dataesr/scientific-paragraphs-categorization huggingface dataset:
These metrics were extracted from a trace report which was computed by nextflow. It contains information about each executed process in the pipeline. The results varied widely across different tasks, which could be attributed to different implementations and differently assigned subtasks of the major data transformations. On the whole, we noticed that Proline and Compomics used CPUs more efficiently and required more memory, while the MaxQuant workflow performed more file read/write operations. In summary, when utilizing the ground truth dataset with UPS proteins spiked into a yeast background, significant differences were observed among the outputs of the various workflows.
Performance on biological data from COVID-19 study Ground truth datasets can be limited in accurately replicating biological samples. Therefore, we decided to also assess the performance of WOMBAT-P using a recent dataset that compared COVID-19 positive and negative subjects  37. The utilization of this dataset led to slightly different results, showing that workflow performance can depend strongly on each particular dataset it processes. When comparing the different benchmarks (Fig. 5 ), the Compomics and TPP workflows consistently exhibited lower peptide quantification numbers, whereas the Compomics workflow demonstrated up to 400 more quantified proteins, when compared to the other workflows. When evaluating the variance and correlation levels within replicates of the same sample type, the correlations were lower compared to the ground truth data.
This disparity can be attributed to the increased biological variability among the samples (from controls and patients) and to the lower number of identified proteins. Notably, the TPP workflow exhibited the lowest coefficient of variance and the highest protein abundance value correlations of non-log-transformed values. It is worth mentioning that we observed significant differences in the dynamic ranges of protein and peptide quantifications, similar to what was observed in the ground truth dataset. These differences could have influenced the high correlation values and the low coefficient of variance for the TPP workflow. Run times showed a diverse picture, similarly to what was observed for the UPS dataset (Suppl. Fig. 4 ). Additionally, computer resources used for the processing of this dataset exhibited very close relative values, compared to the UPS dataset.
Notably, the results from Compomics and MaxQuant workflows were sufficiently similar to separate the two sample types between them. We furthermore compared the protein groups found to be differentially regulated (Suppl. Fig. 6 ). While more than one hundred proteins were detected in at least three of the four workflows, they disagreed to a high degree, leading to more than 500 proteins being uniquely found to be significantly changing between the COVID-19 positive and negative samples. However, further analysis via Reactome pathway enrichment led to considerable agreement between the results of Compomics, Maxquant and Proline workflows for the most enriched pathways (Suppl. Fig. 7 ). In summary, we found both similarities and differences when comparing workflow performances. The noise levels, as expected, were higher for the COVID-19 dataset and showed higher similarity between the different workflows (Figs.
Discussion This study introduces WOMBAT-P as an innovative solution for addressing the challenges of large-scale proteomic data analysis. It relies on the importance of automated data quality control and validation in scaling up to analyzing a large number of files. The scalability of WOMBAT-P using high-performance computing (HPC) environments and its utilization of software containers enable reproducible analyses, making it a valuable tool for both public and in-house label-free data analysis at a large scale. One notable feature of WOMBAT-P is its ability to create SDRF-Proteomics file templates and produce harmonized outputs, facilitating easy benchmarking and comparison of results. This comprehensive approach enhances the robustness and reliability of proteomic data analysis. We found different performances of the workflows when testing them on two selected datasets.
These products are widely used for monitoring natural disasters, initializing numerical weather forecasting models, and evaluating precipitation forecasts. For instance, some products exclusively utilize IR data as input. The Global Hydro Estimator (GHE;  [5] ) employs a fixed relationship between IR data and rainfall rates, calibrated initially with radar data. The PERSIANN Dynamic Infrared-Rain Rate Model (PDIR;  [6] ) calibrates IR data with PMW datasets and climatology data via several machine learning algorithms. On the other hand, Quantitative Precipitation Estimation (QPE,  [7] ) and P-IN-SEVIRI  [8]  use IR data calibrated in real-time with the most recent PMW data through the SCaMPR  [9]  and Rapid Update  [10]  algorithms, respectively. Certain products also incorporate several data types.
