dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, a sky spectral radiometer (model no. CE318; Cimel Electronique, Paris, France) was deployed during both intensive periods of both campaigns as part of the AERONET (AErosol RObotic NETwork) observations. This pointed sun radiometer derived the AOD at several wavelengths, and Holben et al. (1998) provide detailed insights on the working principle of this instrument. It was used to cross-check the lidar retrievals to validate the integrated σ ext (λ) profiles with the AERONET AOD. Directly deriving the LR from nighttime observations with the Raman lidar would also have been a feasible approach. However, as the atmospheric conditions between night and daytime were not homogenous and quite variable, we could not apply the nighttime finding to our daytime observations. However, we used AERONET AOD data to validate our extinction profiles and found good agreement whenever atmospheric conditions allowed.
Measurement platforms During the intensive period of the summer campaign, a set of state-of-the-art instruments, installed on the airborne platform ACTOS  (Siebert et al. , 2006) , determined microphysical and aerosol optical properties. ACTOS was designed as an external cargo under a helicopter, with a 150 m long aerial rope, and was operated at maximum ascend and descend speeds of 6 m s -1. Ambient RH and temperature (T ) were recorded and averaged to a temporal resolution of 1 Hz. A data link was established between ACTOS and a receiver station installed on the helicopter. The scientist on board the helicopter adjusted flight height and track based on the realtime data observation. The measurement strategy is shown in the Supplement, with a typical flight pattern displayed in Fig. S1. On ACTOS, a custom-made silica-bead-based diffusion dryer dried the air sample to ensure an aerosol humidity be-low 40 %, following the recommendations of  Wiedensohler et al.
Aerosol optical properties In summer and winter, the aerosol optical properties were measured on board ACTOS. The Single Channel Tricolor Absorption Photometer (STAP; Brechtel Manufacturing Inc. , Hayward, CA, USA) derived σ abs (λ) at 450, 525, and 624 nm wavelength, respectively. Briefly, the STAP evaluates σ abs (λ) based on light attenuation measurements behind two filters with a spot size of around 1. 75 × 10 -5 m -2. This study used quartz fiber filters (Pallflex membrane filters, type E70-2075W; Pall Corporation, Port Washington, NY, USA). On one filter, the aerosol matter deposits, and one filter spot stays clean downstream of the first filter. A photodetector detects the intensity of light of the given wavelength behind these filter spots. All raw data have been recorded on a 1 s time resolution.
Therefore, the fit for 532 nm at this RH range might be overweighted, leading to an overestimation of γ (532 nm). Also, at 355 nm, Ackermann (1998) has shown a decreasing LR(355 nm) above 90 % RH, which we could not observe in this study solely based on the small number of cases and the observed RH range. The observations follow a trend sim- ilar to the reported parameterization of  Zhao et al. (2017)  but with a different magnitude. Although the LR enhancement was derived similarly, differences can occur because they normalized their observations to RH 0 = 40 %. Also, they used data based on PNSD recorded in the North China Plain (NCP) and a different approach of the aerosol mixing state utilizing a mixture of internally and externally mixed aerosol with a fraction of 51 % externally mixed BC.
Acknowledgements. We gratefully thank the competent help of the technicians Thomas Conrath,  Astrid Hofmann , and  Ralf Käthner. We thank  Holger Siebert , for setting up and building ACTOS. We express our deepest gratitude to all other TROPOS employees, who supported us with energy and passion before, during, and after the campaigns, and we thank all participants for helping to tame the balloon during the winter campaign. Moreover, we are very thankful to the helicopter pilots  Alwin Vollmer  and  Jürgen Schütz , for the secure helicopter flights during the summer campaign. The authors, furthermore, thank  Dieter Schell  of enviscope GmbH, for his expertise. We also thank  Anke Rödger  of  TROPOS , for providing and conducting the filter measurement samples of  Melpitz. Joel C. Corbin  and  Martin Gysel-Beer  received financial support from the  ERC  (grant no.
