dataesr/scientific-paragraphs-categorization huggingface dataset:
Ecdysone is known to coordinate the growth and developmental transitions of the animal; however, it does not seem to be involved in the coordination of NT gene translation with brain development. Another possibility is that NT gene translation is coordinated with the stimulus-independent neuronal activity that occurs just prior to synaptogenesis begins  [66]. This would be an elegant mechanism to keep neurons primed for maturation, until the time comes when their synaptic partners are formed and ready to connect. At the cellular level, translation of NT-related genes could be inhibited by RNA binding proteins or translation inhibitors differentially expressed between Phases 1/2 and Phase 3 of neuron maturation. Supporting this hypothesis, our dataset discloses several RNA biding molecules and translation inhibitors-Syncrip (Syp), musashi (msi), pumilio (pum), brain tumor (Brat), or polyA-binding protein interacting protein 2 (Paip2)-to be expressed in Phase 1 and Phase 2 neurons (S6 Fig).
10x Genomics experimental procedure to generate CB/VNC dataset Approximately 25k of the sorted cells (NB lineages) were used to construct single-cell libraries; libraries were obtained using Chromium Single Cell 3 0 reagent Kits v2 (10x Genomics) standard protocol. Cells were equally divided into 2 samples (duplicates) and loaded in 2 wells of a Single Cell A Chip, aiming for an estimated target cell recovery of approximately 7k cells. Cells were then partitioned into nanoliter-scale Gel Bead-In-EMulsions (GEMs) and reverse-transcribed using an Eppendorf Mastercycler pro Thermal Cycler (Eppendorf), set for 53˚C during 45 min, 85˚C for 5 min and hold at 4˚C. Post-reverse transcription incubation GEMs were then broken, and the cDNA was recovered and cleaned using Silane DynaBeads (Thermo Fisher Scientific).
The generated library went through a new double-sided size selection by SPRIselect and run on a TapeStation for quality control and quantification. Both samples were subjected to paired-end sequencing using the NovaSeq 6000 system (Genome Technology Center at NYU Langone Health). Quantification and quality of cDNA and respective libraries generated for 10x Genomics Data were assessed with TapeStation following the standard protocol available at https://www. agilent. com/cs/library/usermanuals/public/ScreenTape_HSD5000_QG. pdf (Agilent Technologies).
scRNA-Seq dataset preprocessing (CB/VNC dataset) The filtered gene matrices obtained after Cell Ranger processing were analyzed with R package Seurat 3. 1. 5. Only cells that had at least 200 unique feature counts were included in the analysis. Moreover, we only kept cells with a percentage of mitochondrial genes inferior to 20%. Higher percentages of mitochondrial genes are usually indicative of cell damage/rupture and, consequently, of altered overall transcriptional content  [58]. These initial quality control steps resulted in a dataset with 12,671 cells and 10,250 genes. Samples were normalized using the NormalizeData function, and the top 2,000 most variable features were then identified using the FindVariableFeatures function. Next, ScaleData was used to scale all genes; within this step, the percentage of mitochondrial genes was also regressed out, in order to avoid artefacts in subsequent analysis.
scRNA-Seq dataset clustering (CB/VNC dataset) We performed a principal component analysis (PCA), using the previously calculated top 2,000 most variable genes. Next, we used an elbow plot and a jackstraw approach to identify significant PCs. For the complete dataset analysis, we used the first 51 PCs, as not to include PCs that were not significant (p > 0. 05). Within the FindClusters function, the resolution parameter was set to 1. 55 as it resulted in a granularity that allowed the identification of smaller cell populations such as type II NBs and INPs. The combination of these parameters originated 39 clusters. Clusters were annotated into major groups corresponding to the different cell types identified in our dataset, resulting in 10 major clusters. This annotation was performed based on well-described markers for each cell type (Figs 1E and S2C), as well as based on relative cell localization within the UMAP plot.
Differential expression between clusters (CB/VNC dataset) We used Seurat to identify the specific markers for each cluster (S3B Fig). For that, we used the receiver operating curve (ROC) test to find the differentially expressed genes between clusters. Within that analysis, we selected an AUC (area under the ROC curve) >0. 5, to assure that the only hits were from genes with predictive values to classify that cluster. Moreover, to assure that none of the hits is a scarcely expressed gene, we only considered genes expressed in at least 25% of the cells of either one of the groups compared. We established that the average log fold change between the 2 populations being compared should be higher than 0. 25; moreover, the dot plots for the top cluster markers of these analysis only showed genes with a pct.
