dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion Our Python package, dipwmsearch, provides an easy and efficient procedure to find occurrences of di-PWMs in nucleotidic sequences, and well documented snippets. It offers practical advantages compared to an existing solution (like processing IUPAC codes, or an adaptable output-see Supplementary Material). Furthermore, it can be enhanced by combining it with other Python packages (e. g. for processing compressed sequence files). Most of all, the installation is straightforward using pypi or conda. In addition, we presented an original enumeration-based search algorithm that handles di-PWMs even if they contain nonselective positions. Coping with nonselective positions was necessary to make search effective for some di-PWMs, which questions their information content, and in turn their construction process.
Funding This work was supported by  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement [ 956229  to E. R. ]; by  Institut National du Cancer (INCA)   PLBIO  project [ 2020-116  to J. R. and E. R. ]; and by  Labex Solutions Nume  ´riques, Materielles et Modelisation pour l' Environnement et le Vivant  ( NUMEV ) for GEM flagship project [ ANR 2011-LABX-076  to M. M. , J. R. , and E. R. ].
2020). In this work we restrict the analysis to the regions covered by deep NIR imaging (∼5. 5 deg 2 in the COSMOS-E and XMM-LSS fields). The paper is organized as follows. In Sect. 2 we describe both the COSMOS2020 data set used to derive the IRX calibration and the HSC-CLAUDS survey, to which the calibration is applied to measure the SFRFs. In Sect. 3 we describe the estimates of the physical parameters, and in Sect. 4 we perform the calibration of the IRX from direct FIR observations and extend it to low-mass systems using the FIR stacking technique. Section 5 presents the SFR functions derived between 0 ≤ z ≤ 2 and a comparison with previous results from the literature, as well as a comparison with four hydrodynamical simulations, TNG100 from the IllustrisTNG project  (Pillepich et al.
COSMOS2020 Catalog COSMOS is a major extragalactic field with a large multiwavelength photometric coverage over a 2 deg 2 field. It collects ground-based optical observations with intermediate and broadband filters, NIR photometry for a total of 31 filter passbands as described in  Laigle et al. (2016, COSMOS2015). The main improvement of the latest COSMOS2020 catalog with respect to COSMOS2015 is the depth of broadband imaging. A164, page 3 of 28 It combines the deeper CFHT U-band imaging from CLAUDS survey  (Sawicki et al. 2019) , the ultra-deep data from Public Data Release 2 (PDR2) of the HSC Subaru Strategic Program (HSC-SSP;  Aihara et al. 2019 ) and the latest release of UltraVISTA (DR4;  McCracken et al. 2012)  as well as all the mid-infrared (MIR) imaging available with the Spitzer/IRAC channels.
They discuss the processing steps to combine all the above data sets into the HSC grid and the addition of the external bands into the dedicated photometric HSC pipeline  (Bosch et al. 2018). In addition to the catalog produced with the HSC pipeline, a second catalog based on SExtractor software  (Bertin & Arnouts 1996)  is produced using a multiband χ 2 image in dual mode. In the SExtractor catalog we also include the far-UV (FUV; λ ∼ 1500 Å) and near-UV (NUV; λ ∼ 2300 Å) imaging from the GALEX satellite  (Martin 2005). The deblending of the UV photometry was performed with the EMPHOT code  (Conseil et al. 2011 ) by using optical u-band detections as prior down to u ∼ 25 (e. g. ,  Zamojski et al. 2007; Moutard et al. 2016b). D23 gives all pieces of information regarding magnitude, color, depth, and photometric redshift measurements, and detailed comparisons between the two catalogs and external data sets (COSMOS2020, CANDELS, and spectroscopic samples) are discussed.
At high masses, with the COSMOS2020 FIR sample, we still have an overall good agreement. However, we observe a wavy shape of the mean difference with redshift, which contributes to enlarging the scatter reported in each panel, up to 0. 3 dex. We checked that this was not due to the estimates of A164, page 11 of 28 the NrK vector between the HSC-CLAUDS and COSMOS2020 data set. It is rather due to the COSMOS2020 calibration, where our parametric form with redshift does not capture this residual variation at all masses 3  , which does not exceed ±0. 1dex. Since it affects all masses it does not modify the SFR functions but we account for this redshift residual in the cosmic SFRD measurements (Sect. 6) by adding them in quadrature in the error bars. In the highest stellar mass bin, M > 10 11 M , the global shift is due to the fitting procedure, which is slightly above the measurements in Fig.