The Global Satellite Mapping of Precipitation (GSMaP;  [11] ) combines microwave data with IR data and rain gauges through a Kalman filter. The Integrated Multi-satellitE Retrievals for GPM (IMERG;  [12] ) aims to intercalibrate and merge all PMW precipitation estimates with IR estimates, precipitation gauge analyses, and potentially other precipitation estimators. All the mentioned products offer rainfall estimation within an hour, except for IMERG, whose "Early Run" is available four hours post the start of data acquisition. Table  1  summarizes the characteristics of these products, which will be used for comparison. Recent advancements in computing technology and increased availability of large-scale satellite datasets have unveiled new opportunities for enhancing precipitation estimation accuracy with deep learning methods.
Deep learning, a branch of machine learning, has earned significant interest across various scientific disciplines due to its ability to autonomously learn intricate patterns and relationships from extensive datasets. Applying deep learning techniques, particularly deep convolutional neural networks (CNNs), to satellite data has demonstrated promising results in several domains, including image classification  [13] , object detection  [14] , and image segmentation  [15]. Importantly, CNNs are capable of efficiently extracting complex spatial features from images without the need for meticulous feature engineering. Within the context of precipitation estimation, deep learning algorithms hold the potential to leverage the abundant information in satellite imagery for accurate and real-time precipitation estimation, a potential that has already been successfully demonstrated in several studies.
Deep learning has not yet been globally applied to satellite precipitation estimation to investigate if deep neural networks can generalize to various parts of the globe and diverse climates. In this study, the objective is to build upon previous methodologies by training a state-of-the-art DeepLabV3+ architecture on a global precipitation dataset. This dataset is a combination of geostationary satellite data and additional descriptive features as input, with precipitation measurements from the GPMCO satellite serving as the target. Various configurations of input data, loss functions, and hyperparameters will be examined to identify the highest-performing model. The model's performance will be evaluated globally against GPMCO data. Additionally, we will compare our model with six operational satellite-based precipitation products using ground-based radar measurements from the French radar network as the reference.
This evaluation will be conducted across four distinct regions spread across the globe. This paper is structured as follows: Section 2. 1 introduces the datasets and study regions; Section 2. 2 describes the neural network and the experimental protocol; Section 3 evaluates the quality of the model with various input data (3. 1), against a test set of GPMCO data (3. 2), in comparison with other operational products (3. 3), and finally through case studies (3. 4); Sections 4 and 5 presents the conclusions and outlines future prospects.
This process produces over 320,000 precipitation tiles that contain instantaneous precipitation data in mm/h. Figure  1a ) presents an example of such a tile. Tiles to be included in our dataset are carefully selected. Rainfall events, especially heavy rain, are uncommon. Heavy rainfall is particularly rare in regions closer to the poles. Convective rainfall, which usually produces the heaviest precipitation, is more common near the equator. For instance, tiles within latitudes [-70°;-50°] and [50°;70°] account for 42% of all tiles, but they represent only 15% of the tiles with rainfall exceeding 20 mm/h. To ensure the neural network effectively generalizes across the globe, it's essential that heavy rain is uniformly represented in the training dataset across all latitudes.
Preprints Tiles were grouped into latitude bands of 20°and yearly groups, and were sorted based on their maximum precipitation value. For regions closer to the poles, the top 600 tiles with the highest recorded rainfall were selected. For regions closer to the equator, we randomly selected 600 tiles each year from each latitude band with a maximum rainfall exceeding 75 mm/h. This process resulted in a dataset comprising 21,000 tiles. Figure  A1  in Appendix A illustrates the distribution of the maximum precipitation of the selected 21,000 GPMCO data tiles for the seven latitude bands. It can be noted that, despite this selection process, heavy rainfalls are less represented in latitudes closer to the poles. For example, for the band [-70°,-50°], data with maximum precipitations under 50 mm/h had to be included to adequately populate the dataset.
