dataesr/scientific-paragraphs-categorization huggingface dataset:
Nextera XT kit was used to prepare Illumina libraries. Before analyzing the reads, their qualities were analyzed with FastQC software and filtered with Trimmomatic (v0. 39, ILLUMINACLIP : NexteraPE-PE. fa:2:30:10:8: keepBothReads HEADCROP:15 SLIDINGWINDOW:4:25). De novo assembly of parental strains was performed with Unicycler (v0. 4. 8). Contigs smaller than 200nt were removed from the newly formed genomes and Prokka (v1. 14. 6) was used for annotation. Variant calling was carried out using Snippy (v4. 6. 0) using filter reads for each variant, using as reference the genome of variant's parental strain. The analysis pipelines are available on the following GitHub repository: https://github. com/Arnaud-Bridier/BAoBAb.
, 2019)  with the following modifications. Reverse transcription was performed with 1. 3 µg of total RNA, and quantitative PCR reactions were carried out with 2. 6 ng cDNA. All primers were purchased from Sigma-Aldrich (Saint Quentin Fallavier, France), and additional information on oligonucleotide primers are listed in Table  S2. Using NormFinder software, the gene hcaT was chosen as a reference gene since it did not exhibit any significant variation of expression among the samples. Three independent experiments were performed.
Both markers were excited with an Argon Laser set at 488 nm for SYTO ™ 9 and 514 nm for Congo red. Fluorescence emissions were collected using photomultiplier tubes (PMT) at wavelengths of 500-550nm for SYTO ™ 9 and 600-750nm for Congo red. Biofilm three-dimensional structures were scanned with a 63x water objective lens (numerical aperture: 1. 2), with a resolution of 512x512 and a 1 µm Z-step. For each strain, a total of 12 stacks per condition (PHMB-exposed or H 2 O controls) was captured corresponding to 2 Z-stacks per well, with 6 different wells per strain on 2 different plates. Biofilm 3D reconstructions were performed using the Imaris 9. 3. 1 software (Bitplane, AG-Zürich, Switzerland) via the maximum intensity projection (MIP) section view. Quantitative biofilm biovolumes were extracted from CLSM images with BiofilmQ 0.
Statistical analysis Frequencies of occurrence of GenR variants in H 2 O and in every biocide conditions were compared using the Kruskal-Wallis test (P value: * = [0. 05-0. 01]; ** = [0. 01-0. 001]; *** = <0. 001). RT-q-PCR data analysis was performed with the Graphpad Prism software. For each week, a Student t-test was performed between the PHMB and H 2 O condition (P value: * = [0. 05-0. 01]; ** = [0. 01-0. 001]; *** = <0. 001).
Biofilm structural modulation after PHMB exposure The impact of PHMB exposure on the architectural properties of biofilms formed by the seven E. coli strains was also investigated using CLSM to observe potential structural perturbations resulting from bacterial community adaptation. Bacterial cells were stained with SYTO ™ 9 and part of the extracellular matrix was stained with Congo red, a dye known to stain amyloid fibers, including curli, which are essential components of the biofilm matrix. First, CLSM observations revealed that PHMB exposure provoked recurrent structural modifications among most E. coli strains, leading to the formation of dense clusters as shown on CLSM sections in Figure  3. The quantification of biofilm structures following both fluorescent staining (SYTO ™ 9 and Congo red) provided complementary data showing that such architectural modulations in response to PHMB were related to slightly different processes depending on strains.
tion timing was established via petrographic analyses. The diagenetic carbonates were further investigated by fluid inclusion and clumped isotope (Δ 47 ) thermometry. The two thermometric datasets were interpreted by evaluating the possible occurrence of inclusion thermal reequilibration and Δ 47 solid-state reordering, based on the known basin thermal history and the three existing Δ 47 reordering models. By considering the fluid inclusion and Δ 47 datasets obtained and the various Δ 47 reordering models, different carbonate precipitation scenarios, in terms of timing and parent fluid composition (δ 18 O fluid ), were inferred. These results underline that in samples having experienced thermal maximum >100°C, accuracy and interpretation of fluid inclusion and Δ 47 thermometry data (especially on calcite) may be biased by thermal reequilibration and solid-state reordering.
