dataesr/scientific-paragraphs-categorization huggingface dataset:
1, 3, 5 can be found in Figures  11 12 13     The basis of the study was the data obtained by sensors mounted on the prototype station while performing the forming process. During the process reaction forces appearing on joints were observed by an HBM force sensor and a Keyence displacement sensor observed the distance that was traveled by the forming tool. The values observed with both sensors for samples sets no. 1, 3, 5 can be found in Figures  11 12 13      The analysis of the data presented in Figures  11 12 13 determines the maximum forming forces in each case. For the sample set no. 1, it was on the level of 70 kN, for the sample set no. 3, it was on the level of 64 kN, and for the sample set no. 5, it was on the level of 60 kN. The observed reaction forces during the joint forming process for samples sets no.
Using the ATEQ F520 device, the sample sets were subjected to the pressure of 0. 6 MPa several times. The level of leakage that had been set as faulty was 4 [ Pa•L s ]. Table  7  presents the test results. None of the tested sample sets exceeded the established leakage limit. The last test was the tensile strength test, which was carried out with sample sets no. 1, 3, and 5. The tensile strength test was conducted on prototype station, where the force applied to the joint was generated by ball screw manually powered using a crank. Nevertheless, data acquisition was obtained through a HBM force sensor and a Keyence displacement sensor. The samples were fixed with use of internal threads in the connecting block and the pipe. The connecting block was connected directly to HBM sensor, through special threaded adapter.
The pipe was coupled to subassembly of the station, which applied the force to the joint. Tests that were performed on the station were carried out on the basis of standards EN ISO 12996:2013, EN 2591-417:2007, and PN-EN 1993-1-8. The results can be found in Figures  15 16 17    The last test was the tensile strength test, which was carried out with sample sets no. 1, 3, and 5. The tensile strength test was conducted on prototype station, where the force applied to the joint was generated by ball screw manually powered using a crank. Nevertheless, data acquisition was obtained through a HBM force sensor and a Keyence displacement sensor. The samples were fixed with use of internal threads in the connecting block and the pipe. The connecting block was connected directly to HBM sensor, through special threaded adapter.
The pipe was coupled to subassembly of the station, which applied the force to the joint. Tests that were performed on the station were carried out on the basis of standards EN ISO 12996:2013, EN 2591-417:2007, and PN-EN 1993-1-8. The results can be found in Figures  15 16 17      The last test was the tensile strength test, which was carried out with sample sets no. 1, 3, and 5. The tensile strength test was conducted on prototype station, where the force applied to the joint was generated by ball screw manually powered using a crank. Nevertheless, data acquisition was obtained through a HBM force sensor and a Keyence displacement sensor. The samples were fixed with use of internal threads in the connecting block and the pipe. The connecting block was connected directly to HBM sensor, through special threaded adapter.
The FEA allows for determining the behavior of almost every formed joint under various loads and geometrical conditions  [29, 30]. This method was used by other researchers to specify the most advantageous shapes of forming tools in particular cases and to improve the robustness of the forming process  [30]. The FEA can overcome several difficulties that are connected with non-linear nature of the process, such as large deformations, material plasticity, and contact interactions  [28]. In this study, the Finite Element Analysis was performed in test case A with the use of industrial software Solidworks Simulation Premium. According to the figure that is presented above, the destruction of the joint can only be noticed in the case of the pipe element. While testing the flange, the pipe was cut down by the formed flange of the connecting block.
The FEA allows for determining the behavior of almost every formed joint under various loads and geometrical conditions  [29, 30]. This method was used by other researchers to specify the most advantageous shapes of forming tools in particular cases and to improve the robustness of the forming process  [30]. The FEA can overcome several difficulties that are connected with non-linear nature of the process, such as large deformations, material plasticity, and contact interactions  [28]. In this study, the Finite Element Analysis was performed in test case A with the use of industrial software Solidworks Simulation Premium. While investigating the three-dimensional nature of the designed joint with the forming tool that is presented in this study, it was found out that the elements have rotational symmetry along the axis of the tube.
