dataesr/scientific-paragraphs-categorization huggingface dataset:
scRNA-seq: bioinformatics. Data filtering and normalization: Sequenced libraries were demultiplexed, aligned to human transcriptome (hg38) and quantified using Cell Ranger (v_3. 0. 1). Ongoing analysis was conducted using Seurat (V_3. 2. 0)  15  in R (V_3. 5. 2)  16. Quality control filters based on the number of detected genes, number of UMIs and percentage of mitochondrial UMIs were performed to each one of the samples. The thresholds were defined based on the distribution of the previously mentioned parameters and visual inspection of quality control scatter plots. After filtering of low quality cells, a total number of 720 (FA-02004), 1,438 (FA-02006), 1,995 (FA-02008), and 12,549 (Healthy donor) cells were retained. Each single cell dataset was individually normalized, using the Normalize Seurat function.
Feature counts for each cell were divided by the total counts for that cell and multiplied by the scale factor. This was then natural-log transformed. The data was regressed out by cell cycle stadium, number of features and number of counts. Non-linear dimensional reduction was performed (UMAP) to plot the data of each samples. PCA was defined as dimensional reduction to use in the UMAP graph. Each of the FA samples was integrated with the healthy donor sample. Cell annotation: For cell annotation, we use the annotation conducted in three additional human samples of healthy young individuals (3YI). The isolation protocol of 3YI includes the cell types in the 3 FA patients and the HD: shown. The data-analysis processing of those samples was conducted as the protocol describe for FA samples.
this study, FA-02004, FA-02006 and FA-02008, were FA-A patients enrolled in the FANCOLEN-1 gene therapy trial (FANCOLEN-1; ClinicalTrials. gov, NCT03157804; European Clinical Trials Database, 2011-006100-12) and complied with all relevant ethical regulations and approved by the ethics committees at Hospital Vall d'Hebron in Barcelona and Hospital del Niño Jesús in Madrid.
ACKNOWLEDGEMENTS The authors thank  Ramón García-Escudero  for careful reading of the manuscript and helpful discussions. The authors also thank  A. de la Cal  for coordinating the delivery of BM samples from patients with  FA. The authors are also indebted to the patients with FA, their families and clinicians from the  Fundación Anemia de Fanconi. This work was supported by grants from the  European Commission 's  Seventh Framework Program  ( HEALTH-F5-2012-305421 ) to the EUROFANCOLEN Consortium J. B. and  J. Se ; from  Ministerio de Economía, Comercio y Competitividad and Fondo Europeo de Desarrollo Regional  ( RTI2018-097125-B-I00  to P. R. and S. N. ); from  Gobierno de Navarra, Departamento de Desarrollo Económico y Empresarial  ( AGATA 0011-1411-2020-000010 ); from  Instituto de Salud Carlos III (ISCIII)  and co-financed by  FEDER CIBERONC  ( CB16/12/00489  and  CB16/12/00225 );  Redes de Investigación Cooperativa  ( TERCEL RD16/0011/0005 ) and from  Consejería de Educación, Juventud y Deporte de la Comunidad de Madrid  ( AvanCell Project ;  B2017/BMD3692 ).
FA patients included in this study, FA-02004, FA-02006 and FA-02008, were FA-A patients enrolled in the FANCOLEN-1 gene therapy trial (FANCOLEN-1; ClinicalTrials. gov, NCT03157804; European Clinical Trials Database, 2011-006100-12) and complied with all relevant ethical regulations and approved by the ethics committees at Hospital Vall d'Hebron in Barcelona and Hospital del Niño Jesús in Madrid.
No issues calling into question the model's fidelity were identified. No outliers were excluded from the data set because of statistical criteria. Mortality data were transformed for analysis using the arcsin function to allow normality. Pair-wise comparisons with a Tukey's adjustment were performed using the same model and the multicomp package of R  (Pinheiro et al. , 2015). The BW at placement was checked, and it was not significantly different among treatments. Mortality data for individual trials are reported but was not statistically analyzed because of low relevance for individual small-scale trials with low replication and numbers of animals. The percentage of trials that reached different levels of reduction of the cFCR compared with the NC was plotted to evaluate consistency of response for each of the 2 treatments.
