dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The  Institut Laue-Langevin  is acknowledged for providing beamtime on the small-angle scattering machines  D22  and  D33  (data available at: https://doi. ill. fr/10. 5291/ILL-DATA. 9-10-1730 );the PSCM for providing the laboratory infrastructure where all off/line experiments were performed. Fruitful discussions with  Sylvain Prevost ,  Charles Dewhurst , Frederic Nallet, and  Oleg Konovalov  are heartily acknowledged. We thank  Michel Bonnaud ,  Benoit Laurent  and  Abdelali Elaazzouzi  for their help in the instrumental development. O. D. and P. B. acknowledge fundings from  Agence Nationale de la recherche  for  FOAMEX  project, grant number  ANR17-CE008-0016.
• that is an invariant of the state space, it has a constant value in all markings that can be computed as ∈ P • 0 ( ) using the initial marking. A semi-flow only has positive integers as ∈ N coefficients while generalized flows may have both positive and negative integer coefficients ∈ Z. It is possible to compute a generator basis of all generalized flows of a net in polynomial time and space, and the number of flows obtained is itself polynomial in the size of the net. We use for this purpose a variant of the algorithm described in  [8] , initially adopted from the code base of  [9]  and then optimized for our scenario. This provides a polynomial number of simple constraints each only having as variables a subset of places (the are fixed). The constant value of the flow in any marking can be deduced from the initial marking of the net.
Implementation The implementation of the algorithms described in this paper was done in Java and relies on Z3  [1]  as SMT solver. The code is freely available under the terms of Gnu GPL, and distributed from http://ddd. lip6. fr as part of the ITS-tools. Using sparse representations everywhere is critical; we work with transition based column sparse matrix (so preset and postset are sparse), and transpose them when working with places. The notations we used when defining the rules in this paper deliberately present immediate parallels with an efficient sparse implementation of markings and flow matrices. For instance, because we assume that ∀ ∈ • is a sparse iteration we always prefer it to ∀ ∈ P in rule definitions. Our random explorer is also sparse, can restart (in particular if it reaches a deadlock, but not only), is more likely to fire a transition again if it is still enabled after one firing (encouraging to fully empty places), can be configured to prefer newly enabled transitions (pseudo DFS) or a contrario transitions that have been enabled a long time (pseudo BFS).
Today, many nations are calling for protection (MPAs safe from some or all forms of human exploitation) for 30% of the world's ocean by 2030, a call endorsed by a resolution of the IUCN World Conservation Congress in 2016  (25). Even though this "30 × 30" proposal is not based solely on a desire to reduce bycatch, protection of biodiversity is a key objective of this proposed expansion of protected areas, and a key task in protecting biodiversity is reducing bycatch. In this study, we build on the concepts of 30 × 30 by analyzing what might happen to both bycatch and target species if 30% of the fishing areas in a database of fisheries around the world were closed to exploitation. We focus on differences in potential outcomes resulting from protecting a static or dynamic portion-in the range of 30%-of evaluated fishing grounds.
Discussion Sustainable fisheries are critical to securing the livelihoods and resources of millions of people. However, many fisheries, including both those with sustainable and those with unsustainable directed fishing, produce large amounts of bycatch that threaten the future of incidentally captured species. We analyzed target catch and bycatch data from 15 fisheries around the world to evaluate the potential effectiveness of different time and area closures as a bycatch mitigation measure. For these case studies, dynamic mosaic area closures were more effective in reducing bycatch while minimizing reductions in catch of target species, compared to static centroid area closures. For example, closing 30% of a fishing area under a mosaic dynamic approach could reduce bycatch by an average of 57% in comparison to 16% reductions in bycatch achievable under a static centroid closure.
Materials and Methods We analyzed the effect of area and temporal (monthly) closures using logbooks (n = 3 fisheries) and on-board observer data (n = 12 fisheries) collected from 15 fisheries around the globe (SI Appendix, Table  S1 ). Our study included industrial and small-scale fisheries with surface-and bottom-fishing gears. Refer to SI Appendix for a brief description of each fishery. For most case studies, catch data were available at the resolution of individual deployments of fishing gear and for each species or group of species. We aggregated catch data by date (months and year) and spatial location (with a resolution of 0. 5, 1, or 5 degrees of latitude and longitude depending on the resolution available for each fishery). For area closures, a range of 0 to 50% of the total fishing ground was considered for closure.