RNA velocity dynamics (CB/VNC dataset) RNA velocity analysis was performed with the python version of Velocyto v. 0. 17. 17 package  [59]. We used the subcommand "velocity run" to create a loom file for the cells that survived the filtering steps of Seurat pipeline using the Drosophila melanogaster genome annotation file (Drosophila_melanogaster. BDGP6. 88. gtf) and the bam file with sorted reads that was estimated using the default parameters of the Cellranger software (10x Genomics). We masked repetitive regions using the genome expressed repetitive annotation file downloaded from UCSC genome browser. The loom file created separates molecule counts into "spliced," "unspliced," or "ambiguous. " To estimate RNA velocity parameters, we adapted the pipeline used in the analysis of the mouse hippocampus dataset from La Manno and colleagues  [57].
We started by removing cells with extremely low unspliced detection requiring the sum of unspliced molecules per cell to be above the 0. 4 threshold. We also selected genes that are expressed above a threshold of total number of molecules in any of the clusters requiring 40 minimum expressed (spliced) counts in at least 30 cells, after which we kept the top 3,000 highly expressed and variant genes on the basis of a coefficient of variation (CV) versus mean fit that uses a nonparametric fit (Support Vector Regression). We applied a final filter to the dataset by selecting genes on the basis of their detection levels and cluster-wise expression threshold. This filter kept genes with unspliced molecule counts above a detection threshold of 25 minimum expressed counts detected over 20 cells, and with average counts of unspliced and spliced expression bigger than 0.
01 and 0. 08, respectively, in at least one of the clusters. Finally, both spliced and unspliced counts were normalized for the cell size by dividing this value by the total number of molecules in each cell and multiplying the mean number of molecules across all cells. All filtering steps resulted in a dataset of 12,604 cells and 1,086 genes to be used in the RNA velocyto analysis. For the preparation of the gamma fit, we smooth the data using a kNN neighbors pooling approach (velocyto subcommand knn_imputation) and k = 500 with calculations performed in the reduced PCA space defined by the top 99 principal components. Velocity calculation and extrapolation to future states of the cells were performed under the assumption of constant velocity. Analysis pipeline can be obtained from the corresponding author.
Single-cell trajectories (CB/VNC dataset) Pseudotime analysis was performed using R package Monocle v2. 16. 0. The Seurat object, containing all filtering and clustering information, was imported to Monocle; for the analysis of the neuronal population, all clusters annotated as "Neurons" were subset, and within those, only cells with 0 counts for wor and repo were processed. Differences in mRNA across cells were normalized, and "dispersion" values were calculated using the functions estimateSizeFactors and estimateDispersions, respectively. To construct single-cell trajectories, we started by using the differentialGeneTest function to extract the genes distinguishing different clusters; these genes were then marked to be used for clustering in subsequent calls by using setOrderFilter. Afterwards, dimensions were reduced by using the Discriminative Dimension Reduction Tree (DDRTree) method and, finally, ordered using the orderCells function.
Gene annotation lists The list for ion channels was obtained from Gene List Annotation for Drosophila (GLAD)  [98] , obtained from //www. flyrnai. org/tools/glad/web/. For the cadherin super family analysis, we used a gene list from FlyBase, obtained under the group "CADHERINS"-FBgg0000105. For the immunoglobulin super family analysis, genes were selected according to FlyBase FBrf0167517  [99]. In all 3 cases, gene symbols were updated according to our dataset. We used these gene lists to show the correspondence between cluster and cluster top 5 marker genes. To do this, we created chord diagrams using the R package "circlize," adapted according to Allen and colleagues  [31].
Neurotransmitter temporal expression in optic lobe To address the expression of VGlut, ChAT, and Gad1 throughout time, we used a previously generated dataset  [18]  that was downloaded from the Gene Expression Omnibus (GSE156455). Data referring to the DGRP (Drosophila Genetic Reference Panel) dataset were imported to Seurat, and standard protocol was followed as described by Kurmangaliyev and colleagues  [18]. This dataset comprises time points from 0 h APF to 96 h APF with a 12-h interval (duplicates for each time point). QC and integration pipelines were performed as described in  [18]. Samples from 48 h and 72 h were used as references with the FindIntegratio-nAnchors function, and the dimensionality was set to 200 in the IntegrateData function. The first 200 PCs were selected for further analysis, as well as a resolution of 10.
Neuronal cells were annotated based on datasets from the adult animal  [33, 100]. Two types of neurons are shown for each of the represented neurotransmitter families: L5 and Mi1 (cholinergic), L1 and Mi1 (glutamatergic), Dm10 and Mi4 (GABAergic). The average transcriptional profile was calculated for each cell type (cluster) for each time point and for each replicate. Values are shown in TP10k (TP, transcripts per million).
