dataesr/scientific-paragraphs-categorization huggingface dataset:
In  [39] , the equations for the different flow types are used with fixed discharge coefficients derived from tests with a physical scale model. The implementation of the culvert equations into TELEMAC has strongly improved the way discharges through culverts of an FCA are calculated. In  [39]  a physical scale model was necessary to provide the right discharge coefficients, while now it is possible to give physically based head loss coefficients adapted for any shape and size of the culvert construction. Further, in  [39] , the water level inside the FCA had to be calculated based on the amount of water that already entered the area and the topography. Now, the free surface flow of the water in the FCA can be calculated by the TELEMAC software in 2D or 3D. For very small FCAs with only a single in-and outflow, a calculation by hand would still be feasible, but a detailed model of the FCA provides more insight in the complex filling and emptying.
For the FCA Bergenmeersen, the detailed model showed how all of the storm surge water entered through the inflow culverts. It also showed that around 10% of the water entering the FCA daily exits through two older outflow culverts on the east side of FCA Bergenmeersen. This kind of information is important for optimal management of the FCA. Furthermore, the management of FCAs with CRT, where tidal habitat is restored  [31, 33, 34, 39] , demands knowledge about nutrient fluxes, sediment balances, and estimations of flow velocities inside the culverts  [62] , and for this, the discharge data through the culverts are crucial. With the implementation of the culvert flow equations in TELEMAC, models can replace expensive and maintenance intensive ADCPs for the estimation of the discharges.
Physical Scale Model Given the complexity of the culvert structure in the physical scale model, the differences between measured and calculated discharges were small (maximum difference was 6. 8%). The calculated discharges for the test with the trash screen were underestimating the measured values and this might be due to the overestimation of the head loss coefficient for the scale model trash screen. This coefficient was calculated based on the physical properties of the trash screen (Equations (  6 ) and (  7 )) in the scale model and was used as such in the calculation of the discharges. The physical scale model tests added a second and accurate dataset to which the new culvert code implemented in TELEMAC was validated. Because Froude scaling was used for the scale model, but the Reynolds similarities could not be guaranteed for all flow conditions, the scale model results were not scaled up.
TELEMAC-2D (called telemac2d. f). The same is done in the main subroutine for TELEMAC-3D (called telemac3d. f). The total number of source points, given by the variable NSCE, is now the sum of the number of sources (NPTSCE) and twice the number of culverts (a sink and source per culvert). The equations and the conditions when to use which equation, summarized in Table  2  were implemented in the subroutine buse. f. The flow chart in Figure  A1  shows how the code is built up once the OPTION FOR CULVERTS (OPTBUSE) is chosen. The user has to specify all the parameters related to the culverts' dimensions and head loss coefficients in a separate text file. This file is recognized by the program through the keyword CULVERTS DATA FILE. This text file will be read by the subroutine lecbus. f. Both the text file and the subroutine lecbus.
f. They are separated by a tab. The following parameters must be listed in the culverts data file: The computed discharge (DBUS) is positive for the flow from side 1 to side 2. The discharge through the culvert is computed in the subroutine buse. f according to the equations and conditions given in Table  2  and given the parameters for the culverts given in the CULVERT DATA FILE. Two extra assumptions were made when calculating the discharges. The first one is that the critical water level (h c ) inside the culvert is assumed to be equal to two thirds of the culvert height. Secondly, when a wooden weir log is present in front of a culvert, an equivalent culvert bottom elevation is used replacing both the bottom elevations z 1 and z 2 in the equations itself (not in the conditions to determine when which type of flow is occurring).
miniSINEUP-GDNF RNA Selectively Increases GDNF Protein Expression In Vitro To design a specific SINEUP for endogenous Gdnf mRNA, we first evaluated transcription start site (TSS) usage in cells and tissues relevant for PD. We thus interrogated the FANTOM5 collection of Cap Analysis of Gene Expression (CAGE) datasets, which represents the widest catalog of annotated promoters and TSSs in mammalian sam-ples. 19 By using the Zenbu Genome Browser Tool for data visualization,  20  we monitored TSS usage at the mouse GDNF locus and evaluated their expression in a variety of samples. The main TSS was close to the first ATG, suggesting that the first two annotated isoforms (NM_001301332. 1 and NM_010275. 3) were the most widely expressed (Figure  S1 ). Based on this TSS analysis, we designed two GDNF-specific miniSINEUPs (miniSINEUP-GDNF) in antisense orientation to Gdnf mRNA around the first AUG, following the rules of pairing at the sense (S)/AS Uchl1 locus  13  and for other natural and synthetic SINEUPs.
