dataesr/scientific-paragraphs-categorization huggingface dataset:
PHY component skill The skill of the physical analysis fields is assessed over a 3 years period from 2018 to 2020 comparing model results  (Clementi et al. , 2019)  to satellite and in-situ observational datasets by means of root mean square differences (RMSD) and bias (model minus observations) of daily mean analysis outputs, so called Estimated Accuracy Number (EAN) and misfits, calculated at observation time during the forward model integration and evaluated on a sub-set of observations corresponding to the ones assimilated by the system. In EAN, the daily mean analyses are interpolated on daily available observations: this goodness score is somewhat approximated especially at the surface where daily variability is large, but this is a score used by many forecasting systems  (Ciliberti et al.
WAV component skill The quality of the wave analysis and forecast product is assessed over a three-year period from January 2018 to December 2020. Model analysis and first-guess (i. e. just before data assimilation) outputs are compared to in-situ and satellite observations respectively. Significant wave height (SWH) and mean wave period (MWP) measurements are used for data validation from 28 wave buoys in the Mediterranean Sea (Figure  6  lower panel). Data quality control procedures have been applied to the in-situ observations (Copernicus Marine In-Situ Team, 2020) and measurements associated with a bad quality flag are not taken into consideration. Figure  6  depicts scatter plots of the evaluation of the observed SWH and MWP against measurements, obtained from the 28 buoys. The figure reveals small SWH underestimation by the model mainly for very small wave heights (< 0.
BIO component skill The BIO component state variables can be validated at three different uncertainty levels providing a "degree of confirmation"  (Oreskes et al. , 1994)  of different scales of variability based on the availability of reference data. Near real time satellite and Argo float data provide a rigorous skill performance validation data set down to the scales of the week and mesoscale dynamics for a limited set of variables: chlorophyll, nitrate and oxygen. Dataset of historical oceanographic data  (Socat dataset, Baker et al (2016) ; EMODnet data collection,  Buga et al. (2018) ,  Cossarini et al. , (2017) ;  Lazzari et al. , (2016) ) are used to build a reference framework of sub-regions and annual and seasonal climatological profiles to validate model performance to simulate the basin wide gradients, the mean vertical profiles and the seasonal cycle.
However, the conclusions about mesoscale accuracy of the BIO component should be taken with caution since the observations are still relatively few in number (about 1 over 8 w. r. t. the Argo floats have biochemical sensors) and unevenly spaced (e. g. , southern Mediterranean Sea is less observed than northern areas). As explained above, an additional verification of biogeochemical variables can be achieved for additionally 7 variables (not considering chlorophyll) and two other derived variables with climatological data. An example of such comparison is shown in Figure  11  for the carbonate system variables. Average maps and profiles of Alkalinity and DIC in selected sub-regions in the zonal directions (coloured lines) are well superimposed to the range of variability of the historical in-situ data (grey shaded areas) demonstrating the capability of the BIO component to reproduce both horizontal basin-wide gradients and vertical profiles in the different areas.
Conclusions and Future Perspectives In this paper, the Med-MFC components, PHY, BIO and Wav have been described providing an overview of their technical specifications. The system is offline coupled and the PHY component exchanges 3D currents, temperature, salinity with the BIO and WAC components daily, with daily mean values. This approximation is flexible enough that improvements can be carried out separately on the three components, considering different levels of maturity of the numerical modelling parametrizations, the data assimilation components and the validation data sets. Three different data assimilation system are run for each component making the best use of all available data from satellite and in-situ, the effort is to assimilate as much data as possible and use background or model uncertainties to account for the missing couplings.
Overall, the quality of the WAV component stems out of the weakly coupled offline coupling between PHY and WAV. In particular, the WAV component assimilates data with a well calibrated stand-alone OI scheme using satellite altimetry data and implements regular updates and improved parameterization independently from the other components. The BIO system has defined a validation framework  (Salon et al. , 2019)  based on the near real time observations (i. e. , satellite and Argo) showing average errors in the 0-200m layer of 0. 04 mg/m3, 0. 4 mmol/m3 and 16. 8 mmol/m3 for chlorophyll, nitrate and oxygen, respectively. Since 2008 the Med-MFC components have been continuously upgraded and substantially improved. The system evolution will continue also in the future following the main drivers of the three components, the Copernicus Marine Service users.
