dataesr/scientific-paragraphs-categorization huggingface dataset:
assulta (Accession Numbers MF687638-MF687667  22  , Antheraea pernyi (ACT1-ACT22)  23  ). Heliothis virescens and H. subflexa are two closely related species occurring in sympatry. These noctuid moths have recently been reclassified from the paraphyletic genus Heliothis to the monophyletic genus Chloridea 24 but we retain the older name here for continuity with the extensive literature on pheromones in these species. The most striking pheromone difference between these two species is the presence of (Z)-11-hexadecenyl acetate (Z11-16:OAc) and two other acetate esters (Z7-16:OAc and Z9-16:OAc) in the H. subflexa female pheromone, which to our knowledge have never been found in the H. virescens female blend  6. Previous behavioral experiments have shown that this difference plays a critical role in the reproductive isolation of these two species, because the presence of these acetates in the pheromone blend inhibits the attraction of H.
This first filtering revealed 89 differentially expressed contigs, none of which were annotated by Blast2Go with putative functions in pheromone biosynthesis (see Supplementary Table  S2 ). However, among these differentially expressed contigs, five were annotated by Blast2Go as putative lipases (38088_c0_seq4, 38088_c0_seq5, 38579_c0_seq3, 39873_c1_seq2 and 39873_c1_seq3) and one as putative esterase (comp39435_c1_seq17) (Supplementary Table  S2 ). As the GO terms predicted that the genes represented by these contigs are putatively involved in acetate degradation, we considered these contigs as candidate transcripts for explaining the phenotypic difference in acetate levels between VV and SS.
For analysis, samples were reduced to 1-2 µL under a gentle stream of N 2 and injected into a splitless inlet of a HP6890 gas chromatograph coupled with a high-resolution polar capillary column [DB-WAXetr (extended temperature range); 30 m 90. 25 mm 90. 5 Mm] and a flame ionization detector (FID). The area under the pheromone peak was estimated using integration software implemented in Agilent Chem-Station (version B. 04. 03). The absolute amounts (in ng) of the eleven identified components of the H. subflexa sex pheromone blend (14:Ald, Z7-14:Ald, 16:Ald, Z7-16:Ald, Z9-16:Ald, Z11-16:Ald, Z7-16:OAc, Z9-16:OAc, Z11-16:OAc, 16:OH, and Z11-16:OH; see ref. 57) were calculated relative to our 200 ng pentadecane internal standard and divided by the total amount to get the relative amount. To break data interdependency and to correct for individual variation in the amount of pheromone produced that play a role in male response (see ref.
DD23 strain pheromone gland transcriptome assembly For our transcriptome analysis, we extracted RNA from the pheromone glands of 10 females, five of VV genotype and five of SS, using Ambion™ TRIzol™ Reagent (Thermo Fisher Scientific, Waltham, Massachusetts, USA) and Direct-zol™ RNA MiniPrep Kit (Zymo Research, Irvine, California, USA). Previously the pheromones from each of the glands had been extracted with hexane as described above. The RNA was sequenced using Illumina HiSeq2500 by BaseClear (Leiden, The Netherlands). Raw reads which were used to assemble the pheromone gland transcriptome were deposited in the NCBI Sequence Read Archive under the bioproject number PRJNA493752. Resulting reads were trimmed of adapters and for a Phred quality score of 28 or more using Trimmomatic  60. We then removed ribosomal reads using Ribopicker  61.
We also removed reads without a mate pair. The remaining reads were normalized and de novo assembled using Trinity  62. We then aligned the Trinity output with the R1 reads used for assembly with Bowtie2  63, 64  and removed all contigs matching less than five reads. The resulting contigs will be further referred to as the DD23 pheromone gland unfiltered transcriptome. To improve the quality of the transcriptome, we sequentially used three tools as described in ref. 65 and summarized here. We first filtered out the contigs without an ORF of 100 amino or more using Transdecoder (http:// transdecoder. github. io/). We then used RSEM  66  to remove all contigs with an TPM expression value lesser than one. Finally, we clustered and removed redundant contigs using CD-HIT EST (90% similarity threshold and word size of eight)  34, 67.