Statistical analysis Statistical analysis was performed in GraphPad Prism (GraphPad Software). Statistical differences were determined using two-tailed paired Student t test or with 1-way ANOVA, using a Bonferroni's multiple comparison test. Data are presented in scatter plots with bars (mean ± SEM); p-values < 0. 05 were considered statistically significant. dataset. (C) Number of neurons expressing neurotransmitter-associated genes (VGlut, VAChT Gad1, Vmat) independently or simultaneously (counts >0); the respective percentages within the total neuronal population are indicated in bold. Each portrayed gene is associated with a different neurotransmitter: glutamatergic (VGlut), cholinergic (VAChT), GABAergic (Gad1), or monoaminergic (Vmat). The data underlying this figure are contained within GEO database (Accession number: GSE179763).
Two types of neurons are shown for each of the represented neurotransmitter families: L5 and Mi1 (cholinergic), L1 and Mi1 (glutamatergic), Dm10 and Mi4 (GABAergic). Expression patterns shown for these genes range from 0 h APF to 96 h APF, with a 12-h interval; levels of expression are represented in normalized values; the red dashed line indicates 24 h APF, the approximate onset of protein detection in the OL. The data underlying this figure were obtained using the dataset generated by  [18]  and is contained within S2 Data. (G) Quantification of VGlut and ChAT fluorescence intensity in wL3 OL regions when the indicated subunits of the proteosome are knocked down in optic lobe neurons. elav-Gal4 was used to drive the expression of UAS-Pomp RNAi, UAS-Prosβ2 RNAi, and UAS-Rpt5 RNAi. Mean fluorescence intensity of VGlut and ChAT was measured in medulla neurons (W1118, n = 8 brains; Pomp RNAi, n = 5 brains; Prosβ2 RNAi, n = 5; Rpt5 RNAi, n = 5).
Data shown as mean ± SEM; statistical analysis was done using 1-way ANOVA, with a Bonferroni's multiple comparison test; p-values < 0. 05 were considered statistically significant; ns, not significant. The data underlying this figure are contained within S4 Data. ALH, after larval hatching; APF, after puparium formation; ChAT, choline acetyltransferase; Gad1, glutamic acid decarboxylase 1; NB, neuroblast; OL, optic lobe; smFISH, single-molecule fluorescence in situ hybridization; VGlut, vesicular glutamate transporter; wL3, wandering third instar larvae. (TIF).
Acknowledgments We thank  C. Desplan  and his group for all the support with the scRNA-Seq experiments, experimental help, and discussion. We also thank the  NYUAD Center for Genomics and Systems Biology (ADHPG-CGSB)  supported by the  NYU Abu Dhabi Research Institute. We thank  F. Simon  for his help during the scRNA-Seq protocol. We thank  J. Knoblich ,  F. Bonnais ,  H. Aberle , and  C. Mirth  for fly lines and/or antibodies. We thank  A. C. Oliveira  for the EcRDN;UAS-FLP, Ubi-p63E-STOP-Stinger  line. We thank  T. Baptista  for the tub-Gal80ts; VT012709-Gal4 and elav-Gal4; tub-Gal80ts lines. We thank all members of the  C. C. Homem Lab  and  F. Pinto-Teixeira  for helpful discussions. We also thank  C. Mendes  and  R. Teodoro  for revising the manuscript. We also thank  NOVA Medical Schools' Microscopy and Fly facilities  for technical support.
This project has received funding from the  European Research Council  (ERC; https://erc. europa. eu/) under the European Union's  Horizon 2020 research and innovation programme  ( H2020-ERC-2017-STG-GA 759853-StemCellHabitat  to C. C. F. H. ); by  Wellcome Trust  (https://wellcome. org/ grant-funding) and  Howard Hughes Medical Institute  (https://www. hhmi. org/;  HHMI-208581/Z/ 17/Z- Metabolic Reg SC fate to C. C. F. H. );  European Molecular Biology Organization (EMBO)  Installation grant (https://www. embo. org/;  H2020-EMBO-3311/2017/G2017  to C. C. F. H. ); by Fundacão para a  Ciência e Tecnologia  (https://www. fct. pt/;  PTDC/ BIA-BID/0681/2021 ;  IF/01265/2014/CP1252/ CT0004  to C. C. F. H. and  PD/BD/114253/2016  to G. S. M. ). N. K. was supported by the  National Eye Institute  (https://www.
