dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported by  Wellcome Trust/ Department of International Development  (Grant  211309/Z/18/Z ; https://wellcome. org/supporting MM, RR, and FMR),  AXA Research Fund  (https://www. axa-research. org/supporting QTB, BN, JP and SC), and the  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases program  (Grant  ANR-10-LABX-62-IBEID ; https://anr. fr/supporting JPC, QTB, BN, JP and SC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
33 The SF-12 can replicate accurately the two dimensions of the SF-36 while also minimizing response burden. 30 e QoL questionnaire based on the QLSI 32 contains a combination of 25 items of the adolescent and adult versions of the QLSI. Items represent Life Priorities in different subscales (physical health, cognition, social, couple, leisure, work or school, housekeeping, affectivity and spirituality). Participants are asked to provide a score for each item ranging from 1 (essential) to 5 (not very important) based on the importance they attach to each item. Respondents are also asked to identify five of the 25 items in which they consider themselves the happiest at present and five in which they consider themselves as the unhappiest. We present data comparing the Life Priorities of respondents and controls and the areas of life where they considered themselves to be "the happiest" or "the least happy.
| Ethics At the time of the enrollment in studies 58,741, 58,831/2 and 58,881, informed consent was sought according to local practice of each participating center and in accordance with the Declaration of Helsinki. The EORTC study 58LAE (ClinicalTrials. gov Identifier NCT01298388) was approved by the Ethical Committees of the participating institutions and informed consent was obtained from all participants (patients and controls), in accordance with the applicable national legislation.
com/). Control subjects were identified among panel members based on specific pre-specified criteria (age, province, level of urbanization, and sex) to match the patient profile on our study database. Panel members whose profile matched the requirements were invited to participate in our study using a computer-and mobiledevice-based survey through an anonymous link. Controls were asked to confirm their informed consent electronically before starting to answer the QoL questionnaires. The SF-12 and QLSI questionnaires were identical to the ones completed by the survivors (except regarding the items influenced by the childhood ALL). Each ALL survivor was matched one-to-one to a population control sampled with the same age category(18-19, 20-21, 22-23…36-37, 38-39, 40-44,   45-52), region, level of urbanization (urban area vs.
ACKNOWLEDGMENTS The authors thank all the  EORTC Children Leukemia Group members  and the clinicians who participated in the studies. We also thank the EORTC HQ Data Management Department members ( Nicole Duez ,  Livia Giurgea ,  Bart Meulemans ,  Séraphine Rossi  and  Gabriel Solbu ) for their essential support. Finally, we thank  Nancy van Damme  and  Linda Thibaut  ( Belgian Cancer Registry ), and  Dr. Jacqueline Clavel  ( Registre National des Hémopathies malignes de l'Enfant ) for their support in the update of the vital status and of second neoplasms. This work was supported by the  EORTC Quality of Life Group  grant  013/2016 ,  Fonds Cancer  (FOCA) from  Belgium  and the  Kinderkankerfonds from Belgium  (a non-profit childhood cancer foundation under Belgian law) and  Kom op tegen Kanker (Stand up to Cancer) , the  Flemish cancer society from Belgium.
| Experimental overview A field-based experiment with low and high water flow treatments was carried out on the offspring of free-living pairs of orange-fin anemonefish Amphiprion chrysopterus, from a range of water flows in Moorea lagoon, French Polynesia. The experiment linked three life stages: adult; presettlement and postsettlement offspring (grey panels in Figures  1  and 2 ). To establish different water flow categories, flow at the sites of 23 free-living pairs of orange-fin anemonefish was measured over 12 days using Marotte HS (High Sampling Rate) drag-tilt current metres. Measurements at every 10 min were converted to flow velocity by postprocessing with a tilt-to-speed calibration curve using the MarotteHSConfig Software Version 3. 0 (Marine Geophysics laboratory, James Cook University; Figure  S1C ).
| Morphological measures After physiological measurements, adults were gently immobilised within hand nets and photographed using a Sony DSC-RX100 III camera. In addition, at 12 adult sites from which physiological measurements were not taken, adult anemonefish were captured, photographed for morphological measures and immediately released back onto their anemones. Presettlers were anesthetised (0. 1 g of MS222 in 1 L of water) and, on losing equilibrium, transferred to a petri dish containing clean filtered water where lateral photographs were taken with a binocular microscope (LEICA EX4W) before regaining normal activity. Similarly to adults, postsettlers were photographed using a Sony DSC-RX100 III camera. All sample sizes are summarised in Table  S3. The following morphological traits were measured using ImageJ software (Figure  S5 ; Table  S3 ): body elongation, the ratio of either total length (TL; distance from head to end of tail) in pre-and postsettlers or fork length (FL; distance from head to the fork of the tail) in adults to body height (BH; distance from the pelvic fin and the start of the dorsal fin); pectoral fin length ratio, length of the pectoral fin from insertion to the longest tip of the leading edge (PL) divided by fish body length; caudal fin length ratio, the ratio of the length of the caudal fin from the end of the standard length to the longest tip (CL) and fish body length.
