dataesr/scientific-paragraphs-categorization huggingface dataset:
This will be done by integrating a suite of sensors onboard that are capable of collecting environmental data, to automatically acquire data coming from remote sensors placed on the seabed, or to carry out bathymetric surveys. The customized ROV will be tested at the pilot sites in order to ensure that it meets the requirements of underwater operators working in the UCH. The last two objectives, respectively 5 and 6, concern the transfer of skills and knowledge and the raising of public awareness and knowledge about the importance to preserve the underwater historical and archaeological heritage. In this frame, training events, seminars, and conferences will be organized and data collected will be presented to the public in an engaging way, so that people can easily understand the need to preserve UCH for the future generations.
3. The general public: the provided information should help the public to understand the main concepts and approach of TECTONIC and the value that it will bring to society. The expected impact of the proposed measures will be supported from reaching the KPIs, using different channels and dissemination tools such as social media, journals, newspapers, newsletters, press releases, conferences, etc. To meet the KPIs, the following results must be achieved: (a) improve existing materials and experiment with new ones suitable for the protection of remains and for limiting the decay processes underwater (e. g. , marketable coatings, adhesives, protective and consolidating materials); (b) develop more effective forecasting models capable of quantifying degradation phenomena occurring in UCH by exploiting artificial intelligence (AI) methods applied to environmental parameters data and multi-sensors images acquired periodically on underwater pilot sites; and (c) extend the use of virtual and augmented reality technologies for developing applications in which the public can enjoy the reconstructions of the underwater sites by experiencing a virtual dive in the past (especially for non-divers).
These relatively stable and low-nutrient conditions found at depth constrain both the amount of microbial biomass present in the subsurface and the structure of these microbial communities  (11) (12) (13) (14). Many of the microbial taxa that are abundant in these subsurface environments are underrepresented in microbial culture and genome databases  (11). Thus, there are large knowledge gaps in our understanding of the biology of a major fraction of subsurface soil microbes. Because subsurface soils are low-nutrient habitats, part of the challenge associated with culturing and studying the microbes that live belowground may be that they require low nutrient concentrations in order to be isolated or propagated in the laboratory  (15). These microbes-often referred to as "oligotrophs"-are capable of growing under conditions where the supply or quality of nutrition is poor.
By the end of the experiment, we approached ϳ20% culturability across the two medium formulations (Fig. 2 ). In general, microbial culturability was higher for ASM-low than for ASM-high, but this effect was significant only after 4 weeks of incubation (Fig. 2 ; Wilcoxon rank sum test, P Յ 0. 05 at 4 weeks). We assigned taxonomy to each 16S rRNA gene sequence using the SILVA database. All pure cultures isolated on ASM-low and ASM-high belonged to one of two bacterial phyla: Actinobacteria (110 cultures; 83% of the pure cultures) or Proteobacteria (23 cultures, all Alphaproteobacteria; 17% of the pure cultures) (Fig. 3 ). Across all experiments, the genera assigned to bacteria isolated on ASM-low FIG 1 Dilution-to-extinction workflow. Soils were collected and brought to the lab, where they were homogenized in cell extraction buffer, layered over a Nycodenz solution, and centrifuged (A).
We identified phylotypes based on the generation of de novo operational taxonomic units (OTUs) from raw Illumina sequence reads using the UPARSE pipeline at a stringency of 97% identity  (72). Paired-end reads were trimmed of adapter sequences, barcodes, and primers prior to assembly. We discarded low-quality and singleton sequences and dereplicated the remaining sequences before calculating relative abundances. Chimera filtering of the sequences was completed during clustering, while taxonomy was assigned to the OTUs with mothur (73) using version 123 of the SILVA 16S rRNA database (74) as the reference. We generated OTU and taxonomy assignment tables for subsequent analyses. Cell separation. Cells were separated from sieved soils using buoyant density centrifugation with Nycodenz modified from reference 37 to isolate viable cells.
Mean fluorescence calculations. We calculated the mean fluorescence of each culture from the subcultures grown in 25 ml volumes at 12 to 15 weeks after inoculating. Culture aliquots were fixed and stained for 15 to 18 h as described above under "Dilution-to-extinction. " We manually gated histograms of the intensity of SYBR green I fluorescence (in arbitrary units) and extracted the mean fluorescence of the gated peak for each culture using the GuavaSoft software package. "Best hit" genomes were determined by subjecting the full-length 16S rRNA gene sequence of our isolates to a BLAST search against the NCBI Microbial Genomes database using web-blast  (76). We extracted the total genome length from each best-hit genome. Culture identification. Cultures were identified by full-length 16S rRNA gene sequencing.
