dataesr/scientific-paragraphs-categorization huggingface dataset:
Channel-belt aggradation As vertical offset between accretion steps As vertical offset between accretion steps Lithologies • Not currently modelled • Stochastic output automatically • Modelled using a rule-based, • Can be modelled manually by stochastic approach creating a grid in each channel • Not conditioned on well data Ideal modelling applications Sector models of meander-belt reservoirs Sector models of meander-belt reservoirs of any scale, with channel fills and oxbow- at any scale, but especially suitable for shaped abandonments imaged in 3D cases where internal accretion geometries seismic datasets are clearly imaged in 3D seismic datasets Main Inputs • Youngest preserved channel path • Youngest preserved channel path • Migration rate probability • Numerous remnant point-bar distributions deposits used to determine.
, 2014). In a more recent study, a group led by Stephen Krawetz  (Swanson et al. , 2020)  used sperm RNA-seq datasets to identify transcripts of bacterial origin and shed light to the bacterial (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. composition of an ejaculate. They found a diverse bacterial population mostly characterized by members of the phyla Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria  (Swanson et al. , 2020). In pigs, the presence of bacteria in sperm is well documented and bacterial populations in ejaculates are common  (Althouse and Lu, 2005). In pigs, most of the bacteria present in semen ejaculates seem to have an external origin and might thus have contaminated the sperm after ejaculation  (Schulze et al.
Identification of RNA molecules of bacterial origin RNA-seq reads of low quality and adaptor contaminations were removed with Trimmomatic v. 0. 36  (Bolger et al. , 2014). Filtered reads were then mapped to the Sus scrofa genome (Sscrofa11. 1) with HISAT2 v. 2. 1. 0 (Kim et al. , 2015)  with default parameters except "--max seeds 30" and "-k 2". The reads that did not map to Sscrofa11. 1 were screened against the catalogue of porcine Transposable Elements from the Repbase database  (Bao et al. , 2015)  with HISAT2 v. 2. 1. 0 (Kim et al. , 2015). The reads that remained unmapped were taxonomically classified and quantified with Kraken v. 0. 10. 5  (Wood and Salzberg, 2014)  with a threshold score of 0. 15 and using the default database that includes NCBI taxonomic information and complete genomes from RefSeq of archaeal, bacteria, phage and viral domains.
Detection of antimicrobial resistance genes (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Unmapped reads were also subjected to identification and relative abundance quantification of ARGs. ARGs were identified using BLASTN v. 2. 7. 1  (Camacho et al. , 2009)  with 100% percentage identity using the Comprehensive Antibiotic Resistance Database (CARD) v. 3. 0. 0  (Jia et al. , 2017). The number of reads for each ARG was normalized by sequencing depth, as CPM. The read coverage across ARGs of point mutations was individually visualized using R v. 3. 5. 3  (R Core Team, 2017).
To calculate the relationship between the semen quality traits and the bacterial features of the sperm microbiome we used Link-HD  (Zingaretti et al. , 2019) , a tool for the integration of heterogeneous data that considers the compositional nature of microbiome datasets. This analysis included the 7 corrected phenotypes and the bacterial features with average (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. CPM ≥ 1 and representing more than 0. 001% of the total bacterial read counts. The phenotypic values of the clusters obtained by Link-he abundances of the 733 bacterial features of the cluster compromises obtained by Link-HD were then compared using the fitZig function of the metagenomeSeq package v. 1. 28. 2  (Paulson et al.
Relationship between the sperm microbiome and semen quality To identify potential relationships between bacterial abundances and semen quality we employed Link-HD  (Zingaretti et al. , 2019) , a recently developed tool based on STATIS methodology to integrate heterogeneous datasets. This approach analyzes different types of variables measured on the same samples, here bacterial abundance and semen quality phenotypes. To the end, the tool turns each raw data into cross-product matrix, computed on the distances between samples, which are then combined in a common configuration named compromise. A classical Principal Component Analysis (PCA) decomposes the compromise variance into orthogonal components and data structure can be easily recovered using standard clustering techniques. In this study, the samples were clustered into categories according to their microbiome and their semen quality.
