dataesr/scientific-paragraphs-categorization huggingface dataset:
The LOX143 construct is monomeric in solution, whereas LOX129 is dimeric due to an inter-chain disulfide bridge at Cys140  24, 25. Selected cluster representatives were analyzed by multiple biophysical methods to further validate and characterize the identified compounds. As a first method, Saturation Transfer Difference (STD)-NMR was used to demonstrate binding to the receptor. BI-0115 showed clear STD signals in the aromatic region (18. 5% STD, Fig. 2a ) and comparable STD effects for the methyl moiety in the aliphatic region of the 1 H NMR spectrum (data not shown). In addition, similar STD signals of BI-0115 were observed in the presence of LOX143 suggesting that the compound binds to both LOX129 as well as LOX143 under identical conditions (each with n = 1). To enable surface plasmon resonance spectroscopy (SPR), LOX129 was immobilized on a SPR chip by amine coupling and compounds were analyzed for binding.
Based on the individual sensorgrams the compounds were classified as non-binders, overstoichiometric binders or dose-dependent binders. In total four different structural classes could be confirmed as dose-dependent and stoichiometric binders. A representative sensorgram for BI-0115 is shown in Fig. 2b. The compound shows fast binding kinetics. A steady-state analysis of the SPR data indicated a mean Kd of 4. 3 µM. The mean binding ratio is 0. 78 (Supplementary Table  3 ). Assuming a high percentage of active, binding competent protein on the chip surface, this ratio represents less than one BI-0115 molecule per LOX-1 monomer. For additional biophysical profiling of the specific SPR binders, isothermal titration calorimetry (ITC) was used. Due to limited solubility of the compounds, an inverse setup was used by titrating LOX129 into compound solution.
Co-crystallization was performed with a LOX-1 143-273 protein solution containing 2 mM of the compound. Crystals were obtained with a 1:1 mixture of the protein solution and a reservoir solution containing 0. 1 M Bis-Tris pH 7. 5, 30% PEG MME 2000 and 0. 15 M KBr. Data collection, processing and refinement. Crystals were frozen in liquid nitrogen and data were collected at the SLS beam line X06SA (Swiss Light Source, Paul Scherrer Institute) using the PILATUS 6M detector. LOX-1 143-273 apo crystals belonged to space group P 21 21 21 and contained 2 monomers per asymmetric unit. LOX-1 129-273 apo crystals belonged to space group C2 and contained 3 monomers per asymmetric unit. LOX-1 143-273 BI-0115 crystals belonged to space group C2 and contained 8 monomers per asymmetric unit. Images were processed with APRV  41  , XDS  42  and autoPROC  43.
The resolution limits were set using default autoPROC settings. The structures were solved by molecular replacement using MOLREP  44  , Phaser  45  and PDB ID 1YPU  24  as a start model. Subsequent model building and refinement was done using standard protocols using CCP4  46  programs, Coot  47  , Phenix 48 and autoBUSTER  49. A summary of the statistics can be found in Supplementary Table  5. Pictures have been made using the PyMOL 50 software. Isothermal Titration Calorimetry Analysis. Isothermal titration calorimetry experiments were conducted on a MicroCal iTC200 instrument (Malvern) using LOX-1 that had been passed through a PD-10 desalting column (GE Healthcare) equilibrated with 20 mM Tris pH 7. 3 and 150 mM NaCl. Complete saturation of 35 µM, respectively 40 µM LOX inhibitor was typically achieved by injecting 18× 2 µl aliquots of 300 µM LOX-1 at a temperature of 25 °C.
As a control 300 µM LOX-1 was injected in buffer (n = 1, Supplementary Fig. 19 ). The thermodynamic binding parameters were extracted by non-linear regression analysis of the binding isotherms (MicroCal PEAQ-ITC Analysis Software). A single-site binding model was applied yielding the binding enthalpy (ΔH), stoichiometry (n), entropy (ΔS) and association constant (K a ). The binding isotherm of protein in buffer control was not subtracted during data evaluation. Surface Plasmon Resonance Analysis. LOX-1 129-273 was immobilized onto a CM5 chip (GE Healthcare Bio-Sciences AB, Uppsala, Sweden) by amine coupling in 10 mM Na acetate at pH 5. 0. Binding studies were performed using a Biacore T200 instrument (GE Healthcare) at 25 °C in 20 mM HEPES pH 6. 8, 150 mM NaCl, 0. 05% Tween 20, and 1% DMSO at a flow rate of 30 μL/min.