GEO data is projected onto the same 0. 45°grid as the GPMCO precipitation tile and corrected for parallax. Visible images are also corrected for solar angle. These images are then normalized between 0 and 1, using the maximum and minimum values derived from the entire training dataset. Figure  1  provides an example of a precipitation tile and its associated most recent GEO channels. In addition, the following descriptive features are included: the normalized latitude, longitude, sun elevation, and altitude of each pixel within the tile. Finally, seasonal information in the form of two masks representing the sine and cosine of the day in the yearly cycle is included.
Dataset split After merging the precipitation tiles with the GEO data and additional descriptive features, the dataset is split into three parts. The training set includes samples from 2018 to 2020 and the odd-numbered months of 2021. The validation set comprises the even-numbered months of 2021, which enables to monitor the convergence of the training algorithms and evaluate the performance of intermediate experiments, such as the selection of hyperparameters and input data. Data from 2022 is reserved for the test set, which remains untouched throughout the intermediate experiments. This reserved test set is only used at the final stage to evaluate the accuracy of the selected best neural network and compute scores against the GPMCO data.
The deep neural network used in our study is DeepLabv3+  [31]. We selected DeepLabv3+ over the commonly used U-Net  [23, 32]  due to its Spatial Pyramid Pooling module and encoder-decoder structure. These architectural features allow DeepLabv3+ to encode multi-scale information, extract denser feature maps, and generate sharper object boundaries. Despite having a larger receptive field than U-Net, DeepLabv3+ has fewer weight parameters and is less prone to overfitting. We employed the implementation of DeepLabv3+ from Keras Code Examples  [33]  and modified the final activation layer to a ReLU function to transform it into a regressor. Moreover, the dilation rates inside the Spatial Pyramid Pooling module were adjusted to reduce the receptive field of the network, which was originally larger than the size of our images.
Incorporating this weight in the loss function supports addressing the imbalance caused by the relative scarcity of these samples in our dataset. Finally, a threshold is incorporated on this weight, which leads to the following loss function: L = 1 n n ∑ i=1 W i (Y i -Ŷi ) 2 with W i = w i = (1 + l i ) exp ( Y i β + γ) if w i ≤ 20 20 else (1) where Y i is the target precipitation, Ŷi is the estimated precipitation, l i is the normalized latitude, β = 50, and γ = 1. 9. Figure  3  illustrates the experimental training framework, including all the various input features available.
Choice of Best Configuration This section presents investigations regarding the influence of input features on the quality of the output from the neural network. The aim is to identify an optimal configuration to enhance the accuracy of our precipitation estimation model based on satellite observations. Investigations are led methodically: including all available data and then progressively removing different input features. First, we evaluated the effect of varying the number of GEO images used as input. Figure  4a ) illustrates the performance of the model when varying the number of input images. Interestingly, using only the most recent image resulted in an improved FSS for precipitation estimation. As depicted in the figure, the model's performance improved as historical images are removed, although the performance does not strictly follow the number of images.
The POD and FAR, available in Appendix B, support the same conclusions, even if the difference between POD near the poles and at the equator is less marked. These results suggest that while the model is able to accurately detect and estimate precipitation in temperate and tropical regions, it struggles to do so at higher latitudes. Despite precautions to over-sample the dataset and weigh the loss in the higher latitudes, heavy rainfalls in these regions are still too scarce to allow the network to learn the different patterns of rain near the poles. In addition, at the poles, the tropopause is lower and the angle between the ground and the satellite's sensors is greater, leading to a diminished contrast between precipitating and non-precipitating clouds. This makes the task of rain detection more challenging, even to the human eye.
