dataesr/scientific-paragraphs-categorization huggingface dataset:
shape and the difficulty of reproducing the sphere curved surface with a cubic meshing. The oscillation of the cross-section error comes from a slight shift between the Mie and IFDDA cross-section curves; see Fig. 3(d). In Fig. 4 , the same test is conducted with an absorbing sphere, the real part of the relative permittivity being set to 2 and the imaginary part being increased. In this case, we observe that accuracy IFDDA is better than 3%, the absorption damping resonances. To investigate further the limits of IFDDA, we plot in Fig. 5 (a), the errors between IFDDA and Mie for a high refractive index sphere of radius a = λ/2. The real part of the sphere relative permittivity is increased from 1 to 30, with an imaginary part fixed to 1. We observe that the results obtained with N l = 100 and N l = 200 are reasonably accurate (better estimations could be obtained by accounting for local-field corrections  [35, 36] , but this option is only implemented in the code for spherical particles).
USING IFDDA AND IFDDAM IN PRACTICE The source code can be downloaded at Code 1, Ref. [39]. The package comes with a detailed user guide, which explains how to use the interface, the configuration parameters, and the data files. Two versions of the codes are available. IFDDA is optimized for objects in free space and IFDDAM for objects embedded in a multilayer. The free-space version is much quicker than the multilayer counterpart and should be preferred when appropriate. Both versions have a user-friendly interface with a drop-down menu that has been designed so that nonspecialists can easily perform a simulation and obtain meaningful images (microscope images, near-field images, scattering cross sections, among others), within a few minutes; see Fig. 9. All the data can be displayed directly on the interface  [40].
For example, images of all the field components for all the possible cuts, i. e. , x , y , or z within the FFT box are available. The data are also saved in ASCII or HDF5 format. A MATLAB code (ifdda. m) is provided to read the HDF5 or ASCII data. IFDDA comes with many preimplemented examples (see user guide). The objects can be spheres, cuboids, ellipsoids, and even random media. The incident field can be plane waves, Gaussian beams  [15] , or antenna beams. For experts, it is possible to provide custom incident fields and objects using home-made ASCII files. For example, one can study the scattering from inhomogeneous anisotropic objects embedded in a waveguide and illuminated by a (provided) guided mode or study the diffraction by a (provided) hundreds of Fig. 9. Code interface. The drop-down menu on the left allows one to choose the object, illumination, and the wanted simulation (microscopy, optical forces, far-field scattering, etc.
Tutorial wavelength long, highly resonant, 3D subwavelength patterned structure  [41]. Note that many numerical parameters and code options are set by default, but they can be modified in the advanced interface option panel. A parallelized version using openmp (version >= 4. 5) and the fastest Fourier transform in the west  [42]  is available for Linux systems. Another version (without the parallel calculation option)  [43]  is available for Windows.
CONCLUSION Our ambition is that undergraduate students or researchers who are not specialists in electromagnetism try IFDDA or IFDDAM and its preimplemented examples (objects such as spheres or cuboids, incident fields such as Gaussian beams or plane waves) to get useful simulations (microscope images, optical forces) with a minimal effort. Yet, we believe that these codes can also appeal to researchers in electromagnetism and microscopy, as they permit simulations of the light-matter interaction in very complex configurations. APPENDIX A: AUTHORS P. C. developed most of the FORTRAN code and a part of the C++ code. He should be contacted first for any problem concerning the code. D. S. did the main part of the C++ code which manages the graphical interface and the makefile. G. M. and M. R.
This analysis points out the dependence of the image on various parameters and the interest of a rigorous microscope simulator for studying the image formation. Fig. 10. These images were made using MATLAB from the HDF5 files provided by IFDDA, but similar plots are readily available on the IFDDA interface. Disclosures. The authors declare no conflicts of interest. Data Availability. Data underlying the results presented in this paper have all been realized by the IFDDA and IFDDAM codes, which can be downloaded at Code 1, Ref. [39].
