dataesr/scientific-paragraphs-categorization huggingface dataset:
Egg mass To test whether group size interacted with laying order, we fitted a linear mixed model (LMM) with egg mass as response variable and laying order, group size, and their interaction as variables of interest. As covariates, we included clutch size and mother tarsus size, which was previously found to explain egg mass variation  (Fortuna et al. 2021)  In the previous studies, short-term effects of rainfall and temperature on sociable weavers' egg size and composition were not detected  (Paquet et al. 2013; Fortuna et al. 2021 ) and we thus did not include climatic variables in our models, but accounted for inter-annual variation in climate by including a 'season' variable. The single effect of group size and covariates on egg mass will not be discussed here as a previous analysis was performed in an extension of the dataset used here (i.
e. , not including laying order, N = 1928; here N = 779; see  Fortuna et al. 2021). The random terms' structure included nest, breeding female, colony identity, and season. Spearman rank correlation coefficients were never above 0. 26. This and all models described hereafter were run in a Bayesian framework using the MCMCglmm package (Hadfield 2010), because it better accommodated random terms with low estimated variance (see Tables S4-S15 in online Appendix B). We scaled and centered all numerical independent variables to improve interpretation and comparison of effects and to enable interpreting main effects when these are included in an interaction  (Schielzeth 2010) , respectively. This was done by subtracting their mean and dividing by one standard deviation, and numerical response variables were scaled by dividing by one standard deviation.
Latent variables were truncated to prevent under/overflow  (Hadfield 2010). Number of iterations, burn-in, and thinning intervals were adjusted to ensure minimum effective sample sizes of 1000 (see code for details). Plots show raw data and the predicted effects estimated using the 'predict' function in MCMCglmm  (Hadfield 2010). We present means [and 95CrIs] from the posterior distributions of interest in the results.
Hormones: testosterone, A4, and corticosterone We found no support for an interactive effect of group size and laying order on hormonal concentration, namely on testosterone (-0. 03 [-0. 16; 0. 08]; p = 0. 624; N = 122; Figs. 3g  and S3 ; Table  S11 ), A4 (0. 03 [-0. 21; 0. 28]; p = 0. 838; Fig. 3  Relationship between egg components (a to i) and laying order for females with different group sizes. Lines represent the posterior predicted means and 95% credible intervals for three group size values: group size = 2 (no helpers; grey dotted line), mean group size (between 2. 6 and 4 depending on dataset; orange dashed line) and the average between mean and maximum group size (between 3. 3 and 5. 6; blue solid line). Points represent raw data and point colors represent observations for groups without helpers, groups between group size = 2 and mean group size or group sizes above the mean (rounded to the nearest integer) Content courtesy of Springer Nature, terms of use apply.
54; 0. 90] of the times when laid by females with a group size above average (Fig. 4 ). In accordance with the first fledging probability model (larger dataset than here, see Fig. 2  and online Appendix B1), later-laid eggs tended to have lower fledging probabilities  (OR 0. 53 [0. 28; 1. 00] ; p = 0. 047; Fig. 4 ; Table  S15 ).
'Differential allocation' and offspring survival We then assessed if females breeding with more helpers had higher reproductive success, which could be partially mediated by laying eggs with heavier yolks and richer in lipids (i. e. , 'differential allocation';  Russell and Lummaa 2009). For this, we used the long-term dataset to test the interactive effect of group size and laying order on hatching and fledging success. Results showed no effect of this interaction on nestling survival, suggesting that chicks hatching from laterlaid eggs that are raised with more helpers do not appear to have an advantage in terms of fledging success over the remaining chicks. There are several possible explanations for the lack of detectable interactive effects between laying order and group size on hatching and fledging success.
