dataesr/scientific-paragraphs-categorization huggingface dataset:
, A. S. , G. P. T. , A. H. , C. A. S. , L. M. K. , J. G. B. , N. J. W. , M. G. J. , S. A. R. , J. G. , J. S. , and  S. F. M. All  authors approve of the final draft of this manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. DNA and RNA data for this manuscript can be found under NCBI BioProjects listed in table S5. The  R40  and  GG1 v1. 0 genome assemblies can be found on NCBI GenBank under the accessions  JACMSA000000000  and  JACMSB000000000 , respectively. The genome assemblies and annotations can also be found on Phytozome (https://phytozome-next. jgi. doe. gov/). The Lanisha TE has been deposited in NCBI GenBank under MT647524.
The published sequence data used in this study can be found in table S17. Supporting documents for the phylogenomic analysis of sex-linked genes can be found on Dryad under https://doi. org/10. 5061/dryad. v41ns1rsm. Materials and correspondence should be addressed to stuartmcdaniel@ufl. edu. Submitted 24 February 2021 Accepted 14 May 2021 Published  30 June 2021 10. 1126/sciadv. abh2488.
The paper was washed three times in 75 mM H 3 PO 4 and then  32  P incorporation into the peptide was determined by liquid scintillation counting. The Flag-BRI1 cytoplasmic domain was incubated with MBP or MBP-DAY 30 min before the assays. Kinase assays were performed with the BR13 peptide substrate (sequence: GRJKKIASVEJJK). Hypocotyl elongation measurement. These measurements were performed as described  52. Briefly, sterilized seeds were plated on 1/2 MS with 1% sucrose and 0. 8% phyto agar at 22 °C under long-day conditions. After 2 days of incubation at 4 °C, seedlings were irradiated with white light for 6 h to promote germination and then incubated in the dark. Hypocotyl lengths were measured using ImageJ software. Transcriptome analysis. RNA-seq was performed with Illumina Nextseq500.
The Truseq-stranded mRNA prep kit (Illumina) was used for library preparation. The raw data were mapped and aligned with Tophat v2. 0. 13  53  , using the TAIR10 version of the Arabidopsis reference genome. Cuffdiff v2. 2. 0 54 was used to identify differentially expressed genes (DEGs) using a threshold of fold change > 2. Gene set analysis was performed by calculating the enrichment of DEGs in Gene Ontology: Biological Process (GO:BP) terms  55  with the Fisher's exact test. The enrichment was considered significant if P-value ≤ 0. 01 for each GO:BP term. Yeast two-hybrid. The cytosolic domain of BRI1 was introduced into pGBKT7. Two deletion constructs of DAY (ΔcTP and ΔcTP/TM) were cloned into pGADT7. Various combinations of bait and prey plasmids were co-transformed into yeast (AH109). Yeast two-hybrid was performed according to the manufacturer's instructions (Clontech).
Acknowledgements This work was supported by grants from the  Austrian Academy of Science  through the  Gregor Mendel Institute  and by the  Austrian Science Fund (FWF)  (I  3654 ) to Y. B. This research was supported by the following grants to HSP:  Basic Science Research Program  ( NRF-2018R1A6A1A03025607 ) and  Mid-Career Researcher Program  ( NRF-2019R1A2B5B01069573 ) from the  National Research Foundation (NRF) of the Republic of Korea. The research was supported in part by  Brain Korea 21 (BK21) FOUR program ; I. Y. C. is a fellowship awardee of  BK21 FOUR program.
D OCR analysis by Seahorse ® technology of amino acid-starved U2OS cells incubated in the presence (blue) or absence (purple) of LQ during 72 h. OCR was measured either in basal conditions or after the injection of oligomycin, FCCP, and rotenone/antimycin A. Data are mean ± SEM of three biologically independent experiments performed with five replicates. E Basal respiration used to drive ATP production as determined by OCR quantification of data obtained in (D). F Immunoblot of mTORC1 activity markers (S6K, S6, and 4EBP1 phosphorylation) and AMPK phosphorylation of U2OS cells incubated with or without amino acids, in the presence or absence of LQ during 72 h. G Immunoblot of mTORC1 activity markers (S6K and S6 phosphorylation) and AMPK phosphorylation of amino acid-starved U2OS cells incubated in the presence or absence of LQ during 24, 48, or 72 h.