Differential expression analysis and differentially expressed transcripts annotation Differences in expression levels were explored by aligning the reads used for the assembly onto the reference transcriptome, using RSEM  66  , after which we conducted differential expression analysis, using DESeq2  70  and edgeR 71 tools. We selected contigs differentially expressed between SS and VV genotypes by filtering contigs with: a ±2 log2 fold change in expression value (Deseq2) and an associated adjusted P-value (from DEseq2 and edgeR) of 0. 05 or lower. We annotated these differentially expressed transcripts by blasting them against the nr NCBI database  72  , using blastx  73  , and attributed GO terms using Blast2GO  74. Subsequently, an unbiased expression analysis was conducted by mapping SS reads to contigs derived from genes present in SS individuals, and mapping VV reads to contigs derived from genes present in VV individuals (Supplementary Data 1).
Est-KO females were collected from the first generation after outcrossing. Est-Lip-KO and Lip-KO females were from the first three generations after outcrossing. transcriptome data generated in this study have deposited in the NCBI database under accession code PRJNA493752. The sequences resulting from genomic DNA Sanger sequencing generated in this study have been deposited in the NCBI database under accession code OK556469, OK556470, OK556471, OK556472, OK556473, OK556474, OK556475, and OK556476. The pheromone quantification and realtime PCR data generated in this study are available in figshare under the https://doi. org/10. 6084/m9. figshare. 22656718. The Input data for in sillico docking experiment analysis data generated in this study have been deposited in figshare under the https://doi.
Acknowledgements The authors thank  Jesse Kerkvliet  for his help on the DD23 pheromone transcriptome assembly, as well as  Dr. Thomas Chertemps  and  Dr. Camille Meslin  for discussions on the content of the manuscript and advices on the analytical tools and workflow used. A. F. was supported by a  Contrat Jeune Chercheur grant  from the  Institut National de la Recherche Agronomique (France). A. T. G. and A. F. were supported by a  PHC Van Gogh  grant ( n°37955UF ). E. F. was suuported by an  IMPRS grant  from the  Max Planck Society.
Thus, for the last 600 sols, SP and APSS were only occasionally powered on and only the VBB has been powered on continuously, with sampling at 20 Hz. Neither the SP nor APSS were turned on during S1222a. VBB was operating in high-sampling-rate mode and it was possible to also retrieve 100 Hz for the event. SEIS data is archived and released by InSight Mars SEIS Data Service and the data is available to the science community with 3 months delay (InSight Mars SEIS Data Service, 2019). Using InSight seismic data, various Marian seismic velocity models were proposed  (Giardini et al. , 2020; Lognonné et al. , 2020; Stähler et al. , 2021; Khan et al. , 2021; Knapmeyer-Endrun et al. , 2021; Kim, Lekić, et al. , 2021; Drilleau et al. , 2021 Drilleau et al. , , 2022)). MQS uses the suite of models proposed in  Stähler et al.
1-0. 8 Hz. The red and the blue lines refer to P and S arrival times identified by MQS. Glitches identified by MQS are indicated in purple in the bottom. (f) Seismograms filtered between 0. 01-0. 1 Hz. The red and blue lines refer to P and S arrival times identified by MQS. Arrival times of fundamental Rayleigh and Love waves are shown in orange and green. For the surface waves the earliest arrival of all the frequency bands are shown in the figure. For (c)-(f), the signal were rotated to vertical, radial and transverse component using the back-azimuth we obtained (Table1) Figure  2 : Filterbanks of S1222a. To create the filterbanks, both 20 Hz and 100 Hz data were used. Frequencies above 10 Hz used 100 Hz channels and were deglitched using the seisdeglitch tool (https://pss-gitlab. math. univ-paris-diderot.
Acknowledgments We acknowledge  NASA ,  CNES , partner agencies and institutions (UKSA,  SSO ,  DLR ,  JPL ,  IPGP-CNRS, ETHZ, ICL ,  MPS-MPG) , and the operators of  JPL, SISMOC, MSDS, IRISDMC  and  PDS  for providing SEED SEIS data. Marsquake Service (MQS) operations at ETH are supported by  ETH Research  grant  ETH-06 17-02. ETH authors recognise support from the  ETH+ funding scheme  ( ETH+02 19-1 : " Planet Mars "). French co-authors acknowledge support of the  French Space Agency CNES  and  Agence Nationale de la Recherche ,  ANR  ( ANR-19-CE31-0008-08 ). TK, MP, PL, GS acknowledge support of  IdEx Université Paris Cité   ANR-18-IDEX-0001. A. H. is funded by the  UK Space Agency  under grant numbers  ST/R002096/1  and  ST/W002523/1. This research was carried out in part at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ).
