dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2009). The effects of treatment are significant, if the confidence interval (CI) did not overlap with zero  (Borenstein et al. , 2009). We used the following explanatory variables as moderators for the effects of management: (1) irrigation (irrigated or rainfed vineyards); (  2  Plots for mean effect sizes and 95% CIs were produced with the r package plotrix  (Lemon, 2006). Mixed-effects models with restricted maximum likelihood estimations for estimating the random effects were selected based on significant Q-statistics for residual heterogeneity of moderators and a model difference in Akaike's Information Criteria for small sample size (AIC c ) of at least 2 (ΔAIC c >2; cf. Motulsky & Christopoulos, 2003). Models including irrigation had a lower sample size as irrigation data were not available from every study.
| RE SULTS In total, we extracted 181 datasets from 74 articles covering major wine producing regions world-wide except Asian countries, New Zealand and Argentina (Figure  1 , Table  S1 ). The publication dates span from 1992 to 2017. Therefrom, 60 articles originated from the initial search and 14 additional sources from unpublished datasets provided by research colleagues and additional articles from an updated search (see previous chapter and Figure  S1 ). The different categories of ES were well represented in the datasets with a focus on regulating ES (Figure  1 ). About 40% of all datasets originated from irrigated vineyards, 50% were rainfed vineyards and the other studies did not provide information on the use of irrigation (Table  S1 ). Most datasets came from vineyards under Overall, there was a 19•8% increase in biodiversity and ecosystem service provision due to extensive vegetation management in comparison to the control treatment (Figure  2 ).
| D ISCUSS I ON F I G U R E were not related to the use of irrigation, as approximately half of all datasets originated from irrigated Mediterranean vineyards, whereas 83% of all datasets in continental or steppe climates descended from irrigated vineyards. Steppe or semi-arid climates are characterized by rainfall deficiency  (Kottek et al. , 2006) , which increases the need for irrigation. Previous narrative reviews also found overall positive effects of environmentally friendly management on biodiversity and ES provision of inter-row vegetation management in vineyards  (Guerra & Steenwerth, 2012)  and of cover crops in vineyards and olive groves  (Pardini et al. , 2002). However, some studies indicated trade-offs between production and other ES (e. g. Morlat & Jacquet, 2003). The review of  Guerra and Steenwerth (2012)  discussed the relationship of (potential) water stress created by cover crops and concluded that the combination of factors like water regime, cover crop species, management, duration of cover crop establishment, age of vines is very complex and therefore studies show conflicting results.
Despite the potential reduction in wine yield and available soil water, water competition between vines and cover crops also creates benefits from some winegrowers  (Guerra & Steenwerth, 2012). The reduced vine growth may decrease the costs associated with vineyard operations like fruit thinning and leaf pulling for producing high-quality wine  (Guerra & Steenwerth, 2012). In this meta-analysis, we could not detect any overall negative effect of inter-row vegetation cover on grape quantity or quality; nevertheless, in vineyards of dry climates without irrigation grape yields could decrease if vegetation is not carefully managed. We found a significant difference in effect size dependent on the study design of the considered studies. The non-significant response of datasets from randomized block designs in several vineyards is most likely the result of the low number of studies, which mainly cover soil loss and grape yield.
The type of treatment-control slightly altered the effects of extensive vegetation management. However, it should be remarked that also sample sizes differed considerably with F I G U R E 2 Effects of extensive vegetation management in vineyard inter-rows on overall effect size. Significant differences between moderator levels are indicated by whiskers with the associated level of significance (*p <. 05, ***p <. 001). Numbers in brackets show the sample size of the datasets 56% of all datasets from studies comparing organic vs. conventional management investigated biodiversity. These differences might have increased the associated effect size as biodiversity variables responded strongly positive to extensive vegetation management. Regulations for organic winegrowing do not obligate winegrowers to use cover crops, but in our dataset all organic vineyards used vegetated inter-rows in the vineyard (only three studies did not include information on inter-row management).
