dataesr/scientific-paragraphs-categorization huggingface dataset:
Hence, the size class bin width increases with size in a logarithmic fashion. Due to the detection limits of the UVP5, size class bins smaller than 0. 0403 mm ESD are empty and not reported, and the largest size bin covers the size range from 20. 6 to 26 mm ESD. The particle abundance and biovolume of particles with an ESD > 26 mm are also provided as an additional value. Data in this form are available on the EcoPart server. Quality-checked data were downloaded from the server on 26 May 2021 and submitted to PANGAEA. A final update of the dataset was conducted on the 15 February 2022. Apart from the particle abundance and biovolume in different size classes, the dataset contains the cruise ID, the EcoPart project identifier (integer), the profile identifier, the filename of the raw file, the filename of an accompanying CTD profile (if this exists), latitude and longitude (both in decimals), date and time (in UTC), an EcoPart internal station identifier (integer), the depth (in dbar; indicated via the middle value of the 5 dbar depth bin), and the observed volume per depth bin (in L).
The particle size distribution data reported are inclusive of all living and non-living particles across the size range of detection. The dataset (available at https://doi. org/10. 1594/PANGAEA. 924375; Kiko et al. , 2021 ) contains all individual profile data. Moreover, along with the dataset presented here, the values of Aa, Exp, the imaged volume for each data acquisition, links to CTD data and other metadata are archived at PANGAEA in the "Metadata collection for a global marine particle size distribution dataset obtained with the Underwater Vision Profiler 5". Users of this dataset need to be aware that we provide the data "as is", aggregated in 5 dbar depth bins. The particle size that can be quantified reliably with the UVP is limited at the lower end by the optical resolution of the camera and at the upper end by the imaged volume.
The optical resolution differs between the different UVP units used. In most cases, the lower size limit is at 0. 102 mm (UVP5 SD) or 0. 064 mm (UVP5 HD) ESD, and it is sometimes also higher, i. e. 0. 203 or 0. 256 mm ESD, for early SD deployments. Moreover, several datasets exist that have a lower limit of 0. 0403 mm ESD. In these cases, the distance of the camera system to the illuminated field was reduced to increase the image resolution. Projects with project IDs from 33 to  38 (uvp5_sn002zd_cascade2011, uvp5_sn002zd_ccelter_2011, uvp5_sn002zd_gatekeeper2010, uvp5_sn002zd_keops2, uvp5_sn002zd_keops2, uvp5_sn002zd_omer, and uvp5_sn002zd_omer_2)  and from 50 to 51 (uvp5_sn003zp_pelgas2012 and uvp5_sn003zp_tara2012) are concerned. In these cases, the imaged volume was reduced to 0. 48 or 0.
37 L respectively. We would like to note that many UVP users do not use the first bin or even the first two or three bins of the size distribution of a given dataset in their analyses, as the particle size estimates at the lower resolution limit only rely on very few image pixels and, therefore, might be less reliable or noisy. The lower limit can be identified by computing a size spectrum with all depth bins of the profile or with the entire project dataset included. The bin where the differential particle size distribution peaks then represents the limit below which data should be considered as not quantitative. To yield better count statistics at the upper end of the size distribution, UVP users often also combine abundance or biovolume estimates from several depth bins. This is not only possible for the abundance estimates but also for the biovolume estimates, as the reported biovolumes are the sum of the individual spherical volumes computed from each particle.
The differential particle abundance N can be calculated as the total number of objects per unit volume in the given diameter range (e. g. 0. 203-0. 256 mm) divided by the diameter range (in this case 0. 053 mm), and it is given as the number of particles per volume per size. To obtain an estimate of k, which is also referred to as the slope of the PSD, one can then conduct a linear regression of log(N ) vs. log(D) as follows: log(N) = log(b) -k(log(D)). The slope k of the PSD is calculated for the size range from 0. 203 to 2. 05 mm, as this is the size range where the slope is mostly linear. The slope k is only considered if the p value of the regression is < 0. 05, otherwise the value is set to "nan". Existing published datasets (Table  2 ) use different calibration coefficients that are not consistent with the HD intercalibration procedure; therefore, differences may arise when comparing the different dataset versions.
