dataesr/scientific-paragraphs-categorization huggingface dataset:
Among the applications of the multiscale modelling approach in nuclear fuel rod performance, the coupling of integral thermo-mechanical fuel performance codes with lower-length meso-scale modules is of great interest. This strategy allows to overcome correlation-based approaches with mechanistic ones and test their application in accidental conditions. In this work, we explore the coupling between the TRANSURANUS fuel performance code and two meso-scale modules for fission gas/product behaviour: MFPR-F and SCIANTIX. These modules, coupled within TRANSURANUS, are assessed against the IFA-650. 10 loss-of-coolant accident test to analyse their overall impact and highlight future developments toward mechanistic modelling of fission gas during accident scenarios.
Introduction Thanks to the continuously increasing soft-and hardware developments, in combination with the growing availability of more and better (i. e. , more detailed) experimental data  (Cappia et al. , 2022) , highfidelity simulations are becoming mainstream in many fields. In the analysis of the nuclear fuel rod behaviour, attention has been given to the development of multi-scale and multi-physics simulation tools through coupling codes that operate at different length. Conventional fuel performance codes (FPCs), such as the TRANSURANUS code developed at Joint Research Center (JRC) in Karlsruhe  (Lassmann, 1992; Magni et al. , 2021)  or the FRAPCON and FRAPTRAN codes developed at the Pacific Northwest National Laboratory (PNNL) (FRAPTRAN;  FRAPCON-4. 0: 2015) , exploit several models and correlations to describe the complex behaviour of the whole fuel rod in an efficient way from the scale of the fuel and cladding microstructure to the engineering scale of the fuel rod.
e. , Politecnico of Milano (POLIMI) and Institut de Radioprotection et Sûreté Nucléaire (IRSN), respectively. The mechanistic codes for fission gas and product behaviour SCIANTIX and MFPR-F are designed for coupling with integral thermo-mechanics FPCs. For example, the MFPR code (from which MFPR-F is derived) has been coupled with various tools in the SFPR and BERKUT modules  (Veshchunov et al. , 2015; Veshchunov et al. , 2013) , which in turn is used by the EUCLID/V1 FPC for fast reactors  (Veprev et al. , 2018). Recently, the SCIANTIX code has been coupled with the GERMINAL code  (Lainet et al. , 2019) , the TRANSURANUS code  (Zullo et al. , 2022; Magni et al. , 2022)  for simulating FBR MOX fuel in the frame of the INSPYRE project  (Magni et al. , 2022). In the current work, we test the coupling of TRANSURANUS both with SCIANTIX and the MFPR-F code for simulating LOCA tests of pre-irradiated fuel in a commercial light water reactors (LWRs).
, 2020; Khvostov, 2020; Chung et al. ; NEA, 2016). Section 2 introduces the adopted simulations tools as well as their coupling. In Section 3 we apply the coupled code system to one LOCA experiment from the recent coordinated research project Fuel Modelling under accident conditions (FUMAC) of the IAEA (FUMAC-TECDOC;  Veshchunov et al. , 2018) , and discuss the outcomes. In Section 4, we draw the conclusions of this work and outline perspectives for further development and application of the new high-fidelity tools. Lastly, in the Appendix we include a list of the code subroutines that have been modified for the code coupling and is therefore available for the user community of the TRANSURANUS fuel performance code.
The TRANSURANUS code The simulation of a single fuel rod in this work relies on the TRANSURANUS code that was originally programmed in Fortran77  (Lassmann, 1992)  and has been rewritten in modern Fortran for code coupling  (García et al. , 2020; García et al. , 2021). The fuel rod performance code has been extended to deal with loss of coolant accident conditions  (Van Uffelen et al. , 2008) , and has gradually benefitted from the inclusion of more mechanistic models for fission product behaviour  (Pastore et al. , 2013; Pastore, 2012). The latter provided the code with a mechanistic treatment of fission gas atoms at the (spherical) grain boundaries (contained in the FISPRO2 model of the code  (Pastore et al. , 2013; Pastore, 2012) ), in addition to the standard model for the fission gas behaviour based on the conventional saturation concentration of gases at grain boundaries  (White and Tucker, 1983; Forsberg and Massih, 1985; Forsberg and Massih, 1985)  (contained in the FISPRO subroutines of the code  (Lassmann et al.
