dataesr/scientific-paragraphs-categorization huggingface dataset:
Algorithm performance for large numbers of cell neighbour exchanges To assess the performance of the algorithm when applied to tissues exhibiting large numbers of cell neighbour exchanges, we next apply the algorithm to in silico datasets with increasing numbers of cell neighbour exchanges between frames (figure  7 ). The number of correctly tracked cells decreases as the number of cell neighbour exchanges increases. However, the number of incorrectly tracked cells remains below 20% throughout the analysed range of neighbour exchanges, and decreases to zero as the number of edge swaps exceeds 10%. The number of untracked cells increases rapidly as the percentage of cell-cell interfaces that are swapped between successive images increases from 5 to 10%. Note that the percentage of cells involved in these neighbour exchanges is larger than the percentage of cell-cell interfaces that are swapped, as an individual T1 transition changes the cell neighbour relations of four cells, and each cell shares multiple inner edges.
Application of the algorithm to in vivo data Figure  8  shows the first three of 21 segmented image frames of the lateral epidermis of a stage-11 Drosophila embryo to which the algorithm was applied. During stage 11, gross morphogenetic movements do not occur but the tissue is very active with a large number of proliferations occurring within a short duration, making this a much more challenging tissue on which to perform cell segmentation and tracking than the wing imaginal disc, where many previous efforts have been made  [4, 13, 31, 44]. Cell delamination is also more common than in the wing imaginal disc during normal development. This stage of development thus offers a true test of the present algorithm. The images were taken 5 min apart over a time span of 100 min. These first three images comprise 271, 263 and 263 cells, respectively.
Such junctions occur frequently throughout the dataset. Figure  8. Three segmented data frames of an in vivo time-lapse microscopy video of the lateral epidermis of a stage-11 Drosophila embryo. Cells that are tracked across all frames are coloured green or purple, and cells that leave or enter the tissue at the boundary are white. Dying cells are black. The centroids of tracked cells of the respective previous frames are included as yellow dots, and cells that contain only their centroid from the previous frame are coloured green, whereas cells that do not contain their centroid from the previous frame, and cells that contain multiple centroids, are coloured purple. Together, the centroids and the colouring illustrate that it is challenging to track cells between the data frames using solely centroid positions.
Yellow asterisks in the first frame denote higher order junctions where more than three cells meet. rsif. royalsocietypublishing. org J. R. Soc. Interface 13: 20160725 On average, cell centroids move 0. 75 cell lengths between the first and second images, with a maximal displacement of 1. 17 cell lengths. Between the first and second images 36 cells undergo a net gain in edges, whereas 20 cells have a net loss of edges. In total, four cell deaths and no cell divisions are observed across the three data images. Inspection of all individual cell tracks reveals that none of the cells are tracked incorrectly. The data in figure  8  are the first three out of 21 frames. In figure  9 , we show the results of the analysis of the full dataset, including all 21 frames. During the period of measurement, the total number of cells increases from 280 to 330 cells, whereas the total number of tracked cells increases from 270 to roughly 310 cells.
As the number of cells in the tissue rises, the total number of cell rearrangements increases, whereas the average cell area decreases. Here, the number of cell rearrangements is measured by counting how many cells change their cell neighbour number between consecutive frames. For all frames, the number of tracked cells is lower than the number of cells in the tissue. A visual inspection of the tracked data reveals that the difference between the total number of cells and the number of tracked cells is largely due to cells entering or leaving the field of view. The percentage of cells that our algorithm tracks is lowest (84%) when the rates of cell division and cell rearrangement are highest, which occurs at 70 min. Here, the number of tracked cells decreases because the algorithm is not yet able to resolve division events immediately adjacent to rearrangements as well as multiple adjacent divisions.
The tracking of epithelial in vivo data enables quantitative assessment of dynamic changes in cellular morphology. The tracking results in figure  9  reveals that the analysed section of the epidermis undergoes 60 cell rearrangements per 5 min initially and around 100 cell rearrangements per 5 min at the end of the observed time interval. The average ratio of the maximal area and the minimal area observed for individual cells during the period of measurement is 4. 2, indicating that on average cells increase their apical area by a factor of four during mitotic rounding. A total of 18 cell deaths are tracked in the dataset. A striking feature is the level of T1 transitions occurring during this stage of development, even in the absence of gross morphogenetic movements found in earlier or later stages.
