dataesr/scientific-paragraphs-categorization huggingface dataset:
In each plot, the diameter at breast height (DBH; in cm) was measured for all trees with DBH ≥ 10 cm. Trees were identified in the field by expert botanists, and herbarium specimens were collected on each species for cross-identification at the herbarium of Université Libre de Bruxelles (BRLU). In savanna plots, the height (H) of all trees with DBH ≥ 10 cm was measured using a graduated pole for short trees (H ≤ 7 m) and a laser range finder device (Trupulse 360"R) for taller trees (> 7 m). In forest plots, tree height was measured on a subsample of trees per plot (approximately 50 sampled along the DBH range) with their crown apex visible using the laser range finder device. We subsequently used the BIOMASS R package  [32]  to fit plot-level height-diameter allometric models via a three-parameter Weibull function and predict the height of unmeasured trees.
In total, we censused 4309 trees in the forests, belonging to 150 species, and 3487 trees in the savanna from 43 species. In the following analyses, we used the savanna plot size (40 × 40 m) as our minimum mapping unit, and thus split each 1-ha forest plots in four subplots of 40 * 40 m (selecting each time the subplots located on the external edge of the 1-ha plots; Appendix A). The field dataset thus consisted in 62 field plots (18 and 44 plots in savanna and forests, respectively). Table  2  presents a summary statistics of the 62 plots data installed in forest and savanna sites.
Airborne LiDAR Data Airborne LiDAR Scanning data were acquired in 2012 with a Riegl LMQ-560 sensor mounted on an airplane of type Pilatus PC6 with a flight height of 600 m above ground level and an average ground speed of 167 km. h -1. The scan angle was 60 • with a band swath of 690 m and 50% of overlap among adjacent flight lines which resulted in an average point density of 8. 4 points. m -. The ALS point error was 10 cm vertically and 15 cm horizontally. We used the lidR R package (v2. 0. 2; [33]) to process the ALS point cloud and generated a 2-m resolution canopy height model (CHM).
It is primarily coded in the IDL language (Harris) for image processing algorithms, with a core scene model and model inversion engine that has been developed in Matlab (MatWorks) Overland uses look-up tables from LOWTRAN and performs an inversion of a coupled atmospheric scene model  [35]  to estimate atmospheric parameters and discard influences from sky, aerosols, and clouds on the surface reflectance. Another feature of Overland is the ability to partition the reflectance of individual pixels into respective contributions of soil, photosynthetic vegetation (green matter), and the non-photosynthetic matter (dead wood), and characterize the self-cast shadows of the rough vegetation canopies. For this, it implements a vegetation model by combining PROSPECT  [36] , SAIL  [37] , and a soil model.
By inverting this vegetation model, we can notably derive the fractional cover of green vegetation (fCover), the canopy shade factor (CSF), and the leaf area index (LAI). To predict vegetation AGB, we considered the three variables provided by Overland, as well as images spectral bands and vegetation indices from corrected images summarized in Table  3. The dataset of spaceborne optical variables thus consisted of 11 variables for Spot 6/7, 13 variables for Landsat 8 and 24 variables for Sentinel 2.
Field-Based AGB Estimates We used the R BIOMASS package  [32] , which relies on the Global Wood Density Database  [38, 39] , to attribute to each tree a wood density (WD) value based on tree taxonomy. For trees identified at the species or genus level, the average WD of the respective taxonomic level was used. For trees identified at the family level, or unidentified trees, the plot average WD was used. We then computed tree AGB using allometric models based on DBH, H and WD. For forest trees, we used the pantropical AGB model of Chave et al. [40]. For trees from savanna plots, we used an AGB model developed for semi-arid savanna by Colgan et al. [41]. Lastly, we computed plot AGB density (AGB FIELD ) as the sum of individual tree AGBs over the plot area (expressed in Mg. ha -1 ).
