dataesr/scientific-paragraphs-categorization huggingface dataset:
When taking into account the indexing time, LRez is slower than the naive Python-based approach for a single query of 1,000 barcodes. However, applications usually perform hundreds to thousands of such queries. Supplementary Materials Section 5 Runtime depending on the number of queries, Supplementary Figures  S2  and S3  show the runtime depending on the number of queries. These results underline the fact that building the index and querying with LRez is more efficient as soon as 2 or 3 queries are required on BAM or FASTQ files. On the TELL-Seq E. coli FASTQ file, a larger number of queries are required to see the benefit of LRez, which can be explained by the fact that this file is much smaller compared to the others, and that it is thus   3. Runtimes for performing a a query of 1,000 barcodes on BAM files, using LRez and using naive approaches based on grep + SAMtools and on single scans through the whole files using a Python script.
For LRez, we report the overall runtime, including both the indexing and querying steps, as well as the runtime of the querying step alone. Additionally, we also report the average runtime per query, both for the naive approaches and LRez. 1 Times per query were obtained by dividing the overall runtime by the number of queries. In practice, these approaches would still require a whole scan through the file for a single query, and actual runtime for querying a single barcode would be larger. 2 Times per query do not take into account indexing time. much easier for naive methods to scan through it. On gzipped FASTQ files, the number of queries required to see the interest of LRez also raises. This can be explained by the fact that LRez needs to decompress multiple blocks when querying gzipped FASTQ files, and that decompression is more expensive than simply browsing through a regular file.
Conclusion LRez provides a toolkit and an easy to use C++ API, allowing to deal with Linked-Reads data from multiple sequencing technologies, and offering various functionalities. We thus believe it might help both end-users and programmers alike, and be easily integrated to external projects, including structural variant discovery, genome scaffolding or phasing. As previously mentioned, the API is already used in the structural variant calling tool LEVIATHAN, while the FASTQ indexing and querying features of the LRez toolkit are currently used in the gap-filling pipeline MTG-Link. LRez can thus be easily integrated to external projects, allowing to perform various frequent Linked-Reads management tasks in an optimized manner. Using LRez would thus allow developers and users to avoid reimplementing these common tasks, and directly rely on our implementation.
Growing evidence suggests that epigenetic mechanisms like microRNA-mediated transcriptional regulation contribute to the pathogenesis of parkinsonism. In order to study the influence of microRNAs (miRNAs), we analyzed the miRNome 2 days prior to major cell death in α-synuclein-overexpressing Lund human mesencephalic neurons, a well-established cell model of Parkinson's disease (PD), by next-generation sequencing. The expression levels of 23 miRNAs were significantly altered in α-synucleinoverexpressing cells, 11 were down-and 12 upregulated (P < 0. 01; non-adjusted). The in silico analysis of known target genes of these miRNAs was complemented by the inclusion of a transcriptome dataset (BeadChip) of the same cellular system, revealing the G0/G1 cell cycle transition to be markedly enriched.
Out of 124 KEGG-annotated cell cycle genes, 15 were present in the miRNA target gene dataset and six G0/G1 cell cycle genes were found to be significantly altered upon α-synuclein overexpression, with five genes up-(CCND1, CCND2, and CDK4 at P < 0. 01; E2F3, MYC at P < 0. 05) and one gene downregulated (CDKN1C at P < 0. 001). Additionally, several of these altered genes are targeted by miRNAs hsa-miR-34a-5p and hsa-miR-34c-5p, which also modulate α-synuclein expression levels. Functional intervention by siRNA-mediated knockdown of the cell cycle gene cyclin D1 (CCND1) confirmed that silencing of cell cycle initiation is able to substantially reduce α-synuclein-mediated cytotoxicity. The present findings suggest that α-synuclein accumulation induces microRNA-mediated aberrant cell cycle activation in post-mitotic dopaminergic neurons.
Transcriptome/mRNA Expression Analysis Cells were cultured in 10 cm petri dishes until day 6 of differentiation. After washing the cells once with PBS, 500 mL RLT buffer (Qiagen, Hilden, Germany), activated with β-mercaptoethanol (Sigma-Aldrich) according to the manufacturer's instructions, was added to the petri dishes. Total RNA of three replicates was collected in sterile cryotubes (Sarstedt, Nümbrecht, Germany) using a cell scraper (Carl Roth, Karlsruhe, Germany) and stored at -80 • C until use. The expression analysis was performed with Illumina HumanHT-12_V3 bead chips (Illumina, San Diego, CA, United States). Data obtained from Illumina HumanHT-12_V3 bead chips (Illumina, San Diego, CA, United States) measurements was analyzed in order to detect differentially regulated transcripts.