Mediterranean climates (n = 100), oceanic climates (n = 56), and steppe or continental climates (n = 22; three studies included vineyards from different climates). Most studies implemented randomized block designs within one experimental vineyard (n = 113), only few studies implemented block designs in several vineyards (n = 12), whereas 56 datasets used individual vineyards as replicate. The majority of studies investigated the effects of bare soil management (mostly due to tillage, sometimes by use of herbicides or both) compared to cover crops or natural vegetation (n = 137 datasets). We investigated the effects of conventional vs. organic management in vs. extensive vegetation management like the contrast of single to diverse cover crop species in inter-rows or mulching vs. mowing of vegetation.
TA B L E 1 Summary of the ecosystem services (ES) (according to the Millennium Ecosystem Assessment, 2005) and biodiversity datasets extracted from 74 included studies and tillage or mulching as control in comparison to more extensive types of vegetation management did not result in an overall signifi- cant positive effect. Subset (number of datasets ES category/biodiversity ES type/biodiversity included) Variable Biodiversity Biodiversity Flora (6) Plant species richness Fauna (18) Earthworm species richness Spider species richness and abundance Beetle species richness and abundance Grasshopper species richness Insect pollinator species richness and abundance (bees, butterflies) Bird species richness Provisioning Grape quality and Grape quantity (23) Grape yield quantity Grape quality (22) Must quality (sugar content, titratable acidity, yeast assimilable nitrogen) Regulating Erosion protection Soil loss (9) Soil loss Erosion-related soil Water retention parameters (8) Topsoil penetration resistance Aggregate stability Saturated hydraulic conductivity Carbon sequestration Soil carbon (19) Soil carbon content Pollination Pollination (2) Flower visitations Seeds per plant Pest control Natural enemy-related Abundance of potential natural enemies parameters (21) Percentage of parasitism and predation Pest-related parameters (13) Pest abundance Damage per vine and plot Soil water balance Soil water balance (6) Water stress integral, water loss, volumetric soil water content Supporting Soil fertility Soil biota (17) Soil fauna abundance (nematodes, earthworms, springtails, Oribatida, invertebrates) and biological quality indicator Arbuscular mycorrhiza abundance (fungal spores and colonisation) Nutrient cycling processes Soil fauna feeding activity (17) Soil microbial biomass Soil microbial respiration and activity Soil macronutrient content and availability.
Heat transfer analyses The purpose of this section is to present the finite element model of heat transfer to evaluate the temperature fields. The spatiotemporal temperature field is determined considering conductive and convective hear transfer processes ( , ) ( , ) ( ) x x x p c T t k T t s t ρ ∂ = ∆ + ∂ ( 1 ) where T is the temperature distribution in the specimen, ∆ the Laplacian,  the mass density ( •  -3 ),   the specific heat capacity ( •  -1 •  -1 ),  the thermal conductivity ( •  -1 •  -1 ), and  the volumetric heat source accounting for the heat flux induced by Joule effect. Other thermophysical parameters of the material are chosen from data reported in the French design code RCC-MRx  [9]. The natural convection flux across the boundary layer on the surface of the specimen reads ( ) He q h T T = - - ( 2 ) where  is the convection heat flux (/ 2 ), ℎ the heat convection coefficient (/ 2 /°)  [10]  ,   the temperature of He atmosphere (known to be equal to 30°C).
Conclusion The FLASH facility allows thermal fatigue tests to be performed on specimen made of 316L(N) austenitic stainless steel with highly stable thermal amplitudes ranging from 150°C to 200°C prescribed by pulsed laser shocks. Full-field temperature and displacement measurements on the surface of the specimen are carried out by using infrared and visible light cameras. The equivalent Tresca strain range obtained from numerical simulations combined with crack initiation detection via correlation enable the fatigue strength of the studied material to be estimated. The comparison with the French design code RCC-MRx shows a good conservative agreement. The next tests will be conducted with different thermal loading histories to build up an extensive and well-documented database. Furthermore, tests with helium pulses onto hot plates or with static 4-point bending should be performed to investigate the mean bi-tensile stress effect on the fatigue response.
