dataesr/scientific-paragraphs-categorization huggingface dataset:
In C. gigas pediveliger larvae, the transcriptome of the adhesive gland is difficult to obtain due to the small size of the organism and the complexity of this organ. However, the development of high-throughput nucleic acid sequencing methods (DNA and RNA) has led to a significant increase in the number of sequences available in generalist or specific databases (for the transcriptome of C. gigas: Riviere et al. (2015)  [16] ). The use of appropriate informatics tools makes it possible to identify sequences of interest in databases, compare them, analyze them and define their potential biological roles  [17]. Many studies, known as in silico studies, use the available genomic data to identify genes involved in a defined biological process in order to answer to a working hypothesis. Sequence selection criteria (genomic expression rate, specificity of organs or certain stages of development, functional annotations) are defined according to the biological questions raised.
This dataset groups the RPKMs (read per kilobase million) of each gene at each stage of development, and in different tissue of adult oysters. These quantitative data make it possible to visualize the expression rate of each genomic sequence. Nine developmental stages defined by 38 sampling time from hours to days after fertilization and 11 different adult organs were analyzed in this article. The pediveliger stages were named P1 and P2 and correspond to larvae of 18 days old larvae (precisely sampled at 18 days and 45 min and at 18 days, 4 h and 35 min after fertilization). The objective of this study was to identify genes in the transcriptomic data published with the genome of C. gigas that could have a potential role in the adhesion of the pediveliger larvae. The identification of these genes could allow us to suggest the probable protein composition of the adhesive and to pinpoint the biosynthesis pathways and molecular cascades involved in their secretion and cross-linking.
Results Fifty-nine sequences were selected as being specifically expressed at the pediveliger stage of C. gigas (Table  1)   S14  of  Zhang et al. (2012)   [24]. sequences had at least one predicted conserved domain and/or one repeat sequence based on analysis with InterPro  [25]  (Figure  1 ). Forty-two sequences had extracellular localization according to DeepLoc 1. 0  [26]. Twenty-one sequences, or 35. 6% of the selected sequences, were annotated as hypothetical proteins, indicating the absence of known functions from the databases. The number of uncharacterized sequences is slightly lower than the 41. 8% of sequences annotated as hypothetical proteins in the database used as a whole.
Calcifying Sequences Redox enzymes such as tyrosinases (CGI_10009044 | EKC19270, CGI_10014286 | EKC25254 and CGI_10006802 | EKC29117), peroxidase (CGI_10016593 | EKC32997), and the two carbonic anhydrases (CGI_10010889 | EKC18733 and CGI_10011324 | EKC32754) could be related to bio-calcification. Indeed, these enzymes have been classically reported in shell synthesis processes in molluscks and could coincide with the beginning of calcite layer production, which occurs after metamorphosis, at the spat stage. These sequences are poorly expressed at the spat stage, however, compared with the pediveliger stage, although no quantitative relationship has yet been established between the RPKM value observed for any mRNA of sequences in the dataset and the abundance of the corresponding protein. Nevertheless, these sequences are also poorly expressed at the adult stage, particularly in the mantle, the organ responsible for shell synthesis.
• RPKM of stages LU1, LU2 below 70% that of P1 or P2. LU1 and LU2 correspond to "later umbo larvae 1" and "later umbo larva 2" at 14 and 15 days post-fertilization, respectively. The RPKM selection threshold of these two stages was higher than for other larval stages according to the potential heterogeneity of the larval cohort and potential RNA synthesis before settlement at the pediveliger stage. The protein sequences corresponding to the selected genomic sequences were then searched for on the NCBI database. Functional annotation of protein sequences was performed with BLAST2GO 4. 0. 7 software. A BLASTP search was performed with BLASTP2. 7. 1 + (NCBI) from BLAST2GO in the non-redundant protein database (nr) on 23 August 2018. Conserved domains and repetitive sequences were predicted with InterPro  [25] , and subcellular localization was predicted with DeepLoc 1.
Funding: This research received no external funding. Acknowledgments: This work was supported by the  European Institute for Marine Studies (IUEM, France) ,  University of Western Brittany , the  Brittany local government  ( CR Bretagne ) and by the  "Laboratoire d'Excellence" LabexMER  ( ANR-10-LABX-19 ). It was co-funded by a grant from the  French government  under the program " Investissements d'Avenir ".
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  675008. This work was performed using HPC resources from  GENCI  (Grant  2017-A0022A10129 ). We also thank  Mathieu Lorteau ,  Emeric Martin ,  Florent Renac  and  Marie-Claire Le Pape  for valuable discussions.
