dataesr/scientific-paragraphs-categorization huggingface dataset:
Small-Angle Scattering (SAS) and Atomic Force Microscopy (AFM) Small-angle scattering (SAS) and atomic force microscopy (AFM) are low-resolution techniques. SAS, consisting of the small-angle scattering of x-rays (SAXS) and neutrons (SANS), is capable of delivering structural information in the resolution range between 1 and 25 nm. Such information is similar to low-resolution cryo-EM data. SAS samples are dissolved in solution and are exposed to a beam of x-rays or neutrons. Subsequently, scattering data is collected from the detector. SAXS and SANS differ in their scattering particles: SAXS shows the electron density map while SANS shows the distribution of the nuclei of atoms  (Byron and Gilbert, 2000; Koch et al. , 2003; Svergun and Koch, 2003) , both of which are used to determine the size and shape of particles  (Schnablegger and Singh, 2011).
It is possible to map the positions of these reacting nucleotides by detecting the damaged nucleotides. However, finding the damaged nucleotides is tedious and requires specialized equipment. MOHCA-seq  (Cheng et al. , 2015)  is a new development of MOHCA (Figure  2 ), which couples MOHCA with HTS based on RT-stop. MAPseeker is the computational program used to analyze MOHCA-seq data and infer proximal tertiary interactions. Both the basepair information from M2-seq and the tertiary interaction information from MOHCA-seq are key restraints for RNA structure modeling. Besides in vitro structure probing, structure ensembles, and structure dynamics in living cells  (Ganser et al. , 2019)  in vivo probing is being rapidly developed. Apart from in vitro structure probing, methods like DMS, SHAPE, and HRP can also be applied in vivo  (Climie and Friesen, 1988; Moazed et al.
Types of Data-Driven Restraints Most experimental data alone, except x-ray crystallography and high-resolution cryo-EM, is not enough to infer atomicresolution RNA structure. Hence computational modeling is indispensable in interpreting the experimental data into an atomic description of RNA 3D structure. Such computational modeling can be based on existing structure determination software, such as Phenix  (Liebschner et al. , 2019)  for x-ray crystallography and cryo-EM, or CANDID  (Herrmann et al. , 2002)  for NMR, or based on structure prediction programs, e. g. , Rosetta  (Das and Baker, 2007) , Assemble2  (Jossinet et al. , 2010; Jossinet, 2015) , or MC-sym  (Parisien and Major, 2008). The type of data used in structure modeling can be classified into five types: (1) Sequence and sequence alignments are also determined from sequencing experiments.
Sequence alignments can indicate certain co-evolution between base pairs. With the human genome project  (Bentley, 2000)  and advances in sequencing technologies  (Schuster, 2008) , a massive number of metagenome sequences are being deposited in databases  (Sayers et al. , 2011). Sequence covariation gives an alternative to understanding base-pair interactions and even RNAprotein interactions  (Weinreb et al. , 2016). The secondary structures of many significant RNA structures, such as the 16S ribosomal RNA structure  (Noller and Woese, 1981) , were derived from sequence covariations. Besides, sequence covariation also predicts some long-range Watson-Crick base-pairs in pseudoknots: a successful case was illustrated in the group I intron structure  (Lehnert et al. , 1996). DIRECT is a new algorithm reported to improve the prediction in long-range contacts and captures more tertiary structural information  (Jian et al.
It is most difficult to determine non-Watson-Crick base-pairs. The best way besides x-ray crystallography and cryo-EM is to use a combination of chemical probing experiments and sequence covariation. And some successful examples have been reported  (Walczak et al. , 1996). As discussed above, MOHCA-seq has demonstrated its ability in determining both Watson-Crick and non-Watson-Crick long-range base-pairs. In 3D structure modeling, the base-pair information is often used together with the features of a single nucleotide. For example, M2-seq is coupled with DMS to probe RNA structure and has been successful  (Cheng et al. , 2017). (5) Non-canonical interaction-based RNA modules are RNA structural modules that are formed by non-canonical interactions and can be predicted by module prediction methods  (Cruz and Westhof, 2011; Zirbel et al.
