dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: Kashin-Beck disease (KBD) is currently an endemic form of osteoarthritis. In this study, we explored novel KBD diagnostic biomarkers. Methods: The GSE59446 dataset was used to conduct Weighted Gene Co-expression Network Analysis (WGCNA) and differentially expressed genes (DEGs) analysis with peripheral blood samples of 100 healthy individuals and 100 KBD patients. As part of the gene ontology pathway enrichment analysis, genes related to SONFH and DEGs were selected from the extraction module. Then, central DEGs were selected for LASSO analysis, and, based on SVM-RFE and DEG results, overlapping genes were identi ed as key KBD genes. Next, we analyzed the correlations between the selected genes and age, gender, and other factors to eliminate their in uences on gene expression. Finally, we evaluated the diagnostic value of key KBD genes using case information collected by us.
Also, a candidate biomarker gene, or therapeutic target, can be identi ed based on gender and gene set associations  [5]. Finally, the modules corresponding to which features can be identi ed by analyzing the connectivity between them and clinical information. WGCNA has been extensive used for the identi cation of genes related to the different diseases' clinical features, such as Infantile Hemangioma [6], aortic aneurysm  [7, 8] , osteoarthritis  [9] , acute myocardial infarction  [10] , bladder cancer  [11]  and pancreatic cancer  [12]. In this study, we rst retrieved microarray KBD and control PBMC datasets from the Gene Expression Omnibus (GEO) database. After screening DEGs using the "limma" R package, we performed LASSO and SVM-RFE analyses to identify key KBD genes with the highest signi cant correlation levels.
Then, based on the WGCNA screening of the GSE59446 dataset DEGs, we used the Cytoscape software to integrate DEGs into a protein-protein interaction network and de ne the overlapping genes between central genes in key modules and central genes in DEGs. Finally, the GLRX5 gene was identi ed as a new KBD diagnostic biomarker among the GSE59446 dataset key genes. Additionally, other potential KBD diagnostic biomarkers can be further identi ed from our study and used to gain new insights into their underlying mechanisms on KBD.
Methods Subjects data collection and statement. Data from the Gene Expression Omnibus (GEO) database were retrieved from GSE59446, which including 100 controls samples and 100 KBD samples (Table  S1 ). Clinically relevant samples were collected from the patient's peripheral blood and conducted in accordance with the declaration of Helsinki. Our study was approved by The Human Ethics Committee of the First A liated Hospital of Harbin Medical University (Harbin, China) and all subjects gave informed consent to be treated by our hospital (EC-HS-2020112). Patients were diagnosed according to the WS/T 207-2010 criteria, and those presenting any other osteoarthritis-related diseases were excluded. Collected blood samples from 5 healthy laboratory staff volunteers who had not been diagnosed with chronic in ammation or other diseases before, as a reference for the clinical cohort.
Finally, a total of 10 subjects were included in the study, including 5 healthy controls and 5 KBD patients. All subjects provided written informed consent. Differentially expressed genes (DEG) screening. The GSE59446 dataset original data was read and the RMA algorithm was applied for background correction and data normalization. DEGs were ltered using the "limma" R package  [13]. The "ggplot2" R package was used to draw the DEGs' volcano map  [14]. Also, the "gplots" R package was used to create a DEGs' heatmap  [15]. DEGs with p < 0. 05 and |log2FC| > 1 were considered statistically signi cant. WGCNA. After ranking genes from the largest to the smallest according to the median absolute deviation, we used the "WGCNA" R package to list all genes in WGCNA  [5, 16]. Then, we used the "pickSoftThreshold" function (WGCNA software package) to lter out power parameters from 1 to 20.
Protein-protein interaction (PPI) network analysis. The Cytoscape software (v. 3. 7. 1) was used to construct the PPI network with the above key modules genes. Key modules functional enrichment analyses. To evaluate the key modular genes and DEGs biological functions, the annotation, visualization, and comprehensive discovery database tool, DAVID (version 6. 8)  [17, 18] , was used to perform gene ontology (GO) functional enrichment analyses. GO terms with p < 0. 05 were considered signi cant. For the genes in key modules, the functional enrichment of the top 10 GO pathways, sorted by gene count, was visualized. For DEGs between control and KBD patients, all the important functionally enriched GO pathways were visualized. Screening of diagnostic. To perform feature selection and lter OA diagnostic markers, we used the least absolute shrinkage and selection operator (LASSO) logic back (Regression Shrinkage and Selection Via the Lasso) and support vector machine recursive feature elimination (SVM-RFE)  [19].