The results also suggest that a large amount of bacteria may be present in the sperm doses used in artificial insemination. The bioinformatics tool Kraken  (Wood and Salzberg, 2014)  was designed to study meta-genomes and quantify the abundance of bacteria based in their DNA. Kraken has been also used to characterize the sperm microbiome using RNA-seq datasets in human  (Swanson et al. , 2020). While meta-genomics strictly focuses on the abundance of bacterial specimens, metatranscriptomics informs on the expression of their genes and thus the function and activity of these micro-organisms in the sample. Our data provides a quantification of each bacterium based in the overall expression of their transcripts which accounts for both the bacterial abundance and their gene expression activity and have the additional advantage to account for active microorganisms.
Acknowledgements We thank  Sam Balasch  (grup Gepork S. A. ) and  Craig Lewis  ( Genus -PIC ) for providing the blood and sperm samples. This work was supported by the  Spanish Ministry of Economy and Competitiveness (MINECO)  under grant  AGL2013-44978-R  and grant  AGL2017-86946-R  and by the  CERCA Programme/Generalitat de Catalunya. AGL2017-86946-R was also funded by the  Spanish State Research Agency (AEI)  and the  European Regional Development Fund (ERDF). We thank the  Agency for Management of University and Research Grants (AGAUR)  of the  Generalitat de Catalunya  (Grant Numbers  2014 SGR 1528  and  2017 SGR 01060 ). We also acknowledge the support of the  Spanish Ministry of Economy and Competitivity for the Center of Excellence Severo Ochoa   2016-2019  (Grant Number  SEV-2015-0533 ) grant awarded to the  Centre for Research in.
1-Simulation results At first, to evaluate our proposal, we have simulated with Matlab-Simulink a single-stage singlephase grid-connected PV system displayed in figure  4. The system parameters are listed in Table  1. It is composed of a PV array model with parameters listed in Table  2 , a single-phase inverter, a 110V-220V transformer, and an inductance. The latter has been selected, as recommended in  [27]. Because of using the transformer, grid current ( g i ), filter inductance ( L ), and resistance ( R ) are moved to the inverter side. So, they have been defined, as , , L f f i L R. The parameters of the controller are listed in Table  3. They have been set, as explained in the previous section. The Fast Terminal Sliding Mode Control -Direct Power Control (FTSMC-DPC) strategy is evaluated first in a steady-state.
These individuals are tracked over space and time while being transported by the simulated currents or wind. The main biological parameters and processes potentially interacting with the physical transport are also explicitly considered in the Lagrangian model. The resulting Eulerian-Lagrangian (or biophysical) model is used to simulate the trajectories of the larvae or seeds over the spatial domain (Figure  1 ). In this study, we reflect on these converging modeling approaches by first putting them into an historical perspective, and then by comparing the physical and biological processes represented in marine larva vs. terrestrial seed dispersal models, the data used for the models output corroboration (sensu  Augusiak et al. , 2014) , and the tools available to perform simulations. We finally discuss the opportunity that this convergence offers to bridge the gap between two scientific communities which are currently relatively disconnected.
g. , ROMS, MARS for the ocean, RAMS, WRF for the atmosphere). As a consequence, physical processes of small spatial and temporal scales, such as cloud formation, turbulence, and ocean mesoscale eddies, can be represented explicitly in regional models. But they need to be parameterized in global models, because they are smaller that the model horizontal resolution. In order to solve numerically the above equations on a discrete spatiotemporal computational grid, initial conditions and boundary conditions are needed. In ocean simulations, the initial conditions are usually the mean temperature and salinity 3D fields derived from observational based datasets (such as World Ocean Atlas, https://www. nodc. noaa. gov/OC5/woa18/), whereas ocean current velocities are initialized from rest. In atmospheric models initial conditions include the air temperature and relative humidity, and wind velocities are initialized from rest.