Types of Structure Prediction Approaches All of these five types of structural information complement each other. Therefore, the integration of several types of the available information may improve the prediction of RNA structures. When neither a crystalline structure nor high-resolution cryo-EM data is available, the modeling of RNA structure in 3D may rely on RNA 3D structure prediction, which uses some existing knowledge about RNA structure derived from the existing databases. RNA 3D structure prediction has used some computational approaches, such as comparative modeling, fragment assembly, and de novo modeling (Figure  5 ) details can be referred to in the review paper by  Rother et al. (2011a). Comparative modeling is based on the assumption that homologous RNA molecules present similar structures in the relatively conservative regions.
An RNA can be predicted using the known homologous structure in the database as a template. A prediction may either use the pairwise sequence alignment to model based on the template structure  (Rother et al. , 2011b) (ModeRNA)  or translate the template structure into structural restraints in prediction  (Flores et al. , 2010)  (RNABuilder). However, comparative modeling highly depends on the availability of a homologous template, thus it cannot be applied to explore a novel RNA structure. Fragment assembly first decomposes the known RNA structures into fragments and makes a fragment library, and predicts an RNA structure by searching fragments that are similar to the target sequence and assembling them together. Several approaches in this type use secondary structure as a predefined restraint [e.
A good description of these noncanonical interactions both benefit the RNA structure modeling and the comprehension of functional RNA modules. To improve the RNA structure prediction methods, optimizing non-canonical interaction is still inevitable. As non-Watson-Crick base-pairs could include additional chemical probing information, e. g. , the N7G methylation by DMS, new computational methods dealing with these different chemical probing datasets may effectively improve the determination of non-Watson-Crick interactions and achieve better structure predictions. Automatic modeling workflow is another challenge in RNA modeling. Most currently available automatic web servers depend on the sole input of the RNA sequence and optionally the secondary structure information. And the structure modeling accuracy is not ideal because of the lack of experimental data.
However, it will be key to transform various experiment data into structural restraints in a standard manner. Automatic web servers should also promote the input of experimental data as restraints to improve prediction accuracy. With the increasing number of RNA structures being solved, our knowledge of RNA structure will be enlarged. More new RNA folds will be determined, and more structural modules will be explored. Even more structural rules, such as the coaxial rule, will be discovered. Gaining more knowledge about RNA structure will allow for better force-fields, better structure modeling, and a better understanding of the RNA functions. The increasing number of experimentally determined structures in the PDB database will expand the knowledge of existing RNA structure fold databases, e.
Atmospheric radiative transfer models (RTMs) are software tools that help researchers in understanding the radiative processes occurring in the Earth's atmosphere. Given their importance in remote sensing applications, the intercomparison of atmospheric RTMs is therefore one of the main tasks to evaluate model performance and identify the characteristics that differ between models. This can be a tedious tasks that requires a good knowledge of the model inputs-outputs and generation of large databases of consistent simulations. With the evolution of these software tools, their increase in complexity bears implications towards their use in practical applications and model intercomparison. Existing RTM-specific graphical user interfaces are not optimized for performing intercomparison studies of a wide variety of atmospheric RTMs.
In this paper, we present the Atmospheric Look-up table Generator (ALG) version 2. 0, a new software tool that facilitates generating large databases for a variety of atmospheric RTMs. ALG facilitates consistent and intuitive user interaction to enable running model executions and storing RTM data for any spectral configuration in the optical domain. We demonstrate the utility of ALG to perform intercomparison studies and global sensitivity analysis of broadly used atmospheric RTMs (6SV, MODTRAN, libRadtran). We expect that providing ALG to the research community will facilitate the usage of atmospheric RTMs to a wide range of applications in Earth Observation.
of these GUIs are customized for their specific RTM; and none can be used to define and run simulations for multiple RTMs in a consistent manner. In addition, they are not designed to easily precompute large databases, which are important due to the high computational burden for performing statistical analysis  (Verrelst et al. , 2016)  or running these models in a pixel-per-pixel basis  (Gastellu-Etchegorry et al. , 2003; Guanter et al. , 2009). Altogether, these GUIs are not fully offering practical solutions for the implementation of atmospheric RTMs in Earth Observation applications and, in particular, for model intercomparison. Users of atmospheric RTMs are therefore obliged to develop their own specific scripts to create datasets, which are typically: (1) limited to a handful input variables and (2) hardly extensible to other RTMs.
