dataesr/scientific-paragraphs-categorization huggingface dataset:
The study was conducted in accordance with the Helsinki Declaration and was registered in a public trial registry (ClinicalTrials. gov; NCT02671864). This study was approved by local institutions and ethical committees, and the Ethics Committee of CPP Ile-de-France granted approval for all individuals (Ile de France V number 15070). Non-COVID-19 T2D patients were included from the same clinical service prior to the COVID-19 pandemic as part of an ongoing observational study (NCT02671864); patients were matched to the T2D COVID-19 cohort in terms of age, gender, BMI, hypertension and HbA1c. All patients provided informed consent indicating that they understood the nature of their participation in the study (NCT02671864). The principal investigator of this clinical trial is Prof. Gautier Jean-Francois: jean-francois.
Data collection Sampling, clinical data (age, gender, BMI, diabetes duration, history of HTA, COVID duration since 1s symptoms) and biological data (HbA1c, CRP, lymphopenia, D-dimer) were collected at inclusion in a standardised manner; events like introduction of COVID-19 specific treatments, transfer in intensive care unit (ICU) and death were collected at the end of the hospitalisation. Minimum lymphocyte count and maximal CRP were collected at the end of the hospitalisation.
6. 0 (http://www. r-project. org). Statistical significance was set at P < 0. 05. Principal component analysis (PCA) was performed from total lymphocyte and monocyte subpopulation FACS quantification with FactoMineR R package (https://doi. org/10. 18637/jss. v025. i01), and factoextra package (factoextra. bib) was used to construct graphics. Contribution of each individual was analysed to identify outliers biasing the PCA, leading to exclusion of 4 individuals for a technical bias or hospitalisation in the ICU prior to COVID-19 infection. Given the pressing circumstances during COVID-19 pandemic, a power study was not carried out prior to recruitment; sample size was judged sufficient when differences of immunophenotypic were observed. For detailed sample size and exact P-value per figure panel, see Appendix Table  S5.
Acknowledgements F. A. was supported by grants from the  French National Agency of Research (ANR)  for  ANR MitoFLAME  ( ANR-19-CE14-0005 ) and the  European Foundation for the Study of Diabetes (EFSD)/Lilly  grant (Characterisation of monocyte metabolism and bioenergetic responses in type-2 diabetes and risk of cardiovascular disease). N. V. was supported by grants from the  French National Agency of Research (GLUTADIAB and ANGIOSAFE) , the  European Foundation for Diabetes (EFSD)  and the  European Union   H2020 Framework (ERC-EpiFAT 725790 ). The human study was performed at the  Clinical Investigation Centre (Groupe Hospitalier Saint-Louis/Lariboisière, Paris ) and was supported by  Assistance Publique des Hôpitaux de Paris (ANR-DGOS  Project  AngioSafe   T2D ; J. -F. G. , principal investigator) and ASSERADT (a non-profit patient association).
Data and software availability The computer code and software tools used in this study are available: R Software 3. 6. 0 (http://www. r-project. org). R Core Team (2013). FactoMineR R package (https://doi. org/10. 18637/jss. v025. i01) Factoextra: Extract and Visualize the Results of Multivariate Data Analyses 1. 0. 7. https://CRAN. R-project. org/package=factoextra t-Distributed Stochastic Neighbor Embedding (t-SNE). https://jmlr. org/papers/v15/vandermaaten14a. html Raw and source data can be accessed through the Zenodo online data repository https://doi. org/10. 5281/zenodo. 3987326 [Open access as of publication date]. Expanded View for this article is available online.
The accurate representation of spatio-temporal patterns of precipitation is an essential input for numerous environmental applications. However, the estimation of precipitation patterns derived solely from rain gauges is subject to large uncertainties. We present the Random Forest based MErging Procedure (RF-MEP), which combines information from ground-based measurements, state-of-the-art precipitation products, and topography-related features to improve the representation of the spatio-temporal distribution of precipitation, especially in data-scarce regions. RF-MEP is applied over Chile for 2000-2016, using daily measurements from 258 rain gauges for model training and 111 stations for validation. Two merged datasets were computed: RF-MEP 3P (based on PERSIANN-CDR, ERA-Interim, and CHIRPSv2) and RF-MEP 5P (which additionally includes CMORPHv1 and TRMM 3B42v7).
