dataesr/scientific-paragraphs-categorization huggingface dataset:
Accordingly, proteins reported in the literature to interact with CFTR as part of larger complexes were searched for in databases to find the protein components of the complex. During this step, name disambiguation must be considered in order to find all data related to that protein and also to not repeat proteins. For instance, by looking for syntaxin 5, names such as Syn5 and STX5 are also available for the same protein. The same happens for Golgi Associated PDZ And Coiled-Coil Motif Containing protein, known as GOPC although other names such as CAL and FIG are referred to on research papers and used to retrieve as much information as possible. Diagram building. CyFi-MAP was built using yEd graph editor (https:// www. yworks. com/) using the SBGN Palette, and the data was represented based on the SBGN standard  12.
This notation a knowledge representation language used in the illustration of molecular pathways and protein interactions as the standard notation for disease maps  11. Presenting three languages that provide different types of knowledge illustration allow to adapt on the level of detail intended to be highlighted on the map, including the following layers: Activity Flow to depict interactions with process direction, Process Description detailed specific mechanisms, and Entity Relationships which describe the mechanisms without a sequential process  12. CyFi-MAP was implemented following the SBGN Activity Flow, in order to provide a compact, sequential and easy-read format or involving signalling pathways. This language is useful to represent the flow of information in biological sequences/pathways in a way that the information can still be captured for underlying mechanisms of unknown influence  12.
Each subcellular organelle (ER, Golgi, endosome, etc. ) were drawn manually and added to the map as a background image for graphical representation of the different subcellular compartments. Additionally, each interaction provides information on itself. Depending on the selected edge different types of interactions can be found on CyFi-MAP, namely (see Fig. S1  on supplementary material for more details): (1) activation, representing a normal binding, (2) synthesis, when an altered product is released, (3) trafficking, representing movement inside the map, and (4) inhibition, when the interaction inhibits a function. Map exploration via the MINERVA platform. CyFi-MAP diagrams are available in the platform MINERVA accessible through GitHub (https:// cysti cfibr osism ap. github. io/). The project description and key processes (shown side-by-side, represented through images to allow the comparison between wt-CFTR and F508del-CFTR submaps on the CyFi-MAP) are given on the website.
Every CFTR interaction represented in the CyFi-MAP is validated by PubMed references. The user can curate the data by commenting, given they provide the respective reference as well. All suggestions will be analysed by curators and CF domain experts to maintain CyFi-MAP quality and accuracy. The user can contribute to CyFi-MAP by adding comments with questions, corrections or additions to the map. These will be visible to other users and developers. To add a comment to CyFi-MAP during navigation, right-click on the specific location and choose to add a comment. It is possible to choose a specific identity to link the comment, such as the protein or reaction, or to remain 'general' , which will link the comment to the location the user chooses. The remaining fields allow the user to fill in the name and email in order to facilitate communication with the developers and to clarify any questions that may emerge.
Figure9. CyFi-MAP curation process. The curation process developed presents 5 levels. 1st level filter the data to studies with proteins that interact directly with CFTR, meaning that only experimental techniques which confirmed a direct interaction were considered such as e. g. Immunoprecipitation, Surface Plasmon Resonance (SRP), and others. The 2nd level is related to the type of cell culture used in these experiments, focusing on human airway epithelial cells, although other cell types were included when described on review publications. In both levels, if the studie do not agree with the criteria is rejected. The 3rd level consist on finding the location of the interaction inside the cell (e. g. ER, Golgi, cytosol, PM) followed by the type of interaction (binding or inhibition). The 4th level confers confidence to the interaction, consisting on the search for publications that support the information.
Acknowledgements The authors would like to thank the domain experts that participated in stimulating discussions and valuable feedback and to the colleagues from  FunGP-Functional Genomics and Proteostasis  who tested CyFi-MAP and provided valuable suggestions. Work in MDA lab is supported by  UIDB/04046/2020  and  UIDP/04046/2020  centre grants (to BioISI) from  FCT/MCTES Portugal. CP was recipient of fellowship  SFRH/PD/BD/131405/2017  and through funding of  LASIGE Research Unit, ref. UIDB/00408/2020  and ref. UIDP/00408/2020. AM and IB were supported in part by the  Innovative Medicines Initiative Joint Undertaking  under Grant Agreement No. IMI 115446  (eTRIKS), resources of which are composed of financial contributions from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies.
