dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: This work was partially supported by the  "Ministerio de Ciencia, Innovación y Universidades, Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018", Spain , under Grant  RTI2018-096384-B-I00 , and by the  "Programa de Becas SENESCYT" de la República del Ecuador.
Field Sampling Data and samples were collected during RV Meteor cruise 93 to the Peruvian upwelling region in February and March 2013 (2013/02/06 to 2013/03/10, Callao/Peru to Cristobal/Panama), At the time of the cruise, southeasterly winds prevailed (1 -9 m s -1 ,  Thomsen et al. , 2016) , resulting in nutrient upwelling typical of austral summer conditions. At five stations, we took depth-specific mesozooplankton samples conducting 10 vertical hauls (1 m s -1 ) with a Hydrobios MultiNet Maxi (0. 50 m 2 mouth opening, 333 µm mesh size, 9 nets). During each station occupation, a day haul and a night haul were obtained within 24 h and in very close proximity (average distance 0. 15 km, range 0-0. 7 km) to each other, representing a pair of daynight hauls for the assessment of diel vertical migration patterns.
b Formula for general mesozooplankton was used as no specific formula is given in  Lehette and Hernández-León (2009). Biomass was calculated as biomass = BMmultiplicator * area BMexponent. Respi. factor and Excr. factor from  Ikeda (2014). DW to C conversion factors from  Kiørboe (2013). n. a. , not applicable. regulation of oxygen uptake fails, resulting in a drastic metabolic reduction  (Childress and Seibel, 1998). As we only have data on the p crit of these two species from the Peruvian upwelling, we use the mean of these values (0. 6 kPa) as a general p crit for crustaceans in the region. These p crit values were obtained at 13 • C and in order to calculate corrected respiration rates we need to extrapolate the p crit to the environmental temperatures observed. For this, we use Equation (  1 ) by  Deutsch et al.
It is even more important to consider the metabolic suppression when studying DVM-mediated active export of respiratory carbon. Not considering this mechanism leads to 10fold overestimation of DVM-mediated carbon export to below 100 m depth at stations s4 and s5. At station s3 we would assume a considerable export of 5. 08 mg C m -2 d -1 , where there is none. Likewise,  Escribano et al. (2009)  calculated a DVM-mediated active export of 4417. 4 mg C m -2 d -1 via respiration to the 60-600 m depth layer, which should likely be reduced to zero due to the fact that the oxygen concentrations at their sampling location were extremely low between 60 and at least 500 m depth. Another dataset of zooplankton mediated active export from an upwelling region was collected in the California Current system  (Stukel et al.
Implications for Zooplankton Sampling Currently, published datasets are often either taxonomically well-resolved abundance data, lacking information on size and biomass of the identified groups (e. g. ,  Criales-Hernández et al. , 2008  in the same region as the present study), or only contain bulk zooplankton biomass (or biovolume) data, which do not allow for the consideration of the impact of size distribution or taxonomic and functional composition on metabolic rates and fluxes as well as food web interactions  (Ayón et al. , 2011). Physiological rates generally do not scale linearly with body size  (Moloney and Field, 1989)  and therefore changes in the size distribution can go in hand with changes in zooplankton mediated biogeochemical fluxes despite unchanged bulk biomass. Detailed taxonomic analyses are very valuable for diversity studies, but it would be interesting to also integrate size measurements (e.
Optical in situ observations, e. g. , using a VPR or a UVP5, Pelagios, ROVs, etc. will be helpful to ameliorate both problems, but do have the problem of instrument avoidance by optically orienting organisms (e. g. ,  Benoit-Bird et al. , 2010). In general, both approaches should be combined, optimally in an integrated camera-net instrument package to obtain a more complete understanding of zooplankton distribution. We base our calculations on area-biomass functions obtained in the subtropical Atlantic, and the work by  Lehette and Hernández-León (2009)  is to our knowledge the only comprehensive work assessing this issue. Further work on areabiomass relationships is needed and the underlying individual raw data (images, weights and any further information, such as C-, N-, P-content) should be stored in an international database [e.
g. , in EcoTaxa  (Picheral et al. , 2019) ], similar to the NCBI database of genomic sequences. Such data will enable us to better unlock the secrets that are hidden in the image data from the zooscan approach and other optical methods that are currently being collected. Also for these data, EcoTaxa seems to be the currently best developed solution for image hosting, collaborative online sorting and data sharing. Some of these ideas have already been discussed in  Lombard et al. (2019) , an outlook paper that details the needs for a globally consistent strategy in plankton ocean observation, but the need for a database that contains individual high-quality images and data should not be overlooked.
