dataesr/scientific-paragraphs-categorization huggingface dataset:
Analyses We implemented Bayesian Inference (BI) and Maximum Likelihood (ML) phylogenetic analysis to reconstruct the relationships within the studied populations of P. guyannensis and P. cuvieri from northeastern Amazonia using the complete dataset. Sequences of the rodent species Trichomys apereoides (AY083332, AY083341) and Hoplomys (HM544128, NC020657) were obtained from GenBank and included in the analyses as outgroups. The program jModelTest version 3. 7  [54]  was used to select the HKY+G like the best evolutionary model for the datasets based on Bayesian Information Criterion (BIC). The BI phylogenetic tree was reconstructed in MrBayes 3. 2. 1  [55] , using 10 7 Markov Chain Monte Carlo (MCMC) generations, with the first 25% being discarded as burn-in. The ML analysis was run in RAxML in the CIPRES Science Gateway  [56].
Nodal support was estimated with 100 bootstrap replicates. We estimated the divergence time between the lineages using the relaxed lognormal uncorrelated molecular clock and the Yule Process pure-birth speciation model, run in BEAST 1. 6. 2 [57]. The substitution rate used in the analysis was the 3. 6% divergence per million year, derived from Fabre et al. [58]. We used the following times inferred from Fabre et al. [59] : divergence between [Trichomys] and [Hoplomys + Proechimys spp. ] set at 9. 4 Ma (95% CI: 8. 3-10. 9) and divergence between Hoplomys and Proechimys set at 5. 5 Ma (95% CI: 4. 6-6. 7), with these dates being used as calibration for the most recent common ancestor (TMRCA), considering a normal distribution. Since no more recent calibration point was available, we confirmed the absence of substitution saturation on the [Trichomys + Hoplomys + Proechimys] dataset with the Xia test  [60]  implemented with DAMBE  [61].
0  [53]. We considered the isolation effect of different river drainages to evaluate gene flow patterns between geographic areas. We used the program Migrate  [65]  under a Bayesian framework to estimate effective population sizes (Θ) and migration rates M between different populations from north to south in studied area. We ran four chains with thermodynamic integration and a static heating swap scheme (temperatures: 1. 0, 1. 5, 3. 0, 10 6 ), sampling at every 100 th step for a total of 20 6 steps and a burn-in of 50 3 steps. We calculated Fu's Fs  [66] , Tajima's D  [67]  and R2  [68]  to assess potential deviations from neutrality. We used the program DNASP version 5. 0  [69]  and significance was determined from 10,000 coalescent simulations. For each Proechimys species, we analyzed the complete dataset, the clusters from BAPS analyses, and samples separated by main interfluves (Maroni-Oiapoque; Oiapoque-Araguari; Araguari-Jari; Jari-Trombetas).
01 in t-tests and Mann-Whitney Table  1. Haplotype diversity (HD), nucleotide diversity (π) and the number of individuals (N) in the clusters identified by the BAPS for P. guyannensis (G1, G2, G3, G4) and P. cuvieri (C1, C2, C3, C4), the interfluves of the study region: Maroni-Oiapoque; Oiapoque-Araguari; Araguari-Jari e Jari-Trombetas and the complete dataset for each species.
Demographic history We obtained significant results for the R2 (-2. 3781), D (-2. 3781), and Fs (-4. 572) parameters for the P. guyannensis cluster G1. When we analyzed the whole dataset, however, no significant values were obtained, although a significant R2 value was obtained for cluster G4 (Table  4 ). We obtained similar results when we analyzed the data organized by interfluve, with significant values being recorded for all the parameters for the Oiapoque-Maroni interfluve (Fs = -4. 168; D = -2. 3200; R2 = -0. 0617), which has haplotypes similar to the G1 cluster. In the complete P. cuvieri dataset, the only deviation from neutrality was found in Fu's Fs (Table  4 ). In the analysis of the clusters, we recorded significant Fs values for clusters C2 and C4, and significant R2 values for clusters C1 and C3.