, 2016)  and gliders  (Dobrici et al. , 2009)  as well as tidal coastal station at sea level. User needs for the future evolution of the WAV component indicate the increase of the frequency of the wave analyses, making available larger data sets such as the wave spectra and dedicated products (like the directional spread at peak frequency and different parts of the wave spectrum). Extreme waves forecasting requires also a probabilistic approach. The required increased accuracy can be achieved by improving the quality of the wind forcing using downscaled ECMWF forecasts and assimilating additional satellite (wave spectrum from SAR) or higher frequency in-situ wave measurements. Also, for the WAV component, the development of a WAB ensemble prediction system will be necessary. In the light of the user needs and requirements, the BIO component validation framework helped to identify strengths and weaknesses of the model system for future developments.
In the BIO component case, future upgrades should be related to the development of the BFM biogeochemical model process representations and model parameter estimation. This includes increasing the number of phytoplankton functional types and zooplankton compartments to describe diversity of the plankton community and the different energy and matter pathways across the ecosystem. Additionally, the integration of optics and biogeochemistry including new coupled modelling and novel hyperspectral and high-resolution radiometric data will be beneficial for calibrating parameters of key ecosystem processes  (Lazzari et al. , 2021). Regarding the BIO component assimilation framework, the future developments should consider the assimilation of biochemical variables from new sensors such as novel satellite and in situ Argo and Gliders  (Cossarini et al.
Reference data for validation: * EMODnet data collections (Buga et al. , 2018)  integrated with additional oceanographic cruises (Cossarini et al. , 2015). ** Socat dataset (Baker et al 2014). https://doi. org/10. 5194/egusphere-2022-1337 Preprint. Discussion started: 10 January 2023 c Author(s) 2023. CC BY 4. 0 License.
Study design We conducted the study in southwestern France near Bordeaux (44 ∘ 48 ′ N, -0 ∘ 14 ′ W), in a region dominated by vineyards with a temperate oceanic climate (Fig. S1 ). We created a gradient of landscape complexity by selecting 19 landscapes with a range of proportions of semi-natural habitats. In each landscape, we selected two vineyards under conventional versus organic management in order to test for interactive effects between farming systems and landscape structure  (Muneret et al. , 2018b; Ostandie et al. , 2021). Organic vineyards had all been under organic management for at least 6 years prior to data collection. Data collection and field samplings were conducted in 2019.
Landscape context Our landscape gradient ranged from 2 % to 62 % of semi-natural habitats in a radius of 500 m around each vineyard (Table  S2 ). Seminatural habitats were composed of forests (from 0 % to 44 %), grasslands (0-40 %) and hedgerows (0-1 %). There was a strong negative correlation (r = -0. 9, p < 0. 001) between the proportions of seminatural habitats and of vineyards (22-79 %) in our landscapes (Fig. S2 ). Only 7 landscapes out of 19 included other crops than vineyards (from 0. 1 % to 15 %). We calculated several metrics of landscape composition (proportion of each land-use type) and configuration (edge density, mean patch size and distance to the nearest semi-natural habitat) using QGIS 2. 18. 1 and the R package sf  (Pebesma, 2018). Edge density ranged from 0. 03 to 0. 06 m per landscape circle.
Multifunctionality We evaluated the levels of three important ecosystem services in vineyards: wine production, natural pest control and soil quality and fertility. We selected them based on previous reviews of ecosystem services in vineyard systems  (Paiola et al. , 2020; Winkler et al. , 2017; Winter et al. , 2018). Each service was measured using several proxies or ecosystem functions (see the detailed methodology in Supplement D) and raw data in Fig. S5 ). For wine production, we used yield (hl of wine produced per hectare), and leaf chlorophyll content (a proxy of grapevine performance)  (Taskos et al. , 2015). Leaf chlorophyll content reflects grapevine N status, which is crucial for plant vigour, yield, and wine quality  (Verdenal et al. , 2021). Evaluating wine production is complicated by the fact that many winegrowers aim at producing high quality wines rather than maximising yields.
