dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, individual groups and members have received support from  BCKDF ,  CANARIE ,  Compute Canada  and  CRC ,  Canada ;  COST ,  ERC ,  ERDF ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex, Investissements d'Avenir Idex  and  ANR, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF, Greece; BSF-NSF  and GIF, Israel; Norwegian Financial Mechanism 2014-2021, Norway;  La Caixa Banking Foundation ,  CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden ;  The Royal Society and Leverhulme Trust ,  United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from  CERN , the ATLAS Tier-1 facilities at  TRIUMF (Canada) ,  NDGF (Denmark, Norway, Sweden) ,  CC-IN2P3  (France),  KIT/GridKA (Germany) ,  INFN-CNAF (Italy) ,  NL-T1  (Netherlands),  PIC (Spain) ,  ASGC (Taiwan) ,  RAL (UK)  and  BNL (USA) , the Tier-2 facilities worldwide and large non-WLCG resource providers.
IoT System Tuning The queueing models presented in this paper can be employed to properly configure and tune an IoT system. For instance, suppose that an IoT designer is willing to deploy occupancy counters in a smart building. Such a sensor remains inactive (sleep mode) for a certain period; right afterwards, it becomes active to count people in the covered space. IoT designers can leverage the obtained results in Section 4. 1 to either configure the inactive period or to estimate the response time for a default inactive period. For example, given T OFF = 1 min as the inactive period, T ON = 3 s as the active period to count people and D = 0. 1 s as the service time required to count people, IoT designers can estimate the resulting response time. Now, suppose that data flowing from IoT sources are collected via a message broker to be disseminated to interested recipients.
D. Thermodynamic Description The limit of Fe-Cr phase separation and its temporal evolution is determined by the thermodynamic descriptions of the system. The metastable miscibility gap has been re-optimized using the Thermo-Calc software with a second-order Redlich-Kister interaction parameter in the excess energy term. The new description is based on literature data, compiled in Table  VI , and the experimental work presented in this paper, using the thermodynamic model in Section II-D. The thermodynamic Fe-Cr description presented by Andersson and Sundman  [49]  has been used as it is currently implemented in the Thermo-Calc Software TCFE10 Steels/Fe-alloys database. Experimentally, Fe-Cr phase separation by a 0 formation is generally explored at temperatures around 500 °C, motivated by the rate of transformation.
Inden and Dubeil  [50]  Fe 0. 85 Cr 0. 15 10 5 MB Fe 0. 30 Cr 0. 70 35,500 MB Fe 0. 80 Cr 0. 20 35,500 MB Katano and Iizumi  [51]  Fe 0. 60 Cr 0. 40 20 SANS Novy et al. [42]  Fe 0. 80 Cr 0. 20 1067 APT Chandra  [52]  Fe 0. 66 Cr 0. 24 1738 MB Bergner  [53]  Fe 0. 91 Cr 0. 09 Na* SANS Gou et al. [54]  Fe 0. 75 Cr 0. 24 Ni 0. 01 10,000 APT Fe 0. 70 Cr 0. 26 Ni 0. 04 10,000 APT Miller et al. [55]  Fe Bal. Cr 0. 17,0. 19,0. 32, 0. 45 500 FIM-APT Kuwano  [56]  Fe 1-X Cr X 50 MB Williams and Paxton  [57]  Fe *Irradiated samples 0. 6 and 1. 5 dPa. Fig. 8 -CALPHAD re-optimization of the Fe-Cr miscibility gap, using the Thermo-Calc software. [39] One of the findings in this work is that the phase separation through APT Cr composition distribution analysis confirms that the limit of miscibility gap at Fe 0.