This study presents a methodology for solving the classical 1D (one-dimensional) HAM (heat, air, and moisture) hygrothermal transfer model with an implementation in MATLAB. The resolution uses a discretization of the problem according to the finite-element method. The detailed solution has been tested on a plant-based concrete. The energy and mass balances are expressed using measurable transfer quantities (temperature, water content, vapor pressure, etc. ) and coefficients expressly related to the macroscopic properties of the plant-based concrete (thermal conductivity, specific heat, water vapor permeability, etc. ), determined experimentally. To ensure this approach is effective, the methodology is validated on a test case. The results show that the methodology is robust in handling a rationalization of the model whose parameters are not ranked and not studied by their degree of importance.
The study showed that the effect of natural intercellular convection on the thermal resistance of hollow structures cannot be ignored. The FLUENT CFD software was used to solve the coupled equations. Various approaches in the literature address coupled HAM transport. Dos Santos and  Mendes (2008)   [13]  proposed a mathematical model considering the combined twodimensional transport of heat, air, and moisture through unsaturated hollow building bricks. The differential equations are based on the driving potentials of temperature, moist air pressure and water vapor pressure gradients. The discretized equations are solved using the MTDMA (multitridiagonal-matrix algorithm). Li et al. (2009)   [14]  developed a combined model of heat, air, and moisture in building envelopes (HAM-BE) by using COMSOL software to solve the governing partial differential equations.
The equations are simultaneously solved using the MTDMA. Considering coupled HAM transfer problems, the biggest difference between the simulated models is the choice of solving methodology for the HAM model. In the previous literature, there has been no fully virtual evaluation of the hygrothermal behavior of porous building material. Most simulation tools act as black boxes, including a library of predefined blocks to be used to construct and simulate the models. The disadvantage of these methods is that, often, they are quite inflexible in terms of changing or combining models. For this reason, in order to improve the simulation performance, this article presents the finite element method implemented on MATLAB software to solve the HAM hygrothermal transfer model. This software provides high-level visualization of parameters, good algorithm development and a wide range of programming freedom.
The objective of this article is to study and analyze the hygrothermal behavior of hemp concrete. In order to do this, it is necessary to consider coupled heat, air, and mass transfers throughout all components of the envelope. The model considers the temperature, the total pressure, and the water vapor pressure gradients as driving potentials. Based on this model, a one-dimensional numerical simulation is carried out. The results have been simultaneously obtained using MATLAB. Numerical validation of this method is presented, based on experimental results and published numerical benchmarks. The MATLAB model offers more programming freedom and is thus more closely suited to bio-based material studies. In fact, the hygrothermal properties of hemp concrete are not constant; they vary depending on several factors such as the evolution of water content within the wall.
MATLAB Numerical Simulation Tool The complexity of the coupled thermo-aeraulic transfer equation system makes it impossible to solve analytically. Therefore, numerical resolution tools must be used. Simulation tools differ depending on the resolution method and the final objective. However, the HAM model was first implemented on COMSOL; since the form of the equations and the algorithm of resolution are predefined, we merely need to input the coefficients of the model. However, because COMSOL presents it as a black box, it is far from easy to intervene in the resolution process. On the other hand, MATLAB allows better visualization of the parameters and offers greater programming freedom for algorithm development.