Sequences were curated using 4Peaks  (78)  and Geneious Prime v2019. 0. 1  (79). Reads were trimmed and assembled using the moderate setting in Geneious. Forward and reverse Sanger PCR reads that failed to build a full-length 16S rRNA gene with these metrics were considered "mixed" cultures and not analyzed further. Culture taxonomy and determination of taxonomic differences across growth medium formulations. High-quality full-length 16S rRNA gene sequences from the cultures were used to assign taxonomy and reconstruct phylogenetic relationships. We assigned taxonomy to all assembled 16S rRNA gene sequences using the SILVA database SINA aligner v128  (80). A Shapiro-Wilk test of normality was conducted in base R (81) on the distribution of SILVA genus assignments from both medium types. After concluding the data were nonparametric, we performed a Kruskal-Wallis test in R (assigned genus ϳ medium type).
We performed a post hoc analysis (Dunn test, in R) to determine whether culturability within a phylum varied by growth medium type. Taxonomic selection for phylogenetic reconstruction. To reconstruct a phylogeny of full-length 16S rRNA genes, our culture sequences were compared to NCBI's Microbial Genomes and environmental sequence databases using web-blast  (76). The top five hits for each sequence from each NCBI database were chosen based on the highest percent coverage and lowest E value score and included in the reconstruction. Escherichia coli K-12 was used as the outgroup of the alphaproteobacterial phylogeny, and Bacillus subtilis was used as the outgroup for the actinobacterial tree. These sequences aligned with MAFFT  (82)  with turn checking enabled. The alignment was then trimmed using trimAl  (83)  with the "automated1" setting to optimize sequence trimming for maximum-likelihood (ML) phylogenetic analyses.
Environmental contextualization of AZCC isolates. We matched the AZCC isolate full-length 16S rRNA gene sequences against a database of the clustered OTUs from the shallow soil depth profile samples (see "Soil microbial community analysis" above) using the usearch_global command (89) at a stringency of Ն97% identity, in both strand orientations, with maxaccepts ϭ 1 and maxrejects ϭ 0.
ACKNOWLEDGMENTS We thank  Nathan Abramson ,  Jasper Bloodsworth ,  Brenna Bourque ,  Amanda Howe ,  Bridget Taylor , and  H. James Tripp  for assisting with sample collection, culture maintenance, and DNA extractions relevant to this work. Funding from this work came from startup funds provided to P. C. from the  University of Arizona's Technology and Research Initiative Fund  (the Water, Environmental, and  Energy Solutions  initiative) and seed grants from the  Center for Environmentally Sustainable Mining and The University of Arizona College of Agriculture and Life Sciences. Work at  JCVI  was supported by  P01AI118687.
Methods and model CCT offers a framework to quantitatively assess the influence of mechanical stresses and its coupling with other biophysical factors impacting tumor cells proliferation and metabolism. Input data of the mathematical model can be retrieved from the CCT experimental conditions. Furthermore, numerical results in terms of pressure and displacement can be compared with those measured experimentally. This motivated the selection of CCT as reference experiments. For the sake of clarity, the experimental observations reported by  [7]  together with some additional data provided by the authors are briefly summarized in the following subsection. The mathematical model and the in silico reproduction process are then presented.
When confluence was reached (i. e. when R MCTS = R in ), simultaneous monitoring of R MCTS and R out allowed computing the variation of the shell thickness, b, which was averaged over the capsule perimeter. After confluence, the behavior strongly deviated from that of the free MCTS case. Qualitatively, the same phenomenology was observed for all capsule thicknesses. However, capsule thickness appeared to be a significant determinant of MCTS confined growth when quantitative analysis was performed. Finally, we reported radial distributions of cell nuclei, dead cells and proliferative cells. These data were obtained i) by fixing the samples at different time points, typically before and after confluence, following standard protocols, ii) by embedding them in a resin and freezing for cryosection.
