dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, it also captures additional expenses on software  [21]. As a result, we may be overestimating R&D expenditure. However, since all firms in the data we use report R&D in a similar manner, we can reasonably assume that it does not correlate with the independent variables and hence, despite the measurement error in our dependent variable, our OLS estimations have good properties  [32]. Third, we explored lagged relationships by only up to 2 years; it is plausible that price regulation has a delayed effect, longer than the period we envisage and test in the paper. This remains an open avenue for further work with data on a larger number of firms. We were also unable to further explore the role of location in this paper due to the small number of firms we have. Data on more firms and more variation in headquarter locations could add additional insights on the role of firms' domestic versus foreign location in R&D decisions.
Protein Binding Microarray GST-HDP1-DBD binding was analyzed twice on Protein Binding Microarrays (PBMs) as previously described  (47, 48). In this study two different universal PBM arrays (Agilent AMADIDs 016060 v9 and AMADID 015681 v11) were used covering all contiguous 8-mers, as well as gapped 8-mers spanning up to 10 positions. Binding of purified GST-HDP1-DBD fusion proteins was visualized on the PBMs using Alexa-488 conjugated anti-GST antibody. Data analysis was carried out using the PBM analysis software suite downloaded from http://thebrain. bwh. harvard. edu/PBMAnalysisSuite/index. html. Following normalization, enrichment scores were calculated and the "Seed-and-Wobble" algorithm was applied to the combined data to generate position weight matrices (PWMs). An enrichment score cut-off of 0.
Using Illumina TruSeq RNA Sample Library Preparation v2 kit (Illumina, San Diego, CA), Messenger RNA was fragmented into small pieces using divalent cations under elevated temperature. The cleaved RNA fragments were copied into first strand cDNA using reverse transcriptase and random primers. Second strand cDNA synthesis followed, using DNA Polymerase I and RNase H. The cDNA fragments then went through an end repair process, the addition of a single 'A' base, and then ligation of the adapters. The products were then purified and enriched with PCR to create the final cDNA library. Libraries were pooled and sequenced on Illumina HiSeq4000 sequencer with single-end 50 cycles. Read files were checked for quality by using FASTQC v0. 11. 5 (https://github. com/s-andrews/FastQC). Reads were trimmed to remove low-quality positions and adapter sequences using cutadapt (version 1.
16)  (51). Reads were mapped against the P. falciparum 3D7 reference genome v40 (52) using STAR aligner (version 2. 61) (  53 ) and nuclear-encoded genes were analyzed for differential gene expression using cufflinks (version 2. 2. 1)  (54). Genes with a false discovery rate of <=0. 05 with a mean FPKM >5 in at least one strain were called significant. For genes with FPKM > 5 in one strain and no detectable expression in the other, FKPM values were set to 0. 1 for purposes of fold-change calculation. Gene Set Enrichment Analysis was carried out with the FGSEA v1. 16. 0 Bioconductor v 3. 12 package (  55 ) with an FDR cutoff of <=0. 05.
ACKNOWLEDGMENTS General: We wish to thank  Dr. Pawan Malhotra  for the generous gift of anti-PhIL1 antibodies,  V. Carruthers  for the generous gift of anti-Ty1 antibodies, the  Weill Cornell Medicine genomics core  for technical support,  M. Llinás  and  R. Bartfai  for separate HDP1 ChIP-seq attempts with hdp1-gfp and hdp1-ty1 gametocytes, respectively, as well as  K. Deitsch  and  J. King  for valuable feedback on the manuscript.
Specimens Included in the Study Among the small mammals caught (complete data not published), 171 spleen samples were considered in the current work, all belonging to rodents of the family Muridae: i) 50 rodents from outdoor trapping sites, including 15 Arvicanthis niloticus, 20 Gerbillus nigeriae, and 15 Taterillus sp. (most probably corresponding to T. pygargus), these species represented by far the three dominant ones of northern Senegal outdoors rodent communities  [24] ; ii) 121 rodents from indoor trapping sites, including 26 A. niloticus (11 from Diagali and 15 from Fourdou), 44 Mastomys erythroleucus (12 from Diagali, 10 from Fourdou, 1 from Labgar, and 21 from Ranerou) and 51 Mus musculus (17 from Labgar, 16 from Tessekere, and 18 from Yonofere).