SINEUP specificity is given by the BD nucleotide sequence that is unique for the endogenous mRNA target. However, like other nucleic acid-based techniques, off-targets cannot be excluded. Thus, we took advantage of the BLAST to predict potential off-targets, aligning the BD sequence to the mouse mRNA dataset. Three off-target candidates showed the highest sequence similarity: Dad1 (homologous on the 3 0 UTR, 15 nt of similarity), Lrrc14 (homologous in the coding measured by an ELISA (n = 8). (E) Neuro2a cells were transfected with a pLVX-TetOne vector expressing the miniSINEUP-GDNF. After 24 h post-transfection, cells were treated with different concentrations of doxocycline for 24 h to evaluate the correlation between miniSINEUP RNA levels and GDNF protein expression. The induction of the miniSINEUP-GDNF RNA expression by doxocycline (F 4, 29 = 3.
It should be noted that a more comprehensive study on the consequences of the ectopic expression of SINEUP-GDNF in other brain areas, including SNpc, will provide additional information on this important issue. More importantly, miniSINEUP-GDNF was able to ameliorate PD symptoms in the 6-OHDA PD mouse model. We used two behavioral assays widely used to assess the motor performance in this PD mouse model, the cylinder test and the rotational behavior. Mice injected with AAV9 expressing miniSINEUP-GDNF showed an improvement in the usage of the contralateral forepaw and significantly decreased the ipsilateral rotations. These data demonstrated an improvement in the motor behavior, the core symptomatology of PD. By analyzing TH immunoreactivity in the striatum and by counting DA neurons in the SNpc, we also demonstrated that miniSINEUP-GDNF could decrease DA loss induced by the 6-OHDA toxin infusion.
No. NBP1-80049), and anti-CBX4 1:500 (Santa Cruz Biotechnology, Cat. No. sc-517216). The antibody against GDNF detects the mature form of GDNF, with an apparent molecular weight of approximately 17 kDa. Proteins of interest were visualized with the Amersham ECL Detection Reagents (GE Healthcare by Sigma, Cat. No. RPN2105). Western blotting images were acquired with Image Quant LAS 4000mini system, and band intensity was calculated using Image Quant TL software.
Striatal Fiber TH Densitometry The optical densities of the TH immunoreactive fibers in the striatum were measured using the NIH ImageJ program. For each animal the optical density was measured at four rostocaudal levels over the whole striatum. To estimate TH staining density, the optical density was corrected for non-specific background density, as measured from the corpus callosum in each section. Data were presented as the average of the four rostrocaudal slice levels expressed as a percentage of the intact side.
ACKNOWLEDGMENTS We are indebted to all the members of the S. G. lab for thought-provoking discussions. We thank  Eva Ferri  and  Alessandra Sanna  for administrative support. We are grateful to  Luisa Franco  and  Ilaria Zanardi  for experimental advice and  Monica Morini  for experimental assistance at the animal facility. This work has been funded by IIT intramural grants to S. G.
Background: Lung neuroendocrine neoplasms (LNENs) are rare solid cancers, with most genomic studies including a limited number of samples. Recently, generating the first multi-omic dataset for atypical pulmonary carcinoids and the first methylation dataset for large-cell neuroendocrine carcinomas led us to the discovery of clinically relevant molecular groups, as well as a new entity of pulmonary carcinoids (supra-carcinoids). Results: To promote the integration of LNENs molecular data, we provide here detailed information on data generation and quality control for whole-genome/exome sequencing, RNA sequencing, and EPIC 850K methylation arrays for a total of 84 patients with LNENs. We integrate the transcriptomic data with other previously published data and generate the first comprehensive molecular map of LNENs using the Uniform Manifold Approximation and Projection (UMAP) dimension reduction technique.