Despite the relatively limited size of our study area probably these phenomena had distinct impacts on its northern and southern portions contributing to the diversification of the populations of both P. cuvieri and P. guyannensis. In spite of the limitation of mitochondrial data, it has already been shown to be robust  [76]  and has long been considered as a standard for phylogeography and for assessing genetic variation within and between populations  [77]. Further, high ration of copies compared to nuclear genome allows for isolation from long dead tissues and collection-preserved samples, and consequently favor work on large dataset, not requiring new field captures for fresh and/or high quality samples. Here we significantly increased the knowledge about the phylogeography of these two species Amazonian Spiny rats  [29, 34]  confirming the uniqueness of the western portion of the Guyana region.
French Guiana is located entirely within the area of the Guianan Shield, this region Table  3. Estimated migration among Proechimys guyannensis and P. cuvieri populations, based on analysis using the Migrate software. The samples were divided into three regions, the North (from the Oiapoque River to Guyana), Central (Araguari basin in Amapa) and South (Amazon Estuary, Jari and Pararivers). The plus sign (+) after the name of the region indicates a population receiving immigrants. The highest migration rates are marked in bold type. is an ancient region and previous studies regarded it like a forest refugium of northeastern Amazonian  [81]. Van Vuuren et al. [34]  suggested a recent expansion based in the high number of shared haplotypes in P. guyannensis from the regions of Cayenne and Petit Saut (see  Fig 1) , in French Guiana.
Acknowledgments We thank  Claudia Funi ,  Henrique SR Gomes ,  Isai J Castro ,  Romina S Batista  and  Emmanuel Douzery  for the support during the preparation of this article. Chrysoula Gubili  for review and comments on the initial manuscript. Marcio W. Neri  and  Sergio Cunha  helped us with the figures edition. We are very grateful to two anonymous reviewers for their much useful contributions to this article.
We thank  Fundacão de Amparo à Pesquisa do Estado do Amapa(FAPEAP) ,  Fundacão de Amparo à Pesquisa do Estado do Amazonas (FAPEAM)  and  Intitut de Recherche pour le Developpement (IRD)  for financial support through the  Guyamazon II research program  ( Process 250203021/2014 ). The project benefited from the support of an '' Investissement d'Avenir '' grant managed by  Agence Nationale de la Recherche  ( CEBA, Ref. ANR-10-LABX-25-01 ). We also thank  FAPEAP  for support through the  Thesis Funding Programme  ( Process 250203052/2014 ). CRS thanks  Coordenacão de Aperfeicoamento de Pessoal de Nı ´vel Superior (CAPES)  for a scholarship. RNL was supported by  CNPq  ( 150069 / 2014-6; 457267 / 2014-3 ).
The morphological differentiation between the STP and its surroundings is manifested by a thicker crust compared to the rest of the basin particularly north of Saida fault, as implied by the gravity map. Several geological aspects of the STP, however, remain poorly understood and require more in-depth analysis. In particular, more work is needed to unravel the geological nature of the STP and its petroleum systems. Fig. 1. The major structures of the Levant margin and the adjacent Palmyra Basin on TOPEX Free-Air Gravity map backgroundthe mapped structures are based on  Ghalayini et al. (2014)  and published data (e. g. Brew et al. , 2001). The earthquake epicenters are taken between 2006 and 2013 and are from www. cnrs. com. lb. The aim of this contribution is to enhance the characterization of the Levant margin, offshore Lebanon, in general, and the STP in particular.
Crustal modeling Based on recent reviews of the geodynamics of the Eastern Mediterranean region, as well as modern geophysical data, interpreted crustal-scale profiles across the STP could be achieved. This was done through numerical modeling taking into account the free-air gravity anomalies  (Zeyen and Fernandez, 1994; Zeyen et al. , 2005). Figure  7  shows the location of the modeled section on the topography and the free-air gravity maps. Free-air gravity anomalies (1-minute grid spacing) are extracted from publicly available worldwide database TOPEX (www. topex. ucsd. edu). A 50 km wide strip was extracted on both sides of the profile and the values were averaged every 5 km. The variability of data within the strip was considered as data uncertainty (error bars in Fig. 7C ). The modeled lithosphere is divided into bodies representing from top to bottom: different layers of sediments, a continental upper crust, a continental lower crust and a lithospheric mantle.
