dataesr/scientific-paragraphs-categorization huggingface dataset:
SNP Validation The discovery panel was genotyped with the 20 K SNP array following the standard Illumina protocol detailed in the IRSC apple array and its application papers  [3, 5]. In the framework of an ongoing PBA-based QTL mapping study of the FruitBree-domics project, over 1,600 seedlings from 21 full-sib families and their direct parents and additional progenitors were also genotyped and used for the construction of genetic maps. Although this work is not part of the current paper, we will present some of its outcomes, as they relate to the performance of the array. The fullsib families on which these data are based are listed in Table  S1. Genotyping data from the discovery panel and the full-sib families were analyzed using the GenomeStudio software (Illumina) with a GenCall threshold of 0.
SNP Mapping and Validation Within the framework of the FruitBreedomics project, 21 full sib families were SNP genotyped, using the 20 K array, and were used for the generation of linkage maps. This resulted in the genetic mapping of 15. 8 K SNP markers, and included 12,611 (success rate 86%) newly developed SNPs and 3,160 (success rate 96%) from the IRSC array that were informative in this new array and were mapped in at least one of the full-sib families screened (Table  S2 , Dataset S1). Of the non-mapped markers, 271 currently true monomorphic SNP markers may be mapped once screened over a wider germplasm set, as they are based on a single polymorphism in one of the more specific discovery panel members (e. g. M. micromalus or 'Common Antonovka'). Additionally, 747 SNPs that featured complex cluster patterns (suggesting the probes annealed to paralogous genomic regions or contained additional SNPs within the probe binding sites) were found informative by visual inspection but would have required additional elaboration on the calling pipeline or manual annotation as previously reported in  [6].
In addition, since up to 96 samples can be multiplexed in a single lane of Illumina HiSeq sequencing, the genotyping cost per sample can be as low as USD12. 00 at the time of writing (http://www. igd. cornell. edu/index. cfm/page/GBS/ GBSpricing. htm). However, despite clear advantages to the use of GBS under certain experimental conditions for under-resourced species, GBS datasets contain a large proportion of missing values and false homozygote calls, due to low, and uneven genome coverage among individuals (36% in the case of Rubus;  [42] ), and hence data imputation strategies are required for effective data analysis. Moreover sampling chromosomes based on restriction digestion may introduce a bias in allele frequency estimation due to polymorphisms in restriction sites  [47]. Whilst the cost of consumables for genotyping per individual is higher employing WGG arrays than for GBS, and only previously characterized SNPs that are present on the array can be interrogated, the data produced are robust and reliable, typically containing almost no missing values.
Acknowledgments We wish to thank:  INRA-Angers  for providing us with the DNA of the two double haploids,  Yolanda Noordijk ,  Andreas Peil  (JKI),  Anna Pikunova  (VNIIISPK),  Gennaro Fazio (GPRU) ,  Mark Davey  (KUL), and  Stefano Tartarini  ( UNIBO ) for providing leaf material for the 14 genotypes of the discovery panel,  Yolanda Noordijk  ( DLO ) for preparing the DNA samples,  Diego Micheletti ,  Paolo Fontana ,  Pietro Franceschi  and  Ross Crowhurst  for the useful discussions on data analysis,  Sue Gardiner  for manuscript editing, and the  International Doctoral Programme in the Genomics and Molecular Physiology of Fruit Plants (GMPF)  and the  Graduate School of Experimental Plant Sciences of Wageningen University  (The Netherlands) for a fellowship awarded to  Mario di Guardo.
Second, new journals dedicated to methodological advances (not only statistical) have been created and are now having considerable impact (notably Molecular Ecology Resources and Methods in Ecology and Evolution). Third, there are more specialized conferences that provide the opportunity for statisticians to interact with ecologists for mutual benefit. The reasons for this recent rise of statistical ecology are manifold and include the societal demand for scientists to address pressing issues such as global change and the current biodiversity crisis, the need to analyse the massive datasets and the novel data types generated by new technologies, and the popularization of methods through free statistical packages and the increase in computing power. We view the rise of statistical ecology as a sign that ecological and statistical modelling are coming together with the common goal of understanding complex processes in a formal inferential framework for better predictive capabilities.
