dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with software tools It is very common in related works to compare the execution speed of a hardware-accelerated platform against the pure software implementation of the same algorithm being accelerated. But we consider that FIM users are mainly interested in execution speed and/or energy consumption, and not in the nature of the algorithm that is internally used. This position is relevant especially as Borgelt's Eclat actually internally selects between several variants of Eclat and LCM algorithms. Table  VI  gives run times for several recent software FIM tools, and for datasets used in related works. Borgelt's Apriori and Eclat tools are often used as reference software tools in related works about hardware-accelerated FIM. For each tool, we use the latest version available at the time of this writing.
Borgelt's Apriori is used in  [Wang et al. 2015 ][Baker and Prasanna 2005 ][Baker and Prasanna 2006] , we use version  6. 19 (2015. 08. 18 ). Borgelt's Eclat is used in  [Zhang et al. 2013b ][Shi et al. 2013 ], we use version 5. 8 (2015. 08. 18). We use version 6. 8 (2015. 12. 22) of Borgelt's FP-Growth. To the best of our knowledge, LCM 3. 0  [Uno et al. 2005 ] has never been used as reference for hardware-accelerated FIM works, despite it being known for its efficient implementation of the LCM algorithm. In Table  VI , the time of the fastest software tool is highlighted for each dataset. We indicate swap when the memory requirements exceed the 12 GB RAM of our workstation. LCM 3. 0 largely outperforms all other tools on most datasets and this trend can be confirmed on much broader ranges of support values.
Only for the tiny datasets T10I4D100K and T40I10D100K Borgelt's Eclat tool is very close to LCM 3. 0. For this reason, we perform a more detailed comparison of our VC709 platform against LCM 3. 0. The time of our VC709 board is highlighted when it outperforms the software tools. In Figures  11  and 12  the overall execution times of LCM 3. 0 and of our FPGA-accelerated platform are shown. The 4 datasets from  [Wang et al. 2015 ] are used with the same support ranges: pumsb, accidents, webdocs and webdocs5x. LCM 3. 0 is still notably faster than our platform for dataset pumsb. This dataset is known to be exceptionally dense: a huge number of itemsets is produced from a very small dataset. The filtered dataset is actually small enough to entirely fit in the processor cache, which makes the depth-first, memory-efficient LCM algorithm almost unbeatable.
Similarly, LCM 3. 0 clearly leads for dataset accidents for support values below 35%. For higher support values, mining time is negligible compared to dataset loading and filtering time, for both tools. A:20  Nevertheless, our VC709 platform is faster than LCM 3. 0 on a large range of support values for the large and sparse datasets webdocs and webdocs5x, with a maximum speedup of 1. 95x for webdocs (support 12%) and 2. 87x for webdocs5x (support 11%). Our platform also outperforms LCM 3. 0 on the 4 datasets from  [Zhang et al. 2013b] , with speedups ranging from 1. 11x for dataset T40I10D03N500K to 3. 51x for dataset T40I10D03N1000K. However according to our figures, LCM 3. 0 still presents a better scalability for low support values. For the 7% support in Figure  12 , there are more than 5 million generated itemsets, which makes it questionable whether such a high number is of any practical interest.
This is even worse with datasets pumsb, support 52%, and accidents, support 10%, for which the number of frequent itemsets is more than 98 million and 10 million, respectively. We highlight that, with the linear scalability of our FPGA design, using larger FPGAs like the Virtex-7 2000T would directly make our platform 2. 8x faster. This confirms that, even though the Apriori algorithm seems to perform very poor as pure software, FPGA acceleration techniques that exploit this algorithm are still pertinent, particularly for large and/or sparse datasets.
Efficiency of FPGA usage In order to analyze how efficiently our framework uses the FPGA accelerator, Table  VII  gives the distribution of the time spent inside the main processing steps, for several representative datasets and support values. It shows the time related to loading the dataset (all in software), the ideal time for FPGA operations (calculated from the theoretical number of clock cycles), the real PCIe latency overhead, and the software time spent between Apriori iterations (updating the in-memory representation of itemsets). The rest includes potential software time that was not masked by FPGA operation, creation of threads to launch Riffa PCIe transfers, miscellaneous PCIe transfers due to setting configuration registers in the FPGA design, and miscellaneous prints of execution details.
