dataesr/scientific-paragraphs-categorization huggingface dataset:
based on Landsat observations created global databases of the area of rivers (Global River Widths from Landsat -GRWL; Allen & Pavelsky, 2018)  and surface water (Pekel et al. , 2016; Yamazaki et al. , 2015)  which can be used at the basin scale. Based on the decadal-scale monitoring of Landsat missions, the Global Surface Water data set (GSW, Pekel et al. , 2016)  uses 3 million images over 32 years (from 1984 to 2015) at a 30 m spatial resolution to derive a monthly record of water presence in classifying each Landsat pixel as open water, land, or non-valid observation using an expert system. In the Amazon basin, GSW estimates of surface water extent (permanent and total as the sum of permanent and transitory water bodies) are lower than the estimates from other RS-based techniques such as SAR or GIEMS (Figure6b) and comparison of GSW with GIEMS-D3 (see further below) found seasonal water bodies in savannas and forest floodplains were not detected properly (Aires et al.
Acknowledgments The authors thank  Paul Bates  from the  University of Bristol  and two other anonymous reviewers for their time and their constructive and thoughtful comments on our manuscript. The authors thank  Thiago Laranjeiras  and the  Mamirauá Institute for Sustainable Development  for providing the photographs in Figure 1. The authors thank  Dr. Mark Trigg  and  Dr. Conrado Rudorff  for providing topographic mapping of the  Amazon  floodplain.
2 from Advanced SCATterometer soil moisture GPM + SM2RAIN Rainfall inferred from v0. 1 ASCAT H113 H-SAF, SMOS L3 and SMAP L3 soil moisture TMPA-3B42RT v7 TRMM Multi-satellite Precipitation Analysis (TMPA) 3B42RT V7 TMPA-3B42 v7 TRMM Multi-satellite Precipitation Analysis (TMPA) 3B42 V7 TMPA-3B43 v7 TRMM Multi-satellite Precipitation Analysis (TMPA) 3B43 V7 GridSat v1. 0 P derived from the Gridded Satellite (GridSat) B1 thermal infrared archive v02r01 Huffman et al. (2007); https://disc. gsfc. nasa. gov/ datasets/TRMM_3B42RT_7/summary; https://disc. gsfc. nasa. gov/datasets/TRMM_3B42_7/summary.
COVID-19 response in Lebanon and genomic sequencing The 11 cases (Table  1 ) undertaken in this study were detected and then selected randomly between 21 February 2020 and 15 March 2020 and designated as S1-S11. The clinical data were collected as part of the quarantine monitoring measures at the Rafik al Hariri University Hospital with the support of the Lebanese Ministry of Public Health. Required written informed consent was not obtained for the first studied cases due to the pressing need for data collection in the early stages of the outbreak. This study was approved by the Institutional Review Board (IRB) of the Lebanese American University (IRB #:AU. SAS. ST2. 19/May/2020). RNA was extracted from the specimens using the Qiagen QIAamp Viral RNA Mini kit (QIAGEN, Hilden, Germany) by following the manufacturer's instructions.
Consensus sequences Raw sequencing reads were input into FastQC v0. 11. 9 (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/) for quality assessment and subsequent quality control was performed through FQCleaner v3. 0  (Criscuolo & Brisse, 2013)  with a 28 quality score threshold and a minimum read length of 30. Quantified reads were mapped to the Wuhan-Hu-1 reference genome (MN908947. 3) using BWA v0. 7. 17 with the "MEM" alignment algorithm (https://arxiv. org/abs/1303. 3997). Consensus sequences were generated for all 11 SARS-CoV-2 isolates using SAMtools v1. 9 (http://www. htslib. org/) and vcftools v0. 1. 16 (https://vcftools. github. io/index. html).
Phylogenetic analysis Six high quality (N < 0. 5%; Length > 29,000 bp) genomes (S1, S2, S4, S5, S8, S9) were selected for phylogenetic analysis. Reference sequences (n = 55) were chosen based on several criteria including phylogenetic placement of reference genomes, collection dates, history of exposure (travel), GISAID's BLAST feature within the EpiCoV TM browser (https://www. gisaid. org) and overall genome quality/completeness to avoid sequence-based bias (Table  S1 ). Sequences were aligned with MAFFT v7. 467  (Katoh & Standley, 2013). The resulting alignment was used in masking terminal regions and gaps with Nextstrain's custom Python script (https://github. com/nextstrain/ncov/tree/master/scripts). The alignment was input into ModelFinder to assess the best fit substitution model  (Kalyaanamoorthy et al.
