dataesr/scientific-paragraphs-categorization huggingface dataset:
Determination of the bead distance Image analysis was carried out using ImageJ (National Institutes of Health, USA) and Matlab (MathWorks, MA, USA). The in-plane position of each particle is determined by a weighted average of gray levels (fig. S5A ), giving an accuracy of 2 nm as was done for in vitro work  (14, 20). This accuracy can be obtained because the size of the Airy disc spans a few tens of pixels and because we tune the intensity of the incoming light to use the whole range of the 16-bit depth of the camera. A large number of information bits can thus be used to find the subpixel location of the center of the bead. The position of the bead in the vertical direction is measured using the diffraction pattern on several images at different heights  (42). Three images were recorded in quick succession (50-ms interval) at a height distance of 300 nm by moving the objective with a piezo-controlled translator.
Fluctuation and peak analysis The amplitude of fluctuations is computed as the difference between the 90th and 10th decile of a dataset. The asymmetry of a curve corresponds to (ab)/(fluctuations amplitude) (fig. S2A ). To ensure the validity of the fluctuation quantification, we study the cumulative distribution of the measured thickness for being below a certain value (fig. S2B ). The median thickness is removed from each cell, and the shown curve is the mean of the cumulative distribution of each cell. The same trend of large fluctuations for the cortical thickness layer remains apparent. These fluctuations are strongly diminished when looking at a pair of beads inside the cell or at the cortical layer of actin-depolymerized cells. An asymmetry is also apparent in the cortical layer fluctuation, with larger fluctuations increasing the size of the cortical layer and smaller fluctuations decreasing its size.
Confocal image analysis To analyze actin intensity at the cell cortex, each cell is first "unfolded" using a homemade MATLAB algorithm. On each image of the video, the cell is identified with intensity thresholding and isolated from the rest of the image by putting all noncell pixel intensity to zero. The cell center is found using an Euclidian distance transform on the binarized cell image. Then, a coordinate system change is operated from cylindrical to Cartesian. As pixels have a size and do not land perfectly in an (X, Y) grid from an (R, ) grid, each "new pixel" intensity is computed as the weighted average of up to four old pixel intensities. The result is a video where the cell cortex appears linear (fig. S4A ). Cortex intensity value on each image is then taken as the maximum of vertical pixel line (fig.
Acknowledgments: We thank  P. Belska  for preliminary experiments on the magnetic pincher;  R. Voituriez  for useful discussions;  X. Benoit Gonin  and  O. Brouard  for technical support on the setup;  A. Mathur ,  M. Deygas ,  H. Moreau ,  D. Sanseau ,  G. Delgado , and  Z. Alraies  for additional cell culture; and  N. Srivastava  for help on D. discoideum culture and handling. Funding: This work was supported by the  Institut Pierre-Gilles de Gennes-IPGG  ( Equipement d'Excellence, "Investissements d'avenir," program ANR-10-EQPX-34 and Laboratoire d'Excellence ,.
Material and methods Database. We used the database of the Center that coordinates breast cancer screening (CRCDC-OC) in the Hérault department, France. This database registers women with precise, verified, and consolidated information on the occurrence of breast cancer during the follow-up of their screening. The data are also consolidated by the Hérault tumor register which provides an exhaustive information on the occurrence of breast cancer, including interval cancers during the follow-up period. We enrolled women, aged 50 to 60 at inclusion, who experienced a 12-year breast cancer screening from 2006. We extracted a database of 33,858 women, including 805 women who developed a breast cancer in 3-12 years following inclusion. We did not include women who have been diagnosed with breast cancer during the first two years of follow-up to avoid including women who might have already developed a cancer prior to inclusion, but not yet diagnosed at inclusion.
Data were recovered by a telephone survey. It was not possible to interview 33,000 women. Therefore, on one hand we selected all the women who developed a breast cancer during the follow-up period, and on the other hand, we randomly selected 5000 women among those who did not develop breast cancer using the sample function of the R software applied to the list of 32,962 unique identifiers. Power. We hypothesized a risk ratio ≥ 2 , with a ratio of 4 to 1 between women who did not develop breast cancer and those who did during the follow-up period, together with a power of 80%. It gave a required sample size of 543 women with breast cancer during the follow-up period and 2174 women without breast cancer. Study questionnaire. We looked for the risk factors for breast cancer in average risk women reported in the literature prior to creating the study questionnaire.