The interaction analyses were performed in the multi-cycle kinetic mode using 120 s association time and 600 s dissociation time at the following LOX-1 inhibitor concentrations 0. 391, 0. 78, 1. 56, 3. 125, 6. 25, 12. 5 and 25 µM. Data were analyzed using the Biacore T200 Evaluation Software 2. 0. Due to a transient binding, the K D was determined from a plot of steady state response levels against analyte concentration (affinity plot). The mean K D value was determined from four individual experiments (Supplementary Table  3 ). The binding ratio is the average number of compound molecules bound per LOX-1 protein on the surface. NMR spectroscopy. STD NMR binding experiments  51  were performed in 2. 5 mm NMR tubes on a Bruker AVII+ 600 MHz spectrometer equipped with a 5 mm cryo-TCI probe and z-gradients.
Topspin 3. 0 (Bruker Biospin) was used to process and analyze data. Homology Modelling. The homology model of the tetrameric complex has been prepared using the MOE  54  software package. The CTLD part is based on the cocrystal structure of the BI-0115-LOX-1 complex, whereas the neck domain is modeled based on the Homer coiled-coil domain PDB ID 3CVE  55. The Homer coiled-coil sequence showed the highest sequence homology to the LOX-1 neck domain and therefore selected as model basis. Mass spectrometry. Mass spectrometry experiments were carried out on an electrospray time-of-flight mass spectrometer (LCT, Waters, Manchester, UK) equipped with an automated chip-based nanoESI device (Triversa Nanomate, Advion Biosciences, Ithaca, NY). External calibration was performed in positive ion mode over the mass range m/z 500-5000 using the multiply charged ions produced by 0.
PAMPA. The PAMPA assay provides data on the passive permeability of test compounds through immobilized artificial phospholipid membranes. The method used was extensively described previously  56. The number of experiments for BI-0115 is n = 1. High Throughput solubility. The aqueous solubility of the test compound is determined by comparing the amount dissolved in buffer to the amount in an acetonitrile/water (1/1) solution. Starting from a 10 mM DMSO stock solution aliquots are diluted with acetonitrile/water (1/1) or buffer resp. After 24h of shaking, the solutions are filtrated and analyzed by LC-UV. The amount dissolved in buffer is compared to the amount in the acetonitrile solution. Solubility will usually be measured from 0. 001 to 0. 125 mg/ml at a DMSO concentration of 2. 5%. The solubility at the different pH values for BI-0115 were measured with n = 1.
Acknowledgements The authors thank  Adelheid Löhle ,  Yvette Hövels ,  Katja Mück ,  Antje Adomeit ,  Sebastian Eder ,  Karin Guderlei  for expert technical assistance. Christofer Tauterman  helped with building the homology model in MOE. Many thanks to  Claudia Heine ,  Katja Phillipp  and  Anna-Luisa Reiser  for providing the chemical synthesis. We thank  Dirk Reinert  and  Expose GmbH, Villigen  for data collection and the staff at Swiss Light Source (SLS) beamlines PXI and PXIII for their continuous support. Many thanks to  Xavier Espanel  and  Dominique Roecklin  from  Novalix  for fruitful discussions and clear outputs from the native MS study.
Data availability The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files, or from the corresponding author upon reasonable request. The crystallographic data for the LOX-1 extracellular domain in complex with BI-0115 is deposited under accession code 6TL9. The nonliganded LOX143 structure is deposited under accession code 6TL7. The non-liganded LOX129 structure is deposited under accession code 6TLA. Referenced accessions include OLR1_HUMAN, OLR1_MOUSE, OLR1_RAT, Q2HXU8, Q6UXN8, Q9BXN2, 1YPO, 1YPQ, 1YPU, 1YXJ, 1YXK, 3VLG and 3CVE.