| Statistical analyses All statistical analyses were carried out in R version 4. 0. 4 (R Core Team, 2019). To explore the effect of water flow regime on adult fish morphology and metabolism, we used LM with adult elongation, TL ratio, CL ratio and body mass-adjusted metabolic rate (MMR adj , SMR adj or AS adj ) as response variables (see SI-2 for details on body mass adjustments). Explanatory variables were fish sex, parental water flow and anemone surface area (Table  S8 ). To find the best fit model, all covariates were initially included with interactions and subsequently removed if nonsignificant using the likelihood ratio test (LRT) in the lmtest package  (Zeileis & Hothorn, 2002). To reveal differences among groups on model results, we carried out a Tukey post-hoc test (emmeans package;  Lenth, 2020).
In the SGR model, the initial offspring length was also included as a covariate to account for their asymptotic growth (Table  S14 ). We also explored the effect of developmental water flow on each individual fish over time by fitting an LMER with developmental water flow treatment and fish stage (pre-vs. postsettlement) as explanatory variables. Response variables were elongation, PL ratio, CL ratio, CAR or U max. Fish ID was also included as a random variable to account for repeated measures. Parental ID and offspring site were also included as random factors (Table  S13 ). The effect of parental and developmental water flow was also tested on offspring survival by fitting a mixed Cox proportional hazard model via the 'coxme' function in the coxme package  (Therneau, 2020). Parental water flow and developmental water flow were used as explanatory variables, the hazard function (risk of death at time t, which refers to the end of the experiment) used as a response variable, and parental ID was used as a random effect (Table  S15A ).
Funding information Agence National de la Recherche , Grant/ Award Number:  ANR-14-CE02-0005-01/ StayorGo  and  ANR-11-JSV7-012001 / WhereDoWeGoNow; LABEX  Corail, Grant/Award Number: LiveandLetDie ;  Natural Environment Research Council , Grant/Award Number:  NE/J019100/1 ;  Danish Council for Independent Research (now 'Independent Research Fund Denmark') , Grant/Award Number:  DFF-4181-00297.
Financial support was provided by the  Agence National de la Recherche  to  Glenn Almany , SCM and  RB  ( ANR-14-CE02-0005-01 / Stay or Go) and to SCM ( ANR-11-JSV7-012-01/Live  and  Let Die ), and by  LabEx  'CORAIL' to RB and SCM ('Where do we go now?'), by the  Danish Council for Independent Research (now the 'Independent Research Fund Denmark ';  DFF-4181-00297 ) to TN, by  Natural Environment Research Council Advanced Fellowship  to SSK ( NE/ J019100/1 ) and  European Research Council  starting grant to SSK ( 640004 ). We particularly thank  Till Deuss  for his invaluable knowledge in the aquarium facility. We are also thankful to  Marguerite Taiarui ,  Isla Davidson ,  Jules Schligler ,  Nao Nakamura ,  Gilles Siu ,  Pascal Ung ,  Anne Haguenauer ,  Frederic Zuberer ,  Franck Lerouvreur  and the  CRIOBE student community  for field support.
| Design of interface mutants The sequences of trimer-interface variants were based on the sequence of AncCDT-5, an inferred ancestral sequence from our previous work. 21, 23 Unless otherwise stated, the residue numbering used in this study is based on the numbering used in the crystal structure of AncCDT-5 (PDB 6WUP); as such, the numbering convention used here differs from the residue numbering convention used in our previous work. 21, 23 he trimer-interface-forming residues were identified by analyzing crystal structures of the trimeric PaCDT (PDB IDs 5HPQ and 6BQE) and using the InterfaceResidues script in PyMOL (https://pymolwiki. org/index. php/ InterfaceResidues). Sequence and structural alignments between AncCDT-5 and PaCDT were then used to identify positions that differed between AncCDT-5 and PaCDT at these trimer-forming interface positions; this guided the design of AncCDT-5 variants that differed only at these trimer-interface positions.
