dataesr/scientific-paragraphs-categorization huggingface dataset:
Using a modeling chain driven by a CPM over a tropical area in Africa,  Ascott et al. (2023)   In this study, we would like to assess the added value of a CPM regarding the evolution of floods over a Mediterranean catchment prone to intense floods. For this, we perform an analysis of simulated floods magnitudes and characteristics under a historical scenario and under the RCP 8. 5 emission scenario. Two climate datasets, a CPM (CNRM-AROME) and its driving RCM (CNRM-ALADIN), are used as inputs to one lumped, conceptual hydrological model (GR5H) and one distributed, process-based hydrological model (CREST used in EF5). The main aims of this paper are to: • Evaluate the added value of the CPM on extreme rainfall at the scale of a small Mediterranean basin • Evaluate the capacity of a CPM to reproduce Mediterranean floods using two hydrological models • Assess future changes in floods distributions and characteristics between the two models and two climate simulations Section 2 describes the area of interest, the data and the different climate and hydrologic models.
Climate models In this study, we use climate simulations from a CPM and its forcing RCM, both developed and released at the Centre National de Recherches Météorologiques (CNRM), in Toulouse, France. Information about these two climate models is summarized in Table  1. CNRM-ALADIN is a 12-km grid cell RCM that has been run over the continental EUR-11 domain through the EURO-CORDEX initiative. Retrospective simulations are driven by the ERA-Interim global reanalysis dataset  (Dee et al. , 2011) , while historical and future scenarios are forced by the CNRM-CM5 global model  (Voldoire et al. , 2013).
Observations In this study, we compare the hourly simulated data with a high-resolution observed precipitation dataset so-called COMEPHORE. COMEPHORE is an hourly 1-km resolution gridded product covering the Metropolitan French during the 1997-2019 period. COMEPHORE was built by merging weather station rainfall measurements and different radar sources  (Laurantin et al. , 2012; Caillaud et al. , 2021). The dataset used approximately 3000 daily rain gauges and 1200 hourly rain gauges. The number of radars have more than doubled during the first decade of data. In 2019, COMEPHORE was built using data from 29 radars comprising the French radar network (ARAMIS), in addition to foreign radars such as those from the Swiss network and one on Jersey Island. Even though the quality of the dataset is not temporally and spatially homogeneous, COMEPHORE is still considered as the best national reference for studying hourly rainfall at high spatial and temporal resolutions.
Furthermore, the Gardon d'Anduze river catchment is located in a region of high rain gauges and radar density, raising the confidence in this dataset for a benchmark in this study  (Caillaud et al. , 2021). Temperature is the main variable to compute potential evapotranspiration (PET). As a reference, we extracted 3-h temperature measurements from 6 weather stations in the basin. Temperature was then linearly downscaled to the hourly time step and interpolated over the catchment using an inverse distance weighting method. Numerous methods exist to compute PET from climatic variables for hydrological modeling perspectives  (Oudin et al. , 2005). Here, we compute PET using the Hargreaves-Samani (HS) method  (Hargreaves and Samani, 1982). We chose a reliable, widespread applied method that requires the minimum amount of climate variables at the daily time step.
, 2021)  or floods and flash floods in a warmer future  (Li et al. , 2022b, a). In this study, topographic data from the version 1. 1 of HydroSHED database is used for CREST. Resolution of the DEM is 15 arc-sec, hence for this latitude around 300 m in longitude by 450 m in latitude. Drainage direction maps and flow accumulation maps are then produced using the QGIS software and packages. The CREST and GR5H hydrological models have been calibrated using the hourly observed discharge from 2002-2018, using the COMEPHORE hourly rainfall dataset and PET computed from observed air temperatures. For GR5H, all the model parameters have been calibrated. For CREST, most parameters have been fixed  (Li et al. , 2022b)  and the calibration has been performed on a few sensitive parameters. The Nash and Sutcliffe Efficiency (NSE) criteria is used as an objective function for the calibration process.