The Paris Basin represents thus an ideal target to further explore the applicability and limitations of FI and Δ 47 thermometry in rock successions that underwent a more severe thermal history, characterized by a higher thermal maximum. In the present survey, stratigraphically lower reservoirs (Upper Triassic Chaunoy Fm. ), located to the NW of the Paris Basin depocenter, were investigated since, based on the most updated basin thermal model  (Torelli et al. , 2020) , these rocks experienced a thermal maximum >100°C. The main aims were to jointly apply FI and ∆ 47 thermometry on burial diagenetic carbonates within these stratigraphically lower (and hotter) reservoirs to test the consistency of the data from the two techniques and evaluate the carbonate precipitation scenarios derived from the different datasets.
, 2010; Monticone et al. , 2011; Telès et al. , 2014; Torelli et al. , 2020; Uriarte, 1997; Worden et al. , 1999). All these previous data and numerical models make the burial and thermal history of the basin well-constrained. The most recent 3D numerical model of the Paris Basin, furnished by  Torelli et al. (2020) , was developed using the TemisFlow™ software and was built upon the geological model previously constructed by  Telès et al. (2014). The reliability of the model from  Torelli et al. (2020)  is ensured by the fact that it considers multiple parameters, including surface temperature and paleo-bathymetry evolution, a detailed reconstruction of sediment deposition and erosion events, basal heat flow, and thermal properties of the rocks filling the basin. In order to consider the impact of compositional heterogeneities of the basement lithology on the basin thermal state, the model also incorporates an upper crust comprising three domains characterized by different thicknesses, radiogenic heat production and conductivity.
The Linksys 32 software enabled all the operations for FIM. The stage was calibrated with synthetic FI in quartz standards in the -21 to +135°C temperature range. FI was ascribed, where possible, to Fluid Inclusion Assemblages (FIA) following the recommendations of  Goldstein and Reynolds (1994)  and  Goldstein (2003). Size, shape, liquid/vapour ratio, and response under UV-light of the selected FI were determined at room temperature. FIM analysis was executed in two different stages: (1) heating runs to determine homogenization temperatures (Th) and temperatures of gas nucleation after homogenization (Tn g ); (  2 ) cooling runs to determine nucleation temperatures of ice-like phases (Tn i ), apparent eutectic temperatures (Te app ) and final melting temperatures of ice-like phases occurring with stable (Tm i ) and metastable  (Tm i(MET) ) behaviour.
Th and Tm i measurements were accomplished with accuracy of ±1 to ±2°C and of ±0. 1 to ±0. 2°C, respectively. Salinities were calculated from stable Tm i only in the binary H 2 O-NaCl system  (Bodnar, 1993)  and are expressed as %wt. NaCl eq. The software package FLUIDS  (Bakker, 2003 (Bakker, , 2009 ) was used to further characterize the aqueous fluids. The application BULK was employed to calculate bulk FI properties (e. g. , density) using the equation of state of  Krumgalz et al. (1996)  and the measured Th and Tm i. The program LONER32 was used to calculate isochore slopes, according to the thermodynamic model of  Bodnar and Vityk (1994)  and by using the measured Th and salinities.
(2017). The Kiel IV device includes a custom-built PoraPakQ trap held at -40°C to eliminate potential organic contaminants. Prior to each sample run, the pressure-dependent backgrounds were determined on all beams to correct for non-linearity effects in the mass spectrometer. During each run, 18 replicates of 90-110 μg of different samples and 5 replicates of each of the three carbonate standards, ETH-1, ETH-2 and 10 replicates of the standard ETH-3  (Bernasconi et al. , 2018 (Bernasconi et al. , , 2021)) , were analysed for data normalization. One replicate of the international standard IAEA C2 was analysed to monitor the long-term reproducibility of the method. Calcite was reacted for 7 min and 30 s and dolomite for 33 min to ensure a complete reaction. All instrumental and data corrections were carried out with the software Easotope  (John & Bowen, 2016)  using the revised IUPAC parameters for 17 O correction  (Daëron et al.
