dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic Tree The strict core genome of 17 Frankia strains was determined with the Get_Homologs package  (Contreras-Moreira and Vinuesa, 2013). Out of 150,000 amino acid sequences in the Frankia pan genome, 420 proteins were identified as orthologs and part of the strict Frankia core genome. A concatenated phylogenetic tree was constructed. These concatenations were aligned using Clustal W  (Larkin et al. , 2007). The distance matrix was computed by Jukes-Cantor method  (Jukes and Cantor, 1969). The Neighboringjoining method (Saitou and Nei, 1987) was used to build the phylogeny. The percentage of replicate trees in which the associated taxa clustered together was determined using a bootstrap test (1000 replicates)  (Felsenstein, 1985). Streptomyces coelicolor was used as an outgroup.
Each aggregated attribute in the model has a decision table that determines its value for all possible combinations of values of descendant attributes in the hierarchy. The method DEX is currently implemented in the software called DEXi  (Bohanec 2015)  (downloadable from http://kt. ijs. si/MarkoBohanec/dexi. html). DEXi supports the development of DEX models and their application for the evaluation and analysis of decision alternatives. In the model development stage, DEXi checks the quality of decision rules in terms of completeness (providing evaluation results for all possible inputs) and consistency (defined decision rules obey the principle of dominance, i. e. , they monotonically increase with increasing input values). DEX and DEXi have so far been used to support real-world decisions in health care, public administration, agriculture, food production, ecology, land use planning, tourism, housing, traffic control, sports, and finance  (Bohanec et al.
Individual laboratories might even put more weight to specific attributes when selecting a series of methods. Such additions to the model could thus provide a substantial contribution to the final evaluation. Since this manuscript compared only the methods that are already publicly available, it is really important that the model offers the possibility of modifying the attributes with emergence of new methods and requirements. The AnalyticalMeth model as presented here has some clear limitations. It can inform but not conclude on which methods to combine for specific aims of GMO testing (e. g. , detection only, identification, quantification or detection of unauthorized GMOs). It provides information on a general comparison of individual methods based on their purpose in separate steps of GMO analysis.
Acknowledgements We thank  Jeroen van Dijk  and  Bjørn Spilsberg  for sharing information on unpublished NGS-based methods. Funding The research leading to these results has received funding from the  European Union  under  FP7  grant agreement no. 613908  (project  DECATHLON ) and  FP6  contract no. 007158  (Project  Co-Extra ). Complementary financial support was given by the  Slovenian Research Agency  (contract number  P4-0165 ) and the  Research Council of Norway.
Favorable habitats for species The favorable habitat for a species describes the characteristics of the environment in which a population of individual members of a given species can live and flourish. The "Favorable habitat" index is calculated for each work zone for the species present in the administrative district where the work zone is located, and varies between 1 and 2. The value is 1 when the habitat in which the work zone is located is not favorable for the species in question, and is 2 when the habitat is favorable for the species in question. To determine the "Favorable habitat" CORINE Land Cover 2012  [1]  database describing land use in France was used more details will follow" on III. 3.
The count of eosinophils for each animal EC ij is thus modelled by a binomial distribution of size parameter TC ij and probability parameter the latent variable EP ij , the logit of which is defined by the Gaussian mixture: ∼ ∼ ′ ′ C T C E P EP μ σ E Bi nom( , ) logit( ) N( , ) ij ij ij ij ij ij Vaguely informative uniform priors were assigned to each parameter allowing variation within a realistic range and forcing probability parameters (Seth BS , Se BAL , Pinf i and Ppatent) to be between 0 and 1 and θ and ′ θ to be positive (Table  1 ). Monte Carlo Markov-Chain (MCMC) techniques were used to estimate the full joint posterior distribution of parameters from prior distributions and data. Computations were performed using the JAGS software via the R package rjags  (Plummer et al. , 2016 ) (a corresponding R script is provided in Supplementary material both in Appendix A and in a runnable script with corresponding data).