Inflammatory breast cancer (IBC) is a rare but aggressive subtype of breast cancer, mainly characterized using primary tumor samples. Here, using public datasets, we compared the genomic alterations in primary and metastatic samples from patients with metastatic IBC versus patients with metastatic non-IBC. We observed a higher frequency of AURKA amplification in IBC. We further showed that AURKA amplification was associated with increased AURKA mRNA expression, which we demonstrated was higher in IBC. Finally, higher protein expression of AURKA was associated with worse prognosis in patients with IBC. These findings deserve further investigation given the existence of AURKA-inhibitors.
Patient and methods We considered female patients with invasive breast cancer of no special type (NST, commonly referred to as invasive ductal breast cancer) who were either upfront metastatic or relapsed later after primary diagnosis from the Memorial Sloan Kettering-IMPACT  [14]  and the Metastatic Breast Cancer project (MBC)  [15]  cohorts. : 34 patients with IBC (including 2 with both primary and metastatic samples, Figure  1A ) and 602 with non-IBC (including 33 with both primary and metastatic samples) (Supplementary Table  1 ). We further investigated AURKA expression at the mRNA and protein level in the dataset of Van Laere et al. [3]  (n= 67 IBC and 169 non-IBC) and in an institutional cohort of patients with IBC (n= 43 patients), respectively. We applied Firth logistic regression adjusted for hormonal receptor status (positive vs negative), HER2 status (positive vs negative), stage (II vs I, III vs I, IV vs I) and cohort (MSK-IMPACT vs MBC) with genomic alterations or metastatic sites as outcome variables and IBC status as independent variable (yes vs no).
Of note, in our series, 5 out of the 7 AURKAamplified tumors were estrogen receptor (ER)-positive and all but one harbored TP53 mutations (Figure  1A ). Since transcriptomic data were only available for few patients from the studied cohorts, we investigated the correlation between copy number and gene expression levels using publicly available data from The Cancer Genome Atlas  [18]. We observed that breast tumors with AURKA amplification had significantly higher gene expression levels than tumors without amplification (p-value<. 001, Figure  2A ). We then explored the expression levels of AURKA in the dataset from Van Laere, et al. 2013  [3] , confirming the increase of AURKA mRNA expression within patients with IBC as compared to patients with non-IBC, in line with previous reports from patients  [19]  and cell lines data  [20]  (Figure  2  B,C and Supplementary Table  7 ).
We have further shown that AURKA amplification is correlated with AURKA overexpression, and AURKA mRNA overexpression has previously been shown to be associated with tumor proliferation, tumorigenesis, clinical aggressiveness, and tumor progression  [21]  in different cancer types including breast cancer  [22, 23]. However, Staff et al. [24]  showed that AURKA expression as assessed by IHC was not strongly associated with the Ki67 proliferation marker (Spearman's rho= 0. 39). In our study, we further investigated AURKA expression by IHC in a series of samples from primary IBC and observed that higher expression levels were associated with poor prognosis, underlying a possible more complex role of AURKA on tumor progression. Our present observation could be of potential clinical relevance given the existence of several AURKA inhibitors that are currently being tested in patients with breast cancer (NCT02187991, phase 2 with alisertib, and NCT02134067, phase 1 with TAS-119).
These inhibitors have already shown promising results, especially in patients with ER-positive breast cancer  [22, 25, 26]. In our dataset, 5 out of 7 AURKA-amplified IBC were ER-positive, suggesting that AURKA inhibition could potentially be explored in priority as therapeutic option for patients with ER-positive IBC. The clinical relevance of the higher TMB observed in primary IBC compared to primary non-IBC should be further investigated in patients with IBC treated with immune checkpoint inhibitors, in the PELICAN trial for instance (NCT03515798). The main limitations of our study are related to its retrospective nature, precluding centralized revision of IBC diagnosis, and to the relatively small number of IBC samples due to its rare nature, requesting future validation of the findings.
Nevertheless, the IBC samples reported here were originating from two of the largest initiatives collecting and genomically characterizing samples from patients with metastatic breast cancer, emphasizing the difficulty in assembling larger series from rarer disease entities such as IBC. There is therefore a need to further collect and characterize IBC samples, something that will be achieved by increasing patient participation from the IBC community to important initiatives such as the Metastatic Breast Cancer project (www. mbcproject. org) [15, 27]  or post-mortem tissue donation programs, such as the one we recently developed in our institution (NCT04531696). Forest plots for the cox regressions regarding OS in univariable and multivariable models, considering AURKA by category (high vs low around the median) (H) or continuous with a 10unit increase (I).
