dataesr/scientific-paragraphs-categorization huggingface dataset:
In a recent evaluation of the largest dataset in marine clouds in South California  [21] , the parameterization of Boucher and Lohmann (B&L)  [22] , based on the mass concentration of sulfate to retrieve CDNC, was found to provide the best results in comparison to the data. This was also the case in a shorter study off the Canadian coast of the North Atlantic  [23]. There is very limited information on the CDNC over the North Sea region, let alone in combination with sulfate concentrations. Only some data exist on the CDNC in the 1980s  [24] , mostly collected in continental air masses non-representative of the background concentrations. In view of this, we derived an evaluation of the relationship between CDNC and sulfate concentration in a large cloud chamber (CHIEF) at the northwestern seacoast of The Netherlands, in which the conditions of marine stratocumulus were simulated  [25].
In the present investigation, we first tested whether the B&L parameterization was appropriate for the relevant marine clouds over The Netherlands via an investigation in a large cloud chamber (CHIEF). Then, the summer trend of the sulfate concentration was reconstructed using EBAS data to infer the CDNC trend using the B&L parameterization. Finally, the CDNC trend was used to estimate the modification in solar irradiance only due to indirect effect, and will be compared to below-cloud irradiance trend. This will provide information on the magnitude of the aerosol-cloud interaction (ACI) effect on climate  [3]. Before we proceed, it is important to mention that in addition to sulfate, other chemical compounds in aerosols, such as nitrate  [20] , might be of importance as components of the cloud nuclei.
Historic Sulfate Measurements The sulfate mass concentration has been measured since July 1972 in The Netherlands, with many of the data included in the EMEP/EBAS database  [30]. A previous study  [31]  already conducted an analysis comparing the trend of observed sulfate mass concentration to the reflection of radiation during summer over the years 1978-1994. Since the publication of this study, the database has evolved to homogenize the mass concentration values as a function of the method used to measure it. In the early years, sulfate concentrations were measured through X-ray fluorescence (XRF), detecting sulfur, while in the period 1984-1987, a colorization reaction of the leached filter was applied  [31]. Thereafter, ion chromatography was used, though data for the first 4 years of application of the latter method are not in the EBAS database.
However, participants emphasised how negative RAS impacts on the job market could be lessened by "redefin [ing] what we mean by 'full and productive employment' […]. We might consider goals such as 'full unemployment' and the encouragement of leisure instead of work". Unless actions are taken  12  , participants felt that RAS were likely to exacerbate existing inequalities reinforcing preexisting structural biases. Specifically, if artificial intelligence  45  , which is central to many RAS technologies, is trained on biased datasets and decisions are taken without human intervention, those biases and associated inequalities will be amplified  46. There are promising ways to mitigate such threats via ensuring biases in datasets are adjusted for using more appropriate algorithms, however those are yet to be tested in the real world and rely on biases in training datasets being openly acknowledged, which is as yet not the norm  47.
Additionally, participants identified the need to empower more women and those from diverse ethnic backgrounds to engage with RAS development. Currently most RAS researchers are male (84%) and white (67%)  48. This lack of diversity poses a risk that any structural inequalities and preexisting biases in datasets are unconsciously reinforced by RAS developers who may not fully grasp issues facing minority and underrepresented groups. Twenty percent of participants were concerned about the potential negative environmental impacts of RAS (Fig. 3 ). Primarily, these were related to the lifecycle of RAS, including the type and amount of energy required for large-scale RAS deployment  49  , the impact of resource extraction to build RAS and the pollution caused by unrecovered RAS or their disposal  50.
Ethical approval was granted by the University of Leeds Ethics Research Committee (Reference LTSEE-105). The anonymised quantitative data are available on the University of Leeds Institutional Repository (https://doi. org/10. 5518/1078). We piloted the questionnaire and, as a consequence, refined some of the wording to improve clarity. The answers from the pilots were discarded and not included in the analysis. Horizon scan. The first step of the horizon scan comprised an online questionnaire (Fig. 2 ) and aimed to evaluate the overall impact of RAS on each SDGs and extract the main opportunities for and threats towards SDG delivery associated with RAS deployment. Given that there are 17 SDGs covering a very broad remit (Fig. 1 ), we structured the initial questionnaire to allow participants to choose up to three SDGs that best aligned with their expertise.
