dataesr/scientific-paragraphs-categorization huggingface dataset:
Understory and above canopy cameras can also be used to measure the presence and depth of snow  (Dickerson-Lange et al. , 2015). To ensure standardized configuration the Stardot Netcam SCF IR should not be manually configured, instead the configuration of this camera should be executed by using a dedicated installation script (i. e. PhenoCam Installation Tool, or PIT; https://khufkens. github. io/phenocam-installation-tool/). The PIT ensures that the settings of the Stardot Netcam SC5 IR camera adhere to the recommended ICOS settings. In particular, the exposure mode is set to a fixed white balance (in order to minimise in-camera processing of day-to-day changes in illumination and RGB ratios), the date and time settings (set to local standard time when provided an UTC offset during installation), the file format and compression ratio (jpeg -high quality), standardized imprinting of date, time, exposure settings or camera ID on the image as well as a standardised file name convention are all configured by the PIT.
Although data is stored in the jpeg format data quality loss is minimal. Previous research After configuration, a Stardot Netcam SC5 IR camera will transmit data automatically to the ICOS Ecosystem Thematic Center (ETC) File Transfer Protocol (FTP) server, or a local FTP server if a site's network connection is unreliable. The data transmitted includes: an RGB and RGB + IR image and their corresponding meta-data (e. g. exposure) at a set time interval (e. g. 4:00 -22:00 h). If the near-real time transfer of images from the phenocam to the ICOS ETC server is not possible, it is advised that the images are uploaded manually by the PI every two weeks. Deployment of the phenocams will happen throughout the ICOS network, reflecting the spatial coverage and representability in terms of plant functional types of the network.
Post-processing Processed output data will be available through the ICOS data portal which will rely on the python vegindex package (https://github. com/tmilliman/python-vegindex) as a backend to calculate Gcc time series, and the "phenocamr" R package front end for outlier detection, smoothing and the estimation of phenological transition dates (e. g. the start of the season; https://khufkens. github. io/phenocamr/). The vegindex package excludes images taken at a solar angle smaller than 10 degrees (dark images), ingests multiple ROIs and has the ability to quickly generate vegetation colour index time series. A detailed description of the algorithms for both the back and front-end processing are provided in  Richardson et al. (2018a,b)  and  Hufkens et al. (2018) , respectively. It is anticipated that in the near future an application programme interface (API) will be created to access processed data output.
Nonetheless, if PIs wish to process their own digital images a number of algorithms are now freely available to generate time-series of vegetation colour indices. For example, the open source R package, Phenopix has been developed recently by  Filippa et al. (2016)  and can be downloaded freely from the R CRAN network. Phenopix is an easy-to-use R-package that provides a standardised processing algorithm for extracting vegetation colour indices (e. g. the Gcc amongst many) and phenological transition dates calculated or parameterised from a range of different extraction techniques (Fig. 2 ). Multiple ROIs are recommended for ecosystems with heterogeneous canopies (e. g. mixed forests, grazed grasslands or peatlands) in order to capture the phenology of multiple species (or plant functional types) or to capture understory phenology (Fig.
Final dataset The processing of the image data using the vegindex and phenocamr packages will result in a set of standardised products. Data products provided will include, (colour) statistics for predefined ROI for each ICOS site, summarised data at 1 or 3 day intervals and estimates of phenological transition dates at three preset amplitude thresholds (10, 25, 50%). Data will be delivered in the form of rolling releases on an annual cycle.
For example defining ROIs or detecting changes in the field of view can not be automatically defined. Ongoing development of new tools, such as the drawROI tool by Bijan Seyednarollah (https://github. com/bnasr/drawROI) or Machine Learning techniques based on Convolutional Neural Networks (Hufkens, preliminary results; Fig. 3 ) will further facilitate the manual delination of ROI through easy workflows or near complete automation, respectivelly. However, even with consistent instrumentation and quality control the data retrieved remains effectively a unitless measurement and site inter-comparison in absolute Gcc values is often not possible. Normalizing Gcc values between 0 (baseline) and 1 (maximum) and constraining the maximum amplitude using external (climate) variables could be one solution to overcome this problem especially in moisture limited ecosystems  (Hufkens et al.
