dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion In the present study, four collagen hydrolysates were analyzed in different matrices (solvent, TIM dialysate, and human serum) using non-targeted LC-MS, to compare the state of the hydrolysates before and after absorption, either simulated in vitro or in vivo. The presented (data) analytical workflow is especially useful for investigating the behavior of protein hydrolysates. Prior to ingestion, their composition is often already highly complex, and during the course of digestion and absorption, hydrolysate components will be exposed to several enzymes (with broad specificity) and to chemical hydrolysis. Many hydrolysate components will be formed from precursors and be degraded at the same time, which decreases the utility of recording concentrations of intermediates. We illustrated that a general overview of the data set can be obtained by combining non-targeted and targeted data analysis.
X-ray diffraction (XRD) XRD identifies phases within materials based on crystallographic lattice spacing and was chosen to identify the dominate minerals present in the two variants of the MP. Two samples of MP, one light grey and one black, were analysed by powder X-ray Diffraction (XRD) at the XPS Glencore Laboratory (Sudbury, Ontario, Canada). The instrument used to analyse the MPs was a Bruker D8 Advance, equipped with a cobalt X-ray tube (Bruker, Madison, WI, USA). The powders were mounted with grains in random orientations to ensure that all crystallographic directions are sampled by the beam. The resulting diffraction pattern is plotted as 2h (peak spacing) versus intensity (X-ray counts). Identification of mineral phases is determined using EVA software (Bruker); the software compares the XRD spectra of the unknown sample to a published database containing over 200,000 powder diffraction files (International Centre for Diffraction Data).
Acknowledgments We are grateful to  Janice Martell , founder of the McIntyre Powder Project, for her unwavering support and wealth of knowledge and for supplying samples. We would also like to thank  Dave Wilken  and  Kimberly O'Connell  from  OHCOW  for their support and for supplying samples. We would also like to thank  Michelle Kelvin  from  XPS Glencore, Canada  for conducting the XRD analysis. We are grateful to  Christine Levesque  and  Marc Ch  enier of  Health Canada  for conducting the ICP analyses, and we thank  Kristin Macey ,  Larbi El Bilali , and  Daryl Smith  of  Health Canada  for valuable review comments on an earlier version of the manuscript. We are also grateful to  Loïc Hamon  of  SABNP laboratory at Evry University, Paris, France  for conducting the AFM analysis. We thank  Mohamed Sennour  from  Centre des Mat eriaux of MINES Paris Tech, Evry, France  for the TEM analysis.
Anguispira alternata (Say, 1817) (Discidae) (N = 2) and Striatura milium (Morse, 1859) (Gastrodontidae) (N = 2) were selected as outgroups for phylogenetic analysis and also deposited at the Biodiversity Institute of Ontario. All deposited specimens were assigned to the FTMWO or FTMCA project on the Barcode of Life Data Systems (BOLD, http://www. boldsystems. org, Ratnasingham and Hebert 2007). Following protocols in  Layton et al. (2018) , we used samples of foot tissue for molecular analysis including these steps: (1) amplifying the barcode region of cytochrome c oxidase subunit I (COI) with the universal "Folmer" primers (LCO1490/HCO2198)  (Folmer et al. 1994 ); (2) sequencing; (3) sequence editing and aligning; and (4) sequence quality check. The sequence dataset for the genus Vallonia was completed by additional sequence data from GenBank and from various projects on BOLD.
The Barcode Index Number (BIN) algorithm was applied to delineate clusters corresponding to operational taxonomic units at the species level  (Ratnasingham and Hebert 2013). Specimen collection and sequence information are shown in the Appendix (Table  A1 ). Molecular phylogenetic analysis was implemented by the Maximum Likelihood method using the Kimura 2-parameter model  (Kimura 1980). Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Joining and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood approach, and then selecting the topology with the superior log-likelihood value. The analysis involved 36 nucleotide sequences. All positions containing gaps and missing data were eliminated. There were a total of 344 positions in the final dataset.
Acknowledgments: E. P. L. thanks  Gerard Pepe  for its help for the study of Ulva blooms in the Mediterranean seas. E. P. L. thanks  Fabrice Roth  from  Prefecture de Bretagne, Sylvain Ballu from Cente d'Evaluation et de Valorisation des Algues and CRESEB from Conseil Régional de Bretagne  for fruitful discussions. E. P. L. thanks  Ian McBitter  for reviewing the manuscript.