, 2017). A maximum likelihood (ML) tree was generated with IQ-TREE v2  (Minh et al. , 2020)  using the TIM2+F substitution model. Boot-strap support was established through 1,000 iterations for Ultra-Fast Bootstrapping (UFBoot) and SH-like approximate likelihood ratio test (SH-aLRT)  (Guindon et al. , 2010). The consensus tree was visualized with the interactive tree of life v4 (IToL; https://itol. embl. de/) (Letunic & Bork, 2019). We assigned genetic lineages based on three commonly used systems including the recently proposed dynamic classification using Pangolin tool v1. 1. 14 (https://github. com/ hCoV-2019/pangolin)  (Rambaut et al. , 2020) , the Nextstrain classification and GISAID's internal classification.
Comparative genome and spike (S) protein analyses SAM files from read alignment for all samples were converted into BAM files with SAMtools v1. 9 (http://www. htslib. org/). Using bcftools v1. 9 (https://samtools. github. io/ bcftools/), variants were called and extracted through the "mpileup" and "call" commands with ploidy set to 1 and invoking the multiallelic-caller through the "-m" flag. Obtained variants were filtered with the "varfilter" command using the custom perl script vcfutils. pl (http://www. htslib. org/). Consensus genomes were aligned against Wuhan-Hu-1 (MN908947. 3) with MAFFT v7. 467  (Katoh & Standley, 2013)  and the resulting alignment was input in SNP-sites v2. 5. 1  (Page et al. , 2016)  to extract all identified SNPs (Table  2 ). The alignment was visualized (Fig. S1 ) with JalView v2.
Phylogenetic analysis The SARS-CoV-2 isolates recovered from Lebanon clustered, and according to Nextstrain's classification, in three distinct clades namely: 19A (93% bootstrap support), 20A (81% bootstrap support) and 20B (98% bootstrap support) (Fig. 2 ). S5 (EPI_ISL_450512) and S9 (EPI_ISL_450515) were grouped closely to sequences from India (EPI_ISL_435106, EPI_ISL_421667, EPI_ISL_435101) and Kuwait (EPI_ISL_416458) in clade19A. Interestingly, S9 displayed less phylogenetic divergence than S5 based on the distance observed in the phylogenetic tree and polymorphic sites differences. Both patients had different exposure histories (Table  1 ). S1 (EPI_ISL_450508), S2 (EPI_ISL_450509) and S8 (EPI_ISL_450514) clustered within clade 20A. S2 and S8 were closely related to one recovered from Egypt (EPI_ISL_430820).
However, S2 was phylogenetically more related to the isolate from Egypt than S8, with both being linked to local community transfer (Fig. 2 ; Table  1 ). S1 was recovered from a   patient with travel history to Egypt and clustered close to sequences recovered from Saudi Arabia (EPI_ISL_437697), USA (EPI_ISL_447844) and Taiwan (EPI_ISL_444276) (Table  1 ). Additionally, S1 showed lesser evolutionary distance than S2 and S8 (Fig. 2 ) indicating less genomic diversity. S4 (EPI_ISL_450511) on the other hand, was of the same superclade as that of S1, S2 and S8, but clustered under 20B (Fig. 2 ). It also showed proximity to sequences recovered from Europe and more so from England (EPI_ISL_448804), Belgium (EPI_ISL_417422) and Lithuania (EPI_ISL_450496), which was in accordance with the patient's travel history.
Detailed analysis of the European sub-clusters by Nextstrain  (Hadfield et al. , 2018)  revealed that the UK outbreak was largely rooted in B. 1 and B. 1. 1 lineages which was in agreement with the analysis from GISAID's EpiCoV TM database  (Shu & McCauley, 2017). The outbreak in Europe was mainly linked to isolates clustering under clade G and its variants GH/GR. Available data suggested that sequences linked to travel history to the UK represented the early stages of the outbreak with the B. 4 lineage being actively circulating at the time. The predominant lineage in this study was B. 1 (clade GH) (S1, S2, S7, S8, S10, S11). Figure  2  shows that S2 and S8 were closely related to SARS-CoV-2 recovered in Egypt (EPI_ISL_430820) and somewhat distant from S1. S1 was linked to travel history to Egypt, while S2 and S8 to local community spread suggesting potential multiple introductions especially with the observed discrepancies in the number of SNPs in S8 compared to S1 (Table  3 ).