As some eosinophil proportions were estimated to 0, for representing data we fixed the corresponding values to an arbitrary value of -7. In fact, these values are censored and only estimated to be below the detection limit equal to 1/400 (roughly -6 in logit). This censoring was not a problem for modelling as the model directly describes the raw data (eosinophil count EC ij ). We performed a posterior predictive check with the fitted model by comparing predicted and observed values of the numbers of animals in each combination of the four tests, predicted and observed cumulative distribution functions of both quantitative measures (ODR and EP values), and predicted and observed numbers of individuals above and below the chosen cut-off values. In order to check the constant accuracy of tests across population, we also repeat the analysis with the exclusion of each herd of the study one a time  (Mahmmod et al.
Acknowledgments The authors warmly thank the veterinarian and their farmers for their implication in this study, and are grateful to  Catherine Mottet ,  Marie-Thérèse Poirel , and  Elodie Moissonier  for their assistance in the achievement of all the laboratory analyses. We also thank  CEVA  for their financial support and we are grateful to 3 anonymous referees for their relevant comments allowing us to improve this manuscript.
Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability ('p-theory'), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types. The p-theory describes the probability (p-value) that a photon, having intercepted an element in the canopy, will recollide with another canopy element rather than escape the canopy.
Since reflectance values are not saturated within LAI range of 0-2, LAI algorithms perform well within this domain  (Yan et al. , 2016b)  and should be able to provide high quality input data. Importantly, it should be verified if the LAI input indeed corresponds to true LAI. Our findings illustrate that it might be possible to obtain approximate p-values for any location solely from Earth Observation data, given availability of high quality LAI retrievals. In the future, the relationship could be possibly strengthened by further improved CI retrieval algorithms from Earth Observation data (e. g. Wei and Fang, 2016) , by accounting for seasonal variation of clumping  (He et al. , 2016)  and by knowing site specific G-function values  (Raabe et al. , 2015). It is envisioned that our findings provide a stimulus for future applications of the photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data  (Stenberg et al.
Conclusion Our results indicate that the integration of a MODIS LAI product with empiricallybased CI maps is feasible. Their synergy was assessed using the p-theory along with raw LAI-2000/2200 Plant Canopy Analyzer data gathered across a wide range of plant functional types. Importantly, for the first time it is shown that it might be possible to obtain approximate p-values for any location solely from Earth Observation data. This approximation is relevant for future applications of photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data  (Stenberg et al. , 2016). Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space  (Nilson, 1971; Chen and Black, 1992). The CI is an important factor for the correct quantification of true leaf area index (LAI).
The most important improvement in MOD15A2H C6 compared to previous versions is the increase from 1 km to 500 m spatial resolution. In addition, a new version of MODIS surface reflectances (MOD09GA C6) is used to replace the previous 1 km intermediate dataset (MODAGAGG). In C6 the 1 km static land cover input is replaced with new multi-year MODIS land cover product (MCD12Q1) at 500 m resolution. Only MODIS LAI retrievals produced with the main RT algorithm closest to the date of PCA measurements (see Section 2. 4) were used in this study. Engine  (Gorelick et al. , 2017) ; temporal overlap with MODIS LAI product) (Table  1 ). In addition to the retained sites from  Ryu et al. (2010) , PCA measurements from 41 additional sites were included in this study. The available raw PCA data were used to approximate p-value at each site using Eq.
Conclusion Our results indicate that the integration of a MODIS LAI product with empiricallybased CI maps is feasible. Their synergy was assessed using the p-theory along with raw LAI-2000/2200 Plant Canopy Analyzer data gathered across a wide range of plant functional types. Importantly, for the first time it is shown that it might be possible to obtain approximate p-values for any location solely from Earth Observation data. This approximation is relevant for future applications of photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data  (Stenberg et al. , 2016).