Funding: This research was funded by [ Regional Operational Programme for the Wielkopolska Voivodeship  financed by the  Structural Funds of the European Union  and the State budget of Poland] grant number [ RPWP. 01. 02. 00-30-0046/16 ]. Acknowledgments: This work was supported by  Regional Operational Programme for the Opole Voivodeship  financed by the  Structural Funds of the European Union  and the State budget of Poland:  RPOP. 01. 01. 00-16-0017/16.
Due to bead sedimentation, enzyme assay with immobilized InNpXyn11A was performed using a ThermoMixer ® C (Eppendorf) under constant agitation at 1,400 rpm. Aliquot of 50 µL was withdraw and instantly mixed with 200 µL of 1 M Na 2 CO 3. Beads were removed using magnetic stand and supernatant pipetted down to a 96 wells microplate. Absorbance at 401 nm (ε = 22,209 M -1. cm -  ) was measured using a microplate spectrophotometer (Eon Microplate Spectrophotometer, Biotek). activities were determined using beechwood xylan or 4-nitrophenyl-β-D-xylotrioside at concentrations 10 and 2. 5 times over the K m values, respectively (1. 8 mg. mL -1 and 2. 1 mM 44 ). The kinetic parameters were calculated using non-linear regression in SigmaPlot (Systat Software, San Jose, CA). HPAEC-PAD. Quantification of short xylooligosaccharides released over the time from beechwood xylan by free and immobilized InNpXyn11A were determined using aliquots (200 µL) removed at regular time intervals and heated at 95 °C for 10 min to terminate the reaction.
Acknowledgements We thank  Thierry Vernet  for providing plasmid coding for Jo and In. We thank  Nelly Monties  for technical assistance on HPAEC-PAD ICS3000 provided by the  ICEO facility  dedicated to enzyme screening and discovery, part of the  Integrated Screening Platform of Toulouse (PICT, IBiSA ). We thank  Cécile Pouzet  for technical assistance at the TRI-FRAIB imaging facility. We thank also  Gwénaëlle Guittier  for performing N 2 adsorptiondesorption experiment. We thank  A. F. Mingotaud ,  P. Saint-Aguet  and  R. Laurent  from  IMRCP, Toulouse  for performing the SEC-MALS experiments. We finally thank  Harry J. Gilbert  for his valuable comments on the manuscript. This work used the platforms of the Grenoble Instruct-ERIC Center (ISBG:  UMS 3518 CNRS-CEA-UGA-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB).
2015b ) and is a ratio of the number of eggs an average recruit could produce over its lifetime in a fished stock versus the number ASSESSMENT OF ARTISANAL ALBULID FISHERY IN THE SOUTH PACIFIC of eggs an average recruit could produce over its lifetime in an unfished stock  (Brooks et al. 2010). The SPR of the bonefish population and fishery selectivity at Anaa Atoll was estimated with the age-structured methods outlined in  Hordyk et al. (2015a)  with the LBSPRfit function and model type "absel" in the R package LBSPR using the life history characteristics and the two separate estimates of M described above  (Hordyk 2019). The plots of selectivity and the annual length-frequency distributions of the harvested fish compared with a simulated population with an SPR of F 40% were extracted with the plotMat and plot-Targ functions in the R package LBSPR.
2007 (Friedlander et al. , 2015;; Kamikawa et al. 2015). Our estimate of fishing mortality for males is consistently higher than for females, which supports our "differential spawning behavior" theory in which male bonefish engage in spawning events more frequently than females and as a result are more vulnerable to harvest in the trap complex. Previous research indicates that female bonefish exhibit group synchronous oocyte development, with three developmental stages present in the gonads of spawningcapable fish (i. e. , primary growth, cortical alveolar, and vitellogenic 3). Therefore, we suggest that females need time to develop their oocytes between batches, while in contrast, males appear to be capable of spawning during every monthly lunar phase  (Filous et al. 2019a ). These life history characteristics are corroborated by unpublished acoustic tracking data that showed that males visited the spawning grounds more frequently than females (A.