To this end, the output obtained from the Illumina GenomeStudio Software (v. 1. 0. 6) was used as input for further data preprocessing and differential analyses steps, which were implemented as workflow within the KNIME analytics platform ( 1  v3. 6. 2). However, since KNIME allows integration of R code, R capabilities  (lumi v2. 28. 0, affy v1. 54. 0, genefilter v1. 58. 1, limma v3. 32. 10, lumiHumanAll. db v1. 22. 0, and lumiHumanIDMapping v1. 10. 1) were used. All R packages are available from Bioconductor 2. We used the lumi package in order to carry out basic preprocessing of the input data. To this end, the lumiExpresso function of lumi was used with the following settings: For background correction the forcePositive method has been applied, quantile normalization was carried out and log2 transformation was used for variance stabilization.
After data preprocessing, expression values (features) that did not show enough variation to allow reliable detection of differential expression have been removed. Using the nsFilter method of the R package genefilter the interquartile range (IQR) was used as a measure for dispersion and the 0. 5 quantile of the IQR values has been used as cutoff for removal of unnecessary features. For each of the remaining features a two-tailed t-test was performed with the rowttests function from the genefilter package. In order to control the rate of type I errors, when conducting multiple t-tests, the Benjamini-Hochberg method was applied (FDR controlling). Features with adjusted p values < 0. 05 (608 features) are considered differentially regulated and used for further analyses. As additional data processing steps several mappings procedures were carried out in order to annotate the data with additional information.
Bioinformatic analysis was performed with an inhouse developed pipeline. Quality control was performed using fastqc (V0. 11. 3), the sequence reads were streamlined using Trimmomatic (V0. 32), with a sequence quality threshold of Q30 and removing short reads (<15 bp). Reads were mapped using bowtie (V1. 1. 2) against mature and stem-loop miRNA databases (miRbase V21) as well as a proprietary in-house developed database, which takes natural variation of small RNA sequences into account. The expected peak size of mapped reads of a size peaking at 22-23 nucleotides was verified. Counts and TPM of analyzed miRNAs were recorded and filtered for very low expressed miRNAs (TPM < 10) in all samples. The filtered count file, with all miRNA with a TPM < 10 removed, was used for the differential analysis using DESeq2 (V1.
miRNA Target Prediction and Overrepresentation Test Two different online databases were used for identifying target genes of differentially regulated miRNAs (P < 0. 01). 2,458 miRNA targets were predicted using the TargetScan 7. 2 software 3. Additionally, 280 experimentally validated targets were identified using MiRTarBase 7. 0 software 4. For further analysis, a respective transcriptomic dataset (P adj < 0. 05) with 608 differentially expressed genes was included. Genes that were present in at least two of the three gene lists were analyzed for enriched biological processes using PANTHER-GO-Slim version 14. 1. Fisher's exact test was used to calculate p-values. The GO term analysis was corrected for a neutral background. Additionally, the Kyoto Encyclopedia of Genes and Genomes (KEGG) was used to include genes from cell cycle pathway (hsa0411) in the overlap analysis.
Melting curves were recorded. Cycle threshold (C T ) values were set within the exponential phase of the PCR. The correct size of the respective single amplicons was assured by agarose gel electrophoresis. Data was normalized to the four housekeeping genes, ACTB, GAPDH, GPBP1, and RPL22. Comparative normalized relative quantities (CNRQ) were used to calculate the relative expression levels using qBase Plus software (Biogazelle, Zwijnaarde, Belgium). Expression of miRNA hsa-miR-34c-5p was analyzed by miRCURY LNA Universal RT microRNA PCR system (Qiagen). RNA enriched fractions (50 ng) for each sample underwent reverse transcription according to the manufacturer's protocol (Qiagen). Primers were purchased from Qiagen (hsa-miR-34c-5p, ID YP00205659; U6 snRNA, ID YP00203907) and RNU6 was used as a reference gene for normalization.