Molecular Screening All methodological details, such as the type of PCR used, the portion of sequence considered, and the spectrum of species targeted, are provided in Table  1. Below, we focused only on bacteria detected and identified in each host species. The presence of the DNA of Piroplasmida spp. was screened, and four spleens were positive (4/171, 2. 3%) (one from Taterillus sp. , one from A. niloticus, and two from M. musculus). We succeeded in amplifying and sequencing only one sample (of A. niloticus). The phylogenetic analysis showed that this protozoan occupies an intermediate position between the genera Theileria and Babesia (Figure  1 ). Its GenBank accession number is MK484070. The presence of the DNA of Piroplasmida spp. was screened, and four spleens were positive (4/171, 2.
3%) (one from Taterillus sp. , one from A. niloticus, and two from M. musculus). We succeeded in amplifying and sequencing only one sample (of A. niloticus). The phylogenetic analysis showed that this protozoan occupies an intermediate position between the genera Theileria and Babesia (Figure  1 ). Its GenBank accession number is MK484070. In Bartonella spp. screening, 16/171 (9. 35%) of samples were positive (nine from Taterillus sp. , four from A. niloticus, and three from M. erythroleucus). We could successfully amplify 10 positive samples (seven from Taterillus sp. , two from A. niloticus, and one from M. erythroleucus). The comparison of the sequences obtained showed that there were three different genotypes. When blasted, the first genotype (GenBank accession number: MK558846) corresponded to B.
pachyuromydis AB602561, the closest validated species with only 92% of homology. The second (GenBank accession number: MK559409) showed 97% with B. mastomydis KY555067. The third (GenBank accession number: MK559410) was found to be very different, and the closest species is B. tribocorum JF766268 having a query cover of 47% and 85% of homology (Figure  2 ). samples (seven from Taterillus sp. , two from A. niloticus, and one from M. erythroleucus). The comparison of the sequences obtained showed that there were three different genotypes. When blasted, the first genotype (GenBank accession number: MK558846) corresponded to B. pachyuromydis AB602561, the closest validated species with only 92% of homology. The second (GenBank accession number: MK559409) showed 97% with B. mastomydis KY555067.
The third (GenBank accession number: MK559410) was found to be very different, and the closest species is B. tribocorum JF766268 having a query cover of 47% and 85% of homology (Figure  2 ). We found 31 (18. 12%) samples positive for Anaplasmataceae species, i. e. , 7 M. musculus, 1 G. nigeriae, 1 Taterillus sp, 17 A. niloticus, and 5 M. erythroleucus. To identify the species infecting rodents, we used a broad species PCR tool targeting the 23S gene. Only five positive samples have been successfully amplified and sequenced (one from M. erythroleucus, four from A. niloticus). The comparison of the sequences obtained showed that they were all identical, which may mean that all these sequences belong to the same Anaplasmataceae species. When compared with other Anaplasmataceae species, the 520 bps-long amplicons of 23S rRNA gene obtained from Senegalese rodents did not match to any officially recognized species.
Based on its position on the phylogenetic tree (Figure  3 ), it may be attributed to a potentially new species of Ehrlichia. This is clearly reflected by its position on the phylogenetic tree based on the 23S gene and by only 95% of identity with the closest species, Ehrlichia ruminantium NR077002 (Figure  3 ). The obtained GenBank accession numbers for its different genotypes are MK484067, MK484068, and MK484069. When screening for the Hepatozoon spp. harbored by rodents, we found 4/171 (2. 33%) positive spleen samples. The sequencing of a 620-bps-long portion revealed three species of Hepatozoon sp. Two samples corresponded to Hepatozoon sp. closely related to those isolated from snakes in the north of Africa  [35] : KC696569 found in M. erythroleucus and KC696565 that we found in A. niloticus.
Two other sequences belong to two different genotypes of Hepatozoon canis, both of them found in M. musculus (Figure  4 ). by its position on the phylogenetic tree based on the 23S gene and by only 95% of identity with the closest species, Ehrlichia ruminantium NR077002 (Figure  3 ). The obtained GenBank accession numbers for its different genotypes are MK484067, MK484068, and MK484069. When screening for the Hepatozoon spp. harbored by rodents, we found 4/171 (2. 33%) positive spleen samples. The sequencing of a 620-bps-long portion revealed three species of Hepatozoon sp. Two samples corresponded to Hepatozoon sp. closely related to those isolated from snakes in the north of Africa  [35] : KC696569 found in M. erythroleucus and KC696565 that we found in A. niloticus. Two other sequences belong to two different genotypes of Hepatozoon canis, both of them found in M.
