dataesr/scientific-paragraphs-categorization huggingface dataset:
𝐶 = 𝐹 × 𝐷 × 𝑆 (43) Based on the indication of the environmental elements that stimulates each failure type as shown in Table  I , different values of the criticality coefficient were defined based on the weather data. The environmental data was explored by assigning a value between 0 and 5 to each element according to its real value. The mathematical expression of the criticality coefficient as a function of the weather elements was established as in (  46 ) and shown in figure  4. The idea is to amplify the degeneration of reliability if the environmental conditions were more critical and recompense if otherwise. () =     =   0, +∑  ,   (44) Where:   = ( 0, ,  1, ,  2, ,  3, ,  4, ) is the vector of parameters of   () Equation (  46 ) is reconstructed as (47) where the criticality coefficient   () is defined as: 𝑐 𝑗 (𝑍) = 𝐶 𝑗 (𝑍) 𝐶 0,𝑗 = 𝑒 ∑ 𝛽 𝑖,𝑗 •𝑍 𝑖 𝐶 0,𝑗 (45) where  0, is considered the constant of criticality for each component.
Numerical Example Following the numerical optimization procedure based on the algorithm we developed for a finite number of maintenance actions, we propose the following numerical example whose values are arbitrarily chosen. The constant failure rates of the PV system components and subcomponents are tabulated in table 2. A PV system consisting of 3 PV modules connected in series, the output is connected to 2 inverters via DC cables. The inverter output is transmitted to the load via 2 AC cables as well. The reparation rate for each component and the constant corrective maintenance time is also tabulated in table  3. Considering the environmental condition influencing the components/subcomponents, the criticality coefficient data is given in appendix 1 while the corresponding represented values were shown in Figure  4.
Data extraction Data were extracted from the medical file records of each patient included. Co-medications given to the patient within 1 month before and after the first administration of ICI were assessed according to a predefined list of medications known to impact gut microbiota: antibiotics (including class, duration, route of administration, therapeutic or prophylactic use and hospitalisation), glucocorticoids (GCs; including dose, route of administration and indication), metformin and other oral antidiabetic drugs, insulin, PPIs, aspirin, NSAIDs, statins, angiotensin-converting enzyme (ACE) inhibitors/angiotensin II receptor blockers (ARBs), immunomodulators, psychotropic drugs, hormone replacement therapies and morphine. Known prognostic factors for melanoma (Eastern Cooperative Oncology Group (ECOG) performance status, BRAF status, Lactate dehydrogenase (LDH) and stage M1c), lung cancer (ECOG performance status, brain metastasis, the epidermal growth factor receptor (EGFR) status, smoking status and histology) and renal cell carcinoma (IMDC risk score) were collected.
Statistical analysis Qualitative data were analysed between groups using chi-square test or Fisher's exact test. Patients were considered as responders when complete response, partial response or stable disease was obtained and as non-responders in case of progressive disease. Survival data were analysed with univariable Cox regression and presented using KaplaneMeier's graphics. When patients received sequential ICI therapies, we considered the first ICI. Analyses were computed on STATA 13. 1 software (StataCorp LP, College Station, TX) with a statistically significant threshold of 0. 05. Graphs were constructed using GraphPad Prism software version 6. Considering the large number of factors susceptible to influence the treatment response, we performed a factor analysis of mixed data (FAMD), which is a principal component method balancing the influence of all the quantitative and qualitative variables in the construction phase of the dimensions of variability.
Our data on drugs combination are in accordance with such score, revealing the cumulative negative effect of multiple drugs exposure. Of note, the negative impact of concomitant administration of PPIs has been also outlined with other drugs than ICI such as pazopanib in soft-tissue sarcoma patients  [22]. On the other hand, we found that some comedications such as statins or ACE inhibitors/ARBs, clearly not impacted anti-tumoural efficacy of ICI, in accordance to Cortellini et al. [20] , whereas statins have been associated with improved clinical activity of ICI  [23, 24]  and ACE inhibitors with impaired outcomes  [25]. Psychotropic drugs are a heterogeneous group of medications that should be evaluated separately. The synergistic effect of metformin in combination with ICI has been previously suggested but not observed neither in our study nor in those of Omori et al.