Statistical Analysis GraphPad Software Prism 7 (GraphPad Software, La Jolla, CA, United States) was used for statistical analysis. Each experiment includes at least three independent repeats. Data are presented as mean ± standard error of the mean (SEM). Data were generally compared by ordinary two-way ANOVA with Bonferroni's or Sidak's post hoc test, unless otherwise indicated. If statistically significant, pairwise comparisons were evaluated by a two-tailed unpaired t-test.
Study Overview We studied the effect of early phases of αSyn pathology on the miRNome in a human neuronal cell model using a hypothesis-free approach. Figure  1  presents an overview of the analytical process. A miRNome-wide screen was performed in LUHMES neurons overexpressing either αSyn or GFP 4 days post transduction. The resulting differentially expressed miRNAs with significance (P < 0. 01) were analyzed further using two public databases, TargetScan 7. 2 and MiRTarBase 7. 0. Predicted and experimentally validated gene targets of the miRNA hits were compared by an overlap analysis with the respective transcriptome data set (BeadChip). Genes present in at least two overlapping entities were used for enrichment analysis. The most significantly enriched biological process was analyzed by validation and subsequent siRNA-mediated functional analysis in the SNCA overexpression model.
Expression of Cell Cycle Genes Involved in the G0/G1 Superpathway According to the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, CCND1, CCND2, CCNE2, and CDKN1C are required for the G0/G1 cell cycle transition (Figure  4A ). We compared 124 KEGG-annotated cell cycle genes with 154 miRNA target genes of our dataset (Figure  4B ). Out of 15 overlapping genes, 10 genes were annotated for G0/G1 cell cycle phases. We confirmed significant differential expression levels of six genes in SNCA-overexpressing human post-mitotic midbrain-derived neurons that are involved in the G0/G1 cell cycle pathway (Table  1 ). Five genes were upregulated: CCND1, CCND2, CDK4, E2F3, MYC; and one gene was downregulated: CDKN1C. According to MirTarBase, several cell cycle genes are also targeted by hsa-miR-34a-5p and hsa-miR-34c-5p, which have been shown to directly target SNCA.
FUNDING This study was supported by the German Federal Ministry of Education and Research (BMBF, 01KU1403A EpiPD). JT was funded by the French National Agency for Research (ANR, ANR-13-EPIG-0003-02, EPIPD, and ANR-18-RAR3-0001-01, MSAomics). GH was funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy-ID 390857198), Deutsche Forschungsgemeinschaft (DFG, HO2402/18-1 MSAomics), the German Federal Ministry of Education and Research (BMBF, 01KU1403A EpiPD and 01EK1605A HitTau), the NOMIS foundation (FTLD project), and the Parkinson Fonds Deutschland (Hypothesis-free compound screen, alpha-Synuclein fragments in PD). MK was funded by the German Federal Ministry of Education and Research (BMBF) grant "de.
Materials and Methods Implementation and validation of a new tool to quantify HDV RNA editing rates. To implement a new assay to measure HDV RNA editing rates, we used an NGS 'method using the MiSeq ® Illumina sequencer (Illumina, Eindhoven, The Netherlands) to quantify the proportion of edited versus non-edited genome sequences in the plasma of HDV-infected patients. To analyze the large amount of sequence data generated, we took advantage of the patent-protected "in-house" software recently developed for HIV sequence analyses, including the Pyromute, Pyrotrop and Pyrolink modules, which were further developed to create the PyroEdit software. Initial validation of the software was performed using computer-generated edited and non-edited sequences from reference sequences. Briefly, we created a program that generated several synthetic fastq files with the following characteristics: (I) fastq files containing 10 3 , 5 × 10 5 and 10 5 sequences; (II) consensus sequences from all HDV genotypes (HDV-1 to -8); (III) the presence of the W codon on all sequences of each genotype; (IV) sequence variability up to 30%; (V) Phred Quality Score ≥30, and (VI) a lower limit of detection of the W codon of 1%.