Although our data provides only a recent window into the time series changes in this fishery, our results suggest that the stock is being fished at a level that is economically inefficient and at risk of impairing recruitment. While the fishery continues to provide yield, the collapse of fisheries under heavy exploitation is often preceded by a period of stability in catch rates, and these plateau-style collapses, which are often a result of depensation (i. e. , Allee effects from reduced spawner abundance), provide false impressions of fisheries equilibrium  (Mullon et al. 2005). Catch rates are known to be hyperstable in spawning aggregations, and declines are first realized in adjacent sectors of the fishery (De Mitcheson and Erisman 2012). There is some evidence to suggest that this phenomenon is occurring on Anaa, as local ecological knowledge indicates that bonefish have become increasingly harder to catch in the traditional fishing holes in the lagoon, where they were customarily targeted with hook and line.
ACKNOWLEDGMENTS We would like to acknowledge and thank the fishers of Anaa Atoll, who welcomed the presence of A. Filous in their community. Specifically, we would like to acknowledge the help of Toma Teaku, Jean Dexter, Hotea Tevaitau, and Clayton Teaku for their assistance in measuring bonefish captured in the artisanal fish traps. We also gratefully acknowledge the assistance of Raphael Raveino, Raymonde Raveino, Louise Raveino, and the entire Raveino family, who cared for A. Filous both on Anaa Atoll and Tahiti. These individuals provided in-depth local knowledge and logistical support that was instrumental to the success of this research. We would like to thank the children at the school of Anaa Atoll and their director Jean Pierre Beaury, who were indispensable in communicating the results of this work to the larger community and supporting fisheries conservation.
This new small virus was baptized Sputnik virophage  [30]. Sputnik has an 18,343 bp-long double-stranded DNA genome that harbors 21 putative genes (Table  1 ). These genes encode major and minor capsid proteins (MCP and mCP, respectively) and proteins predicted to be involved in DNA replication  [84]. Genomic and structural study of the virophage showed that the MCP, encoded by the V20 gene, contains 595 amino acids, which is in the same magnitude range as for Mimivirus MCP (437 amino acids). In addition, CryoEM analysis revealed that, like for Mimivirus, the MCP of Sputnik has a double jelly-roll fold  [85, 86]. However, the Sputnik MCP was not homologous, neither to the Mimivirus MCP, nor to any other sequences present in databases at the time the Sputnik virophage was isolated. This suggests that Sputnik evolved from other mobile genetic elements before its association with Mimivirus  [85, 86].
We found that its genome was very divergent from the other known isolated virophages. Indeed, the phylogenetic tree based on Sissivirophage MCP (GenBank: MN151334) shows that the closest homolog is from a virophage assembled from metagenomic data from Lake Mendota, USA (see below). Further experiments and analyses are ongoing to characterize the genomic, biological, and ultrastructural features of this new member of the virophage group (Figure  2 ). In addition to these virophages, a second strain of Zamilon (Zamilon virus isolate Z. vigne ) was isolated in France, and sequenced by Jeudy et al. ; while the characterization of the virophage has not yet been published, this virophage has a 17,327 bp double-stranded DNA genome encoding 20 predicted genes (GenBank: MG807318. 2).
Virophages from Metagenomic Datasets Although the first virophage was reported in 2008, 11 years ago, coculture strategies contributed to isolate only ten strains of virophage (described above). In contrast, 57 novel complete or partial virophage genomes were reported from different metagenomic datasets  [98, 99]. Metagenome assemblies not only revealed the diversity of the virophage group, but also suggested a relevant ecological impact of these small viruses and their potential role in the stability of the microbial network  [55, 98]. The first virophage detected by metagenomics was the Organic Lake virophage (OLV). OLV is also the second virophage discovered after Sputnik. Its genome was assembled by metaproteogenomic analyses using samples collected from the Organic Lake, a hypersaline meromictic lake in Antarctica  [55].
Funding: This work was supported by a grant from the  French State  managed by the  National Research Agency  under the " Investissements d'avenir" (Investments for the Future) program  with the reference  ANR-10-IAHU-03  ( Méditerranée Infection ) and by  Région Provence-Alpes-Côte-d'Azur and European funding FEDER PRIMMI. Said Mougari  was supported by the  Foundation Méditerranée Infection scholarship.