Host-pathogens Relationships More details regarding the genotypes of pathogens we identified and their related species are provided in Table  2. For overall prevalence, the best model selected revealed a significant effect of the gender (log-likelihood ratio tests (LRT) = 4. 5226, p = 0. 0335) and the species status (LRT = 18. 2631, p < 0. 0001). Male and invasive rodents appeared to be less infected than female and native ones, respectively. For specific prevalence, model selection was carried out only for Borrelia and Anaplasma as the prevalence of the other bacterial taxa did not exceed 10% in the entire dataset. We exclusively found a positive correlation between host body mass and infection by Anaplasma (LRT = 13. 519, p = 0. 0002). For individual richness, the most parsimonious model contained host gender and species status as explanatory variables.
The obtained sequences were assembled and edited using ChromasPro software (ChromasPro 1. 7, Technelysium Pty Ltd. , Tewantin, Australia). Then, the sequences were compared with those available in the GenBank database by NCBI BLAST (http://blast. ncbi. nlm. nih. gov/Blast. cgi). Phylogenetic analyses and tree construction were performed using MEGA software version 7. 0. 21 [90]  with 100 bootstrap replications.
Statistical Analysis Generalized linear mixed models (GLMMs) were used to evaluate whether host factors (species, gender, body mass), the status (native vs. invasive), and/or the type of habitat (indoor vs. outdoor) influence the infection level of the rodents. We considered individual bacterial richness (number of bacterial taxa recorded in a single individual host) and both specific (infection by a bacterial taxon for which prevalence reached at least 10% in the global dataset) and overall (infection by any bacterial taxon, combining all taxa) prevalence as response variables. We assumed a binomial distribution for prevalence data and a Poisson distribution for richness data, respectively. The sampling site was considered as a random factor. A model selection approach was performed, using the Akaike information criterion with correction for samples of finite size (AICc).
The starting models included all the factors as possible predictors. The most parsimonious model among those selected within two AIC units of the best model was chosen. The significance of explanatory variables and their interactions was determined by deletion testing and log-likelihood ratio tests (LRT). The assumptions of each final model were ensured by checking the model dispersion and normality, independence and variance homogeneity of the residuals. All analyses were performed using the packages MuMIn v1. 15. 1  [26]  and lme4 v1. 1-8  [27]  implemented in R software v3. 2. 1  [28].
Funding: This study was supported by the  Institut Hospitalo-Universitaire (IHU) Méditerranée Infection , the  National Research Agency  under the program « Investissements d'avenir », reference  ANR-10-IAHU-03. This work was co-funded by the  Labex DRIIHM ,  French programme "Investissements d'Avenir"  ( ANR-11-LABX-0010 ), which is managed by the  ANR.
Importantly, the immunemodulatory effect of RANK signaling is confirmed in the D-BEYOND (denosumab, a RANK-ligand (RANKL) inhibitor and its Biological Effects in YOuNg premenopausal women Diagnosed with early breast cancer) clinical trial (NCT01864798), a prospective, pre-operative study evaluating denosumab and its biological effects in premenopausal early-stage BC. Two courses of denosumab induce an increase in TILs and CD8 + T-cell infiltration. Increased activation of RANK signaling pathway in the tumors and circulating serum RL at baseline are identified as predictive biomarkers for the denosumab-driven increase in TILs. Together, these results demonstrate the key role of RANK pathway in the tumor-immune crosstalk and support the use of RL inhibitors, such as denosumab, for enhancing the immune response in poorly immunogenic luminal BC.
None of the RANK +/+ tumors responded to anti-PD-L1 or anti-RL as single agents but their combination significantly reduced tumor growth in 50% of the tumors (Fig. 4f ). Anti-RL did not improve the response to anti-CTLA4 (Fig. 4f ). In tumors lacking RANK, anti-PD-L1 treatment was more efficient than anti-CTLA4, but no improvement was observed after the addition of anti-RL (Fig. 4g ), in contrast with the observations on RANK +/+ tumors. Collectively, these results demonstrate that in this luminal-like BC, RL inhibition improves the anti-tumor response to anti-CTLA4 (in the early setting) and anti-PD-L1 (for established tumors) through inhibition of RANK signaling in the tumor cells. A short course of denosumab treatment in early-stage BC increased TILs. To confirm the immunomodulatory role of RANK pathway inhibition in the clinical setting, we analyzed denosumab-treated BC clinical samples from the D-BEYOND study (NCT01864798): a prospective, pre-operative window-ofopportunity, single-arm, multi-center trial assessing the effect of denosumab in premenopausal women with early-stage BC.