We show that this map captures the main biological findings of previous studies and can be used as reference to integrate datasets for which RNA sequencing is available. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap. ucsc. edu/?p=RCG lungNENomics/LNEN). The data, source code, and compute environments used to generate and evaluate the map as well as the raw data are available, respectively, in a Nextjournal interactive notebook (https://nextjournal. com/rarecancersgenomics/a-molecular-map-of-lung-neuroendocrine-neoplasms/) and at the EMBL-EBI European Genome-phenome Archive and Gene Expression Omnibus data repositories. Conclusions: We provide data and all resources needed to integrate them with future LNENs transcriptomic studies, allowing meaningful conclusions to be drawn that will eventually lead to a better understanding of this rare understudied disease.
Data Description Recently  [7] , we performed the first integrative and comparative genomic analysis of LNEN samples from all histological types, based on newly sequenced data: whole-exome sequencing (WES) data (16 samples), whole-genome sequencing (WGS) data (3 samples), RNA-sequencing (RNA-Seq) data (20 samples), and EPIC 850K methylation data (76 samples), as well as publicly available data. These data correspond to the most extensive multi-omic dataset of LNENs, including the first methylation data for LCNEC and the first molecular characterization of the rarest LNEN subtype (atypical carcinoids)  [7]. This dataset, which provides the missing pieces for a complete molecular characterization of LNENs, has been deposited at the EMBL-EBI European Genome-phenome Archive (EGA accession No. EGAS00001003699).
To facilitate the reuse of the data generated for the previous publication  [7] , we provide here a complementary data descriptor by outlining the pre-processing and quality control (QC) steps performed on each omic dataset available on EGA. Also, other studies have generated sequencing data and performed a molecular characterization of LNEN samples: pulmonary carcinoids (mostly typical carinoids) have been characterized by Fernandez-Cuesta et al. [4]  and Laddha et al. [8] , LC-NEC by George et al. [6] , and SCLC by George et al. [5]  and Peifer et al. [2]. We therefore generate the first pan-LNEN molecular tumor map by integrating the transcriptomic data from Alcala et al. [7]  and the other published LNEN transcriptomic data  [2, [4] [5] [6] 8]. This map provides an interactive way to explore the molecular data and allows statistical interrogation, based on the UCSC TumorMap portal  [13].
WES and WGS data WES and WGS were performed, respectively, on 16 and 3 freshfrozen atypical carcinoids in the Cologne Centre for Genomics and the Centre National de Recherche en G énomique Humaine. For WES, the SeqCap EZ v2 Library capture kit from NimbleGen (44 Mb) and the Illumina HiSeq 2000 machine (Illumina Inc. , San Diego, CA, USA) were used for the sequencing. For WGS, the Illu-mina TruSeq DNA PCR-Free Library Preparation Kit was used for library preparation and the HiSeqX5 platform from Illumina for the sequencing as described in  [7]. The sequencing reads from the 16 atypical carcinoids' whole exomes and the 3 carcinoids' whole genomes were processed using the in-house Nextflow  [15]  workflow available at the IARCbioinfo/alignment-nf  [16]  GitHub repository, revision No. 9092214665.
RNA-Seq data RNA-Seq was performed on 20 fresh-frozen atypical samples. The Illumina TruSeq RNA sample preparation Kit was used for library preparation and the Illumina TruSeq PE Cluster Kit v3 and the Illumina TruSeq SBS Kit v3-HS kits were used on an Illumina HiSeq 2000 sequencer. The data generated were processed in 5 steps (Fig. 1 , middle panel): (i) read trimming using Trim Galore v0. 6. 5 (Trim Galore, RRID:SCR 011847)  [26] , (ii) read mapping to the reference genome (GRCh38, gencode version 33 from bundle CTAT from 6 April 2020  [27] ) using STAR v. 2. 7. 3a (STAR, RRID:SCR 015899)  [28] , (iii) realignment of the reads using ABRA2 v2. 22 (ABRA, RRID:SCR 003277)  [29] , (iv) base quality score recalibration using GATK4 v4. 0. 5. 1 (GATK, RRID: SCR 001876)  [30, 31] , and (v) gene expression quantification using StringTie v2.