The results were identical with the main analysis, except for Coleoptera richness (Table  S12 ). This is in line with previous results showing strong relationships between landscape characteristics at 500 and 1000 m in our study systems, and similar impact on natural enemies and pest control services  (Muneret et al. , 2018b). Other studies also have concluded that landscape characteristics at 500 m described well communities of larger organisms such as birds in our study systems  (Barbaro et al. , 2017; Bosco et al. , 2021). Residuals were checked for normality, variance homogeneity and strong departure from linearity using residual diagnostic plots with the DHARMa package  (Hartig and Lohse, 2020). The following variables were log-transformed to reach normality and variance homogeneity: bird, ground beetle, foliage Coleoptera and Heteroptera richness; the abundance of earthworms, syrphids, bees, ground dwelling spiders, foliage Auchenorrhyncha and Heteroptera; soil C, phosphatase activity, predation rates of pupae, pathogen and pest damage; and the index of agricultural production and of overall multifunctionality.
Acknowledgements This research was funded by the research project  SECBIVIT , which was funded through the  2017-2018 Belmont Forum  and  BiodivERsA joint  call for research proposals, under the  BiodivScen ERA-Net COFUND program , with the funding organizations:  Agencia Estatal de Investigación (Ministerio de Ciencia e Innovación/Spain , grant  PCI2018-092938 ;  MCIN/AEI/10. 13039/501100011033 );  Austrian Science Fund (FWF)  (grant number  I 4025-B32 );  Federal Ministry of Education and Research (BMBF/Germany)  (grant number  031A349I );  French National Research Agency (ANR) ;  Netherlands Organization for Scientific Research (NWO) ;  National Science Foundation  (grant # 1850943 ); and  Romanian Executive Agency for Higher Education, Research, Development , and  Innovation Funding (UEFISCDI).
Data Availability All data and R codes associated with this analysis can be accessed from GitHub (https://github. com/leabeaumelle/MultiBEFVineyards Manuscript) and are archived on Zenodo (https://zenodo. org/badge/latestdoi/472809233). Raw sequence data from Illumina were deposited in the Sequence Read Archive service of the NCBI database (https://www. ncbi. nlm. nih. gov/) (BioProject ID: PRJNA911566).
37, 38 Additional molecular identification was then conducted for specific groups of pathogens. Cadophora and Phaeoacremonium spp. 0] [41]  Identification of Botryosphaeriaceae spp. was performed by analysis of the translation elongation factor 1-⊍ gen amplified using EF1F and EF2R primers. 42 Identification of Cylindrocarpon-like asexual morphs was performed by sequencing part of the histone H3 gene with primers CYLH3F and CYLH3R. 48. 43 2 Biological control agent preparation 2. 2. 1 Bacillus subtilis PTA-271 Bacillus subtilis PTA-271 (GenBank Nucleotide EMBL accession no. AM293677 for 16 S rRNA and DDBJ/ENA/GenBank accession no. JACERQ000000000 for the whole genome), therefore Bs PTA-271, was isolated in 2001 from the rhizosphere of healthy Chardonnay grapevines on a vineyard located in Champagne (Marne, France).
46  was used. For Bs PTA-271, a set of primers were designed targeting the DNA polymerase III (dnaE) gene. The DNA polymerase III gene sequence of Bs PTA-271 full genome was aligned with other Bacillus dnaE sequences retrieved from GenBank nucleotide database using CLUSTALW (1. 82) Multiple Alignment Program. A set of primers was designed (5 0 -TGGATGAAGCGAGACAGCAG-3 0 , 3 0 -TCTTCACTCAGGACAACGCC-5 0 ), using NCBI primer designer tool. 47 A standard curve was prepared to calculate the efficiency of the primers. The curve was constructed using seven consecutive 1:5 dilutions from genomic DNA from a Bs PTA-271 culture in LB medium starting with 50 ng μL -1. One milliliter from each bacterial concentration was subjected to DNA extraction by means of the Wizard® Genomic DNA purification kit (Promega, Madison, WI, USA).
Melting curve assays were performed from 60 to 95 °C at 0. 5°C s -1 , and melting peaks were visualized to check amplification specificity. Elongation factor 1-alpha (EF1) gene was used as reference (5 0 -AACCAAAATATCCG-GAGTAAAAGA-3 0 , 3 0 -GAACTGGGTGCTTGATAGGC-5 0 ). 18 Relative gene expression was determined with the formula fold induction: 2(-11Ct), where 11Ct = [Ct TG (US) -Ct RG (US)] -[Ct TG (RS) -Ct RG (RS)], where the Ct value is based on the threshold crossing point of individual fluorescence traces of each sample, TG is the target gene, RG is the reference gene, US is an unknown sample and RS is reference sample. Integration of the formula was performed using CFX MANAGER 3. 1 software (Bio-Rad, Hercules, CA, USA).
