dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Among the 98 patients from the collection database, only 40 were included (30 were excluded because of incomplete dermoscopy record, 28 were excluded because of the absence of clear-cut histopathological features of regression). The mean (SD) age of included patients was 63. 6 (15. 7) years, and 82. 5% of them presented with melanomas in the local stage (I and II of the AJCC 2018 Melanoma staging; Table  1 ). Within the regression area of the tumor, WSA were observed in 55% of lesions, blue-WSA in 45%, BGG in 97. 5% (Figures  1 2 3 ). Reticular regression was observed in 75% of cases, and was associated with the polychromatic character of the lesion (P = 0. 047) and the stage 2 of the regression in histology (P = 0. 049). Peppering was associated with thin melanomas (< 1mm, P = 0. 012) and positive BRAF mutations (P = 0.
Study design This was a single-center, observational, retrospective study that aimed to evaluate professional practices regarding the prescription of PPIs. We reviewed the medical files of patients aged less than 18 years and hospitalized at the pediatric medical departments of the Bordeaux University Hospital between April 1 and September 30, 2019, in at least one of the following units: post-emergency, gastroenterology, pneumology, neurology, onco-hematology, endocrinology, nephrology, neonatology, and pediatric and neonatal resuscitation. Patients treated with PPIs were included in the study. This study has been registered at the Data Protection Officer of the Bordeaux University Hospital, in accordance with the current regulations in France.
1g ) and absence of 3 × CTCF sites (Extended Data Fig. 2d ), with only a very minor impact on anomalous exponents (Extended Data Fig. 2b, e , by reeling them into loops, or if instead it provides constraints that decrease mobility and prolong the duration of such encounters. Both scenarios have been suggested to be possible theoretically  27, 28  , but it is unclear which effect dominates in living cells. Here we use live-cell fluorescence microscopy to measure chromosome dynamics and its dependence on cohesin and CTCF in mESCs. By combining two live-cell imaging strategies with polymer simulations, we reveal that loops extruded by cohesin constrain global chromosome motion, while also increasing the temporal frequencies and durations of physical encounters between sequences inside the same TAD.
Every monomer represented 8 kb of chromatin, corresponding to the genomic size of the TetO array. Simulation steps were approximated to real-time units by matching the time needed for a monomer to move by its own diameter with the time required by the TetO array to move by its estimated mean physical size (Methods). We sampled an extremely large range of extruder residence times and loading rates (4 orders of magnitude each) centered around a residence time of ~30 min and extruder densities of ~20 per Mb (in line with previous measurements  38, 39  ), and using two extrusion speeds corresponding to in vivo and in vitro estimates (~0. 1 kb s -1 and ~1 kb s -1 , respectively)  20, 38  (Fig. 2b  and Extended Data Fig. 3b ). In the absence of loop extrusion, the polymer underwent subdiffusive behavior with anomalous exponent of ~0.
To enhance our detection efficiency, we employed custom models trained on a combination of the following datasets: smFISH and SunTag datasets provided by deepBlink and in-house manually curated live-cell imaging images. To detect 3D spots, we applied deepBlink to all z-stacks separately followed by linkage of the spots across z-stacks using Trackpy  53. The precise 3D coordinates of the spots were then determined using 3D Gaussian fitting using a voxel of size 6 × 6 × 4 pixels centered at the spot in the brightest z-stack. deepBlink models can be found at https://github. com/zhanyinx/SPT_analysis/tree/main/ models. The parameters and models used for each cell line can be found in Supplementary Table  4. All scripts used for the analysis can be found at https://github. com/zhanyinx/SPT_analysis/.
Tracking and cell motion correction of multi operator data 3D spots coordinates are fed into TrackMate for tracking using linear assignment problem (LAP) tracker. Each track is assigned to manually annotated cell masks (from max z-projection of frame 93) using a custom script (https://github. com/zhanyinx/SPT_analysis/blob/ main/source/spot_detection_tracking/assign_cellids. py), which uses the majority rule. Motion correction is then performed using a roto-translation model. Specifically, for each pair of consecutive time frames, a set of matching spots in every cell is determined by solving the LAP using the Euclidean distance between spots as a measure of distance. Only spots that match across two consecutive frames are then used to estimate the roto-translation model which is then applied to correct for nuclear motion (six matching spots on average across all time frames, trajectories and movies, with a minimum of four spots per pair of time frames).