, 2014). The mechanistic treatment  (Pastore et al. , 2013; Pastore, 2012)  takes into consideration bubble growth and subsequent interconnection to form tunnel-like networks for release of fission gas to the free volume in the fuel rod, preserving the spherical geometry of the fuel grain  (White, 2004). In parallel to these developments for the TRANSURANUS code, independent tools for a more detailed and comprehensive description of the fission product behaviour were developed by partner organisations. They are briefly outlined in the next two sections, along with a more detailed description of the interface applied for their coupling with the TRANSURANUS code that eventually allows to simulate the base irradiation of a nuclear fuel rod in a commercial nuclear power plant (NPP), followed by a test irradiation in an experimental device in a single run.
The physical phenomena governing the behaviour of the nuclear fuel pin under irradiation are thus included in the third level of the overall thermo-mechanical analysis and encompass a wide set of interrelated processes driven by the local temperature, fission rate density, and applied stress. In the third level of the code, the specific call is thus also made to the fission product behaviour model (FISPRO), as illustrated in Fig. 2. For the application of different fission gas behaviour models, the TRANSURANUS code structure thus lends itself perfectly for coupling with an external model. The coupling with the MFPR-F code of IRSN or SCIANTIX of POLIMI serve as good examples.
The MFPR-F code and its plugin for TRANSURANUS The MFPR-F code of IRSN is derived from the MFPR code developed by Veshchunov and coworkers  (Veshchunov et al. , 2006). The code is programmed in modern Fortran, and currently for the coupling with TRANSURANUS it describes the behaviour of fission products, fuel thermochemistry as well as the formation and behaviour of point (vacancies and interstitials) and extended (interstitial dislocation loops and vacancy clusters) defects at each node of the fuel mesh. The validation database of the MFPR-F code  (Veshchunov et al. , 2006; Veshchunov et al. , 2007; Veshchunov, 2000; Veshchunov and Shestak, 2008)  contains separate-effects tests for validation of the individual models, as well as integral tests for LOCA  (Pontillon, et al. , 2004)  and severe accidents  (Ducros et al.
, 2013). For the sake of the coupling with TRANSURANUS, a particular application program interface (API) was developed for use of MFPR-F as alternative to the recommended TRANSURANUS standard model  (Lassmann et al. , 2014) , the TRANSURANUS mechanistic model  (Pastore et al. , 2013; Pastore, 2012) , or the SCIANTIX code  (Pizzocri et al. , 2020) , as it is sketched in Fig. 2. The API permits TRANSURANUS to interface with MFPR-F for initialization, data transfer and to run the MFPR-F driver, without directly accessing MFPR-F internal routines. In doing so, a strict separation between the codes is preserved, thus avoiding any overlapping between them, which might alter future developments. To complement the API, a Fortran module was defined that declares common variables and acts as a buffer to transfer data, such as temperature and FP content, from TRANSURANUS variables to MFPR-F variables and vice versa.
The SCIANTIX code and its plugin for TRANSURANUS SCIANTIX is a 0-D open-source computer code designed to simulate the fission gas behaviour in nuclear fuels (e. g. , UO 2 or MOX), at the scale of the fuel grains  (Pizzocri et al. , 2020). The code can operate both as a stand-alone computer program and coupled to integral thermomechanical FPCs, if a suitable code interface is provided. SCIANTIX has been coupled with TRANSURANUS  (Zullo et al. , 2022; Van Uffelen, 2020) , GERMINAL  (Magni et al. , 2022)  and OFFBEAT  (Scolaro et al. , 2022). The SCIANTIX validation database includes both separate-effect and integral irradiation experiments. In its standalone version, SCIANTIX physics-based models for inert gas behaviour have been validated against separate-effect experiments for intragranular and intergranular gaseous swelling  (Pizzocri et al.