Thus, an increase in effort would produce an increase in catch proportional to the target CPUE. As an alternative, we assumed that the fishing efficiency (∼CPUE) would be reduced by an increase in effort in the open areas, so an increase in effort would produce a decrease in the abundance of the target species outside the closed area and subsequently a decrease in catch (reduced fishing efficiency). Refer to SI Appendix for details on calculations. Most of the datasets used in this study are confidential due to privacy restrictions associated with fishing locations. Thus, analyses were performed by the individual authors of each case study using a set of standardized R functions (code available at https://github. com/maitepons/MPA_tool). If needed, additional analyses can be performed upon request to the authors.
In addition, an interactive web application is available in which users can input their own data to get results with the methods proposed in this paper here: https:// maitepons. shinyapps. io/Spatial_temporal_closures_bycatch_reductions/. Data Availability. R code has been deposited on Github (https://github. com/ maitepons/MPA_tool). Some data cannot be shared directly; most of the datasets used in this study are confidential due to privacy restrictions associated with fishing locations. Guidelines for researchers interested in accessing/ requesting each dataset: 1) Alaskan pollock: anonymized data used in the study is available at https://github. com/jordanwatson/Pons_PNAS; 2) Brazilian tunas/swordfish and Southern Brazilian Pink Shrimp: the datasets used in this analysis are subject to confidentiality requirements, so the raw data cannot be shared publicly.
To request this data from Brazilian fisheries used in this analysis, please contact the Applied Marine Studies Laboratory of the School of the Sea, Science and Technology of the University of Vale do Itaja ı; 3) Californian swordfish: The fisheries-dependent data from the Drift Gillnet (DGN) observer program were collected by the National Oceanic and Atmospheric Administration (NOAA) National Marine Fisheries Service and are confidential US government data. To request access to data from the West Coast Region Observer Program, please contact Charles Villafana (Charles. Villafana@noaa. gov); 4) EU tuna: this data can be shared upon request to miguel. herrera@opagac. org; 5) French tuna: this data are confidential. To request access to it, please contact David Kaplan at david. kaplan@ird. fr; 6) Hawaiian bigeye tuna and Hawaiian swordfish: the data from the Pacific Islands Regional Observer Program used in this analysis are subject to confidentiality requirements, so the raw data cannot be shared publicly.
To request nonconfidential observer data from this program, contact the coauthor Mark Fitchett (mark. fitchett@wpcouncil. org); 7) IATTC tuna: tuna-dolphin associations and free-swimming schools and floating objects datasets: the raw data cannot be shared publicly, but there is a simplified version of the data used in this study available at the following link: https://www. iattc. org/PublicDomainData/IATTC-Catch-by-species1. htm. The raw data with higher resolution used in this study could be formally requested to IATTC at datahandlers@iattc. org; 8) Small-scale tuna/mahi-mahi: these data were collected under confidentiality and mutual trust agreements with individuals, associations, and fishing sectors of nine countries, and their use will require the approval of each of them in each country to be accessed by third parties.
For further analysis and research collaboration, please contact Sandra Andraka (sandrakag@gmail. com); 9) South African tuna: this dataset is restricted and only accessible after approved written request to Sven Kerwath, Department of Forestry, Fisheries and the Environment: Fisheries Research and Development at SKerwath@dffe. gov. za; 10) Uruguayan swordfish: the data from the observer program ("Programa Nacional de Observadores a bordo de la flota atunera uruguaya") of the Uruguayan pelagic longline fishery used in this study are considered confidential under the Uruguayan decree N°115/018, chapter XI, so the raw data cannot be shared publicly. On reasonable request of nonconfidential observer data, please contact Andr es Domingo (adomingo@mgap. gub. uy); 11) US West Coast sablefish: the data from the West Coast Groundfish Observer Program (WCGOP) used in this analysis are subject to confidentiality requirements set forth in The Magnuson-Stevens Act at section 402(b), 16 U.