Nevertheless, these methods are either limited by a low spatial or low temporal resolution. In this work, high-speed atomic force microscopy (HS-AFM) was used to follow the dynamic interaction between Aβ peptide variant and model membranes at the molecular scale. This technique is particularly suitable to overcome dynamics limitation while keeping a high spatial resolution. Indeed, it gives access to dynamic data at a biomolecular scale  27  thanks to its sub-second time acquisition and its high lateral resolution. Thus, HS-AFM is a perfect imaging tool to identify the dynamics of the interaction mechanisms and to better understand the importance and the role of ganglioside GM1 and cholesterol in the interaction between Aβ 1-42 and model membranes. Lipid compositions were selected in order to get relevant models.
Results obtained between the replicates show a perfect reproducibility. Diffusion coefficients were calculated after extracting successive images from raw data of HS-AFM videos with a self-built software working with Igor Pro (Wavemetrics). For each frame serie, (x,y) positions of a given amyloid peptide were recorded manually with the manual tracking plugin of ImageJ software. Amyloid peptide diffusion constant rates were calculated according to a Brownian diffusion model equation: D = 〈 r 2 〉/4t with 〈 r 2 〉 the mean square displacement and t the time interval between each measured position.
9E7. 4 Antibody The 9E7. 4 mAb was obtained by immunization of a rat with a 40-amino-acid peptide (GeneCust, Luxembourg, Luxembourg) derived from the murine CD138 protein (aa 90-130) (GenBank: CAA80254. 1). Its characterization was ensured within the team as previously described  [19]. The isotype of this antibody is IgG2a,κ, and its binding specificity is around 1 × 10 -10 M. 89 Zr DFO is the most common chelator used in 89 Zr-labeled radioimmunoconjugates studies. P-isothiocyanatobenzyl-desferrioxamine B (DFO-SCN) was purchased from Macrocyclics (Dallas, USA). DFO-9E7. 4 was prepared according to published protocols  [20]  by conjugating DFO-SCN to 9E7. 4 mAb to secondly chelate 89 Zr (t 1/2 = 78. 4 h; β + , 22. 7%, E β + max = 897 keV). Briefly, an aliquot of 4 mg/mL of 9E7. 4 was prepared in borate buffered saline solution (0.
Bioluminescence Imaging Mice were serially imaged using 2D bioluminescence imaging as previously described  [14]  to locate tumor progression. The mice were anesthetized with intraperitoneal injection of 100 µL/10g of an anesthetic solution (consisting of 1 mL ketamine at 100 mg/mL (Panpharma); 0. 5 mL xylazine at 20 mg/mL (Bayer); and 8. 5 mL PBS). Mice were injected intraperitoneally with 100 µL of luciferin (Interchim, 12 mg/mL) 5 min before being imaged. Mice were imaged in ventral and dorsal positions using a Photon IMAGER ™ (Biospace Lab, Paris, France) for 30 s. The images were analyzed using the M3Vision ™ software (Biospace Lab, Paris, France).
Digital Autoradiography Femurs of Mice 14, 15 and 20 were removed, fast-frozen in cold 2-methylbutane solution, embedded in optimal-cutting temperature compound and cut into 10 µm sections using a cryomicrotome (CM3050 Leica Biosystems®). Sections were mounted on Superfrost™ slides and digital autoradiography images were obtained on a BeaQuant -Real-time autoradiography (Ai4R, Nantes, France). Image analysis was performed on the dedicated software Beamage ® (version 1. 0, Ai4R, Nantes, France).
Contributions: Conceptualization, C. B. , S. G. and M. C. ; methodology, C. B. , S. G. and M. C. ; software, T. C. and N. C. ; validation, C. B. , S. G. and M. C. ; formal analysis, C. B. , S. G. and M. C. ; investigation, C. B. , S. G. , B. C. and M. C. ; resources, S. G. , F. G. , A. F. -C. , P. R. -L. S. , M. B. , L. R. -V. , F. H. , R. T. and B. C. ; data curation, T. C. ; writing-original draft preparation, C. B. and S. G. ; writing-review and editing, M. C. , F. K. -B. and C. B. -M. ; supervision, M. C. , F. K. -B. and C. B. -M. Funding: This research was funded in part by grants from the French National Agency for Research, called "Investissements d'Avenir" IRON LabEx n • ANR-11-LABX-0018-01, IGO LabEx n • ANR-11-LABX-0016-01, Siric ILIAD, DHU Oncogreffe, ArronaxPlus Equipex n • ANR-11-EQPX-0004 and NExT n • ANR-16-IDEX-0007.
