dataesr/scientific-paragraphs-categorization huggingface dataset:
Detailed After processing, the dataset comprised 49,893 cells passing quality filters, with a median of 3,635 UMIs and 1,343 genes per cell. We used the procedure implemented in Seurat v. 3 to remove batch effects from our sequenced libraries. We used default parameters except for the dimensionality for which we tried the values 100, 150 and 200. We compared the results using the Seurat function LocalStruct with default parameters. The results obtained were 83. 7%, 84. 9% and 83. 6%, respectively. We therefore chose a dimensionality of 150 for the larval dataset. The dataset was then clustered with a resolution of 2. Notably, in this developing structure, cells are clustered both by identity and by differentiation stage. For example, Mi1 cells fall into 2 clusters, an immature (cluster 23) and a mature cluster (cluster 53).
Spatial patterning analysis To focus on the heterogeneity within the neuroepithelial cells, the larval dataset is further subsetted using marker expression with Seurat v3. Expression of neuroepithelial markers shg, tom, and brd were examined for each cluster  64. Clusters with average expression higher than 95 th percentile of normalized expression of Tom and Brd were selected as neuroepithelial clusters. DE-Cadherin (Shg) is known to be enriched in neuroepithelial cells  65  and is enriched in the selected clusters (logFC = 0. 75, adjusted p value = 0). Principal components were calculated using variable features found in the subsetted neuroepithelial cells. Examination of PC1 revealed Tll, an early marker of lamina precursor cells  66  , is expressed in a near-mutually exclusive fashion with Hth (enriched in neuroepithelium and young medulla neuroblasts), suggesting the subset contained both OPC neuroepithelium and lamina precursor cells.
Merging of larval and pupal Mi1 and DE analysis over pseudotime Larval and pupal (P15, P30, P40, P50, and P70) datasets were merged after cells were batch corrected for each stage separately. Standard Monocle workflow was followed to generate trajectories. The L3 and P15 trajectories were ordered manually. Based on the way the developing optic lobe develops, there are cells at the same differentiation stage in the L3 and P15 datasets. We, therefore, decided to align these two datasets in order to get a continuum of expression. We tested different genes and ended up using "Ggamma30A" as a reference gene. Ggamma30A starts increasing in the middle of the L3 trajectory and continues all the way to P15 in linear manner. We adjusted the expression of Ggamma30A in P15 using linear regression, which was then applied to all genes of P15.
GO enrichment analysis We performed GO enrichment analysis and calculated enrichment for 'Biological Process' using The Gene Ontology Resource (http://geneontology. org/) using a Fisher's exact test to calculate p-value. Multiple testing correction was performed by calculating the False Discovery Rate. To find the expression of GO terms over time, we added and normalized the expression of all genes that belong to a specific GO term and plotted it over pseudotime or on the UMAP. We mapped the sequenced libraries to the H. sapiens genome assembly GRCh38 (hg38) using CellRanger 3. 1. 0. For the hashtag oligos (HTO), we used the CITE-seq-Count 1. 4. 2 version to align HTO to 10x barcodes using the following command: CITE-seq-Count -R1 reas1 -R2 read2 -T 1 -t tag -cbf 1 -cbl 16 -umif 17 -umil 26 -cells 40000 -o output --sliding-window # --dense After processing, the dataset comprised 3,363 cells passing quality filters, with a median of 4,736 UMIs and 2,414 genes per cell.
Analysis of mouse cortical data The dataset that was generated by Telley et al. 18  was downloaded from GEO (GSE118953). The raw counts were inputted into Seurat and the standard workflow was followed (log-normalization, followed by clustering and UMAP using 25 PCs, and clustering was done with a resolution of 2). The radial glia clusters (clusters 2 and 3) were identified based on the expression of known radial glia markers, such as SOX2 and PAX6. Radial glia from different embryonic days 12, 13, 14, and 15 were used to generate the violin plots of Extended Data Figure  7A. (C) (Left). Spatial transcription factors (Vsx, Optix, and Rx) are not expressed in medulla neuroblasts, while only Vsx is expressed in some neuronal types (it is unknown whether this expression reflects their origin from the Vsx spatial domain).