, 2022; Scolaro et al. , 2022; Zullo et al. , 2022; Van Uffelen, 2020). This API exploits Fortran and C++ standard intrinsic modules widely adopted scientific computation  (Chapman, 2017) , to ensure the interoperability between TRANSURANUS (Fortran) and SCIANTIX (C++). To minimize the effect on the existing structure of the TRANSURANUS code, and in line with the strategy adopted for the coupling with MFPR-F outlined above, TRANSURANUS interacts with SCIANTIX for initialization, data transfer and code execution, without directly accessing SCIANTIX internal routines, to avoid code overlaps that could interfere with future developments. Moreover, a second Fortran module was defined to declare variables common to TRANS-URANUS and SCIANTIX, such as irradiation history variables (i. e. , local temperature, fission rate density, hydrostatic stress, local burnup, and time step).
Simulation of IFA-650. 10 LOCA test with the new coupled code systems In this section, the Halden IFA-650. 10 LOCA experiment is described. Afterwards, results of the simulations performed with the TRANS-URANUS code, coupled with SCIANTIX and MFPR-F, are presented. The IFA-650. 10, from the international benchmark FUMAC organised by the IAEA, has been selected as representative benchmark case. It represents a case that can be readily simulated with the current versions (and all the implemented features) of both SCIANTIX and MFPR-F, coupled with TRANSURANUS, without the need to consider complicating factors related to fuel relocation (such as in IFA-650. 9). The case is thus a reasonable starting point to add functionalities to simulate cases where FG behaviour is manifestly appreciable, e.
The amount of gas released affects the cladding expansion and the volume available for the new gas composition. The inner volume of the fuel rod being larger for TRANSURANUS//SCIANTIX, the initial gap Fig. 3. Input linear heat rate (blue line) and calculated average fuel temperature (red line), of the IFA-650. 10 base irradiation phase, simulated with the TRANS-URANUS code (FUMAC-TECDOC). pressure is lower, and the same holds for TRANSURANUS and TRANSURANUS//MFPR-F. Details of the integral results obtained at the end of the base irradiation with the three codes are shown in Table  2. The meso-scale codes SCIANTIX and MFPR-F do not significantly affect the burst time predicted by the TRANSURANUS standalone code, i. e. , 265 s after the start of the blowdown, whereas the experimental data was at 249 s.
, 2015). Fig. 6  shows the cladding profilometry at end of the LOCA transient, compared with the experimental data from PIE. The standalone TRANSURANUS versions describe the single ballooning, but its axial location is higher than the experimental one, as it was obtained in the FUMAC project. Discrepancies with the measured cladding profilometry are ascribable to the 1. 5D discretization of the fuel rod, which poorly reproduce axial phenomena, to uncertainties in thermal boundary conditions applied to the outer surface of the cladding, derived from the SOCRAT code (FUMAC-TECDOC;  Kiselev, 2016) , and lastly due to modelling choices, e. g. , use of the small strain approximation. As in the previous discussion regarding the predicted gap pressure, the calculated outer cladding is not appreciably affected by the FGR predictions of both SCIANTIX and MFPR-F.
Conclusions and future perspectives This work constitutes a first step towards improving the predictions of FGR from nuclear fuels under LOCA accident conditions, by exploiting the conventional FPC TRANSURANUS coupled with mechanistic mesoscale modules MFPR-F and SCIANTIX. The main outcomes of this work can be summarised as follows: • The coupling between TRANSURANUS and the mechanistic mesoscale modules for FG/FP behaviour SCIANTIX and MFPR-F has been pursued and assessed against the experimental test IFA-650. 10, representative of a LOCA scenario. • The calculations of the standalone TRANSURANUS version are coherent with calculations of TRANSURANUS coupled with the meso-scale modules, for IFA-650. 10. In other words, the meso-scale codes SCIANTIX and MFPR-F do not alter the TRANSURANUS cladding failure predictions, in line with the outcome of the FUMAC project for the IFA-650.
Acknowledgements This project has received funding from the  Euratom research and training programme  through the  R2CA Project  under Grant Agreement no  847656. Views and opinions expressed in this paper reflect only the author's view and the Commission is not responsible for any use that may be made of the information it contains. The authors gratefully acknowledge  Tommaso Barani  for his valuable contribution to the initial coupling of TRANSURANUS with SCIANTIX, as well as  Tsvetoslav Pavlov  for his contribution to the coupling of TRANSURANUS with MFPR-F.