615922-BLACARAT ), the  ACTRIS2  project funded by the  EU  ( H2020 ; grant no. 654109 ), and the  Swiss State Secretariat for Education, Research, and Innovation (SERI ; contract no. 15. 0159-1 ). Financial support. This research has been supported by the  European Commission, FP7 Ideas: European Research Council  (BLACARAT; grant no. 615922 ), the  European Commission, European Research Council  ( ACTRIS-2 ; grant no. 654109 ), and the  Staatssekretariat für Bildung, Forschung und Innovation  (grant no. 15. 0159-1 ). Review statement. This paper was edited by  Zhanqing Li  and reviewed by three anonymous referees.
Data availability. Q-ACSM, MAAP, the nephelometer, EC, and T-SMPS data are available at the NILU EBAS database under http://ebas-data. nilu. no/default. aspx (last access: 10 November 2021). All other data have been uploaded and are available at https://doi. org/10. 5281/zenodo. 5608560 (Düsing et al. , 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-16745-2021-supplement.
Field studies suggest that sublimation can be a significant component of the snowpack mass balance at the highest elevations of the Himalayas  [10, 11]. The recent release of the High Mountain Asia UCLA Daily Snow Reanalysis (HMASR)  [12, 13]  enables us to advance knowledge on snow-related hydrological processes in the Indus basin. This reanalysis is based on an energy balance model, which computes sublimation unlike previous studies  [14]. Another advantage is that this reanalysis also corrects biases in the precipitation input data by assimilating remote sensing observations of the snow covered area  [15]. Hence, this dataset provides the opportunity to reevaluate snowmelt and to evaluate snow sublimation in the Indus basin. Indus basin map and distribution of population density, annual precipitation and potential evaporation by elevation.
Materials and Methods The HMASR provides several snow cover variables (albedo, snow depth, snowmelt, snow water equivalent and snow sublimation) at a spatial resolution of 16 arc seconds (~500 m) and a temporal resolution of 1 day. The dataset spans the entire High Mountain Asia region including the headwaters of the Indus basin. The lower regions of the Indus basin (tile-average elevation lower than 1500 m) were not processed due to the unlikely occurrence of snowfall  [13]. The dataset covers the period from 1 October 1999 to 30 September 2017. The HMASR products are distributed by tile of 1° longitude by 1° latitude. Each tile is a netCDF file containing the daily estimates of a variable over a water year (starting on 1 October)  [12]. Using netCDF Operators (NCO)  [20] , Geospatial Data Abstraction Library (GDAL)  [21] , Orfeo Toolbox (OTB)  [22]  and GNU parallel  [23] , I merged the tiles containing the daily snowmelt and sublimation estimates, cropped the resulting multiband mosaics to the Indus river basin outline and computed temporal and spatial averages (source code is provided in the Data Availability section).
The snowmelt and sublimation were aggregated to daily basin-scale fluxes (units: gigatons per day, equivalent to km 3 of liquid water per day) and time cumulated specific fluxes (units: millimeters water equivalent since 1 October) using the average of all valid pixels in the basin. The   [16]  and modified in the northeast following  [17]. The population density was sourced from the Gridded Population of the World, Version 4  [18]. The precipitation and evapotranspiration data were sourced from TerraClimate  [19]. Base map from OpenStreetMap.
Materials and Methods The HMASR provides several snow cover variables (albedo, snow depth, snowmelt, snow water equivalent and snow sublimation) at a spatial resolution of 16 arc seconds (~500 m) and a temporal resolution of 1 day. The dataset spans the entire High Mountain Asia region including the headwaters of the Indus basin. The lower regions of the Indus basin (tile-average elevation lower than 1500 m) were not processed due to the unlikely occurrence of snowfall  [13]. The dataset covers the period from 1 October 1999 to 30 September 2017. The HMASR products are distributed by tile of 1 • longitude by 1 • latitude. Each tile is a netCDF file containing the daily estimates of a variable over a water year (starting on 1 October)  [12]. Using netCDF Operators (NCO)  [20] , Geospatial Data Abstraction Library (GDAL)  [21] , Orfeo Toolbox (OTB)  [22]  and GNU parallel  [23] , I merged the tiles containing the daily snowmelt and sublimation estimates, cropped the resulting multiband mosaics to the Indus river basin outline and computed temporal and spatial averages (source code is provided in the Data Availability section).
