dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION MyCLADE is a server that provides an online version of MetaCLADE v2. It gives the user access to a quick way to annotate protein sequences by exploring a wide range of probabilistic models that can achieve more accurate domain annotation than more classic approaches. Also, several options allow to filter out domain hits with E-value greater than the chosen threshold, the reconstruction of the best domain architecture is parametrizable, the user can provide an e-mail address to obtain an identifier and access the data online after job completion, annotation files produced in previous runs of MyCLADE can be provided as input and displayed graphically with the server. With MyCLADE, the user can explore domain annotations to search for new domains and possibly to find hints for a functional annotation.
g. , convection, turbulence) and feedbacks (e. g. , soil, atmosphere, ocean) as well as their nonlinear interaction across scales, (b) lack of knowledge regarding underlying mechanisms, and (c) absence of adequate observations to help us advance our understanding and improve model capabilities. This issue is one of the main objectives of the HyMeX international program and of its associated first special observation period (SOP1;  Ducrocq et al. , 2014; Jansa et al. , 2014; Ferreti et al. , 2014)  from 5 September to 6 November 2012, which was dedicated to heavy precipitation and flash flooding. Because of the large number of instruments deployed, the unprecedented high spatial-temporal coverage achieved, and the quality of the derived observations, SOP1 has offered a unique opportunity to improve understanding and advance documentation of high-impact weather events.
Illustrative case: IOP16 IOP16 is a well-documented and widely investigated event observed in the period 25-29 October 2012 over the WMed region. IOP16 was one of the best-equipped observational periods in terms of instrumental coverage during HyMeX SOP1 (Fig. 1 ). Most ground-based and airborne instrumentation was successfully operational and provided high-quality data, with almost all the on-demand SOP1 instruments involved. Benefiting from this large observational dataset, an extensive number of modeling activities focused on IOP16, with the purpose of investigating different issues related to the occurrence of heavy precipitation, such as the impact of the turbulence representation on the sensitivity of the simulated convective systems  (Martinet et al. , 2017) , the underlying mechanisms of offshore deep convection initiation and maintenance  (Duffourg et al.
Large uncertainties remain regarding the origin, pathways, and timescales of transport of the large amounts of moisture necessary for HPEs in the WMed. Characterization and better understanding of the water vapor supply to HPEs were key aspects of the HyMeX field campaign and subsequent studies. The unprecedented deployment of instruments during SOP1 for the monitoring of water vapor dynamics and the posterior cross-validation studies and synergetic use together with models allowed the many advances achieved in this period, as described in the following. One of the HyMeX observational highlights is the dense network of GPS stations with over 1000 ground-based receivers, providing a reprocessed dataset specially produced for the HyMeX SOP  (Bock et al. , 2016). The large extent and high-density coverage of the reprocessed GPS network allowed for a consistent representation of large-scale features, as well as smaller spatial and temporal scales in agreement with high-resolution simulations  (Bock et al.
(2015b)  demonstrated the consistency and self-coherence of these water vapor datasets during SOP1, pointing out the strong need to assimilate highresolution water vapor profiles in the lowest layers such as those from lidar instruments. In a multi-scale observational investigation of atmospheric moisture variability in relation to HPE formation in the same region,  Khodayar et al. (2018)  profited from the synergetic use of observational datasets, demonstrating that the sampling of spatial inhomogeneities on different scales is crucial for the understanding of the timing and location of deep convection. Furthermore, focusing on the complex island of Corsica during SOP1, multiple observations from the mobile observation platform KITcube  (Kalthoff et al. , 2013)  further demonstrated the benefit of integrated measurement systems  (Adler et al.
The use of ocean-atmosphere coupled systems enables us to consider the ocean 3D structure and its interactive and consistent evolution. Such modeling systems were specifically developed in the framework of HyMeX to improve the realism of weather forecasts for sea surface and atmospheric low levels, and they have been innovatively evaluated thanks to the multi-compartment observational dataset. For IOP16a,  Rainaud et al. (2017)  showed that, due to mixing and heat loss, the progressively lower SST in the coupled model induces lower heat fluxes (-10 % to -20 % of evaporation), local differences in the low-level environment (stability), and cyclonic circulation, with small impacts on the convection organization (convergence) and intensity. Waves also impact the atmospheric low levels by increasing the surface roughness and the momentum flux, and they act as a drag for the low-level upstream flow.