, 2014; Passey & Henkes, 2012). The second model suggests that reordering is given both by an initial diffusion of isotopes through the crystal lattice followed by slow isotope exchange reactions between adjacent carbonate groups  (Stolper & Eiler, 2015). The third model suggests that reordering is the result of a random-walk 18 O diffusion through the carbonate lattice, occurring as a succession of first-order processes that take place simultaneously at different rates  (Hemingway & Henkes, 2021). The first two reordering models (Passey &  Henkes, 2012; Stolper & Eiler, 2015)  can be treated as specific cases of the disordered kinetics, whereas the last model  (Hemingway & Henkes, 2021)  provides a generalized framework to constrain ∆ 47 reordering. Both, the Passey and Henkes (  2012 ) and the Stolper and  Eiler (2015)  reordering models are implemented within the ClumpyCool Python developed by  Lloyd (2020)  and available at https://github.
At the transition between eogenesis and mesogenesis was located eDol locally displaying sweeping extinction, a typical feature of dolomites precipitated during burial  (Spötl & Pitman, 1998). The focus of this contribution is the study of carbonate phases that precipitated during burial diagenesis (mesogenesis) since earlier diagenetic phases could not be investigated with fluid inclusion microthermometry. Specifically, this study focused on Cal1, Cal2 and Dol1 that could be investigated by both FI and Δ 47 thermometry. The relative precipitation order (Figure  9 ) was based on: (1) the observation of rhombohedral eDol being partially Note: Five out of the six carbonate phases (indicated by asterisks) were investigated also for FIM. Fluid inclusion and geochemical data support the petrography-based paragenetic sequence (Figure  9 ).
The Th datasets obtained for each sample reveal not to be fully consistent, since only 70%-80% of the Th measured within individual FIA fall in a range of ±10°C (see Table  S1 ). Conversely, Tm i data from the same samples reveal to be relatively consistent, and the salinities calculated for individual samples vary from 1 to 5 wt. % NaCl eq. at most (see Table  3  and also in Table  S1 ). This means that at least some of the primary FI trapped in Cal1 and Cal2 and to a minor extent also in Dol1 were affected by stretching, though they escaped leakage and refill, therefore preserving the original fluid composition. FI that has behaved like closed though not isochoric systems through time may be used to apply a pressure correction and to determine FI trapping temperatures and pressures (Tt and Pt), considering the minimum Th as the best estimate of the true homogenization  (Goldstein, 2001; Goldstein & Reynolds, 1994).
| Clumped isotope temperature and solid-state reordering Provided a well-constrained thermal history, the three existing Δ 47 reordering models  (Hemingway & Henkes, 2021; Passey & Henkes, 2012; Stolper & Eiler, 2015) , introduced in Section 3. 6, may be used to assess if the analysed carbonates preserved the pristine Δ 47 compositions or if they underwent reordering and to which extent (e. g. Gasparrini et al. , 2023; Mangenot et al. , 2019; Naylor et al. , 2019). The temperature-time history experienced by the samples investigated in this study can be derived from the most updated 3D numerical basin model of the Paris Basin, available under TemisFlow™ software  (Torelli et al. , 2020 ; see Section 2. 2). According to this model, the Upper Triassic (Norian) succession from the three studied wells (LOL1b, AR1 and FEX1) experienced a similar burial and thermal history.
FIM versus ∆ 47 Advances in LA-ICP-MS technique allows at present to achieve in situ absolute dating of low U-bearing carbonate minerals by U-Pb geochronology  (Gasparrini et al. , 2023; Lawson et al. , 2018; Mangenot, Gasparrini, Gerdes, et al. , 2018; Montano et al. , 2021 Montano et al. , , 2022;; Roberts et al. , 2020). U-Pb dating via LA-ICP-MS was attempted for the investigated carbonate phases (see Data S3 in Appendix S1). The analyses revealed to be unsuccessful due to high common Pb and low U contents of the carbonates. In absence of absolute radiometric dating of carbonate phases, diagenesis studies conventionally combine basin thermal histories and carbonate thermometric data to infer the carbonate precipitation timing. This indirect approach to "date" diagenetic mineralizations and fluidflow events is a common practice in basin analysis that requires a well-framed basin thermal history to be applied  (Gasparrini et al.