Acknowledgments: The authors would like to thank the  Radioactivity Technical Platform , and especially  Catherine Maurel  and  Séverine Lambot , the  UTE IRS-UN , and particularly  Sylvia Lambot , and the  Cellular and Tissular Imaging Core Facility of Nantes University (MicroPICell) , and specifically  Myriam Robard  and  Stephanie Blandin , for their help.
Author Contributions: Conceptualization, C. B. , S. G. and M. C. ; methodology, C. B. , S. G. and M. C. ; software, T. C. and N. C. ; validation, C. B. , S. G. and M. C. ; formal analysis, C. B. , S. G. and M. C. ; investigation, C. B. , S. G. , B. C. and M. C. ; resources, S. G. , F. G. , A. F. -C. , P. R. -L. S. , M. B. , L. R. -V. , F. H. , R. T. and B. C. ; data curation, T. C. ; writing-original draft preparation, C. B. and S. G. ; writing-review and editing, M. C. , F. K. -B. and C. B. -M. ; supervision, M. C. , F. K. -B. and C. B. -M. Funding: This research was funded in part by grants from the French National Agency for Research, called "Investissements d'Avenir" IRON LabEx n • ANR-11-LABX-0018-01, IGO LabEx n • ANR-11-LABX-0016-01, Siric ILIAD, DHU Oncogreffe, ArronaxPlus Equipex n • ANR-11-EQPX-0004 and NExT n • ANR-16-IDEX-0007.
When a significant effect of a treatment on stable isotope values was observed, an analytical normalization of non-treated samples was established with linear models. In order to test for species and sex-related differences in linear models, values observed and predicted by the models were statistically compared using either a t-test or Wilcoxon rank sum test, depending whether the dataset adhered to parametric assumptions. Differences in C:N ratios between non-treated and treated samples were assessed for each species. As the data did not follow parametric assumptions, Kruskal-Wallis tests followed by Dunn post-hoc analyses with Bonferroni corrections were used. All statistical analyses were performed using the software R version 3. 4. 3 with a significance level of 0. 05.
Acknowledgements We would like to thank the people who collected the samples and more specifically T. Poirout and  B. Reche. G. Guillou  who analyzed all samples at the isotope facility at  La Rochelle University. U. Martin  was supported by the  Université de La Réunion  and the mobility program Erasmus+ from the  Université Catholique de Louvain. This study is a contribution to the  EcoReco-Run and Eurraica  projects funded by the  SEB DEAL-Réunion  through the  Shark Risk Management Plan of the French Government.
Statistical analysis Data were extracted from the Gynelog clinical database used in our department. This database is approved by the French National Commission for Information Technology and Civil Liberties (CNIL) to be used for clinical research. According to French law (2012-300), patients are aware that their data can be used for anonymous clinical studies unless they specifically state otherwise. This information is detailed in posters in the rooms of the centre, and patients can inform the centre through a letter if they do not want to participate in clinical studies. The measured primary outcome was the live birth rate per attempt. Statistical analyses were performed using StatView software (Abacus Concepts Inc. , Berkeley, CA). Data are means AE SD or median (range) according to the normality of the data.
METHODS From January 2000 to December 2014, patients with LMUM who underwent hepatic resection with curative intent were identified from our prospectively maintained surgical database and retrospectively analysed. This retrospective study had IRB approval, and informed consent was waived. R0 resected patients were first selected. According to our institutional practice, after R0 resection, patients underwent surveillance without chemotherapy. Patients were evaluated by liver MRI every four months during a period of three years and by thoraco-abdomino-pelvic CT scan once a year. When liver recurrence was suspected a new thoraco-abdomino-pelvic CT scan was performed to rule out any extra-hepatic involvement. Patients were then discussed in a multidisciplinary oncological meeting and were offered liver local treatment combining surgery and/or RFA whenever possible.
Data collection The patient charts were retrospectively reviewed, and the following data were collected: age, sex, characteristics of primary UM and treatment, age at time of liver metastasis and time interval from the initial treatment of the primary uveal melanoma to metastasis. For the first metastatic liver event and for hepatic recurrence, the following were recorded: intrahepatic tumour number, location and size of metastases, presence of miliary disease (more than five lesions <5 mm located mainly in the area of the hepatic subcapsular region), type of surgical resection of the first metastases, type of treatment of recurrence of metastases with RFA, recurrence-free interval between the two procedures, treatment of metastatic recurrence after the second procedure, the date of last followup and date of death.