We initialized the models during a one-year warmup period before the starting date. Both hydrological models have been evaluated using the following metrics (see Sect. 4. 2) over the 2002-2018 period: • Nash and Sutcliffe Efficiency  (Nash and Sutcliffe, 1970)  • KGE  (Gupta et al. , 2009)  • Bias on mean flows • Bias on quantile 99. 9 • Bias on Peak Over Thresholds (POT) distributions: we compared the mean of simulated POT distribution to the mean of observed POT distribution. The same period  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018)  is kept to run the hydrological models driven with retrospective climate simulations. For the scenarios datasets, using a 1-year warmup period, we computed simulations over two epochs of 19 years for historical  (1987) (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005).
, 2009). The basis of this method is an extension of a quantile mapping method that allows a change of distribution statistics for corrected variables. Consequently, CDF-t is particularly appropriate to correct future climate datasets in a non-stationary climate  (Michelangeli et al. , 2009; Vrac et al. , 2012)  and it is largely used to correct future climate projections for the sake of climate change impact studies. In this study, we applied the CDF-t correction on hourly precipitation and temperature variables. Due to the distinct seasonality of precipitation, and a strong spatial variability of precipitation in this catchment, we corrected simulated datasets for each calendar month and over each grid cell separately. To take into account differences in the ratio of wet and dry hours, the Singularity Stochastic Removal (SSR) preprocessing method is applied for precipitation simulations  (Vrac et al.
Added value of CPM at the catchment scale We first analyze the climate datasets aggregated at the scale of Gardon d'Anduze catchment. Figure  2  shows the annual cycle for daily minimum (Tmin) and maximum (Tmax) temperature and hourly precipitation, before (raw) and after bias correction (BC-CDFT). AROME and ALADIN in retrospective mode (evaluation) show correct annual cycles despite an overestimation of summer temperature both for Tmin and Tmax. Results in this area are in line with the recent evaluation of the ALADIN and AROME climate models  (Lucas-Picher et al. , 2023). The ALADIN RCM is generally colder than AROME, which can be explained by an effect of resolution. A strong cold bias (3-5 °C) is visible over all seasons for both models in historical scenarios over the period 1986-2005.
This cannot be explained by climate variability or by climate change signals between the decades 1986-2005 and 2000-2018. This is a known cold bias of climate models driven by the GCM CNRM-CM5  (Vautard et al. , 2021). Annual cycles for Tmin and Tmax for future projections (RCP 8. 5) are similar to the evaluation period, i. e climate change signals for temperature are almost of the same magnitude as the cold bias. As expected, this cold bias disappears after bias correction for both AROME and ALADIN historical scenarios. The bias-corrected temperatures under the RCP 8. 5 scenario strongly increase for all seasons. However, the strongest signal is seen for the summer months. Annual cycles of precipitation are well reproduced for ALADIN and AROME for the evaluation period. Both models are a bit too wet in spring and too dry in summer, but the wet season (October to December) on simulations is clearly distinguishable and identical to COMEPHORE dataset (observations).
h⁻¹) for raw (left) and biascorrected (right) precipitation datasets. The upper panels show the retrospective simulations (2000-2018). The lower panels depict the historical (1986-2005) and future (2080-2099) simulations under the RCP 8. 5 scenario. COMEPHORE observed hourly rainfall is appended to all panels (2000-2018). Probabilities under the abscissa axis.
Reproducing floods with the climate datasets The flood distributions of the AROME-and ALADIN-driven simulations with GR5H and CREST are presented on Figure  6. The AROME and ALADIN datasets are retrospective simulations (evaluation) with (CDFT) and without bias correction. We used the same threshold as in Figure  5 , the discharge value (265 m 3. s -1 ) leading to two observed floods per year after the declustering. First of all, when forced by raw climate simulations, POT distributions are largely underestimated by CREST and GR5H. The hydrological models forced with the AROME CPM reproduce floods intensity better than when forced with ALADIN. This can be observed by the shape of the distributions, which is almost flat for ALADIN. The ALADIN-driven flood distributions fail to reproduce the observed flood frequency with 0.
All simulated datasets are compared here with the simulated discharge driven by the observed precipitation (green curve). The ALADIN-driven hydrological simulation shows a slightly better POT distribution than the AROME-driven one for both models, even if the most intense flood event (September 2002) is better simulated with AROME. Bias correction yields an improvement in flood frequency modeling for GR5H with 1. 8 floods per year when using the AROME dataset and 1. 9 for the ALADIN dataset. However, even after bias-correction, the flood frequency with CREST is higher than the COMEPHORE driven simulation (2. 9 and 3. 3 floods per year for AROME and ALADIN respectively). To summarize, the added value found for the CPM compared to the RCM in extreme precipitation in previous sections seems to be transmitted for flash flood simulation.
