dataesr/scientific-paragraphs-categorization huggingface dataset:
From 21 onwards, compartments A04, A07, and A08 ignite and the smoke they generate is added to that of compartment A08. Compartments 03, 04, 11, and 12 are filled with smoke after 25 , co-working spaces 08 after 32 , and finally private offices 01, 02, and 11 after 36. In 36 , the smoke fills the entire available space, i. e. , 49 rooms on the four floors. In order to evaluate the reliability of the results obtained with the simple smoke filling model, the transport of smoke was also simulated using the 3D CFD software SAFIR, validated for many fire scenarios (see for example  [10] ). The simulation covered the first 21′ of fire, before the transmission of the fire to the adjacent rooms of compartment A08. Figure  13  shows the smoke concentration field after 15′ of fire. In order to evaluate the reliability of the results obtained with the simple smoke filling model, the transport of smoke was also simulated using the 3D CFD software SAFIR, validated for many fire scenarios (see for example  [10] ).
The simulation covered the first 21 of fire, before the transmission of the fire to the adjacent rooms of compartment A08. Figure  13  shows the smoke concentration field after 15 of fire. the smoke height is less than 1. 80 m. In order to evaluate the reliability of the results obtained with the simple smoke filling model, the transport of smoke was also simulated using the 3D CFD software SAFIR, validated for many fire scenarios (see for example  [10] ). The simulation covered the first 21′ of fire, before the transmission of the fire to the adjacent rooms of compartment A08. Figure  13  shows the smoke concentration field after 15′ of fire. The filling chronology of the building predicted by SAFIR is given in Figure  14. This chronology is relatively close to the one determined by the network model (Figure  12 ).
2). Most of these data were evaluated during visits aboard French Navy corvettes, with the assistance of DGA naval architects. Note that the ammunition storage magazines and fuel storage tanks are not modelled. These compartments are not susceptible to burning because of the reinforced protection systems they are equipped with. The compartment walls are made of 1 cm steel, covered or not with insulation depending on the type of compartment and its location in the corvette. Table  6  gives the thermal properties of the materials making up the bulkheads of the corvette. An over- First of all, sensitive compartments are classified according to their function into 10 different types. Compartments that do not contain any fuel are referred to as "empty". Figures  19  and 20  show the referencing and distribution of the compartments, as well as the positions and status of the openings (i.
e. , doors, staircases, and access hatches to the decks). Table  5  describes the fuel content of each type of fire-prone compartments, the equivalent fuel(s) used, and, in the last four columns, the values of the parameters needed to construct their design fire curve (see Section 2. 2). Most of these data were evaluated during visits aboard French Navy corvettes, with the assistance of DGA naval architects. Note that the ammunition storage magazines and fuel storage tanks are not modelled. These compartments are not susceptible to burning because of the reinforced protection systems they are equipped with. The compartment walls are made of 1 cm steel, covered or not with insulation depending on the type of compartment and its location in the corvette. Table  6  gives the thermal properties of the materials making up the bulkheads of the corvette.
Conclusions This paper presents a hybrid multiscale approach to produce real-time fire and smoke maps in large multicompartment structures. The proposed approach is based on a network model, whose inputs are determined from two validated models, namely a zone model and a 1D CFD model. Two demonstration cases for a four-story office building and a generic military corvette are presented herein. For the first one, a statistical study is conducted to produce fire risk maps, ranking the building compartments by to their propensity to burn or generate fires of varying severity, and identifying the sensitive and vulnerable areas to be defended. At this stage of development, smoke transport is calculated by a simple filling model whose limits are identified, by comparing results from such a model with results from a validated 3D CFD software for the building application.
A good agreement is found when smoke propagation is not influenced too much by the entire building system and its ventilation. Otherwise, when a strong two-way interaction exists, the agreement degrades. Although further work is needed to improve the modeling of smoke transport, we believe that the hybrid approach could be the basis of a decision support tool, ranging from simple sizing of fire detection and control systems, or design of less vulnerable architectures, to fire risk prevention and real-time fire management in multicompartment structures and the training of firefighting staff. The hybrid approach allows modelers to extend the computational domain and explicitly examine an entire system that may have hundreds or even thousands of compartments. This could only be achieved with a CFD code (due to the computational resources it would require) by simplifying the geometry of the system or by considering only a part of the total system and expand conclusions to the entire system  [1].
