dataesr/scientific-paragraphs-categorization huggingface dataset:
The pivotal role of the complexation processes for the reconstruction of solute concentration time series starting from the EC signal is investigated in two karstic catchments with different geologies and temporal resolution of the available hydrochemical datasets, that is the Kerschbaum dolostone system in Austria and the Baget limestone system in France. The results show that complexation processes are significant and should be considered for the estimation of the total solute concentration in case of SO 4 , Ca, Mg and HCO 3. The EC signal of a karst spring can be used to interpolate and quantify the dynamics of those solutes characterized by large contribution (approximately >6%) to the total EC and low relative variability, that is HCO 3 , Ca and Mg. Moreover, the presented method can be used to estimate concentrations of solutes when applied to karst systems with stationary and hydrogeochemical homogeneous contributing area.
Indeed, some solute species, like SO 4 , Ca and HCO 3 , have high tendency to form complexes, which thus need to be considered in the reconstruction of the total concentration, despite not having a significant influence on the total EC of the water. Moreover, we investigate the uncertainties related to the quantification of each individual solute species both for interpolation and prediction of high-resolution concentration time series. Since different geological structures and rock types of a system determine the flow regime and aqueous chemistry  (Odeh et al. , 2009) , we consider two karstic watersheds with different types of bedrock and temporal resolution of the available hydrochemical datasets, that is the Kerschbaum dolostone system in Austria (quarterly, weekly and 5-h sampling temporal resolutions) and the Baget limestone system in France (sampling temporal resolution from 1 to 4 h).
| Datasets Tables  1  and 2  show the temporal resolution, number of samples and the statistics of the measured EC (μS/cm) and major solute species of interest, that is Ca, Mg, HCO 3 , SO 4 , NO 3 , Cl, Na and K (mg/L), for both the studied areas and available data periods. The EC was measured in the field at the Kerschbaum spring using For all the events, the temporal resolution of the sampling was 1 h during the rising limb of the hydrograph, while it was set to 2 h during the recession phase. To record the baseflow following the end of each precipitation event, we collected composite samples, integrating 8 h The specifications of the laboratory analysis for the datasets of Kerschbaum and Las Hountas are reported in the Supplementary Materials S1 and S2, respectively. During the preprocessing of the data of both the study areas, the charge balance of each sample was checked using the software PHREEQC  (Parkhurst & Appelo, 2013)  and the samples with an error larger than ±5% are considered too uncertain and eliminated from the datasets.
EC ¼ X i EC i ð1Þ The contribution of the species i to the total electrical conductivity (EC i ) is computed from Equations (2S)-(5S) presented in the Supplementary Material S3. As suggested by  Benettin and van Breukelen (2017) , the chemical properties of each ion i can be grouped in a single coefficient a i ((μS/cm)/(mg/L)) as: a i ¼ Λ m Ã γ EC M ð2Þ with Λ m the molar conductivity (μS m 2 /mol), γ EC the electrochemical activity coefficient (-) and M the solute molar mass (g/mol) of the individual ion. T A B L E 2 Summary of the datasets characteristics, including: temporal resolution (TR), number of samples (n), mean, standard deviation (sd), minimum (Min) and maximum (Max) values of the electrical conductivity EC (μS/cm) and major solute species (mg/L) measured at the Las Hountas spring for the events 4/10/2021-14/10/ The PHREEQC file containing the implemented equations to compute the EC i and a i of each ion is reported in the Supplementary Material S4.
| Uncertainty quantification In the present study, we investigate both the uncertainty on the individual solute concentrations resulting from the experimental error on f i , and the uncertainty derived by considering f i as random variables (Section 2. 4. 1). Moreover, we propose to use the described methodology not only to interpolate between the coarse samples, but also to reconstruct the ion content for periods in which only the EC data are available (Section 2. 4. 2). The presented analyses are done for both the Kerschbaum and Las Hountas. However, since for the Kerschbaum spring no ionic measurements were available at peak spring discharge conditions in January 2019, the results of the uncertainty quantification for Kerschbaum are reported in the Supplementary Material.