Parsa et al. (2016)  used the deep fields (HUDF, CAN-DELS, and UltraVista-COSMOS) to measure the UV luminosity functions at 1. 5 ≤ z ≤ 2. 5. To convert it into an SFR function,  Katsianis et al. (2017b)  adopt a luminosity-dependent evolution of the β-slope as proposed by  Smit et al. (2012). These data are the only ones based on a UV selection in this compilation as the authors claimed that this data set provides the best constraint on the slope of the UV luminosity function at this redshift. As a sanity check, we first compared our SFRFs with the COSMOS2020 ones derived with the same NrK method and adopted their photometric redshifts and luminosity estimates. The COSMOS2020-NrK SFRFs are shown as black dotted lines. At all redshifts, they are in excellent agreement with our HSC-CLAUDS SFRFs and within our uncertainties.
The COSMOS2020-(UV+FIR) SFRFs derived with the 24 µm flux-limited sample are shown as shaded orange histograms. The two SFRFs are also in good agreement up to the completeness limit. Even though the NrK method relies on the COSMOS2020 FIR data for the calibration, when applied to the entire HSC-CLAUDS sample, our NrK method reproduces well the SFRF at high SFRs at all redshifts. On the other hand, the UV-optical selection of the HSC-CLAUDS sample spans a wider range of SFRs, extending down to at least a factor of 10 in the low-SFR regime, allowing us to explore the faint end slope. At low redshift, 0 ≤ z ≤ 0. 5, our NrK SFR function is in good agreement with the FIR+UV SFRF obtained in the local volume by  Bothwell et al. (2011, blue line)  and exhibits a comparable faint-end slope (α = -1.
In addition, the use of longer wavelength mid-infrared data would allow the analysis to be extended to higher redshifts. We acknowledge the Virgo Consortium for making their simulation data available. The EAGLE simulations were performed using the DiRAC-2 facility at Durham, managed by the ICC, and the PRACE facility Curie based in France at TGCC, CEA, Bruyères-le-Châtel. We have benefited from the publicly available programming language Python, including the numpy  (Harris et al. 2020) , matplotlib  (Hunter 2007 ) packages, and the Topcat analysis tool  (Taylor 2005).
Acknowledgements. V. P. and S. A. wish to thank  Véronique Buat  for fruitful discussions. We are grateful to the referees for their constructive input. V. P. work was partly funded by  NASA APRA  grant  80NSSC20K0396. This work was supported by the  Spin(e) ANR  project ( ANR-13-BS05-0005 ), the  DEEP-DIP ANR  project ( ANR-19-CE31-0023 ), and by the  Programme National Cosmology et Galaxies (PNCG) of CNRS/INSU  with INP and  IN2P3 , co-funded by  CEA  and  CNES. These data were obtained and processed as part of the  CFHT Large Area U-band Deep Survey (CLAUDS) , which is a collaboration between astronomers from  Canada, France , and China described in Sawicki et al. (2019). CLAUDS is based on observations obtained with MegaPrime/ Mega-Cam, a joint project of  CFHT  and  CEA/DAPNIA , at the CFHT which is operated by the  National Research Council (NRC) of Canada , the  Institut National des Science de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii.
and Mathematics of the Universe (Kavli IPMU), the University of Tokyo, the High Energy Accelerator Research Organization (KEK), the Academia Sinica Institute for Astronomy and Astrophysics in Taiwan (ASIAA), and Princeton University. Funding was contributed by the FIRST program from Japanese Cabinet Office, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), the Japan Society for the Promotion of Science (JSPS), Japan Science and Technology Agency (JST), the Toray Science Foundation, NAOJ, Kavli IPMU, KEK, ASIAA, and Princeton University. The Cosmic Dawn Center is funded by the Danish National Research Foundation under Grant No. 140.
, 2011) , and no tick resistance to this pesticide has been observed  (Agwunobi et al. , 2021). In addition, it was found that spinosad in rotation with amitraz was also effective for the control of the amitraz-resistant strain of the tick R. microplus  (Jonsson et al. , 2010). Consequently, we were interested in studying the repellent effect of the butanolide insecticide, flupyradifurone (FLU), in combination with DEET on the tick I. ricinus. FLU is a new pesticide developed in 2010 by Bayer CropScience, and commercialized under the Sivanto® formulation. It was demonstrated that FLU acts by activating the nAChRs present in the arthropod's central nervous system  (Nauen et al. , 2015) , and is described as a pesticide with a low toxicity for non-target species  (Nauen et al. , 2015). In this study, we developed an easy-to-use laboratory assays coupled to ToxTrac software video recording, and validated our method by evaluating FLU's repellent effect compared to DEET.