Alginate incompressibility also implies volume conservation of the shell. This gives the following constraint equation R 3 out ðtÞ À R 3 in ðtÞ ¼ R 3 out ð0Þ À R 3 in ð0Þ ¼ dðR 3 0 Þ ð2Þ Using this equation, the two time variables R in (t) and R out (t) can be separated and pressure, P (t), written as a function of R in (t) only P conf ðtÞ ¼ 4 3 E 1 À 1 1 þ dðR 3 0 Þ=R 3 in ðtÞ � � uðR in ðtÞÞ R in ðtÞ ð3Þ Experimental input data. First, we considered the denoted training dataset: • for the free MCTS, denoted FG0, the volume was monitored over a time span of 8 days (Fig 1B ); • for the encapsulated MCTS, denoted CCT0, the radial displacement of the capsule (inner radius R = 100 μm and thickness h = 34 μm) was monitored for 8 days (.
We implemented the above model in Python and C++ within the FEniCS framework  [31] , with an incremental monolithic resolution of the mixed finite element (FE) formulation. The simulations have been run with composite Taylor-Hood element P 3 ðR 2 Þ, ½P 2 ðRÞ� 3 (one vectorial and three scalar unknowns), a mesh element size of dh = 5 μm and an implicit Euler scheme with dt = 1200s. All the details and analytical verification of the FE formulation are referenced in Appendix A. Computational framework. All the codes used in this article, analytical verification, integration along the inner radius of the capsule, free growth and confined growth, are available on Github, at https://github. com/StephaneUrcun/ MCTS_mechanics Initial and boundary conditions. The Fig  2  shows the two modeled configurations of MCTS (the free on the left and the confined on the right).
Each mesh is half of a sphere as we also exploit symmetry with respect to a diametrical plane. For the three scalar variables, we prescribed Dirichlet boundary conditions along the outer radius of the domain p l = 0, p tl = 0 and o � nl ¼ 4:2 � 10 À 6 (according to Henry's law, it corresponds to 90 mmHg, the usual oxygen mass fraction in arteries, see  [24] ) and no flux condition at r = 0 and z = 0. For the ECM displacement field u s , slip conditions u s r ¼ 0j r¼0 and u s z ¼ 0j z¼0 are used, and Dirichlet conditions u s = 0 at the outer radius of the domain (see  Fig 2). Local sensitivity analysis. We performed a local sensitivity analysis to estimate Sobol sensitivity indices to assess the sensitivity of the FE solution to the input parameters. They have been performed on the FG0 and CCT0 training datasets, the freely growing and encapsulated MCTS.
S i ¼ y 2 i P i y 2 i þ P ij;i>j y 2 ij and S ij ¼ y 2 ij P i y 2 i þ P ij;i>j y 2 ij ð38Þ Parameter identification and model validation. For both configurations, the identification of relevant parameters was based on sensitivity profiles, as we identified the set of parameters that gathered at least 90% of the variance of the solution. Then, the selected parameters were identified by a Nelder-Mead simplex algorithm (in the Python library SciPy, method minimize, option Nelder-Mead). On the one hand, this algorithm has the advantage of not requiring the computation of the system gradient to the parameters. On the other hand, it generally converges to a local minimum. To avoid this phenomenon, a large range of initial guess was tested: [-20, +20]% around the generic values for m t ; a; g t g ; g nl g and g nl 0 and [-50, +50]% around the initial guess of p 1 and p crit , as we have not previous literature values.
All the parameters having a physical meaning, their values were bound to the physical and physiological values reported in the literature, and we choose not to extend this range. In the CCT0 configuration, considered as the representative case by the team of  [7] , the parameters of the MCTS cell line were identified with the mean experimental value of the alginate stiffness: E alg = 68kPa; it is important to note that this study is not aiming to identify the stiffness of this biomaterial. To evaluate the reliability of the identified parameters, an author of this article and member of the team of  [7]  have jointly provided unpublished experimental results of encapsulated MCTS, CCT1 to 4 and CCTS, namely the validation dataset. As their alginate stiffness is not known (the Young's modulus of the alginate is estimated to be E alg = 68 ± 21kPa), two simulations were run for each capsule with the extreme values of E alg.