Metagenomics Analysis of the Cecal Content The main focus of the overall study was centered on the effect of MMM2 with MMM1 acting as the benchmark. As such, together with a greater magnitude of the cFCR effect for MMM2, it was thought to be sufficient to focus the whole-genome sequencing and metagenomic analysis of cecal samples to explore more deeply the effects of MMM2 on the microbiome function vs. the control. A partial analysis of microbial metagenome data with focus on pathways of interest is presented in this article, but more detailed analyses of functional metagenome effects will be reported separately. Cecum digesta samples were collected (1 bird/pen) from the NC and MMM2 treatment (21 reps/treatment) in trial 12 (AHPharma, Hebron, MD) after cervical dislocation on day 42 and frozen at 280 celsius before DNA extraction.
Metagenomic DNA was obtained using DNeasy PowerSoil HTP96 according to the recommendations of the manufacturer (QIAGEN, Germany). DNA was sequenced at CoreBiome (New Brighton, MN) under a shallow shotgun technology called Booster-Shot that delivers a sequencing depth around 2M-5 M reads per sample (Illumina HiSeq3000, Illumina Inc. , San Diego, CA). After classical treatment of the raw data which included controlling of raw sequence data by FastQC Report software, trimming with CutAdapt to remove adapters and finally mapping to the gene catalog with the Burrows-Wheeler aligner (BWA) were carried out. Gene sequences were then annotated against the chicken gut microbial gene catalog (9. 04 million genes, based on 495 intestinal digesta samples;  Huang et al. , 2018). Using the Kyoto Encyclopedia of Genes and Genomes (KEGG) genomic database (Kaneisha and Goto, 2000; Kaneisha, 2019; www.
genome. jp/kegg), biochemical pathways involved in the TCA cycle and nitrogen metabolism such as amino acid degradations and urea cycle were extracted to build a simplified microbial metagenomic metabolic model. Gene sequences encoding 168 enzymes identified in this model were counted according their annotations. The abundances of the genes were normalized on the abundance of the same DNA sequence in the control sample. Data were analyzed using the nonparametric Mann-Whitney-Wilcoxon test with Pvalue adjust function that applied the fdr correction method (Luz Calle, 2019) using the MultNonParam statistical package of R.
, Cambridge, MA and DSM Nutritional Products, Kaiseraugst, Switzerland) were produced by the catalytic oligomerization of food sugars into tailored glycans  (Geremia et al. , 2016 (Geremia et al. , , 2020a) )  and tested as a feed ingredient in a subset of a total of 19 trials with broiler chickens. Table  5. Effects 1 of 2 different microbiome metabolic modulator products 2 supplemented in the feed in 19 independent trials, on the growth performance of broiler chickens raised in floor pens, evaluated with mixed models. Means with different superscripts differed at P , 0. 05. 1 Linear mixed-effect models considered MMM Treatment as the fixed effect and nested random effects across Study and Block, using the lme function from the nlme package of R  (Pinheiro et al. , 2015). The variance function allowed for different SD by the study site.
Preparation of the oriented mesoporous silica films on ITO electrode The Sol solution was prepared by dissolving 100 mM TEOS and 32 mM CTAB in 1:1 water:ethanol medium containing 0. 1 M NaNO 3. Its pH was adjusted to 3 by adding HCl (1 M) and the sol was hydrolyzed under constant stirring for 2. 5 h. The silica membrane was generated on ITO plates (surface resistivity 8-12 Ω, Delta Technologies) by electroassisted self-assembly (EASA  [27] ) from this aged solution, by applying a constant potential of -1. 3 V for 20 s with a PGSTAT 101 potentiostat operated by NOVA software (Autolab), in a three-electrode cell (ITO working electrode (∅: 8 mm), a silver rod as pseudo-reference electrode and a stainless steel as counter electrode). After deposition, the electrode was quickly removed from the cell, rinsed with deionized water, dried and kept overnight in oven at 130 • C.
Slides were observed using an epifluorescent microscope (Zeiss AxioImager Z1 apotome). For immunohistochemistry staining, endogenous peroxidase activity was quenched with H 2 O 2 /methanol 1:20 for 20 min at room temperature. Secondary antibodies were coupled to peroxidase (Histofine) and staining was developed with Fast DAB (brown precipitate, Sigma Aldrich) and hematoxylin counterstain was used. After dehydration, sections were mounted in Pertex (Histolab). Slides were scanned using NanoZoomer 2. 0 HT ® (Hamamatsu) and analyzed with NDP View (Hamamatsu) software.
