dataesr/scientific-paragraphs-categorization huggingface dataset:
2020 ) and are thus not visible in images from MasCam. MasCam identified inclusions  (Jaumann et al. 2019 ), while the returned samples are depleted in inclusions  (Yada et al. 2022 ), but no equivalent observation could be made by the orbiter because of its lower spatial resolution. Due to the presence of different rock types on Ryugu that have been identified by ONC and MasCam data  (Sugita et al. 2019; Jaumann et al. 2019) , it cannot be excluded that the rock imaged by MasCam is of a different type than the returned samples. In addition, some of the identified inclusions could also be specular reflections  (Yumoto et al. 2022; Schröder et al. 2022). In all cases in which the orbiter and returned sample observations agree, MASCOT confirmed these observations and thus links meter and sub-millimetre scale observations.
The answers to the OH-EpiCap questionnaire are analyzed at the target level for each dimension by averaging the scores across the indicators to get a final score (between 1 and 4), and at the dimension level by averaging targetlevel outputs (the mean scores over all questions are expressed as a percentage). Results are displayed in the form of interactive radar charts and lollipop plots to identify strengths and weaknesses at both dimension and target levels. Users may hover over data points to explore the breakdown of scores for each target and indicator. At the target level, this option displays for each data point the comments provided by the evaluators during the filling of the related question. Finally, users can download a two-page report (in html format) comprising the graphic outputs and comments highlighting the main strengths and weaknesses of the surveillance system examined.
Ethical approval The MATRIX project obtained ethical approval from the ethical advisors of the One Health European Joint Programme. We informed verbally and through email the participants about the following points: (1) the use of the OH-EpiCap tool and application is voluntary; (2) the OH-EpiCap tool does not collect personal information, to comply with the European General Data Protection Regulation; (3) the web application does not keep the data regarding the OH surveillance system evaluated. Results.
Preprocessing of the raw data was performed to remove low-quality ends of the reads with fastp  [16]. Read pairs sharing the same UMI and the same sample barcode were grouped together in silico to make long accurate consensus sequences. Assembly was performed with SPAdes  [17]. The reconstruction of full-length 16S sequences was performed using V-Revcomp  [18]  and V-Xtractor  [19]. On average, 4,812 full-length 16S sequences were reconstructed per sample (range: 1,124-10,511). Based on the UMI redundancy, the LUMI-Seq ™ error rate was assessed at 0. 0047%.
Statistical analysis GA-map ® 131-plex data analysis To account for variable signal levels, the raw signal data (fluorescent intensities) was normalized using a hybridization control, as previously described  [11] , and background noise was subtracted. Variations in the bacterial profiles between and within the groups (pre-T2D, T2D and healthy) were explored using the non-parametric multivariate methods principal component analysis (PCA) and permutational multivariate analysis of variance (PerMANOVA), using Euclidean and Bray-Curtis methods, respectively. Possible confounding effects on the data due to the clinical variables (e. g. , age, BMI, F-cal) were also explored, using the above-mentioned methods. The non-parametric Wilcoxon Rank Sum Test with Benjamini-Hochberg correction (Stats R package  [20] ) was used to determine significant difference (adjusted p < 0.
1) in abundance of the bacterial markers. Microbiota signatures for separation of pre-T2D or T2D and healthy subjects were calculated using the caret (classification and regression training) R-package  [20]. To evaluate the robustness and performance of the classification models, a tenfold cross-validation was performed. 90% of the cohort was used for model training and 10% for model testing. A parameter, "importance", ranging from 0 (no contribution) to 100 (maximum possible contribution for training of the model), was reported for each bacterial marker forming the basis for the microbiota signatures. The best performing model, multi-step adaptive elastic-net (MSA-ENet)  [21] , was chosen. A ROC curve was built, presenting the mean value of the area under the curve (AUC).