Participants assigned to SDG2 and SDG14 did not contribute. Step two resulted in 15 group synthesis redacted by 44 participants. Step three of the horizon scan consisted of an online workshop aiming to highlight the interactions, in terms of both co-benefits and trade-offs, between RAS deployed for delivering different SDGs. During this workshop, one representative from each group presented their synthesis. The 44 participants then discussed the interactions with the other SDGs. Analysis. We used an inductive approach to content analyse the qualitative data extracted from the in-depth answers from the questionnaire (step one), the synthesis (step two) and the transcriptions from the workshop discussions (step three). Data were grouped according to whether they described (a) an emerging opportunity through which RAS could contribute to the achievement of the SDGs or (b) an emerging threat to take into consideration when designing RAS to avoid any negative impact on the achievement of the SDGs.
Acknowledgements We are grateful to  J. Adjima ,  D. Armenteras ,  A. Bowman ,  C. A. Buckley ,  K. Chaudhari ,  L. A. Creencia ,  A. Darzi ,  A. Dickinson ,  H. Dyer ,  N. Flanagan ,  G. Gamula ,  T. G. Ginindza ,  S. Gurung ,  C. Hassall ,  J. N. Kamau ,  P. Kayange ,  Kelvin ,  A. Krishnan ,  P. Kumar ,  I. Kuznetsova ,  D. Mahadevia ,  S. Marvin ,  J. McQuaid ,  L. J. Montoya ,  J. O. Ndege ,  C. Nzeadibe ,  R. J. O'Connor ,  B. Okelo ,  C. Picardi ,  M. A. Post ,  R. M. Protil ,  G. Ramirez-Nieto ,  B. Read ,  A. Redmond ,  D. Rush ,  L. P. Sabogal Paz ,  P. Sammonds ,  G. I. Sanchez ,  M. K. Sanyal ,  A. Sarkar ,  G. Sartoretti ,  D. Spencer ,  W. Tadesse ,  N. J. Talbot  and all other participants who took part in this study. The work of S. G. , P. P. , M. E. T. , B. Y. K. , I. D. R, C.
Validation of this product with other datasets (satellite and ground-based measurements) shows that it is suitable for trend analysis and to check for local and regional variations of temperature in different regions of the world  [19]. In order to achieve a higher resolution for spatial coverage, we averaged the IASI LST using an oversampling method, widely used in other studies for various trace gas concentrations using different instruments  [20] [21] [22]  but until now, never for temperature analysis with IASI. The three instruments are cross validated for temperature and show excellent agreement  [23]. Metop-A was decommissioned in 2017. In this work, a combination of the three instruments' products are used for the summers of 2008-2020.
WBT The WBT is a nonlinear function of temperature and humidity near the surface. In this work we use the formula of WBT provided by Davies-Jones  [24]. Near surface temperature and humidity used to calculate WBT are provided by the European Center for Medium Weather Forecast (ECMWF)'s latest reanalysis ERA5  [25]. ERA5 datasets are at 0. 25 • × 0. 25 • resolution (native horizontal resolution of ERA5 is ∼31 km). All different retrieved variables are interpolated in time and space to match the IASI morning and evening observations. We do not use the LST from ERA5, since the super sampling method of IASI data leads to much finer resolution (of around 0. 01 • × 0. 01 • ) as the figures 1-3 show. ERA5 assimilates IASI radiances, and the two datasets agree very well as shown by Safieddine et al  [19]  and Bouillon et al  [23].
7 • × 0. 7 • ), and irrigation in Al-Jouf is not included in EC-Earth (as for many other climate models). Irrigation forecast remains very challenging as it depends on the evolution of soil, vegetation, water availability, expansion and shifts of agricultural practices. This clearly limits its ability to predict future WBT in Al-Jouf and more generally everywhere else in the Peninsula. Moreover, the spatial resolution of the ERA5 or the EC-Earth dataset might not capture all mesoand micro-scale variations of meteorological conditions, such as mesoscale convective systems  [34] , especially in areas with a complex orography, land cover, or near the coasts  [35, 36]. Even at the smallest grid cell, the simulation output is a collection of values averaged over an area. As such, it depends on grid cell size, the terrain complexity and the type of surface.
Acknowledgments This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 and innovation programme  (Grant Agreement No. 742909 ). The IASI mission is a joint mission of Eumetsat and the  Centre National d'Etudes Spatiales (CNES, France). The IASI L1 data are distributed in near real time by Eumetsat through the Eumetcast system distribution. The authors acknowledge the  Aeris data infrastructure  for providing the IASI L1C data. The authors thank  M D Boucher  and  D Coppens  for the reprocessed L1C IASI radiances used to compute LST, and thank  V Pellet ,  F Aires ,  G Levavasseur ,  Y-W Choi , and  R Döscher  for useful discussions.
