dataesr/scientific-paragraphs-categorization huggingface dataset:
Real-Time Video Analysis During video acquisition, all ImageJ native functions can be used to make manual analysis (e. g. , pixel value calibration, thresholding, contrast and brightness adjustments, lookup tables, and pseudo colors, real-time computing of histogram, line profiles, and image calculation). Moreover, the video capture by the HF_IDS_Cam is fully multi-threaded, taking advantage of multicore CPUs, which is useful for the simultaneous analysis of the video by scripts or another ImageJ plug-ins. For instance, the presented method has been successfully used to record contraction of stimulated cardiac myocytes at a sampling rate of 900 Hz with the IDS UI-1220 video camera  [3]. HF_IDS_Cam has been developed in order to facilitate the synchronization of scripts or macros with each incoming image of the video flow.
Synchronizations The interfacing with serial ports also allows for the easy interaction of analysis with input synchronization and analog signal from other devices (Figure  2 ). When a synchronization signal arrives on the serial port, the pending image is tagged and one or several actions can be executed by macro. For example, as shown in Figure  2 , on each synchronization signal, the mean fluorescence of the rectangular selection is recorded for one second and plotted versus time. The code used to generate the results of Figure  2  is available in a Github repository (https://github. com/PCCV/HF_IDS_Cam).
Limitations and Conclusions Herein, the described software simplifies simple video analysis with ImageJ native functions and more elaborated analysis with macros, scripts, and plug-ins. Even though this HF_IDS_Cam works under MacOS, the drivers for IDS cameras have been developed only for Windows and Linux, which restricts its use to these two operating systems. However, it is useful for all researchers working with video flows or images, and for teachers and students learning video signal processing and analysis, as it provides a cost-effective solution. Acknowledgments: This work was founded by the  University Francois-Rabelais of Tours. We thank  Imaging Development Systems  ( IDS France ) for providing us the UI-3060CP camera. Acknowledgments: This work was founded by the  University Francois-Rabelais of Tours.
Materials and methods Data We used population-based cancer registry data that cover two contiguous Départements [French administrative areas] of West France (Calvados and Manche, nearly 1. 1 million inhabitants). The quality and exhaustiveness of the included reg-istries are certified every 4 years by an audit of the National Institute of Health and Medical Research (INSERM), the "Santé Publique France" agency, and the French National Cancer Institute. The incidence data from those registries are regularly included in the "Cancer Incidence in Five Continents" monograph series of the International Agency of Research on Cancer, where their quality and exhaustiveness are also assessed. We analyzed cancer cases diagnosed between 1997 and 2010 in people aged >15 years at diagnosis. The follow-up of all cases ended on June 30, 2013.
35 he analysis was separately conducted in men and women and used the iterative model-building strategy recommended by Wynant and Abrahamowicz. 36 Starting with the "full model", this strategy eliminates spurious time-dependent and nonlinear EHR functions of the three variables using the likelihood ratio test and 0. 05 as significance threshold. This led to retain a final model for each sex-cancer couple. However, unlike the original proposal,  36  we kept by default the simplest EHR (ie, linear and time-constant) for each of the three variables. To implement the advocated statistical methods, we developed a specific package named mexhaz (version 1. 1), which runs on R software (version 3. 2. 0). Both the software and the package may be freely downloaded from the CRAN repository (https://cran.
We summarize the recommendations we believe important to describe the association between socioeconomic deprivation and the EMH (Table  2 ). Using population-based cancer registry data ensures depicting the full picture of cancer survival inequalities. For decades, the notifications of cancer cases come from many different sources (public and private pathology laboratories and hospital discharge databases as well as databases of the National Health System). Even if the number of data sources has dramatically increased since 1997, it was to collect further information on cancer cases such as treatment, thus not affecting the core of the cancer registry data and their exhaustiveness. For these reasons, we do not suspect any differential ascertainment over the study time period nor between areas of residence or individual and area-level socioeconomic determinants.