Validation Unpublished experimental results of encapsulated MCTS, both thick and thin, were used as validation dataset (Fig  1D ). Each capsule had its own radius R and thickness h and two simulations were run with the extreme experimental values of the alginate stiffness (E alg = 47 kPa and E alg = 89 kPa). Fig 5A  right  shows the range of modeling possibilities of the identified parameters on the training data CCT0, respectively to the alginate stiffness range. The parameter set was identified with the mean stiffness value (E alg = 68 kPa). Fig  5B  shows that the modeling range on two thick capsules, CCT1 and CCT2, is in accordance with the experimental results. Two fits with an alginate stiffness at E alg = 52. 5 kPa and E alg = 70 kPa respectively are proposed. Fig  5C  shows results relative to two thin capsules, CCT3 and CCT4.
Consequently, deeply investigating this range of mechanical constrains could lead to new therapeutic insights. We showed, in this paper, the in silico reproduction of MCTS growth experiments in various physical conditions: free and encapsulated within alginate shells of different sizes and thicknesses. Thanks to a robust validation protocol, variance-based sensitivity analysis, distinct training and validation datasets, all these physical conditions have been successfully simulated by means of a bio-chemo-poromechanical mathematical model. It is important to notice that only one set of parameters, identified on a training dataset (reported in Fig 1B  and 1C ), has been used for all the numerical simulations performed. In the frame of the parameter identification process, a local second-order sensitivity analysis has revealed that the parameters of the model almost become independent under confinement (see Fig  6D ) within a range of ±10% around the usual literature values.
g. , the model bias, annual variability in the produced climatology fields), and data uncertainty  (Hay-wood et al. , 2013). In PlioMIP1, the MPWP is regarded simply as a stable interval despite the climate variability existing over a 300 kyr time slab due to the climate sensitivity and orbital parameters changing, thus the boundary conditions are made as an averaged condition over this long interval, whereas proxy data are representative of some orbital conditions inside this time slab. This boundary condition uncertainty is thus considered as the main contributor to this datamodel discrepancy  (Haywood et al. , 2016a). Therefore, the PlioMIP phase 2 (PlioMIP2) switched to choosing a representative interglacial during the MPWP interval: Marine Isotope Stage KM5c (MIS KM5c; 3. 205 Ma). Thus, boundary conditions (known as PRISM4;  Dowsett et al.
In this case, hydrology and carbon modules are activated, but vegetation is prescribed as the PlioMIP1 study by  Contoux et al. (2012) , using 11 PFTs, derived from the PRISM3 vegetation dataset  (Salzmann et al. , 2008). Therefore, soil, litterfall, and vegetation carbon pools (including leaf mass and thus LAI, leaf area index) are calculated as a function of dynamic carbon allocation.
1 ). The ice sheet mask is changed to the PRISM4 dataset  (Dowsett et al. , 2016)  except in the Eo400_v2 experiment, in which the modern ice sheet is imposed. Topography in these five experiments is calculated based on modern topography used in the IPSL model, on which the anomaly between the PRISM4 reconstructed topography and the modern topography provided by the PlioMIP2 database  (Haywood et al. , 2016a ) is superimposed. Wherever the resulting topography was lower than zero, it was replaced by the absolute PRISM4 topography. Figure  1  shows the resulting topography anomalies in our PlioMIP2 experiments compared to PI and PlioMIP1 experiments. The initial sea surface temperature and sea ice in Eoi400 and Eoi400_v2 are derived from the IPSL PlioMIP1 AOGCM simulation  (Contoux et al. , 2012).
The increase in global mean precipitation rate as well as the monsoon area index (Fig. S2 , calculated based on the method of  Wang et al. , 2008)  in Eoi400 compared to PI is similar to that in PlioMIP1. However, regional discrepancies still exist between these two experiments: the precipitation rates in Eoi400 in the tropics and NH high latitudes are higher than those in PlioMIP1 by 0. 03-0. 05 mm d -1 because of the inwww. clim-past. net/16/1/2020/ Clim. Past, 16, 1-16, 2020 creased warming in Eoi400 in these regions. Regional differences also exist over mountainous regions (e. g. , the Andes, the Rockies, the Tibetan Plateau, the Himalayas, and the Ethiopian Highlands) since the elevation over these regions is modified largely in PlioMIP2 compared to PlioMIP1 (Fig. 1 ). In East Africa, Eoi400 simulates an intensified precipitation than PlioMIP1, which is better consistent with proxy data from East Africa inferring a wet vegetation condition and hydrological systems during this period  (Drapeau et al.