With the present knowledge on calcite Δ 47 reordering it is not possible to favour a scenario between the second and the third and both scenarios can be considered realistic. The datasets here presented wants to point at the different precipitation timing and fluid evolutions that would be inferred by relying on each of the discussed scenarios separately (Figure  13 ). These results underline how processes potentially undermining the reliability of FIM and Δ 47 data, such as thermal reequilibration and solid-state reordering, need to be carefully evaluated for an accurate reconstruction of thermal and fluid-flow histories in sedimentary basins. Consistencies versus discrepancies among Th/Tt and T(Δ 47 ), jointly analysed on the same diagenetic carbonates, have been already reported by previous authors  (Honlet et al.
F Precipitation timing and δ 18 O fluid for Cal1, Cal2 and Dol1 obtained according to different temperature datasets produced from FIM and ∆ 47 thermometry and the thermal curves for the Upper Triassic (Norian) succession of the three studied wells extracted from the available 3D basin thermal model under TemisFlow™ (Torelli et al. , 2020). (a) Trapping temperatures (Tt) derived from minimum Th values from FIM after pressure correction. (b) Precipitation temperatures from measured T(∆ 47 ) when considering the Passey and Henkes (2012) and the Hemingway and Henkes (2021) reordering models. Vertical bars indicate T(∆ 47 ) uncertainties reported at the 95% confidence level. (c) Cal1 and Cal2 precipitation temperatures back-calculated from apparent T(∆ 47 ) when considering the Stolper and Eiler (2015) reordering model.
Inversion In their review, Frisvad et al. [17]  presented several models and ways to estimate the optical properties of translucent materials. Even though our study focuses on the optical properties of highly diffuse materials, their description of the forward and especially inverse model is valuable. When designing an the inverse model, some steps must be validated. The first aspect is to define the type of data measured, then a model for light scattering distribution must be chosen. Afterwards, an objective function is to be selected to give the best match possible between the model and the measurements. Finally, an optimization method is applied to minimize the objective function. In our case, reflectance data profiles are acquired, the diffusion model is chosen as the light transfer model, and we have selected a trust-region nonlinear optimization method to optimize the sum of squares of residuals between the data and the model.
As stated by Kienle et al. [26] , there could be an infinite number of solutions for µ a and µ s if there are no constraints in the optimization process. In their paper, Jensen et al. [30]  used the diffuse equation as reported in Equation (1). For their optimization, they measured the total reflectance, which is the integration of R d over the whole surface considered. Therefore, they added this constraint to obtain estimates of µ a and µ s. Nonetheless, acquiring the total reflectance may not always be possible, which is the case in this work. For this case, we selected milk data from our dataset and we assigned values in the range [0, 0. 2] mm -1 to absorption and reduced scattering coefficients. For each of these values, we computed the sum of square residuals between the data and the model we wanted to fit, and we plotted the contour lines for each channel R, G, and B.
Results and Validation on Milk We assembled two datasets of milk samples to perform our measurements. The first dataset is referred as the Q-dataset and is composed of four types of milk samples from the brand ©Q, a Norwegian milk company whose bottles can be bought in supermarkets. The fat concentration in these milk samples can be found at 0. 1%, 0. 5%, and 1% to 4%. The second dataset is also composed from various samples of milk, ranging from skim milk (0. 1% of fat), light milk (0. 5%, 0. 7%, 1% and 1. 2%), to whole milk (3. 5% and 4%). Since the samples originate from different brands and some variations in the making and dilution processes may have been introduced by the companies, this dataset was named the mix-dataset. For both datasets, we followed a similar protocol of measurement.
A cylindrical plastic glass was filled to its maximal volume capacity with milk, which represented nearly one liter. The glass has a diameter of 9 cm and a height of 16 cm. The ©Dia-Stron was attached to a tripod with an adjustable height. Therefore, we were able to set up the device so that the sensor was in contact with the milk but not entirely immersed into it. All measurements were performed in a dark room to avoid noise and perturbations from other light sources. The value for the refractive index of milk is assumed to be 1. 347  [36]. The results for each dataset are discussed in the following paragraphs.
Q-Dataset Results We first detail the results of the experiment conducted on the Q-dataset composed of milk samples from the Norwegian ©Q brand. Table  1  shows the values of absorption coefficient µ a and reduced scattering coefficient µ s estimated from the reflectance profiles using the inversion method described earlier. A first aspect to note is that µ s values are larger than µ a values by 2-3 orders of magnitudes, which validates the diffusion model utilized as one of its assumption is to have a medium with dominant scattering over absorption. A second positive aspect of the results in Table  1  is the increase in µ a and µ s values with the decrease in the wavelength for each channel. As pointed out by Doornbos et al. [14] , a power-law relationship can be established between the reduced scattering coefficient and wavelengths.