The GSE59446 dataset expression matrix was subjected to quality control to establish a new dataset. Then, an independent dataset was used to verify the diagnostic e ciency of the obtained markers. The LASSO algorithm was used in conjunction with the "glmnet" R package  [20]. Additionally, SVM-RFE is a general machine learning technique based on support vector machines in which feature vectors generated by SVM are removed to nd the best variables  [21]. SVM modules were created using the "e1071" R package to better identify the diagnostic value of these biomarkers. Then, we combined the genes selected by LASSO and SVM-RFE algorithms to further analyze them. A double-sided p < 0. 05 was considered statistically signi cant. Key genes clinical relevance. Use the "ggpubr" software package to analyze the relationship between key genes and age, stage, gender, and experimental groupings.
Results Data normalization processing. Before the GSE59446 dataset standardization, data distribution was uneven (Supplementary Table  S1 ). After standardization, the data median was at the same level without signi cant differences. WGCNA network construction. Following the GSE59446 dataset preprocessing and ltering, data were sorted from largest to smallest according to the median absolute deviation for further analyses. Samples' cluster analyses included in the WGCNA revealed no signi cant differences between them (Fig. 1A ). By setting the power value (β) to 4, a scale-independent value was reached at 0. 95 (Fig. 1B, C ), and the coexpression network achieved scale-free topology requirements (Fig. 1D ). The β = 4 was also used to generate a hierarchical clustering tree and to represent each module with different colors.
2D ). Turquoise module genes functional enrichment analyses. To identify potential biological processes related to KBD, the turquoise module genes were analyzed using GO enrichment analysis. Then, the top 10 functional enriched GO pathways, ranked by gene count, were visualized. The GO enrichment analysis results indicated that genes related to mitochondrial metabolism, protein heterooligomerization, negative regulation of cysteine-type endopeptidase activity involved in apoptotic processes, and alpha-beta T cell differentiation were signi cantly enriched (Fig. 3 ). DEG analysis and identi cation. Subsequently, we screened DEGs between healthy individuals and KBD patients' peripheral blood, based on the GSE59446 dataset. Thus, 36 DEGs were detected, including 20 upregulated and 16 downregulated genes (Fig.
4A  and B , and Supplementary Table  S2 ). The modular biological gene interaction analysis in WGCNA (Fig. 4C ) showed that GLRX5 was related to TNFSF11, BIRC3, ACSL6, ANAPC1, AF116618, HBA2, and abl1 v-abl Abelson genes. Diagnostic markers screening and veri cation. From DEGs, we identi ed 12 genes based on the LASSO logistic regression and 5 based on the SVM-RFE algorithm as KBD diagnostic markers (Fig. 5A  and B , and C, respectively). Overlapping genes obtained by the two algorithms with DEGs are shown in Supplementary Table  S3. Finally, a KBD diagnosis-related gene was obtained (Fig. 5D ). Identify and verify key genes through the database. Among the 36 central DEGs, GLRX5 was identi ed as a key KBD gene and was selected for subsequent analyses. We visualized the GLRX5 expression in the GSE59446 dataset.
We found that the KBD group had a signi cantly lower GLRX5 expression than the healthy group (p<0. 001, Fig. 6A ). Additionally, neither gender nor KBD stage signi cantly affected the GLRX5 expression (p>0. 05, Fig. 6B  and C ). Increasing age was related to GLRX5 gene expression decreases (p<0. 001, Fig. 6D ). In another database, GSE32127, we also found that the GLRX5 relative expression decreased in controls vs. KBD in PBMCs cells. Then, we measured the GLRX5 gene expression in patients' blood. Compared to the control group, the KBD group showed signi cantly low levels of GLRX5 expression in PBMCs (p<0. 001, Fig. 6E ).