Acknowledgements We thank the editors and reviewers for their useful feedback on the manuscript. We are grateful to everyone that contributed to the breeding data collection and annual captures, particularly  Sofia Jerónimo ,  Rita Leal ,  Ryan Olinger ,  Samuel Perret , and  Annick Lucas. We are thankful for the contribution to the egg content analyses of  Olivier Chastel ,  Emmanuelle Grimaud ,  Marie Pallud , and  Cécile Ribout  at  CEBC ,  Maria del Rey Granado ,  Pablo Giovannini ,  Bruno Buatois , and  Raphaelle Leclerc  (PACE) at CEFE. We acknowledge  Pietro D'Amelio  for scientific discussions about the study. De Beers Mining Corporation provided access to  Benfontein Nature Reserve  and logistical assistance.
Funding Open access funding provided by FCT|FCCN (b-on). Data collection was supported by funding from the  DST-NRF Centre of Excellence at the FitzPatrick Institute of African Ornithology  ( South Africa ),  FCT (Portugal)  through grants  IF/01411/2014/CP1256/ CT0007 ,  PTDC/BIA-EVF/5249/2014  and  CEECIND/03451/2018  to RC and the  French ANR  through projects  ANR-15-CE32-0012-02  and  ANR 19-CE02-0014-02  to CD. This work was conducted under the  CNRS-CIBIO Laboratoire International Associé (LIA Biodiversity ) and the  OSU-OREME. R. F. and A. C. F. were funded by  FCT  ( SFRH/BD/130134/2017 ,  COVID/BD/151899/2022 , and  SFRH/ BD/122106/2016 , respectively).
coli CM2372 as a dependent variable can be explained using the two independent variables time (time) and presence or absence of copper (+Cu = 1; -Cu = 0), we set up a linear model (Eq. 1) describing their individual contribution and the contribution of their interaction: relative abundance of E. coli CM2372 = β 0 + β 1 time + β 2 Cu + β 3 time * Cu (1) The model was run for the datasets from each river individually. Time on its own had a significantly negative effect (β 1 < 0) on the abundance of E. coli CM2372 in biofilms from both rivers (HAU: p < 0. 001, HIR: p < 0. 001), mirroring the observation that in the absence of copper the focal E. coli strain decreased in abundance. Copper on its own (β 2 ) had no significant effect on the initial relative abundance of E. coli CM2372 as described above.
Funding This work was supported by the  ANTIVERSA  project ( Bio-divERsa2018-A-452 ) here funded by the  Bundesministerium für Bildung, und Forschung of Germany  under grant number  01LC1904A  and the  French Agence Nationale de la Recherche  under grant number  ANR-19-EBI3-0005-04. KB was supported through a  DAAD scholarship  in the program  Research Grants -Bi-nationally Supervised Doctoral Degrees/ Cotutelle ,  2021/22  ( 57552338 ). UK & TUB were supported by the  Urban Resistome  project funded by the  Deutsche Forschungsgemeinschaft (DFG)  under project number  460816351  and the  PRESAGE  project funded by the  Bundesministerium für Bildung und Forschung  under grant number  02WAP1619. GS was supported through a  Faculty Initiation Grant  ( FIG/100762 ) and the  Indian Department of Science and Technology  under grant number  DST/TM/INDO-UK/2K17/ 46.
1981) , social comparison (adapted from  Gibbons and Buunk, 1999) , and self-reported strategy to evaluate the post (multiple-choice question; if "I followed previous participants' answers" is selected, another multiple choice question followed asking why they did so). This last measure was also used to assess whether the participants felt compelled to use the distribution data during their evaluation, a form of experimenter demand. In addition to responses in the questionnaire, we obtained information about participants from the recruiting platform, such as their level of education, socio-economic status, social media use, and belief in climate change. Analyses. Statistical tests were conducted using R (R Core Team, 2018). We adopted the standard 5% significance level to test against the null hypotheses.