Availability of data and materials Public data used for this study are available as supplementary material in Razavi, et al. 2018  [14] , on cBioPortal (https://www. cbioportal. org/study/summary?id=brca_mbcproject_wagle_2017) and under the accession number E-MTAB-1006 on the ArrayExpress data portal (09/2021). The public datasets along with the R code generated and analyzed during the current study are available in the CodeOcean repository, [PERSISTENT WEB LINK TO DATASETS]. The institutional cohort and the code related to its analysis are available upon request after author approval.
Estimating SST and SIC For both sites, we determined SST within the primary foraging ranges of little auks, as determined by GPS tracking  (Amélineau et al. , 2016; Jakubas et al. , 2020; Karnovsky et al. , 2003 Karnovsky et al. , , 2010)). The foraging range for UK was encompassed by -21. 57 longitude on the west, -17. 47 longitude on the east, 69. 8 latitude on the south, and 71. 65 latitude on the north (~160 × 200 km), and for HS by 13 longitude on the west, 16. 28 longitude on the east, 76. 1 latitude on the south, and 77. 5 latitude on the north (~50 × 150 km). We extracted daily average SST and SIC (for UK only, due to absence of ice at HS) values within these foraging ranges from the National Oceanic and Atmospheric Administration's (NOAA's) interpolated SST (OISST) high resolution daily dataset (1/4 global grid; https://psl.
noaa. gov/data/gridde/data. noaa. oisst. v2. highres. html), and the University of Bremen's Sea Ice Remote Sensing database (https://seaice. uni-bremen. de/sea-ice-concentration/amsre-amsr2/), respectively. Data were averaged across the foraging range and each bird's accelerometer deployment period. Additionally, SST data was obtained from the incubation to late chick rearing period (~June 1 to August 15) between 1986 (year of first DEE measurement at HS by a past study) and 2021 to assess change over time and project future changes. At HS, we only used SST as a metric of environmental variability, since there was no substantial SIC. See Appendix S1: Section S1 for further details regarding processing SST and SIC data.
, 2023 for details). TAB data are presented in Appendix S1: Table  S1  and Figure  S1  for HS and in  Grunst et al. (2023)  for UK. Ste-Marie et al. (  2022 ) derived an equation to estimate DEE from TABs by measuring DEE, using doubly labeled water (DLW;  John, 1998; Lifson & McClintock, 1966) , and TABs, via accelerometery in the UK population, yielding estimates of activity-specific energetic costs: DEE ¼ 9:52 kJ day -1 g -1 %Time fly + dive À Á + 0:97 kJ day -1 g -1 %Time other ð Þ , ð1Þ where %Time fly+dive = %time flying and diving, and %Time other = %time in other activities. We used Equation (1) to estimate DEE, Equation (2), also from Ste-Marie et al. (  2022 ), which divided flight and dive components of EE: DEE ¼ 8:45 kJ day -1 g -1 %Time fly À Á + 10:93 kJ day -1 g -1 %Time dive ð Þ + 0:86 kJ day -1 g -1 %Time other ð Þ , ð2Þ to assess which components of DEE were related to predictor variables.
DEE and SST using historical data We compiled 12 site-year observations of average DEE of breeding little auks using data from  Gabrielsen et al. (1991)  (n = 13),  Grémillet et al. (2012)  (n = 70) and the present study. This dataset spanned 35 years  and three sites (UK, HS, and Kongsfjorden, KF). KF is another little auk colony in Svalbard, which experiences even warmer SST than HS due to influence of the West Spitzbergen current  (Cottier et al. , 2005).
We used this model to predict SST 36 years in the future. The year-by-date interaction was removed when non-significant. We used predicted values of SST from GAMs, and equations generated from our data predicting DEE from SST at UK, to explore the projected increase in average DEE and total EE during chick-rearing (~July 1-August 15) between 1986 and 2021 (36 years; between the first and last year with DEE observations), and 2021 and 2057 (next 36 years). We present values using the equation predicting DEE from SST using UK data. The equation derived from the 12 site-year observations yielded similar results. Finally, we used LMMs to explore relationships between fitness traits (SMI, chick feeding rate), DEE, SST, and Hg at the sites separately. We entered DEE, SST, Hg, and their two-way interactions as fixed effects and year as a random effect.