Data collection. Data were collected by telephone by two trained investigators. An explanatory leaflet (Supplementary material 3) was sent to all participants prior to the phone call and informed consent was obtained from all individual participants prior to the telephone interview. Responses to the study questionnaire were collected between November 2018 and May 2019. The inclusion flowchart is shown on Figure  1 ; 2,198 women were unreachable (mostly unassigned phone number, or reiterated unanswered calls) and 530 refused to participate (mostly alleging lack of time). Thus, this survey enabled collecting 3077 questionnaires, including 597 questionnaires from women who developed a breast cancer. The Case Report Form was developed with the ACCESS software. The analysis database was provided by the CRCDC-OC while having anonymized the data beforehand.
Schoenfeld residuals were computed to check the proportional hazards assumption. Our goal was to define an individual score. Therefore, to estimate 0 , the cumulative base-line hazard func- tion, we used Breslow's estimator 27 defined below. with: Bootstrap. As we performed a random sampling among women without breast cancer, in our database, the distribution between women who developed cancer and the others was no longer representative of the starting population. To remedy this, we performed bootstrap samples. Bootstrap is a resampling method that uses random sampling with replacement. This method allowed to avoid skewing cancer rates compared to the population of interest without introducing bias. We computed 100,000 bootstrap databases using stratified random sampling with replacement among women for whom we obtained answers to all the covariates of the final Cox model, in order to obtain a database close to the initial CRCDC database (i.
e. 33,858 women, including 896 who developed a breast cancer within the 12 years of follow-up). • T 1 < T 2 < • • • < T 12 distinct In these databases, mean risk was identical to the one of the CRCDC database. Then, we estimated, for each woman, � 0 (t) , and calculated F(12|Z) to obtain a risk score of developing a breast cancer during the follow-up period. Thresholds and likelihood ratios. In France, the number of women actually screened is between 5 and 6. 5 million. Among these women, we hypothesized that 10% of them might have a risk different from the "average" risk of breast cancer, either lower or higher. This average 12-year risk was 2. 6% in the organized screening population. From a clinical standpoint, we looked for women that might have a risk two times lower than this "average" risk.
047). Age (HR = 1. 024, 95% CI [0. 997; 1. 05], p = 0. 084) and interaction between age at menarche and menopause (HR = 2. 73, 95% CI [0. 91; 8. 19], p = 0. 073) have been forced in the model. The interaction between age at menarche and menopause was forced in the model to take into account the duration of estrogen impregnation, a notable risk factor in our literature searches  28. Bootstrap. We computed 100,000 bootstrap databases using random sampling with replacement among the 2768 women for whom we had answers to all the covariates of the final Cox model. The risk function F(t) was calculated for all women in the 100,000 databases.
Threshold determination. Pre-test prevalence of the disease was the percentage of breast cancer in the CRCDC-OC database i. e. 2. 6%. So, as described in Supplementary material 2, we looked for : • s 1 such as ∀s ≥ s 1 : • s 2 such as ∀s ≤ s 2 : We then plot LR+(s) and LR-(s) in Fig. 2. We drew the lines y 1 and y 2 with respective equations y 1 = 2. 054 and y 2 = 0. 493. This made it possible to find s 1 , which is the abscissa of the intersection point between y 1 and the representative curve of the LR + function, and s 2 , which is the abscissa of the intersection point between y 2 and the representative curve of the LR -function. This method gave a low threshold at 1. 9% and a high threshold at 4. 5%. On one hand, 9. 39% of women (469,500 women with a participation rate of 50%) presented a 12-year risk lower than 1.