Methods The data for this paper were obtained during two surveys developed to study the behavior of the sand material transported across the channel of this estuarine system. The first survey was planned to determine the bed's physiography by means of a Side Scan Sonar mosaic and was carried out in July 2008. For this study, a Side Scan Sonar CM2000 (CMAX Ltd. , Dorset, England) was used with a frequency of 325 kHz. The resolution for acquisition was 50 meters per band. A geotiff mosaic of images was elaborated using ARCVIEW 3. 2. A second survey was specially developed to quantify the potential and real bedload transport. The data acquisition was carried out between the end of June and the first fortnight of July 2010. During 29 and 30 June 2010, a bathymetry was deployed using an echo sound Valeport Midas Surveyor with a frequency of 210 kHz.
From 1 to 5 July, 20 sediment samples were caught by means of a Van Veen drag (seven in the fluvial studied area and 13 in the marine one). The grain size distributions of the sediment samples were analyzed by the General Research Services of the University of Huelva using a Laser Difractometer Malvern Mastersizer 2000 (Malvern Panalytical, Almelo, Netherlands) able to determine the percentages of grain fractions between 2 and 0. 0002 mm. Both the bathymetry acquisition points and the sediment samples were geo-referenced by means of a dGPS EGNOS with 12 channels to obtain XYZ files with accurate positions. The absolute value of Z was obtained by correcting with a tidal curve from the harbor of Ayamonte (marine area) and a propagation curve from Ayamonte to Sanlucar (fluvial area). The XYZ data were processed by a Delaunay triangulation in the module 3D Terrain model of the software Hypack 2009 (Middletown, CT, USA).
Methods The data for this paper were obtained during two surveys developed to study the behavior of the sand material transported across the channel of this estuarine system. The first survey was planned to determine the bed's physiography by means of a Side Scan Sonar mosaic and was carried out in July 2008. For this study, a Side Scan Sonar CM2000 (CMAX Ltd. , Dorset, UK) was used with a frequency of 325 kHz. The resolution for acquisition was 50 m per band. A geotiff mosaic of images was elaborated using ARCVIEW 3. 2. A second survey was specially developed to quantify the potential and real bedload transport. The data acquisition was carried out between the end of June and the first fortnight of July 2010. During 29 and 30 June 2010, a bathymetry was deployed using an echo sound Valeport Midas Surveyor with a frequency of 210 kHz.
From 1 to 5 July, 20 sediment samples were caught by means of a Van Veen drag (seven in the fluvial studied area and 13 in the marine one). The grain size distributions of the sediment samples were analyzed by the General Research Services of the University of Huelva using a Laser Difractometer Malvern Mastersizer 2000 (Malvern Panalytical, Almelo, The Netherlands) able to determine the percentages of grain fractions between 2 and 0. 0002 mm. Both the bathymetry acquisition points and the sediment samples were geo-referenced by means of a dGPS EGNOS with 12 channels to obtain XYZ files with accurate positions. The absolute value of Z was obtained by correcting with a tidal curve from the harbor of Ayamonte (marine area) and a propagation curve from Ayamonte to Sanlucar (fluvial area). The XYZ data were processed by a Delaunay triangulation in the module 3D Terrain model of the software Hypack 2009 (Middletown, CT, USA).
Acknowledgments: We strongly acknowledge the help of  M. González ,  J. Rangel , and  J. Galvez  during the development of the field work. The help of  M. Cantano ,  I. Delgado ,  G. Flor-Blanco , and  A. Rodríguez-Ramirez  was also essential during the development of this project and processing of the preliminary results. These individuals also participated in the redaction of a preliminary paper of this experiment. The Poliakoff sediment traps could not be built without the kind contribution of  J. Guillén  from the  CSIC, Institute of Sea Sciences (Barcelone). We acknowledge the comments of the anonymous reviewers who have contributed to improving the final version of the manuscript.