, 2016; Dhawale et al. , 2015; Nawar and Mouazen, 2017; Viscarra Rossel and Behrens, 2010; Vohland et al. , 2014). However, other methods can be used with their specific conditions. For instance, the artificial neural network (ANN) and the support vector machine (SVM) require a large dataset. A comparison of different chemometrics methods show that advanced methods such as ANN, SVM, and Multivariate Adaptive Regression Splines (MARS) can slightly improve the performance  (Kuang et al. , 2015; Li et al. , 2015; Viscarra Rossel and Behrens, 2010). Transferring a model between several samples can be very interesting, and PLSR is better than ANN to model local variations, while they are quite similar for global variations  (Wijewardane et al. , 2016). In order to improve model performance and robustness, spectral variable selection is achieved to reduce the spectral collinearity.
As a side effect, this reduction makes chemical interpretation easier  (Peng et al. , 2014; Viscarra Rossel and Behrens, 2010; Vohland et al. , 2014). Furthermore, coupling spectroscopic datasets have been shown to improve model performance  (Clairotte et al. , 2016)  by the increase of the spectral and thus chemical information. Chemometrics methods have been widely used in the biochemistry and the pharmacology domains. These methods are usually based on an experimentally built sample set in which the target concentrations are known or spiked. A direct chemometrics approach is promising to predict concentrations in environmental solids such as sediments, soils, and biological tissues. The challenge is here to propose a procedure designed to build a robust model based on a natural dataset only.
Indeed, we can expect that the range and spread of real target values of a learning dataset is less robust than a model generated with an experimentally designed dataset. The aim of this study is to propose a way to build a PLSR model with hyperspectral data on a natural environmental sediment using the opportunity of its heterogeneity to generate robust models without the constitution of an experimental set. We applied this method to predict the organic matter content in a sediment core by the way of a PLSR model. A sample from the Lake Bourget was used and analyzed with short-wave infrared (SWIR, 900-2500 nm) hyperspectral imaging. The loss on ignition (LOI) was the reference method for the organic matter values. LOI is predicted from a hyperspectral image and a variable selection applied to increase the robustness of the model.
Otherwise, excluding the Rhone, it is 580 km 2. Over the last 10,000 years, the river-borne silicate fraction has ranged between 10 and 40% of the bulk sediment, with the remaining amount composed of carbonate depending on past climate conditions  (Arnaud et al. , 2012 (Arnaud et al. , , 2005;; Debret et al. , 2010; Giguet-Covex et al. , 2010; Jenny et al. , 2014). In 2009, sediment (LDB09-P101, length = 54 cm, width = 9 cm) was cored in the northern basin of Lake Bourget  (N45 45. 334, E5 51. 332, 145 m water depth)  in the frame of the IPER-RETRO program (ANR-08-VUL 005,  (Perga et al. , 2015) ). This core was selected for this study because it contains both seasonally paced bioinduced millimeter lamina as well as interbedded deposits brought by Rhone River flood events.
X-ray fluorescence spectroscopy The relative contents of major elements were analyzed by X-ray fluorescence (XRF) at a 200 μm resolution on the surface of the sediment core with an ITRAX XRF Core Scanner (Cox Analytical Systems) at the CEREGE laboratory in 2010. The split core surface was first covered with a 4-μm-thick Ultralene film to avoid contamination and desiccation of the sediment. The X-ray beam was generated with a molybdenum tube at 35 kV and 30 mA, with a runtime of 15 s. Compton (incoherent) and Rayleigh (coherent) scattering data were extracted from it, and the inc/coh ratio was calculated. It has been used in many studies as a qualitative proxy of organic matter  (Bajard et al. , 2016; Chawchai et al. , 2016; Croudace and Rothwell, 2015; Lintern et al. , 2016).
Data analysis Data acquisition was performed using Specim hardware ENVI 4. 8. The data was then converted, processed and analyzed with MATLAB (R2017a, MathWorks). Several free and open MATLAB toolboxes were used and are detailed afterwards. A key point to predict the LOI550 from hyperspectral imaging is the coupling of the volume of the sample at 5 mm resolution with surface pixels of approximately 0. 2 mm. The proposed method is composed of four main steps. (1) A bootstrapping selection in the hyperspectral spectra of the LOI sampling area was performed to construct several datasets. (2) Then, PLSR was used to model LOI550 thanks to these spectra and the discrete LOI550 measurements for all the datasets. (3) The 10 optimal models were retained based on their performances. (4) Wavelengths highly correlated with LOI550 were identified with variable selection on the retained models in (3).