, 2015). The same system was used to perform an extensive reanalysis of SOP1 by exploiting observations from research instruments deployed during the campaign in addition to the operational observations assimilated in real time  (Fourrié et al. , 2019). For that, processing of observations and systematic comparisons between observations and AROME short-range forecasts were carried out for the following:  Data denial experiments, for which one of the abovelisted datasets was removed from the reanalysis at a time, clearly showed the benefit of assimilating the reprocessed GPS ground-based zenithal total delays as shown in Fig. 9. This result was confirmed in other studies. Lindskog et al. (2017)  demonstrated the benefits of GPS assimilation for the forecast quality. Bastin et al. (2019)  pointed out that the general overestimation of low values of IWV in RCM models over Europe was reduced when using a nudging technique to assimilate IWV information.
, 2015). An observation operator for the airborne RASTA reflectivity observations has also been developed  (Borderies et al. , 2019a). The impact of the assimilation of RASTA data on AROME-WMED analyses and forecasts has been assessed. IOP7a results indicated an improvement in the predicted wind at short-term ranges (2 and 3 h) and in the 12 h precipitation forecasts. Over a longer cycled period, a slightly positive improvement in the 6, 9, and 12 h precipitation forecasts of heavy rainfall has been demonstrated  (Borderies et al. , 2019a). The assimilation of RASTA reflectivity data in AROME-WMED over the whole SOP1 period resulted in an improvement of rainfall forecasts even larger when wind was jointly assimilated  (Borderies et al. , 2019b). Finally, HyMeX has fostered the inception of a collaboration between CNRM (France) and CNR-ISAC (Italy) concerning the assimilation of radar data.
Predictability and ensemble forecasts Despite advances in numerical modeling and data assimilation, the prediction of HP and related floods remains challenging because predictability of intense convective systems is limited, and user expectations are very high given the impact of HPEs. Ensemble prediction techniques can provide solutions through the elaboration of probabilistic HPE warnings. Until the 2010s, regional ensemble prediction systems were mainly limited by the large computational costs of increasing the member resolution and the ensemble size. Ensemble forecasts become even more useful when post-processing techniques are applied to the precipitation fields using statistical methods such as regression or analogs  (Diomede et al. , 2014) , with some difficulties due to the geographically complex forecast error structures of Mediterranean precipitation and the need to prepare very long reforecast datasets in order to adequately sample the statistical behavior of HPEs.
With the availability of more powerful computational resources, operational regional models started to reach the kilometric resolution, leading to physically more realistic convection-permitting ensemble prediction systems (CPEPSs). Studies of HPEs  (Nuissier et al. , 2016)  have shown the added value of CPEPSs over deterministic approaches or lower-resolution ensembles. The enhanced exchange of validation datasets during HyMeX facilitated the objective verification of this kind of result in several ensemble studies such as  Roux et al. (2019). Several studies in the framework of HyMeX have demonstrated that, besides sensitivity to synoptic-scale forcing represented by lateral boundary condition (LBC) perturbations, https://doi. org/10. 5194/acp-21-17051-2021 Atmos. Chem. Phys. , 21, 17051-17078, 2021 CPEPSs were sensitive to multiple error sources, which had to be sampled as specific perturbation in the parameterization schemes.
Some sensitivity of ensemble spread to the model physics (turbulence and microphysics schemes) was demonstrated in  Hally et al. (2014) , who used the HyMeX IOP6 and IOP7a forecast and observation dataset to show that LBC perturbations cannot be neglected and that the relative importance of LBC and physics uncertainties is casedependent, with physics uncertainty being more significant during weakly forced convective events. Vié et al. (2012)  explored the impact of microphysical processes on CPEPS spread and found a relationship between precipitation evaporation and the uncertainty of cold pool formation, which can be relevant to predict the correct location of HPEs. Bouttier et al. (2015)  found a beneficial impact of randomly perturbing surface fields such as SST or soil moisture: the high density of HyMeX SOP1 data gave statistical significance to these results because the objective verification of ensembles at high resolution requires large observational datasets.
