dataesr/scientific-paragraphs-categorization huggingface dataset:
Each series of PCR comprised a negative control with water in place of the NA, and the product of this control for the 1 st PCR was also used as a template for the 1  2 nested PCR. A volume of 10 µL of the final PCR products were loaded on a 9% acrylamide gel (Protogel, National Diagnostics, Nottingham, United Kingdom) in TBE buffer with the MassRuler DNA loading dye (Fisher Scientific) and the DNA molecular weight marker VIII (Roche Diagnostics, Meylan, France). After migration gels were incubated in TBE with 0. 01% of GelRed (Biotium, Fremont, CA, USA) and visualized using a ChemiDoc imaging system and the Quantity One software (Bio-Rad, Marnes-la-Coquette, France). Final RdRp and VP1 amplicons were 193 bp and 344 bp long, respectively. No PCR products were observed in the negative controls.
All bacterial cultures presenting a band at the expected size were used for plasmid extraction using the QIAPrep Spin Miniprep kit (Qiagen) following the manufacturer's instructions. Plasmids were Sanger-sequenced in both directions using the M13F and M13R primers. Chromatograms were analyzed using Geneious 11. 1. 4 (BioMatters Ltd. , Auckland, New Zealand), their ends were trimmed from bases with error probability above 5%, and contigs were built from forward and reverse sequences by de novo assembly. The consensus sequences were genotyped using the Norovirus Typing Tool 2. 0  [31]. Unique VP1 and RdRp sequences from each sample are accessible in GenBank under accession numbers MT582385-MT582411 (VP1) and MT586039-MT586064 (RdRp).
Analysis of Sequences and Phylogeny Using Muscle (v3. 8. 425) implemented in Geneious, sequences of RdRp and VP1 clones and clusters from shellfish samples were aligned together and compared. VP1 unique clones and clusters were aligned to reference NoV GII, GIII and GIV sequences from the Norovirus Typing Tool v2. 0, and to their best hits of a blastn search on the nucleotide database of NCBI. A Bayesian phylogenetic tree was built using Mr Bayes (v. 2. 3. 6) with the GTR evolutionary model, 2 million generations, a sampling frequency of 1/400 and 20% burn-in.
Experimental Measurement: The fabricated KNBNNO ceramic samples were prepared with two electrode configurations. Configuration 1: Approximately 10 µm silver electrode was screen-printed on both sides, followed by firing at 600 °C for 20 min. This configuration made at a high temperature allowed the samples to be measured with elevated temperatures. Configuration 2: 200 nm thick indium tin oxide (ITO) electrode was coated on both sides with sputtering. Visible light could pass through the transparent ITO, and thus this configuration was used for measurements under light. The ferroelectric hysteresis loops and resistivity data were obtained with a ferroelectric test system (Precision LCII, Radiant Technologies, Inc. , USA). The measurement temperature was controlled by a sample stage (LTS 350, Linkam Scientific Instruments, UK).
, 2012)  and scripts developed in Fiji and Matlab (The MathWorks, 2019) for the data analysis process (see supplementary information for a detailed description). For 3D organ level analysis of macromolecule synthesis, AHA staining provided an adequate outline of the humerus, allowing us to segment its 3D morphology (Figure  3A-B ). We quantified organ shape by assessing cross-sectional area (Figure  4A' ) and circularity (Figure  4A" ) of the segmented humerus along the proximodistal axis with BoneJ  (Doube et al. , 2010 ; Figure  4A ). These measurements of 3D organ size can be obtained with other methods such as microCT and focused ion beam scanning electron microscopy (FIB-SEM). However, microCT is unable to image every tissue due to stain limitations and uses hazardous radiation, while FIB-SEM can capture 3D surface topography but not the entire organ morphology.