Each 15-s spin, the data are binned into a ring of 60 6°× 6°pixels. Each pixel contains the LOS integrated hydrogen ENA flux at eight energies for IBEX-Lo and five energies for IBEX-Hi. These single-spin strips are combined and transmitted to ground as 92-spin packets (spanning 23 min). In combination with IBEX observations, in situ magnetosheath and magnetospheric plasma observations are from MMS. MMS is a multispacecraft mission launched into Earth orbit in March 2015 to investigate magnetic reconnection in the near-Earth environment  (Burch et al. , 2016). The Hot Plasma Composition Analyzer (HPCA)  (Young et al. , 2014)  is one of many MMS instruments. HPCA is a time-of-flight mass spectrometer that measures the full 3-D plasma distributions for major solar wind and magnetospheric ion species (H + , He 2+ , He + , and O + ) in 10 s.
Data Availability Statement IBEX-Hi and IBEX-Lo magnetospheric data are available online (http://ibex. swri. edu/researchers/publicdata. shtml#dr12). The MMS science data center (https://lasp. colorado. edu/mms/sdc/public/links/) has the MMS data. Solar wind data were obtained through CDAWeb (https://cdaweb. gsfc. nasa. gov/index. html/). Southwest Research Institute research was NASA funded through MMS contract NNG04EB99C, IBEX subcontract 80NSSC19K1107, and HGI grant NNX17AB98G. ISSI supported this study via the international Cold plasma of ionospheric origin at the Earth's magnetosphere team.
Observational and reanalysis data. Daily temperature observations are from the Global Historical Climatology Network-Daily (GHCN-D) archive  44. Further details on the extensive coverage in the 1930s and the station selection can be found in Cowan et al. 3. Maximum daily Tmax anomalies in Fig. 1a  are taken from the gridded HadEX2 dataset  6  (for a HWF-based version of Fig. 1a , see Supplementary Fig. 1a ). Our hottest heatwave dates were determined from temperatures obtained from the Berkeley Earth Surface Temperature (BEST) dataset  45  , which incorporate the large network of GHCN-D stations (Supplementary Fig. 2a  shows a HWM comparison between BEST and GHCN-D). Given the daily BEST product is considered experimental, we predominantly focus on GHCN-D for the spatial representation of the observed heatwaves.
All model data were interpolated onto a regular 1°grid prior to analysis. Models with at least 400 piControl years of output were considered (all except for bcc-csm1-1-m have 500 years), which allowed for an assessment of multiple occurrences of record-breaking heatwaves over at least four centuries for each individual model piControl experiment. To ensure we captured decadally clustered events as observed during the Dust Bowl decade, rather than outlier record-breaking heatwaves, for each models' HWF, we applied an 11-year (summer) running average over each central US grid point for the entire piControl period. For each century (e. g. , years 1-100, years 101-200, to years 401-500), we selected the maximum HWF value for each grid point and noted the decade of occurrence. We then determined the percentage of central US grid points where record-breaking values fell into the same decade, with the highest percentage selected as the record-breaking decade for that century for that particular model.
The AMO is defined as area-weighted SST anomalies averaged over the North Atlantic (0-60°N, 80°W-0°E), with the global (60°S-60°N) mean SST anomaly removed. No low-pass filtering is performed on the decadal oscillation indices. We included all available CMIP5 models in our multidecadal analysis, although we are aware that the majority of models lack internally generated Atlantic multidecadal variability  53. Significance tests. In Fig. 2 , the non-parametric two-tailed Mann-Whitney U test  28  is used to test the significance in heatwave differences between the ATL HIST and PAC HIST ensembles for each grid point. The null hypothesis tested here is that the data from ATL HIST and PAC HIST have been drawn from the same distribution. The Mann-Whitney U test determines whether the experiment in question is distinguishable from its partner experiment at the 95% confidence level.
Acknowledgements This study forms part of the  Transition into the Anthropocene (TITAN)  project, funded by a  European Research Council (ERC)   Advanced Grant  ( EC-320691 ), and supported by the  EUCLEIA  project funded by the  European Union 's  Seventh Framework Programme  ( FP7/2007-13 ) under Grant Agreement 607085 and the  EUPHEME ERA4CS  grant  690462. T. C. received additional support from the  Northern Australian Climate Program , with funding provided by  Meat and Livestock Australia , the  Queensland Government and University of Southern Queensland. G. C. H. was also supported by  National Centre for Atmospheric Science (NCAS)  and as a  Royal Society Wolfson Research Merit Award  ( WM130060 ) holder. A. S. was supported by  Belmont/JPI-Climate  Project  PACMEDY (Paleo-Constraints on Monsoon Evolution and Dynamics ;  NERC :  NE/P006752/1 ).