Stratigraphic and structural interpretations Seismic interpretation was performed on a high-quality PSDM 3D seismic survey acquired in 2013 by PGS and courtesy of the Lebanese Petroleum Administration. The data covers an area of 3000 km 2 and is located offshore southern Lebanon. Two-way time thickness intervals were depth converted based on stacking velocities and interpretation was performed on depth converted profiles. Key seismic packages and their bounding surfaces were identified (onlap, toplap, downlap and truncations). The seismic facies analysis followed seismic stratigraphic principles presented by  Mitchum (1977) ,  Sangree and Widmier (1977) ,  Roksandi'c (1978)  and  Van Wagoner et al. (1988). The use of such seismic stratigraphic and facies analysis principles allowed to better link the onshore observations and interpretations to the offshore  (Hawie et al.
Acknowledgments. The authors would like to dedicate this work to the memory of  Dr. Carla Mu  ¨ller who passed away in January 2018 and whose work and scientific contribution had a great impact on the current understanding of the geology of the East-Mediterranean region and the support and promotion of young researchers. The two anonymous reviewers and the editorial office of OGST are also thanked for their comments and suggestions that certainly improved the content and form of this contribution.
birostris could negatively impact a species that already has little capacity to withstand directed fishery pressure  [81]. collection, and N. Bourne, S. Cheers, H. Pethybridge, F. Sardenne and P. Nichols for all their invaluable help and in-kind support in the lab. LC is supported by the ''Laboratoire d'Excellence" LabexMER (ANR-10-LABX-19).
A number of campaigns have been carried out to establish the emission factors of pollutants from fuel combustion in West Africa, as part of work package 2 ("Air Pollution and Health") of the DACCIWA (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) FP7 program. Emission sources considered here include wood (hevea and iroko) and charcoal burning, charcoal making, open trash burning, and vehicle emissions, including trucks, cars, buses and two-wheeled vehicles. Emission factors of total particulate matter (TPM), elemental carbon (EC), primary organic carbon (OC) and volatile organic compounds (VOCs) have been established. In addition, emission factor measurements were performed in combustion chambers in order to reproduce field burning conditions for a tropical hardwood (hevea), and obtain particulate emission factors by size (PM 0.
, 2009; Yokelson et al. , 2009; Sharma et al. , 2015). In this context, our study was to conduct several campaigns to measure EFs for the above-mentioned important sources and pollutants. Such a study is fully included in the frame of the Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA) program  (Knippertz et al. , 2015). DACCIWA aims to quantify the influence of anthropogenic and natural emissions on air quality, clouds and rainfall over southern West Africa and assess their impacts on human and ecosystem health as well as agricultural productivity. One of its aims is to develop an emission inventory of anthropogenic sources specific to this region. The work here presented aims to provide a database of EFs for total particulate matter (TPM) mass, elemental carbon (EC), organic carbon (OC) and combustion gases (VOC) for pollution sources specific to Africa.
Description of field measurements Three field campaigns of emission factor measurements were performed as part of DACCIWA work package 2: the first in March 2015 in Abidjan (Côte d'Ivoire), the second in (dry, wet, old or fresh waste), trash-burning plumes were sampled at "Akouédo" landfill, the largest (153 ha) and the official landfill site in the east of Abidjan District. 2. Charcoal-and wood-burning fire: eight samplings were carried out for charcoal-burning EFs, including six in Abidjan and two in Cotonou. For wood-burning EFs, four measurements were carried out in Abidjan using two different species of tropical African hardwood, hevea (Hevea brasiliensis) and iroko (Milicia excelsa). Note that hevea and iroko are both hardwood but with different properties; for example, their density differs (600 and 650 kg m -3 , respectively).
In this way, a larger VOC database was considered, including 12 species of terpenes, intermediate VOCs (iVOCs from C 11 -C 16 n-alkanes), ketones and carbonyl compounds for a reduced number of sources (Table  S4 ). The main differences obtained from this exhaustive speciation were related to the contribution of heavy alkanes (VOC6, with iVOCs the most important fraction, 50 %) and aldehydes (VOC22, 13 %) for HDDV sources. The contribution of heavy alkanes from diesel was also observed in other studies  (Ait-Helal et al. , 2014; Dunmore et al. , 2015). In the same way aldehydes presented a considerable contribution in CH and CHM emissions and terpenes were also significant (14 %) in wood-burning emissions (FW). The determined EFs for gasoline (LDGV) and diesel (LDDV and HDDV) vehicles and wood burning have been compared (Fig.