In an attempt to facilitate the consistent simulation of databases for a wide range of atmospheric RTMs, we developed the Atmospheric Look-up table Generator (ALG). ALG is a Matlab-compiled software package that allows generating Look-Up Tables (LUT) based on a suite of atmospheric RTMs. Namely, a LUT consists of a collection of input atmospheric conditions and corresponding generated RTM spectral outputs (see Section 3. 3 for further details). ALG provides consistent and intuitive user interaction for defining model configuration, running and storing RTM data for any spectral configuration in the optical domain. The main objectives of this paper are therefore: (1) to describe the ALG tool from a functional and sofware design perspective, thereby giving the reader an overview of the implemented features and generated LUT data; and (2) to perform a comparison study between the models implemented in ALG: MODTRAN (v5 and v6), 6SV v2.
1 and libRadtran v2. 0. 2. The remainder of this work is structured as follows: Section 2 gives an overview of the currently implemented atmospheric RTMs and associated graphical interfaces. Section 3 describes the ALG software design and its main features. Section 4 provides a comparative analysis of the implemented atmospheric RTMs. Section 5 summarizes a few applications as examples of the usage of ALG. Finally, Section 6 concludes with an outlook of on-going and planned functionalities to be implemented in future versions of ALG. In this section we briefly describe the key features of the atmospheric RTMs compatible with ALG version 2. 0 and their associated user interfaces.
5 nm. The latest updates of the code account for polarization in the atmosphere  (Kotchenova et al. , 2006; Kotchenova and Vermote, 2007). The only GUI dedicated to 6S known by the authors is its official website (6s. ltdri. org). Under its section Run 6SV, users can define the input configuration and run the code to retrieve the 6S input and output files directly from the web browser. Accordingly, the generation of multi-parametric LUTs is not feasible with this online GUI. In order to overcome this limitation, Py6S was developed  (Wilson, 2013). Py6S is a Python-based Application Programming Interface that provides (1) user-friendly model setting, (2) run and plotting capabilities, and (3) ability to import external data sources (e. g. atmospheric profiles). As such, Py6S can be integrated in any Python code facilitating the direct usage of 6S in data processing algorithms or for LUT generation.
OPAC Despite of not being an atmospheric RTM per se, the OPAC package is a widely used software tool that provides aerosol optical properties in the 0. 25 and 40 µm spectral range  (Hess et al. , 1998; Koepke et al. , 2015). OPAC calculates the extinction, scattering, and absorption coefficients, the single scattering albedo, the asymmetry parameter, and the phase function. These optical properties are calculated for a set of 10 pre-defined aerosol models and user-defined mixtures, thus expanding the existing capabilities of atmospheric RTMs. Similar to the previously defined RTMs, OPAC operates on the basis of input/output files. In order to facilitate its use, several GUIs have been developed that are compatible with OPAC. MOSPMAP is a toolbox, linked with libRadtran, for the optical modelling of complex aerosols, including pre-calculated optical properties of single aerosol particles as those in the OPAC package  (Gasteiger and Wiegner, 2018).
A user-friendly web interface was developed for MOPSMAP facilitating online calculations. The AEROgui tool  (Pedrós et al. , 2014)  is a similar GUI package that can be used to obtain the optical properties of a mixture of aerosol particles. Accordingly, AEROgui expands the current capabilities of OPAC by also providing a user interface to facilitate user definition of new aerosol mixtures. However, in most cases, there is no direct and straighforward way to include the OPAC output data into atmospheric RTM simulations.