Discussion This work was dedicated to explore whether it was possible to offer a personalized risk based screening to the population of women at « average » risk of BC. In a retrospective cohort of French women, aged 50 to 60, at « average » risk of breast cancer and followed-up for 12 years, from 2006 to 2018, who underwent the BC organized screening, we identified two additional levels of risk, either twice lower than the average-risk, or twice higher. Our partition into three groups at risk of breast cancer in these women showed that the level of risk in the socalled « average » risk population is not homogeneous. In a first step we built a score of breast cancer risk that included six variables, two of which (age and breast density) were already included in the databases of the French CRCDCs.
The recommendations on surveillance for women at moderate BC risk are modulated according to age. As the likelihood of getting breast cancer increases with age, all women aged from 50 to their 71st birthday who are registered with a GP are automatically invited for breast cancer screening by mammography every 3 years (Guidance NHS Breast screening programme screening standards valid for data collected from 1 April 2021) (www. nhs. uk). The term « average » risk was retained by the National Comprehensive Cancer Network in the US  30. For the NCCN Panel, increased risk of developing BC is defined by the modified Gail model for women ≥ 35 years of age as a 5-year risk of 1. 7% or greater. The United States Preventive Services Task Force (USPSTF) recommends biennial screening mammography for women between the ages of 50 and 74 years  31.
In this observational survey, the difficulty to contact women is inherent in this type of design and might introduce a selection bias. In our study, among non-participating women, 4 out of 5 were not reachable by phone, however it did not introduce a selection bias since it is at random. Conversely, 1 in 5 women indeed refused to answer the questionnaire which might have introduced a selection bias, even if the majority of them alleged a lack of time to fill out the questionnaire. We considered that it was not obviously linked to the level of BC risk, though limiting a potential selection bias. An internal validation on another database, for example of women aged 40 to 50 in the Hérault department, would be of interest. External geographic validation might be carried out on the databases from other CDCRCs and/or a database from another country  50.
The image of each nematode was cropped out, inverted, their brightness, contrast, and intensity adjusted for both treatment and control (Brightness À17%, Contrast þ 71%, and Intensity À27%), and the number of pixels per nematode that exceeded an empirically derived number of shades of variance (an integer between 0 and 255) from the background gray (Hexadecimal color code 0 Â D1D1D1) were counted using the PixelSearch function in a custom AutoIT script (https://github. com/sebastianevda/ColourCounter). The numerical value assigned to each nematode's fluorescence allowed us to test the significance of the difference between the treatment and the control using an independent two-group Mann-Whitney U test.
Adult males that had emerged were removed each day from the 96-well plate, and stored in the absence of females until measured. Males were imaged under DIC microscopy, and measured using FIJI software (e. g. , Figure  2A ). Notwithstanding large overall variation in absolute body size, the proportion of the body that is occupied by the germline decreases over time from approximately 65%, to approximately 45% (Figure  2B , n ¼ 7-9, Mann-Whitney U, P < 0. 05). The decrease in the proportionate length of the gonad is likely a combination of more modest but individually not significant increases in body length and decreases in gonad length (Figure  2,  C  and D ). As the gonad becomes proportionally shorter, we observed the progressive appearance of large vacuous structures between the head and the distal tip of the gonad in virgin males, with this starting almost immediately after emergence (Supplementary Figure  S1 ).
Using this method, the intensity of fluorescence between mRNA soaked worms and control soaked worms was quantified and compared. From the representative images in Figure  5A , a clear difference was observed in the fluorescence intensity between the control and mRNA soaked worms. Nematodes soaked with lipofectamine MessengerMAX containing mRNA encoding a fluorescent protein excited at 476 nm (eGFP) were on average 3. 9 times brighter than nematodes soaked in lipofectamine containing mRNA encoding a non-fluorescent protein at 476 nm (n ¼ 21 and 22, respectively, Mann-Whitney U P ¼ 5. 703eÀ11). From the marked pixels, we can see that: (1) Most autofluorescence in control nematodes is, as expected, restricted to the digestive system of the nematode and (  2 ) the observed increase in fluorescence in Horizontal line in box plot represents median value, whiskers extend to data points that are less than 1.