Different techniques could be used to generate a representative value of the spectrum used for regression with LOI data, (mean, bootstrap, min or max, mode or median spectrum). The limit of a mean spectrum for each sampling area leads to smooth spectral variabilities; resulting model were not relevant. We applied a bootstrapping procedure rather than a mean spectrum assuming that LOI could be better explained by a specific spectrum of the region of interest than by the mean spectrum of this region. Bootstrapping was used to randomly select 100 spectra in each sampling area to generate 100 datasets of 20 spectra.
Partial least squares regression PLSR was used to establish a relationship between the LOI550 values and SWIR hyperspectral imaging. This method is based on the extraction of orthogonal predictors (also called latent variables, LV) corresponding to the maximum variability in the spectral bands used as predictors linked to one or several predicted variable(s)  (Wold et al. , 1984). Each of the 100 bootstrapped datasets was divided in a calibration set (13 data) and a validation set (the 7 unused data). For each of the 100 bootstrapped datasets, a PLSR model is generated based on the calibration set, and then applied to the validation set in order to assess the performances of the prediction. The optimal number of latent variables was estimated by the use of the Durbin Watson test  (Durbin and Watson, 1950)  which is similar to a signal-to-noise ratio and was used to improve the robustness of the model.
Range, prediction and confidence intervals The PLSR model was made with LOI550 measurements that have a range of 4. 97 wt% to 8. 60 wt%. We assume that LOI550 model intercept at the origin (there is no organic matter spectroscopic signal when there is no organic matter in the sediment). Then we consider that we can predict LOI550 in the range 0 wt% to 8. 60 wt%. 93 % of the data are included in it and have a minimal prediction error of 2. 25 wt% (α = 0. 05) in the center of the calibration (6. 26 wt%). Prediction and confidence intervals were calculated thanks to the complete dataset (figure 5. c). The prediction error map (figure 5. b) shows side effects (right and left), which can be due to surface variation or illumination. This observation is also visible in the concentration map. Obviously, areas with high surface variations (gap, fissures) will not be used in the LOI550 profile, and their prediction errors are more important than other predicted values at the same depth.
This proxy for laminated or varve sediment could be precise at the seasonal scale with the 200 μm pixel size. This methodology must be tested on other cores or heterogeneous samples to verify its relevance. The "universality" of the model generated from this dataset, should be applied on different lake systems in order to test the ability to predict organic matter in other environments and to set the limitations of this. It may be used on other proxies that are characteristic in the basin catchment. Predicting several proxies at the same time may also be studied to create a robust model that is not disturbed by some chemical or physical variabilities. The proposed methodology applied to sediment cores has great possibilities to more precisely infer the paleo-climate and paleo-environment that are recorded in the core inside each sediment structure.
Acknowledgements The core used in this study is stored in the EDYTEM laboratory that also performed the LOI analysis in 2009 during the  IPER-RETRO program  ( ANR-08-VUL 005 ). Hyperspectral imaging was processed at the  University of Normandie-Rouen  and was funded by the  Region Normandie , which supports the scientific consortium SCALE  UMR CNRS 3730.
Statistics The data were analysed using the R software. Male and female offspring were tested separately as there was sexual difference for most phenotypes. For all tests, an adjusted p-value of p < 0. 05 was considered to be significant. For metabolic data, as variances were unequal, data were Boxcox transformed, using the powerTransform and bcPower functions of the car package and analysed with a likelihood-ratio test, using the lrtest function of the lmtest package, and adjusted with the Benjamini and Hochberg (BH) correction for multiple comparisons using the p. adjust function. Linear mixed models were used to model the evolution of metabolic phenotypes with time, using the lmer function of the lme4 package. Estimates of the slopes for each phenotype were reported as "β" and coded as Phenotype = Group + Time + Group × Time+random(ID × Time) and Phenotype = Group + Time + Diet + Diet × Time + Group × Time + Group × Diet × Time + random(ID × Time) for F0 and F1 respectively, where "Group" is the maternal group (CTRL, OB or WL), "Diet" is the F1 post-weaning diet (CD or HFD), "Time" is the timepoint and "random(ID × Time)" is a random intercept and slope component for repeated measures.