LUMI-Seq ™ data analysis After obtaining full-length 16S sequences, QIIME scripts (version 2019. 7. 0) were used for collapse of 100% identical sequences  [22]. Each unique sequence was then assigned to a taxonomy by mapping to a custom 16S database made by BIOASTER, as well as the widely used SILVA reference database. However, due to the lower number of sequences assigned down to the species level, the BIOASTER database was chosen for the downstream analyses (76% with the 16S database vs. 60% with SILVA). Taxa with a total count (summed over all samples) lower than five counts were removed. After normalization of taxa counts, eight samples (four healthy, two pre-T2D and two T2D) showed lower counts and were thus discarded from the analysis to avoid interpretation bias. Principal coordinates analysis (PCoA) and Per-MANOVA, using the Bray-Curtis method, was used to explore variations in bacterial profiles between and within the groups (pre-T2D, T2D and healthy subjects).
The above-mentioned methods were also used to explore any confounding effects on the data due to the clinical variables (e. g. , age, sex, BMI). Differential abundance analyses were conducted with the DESeq2 package from Bioconductor  [23, 24]. Based on a Wald test, taxa with an absolute log-fold change larger than a 0. 5 threshold and an adjusted p-value lower than 0. 05 were considered as differentially abundant. Microbiota signatures for the separation of pre-T2D or T2D and healthy subjects were calculated using one of the most commonly used classifiers, Random Forest  [25] , in a fivefold cross-validation. The sample assignation was repeated until the three groups were equally distributed in the 5-fold range based on the Fisher's exact test. As the majority of the taxa and genes were not associated with the groups, a univariate selection (Welch test, p-value < 1% or 10%) was performed to reduce the dimensionality.
Differences in the targeted 16S regions for the GA-map ® 131-plex and LUMI-Seq ™ (V3-V9 vs. V1-V9, respectively), as well as the selected targets and the pre-determined taxonomic levels of the GA-map ® method, may lead to differences in the phylogenetic resolution. This may be one reason for the two method's limited overlap in genera and species of potential T2D associated bacteria. For instance, Turicibacter sanguinis, found elevated in T2D by LUMI-Seq ™ , cannot be detected directly by the GA-map ® 131 plex -however, may be covered by the broad Bacillota marker. Another limitation may be the use of different statistical methods, chosen to fit the dataset in question. For instance, for the GA-map ® 131-plex data, bacterial abundances were considered as significantly different if p. adj. < 0.
Acknowledgements We would like to thank  Dana Barberio  of  Edge Bioscience Communications  for her writing and editorial assistance in the production of this manuscript. We would also like to thank  Marit Thorkildsen Lia  and  Ragnar Rabe  of Østerås  Legekontor ,  Kristina Ravnås Sunde  and  Kristian Høines  of  Tananger Legesenter ,  Merete Frafjord  of  Forus Helse AS , and  Vibeke Telle-Hansen  of  OsloMet University , for their contribution in recruiting candidates for the study.
This paper describes an original approach for extracting outstanding pharmacophores, named PADs (for Pharmacophore Activity Delta), from a chemogenomic dataset (BCR-ABL in our case). This involves building both a partial order graph (POG) and a condensed POG as a first step to finally land on PADs to be assessed. A pharmacophore is a PAD if its quality deviates at least δ standard deviations from the mean of the quality (growth rate value) of its siblings. From 1479 molecules, 377 PADs were extracted. PADs were summarized afterwards by 130 representative PADs with the MMRFS technique. To analyze these PADs, a pharmacophore network was derived, leading to different areas associated with active and inactive molecules. A discussion of some representative key areas is carried out, pointing out some structure-activity relationships (SARs).
To discover these common anchoring features, the usual method starts with a careful selection of a small subset of ligands known for binding to the same active site with the same binding mode  [5]. We have done several studies with this approach (see  [6]  for an example). In recent years, the integration of large chemical databases  [7]  into the definition of SARs has been clearly explored. With SARs and pharmacophores in mind, we have introduced a method that automatically computes pharmacophores from a large data set of molecules without any prior supervised selection of a small subset of molecules  [3]. That method was based on the computation of the so-called topological pharmacophores  [8, 9]. Considering graph theory, 2D topological pharmacophores represent patterns which are present in a number of chemical structures.