Retrospective RNAseq data analysis Expression of Gast and other genes was analyzed in bulk RNAseq data generated from several mouse intestinal cell types as published by Jadhav et al  [23]. The gene count data was downloaded on 14/09/2018 from the Gene Expression Omnibus (GEO ID: GSE83394). Genes were filtered to only keep those with count-per-million (CPM) > 1 in at least one sample and log(CPM) values were calculated using the edgeR(v. 3. 26. 7) R/Bioconductor package  [24]. We also retrospectively analyzed single cell RNAseq data generated from several flow cytometry purified mouse intestinal cell populations reported by Yan et al  [25]. Yan et al released the data for 4 samples generated in parallel, each with 2 technical replicates. These include Bmi1 + , Prox1 + , Lgr5-high and Lgr5-low samples, generating 4500 single cells.
The data were downloaded on the 14/09/ 2018 from GEO ID: GSE99457. We read the data into R (v. 3. 6. 0) using the read10X function from the edgeR (v. 3. 26. 7) R/Bioconductor package, and stored as SingleCellExperiment object using the SingleCellExperiment R/Bioconductor package (1. 6. 0) The NCBI RefSeq annotation data were used to convert gene aliases to official gene symbols. We filtered out genes that were expressed in fewer than 1% of the total cells, and those with no valid official symbols, as well as duplicated genes. We also removed cells with more than 20% mitochondrial UMI counts which resulted in 3558 cells. We used the scran (v. 1. 12. 1) R/Bioconductor package  [26]  to normalize the gene counts, calculated natural log of the normalized data, and then converted this to a Seurat object using the Seurat (v.
3. 0. 2) R package  [ 27 , 28 , 29 ]  to find 2000 most variable genes and perform principle component analysis (PCA). Using the first 20 principal components (PCs) and the Seurat package, we then performed t-distributed stochastic neighbor (t-SNE -through the Rtsne [v. 0. 15] R package) as well as uniform manifold approximation and projection embedding (UMAP -through umap-learn Python package installed using the reticulate [v. 1. 13] R package). Finally, we converted Seurat object to SingleCellExperiment and performed clustering by running model-based clustering from mclust (v. 5. 4. 5) R package  [30]  on the first two PCs, which resulted in 9 clusters. These clusters were then used with slingshot (v. 1. 2. 0) R/Bioconductor package to infer lineage structure and generate smooth lineage curves.
Statistical analysis For each experiment, data are presented as the mean of a minimum of 3 independent experiments + /-SEM. All statistical tests were 2-sided and a 5% cutoff was used to validate result significance ( * P <. 05, * * P <. 01, * * * P <. 001, * * * * P <. 0001) and were performed using the GraphPad Prism Version 6 software. Tests performed were either Mann-Whitney, Student t-test or Kruskal-Wallisas indicated in figures legends.
To refine our understanding of which intestinal epithelium cell types express progastrin, we analyzed previously published single cell RNA sequencing (scRNAseq) datasets generated in mouse intestinal cells. Since the expression levels for this gene are known to be low in the normal intestinal tissue, we first set out to determine whether Gast expression could be reliably detected in scRNAseq datasets. We were not able to use Gast expression data in some of these datasets, either due to the raw data filtering approach  [37]  or to the Gast gene not being annotated  [38]. However Gast expression was reliably detected in recent scRNAseq data generated by the Cuo lab  [25]. Expression of Gast was restricted to the Bmi1 + /Prox1 + population defined by Yan et al as a reserve stem cell population ( Fig.
, including Gast-expressing cells. However, the highest Uri1 expression was detected in two subsets of Gast -negative/ Bmi1 -positive cells, one of which also expressed Prox1 while the other was Prox1-negative but strongly positive for Chrga and Chrgb expression ( Fig. 4 C ). Althogether, our finding suggests that Gast + cells represent a subset of the Bmi1 + /Prox1 + population of injury-inducible reserve intestinal stem cells identified by Yan et al  [25]  and Jadhav et al  [23]. In addition, to gain further insight into putative progastrin target cell populations in the healthy intestinal mucosa, we also quantified the expression of putative progastrin receptors in the same dataset ( Fig. 4 D ). As expected Annexin-II ( Anxa2 ), one of these candidate receptors  [39]  , was ubiquitously expressed in most intestinal epithelial cells.
In contrast, the recently identified progastrin receptor GPR56  [40]  , a member of the adhesion GPCR family encoded by the Adgrg1 gene, had a more restricted expression pattern. Indeed, Adgrg1 was expressed in a subset of Bmi1 + /Prox1 + cells, in Bmi1 + /Prox1 neg and in a subset of Lgr5high cells ( Fig. 4 D ), suggesting that progastrin may have both autocrine and paracrine roles in the healthy intestine. mRNAs for CCK2R, another suggested receptor for progastrin  [40]  , were not detected in this dataset.