Calculation times To analyse the scaling of the calculation times with tissue size, we repeat the permutation test with tissues of square dimension of varying size on a desktop computer with an Intel i5-6500 T CPU (2. 5 GHz) and 8 GB RAM. We find that the calculation times scale subquadratically with cell number (figure  10 ). The calculation times for the experimental images analysed in figure  8  vary more widely than for the in silico datasets. For the tracking between the first and second frames in figure  8 , the algorithm required 43 s to run, whereas between the second and the third frames the algorithm required 9 s. This is due to differences in the time required to find the first correct mapping; in the first example, 154 cells were searched before the first correct mapping was found, whereas in the second example only 12 cells were searched.
Discussion Cell tracking in epithelial sheets has the potential to generate a vast amount of quantitative data to inform our understanding of the contributions of different cellular processes to tissue morphogenesis. However, cell tracking is notoriously difficult, especially for the complex morphogenetic processes that occur as embryogenesis proceeds. Here, we present an algorithm based on MCS detection for the tracking of cells in segmented images of epithelial sheets. Our algorithm successfully tracks cells in in vivo images of the Drosophila embryonic epidermis, a challenging dataset compared to other tissues, as well as in randomly generated in silico datasets, without the need for the adjustment of tissue-specific parameters such as weights for individual terms in a global minimization scheme  [14].
The use of in silico data to test our algorithm allows us to analyse the performance of our algorithm for a large range of experimentally observed cell rearrangements and tessellations. The tracking of cells in in vivo datasets such as presented in figures 8 and 9 provides quantitative insight into tissue homeostasis. Using our algorithm, we measure example quantities that would not be accessible without a robust cell tracking method. The amount of cell rearrangement, the extent of mitotic rounding and the occurrence of cell death in the observed frames each can be used to learn about tissue homeostasis in developing epithelia. Within the analysed dataset, we find a significant number of T1 transitions despite the absence of gross morphogenetic movements. This may be driven by the large number of proliferation events that occur.
However, it may be possible to apply the algorithm to datasets of epithelial sheets that are embedded in a threedimensional environment, such as the Drosophila imaginal wing disc  [4] , or the Drosophila embryonic epidermis  [6, 9] , including tissues that can be mapped onto a cylinder or ellipsoid, such as the mouse visceral endoderm  [48]. A large number of cell tracking algorithms have been developed for various applications  [10 -27]. Further efforts are required to compare these algorithms with our own, rsif. royalsocietypublishing. org J. R. Soc. Interface 13: 20160725 and to identify the algorithm best suited for an individual dataset. In the cell-tracking challenge  [54] , the authors provide microscopy videos from a variety of in vitro cell cultures, including, for example, mouse embryonic stem cells and human squamous lung carcinoma cells, together with ground truth segmentation and tracking data as benchmarks for cell tracking and segmentation algorithms.
However, many of the published algorithms above have not yet been applied to the challenge, and benchmark datasets for epithelial sheets are not currently available. The fully segmented dataset published within the MCSTracker project can provide a benchmark for future epithelial cell tracking applications. In  [55] , in silico datasets are used as benchmarking datasets for particle tracking algorithms. The proposed algorithm provides a two-dimensional tracking solution specialized for epithelial sheets that attempts to maximize the information that can be gained from the packing that is typical to such tissues. It may, however, be possible to extend this algorithm to applications of cell tracking, where cells are not physically connected by constructing adjacency networks from Voronoi tessellations that use the cell locations as seeds.
Bear data collection and monitoring The data used for this analysis was gathered between 2008 and 2014 by members of the national Brown Bear Network (135 professional members from government agencies and 228 unaffiliated amateur members) under the supervision of the French Game and Wildlife Agency (ONCFS). A systematic monitoring protocol was followed using fixed itineraries along which the agents looked for bear tracks such as hair, scats, claw marks or paw prints. The Pyrenees were broken down in mountain massif subsections using ridgelines and the bottom of valleys. The area of a subsection was 95km 2 on average. This is coherent with the home range of brown bear males and females that is approximately 85-200km 2 and 50-100km 2 respectively  (Huber and Roth, 1993, Preatoni et al. , 2005).
