an Environment department or an Agriculture department in your university / research center? Is it big? Do you work with? Research practices 15.
Centers belonging to clinical networks host a multidisciplinary tumor board (MTB) on sarcoma, which guides practitioners and provides advice for the management of their patients. The networks comprise national referral centers and regional expert centers. National referral centers are responsible for structuring the national network, arranging the double reading of tumor specimens, structuring referral multidisciplinary consultative meetings, contributing to clinical research on these rare cancers as well as drafting recommendations for good practice and their dissemination throughout the network. These centers also have to establish a national database to contribute to the observation of these cancers, organize training for all health professionals involved, work in collaboration with the patient associations, and provide relevant information to patients and their relatives.
National Network Databases A website collecting all patients with a pathological review and all patients discussed in the sarcoma specialized MTB in referral network centers was generated in 2010). The databases contain 60 items divided into four themes: characteristics of the patient and tumor, diagnosis and review, key steps in management and follow-up, and successive presentations of the file and decision making at MTB. The patient's place of residence (French "Commune", municipal scale) at diagnosis is recorded. The prospective collection of the address of the patient, diagnosis and clinical data, place of surgery and patient follow-up are prospectively implemented in the databases. The yearly follow-up of these national databases helps monitor the evolution of clinical practices. A quality assurance program has been established for these databases to ensure the quality of medical data recorded.
IGéAS Cohort Extraction The inclusion criteria of the IGéAS cohort are: The following data were extracted from the database: patient (sex, age, past medical history, address at diagnosis) and tumor (size, grade, stage, localization, type) characteristics and management (clinical situation at RCP). Considering the inclusion criteria, the IGéAS cohort was comprised of 20,590 patients amongst which 488 patients came from overseas territories (French DOM-TOM). Having access to expertise before surgery or after relapse as not considered to be the same, especially for sarcomas, which are mainly aggressive tumors. It was then important to focus our analysis on the ability of the networks to provide early access to expertise to improve patient prognosis. In accordance with sarcoma guidelines and the opinion of sarcoma experts, we considered optimal access to pathological expertise if a review was performed within 30 days of sampling, and optimal access to clinical expertise if the first sarcoma MTB was performed before the first surgery.
Flow Maps Mapping of the flows between patient location and place of the histological review or sarcoma MTB discussion was performed thanks to QGis software (QGis, Grüt, Swiss). Each line represents a relation between the commune and expert center. Numerous communes (mostly urban communes) have several sarcoma patients in the IGéAS cohort and patients of the same commune may be addressed to different expert centers. The size of the flow was calculated according the number of patients comprising each relation. To improve the map reading, we separated soft-tissue, bone, and visceral sarcoma flows as these sarcomas are specific and require different expertise. If necessary, we also distinguished between flows to national referral centers, which present a higher activity, and flows to regional expert centers.
Measuring the Accessibility of the Networks To measure the patient's remoteness to expertise, the calculation of the travel time to the closest expert center was performed using Odomatrix ® software (Source: ODOMATRIX, INRA UMR1041 CESAER, from IGN Route500 ® IGN, Saint-Mandé, France). The travel time was calculated from the town hall of each commune as the complete address of the patients was impossible to collect. Nonetheless, even if the calculation from the town hall address was less accurate than that from the patient's home address, the difference (few minutes at best) should not strongly influence the results as it was for a primary care access study. Median and average travel times to the closest expert center were calculated to assess the overall distance of the French population from the expert centers.
The socio-economic deprivation of district will be evaluated thanks to the European Deprivation Index, a French deprivation index used in cancer inequalities research  [21]. Regarding the remoteness analysis, the data from the networks did not allow us to study the disparities inside urban districts. However, this precision loss is bound to the nationwide dimension of this project, which allows an original and quite exhaustive analysis of the access to sarcoma expertise in France that is based on a database containing tens of thousands of patients crossing clinical and geographical data. We will also produce a geographical classification to analyze sarcoma outcomes according the patients' geographical context. Geographical classification (K-means classification) will be performed using 17 variables estimating environment quality, socio-demographic characteristics, and health care access in each French commune (n = 35,798 communes).
In addition, some clinical variables such as the percentage of R2 surgery, inclusion of a patient in a clinical trial, or access to molecular biology examination will be tested to evaluate the clinical impact on patients of this unequal access to expertise. This delayed access may lead to survival inequalities as patients may have not received the right treatment in the initial phase. After care follow-up will be performed for IGéAS cohort patients diagnosed in 2013. Diagnoses of intermediate malignity tumors, GIST, and low grade sarcoma are excluded from survival studies in order to focus on worse prognosis patients. The population of this nationwide survival study is estimated to be around 2200 patients. Moreover, upcoming data analysis will be useful for better knowledge of the functioning of national sarcoma networks by pointing out the least covered populations and the main impediments to accessing expertise.
PLPP3 Is Repressed in Human Carotid Atherosclerotic Lesions We first evaluated the genetic association of a previously reported rs17114036 variant with PLPP3 levels in human plaques by eQTL analyses. Two suitable proxies were identified (rs6588634 and rs9970807, both R 2 > 0. 8 and D' = 1) that confirmed a marginal association with PLPP3 mRNA levels in plaques from n = 127 patients that were available for these analyses (p = 0. 07; Figure  1A ). Next, gene expression analysis of totally n = 177 lesions showed a significant downregulation of PLPP3 transcript in carotid plaques (CPs) compared to macroscopically NAs in two non-overlapping microarray datasets (n = 127 CP versus n = 33 NA from the larger discovery dataset and n = 50 CP plaque versus n = 5 NA from the smaller validation dataset; Figure  1B ), with mean log2 difference ± SD = À0.
0072; Figure  1D ). To begin delineating the cellular processes associated with PLPP3 expression, correlation analyses were performed in plaques from the Biobank of Karolinska Endarterectomies (BiKE) discovery dataset between PLPP3 and various cellular markers (Table  S1 ). These analyses showed positive correlation (Pearson r > 0. 3, p < 0. 0001) between PLPP3 expression and typical smooth muscle cell (SMC) markers; endothelial marker CD31; T lymphocyte marker CD45RA; pro-inflammatory markers nuclear factor kB (NF-kB) and BMP4; extracellular matrix degradation via SULF1; growth factors TGFB1, IGF1, PDGFB, and PDGFD; and chemokine CCR2. Weak positive correlations were found also with some macrophage markers. To evaluate these bioinformatic results, we further examined PLPP3 mRNA and protein expression levels and localization in various cell types in NAs and plaques in situ.
Overall, PLPP3 protein signal was strong in NAs, particularly in CD31+ ECs, but it was also found in medial SMCs (Figure  2A ). PLPP3 transcript and protein (Figure  2B ) expression was also detected in CPs (n = 10 samples examined), particularly localized in the fibrous cap and in the areas lining the necrotic core. PLPP3 was absent from peripheral, subintimal plaque areas. Double immunostaining with specific cell markers showed PLPP3 protein localization in various cell types, such as SMCs (SMA + ), ECs (CD31 + ), and macrophages (CD68 + ; Figure  2B ), confirming the correlation analyses. These results confirmed overall lower levels of PLPP3 in advanced human plaques, but they indicated a wide expression pattern linked to several major plaque cell types. LPAR6 Is the Most Abundantly Expressed LPAR in Human Plaques, Localized to ECs, and Positively Correlated with PLPP3 Expression of LPAR1-6 transcripts was then analyzed in microarrays from human CPs compared to NAs (discovery dataset) (Figure  3 ).