ACKNOWLEDGEMENTS We thankfully acknowledge the personnel and laboratories who have generated and submitted sequences to the GISAID's EpiCoV TM database. This study does not declare ownership of these sequences. The openly available data was used to compare our results within an international framework and to provide further information about the phylogenomic status and the spreadability of the SARS-CoV-2 in countries with little to non-existing epidemiological and genomic data. We also acknowledge  Dr. Guillain Mikaty ,  Dr. Valerie Caro  and  Dr. Jean-Claude Manuguerra  from  Institut Pasteur , for their technical assistance and support in the sequencing of the SARS-CoV-2 samples.
Data Availability The following information was supplied regarding data availability: Data is available at GSAID: EPI_ISL_450508, EPI_ISL_450509, EPI_ISL_450510, EPI_ISL_450511, EPI_ISL_450512, EPI_ISL_450513, EPI_ISL_454420, EPI_ISL_450514, EPI_ISL_450515, EPI_ISL_450516, EPI_ISL_450517.
11 Hospitalized- Asymptomatic VTM released EPI_ISL_450513 S6 Male - France Nasopharyngeal 33. 7 Hospitalized- Asymptomatic VTM released EPI_ISL_454420 S7 Female 41 Iran Nasopharyngeal 20. 24 Hospitalized- Sore throat and rhinorrhea VTM released EPI_ISL_450514 S8 Female 74 Local- Nasopharyngeal 33. 8 Hospitalized- Dyspnea community VTM released acquired EPI_ISL_450515 S9 Female 25 United Kingdom Nasopharyngeal 34 Hospitalized- Asymptomatic VTM released EPI_ISL_450516 S10 Male 36 Local- Sputum/PBS - Hospitalized- Asymptomatic community released acquired EPI_ISL_450517 S11 Male 22 Italy Nasopharyngeal - Hospitalized- Early stages: dysuria, fever and flu-like VTM released symptoms Followed by severe dyspnea and ARDS Chest CT: bilateral infiltrates with ground glass appearance Notes: * CT, RT-PCR Cycle Threshold.
Develop a Global Health Risk Assessment Toolbox for risk and impact assessment of PPP mixtures, linking exposure to health impacts. 6. Assess integrated risks, costs and benefits of PPP use in different farming systems at microand macroeconomic levels. 7. Propose transition pathways towards sustainable plant protection, provide policy recommendations, and develop a research agenda. This manuscript outlines the methods to achieve objectives 2 and 3 of the project, and contributes to objectives 6 and 7. It describes the field procedures used to collect systematic and comparable data in 11 CSS, located across various landscapes, and covering the main EU crops, comprising conventional and organic farm systems. More specifically, this protocol will be used to collect information on: i) PPP component mixtures and their distribution in study populations relevant to EPAH; ii) health parameters of plants, organisms and humans; iii) agronomic management practices and economic data involved in food production; and iv) PPP risk perception and transition options according to different stakeholders.
Overall design and rationale This manuscript is organized as a descriptive study protocol that will provide the necessary data for modelling of PPP applications related to EPAH health (Fig  1 ). For this we will use a variety of models that describe the fate of PPP components in the environment and the exposure of EPAH. The field and modelling data will be used for an integrated assessment of the impact of PPP applications on EPAH health. The overall design that best serves this aim is a cross-sectional design at different locations in Europe and one in Argentina (Fig  2 ), covering multiple crops of conventional and organic production. The CSS locations cover the different EU regulatory zones of PPP regulation (North, Central and South), most European climate zones and a selection of the main crop types in the EU: viticulture, horticulture, root crops, fruits trees, olives, cereals and oil seeds.
The existence of previous PPP use and monitoring data in the area, and of running/envisioned public discussions on PPP agenda reinforced the inclusion of some CSS. The Netherlands and Portugal-two CSS with contrasting characteristics, and where PPP related projects have been running in the past years-are used as reference CSS for modelling data collection purposes. Argentina was added to the study to include a possible source of indirect contamination coming from soya feed products, which are imported and used in EU conventional farms as livestock feed. Environmental and biological samples will be collected in 2021 during the growing season. The exact timing of the samples' collection is aligned to the middle of the growing season for each crop; when we expect the highest diversity and (bio)monitoring levels of PPP.