ToxTrac software Tick mobility was recorded and analyzed using ToxTrac software  (Rodriguez et al. , 2017; Rodriguez et al. , 2018). For video analysis, three zones were defined on the platform: the area where the tick started the experiment (zone 1); the filter paper in the center of the platform (chemical barrier, zone 3); and the final zone (zone 2), which ticks could access if they crossed the chemical barrier (Fig. 1B ). Tick detection was calibrated on ToxTrac software using size (15-300 pixels) and contrast (55-80 intensity level). After the recording, the software provided data to characterize the ticks' movement on the platform. Thus, the following parameters were automatically recorded: the rate of tick presence inside zone 2 which is the rate of non-repelled ticks relative to the control, the exploration rate (over the whole platform) which is the number of explored areas/total number of areas (the platform is divided into regular 50 mm squares, without any overlaps, and the presence of ticks was recorded for each square), the mobility rate which represents the total distance covered by tick in platform/mean distance covered by the control group under the same experimental condition, and the velocity rate which is the ratio of the instantaneous speed of tick relative to the mean speed of control group under the same experimental condition.
The ToxTrac software could also indicate recording bias, such as the frozen rate (proportion of time during which ticks are immobile), and the invisibility rate (proportion of time during which the software is not able to detect the tick: frame where no tick detection was made during the analysis of the video relative to control group). We performed 10 trials of each experimental condition (10% DEET and 20%, 10% FLU and 20%, combinations of DEET and FLU) on untreated and treated platforms, using the ToxTrac software and video tracking recordings.
Statistical analysis Two types of statistical analyses were performed. For the behavioral study, differences between the control (ticks on untreated platform) and tested conditions (ticks on a treated platform) were analyzed using a One tailed Wilcoxon paired test. Data were shown as means ± SEM of tick groups. All recorded parameters (zone 2 inside, exploration, immobility, frozen, velocity and invisibility rates) were normalized using the mean ± SEM of data under control condition using the same experimental conditions. Data were analyzed using Prism 7 (GraphPad Software, La Jolla, CA, USA). For the electrophysiological results, currents were normalized using the mean current amplitudes under the same control conditions (I/Imean). Data were shown as means ± SEM of the current amplitudes.
Differences between current amplitudes in control conditions and currents recorded under application of DEET, FLU or the combination of both DEET and FLU were analyzed using a one-way Anova and One tailed Wilcoxon paired test. The concentration giving half of the maximum response (EC 50 ) values for ACh, nicotine and FLU were determined using nonlinear regression on normalized data (1 mM ACh or nicotine as maximal response) using Prism 7 (GraphPad Software, La Jolla, CA, USA). The dose-response curves were derived from the fitted curve following the equation: Y = Imin + (Imax -Imin)/ (1 + 10(log(EC 50 -X)H) where Y is the normalized response, Imax and Imin are the maximum and minimum responses, H is the Hill coefficient and X is the logarithm of the compound concentration. For behavioral and electrophysiological experiments, "n" represents the number of ticks and recorded oocytes, respectively.
Repellent effect of DEET and flupyradifurone tested alone and in combination on adult female I. ricinus First, we evaluated the repellent effect of two different concentrations of DEET (10% and 20%), with the aim of validating the platform assay, in particular the ability of ticks to cross the chemical barrier. With 10% DEET, using the video-tracking associated to ToxTrac software video-tracking, we found that ticks were able to cross the chemical barrier as found with ticks for an untreated platform (Fig. 2A  and B ). This finding was confirmed when we analyzed the recorded parameters. Indeed, statistical analysis using parameters from ToxTrac software demonstrated that despite an apparent decrease, no significant differences were found for the parameter zone 2 inside rate (n = 50 ticks, p = 0.