Data availability statement All data that support the findings of this study are included within the article (and any supplementary files). IASI land surface temperature (also called Earth's 'skin temperature') data are available on https://iasift. eu/. ERA5 data (t2m, d2m, skin temperature/LST and surface pressure) used in this work to calculate WBT are available from the Climate Data Store at the following DOI: https://doi. org/10. 24381/ cds. adbb2d47. EC-Earth3 model output prepared for CMIP6 ScenarioMIP are retrieved here: https://doi. org/ 10. 22033/ESGF/CMIP6. 727.
ACKNOWLEDGEMENTS The authors thank  Lorenzo Gorla ,  Herve Capra ,  Maria Alp ,  Davide Vanzo ,  Jan Baumgartner , and  Esther Leitgeb  for early discussions on habitat dynamics in hydropeaking rivers. Feedback from  Nathalie Friese  on an earlier version of the manuscript was greatly appreciated. We also thank the reviewers for the careful review and their very constructive comments. We are grateful for financial support from the  Swiss Federal Office for the Environment (FOEN)  for  Nico Bätz  and from  Electricité de France  ( EDF ) for Clarisse Judes. We thank  Jan Baumgartner ,  Benjamin Berger , and  Steffen Schweizer  ( Kraftwerke Oberhasli AG ) for providing data for the showcase. Open access funding provided by  ETH-Bereich Forschungsanstalten.
Syria's coastal and nearshore zone contains a significant, but under-researched, record of maritime cultural heritage (MCH) ranging from prehistory to the present. This is exemplified by a lack of underwater investigations, but also limited investigation of key onshore maritime sites such as ports and harbours. There is also a lack of specialist in-country management regarding maritime cultural heritage research and protection. This situation has been worsened by the ongoing conflict (since 2011), which has reduced (already limited) field investigation. To assist in the advancement of Syrian maritime archaeology, this paper presents a baseline assessment which makes use of a geospatial database generated from satellite imagery as well as both published and grey literature. This assessment reviews past coastal environment changes affecting the Syrian littoral, and then highlights past disturbances and potential future threats impacting the MCH.
Documentation Results An essential resource for historic environment management is a digital geospatial database of archaeological sites. There has historically been no such database for Syrian maritime sites (Al-Maqdissi pers. comm. ). Therefore, a maritime archaeology-focussed database was compiled in 2017-18 by the HFF-sponsored Syria Benchmarking project  (Westley et al. 2018). This complemented pre-existing and simultaneous documentation in the coastal lowlands by the Endangered Archaeology of the Middle East and North Africa (EAMENA) project  (Rayne et al. 2017). Since 2019, additional documentation has been undertaken by  MarEA (Andreou et al. 2020). All these projects used the same method: a combination of literature review and satellite imagery assessment to identify archaeological sites and assess threats and disturbances.
All results have been uploaded to the open access EAMENA database hosted at the University of Oxford (https:// datab ase. eamena. org/). Key data sources included: (1) Published literature  (Braidwood 1940; Besançon et al. 1994; Riis et al. 2004; Kampbell 2013 ) (2) Unpublished material  (Carayon 2008; Anbar 2020)  (3) Geological/geoarchaeological reports  (Sanlaville et al. 1997; Marriner et al. 2012 ). (  4  This paper considers only sites located offshore, within the Low Elevation Coastal Zone (LECZ: land less than 10 m elevation and in hydrological connection to the coast) or within 500 m of the present coastline. This definition was chosen to capture all documented archaeological material on the seabed, the low-lying coastal plain, and also on elevated land close to the sea (e. g. coastal cliffs, steep slopes).
Acknowledgements We thank former  EAMENA  project members  Jenni Bradbury  and  Bob Bewley  for their advice and assistance in integrating the  HFF Syria Benchmarking  project with EAMENA. We are also grateful to the current EAMENA and MarEA teams for their continued assistance (particularly databaserelated), and to  Naseem Raad  for his thoughts on Classical harbours. Some sites used in this assessment were documented by EAMENA colleagues and the team at the  Archaeology Informatics Lab at Durham University. We thank two anonymous reviewers for their helpful and insightful recommendations which improved the paper. Finally, we thank the  Honor Frost Foundation  for generously supporting the original  Syria Benchmarking  project and the  Arcadia Fund  for supporting MarEA.