Discussion The mean annual snow ablation (sum of snowmelt and sublimation) is 113 Gt (standard deviation 12 Gt), which is close but lower than the annual snowfall value of 121. 8 ± 7. 7 Gt estimated by  [6]  over the period 1979-2019. This discrepancy can be due to (i) different periods of study  (2000-2016 versus 1979-2019) , (ii) differences in the basin delimitation  [17]  and (iii) net accumulation of snowfall in the glacier accumulation areas. The mean annual snowmelt value of 121 ± 13 mm represents a significant but not dominant fraction of the Indus basin water balance (Figure  7 ). In comparison, average basin precipitation from the WorldClim database is 424 mm  [24]. Although there is probably a large uncertainty on this reference precipitation value  [3] , it falls within the range of previous estimates (392 to 461 mm) reported by  [1].
Discussion The mean annual snow ablation (sum of snowmelt and sublimation) is 113 Gt (standard deviation 12 Gt), which is close but lower than the annual snowfall value of 121. 8 ± 7. 7 Gt estimated by  [6]  over the period 1979-2019. This discrepancy can be due to (i) different periods of study (2000-2016 versus 1979-2019), (ii) differences in the basin delimitation  [17]  and (iii) net accumulation of snowfall in the glacier accumulation areas. The mean annual snowmelt value of 121 ± 13 mm represents a significant but not dominant fraction of the Indus basin water balance (Figure  7 ). In comparison, average basin precipitation from the WorldClim database is 424 mm  [24]. Although there is probably a large uncertainty on this reference precipitation value  [3] , it falls within the range of previous estimates (392 to 461 mm) reported by  [1].
Figure1. Indus basin map and distribution of population density, annual precipitation and potential evaporation by elevation. The basin polygon was extracted from the Global Runoff Data Centre [16]  and modified in the northeast following [17]. The population density was sourced from the Gridded Population of the World, Version 4 [18]. The precipitation and evapotranspiration data were sourced from TerraClimate [19]. Base map from OpenStreetMap.
3 NA objective. The lasers used were diodes laser (405, 488, and 532 nm). The microscope was equipped with a galvanometric stage to do z-acquisitions. For cell-associated barcode selection, we computed the knee point and the inflection point of the ranked barcodes distribution plot. The local minimum between these points on the density curve (density base R version 3. 6. 1 function, bw = 500, n = 4096) of the UMI number per barcode was detected using the quantmod package. This local minimum was used as a threshold for cell-associated barcode selections. When no local minimum could be detected between the two points, the nearest local minimum was used. Quality controls regarding abnormal mitochondrial or ribosomal content, UMI number, detected gene numbers, unmapped reads, and putative doublet identification (Scrublet 0.
2. 1-0) were performed, but no data filtering was applied as no important anomalies were detected. In total, for gonadal samples, we obtained 92 267 cells. It included 13 060 cells from E9. 0; 14 962 cells from E10. 5; 16 670 cells from E11. 5; 20 285 cells from E12. 5; 25 794 cells from E13. 5; and 16 994 cells from E16. 5. In total, we obtained 61 755 XY cells and 46 010 XX cells. Adrenal glands were collected only in XY embryos, and 26 639 cells were collected in total with 7296, 11 255, 8822, and 7609 cells collected at E12. 5, E13. 5, E16. 5, and E18. 5 respectively.
| Gene expression normalization UMI counts per gene per cell were divided by the total UMI detected in the cell, multiplied by a scale factor of 10 000 and log transformed. To classify the cell populations present in the developing testis and ovary, we selected all genes detected in more than 50 cells (21 103 genes), log normalized their expression and ran independent component analysis (ICA, Seurat package v2. 3. 0) on these (100 components). ICA extracts non-Gaussian components from data, allowing a better discrimination of small-cell populations in heterogeneous dataset. 44 We set the number of ICs to 100 as we expected to have less than 100 different populations in our total dataset. Another advantage of ICA is its good performance with nearly no gene filtering, such as highly variable genes selection.
| Pseudotime ordering of the cells To order the cells along a pseudotime, we took advantage of the discrete prior knowledge we have about the embryonic day at which each cell was harvested and generated an ordinal regression model (adapted from Ref. 46) to obtain a continuous pseudotime score reflecting the differentiation status of the cells (Bmrm package v4. 1). We trained the model using a tenfold cross-validation schema on the whole expression matrix with a correction for unequal numbers of XX and XY cells. The trained model outputs a weight for each gene according to its ability to predict the embryonic time of each cell in an ordered manner. We then predicted a continuous score for all cells using the weights of the 100 top weighted genes to avoid over fitting of the model. To determine which genes were important for sex-specific progression of the cells along time, we trained two distinct models on XX and XY cells and selected the genes with a significantly higher absolute weight for classification (compared with the complete set of genes).