Tracking and MSD analysis of dual-color data To increase the ability to detect longer tracks, we used an in-house script to stitch multiple tracks belonging to the same cell (https:// github. com/zhanyinx/SPT_analysis/blob/main/source/dual_chan-nel_analysis/utils. py, stitch function). In short, if two tracks from the same cell overlap more than 50% in time, the shortest one is filtered out. We called cell masks using CellPose  56  on the max z-projection of the middle frame of the movie using the GFP channel and used these masks to define cell identity. For tracks with overlaps lower than 50%, the overlapping part of the tracks are randomly removed from one of the two tracks. The resulting tracks are stitched if the distance across the time gap is smaller than 1. 6 µm. To match tracks across channels, we used the following measure to calculate the distance between tracks across channels: ∑ 3 i=1 < (x 1i (t) -x 2i (t)) 2 > t∈T 1 ∩T 2 √ len (t ∈ T 1 ∩ T) 2 Where x 1 are the coordinates from channel 1 and x 2 are the coordinates from channel 2, T 1 contains all the time frames from channel 1 and T 2 contains all the time frames from channel 2, and len is a function that returns the length of an array.
Simulations Polymer simulations were performed using LAMMPS  57. We chose Langevin dynamics with the NVT thermostat. Arbitrary units were set such that thermal energy k B T = 1, where k B is the Boltzmann constant and T is room temperature, corresponding to 300 K. For every set of parameters, we performed ten independent runs. A run consists of an equilibration part of 10 7 simulation steps and a production part of 10 8 simulation steps. For subsequent analysis and calculation of contact maps, we recorded the data every 10 4 simulation steps. In simulations for Fig. 2 , the chain length was 1,125 beads. In simulations for Figs. 5 and 6, the chain length was 1,000 beads. We used PyMOL software (v. 2. 3. 3) to represent snapshots of polymer chain in Fig. 2a. Examples of initial conformations and simulation parameters can be find at https://github.
Statistics and reproducibility No statistical method was used to predetermine sample size. No data were excluded from the analyses. No randomization was performed as the study did not require sample allocation into different groups. Live-cell imaging experiments were performed in 3-7 biological replicates and all replicates showed consistent results. For Capture-C, Hi-C, 4C-seq, piggyBac insertion site mapping and Nanopore sequencing with Cas9-guided adapter ligation, one biological replicate was performed. For flow cytometry measurements two biological replicates were performed. Western blot analysis and genotyping PCR with subsequent agarose gel electrophoresis were performed with 1-2 biological and 2 technical replicates. Blinding was not possible for data collection in live-cell imaging experiments, as data acquisition required identification of the sample for further processing.
Acknowledgements We thank all members of the  Giorgetti laboratory  for help with labeling images for training the deepBlink spot-detection algorithm and feedback on the manuscript;  A. S. Hansen ,  L. Mirny  and members of their laboratories (in particular  S. Grosse-Holz ) for useful discussions of the results;  E. de Wit  for sharing the WAPL-AID-eGFP cells;  H. Kohler  for assistance with flow cytometry experiments; and  R. Grand  and  D. Schübeler  for sharing single-molecule footprinting data. P. M. was supported by a  Marie Skłodowska-Curie Innovative Training Network  (grant no. 813327  ' ChromDesign '). J. T. was supported by a  Marie Skłodowska-Curie Innovative Training Network  (grant no. 813282  ' PEP-NET '). J. Z. was supported by a  Marie Skłodowska-Curie grant  (no. 748091 -3DQuant ).
Data availability All Capture-C, Hi-C, 4C-seq and integration site mapping sequencing fastq files generated in this study have been uploaded to the Gene Expression Omnibus (GEO) under accession GSE197238. The following public database was used: BSgenome. Mmusculus. UCSC. mm9 (https:// bioconductor. org/packages/release/data/annotation/html/BSgenome. Mmusculus. UCSC. mm9. html). The trajectories from imaging data can be found at https://doi. org/10. 5281/zenodo. 6627715. Source data are provided with this paper.
Code availability Custom codes generated in this study are available at: https://github. com/zhanyinx/SPT_analysis/ (image analysis); https://github. com/ giorgettilab/Mach_et_al_chromosome_dynamics/ (4C-seq, Hi-C, nanopore, simulation analysis); https://github. com/polly-code/lammps_le (repository with loop extrusion module for the LAMMPS); and https:// github. com/zhanyinx/hmmlearn (the modified version of hmmlearn).