Model-Based AGB Estimates Imagery products from the different satellite sensors were co-aligned to the AGB ALS raster and aggregated to 40 m resolution. We used the Random Forest (RF) algorithm  [50]  implemented in the randomForest R package  [51]  to model vegetation AGB density from spaceborne optical variables. RF is a popular machine learning technique in remote sensing studies due to its ability to handle high-dimensional datasets, to account for non-linear relationships between response and predictor variables, and to its relative robustness to multicollinearity, model overparameterization, and overfitting  [52]. Here, we built several RF models, considering each time a different set of spaceborne optical variables (i. e. , from Spot 6/7, Landsat 8, or Sentinel 2) and using either field-based AGB estimations (strategy 1, henceforth RF FIELD ) or the LiDAR-based AGB ALS map (strategy 2, henceforth RF ALS ) for model specification and training.
We then evaluated the predictive power of the RF models by comparing RF models' predictions to independent reference AGB estimations. Since RF have shown to overfit training data when predictors are correlated (e. g. [32, 33] ) or spatially auto-correlated  [53] , both properties being expected in our sets of optical variables, we performed a spatial forward variable selection procedure (see Appendix B). This procedure starts with no variable in the model, computes the decrease in model's relative RMSPE (calculated by dividing the RMSPE by the mean of "observed" AGB values) that the addition of each candidate explanatory variable would lead to, and adds to the model the variable leading to the largest relative RMSPE decrease. The procedure is iterated as long as adding a supplementary variable in the model leads to relative RMSPE decrease larger than 1%.
Four-Fold Cross-Validation We evaluated the ability of design-and model-based approaches to predict vegetation AGB outside training areas, using the LiDAR-based AGB ALS map as target AGB density values, and the four folds (blocks) defined in Figure  1c. For design-based approaches, average AGB densities per class were assessed in the three training folds, either using ALS or field data from these folds. In the remaining fold, the mean fold AGB density was computed by multiplying the AGB density of each land cover class by its respective area, relative to the total woody vegetated area of the fold. Validation could only be performed (qualitatively) at the scale of each land cover class or the whole fold. For model-based approaches, in the case of RF ALS , we generated AGB density predictions outside training areas using the four-fold block CV.
Results In model-based approaches, the variables selected varied depending on the training data (calibration on field AGB; RF FIELD or calibration on the intermediate AGB ALS map; RF ALS ; see Appendix B) and satellite sensor used for model calibration. Table  4  shows the variables selected (RF FIELD & RF ALS ) and the model performances in cross-validation for the different models. Independently of the satellite sensor, RF FIELD models gave the poorest performances, with R 2 values around 0. 6, and RMSPE of up to 65 Mg. ha -1 (i. e. , 90%), whereas RF ALS models greatly improved the prediction accuracy (drop of ~30% in RMSPE and relative RMSPE). RF ALS models based on Landsat 8 or Sentinel 2 predictors lead to a decrease of 10% both in RMSPE and relative RMSPE (R 2 = 0. 7; RMSPE of c. 43 Mg.
Discussion Transitional landscapes cover broad extents in Central Africa, and thus significantly affect the carbon budget of the continent  [54]. Achieving unbiased estimation of carbon stocks and fluxes in these highly dynamic environments, characterized by mosaics of very different land uses and covers, is a critical challenge. Major climate change mitigations strategies, such as national greenhouse gas inventories (MRV-REDD+) or high carbon stock (HCS) approaches, depend on our ability to meet this challenge. Despite known limitations, multispectral spaceborne data remain widely used for AGB extrapolations. A widely held assumption is that Airborne LiDAR scanning (ALS) data ensure better model calibration, and hence partly compensates signal limitations  [14, 16, 18, 55]. Our results indeed show that model fit and error can be drastically improved, with an R 2 of 0.
7 and a RMSE decrease of 30 %, when using a Random Forest model calibrated with AGB ALS reference data (RF ALS ) instead of field plots (RF FIELD ). Predictions of the latter moreover proved highly biased (inaccurate) and imprecise along the whole range of AGB densities. Looking closely at the AGB predictions of RF ALS , we can however see that the signal of multispectral data, whatever the sensor, does not in fact allow accurate AGB predictions in low and high ranges of actual vegetation biomass (i. e. , AGB ALS ). Indeed, low biomass ranges are systematically overestimated, and high biomass ranges are underestimated. This behavior has been already evidenced across the tropics  [23, 56]  in similar studies, highlighting the fact that errors in AGB estimations when using optical signals are unfortunately still both unavoidable and crippling for large scale wall-to-wall AGB mapping  [7].