The worst results are for dataset pumsb, this was expected. When filtered, this dataset is so small that the actual support counting operation represents a relatively small part of the overall processing time. The main cause is the PCIe latency to initiate transfers, which is high compared to the theoretical time needed to transfer the data. We highlight that for a support of 80%, the overall processing time is so small (around 90 ms) that the time to read the counters of the last batch of candidates is noticeable (not masked by transfer of a batch of candidates). Our experiments show that any filtered dataset substantially larger than pumsb will lead to a relatively good FPGA utilization efficiency. This is illustrated with datasets accidents and webdocs: apart from the uncompressible loading time, support counting and more generally FPGA utilization represents most of the overall processing time.
We provide extensive and fair comparisons with several previous works, including many types of hardware accelerators and pure software solutions. We discovered that FIM hardware acceleration is only useful for large and low-density datasets. Besides, our Apriori accelerator is well suited for ultra-low latency sequence recognition and signaling from uninterruptible data streams. Using one common FPGA prototyping board, we achieved up to 1 GB/s throughput while monitoring up to 5818 patterns, or 250 MB/s with up to 11636 patterns. As a perspective, when only pattern recognition and signaling on streams are needed, our IP could be further shrunk by removing the support counters. This could bring up to a 100% increase in the number of patterns that can be simultaneously monitored.
Glider Measurements Glider measurements presented here were performed during three campaigns across the NC and along the Nice-Calvi transect (Figure  1 ). The first campaign was realized by ALSEAMAR with one of their glider (code name SEA007) while the two other campaigns were carried out with the Mediterranean Institute of Oceanography's SeaExplorer glider (code name SEA003). The fall campaign was conducted between 28 October and 10 November 2015, the spring campaign between 29 April and 20 May 2016 and the summer campaign between 25 July and 4 August 2016. For the three campaigns, the glider was equipped with a pumped conductivity-temperature-depth sensor (Seabird's GPCTD) from which the conservative temperature ( ), the absolute salinity (S A ) and the density anomaly referenced to the surface (σ 0 ) are derived using TEOS-10 toolbox  (McDougall and Barker, 2011).
This was achieved by subtracting 0. 02µg L -1 (fall campaign) and 0. 08µg L -1 (spring and summer campaigns) from the whole dataset. In a complement to these relatively common glider measurements, the MiniFluo was used on an exploratory basis: never before such high frequency measurements of Trp and Phe were performed from a glider. Only subsets of the glider campaigns are presented here (see Figure  1  caption for dates). They consist of about the first 100 km of the Nice-Calvi transect (red-dashed line in Figure  1 ), with the DYFAMED station located at x = 53. 2 km. This section was chosen because of the relatively good overlap of the glider tracks during the three surveys. Note that while the glider traveled from Nice toward Calvi for the fall and spring transects, the summer transect was realized in the opposite direction.
In order to account for this difference, our strategy is to replace the laboratory blank value (B lab ) from the calibration sheet by a user-defined value specific to the studied environment. In this study, B env values have thus been chosen as the minimum relative-unit of the de-spiked dataset, so that the minimum measured concentration of a certain compound far from dynamical biogeochemical region converges toward zero. Table  2  summarizes the different values used for this study.
, 2015 (Bosse et al. , , 2016)). However, their origins are likely different since SCV reported in these studies are observed deeper (200-500 m) and are associated with high-salinity anomalies. Nevertheless, the new structure revealed here is worth exploring in existing datasets since its physical-biochemical signature suggests a key role in lateral exchanges of biochemical tracers. Again, glider biogeochemical observations, including the one with the MiniFluo, may give useful insights on the origin and the fate of such small-scale processes.