As an example, we calculated the abundances of two size classes and spectral slopes using the datasets from the RV Maria S. Merian cruise MSM23 and several RV Meteor cruises. MiP abundances are 4. 2 (median; interquartile range of 3. 8-4. 7) times larger with the new calibration factors, whereas MaP abundances are 1. 5 (median; interquartile range of 1. 2-2. 0) times larger. Estimates of the slope k of the PSD are 1. 09 (median; interquartile range of 1. 05-1. 12) times larger. These factors were calculated using the datasets from RV Maria S. Merian cruise MSM23 and RV Meteor cruises M92, M96 and M107 for which archived datasets with the relevant data exist. In our view, these changes are related to the increased resolution of the HD version (compared with the SD version) that enabled us to better quantify small particles during the initial laboratory calibration experiment for UVP5 HD serial number 203.
This improved calibration was then propagated to all other units and superseded the earlier calibration experiment done with a SD unit. We do not distinguish UVP5 particle data into distinct categories, such as copepods, aggregates, fecal pellets, or other taxonomic or morphologic classes. For UVP5 data, this is possible for objects > 1 mm ESD, as the UVP5 also retrieves "vignettes" -small thumbnail images of respective regions of interest. Homogeneous identification of these vignettes among different cruises and operators is a time-consuming task and has not yet been achieved for the entire dataset. Data from a subset of profiles are currently being prepared for publication.
Data distribution The global distribution of UVP5 profiles contained in the published dataset is shown in Fig. 2a ; in total, it comprises 8805 profiles collected between 19 June 2008 and 23 November 2020 and between 81. 3695 • N and -75. 29 • S. The dataset represents a compilation of particle data from numerous small regional-scale research cruises as well as several large-scale hydrographic transects with bathypelagic and cross-basin coverage. All major ocean basins as well as the Mediterranean Sea and the Baltic Sea were sampled. Most data are available from the Mediterranean Sea, the tropical Atlantic and Pacific, the Gulf of Alaska, and the Arctic. Information on the number of profiles obtained per year, month and depth level is shown in Fig. 3. The majority of profiles was collected in the upper 1000 m of the water column in June and August.
Between 217 and 1146 profiles per year were obtained between 2008 and 2020. Almost all UVP5 data obtained between 2008 and 2019 are contained in our dataset. We were not able to obtain data from all UVP5 own- ers; therefore, we cannot provide an exact estimate of how many profiles are currently missing from the dataset. Furthermore, some datasets obtained in 2019 and 2020 still require processing and will be added in subsequent updates of the dataset. The sampling effort is biased to the Northern Hemi-sphere summer. Of all 8805 profiles, 1675 (19 %) are shallower than 200 dbar, 7127 (80 %) cover the upper 200 dbar of the water column, 3426 (38 %) cover the upper 1000 dbar and 1018 (11 %) go down to at least 3000 dbar. Deep profiles are mostly full depth profiles. The deepest profile reached 6017.
Global particle abundance patterns The global UVP5 particle dataset enables the characterization of particle abundance and size structure patterns at a global scale, but it also enables specific insights into particle dynamics at several regional study sites (e. g. the Gulf of Alaska; the California, Humboldt, Benguela and Mauretania upwelling systems; and the Mediterranean Sea). Here, we aim to provide a short description of global particle distribution patterns and reference a few existing published studies. We use the terms micrometric particle (MiP) for particles with an ESD of 0. 14 to 0. 53 mm and macroscopic particle (MaP) for particles with an ESD of 0. 53 to 16. 88 mm, as in  Kiko et al. (2017). We thereby also follow an approach used for marine aggregates, in which those larger than 0.
By combining instruments over a micrometre to centimetre size range, it was shown that the value of the slope varies greatly around the typical value of 4  (Jackson et al. , 1995; Stemmann et al. , 2008a). Our study also shows that  the slope k varies greatly in the epipelagic, mesopelagic and bathypelagic regions  (Figs. 12, 13, 14 ). If we constrain the dataset to profiles that go deeper than 3000 dbar, the global mean value of the slope k in the top 200 dbar of the water column is found to be -3. 57 ± 0. 56 standard deviation (minimum of -6. 58 and maximum of -1. 8), with signifi-cant variations from -4 which are likely due to local ecosystem processes and other impacts. The average slope k and the variability remain similar at greater depth (-3. 59 ± 0. 67 standard deviation, minimum of -8.