Code availability The code to generate the figures is available at https://github. com/tcowan80/ Cowan_et_al_2020_DustBowl_HadGEM3, and is based on the NCAR Command Language (version 6. 6. 2; doi:10. 5065/D6WD3XH5). The code to generate climatologies was constructed using Climate Data Operators (CDO) version 1. 9. 8.
77-0. 98 correction range). [25] 2572 reflections were unique (Rint = 0. 045). The structures were solved with SHELXS-1997 by using direct methods and refined with SHELXL-2017 on F 2 for all reflections. [26] Non-hydrogen atoms were refined with anisotropic displacement parameters. 235 parameters were refined without restraints. R1 = 0. 057 for 2572 reflections with I>2s(I) eÅ  3  , and wR2 = 0. 154 for 2878 reflections, S = 1. 081. Geometry calculations and checks for higher symmetry were performed with the PLATON program. [27] CCDC-1968707 contains the supplementary crystallographic data for this paper. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data_request/cif. X-ray crystal structure determination of 8: C28H20NO2P, Fw = 433.
FULL PAPER obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data_request/cif. Computational details: Density functional calculations were carried out with the Gaussian 09 program package. [28] All structures were optimized using the B3LYP functional,  [29]  combined with the 6-31+G* basis set, and these results were discussed throughout. For the conformational search of 3 further calculations were carried out using the M06-2X and the ωB97XD functionals and the cc-pVTZ basis, which gave similar results to B3LYP/6-31+G* (See Table  S1a  in the Supporting Information). At each of the optimized structures vibrational analysis was carried out to check that the stationary point located is a minimum of the potential energy hypersurface (no imaginary frequencies were obtained).
Acknowledgements OTKA  NN 113772  and  BME-Nanotechnology   FIKP grant of EMMI (BME FIKP-NAT ) and the  Alexander von Humboldt Foundation  for a reinvitation grant for (LN),  PICS SmartPAH  ( 08062 ) and MTA -NKM 44/2019 (LN and MH) is acknowledged for financial support. Funding by the  Deutsche Forschungsgemeinschaft  ( MU1657/5-1 ) is gratefully acknowledged.
Chronicles of nature calendar, a long-term and large-scale multitaxon database on phenology Otso Ovaskainen et al. # We present an extensive, large-scale, long-term and multitaxon database on phenological and climatic variation, involving 506,186 observation dates acquired in 471 localities in Russian Federation, Ukraine, Uzbekistan, Belarus and Kyrgyzstan. the data cover the period 1890-2018, with 96% of the data being from 1960 onwards. The database is rich in plants, birds and climatic events, but also includes insects, amphibians, reptiles and fungi. the database includes multiple events per species, such as the onset days of leaf unfolding and leaf fall for plants, and the days for first spring and last autumn occurrences for birds. The data were acquired using standardized methods by permanent staff of national parks and nature reserves (87% of the data) and members of a phenological observation network (13% of the data).
Methods Data acquisition. The data were collected by two research programs: the Chronicles of Nature (Letopisi Prirody) monitoring program, and a volunteer network of phenological observers (Fenologicheskii Klub). The Chronicles of Nature monitoring program  22  is based on the network of strictly protected areas (zapovedniks) and national parks. The program gradually evolved during early 1900s  23  and was formally established in 1940 with the aim of streamlining scientific work in protected areas with standardized methodology among the organizations. The program involves the permanent personnel of each participating organization. The results of the monitoring programs are published annually as Chronicle of Nature books. One printed copy of the books was kept in the office of the participating organization and another copy was sent to the Governmental Environmental Conservation Service (or a corresponding entity depending on the specific point in time).