T P } -[K LP ]{T P (t)}-[K LL ]{T L (t)}] (29) If the boundary conditions are constant, {. T P } = 0. After imposing an initial condition in temperature type {T L (t = 0)}, we can then calculate the {∆T L } for each time step and then work back to the value of {T L } n+1. Through this approach, we rewrite the HAM equations in MATLAB as follows: {∆T}[[C LL1 ] + ∆tθ[K LL1 ]] = ∆t[-[K LP1 ]{T P (t)}-[K LL1 ]{T L (t)}-[K LP2 ]{Pv P (t)} -[K LL2 ]{Pv L (t)}-[K LP3 ]{P P (t)}-[K LL3 ]{P L (t)} +[C LL1 ]{∆ Pv L }/∆t] (30) {T L } n+1 = {T L } n + {∆T L } (31) {∆Pv L }[[C LL3 ] + ∆tθ[K LL4 ]] = ∆t[-[K LP4 ]{Pv P (t)}-[K LL4 ]{Pv L (t)}-[K LP6 ]{T P (t)} -[K LL6 ]{T L (t)}-[K LP5 ]{P P (t)}-[K LL5 ]{P L (t)}] (32) {Pv L } n+1 = {Pv L } n + {∆Pv L } (33) {∆P L }[[C LL4 ] + ∆tθ[K LL5 ]] = ∆t[-[K LP5 ]{P P (t)}-[K LL5 ]{P L (t)}] (34) {P L } n+1 = {P L } n + {∆P L } (35) The algorithm in Figure  3  explains the overall resolution process.
Validation of the Chosen Heat, Air and Moisture (HAM) Model by the MATLAB Code In order to confidently use this model of prediction of hygrothermal transfers, it has been validated both analytically and experimentally. The benefit of this validation is to verify the accuracy of the numerical resolution offered by MATLAB. The results of the HAM model implemented in the MATLAB code are compared with the numerical results obtained by other HAM models, and with experimental results, based on an international benchmark HAMSTAD WP2  [25]. This test consists of determining the spatial distribution of the water content after 100, 300, and 1000 h. Initially, the 20 cm-thick wall is at equilibrium, with a relative humidity of 95% and a temperature of 20 °C. Maintaining the isothermal conditions, the wall is subjected to hydric stresses on both sides.
Validation of the Chosen Heat, Air and Moisture (HAM) Model by the MATLAB Code In order to confidently use this model of prediction of hygrothermal transfers, it has been validated both analytically and experimentally. The benefit of this validation is to verify the accuracy of the numerical resolution offered by MATLAB. The results of the HAM model implemented in the MATLAB code are compared with the numerical results obtained by other HAM models, and with experimental results, based on an international benchmark HAMSTAD WP2  [25]. This test consists of determining the spatial distribution of the water content after 100, 300, and 1000 h. Initially, the 20 cm-thick wall is at equilibrium, with a relative humidity of 95% and a temperature of 20 • C. Maintaining the isothermal conditions, the wall is subjected to hydric stresses on both sides.
The input files and results for each exercise in the international HAMSTAD WP2 benchmark are available. Each file contains folders specifying the material properties and boundary conditions. In one case (exercise 2), the analytical solution is known and the parameters adopted in Table  1  are extracted from this test in order to validate the HAM model based on the MATLAB finite-element method. Table  1. Simulation input parameters for the N • 2 HAMSTAD WP2 test  [25]. The results of the chosen HAM model presented in Figure  5  by the dashed lines, at time 100 h and 300 h, exhibit satisfactory agreement with the numerical results of the international benchmark HAMSTAD WP2  [25]  (shown by the solid line), except the difference at time 1000 h, which is due to an underestimation of the hydric storage capacity.
Thickness For the experimental validation, we looked at the work of Oumeziane  [24]. First, we present the study configuration in Figure  6 -A 30 cm-thick hemp-concrete single-layer wall maintained initially at 23 • C and 40% RH, for 20 days. Therefore, the indoor conditions remain stable at 23 • C and 40% RH. The outdoor relative humidity set points are raised to 80%, but decrease sharply after 10 days to 30% RH. The temperature is always maintained at 23 • C. Note that the conditions throughout the campaign reflect recorded real-world atmospheric conditions, which oscillate significantly around the setpoint value. However, the numerical simulation conducted with the HAM model on MATLAB respects the exact set points shown in Figure  6. The study configuration proposed by  [24]. By examining this humidity record, we are able to observe the behavior of hemp concrete subjected to solicitation in the adsorption and desorption phases.