Tryptophan-Like Fluorophore Dynamics Previous observations of Trp-like fluorescence in marine environment (not under strong anthropogenic pressure) suggest that it is particularly high in region of maximum primary productivity  (Stedmon and Markager, 2005). The high vertical resolution of the present dataset rather suggests that this relationship may be more complex. For example, mean profiles show that the maximum Trp-like fluorescence is always shallower than the maximum Chl-a fluorescence (Table  3 ). This difference may be due to the vertical mismatch between Chl-a fluorescence and biomass, or to the vertical distribution of planktonic communities. Unfortunately, no plankton measurements are available to test these hypotheses. The seasonal dynamics also suggests that Trp-like concentration is not simply proportional to the primary productivity: while both tracers peak in the summer, their dynamics for spring (high Chl-a/low Trp) and fall (high Trp/low Chl-a) are contrasted (see Section 3.
ACKNOWLEDGMENTS The authors would like to thanks  ALSEAMAR , the  Laboratoire d'Océanographie de Villefranche-sur-mer , the  SAM service at MIO  and the Antedon crew who made the three glider deployments possible. Several other contributors who have participated to the development of the sensors, mostly via internships or laboratory work, are listed here in a alphabetical order:  C. Bachet ,  C. Guigue ,  C. Germain ,  P. Joffre , and  G. Wassouf. FC personally thanks the entire SeaExplorer team at  ALSEAMAR  for their support during the field campaigns, including the precious time spent discussing glider technology and piloting strategies. The authors finally thank two reviewers for their helpful comments.
Introduction RNA-protein interactions play fundamental roles in many biological processes, such as regulation of gene expression, RNA splicing, and protein synthesis. The understanding of these processes improves as new structures of RNA-protein complexes are solved and the molecular details of interactions analyzed. As of November 2016, 1880 macromolecular complexes involving both protein and RNA components were available in the Protein Data Bank (PDB), including 1495 solved by X-ray crystallography, 111 by Nuclear Magnetic Resonance (NMR) spectroscopy, 269 by cryo-electron microscopy, and 5 by other methods. However, experimental determination of RNA-protein complex structures at high-resolution that allows the study of position, orientation and interactions of individual atoms is often time-consuming and in many cases, not possible due to sample preparation requirements.
SAXS data can distinguish monomer from dimer, and can perform simultaneous shape reconstruction using the program MIXTURE  [115] , unlike DLS. AUC provides information on the gross conformation of macromolecules (i. e. if the macromolecules are globular or extended); however, SAXS ab initio models can provide a significantly more detailed description of macromolecular shape. In addition, unlike AUC, where it is possible to find out (Yes/No answer) if conformational changes occur with a particular species, by application of SAXS, it is actually possible to visualize conformational changes that occur  [116]. SEC-MALLS is limited to very large macromolecules or larger macromolecular assemblies in terms of determining R g , and typically not very useful for individual proteins, nucleic acids or smaller complexes.
Computational modeling of proteins, RNAs, and their complexes, with restraints from experimental data 2. 4. 1. Classification of macromolecular modeling methods There exist a wide variety of methods for macromolecular 3D structure prediction that are applicable to nucleic acids, proteins, and their complexes. They can be classified in various ways. One classification divides structure modeling methods into those based on the fundamental laws of physics that govern the process of folding (i. e. the ''Greek science" approach), and all others, which typically extensively use information about other structures, available in various databases (i. e. the ''Babylonian science" approach)  [128, 129]. A number of computational methods based on either of these approaches may perform model building with the use of restraints derived from biochemical or biophysical experiments (i.
One type of the knowledge-based ''Babylonian science" approach exploits databases to derive statistical potentials for macromolecular simulations and for scoring of models. The so-called mean force potentials, compiled by using the Boltzmann's principle, can take into account all forces acting between atoms of the molecule under study as well as the influence of the environment, without the need of defining each type of interactions separately  [139]. It is also possible to use existing molecular structures in a different way: instead of modeling the physical process of macromolecular structure formation, one can predict how do the macromolecular structure changes in the course of the evolution. This type of structure prediction relies on empirical observation that evolutionarily related (homologous) macromolecules usually retain the threedimensional structure despite the accumulation of divergent mutations  [128, 140].