The intensities were pre-normalized by the number of scans of each spectrum. Hydration maps were generated using a python script integrated with nmrglue and matplotlib packages  68, 69. Polysaccharide dynamics were probed using  13  C spin-lattice (T1) relaxation, which was examined using a series of 2D  13  C-  13  C correlation spectra with different z-filter durations (0-s, 0. 1-s, 1-s, 3s, and 9-s)  70. The absolute intensity of each peak was quantified and then normalized by the number of scans. Relaxation data were fit using a single exponential function to obtain  13  C-T1 time constants (Supplementary Table  8 ).
Long-range electrostatics are treated using the particle mesh Ewald with a 1. 2 Å grid spacing 84. To enable a 2 fs timestep between force evaluations, covalent bonds involving hydrogen atoms were treated using the RATTLE algorithm  85. The Langevin thermostat was set to maintain a temperature of 300 K using a 5 ps -1 damping coefficient  86. A microsecond-long MD simulation was performed for an NPT ensemble, using a Langevin isotropic barostat that was set to maintain 1 atm pressure 87. The simulation trajectory was analyzed using VMD 1. 9. 4a55, which was enabled with Python 3. 10. 12 and Numpy 1. 24. 2 88. In-house scripts were prepared in Tcl 8. 6 and Python 3. 10. 12 programming software to calculate and quantify the intermolecular interactions between polysaccharides using the coordination number collective variable 89.
Acknowledgments This work was supported by the  National Institute of Health (NIH)  grant  AI173270. The  National High Magnetic Field Laboratory  is supported by the  National Science Foundation  through  NSF / DMR-1644779  and  2128556 , and the  State of Florida. The MAS-DNP system at NHMFL is funded in part by  NIH   S10 OD018519 ,  P41 GM122698 , and  RM1 GM148766. The modeling work was supported in part through computational resources and services provided by the Institute for Cyber-Enabled  Research at Michigan State University. D. S. and J. V. V acknowledge support from  DE-FG02-91ER20021  from the  U. S. Department of Energy, Office of Basic Energy Sciences. The authors thank  Drs. Ivan Hung  and  Zhehong Gan  for technical assistance. Authors Contribution M. C. D. W. prepared fungal samples and conducted the NMR experiments.
Acknowledgments The authors thank  Adrien Aubert  ( Technical support -Laboratoire de Pharmacie Galénique et Génie Pharmaceutique-ICGM UMR5253 -D1 Chimie et Matériaux Moléculaires ) for incorporation of insect larvae in kibbles. For the purpose of  Open Access , a CC-BY public copyright licence (Creative Commons -Attribution  4. 0 International -CC BY 4. 0 ) has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission.
Fig. 4B ). These data suggest that FAS in AML cell lines is prominently regulated by SREBP2, as shown in other models  (31). Decreasing FAS by inhibition of either FASN or ACC or via AMPK activation abolished SSI-4 mediated toxicity (Fig. 3H , Suppl. Fig. 4B-C ). Interestingly, analysis of the Depmap dataset shows that SCD dependency inversely correlates with the expression levels of both FASN and ACACA (ACC) across all cancer cells lines and particularly AML ones (Suppl. Fig. 4D ). Overall these data confirm that modulation of FAS impacts sensitivity to SCD inhibition.
7D ). Moreover, analysis of the BeatAML dataset showed that higher SCD expression correlates with reduced sensitivity in vitro to cytarabine, an antimetabolite known to cause DNA damage and used in combination with anthracyclines to treat AML patients (Suppl. Fig. 8D ). Finally, to validate these findings in vivo we used two aggressive models of fully established AML that are more representative of the scenario routinely encountered in clinic, a cell line derived xenograft (CDX) and a transplant of leukemic iMLL-AF9 cells. The latter reached an average leukemic blasts infiltration in PB of 20% before treatment (Suppl. Fig. 8E ), a common criterion for AML diagnosis. Similarly to what we observed in the PDX, response to SSI-4 therapy as a single agent was varied with significant survival improvement in the CDX model but not in the murine iMLL-AF9 model.