Importantly, the proximity between FUBP1, RUNX1 and CBFB were confirmed in bone marrow cells from all three tested pre-B leukemic patients (Figure  2D ). Finally, comparable results for FUBP1 and RUNX1 proximity were obtained in other pre-B cell lines (BJAB and REH cells) (Supplementary Figure  S1D ). Interestingly, we demonstrated a proximity between FUBP1 and the fusion protein ETV6-RUNX1 in REH cells (Supplementary Figure  S1D ). Moreover, we evidenced a spatial proximity between FUBP1 and RUNX1 in human CD34+ hematopoietic stem and progenitor cells (Supplementary Figure  S1E ). Altogether, these data indicate that FUBP1 is likely to influence RUNX1 transcriptional activity in blood cells.
We then analyzed ChIP-Seq data for RUNX1 and histones marks along the c-KIT gene in pre-B Nalm6 and bone marrow cells from leukemic patients. We identified two strong RUNX1 binding regions in the first intron of c-KIT: one at +700 bp and another one at +30 kb from TSS, that overlapped respectively with H3K4me3 and H3K4me1 marks and displayed H3K27ac marks, indicating active chromatin regions (Figure  3B ). To investigate the binding of FUBP1 on those two chromatin regions, FUBP1 antibody was first validated for IP in HEK293 +FUBP1 (Supplementary Figure  S2C ) and tested for ChIP-qPCR on CDKN1A and MYC loci. Very few target regions, including CDKN1A and MYC loci, are described as bound by FUBP1, with results varying with the models  (19, 20, (56) (57) (58). We found that FUBP1 binds to CDKN1A promoter in Nalm6 control compared to Nalm6 shFUBP1 , but not to the -1.
DISCUSSION In our search for identifying transcriptional co-factors of RUNX1 and deciphering its function, we demonstrated that RUNX1 and FUBP1 co-immunoprecipitated and are physically located close to each other on active chromatin of human hematopoietic cells. Examination of RUNX1 genome-wide distribution together with gene expression data led us to propose that the oncogene c-KIT is commonly activated by RUNX1 and FUBP1. Moreover, we identified two regulatory regions in the first intron of c-KIT (the +700 bp promoter and the +30 kb enhancer regions) that are bound by RUNX1, FUBP1 and present active histone marks. Interestingly, a very recent study in AML1-ETO acute myeloid leukemia spotted identical regulatory regions in c-KIT gene bound by RUNX1 (or AML1) and the fusion protein AML1-ETO  (72).
The phenotype on proliferation associated to c-KIT pathway activation is reinforced when both FUBP1 and RUNX1 are overexpressed. FUBP1 mRNA level was shown to be significantly elevated in leukemia stem  ← ------------------------------------------------------------------------------------------c -KIT FUSE-like sequence within the +30 kb c-KIT intronic enhancer (identified by in silico analysis) is provided. Nucleic acid homology is represented by asterisks (*). The c-KIT FUSE-like sequence showed 48. 2% homology compared to the FUSE sequence according to CLUSTAL 2. 1 Multiple Sequence Alignments software  (83). (Bi) Scheme of the human c-KIT enhancer. Predicted RUNX1 binding sites with more than 80% sequence conservation from Jaspar RUNX1 matrix (MA0002. 1) have been selected with Jaspar software  (61).
ID: inhibition domain, TE/TD: transactivation domain. (Cii) Luciferase assays with a vector containing the +30 kb c-KIT enhancer downstream a minimal promoter and a luciferase ORF, in presence of RUNX1 mutants and truncated FUBP1 expressing vectors were performed in HEK293 shFUBP1 cells. Luciferase levels are represented as in Bii. (Di) Binding energies (kcal. mol-1) of the KH3 subdomain of FUBP1 protein with the c-KIT FUSE-like sequence. Red framed sequence corresponds to the positioning of KH4 subdomain by analogy to the FUSE sequence retrieved on c-MYC promoter associated to the solution NMR structure of KH4 (PDB ID: 1J4W). Maximum binding energy of KH3 is computed for 5 -ATATAAA-3. (Dii) Structural model for the most stable complex formed by the KH3 subdomain with the c-KIT +30 kb enhancer sequence.