Acknowledgments We thank the  IRESP (Institut de Recherche en Santé Publique)  for supporting the study (grant for the ANGEFLEX study, Convention  AAR2013-13  " Soutien à la recherche statistique et mathématique appliquée à la cancérologie "). This work was also partly supported by  Cancer Research UK  grant number  C7923/A18525. The authors thank  Jean Iwaz  ( Hospices Civils de Lyon ) for the revision of the final draft of this manuscript.
Optimizing the SNR We now want to optimize the SN R given by Equation 6, by tuning τ and ∆t. We also add the constraint τ f M ≥ 10 (large number of synaptic inputs), so that the distribution of V is approximately Gaussian  [Burkitt, 2006]. Otherwise, it would be positively skewed 1  , thus a high SN R would not guarantee a low false alarm rate. We assume that L is sufficiently large so that an upper bound for ∆t is not needed. We used the Matlab R2017a Optimization Toolbox (MathWorks Inc. , Natick, MA, USA) to compute the optimum numerically. Figure  4  illustrates the results with P = 2. One can make the following observations (similar to our previous paper which was limited to P = 1  [Masquelier, 2017] ): • Unless f and T are both high, the optimal τ and ∆t have the same order of magnitude (see Figure  4  left).
Vegetation fires are intrinsic ecosystem disturbances of the Earth system. Global burned area products have been delivered from several space-borne instruments, and have recently provided pixel-level information underpinning fire spread processes. Here we present FRY, a global database of fire patches with morphology-based functional traits reconstructed from pixel-based burned areas derived from the MODIS and MERIS imagery using a flood-fill algorithm. Each fire patch is characterized by the geo-location of its center, area, perimeter, the features of the ellipse fitted over its pixel's spatial distribution, and different indices of patch complexity. We obtained a consistent spatial distribution of global fire patch functional traits between the MCD64A1 Collection 6 and the MERIS fire_cci v4.
Furthermore, the spatial accuracy of BA is critically limited by the spatial resolution of the sensors. Despite these limitations, global satellite data products allow for continuous, frequent and global coverage of burned areas, and are suitable for a systematic analysis of fire dynamics over the globe. Global fire patch data derived from pixel-level BA information have recently emerged as an important source of information for climate, vegetation and carbon cycle modeling. They are used to capture the key role of driving factors that influence burn area  11  and benchmark fire module in DGVMs  4. Process-based fire modules in DGVMs simulate BA as the product of the number of ignitions multiplied by the simulated mean spreading area of a typical fire patch  [12] [13] [14]  , and the results of these models can be compared to global BA products.
But this is a less rigorous way to calibrate fire models than a direct comparison between observed and simulated fire patch area, since it is unknown whether a discrepancy between the simulated and observed BA results from differences in the ignition pattern or missing processes in fire propagation. While global or local pyrogeographical analyses could derive significant information from fire patches  [15] [16] [17]  , only a few modeling studies have tried to benchmark fire-DGVM upon fire size distribution  14  based on local fire patch datasets. In addition to fire patch size distribution, fire patch complexity is another piece of information needed to capture the spreading conditions during fire events. The resulting complexity of fire boundaries also affects post-fire recovery rate in forested area, since it is found that remaining islands of living vegetation  18, 19  can accelerate the recolonization process after a fire.
Fire patch size is closely linked to meteorological, fuel continuity and topographical  20  conditions and can modify carbon emissions according to fire observations in Alaskan boreal forests  21. Pyrodiversity  22  , which describes the diversity of fire types in a given fire regime, is a promising approach to investigate continental and global spatio-temporal variations in fire spreading processes, recurrence or rarity of events and their drivers. This new field of research in fire science resembles the emerging topic of functional biogeography  23  , where the functioning of biomes is assessed from the assemblage of continuous functional traits rather than species composition and relies on databases of plant traits  24, 25. Similarly, global databases of fire patch morphological features (or traits) that reflect the underlying spreading processes could be used to explore functional pyrogeography as an analytical and modeling framework.