| SEC coupled with multiple angle light scattering (SEC-MALLS) Samples (100 μl) of each protein were loaded at 5-8 mg ml À1 onto a pre-equilibrated Superdex 200 10/300 GL size-exclusion column (GE Healthcare) attached to multi-angle light scattering (DAWN HELEOS 8; Wyatt Technologies) and refractive index detection (Optilab rEX; Wyatt Technologies) units. A flow rate of 0. 5 ml min À1 was used. The multi-angle detectors were normalized using monomeric bovine serum albumin (Sigma, A1900). A dn/dc value of 0. 186 g À1 was used for each sample. The data were processed using ASTRA 5. 3. 4 (Wyatt Technologies). Unless otherwise stated, data were collected from a single experiment for each variant (n = 1).
| Differential scanning fluorimetrythermostability measurements Differential scanning fluorimetry (DSF) was performed on a QuantStudio 3 Real-Time-PCR-System (Thermo Fisher). In a 96-well PCR plate, reaction mixtures containing approximately 5 μM protein in DSF buffer (50 mM Na 2 HPO 4 ,150 mM NaCl, pH 7. 4) and 1Â Protein Thermal Shift Dye (Thermo Fisher) were heated from 20 to 99 C while monitoring emission at 623 nm with excitation at 580 nm. Measurements were completed as technical triplicates, with melting temperatures (T m s) calculated and determined from the steepest part of the curve (i. e. , peaks in the derivative plot) in the Protein Thermal Shift Software v1. 4 (Thermo Fisher).
The cut-off for short-range electrostatics was 1. 2 Å. For each system, at least four random-seed replicates were performed. All systems were run for 500 ns per replicate. For analysis, frames corresponding to every 200 ps (i. e. , 0. 2 ns) were extracted (using gmx trjconv). RMSD and distance measurements were calculated using Gromacs tools (gmx rms and gmx distance). Data were plotted using matplotlib.
ACKNOWLEDGMENTS This research was conducted by the  Australian Research Council Centre of Excellence in Synthetic Biology  (project number  CE200100029 ) and funded by the  Australian Government. Colin J. Jackson  also acknowledges support from the  ARC Centre of Excellence for Innovations in Peptide and Protein Science (CIPPS). Ben E. Clifton  was supported by a  JSPS   Postdoctoral Fellowship for Overseas Researchers and KAKENHI Grant-in-Aid for Scientific Research  ( 20F20705 ). This research/project was undertaken with the assistance of resources and services from the  National Computational Infrastructure (NCI) , which is supported by the  Australian Government. Open access publishing facilitated by  Australian National University , as part of the  Wiley -Australian National University agreement via the Council of Australian University Librarians.
All generated constructs were confirmed by sequencing (Eurofins). For construction of the conditional PIM1 mutant (Pim1 ON/OFF ), the tetO-CYC1 promoter and Tet transactivator domain from pCM190 were restriction cloned into   9 ) and source data are provided. pFA6a-His3MX6 with EcoRI to create pYM-HIS-N-tetOFF. The whole cassette was PCR-amplified and transformed into yeast, where the resulting promoter exchange enabled inhibition of PIM1 gene expression upon the addition of µg/ml (final concentration) doxycycline (Sigma, D9891). For marker exchange from URA3 to HIS3 to create pHR81-HIS and pHR81-HIS-COQ9 via homologous recombination, HIS3 was amplified from BY4741 genomic DNA, the pHR81-URA and pHR81-URA-COQ9 plasmids were linearized with HindIII and the fragments transformed into yeast.
Data collection was carried out for 1000 s on an S2 PICOFOX spectrometer (Bruker Nano GmbH, Germany) equipped with a molybdenum excitation source (50 kV/600 µA, max. 50 W (max. 50 kV, max. 1 mA)) and XFlash 430 Picofox detector (Be/30 mm 2 ; measured energy resolution 132 eV (Kα-Mn)). Elements were assigned manually and spectra were quantified in the software Spectra v7. 8. 2. To ensure instrumental precision, gain correction was performed daily with 1 µg As on quartz carrier (supplied by the manufacturer). To ensure the accuracy of measurements, gallium (Ga) was added to each sample in appropriate amounts as internal standard. Sensitivity was tested with 1 ng Ni on quartz carrier (measured value sensitivity: 46 counts ng -1 mA -1 s -1 ; measured value (lowest limit of detection): 0. 104 pg).