6 B ). Quantification of GAST and LGR5 -high vs low cells was then performed on multiple crypts per patient sample. Corroborating results obtained in mouse intestinal tissues (see Fig. 3  ) we found that, while progastrin expression is primarily a feature of LGR5-low intestinal cells in the healthy intestinal epithelium, a majority ( > 65%) of Progastrin-producing cells within adenomas are also LGR5high ( Fig. 6 C ). In accordance with this data and with results obtained in mouse tissues, we found that the overwhelming majority of LGR5high cells ( > 80%) did not express detectable levels of progastrin in the healthy colonic epithelium, but that most LGR5-high cells (90%) did express progastrin in colonic adenomas (Supplemental Fig. S5 ). A minority of LGR5-high cells also expressing PG was also observed in the so called normal epithelium distant from adenomas (data not shown).
Fundings FH was supported by research grants from the  Australian NH&MRC  (  GNT1164081  ) and the  Tour de Cure Foundation. JP was supported by the  "region Languedoc Roussillon  " and by  SIRIC Montpellier Cancer Grant INCa_Inserm_DGOS_12553. JG received a scholarship from  "Foundation pour la Recherche Médicale  ", and FG a scholarship from the "Ligue nationale contre le cancer ".
The distribution of clock correction and ephemeris (CCE) parameters in navigation messages broadcast by operational satellite vehicles (SVs) of the Global Navigation Satellite System (GNSS) is studied. For this purpose, two evaluation metrics are proposed, namely atypical navigation message structures and time to complete CCE sets (TTCS). The evaluation metrics are used to analyze three and a half years of operational Galileo F/NAV and GPS LNAV messages. The study considers arbitrary and healthy-only signal-in-space (SIS) health statuses. The frequently used receiver independent exchange format (RINEX) navigation files are shown to not provide sufficient information to determine the CCE parameters transmitted at every time instance. Due to the identified limitations, binary navigation data collected by a worldwide network of monitoring stations are used for the analysis.
During operations, the maximum of the TTCS metric is shown to be impacted by the issue of data (IOD) update periods and structures with mismatched IOD parameters, potentially introducing an additional delay to the time to first fix (TTFF). The message timing requirements of dual-frequency multi-constellation (DFMC) satellite-based augmentation system (SBAS) are shown to be in line with the minimum times needed to obtain multiple complete Galileo F/NAV and GPS LNAV CCE sets.
So far, it has been assumed that only the repetition interval of the CCE sets and the length of the decoding words drive the time needed to obtain a complete set  (Paonni et al. 2010; Delépaut et al. 2020). Additional delays caused by the operational distribution of the CCE parameters have not been considered. Every set of the CCE parameters on a given SIS is uniquely identified by the value of the issue of data (IOD) parameter. To provide their services, some GNSS applications, such as SBAS, broadcast additional clock and ephemeris corrections to augment the CCE sets and use the value of the IOD parameter to link the corrections unambiguously. The future dual-frequency multi-constellation (DFMC) SBAS will enable dual-frequency positioning and allow system designers and service providers to include augmentation of Galileo, GPS, BeiDou, and GLONASS constellations.
The future European DFMC SBAS, named European Geostationary Navigation Overlay Service (EGNOS) V3, considers augmentation of the Galileo and GPS constellations for the avionics users. As a consequence, EGNOS V3 will augment the Galileo F/NAV and GPS LNAV messages. In this context, the potential impact of the statistical distribution of the operational values of the Galileo F/NAV IOD nav parameter on DFMC SBAS was analyzed  (Lapin et al. 2018). A detailed analysis of the times needed to obtain complete CCE sets based on binary data has not yet been performed. The distribution of the CCE parameters in broadcast navigation messages is studied and discussed. A generic model of a navigation message is formulated, and evaluation metrics are proposed. The frequently used receiver independent exchange format (RINEX) (RTCM-SC104 2018) is assessed from the perspective of determining the CCE parameters and SIS health status at every time instance.