(d) Integrated modelling Another trend is the popularization of integrated modellingi. e. combining different datasets in a single, coherent analysis  [19] -to address a wide variety of ecological questions. Current developments deal with the issues of goodness-of-fit testing, model selection, integration of recent developments in demography (e. g. integral projection models) and testing the assumption that data from different sources can be considered independent. From an ecological viewpoint, integrated modelling now scales from populations up to communities  [20].
Implementation (a) Computational algorithms The development of efficient and flexible computational algorithms for complex models and big datasets ([integrated nested] Laplace approximations, Hamiltonian Monte Carlo and standard Markov chain Monte Carlo algorithms) requires tremendous research efforts, as does their implementation in software packages (e. g. R-INLA (http://www. r-inla. org/), AD Model Builder (http://admb-project. org/), Laplaces Demon (http://www. bayesian-inference. com/software), Stan (http://mc-stan. org/), Nimble (http://r-nimble. org/), Open-BUGS (http://www. openbugs. net/w/FrontPage), JAGS (http://mcmc-jags. sourceforge. net/), PyMC (http://pymcdevs. github. io/pymc/), MCMCglmm (http://cran. r-project. org/web/packages/MCMCglmm/index. html)). When a complete likelihood cannot be easily calculated, methods for estimation based only on simulations and summary statistics (Synthetic likelihood  [21] ; Approximate Bayesian Computation  [22] ) are also receiving attention.
1. 3. Behavioral data analysis. Response and reaction time were recorded for each trial in every subject. In the VS task, reaction times were measured as the time difference between the appearance of the numbered labels to when the subject made the button press to indicate his/her choice of the target (1000-4000 ms after stimulus motion onset). A trial was treated as having no response, and discarded, if the subject did not respond within the 3000 ms allocated for response. All subjects performed significantly above chance (25%) (p,0. 01). Most of the correct responses occurred within the first 1000 ms of the response period, discussed in section 1 of the Results (Behavioral Results).
MEG Processing Methods In this section we outline the steps for acquiring and processing the MEG data. Each MEG run was divided into epochs defining the time evolution of each trial. The MEG data were registered to an anatomical MRI volume to map MEG sensor data onto the cortical surface of each subject's brain. Regions of Interest (ROIs) were extracted through finding clusters of activation in the mapped activation and then pruned by using a resolution matrix to eliminate from the analysis regions of high cross-talk (discussed in Section 2. 5 of Methods). 2. 1. MEG data Acquisition. The MEG study was conducted at the Athinoula A. Martinos Center for Biomedical Imaging in Charlestown, Massachusetts. Participants were seated in upright position under the MEG dewar and faced the projection screen placed 80 cm away measured from the eyes.
The MEG data were acquired with a 306-channel Neuromag Vectorview whole-head system (Elekta Neuromag Finland) comprising 204 orthogonally oriented planar gradiometers and 102 magnetometers at 102 locations. The system was housed in a three-layer magnetically shielded and sound-proof room (Imedco AG, Switzerland). During data acquisition the room was darkened. We used an LP350 DLP projector (InFocus, Wilsonville, OR) at a resolution of 10246768 pixels with refresh rate of 75 Hz in the MEG scanning room to present the stimulus. To compute the head position inside the MEG, four head-position indicator (HPI) electrodes were affixed to the subject's head. The positions of the HPI electrodes on the head and at least 80 points sampled on the scalp were entered with a magnetic digitizer (Polhemus FastTrack 3D) in a head coordinate frame defined by anatomical landmarks, which included the nasion and the left and right auricular points.
We truncated the 3000 ms response period because most responses (button box press) occurred in the first 1000 ms (discussed in Section 1 of the Results). Epochs were rejected and removed from the dataset if corresponding gradiometer readings exceeded 2 pT/m peak-topeak or magnetometer readings exceeded 6 pT peak-to-peak. Such large signal fluctuations are due to head or eye motion artifacts and cannot be easily filtered out from the data. In this study, for each of the participating subjects, the maximum number of trials rejected was less than or equal to 3 (below 2% of the total number of trials) leaving between 157-160 total epochs per condition per subject. Eye blink artifacts were removed by first computing an SSP operator from 2200 ms to 200 ms of the DCremoved, raw signal centered at 20 to 30 peaks of vertical EOG channel recordings and then by removing the first principal component of the gradiometer and magnetometer readings.