ACKNOWLEDGMENTS. We thank all the fisheries observers who collected the valuable data necessary for this project. For D. M. K. and the  French Indian Ocean (IO)  purse, we thank the  IRD-Ob7 pelagic observatory of the MARBEC laboratory  for French IO tropical tuna logbook and observer data management and preparation, particularly  L. Floch  and  P. Sabarros. Bycatch data  for the French IO purse-seine fishery were collected under the auspices of the European program " Data Collection Framework" (Reg [ EC] 1543/ 2000  and  199/2008 ) and the  French "Observateur Commun Unique et Permanent" observation program  supported by  French purse-seine companies (Orthongel , a professional organization of the French tropical tuna purse-seine fishery). We would also like to thank  Charles Villafana  and the  West Coast Observer Program  for their help with the California  Drift Gillnet  fishery,  Alan Haynie  for valuable comments on an earlier draft, and  Nicole Baker Loke  for helping us during the submission process.
Mass spectrometry data were acquired from m/z 50 to 1200, using both positive (+) and negative (-) modes. The following parameters were used for the Q-TOF in both ionization modes: end plate offset at 500 V; nebulizer gas (N 2 ) pressure at 3. 5 bar; dry gas flow (N 2 ) at 12 L/min; drying temperature at 200 • C; acquisition rate at 4 Hz. The capillary voltage was set at 3500 V for positive mode and 3000 V for negative mode. A data-dependent acquisition (DDA) protocol was used; therefore, MS/MS fragmentation spectra were obtained automatically for the three most abundant precursor ions using mixed collision energy 20-40 eV (in stepping mode). A solution of Sodium Formate Acetate was used as a calibration to obtain high mass accuracy (2-5 ppm) and was automatically injected at the beginning of each run.
Data Processing and Molecular Network 2. 4. 1. File Conversion Raw datasets obtained from the UHPLC-MS/MS system were calibrated using Bruker DataAnalysis (5. 0 SR1 64-bit) and converted into open format. mzXML using GNPS Vendor 32-bit  [8]. However, the issue regarding a non-calibrated precursor ion value in. mzXML, highlighted by  Zdouc et al. in 2021 , persists in Bruker DataAnalysis software  [10] , which means that the export of raw data sets results in calibrated MS/MS data, with non-calibrated precursor ion information (<precursorMz></precursorMz>) for each MS/MS scan. Zdouc et al. suggested using a script in DataAnalysis software to export to. mgf files, which contain calibrated precursor ion information, and a Perl5 script to correct precursor information in the. mzXML data. The Perl5 script could not be used in the present work because of the difference between Bruker Compass versions used for the acquisition of raw data.
Data Processing Exported. mzXML data were pre-processed using MZmine software, version 3. 0. The processing workflow includes raw data file import, mass detection, chromatogram building, chromatogram deconvolution, feature list deisotoping, alignment between analytical replicates, filtering, and plant species  [8, 11, 12]. The features present in blank methanol runs were removed from the features list. Parameters of each step used for processing can be seen in Table  S1  of Supplementary Materials. Processed data were exported (mgf and CSV files) and applied to MolNotator software. First, the AdNotator module allows the identification of the adducts of a compound, and FragNotator identifies the in-source fragments. Then, the MolNet module allows the construction of a molecular network  [13].
The Adnotator precursor and fragments ion mass tolerance were set to 0. 002 and the retention time tolerance to 6 s. Detailed parameters used for MolNotator software are presented in the Supplementary Materials. Cytoscape 3. 9. 1 software was used to visualize the resulting network. The table with 374 metabolites and their peak areas in the studied Aloe species, generated by MolNotator, was submitted on Metaboanalyst 5. 0 platform for hierarchical cluster analysis in order to produce a dendrogram illustrating molecular similarity between five Aloe species  [14].
A total of six reference standard compounds were injected for MS/MS data acquisition to confirm the identification of major metabolites: chlorogenic acid (8), isoorientin  (31) , isovitexin  (33) , loliolide  (34) , vitexin  (41)  and aloin A (61). All annotated metabolites can be found in Table  1. Spectral data acquired in both positive and negative ionization modes were used to annotate the Aloe species metabolomes.