We merged the two lists of genes and obtained 688 unique genes, of which 128 were common to both XX and XY lineages, 266 were specific to XX germ cells, and 294 were specific to XY germ cells (Figure  S2A, B ). 2. 5. 5 | Gene regulatory network generation GRN was generated using pyScenic package (v0. 9. 14). 47 irst adjacencies were generated using the grnboost function  48  with the whole expression matrix of all the germ cells as input and the transcription factor list provided with the package. Next, modules were generated, associating groups of genes to a putative regulator using modules_from_adjacencies function. Finally, the obtained modules were pruned on the basis of the presence or absence of the master regulatorbinding site footprint in the neighborhood of the TSS of each regulated gene.
AUCell was then ran on the obtained regulons to infer its activity within each cell. 2. 5. 6 | Regulons and genes hierarchical clustering Regulons or genes were ordered using ward. d hierarchical clustering on the AUC or expression matrix with Spearman correlation distance. Modules were determined using cutree with k = 10 for negative regulons and k = 30 for positive regulons (packages dendextend v1. 13. 4, factoextra v1. 0. 7, tidyr v1. 1. 0).
| Spliced and unspliced counts analysis To generate spliced and unspliced counts data, the velocyto. py script from velocyto (v0. 17. 8) package was called on each bam file with aforementioned reference genome annotation. Spliced and unspliced counts were normalized using scvelo package (v0. 1. 19) by dividing all UMI counts of a cell by the total counts in the cell, multiplying the result by the median count number in all cells and applying log1p function. Scatter plots of each specific gene with spliced and unspliced counts were generated using Ms and Mu layers (kNN pooled expression) respectively.
| Web interface to browse single- cell data To allow biologists and other researchers to explore the GC data, we created an interactive website: http://nefla bdata. info/. This allows anyone to easily display the expression of any mouse gene in our matrix on a UMAP representation of the GC data. Other metadata (such as sex, time point, or cluster) can also be shown on the UMAP. Our website uses the Cellxgene data explorer application, which has further functionalities, such as allowing the comparison of the expression profiles of multiple genes, as described in their documentation: https://chanz ucker berg. github. io/cellx gene/posts/ gallery. We packaged Cellxgene in a Docker hosted on Google Cloud Platform (GCP) with our GC data H5AD output file from Scanpy. On GCP, we run a virtual machine instance of two-standard machine type with 2 CPUs and 4-8 GB of memory.
| Pseudotime ordering of the cells and differential expression analysis reveals the dynamics of gene expression across XX and XY germ cell sex determination UMAP projection of the 14 914 GCs permits visualization of the transcriptional landscape (Figure  1C-E  and Material  & Methods). To allow researchers to explore GC expression data, we have created an interactive and user-friendly web site: http://nefla bdata. info/ to easily display the expression of any gene of our dataset on a UMAP representation. The data is also available on the ReproGenomics Viewer, a web-based resource of sequencing datasets related to reproduction freely accessible at http://rgv. genou est. org. 49 Our analysis revealed that, at early stages (E9. 0, E10. 5, and E11. 5), the transcriptomes of XX and XY cells globally overlapped, while more differentiated cells from later stages formed two sex-specific branches (Figure  1C, D ).
| Reconstructing gene regulatory networks that mediate germ cell sex determination reveals the sequential and modular architecture of germ cell development The transcriptional state of a cell emerges from an underlying gene regulatory network (GRN) in which the activities of a small number of key transcription factors and co-factors regulate each other and their downstream target genes. 47 To comprehensively infer the GRNs at play during XX and XY GC sex determination, we applied the pySCENIC pipeline  47  to our single-cell data. In brief, SCENIC links cis-regulatory sequence information together with scRNA-seq data. It first catalogs coexpression (or repression) modules between transcription factors and candidate target genes and then identifies modules for which the regulator's binding motif is significantly enriched across target genes; the identified modules are called regulons.