, 2010 , Martin et al. , 2012 , Mertzanis et al. , 2008). We considered five environmental and anthropogenic covariates for each mountain massif subsection (Table  1 ; Figure  A1 ). Roughness was obtained as the mean of the absolute differences between the altitude of a massif subsection and the value of its contiguous mountain subsections  (Wilson et al. , 2007). We used the IGN BD_ALTI® database (250m resolution) to calculate the mean altitude of each massif subsection. Forest cover and shrub cover covariates were extracted from the CORINE Land Cover® database (U. E -SoeS. Corine Land Cover 2012). Road length was built using the IGN ROUTE 500® database. Human density was obtained from the NASA Socioeconomic data and applications center (http://sedac. ciesin. columbia. edu/data/set/gpw-v3-population-count/data-download).
These analyses were performed in R (RCoreTeam, 2013) with the 'unmarked'  (Fiske and Chandler, 2011) , spaMM  (Rousset and Ferdy, 2014) , rgdal, AICcmodavg, classInt, RColorBrewer and spdep packages. The data and R codes are available on GitHub at https://github. com/oliviergimenez/ursus_Pyrenees_occupancy.
Study protocol and definitions A pilot protocol for the surveillance of CDI in European acute care hospitals was developed by ECDIS-Net participants (epidemiologists and medical microbiologists from various European countries) and ECDC experts in 2012-13. The pilot protocol version 1. 2 specified three options for surveillance: 'minimal', 'light' and 'enhanced'  [14]. In the minimal surveillance, aggregated numerator and denominator data were gathered on all CDI cases. In the light surveillance, basic casebased epidemiological data were included (e. g. age, sex, date of hospital admission and of CDI onset, CDI origin, recurrent CDI) on all CDI cases. In the enhanced surveillance, additional epidemiological data (e. g. comorbidities scored by the McCabe score  [15]  and the Acute Physiology and Chronic Health Evaluation II (APACHE II) chronic health points  [16] , in-hospital deaths) and C.
Microbiological investigation Local laboratories that serviced the participating hospitals used their own diagnostic procedures for CDI. Data on the algorithm used for CDI diagnosis was collected for each patient included in light surveillance. In the enhanced surveillance option, 10 C. difficile isolates (or stool samples, if there was no possibility of anaerobic culture at the local laboratory) from samples from the first 10 episodes of CDI per hospital were sent to the national reference laboratory or appointed study laboratory (collectively referred to as NRL) which performed PCR ribotyping and antimicrobial susceptibility testing, performed according to national procedures. Most NRLs used conventional agarose gel-based PCR ribotyping  [3]  (Finland, France, Hungary, Poland, the Netherlands and UK-Scotland), some used capillary-based PCR ribotyping  [3]  (Austria, Belgium and Germany).
Light surveillance: patient characteristics and diagnostics Patient data were submitted for 1,078 CDI episodes in 37 hospitals (Figure  1 ). Most The median age of patients was 72 years (IQR: 59-80); 38 (4%) CDI episodes were in those younger than 18 years, of whom 13 were younger than two years. The current hospital was reported as being the origin of infection for 66% (n = 673), another hospital for 18%  The 'light' surveillance option included patient data for CDI cases; in the 'enhanced' option, microbiological data on the first 10 CDI episodes per hospital were included. b All 37 hospitals in 14 European countries (Austria, Belgium, Denmark, Estonia, Finland, France, Germany, Hungary, the Netherlands, Norway, Poland, Romania, Serbia and United Kingdom (Scotland only)) tested the light option; 32 hospitals in 13 countries (Austria, Belgium, Denmark, Finland, France, Germany, Hungary, the Netherlands, Norway, Poland, Romania, Serbia and United Kingdom (Scotland only)) tested the enhanced option.
Further standardisation of PCR ribotyping will likely be achieved by regular exchange of new C. difficile strains and build-up of a consistent reference database. The first steps have already been taken by concerted action of ECDIS-Net members with reference laboratories from CDC and the Public Health Agency of Canada  [31]. At the same time, new developments in DNA sequence analyses should be monitored closely for application in ribotyping modifications and considered for implementation in surveillance activities of C. difficile  [32]. In our pilot study, PCR ribotyping of the first 10 strains per hospital in the enhanced option was performed to balance effort, costs and benefits, such as in the national surveillance programme of Belgium  [5]. Despite these positive experiences, further evidence for this approach should be obtained and evaluated at European level.
Acknowledgements This study was funded by the  European Centre for Disease Prevention and Control (ECDC)  through a framework service contract ( ECDC/10/022 ) to  Leiden University Medical Centre, Leiden , the  Netherlands , for supporting capacity building for surveillance of Clostridium difficile infections at European level following an open call for tender ( OJ/2010/07/09-PROC/2010/035 ). The project adopted the name 'European C. difficile  Infection Surveillance Network'  and the acronym ECDIS-Net. The study was performed in collaboration with the European Study Group on C. difficile (ESGCD). We thank  Céline Harmanus  and  Ingrid Sanders  (the  Netherlands ) for.