This is also a good period to study impacts on non-target species, as it is a period of high biological activity. Besides the collection of samples, the following information will be collected in each CSS: natural and socio-economic status, farming system, field and crop characteristics, economic performance, perceptions on PPP use and transitions, and health status information. CSS teams received training on study protocol implementation, namely on sample collection and handling procedures, and on interviewing study participants and stakeholders, in order to safeguard quality and comparability of data collected. The field sampling campaigns will provide new input data for fate and exposure modelling of PPP, but also on EPAH health status, agronomic practices and economic data, and on perceptions regarding PPP use.
3. 1. 6 Cat. In the study the cat is preferably a domestic 'farm' cat that lives outdoors and to some extent reflects the life of a (wild) predator species. It is supposed that the blood samples from the cat will reflect bio magnification of PPP from prey rodents and birds who in turn feed on left-overs in and around barns and on fields near the farm. Cat results will inform on exposures expected to occur in wild predator species higher up in the food chain. 3. 1. 7 Crops. By focusing on most crop types in the EU, this study will contribute to PPP productivity and food safety discussions  [12, 24]. Different parts of the CSS plants will be tested for PPP-these parts are crop type specific, being selected based on what is used or processed for the final product, or used as food or feed. PPP data will be complemented with crop yield, pest incidence, weed infestation, and plant diseases data.
Food and beverages (meals). One participant from each subgroup (farmers, nonfarmers and consumers) will be asked to collect complete duplicate portions of food and beverages consumed during one day, and to collect all urine (a 24-h sample) on the same day. The duplicate food and beverage portions will be processed and analysed for pre-selected PPP residues and these will be compared with the ones recovered from urine. This information will be used to verify to what extent the diaries and questionnaire data provide an accurate reflection of the dietary intake of PPP.
PPP component selection In order to gain insight in (co)occurrence and fate of PPP, environmental media and animal and human samples will be analysed for PPP components (active substances and degradation products and metabolites). For fate studies the most relevant PPP to be measured will be those applied in the crops in the 11 CSSs shortly before the time of sampling. For hazard assessment, knowledge about the occurrence of any (mixture of) pesticide(s) is relevant. The EU Pesticides database  [47]  includes over 1,400 active substances. Although only part of these are authorised (n = 456), many can still be found in the environment or in food and feed and can therefore not be ignored a priori. Due to the high number and the wide variety in nature and physical chemical properties, it is inevitable that a selection has to be made for the substances to be included/excluded in the scope of the sample analysis for SPRINT.
Ecosystems, plants and animal exposure modelling. To estimate animal exposure, we will use the MERLIN-Expo tool  [54] , that involves an extensive list of inputs; some fixed, some following a probability density function  [55] , supplied by literature/existing databases and CSS data. For other non-target organisms we will use the MERLIN-Expo tool and the spatio-temporal exposure description by Roeben, Oberdoerster  [56]. This approach covers environmental parameters, data on the behaviour of the species, information on toxicokinetics on organisms. There is still not yet a well-established method for the calculation of ecosystems exposure. The use of risk quotients appears a valid approach as it considers estimated environmental concentrations from fate modelling and (no-observed) effect concentrations of the most sensitive species in each ecosystem  [57].
Agronomic practices and economic data Farmers will be interviewed before and after the growing season to identify any deviations from planned agronomic practices. Questions cover both natural and human environments, farm and crop characterization, fertilizer and PPP records, and cost and benefits of the assessed crop, including costs of PPP. Information on agronomic management practices and economic input-output data will be used in environmental and economic sustainability assessments as input for farm-level and national-level simulation models. Environmental sustainability models will evaluate both direct impacts on humans and the environment from PPP use as well as life-cycle based impacts of crop protection practices. Empirical analysis of the farm level data involves the determination of trade-offs and synergies between economic performance and PPP risk.
Participant recruitment started shortly after the ethics approval. 8. 1. 2 Personally identifiable information. All personal data will be pseudonymised before any treatment by replacing participant names with a code and protecting all electronic and paper records from unauthorised access (only designated researchers have access to these data). All results will be reported in a strictly non-identifiable format to protect the privacy of every subject. Personally identifiable information will be deleted after completion of the study. The biological specimen and data will be destroyed at the end of the project. The handling of personal data complies with the General Data Protection Regulation (GDPR) and the national legislation on data protection requirements in the country of the CSS.