25 and maximum of -1. 37 at 200-1000 dbar depth; -3. 52 ± 0. 6 standard deviation, minimum of -7. 34 and maximum of -1. 33 at 1000  to 3000 dbar depth). Throughout all of the depth ranges, the steepest slopes are observed in oligotrophic basins such as the eastern Mediterranean Sea and the centre of the South Pacific gyre, whereas flatter spectra are observed in more productive regions such as the western Mediterranean Sea and at high latitudes. These observations confirm earlier work us-ing more restricted datasets  (Guidi et al. , 2009 (Guidi et al. , , 2008a;; Stemmann et al. , 2008c). Earlier work has also shown that the slope of the size spectrum is correlated with the phytoplankton community composition  (Guidi et al. , 2009; Stemmann et al. , 2002)  and can show diel variability related to zooplankton migration  (Stemmann et al.
Potential uses of the data A further, detailed analysis of the provided dataset is beyond the scope of this article. Observation of a particle at a certain depth always generates the question of how it was formed or how it arrived at the given location. Many attempts have been carried out to relate the UVP particle size spectrum to flux measured in sediment traps or using thorium isotope measurements  (Guidi et al. , 2008b (Guidi et al. , , 2015;; Forest et al. , 2013) , sinking speed  (Stemmann et al. , 2002)  and POC  (Stemmann et al. , 2008a) , but deriving biogeochemical properties from particle size is certainly an area for future progress. In this regard, our dataset should enable further regional and global analyses of particle dynamics (see e. g. Bisson et al. , 2021)  Figure  14. k averaged for the 1000 to 3000 dbar depth layer and per 2 • grid box.
Only profiles at least 3000 dbar deep were used for the analysis. and -in combination with flux estimates from sediment traps and/or thorium isotope measurements as well as environmental data from satellites and other sources -enable us to better constrain the particle flux component of the biological pump (see e. g. Clements et al. , 2021 Clements et al. , , 2022)). However, we would like to stress that, although the particles in our dataset are not sinking per se (e. g. living zooplankton are also treated as particles), the particle abundance and size alone are still important information. Therefore, the data are also especially useful to constrain models that explicitly generate a particle size spectrum  (Bianchi et al. , 2018; Niemeyer, 2020; Weber and Bianchi, 2020; Stemmann et al. , 2004; Jouandet et al.
Recommendations for further instrument usage and growth of the dataset This work presents the first attempt to establish a calibrated global dataset of UVP measurements. Our analysis led us to develop a set of recommendations for future expansion of the global UVP dataset. First of all, we recommend that full depth profiles are always taken at locations shallower than 1000 m depth; otherwise, it is recommended that at least the full mesopelagic region down to 1000 m depth be sampled when using the UVP5. This is motivated by the fact that particle processes (indicated via a large range of e. g. MiP and MaP abundance) at these depths are very dynamic and re-quire high-resolution sampling. Below 1000 m depth, particle spatial patterns are less variable. Nevertheless, if sampling during a research cruise is conducted at water depths > 1000 m, full depth profiles or profiles down to the maximum depth rating of the used instruments (typically 6000 m) should be done as often as possible.
The deep sea is not well characterized with respect to the abundance and size of particles, and these comparatively small demands on ship time will generate important added value, as this will, for example, enable us to further assess carbon sequestration in the deep sea. It needs to be reiterated that a larger sampling volume will improve count statistics, especially for larger, rarer particles  (Bisson et al. , 2021). Therefore, sampling programmes should consider conducting repeated profiles at a station to increase the effective sampled volume for a given station. Regions that are currently not well sampled include the Indian Ocean, Antarctic waters and the western Pacific. Furthermore, winter data from both hemispheres are mostly lacking. In general, the UVP should be used during repeat hydrography programmes, as the operational goals of these programmes to cover a representative fraction of the ocean (global and full depth coverage) align with our goals to create a global particle size distribution dataset.
The Scientific Committee on Oceanic Research (SCOR) working group 154 "Integration of Plankton-Observing Sensor Systems to Existing Global Sampling Programs (P-OBS)" recommended the use of the UVP during the GO-SHIP programme and similar seagoing expeditions  (Boss et al. , 2020). Data from the smaller and more versatile UVP6  (Picheral et al. , 2022)  that can also be deployed on gliders, floats, moorings and other vectors should also be integrated in future datasets and will enable the study of particle dynamics at spatial and temporal scales that are not accessible with the UVP5. Ancillary data that are useful for the interpretation of UVP data are temperature, salinity, oxygen and nutrient measurements; measurements of current dynamics; any measurements of particle dynamics and characteristics (e.