L, leachate. The detailed annotations of the fraction names can be found in SI Appendix. (Fig. 2 ). This gives an age ∼450,000 y older than that of Barrat et al. (23). Furthermore, the δ 26 Mg* 0 (initial 26 Mg/ 24 Mg ratio) calculated here (À0. 009 ± 0. 005 &) is slightly lower than in Barrat et al. (23)  (0. 065 ± 0. 080 &), while consistent within uncertainty but more precise. The small discrepancy between these two datasets cannot result from systematic analytical errors. To reconcile the two datasets, the error should be ∼120 & on the δ 26 Mg* measured by ion probe on plagioclase with 27 Al/ 24 Mg ≈ 5,000, or alternatively, the error on the 27 Al/ 24 Mg ratio should be larger than 1,500 with δ 26 Mg* ≈ 200 &. Most likely, the two isochrons date two different events. This might be possible because of the much faster diffusion of Mg in plagioclase than in pyroxene [∼5 orders of magnitude faster; e.
9 and 3. 3 (see SI Appendix for details). In addition, three widely available terrestrial samples (United States Geological Survey, basalt BHVO-2, BCR-2, and andesite AGV-2) were also analyzed to monitor data quality. All the fractions were passed through columns with ion exchange and specific resins in several steps to discard matrix and purify solutions for Nd and Mg (see details in SI Appendix). Neodymium and Sm isotope compositions were analyzed at the Laboratoire Magmas et Volcans, and detailed methods can be found in the SI Appendix. The Mg isotopic composition and 27 Al/ 24 Mg ratio were analyzed at the Institut de Physique du Globe de Paris (IPGP) following the method described in the SI Appendix. Both 146 Sm-142 Nd, 147 Sm-143 Nd and 26 Al- 26 Mg internal isochrons were calculated using IsoplotR, Model1  (50).
ACKNOWLEDGMENTS. We thank the editor for his efficient editorial handling. We greatly appreciate the constructive comments from the two reviewers (anonymous and  Klaus Mezger ) that greatly improved the quality of the paper and helped us build a more balanced discussion. We thank  Zhengbin Deng  and  Haoxuan Sun  for their suggestions on chemistry work and data processing and  Romain Tart  ese for discussions. This work was supported by the  European Research Council (ERC)  under the European Community's  H2020 framework program / ERC  Grant agreement No. 101001282  (METAL) awarded to F. M. and under the European Union's  Horizon 2020 research and innovation program / ERC  Grant agreement No. 682778  awarded to M. B. Additional support was from the  UnivEarthS Labex program  (Grant agreement Nos.
ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ). Parts of this work were supported by  IPGP  multidisciplinary program PARI, by  Region Île-de-France SESAME  Grant Nos. 12015908  and  EX047016 , and the  IdEx Universit e de Paris Grant Nos. ANR-18-IDEX-0001  and the DIM ACAV+. L. F. thanks the  China Scholarship Council  for a PhD fellowship (# 202004910297 ).
Quantification of nitrites. Nitrites in culture supernatants were quantified by colorimetry using the Griess assay kit (Thermo Fisher Scientific, G7921), according to the manufacturer's instructions. RT-qPCR analysis. Total RNA isolation, quality control, and reverse transcription were performed as previously described (de La Llave et al. ). RT-qPCR was carried out in 384-well PCR plates (Framestar 480/384, 4titude, Dominique Dutscher) using the iTaq Universal SYBR® Green Supermix (Bio-Rad) and 0. 5 μM primers with a LightCycler® 480 system (Roche Diagnostics, Meylan, France). Primer information for all targets is detailed in the supplementary table  S1. Crossing Point values were determined using the second derivative maximum method (LightCycler® 480 Basic Software). The relative expression software tool (REST©-MCS)  (Pfaffl et al.
Proteome Normalization was performed with the geometric mean of the two best reference genes Ppih and Mau2 as determined by the GeNorm and Normfinder programs (data not shown). For statistical analysis of gene expression levels, Cp values were first transformed into relative quantities (RQ) and normalized  (Lecoeur et al. , 2020). Nonparametric Kruskal-Wallis tests were performed on Log transformed Normalized Relative Quantity values. Results were expressed as Z-scores calculated by relative expression compared to the M0 group (short-term analyses) or the day 0 unpolarized group (long-term analyses). Heatmaps of Z-scores were generated (R, version 4. 1. 2) using homemade scripts based on native graphical functions. RNASeq analysis. RNA isolation was performed with the RNeasy+ isolation kit (Qiagen) according to the manufacturer's instructions.