Acknowledgments: We would like to express our gratitude for the collaborative effort between the  AI Lab of Météo-France  and the  Centre de Météorologie Spatiale , which was crucial to the success of this work. Specifically, we want to acknowledge the significant contributions of several individuals:  Laurent Perier  for his instrumental role in software development, particularly in data preprocessing;  Laura Bouzid ,  Célestin Cadoret ,  Coralline Nicolas  and  Louis Soulard-Fisher  for their diligent work in comparing Espresso with other operational products, carried out as part of their internships. We also extend our appreciation to  Sylvain Le Moal ,  Jean-Baptiste Hernandez ,  Olivier Membrive , and  Philippe Chambon  for their valuable insights and advice, which enriched the quality of our research.
It also seems difficult for some of them to differentiate between the notions of sport in general and APA. Conversely, after specific training, the majority (64%) of GP questioned by RUCAR felt able to prescribe APA to their patients. As for training in APA, the vast majority (more than 90%) of GP thought it should be included in the curriculum (CHEVALLIER & BULTEZ, PITARCH), almost half (45-50%) thought it should be incorporated into face-to-face continuing medical education (LESA-GE, EDOUARD, DE LARQUIER), and just under a third (27%) felt it should be organized in the form of seminars with specialists in APA (LESAGE). Alternative proposals identified in other studies included the creation of online courses (23. 4%; LESAGE), the establishment of a reference framework to help with written prescriptions (BARTHEL, LESAGE, RUCAR), and the creation of an IT tool connected to existing medical software (RUCAR).
High-throughput 16S rRNA gene amplicon analysis Genomic DNA from snap-frozen fecal samples and the amplification of hypervariable V3-V4 regions of their 16S rRNA gene were prepared as described previously. [34]  Libraries preparation and sequencing (Illumina Miseq cartridge) were performed by the Genotoul facility (Get-Biopuces, Toulouse). Raw sequences were processed using the FROGS pipeline (Galaxy Version 3. 2. 3) and analyzed using the R package Phyloseq (v1. 34. 0) as follows  [35]  : Each pair-end valid denoised sequences were filtered, merged and clustered with the swarm fastidious option using a maximum aggregation distance of 1. [36]  Putative chimera were removed (vsearch) and clusters (i) whose abundance represented at least 0,005 % of all sequences, (ii) presents in at least 2 times in a minimum of 5% of total samples with a prevalence threshold of 5% of all samples, were retained, yielding to 318 final clusters.
The silva 138. 1_16S reference database was used for cluster affiliation into Operational Taxonomic Units (OTUs) using Blast+with equal multi-hits, taxonomic multi-affiliations were checked manually. Within sample community richness and eveness ( diversity) were estimated using both the Chao-1 and Shannon indexes respectively, and examined by one-way ANOVA analysis and Tukey's multiple comparisons test. Divergence of bacterial composition between samples ( diversity, Phyloseq) was explored using the Unifrac distance matrices, and statistically tested using permutational multivariate analysis of variance (MANOVA using Adonis test with 9999 permutations followed by pairwise multilevel comparisons). OTUs were agglomerated at the order and genus ranks to further analyze differential abundances according to diets using the Deseq2 package (v1.
Statistical analyses For the animal experiment, once outliers were identified/removed by a ROUT test, and normality was checked, ANOVA was performed using GraphPad Prism software (version 9. 5. 0), followed by a Dunnett's or a Tukey's mean comparison test, in case of comparison to a control group (sodium nitrite effect), or comparison of all means (alternative effect), respectively. In case of heteroscedasticity (tested by Bartlett's and Brown-Forsythe tests), data were log transformed before ANOVA (DHN-MA and NOCs). In case of nonnormality of residuals, a nonparametric test (Kruskal-Wallis) was performed followed by Dunn's mean comparison test (some RT-qPCR and bacterial taxa). Finally, if equal SD cannot be assumed despite data transformation, a Welch's ANOVA was performed (cytotoxicity test).