Acknowledgements We are indebted to the fly community for gifts of antibodies and fly lines;  Cheng-Yu Lee ,  Deborah Hursh ,  John Nambu ,  Andrew Tomlinson , and  Mitshuhiko Kurusu  for fly lines,  Peter Gergen ,  Kwangwook Choi ,  Hermann Aberle , and  Dorothea Godt  for antibodies. We thank  Isabel Holguera ,  Sergio Cordoba ,  Chris Doe ,  Stein Aerts , and  Tzumin Lee  for constructive feedback on the manuscript. We thank  Sergio Cordoba  for the optic lobe illustrations and  Kazi Hossain  and  Albert Tadros  for help with preliminary experiments. Finally, we thank all members of the  Desplan Lab  for helpful discussions. This work was supported by  NIH  grant  EY019716  to C. D. N. K. was supported by the  National Eye Institute  ( K99 EY029356-01 ). A. M. R was partly supported by funding from  NIH  ( T32 HD007520 ), and by  NYU 's  GSAS MacCracken Program  and a  Dean's Dissertation Fellowship.
Data Availability All Drosophila raw and processed data referenced were uploaded to GEO: accession number GSE167266. The human source data described in this manuscript are available via the PsychENCODE Knowledge Portal (https://psychencode. synapse. org/). The PsychENCODE Knowledge Portal is a platform for accessing data, analyses, and tools generated through grants funded by the National Institute of Mental Health (NIMH) PsychENCODE program. Data is available for general research use according to the following requirements for data access and data attribution: (https://psychencode. synapse. org/DataAccess). CC-BY 4. 0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made bioRxiv preprint doi: ; https://doi. org/10. 1101/2021. 06. 13. 448242 this version posted November 7, 2021. The copyright holder for this preprint CC-BY 4. 0 International license available under a. scripts and related R objects can be found here: https://drive. google. com/file/d/1geoY8AmtqFnJNi2OF7TlaJin8V9Nf- 2C/view?usp=sharing Mapping and integration of larval (L3) and pupal (P15) datasets We mapped the sequenced libraries to the D. melanogaster genome assembly BDGP6. 88 using CellRanger 3. 0. 1. We kept only genes that were expressed in at least 3.
, 2014; EMEP/EEA, 2016; Kurokawa et al. , 2013). Moreover, these inventories are often provided at the annual or monthly scale with typical temporal profiles to build the weekly, daily and hourly variability of the emissions. The combination of uncertain activity data, emission factors and emission timing can be a large source of uncertainties, if not errors, for forecasting or analyzing air quality  (Menut et al. , 2012). Finally, since updating the inventories and gathering the required data for a given year is costly in time, manpower and money, only a few institutes have offered estimates of the gaseous pollutants for each year since 2011 (i. e, European Monitoring and Evaluation Programme EMEP updated until the year 2017, MEIC updated until the year 2017 to our knowledge). Nevertheless, using knowledge from inventories and air quality modeling, emissions have been mitigated.
, 2008; Lin et al. , 2012; Ding et al. , 2017). Variational inversion systems allow for the solving of high-dimensional problems, typically solving for the fluxes at high spatial and temporal resolution, which can be critical to fully exploit satellite images. Here, we present the Bayesian variational atmospheric inversion system PYVAR-CHIMERE for the monitoring of anthropogenic emissions of reactive species at the regional scale. It is based on the Bayesian variational assimilation code PYVAR  (Chevallier et al. , 2005)  and on the regional state-of-the-art CTM CHIMERE  (Menut et al. , 2013; Mailler et al. , 2017). CHIMERE is dedicated to the study of regional atmospheric pollution events (e. g. ,  Ciarelli et al. , 2019; Menut et al. , 2020) , included in the operational ensemble of the Copernicus Atmosphere Monitoring Service (CAMS) regional services.
PYVAR adapted to CHIMERE The PYVAR-CHIMERE inverse modeling system is based on the Bayesian variational assimilation code PYVAR  (Chevallier et al. , 2005)  and on a previous inversion system coupled to CHIMERE  (Pison et al. , 2007). PYVAR is an ensemble of Python scripts, which deals with preparing the vectors and the matrices for the inversion, drives the required Fortran codes of the transport model and computes the minimization of the cost function to solve the inversion. Previ- ously used for global inversions with the LMDz model (e. g. ,  Pison et al. , 2009; Chevallier et al. , 2010; Fortems-Cheiney et al. , 2011; Yin et al. , 2015; Locatelli et al. , 2015; Zheng et al. , 2019) , PYVAR has been adapted to CHIMERE with an adjoint code without chemistry by  Broquet et al. (2011). In order to couple PYVAR to the new state-of-the-art version of CHIMERE (see Sect.
