dataesr/scientific-paragraphs-categorization huggingface dataset:
The sample R, MATLAB, and Python codes provided with this dataset (in the Supplement) provide a similar example to users. 6. 3 Database updates, versioning scheme, and submission of new or updated datasets This publication marks Version 1. 0. 0 of the Iso2k database. Following publication, the database will continue to evolve, as new datasets are added (both new studies and previous records that have been missed) and existing data or metadata are extended or, as necessary, corrected. Readers who know of missing datasets are asked to submit them directly through http://lipd. net/playground (last access: 30 July 2020). Database users who find errors in individual datasets can submit proposed edits using the "Edit LiPD file" function at http://lipdverse. org/iso2k/current_version/ (last access: 30 July 2020), or they can use the "Report an issue" option for errors that apply to multiple datasets.
More detailed instructions for dataset submission and a link to a LiPD entry template hosted through http://lipd. net/playground (last access: 30 July 2020) will be added to the WDS-NOAA landing page (https://www. ncdc. noaa. gov/ paleo/study/29593, last access: 30 July 2020) when they become available. As the database updates, it will be versioned following the scheme used by other PAGES data collections  (Kaufman et al. , 2020; McKay and Kaufman, 2014; PAGES 2k Consortium, 2013 , 2017) , with the following format: X1. X2. X3, where X1, X2, and X3 are incrementing integers. When X1 increases, X2 and X3 reset to zero. When X2 increases, X3 resets to zero. X1 represents the number of publications describing the database. X2 increments each time the set of records in the database changes (addition or removal of a dataset).
Citation This Iso2k data descriptor should be cited when the database is used in whole or in part, including its metadata fields, for subsequent studies. We encourage users of the database to not only cite the Iso2k data product but also the original publications of the underlying primary data (Tables  S2  and S3 ). Citation of both the Iso2k data product and the underlying studies is particularly encouraged when analyses make explicit use of individual records or small subsets of records, https://doi. org/10. 5194/essd-12-2261-2020 Earth Syst. Sci. Data, 12, 2261 Data, 12, -2288 Data, 12, , 2020  even though citation of > 400 original studies may not be practical if the entire Iso2k database is used.
Conclusions and anticipated applications of the Iso2k database The global extent, quantity, and quality of metadata included in the Iso2k database allow examination of the multiple variables that impact water isotopes, including moisture source and transport history, temperature, and precipitation amount. These multivariate controls mean that water isotopes contain a wealth of information about climate. Importantly, water isotope signals contained in proxy archives can be modified by local environmental processes, such as evaporation, biosynthetic fractionation, bioturbation in sediments, or diffusion. These archive-or proxy-specific transformations therefore additionally allow for reconstruction of water balance (P -E), different forms of drought (e. g. , meteorological, hydrological, or soil moisture), and relative humidity  (Rach et al.
, 2017). It is difficult to tease apart the effects of multiple variables in a single proxy record, but this global compilation of water isotope proxy records from a range of archives will help to overcome this barrier, facilitating extraction of common signals from the noise of individual proxies and providing insights into different aspects of the hydrological cycle at a range of spatial and temporal scales. The Iso2k database also provides an unprecedented direct comparison for state-of-the-art water-isotope-enabled climate models. Many data-model comparison efforts com-pare climate model variables such as temperature and precipitation to paleoclimate data; the latter is often a complex and nonlinear signal integration of multiple climate influences, and uncertainties arise from the assumptions that must be made  (Dee et al.
, 2016; Evans et al. , 2013). Comparing water isotope fields from climate model outputs to isotope proxy records of the same components of the water cycle circumvents these uncertainties, providing a more direct comparison of proxies and model simulations in the same units. Model validation on this relatively level playing field will improve estimates of climate models' ability to simulate changes in hydroclimate on long timescales. For those archives that further filter the isotopic signal, proxy system models can aid data model comparison  (Dee et al. , 2015 (Dee et al. , , 2018;; Jones and Dee, 2018). Therefore, the Iso2k database will not only enable global-scale comparisons with isotope-enabled climate models but may also serve as an input database for paleoclimate data assimilation reconstructions such as the Last Millennium Reanalysis  (Hakim et al.