Data availability NGS reads deposited under ENA accession PRJEB39266. Scripts available at github repositories https://github. com/sebastia nevda/ColourCounter and https://github. com/OlafKranse/ Selective-analyses-of-areas-of-interest-for-next-generation-se quencing. Gene sequences available at DRYAD under accession doi: 10. 5061/dryad. r4xgxd296. Supplementary material id available at figshare DOI: https:// doi. org/10. 25387/g3. 13186817.
Statistical analysis Primary ToxTracker data were generated as flow cytometry (. fcs) files. Mean GFP expression and viable cell concentrations after treatment were exported as text files (. csv) that are imported into Microsoft excel for calculation of reporter gene induction and cytotoxicity. Tox-Tracker is considered to give a positive response when a compound induces at least a 2-fold increase in GFP reporter gene expression in any of the six cell assay systems. GFP induction at test substance concentrations that do not cause more than 75% cytotoxicity are used for the Tox-Tracker analysis.
Such methods have seen widespread use in cosmology where the signal covariance is often sparse due to the statistical isotropy of the underlying signal  (Jasche & Lavaux 2015; Alsing et al. 2017; Jeffrey et al. 2018a). For a Wiener filter messenger field implementation on the sphere we the Dante 1 package (Kodi  Ramanah et al. 2019) , which uses an optimized novel messenger field implementation to perform Wiener filtering on the sphere for spin-2 fields. We test convergence by doubling the Dante precision (with precision parameter from 10 -5 to 5 × 10 -6 ), which effectively corresponds to increasing the number of iterations, and showing a negligible MSE change of 3 × 10 -5 per cent with simulated data. The signal covariance matrix in harmonic space is diagonal, with elements given by an assumed fiducial power spectrum.
Our fiducial E-mode power spectrum is taken as the power spectrum of the convergence truth map from the simulated data (see Sec. 4) which was corrected for the mask using the NaMaster 2 pseudo-C estimation code  (Alonso et al. 2019 ). 1 https://github. com/doogesh/dante 2 https://github. com/LSSTDESC/NaMaster We explicitly provide a B-mode power spectrum set to zero, thus simultaneously achieving the null B-mode prior equivalent to Sec. 3. 3.
g. due to point-spread-function residuals). In particular, the catalogue is characterized by a non-zero mean shear whose origin is unknown and which is subtracted at the catalogue level before performing any analysis. A two-stage blinding procedure was used in the DES Y3 analysis to mitigate confirmation bias. The first level of this procedure blinded the shear catalogue by means of a multiplicative factor, in a fashion similar to what has been adopted in the Y1 analysis  (Zuntz, Sheldon et al. 2018 ). The second level of blinding  (Muir et al. 2020 ) was applied to the summary statistics under examination (e. g. cosmic shear, galaxygalaxy lensing, galaxy-galaxy clustering). Since in this work we do not directly measure any summary statistics from the data maps, only the first level of blinding has been considered.
Simulated mock galaxy catalogue To build our simulated galaxy catalogue, we use a single realization of the 108 available  Takahashi et al. (2017)  simulations. These are a set of full-sky lensing convergence and shear maps obtained for a range of redshifts between z = 0. 05 and 5. 3 at intervals of 150 h -1 Mpc comoving distance. Initial conditions were generated using the 2LPTIC code  (Crocce et al. 2006 ) and the N-body simulation used L-GADGET2  (Springel 2005)  with cosmological parameters consistent with WMAP 9 year results  (Hinshaw et al. 2013 ): Ωm = 0. 279, σ8 = 0. 82, Ω b = 0. 046, ns = 0. 97, h = 0. 7. The simulations begin with 14 boxes with side lengths L = 450, 900, 1350,. , 6300 h -1 Mpc in steps of 450 h -1 Mpc, with six independent copies at each box size and 2048 3 particles per box.