Discussion KBD is characterized by a localized cartilage necrosis area in the deep articular cartilage part and cartilage plate growth. The pathogenesis of KBD is multifactorial, with both genetic and environmental factors at play  [22]. In clinical terms, KBD can be di cult to diagnose or predict since its early symptoms are not apparent. Therefore, some KBD patients have osteoarthropathy with a high permanent disability rate. Although current treatments presented only limited KBD curative effects, food replacement treatment can be effective at the early stages. Consequently, new KBD biomarkers are required for early diagnosis. Advances in genomics and proteomics have made important contributions to the diagnosis and treatment of many rare diseases and pathological conditions  [23]. In this study, 100 peripheral blood samples from KBD patients and 100 samples from healthy individuals were retrieved from the GSE59446 biochip.
13b vs. Equation  12a ), which now balances traits affecting attraction (first term of Eq. 13b) with the costs of reduced competitive ability (second term of Eq. 13b). Co-option of the antagonist also modifies selection on defense (which is still given by Eq. 12b) by changing both trait values and equilibrium densities. Model parameterization. All ecological parameters are estimated from empirical data. Here we parameterize the saturation constant H, maturation efficiency m i , larval mortality d i , and adult mortality d A as well as the parameters for the alternative larval host plant and the alternative nectar source (see "Model validation" for other parameters). We cannot fit the saturation constant H to data because seed set saturates with even a single floral visit. We therefore estimate H as follows: D.
Acknowledgements We thank  Julie Johnson  ( Life Science Studios ) for the beautiful illustrations in Fig. 1 and for help designing all of the Figures. We thank  Olivia Brinkerhoff ,  Kimberly Kopplin ,  Nico Lorenzen ,  Keane Sullivan , and  James Wadsworth  for their help in growing plants and rearing larvae as well as  Heather Costa  for colony maintenance. This work was supported by  Science Foundation Arizona  grant  BSP 0528-13  to C. A. J,  U. S. National Science Foundation  grant  IOS-1053318  to G. D. ,  U. S. National Science Foundation ,  Dimensions of Biodiversity program  ( DEB-1831493 ) to R. F. , and the  Keep Engaging Youth in Science (KEYS) program  of the  University of Arizona BIO5 Institute.
Here, we use the term ''circle'' to mean these circle pairs, unless specified otherwise. Stevens et al. (2019a)  provide more information about the campaign and the instruments employed for measurements, and the circle flight strategy is explained in detail by  Bony and Stevens (2019). Vaisala RD-94 dropsondes were used during the campaign [refer to  Wang et al. (2015)  for details]. For processing the dropsonde raw data, we use the Atmospheric Sounding Processing Environment (ASPEN), version 3. 3-297, software and interpolate the measurements to a uniform 10 m vertical grid. These processed data are used to estimate kinematic parameters such as D, v, and vertical velocity (W) following  Bony and Stevens (2019) , as well as thermodynamic parameters such as static energy, precipitable water (PW), free-tropospheric humidity (FTH), surface wind speed, lifting condensation level (LCL), LTS, inversion height, and surface fluxes.
MEASUREMENTS To quantify cloudiness, we use the unified dataset by  Konow et al. (2019)  for measurements from the HALO Microwave Package (HAMP;  Mech et al. 2014) , which includes microwave radiometers and a 35 GHz cloud radar, all looking nadir from the aircraft. As we focus here on low-level clouds, we also extract profiles of shallow cumulus clouds from the HAMP radar, with a cloud mask developed by segmenting the radar cloud data into individual two-dimensional cloud objects along time and height, which we refer to here as the Konow mask. All objects derived from the Konow mask with base below 1 km and top below 4 km are classified as shallow cumulus (ShCu) clouds. We estimate the fraction of radar echoes. 250 dBZ as a proxy for cloud fraction (CF) for the ShCu cloud objects as well as for all hydrometeors detected by the radar, and call them CFShCu and CFtotal, respectively.