(Konecky and McKay, 2020)oys the Linked Paleo Data (LiPD) format(McKay and Emile-Geay, 2016), with serializations available for R, MATLAB, and Python. The LiPD format is machine-readable, with code bases to facilitate input, output, visualization, and data manipulation in R, Python, and MATLAB. Simple visualization and data access (both as LiPD and csv files) is available through the LiPDverse at http://lipdverse. org/iso2k/ current_version/ (last access: 30 July 2020) (iso2k1_0_0, 2020). The LiPDverse additionally houses other paleoclimate records and compilations that may be of interest to users of the Iso2k database. The serializations contain all LiPD files included in the current version of the Iso2k database. Serializations of the database can be downloaded from https://doi. org/10. 25921/57j8-vs18(Koneckyand McKay, 2020)and from the WDS-NOAA Paleo Data landing page: https://www.
Unique record ID paleoData_iso2kUI Unique Iso2k identifier assigned to each 1 isotope record to distinguish among records when more than one record exists in the original publication. LiPD ID paleoData_TSid Unique LiPD file identifier for each time 1 series in the database. Variable namepaleoData_variableName Variable measured (e. g. , δ 18 O, δ 2 H). See 1.
Smallholder agriculture provides 90 % of primary food production in developing countries. Its mapping is thus a key element for national food security. Remote sensing is widely used for crop mapping, but it is underperforming for smallholder agriculture due to several constraints like small field size, fragmented landscape, highly variable cropping practices or cloudy conditions. In this study, we developed an original approach combining remote sensing and spatial modelling to improve crop type mapping in complex agricultural landscapes. The spatial dynamics are modelled using Ocelet, a domain-specific language based on interaction graphs. The method combines high spatial resolution satellite imagery (Spot 6/7, to characterize the landscape structure through image segmentation), high revisit frequency time series (Sentinel 2, Landsat 8, to monitor the land dynamic processes), and spatiotemporal rules (STrules, to express the strategies and practices of local farmers).
The second consists in using machine learning methods (such as deep learning and neural network) to free from the limitations of expert knowledge. The direct integration of context data into the classifier allows the latter to learn automatically from the ground truth data. Nevertheless, this approach is particularly suitable to address one issue at a time, such as for crop rotation in  Bailly et al. (2018)  and with a large and diversified dataset. To go beyond traditional approaches in remote sensing, this research aims at investigating the complementarity of remote sensing and spatio-temporal modelling to study: • How can spatial modelling help converting remote sensing data into more reliable thematic products? • How can remote sensing provide evidence of the landscape processes expressed as formalized knowledge in models? Such an approach is expected to be useful for a better understanding of complex fragmented and heterogeneous landscapes, such as those shaped by smallholder tropical agriculture in developing countries.
Remote sensing data and pre-processing: The remote sensing dataset corresponds to (i) one very high spatial resolution SPOT 6 image, and (ii) a high spatial resolution (10m-30m) time series of 37 Sentinel 2 and 9 Landsat 8 images, for the period between October 1 st , 2017 and April 29 th , 2018, corresponding to the main growing season. The SPOT 6 image was acquired on February 14 th 2017 around the peak of the growing season. The image was ortho-rectified, converted to Top Of Atmosphere reflectance and pansharpened using the open-source Orfeo ToolBox (OTB;  Grizonnet et al. , 2019)  to obtain multispectral images at 1. 5 m spatial resolution. Sentinel-2 image time series  (Donadieu, L'Helguen, 2016)  were acquired from THEIA website (https://theia. cnes. fr). These images are provided in surface reflectance, and with a cloud mask, thanks to the MAJA (MACCS ATCOR Joint Algorithm) processing chain  (Hagolle et al.
In situ data on land use: A ground truth database on actual land use was built during field surveys around the peak growing season (beginning of February to end of March 2018). GPS waypoints were recorded following an opportunistic sampling approach. Waypoints were chosen according to their accessibility (but some parts of the area could not be reached due to heavy rains affecting the road conditions), and the size of the object (at least 20 * 20 m, so that they can be identified on satellite images). We recorded land uses according to a modified version of the JECAM nomenclature (see http://jecam. org/wpcontent/uploads/2018/10/JECAM_Guidelines_for_Field_Data_ Collection_v1_0. pdf) to better take into account the specificities of tropical agriculture. In the case of intercropping, we recorded the main and the second crop in the field.