Being our aim to derive high resolution time series for the solute concentration from the EC measurements, and not vice versa, we can conclude that karst system studies require to account for the role of complexation processes when applying EC decomposition methods to retrieve the concentrations of those solute species which form complexes. The uncertainty quantification was investigated for both watersheds. Due to the lack of EC and concentration measurements at high flow conditions for Kerschbaum, the analysis of this spring is to some extent limited. The inclusion of Kerschbaum in the present work was chosen to investigate the relevance of complexes in karst systems with different geologies. Moreover, the available data show that for catchments with stationary and homogeneous geology the method performs well both for interpolation and prediction purposes.
ACKNOWLEDGEMENTS This research is part of the  ROCKAT  project ( RObust Conceptualisation of KArst Transport) , funded by  DFG ,  CH 981/6-1. The authors further refer to the  Interreg Central Europe  project  boDEREC-CE , funded by  ERDF. The water works in Waidhofen a. d. Ybbs provided the orthophoto and the data recorded in the Kerschbaum spring recharge area. Paolo Benettin  thanks the  ENAC school  of the  Ecole Polytechinque F Ed Erale de Lausanne (EPFL)  for financial support. On behalf of all authors, the corresponding author states that there is no conflict of interest. Open Access funding enabled and organized by  Projekt DEAL.
DATA AVAILABILITY STATEMENT The data for the Kerschbaum spring for the periods 2000-2016 and 2018-2019 were provided by the waterworks of Waidhofen a. d. Ybbs, whereas the data for the event in January 2022 were collected during an event-based sampling campaign. For the Las Hountas spring the data were collected during the fieldwork in October-November 2021 and in November 2022. The datasets can be downloaded from the Hydroshare repository at the link http://www. hydroshare. org/resource/fb92daaffced415fb7a991747e73adfa.
Figures Figures 3a,b and 3e,f presents the EC and charge balance errors (%), respectively, computed by PHREEQC at the same temporal resolution as the measured water samples for the Kerschbaum (period 2018-2019) and Las Hountas (event 4 October 2021-14 October 2021) springs. The low frequency computed EC (dashed line) and the continuous observed EC (solid line) are plotted together in Figure 3a,b. For the case of Kerschbaum, the computed EC does not fully capture the dynamic of the system due to the low temporal resolution. For example, in December 2018 and January 2019 no water samples were collected and hence the manual sampling does not cover the period with the highest EC values. Considering the Las Hountas dataset with a higher temporal resolution of hydrochemical measurements, the computed EC time series better matches the short dynamic of the system.
To investigate the relevance of complexation processes, we performed speciation calculations considering two karstic catchments with different geologies and temporal resolution of the available hydrochemical datasets, that is the Kerschbaum dolostone system in Austria and the Baget limestone system in France. The results of the uncertainty quantification performed within this work show that our method can successfully be applied in case of karst systems with a homogeneous geology, as in the case of Kerschbaum. For the latter the EC signal can be used to interpolate and predict with less uncertainty the temporal dynamics of those solute species with large contribution to the total EC and low variability of the weight factor f i. In case of heterogeneous systems as Baget, the method cannot represent the mixing of the water contributions from the different geological areas of the catchment.
Note however that the hydraulic properties estimated from GPR data are subject to an inherent compromise between a deep investigation and a fine spatial resolution. For instance, the lowest frequencies (typically from 1 GHz down to 100 MHz) allow deeper penetrations (until a maximum depth between 1 m and 3 m in most organic media). Saintenoy and Hopmans (2011)  performed a sensitivity analysis to evaluate the importance of the soil parameters on the reflections caused by the water table. They found that the detectability of the GPR reflections is mainly dependent on the slope of the retention curve in the capillary zone. Moysey (2010)  showed that the Mualem-van Genuchten parameter n is the most poorly constrained among all unsaturated soil parameters when considering the two-way travel time (TWT) from various sources of reflection for a laboratory infiltration experiment conducted in a sandbox.