Mix-Dataset Results Similarly to the Q-dataset results, this section highlights the results of the mix-dataset which is composed of milk samples coming from various brands. Acquisitions were performed identically to the Q-dataset but are separated by a month in time. In Table  2 , the results of the inverse algorithm are registered for estimating the optical properties following the acquisitions of reflectance profiles with the TLS850 device. Once again, one can notice that µ s values are larger than µ a values, which is expected in the case of a highly scattering material. Actually, as mentioned by Leyre et al. [2] , the fact that scattering is more dominant in such a diffuse material as milk lowers the accuracy which can be reached for the estimate of the absorption coefficient. Furthermore, absorption is very low for milk.
Repeatability of Measurements Another focus on the results for the milk material is their repeatability. It occurs that between the Q-dataset and the mix-dataset, there are common samples of milk containing the same fat content while measurements were conducted at separate instances of time. Thus, a comparison can be made for µ a and µ s values for the samples of 0. 1%, 0. 5%, 1%, and 4% of fat content. These results are reported in Table  4 , computed as the difference between values for the Q-dataset and values for the mix-dataset and then divided by the Q-dataset values taken as the references. When first observing the relative difference for the reduced scattering coefficients, one can notice the resulted differences are low. It is a clear indication our inversion method performs well for repeated measurements regarding µ s values, especially in the case of highly diffuse materials with high scattering properties.
Discussing the Inversion Method One contribution of this paper is the inversion method. The process with the nonlinear fitting was changed as explained in the Section 2. Nonetheless, the algorithm procedure and the management of the reflectance data have their importance on the final estimates. For each sample of the materials considered, several records (between 10 to 15) were acquired each time. The inversion method for estimating the optical properties was implemented with two variations. The first variation considers all the records for a material sample. Then, the inversion algorithm is applied on each of them, giving individual (µ a , µ s ) values for each record. Afterwards, the final estimate of (µ a , µ s ) is the average of all the individual estimates. The second variation considers the average of all the records, which removes potential noise from the sensor, and then perform the inverse algorithm to estimate (µ a , µ s ) values.
Therefore, the main difference between the two variations is when the average is performed, after or before the inversion, and on which quantities it is applied, the estimates from all the records or the records themselves. We compared the two variations by using the data of the milk dataset and computing estimates of absorption and reduced scattering coefficients. Values reported in Tables  1  and 2  are obtained using the second variation of the algorithm as previously mentioned. The relative difference between each value of (µ a , µ s ) for all milk samples from the two variations were then computed by taking the second variation as a reference. For this comparison, the criteria of the dataset (Q or mix) and the colour channel are discarded, i. e. , we have 33 values of µ a and 33 values of µ s to be compared together for each variation.
Since all the paper sheets were compactly stacked together, we assume the air interface between each sheet is too thin to be considered, and then it allows us to utilize the diffusion model. Further information required for our inversion method is the knowledge of the real part of the refractive index for the material considered. In the case of white paper, we used the study of Fabritius et al. [41]  to obtain the value η = 1. 557. Due to the anisotropic nature of the surface of paper and its directionality, we divided our sets of acquisitions by changing the position of the rectangular sensor in relation to the orientation of the stack. Therefore, we defined two datasets for our measurements. One is called the Vertical dataset and it corresponds to the sensor being orthogonal to the orientation of the paper stack.
Vertical Dataset Results Using our implemented inversion method coupled with the diffusion model, we were able to fit our data and obtain estimates of the absorption and reduced scattering coefficients for the paper sheets with the Vertical configurations. Results of these estimates for each class of paper and each R, G, and B channel are presented in Table  5. The first observation which can be made is that absorption values are largely lower than the reduced scattering values, which follows the assumption of highly diffuse materials. When comparing µ s values between channels, one can note the red channel values are larger than the blue, which in turn are larger than the green. This could be interpreted as paper having a higher scattering capacity for the red wavelength over the blue and the green.