Finally, the activity of each regulon is attributed to each cell, using the AUCell method. 47 For GCs, we identified 950 regulons (487 positive and 463 negative regulons) containing 11 336 genes (Table  S2 ). To compare how XX and XY GCs acquire their sex-specific identity, we selected the 406 positive regulons with binarized AUCell activity in more than 100 cells that contain more than five genes and classified them according to their expression pattern into 30 modules (M1-M30) along pseudotime (Figure  2 ). We represented the smoothed regulon expression level of XX and XY GCs with a mirror heatmap. Each module was also functionally annotated via enrichment analyses using GO biological process and Reactome Pathway terms. The most relevant enriched categories are displayed on Figure  2. Strikingly, the expression patterns revealed numerous stage and sex-specific regulon profiles, mostly at late developmental stages (late E13.
58 Tbrg4 gene encodes a known mitochondrial RNA-binding protein that may also have a role in cell cycle regulation,  59  whereas Rbm38 is coding for an RNA-binding protein involved in cell cycle arrest and mRNA splicing. 60 RNA-Scope in situ hybridization analysis of these three genes, followed by immunostaining for the DAZL GC marker, reveals that (A) (B) (E) (C) (D) these transcripts were colocalized with the DAZL protein and were specifically enriched in XY GCs, consistent with our scRNA-seq data (Figure  3C ). The testis specific expression of RNA processingassociated genes as well as its splicing pattern has already been investigated during adulthood in many species and is known to play a crucial role for multiple cellular decision such as mitosis-meiosis transition and stem cell population maintenance (For reviews see Ref.
61 and Ref. 62). However, to our knowledge, the presence of a sex-specific splicing pattern during embryonic development has not been thoroughly investigated. DEGs overexpressed in XY GCs at E13. 5 were associated with terms such as negative regulation of mRNA processing, negative regulation of mRNA splicing, and other terms figuring a decreased splicing process. The term "negative regulation of translation" was enriched as well, indicating most likely a global posttranscriptional silencing of gene expression. We therefore formulated the hypothesis that more transcripts should be unspliced in XY GCs at E13. 5 compared with XX GCs. Consequently, when looking at each gene in XY and XX GCs, the ratio of intronic/exonic reads should be higher in XY GCs. To test this hypothesis, we counted intronic reads in our dataset and calculated the ratio of intronic counts over total counts (exonic + intronic) for each gene.
| The Nodal/Activin and BMP pathways are important early players in the sexual differentiation of GCs BMP signaling and the BMP target Zinc Finger GATA Like Protein 1 (ZGLP1) have been reported to be essential for the oogenic fate determination and meiosis entry. 12, 56 It has also been shown that the BMP pathway can be repressed at the ligand-receptor level by Nodal/Activin (NA) activity under certain conditions, notably in stem cells. 67 In addition, the NA pathway is specifically active in XY GCs from E11. 5 to E13. 5, and its inhibition allows meiosis of TGC in cultured gonads. 21 We therefore investigated the expression of NA and BMP downstream genes in our dataset. As illustrated on Figure  4A , we found that NA and BMP target genes are specifically expressed as early as E11. 5 in XY GCs and XX GCs, respectively, and are reciprocally silent in the other sex.
ACKNOWLEDGMENTS We thank  Luciana Romano  and  Deborah Penet  for the sequencing,  Christelle Borel  ( GEDEV Department, University of Geneva ) for her advice and help with 10X technology,  Cécile Gameiro  and  Gregory Schneiter  ( Flow Cytometry Facility, University of Geneva ), the team of the Animal Facility ( Faculty of Medicine, University of Geneva ),  Julien Prados  ( Basic Neuroscience, University of Geneva ) for his help for pseudotime computation, and  Valentin Durand Graphic Design  for help with artwork. We thank also  Andy Greenfield , the members of the  Nef and Dermitzakis laboratories , for helpful discussion and critical reading of the manuscript.
Two datasets with three different resolution images are used to test our proposed NPAE algorithm on both GPU and CPU platforms. The RetinaNet is chosen as the reference pedestrian detector. Our algorithm increases the detection speed with 42% on the GPU platform and with 52% speed increase on the CPU platform, for the test images with a resolution of 1920×1080 pixel. In terms of detection accuracy, NPAE improves by up to 37. 94% compared to classical data optimization acceleration methods. The detection accuracy results are well preserved in both platforms.