Yet, the increasing accessibility of high-and super-resolution imaging methods fuels the demand for user-friendly image analysis workflows. Here, we provide a compendium of strategies developed by participants of a training school from the COST action INDEPTH to analyze the spatial distribution of nuclear and chromosomal signals from 3D image stacks, acquired by diffraction-limited confocal microscopy and super-resolution microscopy methods (SIM and STED). While the examples make use of one specific commercial software package, the workflows can easily be adapted to concurrent commercial and open-source software. The aim is to encourage biologists lacking custom-script-based expertise to venture into quantitative image analysis and to better exploit the discovery potential of their images.
This resource paper contributes to these efforts by providing a compendium of image analysis workflows for nucleus, chromatin and chromosome studies, taking seven case-studies as examples developed by participants of the training school of the INDEPTH COST action  [14]. The workflows are based on a user-friendly, commercial image processing software (Imaris, Bitplane, Switzerland) but are conceptually applicable to concurrent (commercial or open source) software as discussed in this paper. In addition, although they largely borrow examples from plant nuclei and chromosomes, they remain transferable to the study of animal nuclei. Indeed, the organization of the nucleus, including the nuclear envelope, chromatin domains and chromosomes, share common organizing principles in plants and animals  [15] [16] [17] [18]  The workflows associated with each case study are briefly described below and are illustrated in the related figures.
Sample preparation and imaging are described elsewhere  [23]. Deconvolved STED images are segmented using the Imaris software (Bitplane, Switzerland) to create digital objects corresponding to the nucleus, the nucleolus, the chromocenters and the RNPII signals (Figure  1b-d , Supplemental File 1 -Video 1). The surface object corresponding to the nucleus is also used to apply a 3D mask to separate the true image from the background signal (compare the framed regions in Figure  1bc ). While the nucleus and nucleolus are segmented based on smoothed, manual contours, heterochromatin is segmented using the supervised automated tool. Chromocenters (CCs) are typically large, brightly stained regions. In Arabidopsis nuclei, these are discrete and relatively easy to segment (Figure  1d , inset d1).
Variables of interest, such as signal intensity per channel, object size and shape and distance between objects (spot-tospot, spot-to-surface) were exported for each object type and channel. The high number of variables, object type, channels, image replicates and levels of comparison (such as genotypes and treatment) dramatically increases data complexity. To facilitate data exploration, we built a stand-alone data visualization interface named DataViz (https:// github. com/barouxlab/DataViz) which allows one to interactively plot all, or a subset of, data. This also enables custom variable creation for the normalization of distances and intensity per image (Figure  1g -i, Supplemental File 1 -Dataviz_guidelines). Here, we provide a few examples of violin plots (Figure  1g ), density distributions (Figure  1h ) and scatter plots with   1i ).
(f) Fully segmented image containing surface (nucleus, nucleolus, CC and nanoCC) and spot objects (RNPII-ser2P, abbreviated S2P). (g-i) Data exploration using DataViz (github. com/barouxlab/DataViz, Supplemental File 1-Dataviz_guidelines). (g), Violin plots showing a similar DNA density distribution in CC and nanoCC but much lower density in S2P clusters (intensity mean, DNA channel, normalized per image) and a sharp peripheral location of CC as formerly described  (Andrey et al. , 2010; Fransz et al. , 2002) , contrasting with the more dispersed distribution of nanoCC and S2P clusters (distance to nucleus surface (0) normalized using the nucleus center of mass as reference);. We imaged Arabidopsis root nuclei expressing a GFP-tagged protein associated with the NE (NE-GFP, Tatout, Mermet, Boulaflous-Stevens, unpublished) from 1 week old seedlings using a confocal microscope equipped with an Airyscan module  [27]  (Supplemental File 2-Figure  2 ).