ACKNOWLEDGEMENTS This study has been carried out on the  MicroMic  project with financial support from the  French National Research Agency (ANR)  in the frame of the Investments for the future Programme, within the Cluster of  Excellence COTE  ( ANR-10-LABX-45 ). We acknowledge all active members of the ICOS ecosystem working groups and all other people who contributed to the development of the ICOS ecosystem domain and the measurement protocols. We especially thank  Daniella Franz  for her encouragement and perseverance in bringing together all the ICOS protocols. The development of PhenoCam has been funded by the  Northeastern States Research Cooperative ,  NSF 's  Macrosystems Biology program  (awards  EF-1065029  and  EF-1702697 ), and  DOE's Regional and Global Climate Modeling program  (award  DE-SC0016011 ).
Crossa et al. (2016)  extended the M•E model using priors that produce shrinkage (Bayesian ridge regression) or variable selection (BayesB), and reported better prediction performances for these models compared to single environment and across-environment models. The latest multi-environment genomic prediction models fall back on a Bayesian approach  (Cuevas et al. 2017). Application of these methods to one maize and four wheat CIMMYT data sets showed that models with G•E always have higher prediction ability than single-environment models, regardless of the genetic correlation between environments. The predictive ability of these Bayesian methods was also generally better than that obtained with the G•E models proposed by  Lopez-Cruz et al. (2015)  and  Cuevas et al. (2016) , when applied to the same datasets.
The analysis was performed with the proc mixed procedure of SAS 9. 2 (SAS Institute, Cary NC, USA); the method of estimation for the variance components was the restricted maximum likelihood (REML). The formula by  Holland et al. (2003)  was used to estimate broad sense heritability (H 2 ) as well as the corresponding standard error (SE) for each trait and each water management system in each population: H 2 ¼ s 2 g s 2 g þ s 2 gy ny þ s 2 e nr ; where s 2 g , s 2 gy and s 2 e are the variance components associated with the genotype, the interaction between genotype and year and the residual, respectively. ny is the harmonic mean of the number of years per accession and nr, the harmonic mean of the number of plots across years per accession. Conditional coefficients of determination (R 2 ) were also computed using the methodology described by  Nakagawa and Schielzeth (2013).
Genotypic data The method used to genotype both RP and PP populations is detailed in Ben  Hassen et al. (2017). It relies on the genotyping by sequencing protocol developed by  Elshire et al. (2011). Sequencing was performed with a Genome Analyzer II (Illumina, Inc. , San Diego, USA). The different steps of analysis (raw data filtering, sequence alignment, SNP calling and imputation) were performed with TASSEL v3. 0 and the associated GBS pipeline  (Glaubitz et al. 2014). A working set of 32,066 SNPs was obtained with a heterozygosity rate , 5% and minor allele frequency (MAF). 5%.
u, f and e are independent and normally distributed. For more methodological details concerning the extended GBLUP, RKHS-1 and RKHS-2 statistical models please refer to Lopez-Cruz et al. Implementation of the models: Analyses were performed in the R 3. 4. 2 environment (R Core Team 2017) with the R packages BGLR 1. 0. 5 (Pérez and de los Campos 2014) and MTM 1. 0. 0 (De los Campos and Grüneberg 2018). For both packages, 35,000 iterations for the Gibbs sampler were used. For the inference, 3,000 samples were used after removing the first 5,000 samples (burn-in) and keeping one in ten samples to avoid auto-correlation (thinning). Convergence of Markov chain Monte Carlo algorithm was assessed for all parameters of the models with Gelman-Rubin tests (Gelman and Rubin 1992) using the R-package coda 0.
ACKNOWLEDGMENTS This work was funded by  Agropolis Foundation  (http://www. agropolisfondation. fr/) and  Cariplo Foundation  (http://www. fondazionecariplo. it/),Grant no  1201-006. This work was supported by the  CIRAD -UMR AGAP HPC Data Center of the South Green Bioinformatics platform  (http://www. southgreen. fr/). The authors declare that they have no conflict of interest.
This paper is organized as follows: section 2 describes the datasets used, methods, and TRENDY models selected. Section 3 reports the results regarding the relationship between ENSO and CGR and EP and CP El Niño events, in addition to a composite analysis on carbon cycle behaviors, and terrestrial mechanisms. Section 4 contains a discussion of the results, and section 5 presents concluding remarks. (1) where ' = 2. 1276 Pg C ppm -1 ; *"+ , is the atmospheric partial pressure of CO 2 in ppm; and t is the time in months. The detailed calculation of the conversion factor, ', can be found in the appendix  (Sarmiento et al. , 2010).