Development and parallelization of the adjoint and tangent-linear codes of CHIMERE To compute the sensitivity of simulated atmospheric concentrations to corrections to the fluxes, the adjoint of CHIMERE has been developed. Originally, the sequential adjoint was coded  (Menut et al. , 2000; Menut, 2003; Pison et al. , 2007). The adjoint has been coded by hand line by line, following the principles formulated by  Talagrand (1997). It contains exactly the same processes as the CHIMERE forward model. The code has been parallelized, which required a redesigning of the entire code, associated with a full testing scheme (see Sect. 3. 3). Furthermore, the TL code has been developed and validated (see Sect. 3. 3). Changes have been implemented in the forward CHIMERE code embedded in PYVAR-CHIMERE to match requirements of the studies conducted with this system.
PYVAR-CHIMERE is currently implemented with a full module of gaseous chemistry. As a compromise between the robustness of the method for reactive species, the time required to code the adjoint and the computational cost with a full chemical scheme, the aerosols modules of CHIMERE have not been included in the adjoint of CHIMERE yet and are therefore not available in PYVAR-CHIMERE. The development and maintenance of the adjoint means that the version used is necessarily one or two versions behind the distributed CHIMERE version (http://www. lmd. polytechnique. fr/chimere/, last access: March 2019). It should also be noted that PYVAR-CHIMERE only infers anthropogenic emissions at this stage. The optimization of biogenic emissions, which are linearly interpolated at the sub-hourly scale in CHIMERE, is currently under development.
e. , the diagonal matrix of standard deviations ; Table  1 ) which can be made in terms of fixed values ("fx" in the code) or percentages (%, "pc" in the code). For correction types "mult" and "scale", as well as for correction type "add" with a fixed value, the value is directly used as the uncertainty in the corresponding components of the control vector. For correction type "add" with a percentage provided, maps of standard deviation of uncertainty are built by applying this percentage to the matching input fields (fluxes, initial conditions, boundary conditions). The user may also provide a script to build personalized maps of variances. Potential correlations between uncertainties in different types of control variables (e. g. , between fluxes and boundary conditions) and correlations between uncertainties in different species (e.
Equivalents of the observations During forward simulations, the equivalents of the components of y (i. e, the equivalents of the individual data) are calculated by PYVAR-CHIMERE. It includes the CTM and an interpolation (see below the vertical interpolation from the model's grid to the satellite levels) or an extraction and averaging (e. g. , extracting the grid cell matching the geographical coordinates of a surface station and averaging over 1 h). As a compromise between technical issues such as the time required for reading and writing files, the observation operator H that generates the equivalent of the observations by the model (i. e. , H (x)) has been so far partly embedded in the code of CHIMERE. It makes it easier to use finer time intervals than available in the usual hourly outputs of CHIMERE to compute the required information (e.
Numerical language The PYVAR code is in Python 2. 7, the CHIMERE CTM is coded in Fortran90. The CTM requires several numerical tools, compilers and libraries. The PYVAR-CHIMERE system was developed and tested using the software versions as described in Table  2. PYVAR-CHIMERE's computation time for one node of 10 CPUs is about 4 h for 1 d of inversion (with ∼ 10 iterations) for the European domain size of 101 (longitude) × 85 (latitude) × 17 (vertical levels) used in Sect. 4. As described in  Menut et al. (2013)  for CHIMERE, the model parallelization results from a Cartesian division of the main geographical domain into several sub-domains, each one being processed by a worker process. To configure the parallel subdomains, the user has to specify two parameters in the model parameter file: the number of sub-domains for the zonal and meridian directions.
Potential of PYVAR-CHIMERE for the inversion of CO and NO x emissions The potential of the PYVAR-CHIMERE system to invert emissions of reactive species is illustrated with the inversion https://doi. org/10. 5194/gmd-14-2939-2021 Geosci. Model Dev. , 14, 2939-2957, 2021 of CO and NO x anthropogenic emissions in Europe based on MOPITT CO data and OMI NO 2 data, respectively. We have chosen to present an illustration of CO inversion over 7 d, the first week of March 2015. Considering the short lifetime of NO x of a few hours  (Valin et al. , 2013; Liu et al. , 2016) , we have chosen to present illustration of NO x inversion over 1 d, 19 February 2015. These particular periods have been chosen as they present a representative number of super-observations during winter, and as the emissions are high during that period.