The first in-human studies of faecal microbiota transplant (FMT) before anti-PD-1 rechallenge in refractory melanoma patients were recently published, inducing rapid changes in gut microbiota with clinical response in some patients  [30, 31]. They represent a proof-ofconcept that manipulation of gut microbiota may overcome resistance to anti-PD-1 treatment, and other clinical trials are ongoing (NCT04130763, NCT04729322, and NCT03341143). Probiotic therapy and high-fibre, plant-based diet are also studied, as more acceptable ways to induce favourable changes in gut microbiota to enhance ICI efficacy  [32, 33]. Finally, introducing or withdrawing co-medications according to their positive or negative impact on ICIs response may represent another feasible option in clinical practice, therefore subject of active research highlighted by the growing number of publications during the last 2 years.
Study design and selection of patients This retrospective study was performed in eight French referral hospitals. The medical files of all patients with AOSD suspicion between January 2000 and January 2018 were collected from the hospital databases. The project was approved by the institutional review board. The inclusion criteria were: (i) AOSD diagnosis on the basis of the Yamaguchi (3) and/or Fautrel (4) criteria and (ii) exclusion of all differential diagnoses, including infection, cancer or another type of inflammatory rheumatism. The exclusion criteria were: (i) early onset of the disease, before the age of 16 years and (ii) insufficient available medical data. The patients included in the study were classified into two groups: the AOSD+C group, which contained all patients presenting AOSD with cardiac involvement, and a control group of patients with AOSD but without cardiac involvement.
Published data for the use of TNFα inhibitors in cardiac impairment are very limited and not in favor of their use  (39, 40). None of the patients evaluated remotely by TTU in our study had any functional sequelae and no deaths related to cardiac complications were recorded. Our results are consistent with those of a previous review reporting a good short-term prognosis in 23 of 24 cases of myocarditis, in which only one death occurred after cardiogenic shock  (6). Both pharyngitis and cardiac involvements are more common in AOSD with systemic form with a high inflammatory blood level. Pharyngitis is an important early diagnostic symptom when the intensity of inflammation is high. Therefore, its association with cardiac damage was not surprising since patients with cardiac injuries are more inflammatory.
This new unit relies on physical models allowing the numerical estimation of decay heats, α doses and the mass of fission products and actinides, needed for the calculation of canister production. The first part of this paper describes the new models, based on neural networks trained on dedicated databases built with the SMURE package, see  [5]. The second part presents the CLASS developments carried out for the integration of the new simplified vitrification unit. The third and the fourth parts are dedicated to the scenario modelling with the CLASS code and the results concerning the canister production. Two main discussions are proposed: the first one develops the influence of spent fuel management, and more specifically the reprocessing priorities, while the second one focuses on canister production sensitivity to vitrification parameters.
Vitrified canister modelling The canister production related to waste vitrification depends on several properties of the materials to be vitrified. In this work, only three global quantities are considered: the decay heat released at the canister production time, the total number of α decay over 10 000 years per glass gram and the weight proportion of fission products and actinides in the canister. These data are not directly available in CLASS simulations as these focus on actinide inventories in each unit of the simulated nuclear fleet. To resolve that, new databases, dedicated to post-irradiation data, are built to calculate those three characteristics for material coming from reprocessed spent fuels. This section presents how the SMURE package, see  [5] , is used to produce data for the following PWR fuels: -UOX and ERU (Enriched Reprocessed U oxide fuel) respectively for enriched natural uranium oxide and enriched reprocessed uranium oxide, -MOX and MIX respectively for plutonium oxide coming from reprocessed UOX spent fuels mixed homogeneously with depleted uranium oxide and multi-recycled plutonium oxide (from UOX, MOX or MIX reprocessed spent fuels) mixed homogeneously with enriched uranium oxide.