Protein is shown as a molecular surface colored according to hydrophobicity (84) (white for hydrophilic, yellow for hydrophobic), electrostatic potentials are represented as iso-surfaces (-50kTe in red and +50kTe in blue). Tilt observed between second (dT) and third (dA) bases originates from the position of the KH3 first alpha-helix, as it was observed in the experimental structure used as structural template for modeling. (Diii) Schematic representation of the amino-acids of KH3 subdomain of FUBP1 involved in the binding to the FUSE sequence. cell-enriched cell populations and in some lymphoblastic leukemia subtypes (data from St. Jude Children's Research Hospital)  (67, 79)  as well as RUNX1  (80). Here, we proposed for the first time a rationale for oncogenic function of FUBP1 in leukemia.
ACKNOWLEDGEMENTS We thank all the members of  Gene Expression and Oncogenesis team  for insightful discussions and for providing scientific expertise. We are most grateful to patients and patients' families from  Rennes University Hospital, CHRU Rennes. We thank the following core facilities from  BIOSIT SFR UMS CNRS 3480 -INSERM 018 :  MRic-Photonics (Stéphanie Dutertre ), Flow cytometry and cell sorting ( Laurent Deleurme, Gersende Lacombe), ImPACcell (Rémy Le Guével ), ARCHE animal housing ( Laurence Bernard-Touami ),  Human and Environmental Genomics of Rennes facilities (GEH) (Marc Aubry ).
Results will be published in accordance with the CONSORT statement in a peer-reviewed scientific journal. trial registration number NCT02804074; Pre-results. strengths and limitations of this study ► The MASked-unconTrolled hypERtension management based on office BP (OBP) or on ambulatory blood pressure measurement Study is the first randomised controlled study so far addressing an important and still controversial issue, that is, whether using ambulatory BP monitoring rather than OBP measurements as a guide to antihypertensive treatment confers any benefit in terms of cardiovascular prevention. ► For practical reasons, a double-blind study design could not be implemented. ► Due to the multicentre nature of our study which includes hypertension centres from all over the world, there is the theoretical possibility of a dropout rate larger than expected.
blinding (masking) Because the MASTER is an open-label study, blinding will apply only for the endpoints. Thus, both participants and investigators will not be blinded to the allocated intervention. In order to guarantee the blind assessment of the coprimary end point of LVM, the personnel involved in its assessment (ie, sonographers) will be blinded to the patient's allocated management strategy. In addition, ABPM data of patients enrolled to group 1 will be directly uploaded on the e-CRF and will not be available to the investigators in charge of treatment decisions. The role of the Event Adjudicating Committee (EAC) of the MASTER Study is essential in a trial such as MASTER, necessarily designed as a PROBE trial. EAC within the MASTER study will be responsible for verifying and adjudicating in a blinded fashion all events representing primary, secondary and tertiary outcomes of the study, as well as all serious adverse events occurring in the course of the trial.
Interventions 7] [8]  Indications on how to measure BP in the office, at home and in ambulatory conditions over 24 hours are provided in the supplemental operation manual. In order to standardise ABPM and OBPM data collection and analysis as much as possible, 24 hours ABPM in MASTER Study will be preferentially performed with A&D TM2430 device and OBP levels will be measured with A&D UA-651BLE device provided by the coordinating centre, thanks to a non-conditioning support given by the A&D Company (A&D Engineering, San Jose, California, USA). For HBPM, any of the currently available validated devices can be employed. Patients randomised to be managed based on OBP (group 1) will have their BP measured by the office method at all scheduled visits (at 0, 3, 6, 12, 18, 24, 30, 36, 42 and 48 months).
In these patients, ABPM will only be performed at randomisation visit and at follow-up visits 3, 5, 7 and 9 and in case of premature discontinuation (ie, patients' decision to withdraw, non-compliance with the study protocol) at any time. However, ABP values will not be used by the investigator to take treatment decisions, but treatment will be based on OBP only. According to the ABPM procedures manual of the MASTER Study, data of these patients will not be made available to the investigators in charge of treatment decisions, but will be directly uploaded on the study website (table  1 ). As indicated in table 1, additional tests and examinations will be performed at different time points during the study follow-up. 6] [17] [18]  LVM will be calculated with the formula for estimation of LVM from LV linear dimensions.