Using several analytical figures of merit, as previously recommended by El Haddad et al. [17]  such as the determination coefficient, uncertainty, limit of quantification and prediction ability (i. e. , trueness), it is interesting to observe that, depending on the type of data extraction, baseline modeling as well as the calibration model, the quantification performance of the technique can be dramatically different. Thus, these two papers  [17, 60]  aim to optimize any LIBS analysis through methodological steps toward a standardization of LIBS. For mapping interpretation Motto-Ros et al. [61]  recently pointed out the importance of this data processing and in particular signal extraction to provide robust and reliable results. Acquisition of elemental mapping provides thousands to millions of spectra, corresponding to complex LIBS datasets.
They proposed a new conditional data extraction procedure relying on the statistical uncertainty associated with the extracted signal. A synthetic spectral dataset is built with controlled spectral features to test the linearity, dynamic range and operating speed of different extraction approaches. This procedure is of particular relevance for the extraction of weak line intensities and in cases where the presence or absence of certain elements is critical (i. e. , biomedical applications or trace analysis). In addition, the proposed conditional approach offers new insights into the means of providing LIBS imaging results. To cope with the very big amount of data recorded by LIBS, preventing manual opening and processing of each single spectrum, it is strategic to build reliable and robust numerical tools for future routine use, as it is the case for the other analytical techniques used in laboratories, such as SEM, EMPA, ICP-MS, and XRF.
Sourcing and discrimination of minerals and gems The number of articles on rocks analysis has increased since 2007 and the major part of these   [64, 76] ). These two departments have published remarkable papers on the classification of minerals and gems to determine their provenance according to their LIBS signatures. This kind of study was already demonstrated through molecular techniques (IR, VNIR, Raman…) or analyzing their fluid inclusions (in terms of gas and liquid). Numerous broadband LIBS spectra of beryls  [65] , garnets  [67] , conflicts minerals  [77]  sapphires and rubis  [70] , diamonds  [71] , tourmalines  [72, 78] , are undertaken to obtain a database and compare their LIBS signatures. Because of social political or human rights abuses concerns, the provenance of gems is now essential for the systematic controls in legal laboratories.
However, the cited sentence as "the analyst needs no rigorous training in spectroscopy or gemology" should be taken with carefulness. For this aspect, it validates the development of a new software in 2018, so-called Quantagenetics®  [75]. In this last studied case, more than 768 million data points have been investigated, regarding the entire library that is composed of 19 200 spectra. As their studies demonstrated that chemometric analysis of LIBS spectra makes accurate, rapid provenance determination possible with a success rate higher than 95%, two US patents have been deposited (US Patents 9,063,085 and 8,699,022) about the use of multivariate statistical method based on Bayesian statistics using the Euclidian distance between LIBS spectra. Finally, mineral/rock discrimination through LIBS strongly depends in the first place on the measurement itself, in particular in the case of field measurements for which the sample surface may exhibit significant roughness and a lack of planarity and secondly, on the lack of reliable and robust algorithms to make the decision univocally, directly from raw data.
This difficulty, reported for tailing slurries, is also an essential aspect for any geological purposes, as the signature of LIBS spectra does not give any information about the molecular structure of the minerals. Then, the information for the anions are also hard to interpret as C and O can be related to the atmospheric air, and S is hardly detectable. In mineralogical study, the elemental information (cations and anions) is related to the mineral composition of the sample. New publications in the mining industry field, published over the last decade, report LIBS applications to characterize the spatial distribution of elements in ores, providing key information about their sources and depositional processes. These data assist the decision makers for further prospects in the field and in the evaluation of economic viability for extraction and refining.