We normalized the signal by subtracting the mean and dividing by the standard deviation of the signal in the prestimulus. 2. 3. Anatomical MRI and MEG spatial registration. T1 weighted structural MRI scans were acquired on a separate day using an 8-channel phase array head coil in a 3T scanner (Siemens-Trio, Erlagen, Germany). Parameters of the sequence were: distance factor: 50%; slices per slab: 128; FOV: 256; FOV phase: 100; slice thickness: 1. 33 mm, TR: 2530 ms, TE: 3. 39 ms. Freesurfer software (http://surfer. nmr. mgh. harvard. edu) [32] [33] [34] [35]  was used for cortical reconstruction and volumetric segmentation of the T1 weighed whole brain images for each subject. The individual brain scans were motion corrected, spatially co- First, the fixation mark appeared on a blank screen and lasted 300 ms.
Next, the 9 textured spheres faded in from the background over a 1000 ms period. Then, the spheres remained static for 1000 ms. This was followed by displaying the stimulus motion for 1000 ms, 8 spheres simulating forward observer motion and the other (target) had independent motion, forward or backward with different speeds than that o the observer. Finally, the response period, lasting 3000 ms, displayed the spheres static, four of which were labeled with numbers 1-4, one corresponding to the target. The subject's task was to chose the number corresponding to target. doi:10. 1371/journal. pone. 0107715. g001 registered by morphing into the Freesurfer average brain through spherical surface mapping  [36]  and spatially smoothed with a 5 mm FWHM (Full with at Half Maximum) kernel. To perform the alignment of each individual subject MEG data onto their corresponding structural MRI of the brain, we used the MNE software (http://www.
nmr. mgh. harvard. edu/martinos/ userInfo/data/sofMNE. php)  [37]. We matched the fiduciary landmarks to their respective locations within the reconstructed skin surface from the anatomical MRI scan. The alignment was refined by applying the iterative closest point algorithm  [38]. 2. 4. MEG source estimates. Spatial distributions of cortical currents underlying the MEG signals were estimated using the L2 minimum-norm approach computed with the MNE software. The sources were restricted to the grey matter surface extracted with the FreeSurfer software. The triangulation of the cortex was decimated to about 9000 sources per hemisphere resulting in an average distance of 4 mm between adjacent sources. The orientations of the sources were approximately constrained to the cortical surface normal direction using the loose orientation constraint approach.
Data Analysis Methods In this study we investigated the spatiotemporal profile of the alpha band power at 10 Hz and its role in the functional linking between visual motion processing regions and frontoparietal regions in a complex visual motion task (VS). First we used wavelet decomposition to compute the oscillatory power in time at the 10 Hz alpha frequency (discussed in Section 3. 1 of Methods). Second, in order to measure how the 10 Hz oscillatory power is synchronized among ROIs, we computed the Weighted Phase Lag Index (discussed in Section 3. 2 of Methods). 3. 1. Frequency Bands (Wavelets). To compute timevarying 10 Hz alpha-band oscillatory power, we applied the complex Morlet wavelet filter  [41]  to the ROI's time courses using the Matlab (MathWorks, Natick, Ma) package Fieldtrip (http:// fieldtrip.
Behavioral results Behavioral performance was assessed as a percentage of correct responses for object motion (target) detection in the VS task, and for discrimination of the direction of the pattern of radial motion (expansion or contraction) in the MT+ Localizer (MTLoc) task. We also recorded the reaction time in each trial. In the MTLoc task, the detection rate was close to 100% correct in all subjects. In the VS task the mean percent correct of target detection was 50. 8%+/27. 6% (2 SE), which is significantly above chance (p, 0. 01). Most (78. 2%) correct responses in the VS task occurred between 1400 ms and 2000 ms after the start of the motion stimulus (400-1000 ms relative to the beginning of the response period). Therefore, for all data analysis, we truncated the MEG time course at the 2000 ms mark (1000 ms stimulus motion period, and 1000 ms of the response period).