Galileo F/NAV message Galileo SVs broadcast F/NAV message data as a continuous stream of 600 s long F/NAV frames (European Union (2020)). The F/NAV frame is composed of twelve F/NAV subframes, each taking 50 s to transmit. The F/NAV subframe is further composed of five 10 s long F/NAV pages. The structure of the F/NAV page is uniquely identified by its page type. The complete set of CCE parameters is contained in page types 1, 2, 3, and 4. The CCE set is identified by a parameter called IOD nav. Although the higher hierarchical structure of the Galileo F/NAV page is the F/NAV subframe, there are no management data transmitted within the F/NAV message to indicate subframe and frame structures (European Union (2020)). As further clarified in the interface specification, the higher-level structures should be considered as the typical flow of pages reflecting the current Galileo navigation message design, which may evolve together with the future evolutions of the system.
This implies that the F/NAV pages can be transmitted in an arbitrary order and without any guaranteed repetition interval. The user is thus requested to always decode the page type identifier. The minimum achievable duration needed to obtain a complete CCE set in the F/NAV subframe structure is 40 s. The structure of a typical F/NAV subframe containing a complete CCE set is shown in Fig. 1. The SIS health status is determined through three SIS status flags that are encoded in the F/NAV page type 1 (European Union 2019): signal health status (SHS), data validity status (DVS), and signal-in-space accuracy (SISA).
The study considers arbitrary and healthy-only SIS health statuses. The arbitrary health status includes healthy, unhealthy, and marginal statuses. In total, over 218 million F/NAV pages and over 590 million LNAV subframes are identified in the data set. The number of missing and discarded elements is summarized in Table  2. The data gap is determined when an element  should have been received but is not recorded by any station or when the error checking fails. The ambiguous content filter is triggered when the binary content is not unanimously confirmed by all stations monitoring the SIS. The single station filter is triggered when the SIS is monitored by only one station. The single station filter is only applied to the LNAV message due to its lower robustness to the burst bit errors. After the consolidation step, over 218 million F/NAV pages and 582 million LNAV subframes with arbitrary SIS health statuses remain.
Atypical navigation message structures The observed structures with mismatched IOD parameters of both evaluated navigation messages are summarized in Table  3  for the arbitrary SIS health status. As can be seen, the mismatched IODE parameters are quite common for the LNAV message with the probability of occurrence P occ = 2. 11 × 10 -4. This is attributed to the data set cutovers as the IODE mismatches occur in the LNAV subframes 2 and 3 belonging to the new CCE set, while the LNAV subframe 1 still belongs to the old set. An example of such a frame transmitted by the SV with the pseudo-random noise (PRN) number 12 on June 30, 2020, is shown in Fig. 8 , where subframe 1 contains IODE 42 and subframes 2 and 3 contain a mismatched IODE 7. Considering the interface specification is not explicit in which cutovers are to be considered normal and which not (U.
Time to complete clock correction and ephemeris sets The TTCS n metric is evaluated for n ∈ {1, 2, 3, 4, 5} complete CCE sets. The starting point of the reception is assumed to be random and uniformly distributed along the navigation data stream. The cumulative density functions (CDFs) of TTCS n of both evaluated messages are shown for the arbitrary SIS health status in Fig. 9 , and the healthy-only SIS in Fig. 10. Figures  9  and 10  show that the statistical distributions of TTCS n are almost identical for the arbitrary and healthy-only SIS health statuses. The only notable difference is between the maxima of TTCS n for n > 1. Unhealthy and marginal SIS health statuses are characterized by lengthier periods during which IOD parameters are not updated, resulting in larger maxima of TTCS n in Fig.
Patients who had received stem cell transplant following relapse, had been diagnosed or treated for another malignancy within 2 years before MM diagnosis, or had any evidence of residual disease from a previously diagnosed malignancy were excluded. Patients were also excluded if they were diagnosed with a clinically relevant medical or psychiatric condition that could interfere with completing the study, or had an uncontrolled/active infection or concurrent medical condition with symptoms that could confound their description of their experience with RRMM. Purposeful sampling was used to enroll patients with RRMM from 6 clinical sites in the US. The participating sites were selected based on their expected ability to recruit eligible patients within the needed timeframe. Sites identi ed potential patients by screening medical records and databases to abstract key variables such as disease background and current treatment regimen.
Potentially eligible patients were then contacted by phone or in person by site personnel using a prepared script summarizing the goals of the study and inviting them to participate in a one-time face-to-face interview. If the patient was interested, the site began the informed consent process. All participants provided written informed consent and agreed to the audio-recording and transcription of the interview. The site staff then abstracted additional descriptive demographic data about participants from the patient medical records. The study protocol was approved by Advarra IRB (Columbia, MD). Participants were compensated for time spent on interview completion.