[34]. In the work of Gao et al. [35] , the authors introduced a Chebyshev surrogate model to characterize the dynamic buckling of beam-column structures. Last but not least, buckling uncertainty of carbon nanotube-reinforced composite under compressive stress was reported in the work of Pouresmaeeli et al. [36] , using Galerkin's method. Despite all of these efforts, it is not always possible to investigate the highly nonlinear relationship between load-lateral deflection of columns under axial loading, as reported in the literature  [1, 2, 5, 6, 11, [37] [38] [39]. Such nonlinear phenomenon (large displacement when the load increment is small) made the instability analysis of structural components under compressive stress more complicated  [40, 41]. Yang et al. [4]  used the commercial finite element software Abaqus to investigate the buckling problem for I-section steel columns under axial loading.
Jiang et al. [39]  applied Abaqus in order to simulate the buckling behavior of pseudo-elastic Nicken Titanium alloy tubes. Shi et al. [5]  employed another commercial software, namely Ansys, to generate 60 finite element buckling configurations based on 24 initial experimental tests. However, commercial software programs are not compatible with large datasets when propagating material uncertainty on buckling responses  [42, 43]. Within the context of uncertainty propagation through partial differential equations, the development of code in programming language is indispensable. The main purpose of this study is to quantify the impact of uncertainties in material properties on the critical buckling load of columns under axial compression. With this aim, uncertainty in material properties was modeled using the ensemble of random matrices, constructed by Soize  [21, 22, 44].
Methodology for Modeling In this study, the methodology for modeling and simulation (Figure  2 ) was constructed, involving: (i) a probabilistic model for generating uncertainties in material elasticity matrix; and (ii) a finite element model for tracking equilibrium of column under axial compression using the Newton-Raphson method. Dispersion coefficient δ G and elastic constants of materials such as Young's modulus and Poisson's ratio are the inputs of the probabilistic model. The construction of this probabilistic model is presented in Section 2. 2. Regarding the finite element tool, the code is developed in Matlab for solving the boundary value problem of partial differential equations defined in Section 2. 3. The Newton-Raphson method was implemented in order to solve nonlinear equations for the column under compressive stress.
g) 3 0 ; losses rad ref RT Bi k ε σ = (5. h) * 0 0 ref q q q = (5. i) * s s ref ref q S Φ Φ = (5. j) with, 0 ref ref k T q R = (5. k) 2 2 (1 ) (1 ) ( , ) exp 2 ref spot S R π µ = - ( (1 ) g µ µ µ µ   - = -   -   (6) for the spatial distribution, while the temporal profile is either a square pulse represented by ( ) ( ) 1 1 ( , ) 1 ( ( )) 1 ( ( )) 2 2 h a d u erf erf τ τ φ τ τ φ τ τ = + - - + - (7. a) or an exponential decaying pulse 2 2 ( , ) exp 2 4 h h h u τ τ τ τ τ τ   = -     (7. b) In the above equations, φ is a smoothing parameter (φ=2700), while subscripts "a" and "d" refer to the ascent and descent time of the square pulse. For the solution of the complete model given by equations (3), the finite element method was used through the COMSOL Multiphysics® 4. 3b commercial package.
a) ( ) ( ) * 2 ( ) ' 0 , np h np u L e d τ α λ τ τ τ τ τ λ τ τ ′ - - = ′ ∂ ′ = ′ ∂ ∫ (15. b) ( ) ( ) 2 * 2 1/2 2 2 * 1 2 2 ( 1/ 2) np np n np eff np k N J Bi n k λ λ λ λ + = - + -+ (15. c) where * 1/2 ( ) n np J r λ + is the Bessel function of order n+1/2, and the terms ( ) g n , ( ) p n are given by equations  (13). The eigenvalues n p λ are obtained from the following transcendental equation  [41] : ( ) ( ) * 1/2 1/2 * 2 0 eff n np np n np np k Bi dJ J d k λ λ λ λ + + - + = (16) It is worth mentioning that, for computational efficiency, the double summation in equation (  14 ) was rearranged in the form of a single summation by reordering the eigenvalues 2 np λ in increasing order  [44]. Furthermore, the different integrals appearing in the analytical solution were precomputed using MATLAB® symbolic computation capabilities.