03% (right panel). None of the samples presented >1% of over-represented sequences, which ensures a proper library diversity. RSeQC was used to control the alignment quality and to assign mapped reads to different genomic features (coding regions, introns, intergenic regions, TSS, TES). Fig. 2D  (left panel) shows that every sample had >70% of reads uniquely mapped and the read distribution for each sample is represented in Fig. 2D  (middle panel). All samples had >75% reads mapped in coding regions (CDS-exons, 5 and 3 untranslated transcribed region exons). The read counting was performed at the gene level for 59,607 genes (genecode annotation, release 33) using HTSeq  [34]. Fig. 2D  (right panel) shows the read assignments; the percentage of assigned reads ranges from 71. 3 to 87. 3%. STAR, RSeQC, and HTSeq metrics for each sample are provided in Supplementary Tables  2 3 4.
Note that 3 samples, LNEN008, LNEN014, and LNEN017, have a higher proportion of reads classified as "Unmapped too short" and "Mapped to multiple loci" (Fig. 2D , left panel), reads mapped in intronic regions (Fig. 2D , middle panel), and a lower proportion of reads assigned by HTSeq (Fig. 2D , right panel) in comparison with the other samples. Unexpected results concerning those samples should thus be considered with caution. Finally, to apply dimensionality reduction methods to the RNA-Seq data (see below), the DESeq2 package v1. 26. 0 (DESeq2, RRID:SCR 015687)  [39]  was used to transform the read counts obtained using StringTie to variance-stabilized read counts (vst), enabling the comparison of samples with different library sizes. To reduce sex influence on expression profiles, the genes located on sex chromosomes were not considered for subsequent analyses.
Methylation data The methylation analyses were performed on the basis of the EPIC 850K methylation arrays and the Infinium EPIC DNA methylation beadchip platform (Illumina) for 33 typical carcinoids, 23 atypical carcinoids, 20 LCNECs, and 19 technical replicates in total. These arrays interrogate >850,000 CpGs and contain internal control probes that can be used to assess the overall efficiency of the sample preparation steps. The raw intensity data (IDAT files) were processed using the R package minfi v. 1. 24. 0 (minfi, RRID:SCR 012830)  [40]. Fig. 1  (right panel) provides the packages, functions, and publication used for the data processing, QC, and filtering steps as implemented in the IAR-Cbioinfo/Methylation analysis scripts  [41]  GitHub repository. Fig. 2E  shows that no outliers were detected: (i) the left panel, representing the median log 2 of the methylated and unmethylated intensities, indicates that all samples cluster together with a log median intensity >11 for both channels, which supports the absence of failed samples; (ii)in the right panel, the multidimensional scaling plot shows that the samples cluster together by histological groups.
We used the depectionP function (minfi package), which compares the DNA signal to the background signal based on the negative control probes to provide a detection P-value per probe, lower P-value indicating reliable CpGs. Fig. 2F  represents the mean detection P-values per sample and shows that all samples' mean detection P-values were <0. 01. To correct for the variability identified in the control probes, a normalization step was applied to the raw intensities using the prepro-cessFunnorm function from minfi. After between-array normalization, different sets of probes that could generate artifacts were removed successively from the methylation dataset: (i)  19 ,634 probes on the sex chromosomes, in order to identify differences related to tumors but unrelated to sex chromosomes; (ii) 41,818 cross-reactive probes, which are probes co-hybridizing with multiple CpGs on the genome and not only to the one for which it has been designed  [42] ; (iii) 10,588 probes associated with common SNPs (present in dbSNP build 137); (iv) 24,363 probes with multi-modal β-value distribution; and (v) 9,697 probes having a detection P-value >0.
Generation of an integrative molecular map Here we have generated a pan-LNEN molecular map with the whole-transcriptomic (RNA-Seq) data available from individual studies of each LNEN tumor type  [2, [4] [5] [6] [7] [8]. This dataset includes the RNA-Seq data for a total of 51 SCLCs, 69 LCNECs, and 118 carcinoids including 40 atypical and 75 typical carcinoids. The different data underwent the same processing steps described above because the generation of the molecular map requires a homogenized dataset.