These new VOC data were also used to evaluate the emission inventories in Côte d'Ivoire as a first case study. VOC emissions were calculated by combining the new speciated VOC EFs and statistical activity data provided by International Energy Agency (IEA) and then compared with those provided by the EDGAR inventory (EDGAR v4. 3. 2) for the road transport sector. For that purpose, the 15 VOCs were aggregated according to GEIA VOC groups (Table  S4 ,  Huang et al. , 2017). In Fig. 6  the road transport emissions for the year 2012 in Côte d'Ivoire are compared. Notwithstanding the selected number of VOC species, a large discrepancy can be noted between both profiles (Fig. 6a ): the EDGAR inventory underestimates VOC emissions by a factor of 50. In terms of composition, the main differences are observed for the VOC6 group (> C 6 alkanes), which presents a greater contribution in the EDGAR inventory.
These EFs showed that emission profiles are dominated by aromatics (up to 80 % for some traffic-related sources). The greatest emissions are observed for two-stroke two-wheelers, which can be higher than 3 orders of magnitude compared to the EFs observed from LD and HD vehicles. The presence of terpenes in biofuel burning emissions was considerable, as were heavy alkanes (iVOCs), reaching up to 50 % in diesel-related sources. Comparison to recent literature worldwide points out the greatest levels of anthropogenic African EF for VOC and the relevance of in situ measurements to derive realistic and representative emission factors. Such measurements should be complemented with experiences under controlled conditions. This original database will be useful for improving and updating African emission inventories, which will reduce emission uncertainties and help to better assess impacts on climate, air quality and health.
Acknowledgements. This work has received funding from the  European Union   7th Framework Programme  ( FP7/2007-2013)  under grant agreement no. 603502  ( EU  project  DACCIWA: Dynamics-aerosol-chemistry-cloud interactions in West Africa ). Sekou Keita would like to thank the  AUF (Agence Universitaire de la Francophonie ) for funding his stays in Laboratoire d'Aérologie of Toulouse (3 months per year during 3 years). Part of the analysis of VOC tubes was graciously performed at the  SAGE Department at IMT Lille Douai (France)  by  Thierry Leonardis  and  Stéphane Sauvage. Edited by: Mathew Evans Reviewed by: two anonymous referees.
2 ; Supplementary Protocol S1). Three types of respiration were ) a Parameters were estimated in four complementary methods: (i) directly estimated from experimental data described above (experiment); (ii) directly taken from the literature; (iii) taken from the literature first but then adapted for grapevine based on the trends published in the literature or in our data collection (exploration); and (iv) taken from the literature first but then calibrated for our data through numerical optimization (calibration). The data sets of  Dai et al. (2009)  and  Bobeica et al. (2015)  were used for calibration. Table  1. Continued considered (Table  1 ), namely phloem loading and unloading respiration (q mobile for each process), maintenance respiration (q m ), and growth respiration (q g ). Growth respiration represents the carbon losses associated with the synthesis of new biomass.
Total osmolarity was measured with a micro-osmometer (Roebling 13/13DR-Autocal, Berlin, Germany;  Lechaudel et al. , 2007). An additional data set used for calibrating the response of photosynthesis and transpiration to soil water potential was described in  Peccoux et al. (2017)  and the calibration results were shown in  Zhu et al. (2018). Parameters linked to canopy architecture, and the sizes and weights of internodes and leaves at different ranks were measured in a fruiting-cutting experiment in 2015 (Supplementary Protocol S2). The second series of experiments was conducted using 4-year-old potted cv. Sangiovese vines with a 1 m long fruiting cane with eight or nine dormant buds. Detailed whole-canopy photosynthesis and transpiration, and the berry developmental profile were measured  (Bobeica et al.
Calibration of the berry growth module The berry growth module was calibrated using the data sets of  Dai et al. (2009)  and  Bobeica et al. (2015). The contributions of acids and other ions to total osmotic pressure at different soluble sugar concentrations were estimated using an exponential decay curve (E13 in Fig. 2 ; Supplementary Fig. S2 ; Supplementary Protocol S3). The Cabernet Sauvignon berry surface area was estimated using the recorded diameter of the berry by considering it as a sphere, and Sangiovese area was estimated using diameter and length from the proximal to distal position of the berry and assuming it to be ellipsoid. The relationships between berry surface area and FW (E14 in Fig. 2 ) were estimated by the non-linear least square method in the 'stats' library in R (R Development Core Team, 2017).