The authors combined chemical, mineralogical and textural information from μ-EDXRF, LIBS and HSI for petrographic analysis of plutonic rocks. The LIBS technique provides very low detection limit for lithium and obviously enables the identification of Li-enrichment in biotites or plagioclases. Thus, using supervised classification of spectral information, mineral distribution maps are obtained for image analysis including geometrical data of each grain, such as grain size, grain orientation and grain location for subsequent targeted analysis. The authors obtained the modal mineralogy for plutonic rock classification for 20 rock slabs but only 4 slabs were scanned by LIBS.
b. Economic and strategic elements Mineralogical investigations applied for exploration of mineral deposits and mining residues require a good database regarding mineralogy, geochemistry, as well as the access of the rock texture for the evaluation of the economic potential of a deposit. Current standard procedures accepted by the industry are time consuming, expensive and personnel intensive. Due to the current crisis hitting the mining industry, an elevated demand exists for new exploration strategies and methods. They are aimed at providing a reference database in shorter time, at comparable accuracy and information level, but at definitely lower costs. As already mentioned, it is sometimes hard to find the actual data obtained on a mining area. Some nice in situ studies are not published as scientific paper, but they are more largely published as notes in the company's websites or only presented in specific mining meetings or even as internal documents.
Hyperspectral LIBS imaging Geological studies increasingly require highly sensitive elemental techniques able to image the distribution of elements in minerals with microscopic-scale resolution. Meanwhile, very fast highresolution multi-elemental LIBS technique for the mapping of geological samples has been developed. The repetition rate is up to 1 kHz and the spatial resolution from 15 μm to 50 μm  [44, 92]. A new methodology of data treatment was adapted to a multi-phase material. This megapixel LIBS imaging, done on a sample surface of 5 cm 2 by Fabre et al. [100] , is based on mask spectra according to each mineral phases. The study is conducted on a hydrothermal ore sample with a complex mineral structure involving five different mineral phases (different sulfur phases, quartz and carbonate).
Rossi et al. [105]  proposed a pioneering study using CF-LIBS (calibration-free LIBS) on gem-quality alexandrite, hiddenite and kunzite, elbaite and topaz minerals. Due to the presence of chromophore elements at minor levels and/or as traces, light lithophile elements (Li, Be and B) and differences in their crystal chemistry, these minerals were studied through a multi-methodological investigation based on EMPA-WDS, LA-ICP-MS, and CF-LIBS. For the first time in a real geological purpose, the authors demonstrated that the results from calibration-free LIBS were in a good line with EMPA, and LA-ICP-MS data for major elements such as Si, Al, Be and B. Very promising results were also obtained for chromophore elements such as Fe, Cr, V, and Mn. However, due to probable selfabsorption of the emission lines of the lightest elements (namely Na and Li) the concentrations found by LIBS were not in good agreement with those found via other conventional analysis methods.
Comparing to any other resources samples previously presented (solid rock without any water content), here the analysis of bitumen ore samples is very challenging due to the presence of several phases of wet or dry particles and heterogeneous mixture of clay, bitumen, water, and solid contents. Regarding to this complex topic, Harhira et al. [107]  present a new enabling method using LIBS technique to determine rapidly and without sample preparation the bitumen content in oil sands ores. Using PCA and PLS methods on LIBS data for quantitative purpose, they assess bitumen content and its properties. They obtained a good correlation between LIBS spectra and bitumen content and a prediction averaged absolute error around 0. 7 wt % is obtained. This statistical method demonstrates that LIBS is a real tool for rapidly assessing oil sands ore grades either in the lab, at-line, or online.
In 2018, after AGU Fall meeting (Washington), Jain et al. [98, 97]  proposed LIBS-based imaging for the analysis of hydrocarbon-bearing shale samples from the so-called Marcellus gas well  [97]. The data collected from these experiments were used to construct 2D elemental maps including hydrocarbon-forming elements, such as C and H elements. Shale samples were analyzed by LIBS using an 81 × 81 grid pattern covering an 8 × 8 mm 2 area. Spatially resolved elemental composition for H (0-0. 7 wt%), C (0-13 wt%) and H/C are obtained reflecting the probable differences between the matrices of the samples and the depth of their sampling along the log. A larger number of reference standards would have much improved the validation of the univariate calibration curve for C and H estimates. In this study, only three points/standards were used and no RSD value was given for each of them, but these results are satisfying and intensity maps are very instructive for the geologists.