Genomic analysis of LMUM and ocular UM DNAs were obtained from snap-frozen samples according to standard procedures (proteinase K/RNAseA treatment and phenol/chloroform extraction using PLGL (Eppendorf, Hamburg, Germany), then qualified and quantified with a Nanodrop and a Qubit dsDNA BR Kit (Thermo Scientific, Wilmington USA). Up to 1μg tumour and reference DNA were labelled and cohybridised to the NimbleGen or Agilent Microarrays. The slides were washed and scanned according to the manufacturers' instructions. Images were acquired, data extracted and produced files analysed on suitable devices and softwares (For Nimblegen: GenePix 4000B, V. 6. 6 Software, NimbleScan V. 2. 5, SignalMap V. 1. 9 (Roche NimbleGen Inc. Madison USA); For Agilent: SureScan, CytoScan V. 2. 7, CytoGenomics V. 2. 7 (Agilent Technologies, Santa Clara, USA).
Statistical analysis Quantitative data are reported as median and ranges, and qualitative data were presented as numbers and proportions; missing data were not taken into account. The survival curves were constructed according to the Kaplan-Meier method. Overall survival was defined as the time from metastasis diagnosis to the occurrence of death from any cause. Two recurrence-free intervals were estimated. The first one was defined as the time from the treatment of metastasis to a first recurrence. The second one was defined as the time from radiofrequency ablation to a second recurrence (patients without post-RFA recurrence were censored at the date of last news. ) All analyses were performed using R software (version 3. 22; http://cran. r-project. org).
91 and 2. 77, reaching no statistically significant difference. In addition, two independent flow cytometry experiment measuring the total excitation on 10 000 cells did not show differences in the geometric mean fluorescence intensity between STAT and VBNC. The β-oxidation pathway described in the KEGG database was used to visualize the 63 cellular proteins differentially produced in VBNC and the 8 proteins for fatty acid degradation metabolism (Fig  8C ). The fatty acid degradation pathway (β oxidation) is well-characterized in Saccharomyces cerevisiae with several genes found also in C. neoformans genome (Fig  8D ). Interestingly, the corresponding proteins in C. neoformans were known to be under produced in VBNC. The 9 deletion mutants of proteins identified from the KEGG database and by the orthology with Saccharomyces genome available in the Madhani collection were tested in the model for their ability to generate VBNC.
neoformans infection, the relationship between acetyl-CoA, PA-a precursor of acetyl-CoA-and dormant cells should be further analyzed. The fact that PA was able to resuscitate VBNC cells, confirms the potential relevance of the link made by the proteomic study between VBNC and metabolic pathways of acetyl-CoA. Qualitative and quantitative changes in fatty composition occurs in C. neoformans depending on the growth phase, with an increase during growth progression  [83]. In addition, KEGG pathway mapping of the 63 proteins regulated in hypoxia showed an involvement of 8 proteins involved in fatty acid degradation. The 8 proteins involved were downregulated in hypoxia compared to normoxia. Another reconstruction of the fatty acid degradation pathway using Saccharomyces genome database involved the 4 proteins also downregulated in hypoxia FAA2 (CNAG_03019), POX1 (CNAG_07747), FOX2 (CNAG_05721), POT1 (CNAG_00490).
Lipid droplets were stained using Bodipy 505/515 (Invitrogen, Carlsbad) as already used in Saccharomyces cerevisiae  [89]  at 1μg/mL with 10 7 cells for 20 min at room temperature. To assess viability, LIVE/DEAD Fixable Violet Blue (416 nm/451 nm) diluted at 1:1000 and 250 μL was added to 10 7 yeasts pellets and incubated at 30˚C in the dark. Controls were dead yeasts generated upon exposure to 200 mM of oxygen peroxide (H 2 O 2 ) at 37˚C, under 650 r/min agitation or heat killed 1h at 65˚C. Yeasts were analyzed by fluorescence microscopy and pictures taken using an AxioCam MRm camera (Carl Zeiss, Oberkochen) on interferential contrast microscope (DMLB2 microscope; Leica, Oberkochen) with Zeiss Axiovision software. Mitochondrial mass and polarization were evaluated with Mitotracker Green (Invitrogen) at 40 nM, JC-1 (Invitrogen) at 10 μg/ml and TMRE at 200nM in PBS on 10 7 yeasts for 30 min at 37˚C and then washes 3 times in PBS.