ACKNOWLEDGMENTS The financial support from  Centre National de la Recherche Scientifique (CNRS) ,  Region-Normandie , and  Carl Tryggers Research Foundation  is gratefully acknowledged. This work was partly carried out owing to the experimental GENISIS platform. GENISIS is supported by the  region Haute Normandie , the  Metropole Rouen Normandie , the  CNRS via LABEX EMC , and the  French National Research Agency  as part of the  program ''Investissement d'avenir' '  ARN-11-EQPX-0020. This work was also partly performed within the Competence Center Hero-m 2i financed by  VINNOVA , the  Swedish Governmental Agency for Innovation Systems, Swedish Industry , and  KTH Royal Institute of Technology. One of the authors (MS) would like to thank  Prof. Wei Xiong  for useful discussions.
Transcriptome sequencing, database search and sequence comparison Transcriptomic data from T. molitor [BioProject: PRJNA608239; Sequence Read Archives (SRA): SRR11184806] from the brain and retrocerebral complex of adult beetles obtained by  Marciniak et al. (2020)  were reanalyzed and used for database search and sequence comparison by local tblastn. For local tblastn, a sequence of Tribolium castaneum TRPR (XP_008194527. 2) was used to identify a sequence of T. molitor TRPR. For the analysis of putative transmembrane regions in the predicted protein sequence of the Tenmo-TRPR ORF (open reading frame) PSI-PRED-MEMSAT software (https://bioinf. cs. ucl. ac. uk/psipred/) was used  (Nugent and Jones, 2009). Prediction of glycosylation, palmitoylation and phosphorylation was performed using, respectively, NetN-Glyc 1.
0, CSS-PALM 4. 0 and NetPhos 3. 1 (https://www. cbs. dtu. dk/ services/NetNGlyc/, https://csspalm. biocuckoo. org/online. php, https ://www. cbs. dtu. dk/services/NetPhos/) (Blom et al. , 2004; Ren et al. , 2008). In addition, a two-dimensional representation of the receptor was created in TOPO2 (Johns S. J. , TOPO2, transmembrane protein display software, https://www. sacs. ucsf. edu/TOPO2/). Protein sequence alignment of Tenmo-TRPR with other Coleopteran TRPRs was performed with Clustal W (https://embnet. vital-it. ch/sof tware/ClustalW. html) and includes T. castaneum (XP_008194527. 2), Aethina tumida (XP_019866300. 1), Nicrophorus vespilloides (XP_017769025. 1) and Rhynchophorus ferrugineus (QGA72520. 1). To compare Tenmo-TRPR sequence with other insect species and human neurokinin receptors (NKR) the following sequences were used: NP_524556.
Reverse transcription of the same amount of isolated RNA (200 ng) to cDNA was accomplished using the RevertAid™ Reverse Transcriptase kit (Thermo-Fisher Scientific, USA) according to the manufacturer's protocol. RT-PCR analyses were conducted using a T100™ Thermal Cycler (Bio-Rad, USA). The primers were designed based on sequences of Tenmo-TRPR using Primer3 software  (Untergasser et al. , 2012). The primer pair was created to amplify fragments of 177 bp with the following sequences Fw 5 ′ -TTATCCAGAATGGCCCGACG -3 ′ and Rev 5 ′ -CATTTGCCTCTGCGTGCATT -3'. The primers were synthesized by the Institute of Biochemistry and Biophysics of the Polish Academy of Science (Warsaw, Poland). RT-PCR was performed in a 10 μL reaction volume containing: 3. 95 μL of DNase⁄RNase-free water, 1 μL of Dream-Taq™ Green Buffer (Thermo-Fisher Scientific, USA), 1 μL of 2 mM dNTP, 1 μL of 10 μM forward primers, 1 μL of 10 μM reverse primers, 0.
Total haemocytes count The number of circulating haemocytes was determined following  Urbański et al. (2018). In this assay, 2 μL of haemolymph were mixed with 20 μL of physiological saline (PS) appropriate for beetles (274 mM NaCl, 19 mM KCl, 9 mM CaCl 2 ) and an anticoagulation buffer (4. 5 mM citric acid and 9 mM sodium citrate, 5:1 v/v). The total haemocyte count (THC) was then determined using a Bürker chamber and a Zeiss Pri-moStar light microscope equipped with an Axiocam 105 digital camera. In each replicate, 24 randomly selected fields of the Bürker chamber were analysed and counted using ImageJ (version 2) software. At least 10 individuals were used in each of the treatments and three independent replications were conducted.