Data Temperature and precipitation datasets for 1960 through 2013 were obtained from CRUNCEPv6  (Wei et al. , 2014). CRUNCEP datasets are the merged product of ground observation-based CRU data and model-based NCEP-NCAR Reanalysis data with a 0. 5°×0. 5° spatial resolution and 6-hour temporal resolution. These datasets are consistent with the climatic forcing used to run dynamic global vegetation models in TRENDY v4  (Sitch et al. , 2015). The sea surface temperature anomalies (SSTA) over the Niño3. 4 region (5°S-5°N, 120°-170°W) were obtained from the NOAA's Extended Reconstructed Sea Surface Temperature (ERSST) dataset, version 4  (Huang et al. , 2015). The inversion of F TA from the Jena CarboScope was used for comparison with the TRENDY multi-model simulations from 1981 to 2013. The Jena CarboScope Project provided the estimates of the surface-atmosphere carbon flux based on atmospheric measurements using an "atmospheric transport inversion".
TRENDY simulations We analyzed eight state-of-the-art dynamic global vegetation models from TRENDY   (Oleson et al. , 2013) , ISAM  (Jain et al. , 2013) , JSBACH  (Reick et al. , 2013) , JULES  (Clark et al. , 2011) , LPX-Bern  (Keller et al. , 2017) , OCN  (Zaehle and Friend, 2010) , VEGAS  (Zeng et al. , 2005) , and VISIT  (Kato et al. , 2013)  (Table  1 ). Since LPX-Bern was excluded in the analysis of TRENDY v4, due to it not fulfilling the minimum performance requirement, the output over the same time period of a more recent version (LPX-Bern v1. 3) was used. These models were forced using a common set of climatic datasets (CRUNCEPv6), and followed the same experimental protocol. The 'S3' run was used in this study, in which simulations forced by all the drivers including CO 2 , climate, land use, and land cover change  (Sitch et al.
, 2005; Wang et al. , 2016; Wang et al. , 2013). This phenomenon can be clearly detected in the EP El Niño composite (Fig. 3b ). Therefore, the composite analyses in this study can still give us some insight into the interannual variability of the global carbon cycle. Another caveat is that the TRENDY models seemed to underestimate the amplitude of the F TA anomaly during the extreme EP El Niño events (Fig. 1b ). This underestimation of F TA may partially result from a bias in the estimation of carbon releases induced by wildfires. As expected, the carbon releases induced by wildfires in such 1997/98 strong El Niño event played an important role in global carbon variations  (van der Werf et al. , 2004; Chen et al. , 2017)  (Supplementary Fig. S6 ). However, some TRENDY models (ISAM, JULES, and OCN) do not include a fire module to explicitly simulate the carbon releases induced by wildfires (Table  1 ), and those TRENDY models that do contain a fire module generally underestimate the effects of wildfires.
Acknowledgements. We gratefully acknowledge the  TRENDY DGVM community , as part of the  Global Carbon Project , for access to gridded land data and the  NOAA ESRL  for the use of Mauna Loa atmospheric CO 2 records. This study was supported by the  National Key R&D Program of China  (grant no. 2017YFB0504000  and no. 2016YFA0600204 ), the  Natural Science Foundation of Jiangsu Province, China  (Grant No. BK20160625 ), and the  National Natural Science Foundation of China  (Grant No. 41605039 ). Andrew Wiltshire  was supported by the  Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). We also would like to thank  LetPub  for proving linguistic assistance. References Ahlstrom ,  A. , Raupach , M. R. , Schurgers, G. , Smith, B. , Arneth, A. , Jung, M. , , and us have for Young Scientists Nino different?,  Geophys Res Lett ,  44 ,  10.
Data availability. The monthly atmospheric CO 2 concentration is from NOAA/ESRL (https://www. esrl. noaa. gov/gmd/ccgg/trends/index. html). The Niño3. 4 Index is from ERSST4 (http://www. cpc. ncep. noaa. gov/data/indices/ersst4. nino. mth. 81-10. ascii). Temperature and precipitation are from CRUNCEP v6 (ftp://nacp. ornl. gov/synthesis/2009/frescati/temp/land_use_change/original/readme. ht m). TRENDY v4 data are available from S. Sitch (s. a. sitch@exeter. ac. uk) upon your reasonable request.