Finally, cells were analysed using BD FACSCalibur BD Biosciences flow cytometer. The analysis of the data was performed using the BD CellQuestTM Pro Analysis software. Real-time PCR. The total messengers RNAs from cells were isolated using TRIzol reagent (Invitrogen). All samples displayed RIN greater than or equal to 8 as determined using the RNA 6000 Nano Kit and the bioanalyzer 2100 (Agilent Technologies). After quantification and normalization, 1 μg of total mRNA was retrotranscribed using GoScript Reverse Transcription System (Promega). Realtime PCR was performed in triplicate for each sample with SSO Advanced Universal SYBR Green Supermix (BioRad), 1 ng of cDNA and transcript-specific primers using Step-One Plus Real-Time PCR system (Applied Biosystems). Specific primers were purchased from Sigma-Aldrich.
A Sequant ZIC-pHILIC column (150 × 2. 1 mm, 5 μm) and guard column (20 × 2. 1 mm, 5 μm) (Merck Millipore) were utilized for the chromatographic separation  33. The column oven temperature was maintained at 40 °C. The mobile phase was composed of 20 mM ammonium carbonate and 0. 1% ammonium hydroxide in water (solvent A), and acetonitrile (solvent B). The flow rate was set at 0. 2 ml/min with the following gradient: 80% B for 2 min, linear decrease to 20% of B 15 min. Both solvents were then brought back to initial conditions and maintained for 8 min. The mass spectrometer was operated in full MS and polarity switching mode. The acquired spectra were analysed using XCalibur Qual Browser and XCalibur Quan Browser software (Thermo Fisher Scientific) by referencing to an internal library of compounds.
One field was acquired from each coverslip and the data were pooled from three independent coverslips on three independent days. Data were processed using OriginPro 7. 5 software (Origin Lab, Northampton, MA, USA). On graphs, data were summarized as the mean ± standard deviation (SD). Statistics. The results are expressed as a mean ± standard error of the mean of at least three independent experiments. One-way ANOVA followed by Bonferroni's comparison as a post hoc test were used to evaluate the statistical difference of the results between more than two groups. Statistical significance was estimated when p < 0. 05. Immunoblots, micrographs, and clonogenic assays are representative of at least three biologically independent experiments.
Acknowledgements This work was supported by funds from the following institutions:  Agencia Estatal de Investigación/European Regional Development Fund, European Union  ( PGC2018-096244-B-I00 ,  SAF2016-75442-R ),  Ministry of Science, Innovation and Universities of Spain ,  Spanish National Research Council-CSIC ,  Institut National de la Santé et de la Recherche Médicale -INSERM ,  Université de Bordeaux ,  Fondation pour la Recherche Médicale , the  Conseil Régional d'Aquitaine, SIRIC-BRIO, Fondation ARC , and  Institut Européen de Chimie et Biologie. C. B. was recipient of fellowships from the  Minister of Higher Education, Research and Innovation (France)  and the  Fondation ARC (France). We thank  Prof. Patricia Boya  ( Centro de Investigaciones Biologicas, Madrid, Spain ) for kindly providing with the ATG5 +/+ and ATG5 -/-MEFs.
Introduction: Atypical hemolytic uremic syndrome (aHUS) is a rare, complex, multisystem disease of dysregulated complement activity, characterized by progressive thrombotic microangiopathy (TMA), acute kidney injury, and multiorgan dysfunction, which often progresses to chronic kidney disease. Results from the prospective clinical trial of ravulizumab (NCT02949128) reveal rapid resolution of TMA in patients with aHUS, with sustained efficacy and safety in a 26-week initial evaluation period.
METHODS Trial Oversight and Design ALXN1210-aHUS-311 (NCT02949128; EudraCT 2016-002027-29) is a phase III, single-arm, global study evaluating the efficacy and safety of ravulizumab administered by i. v. infusion to adults ($18 years of age) with aHUS who are naive to complement inhibitor treatment. The study methodology and results from the initial 26-week evaluation period have been previously reported. 16 This report details the results of all patients available for follow-up (median: 76. 7 weeks; range: 0. 6-118. 3 weeks) from the currently ongoing 4. 5-year extension period. The dosing regimen for ravulizumab used in this study has been described previously. 16 Bodyweightdependent maintenance doses ($40 to <60 kg, 3000 mg; $60 to <100 kg, 3300 mg; $100 kg, 3600 mg) were repeated every 8 weeks in the extension period.