Key software functionalities The primary goal of ALG is to provide a scientific software package that fills the gaps observed in the previously analyzed tools. In particular: 1. Each existing GUI is compatible with only one specific atmospheric RTM (e. g. , PcModWin for MODTRAN) and cannot be used to configure and run simulations for other RTMs. 2. These tools are not intended to run a large number of simulations and thus creating LUTs. 3. The inputs and outputs of each atmospheric RTM are generally not consistent between each other, adding an extra layer of complexity when using or comparing various models. Accordingly, ALG offers the following key functionalities: 1. ALG functions as a wrapper for running atmospheric RTMs, providing a graphical tool in which users can select the input configuration (i.
e. , atmospheric, geometric and spectral). In this way, ALG keeps the same functionality as all the previously described tools. 2. ALG facilitates the integration of additional atmospheric RTMs. In its current version 2. 0, ALG is compatible with MODTRAN5  (Berk et al. , 2006) , MODTRAN6  (Berk et al. , 2014) , 6SV version 2. 1  (Vermote et al. , 1997)  and libRadtran version 2. 0. 2  (Emde et al. , 2016). 3. The GUI is common to all the implemented models so that it facilitates the configuration of a wide variety of atmospheric RTM. 4. LUT design with ALG is a flexible process in which users can select a RTM, its input atmospheric variables and values. 5. ALG automatically processes and harmonizes all the RTM input and output data into the final LUT file. With this functionality, ALG facilitates the intercomparability between atmospheric RTMs and the possibility to alternate between models in a data processing algorithm (e.
ALG graphical interface ALG was developed following the high-level architecture design shown in Fig. 1 , which separates the tool into its GUI and its internal functions. ALG's graphical interface provides users the tools to configure the software, to run the RTM simulations and to construct the final LUT. In its core interface (LUT configuration) users can select the RTM input variables and values used to run the simulations and to store the spectral outputs into the LUT. This GUI is based on the commonalities found in Section 2, with extended functionalities that allow running a large set of simulations. The LUT configuration GUI is divided in five main subsequent steps as shown in Fig. 2  and further described in the paragraphs below. LUTs  (Brazile et al. , 2008). Secondly, ALG allows users to introduce selected atmospheric and geometric variables and their values (see Fig.
3 ). In ALG,  Thirdly, ALG implements a back-end interface with OPAC (v3. 1) database, expanding the pre-defined aerosol models with a comprehensive database of aerosol optical properties (i. e. , extinction, absorption and phase function). For OPAC aerosol models, users can create new aerosol mixtures described by their particle number density from a set of basic components. Fourthly, the spectral configuration of the RTM simulations is introduced. Users can set the desired spectral range and resolution, eventually at non-contiguous spectral intervals, saving computation time and disk storage of unwanted wavelengths. A set of predefined spectral configuration of common satellite instruments or user-defined sensors can be loaded. Finally, the user has access to advanced RTM configuration parameters (e.
g. , selection of radiative transfer solver, printed output files). These parameters largely depend on the selected RTM. All these LUT configuration parameters are stored in a. xml file that is later used by ALG's internal functions to automatically run the RTM simulations and construct the final LUT. This configuration. xml file can be loaded by ALG, allowing users to edit and re-run previous simulations e. g. , by adding new atmospheric variables, changing the spectral range/resolution or modifying advanced settings. It worth also noticing that ALG interface is common for all implemented atmospheric RTMs and the software harmonizes the naming and definition of atmospheric and geometric parameters to all models. Additionally, ALG's GUI provides access to the help system with information about:.
Other applications As described in the section 3, ALG facilitates the usage of atmospheric RTMs and the generation of large LUTs of atmospheric transfer functions. Users can integrate these LUTs into a wide range of applications. One of these applications is in End-to-end mission performance simulators (E2ES). E2ES are software tools that reproduce all aspects of satellite missions including the platform orbit/attitude, synthetic scene generation, sensor behavior, ground image processing and product evaluation  (Kerekes et al. , 1999; Segl et al. , 2012). These tools are used by remote sensing scientists and engineers to support trade-off studies, to prepare of system calibration tests and to optimize data processing algorithms. As part of the European Space Agency's FLEX E2ES  (Vicent et al.