(E) Dot plot showing genes that were used to identify each cell type. Dot size indicates the percentage of cells expressing the gene in each cell type; color variation represents the average expression of the gene in each cell type. The data underlying this figure are contained within GEO database (accession number: GSE179763); see also S1 and S2 Figs. CB, central brain; dINP, developing INP; GMC, ganglion mother cell; INP, intermediate neural progenitor; NB, neuroblast; OL, optic lobe; scRNA-Seq, single-cell RNA sequencing; VNC, ventral nerve cord. https://doi. org/10. 1371/journal. pbio. 3002115. g001.
The data underlying this figure are contained within GEO database (accession number: GSE179763); see also S3 Fig and S1 and S2 Tables. ChAT, choline acetyltransferase; Gad1, glutamic acid decarboxylase 1; nrv3, nervana 3; nSyb, neuronal Synaptobrevin; UMAP, uniform manifold approximation and projection; VAChT, Vesicular acetylcholine transporterAU : PleaseprovidefullspellingforVAChTandVmatinFig2abbreviationlist; iftheseindeedareabbreviations: ; VGlut, vesicular glutamate transporter; Vmat, Vesicular monoamine transporter. https://doi. org/10. 1371/journal. pbio. 3002115. g002.
(B) Immunofluorescence images for VGlut or ChAT antibody staining (magenta). Neural cells in clones are labeled with nuclear GFP (green); outlines indicate examples of lineages with GFP-positive cells; yellow asterisk identifies neuroblast. Scale bar, 20 μm. (C, D) 72-h clones induced at 87 h ALH and analysed at 159 h ALH (approximately 48 h APF) using G-TRACE; oldest neurons in the clone are approximately 66. 5h. (C) Schematic representation of the temporal strategy used to induce 72-h clones in neural lineages represented in green. Timings used for the scRNA-Seq dataset (18-h clones induced at 87 h ALH and analysed at 105 h ALH) are represented in grey. (D) Immunofluorescence images for Vglut or ChAT antibody staining (magenta). VT201094-Gal4 drives nuclear GFP (green) expression in NBs, daughter cells generated by NBs during clone period inherit GFP expression.
No. A2156 MG-132 [Z-Leu-Leu-Leu-CHO] (CAS 133407-82-6) Santa Cruz Biotechnology Cat. No. sc-201270 DMSO (dimethyl sulphoxide, CAS 67-68-5) Sigma Cat. No. D8418 Critical Commercial Assays DynaBeads MyOne Silane Beads Thermo Fisher Scientific Cat. No. 37002D Chromium Single Cell 3 0 Library and Gel Bead Kit v2, 4 rxns 10x Genomics Cat. No. 120267 Chromium Single Cell A Chip Kit, 16 rxns 10x Genomics Cat. No. 1000009 Stellaris RNA FISH Hybridization Buffer BioSearch Tecnhologies Cat. No. SMF-HB1-10 Deposited Data Single-cell RNA sequencing of Drosophila melanogaster this study GEO: GSE179763 Central Brain and Ventral Nerve Cord (raw and analyzed data) Experimental Models: Organisms/Strains Fly: D. melanogaster: w 1118 gift from Antonio Jacinto N/A Fly: D. melanogaster: VT012709-Gal4 gift from Juergen Knoblich N/A Fly: D.
Lakes and reservoirs are ubiquitous across global landscapes, functioning as the largest repository of liquid surface freshwater, hotspots of carbon cycling, and "sentinels" of climate change. Although typically considered as lentic (hydrologically stationary) environments, lakes are an integral part of global drainage networks. Through perennial and intermittent hydrological connections, lakes often communicate with each other, and these connections actively affect water mass, quality, and energy balances in both lacustrine and fluvial systems. Deciphering how global lakes are hydrologically interconnected, or the so-called "lake drainage topology", is not only important to lake change attribution, but also increasingly critical to discharge, sediment, and carbon modeling. Despite the proliferation of river hydrography data, lakes remain poorly represented in routing models, partially because there has been no global-scale hydrography dataset tailored to lake drainage basins and networks.