Acknowledgments The authors thank  Xavier Blanc  ( UE 1298 SAAJ, Sciences de l'Animal & de l'Aliment, INRAE)  for providing custom experimental diets and all members of the  EZOP (Animal facility)  for assistance with the animal experimentation. The authors thank the  Genotoul bioinformatics platform Toulouse Occitanie  and  Sigenae group  for providing help and storage resources thanks to Galaxy instance (https://galaxy-workbench. toulouse. inra. fr). The authors thank  Bettina Seeger  ( University of Veterinary Medicine, Hannover ) for providing the CLD88 et NO-Analyzer for NOC quantification. The authors thank  G. Kuhnle  for fecal NOC analyses in the supplementary experiment.
Evaluation of the energy demand The definition of a social housing apartment, as prescribed in the General Moroccan law, (code des impots) does not specify any integration of any cooling and heating technologies. This side is left to the appreciation of the end consumer. The energy demand is then evaluated using an ideal HVAC system, other electrical consumption is taken into consideration in the simulation but is not part of the analysed outputs. The set use schedules follow an approximate interpretation of the behaviour of a standard Moroccan family. Once all simulations are finalized, we set up an initial filtering process using the overall yearly energy consumption per m² thresholds as defined in the thermal regulation of construction in Morocco. These thresholds in our case are used to filter resulting data into a readable analysis diagram.
Overview and Method The weather analysis has been carried out for four distinct locations. Standard weather data have been exported from the software tool EPWMap ladybug website. The data have been analysed using Grasshopper as well as custom Python scripts. Weather data have been exported from EPWMap ladybug website in EPW format. This way, the analysis could be carried out for the exact same weather data as were used for simulation. For Meknes, Agadir and Marrakesh, weather station locations were used. Weather data were calculated for a typical recent period, with radiation data based on recordings between 1991 and 2010 and temperature data based on recordings between 2000 and 2009. The analysed weather locations are summarized in Table  2. Table  03. Location Characteristics for the analysed regions  (1)  g represents energy generation values, l is the energy load, i represents the energy carrier and t is the time interval used (hours, days or months) N corresponds to the number of data samples in our case 12.
For an analysis of morphometric characters, we also took 29 different measurements of the hard parts of the haptor, the male copulatory organ (MCO), and the vagina (Fig. 2 ). The terminology was based on Pariselle et al. [78]. The marginal hooks are counted according to Llewellyn  [60]. The monogeneans were identified to species level based on shapes and sizes of the sclerotised structures. However, to assess additional morphological variation in similar species, we conducted principal component analyses (PCA). Standard PCA approaches require a dataset without missing information, but often measurements of monogenean flatworms are incomplete due to the fragility of the worms and damage caused to the samples by the mounting process. Multiple methods have been proposed to address missing data in PCAs (see  [119] ).
Phylogenetic position: maximum parsimony and machine learning We used the morphometric data to infer the phylogenetic position of the new species of Cichlidogyrus based on the dataset published by Cruz-Laufer et al. [12]. Cruz-Laufer et al. [12]  demonstrated that morphometric data of the attachment and reproductive organs can indicate phylogenetic relationships between species of Cichlidogyrus, albeit limited to certain measurements and groups of related species. Here, these data were reanalysed by expanding the parsimony and machine learning approaches to the new species found on chromidotilapiine cichlids. First, phylogenetic positions were inferred based on the morphometric measurements (Fig. 2 ) and the morphological discrete characters for the reproductive organs suggested by Cruz-Laufer et al.
Acknowledgements. We thank  Miguël Parrent  and  Jos Snoeks  for providing access to the ichthyological collection of the  Royal Museum  for Central Africa (RMCA). We thank  Nnoye Scholastica Mbanu  and  Jennifer Mbanu  for suggesting species epithets in Igbo. Part of the research leading to results presented in this publication was carried out with infrastructure funded by the  European Marine Biological Research Centre (EMBRC) Belgium ,  Research Foundation -Flanders (FWO-Vlaanderen)  project  GOH3817N. AJCL ( BOF19OWB02 ), NK ( BOF21PD01 ) and  MPMV  ( BOF20TT06 ) received support from the  Special Research Fund of Hasselt University. We thank the anonymous reviewers and the editor for their suggestions.