Observations y We use CO data from the MOPITT instrument  (Deeter et al. , 2019). MOPITT has been flown onboard the NASA EOS-Terra satellite, on a low sun-synchronous orbit that crosses the Equator at 10:30 and 22:30 LST. The spatial resolution of its observations is about 22 × 22 km 2 at nadir. It has been operated nearly continuously since March 2000. MO-PITT CO products are available in three variants: thermalinfrared TIR only, near-infrared NIR only and the multispectral TIR-NIR product, all containing total columns and retrieved profiles (expressed on a 10-level grid from the surface to 100 hPa). We choose to constrain CO emissions with the MOPITT surface product for our illustration. Among the different MOPITTv8 products, we choose to work with the multispectral MOPITTv8-NIR-TIR one, as it provides the highest number of observations, with a good evaluation against in situ data from NOAA stations  (Deeter et al.
, 2019). The MOPITTv8-NIR-TIR surface concentrations are subsampled into "super-observations" in order to reduce the effect of errors that are correlated between neighboring observations: we selected the median of each subset of MOPITT data within each 0. 5 • × 0. 5 • grid cell and each physical time step (about 5-10 min). After this screening, 8437 "superobservations" remain in the 7 d inversion (from 10 667 raw observations). It is important to note that the potential of MO-PITT to provide information at a high temporal resolution, up to the daily scale, is hampered by the cloud coverage (see the blanks in Fig. 5b ). The observational constraint on NO 2 emissions comes from the OMI QA4ECV tropospheric columns  (Muller et al. , 2016; Boersma et al. , 2016 Boersma et al. , , 2017)). The Ozone Monitoring Instrument (OMI), a near-UV-visible nadir solar backscatter spectrometer, was launched onboard EOS Aura in July 2004.
The TNO-GHGco-v1 inventory has been aggregated to the CHIMERE grid. The prior anthropogenic emissions for VOCs are obtained from the EMEP inventory  (Vestreng et al. , 2005 ; EMEP/CEIP: https://ceip. at/ms/ceip_home1/ceip_home/ webdab_emepdatabase/emissions_emepmodels/, last access: March 2019). Biogenic emissions come from the Model of Emissions of Gases and Aerosols from nature (MEGAN)  (Guenther et al. , 2006). Different climatological values from the LMDZ-INCA global model  (Szopa et al. , 2008)  or from a Monitoring Atmospheric Composition and Climate (MACC) reanalysis are used to prescribe concentrations at the lateral and top boundaries and the initial atmospheric composition in the domain. Full access to and more information about the MACC reanalysis data can be obtained through the MACC-II web site (http://www.
g. , over the Netherlands and Po Valley). These discrepancies might be due to different causes, which can all interact. A source of uncertainties is related to the observations. For example, satellite data inter-comparison studies reveal large differences between different retrievals of the same compound  (Qu et al. , 2020). This can be explained by uncertainties from the CTM (e. g. , through the underestimation of the atmospheric production or the underestimation of the species lifetime). It could also be explained by an underestimation of the anthropogenic emissions in the BU inventory. For the CO inversion, the control vector x is as follows: the CO anthropogenic emissions at a 7 d temporal resolution, at a 0. 5 • × 0. 5 • (longitude × latitude) horizontal resolution and over the first 8 vertical levels, i.
However, a positivity constraint should be implemented in future versions of the system. Based on the sensitivity test in Fig. 4 , the errors assigned to the CO lateral boundary conditions and to their initial conditions are set at 15 %. As these relative errors are significantly lower than those for the emissions and as variations in the CO surface concentrations are mainly driven by emissions (Fig. 3 ), we assume a small relative influence of the correction of initial and boundary conditions on our results. The variance of the individual observation errors in R is defined as the quadratic sum of the measurement error reported in the MOPITT and the OMI data sets, and of the CTM errors (including chemistry and transport errors and representativity errors) set at 20 % of the retrieval values.