After washing, the haemocytes were fixed with a 4% paraformaldehyde solution for 10 min. Next, the samples were mounted with mounting medium (glycerol for fluorescent microscopy, Sigma Aldrich 1. 04095) and analysed with a Nikon Eclipse Ti2 microscope equipped with Nikon DS-Qi2 digital camera. For a better recognition of phagocytosis, cellular responses were confirmed with light microscopy. Five images for each sample were taken. Photos were analysed with ImageJ (version 2) software, and the results were expressed as the mean percentage of cells with fully phagocytosed beads of the total number of haemocytes on the images. For the minimization of error related to the individual variance between beetles, samples were compared to the control samples performed always in the same day as the groups treated by Tenmo-TRP-7.
1% Triton-X 100 (Sigma-Aldrich T8532). Then, the haemocyte F-actin cytoskeleton was stained with Oregon Green® 488 phalloidin (Invitrogen) for 25 min in the dark at room temperature. To visualize nuclear DNA, a solution of DAPI in physiological saline (1:50 v/v) was used. After staining, the samples with mounting medium (90% glycerol with 2. 5% DABCO) were mounted. Haemocytes were analysed with a Nikon Eclipse TE 2000-U fluorescence microscope. The photos were taken using a Nikon DS-1QM camera. Based on the obtained micrographs, the surface area of T. molitor haemocytes was determined using AxioVision software (ver. 4. 8. 2. ). To minimize the individual variation between insects, treatment groups were compared to the control samples performed on the same day. To quantify changes observable under the microscope, at least 220 haemocytes from 7 individuals were measured.
For each treatment, 5-6 different treated animals were used, each produced one slide of haemocytes to analyze. At least 50 nuclei from 10 randomly captured images were analysed per slide (i. e. 10 images/slide and 5-6 slides/treatment). Assessment of DNA integrity of haemocytes was determined using OpenComet software according to the method described by  Gyori et al. (2014). DNA integrity was evaluated based on the three parameters: tail moment (tail length x % of DNA in the tail), % of DNA in the tail and % of DNA in the head.
Phenoloxidase activity Phenoloxidase was assayed as previously described by  Sorrentino et al. (2002)  and  Urbański et al. (2018). Haemolymph samples (1 μL) were placed on white filter paper soaked with DL-DOPA (2 mg/1 mL) in a 10 mM phosphate buffer and incubated for 30 min at room temperature in the dark. For each of the tested individuals two technical replicates were analysed. After incubation, the filter papers were air-dried, scanned by SHARP AR 153 EN (600 dpi, 8 bits, grayscale), and analysed with ImageJ (version 2) software. Results are expressed as mean pixel value measured for the centre part of each sample (40 pixels). At least 10 individuals were used in each treatment.
Presence of immune-related genes in nervous tissues The possible presence of immune-related genes in selected nervous tissue of T. molitor was analysed based on previously mentioned transcriptomic data from the brain and retrocerebral complex of adult beetles. Moreover, this analysis was confirmed by RT-PCR analysis of expression of genes encoding selected immune-related receptors and antimicrobial peptides. The receptors and AMPs sequences used for local tblastn were mainly based on the sequences previously published by  Jacobs et al. (2017)  and  Johnston et al. (2014). RT-PCR analyses were performed according to the method described above. The primers for immune-related genes were synthetized according to sequence used before by  Jacobs et al. (2017). Only primers for domeless were designed based on available data (NCBI Reference Sequence: XM_001807008.