Briefly, beads were combined with 1 ml of re-suspended cells and shaken 8 times (30 hz, 4 min) with 1 min break. Proteins were digested and prepared for mass spectrometry analysis using a modified version of the SP3 protein clean-up and digestion protocol  67. Peptides were labeled with TMT10plex reagent according to the manufacturer's protocol (Thermo Scientific). Each sample was separated using a Thermo Scientific Dionex nano LC-system in a 4 hr 6-40% ACN gradient coupled to Thermo Scientific High Field QExactive. The software Proteome Discoverer vs. 1. 4, including Sequest-Percolator for improved identification, was used to search the S. cerevisiae Uniprot database (UP000002311) for protein identification, limited to a false discovery rate of 1%.
Measurement of O 2 consumption in intact cells O 2 consumption in whole cells was measured in 2 ml of cell culture at 28 °C with a Clarke-type electrode (Rank Brothers Ltd, Cambridge, UK) calibrated with 1x PBS and 30 g/l Na 2 SO 3 for 5 min each. The decline in O 2 concentration in the electrode was monitored over 5 min/sample. Data was collected and analyzed with LabChart v7. 3. 8. For accurate comparison between strains (WT and Δpmr1), treatments (control and MnCl 2 ), and conditions (vector control and expression construct) the same amount of propidium iodide (PI) negative (live) cells were assayed. Cultures with less PI negative cells were diluted with spent media from the same culture.
Flow cytometry Propidium iodide (PI) staining indicative of loss of membrane integrity was used to assess cell death via flow cytometry as previously described  68. In brief, about 1 × 10 6 cells were collected in 96-well plates, incubated for 5 min with PI (Sigma 81845; final concentration 500 ng/ ml in PBS) and analyzed via flow cytometry using a Guava easyCyte 8HT B/R equipped with a 150 mW 488 nm laser (blue) and a 642 nm laser (red) and the following filters: 488/16 nm (SSC), 525/30 nm (green), and 695/50 nm (red) (Luminex/Merck group). Per sample, 5000 events were recorded. Data was acquired and analyzed using InCyte v3. 1, and respective gating strategy is shown in Supplementary Fig. 5a. For determination of mitochondrial mass, 0. 2 OD 600 of cells were pelleted, stained in 250 µl 100 nM MitoTracker Deep Red (Invitrogen TM M22426) in PBS, incubated for 10 min, pelleted and resuspended in PBS and evaluated via flow cytometry using the gating strategy shown in Supplementary Fig.
RNaseOUT TM (10777019, Invitrogen) was added to the reaction. DNA contamination was assessed via PCR with primers amplifying UBC6. qPCR was performed in a 20 µl reaction with KAPA SYBR® FAST qPCR Master Mix (KM41001, Merck) according to the manufacturer's instructions in a Rotor-Gene Q (QIAGEN) PCR cycler. All primers for qRT-PCR are listed in Supplementary Table  8. Data are represented as fold changes to WT after COQ mRNA levels were calculated relative to UBC6 mRNA levels with the comparative CT method (ΔΔCT)  75.
Following the last wash, beads were resuspended in 40 µl ABC buffer and an aliquot was taken for Coq7 protein quantification via immunoblotting (bound fraction). Samples were incubated with 1 µg trypsin (T8658, Sigma) for 16 h (37 °C). The digested samples were separated from the beads with spin-columns (BioRad, #7326207), the volume was determined and 5 µl of gallium standard (1 mg/l) were added. The complete volume was applied to TXRF quartz glass carriers in aliquots of 5 µl, which were repeatedly dried on a hot plate to increase sample concentration on the surface. See section on TXRF spectrometry for data collection.
Data preparation and statistical analysis Data were analyzed and graphs were generated with R v3. 6. 2 (base, ggplot2, tidyverse), RStudio v1. 2. 5  76  and GraphPad Prism v8. 0 (developmental assay). Figures were prepared in Adobe Illustrator v26. 5. Data sets with n ≥ 8 are presented as box plots with mean (x), median (line), first/third quartile (lower/upper bound), minimum/ maximum within 1. 5-fold IQR (lower/upper whisker) and outliers outside 1. 5-fold IQR (circle/o). Other data are presented as dot plots or line graphs with means and error bars showing standard error of mean (SEM). Principal component, differential protein expression and heatmap analysis were performed on the log2 transformed proteomics data set with stats::prcomp function, DeqMS package  77  and gplots package, respectively.