IFDDA, an easy-to-use code for simulating the field scattered by 3D inhomogeneous objects in a stratified medium: tutorial Patrick C Chaumet, Daniel Sentenac, Guillaume Maire, Md Rasedujjaman, Ting Zhang, Anne Sentenac To cite this version: Patrick C Chaumet, Daniel Sentenac, Guillaume Maire, Md Rasedujjaman, Ting Zhang, et al. IFDDA, an easy-to-use code for simulating the field scattered by 3D inhomogeneous objects in a stratified medium: tutorial. Journal of the Optical Society of America. A Optics, Image Science, and Vision, 2021, 38 (12), pp. 1841. 10. 1364/josaa. 432685. hal-03462189.
, 2018b ; Figure  1C ). The inter-division process could be the accumulation of a molecule produced since birth, as summarized above. According to this 'concurrent-cycles' model, the slowest process sets the timing of cell division at the single-cell level. Based on recent experimental evidence  (Si et al. , 2019; Witz et al. , 2019) , DNA-replication initiation is controlled through an adder-like process between subsequent initiation events, which could also stem from a molecule accumulating during replication events  (Ho and Amir, 2015; Sompayrac and Maaloe, 1973). Micali et al. showed that single-cycle models proposed  (Wallden et al. , 2016; Ho and Amir, 2015; Harris and Theriot, 2016)  fail to explain experimental data on the B and C+D subperiods in single cells, while the concurrent-cycles model is able to fit the previously available experimental datasets  (Micali et al.
, 2010; Long et al. , 2013; Long et al. , 2014; Si et al. , 2019; Witz et al. , 2019). To reliably distinguish subsequent rounds of DNA replication, we grew cells in minimal medium (M9+NH4Cl+glyccerol), such that subsequent replication rounds do not overlap. We segmented single cells using the Oufti cell-segmentation tool  (Paintdakhi et al. , 2016)  and constructed cell lineages using the Schnitzcells package  (Young et al. , 2012). We then used the YPet-DnaN signal to measure periods of DNA replication (Figure  2 -figure supplement 1). In unperturbed cells, we found an average C period of 51 AE 1 min and a D period of 22 AE 4 min (Supplementary file 1), in agreement with previous bulk measurements  (Michelsen et al. , 2003). Since DnaN stays bound to DNA for about 5 min after replication termination  (Moolman et al.
A replication-independent adder-like process is increasingly likely the bottleneck process for cell division As described in the introduction, a range of different single-process models were proposed in the past to explain correlations between DNA replication and cell division  (Si et al. , 2019; Harris and Theriot, 2016; Witz et al. , 2019; Wallden et al. , 2016; Ho and Amir, 2015). Some of us recently argued that existing single-process models are incapable to reconcile correlations observed in previous experimental datasets  (Micali et al. , 2018b) , which led us to propose the concurrent cycle scheme illustrated in Figure  4A. The model assumes two processes that must both finish for cell division to occur, one replication/segregation process related to the size at replication initiation and one inter-division process related to the size at birth.
, 2019). We therefore tested the performance of both of these models on our experimental data of unperturbed cells, by jointly comparing the predicted couplings of the interdivision period and the C+D period. We found that both frameworks appear to be incompatible with our data (Figure  4C ). We also verified that the concurrent-cycles scenario generally shows better agreement with recently published data  (Si et al. , 2019; Witz et al. , 2019)  than single-process models (Figure  4 figure supplement 3). Interestingly, when fitting our model to all datasets including our own, we found that p H » 0:5 at slow growth (if the average doubling time is smaller than 1. 4 hr), while p H increases with decreasing doubling time. This trend is in qualitative agreement with recent work from Tiruvadi-Krishnan et al.
Image analysis Image analysis was based on published or custom Matlab scripts. Cells were segmented using the Oufti package  (Paintdakhi et al. , 2016). Dimensions of cells grown in liquid culture and imaged on agarose pads were extracted using Oufti. For cells grown in mother machine channels, we considered all channels that contained cells growing for the whole duration of the experiment. As the cells are trapped in channels and their long axis is aligned with the channel direction, we computed cell length as the distance between the two extreme points of the cell contour (obtained with Oufti) along the channel axis. We subsequently reconstructed cell lineages using the Schnitzcells software  (Young et al. , 2012) , and we considered only cells with at least four ancestors for further analysis.