ALG was used to simulate several datasets with varying aerosol types, optical properties and vertical distribution. The applicability of ALG is not limited to spaceborne instruments, but they is also suitable for the analysis of airborne and proximal sensing (e. g. , flux towers, unmanned aerial vehicles). In our recent publication  (Sabater et al. , 2018) , we studied the impact of path length in proximal sensing measurements of downwelling irradiance and at-sensor radiance, and their impact on sun-induced fluorescence retrieval. The study focused on remote sensing instruments placed at 2-50 m height over the surface. ALG was used to facilitate the running of MODTRAN simulation, which varied the instrument height and the SZA for standard atmospheric conditions. Altogether, these few examples demonstrate the versatility of ALG to address multiple remote sensing applications based on the use of atmospheric RTMs.
Conclusions and future work In this paper the main design concept and features of ALG have been described along with an intercomparison study for the atmospheric RTMs 6SV, MODTRAN and libRadtran. The a priori tedious tasks of (1) writting consistent input files, (2) running the RTMs in an efficient manner, (3) compiling and harmonizing the various model output files into ready-to-use LUT files, and (4) perform a model sensitivity analysis was largely simplified using the developed ALG tool and its compatibility with the ARTMO software framework  (Verrelst et al. , 2012). The sensitivity analysis results indicate that, overall, the various atmospheric RTMs simulate similarly the absorption and scattering processes for the selected atmospheric variables. However, there are still important differences in the sensitivity analysis that must be analysed in more detail.
Other practical applications, such as scene generation, atmospheric data analysis and atmospheric correction  (Thompson et al. , 2018) , can also benefit from the use of ALG. A few application examples were presented, demonstrating the software capabilities to generate consistent LUTs for several atmospheric RTMs, with a wide range of input atmospheric variables, nodes distribution and spectral configurations. ALG is an ongoing work and regularly updated with new added functionalities and tools. The following upgrades are in the pipeline: (1) including the polarization data calculated by the 6SV code and the polRadtran and Mytic solvers in libRadtran, (2) implement functions to develop emulators of atmospheric transfer functions, (3) generation of LUTs of TOA radiance spectra for non-Lambertian and non-homogeneous surfaces, (4) implementation of additional RTMs such as RRTOV and SOS, and (5) compatibility with Linux and MacOS systems.
Acknowledgements. This work was partially carried out in the frame of  ESA 's project  FLEX L1B  to L2 Algorithms Development study with  ESA-ESTEC  contract no. AO/1-8897/17/NL/MP. Jochem Verrelst  was supported by the  European Research Council (ERC)  under the ERC-2017-STG  SENTIFLEX  project (grant agreement  755617 ).
Code availability. ALG can be freely downloaded from the ARTMO website (http://ipl. uv. es/artmo) after registration and agreement of the use license. The software package has been developed in Matlab ® R2018a and its current version v2. 0 is compatible with Windows operating systems. The tool is provided as a stand-alone compiled executable file so that users not having a licence of Matlab can still run the software. Accordingly, users must first install the corresponding Matlab Runtime (MCR version 9. 5, 64-bits). In addition, the help system of ALG includes a set of guidelines to install and compile the compatible atmospheric RTMs.
THE MICROBIOME RESPONDS TO AND INTERACTS WITH CONTAMINANTS Data on effects of environmental contaminants on microbiomes has been published over the last years, with a focus on animal gut-associated bacteria  (19) (20) (21) (22). Published studies include controlled exposure of model organisms to various types of contaminants including pesticides, antibiotics, heavy metals, xenobiotics, or nanoparticles  (23, 24). Bacterial community composition is typically assessed using the 16S rRNA sequence as a taxonomic marker that identifies Operational Taxonomic Units (OTUs), a commonly used proxy for species. The effect of exposures on OTU richness and diversity is then evaluated using multivariate statistics to test whether contaminants interfere with microbiome composition  (25, 26). Many studies include complementary analyses on host parameters of toxicological relevance such as markers of the immune system, tissue histology, and developmental markers.