Dataset In line with our previous paper  [12] , we retrieved a ChEMBL compound data set of BCR-ABL ligands  [16, 17]  (target ChEMBL ID: CHEMBL1862, ChEMBL-24). After discarding compounds with molecular weight above or equal to 800 g/mol, we obtained a data set of 1479 molecules with either Ki or IC50 information. Of these 1479 molecules, 773 were designated active compounds (meaning their Ki or IC50 value was below or equal to 100 nM).
SEC/generators/parents Of the 15477 SECs, 1745 are associated with at least two generators (vide supra for the definition). These 1745 SECs cover 1301 out of 1479 compounds. Of these 1745 SECs, 443 are associated with at least 5 generators, 25 with at least 30 generators, 10 with at least 50 generators and 1 with 271 generators. To give a first explanation of these results, the size of the molecules and the associated number of pharmacophoric functions were analyzed. In the initial dataset, 99 compounds have a molecular weight ≥ 500 g/mol and a number of pharmacophoric functions ≥ 20. Of these 99 compounds, 51 are associated with an SEC with at least 30 generators. So, the molecular weight and the number of associated pharmacophoric functions give a first and clear explanation for the observed number of generators associated with some SECs.
Cross-validation studies and stability of PADs A 10-fold cross-validation study was carried out on the initial dataset (scikit-learn/Kfold  [26] ). The method (extraction of pharmacophores and definitions of POG/SECs/PADs) was applied independently to each fold. The objective was to find PADs that show up repeatedly in different folds, which indicates their stability in the data set. As our method is local and depends on the available SECs in the partial order graph, relatively low stability can be expected. A total of 364 active PADs were obtained by considering the 10 folds. The method was found to be more stable than we expected. Indeed, of these active PADs, 61% are present in at least 5  folds.
Conclusions Aiming to develop a tool that will provide quick information from a data set of molecules regarding active or inactive compound characteristics, we explored structural elucidation from a completely annotated data set of molecules extracted from the ChEMBL database. We gathered 1479 molecules, and nearly half of them were active towards the BCR-ABL target. Then, we processed those 1479 molecules with our in-house Norns software to generate topological pharmacophores containing 1 to 7 motifs with the support of at least 10 molecules. Those constraints let Norns provide us with 112 291 pharmacophores. Based on our goal of involving a human expert in the pharmacophore elucidation, we set up a particular method to look for a new view of the data corresponding to outstanding pharmacophores named PADs.
Availability of data and materials The datasets supporting the conclusions of this article and the main programs related to this work are available at https://osf. io/pj8n3/?view_only=f49d5a0af5114b568f327c11d46bdfd3. The main program is also available on GitHub: https://github. com/Etienne-Lehembre/Pharmacophores-Activity-Delta. Image_Dockers for Norns are available by contacting the main author: ronan. bureau@unicaen. fr. Pipeline Pilot (BIOVIA Pipeline Pilot, Release 7. 5, San Diego: Dassault Systems) is commercial software. Data Set. J Med Chem 61:3551-3564. https://doi. org/10. 1021/acs. jmedchem. 7b01890 4. Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA (1998) Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure Appl Chem 70:1129-1143. https://doi. org/10.
pharmacophore p in the dataset D, the sibling mean µ(S(p, G), D) is: μ(𝑆(𝑝, 𝐺), 𝐷) = ∑ 𝑓(𝑠, 𝐷) 𝑠∈𝑆(𝑝,𝐺) 𝐶𝑎𝑟𝑑(𝑆(𝑝, 𝐺)) Then, σ(S(𝑝, 𝐺), D) is defined as the standard deviation of the siblings: σ(𝑆(𝑝, 𝐺), 𝐷) = √ ∑ (𝑓(𝑠, 𝐷) -μ(𝑆(𝑝, 𝐺), 𝐷)) 2 𝑠∈𝑆(𝑋).
After using DEVINE, they noted lower errors: thanks to the ability of the downscaling model to simulate terrain forced flow at a local scale and notably strong wind accelerations over summits and crests, the initial NWP underestimation is reduced. Since some wind speed underestimation remains, it is not clear whether the downscaling model does not accelerate sufficiently the input wind, or if the initial NWP wind speeds are too low. Whatever their origin, deep learning technics jointly with in situ measurements and ancillary atmospheric and toporaphic data may enable an a posteriori compensation of such systematic biases. In this context, we design and present a strategy, based on deep learning, that corrects NWP input wind fields upstream of DEVINE downscaling method. Indeed, the correction is made before the downscaling step, but the effect of downscaling is accounted for in the calculation of the correction.