Here, we introduce the global Lake drainage Topology and Catchment database, or "Lake-TopoCat", which reveals detailed lake hydrography information with a careful consideration of possible multifurcation. Lake-TopoCat contains the outlet(s) and catchment(s) of each lake, the inter-connecting reaches among lakes, and a wide suite of attributes depicting lake drainage topology such as upstream and downstream relationship, drainage distance between lakes, and a priori drainage type and connectivity with river networks. Using the HydroLAKES (v1. 0) global lake mask, the Lake-TopoCat v1. 0 identifies ~1. 46 million outlets for ~1. 43 million lakes larger than 10 ha and delineates 77. 5 million km 2 of lake catchments covering 57% of the Earth's landmass except Antarctica. The global lakes are interconnected by ~3 million reaches, derived from MERIT Hydro (v1.
g. ,  Stieglitz et al, 2003) , and energy along connecting rivers. Since lakes sequester a large amount of organic and inorganic carbon  (Tranvik et al. , 2009; Mendonça et al. , 2017) , deciphering how global lakes are connected through drainage networks will complement routing models, which currently emphasize rivers  (Leibowitz et al. , 2018; Liu et al. , 2022) , to better constrain the terrestrial carbon cycling  (Cardille et al. , 2007). The drainage paths between lakes, including their distances, gradients, connectivity, and climatic zonation, also determine the migration pattern for lacustrine species, which is crucial for monitoring ecosystem health and services under climate change  (Woolway et al. , 2020). For these reasons, a hydrography dataset tailored to global lakes, which provides fine spatial details of the boundary of each lake catchment and how lakes are topologically interconnected, is overdue and has the potential for high hydrological and ecological significance.
Similar issues were also acknowledged in the Global Lake area, Climate, https://doi. org/10. 5194/essd-2022-433 Preprint. Discussion started: 18 January 2023 c Author(s) 2023. CC BY 4. 0 License. and Population dataset (GLCP), where HydroBASINS was also used to match lakes to the surrounding sub-basins  (Meyer et al. , 2020). In HydroLAKES  (Messager et al. , 2016)  and the most recent LakeATLAS dataset  (Lehner et al. , 2022) , the outlet and the upstream drainage area associated with each lake were derived from the 15 arc-second HydroSHEDS hydrography dataset  (Lehner et al. , 2008). Their methods of lake outlet delineation, however, excluded the possibility of lake bifurcation or multifurcation (i. e. , a lake flowing out in two or more directions), and the 15 arc-second resolution (about 450 m at the equator) of HydroSHEDS might be too coarse to derive reliable catchment boundaries for many small lakes in HydroLAKES (with a minimum size of 0.
1 km 2 ). In addition, lake catchments in HydroLAKES or LakeATLAS were provided with only area values, not geometric boundaries, meaning that spatially explicit applications of such catchment information remain inconvenient. In fact, despite the proliferation of river basin datasets (e. g. ,  Lehner and Grill, 2013; Lin et al. , 2021) , there has been no globalscale catchment data tailored specifically for lakes. To our knowledge, only a few regional lake watershed datasets are available so far, and examples are the National Hydrography Dataset Plus Version 2 (NHDPlusV2)  (McKay et al. , 2012)  and the Lake-Catchment (LakeCat) Dataset  (Hill et al. , 2018)  for the US, the COmprehensive Data set for China's Lake Basins  (CODCLAB)  for 767 large lakes (>10 km 2 ) in China  (Chen et al. , 2022a) , and several lake catchment datasets for the endorheic Tibetan Plateau (e.
On the other hand, usually only large lakes have been resolved in continental-or global-scale modeling networks (e. g. ,  Tokuda et al. , 2021) , due to the constraints of model and hydrography data resolutions, computing power, and additional workload. To address these gaps, we introduce the global Lake drainage Topology and Catchment database or Lake-TopoCat. Lake-TopoCat v1. 0 was constructed for nearly all 1. 4 million lakes in HydroLAKES v1. 0, but our automated algorithm is generic and applicable to other global lake inventories (see Section "Database availability"). Lake-TopoCat offers an unprecedented detail of global lake hydrography, including spatially resolved perimeters of the lake catchments, drainage outlets from the lakes, perennial, intermittent, and potential drainage reaches between the lakes, and importantly, the topological relationships among the lake, catchment, and reach structures.
Lake boundaries Lake boundaries in TopoCat are geometrically the same as the HydroLAKES lake polygons, except that the former includes new attributes informing lake drainage relations. About 99. 95% of the ~1. 4 million Hydro LAKES polygons were located within or on the MERIT Hydro boundary. These lakes were used to construct TopoCat (Fig. 2a-b ). For consistency, we used the original HydroLAKES lake IDs ("Hylak_id") to index lakes and their associated topological attributes (Table  1 ). This allows users to link TopoCat features or attributes to HydroLAKES, as well as to other databases derived from HydroLAKES, such as the GLCP  (Meyer et al. , 2020) , LakeATLAS  (Lehner et al. , 2022) , the Global Lake Evaporation Volume (GLEV) dataset  (Zhao et al. , 2022) , and the GLObal lakes Bathymetry dataset (GLOBathy)  (Khazaei et al.