5f ), as observed in the mouse models. No significant changes in NK cells, dendritic cells, mast cells, neutrophils, and eosinophils were noted, because these populations may be too scarce to be captured properly by this method (Supplementary Fig. 5f ). Of note, after denosumab treatment, neutrophils correlated negatively with sTILs (Supplementary Fig. 5g ), and the neutrophil chemotaxis and migration pathways were modulated after denosumab treatment (Supplementary Data 9), supporting the preclinical findings. To ensure that these changes are specific to denosumab treatment and not a consequence of the presurgical biopsy procedure, we interrogated the publicly available gene expression data of patients from the control arm (untreated) of the Peri Operative Endocrine Therapy -Individualizing Care (POETIC) study, a large BC window-of-opportunity study evaluating the role of perioperative aromatase inhibitor, for which gene expression data were obtained from presurgical biopsies and surgical specimens.
Similar to the D-BEYOND study, biopsies were taken at diagnosis and 2 weeks later, at the time of surgery. The comparison of surgery and biopsy samples from the POETIC study did not reveal any enrichment of immune cells assessed by CIBERSORT or an immune pathway, as assessed by GAGE analyses (Supplementary Fig. 5h  and Supplementary Data 11). Together, our results indicate that a short course of denosumab enhances immune infiltration as determined by the increased levels of TILs, B and T lymphocytes, and CD4 + and CD8 + T cells in luminal and HER2 + breast tumors, validating the clinical relevance of the findings in the preclinical models. RANK pathway activation in tumors and circulating sRL levels predict denosumab's immune effect. Finally, we investigated the baseline features associated with the immunomodulatory effect of denosumab.
The D-BEYOND trial has some limitations, such as the small sample size, the inclusion of only premenopausal patients, and the limited number of triple-negative and HER2 + cases. Whether the immunomodulatory response associated with RL inhibition could also be effective in postmenopausal patients will be addressed in the ongoing trial: D-BIOMARK (NCT03691311). It will be also worth reassessing the clinical outcome of two recent large phase III trials of adjuvant denosumab in early BC, D-CARE, and ABCSG-18, according to the predictive biomarkers we defined as follows: baseline RANK metagene, sRL levels, and the presence of Tregs. The D-CARE study reported no differences in disease-free survival (DFS), whereas the ABCSG-18 trial showed DFS improvement in postmenopausal patients  [49] [50] [51].
Importantly, the combined treatments show no increased benefit in RANK -/- tumors, indicating that it is driven by the inhibition of RANK signaling in tumor cells. This is a novel mechanism of action, as previous preclinical studies reporting the benefit of the combination were done in melanoma and colon cancer cell lines highly responsive to immunotherapy but lacking RANK expression  54, 55. Although we cannot rule out that denosumab may have additional systemic effects, our findings support that a tumor celldriven effect contributes to the immunomodulatory effect of denosumab in BC. The benefit of the combined effect of anti-RL and immunecheckpoint inhibitors will be investigated in the CHARLI trial (NCT03161756), a phase I/II study of the effect of denosumab in combination with nivolumab (an anti-PD-1), with or without ipilimumab (anti-CTLA4), in metastatic melanoma patients, and in the POPCORN trial (ACTRN12618001121257), which will evaluate immune changes in NSCLC patients treated with nivolumab alone or in combination with denosumab.
Twenty-seven patients were enrolled in the D-BEYOND trial: the first patient enrolled on 2 October 2013 and the last patient enrolled on 9 June 2016. D-BEYOND was a prospective, single-arm, multi-center, open label, pre-operative "window-of-opportunity" phase IIa trial (NCT01864798). All patients received two injections of denosumab 120 mg subcutaneously, administered 7-12 days apart, prior to surgical intervention. Surgery was performed 10-21 days after the first dose of denosumab (median, 13 days). Post-study treatment was at the discretion of the investigator. Snap-frozen and formalin-fixed, paraffin-embedded (FFPE) tumor and normal tissues were collected at baseline (pretreatment) and at surgery (posttreatment). Normal tissues (snapfrozen and FFPE) were defined as being at least 1 cm away from tumor, another quadrant, or contralateral breast biopsies.