contents, and the Mualem-van Genuchten shape parameters α and n) on the GPR TWT data of the three reflectors using Global Sensitivity Analysis (GSA). -Performing statistical calibration of soil parameters using the Markov Chain Monte Carlo (MCMC) method and evaluating the reliability of the estimated parameters by analyzing not only the calibrated model parameters but also their associated uncertainty. -Evaluating the impact of the type of reflector (moving or fixed) by analyzing the calibrated model parameters and their confidence intervals for different scenarios. The plan of the paper is as follows: Section 2 describes the test case as well as the mathematical and numerical hydrogeophysical models. Section 3 reports on the GSA results of the different TWT signals. Then, Section 4 discusses the results of soil parameter estimation with MCMC for different scenarios.
y(X) = |β|≤D s β Ψ β (X) ( The number of coefficients for a full PCE representation is P = (p + D)!/p!D!. A training dataset of M realizations of the forward coupled hydrogeophysical model is used to build the PCE surrogate model of order D  (Fajraoui et al, 2011; Shao et al. , 2017; Younes et al. , 2013). The coefficients of the PCE are obtained by searching the best fit (in the least square sense) of the PCE surrogate model to the hydrogeophysical model for the M realizations. To work with low-discrepancy sets, the M realizations correspond to sets of input parameters sampled from their prior probability distributions, using quasi-random Sobol sequences  (Shao et al. , 2017). We illustrate the principle of the construction of the PCE with our hydrogeophysical model in   2  show that the relative difference between the two variances is very small for all investigated times.
Bayesian soil parameter estimation from the TWT signals In this section, we estimate the unsaturated soil parameters in a Bayesian framework using the Markov Chain Monte Carlo (MCMC) sampler  (Vrugt and Bouten, 2002; Vrugt et al. , 2008). The statistical calibration is performed for a GPR monitored infiltration experiment in order to address the following questions: 1. Can we obtain an appropriate estimation of all unsaturated soil parameters from TWT data? 2. What is the impact of the kind of TWT data (moving/fixed reflectors) and of the number of reflectors on the calibrated model parameters and their confidence intervals? 3. What is the optimal set of TWT measurements that yields good reliability of all unsaturated soil parameters? https://doi. org/10. 5194/egusphere-2022-936 Preprint. Discussion started: 29 September 2022 c Author(s) 2022.
CC BY 4. 0 License. The MCMC method has been successfully employed in various inverse hydrological problems (e. g. ,  Fajraoui et al. , 2011; Younes et al. , 2016; Younes et al. , 2017; Younes et al. , 2018). The method generates random sequences of parameter sets that asymptotically converge toward the target joint posterior distribution by searching the ensemble of possible parameter sets that satisfactorily fit the observations. The converged sets can then be used to assess the quality of the parameter estimation such as the optimal parameter values and the 95% Confidence Intervals (CIs) which allow for evaluating the reliability of the parameters via uncertainty quantification. In the sequel, the MCMC method is performed with the DREAM (ZS) software  (Laloy and Vrugt, 2012) , which is an efficient MCMC sampler.
The vector of unknowns is formed by the five unsaturated soil parameters (K s , θ s , θ r , α, n). A reference solution is generated by simulating the hydrogeophysical problem formed by the system of equations (  1 )-(  6 ) using the following reference parameter values K * s = 0. 08 cm/s, θ * s = 0. 4, θ * r = 0. 07, α * = 0. 145 cm -1 , n * = 2. 68, as shown in Table  3. The TWT f , TWT 50 , and TWT 120 signals used as calibration data are deduced from the results of the simulation using the reference parameter values. These TWT signals are then independently corrupted using a normally distributed noise with a standard deviation σ = 0. 5 ns. The TWT f , TWT 50 and TWT 120 calibration signals, illustrated before noise corruption in Fig. 2 , increase almost linearly until reaching a plateau.
The reliability of the unsaturated soil parameters is assessed for 5 different scenarios of measurement sets. In the first scenario, only data of the wetting-front TWT f signal are used for the calibration. The second and third scenarios use only the TWT 50 and TWT 120 signal, respectively, obtained from reflection on the fixed reflector R50 and R120. The fourth scenario uses both data of TWT f and TWT 120 as fitting data. The last scenario investigates the benefit of adding a fixed reflector by using data of the TWT f , TWT 50 and TWT 120 signals as conditioning information. In the five scenarios, the MCMC sampler uses three parallel chains and a total number of 50000 runs. The last 25% of the runs that adequately fit the model onto observations are used to estimate the joint posterior distribution.