7 ). When compared to the PI condition, sea ice extent in the Eoi400 decreases by 5. 4 and 3. 8 Mkm 2 respectively for the winter and summer season in the NH. In the Southern Hemisphere (SH), sea ice extent reduces by 8. 8 Mkm 2 for the winter sea-son and is nearly extinct during the summer. In comparison with PlioMIP1, NH sea ice cover in Eoi400 reduces by 2. 1 and 0. 8 Mkm 2 , respectively, for cold and warm season, but there is no large difference in SH between these two experiments. The largely decreased sea ice extent can amplify the warming in the high latitudes through its role as an insulation between the ocean and the atmosphere as well as positive albedo temperature feedback  (Howell et al. , 2014; Zheng et al. , 2019). Reconstructed data in the Arctic Basin suggest the presence of seasonal rather than perennial sea ice in the Pliocene Arctic  (Polyak et a.
12b ). Of the three experiments (PlioMIP1, Eoi400, Eoi400_v2), both Eoi400 and Eoi400_v2 show increased warming in the mid-to high latitudes as compared to the PlioMIP1 result. However, despite the increased warming exhibited by our PlioMIP2 simulation, there is still an obvious disagreement between model and proxy, for which model performance is partly to blame. However, the interpretation of the reconstructed data can also affect the data-model comparison. Conventionally, SSTs are reconstructed from U k 37 paleothermometry assuming they represent annual mean values, whereas it has been shown that they can represent seasonal temperatures, for example representing the warmest summer month in the North Atlantic (NATL)  (Leduc et al. , 2017)  and in the Benguela Current  (Leduc et al. , 2014).
Data availability. Climatological averages of each simulation in NetCDF format will be uploaded to the PlioMIP2 data repository soon. It is accessible in the meantime by contacting the authors. Specific data requests should be sent to the lead author (ning. tan@mails. iggcas. ac. cn). All PlioMIP2 boundary conditions are available on the USGS PlioMIP2 web page https://www. usgs. gov/centers/fbgc/science/ pliocene-research-interpretation-and-synoptic-mapping-prism4? qt-science_center_objects=0#qt-science_center_objects (last access: 28 November 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-16-1-2020-supplement.
Introduction Reliable thermal scattering cross-section data, often termed as Thermal Scattering Law (TSL) or S (α, β) for light water are important for reactor physics and criticality safety applications. Pressurized Water Reactors (PWRs) operate with light water at high temperature and pressure, i. e. , around 550 K and 150 bar. Hence, an accurate estimation of TSL both at the room temperature and reactor operating temperature is essential. The two most recent TSL library for light water that are widely used are the one in the JEFF-3. 3 [1] , based on time-of-flight (TOF) experiments measured in the 60s  [2]  and a recent one available in the ENDF/B-VIII. 0, based on Molecular Dynamics (MD) simulations using GROMACS and TIP4P/2005f water potential  [3]. Irrespective of the TOF approach or MD simulation approach, TSLs are generated using the LEAPR module of the NJOY code  [4]  that takes frequency spectrum of the material as an input.
The presence of rings around a transiting planet can cause its radius to be overestimated and lead to an underestimation of its density if the mass is known. We employed a Bayesian framework to show that the anomalously low density (∼0. 09 g cm -3 ) of the transiting long-period planet HIP 41378 f might be due to the presence of opaque circum-planetary rings. Given our adopted model priors and data from the K2 mission, we find the statistical evidence for the ringed planet scenario to be comparable to that of the planetonly scenario. The ringed planet solution suggests a larger planetary density of ∼1. 23 g cm -3 similar to Uranus. The associated ring extends from 1. 05 to 2. 59 times the planetary radius and is inclined away from the sky plane by ∼25 •. Future high-precision transit observations of HIP 41378 f would be necessary to confirm/dismiss the presence of planetary rings.
Transit data The star HIP 41378 was observed in long-cadence mode (LC) during K2 C5 and then in short-cadence mode (SC) in C18. We used the reduced HIP 41378 light curves from  Santerne et al. (2019) , which were produced with the K2SFF pipeline (Vanderburg & Johnson 2014) without significant modification of the in-transit data. Searching for ring signatures in light curves requires high time resolution data, so we performed our analyses on the C18 SC light curve of HIP 41378 f (1933 transit data points) and checked the consistency of the result with the C5 light curve. A cursory fit of a spherical planet transit model to the light curve (Fig. 2 ) reveals no visual sign of the characteristic residual ingress and egress anomalies that can be caused by the presence of rings 1   (Akinsanmi et al.