Fire regimes could be more precisely described and modeled with an assemblage of fire traits, rather than simple burned area. This paper presents FRY, a global database of fire patch functional traits that includes patch areas and other morphological features derived from burned areas of the MODIS and MERIS sensors. We first describe the algorithm known as flood-fill 26 that we use to group individual burned pixels with a burn date into fire patches, defined as groups of adjacent pixels with temporal coincidence parameterized by a temporal cut-off value (section Methods). We extensively compared the functional traits of individual fire patches obtained from the two sensors (section Technical validation), and thoroughly investigated the influence of the cut-off parameter on the measured fire patch functional traits.
Original burned area input data The MCD64A1 Collection 6 BA product  27  is derived from the MODIS sensors onboard the Aqua and Terra satellites. The Terra satellite has a morning overpass time (10h30am), and the Aqua satellite has an afternoon (1h30pm) overpass time. It provides global coverage of burned area with daily revisit at the equator at a resolution of 463x463m over the period 2000-2017. The MCD64A1 Collection 6 algorithm detects BA from daily changes with a burn-sensitive vegetation index computed from the difference between channels 5 and 7 of the spectrometer (corresponding to infrared light) and combined with MODIS active fire observations to discriminate unburned from burned pixels. A burn date, with its uncertainty, is then attributed to each detected burned pixel  27. The MERIS fire_cci v4.
Both MCD64A1 Collection 6 and MERIS fire_cci v4. 1 datasets are available in separated GeoTIFF tiles, providing the burn date of each burned pixel. Using both datasets allows for comparison of fire patch functional traits calculated from BA. The two burned area products have different advantages, namely a finer spatial resolution for MERIS fire_cci v4. 1, and a smaller temporal resolution and longer time series for MCD64A1. The half-axes (σ X and σ y ) and the azimuthal (θ) angle are displayed in red. The so-called core areas (A core , Table  1 ), corresponding to the area of pixels totally surrounded by other burned pixels in a given patch, are displayed in light blue for the four considered fire patches. Computed values for the functional traits of these patches are shown in Table  2.
These values were chosen as the possible time lapse between two detected neighboring burned pixels belonging to the same fire event considering uncertainties, so they span the majority of cut-off values used in previous studies  17, 26, 32. The flood-fill algorithm processing daily global burned area data were implemented using the R CRAN package 'raster'. To optimize the processing time, we made use of the 'clump' function which allows pixels of burned areas to be efficiently combined into clumps, defined here as a group of neighboring pixels (in a so-called queen scheme). Working directly at the clump level was more efficient than working at the pixel level and enabled the use of parallelized features of the 'raster' package. The algorithm proceeded as follows. A burn date t bd was identified in the BA product, and only the pixels within the time span t bd o t o t bd + Δt co were selected, where Δt co is the cut-off parameter.
The clump routine from the R-CRAN raster package was applied to these pixels to attribute a unique ID number to each clump, defined as a group of burned pixels totally isolated by unburned pixels during the considered cut-off dependent time span. We performed the same two previous steps for the next burn date (t bd + 1). When a new clump overlapped with one or more clumps from the previous step, these clumps were considered to belong to the same patch and therefore were merged. For every month, the latitude/longitude coordinates, IDs and burn dates of pixels belonging to a detected fire patch were saved. This information was saved on a monthly time step rather than on a yearly time step since some pixels were susceptible to be detected as burned more than once per year. The resulting fire patches in the same 0.
Calculation of fire patch functional traits From the monthly patches described above, we used the R CRAN package 'SDMtools' (Species Distribution Modeling Tools) to compute all fire patch functional traits including morphological features and spatio-temporal information  32  (Table  1 ). The perimeter (P), the total number of burned pixels (N pixels ) and the number of core burned pixels (N core pixels ) were computed for each patch, where a core burned pixel is defined as a pixel which is totally surrounded by other burned pixels. These three quantities were computed in pixel units. The corresponding patch areas (in hectares) were also computed (A for N pixels , and A core for N core pixels ). Traits that describe the fire patch complexity are the Perimeter to Area Ratio P. A. R. ( = P/N pixels ), the Shape Index S.