On the other hand, the extracts that had antioxidant activity in 96-well plates also showed radical scavenging properties with the On-Line approach. A. macra, A. ferox, and A. purpurea showed 5 to 6 negative peaks on the DPPH Vis 515 nm chromatogram. On the other hand, the extracts that had antioxidant activity in 96-well plates also showed radical scavenging properties with the On-Line approach. A. macra, A. ferox, and A. purpurea showed 5 to 6 negative peaks on the DPPH Vis 515 nm chromatogram. The aim of the On-Line RP HPLC DPPH assay was to identify compounds responsible for the antioxidant properties of the extracts. The extracts were also analyzed by LC-MS/MS using the same analytical method as On-Line DPPH. The acquired MS data led to the annotation of active metabolites by comparing their MS/MS spectra to those from previously annotated metabolites (Section 2.
Acknowledgments: We are grateful to  Alizé Riou  and  Johnny Férard  from  CBNM  for the preparation of the voucher specimens, to  Miharisoa-Mirana Gauche  from the  Reunion University Herbarium  for registration of the deposits, and  Alrick Dias  and  IT department of IMBE  for their help in the preparation of Perl script, allowing the correction of exported data.
Data Availability Statement: The data presented in this study are openly available. Data is available in a publicly-accessible repository. Raw LC/MS data is available at: ftp://MSV000089679@massive. ucsd. edu (accessed on 13 November 2022) and treated LC/MS data at: https://doi. org/10. 5281/ zenodo. 6884839 (accessed on 13 November 2022). GNPS Feature-Based Molecular Networking Jobs are available online at https://gnps. ucsd. edu/ProteoSAFe/status. jsp?task=62db3daa7df74ad8a273 eb51fc27b3e0 (accessed on 13 November 2022) and at https://gnps. ucsd. edu/ProteoSAFe/status. jsp? task=9048ac276e6841d08a858c46088f0c40 (accessed on 13 November 2022) for positive and negative modes, respectively.
Dataset Download Image data used in this chapter can be downloaded from our Zenodo repository (7 http://doi. org/10. 5281/zenodo. 3975644) or from the GitHub repository associated with this book. The ZIP package contains several folders. The first one called "Data" contains two folders relative to the two different investigated mutants. A second folder called "Processfiles" contains some files used in preprocessing for white balancing and dead pixel corrections, the ilastik training file and the full code. Note that due to limitations of the ilastik plugin it is important to save the ilastik-project file (. ilp) in a folder structure without spaces in the path. Once the data is downloaded, we can do a quick pre-assignment before we dive into the workflow. Looking at the raw non-processed sample images, appreciate the variability of patchiness of the fly eye; as it is commonly said-"beauty is in the eye of the beholder.
Step 3. Segmentation by Using Ilastik With the cropped images now saved, we can move forward with segmentation by using ilastik. We use here the simple Pixel-based Segmentation option in the ilastik software  (Berg et al. , 2019) , which can be run with the following command: 1 run("Run Pixel Classification Prediction", "projectfilename=" + DirCorr + "\\" + TrainingFileName + "inputimage=Left pixelclassificationtype=Segmentation"); → → It will take a few seconds to get the segmented image, but afterwards is where things get interesting; we can now start extracting information from the images. An example of segmentation is shown in. Fig. 7. 3. We are not going to go into details regarding using ilastik itself here, because great tutorials are available on the ilastik website. 3 We do not provide a training set for the performed segmentation, but instead provide a pre-trained ilastik model as part of the accompanying materials to this chapter.
We note that training is laborious and requires some Drosophila domain knowledge in recognizing features in the images. However, we will make some general points about the training process itself and how segmentation works in ilastik. ilastik segmentation is based on machine learning and requires selecting features, and training a model to categorize pixels into different segmentation classes. Training ilastik models requires an annotated training set of images, which can be created by the user within the software. In short, the user assigns labels to pixels using a very intuitive interface where they simply draw or 'color' on a training image. We 7 found that the accuracy of the annotation was dependent on how familiar the user was with the subject matter in the images, with more experience leading to more accurate annotation.