The second image, shown in Figure  3h -k and provided in Supplemental File 3 -Image 3b, shows a pachytene stage nucleus labeled with ASY1 (white), ZYP1 (green), HEI10 (red) and counterstained with DAPI (blue), was acquired using 3D-SIM similar as for rye  [35]. The aim of both images was to segment HEI10 or DMC1 foci and to analyze their distribution relative to the SC, their size and their intensity. For this, the chromosomes, SC complex and HEI10 or DMC1 foci are segmented separately. The detailed strategies used for filtering and classifying the HEI10 or DMC1 foci are explained in the detailed workflow descriptions (Supplemental Files 3). In brief, the image analysis followed the steps of deconvolution (optional depending on the imaging method), chromosome segmentation, SC segmentation, CO foci segmentation and classification before data export (Figure  3a ).
Finally, DMC1 foci were segmented as spot objects using an estimated seed size of 200 nm (Figure  3f ). The algorithm detects all possible foci with both low and high intensities. Classical studies have so far focused on highintensity foci, whose abundance falls within a few hundred  [31, 33]. By contrast, the workflow described here enables one to capture all foci, first, irrespective of their intensity, and to classify them according to intensity, during the creation process (Figure  3f , right panel). In this example, three classes were created (Figure  3f  plot, yellow, magenta and cyan classes). Alternatively, spots can be classified after data export based on normalized signal intensity in a third-party software application (for instance, using DataViz, see Workflow1). In an intensity sum-based classification, we scored 217 DMC1 spots with medium-to-high intensity (Figure  3f  plot, magenta and cyan classes) as previously reported for a similar stage of meiosis  [31, 33].
200 nm diameter. Segmentation data of several images were exported and plotted using DataViz (see workflow 1, Supplemental File 1 -Dataviz_guidelines). SSSU-YFP speckles showed a broad spatial distribution, with no clear preferential enrichment toward the periphery (Figure  4d ), in contrast to chromocenters as previously shown  [24, 25]. We found, however, that SSSU speckles are not uniform: they differ in their relative enrichment (SSSU:DNA ratio), which correlates with the proximity to CC (Figure  4e ). Our analysis demonstrates that the nuclear speckles formed by SSSU are not preferentially enriched at the nuclear periphery as would have been expected from their biochemical interaction with CRWN1. The analysis suggests a differential enrichment depending on the proximity to other nuclear bodies, the CC, a relationship whose functional relevance remains to be investigated.
In this example, centromeres, 45SrDNA (NOR, nucleolus organizing regions) telomeres, and subtelomeres of somatic metaphase chromosomes were labeled with specific FISH probes as described  [49]. In addition, halfand full helical turns of the chromonema were painted by oligo-FISH at the long arm of chromosome 5H (Figure  5 , probes were named according to birds: Stork, Eagle, Ostrich, Rhea, Moa, Flamingo)  [49]. 3D-SIM raw data image stacks (Figure  5b ) were acquired using an Elyra PS. 1 microscope system equipped with a 63×/1. 4 Oil Plan-Apochromat objective, processed with the software ZENBlack (Carl Zeiss GmbH) (Figure  5c )  [50]  and converted into an Imaris file. DAPI and FISH signal intensities were adjusted for improving the visualization using the 'Display Adjustment' tool (Figure  5d ).
Analyzing the orientation of mitotic chromosomes during cell division is thus relevant to understand this intricate relationship. We designed a 3D microscopic image analysis workflow described in Figure  7a  and detailed in Supplemental Files 7 containing a protocol and troubleshooting tips. We used barley chromatin and centromere fluorescent marker lines (FMLs) expressing translational fusions of histone H2B with CYAN FLUORESCENT PROTEIN (CFP-H2B) and -CENTROMERIC HISTONE H3 with RED FLUORESCENT PROTEIN (RFP-CENH3), respectively (Kaduchová, Pecinka et al. , in preparation). Z-stack images were acquired using a Leica TCS SP8 STED3X confocal microscope equipped with a Leica Application Suite X (LAS-X) software version 3. 5. 5 with a Leica Lightning module (Leica, Buffalo Grove, IL, USA) (Leica Microsystems, Wetzlar, Germany).