To that end we identified fluorescent spots of Ypet-DnaN: First, a bandpass filter was applied to the YPet fluorescence image (Matlab function bpass with 0. 8 px and 20 px for the characteristic length scales of noise and objects, respectively). We then considered all local intensity maxima (Matlab function regionprops) inside cell contours with peak intensity above a manually defined threshold. We then obtained the average time points of initiation/termination as as inflection points along the x-axis in probability density maps (see Figure  2C ). For the detection of DNA-replication initiation and termination in single cells, we did not consider spots but took advantage of the heterogeneous Ypet signal during replication (as illustrated in Figure  2-figure supplement 1 ). After bandpass filtering of the YPet image, we subtracted the median intensity I med for every pixel and took the sum: I tot ¼ P i ðI i À I med Þ, where i runs over all pixels inside the cell contour.
To test for the influence of division asymmetry on the adder slopes, we corrected the added lengths for the asymmetries of grandmother-mother and mother-cell division events. For example, to correct for the asymmetry in the calculation of the inter-initiation added length, if subsequent initiations happen in mother and daughter cell, we obtain D asym I ¼ D I þ ð1 À aÞL $ d , where L $ d is the ori-normalized length of the mother cell at division, and where a ¼ L sibling 0 À L 0 = L sibling 0 þ L 0 is the division asymmetry between the daughter cell with birth length L 0 and its sibling with birth length L sibling 0. Comparing the simple and the more accurate calculation revealed no significant difference for both I and C+D periods, respectively (Figure  4 -figure supplement 2). Adder slopes were estimated from a robust fit on the cloud of points using iteratively re-weighted least squares (Matlab, robustfit function) to avoid the contribution of occasional outliers.
Description of the analysis of data from the literature To compare our findings with data available in the literature, we downloaded data of untreated conditions from  Si et al. , 2019  (downloaded at https://www. sciencedirect. com/science/article/pii/ S0960982219304919) and  Witz et al. , 2019  (downloaded at https://zenodo. org/record/3149097#. X7PKA9NKhBx). Excel files are imported in MATLAB using the function readtable and all the subsequent analysis has been performed with MATLAB. Since this manuscript is focused on slow-growth conditions, we restrict our comparision with  (Si et al. , 2019)  to their slow-growth conditions (MG1655 acetate and NCM3722 MOPS arginine) (see Figure  4 -figure supplement 3). Note that Si et al. , 2019 report the initiation size per origin without specifying the number of origins and without providing the added size during C+D.
The slopes z G , z I and z CD were calculated by applying the robustfit function in MATLAB to the clouds of points of the inter division, inter initiation and C +D adder plots, respectively. The data from  Witz et al. , 2019  are in a different format which provides the inter-division, interinitiation and C þ D added size as well as size at birth and size at initiation. For this reason, we were able to calculate the z G , z I , and z CD directly using the added quantities and the robustfit function in MATLAB.
Acknowledgements We thank the  Biomaterials and Microfluidics core facility of Institut Pasteur , and particularly  Samy Gobaa , for the help in the fabrication of microfluidic chips, former student  Ewen Corre  for first investigations of DnaN-Ypet behavior, members of the  Microbial Morphogenesis and Growth lab  for discussion and technical assistance,  Rodrigo Reyes-Lamothe  for advice and for sharing the DnaN-Ypetcontaining strain. We also thank  Pietro Cicuta  and  Mia Panlilio  for sharing microfluidic devices and advice about microfluidic protocols. This work was supported by grants to SVT from the  European Research Council (ERC)  under the Europe Union's  Horizon 2020 research and innovation program  [Grant Agreement No. (679980)], the  French Government 's  Investissement d'Avenir program Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  ( ANR-10-LABX-62-IBEID ), the  Mairie de Paris 'Emergence(s)' program , and the  Volkswagen Foundation.
Introduction The web has become a legitimate field of study for the social sciences  [Ackland 2013 ]. In qualitative as well as quantitative social sciences, an increasing number of researchers extend their traditional fieldwork towards digital media, as a complement or even as the primary way of collecting data  [Rogers 2013 ]. Social networks like Facebook and Twitter have gained a lot of attention thanks to the possibility of extracting data from their API. These platforms, however, are far from covering all social phenomena on the web. The generic web, although more difficult to harvest and analyze, still represents an important part of actors' presence on the web. For three years, we worked with sociologists to determine which obstacles prevent them from mining the generic web  [Diminescu 2012 ].