In the numerical approach, the Dirichlet boundary conditions are used. Hence, this approach does not take account of the surface exchanges or the quantities of heat and moisture actually entering and leaving the wall. Thus, it provides only a partial view of the hygrothermal behavior of the wall. This may also be due to an underestimation of the storage capacity in the desorption phase. The study configuration proposed by  [24]. By examining this humidity record, we are able to observe the behavior of hemp concrete subjected to solicitation in the adsorption and desorption phases. Figure  7  presents the comparison of the numerical results obtained by the HAM model, computed by the finite element method on MATLAB, with the experimental results from the literature  [24]. The numerical results show close agreement with the T and RH profiles obtained experimentally, during the adsorption phase.
This may also be due to an underestimation of the storage capacity in the desorption phase. The study configuration proposed by  [24]. By examining this humidity record, we are able to observe the behavior of hemp concrete subjected to solicitation in the adsorption and desorption phases. Figure  7  presents the comparison of the numerical results obtained by the HAM model, computed by the finite element method on MATLAB, with the experimental results from the literature  [24]. The numerical results show close agreement with the T and RH profiles obtained experimentally, during the adsorption phase. It seems, however, that the evolution of temperature and relative humidity during the desorption phase is less well reproduced than during the adsorption phase. Concerning the temperature distributions, a phase shift between numerical and experimental results is evident at all positions within the wall.
This increase in temperature might be due to an underestimation of thermal dispersion with the outer atmosphere, leading to fluctuations in the evolution of RH within the wall due to the phase change effect. In the numerical approach, the Dirichlet boundary conditions are used. Hence, this approach does not take account of the surface exchanges or the quantities of heat and moisture actually entering and leaving the wall. Thus, it provides only a partial view of the hygrothermal behavior of the wall. This may also be due to an underestimation of the storage capacity in the desorption phase. In conclusion, the HAM model described by the finite element method on MATLAB is able to predict the hygrothermal behavior of a hygroscopic wall subjected to stresses of T and RH on both sides. In conclusion, the HAM model described by the finite element method on MATLAB is able to predict the hygrothermal behavior of a hygroscopic wall subjected to stresses of T and RH on both sides.
Conclusions Coupled HAM transfer through a porous building envelope has a major influence on energy consumption and indoor hygrothermal behavior. A literature review revealed divergences as to the numerical methods used to solve the classical hygrothermal transfer model. In addition, most programs do not offer full freedom of programming for model representation. In response to this situation, the current paper presented a phenomenological formulation for simulating HAM transfers through hemp concrete, considering temperature, water content, and total pressure gradient as driving potentials. The three non-linear coupled equations of the macroscopic model are based on the microscopic equations of mass and energy conservation. The discretized equations have been solved by the finite-element method using MATLAB.
The numerical results show close agreement regarding the variation of the temperature and relative humidity profiles obtained experimentally, during the adsorption phase. It seems, however, that the evolution of temperature and relative humidity during the desorption phase is less well reproduced than that during the adsorption phase. This numerical simulation tool differs from others in that it offers a significant degree of programming freedom, in order to create a virtual representation of the model and achieve acceptable performance. The model exhibits good concordance with the one-dimensional numerical benchmark case No. 2 from the literature, and the experimental results obtained by Oumeziane  [24]. This agreement demonstrates that the model implemented here is pertinent, and that the numerical resolution software (MATLAB) is efficient.
Qualitative Methods of Comparison Qualitative approaches are based on some comparison criteria in order to see the ability of software to meet these criteria. Ejercito et al. (Ejercito et al. , 2017 ) performed a comparison of traffic simulators in order to choose the most reliable and suitable to allow researchers to simulate road traffic in the EDSA (Epifanio de los Santos Avenue) in Manila (Philippines). The simulators studied were MATSim, SUMO, AIMSUN, and PTV VISSIM. Several criteria were considered in this work such as nature of software (e. g. , free, opensource, commercial), portability operating systems, creation of road traffic networks and associated vehicle models, and graphical simulation and quality of graphical representation. The authors only focus on how the different functionalities in the simulators are defined based on the comparison criteria.