It is thus important to better benchmark the values and limitations of optical signals in varying contexts through landscape-scale studies integrating highly informative ALS data. Here we showed that improved calibration of spaceborne models by ALS data did ensure unbiased estimation of AGB overall. In other words, although the signal does not allow accurate predictions of AGB in a given forest or savanna location, RF ALS models still provide accurate prediction of average AGB levels across the landscape. This is only true, however, inasmuch as the balance between land cover types in the predicted landscape is comparable to that of the training area. Notably, the underestimation of total AGB will plummet with the share of land harboring high AGB forests, above the hinge point of saturation of about 225-250 Mg.
Conclusions We showed that airborne LiDAR-based AGB data can significantly improve the calibration of prediction models from spaceborne multispectral data, with an error reduction of ~30% compared to a field-based AGB calibration. It is however crucial to acknowledge that, due to signal limitations, irrespective of the multispectral sensor and mix of spectral indices used, predictions are only unbiased at the landscape or regional level, and for land cover conditions similar to the training area. Data from upcoming new radar-based (BIOMASS, NISAR) and LiDAR-based space missions (GEDI, ICESat-2, MOLI) are expected to improve our extrapolation capacities by providing both global coverage and signals with better relationships to vegetation structure  [7, 57]. However, some of those valuable data will need spatial interpolation (GEDI or Ice) from optical data or will not be available for long-term monitoring.
Acknowledgments: We thank  Moses Libalah  and  Eric Ngansop  for botanical identification of species. We thank  Bertrand Akouemo ,  Donatien Zebaze ,  Gislain Mofack ,  Narcisse Kamdem  and  Nelly Sirri  for their support to field data collection. We also thank  Gaëlle Viennois  for image data processing in Overland software.
The following result was generated with 4 wavelengths only because the 12 other lasers were unfortunately not available at that time. The raw data, from the ramp compression experiment, are shown in Fig. 4 (a) for the first two μs. The four signals are truly four independent series of fringes. This dataset was processed by STFT, and the four velocity profiles obtained are plotted in Fig. 4(b). A peak at ∼86 m/s is reached in 0. 5 μs, and then, there is a plateau at ∼70 m/s. The figure inset zooms on the peak, and no difference can be seen between the four wavelengths. In order to evaluate the differences in the velocity profiles between the four wavelengths [Fig. 4(b)] , channel 1 was set to be the reference velocity signal and the three other signals were subtracted. The differences in velocities obtained are plotted around the velocity peak between 3.
| Study design and setting This was a retrospective multicentre study in which four hospitals participated. Data of adult solid organ transplant (SOT) and hematopoietic stem cell transplant (HSCT) patients diagnosed with HEV infection, who had been treated with RBV monotherapy between 01-03-2008 and 01-08-2018, were collected. For all patients, socio-demographic, clinical parameters and laboratory results were collected. The decision to treat HEV with RBV, the starting and maintenance dose of RBV and the timing of RBV plasma concentrations measurements were determined by the treating physician. No formal therapeutic drug monitoring (TDM) protocol for RBV was implemented in any of the participating hospitals. A waiver was given for this retrospective study by the Medical Ethics Committee of the Erasmus MC (MEC-2018-1326).
Multiple sclerosis patients data The 353 patients in our study are part of the alSacEP network following MS diagnosed patients living in the Alsace region. All patients records were managed with the standardized European Database for Multiple Sclerosis (EDMUS). 27 We focus on the period between 1 January 2000 and 31 December 2009. Two types (1 and 2) with two subtypes (A and B) of patients can be distinguished in the study population (see Figure  1 ). For Type 1 patients, their relapse history is known from some time post-MS onset, until the end of our study period (Type 1A), or until last patient information (Type 1B). For Type 2 patients, their relapse history is available from MS onset, until the end of our study period (Type 2A), or until last patient information (Type 2B). In epidemiology, Type 1 patients are prevalent cases, that is, they were diagnosed with the disease before the study period started.