Statistical analysis For the statistical analysis GraphPad Prism 5 software was used (Adam Mickiewicz University license). The normality of the distribution in each tested research variant was determined with the Shapiro-Wilk test. For checking of homogeneity of variance Brown-Forsythe test and the Levene test were used. Normally distributed data were analysed with the Student's t-test with Welch's correction. Non-normally distributed data were analysed with the Kruskal-Wallis test and the Mann-Whitney U test.
Analysis of T. molitor TRPR sequence Based on the BLAST search using the local database the transcriptomic assembly of T. molitor brain and retrocerebral complex yielded an open reading frame with 1287 bp which encodes a putative tachykinin-related peptide receptor (Fig. 1 ). The Tenmo-TRPR displays the seven transmembrane domains typical for GPCRs (G-protein coupled receptors)  (Bass et al. , 2014)  with N-terminal ligand binding tail and C-terminal intracellular region (Fig. 2 ). The predicted post translational modifications of the receptor protein include the typical glycosylation of N-terminal region and extracellular loops, phosphorylation by protein kinase C and cAMP and cGMP dependent kinases and palmitoylation at the C-terminal tail (Fig. 2 ). Protein sequence alignment with other beetles showed a high degree (>67%) of identity (Fig.
, 2011). Moreover, neurokinin receptors (vertebrate receptors for tachykinins) are present on vertebrate immune-related cells, like macrophages  (Ho et al. , 1997). Also, the research conducted by  Nagai-Okatani et al. (2016)  on Bombyx mori partially support this assumption, because the transcript for gene encoding TRP was also present in silkworm haemocytes. The transcriptomic data also allows us to predict the sequence and structure of Tenmo-TRPR. The first evidence of presence of receptor for TRP in insects was shown by  Li et al. , in 1991. These authors, using the probes from the bovine substance K receptor (one of the neurokinin receptors), found it in the D. melanogaster embryo. Until now, the presence of TRPR were shown in different tissues of many insect species, including another representative of Tenebrionidae, the model species T.
Acknowledgement AU was supported by a scholarship from  Polish National Agency for Academic Exchange (NAWA) , within the  Bekker Programme, 2019 (personal stipend ,  PPN/BEK/2019/1/00167 ) and scholarship from the  Deutscher Akademischer Austauschdienst (DAAD) , within the program for  Research Stays for University Academics  and Scientists, 2018 (personal stipend, 91696887).
1f-g ; Supplementary Table  1g ). Thus, cell-intrinsic genomic crosstalk between AR and ER signalling pathways is highly feasible in non-malignant and malignant breast epithelial cells. To define the functional interplay between AR and ER at the chromatin level, ChIP-seq experiments were performed using the estrogen-dependent ZR-75-1 breast cancer cell line, which endogenously expresses both receptors and is growth restricted by androgen in vitro  22, 23  , akin to what was observed in clinical samples (Fig. 1b-c ). Cells were cultured under steroiddepleted conditions for 3 days and then treated 4 hours with E2 or E2+DHT. Consensus AR and ER cistromes were generated for each hormone condition using ChIP-seq datasets derived from 3 consecutive passages of cells (Extended Data Fig. 3a ); a DNA binding event was only included if identified in at least 2 of 3 replicates.
The data also accords with data generated in PDEs (Fig. 1 ). The AR gene signature had highly significant prognostic performance in the METABRIC 15 and ROCK 50 cohorts, as well as in an ER+ tamoxifen-treated dataset  51  (Fig. 5d ). In an independent assessment, the AR signature had greater prognostic power than cancer-associated signatures (MSigDB) and gene sets derived or annotated from the MammaPrint gene signature (Fig. 5d ), as well as other commercially available genomic tests (OncotypeDx, EndoPredict and PAM50) used for breast cancer prognostication (Supplementary Table  3e ). Thus, endogenous AR activity is evident in primary ER+ breast cancers, is positively associated with longer disease-free survival, and has significant prognostic power. This signature may be useful in breast cancer prognostication or for patient selection and assessment of treatment response in future trials testing an AR agonist target therapy.