3D visualization and analyses of membrane protein diffusion reveal no directionality bias We wished to visualize tracks of membrane proteins on the bacterial wall surface in 3D and to analyze if movement may have a directional bias, e. g. , possibly a preferential movement perpendicular to the long axis of the cells. This idea stems from the inhibition of filament formation of E. coli MreB protein, an actin-like protein that forms filaments directly underneath the cell membrane along the short axis of the cell, that was shown to increase diffusion coefficients of membrane proteins  [3]. This finding implies that the filaments generate a diffusion barrier, which would disfavor diffusion along the long axis of the cell. To visualize tracks, we wrote a script in MATLAB to extract the track lengths from the raw data.
To ensure continuous nutrition supply and to immobilize the cells, 4 μl of cell culture were spotted on a cover slip and covered with a pad consisting of S750 minimal medium and 1% (w/v) agarose. Epifluorescence microscopy was done using a Zeiss Axio Imager A1 equipped with an EVOLVE EMCCD camera (Photometrics) and a TIRF objective with an aperture of 1. 45, acquiring images with VisiView (2. 1. 2, Visitron, Munich) software and using a 515-nm laser for YFP detection. The resulting images were classified using MicrobeTracker  [37]. Cell outlines were identified, cell length was measured, and intensity profiles were calculated for each cell (Additional file 1). Fusions from selected strains containing localized proteins were confirmed by sequencing.
Single-molecule tracking For single-molecule microscopy, a Nikon Eclipse Ti with a TIRF objective (× 100, Apo, NA: 1. 49) was used. Image acquisition was accomplished using a back-illuminated EMCCD camera (Hamamatsu). The center of a 20-fold expanded beam from a 100-mW multiline argon laser (JDS Uniphase, laser head: 2219-G5MLS) was focused on the back focal plane and operated during image acquisition with 150 to 200 W/cm 2. A 514-nm laser diode was used as excitation source. For image acquisition, the program Andor Solis 4. 21 was applied. Streams of 1500 frames of 20 ms were acquired. Cells continued to grow after imaging, showing that there is little to no photo damage during acquisition. Acquired streams were loaded into Fiji ImageJ  [75] , and pixel sizes (106 nm) and time increments were calibrated.
To find best candidates, we analyzed the density of proteins over average cells. Taking into account the depth of focus of 0. 125 μm, the polar regions contribute for less than 10% of the total correction factor. We implemented a script in Wolfram Mathematica to automatically reconstruct cell meshes and realistic tracks from microscopic data into three dimensions (Fig. 3c ). Finally, density maps revealed that MinJ satisfies at best the request of spatial homogeneity (Additional file 5) and it was used as a calibration protein. Since the observed area does not present a very high curvature, we do not expect a strong correction factor. The correction coefficient C was indeed 1. 23, corresponding to a correction of 23% on the diffusion coefficients computed from randomly oriented cells. If cells are aligned to the coordinate system, w x and w y represent the independent corrections to the x and y components of each track.
P and B c in Eq. 9 represent weights that estimate the total distance that each protein travels in a specific region. For correcting diffusion of rotated cells, P and B c can be specific from every single dataset. In such a way, only the factor w cyl is needed from the calibration protein. Thus, the only requirement that a calibration protein has to satisfy is to diffuse homogeneously along the short axis direction. We used Eqs. 8 and 9 to correct the apparent directionality histograms that describe how many tracks move along a specific direction (left panels of Fig. 5 ).
The Superposition of Functional Contours (SFC) prosody model decomposes the intonation and duration contours into elementary contours that encode specific linguistic functions. It can be used to extract these functional contours at multiple linguistic levels. The PySFC system, which incorporates the SFC, can thus be used to analyse the significance of including the neighbouring syllables in the scope of the tone functional contours in spoken Chinese on the modelling of prosody. Our results show that significant improvements of modelling tone functional contours are obtained by including the right syllable in the scope, but not the left one. We thus show that there is a larger carry-over effect for Chinese tones in contrast to an anticipatory one. This finding is in line with the established state-of-the-art.