Discussion The spatial complexity of the Mediterranean region, the intricacy of the dynamical and physical processes involved including the multiple interactions across scales, and the technical and observational limitations in the past have made HP understanding and modeling in the Mediterranean region very challenging issues. To try to advance in this direction the Hydrological Cycle in the Mediterranean Experiment  (HyMeX, 2010 (HyMeX, -2020) )  has put major effort into investigating the predictability and evolution of extreme weather events. Within this framework and profiting from state-ofthe-art observational datasets and modeling capabilities that have recently become available and been developed within the program, important achievements towards improved un-derstanding of the mechanisms leading to HP in the WMed have been accomplished.
These interdisciplinary efforts were crucial to come towards improved understanding of the mechanisms leading to HP in the WMed. The links and networks originated in the framework of HyMeX must continue and even be enlarged in the future to progress together https://doi. org/10. 5194/acp-21-17051-2021 Atmos. Chem. Phys. , 21, 17051-17078, 2021 towards more integrated approaches. Novel integrated multidisciplinary research partnerships based on cross-sectional collaborations will indeed be needed for more efficient research on impacts. Code and data availability. Given that this is a review publication, the data and code availability are provided in each of the referenced publications. Author contributions. All authors collaborated and contributed to drafting, reviewing, and editing the paper.
Acknowledgements. This work is a contribution to the  HyMeX program , supported in France by  MISTRALS (Météo-France ,  CNRS, INRAE)  and the  Agence Nationale de la Recherche  ( ANR MU-SIC  grant  ANR-14-CE01-0014 ,  ANR IODA-MED  grant  ANR-11-BS56-0005 ). We would like to thank all HyMeX contributors, particularly all members of the scientific team heavy precipitation (ST-HP, with more than 100 members in the last 10 years), who have effectively participated in advancing knowledge regarding heavy precipitation in the Mediterranean region. Without their work this study would not have been possible. We further acknowledge the HyMeX database developers and all data providers. We also thank  all HyMeX  scientific team coordinators for the close cooperation during these years. We acknowledge  Hélène Roux  and  Eric Defer  for their input on research activities of the ST-ffv and ST-lightning.
The authors thank  Marie-Noëlle Bouin  ( CNRM & LOPS ), who provided us with Meso-NH simulation data related to IOP16a sensitivity to waves. The contribution of  Paolo Di Girolamo  to this work was possible based on support from the  Ital-ian Ministry for Education, University, and Research  under grants  OT4CLIMA  and  FISR2019_01711  CONCERNING. The contribution of the first author Samira Khodayar Pardo was supported by the program  Generacio Talent of Generalitat Valenciana  ( CIDE-GENT/2018/017 ). Thank you to the two anonymous reviewers who helped us improve this paper with their comments. Financial support. This work is a contribution to the HyMeX pro30 gram, supported in France by  MISTRALS (Météo-France ,  CNRS, INRAE)  and the  Agence Nationale de la Recherche  ( ANR MUSIC  grant  ANR-14-CE01-0014 ,  ANR IODA-MED  grant  ANR-11-BS56-0005 ).
Data Pre-Processing Raw fastq files were aligned to the reference human genome GRCh38 using Bowtie2 (v2. 4. 1). Aligned datasets were qualified for read count saturation using the next generation sequencing quality control generator (NGS-QC Generator)  [33, 34]. Furthermore, read counts associated with annotated transcribing regions were assessed with the tool featureCounts (v2. 0. 1)  [35]  and were assembled within a matrix composed by genes in rows and all datasets (issued from all protocols) in columns. The raw matrix was cleaned by removing genes devoid of read counts, and then one read was added over the whole matrix to avoid division by zero errors. Finally, technical amplitude differences between compared datasets were corrected by applying a quantile normalization procedure. Data sets were regrouped by cell type and genetic background into subgroup or replicate datasets in each protocol.