Conclusions Here, we provide the first global particle size spectra dataset containing 8805 profiles that were obtained with the UVP5 between 2008 and 2020. All of the UVP5 units used were inter-calibrated with a standard procedure, calibration coefficients and metadata were checked, and all profile data were reprocessed. Therefore, this dataset is internally consistent and supersedes earlier versions of cruise-specific UVP5 particle size spectrum data. The analysis of this global dataset shows that particle abundances are high in regions of high primary productivity and in coastal areas. Further analysis of the dataset should enable insights into different aspects of particle dynamics such as the effects of mesoscale features and oxygen minimum zones, the fate of particulate matter in the deep sea, and many other important aspects of the oceans' biogeochemistry.
Author contributions. RK, MP and LS formulated the goals for data aggregation, quality control and the publication of a global UVP5 dataset. RK and MP led the quality control endeavours, supported by all co-authors. RK and LS conceived and drafted the article. All authors participated in writing the article. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements. The authors would like to thank Hela Mehrtens (GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany) for her help with the submission to the PANGAEA data archive. Financial support. Nelson Mandela University research was supported, in part, by the National Research Foundation of South Africa (grant no.
120174). Lionel Guidi and Fabien Lombard received support via a EU H2020 grant (AtlantEco project; grant no. 862923). Rainer Kiko received support from a "Make Our Planet Great Again" grant from the French National Research Agency within the "Programme d'Investissements d'Avenir" (grant no. ANR-19-MPGA-0012) and from an EU H2020 grant (grant no. 817578; TRIATLAS project). Rainer Kiko and Helena Hauss also received support from the DFG-funded Collaborative Research Center 754 "Climate-Biogeochemistry Interactions in the Tropical Ocean" project (work package B8) and the "CUSCO -Coastal Upwelling System in a Changing Ocean" project (grant no. 03F0813A; work package 5) funded by the Federal Ministry of Education and Research (Germany). The McDonnell Laboratory at the University of Alaska Fairbanks received support from the US National Science Foundation (grant nos.
pangaea. de/10. 1594/PANGAEA. 846153 uvp5_sn010_2014_m106 https://doi. org/10. 1594/PANGAEA. 874870 uvp5_sn010_2014_m107 https://doi. org/10. 1594/PANGAEA. 885759 uvp5_sn010_2015_m119 https://doi. org/10. 1594/PANGAEA. 874872 uvp5_sn003_cassiopee_2015 https://doi. org/10. 1594/PANGAEA. 876216 uvp5_sn009_2015_p16n https://doi. org/10. 1594/PANGAEA. 874875 uvp5_sn202_ps99_21_06_3_filtered https://doi. pangaea. de/10. 1594/PANGAEA. 896047.
Statistical Data Analysis Statistical analyses for the antibiofilm assay and the fruit bioassays were performed with JMP Pro 16 (SAS, Cary, NC, USA). For all analyses, a confidence level of 0. 05 was applied. Normality of data was assessed with a Shapiro-Wilk test, while equality of variances was verified using an O'Brien test (for non-normally distributed data sets) or a Bartlett test (for normally distributed data sets). When the variance was equal, a one-way ANOVA was run with a Student's t-test to compare the means of the different treatment groups with the positive control in the case of normal data, while the Games-Howell test (a Tukey HSD post hoc analysis assuming unequal variances) was used for non-normal data. For unequal variances, the Wilcoxon Each Pair test was calculated.
Those can be used within existing parameterized algorithms to yield efficient heuristics, a posteriori approximations or even exact solutions. graph models to represent complex topological motifs called pseudoknots  [41] , inducing the hardness of several tasks, such as structure prediction  [1, 23, 29] , structure alignment  [5] , or structure/sequence alignment  [25]. Such motifs are functionally important and conserved, as witnessed by their presence in the consensus structure of 336 RNA families in the 14. 5 edition of the RFAM database  [18]. Moreover, methods in RNA bioinformatics  [28]  are increasingly considering non-canonical base pairs and modules  [20, 24] , further increasing the density of RNA structural graphs and outlining the need for scalable algorithms. A parameterized complexity approach can be used to circumvent the frequent NPhardness of relevant problems.