The experiments are repeated for two protein concentrations of 5 and 0. 1 nM, allowing to report the evolution with the protein concentration. With the Atto647N label, the fluorescence intensity time traces show fast step decays typical of single molecule fluorescence photobleaching (Fig. 2a, b ). These traces are analyzed using the automated software AutoStepfinder  36  to count the number of bleaching steps from which we deduce the number of proteins. The histogram of the number of molecules per horn antenna follow the expected Poisson distribution, with an average number of molecules of 2. 3 for the 5 nM concentration and 0. 7 for the 0. 1 nM case. The evolution of the average number of molecules does not scale exactly with the concentration (although there is an obvious dependence) as the 30 minutes incubation time and possible steric hindrance between neighboring proteins may limit the number of proteins able to bind on the horn antenna surface.
Fluorescence time trace analysis The fluorescence data is analyzed with Symphotime 64 (Picoquant) and Igor Pro 7 (Wavemetrics). For the fluorescence time traces from immobilized label-free single proteins, the temporal bin width is set to 300 ms to optimize the signal to noise ratio while still providing enough time resolution. Each trace stemming from a different horn antenna is fitted using and exponential decay model   -/  +  0 where A is the decay amplitude (used for the histrograms),   is the bleaching time and  0 is the background level set by the dark counts of the photodetector and the residual photoluminescence from the nanostructure. For the control experiments using Atto647N labels, the traces are analysed using AutoStepfinder, a recently developed software for automatic step detection.
Numerical simulations The electric field intensity radiated by a point dipole is calculated with finite-difference time-domain (FDTD) method using RSoft Fullwave software. We set a fixed emission wavelength at 350 nm. Horizontal and vertical orientations of the source dipole are computed separately and averaged for the final output to represent the emission of a molecule with nanosecond rotational time. Each simulation is run with 2 nm mesh size and is checked for convergence after several optical periods. To compute the enhancement displayed on Fig. 1g , 10 horn antennas with different cone angle are simulated. For each of them, we compute the gain into the 0. 8 collection NA of the microscope objective as compared to the emission from a single aperture without horn antenna (Fig. S2 ). The data points are then fitted with a Gaussian function.
S4. FCS and lifetime data tables for p-terphenyl Table  S1. Fit parameters for the FCS data displayed on Fig. 1d ,e. We do not observe any fast blinking contribution for p-terphenyl, so   and τ T are set to zero. The shape parameter κ is fixed at 8 for the confocal case based on the calibration of the microscope point spread function (PSF). For the aperture and horn antennas, we use κ = 1 based on our previous work using nanoapertures and fluorescent dyes in the visible. 3 CRM = (F-B)/Nmol stands for the fluorescence count rate per molecule (average brightness per emitter). A similar reduction in the translational diffusion time was observed for nanoapertures in the visible spectral range as compared to the diffraction-limited confocal reference. As this approach is only based on statistical analysis and no numerical fit is performed, this dataset independently confirms the fluorescence enhancement of the brightness per molecule deduced using FCS fitting in Fig.
This multiexponential response is typical of protein autofluorescence. 11 Owing to the limited total photon budget from a single protein, the statistical noise is large for the component with the longest lifetime. In the lifetime analysis, we have decided to fix the long lifetime component to its 1. 6 ns value obtained from the averaging at 1. 7 µM (Fig. S15b , Tab. S5). The lifetimes are expressed in ns, the intensities are normalized so that their sum equals. This data is used to compute the hydrodynamic radius of the protein upon unfolding and denaturation shown in Fig. 4b  following the approach described in  10. As the hydrodynamic radius also depends on the viscosity of the solution, we have calibrated the viscosity increase due to the addition of urea (blue trace, right axis). This calibration is performed separately using FCS on a visible-light confocal microscope with Alexa 647 fluorescent molecules.
The standard deviation is then normalized by the number of molecules Nmol deduced from the FCS fit to obtain a concentration-independent quantity representing the noise in an FCS acquisition. These data quantify the noise reduction seen in (a) and the different cases can be directly compared. (c) The normalized standard deviation in (b) is used to compute the signal to noise ratio (SNR) improvement as compared to the confocal case. The noise in FCS depends linearly on the fluorescence brightness per molecule. 4 As this approach is only based on statistical analysis and no numerical fit is performed, this dataset independently confirms the fluorescence enhancement of the brightness per molecule deduced using FCS fitting in Fig. 1g.
Acknowledgments The authors thank  Antonin Moreau ,  Julien Lumeau ,  Satyajit Patra ,  Manos Mavrakis ,  Redha Abdeddaim  and  Marco Abbarchi  for stimulating discussions and technical help. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  723241 , A. B. , P. R. , J. -B; C. , J. W. ).