The calibration curves undertaken for the REE elements also display figures of merit (slope, Offset, RMSE, R-square) allowing the readers to better evaluate the quality of the calibration. Those data support that LIBS has potential to quantify REEs in geological minerals/ores. The powerfulness of the LIBS technique for the detection of REE has also been validated in a complete study for the geological formation of the Norra Kärr Alkaline Complex in southern Sweden  [117]  in an elemental mapping. Yttrium was detected as trace elements in eudialyte (Na15Ca6(Fe,Mn)3 On the same kind of geological samples, Chirinos et al. [103]  published a nice paper on a 3D geological imaging involving REE. The authors presented 2D layer-by-layer mapping, 2D crosssectional imaging and 3D volume rendering of elements and isotopes in a Bastnäsite matrix.
Thus, concerning the industrial megapixel LIBS imaging for the geological characterization of minerals, most of these publications appear extremely promising for the geological domain and should pave the way for innumerable LIBS applications. Here, it is important to add a comment on the general processing of data, both for spectrum analysis and for processing large amounts of data generated in the case of imaging or sourcing. Publications rarely report the software or at least algorithm used for the treatment of the LIBS spectra. This first part of the study which can be time consuming due to the high number of emission lines, their identification and their treatment can be made by two different softwares, one for the detection/identification and one for the quantification/signal treatment.
Nevertheless, the multivariate analyses are commonly done using software such as R or OsiRis, but we can report for these geological purposes the use of PYMCA®, SpectraGryph®, or some specific development like the software Quantagenetics®. Indeed, we can underline the necessity to obtain reliable results from userfriendly software. Commercial ones, provided with the detector, are usually developed for the acquisition of the signal and not allow to accomplish expert spectral treatment, and large part of the LIBS instruments can be considered as 'home made' ones. Obviously, the portable LIBS instruments are now of first importance and it can be an essential part of the geologist toolkit. But geologists should use them properly and the manufacturers should produce instruments that can be internally calibrated to provide results that can be shared and understood on a high scientific level.
Thus, due to the potential of alkaline quantification on small minerals, the last ten years a new approach of LIBS applications have been developed to investigate the feasibility of in-situ K-Ar dating  [121] [122] [123] [124] [125] , for the estimates of K amount, as the 40 Ar is obtained through a mass spectrometer. In this thematic of extreme conditions, we can underline the proposals for future spatial missions involving LIBS apparatus  [121, 126, 127]. Finally, considering that LIBS measurements only require very little sample pre-treatment, meaning no metallization, no vacuuming and no addition of hazardous substances as acid, or gas, and considering also that LIBS is a fast all-optical multi-elemental technique, easy to set up even out of the laboratory, it is undoubtedly the ideal technique to achieve a first quick screening and then provide valuable data (smart sampling) prior to further laboratory analyses when needed.
[66], I would like to focus on one of the first papers of McMillan et al. [66]published in the special issue of Spectrochimica Acta B following the 4 th international conference on LIBS(Montreal, 2006). It is the first geological paper arguing with a very impressive level of information of the chemical structure and the interest in the LIBS specificities, and not a systematic blind analysis of minerals. McMillan et al. underline the subtle chemical difference between close minerals, not always observable by classical analytical techniques. The carbonate and silicate panel of database of composite broadband spectra (52 minerals) represents a very reliable base to test the potential of the LIBS for the identification of minerals. The authors already pointed out the high potential for field application even if "LIBS is yielding compositional and not stoichiometric information" (as previously published byHarmon et al.