It covers fuel discharged burn-up  from 0 to 55 GW d/t. ENDFB7. 1 library is used for the nuclear data. After the depletion calculations, post-irradiation data may also be calculated with SMURE. For instance, contributions of fission products and minor actinides to the decay heat of typical UOX and MOX spent fuels are presented in Figure  1  α activities of such spent fuels are presented in Figure  2. Vitrification modelling must not consider the total spent fuel to evaluate post-irradiation data as chemical separation is performed to recover plutonium and uranium. Very little data of industrial chemical separation efficiencies are available in the literature and for this paper, some approximations have been done. Considered separation efficiencies are presented in Table  2. Note that, the chosen separation efficiencies are lower than typical efficiencies achieved in industrial plant which may affect the estimated canister production.
Database construction To be flexible, only two databases are built: one for UOX and ERU fuel, one for MOX and MIX fuel; for each database calculation options are shared. The plutonium content and the enrichment are sampled as presented in Table  3. The sampling space is deliberately very large to ensure good accuracy of neural network predictions. Indeed, as those are interpolators, all the possibilities that may be simulated in further scenario studies have to be inside the training space. For this work, the reprocessing of very old UOX spent fuels as well as MIX fuels with a low cooling time are considered. Intervals for plutonium isotopic composition were inspired from  [10]. Respectively, 200 and 500 initial compositions for UOX/ERU and MOX/MIX databases are identified with the Latin Hyper-cube Sampling technique.
5 × 10 19 α disintegrations over 10 000 years per gram of glass and a limit of 18. 5 wt. % for the total weight proportion of nuclear wastes (fission products and actinides: FPA) in each canister. For each criterion, the number of canister is deduced by the increased ratio of the SMURE specific quantity and the chosen limit value. Figure  3  represents the canister production, while reprocessing 100 tons of all spent fuel compositions, calculated in both databases (coloured by the discharged burn-up) regarding two criteria: the heat at canister production and the integrated α dose over 10 000 years. In this figure, two different behaviours may be attributed to the UOX/ERU data and the MOX/MIX data. The dots related to the UOX/ERU data are indicated on the plot. Regarding those, decay heat is the main limit, besides, after several years of cooling, the mass criterion is always dominant for canister production, as shown in Figure  4  that presents the number of canister produced by the reprocessing of 100 tons of UOX/ERU regarding mass and heat criteria.
In the MIX/MOX data, there is a competition between decay heat and cumulative α dose, depending on reactor's burn-up, plutonium isotopic composition and spent fuel cooling time. The higher the cooling time is, the higher the 241 Am inventory is, and therefore, the higher the cumulative α dose becomes, while the lower the decay heat becomes. From the MOX/MIX database, it should be noticed that cumulative α dose is always the sizing criterion as soon as the cooling time exceeds 15 years. For lower cooling time, no strong correlations are observed. Finally, as the first limit reached is different for uranium fuel and plutonium fuel, this CSD-V production estimation, using the databases, suggests that some mix of UOX/ERU and MOX/MIX spent fuels in fuel cycle simulations can reduce the total amount of canister produced by optimising the containers' load.
Here, it is worth noting that those calculations are based on several hypotheses, which strongly limit the assessment of canister productions, specially the assembly depletion calculations and the chemical separation efficiencies (see Tab. 2). However, this work aims at studying, for different trajectories, the sensitivity of produced high-level wastes to fuel cycle parameters, specially the spent fuel management described by the reprocessing and fresh fuel fabrication priority order. Using both databases, neural networks are trained to predict the three criteria of interest (FPA mass, decay heat and cumulative α dose), the number of produced CSD-V being directly deduced from those values during CLASS simulations.
Model validation Neural networks are built for both databases. Their inputs are the fresh fuel (uranium and plutonium if needed) isotopic composition, the burn-up and the cooling time before vitrification. They allow, for each fuel type, the calculation of initial decay heat, cumulative α dose over 10 000 years and FPA mass to be incorporated in glasses, from 100 tons of heavy metal before irradiation. The predictor accuracies are here estimated by comparing the predicted values with a new set of data, completely independent from the training set. Verification is shown here for the predictors dedicated to the MOX/MIX database. In fact, the sampling space is wider than the one used for the UOX/ERU database, consequently these last predictors are more accurate and their precision's are not presented here.