ACKNOWLEDGEMENTS This work was supported by a  French grant  from the  region GRAND-EST France  and the city of  GRAND-REIMS France  through the  BIOVIGNE PhD program , whose functioning is supported by  BELCHIM Crop Protection France , and by Chaire Maldive. DG was supported by the  Ramón y Cajal program ,  Spanish Government  ( RyC-2017-23098 ). Part of this study was financially supported by  BELCHIM Crop Protection France. We also acknowledge the availability of nursery staff and vineyard farmers to facilitate nursery and field work, respectively.
By the abovementioned genetic operators, these individuals are combined to produce a new generation of candidate solution groups, and the process is repeated until some convergence index is satisfied. GA acquire and accumulate knowledge about the search direction during the search process and, thus, automatically control the search direction to generate the optimal solution  [44] , which has been successfully applied to remote sensing image classification and have achieved good results. Many improved GAs have emerged in recent years  [45]. However, there are still unresolved issues. 1) Extensive calculations. The convergence of GA deteriorates when the dataset becomes larger. 2) The poor antinoise performance simple GA focusing on spectral features is usually sensitive to noises in the image.
A. Introduction to Data Three datasets of remote sensing images acquired by the Beijing 2 satellite are used to verify the accuracy and effectiveness of the proposed algorithm. Beijing-2 is a civil commercial remote sensing satellite constellation (DMC3) consisting of three high-resolution satellites. It was launched on July 11, 2015 from India and delivered to a 651 km sun-synchronous. The Beijing-2 constellation system provides approximately 24 km wide, 0. 8 m resolution panchromatic, and 3. 2 m resolution blue, green, red, and near-infrared multispectral images in orbit, which can provide remote sensing satellite data and spatial information products with global coverage and excellent spatial and temporal resolution, capable of revisiting any location on the globe for one or two day. Experimental images were obtained in 2018.
C. Classification Results Figs. 3 4 5 show LULC classification results in color maps corresponding to the different models when tested on each dataset. Since the three remotely sensed images contain different types of land use, the color representation of the classification chart is not uniform. According to these classification results, for three figures, the color maps obtained by the proposed method are more clear and smooth. Table  I  shows times (running time) and OA achieved by the 12 models and when tested on research areas to compare the classification performance with different methods. The best experimental results are shown in bold. One can conclude that the proposed method presented the best overall results, with the highest OA of 88. 72% and relatively few wrong pixels. For Area 3, when using the neighborhood windows, the classification accuracy of LULC is increased by 28.
This work was supported in part by the  National Natural Science Foundation of China  under Grant  62201438 , Grant  61772397 , and Grant  12005159 , in part by the  Shaanxi Forestry Science and Technology Innovation Key Special  under Grant  SXLK2022-02-8 , in part by the  Natural Science Basic Research Program of Shaanxi  under Grant  2021JC-23 , and in part by the  Science Technology and Development Project of Yulin Science and Technology Bureau  under Grant  CXY-2020-094. ( Yang Cao  and  Wei Feng  contributed equally to this work.
The elimination of OTUs with less than 50 reads (after removing contaminating and false-positive OTUs) allows us to reduce our dataset from 3282 to 64 OTUs by losing only 4% of the reads, which led us to 7,892,262 sequences. Of these sequences, 92% of the reads are from 5 major taxons (Chlamydiales, Solilurobacteriales, Leptospirales, Pseudomonadales and Burkholderiales). Relative abundance values presented in Fig. 3  illustrate the presence and absence of different bacterial genera detected in the control and the tumorous hydras of the four studied strains. First, we detected a majority of Pseudomonadales in the control individuals from St Petersburg, while Spirochaetales are the most represented in the tumoral individuals of the same strain. This is consistent with the previous literature, with the exception that we do not detect co-infection in all individuals or in a lesser extent than reported previously  19.