Acknowledgements This work was supported by the  Swedish Research Council  ( 2015-05468  and  2019-05249  to S. B. ,  2018-03694  to M. O. ), the  Knut and Alice Wallenberg Foundation  ( 2017. 009  to S. B. and M. O. ),  Stiftelsen Olle Engkvist Byggmästare  ( 194-0681  and  207-0527  to S. B. ), the  Austrian Science Fund FWF  ( J4342-B21  to V. K. ),  Deutsche Forschungsgemeinschaft DFG under Germany's Excellence Strategy  ( CIBSS-EXC-2189 , Project  ID 390939984  to FNV), SFB1381 (Project  ID 403222702  to F. -N. V. ),  423813989/GRK2606  (to F. -N. V. ) and the  Emmy-Noether program  (to F. -N. V. ), the  Medical Research Foundation  ( DPM2012112553  to F. P. ), and the  ANR  ( ANR-19-CE44-0014  to F. P. ). Mass spectrometry analysis was performed by the  Clinical Proteomics Mass Spectrometry facility ,  Karolinska Institutet/Karolinska University Hospital/Science for Life Laboratory.
Fig. 2  Weighted gene co-expression network analysis (WGCNA). The gene co-expression network is displayed with genes as nodes and edges representing the co-expression relationships between genes. Nine modules were detected within which gene expression was especially strongly correlated (see Supplementary Fig. 2  for more details. ) Shown are the seven largest modules, represented by colors. For each module, information is supplied on the caste or queen stage with which module expression is significantly correlated; upward arrow indicates a significant increase, downward arrow a significant decrease, in expression. The top three enriched GO terms associated with the gene members of each module are displayed (for full lists of significant GO terms, see Supplementary Data 1). This network was created with Cytoscape (version 3.
Taken together, these data suggest that QT2 is a transitory stage where the final phase of fat body maturation is prepared or initiated in many processes. Our PCA results support these effects (Supplementary Fig. 1b ). In QT3, the abdomen has become enlarged. We found that over 90% of all cells in the fat bodies of QT3 and QT4 have 4C. The tree was constructed with iqtree2 (Q. pfam+R4 model and 10,000 bootstraps) and visualized with iTOL (v. 6). Branch labels are bootstraps, the tree is rooted at the Strigamia maritima gene. Fig. 5  Heatmap with the expression of genes in fat bodies during adult queen maturation involved in lipid metabolism. Heatmap representing standardized gene expression (blue = low; red = high) at each of the five queen stages (QT0-QT4). Annotations to the right of the heatmap include the WGCN module (Supplementary Fig.
Queens were sampled after 3 months (QT1), 9 months (QT2) and after 31 months (QT3). During the first years of a colony's life, larvae emerge which become sterile workers or soldiers  35. At QT1, the number of minor and major workers, soldiers, presoldiers, and larvae were counted in colonies where queens were sampled. At QT2, these were also counted and the fungus was weighed. Species identity. Total DNA was isolated from the head and the legs of one imago of each of the nine colonies. PCR was performed using the cytochrome oxidase I gene primers: LCO 5'-GGT CAA CAA ATC ATA AAG ATA TTG G-3' and HCO 5'-TAA ACT TCA GGG TGA CCA AAA AAT CA-3'  73  and the 650-bp amplified fragment was sequenced and analyzed using the Barcode of Life Database identification system (www. barcodinglife. org). Species identity of each colony was confirmed to be M.
Ligation products were PCR-amplified. All libraries were subjected to size profile analysis conducted by Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) and qPCR quantification (MxPro, Agilent Technologies, Santa Clara, CA, USA) using the KAPA Library Quantification Kit for Illumina Libraries (KapaBiosystems, Wilmington, MA, USA), then sequenced using 150-bp paired-end read chemistry on a HiSeq 4000 Illumina sequencer (Illumina, San Diego, CA, USA). An Illumina filter was applied to remove the least reliable data from the analysis. The raw data were filtered to remove any clusters with too much intensity corresponding to bases other than the called base. Adapters and primers were removed on the whole read and low-quality nucleotides were trimmed from both ends (when the quality value was lower than 20).
Sequences between the second unknown nucleotide (N) and the end of the read were also removed. RNA-seq analyses. The M. natalensis genome  49  was downloaded from the gigadb database (http://gigadb. org/dataset/100057; accessed March 2019). RNA-seq reads were mapped against the genome using hisat2 (version 2. 1. 0  74 ) at default settings. Gene expression levels were then generated by counting reads mapping to each gene of the M. natalensis genome (annotation version 2. 3) using htseq-count  75. Differential expression analyses were carried out in R (3. 5. 1) with the DESeq2 package  76  , comparing between all pairs of castes and queen stages, as well as comparing each caste and queen stage against all others. Genes were considered significantly differentially expressed if the adjusted P value was less than 0.