Simulated galaxies are then randomly drawn within the DES Y3 footprint according to the DES Y3 number density. Each simulated galaxy is assigned a shear and convergence value depending on its position (i. e. by looking at the value of that particular pixel of the convergence and shear maps into which they fall). To assign realistic shape noise and weights to the simulated galaxies, we make use of the fiducial DES Y3 shape catalogue. In particular, we randomly rotate the ellipticity of each galaxy in the data such that it can be used as intrinsic ellipticity. This intrinsic ellipticity is added to a random galaxy of the simulated catalogue, using the shear addition formula (e. g. Seitz & Schneider 1997). We also assign to the simulated galaxy the inverse variance weight from the same real galaxy we used to obtain the intrinsic ellipticity.
Mask effects To demonstrate the effects of the mask and missing data, we generate a mock catalogue with no shape noise. Fig. 2  shows the input true convergence map (top left), the KS Emode reconstruction (top left), the KS residual map (bottom left), and the KS B-mode map (bottom right). The residual is defined as the difference between the input true map and the reconstructed E-mode map. In these figures the maps have been smoothed with a Gaussian kernel with σ = 10 arcmin for visualization. In the noise-free case all methods other than KS (including Wiener and Glimpse) have a null B-mode prior and are thus equivalent. In this noise-free limit, the noise covariance becomes a binary matrix (for the mask) and the signal factors can divide out (although our code implementations of Wiener and Glimpse would not be able to deal with this zero limit in practice).
Reconstruction from realistic mock data Fig. 5  shows the reconstructed maps from the simulated realistic noisy shear catalogue using the four methods for comparison. Again, the KS and the null-B-mode reconstruction have been smoothed at 10 arcmin. The Glimpse reconstruction uses a sparsity parameter of λ = 3 (discussed below). Recall that all the map making methods take into account the noise covariance matrix of the data, thereby characterising the noise amplitude and distribution across the observed area. As a result, all methods naturally take into account inhomogeneities in the noise properties across the DES Y3 footprint. The KS E-mode map is now noticeably degraded compared to the noise-free example (Fig. 2 ). Though the most significant features of the input convergence field can still be spotted by eye, a number of noise-induced small-scale peaks dominate the reconstructed map.
We note that different mass map versions show consistent signals (within the quoted uncertainties). While these void lensing results remain open to much further quantitative work, there is certainly clear detection of correlations between underdensities of galaxies and matter; this will motivate further studies using DES Y3. We finally remark that the typical convergence signal associated with local underdensities can be affected by the void definition and selection. We explore alternative void samples extracted from DES Y3 data in Appendix B. Figure  14. The line-of-sight density of redMaGiC galaxies aligned with a significant "vole" in the KS mass map. At low redshifts, we find evidence for an extended system of underdensities.
SUMMARY In this work we constructed weak lensing convergence maps ('mass maps') from the DES Y3 data set using four reconstruction methods. The first method considered is the direct inversion of the shear field, also known as the Kaiser-Squires method, followed by a smoothing of small angular scales. The second method uses a prior on the B-modes of the map, imposing that the reconstructed convergence field must be purely an E-mode map (null B-mode prior); this method also includes smoothing at small scales. The third method, the Wiener filter, uses a Gaussian prior distribution for the underlying convergence field. Lastly, the Glimpse method implements a sparsity prior in wavelet (starlet) space, which can be interpreted as a physical model where the matter field is composed of a superposition of spherically symmetric haloes.
Based in part on observations at Cerro Tololo Inter-American Observatory at NSF's NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants  ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016 -0588, SEV-2016 -0597, and MDM-2015-  NJ has been supported by funding from l'Ecole Normale Supérieure, Paris. OL and NJ acknowledge support from a European Research Council Advanced Grant TESTDE (FP7/291329) and STFC Consolidated Grants ST/M001334/1 and ST/R000476/1.
0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U. S. Department of Energy, Office of Science, Office of High Energy Physics.
ACKNOWLEDGMENTS Funding for the DES Projects has been provided by the  U. S. Department of Energy , the  U. S. National Science Foundation , the  Ministry of Science and Education of Spain , the  Science and Technology Facilities Council of the United Kingdom , the  Higher Education Funding Council for England , the  National Center for Supercomputing Applications  at the  University of Illinois at Urbana-Champaign , the  Kavli Institute of Cosmo-.