Data In this study, we used forecasts from the AROME NWP system as inputs to our new downscaling strategy. We rely on forecasts from AROME both for large scale wind fields and other atmospheric variables used in the corrective step. Our models also make use of high resolution topographical information (30m). Quality-controlled wind observations acquired over a large network of automatic weather stations (AWS) are used for model training (train set) and evaluation (test set). We finally compared the performance of our models to the operational analysis of the AROME system.
For this study, we built two different products from the aforementioned cycles. Firstly, we built a continuous time series by extracting +6 to +29h AROME forecast lead times, initialized with the analysis of 00:00 UTC, as in  Quéno et al. (2016). This way, we were able to construct a continuous time series of 11 variables from AROME forecasts between the 1 st of September 2017 to the 1 st of October 2020 at an hourly time step (AROME f orecast ). The variables are detailed in Table  2  and their respective use described in Sect. 3. 3. For the same period, we extracted the same variables, from the analysis cycles (AROME analysis ) also at an hourly time step. Finally, we dispose of two datasets from AROME: AROME f orecast are representative of forecasted atmospheric and surface conditions and AROME analysis are representative of an a posteriori product, giving the most plausible state of the atmosphere at the considered date.
Observations We gathered hourly wind field observations from automatic weather stations originating from different observation networks in Switzerland and France in order to train and evaluate our models. In detail, we used a total of 273 observation stations. Among them, 214 stations are located in Switzerland, and correspond to data provided by MeteoSwiss, the Swiss Federal Office of Meteorology and Climatology. Then, 59 stations are located in France among which 54 are from Météo-France observational networks and 5 from the GLACIOCLIM network ("Les GLACIers un observatoire du CLIMat" -"Glacier: an observatory of the climate"). We note the use of three AWS from the Col du Lac Blanc instrumental site, a high-altitude observatory specifically dedicated to the study of mountain meteorology and drifting-snow  (Vionnet et al.
Observa on sta ons Test Training Observa on sta ons observational sites are located in various types of environments, all representative of alpine terrain. This includes snow covered areas, slopes, exposed terrain but also lower elevation valleys and some stations localized around urbanized terrain. Since most of the wind observations used in this study were obtained in complex terrain and frequently under challenging meteorological conditions, we applied a quality check procedure to our observational dataset, inspired from  Lucio-Eceiza et al. (2018a, b). As extensively detailed in Le  Toumelin et al. (2022) , this procedure first asserts a correct data compilation and storage including chronological sorting, a search for eventual repeated dates and the distinction between true North (360°) and undefined direction when wind speed is null (0°).
(2017). These simulations were run over a large range of synthetic Gaussian topographies of diverse complexity, using similar constant initial atmospheric conditions across all simulations. The trained model showed good behavior at reproducing ARPS simulation on an evaluation dataset. As a case study, Le Toumelin et al. (  2022 ) applied DEVINE to downscale AROME forecasted wind fields in the french Alps and used observations from 61 in situ stations for model evaluation. Qualitatively, the model simulates coherent spatial structures, characteristic of terrain forced flows. Notably, the model is able to detect ridges and summits and to simulate acceleration as observed within ARPS simulations that served as labels. Similarly, DEVINE shows good behavior at detecting windward and leeward areas and is able to modify speed accordingly.
Training In order to adapt the weights and biases of the ANNs, we adopted a sequential approach. First, we optimized ANN direction for wind direction, and then optimized ANN speed for wind speed. This order is motivated by the fact that an erroneous direction can translate into erroneous high-resolution wind speeds with DEVINE as a result of wrong topography adjustments, whereas the opposite will have less impact. We selected 218 training observation stations in the french Alps measuring wind speed and direction, and used topographic maps centered around each station. Additionally, we used data from the nearest grid cell of AROME at each of these stations to take into account large scale atmospheric conditions. Since our network outputs wind field    2 ).