The MCMC results of the five studied scenarios are given in Table  3  which depicts, for each parameter, the mean estimated value, its posterior CI size, and the ratio of prior to posterior intervals. Note that the CI and the last indicator are calculated from the standard deviation by assuming a Gaussian posterior distribution. A small CI indicates an accurate estimation of the parameter. A significant difference between the prior and posterior intervals is a sign of the high sensitivity of the model to that parameter  (Dusek et al. , 2015). Results of table 3 for scenario 1 using only data of the TWT f signal for the estimation of the unsaturated soil parameters show that: (bold), size of the posterior confidence intervals (CIs) (between brackets), and ratio of prior to posterior intervals (italic).
167 cm -1 and its posterior interval size is half the prior one. -The TWT f signal yields a mean estimated value n = 2. 75 ± 0. 47 which is close to the reference value n * = 2. 68. The parameter n is quite well identified since its posterior interval is 9 times smaller than the prior interval. This is relatively surprising since the sensitivity of n is negligible (Fig. 4a  and 6a5 ). In summary, using only data of the TWT f signal as conditioning information for the hydrogeophysical model calibration yielded well mean estimated parameter values, close to the reference values for all unsaturated soil parameters. However, the examination of the associated uncertainties, showed that only K s , θ s , and n are correctly identified (with narrow posterior intervals with respect to the prior ones).
This points out the importance of statistical calibration methods for highly nonlinear problems to investigate not only estimated parameter values but also the associated uncertainties. The estimation of the unsaturated soil parameters for scenarios 2 and 3, using only data of the TWT 50 or TWT 120 signal for the calibration shows that: -The parameters K s and θ s , which are the most sensitive parameters during most of the experiment (Fig. 4b  and 4c ), are well identified with small CI size and strong reductions by at least 6 for K s , and 19 for θ s , of their intervals of variation. We note that the TWT 120 signal allows a much better estimate of both K s and θ s as their CIs are smaller than the ones estimated with TWT 50. It is especially true for K s where there is almost a factor 2 between the reduction ratios.
-The soil parameters θ r , α, and n, although sensitive (Fig. 4b  and 4c ), cannot be identified from the TWT 50 and TWT 120 signals since their posterior intervals are as large as, or at best two times smaller than their prior ones. The results of scenario 4 which combines data of TWT f and TWT 120 signals show that: -Both parameters K s , θ s , and n are very well identified, with very narrow posterior intervals showing a strong reduction by 46, 37, and 17 of their prior intervals, respectively. -The parameters θ r and α are reasonably well estimated with mean values very close to their reference and intervals of variation reduced by 8 and 9, respectively. Finally, the results of the last scenario which combines data of TWT f , TWT 50 and TWT 120 signals, show performances very similar to scenario 4.
The van Genuchten parameter n has a high sensitivity for n < 3. 5 and a poor sensitivity for n ≥ 3. 5. The reliability of the unsaturated soil parameters has been assessed for 5 different scenarios of TWT measurement sets. When only data of the TWT f signal are used as conditioning information for the model calibration, all estimated parameter values were very close to the reference values. However, analyzing the associated uncertainties showed that only K s , θ s , and n were correctly identified (with narrow posterior intervals). Further, using only data of the TWT 50 or TWT 120 signals for the calibration allows also only a good identification of K s and θ s with a strong reduction of their intervals of variation. The best results, in terms of parameter reliability, are obtained with the combination of TWT f with fixed reflectors.
Acknowledgements The doctoral position of the first author is co-funded by the  Grand-Est Region  and the  University of Strasbourg. This research work is partly funded by the  French National Research Agency  through the  Exciting  project  ANR-17-CE06-0012-01. Finally, computing time was provided by the  HPC-UdS. Acknowledgements. The doctoral position of the first author is co-funded by the  Grand-Est Region  and the  University of Strasbourg. This research work is partly funded by the  French National Research Agency  through the  Exciting  project  ANR-17-CE06-0012-01. The authors.