By construction, using a maximum bipartite matching algorithm on this graph as implemented in the R package igraph produces the largest set of matched pairs that satisfy the unit-specific proximity constraints set by our thresholds. The diagnostics for balance show that, the within-patient pair matching algorithm described above was successful in constructing "similar" control (polluted) and treated (clean) days (see the distributions of X i, s, t in both groups in Figure  3 ). Given the available covariates, our attempt to mimic the randomized intervention from the Alsace city council was successful at creating comparable groups of polluted vs less polluted days.
RESULTS Our balanced subset of the data was analyzed as a whole, and within patient Clusters 1 and 2 to assure we study patients that are in similar phases of their disease history. Recall that Cluster 1 patients are older, thus at a later stage of their disease progression, and have a longer follow-up period than Cluster 2 patients (see Table  1 ). Accordingly, we anticipate Cluster 1 patients to develop fewer relapses than Cluster 2 patients, regardless of their environmental exposure. In the matched population, we estimated the log odds of a relapse after the patients are subject to a hypothetical intervention decreasing the air pollution levels vs higher pollution levels. These estimates and their associated approximate Fisherian P-values, based on 1000 draws of the permuted treatment assignment are presented in Table  2.
The null hypothesis of no effect of air pollution reduction intervention is not rejected in the overall study population, which differs from previous studies,  [17] [18] [19] [20] [21] [22] [23]  and highlights the statistical conclusion differences between studies using causal inference methods vs directly modeling the observed data (eg, using regressions). The secondary analyses indicate an effect of the intervention that is worthy of attention in the subgroup of women with relapsing-remitting MS; such question was not examined in previous studies. The effects of air pollution may be different between men and women. 52, 53 It has to be reminded that this subgroup of women was not the primary focus of our study, this result has to be confirmed in a study designed for this subgroup. A limitation of our study is that we had to focus on the patients for whom we had a complete disease history and omit the patients whose relapses were recorded on a doubtful date.
Ideally, we should have imputed the dates of these relapses by following a multiple imputation procedure for outcome data as suggested by Little and Rubin. 54 However, the causes of MS relapses, a rare outcome, remain unknown, which makes their timing nearly impossible to predict accurately. This issue motivates why we decided to analyze a complete-case subset of MS patients. Furthermore, we considered only one pollutant, PM 10 , which constitutes another limitation of our study. The environmental epidemiology literature suggests that a pollutant mixtures may be more relevant to study. ] [57]  2. We removed the patients who recorded relapses only on doubtful dates. Patients who only have their disease onset date unclear were kept because we are analyzing relapses post-onset for all patients.
3. For each patient, we observe their daily data as from 30 days post-MS onset until their last available information. Also, we do not observe patient data 30 days post-relapse because according to McDonald et al's  14  definition, relapses are only recorded when they occurred at least 30 days after the last relapse. 4. We observe days where the 5 days average PM 10 level are smaller or equal to 15 μg∕m 3 , and bigger or equal to 25 μg∕m 3. 5. For six patients, because their number of observed days is small (between 9 and 33 days), no match according to our matching criteria has been found (Table  A1 ).
ACKNOWLEDGEMENTS Research reported in this publication was supported by the Office of the Director,  National Institutes of Health  under Award Number  DP5OD021412  and the  John Harvard Distinguished Science Fellows Program  within the  FAS Division of Science of Harvard University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank  Prof D. B. Rubin  for many helpful discussions and suggestions,  Dr Stéphane Shao  for his help with the implementation of the matching algorithm,  Prof de Sèze  and  Dr. Ongagna  for sharing the data set,  Mrs Berthe  and  Mr Senger  for their help during the data collection and quality control, all the neurologists contributing to the alSacEP network, and  Dr Bard ,  Dr Roux , and  Dr.
Experimental setup and data selection A detailed description of the ALICE detector and its performance can be found in  [24, 25]. In this section, we briefly outline the main detectors used for the measurements presented in this paper. The ALICE apparatus comprises a central barrel used for vertex reconstruction, track reconstruction and charged-hadron identification, complemented by specialised forward detectors. The central barrel covers the pseudorapidity region |η| < 0. 9. The main central-barrel tracking devices used for this analysis are the Inner Tracking System (ITS) and the Time-Projection Chamber (TPC), which are located inside a solenoidal magnet providing a 0. 5 T magnetic field. The ITS is composed of six cylindrical layers of high-resolution silicon tracking detectors. The innermost layers consist of two arrays of hybrid Silicon Pixel Detectors (SPD), located at an average radial distance r of 3.