Description As all web crawlers, Hyphe allows its user to harvest the content and hyperlinks of a series of web pages. It performs a series of "crawl jobs" demanded by the user. Pages are aggregated as "web entities" (roughly corresponding to websites) in order to reduce the complexity of data. The typical output is a network of web entities to be analyzed through network analysis software such as Gephi  [Bastian 2009]. Approach. Hyphe is user-centric. It does nothing unless commanded by the user, and provides a lot of feedbacks for monitoring its processing. We invested a lot of time and energy on interaction design and software engineering to make it usable by people with limited computer skills. Our ambition is to make the whole data management process traceable and understandable by the users, which supports a higher interpretability of the results.
We call Hyphe a "curation-oriented crawler" because it uses the concept of "corpus" as a pivot to articulate web-mining constraints with social science methodology, as explained in the next sections. In particular, we replaced the notion of website with the more flexible notion of "web entity". Hyphe's demo and source code are available online at: http://hyphe. medialab. sciences-po. fr Use cases. Hyphe is too recent to appear in many published papers but we briefly mention two of them as empirical examples. Tournay identified and studied the community of stem-cells on the web, displayed in figure  1 , observing how the link structure impacts the "propagation of meaning between sociologically disparate actors"  [Tournay 2015 ]. Pedroja and a team of librarians and sociologists carefully delineated the debate on climate change on the web, notably measuring the unexpectedly large presence of climate skeptics  [Pedroja 2016 ].
Implementing the concept of corpus Issue. When considering the web as a field of investigation, social scientists expect their data to have certain methodological properties that make it a corpus, something more complex than the simple concept of dataset  [Wynne 2005]. It aims at representativeness and has important properties like selection criteria, balance or homogeneity. It can be found everywhere in social sciences, from survey panels in quantitative sociology to document space in archive investigation. When operationalizing their research questions, social scientists use the concept of corpus as a tool to frame the risk of methodological biases and errors. When scholars use a dataset in place of a corpus, they lose the crucial validity metrics that support interpretability. Solution.
A 3% H 2 O 2 solution (Gilbert, cat# 6553036) was added for blocking of endogenous peroxidase, and nonspecific binding was inhibited by protein blocking (Dako, cat# X0909). For IHC, 3-amino-9ethylcarbazole substrate (Vector Laboratories, cat# SK4200) was used. Slides were then counterstained with haematoxylin (Dako, cat# CS700), mounted with glycergel (Dako, cat# C0563) and scanned with a Nanozoomer (Hamamastu). For IF, the nucleus was stained with DAPI; slides were mounted with Prolong Glass antifade reagent and scanned with AxioScan (Zeiss). Calopix software was used to quantify PD1, CD8, and CD163. In all cases, isotype controls were used. The whole slide was always analyzed, avoiding the bias of choosing particular areas. Staining specificity. A competition test on liver sections was performed to verify the specificity of C1s staining (Supplementary Figure  S1A-B ).
Analysis of differentially expressed genes For RNASeq analysis, three biological replicates for each condition (siC1s vs siC) were used. The libraries were prepared with NEBNext Ultra II Directional RNA Library Prep Kit (New England BioLab, cat# E7760) according to the manufacturer's protocol: polyA purification using poly-T oligo attached magnetic beads, followed by 300-bp fragmentation with divalent cations under elevated temperature, synthesis of double-stranded cDNA and amplification by PCR. Sequencing was then carried out using a paired-end 75b of lllumina HiSeq 4000 instrument. The quality of FASTQ files was verified by using FASTQC and then aligned to a reference genome using STAR tool. Once the data were mapped on the genome, HTSeqCount was used to count and assign reads to a given exon and generate a count table.
Before differential analysis, non-expressed genes with a count equal to 0 were removed. R software and the DESeq2 package  [18]  was used to examine differential expression between siC1s-and siC-transfected cells. Results were considered statistically significant at an adjusted p value of < 0,05. The top 50 most significantly up-or down-regulated genes were sorted by the adjusted p-value; volcano plots were produced, as was a heatmap using heatmap. 2 and EnhancedVolcano packages in R. Gene Ontology enrichment analysis was performed with WEV-based Gene SeT AnaLysis Toolkit (WebGestalt) with the GO biological process category. The raw data are available under the GEO accession number ID PRJNA718732.