Reactive oxygen (ROS) nitrogen species (RNS) measurements were also performed. Microplate wells were filled with 10 4 yeasts in 100 μL PBS. Briefly, the probes for ROS (2',7'-dichlorofluorescein diacetate, Sigma) and RNS (dihydrorhodamine 123, Invitrogen) were diluted at 100 μM in PBS and methanol, respectively, and 20 μL were added to wells of a microtiter plate containing 10 4 yeasts in 100 μL of PBS. Hydrogen peroxide (4M) was included as a positive control. The plates were incubated at 37˚C in the dark. After 1 hour, fluorescence was measured with a fluorometer (Fluoroskan ascent fluorometer, Thermos Fischer Scientific, Waltham, MA, USA) using excitation 485 nm and emission wavelengths of 530 nm  [90]. The data were expressed as arbitrary units of fluorescence ±SD.
Flow cytometry measurements and cell sorting Flow cytometry was used to quantify MDY64 staining, viability and apoptosis assays, yeastsusing the Guava easyCyte 12HT Benchtop Flow Cytometer (Guava, MERCK, Kenilworth, New Jersey). Multispectral flow cytometry (ImageStreamX, Amnis Corporation) was used to quantify mitochondrial fluorescence as described before  [23, 88]. For each population of interest, the geometric mean was calculated using the IDEAS software. In specific experiments, yeasts were sorted on a BD FACS aria II flow cytometer. The gating strategy included the exclusion of the aggregates and the doublets. Subsequently, the sorting was carried out according to the intensity of CMFDA in 3 populations (CMFDA high , CMFDA medium and CMFDA low ).
Digestion was stopped by adding formic acid to 5% final concentration and peptides were desalted and concentrated on Sep-Pak C 18 SPE cartridge (Waters, Milford, MA, USA) according to manufacturer's instructions. Mass spectrometry data analysis. Tryptic peptides were analyzed on a Q Exactive Plus instrument (Thermo Fisher Scientific, Bremen) coupled with an EASY nLC 1000 chromatography system (Thermo Fisher Scientific). Sample was loaded on an in-house packed 30 cm nano-HPLC column (75 μm inner diameter) with C18 resin (1. 9 μm particles, 100 Å pore size, Reprosil-Pur Basic C18-HD resin, Dr. Maisch GmbH, Ammerbuch-Entringen, Germany) and equilibrated in 98% solvent A (H2O, 0. 1% FA) and 2% solvent B (ACN, 0. 1% FA). Peptides were first eluted using a 2 to 18% gradient of solvent B during 112 m, then a 18 to 30% gradient of solvent B during 35 m, a 30 to 45% gradient of solvent B during 15 min and finally a 45 to 60% gradient of solvent B during 5 min all at 250 nL.
m -1 flow rate. The instrument method for the Q Exactive Plus was set up in DDA mode (Data Dependent Acquisition). After a survey scan in the Orbitrap (resolution 70 000), the 10 most intense precursor ions were selected for HCD fragmentation with a normalized collision energy set up to 27. Charge state screening was enabled, and precursors with unknown charge state or a charge state of 1 and >7 were excluded. Dynamic exclusion was enabled for 45 s. Data processing and analysis. All data were searched using Andromeda  [100]  with Max-Quant software  [101, 102]  version 1. 5. 3. 8 against the H99 strain proteins database (6980 entries) and the H99 strain mitochondria proteins database (13 entries) concatenated with usual known mass spectrometry contaminants and reversed sequences of all entries.
The latter interaction was useful to model the pairing between measurements corresponding to the same condition. The proteins differentially expressed over time are selected according to the following criteria: change with respect to the initial value of log2 and a p <0. 05. The p-values obtained were adjusted by the method of Benjamini-Hochberg  [107]. Analysis of enriched processes for secreted proteins "Gene Ontology (GO) enrichment analysis". The fungifun2 v2. 2. 8 BETA  [108]  site (https://elbe. hki-jena. de/fungifun/fungifun. php) was used by selecting for each sample of secreted proteins the corresponding cellular protein background and to study the cellular proteins the background was the whole database. For this analysis we selected only the proteins identified in the 3 replicates per condition.
Differential expression analysis To identify the expression differences across conditions, the read counts were filtered and normalized using the R package "DESeq2". Genes with read counts below a threshold of 10 in all samples were filtered out. After normalization of read counts, differentially expressed genes for each condition were identified using the generalized linear model, which perform pairwise comparisons among each of the conditions (STAT, 8D-HYPOx, 8D-NORMOx and LOG). Genes passing the threshold, (FDR <5%), were considered significantly differentially expressed and were then considered to be up or down-regulated according to the direction of the fold change. The k-means clustering analyses were performed using R tools, "kmeans" and "heatmap. 2". To discover the underlying pathways of these differentially expressed genes, we also carried out functional annotation analysis by summarizing the data, including known H99 gene annotation, S.