In the Chronicles of Nature monitoring program, bird phenological events are extracted from route-based observations conducted regularly by ornithologists or professional rangers of the protected areas. Plant phenological events are reported either by botanists who visit permanent monitoring plots or transect, or by rangers who conduct regular walk-throughs within the strictly protected area or national park. The insect phenological data are extracted from standardized trapping data collected by entomologists on permanent plots or transects. The amphibian and reptile data are extracted from standardized trapping data collected by herpetologists. The fungal phenological data are collected by mycologists on permanent plots or transects. The weather event data are collected following a list of pre-defined events (e.
g. first day of snowfall) by dedicated personnel or sourced from observations made on a local meteorological station. The types of data collected by each organization depends on the expertise of different taxonomic groups in the scientific personnel. For more details on how the data were collected, see  22, [24] [25] [26] [27] [28]. The network of phenological volunteer observers was established by the Russian Geographic Society in 1848 with questionnaires sent out to selected contacts among scientific community, including teachers and general public  29. The participants of the volunteer observation network make observations throughout the year to collect data on a pre-defined limited set of phenological events related to plants, animals, and weather. The species included in the pre-defined lists were selected so that they could be identified reliably without specific taxonomical training.
Data digitalization and unification. The compilation of the data in a common database was initiated in the context of the project "Linking environmental change to biodiversity change: long-term and large-scale data on European boreal forest biodiversity" (EBFB), funded for 2011-2015 by the Academy of Finland, and continued with the help of other funding to OO since 2016. We organized a series of project meetings that were essential for data acquisition, digitalization and unification. The compilation of the data into a common database was conducted by the database coordinators (EM and CL) in Helsinki (Finland). Those participants that already held the data in digital format submitted it in the original format, and those that had the data only in paper format digitized it using Excel-based templates developed in the project meetings.
Submitted data were processed by the database coordinators according to the following steps: 1. The data were formatted so that each observation (the phenological date of a particular event in a particular locality and year) formed one row in the data table (e. g. un-pivoting tables that involved several years as the columns). The phenological event names were split into event type (e. g. "first occurrence") and species name. 2. The event type names (provided originally typically in Russian) were translated into English and the species names (usually provided in Russian) were identified to scientific names, using dictionaries that were partly developed and verified in the project meetings. All scientific names were periodically verified by mapping them to the Global Biodiversity Information Facility (GBIF) backbone taxonomy  30.
3. We associated each data record with the following set of information fields: (1) project name, i. e. the source organization, (2) dataset name, (3) locality name, (4) unique taxon identifier, (5) scientific taxon name, and (6) event type. 4. We imported the data records in the main database (maintained as an EarthCape database at https:// ecn. ecdb. io). During the import, the taxonomic names, locality names, and dataset names were matched against already existing records. 5. The database was published in Zenodo  31. Updates and limitations. There are at least 150 National Parks and Nature Reserves that collect Chronicles of Nature Book data (in Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine and Uzbekistan). Out of these, the current database covers data from 62 organizations, with the highest coverage in European Russia (Fig.
2 ). The collection of new data continues in most parks. Thus, the database is not complete, and we aim to support the database with updates, depending on the interest of new partners to join, as well as resources and funding. The technical validation procedures described below will also be applied to any new information included in the database. The resulting new versions of the database will be released through the Zenodo repository to ensure the long-term availability of the database. The Chronicles of Nature programme involves several kinds of systematically collected data beyond phenology data: e. g. trapping data on small mammals, count data on birds, and yield data on berries and mushrooms  22. We aim to publish these data as separate data papers.
Data Records The database is organized in six datasets: (1) a classification of taxa included, (2) a list of phenological events included, (3) a list of climatic events included, (4) information on the study site, (5) the phenology data, and (6) an information sources table for phenology data  31. All tables are in csv format (columns separated by commas), and their fields are described in Tables  1 2 3 4 5 6. The tables can be linked to each other using the unique study site names and the unique identifiers for species and climatic evens.
technical Validation We asked the contributors to carefully check the validity of the phenological dates prior to submission. While uploading the submitted data to the database, we did manual validation checks to pinpoint data records that were suspicious (e. g. summer events recorded in winter), and sent the suspicious data records back for the contributors for correction or validation. However, given the extensive size of the database, it is likely that the database contains a number of erroneous records. Thus, we performed a series of checks to identify spurious data points and to examine for the strength of biological signal in the data. First, we fitted for each (site -climatic/species name -event type) triplet a von Mises distribution, i. e. the circular normal distribution, where the circularity was used to connect the last day of the previous year to the first year of the next year.