Proofs of concept We now illustrate the relevance of our approach, and the practicality of our algorithm for Tree-Diet, by using it in conjunction with FPT algorithms for three problems in RNA bioinformatics. We implemented in C++ the dynamic programming scheme described in Theorem 8 and Appendix E. Its main primitives are made available for Python scripting through pybind11  [17]. It actually allows to solve a generalized weighted version of Tree Diet, as explained in Appendix E. This feature allows to favour the conservation of important edges (e. g. RNA backbone) during simplification, by assigning them a much larger weight compared to other edges. Our implementation is freely available at https://gitlab. inria. fr/amibio/treediet.
In particular, Rinaudo et al  [25]  describe a Θ(n. m tw+1 ) algorithm for optimally aligning a structured RNA of length n onto a genomic region of length m. It optimizes an alignment score that includes: i) substitution costs for matches/mismatches of individual nucleotides and base pairs (including arc-breaking) based on the RIBOSUM matrices  [19] ; and ii) an affine gap cost model  [26]. We used the implementation of the Rinaudo et al algorithm, implemented in the LicoRNA software package  [37, 38].
We simplified our the initial tree decomposition T 4 , and obtained simplified models T 3 , and T 2 , having width tw = 3 and 2 respectively. As controls, we included tree decompositions based on the secondary structure (max. non-crossing set of BPs; T 2D ) and sequence (T 1D ). We used LicoRNA to predict an alignment a T ,w of each original/simplified tree decomposition T onto each sequence w of the c-di-GMP-II riboswitch family in the RFAM database  [18]  (RF01786). Finally, we reported the LicoRNA runtime, and computed the Sum of Pairs Score (SPS)  [35]  as a measure of the accuracy of a T ,w against a reference alignment a w : D -Second best hit (c = -9. 9) SPS(a T ,w ; a w ) = | MatchedCols(a T ,w ) ∩ MatchedCols(a w ) | | MatchedCols(a w ) | , Figure  9  Corrected costs associated with the search for structured homologs of the Twister ribozyme in chromosome 5 of S.
Coverslips-attached cells were washed again and colored 5 min with Dapi 1µg/mL final concentration. After two rinses with PBS 1X, the glass slides were placed on microscope slides (Thermo Scientific, Waltham, MS, USA) using a Vectashield mounting medium (Catalog number : H-1000, from Eurobio, Paris, France) and imaged using a Zeiss LSM 800 confocal microscope (Zeiss, Jena, Germany). The images were processed using the ImageJ software.
Measurement of Cytokines and Chemokines Secretion Tumor necrosis factor (TNFa) and interleukin 6 (IL-6), macrophage Inflammation Protein (MIP-1a) and monocyte chemoattractant Protein-1 (MCP-1) were measured in the supernatant from cultured cells exposed to pigments and/or activated by LPS 200 ng/ml, using the Mouse Inflammation Cytometric Bead Array kit (reference: BD Biosciences, Rungis, France) according to the manufacturer's instructions. Measurements were performed on a Facscalibur flow cytometer, and the concentrations of cytokines secreted were assessed using FCAP Array Software.
Electrophoresis was performed in a vertical electrophoresis tank (Cleaver Scientific) at 0. 7 V/cm for 30min. Slides were then neutralized in PBS and stained with 50 µL of GelRed (Thermofisher Scientific). The analysis consisted in recording the % tail DNA on 50 cells per gel using the Comet IV software (Perceptive Instruments, Suffolk, UK). Experiments were performed three times independently, with two technical replicates per condition in each independent experiment. The cells exposed for 24 h to 100 µM of methyl methanesulfonate were used as positive control, and one control slide exposed to H 2 O 2 was used as positive control for the electrophoretic migration.
Physical Characterization of Pigments via Dynamic Light Scattering and Transmission Electron Microscopy The size (hydrodynamic radius) and the size distribution (polydispersity percentage) of the pigments in suspension were characterized using Dynamic Light Scattering (DLS). Size distribution is deemed monodisperse when the polydispersity percentage is inferior to 15%. The results in Figure  2A  and Supplementary Data 1 show a high diversity of sizes for all the pigments tested. Consequently, dispersity cannot be measured. However, the average hydrodynamic radius of each pigment tested was very different. Indeed, the average hydrodynamic radius of PV14 was approximately 994 nm in H2O medium and 860 nm in DMEM culture medium. For PG19, the radius size was 196 nm in H2O and 98 nm in DMEM. For PB28, the radius size was 517 nm in H2O and 441 nm in DMEM.