Although the presence of tumors in males seems to contradict this idea, recent evidence in Cnidarians indicates that components of the microbiome can be transmitted through sperm as well  37. Secondly, the accumulation of germline stem cells in tumorous hydra might be an adaptive response from the side of the host to the presence of bacteria. For instance, female germline stem cells express the peptide periculin, which has potent antimicrobial effects  38  , and this could help to reduce microbial loads in heavily infected hydra. The presence of tumors in males is again enigmatic, since sperm precursors are not known to have a specifically high expression of antimicrobial molecule, studying existing expression datasets could probably bring us evidence. Alternatively, the increased number of germline stem cells in tumorous individuals could be an adaptive response from the side of the host to preserve its own fitness through terminal investment, i.
Morphological and histological analysis. Hydras used for histological slides were photographed using a binocular magnifier connected to OLYMPUS© EP50 camera and EPview software (v2. 9. 6_20201224; Olympus, Tokyo, Japan). The individuals were first immobilized using a 5% urethane solution and then immersed into a 2. 5% Glutaraldehyde solution at 4 °C. Postfixation was achieved in FB-1% osmium tetraoxide for 1 h at 4 °C in the dark (same blocks were used for the TEM preparation). Excess fixing agent was eliminated during the dehydration step in a successive series of water solutions containing increasing amounts of acetone until reach-ing 100%. Finally, samples were embedded in epoxy resin (TAAB 812). Sections (80 nm thick) were stained with toluidine blue and observed, pictured, and measured under a microscope OLYMPUS©, the EP50 camera, and EPview software (v2.
Data availability The datasets generated and/or analysed during the current study are available in the Mendeley Data Repository: https:// doi. org/ 10. 17632/ 453g8 p489j. 2. Microbiota 16S data are available in the Short Reads Archive under BioProject Accession No. PRJNA890148 (https:// www. ncbi. nlm. nih. gov/ biopr oject/? term= PRJNA 890148).
S4b-d ). Better resolution of the production level and timing of sfGFP fusions with respect to the ss-to-dsDNA conversion (appearance of the mCh-ParB focus) in individual transconjugant cells was obtained using time-lapse imaging of conjugation performed in the microfluidic chamber (Movies S1, S2). We performed transconjugant cell detection and quantification of the intracellular sfGFP SNR cells over time (Fig. S5a-d ). When the transconjugant cell divided, we continued fluorescence quantification in the resulting daughter cells to monitor sfGFP production over a longer period. From this raw data, we calculated the fold-increase in SNR per 10-min interval, where a foldincrease superior to one reveals that the fusions are being produced in the transconjugants (Fig. S5a-d ). These data were finally translated into a comprehensive diagram presenting the production time windows for each fusion in transconjugant cells relative to the ss-to-dsDNA conversion event (Fig.
45 oil Plan Apo Lambda phase objective, ORCA-Fusion digital CMOS camera (Hamamatsu), and using NIS software for image acquisition. Acquisitions were performed using 50% power of a Fluo LED Spectra X light source at 488 and 560 nm excitation wavelengths. Exposure settings were 100 ms for Ypet, sfGFP and mCherry and 50 ms for phase contrast. Image analysis. Quantitative image analysis was done using Fiji software with MicrobeJ plugin  99. For snapshot analysis, cells' outline detection was performed automatically using MicrobeJ and verified using the Manual-editing interface. For time-lapse experiments, detection of cells was done semi-automatedly using the Manualediting interface, which allows to select the cells to be monitored and automatically detect the cell outlines. Within conjugation populations, donor (no mCh-ParB signal), recipient (diffuse mCh-ParB signal) or transconjugant (mCh-ParB foci) categories were assigned using the 'Type' option of MicrobeJ.
By contrast with the total amount of fluorescence per cell, which is depending on the cell size/ age and accounts for the background, SNR quantitative estimate is more appropriate for unbiased quantification of intracellular fluorescence over time. Ssb-Ypet, Ssb F -mCh and mCh-ParB foci were detected using MicrobeJ Maxima detection function, and foci localisation and fluorescence intensity were extracted and plotted automatically. Plots presenting time-lapse data were either aligned to the first frame where the transconjugant cell exhibits a conjugative Ssb-Ypet focus (ssDNA acquisition) or an mCh-ParB focus (ss-to-dsDNA conversion) as indicated in the corresponding figure legend. Importantly, because conjugation is asynchronous in the population, time-lapse movies do not always capture the entire sequence of DNA transfer, i.