However, the uncertainty of those have been estimated to 0. 9% for the cumulative α dose, 1. 0% for the heat and smaller than 0. 1% for the metal mass. 160 new compositions are randomly sampled over the same space (see Tab. 3) to build the training set. Each of them were used for SMURE depletion simulations and post-irradiation data are calculated for two random cooling times, leading to a set of 2 590 new data for metal mass, decay heat and cumulative α dose of material left after separation. This new independent MOX/MIX database is compared with the artificial neural network estimations, which results are presented in Table  4  for different burn-ups (BU). Clearly, the accuracy depends on the discharged burn-up, and the prediction may be uncertain (up to several percent) for low burn-up values.
It may be because neural networks are built on a database describing two different fuel types. However, the neural networks' precision seems much better than all the uncertainties that must be coped with this approach, e. g. the chemical separation efficiencies or the depletion simulation options. Those latest may bring several percent of biases in plutonium or minor actinide inventories at the reactor Fig. 5. Precision of decay heat at production and cumulative alpha dose over 10 000 years estimation for spent fuel burn-up higher than 30 GW d/t. discharged as shown in  [11]. It is also important to note that the neural network accuracy is directly the canister production precision regarding each limit. Thus, with this model, the CSD-V production uncertainty due to the use of neural networks' models is approximately 1.
In addition, chemical extraction efficiencies for uranium and plutonium reprocessing were chosen. Thousands of different decay heat, α activity and mass estimations as a function of all these previous parameters were calculated with the SMURE package in order to build dedicated numerical databases. Artificial neural networks were then generated to estimate efficiently the three relevant criteria used in this study for the waste canister production calculation. Once the new CLASS vitrification model developed, the investigation of how fuel cycle parameters impact the CSD-V production was performed based on a transition scenario inspired from  [1]. This scenario considers a transition from a plutonium mono-recycling PWRs fleet to a plutonium multi-recycling PWRs fleet using MIX fuels. The study shows the canister production evolution as a function of some stock management strategies for fuel reprocessing, fabrication and vitrification.
In this section, the optimization tools are then presented to define the dimensions of the rectangular strips and their orientation in order to reduce energy consumption and material waste. Secondly, a thermoelectric model, using COMSOL Multiphysics ® software, is presented in section 3. This model is employed to check numerically the feasibility of this new method with regards to the technological constraints and difficulties such as the imperfection of the contact between the blank and the electrodes. In section 4, the heating demonstrator and the experimental results are presented.
To minimize the energy consumption, it is necessary to increase the ratio of the strip length to the cross section  [48]. Thus, the method involves two steps to minimize the manufacturing cost: 1. The first step lies in finding the best orientation of the blank in relation to the current direction. As shown in figure  5 , the initial orientation with an angle θ a is modified to find the minimum width w that corresponds to the optimal orientation obtained with an angle θ c = 119 •. 2. The second step is the rectangular discretization that comprises a minimization of the added material that is to say the surface area outside the blue dotted lines. The minimization is performed using Matlab software with an Inverse-PageRank-Particle Swarm Optimizer algorithm (I-PR-PSO algorithm)  [43]. For a given shape, the orientation is determined in the first step and the maximum number of generators or rectangular strips is imposed by industrial constraints.
The full protein list is presented in the Supplementary dataset 1. These proteins include several intracellular proteins that may originate from dead cells and cell debris, which are collected with the secretome during washes. Extracellular proteins were selected using the Proteome Discover database. We identified 408 extracellular proteins in the apical secretome of Calu-3 and NHBE cells (Fig. 3 ). Each protein was identified in at least two biological replicates, showing that little difference exists between cell batches (in the case of Calu-3 cell lines) or donors (in the case of NHBE primary cells). Interestingly, the same proteins were identified in the secretome of Calu-3 and NHBE cells, indicating a very high similarity in the epithelial cell secretome in the Calu-3 and NHBE model over time (Fig.