Patient and public involvement Neither patients nor public have been involved during the design of MASTER Study. We only explored, in a pilot survey, how often OBP control was accompanied by persistence of elevated BP values at home in a number of patients referred to our hypertension centre. Results of MASTER study will be published in peer-reviewed scientific journals regardless of the outcome. Besides, MASTER study results will be available at https:// clinicaltrials. gov/ either to patients or general public. Finally, assessment of the burden of the intervention has not been foreseen in the present study. data collection and data statement All data collected locally at each enrolling unit will be recorded into an electronic e-CRF on the website of the trial (https:// master. digitalcrf.
eu). Clinical data to be input into the e-CRF include demographic information, results on blood and urine laboratory tests performed locally (ie, plasma creatinine and eGFR, fasting glucose, uric acid, Na+, K+, Lipid profile, HbA1c, urinary sediment), ongoing antihypertensive treatment (number and dose of antihypertensive drugs), report forms for adverse events during follow-up. Digital files containing raw data from imaging (echocardiograms) and device-based examinations (ABPM, HBPM data, ECG tracings) will also be uploaded on the study website linked to the study e-CRF for further analysis by the central reading sites.
data management Clinical data and recordings obtained with the various technologies will be uploaded in the study e-CRF and on the study website under online control by the Data Management Coordinating centre, which includes an audit trail system. The quality of data obtained with the different technologies (ie, echocardiograms, ECG, ABPM, HBPM) will be validated by expert personnel at Open access the central reading sites. The occurrence of CV events (stroke, myocardial infarction, all major CV events, CV death, all cause death) and renal outcomes reported by each centre and supported by clinical source documents (ie, diagnostic tests, medical reports) will be validated by an adjudicating committee of experts blinded to assigned management strategy, BP values and organ damage measurements.
data analysis plan Descriptive analysis Continuous data will be summarised by means of mean value, median, minimum, maximum, SE, SD and number of observations. Moreover, a 95% CI for the mean value will be provided. Categorical data will be summarised by means of absolute and relative frequencies. All these analyses will be performed per management group as well as for the study population as a whole. Unpaired t-test (or Mann-Whitney U test in case of non-normal distribution) for continuous variables or 2 test for categorical ones in order to evaluate significant between-group differences at baseline will be applied. The incidence of adverse events will be tabulated by treatment group.
For endpoints 4-12, we will apply the χ 2 test for dichotomous endpoints, the t-test (or median test whenever necessary) for continuous endpoints and, specifically for variation coefficients, the large sample Z test as suggested by Bhoj et al. 26  For endpoints 13 and 14, the differences between the Pearson correlation coefficients of two groups will be calculated and reported with their 95% CI using the approach proposed by Zou. 27 The handling of missing data will be based on a multiple imputation approach. 24 All statistical tests will be interpreted at the 5% significance level considering two-sided test, unless specified otherwise. All statistical analyses will be performed by the Statistical Centre of Istituto Auxologico Italiano, Milan. Statistical packages will be used for the analyses, chosen among Stata (STATA data analysis and statistical software, Texas, USA), R (The R project for statistical computing, free software) and SAS (SAS Institute, Cary, North Carolina, USA).
Acknowledgements The names of the remaining investigators of the MASTER Study are listed in the supplementary file 1. Contributors GP, RS, GS, RM, JR, SO, GLB, GM and AZ: contributed to the conception and design of the study. GP, RS, GM and AZ: drafted and wrote the protocol in accordance to the coauthors' contributions. TF and PW: the study managers in charge of database management and study monitoring. All authors contributed to the writing and the reviewing of this article, and approved the final draft of the protocol.
How is optimization of obtained data (kinetic traces) using stopped-flow technique executed? To optimize the kinetic scheme, which would be describe the kinetic traces obtained by stoppedflow technique  [4] , the proposed mechanisms should be examined by adding a gradual stage of the enzyme-substrate complex transformation, with replot and analysis of residuals being carried out. Global nonlinear least-squares fitting of the data obtained was performed in the DynaFit software (BioKin Ltd. , USA)  [5]. The scree test was conducted for validation of the proposed kinetic scheme (Fig. 1 ). Two-or three-step binding mechanisms describing Apn1's interaction with substrate DHU(2-aPu) in BER buffer are represented as Schemes 1 and 2, respectively.