However, it is still not feasible to predict, with the use of pure theory, structures of large macromolecules, unless they have related structures in the database. On the other hand, the use of the knowledge-based approach is limited only to target macromolecules for which a structurally similar template exists in the database, but this target-template relationship must be correctly identified. This limitation can be partially overcome by incorporation of experimental restraints, which can be used either explicitly in the modeling process or for scoring and ranking of alternative models obtained. One of the problems that plagues computational structure prediction according to the physics-based approach is the large number of local minima of the scoring function that need to be sampled, and as a result -a large number of alternative conformations that have to be analyzed.
Restraints on the shape of protein and RNA macromolecules and their complexes can be obtained from small angle scattering (SAS) experiments that utilize either X-ray (SAXS) or neutron (SANS) radiation. SAS data can be used for ranking the model structures obtained by large-scale computational simulations (e. g. , from RNA-protein docking experiments) to identify structures that best explain the observed data using, for example, the FOXS server  [144]. The prediction of scattering curves with explicit solvent Molecular Dynamics simulations can be used to take into account the hydration layer and the excluded solvent, thereby avoiding any solvent-related fitting parameters, while naturally accounting for thermal fluctuations (e. g. , as implemented in the WAXSiS server  [145] ). Although the calculation of theoretical scattering curves is computationally expensive, it can be vastly reduced by employing proper representation of the atomic model like spherical harmonics or Zernike polynomials expansion (as implemented in CRYSOL  [146]  and SASTBX  [147]  respectively).
The availability of fast methods for estimating SAS curves from atomic models also enables studies of conformationally heterogeneous structures. If a single model cannot explain experimental data, tools like Minimal Ensemble Search  [148]  or Ensemble Optimization Method  [149, 150]  can be used to identify a small set of most probable components from a pool of states generated with bioinformatics modeling tools (see Sections 2. 5. 2 and 2. 5. 5). SAS data can also be used to obtain ''ab initio" shape reconstructions at low-resolution, with several computational methods available for this purpose, e. g. DAMMIN/F  [151, 152]. Various modeling programs can be then used to perform fitting of pre-modeled protein and RNA 3D structures into these shapes, and best-fitting structures may be selected as the most promising models, e.
Software for 3D shape modeling based on SAS data Small angle scattering is a low-information content technique. The actual number of independent observations depends on the data quality, maximum resolution, shape and size of the studied particles. Theoretical analysis of the SAS curve information content for an idealized experimental setup suggests that the number of independent observations recorded for biological samples is in the order of few tens  [178]. A more accurate estimate for a specific case that takes into account data quality can be obtained using recently developed SHANUM program from the ATSAS suite  [179]. Nevertheless, one should always bear in mind that there is a large danger of overfitting in the macromolecular modeling based on SAS data. Therefore, the number of parameters of a model should be reduced as much as possible.
Software for modeling of RNA-protein complex structures with the use of experimental data With atomic models of protein and RNA components one can proceed with the docking of the two structures in order to build a complete model of the complex  [209, 210]. In general, two approaches are available. One is to generate a large pool of plausible complex models with docking tools that enable RNA-protein docking like NPDock  [211] , PatchDock  [212] , or HADDOCK  [213]  and score them against experimental SAXS data using e. g. FOXS  [144]  or CRYSOL  [146]. Another possibility is to build complex models with tools that can directly use SAS data to restrain docking process. Currently available programs that can be used for that purpose include IMP  [144] , pyDockSAXS  [214] , and FoXSDock  [191]  and PyRy3D (developed in the Bujnicki laboratory and available at http://genesilico.
For this reason, we have developed a new protocol for modeling RNA-protein complexes that exploits modular architecture of RNA. In this approach, individual helices of RNA (together with hairpin loops at their ends) were modeled as rigid bodies, restrained at the junction to maintain the continuity of the nucleotide chain. Such a ''marionette" model of RNA can be modeled together with the protein component (potentially also divided into small parts) to build a complex that agrees with experimental data restraints. (Fig. 2 )  [6]. Initially, we used this modeling strategy for docking, with the use of PyRy3D, the model components into low-resolution ab initio reconstructions of the complex shape (obtained e. g. with DAMMIN program, Fig. 2 panels  3  and 4 ). With the recent versions of PyRy3D, however, one can use SAS curve to directly restrain the docking simulation, which is highly recommended.