Differential Gene Expression Analysis Differential gene expression analysis between samples was performed using Deseq2 (v. 1. 32. 0) on the quantile normalized count matrix. Datasets associated with a given protocol were compared to their corresponding iPSC readouts when available. When the iPSC data were not provided  (Muffat et   2017 ) were used as a reference for the differential expression analysis. Differentially expressed genes were considered for foldchanges > 2 and presenting an adjusted p-value (padj) < 0. 01 (Supplementary Table  S2 ). To determine, the composition of the microglia identity of each iMGL population, upregulated genes were compared to their in vivo counterpart (fetal and adult) to determine the percentage of commonly up regulated genes between each iMGL population compared to two in vivo controls (fetal and adult microglia from  Abud et al.
). This value of similarity was tempered by the divergence between the in vivo microglia and the iMGL (the percentage of up regulated genes in iMGL that were not differently expressed in their in vivo counterpart). This value allowed us to take the variability of the number of up regulated genes between samples into account. Microglia-specific markers were defined by selecting upregulated genes that were present in the fetal microglia dataset (6154 genes) but that were absent from all other control samples (CD14+/CD16-monocytes, CD14+/CD16+ inflammatory monocytes, myeloid dendritic cells, neuronal precursors, and neuronal-related datasets). This Microglia-specific marker subset (201 genes; Supplementary Table  S3 ) was further confirmed by gene ontology analysis using ENRICHR  [36]  and was used to profile gene expression changes within the iMGL samples.
Gene ontology analysis on the common master regulatory TFs was performed using ENRICHR  [36]. Inferred consensus master transcription factors were compared with master players described in three further studies: Gosselin et al. [39] , which was based on in vivo microglia isolated from surgically resected brain tissue and that had been cultured in an in vitro environment; Banerjee et al. [40] , who described a protocol for iMGL generation from peripheral monocyte samples; and Grubman et al. [41] , who described an enhanced maturation of iMGL precursors when co-cultured with human neuronal precursors (ReN) (Supplementary Figure  S4 ). Specifically, the raw read counts provided by Grubman et al. (GSE125872) were used to query for genes being over-expressed in the iMG + ReN conditions relative to their corresponding iPSC sample.
iMGL Transcriptome Datasets Issued from Different Protocols Present Variable Degree of Similarity with Control Microglia Samples With the aim of evaluating the microglia differentiation performance between the compared protocols, we have first evaluated the gene expression levels of well-known microglia markers within the analyzed transcriptomes. As illustrated on Figure  2 , significant gene expression is observed for the G protein-coupled receptor GPR34 and the transcription factor SPI1 in all iMGL transcriptomes, which is in agreement with the transcription levels observed on the fetal and adult microglia readouts. On the contrary, expression differences are observed for the gene CD45 and TREM2, where no signals were observed in datasets generated by Chen et al. Similarly, TMEM119 is devoid of readouts on the McQuade and Abud's iMGL datasets; P2RY12 do not present read counts on Muffat, Douvaras, Chen and Trudler's iMGL datasets, and the expression of the CX3CR1 gene can be Each of these studies generated whole transcriptome readouts associated with the iMGL state, their hematopoietic intermediates (HPC) when available, their related iPS precursors as well as positive controls issued from fetal and adult microglia cells, and negative control samples corresponding to CD14+/CD16-monocytes (CD14M), CD14+/CD16+ inflammatory monocytes (CD16M), myeloid dendritic cells (Blood DCs), neuronal precursors (NPC), and neuronal transcriptome data.