Solution verification The solution of the complete forward model given by equations (  3 ) is obtained with the finite element software COMSOL Multiphysics®. In order to verify the numerical solution of the problem and to ensure the correct choice of the mesh parameters  [56] , the numerical solution is compared with the analytical solution of the problem, given by equations  (12) (13) (14) (15) (16)  by assuming a global heat transfer coefficient heff = 40 Wm -1 K -1 , for both solution methodologies. The analytical solution is implemented with MATLAB®. Tables  1  and 2  illustrate the convergence behaviour of the temperature eigenfunction expansion with the increasing number of terms used in the series solution (equations 12-16), for selected angular positions at r = R, at times t = 6 ms and t = 16 ms.
Although the analytical solution of the reduced model is evaluated only at the radial position r = R for different angular positions, its computational cost is higher as compared to the numerical solution of the complete model obtained with COMSOL Multiphysics®. It is worth mentioning that the commercial code COMSOL Multiphysics® makes an optimal use of the processing unit. We note also that, attempts in using the numerical solution obtained with COMSOL Multiphysics® for the solution of the inverse problem of interest have resulted in a high computational cost. With the aim of reducing this cost for the solution of the inverse problem, the analytical solution given by equations  (12) (13) (14) (15) (16)  is used for the solution of the direct problem, by using a preset fixed low truncation order in the eigenfunction expansion.
The convective heat losses can thus be computed from the conjugate heat transfer model with the estimated emissivity. Such a detailed physical model of the cool-down experiment of the levitated sample of metal was developed in COMSOL Multiphysics and solves a non-isothermal flow problem with temperaturedependent properties. As a consequence, one evaluation of the direct problem presents a high computational cost, thus, the Bayesian inversion algorithm proposed in  [61]  is being investigated for computational efficiency. Irrespective of the approach adopted to obtain the heat losses, the heat capacity and the density of the material being characterized are required. On the other hand, some data on heat capacities of metallic materials at high temperatures can be found in the literature  [8, 9, 11, 55].
We then introduce in Section 3, results of simulated IASI observations over the full spectrum and the pre-selection of all ozone-sensitive channels using a physical method based on non-linear sensitivity. The diagnosed observation error covariance matrix taking into account the IASI inter-channel error correlations is computed and the background error covariance matrix including ozone is also investigated with a lagged forecast method. Section 4 describes the channel selection method to extract information content of IASI ozone-sensitive data. Then, channel selection are evaluated with 1D-Var experiments on top of the operational IASI channel dataset and the final selected ozone-sensitive channel selection for NWP are presented. Finally, Section 5 summarizes the results and opens discussion.
, 2015). Photolysis rates are calculated in MOCAGE via a look-up table computed off-line. This table was updated using TUV (The Tropospheric Ultraviolet and Visible) software version 4. 6  (Madronich, 1987). Also the photolysis of PAN (Peroxyacetic Nitric Anhydride) was added. All these changes provide improvements to ozone in MOCAGE, particularly in the Upper Troposphere and Lower Stratosphere (UTLS) where ozone plays an important role on the radiation. Ozone profiles from MOCAGE are used in this study for two purposes: firstly, as input to the RTM for the simulation of IASI radiances, and secondly, to calculate the background error covariance matrix with a lagged forecast approach.
Pre-selection of O 3 channels based on a physical method First step of channel selection is the identification of IASI ozone-sensitive channels over the full spectrum. For this purpose, we have used the methodology developed by  (Gambacorta and Barnet, 2013)  for the Cross-track Infrared Sounder (CrIS) instrument. This method involves assessing sensitivity of the brightness temperature response ∆BT, represented by the difference between RTTOV simulations with unperturbed and perturbed profiles. Only the vertical ozone profile was modified with a constant perturbation, typically 10 % for the perturbed simulations. Same background datasets from previous simulation experiments were used. The ∆BT indicates the sensitivity of each channel to ozone in all atmospheric cases (345). The largest ∆BT sensitivity of IASI channels is located between 645 -770 cm -1 and 970 -1095 cm -1 , with values around 0.