STUDY DESIGN This study aimed to reveal whether SCD represents a promising therapeutic target in acute myeloid leukemia and determine toxicity and efficacy of clinical grade inhibitor SSI-4 in AML setting. Therefore we used the full spectrum of AML models: patient datasets, cell lines, a cohort of 47 primary AML samples from two independent institutions, murine AML models, cell lineand patient-derived xenograft models. Initial epidemiological studies that attempted to evaluate SCD association with AML prognosis and response to therapy were performed on datasets comprising more than 1000 patients. Cell line studies on a panel of 8 genetically heterogeneous AML cell lines were designed to determine SSI-4 efficiency in inducing anti-leukemic effects, specificity to SCD inhibition, cellular metabolic response and modes of action.
Flow cytometry Briefly, Cell viability was determined using anti-Annexin V antibody with propidium iodide, 7-AAD or Zombie NIR™ stains. Cell cycle progression was determined using PI solution. Lipid uptake measurement was performed using C1-Bodipy C12 500/510, while lipid peroxidation was measured with Bodipy 581/591 and determined as the ratio of MFI in the green and red channel. Immunophenotypic analyses were performed using antibody cocktails as described in Figure  legends  and Supplementary methods. Flow cytometry analyses were performed using LSRFortessa and FACSymphony A3 (BD) instruments and all data analysis was performed using FlowJo 10. 0 software.
Metabolomics experiments For lipidomics analysis, lipid species were extracted using monophasic isopropanol extraction and analyzed using liquid chromatography-mass spectrometry following the methodology described before  (61). Lipid annotation was performed with LipiDex software using the parameters used in the original publication  (62)  and additional analysis of the lipidomics dataset was performed with the LipidSuite webtool (https://suite. lipidr. org). For fatty acid profiling, apolar metabolites were isolated from cells using chloroform:methanol extraction and fatty acids partitioned from polar metabolites by resuspension of dried extracts in chloroform:methanol:water. Data acquisition was performed using gas chromatography -mass spectrometry. Data was acquired using MassHunter software (version B.
STATISTICS AND REPRODUCIBILITY All data was analyzed and visualized in Prism 9. 0 (GraphPad) and all data are shown as mean ± standard error of the mean, unless otherwise stated. The cohorts were dichotomized into groups with high and low gene expression after calculating the optimal cut point value using the receiver operating characteristic (ROC) curve for censored overall survival data. Overall survival was plotted using Kaplan-Meier plots, using Cox proportional hazard regression to compare the differences between the curves, providing the Hazard ratio (HR) and the 95% confidence interval (CI). According to data availability, we adjusted prognosis prediction for confounders as follows: age (as continuous variable), sex (male vs. female), white blood cell counts (WBC, as continuous), and European LeukemiaNet categorization (ELN2010 or ELN2017).
Data and materials availability: All regents and materials in this study are listed in Supplementary table 1. SSI-4 can be obtained through material transfer agreement (MTA) with Modulation therapeutics Inc. All cell lines generated in this study can be obtained upon request. The RNA-sequencing data generated in this study have been deposited in the ArrayExpress database and are available at E-MTAB-13174. Data from free fatty acid profiling and lipidomics analyses are available in Supplemental data 1, 2 and 3. DNA sequencing, RNA sequencing, proteome and phosphoproteome data on BCI primary AML samples were derived from previously published studies from Barts Cancer Institute  (52, 64). Publicly available clinical and transcriptomic data of five adult AML cohorts whose patients were treated with intensive chemotherapy were used to investigate the prognostic role of SCD expression: AML TCGA (data obtained from https://www.
cbioportal. org/), GSE6891, GSE425 (Bullinger), GSE10358, GSE14468. Normalized gene expression data were retrieved from the Gene Expression Omnibus (GEO) database (www. ncbi. nlm. nih. gov/geo/). SCD signature was generated by dichotomizing patients in TCGA dataset in high and low expressing samples based on median expression. Dataset GSE97631 was used to determine SCD signature expression in MRD stage. Data from manuscript 10. 1056/NEJMoa1808777 (NEJM1808777),(  65 ) and GSE66525 were used to perform comparative RNASeq analyses on paired diagnosis-relapse samples in human cohorts and murine models. BeatAML dataset (data obtained http://www. vizome. org/), was used to determine association of SCD expression with sensitivity to cytarabine ex vivo. Data from manuscript 10. 1016/j. cell. 2019.