Visualizing Results: Presentation and Discussion At the end of the workflow, we will get three tables: 5 Area Distribution. csv contains the area of each patch for each analysed eye; 5 Intensity Distribution. csv contains the average intensity of each patch for each analysed eye; 5 FinalTable. csv contains the different metrics collected along the way (Number of Patches, Average Size, Average Intensity of the whole cropped eye image, Percentage of Area, Crowdedness, Ideal Ratio of distances, Deviation from Ideal, Deviation from Random Distribution) The tables are a convenient output from Fiji. However, a graph can speak a thousands words (or numbers). We suggest using plotting packages in R, such as ggplot2, or in Python, such as the seaborn library or if one is extra ambitious and nitpicky, the matplotlib library.
Acknowledgements We would like to thank the  NEUBIAS community  for their input on the workflow over the years as well as the lab of  Ernst Hafen  at the  Institute of Molecular Systems Biology and ScopeM of ETH Zürich  for their advice and instruction on imaging methods and technologies. This work was made possible in part by  NIH  grants  1F32GM116321-01A1  and  R35GM118170. Further Readings If packing, organising objects inside a space in 2D or in 3D are of interest, we recommend the first chapters of a book dealing with perception and spatial organization:  L. C. Robertson ,  Space, Objects, Minds and Brains. To advance to subjects with more metrics, Andrey et al. (2010) is a must read. An ImageJ plugin for spatial statistics already exists (7 https://imagejdocu. tudor. lu/plugin/analysis/spatial_statistics_2d_3d/start.
The assessment of tropical cyclone (TC) statistics requires the direct, objective, and automatic detection and tracking of TCs in reanalyses and model simulations. Research groups have independently developed numerous algorithms during recent decades in order to answer that need. Today, there is a large number of trackers that aim to detect the positions of TCs in gridded datasets. The questions we ask here are the following: does the choice of tracker impact the climatology obtained? And, if it does, how should we deal with this issue? This paper compares four trackers with very different formulations in detail. We assess their performances by tracking TCs in the ERA5 reanalysis and by comparing the outcome to the IBTrACS observations database. We find typical detection rates of the trackers around 80 %.
The few high-resolution model results already published clearly demonstrate a dramatic improvement in simulating TCs  (Manganello et al. , 2012; Murakami et al. , 2015; Walsh et al. , 2015; Roberts et al. , 2020a). This avenue is raising hopes in our capacity to better understand these storms and to better predict their future evolution. Studying TCs in global simulations spanning several decades requires their objective and automatic detection and tracking, which is accomplished by so-called TC trackers. Trackers are algorithms that are able to detect cyclonic structures associated with a warm core in a gridded dataset and link them together into a trajectory. Many modeling and operational centers have developed such trackers independently, and there is now a wealth of such algorithms available to the community and described in the literature (see for Published by Copernicus Publications on behalf of the European Geosciences Union.
The OWZ tracker was found to produce better results across a wide range of resolutions, while the CSIRO tracker performed better for the high-resolution datasets. These studies confirm the naive expectation that different tracking algorithms inevitably have different TC detection skills. As a result, it is often difficult to compare different studies because they use different trackers. For example, future projections of TC frequencies in CMIP5 as reported by  Tory et al. (2013b)  and  Camargo (2013)  are difficult to compare because they used the OWZ tracker and that of  Camargo and Zebiak (2002) , respectively. Two recent papers by  Roberts et al. (2020a)  have tried to circumvent this problem using multiple trackers when analyzing a given dataset and check whether the result is robust, i.
This paper reports the results of an intercomparison of four different trackers with properties as different as possible from one another in terms of their formulation. The report is based on a comparison between the tracks detected by these trackers on a reanalysis (ERA5,  Hersbach et al. , 2020)  and those recorded in an observation database, i. e. , the International Best Track Archive for Climate Stewardship (IB-TrACS,  Knapp et al. , 2010). This study uses the reanalysis as a bridge between observations and simulation. Our main goal is not to provide an assessment of ERA5 performances in reproducing a given TC climatology but to compare the trackers with one another. Numerous studies have undergone such an assessment on several other reanalyses, including ERA5's predecessor ERA-Interim  (Hodges et al.