Once the waypoints were acquired, the boundaries of each field were digitized on the SPOT 6 image, and the class labels (and other attributes) were joined to the polygon database. Additional non-cropland polygons were also added from photo-interpretation of the SPOT 6 image for easily recognizable classes such as built-up surfaces, water bodies, wetlands, mineral soils, or natural forests. At the end, 2170 polygons were obtained (1759 crop and 511 non-crop).
Remote sensing The preliminary land use map was produced from remote sensing. This involved the following steps: (i) segmentation of the very high spatial resolution (VHSR) image into homogeneous objects, (ii) extraction of spectral and textural features from satellite images and other products (DEM), and (iii) training and validation of a Random Forest classifier which was then applied to the whole study area. For this study, all steps were performed using the MORINGA processing chain  (Gaetano et al. 2019) , which uses OTB and Gdal functions driven by Python scripts. Figure  1 : Framework of the method. The processes are presented in red color. CMVPsat corresponds to class membership vector probabilities obtained after random forest (RF) classification of satellite images. CMVPrules refers to the same vector obtained after spatio-temporal rules (STrules) implementation in the model.
Feature extraction: Satellite features were extracted for each entity of the ground truth database to build a learning database. This step involved the extraction of the following features: (i) reflectance from the Sentinel-2 and Landsat-8 time series (471 features), (ii) spectral indices from the same time series (267 features), (iii) textural indices from the Spot 6 panchromatic image (6 features), and (iv) altitude and slope from the digital elevation model (2 features). Those features were chosen according to a previous study by  Lebourgeois et al. (2017). Concerning the spectral indices, we computed the most common ones: NDVI  (Rouse et al. , 1974) , NDWI  (Gao, 1996) , MNDWI  (Xu, 2006) , MNDVI  (Jurgens, 1997) , Brightness Index, and RNDVI  (Jorge et al. , 2019)  for each image of Sentinel-2 and Landsat 8.
Classification: In the final step of the remote sensing approach, the objective was to obtain a land use map and to extract and a class membership vector of probabilities for each object (CMVPsat). This vector is useful for understanding the probabilities distribution between classes, and in the following steps, for redefining class membership probabilities for selected objects. The Random forest (RF) algorithm  (Breiman, 2001)  is widely used in remote sensing (e. g. Lebourgeois et al. , 2017; Debats et al. , 2016)  and has been shown to outperform other classifiers in benchmark studies (e. g. Fernández-Delgado et al. , 2014; Inglada et al. , 2015). The RF classifier was used to produce a land-use classification based on the learning database at the end of the growing season. The main output, the class membership vector of probabilities (CMVPsat), was extracted for each object.
ACKNOWLEDGEMENTS This study was conducted as part of the  SAMSAM  project funded by  CNES , the  French Space Agency  ( TOSCA APR 2017 ). This work was also supported by the  French National Research Agency  under the Investments for the Future Program, referred to as  ANR-16-CONV-0004. The authors would like to thank  Eloise Rasoamalala  and  Valérie Andoniaina  for their contributions to the field surveys, and the researchers of SPAD, the CIRAD platform in partnership on "Highland production systems and sustainability in Madagascar".
Analysis To assess area-level social vulnerability, a Material Deprivation Index (MDI) was created, drawing on three census data variables: population unable to read or write (per cent of individuals aged 10 and over), unemployment rate (per cent of unemployed among the active population) and substandard housing rate (per cent of houses without a toilet). This index was preferred to the EDI-PT (Portuguese version) -a more complex indicator adapted from the European Deprivation Index (EDI)  21  -because some of the variables used for building it are not free of charge at the statistical section level at the National Statistics Institute. The index used here has been associated to a variety of health outcomes and behaviours. 3 8 9 22 ll three indicators were standardised to the LMA means and then added together to form the Multiple Deprivation Index, in accordance to the z-score-based Carstairs and Morris' method.