FIRST WORKSHOP ON WATER-MASS TRANSFORMATION ANALYSIS FOR OCEAN PHYSICS, BIOGEOCHEMISTRY, AND CLIMATE wHaT: An international cohort of oceanographers, marine biogeochemists, and climate modelers gathered to expand the use of water-mass transformation diagnostics in studies of ocean physics, biogeochemistry, and climate. Led by early-career scientists, the group laid out avenues to leverage growing oceanic observational databases and new model capabilities, using fundamental understanding of the ocean's layering. wHen: 4-6 February 2019 wHere: Sydney, New South Wales, Australia T he opacity of seawater prevents radiation from penetrating into the ocean interior. Surface waters accumulate solar radiation, augmenting their buoyancy relative to deeper waters. Solar heating thus strengthens vertical stability and restrains vertical mixing of the ocean.
g. , chlorofluorocarbons or trace elements) that combined can constrain mass and tracer fluxes into the ocean interior. By performing budgets of multiple tracers in the mixed layer, estimates of subduction and ventilation rates are achievable. The rapidly growing GEOTRACES (www. geotraces. org) and Biogeochemical-Argo (BGC-Argo; Johnson and Claustre 2016) databases increase the feasibility of such budgets. Progress may also be achieved by approaching the problem from below. For some tracers (e. g. , buoyancy or chlorofluorocarbons), it may be easier to quantify WMT in the deep ocean, where there is less temporal variability in the tracer fields and fewer processes ES244 leading to WMT. The requirement for matching transformation in the upper ocean can then be used to narrow uncertainties in subduction and ventilation processes.
AC K N OW LE D G M E NT S. The aut hors t ha n k Bernadette Sloyan and  Stephen Griffies  for reading and checking the content of this summary, and all participants for their contributions to the meeting. We acknowledge support from the  School of Mathematics and Statistics and ARC Centre of Excellence for Climate Extremes of the University of New South Wales, Sydney, Australia. Sjoerd Groeskamp ,  Ryan Holmes ,  Casimir de Lavergne , and  Geoffrey J. Stanley  acknowledge support from the  Australian Research Council  Grant  FL150100090. A repository can be found online (https://github. com/water-mass-transformation-tools).
g. ,  Renard, McBeck, et al. , 2019). In the present study, we analyzed in situ time-resolved three-dimensional X-ray microtomography data acquired on centimeter-scale samples of Carrara marble subjected to triaxial compression tests under stress conditions relevant for earthquake nucleation. This imaging technique produced a 3-D digital map of the spatial distribution of microfracture populations and their evolution toward failure. We developed a novel tracking algorithm to image not only the cumulated microfracture network but also the dynamics of increments of fracture growth, allowing quantifying tomography data beyond what has been previously achieved for other rocks  (Renard, McBeck, et al. , 2019; Renard, Weiss, et al. , 2018). Our data sets provide highresolution quantification of the dynamics of precursors prior to system-size brittle failure.
Journal of Geophysical Research: Solid Earth 2. The gray level in each volume was normalized to enhance the details of the microfractures (Figure  3b ) and saved into 8 bit. 3. A nonlocal means filter  (Buades et al. , 2005)  was applied to average out the speckle noise and sharpen the microfractures (Figure  3c ). 4. We used a Matlab package for ring artifact correction  (Jha et al. , 2014)  to remove these artifacts in the three-dimensional volumes. A resulting image is shown in Figure  3d. 5. We employed a background subtraction algorithm  (Sternberg, 1983)  to remove a smooth continuous background across the image (Figure  3e ). This step is performed by choosing a "rolling ball" radius for each image. At every voxel is assigned a local background value by averaging around the voxel over of the rolling ball.
This averaged intensity is subtracted from the original image. To remove large spatial variations in the background intensity, we have used the background subtraction filter of the software AvizoFire. 6. Each image was then thresholded to select microfractures. Varying slightly this threshold did not change quantitatively the results (Figure  S2  in the supporting information). Microfractures with a volume less than 100 voxels were limited by the resolution of the segmented images and were removed. Therefore, the value of 100 voxels is the lower limit of microfracture size considered here. At the end of this last segmentation step, we obtained a binary image with microfractures quantified as intensity value 1 and the rock with intensity value 0 (Figure  3f ). 7. Finally, we have used dilation-erosion filtering to connect voxels that belong to the same microfracture.