Mean Sample of pre-selected O3 IASI channels Figure  5. Mean of differences between simulations with the unperturbed and perturbed ozone profiles (∆BT) w. r. t. IASI channels and wavenumbers [cm -1 ] using a set of 345 profiles (a). Sample of pre-selected ozone-sensitive channels between 970 and 1095 cm -1 for ∆BT using total datasets (b). The shaded area represents IASI instrumental noise at 280 K.
Code availability. Codes of the Radiative Transfer Model RTTOV and the uni-dimensional data assimilation system 1D-Var used is this study are all available on https://www. nwpsaf. eu/site/software/rttov/download/. Data availability. Data of radiosondes are available through this website: http://www. woudc. org. IASI data are available from EUMETSAT or AERIS: https://www. aeris-data. fr/. Model data are available upon request.
These systems have core homologous components, sometimes in multiple copies, and present similarities in terms of macromolecular architecture throughout Bacteria and Archaea (Fig  1 )  [12] [13] [14]. They include AAA+ ATPases, among which the T4aP PilT is the most powerful molecular motor known  [15] ; an integral (cytoplasmic) membrane (IM) platform; and a prepilin peptidase that matures a set of specific pilins or pseudopilins (in T2SS)  [16]. Bacteria with two cell membranes (diderms) also encode a secretin that forms an outer-membrane pore  [17]. Other the drawing indicates the colour code and the name of the different components in each type of system. For the Archaeal-T4P, the representation of the systems is based on the representation of the archaellum, and the genes mentioned in the legend are the names of the genes used in the literature (not the arCOG database's names).
The rooted tree shows two basal clades of Archaeal-T4P systems of unknown function, mostly found in methanogens (group 1 from  [35] , which is separated by the root in our tree). Unexpectedly, the position of the root places Tad as a system derived from Archaeal-T4P systems. This feature is found in the trees of ATPase, IM platform, and major pilin with high confidence. Furthermore, all these trees showed a monophyletic clade, including the Tad and the 'EppA-dependent' (Epd) pilus (clade 'Epd-like'), whose major pilins have similarly short sequence lengths when compared to the others from Archaeal-T4P (S7 Fig). Both Epd-like pili and Tad have two homologous genes encoding the IM platform, suggesting that their common ancestor already contained them both. We examined the domain structure of these two genes and found that each has one 'T2SSF' domain (PFAM domain PF00482), whereas most other Archaeal-T4Ps have two such domains and longer IM platform proteins.
The major pilin of this system is very divergent compared to major pseudopilins from Proteobacteria. Our analysis raises the exciting possibility that it might represent a novel type of protein secretion system derived from the T4aP independently of the T2SS. All trees show that the widely studied T4aP systems are very diverse and form several different clades in the tree, whereas the one with the PilU ATPase, the most widely studied, accounts for a minority of the identified systems. Most of the other T4aP are poorly characterised and may represent systems with novel properties. Finally, the results obtained with the final improved models showed few systems identified as generic. This suggests that there may be few novel families of systems to be discovered in the superfamily that contain the three key components (ATPase, IM platform, major pilin) and are present in the phyla represented in the genome database.
Detection of the TFF superfamily All the systems of the family were detected using MacSyFinder v1. 0. 2  [44]. This program uses a model to identify a type of system in a DNA sequence (typically a replicon). The model specifies the components of the system, each represented by an HMM profile, and how their systems are organised in the sequence. A full description of the program and the models can be found in http://macsyfinder. readthedocs. io/en/latest/index. html. Briefly, the components can be mandatory, accessory, or forbidden. This does not represent a biological classification. The classification is made to distinguish between components that are ubiquitous and easy to identify (mandatory) and those that are either frequently absent or easily missed (accessory). A system is only validated if it fulfils a quorum of mandatory (minimum mandatory genes required [MMGR]) and/or mandatory + accessory genes (minimum genes required [MGR]).