Overall, as part of this comparative analysis, we collected 148 transcriptomes collected from the public gene expression omnibus (GEO) repository and reprocessed them with the help of our in-house qcGenomics pipeline  [33]. As part of the primary analysis, we evaluated the quality of the collected datasets. This was performed using our previously described QC STAMP-scoring methodology, which assess the sequencing-depth saturation point by comparing the level of read-count pattern reproducibility between the original RNA-seq profile and those where only a subset of the total mapped reads are used for reconstruction  [34, 42]. As illustrated on Figure  1B , most of the reanalyzed datasets present QC STAMP scores comprised between 15 and 25 despite of their mapped reads, while the transcriptomes generated by Douvaras and colleagues present a relatively lower quality scoring level.
iMGL Transcriptome Datasets Issued from Different Protocols Present Variable Degree of Similarity with Control Microglia Samples With the aim of evaluating the microglia differentiation performance between the compared protocols, we have first evaluated the gene expression levels of well-known microglia markers within the analyzed transcriptomes. As illustrated on Figure  2 , significant gene expression is observed for the G protein-coupled receptor GPR34 and the transcription factor SPI1 in all iMGL transcriptomes, which is in agreement with the transcription levels observed on the fetal and adult microglia readouts. On the contrary, expression differences are observed for the gene CD45 and TREM2, where no signals were observed in datasets generated by  Chen   appeared distant from the microglia control datasets and rather proximal to the hematopoietic precursor (HPC) and hematopoietic datasets.
This set of 201 genes also appeared to be strongly upregulated (>4-fold change) in the iMGL samples issued from Muffat, Douvaras, and Trudler's protocols, where ~70% to 80% of the microglial genes were upregulated (Figure  3C  and Supplementary Table  S3 ). The iMGL samples issued from Abud and McQuade's protocols presented 36% upregulated genes, while the iMGL samples issued from Chen's protocol only presented 23% of these selected microglia-specific upregulated genes. Remarkably, one of the iMGL replicate datasets issued   3C ). A gene ontology analysis performed on the set of 201 genes confirmed their enrichment on microglia-related terms at a high confidence level (Figure  3D ). These major differences observed on a small subset of genes strongly support the need for large scale analyses able to decrease potential technical differences and to highlight the relevance of each of these protocols for generating iMGL cells.
For this, a principal Brain Sci. 2021, 11, 1338 6 of 12 component analysis (PCA) was used to compare all of the iMGL datasets as well as the various positive and negative control samples (Figure  3A ). The use of a 3-dimensional PCA representation (PC1: 60. 9%; PC2: 10. 4%; PC3: 6% variance, respectively) allowed us to reveal the similarities between the microglia control datasets and the iMGL samples issued from Muffat, Douvaras, and Troudler's protocols, which are located far away from the human iPS, neuronal precursors (NPCs), and neurons as well as the hematopoietic datasets (CD14, CD16, Blood DC). Surprisingly the iMGLs issued from Chen's protocol appeared distant from the microglia control datasets and rather proximal to the hematopoietic precursor (HPC) and hematopoietic datasets. Similarly, most of the iMGLs issued from Abud and McQuade's protocols appeared to be preferentially distal from microglia control samples, proximal to HPCs, but distant from hematopoietic samples, suggesting that they are more similar to microglia than hematopoietic samples.
This set of 201 genes also appeared to be strongly upregulated (>4-fold change) in the iMGL samples issued from Muffat, Douvaras, and Trudler's protocols, where ~70% to 80% of the microglial genes were upregulated (Figure  3C  and Supplementary Table  S3 ). The iMGL samples issued from Abud and McQuade's protocols presented 36% upregulated genes, while the iMGL samples issued from Chen's protocol only presented 23% of these selected microgliaspecific upregulated genes. Remarkably, one of the iMGL replicate datasets issued from the protocol described by  McQuade et al. (McQuade. 5) showed a strong similarity with the fetal and adult microglia control samples. On the contrary, one of the replicates generated by Douvaras et al. (Douvaras. 1) showed a lowered similarity to in vivo control samples (Figure  3C ).
This microglia-related TFs enrichment is also supported by a comparison with master players retrieved in three other studies, either issued from in vivo microglia collected from surgically resected brain tissue  [39] , from matured microglia due to their co-culturing with human neuronal precursors  [41] , or differentiated from peripheral monocytes  [40]  (Supplementary Figure  S4 ). as well as those related to the fetal and adult microglia control datasets (Figure  4D ). This shorter CEBPA transcript coincides also with a lower molecular weight being reported by the authors (268 amino acids) relative to the molecular weight described in public repositories (358 amino acids).