Conclusions We describe 3-D experimental data that reveal the spatial distribution of nucleating, propagating, and coalescing microfractures during the brittle compressive failure of Carrara marble rock samples. Our novel experimental setup allows in situ imaging of the sample during deformation and so enables the computation of statistical properties of the volume of microfractures and the derivation of scaling laws through which we quantify the critical nature of the failure process. We quantified the behavior of precursory microfracture events preceding macroscopic failure. The incremental damage showed power law acceleration with increasing stress up to ~98% of the failure stress and then transitioned to an exponential evolution in the vicinity of system-size failure. This evolution from power law to exponential appears therefore as the ultimate precursory signal of material failure.
The vessel's speed was typically 2-3 m/s for the majority of the tracks. The ADCP data were merged with navigation data provided by onboard GPS system also operating at 1 Hz. Underway velocity measurements were performed during neap tide, under very weak wind (mean speed 2 m/s, max speed 5 m/s) and low waves (Hs < 0. 4 m). Neap tide conditions were specifically targeted as the fieldworks were devoted principally to the deployment of three bottom-mounted ADCPs and drifting buoys in the study area. The availability of the R/V "Thalia", between different steps of the sea trials, allowed to carry out a total of four towed ADCP surveys during different stages of the tidal cycle (Fig. 2 ). The results of the two longest surveys, S3 and S4, separated by two tidal cycles are presented in this paper.
Survey 3 was performed on April 21, 2017 at 12:30-15:00 UTC, before reaching high water (HW) in Goury: from HW-2. 5 hours to HW. Survey S4 was performed the next day, on April 22, at 07:00-12:15 UTC, during the ebb flow and lasted slightly more than 5 hours: from low water (LW) -3 hours to LW+2. 25 hours. Shorter surveys (S1 and S2) provided complementary data which were used for comparison with the model output and misfit estimation. Current velocities recorded by ADCP were corrected for boat motion and averaged within 1 minute intervals. GPS coordinates were also sub-sampled every minute, so that the distance between the thinned along-track data points varied within 120-180 m, depending on the towing speed. Velocities were then vertically averaged for comparison with the model output. Survey tracks and measurement locations are shown in Fig.
Model-data comparison In order to assess the model skill, the velocities recorded by towed ADCP along the track were vertically averaged and compared to the velocities derived from the model output. For all surveys, underway ADCP measurements started just after the current reversal. Therefore, velocities recorded during the first 30-60 minutes were very low and were not used in comparison. Only vertically averaged velocities recorded from 13:00 to 15:00 on flood tide (survey S3) and from 08:15 to 12:15 on ebb tide (survey S4) were compared with the model output at the mid-time of the respective periods. Vector maps of modeled velocities corresponding to that mid-time on flood and ebb tide are shown in Fig. 6. The first comparison is performed with the observed velocities varying in time and space.
, 2012 for more details). Another source of discrepancy is a limited capacity of the model to reproduce the non-linear effects of tidal wave propagation over highly irregular seabed. Even if the model is sought to be extensively validated, large model-data misfit was found in the north-eastern part of the domain where strong bathymetry gradients are found. The OI technique proposed in this study is a valuable way to bypass the actual limits of numerical modeling. After performing OI, the level of velocity overestimation by the model drops down to 9% for both tidal phases. In contrast to pure model simulations, peak flow velocity was found larger on ebb tide. A migration of tidal current jet offshore was confirmed and the spatial extension of largest velocity area was assessed. This important feature of local tidal dynamics was demonstrated by towed ADCP velocity measurements for the first time.
Conclusion Underway ADCP measurements coupled with the 2D hydrodynamic model MARS post-simulation were used to assess the tidal circulation and tidal stream resource in Alderney Race -a highly energetic area where reliable, good quality measurements of tidal flow are scarce. Optimal interpolation was applied to process the velocity measurements from transect surveys at neap tide flood and ebb flow. The presented synthesis of the observed velocities with the model output provided a significant improvement in processing underway tidal velocity surveying both in terms of the reduction of model-data misfit and better consistency with independent (static point) velocity observations. As a consequence, a significant improvement in representation of the temporal and spatial variability of the tidal circulation was achieved.