Acknowledgements JP was supported by  Estonian Research Council  Grant  PUT1355  and  Mobilitas Pluss MOBERC11. The global clumping index map by He et al. (2012) is available for download through the following link: https://www. researchgate. net/publication/314151326_Global_Clumping_Index_Map. The MODIS LAI C6 data have been accessed through Google Earth Engine. Dr. Youngryel Ryu  kindly shared his compilation of raw PCA data. Original providers of the raw PCA data to  Dr. Ryu  are appreciated:  Drs. Jing Ming Chen ,  Michael Gavazzi ,  Mark Heuer ,  Taehee Hwang ,  Joon Kim ,  Soo-Hyung Kim ,  Hideki Kobayashi ,  John Kochendorfer ,  Hyojung Kwon ,  Beverly Law ,  Craig Macfarlane ,  Francesco Mazzenga ,  Mark Mesarch ,  William Munger ,  Kenlo Nishida Nasahara ,  Asko Noormets ,  Jean-Marc Ourcival ,  Dario Papale ,  Serge Rambal ,  Andrew Richardson ,  Julie Talbot ,  Shashi Verma , and  Leland Werden.
(2011) 2. 18 0. 175 0. 65 1. 01 functional type. Lat is latitude (in degrees). Lon is longitude (in degrees). PCA is Plant ENF USA 527 46. 22N 116. 79W Fir, pine, spruce, larch Jensen et al. (2011) 1. 94 0. 189 0. 59 1. 01 In addition, a new version of MODIS surface reflectances (MOD09GA C6) is used to replace the previous 1 km intermediate dataset (MODAGAGG). In C6 the 1 km static land cover input is replaced with new multi-year MODIS land cover product (MCD12Q1) at 500 m resolution. Only MODIS LAI retrievals produced with the main RT algorithm closest to the date of PCA measurements (see Section 2. 4) were used in this study.
S1 ). We used yield and weather data from cultivar trials from 1991 to 2014 at 636 locations for 991 cultivars of winter wheat, spring wheat, and durum wheat in nine countries, as well as their cultivation areas in eight countries. First, we determined the responses of wheat yield to weather by identifying the agroclimatic variables critical to yield (step 1)  (17) , estimating the cultivar yield responses to the variables (step 2)  (17) , and grouping the weather variables based on the responses using principal component analysis (PCA, for R 2 values see SI Appendix, Tables  S1  and S2 ) (step 3)  (18). Second, we estimated the response diversity by clustering the cultivars based on the component scores (step 4)  (18)  and assessed the annual diversity indices for the clusters (response diversity) and individual cultivars (type diversity) in farmers' fields and trials by country (step 5)  (17).
Materials and Methods Data. Long-term data series of cultivar trials between latitudes 37. 21°and 61. 34°and longitudes -6. 02°and 26. 24°in nine countries across Europe (Finland, Denmark, Germany, Belgium, Czechia, France, Slovakia, Italy, and Spain) were used. In some countries (Fig. 1 ), the period was limited by data availability. The trials had a randomized complete block design or an incomplete block design with two to four replicates, and their management was similar to farmer practices. The cultivars in the experiments differed over time, but standard reference cultivars were used over long periods and across several countries. Cultivars with at least 20 yield observations were included in the assessment. The total number of clustered cultivars (minimum number of observations was 20) was 36 for Finland, 90 for Italy, 113 for Belgium, 139 for France, 140 for Germany, 169 for Czechia, 186 for Spain, 188 for Slovakia, and 265 for Denmark.
Annual grain yield (kilograms per hectare) was used as the response variable. The entire cultivar yield data set comprised 100,985 observations. Step 1: Identifying Agroclimatic Variables Critical to Wheat Yield. The agroclimatic variables that were potentially critical to yield (SI Appendix, Table  S1 ) were selected based on responses reported in the literature  (5, 22, 23). Data on these agroclimatic variables during crop phenological stages  (24)  were obtained from the stations closest to the cultivar trial sites. Missing data on sowing, heading, or maturity dates were estimated based on the corresponding dates for all of the cultivars from the same site and year. If a sowing date was missing, then it was assumed that the sowing dates of all cultivars did not differ (unless stated otherwise in the metadata).