The performances of the two merged products and those used in their computation were compared against MSWEPv2. 2, which is a state-of-the-art global merged product. A validation using ground-based measurements was applied at different temporal scales using both continuous and categorical indices of performance. RF-MEP 3P and RF-MEP 5P outperformed all the precipitation datasets used in their computation, the products derived using other merging techniques, and generally outperformed MSWEPv2. 2. The merged P products showed improvements in the linear correlation, bias, and variability of precipitation at different temporal scales, as well as in the probability of detection, the false alarm ratio, the frequency bias, and the critical success index for different precipitation intensities. RF-MEP performed well even when the training dataset was reduced to 10% of the available rain gauges.
Data acquisition First, the selected covariates and ground-based measurements are acquired. The spatial covariates are: i) the selected P products, and ii) topography-related datasets such as digital elevation model (DEM), aspect, rate of elevation change, or slope, which are used to account for the P gradient related to elevation (not to be mistaken with altitude, see  McVicar and Körner, 2013). The ground-based measurements are quality controlled and checked for homogeneity.
Data processing The selected rain gauge stations are divided into two groups: a training set (to train the RF model) and a validation set (to assess the performance of the merged product). The selected P products and topography-related datasets are resampled to a selected spatial resolution to ensure identical raster geometry (spatial resolution, spatial extent, and origin). The traditional RF algorithm ignores sampling locations which could lead to sub-optimal predictions  (Hengl et al. , 2018) ; therefore, covariates that account for geographical proximity are incorporated. The use of only geographical coordinates as spatial predictors can cause unnatural surfaces in the merged product  (Behrens et al. , 2018; Hengl et al. , 2018). Instead, RF-MEP uses gridded layers of Euclidean distances from each rain gauge in the training set to the centroid of all the grid-cells in the selected study area.
This process is repeated for each time step, implying that the RF model will vary temporally. Fig. 2  illustrates an example of the merging procedure process using two P products, a digital surface model (DSM), three rain gauge stations, and the three correspondent Euclidean distance layers  (ED (a) , ED (b) , and ED (c) ). An R package to implement RF-MEP is freely available online at https://github. com/hzambran/RFmerge.
Ground-based precipitation Time series of ground-based daily P for 1900-2018 were downloaded from a database of 816 rain gauges from the Center of Climate and Resilience Research (CR2; http://www. cr2. cl/recursos_y_ publicaciones/bases-de-datos/). These data are provided by Dirección General de Aguas (DGA) and Dirección Meteorológica de Chile (DMC), the Chilean water and meteorological agencies, respectively. In Chile, daily P is recorded at 08:00 local time (11:00-10:59 UTC).
, 2015 , Sorooshian et al. , 2000) ; and the Climate Prediction Center (CPC) Morphing technique version 1. 0-BLD, gauge-satellite blended precipitation product (CMORPHv1;  Joyce et al. , 2004 , Xie et al. , 2017). The Multi-Source Weighted-Ensemble Precipitation (MSWEPv2. 2; Beck et al. , 2017a Beck et al. , , 2019) )  was only used in the validation step as a benchmark product because: i) it is the first fully global P dataset derived by optimally merging a range of gauge, satellite, and reanalysis estimates  (Beck et al. , 2019) ; ii) it has shown more realistic spatial P patterns in mean, magnitude, and frequency than other state-of-the-art global precipitation products at the global scale  (Beck et al. , 2017b (Beck et al. , , 2019)) ; iii) it uses the same rain gauge dataset within Chile; and iv) it recently outperformed other state-of-the art P products over Chile  (Zambrano-Bigiarini, 2018).
Data processing All selected P products that are sub-daily (Table  2 ) were aggregated to the daily scale. MSWEPv2. 2 was obtained at daily temporal scale because the 3-hourly version is not freely available. We downscaled PERSIANN-CDR, ERA-Interim, CMORPHv1 and TRMM 3B42v7 to the same spatial resolution as CHIRPSv2 (0. 05°) using the nearest neighbour method (to avoid any improvements in the products performance prior to the merging procedure), while the DSM was upscaled from its original spatial resolution (250 m) to 0. 05°using bilinear interpolation. The reason for resampling all the covariates to 0. 05°(the highest spatial resolution of the selected P products) was to obtain a merged product that can be fairly compared to all selected P products. We selected the 369 rain gauge stations that had < 5% of missing values and showed consistency when evaluated using the double-mass curve method to identify abnormalities comparing each station with the  neighbouring stations, assuming homogeneity  (Weiss and Wilson, 1953).