Validation of the bioassay method on adult female I. ricinus In the present work, we validated a new in vitro bioassay to study the repellent effect of the combination of DEET and FLU on adult female I. ricinus. The device developed here allowed the study of the explorative tick behavior on a horizontal platform to be exploited using ToxTrac software analysis  (Rodriguez et al. , 2017; Rodriguez et al. , 2018) , which made it possible to optimize the exploitation of the data by using automatic recording  (Adenubi et al. , 2018). Several parameters were selected in the tick behavioral assays and those described in the present manuscript are the most representative of the trajectories, immobility, and exploration of the ticks. Moreover, the parameter illustrating the time spent where the individuals were not visible to the camera allowed to demonstrate that this phenomenon is not a bias as no significant difference was observed between the control and tested groups.
2a  and Extended Data Fig. 3f, g ). We then quantitatively evaluated the T cell dynamics using the previously developed STARTRAC indices upon TCR tracking  18  (Methods). T cells carrying repetitive TCRs are defined as clonal cells. The presence of clonal cells across several different tissue sites within the same cluster implies the tissue migration (STARTRAC-migr) of indicated T cell subtypes. And clonal cells found within a T cell cluster were quantified with STARTRAC-expa index, whereas clonal cells between two different T cell subtypes referred to cell state transition (STARTRAC-tran). Among all CD8 + T cells, T eff cells showed the highest clonal expansion, migration and transition index (Fig. 2c ), as expected. Additionally, expa index pointed out that clonal expansion might be a possible explanation for the T EX enrichment in tumor sites (Fig.
Similar analyses were also performed on CD4 + T cells to quantify their tissue distributions and TCR sharing. In contrast to CD8 + T cells, CD4 + T cells showed an overall lower clonal expansion. Among these clusters, CD4 + T eff cells exhibited the highest clonal expansion, migration and transition indexes (Fig. 3a ), similar to the observations in CD8 + T cells. The inferred developmental trajectories also exhibited a similar branched structure. T N (T01), T H 1-like (T05) and T reg (T03) cells were positioned at three different branches whereas T CM (T02) cells were located in the middle (Fig. 3b  and Extended Data Fig. 5a ). In addition, pairwise transition analysis based on TCR sharing (Fig. 3c  and Extended Data Fig. 5b ) and the shared TCR pattern among T02, T03 and T05 (Extended Data Fig.
6a ), we speculate that LAMP3 + DCs might also help potentiate the infiltration and differentiation of T H 1-like cells in ovarian tumors. This could explain the higher enrichment indexes of both LAMP3 + DCs and T H 1-like T cells in Met. Ome than in Pri. OT (Fig. 4b ). Notably, we did not detect many conventional DCs (cDCs) in tumor tissues as reported in recent studies  27  , but instead observed their specific relative enrichment in malignant ascites (Fig. 4b  and Extended Data Fig. 6b, c ). To further elucidate the functions and relationships between different myeloid clusters, we performed similarity analysis of myeloid cells in our dataset with those reported in colorectal cancer (CRC)  28  and hepatocellular carcinoma (HCC)  27  (Fig. 4c ). As expected, both cDC1 and cDC2 from different cancer types or tissue sources were clustered together, indicating their conserved phenotypes (Fig.
4a, c  and Extended Data Fig. 6d ). Among AeM cells, FABP5 + AeM (M09), FOLR2 + AeM (M14) and FN1 + AeM (M08) were all clustered into the same branch with HCC ascites-enriched C6-MARCO, likely reflecting the environmental plasticity of macrophages. Of note, VCAN + AeM (M11), characterized by high expression of transcripts associated with monocytes (VCAN, S100A9 and S100A12)  34  , was clustered into the same branches with tumor-enriched C5-VCAN and ascites-enriched C1-THBS1 in HCC dataset and FCN1 + mono-like cells in CRC (Fig. 4c  and Extended Data Fig. 6d ). These two macrophages in HCC were defined as myeloid-derived suppressor cells (MDSCs) in the same differentiation lineage  27. Therefore, VCAN + AeMs (M11) in our study were more likely to be MDSCs distributed in ascites. We next investigated the different functional states of TeMs (M07, M10 and M12) and AeMs (M08, M09, M11 and M14).