Acknowledgements The authors wish thank the laboratories which participated in the laboratory visits, the  ESP Lung EQA  scheme of 2019 and the Gen&Tiss scheme of 2018-2019. In addition, dr. Cleo Keppens, dr. Inne Nauwelaers, prof. dr. Patrick Pauwels, prof. dr. Ales Ryska , prof. dr. Izidor Kern , dr. Federica Pezzuto , dr. Alexandre Harlé , dr. Aude Lamy and dr. Ludovic Lacroix  are thanked for their expertise as an assessor of the EQA schemes and dr. Raed Al Dieri as the general direct of the  ESP. We are grateful for the nancial support provided by  AstraZeneca Belgium Nv.
The trial met its primary efficacy endpoint but not the primary PFS-related biomarker objective. However, incremental increases in ORR were observed with increasing bTMB cutoffs. The Blood First Assay Screening Trial (BFAST, NCT03178552, ClinicalTrials. gov) is a global, open-label, multicohort trial evaluating the safety and efficacy of targeted therapies or immunotherapy in patients with previously untreated unresectable Stage IIIB-IV NSCLC selected for several different biomarkers, including driver mutations and TMB, using a blood-based next-generation sequencing assay (Extended Data Fig. 1 ). BFAST cohort C is the first prospective randomized study to assess bTMB as a predictive biomarker for immunotherapy. Patients were selected by bTMB status, and atezolizumab monotherapy was compared with platinum-based chemotherapy.
05). Concordance of the F1L CDx assay with the bTMB CTA. Of the 471 patients enrolled in cohort C, 426 had samples available for analysis with the F1L CDx assay (Extended Data Fig. 3 ). Of these, 403 samples passed laboratory processing and 6 failed quality control, leaving 397 samples for inclusion in the analysis. In addition to the 397 cohort C samples, 130 CTA-negative samples (patients screened for BFAST but not eligible for cohort C) were tested on the F1L CDx assay to evaluate assay concordance. The bTMB CTA ≥16 cutoff was determined to be equivalent to a F1L CDx value of 13. 6 mutations per megabase and ten mutations was equivalent to 8. 3 mutations per megabase (Methods). The 20 most commonly mutated genes tested by the F1L CDx assay are shown in Extended Data Fig. 4a. This exploratory analysis showed that the two assays were highly concordant at their respective cutoffs (Extended Data Fig.
Adverse events of special interest All-grade adverse events of special interest 95 (  42  Cancer driver genes were defined based on their status in the Catalog of Somatic Mutations in Cancer  46  or evidence from literature supporting their pathogenicity status. This literature evidence must demonstrate that the mutations in the tumor suppressor gene results in loss of function. The cancer driver genes are maintained in a database at Foundation Medicine Inc. (FMI) that is updated over time as new clinical evidence emerges. Pathogenic mutations in CHIP-related genes were filtered from the bTMB calculation. Single nucleotide substitutions detected at an allele frequency of ≥0. 5% were filtered for germline and driver mutations and were included in the bTMB score  17. Samples with low tumor content within a circulating free DNA sample as measured by MSAF < 1%, were below the limit required to make reproducible bTMB determinations.
If significant, the secondary endpoints were hierarchically tested at the two-sided α = 0. 05 significance level (for each endpoint) in the order of OS in the bTMB ≥16 population, PFS in the bTMB ≥10 population then OS in the bTMB ≥10 population. Clinical analyses were performed using SAS (v. 9. 4). Biomarker exploratory analyses were performed using R (v. 3. 6. 1).
However, due to the negative outcome of the primary endpoint, only an analytical concordance was performed with the bTMB CTA, and efficacy analyses were exploratory. The analysis set for the concordance study comprised available samples from all enrolled patients, excluding those who withdrew consent, for a F1L CDx biomarker-evaluable population of 426 (Extended Data Fig. 3 ). CTA-negative (bTMB <10) samples from patients screened for BFAST who were ineligible for enrollment in cohort C were also tested with the F1L CDx assay. The bTMB CTAdetermined bTMB scores were converted to mutations per megabase (the metric used in the F1L CDx assay) by factoring the size of the T7 bait set (FMI) targets (~1. 14 Mb) and then adjusting for ambiguity related to rounding using a -0. 5 correction factor.