Software for validation of 3D structure models against SAS data FoXS (Fast X-ray Scattering) is a web server available with an intuitive interface at http://modbase. compbio. ucsf. edu/foxs/index. html. It is used for rapidly computing a SAXS profile of a given molecular structure and its fit with experimental SAXS profiles if provided  [144]. The algorithm to compute SAXS profile is based on the Debye formula. As an input, it takes a mandatory one structure file in the PDB format (or a zip archive with multiple PDB files) to compute its SAXS profile. The optional input includes an experimental SAXS profile (as. dat file). Computing SAXS profile of a molecular structure and its fitting with SAXS experimental profile is performed rapidly in real time. The output page displays a plot of the computed profile as well as a plot of the computed profile fitted to the experimental profile.
In addition, the values of fit parameters (v, excluded volume, hydration layer density) and R g for the current profile are displayed. Closer the value of v to 1, better the molecular structure fits the experimental SAXS profile. CRYSOL calculates solution scattering profile from the macromolecular structure and fits it with the experimental SAXS profile  [146]. The software can be downloaded as an ATSAS package from https://www. embl-hamburg. de/biosaxs/download. html. Alternatively, CRYSOL web server can be accessed from https://www. embl-hamburg. de/biosaxs/atsas-online/crysol. php. As an input, one can use a structure of a protein or a protein-DNA(RNA) complex (in PDB format) and an optional experimental SAXS profile (as. dat file). CRYSOL uses multipole expansion of the scattering amplitudes to calculate the spherically averaged scattering pattern and takes into account the hydration shell.
Software to study disordered and flexible systems In a biological system, there are many cases where the RNA, protein and/or their complexes are flexible in nature. Conversely, the protein of interest may be disordered in solution. Significant progress has been made over the last few years to study disordered and flexible systems. For example, the shape reconstruction based on SAXS data of disordered and flexible macromolecules can be performed by means of Ensemble Optimization Method  [149, 215]  and ENSEMBLE program  [216]. The BILBOMB minimal ensemble search algorithm combines the SAXS data analysis for disordered and flexible system with molecular dynamics calculations  [114, 148]. On the other hand, the Basis-Set Supported SAXS algorithm SAXS data analysis with coarse-grained analysis can also be employed  [217].
Furthermore, the Bayesian ensemble SAXS package  [218]  and Ensemble Refinement of SAXS package  [219]  can also be used to obtain structural information on flexible and disordered systems. In addition, the programs such as MONTEHY-DRO  [220]  and SIMUFLEX  [221]  can be used to calculate hydrodynamic parameters and scattering intensities from disordered and flexible macromolecules and their complexes  [222].
Computational models using the SAXS data and RNA secondary structural data as restraints (using SimRNA/PyRy3D modeling pipeline) suggested a bent and extended A-RNA helical structure with the bottom of a long and short-stem loop stacking on each other. OAS1-3 0 -TR complex formation studied by SAXS and computational modeling of the existing 3D structure of OAS1 was consistent with biochemical data demonstrating interaction with the long stem loop, and does not significantly perturb the RNA structure (Fig. 3 ). The experimentally determined structure of OAS1  [246]  and refined model of the 3 0 -TR RNA were treated as rigid bodies and additional restraints were used to keep OAS1 residues involved in protein-RNA interactions close to the RNA chain.
Acknowledgements We thank  Krzysztof Skowronek  for critical reading of the manuscript and useful comments. T. R. P. was supported by the  University of Lethbridge  Start-up fund. J. M. B. and A. were supported by the  Polish National Science Centre  (grant  2012/04/A/NZ2/00455  to J. M. B. ). J. M. B. was additionally supported by the  European Research Council  (grant  RNA+P=123D  to J. M. B. ) and by the '' Ideas for Poland" fellowship  from the  Foundation for Polish Science (FNP). S. A. M. was supported by a  Discovery Grant  from the  Natural Sciences and Engineering Research Council of Canada  ( RGPIN-2015-06142 ) and the  Cancer Research Society.