Climate change impact on flood characteristics Figure  8  shows boxplots of the simulated rainfall events (a)) and flood characteristics produced by these rainfall events (b)) driven by historical and future bias-corrected climate simulations. Every boxplot is made of all values of a specific metric relative to all extracted flood events or the associated rainfall events for one dataset. Firstly, most simulated datasets show a negative future signal for the baseflow component of future floods. Only the GR5H model driven by ALADIN shows a little increase of the median, but this signal is not clear because the distribution becomes wider for future baseflows. The same future positive signal can be found on the ratio metric: all datasets yield a negative trend, reflecting an increase of the runoff part of the streamflow during floods.
Acknowledgements Thanks are given to the  CNRM/MOSCA  team for their technical help, in particular to  Erwan Brisson ,  Cecile Caillaud  and Edith Cortez. https://doi. org/10. 5194/nhess-2023-82 Preprint. Discussion started: 5 June 2023 c Author(s) 2023. CC BY 4. 0 License.
Acknowledgments This work has been supported by the  APPROve  project funded by the  ANR  ( ANR-18-CE34-0013-01 ). We thank the  « Radioecology lab » of the LIENSs and Competent Radiological Protection Persons  ( Christine Dupuy  and  Thomas Lacoue-Labarthe ) for their technical support. This work benefitted from the  French GDR "Aquatic Ecotoxicology " framework which aims at fostering stimulating  scientific discussions and collaborations for more integrative approaches.
Ethical approval The Cog-Tab-Age study was approved by the local ethics committee [Ref. CNRIPH: 19. 12. 16. 66820, Comité de protection des personnes Ile de France IV, France]. It is registered as ID-RCB 2019-A03112-55, ClinicalTrials identi er: NCT04261153, registered on 07/02/2020. All included patients gave their written informed consent to participate before any study-related assessment. They could withdraw their consent at any time. The study was conducted in accordance with Good Clinical Practice Guidelines and the ethical principles of the Declaration of Helsinki.
Intervention Tool: The HappyNeuron PRESCO software App The HappyNeuron PRESCO software® is a digital cognitive therapy tool. For this study, it was installed in the app format on a tablet and for use o ine. By creating an account, the user's progress can be recorded in the databases. Developed by a neurologist, this software is designed to improve 12 cognitive domains, including attention, processing speed, memory, and executive functions, which are common in CRCI. The software offers 41 exercises divided into nine levels of di culty and is available in 11 different languages. It has been used in numerous studies in the eld of mental health and in two studies with young cancer patients, with positive results on cognition  [28] [29] [30]. Users are free to choose from the 41 exercises and levels of di culty, or the therapist can customize the rehabilitation session by selecting the cognitive domains to be trained, the level of di culty, and the session duration.
Turbine speed control design -MPPT mode-(a) ANFIS Speed optimizer For a given wind speed, the maximum power point s achieved for a specific speed of the turbine rotor, called optimal rotor speed (see Fig. 3a ). The MPPT optimizer employed in this study to track the optimal reference speed makes use of an intelligent learning approach  [40, 41]. We propose an Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm that provides online the optimal turbine speed 𝛺 𝑜𝑝𝑡 , in the sense of MPPT. Then, we let the reference signal of the turbine speed regulation loop be equal to the optimal speed, i. e. 𝛺 𝑟𝑒𝑓 = 𝛺 𝑜𝑝𝑡. The ANFIS optimizer structure, see Fig. 3b , was developed in  [25]. The training dataset for the first network includes the input data formed by the turbine speed, the Vienna rectifier output voltage and current to determine the output power of the synchronous aero-generator.