PLX4720 is an analog of PLX4032. E IC 50 values of PLX4032 (lM) in the panel of BRAF V600 melanoma cells as determined by ATP assay (n = 3, mean AE SD). For SKMEL24 and WM793, IC 50 was > 8 lM. Source data are available online for this figure. We then confirmed these results by conducting quantitative PCR (Q-PCR) and Western blot analyses in a panel of 14 BRAF V600 -mutated human melanoma cell lines, including two shortterm cultures established from patients with melanomas displaying similar mutations  Fig 1B and C). We observed an inverse correlation between the levels of ZEB1 and those of ZEB2 and MITF, while TWIST1 protein levels were generally high in all of these cell lines and were not correlated with MITF (Fig 1B  and C ). An increase in the expression of MITF was previously reported to contribute to tumor progression and resistance to BRAFi in a subset of melanomas  (Johannessen et al, 2013) , whereas low levels of MITF expression were shown to predict intrinsic MAPKi resistance  (Konieczkowski et al, 2014; Muller et al, 2014) , highlighting the dual function of this factor.
As previously suggested  (Konieczkowski et al, 2014; Muller et al, 2014) , low MITF levels were associated with intrinsic resistance to MAPKi in these cell lines. We then validated these findings in our panel of BRAF V600 -mutated melanoma cell lines, by determining the IC 50 of PLX4032. To do so, we performed ATP assays, in which melanoma cells were treated with this drug, at a dose ranging from 1 nM to 10 lM, for 72 h. The IC 50 for PLX4032 was generally higher in the ZEB1 high /MITF low cell lines, compared to the ZEB1 low /MITF high cell lines (Fig  1E ). Overall, these in silico and in vitro data demonstrate that cell lines intrinsically resistant to MAPKi exhibit a ZEB1 high /MITF low profile. High ZEB1 and low MITF levels are associated with inherent resistance to MAPKi in BRAF V600 -mutated melanoma tumors in patients We then investigated the relevance of these in vitro observations in human melanoma samples.
Interestingly, most ZEB1 high melanoma samples were in the primary ▸ Figure  2. High ZEB1 and low MITF levels are associated with inherent resistance to MAPKi in BRAF V600 -mutated melanoma tumors. A MITF mRNA expression levels according to ZEB1 expression in 467 melanoma tumors from the TCGA data set (Pearson correlation test). B Representative pictures of ZEB1 and MITF immunostaining in primary melanomas. Scale bar = 40 lm. The aberrant activation of ZEB1 in melanomas is correlated with a MITF low phenotype. C Representative pictures of ZEB1 immunostaining in BRAF V600 tumors from patients, classified into ZEB1 high , ZEB1 int , and ZEB1 low subgroups based on the intensity of ZEB1 staining and on the percentage of cells positive for ZEB1. Scale bar = 80 lm. D Pie charts representing the distribution of ZEB1 alone (upper part), or ZEB1 and TWIST1 (lower part) immunohistochemical staining in tumors according to their initial response to vemurafenib AE cobimetinib treatment.
Human tumor samples and immunohistochemical analyses Melanoma tumor samples were obtained through the Biological Resource Center of the Hopital Lyon Sud, Hospices Civils de Lyon, and of the Hopital Saint Louis, AP-HP, Paris. Human tumor samples were used with the patient's written informed consent. This study was approved by the scientific board of the Hospices Civils de Lyon. Immunohistochemical studies were conducted on a cohort of 70 melanomas with a BRAF V600 mutation, for which clinical data and response to vemurafenib AE cobimetinib treatment were known. Of the 70 patients, 16 received the combination therapy. Patients were referred to as "initially responding" when regression of the tumor was observed (40) and referred to as "initially non-responding" when the tumor progressed or remained stable (30) according to RECIST criteria.