Acknowledgements The authors are grateful to the  Portuguese National Institute of Medical Emergency  for graciously providing the data. The authors are also grateful to  Marcos Correia  for providing the road network that was used in the study and  Archie Davies  for the linguistic reviewing of the manuscript. The authors also thank  Luís Henrique Gama Dore de Araújo  and  Bruna Bronhara  for the statistical review of the methods and findings. Finally, the authors are grateful to the reviewers for their helpful comments. Contributors KSdNS conceptualised the study and undertook the literature review, collected the data, built the database and analysed the general results. MP designed the statistical analysis and analysed the results. At different stages KSdNS and MP were involved in the development of the methods, data analysis and interpretation.
Funding This work was supported by the  Centre of Geographical Studies of the Institute of Geography and Spatial Planning at the University of Lisbon  funded by the Fundação para a Ciência e aTecnologia (FCT, I. P. ), under the grants  «UIDB/ GEO/00295/2020 » and « UIDP/GEO/00295/2020 »; and the  Coordination for the Improvement of Higher Education Personnel , a Foundation within the  Ministry of Education in Brazil  with the research project  Health and Spatial Justice: The Geography of the Emergency Services in the Lisbon Metropolitan Area approved  under the agreement number  1685-13-9. by copyright. on November 11, 2020 at  Universidade de Lisboa. Protected http://bmjopen. bmj. com/.
1C). To determine whether the HSP90 metabolic interactome was constituted of particularly thermally unstable proteins, we compared their general structural and biochemical properties  [32]  obtained from protein databases. There were no common features in this subset of proteins (Fig. S1 ), and neither an enrichment in proteins classified as unstable as determined by their melting temperature  [32]  (Fig. S1 ). This suggests that this is not a subset of particularly unstable proteins. In accordance with this notion,. CC-BY-NC-ND 4. 0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted May 31, 2020. ; https://doi.
Because these are nonmembrane-bound proteins, our data suggests that HSP90 may be facilitating the spatial organization of these enzymes in the cytosol of DLBCL cells. HSP90 topologically organizes its metabolic interactome into metabosomes. Under high nucleotide demand conditions, the enzymes IMPDH2 of the purine biosynthesis and CTPS1 of the pyrimidine biosynthesis form homo-and heteropolymers to increase their efficiency and maintain the synthesis of purines and pyrimidines nucleotides  [23]. The molecular mechanism that facilitates these complexes has not been fully elucidated. Because these two enzymes are components of the HSP90 metabolic interactome (Fig. 1B-C ), we investigated the role of HSP90 in the cytosolic topology of IMPDH2-CTPS1 multi-enzymatic metabolic megacomplexes. We treated OCI-LY1 cells with the HSP90 inhibitor PU-H71 (500 nM) for 6 h and assessed the endogenous interaction of IMPDH2 and CTPS1 by a protein proximity ligation assay.
HSP90 inhibition decreases nutrient utilization in DLBCL cells. To determine the metabolic advantage provided by active HSP90 in the cytosol of lymphoma cells, we evaluated the effects of shortterm HSP90 inhibition on nutrient utilization. Central to maintenance of the biomass in cancer cells is the oxidation of glucose via glycolysis. We measured the glucose uptake from the culture medium and the secretion of lactate as the end-product of glycolysis into the culture medium, in OCI-LY1 and OCI-LY7 cells treated with 200 nM and 500 nM PU-H71 or vehicle for 6 and 14 h. We found a decrease in glucose uptake (Fig. 2A ) and lactate secretion (Fig. 2B ) in both cell lines. As expected, due to an accumulative effect, the differences were more significant after 14 h. This data indicate HSP90 inhibition impairs glycolysis.
These patients from the clinical trial NCT01393509 were administered the human equivalent dose of 75 mg/kg in mice of PU-H71 i. v. and plasma was obtained before and 4 and 24 h after a single dose of the drug. We found that, for each metabolite, 3 out of 4 patients had either a decrease in inosine and/or lactate and/or increase in L-arginine as soon as 4 h after PU-H71 (Fig. 4G ). In most cases the changes plateau at the 24 h time point (Fig. 4G ). This finding indicates that immunometabolites are susceptible to low-dose HSP90 inhibition in DLBCL patients. Taken together these data indicate that the activity of the HSP90-dependent metabosomes contributes to sustaining cellular and secretory metabolic needs of DLBCL.