The R CRAN package 'aspace'  35  was used to compute the center position of the SDE (X and Y), its azimuthal angle (θ), and the minor and major half-axes (σ X and σ Y ) of the fitted ellipse to each patch (Table  1 ). The ellipse half-axes and azimuthal angle, initially computed in latitude/ longitude coordinates in degrees, were also computed after projecting the fire patch in a local flat projection (σ X,km and σ Y,km , in kilometers, and θ km ). The eccentricity (E SDE ) and ratio of half-axes of the ellipse (R SDE ) were also computed. As an example, the values of these different metrics are provided for the patches highlighted in Fig. 1  (Table  2 ). The fire patch functional traits were only computed for patches composed of at least 5 burned pixels. This arbitrary threshold allowed us to lighten the database files by removing very small fire patches with unreliable information about the fire patch complexity or orientation.
We then fitted the power law by using the Minuit minimization algorithm (https://seal. web. cern. ch/seal/snapshot/work-packages/mathlibs/minuit/) which provides an efficient way to minimize χ 2. As a level 2 product, the maps of β and σ β , the standard deviation of β obtained from the fit (computed from the β values at χ 2 ± 1), are also provided because they assess the quality of the fit and allow intercomparisons between the different datasets. We also produced maps of the mean values and standard deviation for the perimeter-to-area ratio, shape index, fractal correlation dimension, ellipse ratio and ellipse eccentricity, and maps of the number of fire events within each grid cell.
Data Records The FRY database is organized in 8 datasets (2 burned area products for 4 cut-off values), which can be downloaded from the OSU-OREME repository. For each survey SURVEY (MERIS for MERIS fire_cci v4. 1 or MODIS_6 for MCD64A1) and each cut-off value N (3, 5, 9 or 14), values for all traits listed in Table  1  are provided for each fire patch in Comma-Separated Format (csv) under the name fire_patches_SURVEY_final_co_N. csv (Data Citation 1). The names and order of the different fields are described in the header of each csv file and in the meta-data associated with the repository. The NetCDF maps of the parameters described at the end of the Methodology section for the survey SURVEY and the cut-off value N are provided under the name trait_map_SURVEY_1. 0deg_co_N. nc (Data Citation 1).
Technical Validation In this section, we compare fire patch functional traits between the MERIS fire_cci v4. 1 and the MCD64A1 products for 2005-2011. Among the different traits listed in Table  1 , we focus on the spatial distribution of the density of individual fire patches (Fig. 2 ), defined as the number of individual fire patches per square kilometer, the slope of the patch size distribution (β parameter, Fig. 3 ), and the shape index (S. I. , Fig. 4 ) as an indicator of fire patch complexity. We display 1º ×1º grid maps of these three metrics for the MCD64A1 and MERIS fire_cci v4. 1 with a cut-off value of 5 days. We focused first on the 5 days cut-off value because it corresponds to an intermediate value of the considered cut-off range. We removed from the database all fire patches with less than 5 pixels (Methods section).
1 patch data, the fire patch density of the product is larger in all regions. However, the MCD64A1 patch data still indicate more fires in the aforementioned regions. The differences in South America and South-Western Africa most probably arise from the lower number of available MERIS fire_cci v4. 1 images in these regions  39. In boreal regions, the slight discrepancy between the two products is also found when comparing burned area  30. The lower total number of detected fire patches for MERIS fire_cci v4. 1 could arise from missed seeds by the fire_cci algorithm. The 3-day time of overpass can lower the probability of matching burned pixels detected by MERIS imagery with active fire pixels from MODIS if there is a long delay between the two detections. This suggests that the temporal resolution of the sensors is an important parameter for detecting individual fire events when using a two-step algorithm to detect BA.