Determination of the Secondary Structures of the RNA Editing Region To analyze secondary HDV RNA structures, especially in the region surrounding the amber/W editing site, we determined the full-length genome sequence of 32 out of the 219 strains from the initial cohort (10 HDV-1, 7 HDV-5, 5 HDV-6, 5 HDV-7 and 5 HDV-8), as previously described  [9]  (Being published in EMBL). The more stable predicted secondary structures (RNAfolding) of the RNA editing site regions were determined using two online software applications with default setting from the entire antigenome sequences: RNAfold WebServer (http://rna. tbi. univie. ac. at/cgi-bin/RNAWebSuite/RNAfold. cgi accessed on 1 November 2019) and UNAfold WebServer (http://www. unafold. org/mfold/ applications/rna-folding-form. php accessed on 1 June 2021).
Statistical Analyses Statistical analyses were carried out using the R software (version 3. 5. 1, 2018 Free Software Foundation, Boston, MA, USA). The non-parametric Kruskal-Wallis test was used to compare population distributions. The Wilcoxon test was used to analyze paired samples of non-categorical data, while Pearson's test was used for statistical correlations. p values of <0. 05 denoted statistical significance. Figures were carried out under ggplot2.
Provision of a New Tool to Quantify HDV RNA Editing Rate We developed a new tool to measure the editing capacity of HDV strains in samples from infected patients. The workflow is based on NGS and quantifies editing of the amber/stop codon at positions 1010-1013 of the antigenomic RNA. Previously created in-house software was adapted to develop the PyroEdit application, that was subsequently validated to be used regardless of the number of sequences to analyze, threshold values, virus variability and HDV genotype. Firstly, the validation process was conducted by using computer-generated fastq files, as described in the "Methods" section. The mean difference between the expected values of edited genome rates (input) and the values calculated by PyroEdit (output) was less than 0. 1%, whatever the HDV genotype is (data not shown).
Altogether, HDV RNA editing is a key step in the virus life cycle that plays an important role in the natural history and clinical outcomes of human infection. In this study, we report the development and validation of a new NGS-based assay using the MiSeq ® Illumina device, together with a new in-house software, PyroEdit, which allowed us to measure the viral editing rate in plasma samples from treatment-naïve patients infected with different HDV genotypes. We also analyzed the predicted RNA structural features of the editing region of several strains from this cohort according to editing rate and HDV genotype. Our results show that our assay was able to: (I) be robust in spite of the high variability of the HDV genome, characterized by a nucleotide dissimilarity rate ranging from 20% to up to 35% over the entire genome sequence between the different HDV genotypes; (II) individualize the sequence of interest of 251 nt in length with a Phred Quality Score > 20% and with 99% accuracy; (III) correctly align each consensus sequence; (IV) check which nucleotide was present at the amber/W codon position and characterize the codon; and (V) calculate the editing rate and provide a final report.
After pre-processing of radiographs to generate high-quality local tomography (see Online Methods for further details), image reconstruction was performed using a filtered back-projection algorithm, coupled with a singledistance-phase-retrieval  20  , as implemented in the software PyHST2  21. Subsequently, ring artefacts were corrected on reconstructed slices  22. The entire pipeline, from obtaining fixed organs to 3D reconstruction of intact human organs scans at multiple resolutions is 2-4 days depending on the size of the organ with scanning time for HiP-CT is significantly faster than other similar techniques: ~16hrs for a whole brain at 25 µm per voxel and ~3. 5hrs for a whole kidney at 25 µm per voxel (see Online Methods and Table  1. for further details). To validate the utility of HiP-CT for imaging human organs, we scanned the intact human lung of a 94-year-old female (Donor 1 -clinical details in Online Methods) beginning with the whole organ at 25 µm voxel resolution, before randomly selecting VOIs for high-resolution imaging at a 2.
HiP-CT enables imaging of organotypic functional units at the scale of whole human organs Next, we sought to apply HiP-CT to image multiple length scales in a variety of human organs. Intact brain (Donor 2), lung, heart, kidney and spleen (Donor 1) were acquired from two donors (demographic and clinical details in the Online Methods), processed and imaged using the HiP-CT pipeline. Images were sequentially acquired at low resolution (25 µm per voxel) to capture the entirety of each organ before high-resolution (6 µm and 1. 3 -2. 5 µm per voxel) imaging of selected VOIs spanning each organ's breadth, (Supplementary Data 2, Supplementary Videos 1-3). At 25 µm voxel resolution, macroscopic features of each organ are visible including sulci and gyri of the cerebral cortex (Figure  2Ai  and Aii), individual lobules of the lung (Figure  2Bi  and Bii), the four chambers of the heart and associated coronary arteries (Figure  2Ci  and Cii), the pelvis and calyces of the kidney (Figure  2Di  and Dii) and the pulpa of the spleen (Figure  2Ei  and Eii).