Activated PR directly interacts with ER, resulting in a widespread gain of ER binding events in vitro and in vivo. In contrast, a direct interaction between AR and ER has not been detected to date ( 20 ; data not shown), and activation of AR caused a more prevalent loss of ER binding events, especially in vivo. The differences between AR and PR interaction with ER make biological sense considering that androgen inhibits pubertal breast development whereas progesterone stimulates functional differentiation (e. g. lactation). While stimulation of PR has therapeutic potential in primary ER+ breast cancers  54  , loss of PR expression is a common event 17. Conversely, AR expression is largely maintained in primary and metastatic lesions 1 (Fig. 1f-g ) and therefore represents a more prevalent therapeutic target, especially in the endocrine resistant setting.
Statistical analyses were performed using STATISTICA (Stat Soft Ltd, Tulsa, USA) and SPSS (version 17, Chicago, USA). Where appropriate, Pearson's chi-squared; student's t-test and ANOVA tests were used. Cumulative survival probabilities and 10-year BCSS and DFS were estimated using the univariate Cox proportional hazards models and the Kaplan-Meier plot method where appropriate, and differences between survival rates were tested for significance using the log-rank test. Multivariable analysis for survival was performed using the Cox proportional hazard model. The proportional hazards assumption was tested using standard loglog plots. Hazard ratios (HR) and 95% confidence intervals (95% CI) were estimated for each variable. All tests were two-sided with a 95% CI and a p-value <0. 05 was indicative of statistical significance.
Clinical samples used in Breast cancer progression TMA. A TMA was constructed to represent stages of breast cancer progression, comprised of thirty cases each of the following: non-malignant breast tissue of normal histology; low-, medium-, and high-grade ductal carcinoma in situ (DCIS); grade I, II, and III invasive ductal carcinoma (IDC), and metastatic tumour in lymph nodes (LN metastasis) (Supplementary Table  1f ). Cases were identified via the South Australian Health Database, and tissue slides and FFPE blocks retrieved from the Royal Adelaide Hospital archives, under approval by the Royal Adelaide Hospital and University of Adelaide Human Research Ethics Committees. Pathological review of the slides was performed by a pathologist (S. J. ) and relevant diseased areas selected. The TMA was constructed using the Chemicon Advanced Tissue Arrayer (ATA100; Merck Millipore, Germany), with duplicate 1 mm breast tissue cores per area randomly allocated to the TMA layout.
For colony formation assays, cells were seeded into 12-well plates the day before treatment. Indicated treatments were refreshed every 3 days and colonies were visualized with a crystal violet staining solution (0. 05% w/v crystal violet, 1% formaldehyde and 1% methanol). Images were scanned using the Perfection V800 Scanner (Epson). Area occupancy was analysed using the ColonyArea FIJI plug-in  56. In vitro proliferation data was analysed in GraphPad Prism where appropriate. To determine kinase activity, immunoprecipitates of CDK2 were collected with protein A-Sepharose beads (Zymed, Invitrogen) from 100 μg of cell lysate, and the activity of each toward histone H1 substrates was determined in kinase reactions with [γ-32 P]ATP followed by polyacrylamide gel electrophoresis and exposure of dried gels to X-ray film as described  57.
DHT and Tamoxifen pellets were surgically implanted sub-cutaneously. Upon reaching ethical or predefined experimental endpoints, mice were euthanized, and the primary tumour collected. After weighing, the tumour was cut into pieces that were allocated to be snap-frozen, fixed overnight at 4 C in 10% neutralbuffered formalin, or embedded in cryo-protective optimal cutting temperature (OCT) compound before being snap frozen. Frozen samples were kept at -80 C. Formalin-fixed samples were sent to the Garvan Institute Histology Core Facility for paraffin embedding. Tumour growth curves were analysed in GraphPad Prism (GraphPad Software) by two-tailed, unpaired t-test. Tumour mass at end-point was analysed by two-tailed Mann-Whitney t-test as per figure legends unless otherwise specified. Mammary intraductal (MIND) xenografts were established as described 38 by injecting Tam R cells (1 x 10 5 cells/10 µL) into the 4 th inguinal mammary ducts of NSG mice (Australian BioResources).