, over countries rather than over Europe) as a compromise between resolution and the computational cost. Moreover, a step forward in the joint assimilation of co-emitted pollutants will be possible with the PYVAR-CHIMERE system and the availability of TROPOMI co-localized images of CO and NO 2. This should improve the consistency of the inversion results and can be used to inform inventory compilers and subsequently improve emission inventories. Moreover, this development will help in further understanding air quality problems and addressing air-quality-related emissions at the national to subnational scales. made by GENCI. Finally, we wish to thank François Marabelle (LSCE) and his team for computer support. Financial support. For this study, Audrey Fortems-Cheiney was funded by the H2020 VERIFY project, funded by the European Commission Horizon 2020 research and innovation programme, under agreement number 776810.
Acknowledgements. We acknowledge  L. Menut  and  C. Schmechtig  for their contributions to the development work on the adjoint code of CHIMERE and its parallelization. We acknowledge the  TNO team  for providing their inventory of NO x and CO emissions over Europe. We also acknowledge the free use of tropospheric NO 2 column data from the OMI sensor and the free use of CO surface concentrations from the MOPITT sensor. A large part of the development and analysis was conducted in the frame of the  H2020   VER-IFY  project, funded by the  European Commission   Horizon 2020 research and innovation programme. We wish to thank all the persons involved in the preparation, coordination and management of this project. This work was also supported by the  CNES  ( Centre National d'Etudes Spatiales ), in the frame of the  TOSCA AR-GOS  project.
Code and data availability. The OMI QA4ECV NO 2 product can be found here: http://temis. nl/qa4ecv/no2. html (last access: March 2019,  Boersma et al. , 2017). The MOPITTv8-NIR-TIR CO product can be found here: ftp: //l5ftl01. larc. nasa. gov/MOPITT/ (last access March 2019, Deeter et  al. , 2019). The CHIMERE code is available here: https://www. lmd. polytechnique. fr/chimere/ (last access March 2019, CHIMERE, 2019). The associated documentation of PYVAR-CHIMERE is available on the website https://pyvar. lsce. ipsl. fr/doku. php/3chimere: headpage (last access March 2019, PYVAR-CHIMERE, 2019). The documentation includes a whole description of PYVAR-CHIMERE and several tutorials on how to run a first PYVAR-CHIMERE simulation or how to run an inversion.
MetamORF enables new analyses at locus, gene, transcript and ORF levels, which should offer the possibility to address new questions regarding sORF functions in the future. The repository is available through an user-friendly web interface, allowing easy browsing, visualization, filtering over multiple criteria and export possibilities. sORFs can be searched starting from a gene, a transcript and an ORF ID, looking in a genome area or browsing the whole repository for a species. The database content has also been made available through track hubs at UCSC Genome Browser. Finally, we demonstrated.
Very interestingly, uORF-encoded peptides have also been shown to form protein complexes with the protein encoded by the main CDS of the same mRNA  (31) , and it has been suggested that polycistronic sequences may exist in eukaryotes  (24, 31). Furthermore, given the increasing evidence on the regulatory functions of peptides encoded by sORFs located within mRNAs, introns of pre-mRNAs, lncRNAs and primary transcripts of microRNAs or ribosomal RNAs  (2, (8) (9) (10) (11) (12) (13) (14) (15) 26) , there is an urgent need to study sORFs (i) individually and (ii) at the whole proteome scale. Indeed, the latter should reveal important features of sORFs, thus enabling the characterization and the identification of their functions. However, the fact that (i) the publicly available data are scattered across different databases and (ii) datasets are aligned on different genome builds, differently annotated and formatted, calls for an uniformed resource where each sORF is individually described.
With this in mind, we have built a resource database of publicly available sORFs identified in the human and mouse genomes, by gathering information from computational predictions and Ribo-seq and proteomic experiments. The curation of data, their homogenization in order to merge the redundant information into unique entries, the completion and computation of missing information (e. g. sequences and Kozak contexts) and the re-annotation of sORF classes represent the added value of this database. Notably, this enables the analyses at locus, gene, transcript and ORF levels. In this work, we propose (i) a pipeline to regularly update the content of the database in a reproducible manner, (ii) a database content that can be fully downloaded for custom computational analyses and (iii) an user-friendly web interface to ease data access to biologists.