Additional samples were therefore collected every 3 days during this period. They were fixed in 0. 2% glutaraldehyde and stored at -80 • C until analysis with a second CytoSense (CytoBuoy, b. v. , NL) instrument provided by the PRECYM flow cytometry platform of MIO in order to record the abundances of the main phytoplankton clusters. Trigger levels and flow rates were set in order to obtain a population resolution similar to the buoy CytoSense settings. CytoSense data were processed and analyzed with the CytoClus® software (CytoBuoy). Phytoplankton clusters were resolved using several two-dimensional cytograms of retrieved information (descriptors) from the 5 pulse shapes (FWS, SWS, FLO, FLR, FLY) obtained for each single cell, mainly the area under the curve and the maximum of the pulse shape signal.
METEOROLOGICAL AND HYDROLOGICAL DATA Meteorological information was collected from a nearby Météo-France weather station (Nice airport station, 43. 648 • N, 7. 208 • E). Daily precipitation (mm), daily averaged wind speed (m. s -1 ) and daily solar radiance (J. cm -2 ) were the parameters selected to assess the influence of external events on marine water properties and phytoplankton community composition and dynamics. Temperature ( • C) and salinity were recorded every minute using a STPS sensor (nke instrumentation®) immersed at 2 m depth under the EOL buoy. This temperature/salinity data-set was compared with the SOMLIT Point B data-set collected weekly at 1 m (SBE25).
STATISTICAL ANALYSIS Statistics were run under the R software (CRAN, http://cran. r-project. org/). Time series of phytoplankton abundances, nutrient concentrations, and hydrological and meteorological variables were smoothed using a loess method (library stats, function loess) followed by a predictive procedure (function predict;  Cleveland and Devlin, 1988)  in order to cover the periods of missing data and generate regular discrete time series with similar frequencies. The loess function corresponds to a local polynomial regression fitting. The weighted least squares local fitting uses neighborhood points with a tricubic weighting. Cross-correlation function (CCF) was computed between the environmental variables and the phytoplankton abundance's computed loess curves. Boxplot function was used to plot abundance variations over time.
ACKNOWLEDGMENTS We wish to thank  J. -Y. Carval  and  J. L. Prevost , the  Captains of the scientific vessels "La Sagitta" and "La Velelle. "Chlorophyll data were provided by the  Service d'Observation en Milieu Littoral, INSU-CNRS , of Villefranche-sur-mer. We are also grateful to the director of the Observatoire of Villefranche sur mer,  G. Gorsky , for giving us the opportunity to carry out our experiments. We gratefully acknowledge the financial and administrative support of DYMAPHY (Development of a DYnamic observation system for the assessment of MArine water quality, based on PHYtoplankton analysis) INTERREG IVA "2  Mers Seas Zeeën ," a European cross-border project co-funded by the  European Regional Development Fund (ERDF)  and  French  ( ULCO-CNRS-UL1 ),  British (Cefas)  and Dutch (RWS) partners, supervised by  Luis Felipe Artigas at the Oceanology and Geosciences Laboratory in Wimereux (France).
Among the methods to quantify texture on images based on optical data, those grounded on image texture analysis, like the FOTO Method  [6, 22] , showed promising results for applications to biomass predictions in wet tropical forests, notably because the texture indices showed no evidence of saturation at high biomass levels  [20, 24]. The FOTO Method is a 3-step process: (1) 2-D Fast Fourier Transform and r-spectra computation applied on very high resolutions images, 2) ordination of r-spectra using Principal Component Analysis (PCA), and (3) Linear Regression (LR) model for estimating biomass values or stand parameters from reference datasets (field data or data-derived simulations). PCA and LR are the two mathematical models used for inferring the output of the method. Most of the models used in the published applications found in the FOTO literature are linear  [5, 6, 22, 2, 20, 21, 3]  and cannot capture all aspects of problems that are inherently nonlinear.
Dataset description Reference data have been provided by N. Barbier using a simulator of 3D forest mockups "Allostand" as in  [2]  in order to roughly mimic in terms of tree densities and dbh distributions forest types that have been locally observed in three countries of Central Africa. Canopy images were simulated from mock-ups using a radiative transfer model (DART,  [11] ). DART is a software tool based on an approach that combines a ray-tracing model and discrete ordinate methods to simulate, simultaneously in several wavelengths of the optical domain, remotely sensed images of heterogeneous natural and urban landscapes. DART software is made at CESBIO and can be downloaded for free at their website http://www. cesbio. ups-tlse. fr/fr/dart. html. The data is a set of gray scale square images of size 99 × 99 pixels, analogous to 1ha of forest in the field, with their corresponding AGB which is given in kilograms per hectare.