Image segmentation delivers a wealth of information related to signal intensity, distribution pattern (texture), shape, size and distance relationships between segmented objects  [1]. Thus, images become associated with many variables and entry types, generating big data. Those can either be explored in a non-hypothesis-driven way using multidimensional data analysis  (Bagheri et al. , 2022)  or in a hypothesis-driven manner following a careful choice of data for export. Even when exporting a selective number of image descriptors, the analysis of replicate image datasets, in different conditions (treatments or genotypes), labeled for multiple components, quickly generates a large numerical dataset. Versatile data visualization interfaces become handy at this stage. Here, we provided some examples among the numerous available solutions.
An example is shown in workflow 4 with the analysis of nuclear bodies in mouse embryonic stem cells. In addition, these image analysis workflows are expected to inspire cell biologists beyond the study of the nucleus and its constituents. For instance, transposed at the cellular scale, workflow 1 or 4 could be applied to analyze the spatial distribution of vesicles or cytoplasmic bodies within a cell, using cell segmentation modules to create the initial surface object (see, for instance, but not exhaustive, references  [66] [67] [68] ). Finally, while based on a particular (commercial) software piece, the concept of these workflows is expected to be transferable to other concurrent software offering similar image analysis tools (Supplementary File 8 -Table  1 ). One example is the 3D ImageJ Suite  [8, 10]  popularized by the NEUBIAS COST action  [8]  which also offers a set of Fiji-based plugins for analyzing the spatial distribution of nuclear signals.
In a semi-automated, userguided segmentation such as proposed here, how to decide on a specific threshold or cutoff values can be difficult (of note, this type of decision is similar to those met in bioinformatics analyses to select sequencing reads based on their quality, replication and cutoff levels). Threshold values must be justified with sufficient criteria to be reproduced and understood by peer users. Alternatively, and when image quality is relatively homogeneous in a dataset, it is possible to use values automatically proposed by the algorithm as those are usually derived from image-based statistical parameters. We address this issue and propose solutions for each workflow in their detailed description (supplemental files 1-7). Yet, the rapid emergence of machine-learning (ML) based segmentation algorithms is expected to ease the application of optimal segmentation parameters, although an initial investment is required to train the algorithm with ground-truth images (discussed in  [1, 12] ).
Eventually, and perhaps most importantly, the image analysis becomes only relevant when two or more biological conditions are compared. Sample preparation and image analysis done in the same conditions and by the same user, ideally in a blind analysis design, will average possible technical and cognitive biases throughout the datasets. This will, in turn, allow us to draw relevant conclusions relative to the type and the order of magnitude of changes correlated with a treatment, a genotype or cell type, for a given spatial pattern describing nuclear, chromatin or chromosome organization. The compendium of workflows presented here, with its illustrations, training images and detailed guidelines, aims at inspiring experimentalists in the field of chromatin, chromosome and nucleus organization studies, with no or little expertise in image processing.
Acknowledgments We acknowledge the microscopy imaging facilities of the  University of Zürich  ( Jana Doehner ,  Urs Ziegler ; ZMB), of the CEITEC at the  Masaryk University  (CELLIM, supported by the  MEYS   CR  project no. LM2018129 ), and of the Auvergne Bioimaging platform (CLIC microscopy facility). The authors acknowledge the 'INDEPTH'  COST Action   CA16212  for funding of the Training School during which the workflows were developed,  Peter Majer  and  Michael Mahlert  ( Bitplane AG ) for suggestions during the training school.
Funding This work was funded by grants from the  Swiss National Science Foundation  [ 310030_185186  and  IZCOZ0_198171  to CB,  310030_201268  to RS]; the  Velux Stiftung  (project  1107  to CB); the  Ricola foundation  (to CB); the  ERC Grant Shuffle  (Project ID:  669182 ) and  Biotechnology  and.