Slope of the fire patch size distributions (β) The maps of the fitted β parameter for MCD64A1 and MERIS fire_cci v4. 1 describing fire patch size distribution were computed with their uncertainties, the difference of β between the two products, and their degree of agreement in each cell (Fig. 3 ). The degree of agreement is measured in units of standard deviation (also noted σ β ) and is equal to N if the fitted values of β for a given grid cell are compatible within N σ β for the two products. The spatial distribution of β is similar between the two fire patch datasets, with higher values in Africa, Eastern Europe (Ukraine and Western Russia) and South Eastern Asia, and lower values in northern Australia, Russia and North America. The uncertainty of the β parameter is intrinsically smaller than in regions with a large number of fire events.
The authors would like to remind potential users of the FRY database that the perimeter of the fire patches are computed from pixel data. All diagonal border lines (NW, NE, SE and SW directions) of the perimeters will appear as 'stair-step' lines because of aliasing, and will therefore be biased upward with respect to the real fire patch perimeters. Since this bias depends on the spatial resolution of the pixel product, this renders difficult the direct comparison of the complexity indices between MERIS fire_cci v4. 1 and MCD64A1. However, it is still possible to discriminate regions with more complex fire patches from regions with simpler fire patches for products with similar resolution. Sensitivity to varying the cut-off parameter on regional statistics of fire patch functional traits We next compare the values of β, S.
I. and R SDE averaged over the 14 Global Fire Emissions Database (GFED) regions for the 4 considered cut-off values (Fig. 5 ). To compute the average, the mean values in the 1º × 1º cells are weighted by their uncertainties. We also display the ratio of the average values obtained for MERIS fire_cci v4. 1 and MCD64A1. The cut-off value is arbitrarily chosen to reconstruct the fire patches. Here, we use the dispersion of the different cut-off values as a rough estimation of the uncertainty due to the arbitrary choice of the cut-off parameter value to reconstruct fire patches. The value of the β parameter decreased as the cut-off value increased. Increasing the cut-off tends to group together fire patches which would have been separated with a lower cut-off. As a result, the number of small fires decreases and the number of large fires increases for increasing cut-off values (Fig.
Acknowledgements The authors would like to thank  Cathy Nangini  for helping them check the language quality of the article, and the  OSU OREME (Montpellier, France)  for providing the authors the data repository within the  Observation task "Mediterranean_Forest/Forest_Fires ". Pierre Laurent work was supported by the  ESA   Fire_cci  project. M. Vanesa Moreno work was supported by  UM Post-Doc program ,  University of Montpellier (France) , and co-financed by the  ESA   Fire_cci project. The  Coordenação de Aperfeiçoamento de Pessoal Superior (CAPES, Brazil)  provided a PhD grant to  Joana MP Nogueira  ( BEX-1185-13-6  process).
Code availability The R-CRAN 'raster' (https://CRAN. R-project. org/package=raster), 'SDMTools' (https://CRAN. Rproject. org/package=SDMTools) and 'aspace' (https://CRAN. R-project. org/package=aspace) packages can be found on CRAN, the global repository of open-source packages for R. Due to the heavy CPU resources required to create the FRY database, the full code has been designed to run on a specific cluster architecture, and providing it would be of limited interest. However, any user interested in creating another database from a pixel BA product can contact the corresponding author who can share snippets of the code and provide help in adapting the code to another architecture.
Recent technological advancements have permitted high-throughput measurement of the human genome, epigenome, metabolome, transcriptome, and proteome at the population level. We hypothesized that subsets of genes identified from omic studies might have closely related biological functions and thus might interact directly at the network level. Therefore, we conducted an integrative analysis of multi-omic datasets of nonsmall cell lung cancer (NSCLC) to search for association patterns beyond the genome and transcriptome. A large, complex, and robust gene network containing well-known lung cancer-related genes, including EGFR and TERT, was identified from combined gene lists for lung adenocarcinoma. Members of the hypoxia-inducible factor (HIF) gene family were at the center of this network. Subsequent sequencing of network hub genes within a subset of samples from the Transdisciplinary Research in Cancer of the Lung-International Lung Cancer Consortium (TRICL-ILCCO) consortium revealed a SNP (rs12614710) in EPAS1 associated with NSCLC that reached genome-wide significance (OR = 1.