Therefore, conclusions drawn by the authors are not precise enough to guide the choice of a given modeler in the selection of a traffic simulator for another specific case. Saidallah et al. (Saidallah et al. , 2016)  and Ghariani et al. (Ghariani et al. , 2014)  start with a presentation of the simulators studied by highlighting their different characteristics. Then, they perform a comparative study (still qualitative) based on a set of selected criteria such as general characteristics (e. g. , software category), integration of transit components (e. g. , roads, stops), and simulation models (microscopic or macroscopic). The work in  (Ghariani et al. , 2014)  focused on the study of seven simulators (SUMO, TRANSMIS, ARCHISIM, AIMSUN, Paramics, VISSIM, and CORSIM) according to their ability to simulate public transport.
The study carried out in  (Saidallah et al. , 2016)  focused on four simulators (MATSim, SimTraffic, MITSIMLab, and TransModeler) in addition to the seven previously mentioned. The qualitative comparison framework proposed by these authors  (Saidallah et al. , 2016; Ghariani et al. , 2014)  is quite explicit. Through the summary table, one is briefed about the functionalities that simulators can satisfy. However, this information is only limited to the functionalities defined by the authors. Moreover, some features have not been studied such as portability, generation of traffic demand, and software scalability. The main drawback of the above-mentioned approaches (qualitative) is that they are subjective since the evaluation criteria are too specific in most cases to the project or to the modelers (authors).
Studied Traffic Simulators Several simulation software have been considered to test the comparison approach. They can be organized into two groups defined as follows: 1. Microscopic Agent-based Road Transport Simulators. These are specific simulation platforms (intended only) for road traffic. The simulators chosen are: MATSim (Multi-Agent Transport Simulation Toolkit) -Version 0. 10. 1  (Horni et al. , 2016) , SUMO (Simulation of Urban Mobility) -Version 1. 0. 1  (Lopez et al. , 2018) , Aimsun Next (Advanced Interactive Microscopic Simulator for Urban and Nonurban networks) -Version 8. 1. 4 3 , and PTV Vissim (Planung Transport Verkehr AG Verkehr In Städten -SIMulationsmodell) -Version 10 4. 2. Generic Multi-Agent System (MAS) Simulators. Being generic, they can be adapted for modeling several systems including road traffic.
Short Listing of the Solutions Table  7  proposes an overview through the five categories. It makes it clear that no simulator is ideal to meet all the needs and requirements of the modeler. The simulators dedicated to traffic, outperform our test with a score of up to 7. 4. The delimitation between commercial and OpenSource software is also clear. Commercial software are well integrated into the GIS solution to build and set up simulation and provide qualitative GUI and simulation view. However, those solutions remain obscure considering the possibility to enter into the simulation source code and provide customized behaviors and simulation. The simulators chosen to be more deeply investigated by implementing intermodality scenarios are MATSim and SUMO. For this work, we were interested in the intermodal routing module in particular in the cost function of the modal choice (intermodal) implemented by these two simulators.
Traffic Simulation Scenario Generation Framework The use of both SUMO and MATsim relies on several tools to generate simulation configurations from classical data format. Figures  1  and 2  present a global framework of the generation of a traffic scenario from OpenStreetMap (OSM) 10 and mobility data (usually the Households Travel Survey (HTS) and/or the Census of the population) in SUMO and MATSim respectively. The road network is generally produced from the OSM data of the study area extracted from OpenStreetMap. Moreover, it is possible to include GTFS 11 data, allowing a better description for the public transport supply in MATSim context. Details on the network generation tools are presented in the subsections dedicated to each simulator. 10 OpenStreetMap is an open data platform that provides map information of roads, trails, etc.
Availability of Transportation Modes The definition of the transportation modes available or accessible by the traveler is essential to properly model an intermodal transport supply. For example, it would not be possible to use a bicycle or a car and then take the train if the traveler does not know these modes. SUMO takes into account the transportation modes available in an intermodal trip through the modes attribute in the personTrip module, which will contain the name of these modes. For example, modes="car public" means that the agent can use the car, or public transport or a combination of both. Walking is assumed to be available to all agents while the other modes need to be supplied explicitly.