Introduction Mandarin is a tone language in which every syllable carries a tone that can distinguish words that are segmentally identical  [1, 2]. To model Mandarin intonation, it is critical to generate fine details of F0 contours associated with the lexical tones. The Superposition of Functional Contours (SFC) model is a prosodic model that can decompose the prosody, including the pitch contour, into functionally relevant elementary contours  [3, 4, 5]. It has been successfully used to model different linguistic levels, including: attitudes  [6] , grammatical dependencies  [7] , cliticisation  [5] , focus  [8] , as well as tones in Mandarin  [9]. The experiments were carried out using PySFC, which is a prosody analysis system based on the SFC model, implemented in the scientific Python ecosystem  [10].
Objectives and State-of-the-art The existence of the carry-over effect is well established in literature  [15, 16, 17]. Due to articulatory inertia  [18] , in connected speech, a large portion of each tone consists of a movement away from the final state of the preceding tone and toward the underlying properties of the current tone  [15]. It is our objective to validate these facts through the effects of scope on the modelling accuracy of the SFC. The SFC is able to separate the influences of the different elementary contours and thus does not require an especially tailored database for this purpose. We hypothesise that because of the established dominance of the carry-over effect over the anticipatory effect, the inclusion of the right syllable in the tone function scope will lead to significant improvements in intonation modelling, while the inclusion of the left one will conversely not.
Using these different settings we fit the SFC model to the data and analyse its performance using the weighted root mean square error (WRMSE), this : W RM SE[semitones] = N -1 n=0 w[n](f0[n] -f0[n]) 2 N -1 n=0 w[n] , ( 2 ) where f0 is the original pitch contour, f0 is its reconstruction output by the SFC model, w is the weighting function and N is the number of samples in the utterance. Since the SFC works on predicting pitch targets within the vowel nucleus, we choose a binary w that is 1 only within vowel regions, thus limiting the WMRSE calculation to f0 in vowels. The analysis was based on a database of read Chinese from a single speaker comprising 110 carrier utterances ranging from 6 to 38 syllables in length  [9]. The database was annotated with the following functional contours: declaration, question, tones, word boundaries, interdependent and independent clauses.
We can see that using a narrow scope gives the worse results, adding the left scope gives a slight improvement, while using the right scope and finally the double scope gives the best results. The distribution of the WRMSE across the files in the database is given using a boxplot in Fig. 6. We can see that the change in the scope parameter does not influence the spread of the distribution, as was evident in the standard deviation in Table  3 , but only offsets the median. To assess the significance of the change in WRMSE we conducted a repeated-measures analysis of variance (RM-ANOVA), since we were measuring the WRMSE on the same.
Conclusions We have demonstrated that the scope of the elementary intonation contours used to communicate the four tones of Chinese is not contained within the syllable that carries the tone, but in fact it also includes the following syllable. This carry-over effect might be due to the inertia of the laryngeal system that effectuates the pitch change. We have also shown that there is not a significant anticipatory component in the realisation of tones in our Chinese data. The described methodology can be used to assess these effects in other tonal languages as well. Also, the PySFC can be used to explore the impact of neighbouring tone context on the tone shapes. A limitation of the proposed approach is that it can be applied only when the prosodic structure of the utterance matches its syntactical structure, on which the annotations and thus the input ramps of the NNCGs are based.
Combined Traffic and Network Simulators Several platforms that simulate V2X have been developed in recent works. Some example simulation models are described in  [23]. The authors in  [24]  combined the network simulator ns-2  [25]  with SUMO  [13]  to evaluate VANETs and developed a TraCI in which SUMO and ns-2 communicated over a Transmission Control Protocol (TCP) to simulate V2V connections. The integrated platform that links SUMO and ns-2 by using the TraCI interface is known as Traffic and Network Simulation (TRANS)  [26]. TRANS can mimic traffic congestion and road collisions at a specific location or vehicle. SUMO was also integrated with the network simulator OMNeT++ in a further work to evaluate the Inter-Vehicle Communication (IVC) protocol  [6]. In the same line of research, a communication module to integrate ns-2 and the Simulation of Car-to-Car Messaging (CARISMA) developed by BMW was implemented to simulate and evaluate V2V communication scenarios  [27].