Safety Endpoints The long-term safety and tolerability of ravulizumab were evaluated by physical examinations, vital signs, electrocardiograms, laboratory assessments, and by assessing the incidence of AEs and serious AEs. AEs were coded using MedDRA version 21. 0, and the severity of AEs was graded using the Common Terminology Criteria for Adverse Events version 4. 03. The proportion of patients who developed antidrug antibodies was also evaluated. In the previous report of the primary data from the initial 26-week evaluation period, safety analyses were performed on all safety data available at the time (median of 40 weeks). 16 In this study, safety analyses were conducted on all data until the last available follow-up and compared with safety data specifically from the initial 26 weeks.
Genetic Analysis For patients who provided informed consent for genetic analyses, complement genetic variant analysis was performed by whole-exome sequencing using the Novaseq 6000 platform with a 2 Â 150-base pair pairedend module (Illumina, Inc. , San Diego, CA), with pathogenesis determined as previously described. 16 For patients who provided consent to local centers, local site investigators were contacted and asked if they would agree to provide available data on complement gene variants. Variants were reviewed against previously reported variants in the literature and classified as pathogenic, likely pathogenic, nonpathogenic, or variant of uncertain significance. The known pathogenic and likely pathogenic variants are reported.
CLINICAL RESEARCH T Barbour et al. : Ravulizumab Long-Term Outcomes but did not satisfy all criteria simultaneously in the initial evaluation period. This highlights the prospect that continued therapy beyond 6 months may be required to achieve maximum therapeutic response in some patients. Furthermore, the patients also maintained the clinically meaningful responses in hematologic, renal, and quality of life (as measured by Functional Assessment of Chronic Illness Therapy-Fatigue) outcomes observed during the initial evaluation period, suggesting some patients may continue to benefit beyond 6 months of treatment. Taken together, these data reveal that ravulizumab is effective in longer-term prevention of TMA. In the current study, genetic testing by whole-exome sequencing was performed as an exploratory analysis in consenting patients, and the initial results have been published previously.
DATA SHARING Alexion will consider requests for disclosure of clinical study participant-level data provided that participant privacy is assured through methods like data de-identification, pseudonymization, or anonymization (as required by applicable law), and if such disclosure was included in the relevant study informed consent form or similar documentation. Qualified academic investigators may request participant-level clinical data and supporting documents (statistical analysis plan and protocol) pertaining to Alexionsponsored studies. Further details regarding data availability and instructions for requesting information are available in the Alexion Clinical Trials Disclosure and Transparency Policy at https://alexion. com/our-research/research-anddevelopment. Link to Data Request Form (https://alexion.
ACKNOWLEDGMENTS Medical writing support was funded by Alexion Pharmaceuticals, Inc. , Boston, Massachusetts, USA, and was provided by Leia Silvagnoli-Compston, MSc, and Alexander T. Hardy, PhD, of Bioscript, Macclesfield, UK. The authors acknowledge Åsa Lommelé, PhD, and Jonathan Mathias, PhD, of Alexion Pharmaceuticals, Inc. for their critical review of the manuscript. The authors also acknowledge Marc Vallee and Jimmy Wang for their assessment of all data and statistical analyses and.
DATA AVA I L A B I L I T Y S TAT E M E N T The time series used in this analysis (see File S1) come from the published BioTIME data set  (Dornelas et al. , 2018)  or other publicly available sites BioTIME data are open access and can be accessed on Zenodo (https://doi. org/10. 5281/zenodo. 2602708) or through the BioTIME website (http://bioti me. st-andre ws. ac. uk/). The four studies not included in the BioTIME database are accessible via the references and sources listed in Data S1 (metadata_for_marine_fish_ assemblages. xlsx). The BioTIME level studies were subset into grid cells to maintain spatial extent throughout; information on this process can be found in the supplementary materials and in the supplement for Blowes et al. (2019). Code for this process is available on Zenodo at https://zenodo.
DNA metabarcoding is an increasingly popular technique to investigate biodiversity; however, many methodological unknowns remain, especially concerning the biases resulting from marker choice. Regions of the cytochrome c oxidase subunit I (COI) and 18S rDNA (18S) genes are commonly employed "universal" markers for eukaryotes, but the extent of taxonomic biases introduced by these markers and how such biases may impact metabarcoding performance is not well quantified. Here, focusing on macroeukaryotes, we use standardized sampling from autonomous reef monitoring structures (ARMS) deployed in the world's most biodiverse marine ecosystem, the Coral Triangle, to compare the performance of COI and 18S markers. We then compared metabarcoding data to image-based annotations of ARMS plates. Although both markers provided similar estimates of taxonomic richness and total sequence reads, marker choice skewed estimates of eukaryotic diversity.