-Congo dataset: 176 images. The AGB values for this dataset range from 68184kg/ha to 480649kg/ha with a mean of 255805kg/ha and a standard deviation if term > 0 then 6: L k ← all sets of k features containing S end if 21: end for 22: return S of 93192kg/ha. -Democratic Republic of Congo dataset: 105 images. The AGB values for this dataset range from 55311kg/ha to 328223kg/ha with a mean of 167655kg/ha and a standard deviation of 55476kg/ha. -Central African Republic dataset 109 images. The AGB values for this dataset range from 3259kg/ha to 523208kg/ha with a mean of 287586kg/ha and its standard deviation is 103148kg/ha. These datasets are used as original training examples.
Attribute selection The original dataset has 67 features labeled F 1, F 2,. , F 67 resulting from the rspectra calculation step. The attribute selection process applied on the Central Africa Republic dataset produced 12 features. The same process applied on the Congo and the Democratic Republic of Congo datasets produced respectively 14 and 31 features. These features are used only for SVR and k-NN predictions. In the case of Random Forests, prediction is performed on the entire dataset because the algorithm already includes a feature selection.
test) set becomes the test (resp. training) set. This 2-fold approach can be generalized to obtain a k-fold cross-validation : this method partitions the dataset into k equal-sized subsets. At the i th iteration, the i th subset is used as test set while the union of the other subsets serves as training set. The performance of the classifier is estimated by considering the average accuracy of the k models constructed during the k runs. The advantage of this approach is that each example is used exactly once for testing and k -1 times for training. This ensures that the estimate of predictive accuracy is less biased.
Results For each classifier and each dataset, we used a ten-fold cross validation scheme to estimate the error. The evaluation criterion is the average mean absolute error (AMAE) i. e. the average of the absolute value of the difference between the real and the predicted AGB values for all the images of the dataset. The results are presented in Table  2 , Table  3     We see from these results that the FOTO method is improved by using Support Vector for Regression with attribute selection. Depending on the dataset we can have an improvement of more than 19%. In fact the regression method must be selected according to the dataset in presence. For some datasets, the linear regression can be a good choice but for other datasets it can be a bad one. For the Congo dataset, we obtained the major improvement with k-NN combined with attribute selection using k = 7 and a gain of 19.
99% followed, in the quality of result by SVM with attribute selection with a gain of 19. 85% For the DRC dataset, the algorithms that yield the best improvements are SVM with attribute selection and Random Forests (RF) with respective gains of 23. 24% and 21. 95%. For the CAR dataset, the algorithms that yield the best improvement are SVM with attribute selection and Random Forests with respective gains of 18. 01%. We notice from these results that the new algorithms presented here improve the original FOTO Method except Random Forests applied to Congo dataset, where the FOTO Method gives better results.
Discussions In this work, we have explored some non-linear machine learning methods for biomass estimation in tropical forest areas based on canopy image texture. The dataset was posed of simulated images relating to three different forest types yielding diversified image textures and canopy aspects (open vs. closed canopy, fine vs. coarse texture). The relationship between tree biomass and crown dimensions is non-linear and the ensuing relationship between stand biomass and features of canopy texture is liable to be nonlinear in many situations. We observed here that nonlinear methods applied on textural indices yield better results compared to the linear method based on PCA and linear regression. Average prediction errors as presented in tables 2, 3 and 4 confirm this assertion. In average, nonlinear methods have a gain of 19.
We thank  Nicolas Barbier  ( IRD, UMR AMAP ) for providing the simulated images used in this paper as test dataset and  Jean-Philippe Gastellu-Etchegorry (Univ. of Toulouse, UMR CESBIO)  for granting the free use of the DART software to carry out such simulations. We also thank  Pierre Ploton  ( IRD, UMR AMAP and ENS Yaoundé, Department of Biological Sciences ) for fruitful discussions. We thank  Professor Maurice Tchuente  ( UMMISCO, Yaoundé ) for his valuable and constructive suggestions and inspiring comments. His willingness to give his time so generously has been very much appreciated.