A traditional drumline was modified by the addition of a GPS buoy connected to the Iridium satellite. When a fish is hooked, it triggers a magnet that allows the GPS buoy to emit a signal, the "Catch-A-Live" system. This real-time alert system of this SMART drumline informs the fisherman on duty within just a few minutes after a fish is hooked, enabling rapid intervention that ensures a very high survival and release rate of non-targeted species. In addition, due to the rapid handling of the fish after its catch, this fishing gear allows fishers to tag and measure the specimens before their release and also provides a very accurate (<5 min) timing of the capture  [33]. To compare the catch composition, its seasonal variability, and the size of the animals with the catches of recreational fishers along the coast, data from the fishing operations of the shark control program were extracted from the database for the same time period as the survey on Facebook, between January 2016 and December 2021.
Sequences were checked for quality and edited using Geneious 8. 1. 2  [36]. Species identification of the samples was performed by comparing sequences to DNA records on the Barcode of Life Data System (BOLD,  [37] ) and BLAST identification system (https://blast. ncbi. nlm. nih. gov/Blast. cgi, accessed on 15 February 2023). A ~98% sequence similarity match was considered reliable for species identification  [38].
Data Availability Statement: Data from barcoding will be made available on GenBank. Acknowledgments: D. Guyomard and C. Perry initiated the shark control program. Grateful thanks to all professional fisher involved in the shark control program and to all recreational fishers that provided information and samples. J. F. Nativel provided additional samples collected within its own network of fisher. G. Chandelier provided the drawing of the species. We are grateful to the two anonymous reviewers who improved the manuscript with their comments.
We demonstrate a general alternative strategy, which consists in connecting small and stable "brick" αRep proteins comprising a few motifs with a "staple" protein that forces their head-to-tail assembly into a predefined geometry that mimics the covalent αRep superhelix. Our general concept is to create a heterotrimeric αRep junction in which a staple protein (Figs. 1a, b, yellow) mediates the assembly of two consecutive bricks (Figs 1a, b, blue) with the same geometry as an αRep with twice as many repeats. The propagation of this trimeric junction from an equimolar brick:staple mixture leads to infinite helical supramolecular architectures. In order to generate such junctions, we took advantage of the known crystal structure of a face-to-back complex between two αReps (PDB code 8AW4 and Supplementary Information Section 1).
CryoTEM sample protocol. 3 µL of sample were deposited onto glow-discharged lacey carbon grids and placed in the thermostatic chamber of a Leica EM-GP automatic plunge freezer, set at 20°C and 95% humidity. Excess solution was removed by blotting with Whatman n°1 filter paper, and the grids were immediately flash frozen in liquid ethane at -185°C. The frozen specimens were placed in a Gatan 626 cryo-holder, and cryoEM was carried out on a Jeol 2100 microscope, equipped with a LaB6 cathode and operating at 200 kV, under low dose conditions. Images were acquired with SerialEM software, with defocus of 1-2 μm, on a Gatan US4000 CCD camera. This device was placed at the end of a GIF Quantum energy filter (Gatan, Inc. ), operated in zero-energy-loss mode, with a slit width of 25 eV. Images were recorded at a nominal magnification of 4 000x corresponding to calibrated pixel sizes of 1.
Cryoelectron tomography (cryoET). Tilt-series were acquired on a Talos Arctica (Thermo Fisher Scientific) operated at 200 kV in parallel beam condition, a K2 Summit direct electron detector and a BioQuantum energy filter (Gatan Inc. ) operated in zero-loss mode with a slit width of 20 eV. Data were collected using Tomo software (Thermo Fisher Scientific), at a nominal magnification of 63 000x with a calibrated pixel size of 2. 8 Å and between -5 and -8 µm defocus. Tilt series were acquired following the dose symmetric scheme between +60° and -60° with a step size of 2°. (  55 ) Each tilt image was acquired in electron counting mode with a total dose of 70 e -/A 2. Tomogram reconstruction and modelling. Frames were aligned using MotionCor2 to correct for beam-induced motion and reconstruction was performed in IMOD.