The period 2000-2016 was chosen because of ground-based data availability over the period of record of the selected P products. A random sample of 70% of the selected rain gauge stations (258) were used as ground truth data to train the RF model (training set), while the remaining 30% of the stations (111) were used to assess the performance of the merged products (validation set). Past studies have typically selected 80% or more stations for training purposes (e. g. ,  Li and Shao, 2010 , Ma et al. , 2018 , Rozante et al. , 2010 , Woldemeskel et al. , 2013 , Yang et al. , 2017) ; however, we selected 70% to be more thorough in the evaluation of the method. We computed the 258 layers of Euclidean distances using the GSIF R package  (Hengl, 2019).
Performance evaluation We evaluated the performance of both merged products, MSWEPv2. 2, and the individual P products used as covariates, through a point-to-pixel analysis with the indices of performance described in Section 2. 4, applied for the stations included in the validation set. The evaluation process was performed at multiple temporal scales: 3-day, monthly, annual, DJF, MAM, JJA, and SON. Because no sub-daily measurements are available to transform the ground-based P dataset (see Section 3. 2. 1) to the 0:00-23:59 UTC daily period used by all the P products, we used 3-day accumulations as a proxy for evaluating daily performance. This approach reduces likely biases in the performance of the P products at this temporal scale by considering the influence of reporting times. The categorical indices were evaluated using P intensities (Table  3 ; Zambrano-Bigiarini et al.
This demonstrates that the method is able to substantially improve the correlation of the P products for the Chilean case study. Fig. 5b  plots the performance of the β component of the KGE', showing that RF-MEP 5P , RF-MEP 3P , MSWEPv2. 2, and CHIRPSv2 were close to exhibiting no bias. Both merged datasets present lower dispersion than MSWEPv2. 2 and CHIRPSv2 for all temporal scales except DJF. This result shows that the evaluated products are generally biased but contain useful information that can be combined with ground-based measurements to derive improved P estimates. In DJF, both merged products presented a β > 1 and were outperformed by MSWEPv2. 2. Fig. 5c  shows the γ component of the KGE', highlighting that all datasets underestimated the variability of P at all temporal scales. MSWEPv2.
2 best represented the variability of the ground-based measurements, followed closely by both merged datasets. The high values of γ obtained for MSWEPv2. 2 were expected because this product uses the same daily ground-based Chilean dataset in its computation and accounts for the difference in reporting times. Both merged products showed a reduced dispersion of the KGE' components at the 3-day, monthly, MAM, JJA, and SON scales; however, the dispersion at the annual scale increases due to the reduced performance in DJF.
RF-MEP 5P RF-MEP 3P performed better in the high elevations of the Far North region compared to the low elevations (see Figs. 3  and 7 ). These high elevations correspond to the alpine tundra climate (ET), while the cold and arid desert climate (BWk) dominates the lower areas of the Far North, where the P datasets presented their worst performance. This suggests that arid climates present a great challenge for existing P products.
Assessment of precipitation intensities Fig. 8  plots the median values of the four categorical indices for the five classes of daily P intensity described in Table  3. All datasets, with the exception of RF-MEP 3P and RF-MEP 5P , obtained POD values lower than 0. 45 for P events higher than 1 mm, while the no-rain events were well captured by all products. Similar results were observed for the FAR and CSI, where RF-MEP 3P and RF-MEP 5P presented the best performance of the evaluated products. FAR values were consistently the worst for the light rain intensities ([1, 5) mm d -1 ), highlighting that the products remain unable to adequately capture low P values. The CSI presents the best performance for no-rain events followed by extreme events (≥ 40 mm d -1 ), as a result of the decreased FAR compared to the other P intensities.