Therefore, HiP-CT has the potential to quantify functional units and their 3D morphology with histological resolution within intact human kidneys, providing morphometric insights across entire organs. Ai) The three resolution datasets of HiP-CT taken of a human kidney aligned and overlaid. Aii) shows the measurement of the parenchymal volume semi-automatically segmented (green). Aiii) shows the 6µm dataset with the virtual biopsy cylinder in white. 853 glomeruli that were within this cylinder were counted (blue dots) and the parenchymal volume within the cylinder measured. Aiv) the 1. 3µm dataset with virtual biopsy (red cylinder) shown. The 13 glomeruli that abutted this cylinder were cropped from the data and segmented (yellow). B) Comparison of HiP-CT with aligned histopathological sections (Haematoxylin and Eosin (H&E) stained) taken after all HiP-CT scanning was finished.
Image quality comparison (Figure 1Cii and Ciii) Image quality comparison was performed using the structural similarity index  23   Equally sized subvolumes from the two columns were used in their original 32-bit form (999 slices in each case). Histograms of the two subvolumes were calculated with fixed bin width of 0. 001. Skew, kurtosis and mean pixel intensity were calculated via Pyradiomics v3. 0. 54. Structural similarity index was calculated between pairs of xy slices from the two subvolumes. Two images were randomly selected (by slice number); either both images were from the same subvolume (1-1) and (2-2) or the images were from different subvolumes (1-2) and (2-1). 200 pairs of images were analysed for each group. One-way anova of the output was performed in Matlab 2020b using the anova1 function.
Kidney Glomerulus analysis Using the 25 µm per voxel scans of the whole kidney, the parenchyma was semi-automatically segmented in Amirav2019. 6. After manually aligning all three resolution datasets, two cylinders, one through each of the 6µm and 1. 3µm columns, were defined perpendicular to the kidney surface. The cylinders had radii, lengths, and volumes of (1300µm, 40,811µm and 2. 17x10 11 µm 3 ) and (203µm, 9814µm and 1. 3x10 9 µm 3 ) for the 6µm and 1. 3µm datasets respectively. These cylinders were used as virtual biopsies. In the 6µm case any glomeruli that fell within the cylinder were counted (blue dots in Figure  3Aiii ). In the 1. 3µm dataset any glomeruli that touched the cylinder were manually segmented in Amira v2019. 6 in 3D. The volume, surface area and sphericity were calculated using the binary mask from the segmentation in ImageJ using the MorphoLibJ plugin-(analyse regions 3D)  58, 59.
2 m datasets of the COVID-19 and control lung, were chosen, (randomly in the case of the control lung, and in the COVID-19 case, randomly within larger areas designated by pulmonary histopathologists as structurally preserved or consolidated i. e. from areas where alveoli morphology was identifiable verses areas where there was widespread consolidation of the parenchyma). Binary images separating airspace from alveoli septea and small vessels were produced in ImageJ by thresholding the images used one two methods: 'triangle' method for Control and COVIDS and 'Yen' method for COVIDC  58. A 3D Chamfer distance from the MorpholibJ plugin  59  was used (using Svensson 3,4,5,7) to create the distance maps (Figure  4Di -iii). The BoneJ plugin (V2) was used to perform morphological analysis including connectivity, surface area to volume ratio, airway diameter and septal thickness  60, 61.
The breakthrough in the ability to recover DNA from environmental samples (eDNA) using metabarcoding technology has led to an easier, cheaper, faster, and noninvasive method of detecting and monitoring organisms  (Cordier et al. , 2020; Taberlet et al. , 2012; Yamamoto et al. , 2017). This method allows for the identification of species composition using predesigned molecular primers that target specific clades  (Deiner et al. , 2017). The recovered sequences can then be compared with a genetic reference database and used to identify the taxa present in the environmental samples  (Valentini et al. , 2016). The application of eDNA metabarcoding increases the species detectability and permits more exhaustive ecosystem monitoring compared to previous approaches  (Cilleros et al. , 2019). eDNA metabarcoding was shown to be particularly adequate to monitor fish species in environments where they are difficult to detect, like in large rivers  (Blackman et al.