(F) Tumor burden and SCD signature expression in Ara-C-treated PDX models (GSE97631) and GSEA for SCD signature at MRD stage (E) A panel of eight AML cell lines was treated with SSI-4 (0. 01 -10 µM) or corresponding vehicle for 72h. Cells with less than 10% decrease in viability were designated resistant. Results are presented as non-linear regression of normalized response and data points are mean ± SEM. * p < 0. 05, ** p < 0. 01, *** p < 0. 001, **** p < 0. 0001. 13 C-Glucose (2 g/L) and treated with SSI-4 (1 µM) or vehicle control for 24h. Graphs represents percentage of 13 C-glucose incorporation in palmitate (C16:0), stearate (C18:0), palmitoleate (C16:1) and oleate (C18:1). Data are mean ± SEM. * p < 0. 05, ** p < 0. 01, *** p < 0. 001, **** p < 0. 0001. Figure  3. SFA/MUFA ratio and lipotoxicity in response to SCD inhibition depend on the activity of FAS pathway.
Data are mean ± SEM. * p < 0. 05, ** p < 0. 01, *** p < 0. 001, **** p < 0. 0001. (A) Significantly enriched KEGG pathway signature in MV-4-11 cells treated with SSI-4 (1 µM) or vehicle control for 24h. (B) Sensitive K562, MOLM-13, MV-4-11 and resistant OCI-AML3, THP-1, HL-60 and Kasumi-1 cell were treated for 24h with SSI-4 (1 µM) or vehicle control. Lipid peroxidation was measured using Bodipy C11. (C) Lipidomics analysis on MV-4-11 cells treated for 24h with SSI-4 (1 µM) or vehicle control. Upper graph represents enrichment analysis per lipid groups of treated cells vs. control (Q1-Q3 with line at median value) with significant lipid groups marked in red. Lower graph represents significant differentially expressed individual lipids with upregulated lipids presented on the right hand-side and down-regulated lipids on the left hand-side of the graph.
Red bars: padj < 0. 05. (D) MV-4-11 and MOLM-13 cells were treated for 72h with SSI-4 (1 µM) or vehicle control with or without addition of Ferrostatin-1 (5 µM). (E) MOLM-13 cells in early apoptosis (Annexin-V + /PI -) after 72h treatment with SSI-4 (1 µM). (F) Representative western blot of MOLM-13 and OCI-AML3 cells treated for 72h with SSI-4 (1 µM) or vehicle control with or without addition of oleate (100 µM) or palmitate (100 µM). (G) MOLM-13 and MV-4-11 cells were treated for 72h with SSI-4 (1 µM) or vehicle control with or without addition of Q-VD-OPh (50 µM). Viable cells were determined as Annexin-V -/PI -. Data are mean ± SEM. * p < 0. 05, ** p < 0. 01, *** p < 0. 001, **** p < 0. 0001. Figure  5. SSI-4 has antileukemic effects on a subset of primary AML patient samples both in vitro and in vivo with sensitive samples showing higher levels of MUFA.