Acknowledgments This work benefitted from the funding support from  France Energies Marines  and the  French Government , operated by the  National Research Agency  under the  Investments for the Future program :  Reference   ANR-10-IEED-0006-07. The study represents a contribution to the project  HYD2M  of the above program. We would like to acknowledge our colleague  Max Yaremchuk  from  NRL  for his help and valuable comments on the manuscript. We also acknowledge the skill of the scientific team, in particular, the head of the team,  Louis Marié  (Ifremer), the head of the research project,  Anne-Claire Bennis  (UNICAEN), the skipper and the crew of the  R/V Thalia.
Data Collection Soil moisture data were automatically measured at two monitoring stations (S1 and S2, Figure  1 ) by using TDR probes (CS616, Campbell Scientific). Data from monitoring stations S1 and S2 were collected between March 2014 and December 2016. One TDR probe per site was vertically inserted in the soil to indirectly represent the average water content along the first 30 cm downward from the surface, i. e. , over the depth of 0-30 cm. The two TDR probes were calibrated in the laboratory, with the reproduction of the field conditions in soil column tests. Table  1  shows some soil characteristics of monitoring stations S1 and S2, such as the soil type, textural characterization, bulk density, and particle density. Overall, these two sites present similar physical properties of the soil and homogeneity of the profiles along the depths of 0-10, 10-20, and 20-30 cm.
During the rainy season, the groundwater levels are approximately 1 and 4 m above the soil surface in stations S1 and S2, respectively. Both monitored sites are covered by sparse grass frequently trimmed, with root depth inferior to 10 cm. Daily meteorological data were also used in this study to calculate the potential evapotranspiration (ET p ) by Penman's formulation  [57]  modified by  Shuttleworth [58]. This formulation is a physically-based form of ET p , which means that all key variables that govern the evaporative process are explicit in the equation  [59]. Donohue et al. [60]  demonstrated that this form of ET p is the most appropriated when considering a changing climate, which was also used by  Coelho et al. [61]  in a study carried out in the Brazilian Northeast region. The data necessary to calculate the ET p by the Penman's formulation were acquired from a meteorological station (named S3) operated by Pernambuco Water and Climate Agency (APAC).
The difference between the three methods lies in the way in which the cumulative infiltration data are processed. In this study the BEST-slope and BEST-intercept algorithms were used; however, the results of the first algorithm were discarded due to divergent values of K s. According to Yilmaz et al. [68] , the BEST-slope may lead to erroneous values of K s , especially when a very high level of precision relative to the steady-state infiltration rate cannot be obtained. In this study, the infiltration experiments were performed at depths of approximately 10 cm from the soil surface in order to represent the range of soil moisture obtained by the probes installed in the field. For the experiments, fixed water volumes (70 mL) were poured into a 75-mm-diameter ring, leading to a ponded water thickness of a few millimetres.
Results In this study, we propose an alternative framework to disentangle the dynamic and thermodynamic contributions. Changes in extreme precipitation are decomposed using a robust physical approach based on the atmospheric water budget (see Methods). This framework has been widely used in the tropics to relate local changes in precipitation to changes in atmospheric water vapor and circulation [e. g. 16, 20, 21 ]. This method is applied to January 2014 precipitation to understand the physical drivers of this extreme event. It also provides a physically-based quantification of dynamic and thermodynamic contributions that might be useful for extreme event attribution. The analysis is carried out using the ERA-Interim (ERAI) reanalysis  22  , motivated by the horizontal resolution of this dataset (0.
Data from loggers February-March 2017 The three ropes installed in February (see Fig. 7 ) make it possible to monitor the temporal evolution of water temperature at different depths, as well as current speed and direction between mid-February and the end of March inside and outside the eddy (Fig. 9 ). Measurements with the Alec current meter in the region of the eddy for 16-17 February show currents with speeds of 3-4. 4 cm s -1 and direction 208 at 45 -m depth. The SeaHorse current meter installed at the same depth shows similar variability of current speed (3-4. 5 cm s -1 ) and current direction (160-200 ) at the beginning of the measurements. Thermistors installed on rope 1 show several periods with temperature variations. The first period (days 49-57, 18-26 February) shows a gradual decrease of vertical spread between the isotherms.