3a ). The abundance of each protein was analyzed so that we could determine whether one protein was more or less secreted in one condition, either between the Calu-3 and the NHBE model, or as a function of time. To get an overview of the proteomic pattern for each condition, a principal component analysis (PCA) of the quantitative proteomic data was performed. The plot of the two main eigenvectors PC1 and PC2 is shown in Fig. 3b. This analysis shows that the secretomes of NHBE cells form a single cluster that differs from the secretome of Calu-3 cells at all time points. No difference in the secretome of NHBE cells with time or between donors was observed in this analysis, suggesting a high stability of the NHBE culture over time and little difference in the secretions of the epithelial cells between donors.
The potential contamination of the apical secretome by bovine serum albumin from the basal medium was checked using two methods: first, the peptide sequence analysis showed higher sequence homology with Homo sapiens versus Bos Taurus serum albumin using the Swissprot database; secondly, up-regulation of HSA expression in Calu-3 cells was evidenced by qRT-PCR in Calu-3 cells (Fig. S4 ). Serum albumin is a major component of lung lining fluid  47. We hypothesize that the hypersecretion of serum albumin by Calu-3 cells a few days after ALI is related to the control of the osmotic pressure in an immature epithelium. This is supported by the secretion of Hsp90, a heat shock protein secreted by normal cells in case of tissue injury that also promotes cancer cell motility  48. A basal level of secretion of serum albumin was then observed in both models.
Biological pathways associated with the secretome of Calu-3 and NHBE cells. The biological pathways associated with the secreted proteins were analysed using the Reactome database. We selected the proteins secreted both at day 11-12 and 18 after ALI for this analysis, which correspond to the proteins forming a stable and mature secretome in Calu-3 and NHBE models. The Reactome pathways associated with the secretome of Calu-3 and NHBE cells in long-term cultures are presented in Fig. 5b  and Table  1. The secreted proteins are strongly associated with the immune response both in Calu-3 and NHBE models. This response involves the innate immune response, the neutrophil degranulation, and antimicrobial peptides (bpifa1, bpifb1, clu, lcn2)  56, 57. The proteins secreted by the epithelial cells, such as lipocalin 2, BPI fold containing family A and B members, and clusterin, are essential elements of the mucosal immune defense against pulmonary infections  58.
Expression of SARS-CoV-2 receptors in preclinical 3D model of the human bronchial epithelium. The infection of Calu-3 cells by SARS-CoV-2 was recently reported  60, 61. The ACE-2 (angiotensin-converting enzyme 2) receptor and the neuropilin-1 receptor, which are involved in SARS-CoV-2 infection of the lung epithelial cells  62, 63  , were both expressed by Calu-3 cells for 4 weeks in our 3D model of human bronchial epithelium (Supplementary dataset 1). Proteomic analysis of cell extracts and qRT-PCR will be performed to confirm the levels of expression of these receptors in long-term Calu-3 cell cultures. The presence of these receptors could support the use of this preclinical model for testing novel therapeutic strategies against COVID-19  [64] [65] [66].
Finally, tetramethylbenzidine (TMB) substrate reagent (555214 BD OptEIA, BD Bioscience) was added and incubated for 20 min in the dark at room temperature. The enzymatic reaction was stopped with 2 N H 2 SO 4 (84727, Sigma-Aldrich) and the absorbance at 490 nm was measured. Each analysis was performed in duplicates. MUC5AC and ZO-1 immunolabelling. qRT-PCR. RNA was isolated from Calu-3 cells 20 days after ALI using NucleoSpin RNA kit (740955. 250, Macherey-Nagel) and converted to cDNA using the high-capacity cDNA reverse transcription kits (4368814, Applied Biosystems). Primers for the selected genes of interest (muc5ac, muc5b, zo1, alb) were designed with Primer-Blast software (NCBI) and synthesized by Eurofins Genomics (zo-1, hprt, muc5ac), Oligo (tbp) and Invitrogen (muc5b). The sequence of the primers is detailed in Table  S1.