Data use and management This protocol was prepared as a comprehensive and reference framework to assess the impact of PPP mixture residues, their distribution and exposure across CSS. Data collected with respect to PPP residues distribution in soil will be linked to terrestrial ecosystem health indicators, PPP residues in surface water bodies, and to aquatic ecosystem health. Ecotoxicological tests will be used to investigate these associations in depth. PPP residues detected in animal and human samples will provide first indications on potential linkages between the use of PPP and EPAH systems. Collected data are used as input for modelling and upscaling, and for socio-economic analyses. The main outputs of the fate models are time-resolved PPP concentrations in soil, surface water, sediment, airborne particulate matter, and in the gas phase.
These will be further used for the exposure assessment of earthworms, phytoplankton, fishes, animals and humans. Uncertainties in the estimates will be included in the models where possible. CSS and modelling data will be used to study extrapolation of predicted PPP concentrations from local to larger settings, one of the foundations for the development of the global health risk assessment toolbox to be developed by SPRINT. Finally, also innovative agronomic management practices will be identified through CSS data and stakeholder discussions to develop new strategies for reducing PPP use. For one or two selected case(s), using CSS data, stakeholder inputs and agent-based bio-economic modelling, key strategies will be tested in terms of their wider economic and environmental impacts. In order to manage data efficiently, effectively and securely, a data management plan was drafted in an early stage of the project and updated regularly.
All data generated or collected during the SPRINT project will be stored at the SPRINT Nextcloud repository (SPRINT-data. eu) hosted by Netcup (Germany). The database will be secured against unauthorised access by means of a password-protected login process. The project data repository and maintenance of the database will be done by two SPRINT data managers. They will safeguard the data quality, set up required infrastructure for storage and backup, and facilitate data archiving and data sharing. CSS data will be published in peer reviewed open-access scientific journals, and main findings will be disseminated to various target audiences, such as farming communities, rural populations, advisory services, industry, consumers, environmental regulators, and policymakers. First results on PPP determinations should be available around February 2022.
One of the main difficulties to analyze modern spectroscopic datasets is due to the large amount of data. For example, in atmospheric transmittance spectroscopy, the solar occultation channel (SO) of the NOMAD instrument onboard the ESA ExoMars2016 satellite called Trace Gas Orbiter (TGO) had produced ∼10 millions of spectra in ∼20000 acquisition sequences since the beginning of the mission in April 2018 until 15 January 2020. Other datasets are even larger with ∼billions of spectra for OMEGA onboard Mars Express or CRISM onboard Mars Reconnaissance Orbiter. Usually, new lines are discovered after a long iterative process of model fitting and manual residual analysis. Here we propose a new method based on unsupervised machine learning, to automatically detect new minor species. Although precise quantification is out of scope, this tool can also be used to quickly summarize the dataset, by giving few endmembers ("source") and their abundances.
The methodology is the following: we proposed a way to approximate the dataset non-linearity by a linear mixture of abundance and source spectra (endmembers). We used unsupervised source separation in form of non-negative matrix factorization to estimate those quantities. Several methods are tested on synthetic and simulation data. Our approach is dedicated to detect minor species spectra rather than precisely quantifying them. On synthetic example, this approach is able to detect chemical compounds present in form of 100 hidden spectra out of 10 4 , at 1. 5 times the noise level. Results on simulated spectra of NOMAD-SO targeting CH 4 show that detection limits goes in the range of 100-500 ppt in favorable conditions. Results on real martian data from NOMAD-SO show that CO 2 and H 2 O are present, as expected, but CH 4 is absent.
Introduction In modern exploration science, one has to face a major challenge : how to learn something new from analyzing a large dataset collection while taking into account what we already know. If the current knowledge overrides the analysis, the discovery of new elements may be difficult. Usually, in the field of spectroscopy, one can compare laboratory spectra, model and observation spectra. Going back and forth leads to discovery of new lines by identifying unexpected residuals in the observation data (not expected by the model). Sometimes, initial identification of lines can be wrong. As an example, spectroscopic evidence of atmospheric CO 2 ice cloud was reported after the discovery of an emission spike at a wavelength of 4. 3 μm from Mariner 6 and 7 infrared probings of the bright martian limb  (Herr and Pimentel, 1970) , but this spectral feature was mistaken for a resonant scattering band of CO 2 fluorescence  (López-Valverde et al.