The FMI database of known driver mutations can Extended Data Fig. 6  | Exploratory analysis of OS in subgroups defined by the F1L CDx assay OS survival curves for the bTMB ≥13. 6 mut/Mb population atezolizumab arm vs chemotherapy arm and OS HRs at various bTMB cutoffs. exploratory analysis of OS in subgroups defined by the F1L CDx assay (A) OS survival curves for the bTMB ≥13. 6 mut/Mb population atezolizumab arm vs chemotherapy arm. Statistical analysis used a stratified log-rank test at the two-sided 0. 05 level and was not adjusted for multiple comparisons. (B) Forest plot of OS HRs at various bTMB cutoffs. Atezo, atezolizumab; bTMB, blood-based tumor mutational burden; Chemo, chemotherapy; F1L CDx, FoundationOne Liquid Companion Diagnostic assay; HR, hazard ratio; OS, overall survival; PFS, progression-free survival.
Acknowledgements Todd Riehl  was a founding member of the  BFAST program  and is now deceased. The authors would like to acknowledge  S. Gong  of  FMI  and  X. Hou  of  Genentech, Inc , for conducting the bTMB concordance and related clinical outcomes analyses and  V. Prasad  of  Genentech, Inc , for his review and insights in the writing of this manuscript. Support for third-party writing assistance for this manuscript-by  C. Lum, PhD , of  Health Interactions, Inc -was provided by  F. Hoffmann-La Roche, Ltd. This study was sponsored by  F. Hoffmann-La Roche, Ltd. The authors declare the following competing interests: S. P. has received institutional support for consulting or advising from  AbbVie, Amgen, AstraZeneca, Bayer, BeiGene, Biocartis, Boehringer Ingelheim, Bristol Myers Squibb, Clovis, Daiichi Sankyo, Debiopharm , eCancer,  Eli Lilly ,  Elsevier, Foundation Medicine, Illumina ,  Imedex ,  IQVIA ,  Incyte ,  Janssen ,  Medscape ,  Merck Sharp & Dohme ,  Merck Serono, Merrimack ,  Novartis, Oncology Education, PharmaMar ,  Phosplatin Therapeutics ,  PER ,  Pfizer ,  PRIME, Regeneron, RMEI, Roche/Genentech ,  RTP ,  Sanofi ,  Seattle Genetics and Takeda ; institutional fees for speaking at company-sponsored public events for  AstraZeneca ,  Boehringer Ingelheim ,  Bristol Myers Squibb , eCancer,  Eli Lilly ,  Illumina ,  Imedex ,  Medscape ,  Merck Sharp & Dohme ,  Novartis ,  PER ,  Pfizer ,  Prime ,  Roche/Genentech ,  RTP ,  Sanofi and Takeda ; and received institutional grants and research support for the conduct of clinical trials from  Amgen ,  AstraZeneca ,  Biodesix ,  Boehringer Ingelheim ,  Bristol Myers Squibb, Clovis ,  GlaxoSmithKline ,  Illumina ,  Lilly ,  Merck Sharp & Dohme ,  Merck Serono, Mirati ,  Novartis ,  Pfizer ,  Phosplatin Therapeutics  and  Roche/Genentech.
All analyses were performed by intention to treat. This trial was registered in ClinicalTrials. gov NCT03191201. Findings Iron infusion did not affect first and second phase insulin release. For first phase, the between group mean difference from baseline to day 28 was 0 mU £ 10 min/mL [95% CI, -22 to 22, P = 0. 99]. For second phase, it was -5 mUx10min/mL [95% CI, -161 to 151; P = 0. 95] at the first plateau of the clamp and -249 mUx10min/mL [95% CI, -635 to 137; P = 0. 20] at the second plateau. Iron infusion increased serum ascorbyl/ascorbate ratio, a marker of plasma oxidative stress, at day 14, with restoration of normal ratio at day 28 relative to placebo. Finally, high-sensitive C-reactive protein levels remained similar among groups. Interpretation In iron deficient women without anaemia, intravenous administration of 1000 mg of iron in a single sitting did not impair glucose-induced insulin secretion despite a transient increase in the levels of circulating reactive oxygen species.
Study design The DIAFER study was an investigator-led, randomized, double-blind, parallel-group, placebo-controlled, proofof-concept trial conducted at the Lausanne University Hospital, Switzerland, from June 21, 2017 to March 9, 2020. The Clinical Research center monitored the trial and ensured compliance with the International Conference on Harmonization guidelines for Good Clinical Practice (ICH E6(R2)) and the Declaration of Helsinki. The trial was registered at ClinicalTrials. gov (NCT03191201) and approved by the regional ethics committee (2016-01449). No changes were made to eligibility criteria and design after the trial started. In the original protocol of this study, insulin secretion, insulin sensitivity, and endogenous glucose production were listed as co-primary outcomes although our primary hypothesis was that iron would impair glucose-induced.