No end repair step was necessary. An adenine was added to the 3'-end and specific Illumina adapters were ligated. Ligation products were submitted to PCR amplification. The obtained oriented libraries were controlled by Bioanalyzer DNA1000 Chips (Agilent, # 5067-1504) and quantified by spectrofluorimetry (Quant-iT™ High-Sensitivity DNA Assay Kit, #Q33120, Invitrogen). Sequencing was performed on the Illumina Hiseq2500 platform to generate single-end 65 bp reads bearing strand specificity. Reads were cleaned using cutadapt version 1. 11 and only sequences at least 25 nt in length were considered for further analysis. STAR version 2. 5. 0a (Dobin et al. , 2013) , with default parameters, was used for alignment on the reference genome (GRCm38 from Ensembl database 94). Genes were counted using featureCounts version 1.
4. 6-p3 (Liao et al. , 2014)  from the Subreads package (parameters: -t gene -s 0). Transcriptomic data are made publicly available at the NCBI's Gene Expression Omnibus repository (Subseries GSE205856). In silico analyses. "ClusterProfiler" package (version 4. 2. 2) of R was used for gene set enrichment analysis (GSEA) and for data mapping on metabolic pathways using the Kyoto encyclopedia of genes and genomes (KEGG) database. Results were visualized by the "enrichplot" (version 1. 14. 1) and the "pathview" packages (version 1. 34. 0). Protein-protein interaction networks were performed in Cytoscape (version 3. 9. 1) using STRING application (version 1. 7. 1). Transcription factors and targets data derived from Transcriptional regulatory relationships unravelled by sentence-based text mining (TRRUST, version 2)  (Han et al.
ACKNOWLEDGEMENTS We acknowledge the financial support of the  Institut Pasteur (Paris) , the  Région Ile-de-France  (program DIM1Health), the  Institut Pasteur International Direction  to the  International Mixed Unit "Inflammation and Leishmania Infection ", and the  Institut national de la santé et de la recherche médicale (INSERM). Sheng Zhang  is part of the  Pasteur -Paris University (PPU) International PhD program. This project has received funding from the  CNBG company, China.
Head bar surgery Because we noticed that surgical anesthesia limited to 90 min is better tolerated by the rodents, we split the surgery into two different sessions to allocate more time for each of these procedures and nearly abolish mortality due to surgery. We first placed the head bar, waited a minimum of 7 days, and then performed the cranial window surgery [Fig. 1(d)]. A complete list of tools, drugs, and supplies is provided in Table  S1  in the Supplemental Material, and a detailed step-by-step protocol is provided in the Supplemental Material. Sterile surgical techniques were done with sterile instruments, surgical supplies, and disinfected stereotaxic frame. The design and dimensions of the U-shape head bar are provided in Fig. S1  in the Supplemental Material and in the following Github repository available at: https://github.
fUS imaging fUS imaging was performed using an ultrasound scanner (Iconeus One, Iconeus, Paris, France) with a linear ultrasound probe (128 elements, 15 MHz central frequency, Vermon, Tours, France) placed 2 mm above the cranial window. Custom ultrasound sequences were written in Matlab (Mathworks). Ultrasound images of the brain were produced after the backscattered echoes of US plane waves were collected and beamformed. To increase the contrast, the US images were compounded by transmitting several tilted plane waves and adding their backscattered echoes. The compounded sequence resulted in enhanced US images, thereby increasing the sensitivity of the Doppler measurement without aliasing in the mouse brain. In this study, the ultrasound sequence consisted of transmitting 11 different tilted plane waves (from -10 deg to 10 deg in 2 deg increments) with a 5500-Hz pulse repetition frequency (500 Hz frame rate of reconstructed images).
Tissue signal was removed from the data using singular value decomposition (SVD) and eliminating the 40 first SVD. The power Doppler (PD) was further filtered with a Butterworth filter (fifth order). Each voxel signal was obtained by the incoherent temporal average of the blood signal. The voxel size at the focal plane was: 110 × 100 μm (x and z directions) and 400 μm (y direction, i. e. , slice thickness). Data are shown as PD or delta power Doppler (ΔPD) over PD responses (ΔPD∕PD), with a baseline of 8 s (from 2 to 10 s). Filtering of axial velocity was done as previously described. 11.