Acknowledgments. We thank the entire team of  NARVAL2  for making the measurements. We thank  Chris Fairall  for sharing his code to compute the surface fluxes from the dropsondes. We thank  Anna Lea Albright ,  Cathy Hohenegger ,  Theresa Mieslinger ,  Robert Pincus , and  Hauke Schulz  for their insights and helpful discussions. We thank  Heike Konow  for help with the cloud objects from the HAMP radar, which we refer to as the Konow mask in this study. We thank  Manuel Gutleben  and  Martin Wirth  for their help with the WALES lidar data and cloud-top height estimates using the same. This project has received funding from the  European Research Council (ERC)  project  EUREC 4 A (Grant 694768 ) under the European Union's  Horizon 2020 research and innovation programme. ERA5 data were retrieved from the  IPSL mesocenter ESPRI facility , which is supported by  CNRS ,  Sorbonne Universite , Labex L-IPSL, CNES, and  Ecole Polytechnique.
Data availability statement. The NARVAL2 measurements from the dropsondes, HAMP radar, and the HAMP microwave radiometers are available as unified data products from the CERA database at https://doi. org/10. 1594/WDCC/HALO_ measurements_3. The LWP and RWP products from the microwave measurements are available at the CERA database at https://doi. org/10. 26050/WDCC/HALO_measurements_6. The WALES lidar data and the dropsondes raw data can be accessed from the HALO database http://doi. org/10. 17616/R39Q0T. The GridSat-B1 satellite products are publicly available at https:// www. ncei. noaa. gov/data/geostationary-ir-channel-brightnesstemperature-gridsat-b1/access/. The ERA5 used in this study can be accessed from the ECMWF website (https://www. ecmwf. int/ en/forecasts/datasets/reanalysis-datasets/era5).
The African continent is probably the one with the lowest density of hydrometric stations currently measuring river discharge despite the fact that the number of operating stations was quite important until the 1970s. This new African Database of Hydrometric Indices (ADHI) provides a wide range of hydrometric indices and hydrological signatures computed from different sources of data after a quality control. It includes 1466 stations with at least 10 years of daily discharge data over the period 1950-2018. The average record length is 33 years, and 131 stations have complete records over 50 years. With this new dataset spanning most climatic regions of the African continent, several hydrometric indices have been computed, representing mean flow characteristics and extremes (low flows and floods), and are accessible to the scientific community.
, 2010; Mahe et al. , 2013; Stewart, 2015; Dixon et al. , 2020). African countries are largely underrepresented in largescale databases such as the Global Runoff Data Center (GRDC) or the recent GSIM initiative  (Do et al. , 2018; Gudmundsson et al. , 2018) , and/or the time series are mostly not updated. At the African scale, there is still a lack of coordination for hydrological data collection and dissemination despite the launch in 1975 of the UNESCO Intergovernmental Hydrological Program (IHP) dedicated to water research and water resources management, as well as education and capacity building. This initiative enhanced the setup and management of international rainfall and runoff databases at the regional scale of the FRIEND programs  (Van Lanen et al. , 2014) , but these are still largely not updated.
, 2018; McMillan et al. , 2017) , and for assessing the potential impact of climate change and human activities on river regimes  (Mahe et al. , 2013). They can be used for various purposes, including basin classifications, aquifer properties characterization, and hydrological predictions in ungauged catchments  (Westerberg et al. , 2016; Gnann et al. , 2020) , and to investigate long-term trends for different hydrological processes  (Do et al. , 2017; Nka et al. , 2015). We introduce here the African Dataset of Hydrometric Indices (ADHI) that aims at giving access to an ensemble of hydrometric indices computed from an unprecedentedly large ensemble of stations with daily discharge data  (Tramblay et al. , 2020; Tramblay and Rouché, 2020). Thus, useful information regarding the African rivers' variability over the last 68 years can be shared with the international community while respecting the confidentiality of the original records when these are not allowed to be publicly shared by the national authorities.
Data collection The database used in the present work is based on the collection of stations from the Global Runoff Data Center (GRDC) and the SIEREM database  (Boyer et al. , 2006; Dieulin et al. , 2019). The hydro-climatological data contained in SIEREM are the legacy of the former Laboratoire d'Hydrologie of the Office de la recherche scientifique et technique outre-mer (ORSTOM; now Institut de Recherche pour le Développement, IRD, France). It must be noted that in addition to the daily data, the SIEREM database also contains instantaneous rainfall and discharge for hundreds of experimental small catchments mostly established in the 1950s and 1960s. The criterion to include a station in the ADHI is that it has a minimum of 10 full years, not necessarily consecutive, of daily discharge data between 1950 and 2018.