Simulation protocol The performances of the adaptive controller of Table  2  and the achievement of MPPT and APPT objectives are highlighted by simulation using MATLAB/Simulink, referring to the setup depicted in Fig. 5. In addition to the adaptive controller of Table  2 , the control system also contains an optimizer and a management algorithm shown in Fig. 6. The proposed algorithm aims to instantly balance the flow of extracted wind energy with the needs of the DC grid. It ensures a smooth transition between both operating modes (MPPT and APPT), improving DC grid stability and ensuring the efficient use of wind energy. The algorithm is based on available wind power and grid voltage level 𝑉 ′ 𝐷𝐶. It uses an adaptive observer to estimate the end  HVDC cable current î𝑜𝑢𝑡 and equivalent load R𝐿𝐷 (grid required power).
Depending on the estimated HVDC line voltage V ′ 𝐷𝐶 = R𝐿𝐷 î𝑜𝑢𝑡 , two operating modes are considered. If the voltage 𝑉 ′ 𝐷𝐶 is less than the maximum DC grid voltage, the equivalent load requires maximum energy, so the MPPT mode is suggested. If 𝑉 ′ 𝐷𝐶 exceeds the maximal admissible value, a limited energy amount is injected just to meet energy need (APPT mode), this is performed through line voltage regulation. The output feedback controller of Table  2 , is implemented using Matlab/Simulink and the Simscape toolbox. The parameters of the system and the controller are given the values of Table  6. These values were determined using a minimizing-error technique in Python and trial-and-error.
To accelerate the interpretation of 'omics' data from a description of microbial, transcript, or metabolite identities or abundances into a functional understanding of the interplay between the individual entities of the biological system, we designed a novel multi-omics strategy for the annotation and integration of metabolomics and metagenomics data. We generated metabolome and microbiome datasets by LC-MS/MS based metabolomics profiling and metagenomic sequencing, respectively. Comprehensive metabolite annotations were obtained by molecular networking and computational annotation of fragmentation spectra. Associations between microbes and GNPS molecular networks were predicted by machine learning and visualized as an extensively annotated, nested interaction network in Cytoscape. As a proof of concept, we applied this strategy to scalp swabs from a cohort of healthy volunteers with varying scalp sebum levels and were able to elucidate the antagonistic interaction between two well-characterized microbes, Staphylococcus epidermidis and Cutibacterium acnes.
Microbial profiling is performed by metagenomic sequencing, and the MO integration is achieved by predicting conditional probabilities of microbe-metabolite associations. Finally, the results of interactions are visualized as a nested molecular/microbial network that facilitates the analysis of the microbial and host-microbe interactions. The MO strategy was applied to the study of the metabolomics and microbiome profile of scalp samples from human volunteers with either normal or oily scalp to understand the composition and function of different microbiome profiles on different scalp environments. The combination of GNPS spectral library searches  (9)  and SIRIUS  (8)  in silico annotations yielded a highly annotated metabolomics dataset that was correlated with physical parameters of the scalp (oiliness and moisture), describing the "scenery" on which the microbial players interact with each other and with the human host.
The results of GNPS and SIRIUS were combined and visualized as annotated molecular networks. The annotation of the molecular networks was further enriched with statistical analysis results by displaying the correlation of feature intensity (chromatographic peak area, log scale) with the oiliness of the skin (Sebumeter value). Metabolite annotations from spectral libraries and molecular networking. To examine the annotation coverage obtained by spectral library matching with the GNPS and the NIST spectral libraries, the sets of annotated features and their distribution in datasets were visualized as UpSet plot (Figure  2A ). HILIC provided the largest number of spectral matches (286) in positive ionization mode (HILIC+), but only 84 in negative ionization mode (HILIC-). Reverse phase chromatography gave 105 matches in positive ionization mode (RP18+) and 21 in negative ionization mode (RP18-).
The highest overlap of spectral library annotations was observed between the HILIC+ and RP18+ (78) and between the HILIC+ and HILIC-(57). Overall, these results illustrate that each metabolomics dataset contributes to a different metabolic space, depending on the metabolites' polarity and ionization preference. When comparing the number of annotations to the total number of features, the annotation rate was 2-4 % in each dataset (Figure  2B , Supplementary Figure  S1  and Supplementary Table  S1 ). This proportion was increased to 9-10 % by performing spectral library matching in analogue mode, which tolerates a spectral shift for the fragment and precursor ions between the experimental and the library spectra. Yet, this analogue mode is known to have a higher chance of providing incorrect matches.