Light sheet microscopy All 3D images were acquired using a Zeiss light sheet Z. 1 microscope paired with Zen software. Unless otherwise indicated, samples were cleared and imaged in 67% TDE. Post-processing for visualization purposes was performed with Arivis Vision4D v3. 1. 4 on a workstation with a 64-bit Windows Embedded Standard operating system, and an Intel(R) Xeon(R) CPU E5-2620 v3 @ 2. 40 GhZ (two processors), 128 GB RAM, and NVIDIA Quadro K2200 GPU. Sub-volumes were stitched together with the Tile Sorter in Arivis, using the manual projection option. Volume fusion was performed through automatic landmark registration of manually selected points for alignment. For visualization, background intensity was corrected using the automatic functionality.
The 3D Objects Counter then provided a list of identified cells as well as their volume and position, among other information. In addition, the surfaces of the segmented cell nuclei were exported and processed in MeshLab, similarly to the process followed with the organ segmentation, to be visualized in Matlab (Figure  3E ). The centroid coordinates and corresponding object volumes identified in Fiji were imported into Matlab for further processing and plotting. We computed the number of cell centroids within 50 mm thick slices perpendicular to the proximodistal axis to obtain a densitylike measure of proliferating cells. Slice thickness was selected as slightly larger than the average cell diameter to ensure cells were not sampled across more than two slices.
Molecular level The goal of the molecular level analysis was to determine molecular activity, characterized by fluorescent signal, which in our study represented DNA replication (EdU). The aligned and cropped image stack used as a starting point of the cellular-level analysis (Figure  3C ) was also analyzed at the molecular level. We resliced the image stack along the proximodistal axis in the organ under study (Figure  3F ). The histogram and pixel intensity statistics were listed for each slice via the getRawstatistics function. These allow for further quantification of the fluorescent signal in Matlab, for example, we calculated the mean intensity of each plane along the proximodistal axis and the histogram of the whole organ. For the example showing nerve-dependent regeneration in axolotl blastemas (Figure  6A ), we compared overall pixel intensity of the left (denervated) and right (control, innervated) forelimbs of the same animal.
To ensure full repeatability of our data analysis, we selected the same cubic volume in all blastemas processed: a cube with 175 mm sides, centered along the proximal-distal axis and at a distance of 250 mm from the distal tip (Figure  6A ). The cube size was adjusted to maximize the cube volume for all blastemas processed, while ensuring that the entire cube was contained within the blastema. Histograms of each cubic volume and the ratio between the innervated and denervated limb of the same animal were computed and plotted with Matlab (Figure  6B ) to quantify the changes in proliferation in innervated versus denervated limbs at different stages of regeneration. All scripts for blastema cube quantification are provided in the supplementary materials.
Acknowledgements The authors would like to acknowledge  Tyler Jensen  for his early assistance in developing the wholemount staining protocol, and both Alex Lovely and  Gouxin Rong  for their microscopy and image analysis assistance. Images were obtained from the  Harvard University Center for Biological Imaging  and the  Northeastern University Chemical Imaging of Living Systems core. We thank the  Institute for Chemical Imaging of Living Systems at Northeastern University  for consultation and imaging support. We acknowledge animal support from the  Ambystoma Genetic Stock Center  funded by  NIH  grant  P40-OD019794.
In 2004 it was tested in vitro against SARS-CoV  [17]  and caused a 99% reduction of viral replication after 3 days at 16 μM. Moreover, tests in vitro have shown inhibition of viral replication on SARS-CoV 2 detected by PCR and by CCK-8 assay  [18]. Hydroxychloroquine (hydroxychloroquine sulfate; 7-Chloro-4-[4-(N-ethyl-N-bhydroxyethylamino)-1-methylbutylamino]quinoline sulfate) has shown activity against SARS-CoV2 in vitro and exhibited a less toxic profile  [19]. This drug is well known and currently used mostly to treat autoimmune diseases and also by our team to treat Q fever disease  [20, 21]  and Whipple's disease  [22, 23]. In those clinical contexts, concentrations obtained in serum are close to 0. 4-1 μg/mL at the dose of 600 mg per day over several months  [24]. Clinical tests of chloroquine and hydroxychloroquine to treat COVID-19 are underway in China  [25] , with such trials using hydroxychloroquine in progress in the US (ClinicalTrials.