Article https://doi. org/10. 1038/s43018-023-00599-8 suggesting that DES + MCs were more likely to fall off into the ascites from tumor tissues. Meanwhile, we observed a significantly decreased cell adhesion potential of MCs in Met. Ome compared to that in Pri. OT (Fig. 6f ). These analyses indicated that the loss of cell-cell adhesions could be a reason for MCs to shed from the omentum into ascites, which provides a favorable condition for tumor cell metastasis and colonization. Notably, DES + MCs showed high expression of CXCL12, CXCL13 and CXCL16 (Extended Data Fig. 8e ), reminiscent of the recently reported immunomodulatory cancer-associated fibroblasts (CAFs) identified in ascites  15. By integrating our dataset with that of CAFs in OC ascites, we further confirmed the similarities between DES + MCs in our study and the immunomodulatory CAFs  15  (Fig.
We found that patients highly expressing the top 20 signature genes of IL13RA1 + E02 had shorter overall survival (Fig. 7h ), further confirming their functions in tumor angiogenesis; however, signature genes of VCAM1 + E06 were not significantly correlated with clinical outcomes of patients with HGSOC (Extended Data Fig. 9d ). We also used another independent microarray dataset to validate these results (Extended Data Fig. 9e, f ).
Patients HGSOC3, HGSOC6, HGSOC7 and ECO1 were platinum-resistant (nonresponsive), whereas the other patients, except UOC1 were platinum sensitive (responsive). Patients ranged in age from 43 to 82 years old, with a median age of 62 years. Five more patients pathologically diagnosed with HGSOC (patients HGSOC11-HGSOC15) were enrolled in this study for flow cytometry analysis of T cells. The available clinical metadata of these patients are summarized in Supplementary Table  1.
ScRNA-seq data processing Low-quality cells were filtered out if cells had fewer than 200 genes expressed or >10% unique molecular identifiers (UMIs) linked to mitochondrial genes. The gene expression matrices of the remaining cells were generated with log normalization and linear regression using the NormalizeData and ScaleData functions of the Seurat package (v. 3. 1. 4). Cells with expression of more than one major cell marker were considered as doublets and removed from each cluster individually. The remaining cells that passed the filtering criteria were considered single cells. We also identified 2,010 platelets with high expression of pro-platelet basic protein. Almost all platelets were found in PB mononuclear cell samples and they are not discussed in this study. For visualization, the dimensionality of each dataset was further reduced using UMAP with the Seurat function Run-UMAP.
Unsupervised clustering and identification of cell subpopulations After the main cell populations were identified by first-run clustering, we ran the Seurat pipeline for a second time. Unwanted effects caused by percentage of mitochondrial UMI counts were removed by regression in this run. The selection of the resolution on the characteristics of each dataset and the top n PCs from principal-component analysis were used for identification of clusters. For T lymphocytes, we performed extra batch correction across different samples with Harmony (v. 1. 0) at the default settings. Small clustering groups with expression of dual-lineage signatures, including EPCAM-PECAM1-CD3D, EPCAM-CD79A, PECAM1-CD79A and CD79A-CD3D, were removed from downstream analysis. For other cell types, we did not conduct any batch correction as no obvious clustering bias using raw transcripts per million-like expression data would affect our downstream analyses.
TCR analysis The TCR sequences for each single T cell from 10x Genomics were processed using CellRanger (v. 3. 0. 2) with the manufacturer-supplied human VDJ reference genome 'GRCh38-alts-ensembl'. If two or more cells had the same identical α/β chain pair, the α/β chain pair were identified as clonal TCRs and these T cells were considered to originate from the same clonotypes, identified as clonal cells. After integrating TCR results with the gene expression data of 10x Genomics data, we identified TCR α/β-chain pairs for 59,334 cells. We then presented three STARTRAC indices to analyze different aspects of T cells based on paired single-cell transcriptomes and TCR sequences using STARTRAC (v. 0. 1. 0) as previously described  18. STARTRAC-expa, STARTRAC-migr and STARTRAC-tran are designed to measure the degree of clonal expansion, tissue migration and state transition of T cell clusters upon TCR tracking, respectively.
Developmental trajectory inference PAGA. To characterize the developmental origins of CD4 + and CD8 + T cells, respectively, we performed the partition-based graph abstraction method PAGA  23  , a part of the single-cell analysis package Scanpy (v. 1. 7. 2) in Python (v. 3. 6. 13)  63  , to infer the potential differential trajectory. Moreover, we used PAGA to assess the most likely trajectories of cell progression among endothelial cells in OC. The computations were carried out using default parameters. The edge connectivity between each subpopulation node for all edges are further compared by using an unpaired two-sided Student's t-test. Palantir. We also applied Palantir  24  to complement the trajectory analysis using default parameters.