Simulations Unless specified otherwise, the simulations correspond to 200 replicates per scenario, a population size of 1000 individuals and a sample size of 30 individuals. Genomes were made of one chromosome of 10 7 bp in length, with a recombination rate and mutation rate of 10 -8 per bp, which are commonly used parameters in human population modeling. We used the geometric-like model (b = 1) since  Austerlitz and Heyer (1998)  showed it was more realistic than the Poisson-like model (b = ∞) in the population of Saguenay-Lac-Saint-Jean where CTRS is documented from pedigree datasets. Coalescent trees are built in two steps: (1) forward-in-time simulations using our model implemented in SLiM  (Haller and Messer, 2019)  starting before the beginning of CTRS, resulting in trees that did not fully coalesce when the CTRS period is short, (2) a backward neutral coalescent process in order to complete the trees from the first step (i.
Assessing demography inference bias To assess the bias in SFS-based demography inference, we used the software δaδi  (Gutenkunst et al. , 2009)  with a one-event model. Two scenarios were studied: (1) a sudden fivefold expansion in population size that occurred 280 generations before a short period of CTRS (20 generations); and (2) a sudden fivefold expansion in population size that occurred during CTRS, after the first 1200 generations of a 1500-generations period of CTRS (Figure  1 ). We chose a fivefold sudden expansion as a simple illustration of a demographic event, which has the advantage of mimicking the past Neolithic expansion in human population history. From 30 diploid individuals sampled 300 generations after the demographic event, we inferred two parameters: the growth factor (expected value of 5) of the population and the number of generations since the event (expected value of 300 generations).
Conclusions Many studies evaluating CTRS strength in human populations rely on the computation of correlations between parents and children progeny size from pedigree datasets  (Murphy, 1999). However, we show here that this measure cannot by itself account for the magnitude of CTRS effects on population genetics. Indeed, under the high variance in progeny size model (b = 1), correlations are lower than under the low variance model (b = ∞), while the impacts on population genetics are increased. Thus, a more precise evaluation of CTRS from pedigree data would require considering the distributions of parents and child progeny sizes in addition to the correlation values. Furthermore, the higher correlations under the low variance model (b = ∞) could explain the higher correlations observed in populations that exhibited a demographic transition  (Murphy, 1999; Jennings et al.
, 2018) , population structure  (Mazet et al. , 2016) , selection, gene conversion, and biased sampling in microbial populations  (Lapierre et al. , 2016) ). To disentangle the effects of demographic events from CTRS, imbalance indices that are unaffected by variations in the census population size can be used. We showed that the power of detection of CTRS from genomic data is less impacted by the number of independent regions than by the number of sequenced individuals that should be high enough, a condition easily achieved with modern datasets. However, these indices are computed from coalescent trees which first need to be reconstructed from genomic data (e. g. , using tools such as ARGweaver  (Rasmussen et al. , 2014) , tsinfer  (Kelleher et al. , 2019) , or relate  (Speidel et al. , 2019) ).
This tree reconstruction step might not be able to infer a perfectly accurate topology, yielding potential biases in the estimated (im)balance indices. Moreover, in addition to the expected imprecision of the reconstruction of neutral trees, the behavior of these tools under CTRS remains to be checked. Another possibility would be to build and train deep learning networks directly on raw genomic data without reconstructing coalescent trees, as in Sanchez et al. (2021), which would prevent the introduction of biases due to tree reconstruction, but might require a larger amount of simulated data for training. To generate this large dataset, it would be useful to develop a backward coalescent model of CTRS, as forward-in-time simulations are particularly time-consuming. Finally, we should address the question of the similarity between CTRS and natural selection: in both cases, some individuals have more offspring than others and transmit this higher fertility to their descendants.
Fermentation-based system -SHIME® Fermentation-based systems are powerful in vitro tools mimicking the human gut properties. They consist in a single static  [64]  or multistage fermentation models  [65] [66] [67]. Continuous fed-batch models have already been used to demonstrate the anti-C. albicans activity of Lactobacillus plantarum  [65, 66]. The SHIME® (Simulator of Human Intestinal Microbial Ecosystem) is a multicompartment semicontinuous fed-batch system, originally developed in Ghent University and currently further developed and commercialized by the company ProDigest, that simulates the different sections of the GI tract from the stomach or small intestine to the distal colon  [68]. It can also include a mucosal compartment  [7, 68]  and a host-microbiota interaction module that allows coculturing complex bacterial communities with a monolayer of enterocyte human cells  [69].