All of the above effects are fixed in the model. Country l , site m , year n , and the interaction of treated × country × site × year (category) jklmn are random effects of the lth country, mth experimental site, and nth year within the jth category. For each cultivar and agroclimatic variable, the relative difference in yield between the extreme categories (high-low) was calculated. The relative difference was used to balance the differences between the yield levels of cultivars, although using the simple difference led to similar results. These data consisted of the grain yield responses of 991 cultivars to 43 agroclimatic variables. For example, the relative difference of the estimated yields of each cultivar that experienced fewer than 56 rainy days from sowing to maturity and the estimated yields of each cultivar that experienced more than 68 rainy days from sowing to maturity was calculated.
Clustering was based on the PC scores, calculated as a byproduct of PCA (step 3). The cultivars were clustered with Ward's (33) method, which starts with n clusters of size one and continues until all of the observations are included in one cluster. The squared Euclidean distances between data points were used. The number of clusters (nine) was selected based on the dendrogram, the pseudo t 2 criterion, , and the variation in R 2 values  (34). PC loadings (obtained in step 3) were used to weight the average yield responses to the agroclimatic variables  (18). The PC loadings were squared and divided by the eigenvalue of each PC. Therefore, P n i=1 w i = 1, where n is the number of agroclimatic variables and component loadings. The weighted means and SEs were calculated according to the following equation: x = P n i=1 w i x i P n i=1 w i , SE = ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P n i=1 w i ðx i - xÞ 2 n s , where w i is the weight and x i is the average yield response to the ith agroclimatic variable.
ACKNOWLEDGMENTS. We thank the data providers ( SI Appendix ) and  Miina Porkka  for executing the figures. This work was supported by the  Joint Programming Initiative for Agriculture, Climate Change and Food Security (FACCE)  through the project  Modelling European Agriculture with Climate Change for Food Security (MACSUR) ,  Innovation Fund Denmark, Belgian Science Policy Belspo  Contract  SD/RI/03A ,  Slovak Research and Development Agency  Contract  APVV-15-0489 ,  German Federal Ministry of Education and Research  (Grant  031B0039C ),  Italian Ministry for Agricultural, Food and Forestry Policies (Ministerial Decree , Grant  24064/7303/15 ), the  French National Institute for Agricultural Research through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) , the  Spanish National Institute for Agricultural and Food Research and Technology and Agencia Estatal de Investigación Grant  through  MACSUR02-APCIN2016-0005-00-00 , and the  Czech Adaptation Strategies for Sustainable Ecosystem Services and Food Security under Adverse Environmental Conditions  project (SustES, Contract  CZ.
BdM was funded by grants from the  Centre National pour la Recherche Scientifique (CNRS) , the  European Research Council Executive Agency  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013 Grant Agreement no. [322788 ]), by the  Agence Nationale de la Recherche  ( ANR-15-CE12-0010-01/DaSiRe ), and by the  Fondation Bettencourt-Schueller. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgements This work was funded by the  joint Food and Agricultural Organization (FAO)  of the  United Nations/International Atomic Energy Agency (IAEA) Division of Nuclear Techniques in Food and Agriculture , the  Department of Technical Cooperation  and the  United States Agency for International Development (USAID - Award No. AID-OAA-F-16-00118 ) and by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 682387-REVOLINC ). This paper reflects only the authors' views and the Agency is not responsible for any use that may be made of the information it contains. The University of Liverpool covered the tuition fees of N. C. under a bursary scheme.