Most of the hydrological stations in French-speaking countries have been set up and managed for decades by the ORSTOM institute  (Mahé and Olivry, 1999). At the time the data were processed, the SIEREM database included a total of 1046 series, with several of them being duplicates of the same monitoring station but for different time periods. There are a total of 101 stations with two times series, 42 stations with three time series, 24 stations with four time series, and 7 stations with five time series. In most cases, one time series includes the longest record, and that one was kept for the analysis in the present paper. For some stations, the different time series were differing substantially during the same period due to different rating curves. A visual inspection of these series   (Peel et al.
, 2007). led to the elimination of erroneous or doubtful data. Only for 17 stations were the time series concatenated after making sure the rating curve(s) applied on the different time periods to compute river discharge were adequate by comparing daily runoff on a common period. Additionally, to these 1046 series, 933 stations have been retrieved from the GRDC database. For 106 of these stations, there was a duplicate station in the SIEREM database with longer time series, and the latter was selected. After this data quality processing step, 672 stations were kept for SIEREM and 794 for the GRDC database for a total of 1466 stations (Fig. 1 ). The stations from SIEREM mostly cover the western, central, and northern African regions and stations from the GRDC the eastern and southern parts of Africa.
In addition, through visual inspection it was possible to identify stations to which some gap filling methods have been applied (13 stations) or for which the data are suspicious (28 stations). A flag has been added in the metadata to identify these stations. It is worth noting that, for the stations of the SIEREM database, most of the data were analyzed and criticized prior to the inclusion in the database by the former ORSTOM hydrology laboratory with therefore a reduced level of error in the archived data. In addition, to detect possible shifts in the data due to nonnatural influences, such as an artificial drift in the monitoring devices, changing instrumentation, recalibration of the rating curve, or river regulation by dams or reservoirs, the Pettitt test  (Pettitt, 1979)  was applied to mean annual runoff series.
Climate characteristics This data collection results in the largest ever database built of daily discharge data in Africa. These stations belong to different climate zones (Fig. 1 ) according to the Köppen-Geiger climate classification  (Peel et al. , 2007). The main climate zone represented is savannah (class Aw) for 687 stations corresponding to the western and central African basins. The second most represented climate zone is steppe-hot (Bsh) for 207 stations located in the Sahel region and southern Africa (Botswana, Namibia). The temperate with dry winter classes (Cwa and Cwb) include 187 and 125 stations, respectively, located in southern Africa (Zambia, Angola, Rwanda, Mozambique, South Africa, and Zimbabwe). The 98 stations belonging to the desert-hot class (Bwh) are mostly located in the northern and southern boundaries of the Sahara Desert.
A total of 87 stations under a temperate climate with dry hot summer, corresponding to Mediterranean climate (Csa), are found in North Africa and the southwestern part of South Africa. Thus, the selected river basins are representative of most of the climate zones in Africa. It must be noted that for large basins, such as the Congo, Niger, or even the Orange rivers, the climate type at the outlet may not be representative of the whole catchment that may span over diverse climate zones. To document the mean annual precipitation and evapotranspiration at the catchment scale, the CRU4 dataset has been considered  (Harris et al. , 2020). However, without longterm and homogeneous ground monitoring networks over the https://doi. org/10. 5194/essd-13-1547-2021 Earth Syst. Sci. Data, 13, 1547-1560, 2021 African continent, no best precipitation database could be identified for Africa as a whole  (Sylla et al.
, 2013; Beck et al. , 2017; Awange et al. , 2019; Satgé et al. , 2020). For some regions, such as northern or equatorial Africa, there are large differences between different remote-sensing-or gaugedbased precipitation products  (Gehne et al. , 2016; Harrison et al. , 2019; Nogueira, 2020) , in particular for extreme precipitation events. This is the reason why we choose to provide only mean annual precipitation, evapotranspiration, and temperature. This implies that the ADHI dataset does not provide metrics relying on time series of precipitation or evapotranspiration, such as the runoff ratio, streamflow precipitation elasticity, or catchment response time. To calculate these indices requiring climatic time series for a given catchment, the user is advised to check first the best available data for that area.