ACKNOWLEDGMENTS The  French Polar Institute  (ADACLIM [388];  Jérôme Fort ,  David Grémillet ),  Contrat de Projet Etat-Région  and  Fonds Européen de Développement Régional ,  Horizon 2020 programme (Marie Curie IFs :  101025549 ,  896866 ,  101024166 ;  Melissa L. Grunst ,  Andrea S. Grunst ,  Marta Cruz-Flores ),  Institut Universitaire de France (PB) ,  French Ministry of Higher Education and Research (CA) , and  Northern Scientific Training Program of Canada  provided funding. This work contributes to projects ARCTIC-STRESSORS and  ILETOP (French National Research Agency ;  ANR-20-CE34-0006 ,  ANR-16-CE34-0005 ), the  ARCTOX initiative , and  OPUS13  (Polish National Science Center;  2017/25/B/NZ8/01417 ).
Funding information Grant/Award Numbers :  ANR-16-CE34-0005 ,  ANR-20-CE34-0006 ;  Contrat de Projet Etat-Région ;  European Regional Development Fund ;  Excellence Chair ECOMM (Region Nouvelle Aquitaine) ;  Horizon 2020 Framework Programme , Grant/Award Numbers:  101024166 ,  101025549 ,  896866 ;  Institut Polaire Français Paul Emile Victor , Grant/Award Number: ADACLIM program ( 388 );  Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche ;  Northern Scientific Training Program of Canada ; Polish National Science Center, Grant/Award Number:  2017/25/B/NZ8/01417.
Code availability. The Cidre source codes are available here https://gitlab. com/geomorphotoulouse/cidre under the opensource CeCILL v2. 1 licence. The code is also permanently deposited on the HAL repository with the number hal-04141239v1 (https://hal. science/hal-04141239). Author contributions. S. Carretier and V. Regard designed the study and the cosmogenic nuclide model, S. Carretier and Y. Abdelhafiz implemented the cosmogenic nuclide model with the help of B. Plazolles. S. Carretier wrote the paper with inputs of all the co-authors.
In sociable weavers, there is a size hierarchy among chicks due to hatching asynchrony. We then examine how egg mass and content vary with laying order and group size. We collected egg mass data during eight breeding seasons and, for egg content, we collected 59 clutches in two breeding seasons and measured nine egg components, representing three groups of compounds: yolk mass, lipids and proteins (macro-nutrients group), carotenoids, vitamin A and vitamin E (micronutrients group) and testosterone, A4, and corticosterone (hormones group). For nutrient allocation, we had opposite predictions according to the 'load-lightening' or the 'differential allocation' hypotheses (see Fig. 1 ). Finally, as a post hoc test given our results, we examine whether group size interacts with laying order effects on offspring hatching and fledging success.
We monitored the breeding activity of 16 sociable weaver colonies during 8 breeding seasons (from 2010/2011 to 2017/2018) to obtain data on egg mass, egg laying order, and fledging success (see online Appendix A1 for details on data collection; D'  Amelio et al. 2021; Fortuna et al. 2021). We obtained a sample of 779 eggs (in 326 nests from 14 colonies) with known mass and laying order, and for which breeding female's identity, tarsus size, and group size were identified (see below). The sample of hatched eggs with known chick fate (fledged or not; see below), known egg mass and laying order, and known mother identity was of 419 (for 258 nests from 16 colonies; see below).
3 as the mean number of birds seen visiting the nests over all observations of each breeding attempt. Only birds that appeared at least 3 times (in the same day or different days) were considered, to avoid including prospecting individuals that do not share the workload with the parents. Nest building visits were excluded and unringed birds were included (counted as 1 bird). To identify breeding females, we used a combination of criteria: incubation video recordings (for collected clutches, recorded before collection) and video recordings or direct observations of feeding visits in current and/or posterior breeding attempts in the same nest and colony. We then used information from genetic analysis from blood samples  (Paquet et al. 2015)  and field data  (Silva et al. 2018)  to attribute parentage to the birds seen (see online Appendix A7 for details;  Fortuna et al.
2021). For the collected clutches, we identified the breeding female of 51 out of 59 clutches. The group size of these females could not be estimated during rearing due to collecting the eggs and was instead estimated from their subsequent breeding attempts for 46 out of 51 females. We expected that group size would not severely change in their next breeding attempt, as no juveniles were produced (since all eggs were collected) and most replacement clutches were laid within 2 months. Furthermore, we found a correlation of 0. 57 (95 CI [0. 33; 0. 75]; p < 0. 001; N = 50) between the size of two consecutive groups of the same breeding female, when using the long-term database (see details in online Appendix A8).