Machine learning could also be used to search for fungal species with a potential antagonistic activity against C. albicans. Indeed, due to the low diversity and high variability of the fungal microbiota  [6, 81] , and the fact that most fungal species are likely not to be true colonizers of the human gut but rather transient species introduced by food and/or the environment  [82, 83] , it can be tedious to identify fungi that are strongly negatively associated with C. albicans abundance in the gut. However, by employing machine learning, bias-inducing parameters such as the diet of the subjects could be considered, thus increasing the likelihood of identifying true fungal signatures. However, OMICs-based approaches, especially if they rely on complex algorithms, require large datasets, which are often costly and difficult to obtain.
This is why we need to develop public databases of human microbiota and mycobiota, which could limit the risk of identifying cohort-or study-specific anti-C. albicans bacterial signatures. Overall, although the combination of optimized in silico pipelines and experimental procedures could allow the identification of additional antifungal species, the current research has already identified dozens of bacterial species in silico with a potential anti-C. albicans activity, and several have been validated in vitro. While additional validations need to be performed to confirm the antifungal properties of these species, these discoveries might open the way to the development of a consortium of bacteria that would allow the recovery of a microbiota limiting C. albicans overgrowth, thus preventing the emergence or recurrence of vulvovaginal candidiasis, or life-threatening systemic infections.
Outstanding questions How can machine-learning approaches be used to extend our understanding of the interactions between the microbiota and C. albicans? How can OMICs-based strategies be combined to machine-learning approaches to develop a consortium of bacteria able to curb C. albicans growth in the intestinal and vaginal niches? Can OMICs-based strategies be adapted to identify bacterial species with an antagonistic activity against other fungal species, notably in the context of invasive infections or of fungi-associated cancers? What will be the role of microbiota data contained in public databases in the context of the identification of antifungal microbial species and compounds? How to develop an ex vivo or in vitro model that mimics the key characteristics of the human gut, including host immune response and oxygen gradient?.
EXPERIMENTAL RESULTS We implemented the proposed approaches on top of Minisat  (Eén and Sörensson, 2003). We disabled database simplification to keep the original clauses during the whole search. We also disabled restarts which build a sequence of search trees. For the DPLL approach, we also disabled clause learning and conflict analysis. The latter is replaced by a dedicated procedure used to collect the sources. The activity of a variable, which is updated for each new learned clause in classical CDCL solver, is updated each time a new clause is added to the sources in our context. For the CDCL approach, we have both the conflict analysis procedure and the sources computation procedure (the heuristics and clause learning is unchanged compared to Minisat). The Glasgow Subgraph Solver (GSS for short)  (McCreesh et al.
Our findings revealed a relationship between the average thickness of the left and right second transverse temporal gyrus (TTG) and participants' language experience. Model comparisons demonstrated that the inclusion of phoneme-level information in the language experience index accounted for the greatest variance in average TTG thickness (bilaterally). The direction of this effect was negative, indicating that a more extensive and more phonologically diverse language experience was associated with thinner cortices in the second TTG. Across two independent datasets, we provide robust evidence linking the degree of language experience and typological distance between languages to cortical thickness in early auditory brain regions. We propose that this phenomenon may reflect experiencedriven pruning and neural efficiency, warranting further investigation through longitudinal studies of language acquisition.
Given our focus on auditory brain regions, we quantified how the languages spoken by the participants differed from each other in terms of their phonological systems. Specifically, we investigated whether the neuroanatomical indices describing the auditory cortex regions were related to cross-linguistic phonological information at different levels: acoustic and articulatory feature-level, phoneme-level, or (more abstract) counts of phonological classes. To investigate the relationship between the individual variability in the morphology of the auditory cortex and variability in language experience, we analyzed the anatomical brain scans of 204 healthy, right-handed participants from the PLORAS database  (Seghier et al. , 2016). The sample was split into two groups according to the date of data acquisition.