, 2018). Sources of biases include sampling methods  (Elbrecht et al. , 2017; Ransome et al. , 2017) , DNA extraction method  (Deiner et al. , 2015) , PCR amplification protocol and primer choice  (Clarke et al. , 2014 (Clarke et al. , , 2017;; Cowart et al. , 2015; Piñol et al. , 2019; Tragin et al. , 2018; Zhan et al. , 2014) , library preparation techniques  (Braukmann et al. , 2019; Zizka et al. , 2019) , sequencing error  (Deagle et al. , 2019; Elbrecht & Leese, 2015) , bioinformatics biases  (Brannock & Halanych, 2015; Brown et al. , 2015; Flynn et al. , 2015) , and the unavailability of an adequate reference database  (Cristescu, 2014; DiBattista et al. , 2020; Gold et al. , 2020; Ransome et al. , 2017). One the greatest sources of bias in metabarcoding studies is associated with marker choice.
Notably, a marker may fail to identify an organism or group of organisms due to issues with primer universality or reference database gaps. In studies focused on eukaryotic biodiversity (e. g. , ARMS sampling), the mitochondrial cytochrome c oxidase subunit I (COI) and small subunit (SSU) 18S ribosomal RNA (rRNA) gene regions are two common targets that are considered "universal markers" that capture a broad range of eukaryotic taxa  (Hebert et al. , 2003; Leray & Knowlton, 2016; Tragin et al. , 2018). Of these, 18S is the more commonly selected universal marker (59% and 28% of studies target the 18S and COI gene, respectively; van der Loos & Nijland, 2020). While both are considered universal markers that perform well across many eukaryotic taxa, they have known limitations that impact their ability to faithfully detect taxonomic diversity.
The COI marker is often selected due to the purported availability of well-curated reference databases  (Hebert et al. , 2003)  and higher taxonomic precision; yet, it is ineffective across certain taxa. For example, while the high substitution rate in COI can provide excellent resolution in terms of taxonomic assignments  (Andújar et al. , 2018; Leray & Knowlton, 2016) , species level discrimination is poor for some early diverging metazoan groups, such as Porifera and Anthozoa  (Hebert et al. , 2003; Huang et al. , 2008; Shearer et al. , 2002). The comparatively fast mutation rate also makes it difficult Bryozoa and Porifera and lower estimates of Chordata as compared to 18S, but 18S recovered 25% more phyla than COI. Thus, while COI more reliably reflects the occurrence of dominant sessile phyla, 18S provides a more holistic representation of overall taxonomic diversity.
, 2017). Conversely, due to the relatively conserved nature of 18S, it provides noteworthy taxonomic coverage across a broad range of eukaryotes  (Creer et al. , 2010; Fonseca et al. , 2010; Hadziavdic et al. , 2014; López-García et al. , 2001; Moon-van der Staay et al. , 2001; Zhan et al. , 2013) , but this comes at the cost of inferior taxonomic resolution compared to COI  (Leray & Knowlton, 2016; Wangensteen et al. , 2018). While broad-range biodiversity assessments (currently dominated by plankton studies) commonly target the highly diverse V4 and V9 regions of 18S to maximize coverage across a wide assortment of eukaryotic groups  (Casas et al. , 2017; DiBattista et al. , 2020; Pearman et al. , 2014; Pearman & Irigoien, 2015; Tragin et al. , 2018; de Vargas et al. , 2015) , the 18S gene region considerably underestimates eukaryotic species, especially cryptic microbial metazoans that are unaccounted for in DNA reference databases  (Piganeau et al.
| Sequence bioinformatics Unlike COI bioinformatic pipelines, 18S pipelines are well-established in the metabarcoding literature. Given the intrinsically distinct nature of these two primer sets, different bioinformatic pipelines were used to process the COI and 18S sequences. To process the COI sequences, we first merged paired-end reads using PEAR  (Zhang et al. , 2013) , demultiplexed sequences using the FASTX-Toolkit FASTQ Barcode splitter (http://hanno nlab. cshl/ fastx_toolk it/), and removed tags/indexes with FLEXBAR  (Dodt et al. , 2012). We then conducted quality filtering with Trimmomatic (removing sequences <200 bp;  (Bolger et al. , 2014) , and additional sequence cleaning was performed via the Multiple Alignment of Coding Sequences (MACSE;  Ranwez et al. , 2011) , which explicitly accounts for the underlying codon structure to filter out inconsistent sequences.