Data set analyses The levels of mRNA expression of 61 melanoma cancer cell lines obtained from the "CCLE_Expression_Entrez_2012-10-18. res" file available through the Cancer Cell Line Encyclopedia (CCLE) were analyzed. In addition, the drug response profile of 28 BRAF V600mutated melanoma cancer cell lines obtained from the "CCLE_NP24. 2009_Drug_data_2015. 02. 24. csv" file (CCLE) was also analyzed  (Barretina et al, 2012). Furthermore, the levels of mRNA expression of 467 human melanomas obtained from the level 3 data UNC Illu-minaHiSeq_RNASeqV2 of the Skin Cutaneous Melanoma study provided by The Cancer Genome Atlas (TCGA) were analyzed. The mutational status of BRAF and NRAS genes of 341 human melanomas obtained from the cBioPortal (http://www. cbioportal. org/) were also analyzed  (Cerami et al, 2012; Gao et al, 2013; The Cancer Genome Atlas Network, 2015).
Q-PCR Total RNA was isolated using RNeasy Kit (QIAGEN) and reverse-transcribed using a high cDNA capacity reverse transcription kit following the manufacturer's instructions (Fisher Scientific). Real-time PCR intron-spanning assays were designed using the ProbeFinder software (Roche). All reactions, including no-template controls and RT controls, were performed in triplicate on a CFX96 (Bio-Rad) and were analyzed with the Bio-Rad CFX manager software. Human HPRT1 was used for normalization. The primers used were as follows: human ZEB1 AGG GCA CAC CAG AAG CCA G and GAG GTA AAG CGT TTA TAG CCT CTA TCA; human ZEB2 AAG CCA GGG ACA GAT CAG C and GCC ACA CTC TGT GCA TTT GA; human TWIST1 GGC TCA GCT ACG CCT TCT C and CCT TCT CTG GAA ACA ATG ACA TCT; human MITF CAT TGT TAT GCT GGA AAT GCT AGA and TGC TAA AGT GGT AGA AAG GTA CTG C; human p75, TCA TCC CTG TCT ATT GCT CCA and TGT TCT GCT TGC AGC TGT TC; human ABCB5 TTG AAA CCT TCG CAA TAG CC and TGG AAA AGT TAT CTA TAC TGG GTT TCT; human JARID1B AGC AGA CTG GCA TCT GTA AGG and GAA GTT TAT CAA CAT CAC ATG CAA; and HPRT1 TGA CCT TGA TTT ATT TTG CAT ACC and CGA GCA AGA CGT TCA GTC.
Viability assays For short-term viability assays, the CellTiter-Glo Luminescent Cell Viability Assay (ATP assay) (Promega) was used, based on quantitation of the ATP present, which signals the presence of metabolically active cells. 1,000 cells in 96-well plates were treated with the indicated drugs for 72 h in a final volume of 100 ll. Three by threefold PLX4032 dilutions resulted in concentrations ranging from 1 nM to 10 lM. After 72 h, the CellTiter-Glo reaction solution (Promega) was added and luminescence was measured (Tekan). Control wells with DMSO were used for normalization. IC 50 values were determined with the Compusyn software. For long-term viability assays, 800 cells in 6-well plates were treated with the indicated drugs at the IC 50 concentration. Medium was changed every 2-3 days, and colonies were fixed and stained with crystal violet after 10-15 days.
Statistical analyses All statistical analyses were performed using GraphPad Prism 6 software and Microsoft Excel 2010, except statistical analyses of TCGA and CCLE data that were performed using the R software (version 3. 1. 2; the R core Team R: a language and environment for statistical computing, 2008; http://www. R-project. org). To assess the significant correlation between ZEB1, MITF, and NGFR expression in the CCLE and TCGA data sets, a Pearson's correlation coefficient was performed. To assess the significant associations between the level of ZEB1 expression and BRAF-or NRAS-mutated status in TCGA data set, a Wilcoxon signed-rank test was performed. All experiments were performed at least in triplicate. Data are presented as mean AE s. d. or AE s. e. m. as specified in the figure legends.
Acknowledgements The authors would like to thank  Brigitte Manship  for critical reading,  Michelle Houang ,  Sophie Leon , and  Nicolas Gadot (Anipath)  for their help with immunohistochemical stainings, and  Frédérique Fauvet  for her help with the generation of cell lines. Our team is funded by the  Ligue Nationale contre le Cancer. The work was additionally supported by the  Institut National du Cancer (INCa PAIR  melanoma  D22044 ), the  Fondation de France  (project no. 2012 00029143 ), and the  Société Française de Dermatologie , SFD (JC). GR and ML were supported by the  LABEX DEVweCAN of the University of Lyon  ( ANR-10-LABX-0061 ) and by a fellowship from the  Ligue Nationale contre le Cancer (GR). AB and MPM are recipients of fellowships from the  Fondation pour la Recherche Médicale (FRM).