gov Identifier: NCT04307693) and in Europe with the Discovery Trial. In this drug repurposing effort, antibacterial components have also been tested. Teicoplanin, a glycopeptide, was demonstrated in vitro to inhibit cellular penetration of Ebola virus  [26]  and SARS-CoV 2  [26, 27]. Azithromycin (azithromycin dihydrate), a macrolide, N-Methyl-11-aza-10-deoxo-10-dihydroerythromycin A, has shown antiviral activity against Zika  [28] [29] [30]. Azithromycin is a wellknown and safe drug, widely prescribed in the US, for example, with 12 million treatment courses in children under 19 years of age alone  [31]. A recent study has identified these two compounds (azithromycin and hydroxychloroquine) among 97 total potentially active agents as possible treatments for this disease  [32]. In a preliminary clinical study, hydroxychloroquine and, with even greater potency, the combination of hydroxychloroquine and azithromycin were found effective in reducing the SARS-CoV-2 viral load in COVID-19 patients  [33].
Funding This research was funded by the  French Government  under the " Investissements d'avenir" (Investments for the Future) program  managed by the  Agence Nationale de la Recherche  ( ANR ,  French National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ), by  Région Provence-Alpes-Côte d'Azur and European funding FEDER PRIMI.
Funding This work was financially supported by the  National Science Centre, Poland , grant no. NCN/2016/21/B/ST5/ 01365  (AZW). Analyses were performed in the  Centre of Synthesis and Analysis BioNanoTechno of the University of Bialystok. The equipment in the Centre was funded by the  EU  as a part of the  Operational Program Development of Eastern Poland 2007-2013 , projects:  POPW. 01. 03. 00-20-034/09-00  and  POPW. 01. 03. 00-20-004/11. This manuscript is dedicated to  Professor Jacek W. Morzycki  from the  University of Bialystok  on the occasion of his 70th birthday.
This paper reviews new evidence on the trends and patterns of migration between Africa and Europe since the mid-1970s, and discusses their congruency with the changing context of migration policy. Using data from the Determinants of International Migration (DEMIG) and the Migration between Africa and Europe (MAFE) projects, we compare flows and policies of three African and six European destination countries (Democratic Republic of Congo, Ghana, and Senegal, on the one hand; and Belgium, France, Italy, Spain, the Netherlands, and the UK, on the other). The paper focuses on topics that quantitative studies usually overlook due to the lack of data, namely the propensity to out-migrate, legal status at entry, routes of migration, and propensity to return. We show that times of restrictions in Europe do not correspond to less African out-migration, but rather to more unauthorized migration and fewer returns.
The analyses rely on quantitative data from the DEMIG (Determinants of International Migration) and MAFE (Migration between Africa and Europe) projects; the former allowing to describe policy changes, while the latter informs us on migration behaviours. 1 After this introduction, the article is divided into three sections. The first briefly presents a history of migration policies in Europe based on the analysis of the DEMIG database. It discusses the notion that European policies have become uniformly restrictive since the 1960s, by providing an overview of the contrasting policy opportunities that non-European migrants have had to adapt to over time and across countries. The second section turns to migration trends and examines migrants' behaviours. It reviews the evolution and patterns of sub-Saharan migration between 1975 and 2008, focusing on measures that are expected to be affected by policy changes.
They experienced different political histories since decolonization, with a very stable country, such as Senegal, contrasting with the political and military unrest in DR Congo since the early 1990s. The three countries also differ in their ecological conditions, Senegal being subject to severe droughts since the mid-1970s, while DR Congo is an equatorial country, and Ghana being in an intermediary geographical position. Finally, the three countries experienced contrasted economic trajectories, especially since the early 1980s. At that time, the Congolese and Ghanaian GDP per capita that were so far equivalent  (around 350 USD in 1985)  drastically diverged, with a steep decrease of the former (down to 150 USD in 2010) and a steady increase of the later (up to 600 USD in 2010). Over the same period, the Senegalese GDP per capita fluctuated from 700 USD in 1985 down to 635 in 1994 and up to 800 USD in 2010 (source: The World Bank, World Development Indicators extracted from the MAFE contextual database).