BC subtypes were defined according to the St Gallen 2015 Consensus Meetings 57 using immunohistochemical surrogates as follows: Luminal A: ER and/ or PR(+), HER2(-), Ki-67 < 20%; Luminal B: ER and/or PR(+), HER2(-), Ki-67 ≥ 20; Basal: ER(-), PR(-), and HER2(-), irrespective of Ki-67 score; and HER2: HER2(+), irrespective of ER, PR, or Ki-67. All 4 HER2+ patients included in the study were ER+ PR+. The full study protocol is available as Supplementary Note 1 in the Supplementary Information file. Serious and non-serious AEs were collected from the day of signed informed consent until one month after the final administration of the study drug, except for the project-specific AEs, for which the reporting was extended to 3 months after the final dose of denosumab. Safety data were evaluated using the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE v 4.
0). AEs were coded according to the Medical Dictionary for Regulatory Activities (version 20. 1). All non-serious AEs are summarized in Supplementary Data 6, the most frequent one being arthralgia (4/27, 14. 8%). This study was approved by the Ethics Committee of the trial sponsor; the Medical Ethics Committee of the Institute Jules Bordet (IJB No. : 2064) and and the Melbourne Health Human Research Ethics Committee. All patients provided written informed consent prior to study entry. One patient was excluded because she had a ductal in situ carcinoma and two patients were excluded because of lack of available tumor tissue. Another patient was excluded from TIL evaluation due to tissue exhaustion. The primary study endpoint was a GM decrease in the percentage of Ki-67-positive cells assessed by IHC.
Additional secondary endpoints include: change in RANK/RL gene expression and signaling, change in tumor proliferation rates using gene expression, change in expression levels from genes corresponding to mammary progenitor populations, estrogen pathways, immune pathways, and gene expression changes in the paired samples of surrounding normal tissue when available. All primary, secondary, and exploratory endpoints performed are summarized in Supplementary Data 17. Enzyme-linked immunosorbent assay. Serum concentrations of human sRL were centrally assessed at IJB in triplicate, using an ELISA according to the manufacturer's instructions (Biomedica, Austria). sRL bound to denosumab is not be detected by this assay. Serum CTX levels were routinely evaluated in each center by ELISA. Pathological assessment and immunohistochemical staining of human tumor samples.
Tumor cellularity was centrally assessed on HE-stained tissue sections from FFPE and frozen human tumor samples. For patients with multiple samples, the sample with the highest tumor content was chosen for further analyses. The percentage of intratumoral and sTILs was independently evaluated by two trained pathologists (R. S. and G. V. D. E. ) who were blinded to the clinical and experimental data on the HE slides, following the International TIL Working Group 2014 methodology, as described elsewhere  58. Median tumor cellularity ranged between 35% and 90%. TIL proliferation was assessed as the percentage of Ki-67 + TILs among all TILs. Tissue sections (4 μm) from FFPE tissues of human primary breast tissue were used to assess RANK and RL. For each patient, representative unstained slides of the primary tumor were shipped to NeoGenomics Laboratories (California, USA) for immunohistochemical staining of RANK (N1H8, Amgen), RL (M366, Amgen), blinded to clinical information.
Indexed cDNA libraries were obtained using the TruSeq Stranded Total RNA Kit (Illumina) following the manufacturer's recommendations. The multiplexed libraries were loaded onto a NovaSeq 6000 apparatus (Illumina) using a S2 flow cell and sequences were produced using a 200 Cycle Kit (Illumina). Mouse microarray data were feature-extracted using Agilent's Feature Extraction Software (v. 10. 7), using the default variable values. Outlier features in the arrays were flagged by the same software package. Data were analyzed using the Bioconductor package in the R environment. Data preprocessing and differential expression analysis were performed using the limma and RankProd packages, and the most recently available gene annotations were used. Raw feature intensities were background-corrected using the normexp background-correction algorithm.
All correlations were measured using the Spearman's non-parametric rho coefficient. All reported P-values were two-tailed. All analyses were performed using R version 3. 3. 3 (available at www. r-project. org) and Bioconductor version 3. 6. No correction was made for multiple testing for exploratory analyses, except for the gene expression analysis, for which the false discovery rate was used. Mouse experimental data were analyzed using GraphPad Prism version 5. Differences were analyzed with a two-tailed Student's t-test, an F-test, or an unpaired-samples t-test against a reference value of 1. Tumor growth curves were compared using two-way analysis of variance. Frequency of tumor initiation was estimated using the extreme limiting dilution assay (http://bioinf. wehi. edu. au/ software/elda/).