Acknowledgements We thank  Nabil Girollet  for assistance in running the model on the computing facilities  MCIA (Mésocentre de Calcul Intensif Aquitain , on the cluster AVAKAS) of the  University of Bordeaux , and the PFR internal science publication office for their help in the preparation of the manuscript. We greatly acknowledge financial support from the  European Community 's  Seventh Framework Program  ( FP7/2007-2013 ) under the grant agreement no. FP7-311775 , Project  INNOVINE , the project  Frimouss  (grant no. ANR-15-CE20-0009 ) from the  Agence Nationale de la Recherche, France , and the  PFR Grape and Wine Research programme , funded by the  New Zealand Ministry of Business, Innovation and Employment Strategic Science Investment Fund , delivered by PFR in consultation with  New Zealand Winegrowers.
Then the two models with new CUE formulation were evaluated based on data from laboratory litter incubation experiments and largescale in situ observations of present-day soil organic C (SOC) stock in Europe and China. Results indicated that the CENTURY model with new CUE d formulation was able to reproduce differences in respiration rate of litter with contrasting C:N ratios and under different levels of mineral N availability, whereas the default model with fixed CUE d could not. Using the CENTURY with flexible CUE d , we also illustrated that litter quality affected the long-term SOM formation. Litter with a small C:N ratio tended to form a larger SOM pool than litter with larger C:N ratios, as it could be more efficiently incorporated into SOM by microorganisms. Both the CENTURY and MIMICS model with flexible CUE can reasonably capture the observed present-day SOC stocks at European and China forest sites, although a large bias between the observed and simulated SOC stock, which was potentially caused by the uncertainties in litterfall inputs, historical land use change, soil erosion and some other factors (e.
Fourier transformation infrared microscopy FTIR measurements were carried out at the Institut de Physique du Globe de Paris, France with a Thermo Scientific iN10 MX1 (Ever-Glo infrared source) equipped with a ×15 objective (NA = 0. 7) and a liquid nitrogen cooled MCT-A detector in transmission and reflection mode. We acquired spectra in the range 4000-710 cm -1 by mapping over areas of several mm 2 with individual spot sizes of 30×30 μm and steps of 30 μm. Each spectrum corresponded to 128 summed scans collected with a spectral resolution of 4 cm -1. The raw data was converted into-ASCII files with the Omnic Atlus1 imaging software by Thermo Scientific TM and afterwards normalized and background-reduced using the ChemoSpec package for the software R  [38]. The vibrational bands summarized in Table  1  were considered as indicative of bonds or functional groups in organic molecules, which potentially originate from biological material  [39;40].
Environmental DNA (eDNA) analysis of water samples is on the brink of becoming a standard monitoring method for aquatic species. This method has improved detection rates over conventional survey methods and thus has demonstrated effectiveness for estimation of site occupancy and species distribution. The frontier of eDNA applications, however, is to infer species density. Building upon previous studies, we present and assess a modeling approach that aims at inferring animal density from eDNA. The modeling combines eDNA and animal count data from a subset of sites to estimate species density (and associated uncertainties) at other sites where only eDNA data are available. As a proof of concept, we first perform a cross-validation study using experimental data on carp in mesocosms. In these data, fish densities are known without error, which allows us to test the performance of the method with known data.
We then evaluate the model using field data from a study on a stream salamander species to assess the potential of this method to work in natural settings, where density can never be known with absolute certainty. Two alternative distributions (Normal and Negative Binomial) to model variability in eDNA concentration data are assessed. Assessment based on the proof of concept data (carp) revealed that the Negative Binomial model provided much more accurate estimates than the model based on a Normal distribution, likely because eDNA data tend to be overdispersed. Greater imprecision was found when we applied the method to the field data, but the Negative Binomial model still provided useful density estimates. We call for further model development in this direction, as well as further research targeted at sampling design optimization.
e. , SE and 95% C. I. ). After introducing the method, we assess its performance using experimental data from a controlled study on common carp (Cyprinus carpio). In this proof of concept study, the model was implemented on eDNA data collected in mesocosms where the number of carps was perfectly known, thus providing an ideal situation to evaluate the method's accuracy under controlled conditions. Finally, we implemented the approach on field data from a study on Idaho giant salamanders (Dicamptodon aterrimus) to assess its potential to work in natural settings.