(F) In hCD45 + hCD33 + cells isolated from mice transplanted with AML5 sample lipid peroxidation was measured using Bodipy C11. Viable cells were determined as Annexin-V -/PI -. Data are mean ± SEM. * p < 0. 05, ** p < 0. 01, *** p < 0. 001, **** p < 0. 0001. Figure  6. SSI-4 demonstrates no hematopoietic toxicity but, as single agent, does not prolong survival in a mouse AML model. (A) C57BL/6 mice (n=15) were treated with 10 and 30 mg/kg SSI-4 or corresponding vehicle orally for a total of 21 days with 2 days break after each 5 days of continuous treatment. PB counts of control or SSI-4 treated mice. WBC -white blood cells, RBC -red blood cells, HGB -hemoglobin concentration, HCT -hematocrit, MCV -mean cell volume, MCH -mean cell hemoglobin, MCHC -mean cell hemoglobin concentration, PLT -platelets.
2 + /Mac-1 + /Gr-1 -cells in the BM at the end of the experiment. (F) In CD45. 2 + cells lipid peroxidation was measured using Bodipy C11. Viable cells were determined as Annexin-V -/PI -or Annexin-V -/7-AAD -. Data are mean ± SEM. * p < 0. 05, ** p < 0. 01, *** p < 0. 001, **** p < 0. 0001. 14 days after transplant animals were treated for 9 days with 10 mg/kg SSI-4 or corresponding vehicle orally with or without conventional chemotherapy protocol (3 days 1. 0 mg/kg doxorubicin i. v. , 5 days 50 mg/kg cytarabine i. v. ). (F) CD45. 2 + leukemic iMLL-AF9 cells were transplanted into CD45. 1 + NBSGW mice (n=18). When leukemic burden in PB reached 20%, animals were treated for 7 days with 10 mg/kg SSI-4 or corresponding vehicle orally with or without conventional chemotherapy protocol. Kaplan-Meier curves represent overall survival of animals treated with SSI-4 and corresponding vehicle with our without conventional chemotherapy.
When examining each setsize separately, we found that overconfidence was significantly positive when the setsize was 1 (Mean=6. 57, t(16)=2. 50, p=. 024), but not when it was 4 (Mean=. 51, t(16)=0. 20, p=. 842) or 8 (Mean=-1. 87, t(16)=-0. 73, p=. 476). In other words, overconfidence was present only for local confidence judgments in this dataset.
e. no difference between the two intercepts, Mean=. 10, Std=. 62, t=. 164, p=. 871) and nos significant interaction ∆ between condition and the effect of performance (i. e. no difference between the two slopes, Mean=. 004, Std=. 123, t=. 034, p=. 973). In other words, variability affected overconfidence ∆ in the same manner at the global and at the local level. Given this result, and the link from local to global confidence, it is possible that variability over trial difficulty within a set had an initial effect on local judgments, which then spread on global judgments. A Bayesian hierarchical mediation analysis, using the "bmlm" package in R  (Vuorre, 2017)  confirmed this hypothesis (Figure  7C ). Variability affected local confidence (Mean coefficient=2. 57, 95% CI =[1. 80,3. 38]), which in turn affected global confidence (Mean coefficient=.
Monovalent binding of α1,6-core-fucosylated oligomannose-3 to the FimH lectin To visualize the interaction between FimH and corefucosylated oligomannose-3, we have performed cocrystallization and obtained a crystal structure at 1. 4 Å resolution (Table  S1 ). The ligand Man3Gn2F1  [6]  interacts with FimH via its α1,3-arm, and the mannose binds in the monosaccharidebinding pocket (M) of FimH via the conserved ligand residues Phe1, Ile13, Asn46, Asp47, Tyr48, Ile52, Gln133, Asn135, Tyr137, and Asn140. The α1,6-fucosylated N-acetylglucosamine 1 in one of the FimH monomers of the asymmetric unit has a very high motility and was modeled in two alternate conformations, altA and altB (Fig. 2B ). The altB binding mode is conserved over the two FimH monomers (Fig. 2, C  and D ). We compared this new complex with the previously published crystal structure of the FimH-Man3Gn2 complex (PDB entry 2VCO (  21 )), to evaluate the effect of the addition of fucose.