Nevertheless, their design method and performance analysis are rarely known in the literature and keep a high experimental component similar to most pumping equipment. This paper proposes a numerical study and the optimization of a booster multiphase jet pump system installed in a heavy oil conventional loop of a gathering system. First, the optimization of a traditionally designed jet pump, combining CFD simulation and optimization algorithms using commercials software (ANSYS CFX ® and PIPEIT ® tool), has been carried out. This method allowed evaluating the effect of multiple geometrical and operational variables that influence the global performance of the pump to run more than 400 geometries automatically in a reduced time frame. The optimized pump offers a substantial improvement over the original concerning total flow capacity (+17%), energy, and flow distribution.
Jet Pump Design and Optimization An optimization routine to iterate over the main dimensions, combining their performance analysis through the CFD, was implemented to design an optimal jet pump configuration. CFD commercial code ANSYS CFX ® (v-14, Canonsburg, US) and the optimization algorithms of the Pipeit ® (Version 1. 0, Throdheim, Norway) tools have been used. Pipeit ® is an Integrate and optimization software that originally conceded to integrate models and optimize petroleum assets. As an integrator, Pipeit ® allows to chain together an unlimited number of applications, run sequentially and in parallel  [11]. The proposed methodology is schematized according to Figure  8. After fixing the variables, constraints, and the objective function, using the IBM non-linear IPOPT (Interior Point Optimizer for large-scale nonlinear optimization) optimization solver, several geometrical combinations for the pump were obtained, and its performances were predicted through CFD simulations  [2].
Jet Pump Design and Optimization An optimization routine to iterate over the main dimensions, combining their performance analysis through the CFD, was implemented to design an optimal jet pump configuration. CFD commercial code ANSYS CFX ® (v-14, Canonsburg, PA, USA) and the optimization algorithms of the Pipeit ® (Version 1. 0, Throdheim, Norway) tools have been used. Pipeit ® is an Integrate and optimization software that originally conceded to integrate models and optimize petroleum assets. As an integrator, Pipeit ® allows to chain together an unlimited number of applications, run sequentially and in parallel  [12]. The proposed methodology is schematized according to Figure  8. After fixing the variables, constraints, and the objective function, using the IBM non-linear IPOPT (Interior Point Optimizer for large-scale nonlinear optimization) optimization solver, several geometrical combinations for the pump were obtained, and its performances were predicted through CFD simulations  [2].
tion algorithms of the Pipeit ® (Version 1. 0, Throdheim, Norway) tools have been used. Pipeit ® is an Integrate and optimization software that originally conceded to integrate models and optimize petroleum assets. As an integrator, Pipeit ® allows to chain together an unlimited number of applications, run sequentially and in parallel  [11]. The proposed methodology is schematized according to Figure  8. After fixing the variables, constraints, and the objective function, using the IBM non-linear IPOPT (Interior Point Optimizer for large-scale nonlinear optimization) optimization solver, several geometrical combinations for the pump were obtained, and its performances were predicted through CFD simulations  [2]. Keeping in mind that the objective of the jet pump of the present case study is to increase the total flow capacity of fluid handled by the main and the loop pipeline system, a dimensionless flow function was defined as follow: Keeping in mind that the objective of the jet pump of the present case study is to increase the total flow capacity of fluid handled by the main and the loop pipeline system, a dimensionless flow function was defined as follow: X = 50 -Q * 3 50 -1 • Q 1 Q 4 ( 7 ) where: Q * 3 = 100• Q 3 Q 1 (8) The new variable X proposes to be an objective function that will maximize the total flow in the system, minimizing the jet pump driving fluid Q 1 guarantying the evenest flow distribution.
That will result in an 8% increment of total handled fluid with a significant power reduction, guaranteeing an even flow distribution at the loop exit. Figure  12  shows the result from almost 400 geometric combinations proposed by the optimization program and simulated by CFD software. This figure illustrated the evolution of the objective function against geometrical and operational variables. Red points represent the pumps configurations with results in and exit flow distribution almost symmetrical, which means, X value is bigger than 45. The green and yellow points correspond to the optimum pump geometries, implying the objective function has at least two local maximums. Again, as can we appreciate in this figure, dimensions between best pump 1 and 2 have no significant differences. These two pumps (Best pump 1 and 2) have similar geometries, but they are pretty different from the other analyzed pumps  [13].