Peaks were called using MACS2 (default settings)  64, 65  against a pooled input sample. A modified MACS2 cut-off (p<0. 005) was used to call peaks in data associated with GSE141582 (Extended Data Fig. 6c, d ). Only peaks found in 2 out of the 3 replicates were kept for the consensus peakset. 'Lost' and 'Gained' sites were identified using BEDTools  66. For xenograft ChIP-seq data, FASTQ files were aligned to hg19 using bwa (v0. 7. 15; default parameters)  67  and mapped reads with a minimum MAPQ <15 were removed using SAMtools. Peaks were called using MACS2 (default settings) and consensus peaksets were obtained by including any peak represented in at least two or more replicates using DiffBind as previously described 68. ChIP-seq data was visualized using the Integrative Genomics Viewer 69 ; whereby an average enrichment signal from all replicates was generated by merging mapped reads for a given condition, followed by subsequent conversion and normalisation to bigwig format using deepTools 70 or HOMER 71.
Heatmaps were generated using deepTools and tag density plots were generated using HOMER or deepTools. For two-factor MA plots, average enrichment scores were calculated using deepTools multiBigwigSummary and plotted in R using the ggplot2 and MASS packages. Down-and up-regulated genes associated with ER/AR binding events were identified using BETA 72 , or by annotating peaks within 100 kb of a transcription start site with CisGenome 73 followed by data integration using BEDTools. Differential de novo motif analysis was determined for each peak set (ER lost or ER gained), using the opposing dataset as background, in the Homer 71 findmotifsgenome. pl function (-bg flag; hg19 genome build; 200 bp window flanking the center of each peak). ChIP-PCR reactions were prepared using iQ SYBR Green Supermix (BIO-RAD) and primers as listed in Supplementary  Workbench).
For RNA-seq data generated from xenografts, Fastq files were quality checked (FastQC v0. 11. 4; fastq-screen v0. 5. 2). Sequence adapters were trimmed using cutadapt v1. 9. 1 and reads were processed with Xenome v1. 0. 1 76 to remove host reads. Human sequences were aligned to hg38 using STAR v2. 4. 2a 77. Gene feature counting was performed with RSEM v1. 2. 21 78. Transcripts with zero expression counts across all samples were removed. TMM normalized counts 79 were log transformed using voom 80 and differential expression was performed with limma 81. Moderated F-statistics were calculated and genes that were significantly differentially regulated (with a Benjamini-Hockberg adjusted p-value (BH) <0. 05) between the treatment groups were identified. For RNA-seq in PDX tumours from Gar15-13 and HCI-005; transcripts were aligned to hg38 using STAR v2.
5 and feature counting was computed in R v3. 4. 3 using Rsubread 1. 28. 0 82. Gene expression analysis was conducted using edgeR 3. 20. 1 79,83 and limma 3. 34. 1 84. For validation RT-qPCR, RNA was extracted from consecutive passages of cell lines (MCF7 n = 3; ZR-75-1 and T-47D n = 4) or tumours harvested 5 days post-treatment in vivo (Vehicle, Enzalutamide, Enobosarm n = 4; DHT n = 5) and quantified as described above. RNA was DNase treated using the TURBO DNase Kit (Invitrogen) and reverse transcribed using the iScript cDNA Synthesis Kit (BIO-RAD). RT-PCR was performed as per ChIP-qPCR but using primers outlined in Supplementary Table  4b. Gene expression was calculated by the 2 -ΔΔCt method and normalised to the expression of GAPDH (cell lines) or IPO8 and PUM1 (tumours), using the CFX Manager Software (BIO-RAD).