Specifically, we aligned sequences to a high-quality library of COI barcodes from the Mo'orea BIOCODE database  (Meyer, 2016)  to remove sequences with stop codons, frameshifts, insertions, and more than three deletions  (Leray et al. , 2012). We then clustered dereplicated sequences into operational taxonomic units (OTUs) at 97% similarity using the uclust command in USEARCH  (Edgar, 2010) , discarding all singleton OTUs (OTUs that occur only once across the dataset). We assigned OTUs to phylum using the basic local alignment search tool (BLASTn;  Altschul et al. , 1990) , comparing OTUs against two databases: a local download (on April 14, 2020) of COI data from NCBI excluding environmental samples (but including prokaryotes) and a local BIOCODE database  (Meyer, 2016)  using the Smithsonian Institution High Performance Computing Cluster (https://doi.
org/10. 25572/ SIHPC). We assigned phylum when sequence similarity was ≥85% (following  Ransome et al. , 2017) , and OTUs with a similarity threshold below that threshold were designated as "unknown. " In cases where a different result was obtained from the NCBI and BIOCODE databases, we selected the result from the BIOCODE database because all BIOCODE samples have associated vouchers. Phylum names were based on a recently created hierarchical classification of life  (Ruggiero et al. , 2015). For 18S sequences, we used USEARCH for bioinformatic processing. First, we merged pair-end reads and performed quality filtering. We then removed reads shorter than 380 bp and longer than 440 bp. After primer removal, sequences were clustered at 99% similarity. We used VSEARCH for reference-based and de novo detection of chimeras, which were subsequently removed, with the Protist Ribosomal Reference (PR 2 ) database (v4.
5; Guillou et al. , 2013). After discarding all singleton OTUs, we assigned taxonomy at ≥90% sequence identity with BLASTn using the PR 2 database and the SILVA database (release 128;  Pruesse et al. , 2007). Phylum-level taxonomy was accepted when sequence similarity was ≥90%, and OTUs below that threshold were designated as "unknown. " Again, we named phyla based on a recently created hierarchical classification of life  (Ruggiero et al. , 2015).
| Data analysis We ran all analyses with the statistical software R (version 3. 6. 1; R Core Team, 2019). Data wrangling was performed with the tidyverse package  (Wickham, 2017) , and all visualizations were made with the packages ggmap  (Kahle & Wickham, 2013)  and ggplot2  (Wickham, 2016). From the raw OTU tables, we produced rarefaction curves for the COI and 18S datasets to visualize sampling effort and OTU richness across sequences, as well as to simulate extrapolated projections with additional sampling with the iNEXT package  (Chao et al. , 2014). For both markers, we generated rarefaction curves for each fraction (500 μm, 100 μm, and sessile fractions) of each ARMS, as well as for each ARMS as a singular unit after summing across fractions. Next, we converted the raw sequence read data to relative read abundances (RRA; in accordance with  Deagle et al.
, 2019) , which we used for the majority of the analyses and is a fitting metric for the analysis of hyperdiverse metabarcoding data  (Casey et al. , 2019). Using the RRA data, we generated non-metric multidimensional scaling (nMDS) ordinations based on Bray-Curtis dissimilarity matrices with the vegan package  (Oksanen et al. , 2018)  to examine the clustering relationships between ARMS and fractions for the COI and 18S markers. The nMDS ordinations were also used to visualize the potential impact of spatiotemporal variables (year and site) on these assemblages. All taxonomy-based analyses were conducted at the level of phylum. We found a higher percent of unidentified sequences in the COI dataset as compared to the 18S dataset (COI = 41. 1%; 18S = 0. 6%), which substantially impacted RRAs across identified phyla.
Thus, for all phylum-based analyses, we removed all unidentified OTUs from the raw dataset and recalculated RRAs using only OTUs with phylum-level taxonomic assignments. Furthermore, we maintained the separation between spatiotemporal variables across all analyses to avoid confounding factors. To visualize the performance of each marker, we calculated and ranked the 10 phyla with the highest average relative abundances across the ARMS from each spatiotemporal treatment (2012_Site1, 2012_Site2, and 2013_Site1), as well as creating an "Other" category to summarize all remaining phyla not included in the top 10 (see list of phyla in the "Other" category; Table  S2 ). To analyze the taxonomic differences between the COI versus 18S taxonomic data from the ARMS summed across fractions, we ran a Bayesian mixed model with the packages brms  (Bürkner, 2017) , tidybayes  (Kay, 2019) , and rstan  (Stan Development Team, 2019).