The HSP90 metabolic interactome supports the metabolic program of MYC in lymphomas. To understand the mechanistic relevance of these findings to the reprogramming of lymphoma metabolism, we analyzed putative transcription factors regulating the expression of the HSP90 metabolic interactome. We found that the promoter of genes coding for the HSP90 metabolic interactome (n = 90, Fig. 1A ) were significantly enriched for consensus binding sites of MYC and MYC/MAX, SP1, E2F family and ARNT of the HIF1 family (Fig  5A ), suggesting that these transcription factors may be more reliant on HSP90 to reprogram cell metabolism. Since MYC is known to carry out a transcriptional program that includes numerous metabolic enzymes  [38] , we compared the MYC targets in our dataset of HSP90 metabolic interactome with the universe of MYC targets in the KEGG metabolic pathways.
We found a significant overrepresentation of MYC targets in the HSP90 metabolic interactome (Fisher's exact p < 0. 0001, Fig. 5B ). MYC is an important prognosis-associated oncogene in DLBCL  [1, 39, 40]  that regulates a myriad of cellular pathways. To determine the relevance of the HSP90 metabolic interactome in the metabolic transcriptional program of MYC in DLBCL, we calculated the transcript expression correlations of MYC with the subset of transcripts corresponding to the HSP90 metabolic interactome proteins. We used two independent cohorts of DLBCL patients; our 80 DLBCL RNA-seq samples (WCM cohort) and a publiclyavailable set of 48 DLBCL RNA-seq samples (TCGA cohort). There were 37 HSP90 metabolic interactome genes that correlated significantly (p-value < 0. 05) with MYC expression in both datasets, with 35% of them belonging into the "nucleotides" pathway (Fig.
Human studies Serum from de-identified healthy volunteers (n = 25) and DLBCL patients (n = 50) were obtained with the approvals of the IRBs of Weill Cornell Medicine (New York, USA) and Saint Louis University Hospital (Paris, France). Serum from healthy volunteers were selected to matched the gender and age distribution of DLBCL patients in a 1:2 ratio. Sera were kept at -80 C till the moment of processing for metabolomics. De-identified BL tissue (n = 1) and DLBCL tissues (n = 80) were obtained with the approvals of the IRBs of Weill Cornell Medicine (New York, USA) and University of Turin (Turin, Italy). Tissues were kept at -140C till processing. Plasma from DLBCL patients enrolled in the clinical trial NCT01393509 were obtained with the approval of the IRB of Memorial Sloan Kettering Cancer Center (New York, USA).
The Q Exactive operated in polarity-switching mode. A Sequant ZIC-HILIC column (2. 1 mm i. d. × 150 mm, Merck) was used for separation of metabolites. Flow rate was 150 μL/min. Buffers consisted of 100% acetonitrile for A, and 0. 1% NH4OH/20 mM CH3COONH4 in water for B. Gradient ran from 85 to 30% A in 20 min followed by a wash with 30% A and reequilibration at 85% A. Metabolites were identified on the basis of exact mass within 5 ppm and standard retention times. Metabolites and their 13C isotopologues were identified on the basis of standard retention times and exact mass within 5 ppm. Relative quantitation was performed based on metabolite peak area. The studies were conducted by the WCM Metabolomics and Proteomics Facility and data analysis was done using in-house written scripts.
Statistical analysis Metabolomics dataset analyses: All metabolite concentrations were converted to log2 prior to statistical analysis, since previous studies have demonstrated that metabolites are largely log-normally distributed. Identified metabolites with more than 20% missing values were excluded. The remaining missing values were imputed by drawing from a normal distribution with the same mean and standard deviation as the non-missing values from the respective metabolite. No named metabolites and exogenous metabolites were excluded from analysis. Each metabolite was annotated with a) one of seven major biochemical super-pathways ("amino acid", "peptide", "lipid", "energy", "carbohydrate", "nucleotide" and "cofactors & vitamins"), and b) one sub-pathway. For each group of metabolites belonging to a pathway, mean zscores were computed as a measure of average activity over all members of the pathway.