The interaction probability per single bunch crossing ranges between 2% and 14%. The contamination from beam-induced background is removed offline by using the timing information in the V0 detectors and taking into account the correlation between tracklets and clusters in the SPD detector, as discussed in detail in  [25]. The contamination from in-bunch pile-up events is removed offline excluding events with multiple vertices reconstructed in the SPD. Part of the data used in this paper were collected in periods in which the LHC collided "trains" of bunches each separated by 50 ns from its neighbours. In these beam conditions most of the ALICE detectors have a readout window wider than a single bunch spacing and are therefore sensitive to events produced in bunch crossings different from those triggering the collision.
9. 5  [34]. It was found that a correction factor < 5% is needed for the efficiency of Λ, Ξ + , Ω + for p T < 1 GeV/c, while the effect is negligible at higher p T. Events generated using PYTHIA 8. 210 (tune Monash 2013)  [35, 36]  and EPOS-LHC (CRMC package 1. 5. 4) [37]  and transported in the same way are used for systematic studies, namely to compute the systematic uncertainties arising from the normalisation and from the closure of the correction procedure (details provided in the next sections). The acceptance-times-efficiency changes with p T , saturating at a value of about 40%, 30%, 30% and Multiplicity dependence of strangeness in pp at √ s = 13 TeV ALICE Collaboration 20% at p T 2, 3, 3 and 4 GeV/c for K 0 S , Λ, Ξ and Ω, respectively. These values include the losses due to the branching ratio.
The core is then hadronised in terms of a grand-canonical statistical model. The relative amount of multi-hadron production arising from the core grows with the number of MPIs. The EPOS-LHC model adopted in this analysis (distributed with the CRMC package 1. 5. 4) shows a pronounced increase of the strangeness yields as a function of the charged particle multiplicity at mid-rapidity. Despite the differences in the underlying physics, the comparison to data leads to a similar conclusion for EPOS-LHC and DIPSY. | < 0. 5 η | 〉 η /d ch N d 〈 1 10 〉 y /d N d 〈 3 - 10 The comparison of the Monte Carlo model predictions to the data indicates that the origin of the strangeness enhancement in hadronic collisions and its relation to the QCD deconfinement phase transition are still open problems for models.
The colour reconnection effects implemented in PYTHIA 8 and DIPSY as well as the collective hydrodynamic expansion implemented in EPOS-LHC could not account quantitatively for all the reported results. Some of the qualitative features of the data are described by these models, including the indication of a non-linear increase of the yields of strange hadrons at high p T , similar to what was previously reported in  [52]  for J/ψ and D meson production. Although none of the tested models can reproduce the data from a quantitative point of view, EPOS-LHC and DIPSY do provide a better qualitative description of the strangeness enhancement.
Antibodies used for HRP revelation were: Akt-pT308 (Cell signaling Technology, CST13038), Akt-pS473 (CST4058), Akt(pan) (CST9272), REDD1 (Proteintech, 10638-1-AP), Tubulin (Sigma-Aldrich, T6199). Immunoblot quantifications were realized using Image Studio R or Fiji softwares  (Schindelin et al. , 2012). Actin and aP2 proteins are used as loading controls. Entire crude blots are presented as Supplementary Material.
Electron Microscopy Epididymal WAT tissues were fixed with 2. 5% glutaraldehyde in 0. 1 mol/L cacodylate (pH 7. 4) and processed as previously described  (Briand et al. , 2014). Semi-thin sections were prepared (2 µm thick) to validate adipose tissue integrity and were used to measure adipocyte diameters, using Image J software on images captured with an Olympus AX60 microscope equipped with a QIClick Color light camera (QImaging) using QCapture Pro software. Ultra-thin sections were observed with a JEOL JEM-1400 microscope equipped with a digital camera. A minimum of 50-µm membrane stretches, measured at least on 25 different images, has been used for each mouse for caveolae quantification using Image J software.