These destinations were chosen because they attract a significant number of migrants and because they display features distinct from the historical destination, especially in matters of migration policies, economic context, and sociocultural conditions (especially language and education system). 1 All MAFE (Migration between Africa and Europe) data are available free of charge. For more details, see: http://mafeproject. site. ined. fr/en/. The MAFE project was coordinated by INED (C. Beauchemin); other participants include the Université catholique de Louvain (B. Schoumaker), Maastricht University (V. Mazzucato), Cheikh Anta Diop University (P. Sakho), the University of Kinshasa (J. Mangalu), the University of Ghana (P. Quartey), Pompeu Fabra University (P. Baizan), the Spanish National Research Council (A.
González-Ferrer), the Forum Internazionale ed. Europeo di Ricerche sull'Immigrazione (E. Castagnone), and the University of Sussex (R. Black). The MAFE project received funding from the European Community's Seventh Framework Programme under grant agreement 217,206. The MAFE-Senegal survey was conducted with the financial support of INED, the Agence Nationale de la Recherche (France), the Région Ile-de-France, and the FSP programme 'International Migrations, territorial reorganizations and development of the countries of the South'. the last section discusses the diversity and commonalities between the three migration sub-systems at stake. A short history of migration policies in  Europe (1960 Europe ( -2010) )  The DEMIG data The objective of this paper is to assess the congruency of migration trends and policy changes to speculate on possible effects of the policies on migrants trajectories.
Two databases are used in this paper. One of them, the DEMIG VISA database, is a global panel of bilateral travel-visa requirements covering the years 1973-2013  (Czaika and de Haas 2017) , and provides insights on the evolution of visa requirements in Europe. The other, the DEMIG POLICY database, tracks changes in migration policies, by following a rights-based approach: codes indicate whether each policy change increased or decreased migrants' rights according to their legal status  (de Haas et al. 2015). Importantly, for this paper, we recoded the information available in the database to correspond to our population of interest 2 : the results presented in this paper only relate to non-European migrants, whose circulation rights are obviously different from European citizens who benefit from a right of free circulation.
We also unmerged the category of 'migrant workers' in the categories of 'low-' and 'high-skilled workers', and excluded specific categories, such as foreign criminals and terrorist suspects. (renouncing to migration, changing routes …). Yet it is unclear whether migrants have information about their effective rights or about the changes implemented that affect their rights. Another limitation is related to the implementation gap in migration policies  (Czaika and de Haas 2013). The database takes into account the measures 'on paper', but there is often a gap between the policies 'on paper' and the actual outcomes, which are still more difficult to measure. Still, DEMIG POLICY gives a unique view on policy changes over time and makes it possible to examine whether these changes coincide with transformations of migrants' behaviour and profiles.
4% vs. 0. 8% of the whole population), they were submitted to a much higher refusal rate (16% vs. 6%). Figure  1  confirms that European countries have become consistently more restrictive over the last decades. While a visa was needed to travel from Africa to Europe in 80% of the cases until the mid-1980s, the proportion rose to 100% in 2010. This European trend reflects national preferences for more control, but it also results from the creation of a free-circulation zone within Europe (i. e. , the Schengen Area, the EU countries of which signed the 1997 Treaty of. Source: DEMIG VISA database. Computation method: a yearly index is calculated by computing the percentage of African countries that need a travel visa to enter a destination country in Europe. The average value for all destination and origin countries within the region of interest for every year was subsequently calculated.
Source: DEMIG POLICY database recoded by the authors construction, and domestic work. Also, French and Spanish strategies differ especially in the policies they adopted to manage unauthorized migration. Although unauthorized migrants in France were not targeted more than other migrants in the growing restrictions regarding entries (Fig. 3 ), their rights after entry declined very steadily since the 1980s, more than in any other country (Fig. 4 ). As a point of entry to Europe, Spain regularly adopted restrictive measures since the early 1990s to discourage unauthorized entries (Fig. 3 ) but did not significantly reduce rights of unauthorized migrants once they were in Spain and offered opportunities for regularisation (Fig. 4 ). Although public debates in Europe convey the idea that Mediterranean countries are merely portals through which African migrants enter before going to northern countries, these opposite policy trends suggest there might be a more complex pattern of migration.