The final model was relaxed to identify a larger fraction of the systems. We reduced the MMGR to 4 and the MGR to 5. To fit the data better, we added the prepilin peptidase of ComM (comC) as another exchangeable gene of gspO. We set the gspC gene as mandatory and gspM as accessory, and gspD was set as a loner to better fit the data. ComM. In this initial model, only the genes that compose the pilus were used in the model, not the genes that encode DNA uptake system, such as comEA, comEB, and comEC  [31, 78, [94] [95] [96]. The minimal distance between genes was set to 5. The MMGR was set to 3 and the MGR to 5, and the system was set as multi_loci because some genes are loners. The genes comC, comGA, comGB, comGC, and comGD were set as mandatory, and the other ones were set as accessory in relation with their presence in experimentally validated systems, curated with an exploratory phase to know the relative abundance of the genes in the systems (the genes with more than 80% of presence in the detected systems were set as mandatory and the others as accessory).
2. 10-p1 (default settings)  [102]. We selected one sequence from each family as a representative. We aligned all the representatives using MAFFT v7. 273 (-auto, automatic selection of the parameters depending of the size of the alignment, default values for the other parameters)  [98]. With SEAVIEW  [103] , the poorly aligned regions at the extremities were manually trimmed in the alignment. The trimmed alignment was used to build the HMM profile using hmmbuild (default parameters) from HMMER package v3. 1b2  [104]. For the HMM profiles of the final model, we used the sequences of the profiles described above. Using the information of the phylogeny of the systems, we added the sequences of the systems that were annotated as generic but that clustered in a group of experimentally validated systems.
We aligned all the representatives using MAFFT v7. 273 (-auto, automatic selection of the parameters depending of the size of the alignment, default values for the other parameters)  [98]. With SEAVIEW  [103] , the poorly aligned regions at the extremities were manually trimmed in the alignment. The trimmed alignment was used to build the HMM profile using hmmbuild (default parameters) from HMMER package v3. 1b2  [104].
The 16S sequences were retrieved from genome sequences using RNammer v1. 2  [108]  (options -S set to bac and the -m to ssu). We aligned archaeal and bacterial 16S rRNA separately using the secondary structure models with SSU_Align v0. 1. 1 (http://eddylab. org/software/ssu-align/, default options). Poorly aligned positions were masked with ssu-mask. The alignment was trimmed with trimAl v1. 4rev15  [109]  (-noallgaps, which allows for removing regions that are only composed of gaps from the alignment). The maximum likelihood trees were inferred using IQ-TREE v1. 6. 7. 2  [52]  (using the bestselected model SYM + R6 for the archaeal tree and SYM + R10 for the bacterial tree, -bb 10,000 ultrafast bootstrap  [106] , -wbtl to conserve all optimal trees and their branch lengths).
2. When there were multiple copies of a family of clade-specific paralogues, the system was represented multiple times in the phylogeny and in the distance matrix. To solve the problem and to have only one distance between two systems, we chose the minimal distance between the paralogues of the systems. 3. Each core protein family has a different rate of evolution. To compare them, we normalised each matrix by the sum of all the branch lengths in the tree of the family. We then built a matrix that is the average of all normalised matrices. This average matrix was used to infer a tree with bioNJ  [110]. The tree was rooted at the midpoint. 4. We used the bioNJ tree to define monophyletic groups of similar systems. We iteratively used the R function 'cutree' from the stats package by gradually decreasing or increasing the heights at which the tree should be 'cut' until we obtained between 200 and 300 groups.
The proteins of each cluster were aligned with MAFFT and used to build HMM protein profiles as described above. To annotate the protein clusters, we used two methods. First, we searched for similarities of their HMM profiles with the profiles used to identify the TFFs' components using HHsearch (v3. 0. 3, p-value < 1 × 10 -5 ). Second, we searched for homologies between the remaining clusters and the profiles of the PFAM database (v31. 0, same method). To test whether a given cluster is significantly positively associated with a given type of system, we made the following analysis. We counted the occurrences of the elements of the cluster associated with a given type and made a contingency table in which the columns are the type versus all other types and the lines are presence or absence of an element from the cluster.
Acknowledgments We are much indebted to  Olivera Francetic  for comments, encouragement, and guidance during the development of this study. We thank  Simonetta Gribaldo  for comments and suggestions on a previous version of the manuscript and  Bertrand Neron  for continuous support of MacSyFinder. We thank  Simonetta Gribaldo  and  Panagiotis Adam  for providing a cladogram symbolising the relationships between the clades analysed in this study, which we used as support for Figs 4,  S2  and  S11.