Because both merged products were computed using daily gauge data from the national water agencies they represent daily accumulations from 11:00-10:59 UTC, whereas all other selected P products represent daily P accumulations from 0:00 to 23:59 UTC (~11 h difference; for discussion, see  Beck et al. , 2019). This time difference must be considered for the evaluation of the P products at the daily temporal scale. Among the evaluated P products, only MSWEPv2. 2 incorporates daily gauge data and applies corrections to account for the reporting times of the rain gauges. Fig. 12  shows the evaluation of the P products for 1-day and 3-day periods. Both merged products performed similarly well with a median KGE' of 0. 83 because they use the Chilean rain gauges; however, the five P products used in their computation performed slightly worse in the 1-day evaluation due to the 11 h difference in the reporting times.
Therefore, based on the similar strong performances of both merged products (see Section 5. 1), we prefer RF-MEP 3P for the Chilean case study, as the benefits of including CMORPHv1 and TRMM 3B42v7 to generate RF-MEP 5P are outweighed by the loss of 15 years of record. Although RF-MEP was only applied over Chile, we are confident that this method could be successfully applied over other areas, due to its outstanding performance in a region with notable heterogeneity in topography and climate, and because it was able to improve the spatiotemporal characterisation of P even when the training set was largely reduced. However, some limitations apply to this method: i) since ground-based data are necessary, it would be difficult to apply the proposed method globally and in near-real time; ii) it can be computationally intensive when applied to large areas; and iii) it has problems predicting values that are completely out from the training range.
Conclusion Satellite and reanalysis-based P estimates provide an unprecedented opportunity for numerous hydrological, meteorological, and other environmental applications. Despite the continuous improvements of P products, different types of mismatches still exist in most of them. Here we present RF-MEP, a novel method capable of deriving improved P estimates by merging information from (near-)global and publicly available P products, rain gauge stations, and topography-related data. Two merged products (RF-MEP 3P and RF-MEP 5P ) obtained with the proposed method showed improved r, β, and γ values at all temporal scales compared to all the individual P products used as covariates. Furthermore, both merged datasets exhibited improved POD, FAR, fbias, and CSI for different P intensities. Finally, both merged products performed better than the benchmark dataset MSWEPv2.
Therefore, including CMORPHv1 and TRMM 3B42v7 as covariates in the merging procedure only led to a minor increase in the overall performance of the final merged product. Consequently, for the Chilean case study, it is preferable to use RF-MEP 3P and gain 15 years of data (1983 as the starting date instead of 1998). (e) The performance of RF-MEP increases when more rain gauge stations are used to train the model; however, it is still able to improve P characteristics even with relatively few stations in the training set. (f) RF-MEP showed better performance than the results obtained using Kriging with external drift and one-outlier-removed arithmetic mean. (g) The difference in reporting times between the P products and the ground-based measurements must be taken into account when assessing the performance of P products at the daily temporal scale so that their performance is not underestimated.
This issue constitutes a major limitation of most P evaluation studies carried out far from 0:00 UTC. (h) The KGE' proved to be a versatile performance index because of its ability to decompose the performance of the P products into r, β, and γ. Therefore, the KGE' helps us understand the sources of mismatches between the P products and ground-based observations. In addition, the use of categorical indices provides crucial information about the performance of these P datasets for capturing different P intensities. RF-MEP was developed to improve the characterisation of the spatio-temporal variability of P by merging multiple P products, topography-related datasets, and ground-based information. The P products used in this study are publicly available and have a (quasi-)global spatial coverage.
Acknowledgements The authors thank the  Centers for Natural Resources and Development (CNRD) Ph. D. program for their financial support to the main author. The daily RF-MEP 3P will be made available upon request for 2000-2016 at a spatial resolution of 0. 05°. In addition,  Dr. Zambrano-Bigiarini  thanks  Conicyt-Fondecyt 11150861  for the financial support from  2016  to 2018 and the  Center for Climate and Resilience Research  ( CR2 ,  Conicyt-FONDAP 15110009 ) for providing the rain gauge data. We particularly thank  Tim R. McVicar  and the three anonymous reviewers, whose constructive comments helped to improve the quality of the final manuscript. We would also like to thank the  RSE editorial team  for their support.