We explore in the next sections whether there was indeed such country deflections of unauthorized migrants and discuss other possible explanations for such shifts. Africa and Europe (1975-2008)  The MAFE data Although migration acquired a high position in the policy agenda of both European and African countries, the lack of data on African migration was a widely recognized fact in the early 2000s  (Lucas 2006; Hatton 2004 ). The multisite MAFE project, inspired by the Mexican Migration Project, has produced new data on African migration  (Beauchemin 2015). The primary objectives of the MAFE project were to measure trends and patterns of migration, to study factors of departures and returns, and to analyze the consequences of international migration on economic and family outcomes. Both household and individual data were collected in 20,082,009 on three migration systems, each including a country of origin in Africa (Ghana, Senegal, and DR Congo), the former colonial metropolis (respectively the UK, France, and Belgium), and one or two new destination countries in Europe (Fig.
6 ). The same questionnaires were used in all settings, making data comparable across countries. 4 ousehold surveys were conducted in sending countries among representative samples of households of selected cities (Accra and Kumasi, Ghana; Dakar in Senegal; Kinshasa in DR Congo). 5 The samples were limited to these cities because of budget and time constraints. Dakar represents around a quarter of the total population of Senegal, Kinshasa around 12% of the population of DR Congo, and Accra and Kumasi about 17% of Ghana's population  (Schoumaker and Beauchemin 2015). In the household questionnaire, data were collected on all the regular members of the household, as well as on a series of people related to the household but who were not household members at the time of the survey. Questions on first departure (year, destination) and return (year) among these people are used to compute trends in rates of departure and return.
Migration trends between Individual retrospective data collected both in Africa and in Europe among people aged 25 and over are also used in this paper. Non-migrants and returnees in Africa were randomly selected in the surveyed households. In Europe, a quota approach was used to sample migrants in five of the six countries. 6 Along with employment and family histories, detailed migration histories were collected, including questions on the motives to migrate and the reasons for destination choices. A full module was also dedicated to the routes followed by migrants when they left their country of origin (e. g. , transit countries, means of transportation, people they travelled with). Legal status trajectories were also collected, indicating whether migrants had the right to stay and/or work at any time.
In addition, questions were asked about migration intentions, both to register the steps that interviewees (who actually migrated or not) had taken to prepare an international migration, and to measure the intended duration of stays abroad among those who actually migrated. Although the MAFE data offer rich information on migration patterns, they suffer from a number of limitations. First, the migration systems are not fully covered. In particular, transit countries in Africa (especially in the Maghreb) were not included in the destination countries. Second, samples are not fully representative at the national level, and sample sizes are limited (Fig. 6 ). Despite these limitations, the MAFE data make it possible to generate results on phenomena that are overshadowed in conventional data sources.
Acknowledgments We acknowledge the contributions of  Erin Amerson ,  Raul Cabrera ,  Roberto Estrada ,  Giaconda Gaudiano ,  Jorg Heukelbach ,  Janet Hickman ,  Richard Keller ,  Arum Krismi ,  Stefania Lanza ,  Kieron Leslie ,  Harvey Liu  and  Fatimata Ly  to the expert panel. Hilary Bruce  assisted with the figures.
, 2017). Although NO x emissions in the Red Sea and Arabian Sea areas have been reported to be 3 and 5 times smaller than for the Arabian Gulf, respectively, these values are still 50-100 times larger than the emission density reported for areas such as the South Pacific Ocean  (Johansson et al. , 2017). In the present study, we characterize photochemical NO-PRs in the MBL around the Arabian Peninsula. In Sect. 2, descriptions of the campaign, the instruments, the data processing and the methods used in this study are presented. In Sect. 3, mixing ratios of NO x and O 3 around the Arabian Peninsula are reported. Based on concurrent measurements of HO x , the actinic flux, temperature and pressure, noontime (HO 2 + RO 2 ) mixing ratios are estimated and used to calculate NOPRs in the different regions around the Arabian Peninsula.