Introduction The Asian tiger mosquito Aedes albopictus has been recently considered as one of the "100 World's worst invasive alien species" (Global Invasive Species Database)  [1]. Originating from Asia, Ae. albopictus has spread over 5 continents during the last decades  [2]. Though the mosquito shows a poor active dispersal ability by flight (less than 300 m. ), passive dispersal in goods (tyres or Lucky Bamboo) due to increasing global trade has been largely involved in its spread  [3]. Ae. albopictus has also been considered as one of the most important disease vectors and has already been identified as a potentially competent vector for more than 22 viruses in the laboratory and being responsible for the epidemic transmission of chikungunya, dengue and zika  [4]. Viruses acquired by the mosquito through blood meal need to accomplish replication cycles inside the insect before being transmitted to a vertebrate host.
Wing size measurement Two wings from each mosquito individual were fixed on a glass slide within 50 μl of Eurapal (Carl Roth, Germany). Pictures of the wings were taken under a stereomicroscope x 20 (Leica, Germany) and processed with the Leica LAS software (Leica, Germany). The wings size was estimated for one wing per individual from the intersection of the 2 nd and 3 rd vein to the intersection of the 7 th vein and the apex of the wing (S1 Fig) with the imageJ software (https:// imagej. nih. gov/). A total of 43 individuals out of the 313 presented a deterioration of their wings and were referred as missing data points (NA) in the database (S1 Table ).
Data analysis Reduction in genetic diversity within the inbred lines was assessed for each cohort based on the expected heterozygosity (He). To test the effects of population origin and inbreeding on the He levels, we used a beta regression and a likelihood ratio test for nested models comparisons with the R packages betareg and lmtest  [35, 36]. The midgut's bacterial α (Richness and Shannon index) and β (Bray-Curtis dissimilarity) diversity indices were estimated with the vegan package in R  [37]. The comparative analysis of the microbiota α-diversity was only based on the Shannon index (H') which reflects the uncertainty to sample similar bacterial Operational Taxonomic Units (OTU) out of a given individual. This value is directly impacted by the number of taxa (richness) and their relative abundance (evenness).
Acknowledgments This publication is dedicated to the memory of our beloved colleague and friend Florence H. TRAN. We acknowledge the contribution of the DTAMB platform of the  FR41 Bio-Environment and Health (University Lyon 1 ) and the  Academy of Finland  (Decision numbers 273098 and 265641). We also acknowledge  Marion Journiac  and  Morgane Guegan  for their help with wings measurements and insectary management respectively.
This research plan was funded by  ERA-NET BiodivERsA  with the national funders  ANR-13-EBID-0007-01 ,  FWF I-1437 , and  DFG KL 2087/6-1  as part of the 2012-2013 BiodivERsA call for research proposals (http://www. biodiversa. org/)and recieved by PM. The authors acknowledge the contribution of the DTAMB platform of the  FR41 Bio-Environment and Health (University Lyon 1 ) and the  Academy of Finland  (Decision numbers 273098 and  265641 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
They undergo complete metamorphosis and their larvae are predacious and feed on insect immature stages. The average development of this family from egg to adult is 20 days at 30 °C  (Fakoorziba et al. 2017 ). Our study focused on adults of Saprinus and immature stages were unfortunately not collected, albeit we admit that their presence might be informative for the determination of the post-mortem interval. Based on our data collected from pig carcasses, autopsies, and field trips, Saprinus species were the most common and diverse within the histerids. This paper aims to clarify the diversity, abundance, and association of Saprinus species with decomposing carcasses in Lebanon. In the Palaearctic Region, 357 species of the Saprininae subfamily have been reported so far  (Lackner et al. 2015).
Acknowledgements The first author would like to thank the  CNRS (National Council of Scientific Research-Lebanon)  and  Walid Joumblatt Foundation for University studies  for their financial support. This paper is a contribution of the team project " Biodiversity: Origin, Structure, Evolution and Geology" and to the activity of the laboratory "Advanced Micropalaeontology, Biodiversity and Evolution Researches (AMBER) " at the  Lebanese University , the support of taxonomists and to the  Institut de Systématique, Évolution, Biodiversité in the Muséum National d'Histoire Naturelle, Paris, France  and.