*p < 0. 05, paired t test (Bsl versus LTPi). (H) Average population data of the membrane resting potentials of LTPI-FFI (circles) and no LTPi cells (diamonds). Population data are illustrated as mean ± SEM. These results indicate that potentiation of feedforward perisomatic inhibition alters the temporal association of L5 PNs during g-oscillations. Bursts of APs Decrease the Firing Rate in a Subset of L5 PNs In Vivo Do LTPi-inducing bursts also decrease L5 PN spikes in vivo? We performed whole-cell recordings from L5 PNs in the barrel cortex of anesthetized mice (STAR Methods) during spontaneous activity and in the presence of sensory stimulation induced by air puffs to the contralateral whisker pads. Location and PN identity were confirmed by anatomy in some experiments. Notably, 100% of recordings were in L5, as confirmed by labeling L4 in scnn1acre crossed with a td-Tomato reporter line (Figure  S3 ).
In current-clamp experiments, input resistance was monitored with small current steps (À30 pA for 600 ms) and cells were excluded if it changed by > 25%. ODQ was obtain from R&D Systems Europe. In vitro data analysis Signals were amplified, using a Multiclamp 700B patch-clamp amplifier (Axon Instruments, Foster City, California, United States), sampled at 50 kHz and filtered at 4 or 10 kHz for voltage and current-clamp mode, respectively. Data were analyzed using pClamp (Axon Instruments), IGOR PRO 5. 0, (Wavemetrics), MATLAB (MathWorks) and GraphPad Prism software. IPSCs during rhythmic activity in vitro Custom written software (Detector, courtesy J. R. Huguenard, Stanford University) was used for analyzing GABAergic events, as previously described  (Manseau et al. , 2010; Ulrich and Huguenard, 1996).
Spike probability of L5 PNs during rhythmic activity in vitro Analysis of the temporal relationship between L5 PNs spikes and ongoing rhythmic activity was analyzed using custom written scripts in MATLAB (The MathWorks, Inc. , Natick, Massachusetts, United States). Briefly, spikes were extracted using a threshold of À10 mV on the membrane potential trace, and the times of the action potential peaks were extracted after cubic spline data interpolation of the waveforms around the spikes. Next, IPSC peak positions from the detector (see above) were adjusted with a 50-samples smoothing of the current waveform, and delays between action potential timing and IPSC occurrence were computed. Finally, histograms between spike times and IPSC occurrences were generated using 2 ms binning and converted into probability distributions.
A histogram was used as polar-plot or equivalently as a Cartesian-plot to display the distribution of AP-LFP phases. The confidence interval was estimated by Monte Carlo methods from 100 independent surrogate dataset, obtained upon randomly jittering each AP times (i. e. , Gaussian distributed, zero mean, 10 ms standard deviation) and again estimating the distribution of the corresponding LFP phases from the Hilbert instantaneous phase waveform. Computational model L5 PNs were modeled as integrate-and-fire neurons, with membrane potential dynamics given by c m dV dt = À g m V + IðtÞ where the membrane potential V is determined with respect to the resting potential of the cell. The membrane capacitance was c m = 100 pF, and the membrane conductance g = 10 nS. An action potential was emitted when the membrane potential reached a threshold value V T = 40 mV.
QUANTIFICATION AND STATISTICAL ANALYSIS Analysis of LTP was performed by comparing the mean amplitude of light evoked inhibitory postsynaptic currents in the 5 last minutes of the plasticity to the baseline period (Figure  1 ). Unless otherwise indicated, statistical comparisons were done between raw values. Normality of the data was assessed (D'Agostino & Pearson omnibus normality test). Normal distributions were statistically compared using paired t test two-tailed and Wilcoxon matched-pairs signed rank test was used as a non-parametric test. Group data was analyzed using the either Friedman test followed by Dunn's multiple comparison test when comparing repeated-measurements (e. g. , Figure  2D  for different frequencies and Figure  3D ), or Kruskal-Wallis Test (e. g. , Figure  4F ). Differences were considered significant if p < 0.
DATA AND CODE AVAILABILITY The code used to generate the model in Figures 7 and S7 is available at https://github. com/MathildeBigot. All the other scripts supporting this study have not been deposited in a public repository, because they are standard analysis routines. However, they are available from the corresponding author upon request. The source datasets is available in Mendeley Data (https://data. mendeley. com/datasets/75t97ncb24/2).