UMAP method The pan-LNEN map was obtained using the Uniform Manifold Approximation and Projection (UMAP) method  [43]  on the genes with the most variable expression (genes explaining 50% of the total variance). UMAP is a dimensionality reduction method based on manifold learning techniques, which are adapted to non-linear data in contrast with the commonly used principal component analysis (PCA) method. First, it builds a topologi-cal representation of the high-dimensional data, and second it finds the best low-dimensional representation of this topological structure  [43]. UMAP representations were generated using the umap function from the R package umap (v. 0. 2. 5. 0)  [44]. All the parameters were set to their default values except the n neighbors parameter. This parameter defines the number of neighbors considered to learn the structure of the topological space.
The mean MI across k values was computed for all gene expression features for: (i) the original space, (ii) the PCA-5D projection, and (iii) the UMAP projection (with n neighbors = 238). We used the implementation of MI from the Moran. I function of R package ape (v. 5. 3)  [49]. To evaluate the preservation of the spatial auto-correlations, we ranked the top N genes based on the mean MI values for these 3 cases and calculated the overlap between the lists (Fig. 4B ). We found that the PCA-5D is only slightly more conservative of the spatial auto-correlations found in the original space than UMAP (unilateral paired t-test P = 2. 2 × 10 -16 ). For example, for N = 1,000 (see Euler diagram inserted in Fig. 4B ), 88. 8% of the genes with the highest MI overlap between the PCA-5D, UMAP, and the original space.
An interactive TumorMap Newton and colleagues have recently developed a portal called TumorMap  [13, 50] , an online tool dedicated to omics data visualization. This new type of integrated genomics portal uses the Google Maps technology designed to facilitate visualization, exploration, and basic statistical interrogation of high dimensional and complex datasets. The pan-LNEN molecular map that we generated in this work (Fig. 3 ) has been shared on the Tu-morMap platform. Along with the molecular map, the main clinical, histopathological and molecular features highlighted in the previous studies were uploaded as attributes. The interface enables users to explore and navigate through the map: zooming in and out, coloring and filtering samples based on attributes. The users can also create their own attributes based on pre-existing ones by using operators such as union or intersection.
An interactive computational notebook In the first part of the article, we described the pre-processing and QC steps applied on the recently published LNEN multiomics dataset  [7]  in order to facilitate its reuse. To generate the pan-LNEN molecular map, the same pre-processing steps were followed to homogenize independently published transcriptomic data  [2, [4] [5] [6] [7] [8]. For that purpose, reproducible pipelines, developed in house, were used and are available for reuse to the scientific community on GitHub [51] (see the "data description" section). In addition, the code used to generate the molecular map and to evaluate the quality of the dimensionality reduction is provided as a notebook published on Nextjournal  [52]. Along with the code, the notebook provides the data and the dependencies required to run the analyses performed in this paper.
Integration of new samples The homogenized read counts of the pan-LNEN data are available on GitHub  [14]. Along with the available code, these data could be used to integrate new samples for which RNA-Seq data are available. The raw read counts of the new samples should firstly be generated following the same processing steps described in the section "Data quality controls" (Fig. 1 , middle panel) and integrated to the pan-LNEN read counts. We also provide in the Nextjournal notebook, the Nextflow command lines allowing to obtain the read counts. The vst (DESeq2  [39] ) should then be applied on the combined dataset and UMAP should finally be rerun to project all samples together in a 2D space. All together, we provide the resources to integrate additional samples into our molecular map, starting from raw sequencing read counts.
Discussion Genomic projects focused on rare cancers encounter the limitation of availability of high-quality biological material suitable for such studies. This translates in small series of samples usually underpowered to draw meaningful conclusions. Thus, tools facilitating the integration of independent datasets into larger sample series will lead to more informative studies. Recently, the first multi-omic dataset for the understudied atypical pulmonary carcinoids and the first methylation dataset for LCNECs was published  [7]. Here we provide a parallel description of the pre-processing of these molecular data and provide evidence of the good quality of the different 'omics data generated. This data collection associated with previous datasets  [2, [4] [5] [6] 8]    source to improve the molecular characterization of LNEN tumors.