Flow cytometry Caki-1 cells untreated or siC or siC1s were cultured for 24 hours in the presence or absence of IFN (500ng/ml). Expression of MHC class I, C3aR and C5aR on tumor cells was studied by flow cytometry. Single-cell suspensions were resuspended in PBS + 10% heat-inactivated FCS. To block nonspecific antibody binding to Fcγ receptors, with Fc block (BD Pharmingen, cat#564219). Then cells were incubated with a viability marker (Invitrogen, Live/Dead fixable yellow dead cell, cat# L34968), MHC class I ABC-FITC (BD Pharmingen, cat# 555552), C3aR-PE (BioLegend, cat# 345804) and C5aR (BioLegend, cat# 344310) for 30min at 4°C. After a washing step with PBS + 10% FCS, cells were fixed with a solution of 0. 4% PFA. Flow cytometry data were acquired using a BD LSRFortessa X-20 (BD Biosciences) and analysed using FlowJow software.
Bioinformatic analysis Bulk RNAseq. C1S gene expression, as measured as transcripts per million (TPM), was determined by using RNAseq data from 28 TCGA cohorts of patients with different cancer types available at cBioPortal  [19] ,  [20]. No patients were excluded. In this database, TPM values were generated with the RSEM method (RNA-Seq by Expectation Maximization). Tumor types were arranged according to median expression values. Hepatocellular carcinoma (LIHC) was excluded from the analysis to avoid hiding the potential differences between tumor types linked to the very high expression of complement genes in the liver. Survival analysis was conducted with the Protein Atlas Interactive survival scatter plot & Survival analysis tool (https://www. proteinatlas. org/) using the optimal cut-off and the Gene Expression Profiling Interactive Analysis (GEPIA) tool (http://gepia.
cancer-pku. cn/); the results are represented as the log2 Hazard ratio of C1s for overall survival, with an optima cut-off or median cut-off calculated with the Cox pH Model respectively. A log-rank test was applied to evaluate differences between the curves; surrounded boxes represent a log-rank p-value < 0,05. Because of the low number of survival events, the testicular germ cell tumor cohort (TGCT) was also excluded from the survival study. The KIRC cohort of TCGA was used to compare expression (log2(1+TPM)) of complement genes (C1QA, C1R, C1S, C2, C3 and C4A) among patients. Heatmaps were generated with R software 3. 4. 2 and the heatmap. 2 package. Differential expression of C1S between tumors versus paired normal tissue from TCGA normal and GTEx normal databases (n=100) were investigated with GEPIA.
A subgroup of ccRCC patients displays a complement-rich phenotype Local production of complement proteins is key for intratumoral complement activation. In the ccRCC (KIRC) cohort of TCGA, approximately half of the patients displayed a complement-rich tumor environment with simultaneous expression of genes related to the classical complement pathway (C1QA, C1R, C1S, C2, C3 and C4A) (non-supervised classification, Figure  2A ). All these genes were expressed at higher levels in ccRCC tumors, compared with normal tissue, according to the TCGA database (p<10 -10 ). Analysis of the available scRNA-seq dataset of a fresh tumor sample  [22]  (Figure  2B  and Supplementary Figure  S2 ) showed that C1S, C1R, C2, C3 and some extent C1Q genes were expressed in tumor cells but C1QA, (also C1QB, C1QC) and C3 were expressed by macrophages (Figure  2C ).
C1s modulates the transcriptional program of ccRCC tumor cells Considering that the prognostic value of C1s may be independent of complement activation, we investigated C1s effects on tumor cells. At the single-cell level (in a dataset from a fresh ccRCC human tumor  [22] ), we searched for genes that discriminated tumor cells expressing C1S from those that did not (Figure  5A ). Gene Ontology analysis showed that genes enriched in C1s + tumor cells are implicated in various pathways such as vesicle mediated transport, response to oxygen-containing compounds, response to hormones, myeloid activation, neutrophil activation and degranulation, response to endoplasmic reticulum stress, extracellular matrix organization, complement proteins, regulation of IGF transport and uptake, and folding assembly and peptide loading of MHC class I (Figure  5B-C ).