T P I 500m in the second and third quartiles), we note a positive speed bias, less intense than its negative counterpart. Numbers in Fig. 3  indicate the number of observation stations in each group and inform on the topographic characteristics of our observational dataset. Notably, we observe that elevated stations coincide with high absolute value of T P I 500m. High positive values of T P I 500m indicate that the observation station dominates its neighborhood, and is to some extent "exposed": a feature that occurs mainly in high elevation areas in our dataset. Similarly, most of the lowest elevation stations have T P I 500m in the second and third quartiles. T P I 500m close to zero characterize stations on average at the same elevation than their neighborhood in a radius of 500m, a definition that includes flat terrain.
Model evaluations In this section, we evaluate the performances of different wind products, including AROME f orecast and AROME analysis but also the results of our deep-learning corrections and/or downscaling models (DEVINE, Neural Network, and Neural Net-work+DEVINE). Consequently, we use the test dataset, which was not used to train the deep learning models. We remind that AROME f orecast serves as input for DEVINE and Neural Network+DEVINE, while both deep learning models did not use directly any data from AROME analysis as input. Integrated evaluation metrics first highlight improved RMSE, MAE, mean bias and coefficient correlation with DEVINE over AROME f orecast (Table  3 ). Such improvements are not able to bridge the gap between AROME f orecast and AROME analysis , the latest showing largely improved evaluation metrics.
Interestingly, the T P I 500m , which was also a variable we identified to possibly account for AROME f orecast biases, presents diverse effects on the outputs. As Neural Network+DEVINE was trained after comparing observed values to downscaled simulations, the effects in Fig. 10  do not only compensate for biases in AROME f orecast but can also relate to local scale effects in the downscaling module, i. e. counterbalancing missing or incorrectly represented local processes in DEVINE. Finally, we observe that input variables related to the state of the atmosphere (green shaded areas in Fig. 10 ) have a lower influence on the output and tend to be less dispersed. Interestingly, we see that net shortwave radiations at the surface (SW net , Fig. 10 (c  re-training the model, did not impact the evaluation metrics.
Such a project could include the use of dense observational networks to assess precisely the behavior of wind at a local scale. This exercise could help listing the pro and cons of each methods, often developed over different areas and targeting distinct end user application cases, and reveal each method value for operational applications. The wealth of near-surface observations to be acquired at high spatial resolution in the central European Alps within the TeamX campaign  (Serafin et al. , 2020) , complemented with the observations routinely acquired by the local metservices, will provide an adequate database for this venture.
Elucidating the mechanism of formation, functions and regulation of LLPS systems remains a challenging task  [19]. Nonetheless, many such systems have already been described, and several dedicated LLPS databases became available  [5, [20] [21] [22]  providing rich annotations enabling potential generalizations on the associated proteins  [23, 24]. Numerous crucial cellular processes rely on phase-separated condensates, for instance, transcription and its regulation rely on RNA polymerase II condensates  [25] , super enhancers  [26]  and chromatin compartments with distinct histone modification patterns  [27, 28]. Therefore, perturbations of LLPS and the associated condensates can readily lead to the development of various diseases  [29, 30]. Phaseseparated liquid-like structures can make a transition into less dynamic hydrogels or amyloid-like protein aggregates that are associated with certain neurodegenerative diseases  [31, 32] , such as amyotrophic lateral sclerosis  [33, 34]  and Alzheimer's disease  [35, 36].
Calculations done on a larger, but less confident dataset of LLPS scaffolds derived from PhaSepDB (Table  S4 ) used as an independent alternative of our scaffold dataset also confirmed the observed tendencies (Figure  S1 ). This shows that biological condensation is central to the development of somatic cancer in general. Proteins that regulate LLPS or partake in condensation in a client role only have a more moderate overlap with around 3 standard deviations above expectation level (Figure  S2 ). Also, this effect is specific to condensation through LLPS, as proteins prone to aggregate through amyloid formation (Table  S5 ) do not show a significant overlap with somatic cancer (Figure  S3 ).