For molecular networking results, the number of nodes in networks was lower for the HILIC+ (16%) and HILIC-(27%) datasets and higher for the C18+ (29%) and C18-(53%). These differences are likely due to different fragmentation behavior for chemical classes preferentially observed in each dataset. Expanding metabolite annotations with SIRIUS. To increase the annotation coverage, we computationally annotated molecular formulas  (14) , putative structures  (15) , and chemical classes (8) using the SIRIUS tools. As observed for the spectral library annotation and molecular networking results, the proportion of annotations with SIRIUS varied per dataset (Fig. 2B , and Supplementary Table  S1 ). Nevertheless, the annotation rate was considerably expanded for all datasets, with a 23. 5-fold increase in annotation coverage for the C18+, a 45-fold increase for C18-, a 7.
6-fold increase for HILIC+ and a 13. 5-fold increase for HILIC-. Chemical class predictions were obtained with CANOPUS for 26% and 29% of the features in the C18+ and C18-datasets, respectively. The rate was lower in the HILIC datasets with 16% and 5% for the HILIC+ and HILIC-, respectively. The source of this difference is likely due to the machine-learning nature of the class prediction method. Indeed, the number of reference spectra available in public spectral libraries acquired in negative ionization mode or observed in HILIC is relatively limited. Thus, fewer reference spectra can be used for the training of the CANOPUS neural network and fewer classes can be predicted reliably in these modes. For putative structure prediction with CSI:FingerID, the annotation rate ranged from 11% (HILIC+) to 30% (HILIC-).
Overall, compared to spectral library matching-based annotation, CSI:FingerID increased the network annotation rate by 1. 75 (C18+ and HILIC-) to 15. 5 fold (C18-). When combining the annotations from spectral library matching (both direct and analogue mode) with the SIRIUS annotations, the total feature annotation rate reached 52%, 90%, 24%, and 54% for the C18+, C18-, HILIC+, and HILIC-datasets, respectively. Furthermore, when combining these annotations with molecular networking, 67% (+22. 4%), 95% (+5. 3%), 28% (+14. 3%), and 57% (+5. 3%) of the features were either directly annotated or connected in an annotated molecular network. This illustrates how combining multiple annotation strategies allows us to better apprehend the chemical diversity observed in metabolomics datasets. Evaluating the consistency of the SIRIUS predictions.
To evaluate the consistency of the data processing and annotation performed on each dataset, we relied on the following methodology: high-quality spectral library matches were considered ground truth for comparing SIRIUS-based molecular formula and class prediction. Results showed that the predicted molecular formula matched the spectral library annotation in 66% (n= 44), 70% (n = 10), 90% (n =125), and 95% (n = 59) of evaluation pairs for the C18+, C18-, HILIC+ and HILIC-datasets, respectively (Supplementary Figures  S2-S5 ). Inspection of incorrect molecular formula prediction revealed that often an incorrect adduct assignment was involved (in particular sodiated instead of protonated for the positive ionization mode). This is also supported by the higher annotation consistency for the negative ionization.
In addition, the lower consistency rate in C18 datasets could also be related to the high number of isobaric lipid species observed in this chromatographic condition, which are challenging to annotate unambiguously even in high-resolution mass spectrometry. An assessment of the chemical class prediction accuracy was not feasible for the datasets in negative ionization mode, as only 10 pairs of high-confidence spectral library and chemical class prediction were available for each. However, for the positive ionization mode, 44 and 120 pairs were available for C18+ and HILIC+, respectively. The chemical class was correctly predicted for C18+ and HILIC+ datasets, respectively, at the following accuracy: 84% and 92% for the superclass level; for 80% and 90% at the class level; and 57% and 86% for the subclass level.
Metabolomic differences between normal and oily scalp. We next assessed the correlation of each observed feature's intensity with oily or normal scalp. After a filtering step to remove features that contributed little to biological information (high feature intensities in blank samples or high technical variability), the feature intensities were correlated with scalp oiliness (assessed with a Sebumeter) and scalp moisture (skin conductance measured using a Skicon device) by ANOVA. The statistical analysis comprised 44,570 features from the four datasets combined. Figure  3  shows selected molecular networks from the C18+ (Figure  3A ) and HILIC+ (Figure  3B ) datasets. Symbols represent the annotation method of the feature while colors highlight statistically significant differences between skin types.