dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors would like to thank all doctors, nurses and other staff members at  Mnazi Mmoja Hospital, Zanzibar  and the Zanzibar Integrated HIV, TB and  Leprosy Control Programme  for supporting the study, as well as  Professor Harald Wiker  at the  Department of Clinical Science, University of Bergen  for his advice in the development of the methodology protocol. The authors would also like to thank  Dr. Bram Burger  at the  Department of Population Health Sciences at the University of Newcastle, UK , for help with statistics.
ACKNOWLEDGEMENTS We thank  Institut National de la Santé et de la Recherche Médicale (Inserm) ,  Ligue Contre le Cancer, Institut National du Cancer  ( INCA_16697 ),  Fondation ARC  and the  National Research Agency (ANR  through the  "Investments for the Future "  LABEX SIGNALIFE : #  ANR-11-LABX-0028-01 ) for their financial support. The financial contribution of the  Conseil Départemental 06 ,  Canceropôle PACA and Region Provence Alpes Côte d'Azur  is also acknowledged. S. D. was a recipient of doctoral fellowship from the  LABEX SIGNALIFE and Fondation pour la Recherche Médicale.
Rupture Process from Backprojection and Finite-Fault Modeling Using far-field teleseismic data, we estimated the space-time rupture evolution via backprojection (e. g. ,  Ishii et al. , 2005; Koper et al. , 2011; Li and Ghosh, 2017)  and finite-fault modeling (e. g. ,  Mendoza and Hartzell, 2013, and references therein). For this purpose, we compiled two datasets. For the backprojection, we used seismic stations in Alaska and selected only stations with average cross correlation (CC) above 0. 6 for the first 25 s around the P-phase arrival filtered in the range 0. 1-2 Hz. This results in 205 and 201 stations for the M w 7. 8 and 7. 6 events, respectively. The targeted backprojection region extends from 35°to 39. 5°, both in latitude and longitude, with 0. 01°grid spacing in both the directions.
Ground-Motion Observations and Shaking Levels We collected strong-motion recordings from the AFAD (see Data and Resources) for first M w 7. 8 earthquake at 254 stations based on the following selection criteria: (1) instrument response removed, band-pass filter applied (low cutoff frequency: 0. 025-0. 1 Hz and high cutoff frequency: 25-40 Hz), and baseline corrected; (2) no abnormal recordings (e. g. , no pre-event signals and no obvious peaks); (3) three-component recordings available; and (4) V S30 values available. Moreover, we obtained regional-distance seismic waveforms from several sites located along the southward extension of the EAF, the Dead Sea fault, and the Gulf of Aqaba, including stations in Saudi Arabia. We removed the instrument response from waveforms, and then filtered them between 0.
These observations are consistent with  Gülerce et al. (2016)  who modified the Next Generation Attenuation-West1 (NGA-W1) GMMs using the Turkish strong-motion database (so-called "TR-adjusted models"). These TR-adjusted GMMs better replicate recorded ground motions, because they adopted the well-constrained largemagnitude scaling of the global dataset in the NGA-W1 models. ShakeMaps for two spectral periods (T = 0. 2 s and T = 1. 0 s) of spectral acceleration SA(T) reveal the concentrated strong shaking in several areas (Fig. S13a-d ). The regions of particularly high shaking levels correspond approximately to fault areas with high slip, whereby extended strong shaking in the Hatay-Antakia region can be explained by a combination of strong seismic radiation and local site effects. At several sites (e.
pdf. High-resolution aftershock locations are available at "A. Lomax (2023). Precise, NLL-SSSTcoherence hypocenter catalog for the 2023 Mw 7. 8 and Mw 7. 6 SE Turkey earthquake sequence. (v2. 0) [Data set]. Zenodo, doi: 10. 5281/zenodo. 7727678". The supplemental material provides additional material to explain results and support discussions in the main paper. Specifically, the supplemental material includes figures on the processing of Sentinel-1 radar images, further information on the teleseismic back-projection and finite-fault inversions, and detailed comparisons of the observed ground motions with empirical ground-motion models and the design spectra of the Turkish building code. All websites were last accessed in February 2023.
Data and Resources Real-time aftershock locations are provided by the Disaster and Emergency Management Authority (AFAD) available at https:// deprem. afad. gov. tr/event-catalog. Focal mechanism solutions of the two mainshocks are available at https://geofon. gfzpotsdam. de/old/eqinfo/list. php?mode=mt. Focal mechanisms of significant seismicity during the period 1 January 2018-6 February 2023 can be downloaded at https://www. globalcmt. org/. Satellite data are available via the Sentinel data hub at https://scihub. copernicus. eu. Teleseimic waveforms for backprojection were downloaded from the Incorporated Research Institutions for Seismology (IRIS) at https://ds. iris. edu/wilber3/ find_stations/11654089 for the M w 7. 8 event and https:// ds. iris. edu/wilber3/find_stations/11654205 for the M w 7.
6 event. Teleseismic waveforms for finite-fault inversion were obtained from the IRIS at https://www. iris. edu, Geoforschungsnetz (GEOFON) at https://geofon. gfz-potsdam. de, and Observatories and Research Facilities for European Seismology (ORFEUS) at https://www. orfeus-eu. org data centers, respectively. Broadband waveforms of Figure  5  are from King Abdullah University of Science and Technology (KAUST) seismic network (COLA, ASCO, available upon request to the authors), GEOFON (EIL,.
Declaration of Competing Interests The authors acknowledge that there are no conflicts of interest recorded. eşyalarını kaybeden herkese başsağlığı diliyoruz. (We share with all our hearts the suffering of the citizens of Türkiye and Syria during these difficult days. Our condolences to all those affected by these devastating earthquakes, who lost their families, friends, neighbors, homes, and belongings. ) We are indebted to the Saudi Geological Survey (SGS) and the National Center for Geophysics from Centre National de la Recherche Scientifique-École Normale Supérieure (CNRS) of Lebanon for sharing regional distance seismic waveforms. Comments and constructive criticism by two anonymous reviewers and the Associate Editor helped to improve our work. The research presented in this article is supported by King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia, by Research Grant Numbers BAS/1/ 1339-01-01 and BAS/1/1353-01-01.
Final elution step was repeated twice in 50 µl elution buffer to increase DNA yield. DNA concentration was quantified using the NanoDrop 1000 Spectrophotometer (ThermoFisher Scientific, Illkirch-Graffenstaden, France). We amplified a mitochondrial fragment of 402 bp from cytochrome b (cyt b) able to differentiate among the six WBP lineages, following  Gaubert et al. (2016). PCR products were sequenced at Genoscreen (https://www. genoscreen. fr/en; Lille, France) and Macrogen Europe (https://dna. macrogen-europe. com/en; Amsterdam, the Netherlands). Sequences were manually aligned in BioEdit v7. 0. 5  (Hall, 1999). All the sequences produced in this study were deposited in Genbank under accession numbers OP524750 -OP525226. We amplified 20 microsatellite markers developed from the genome of WBP using four multiplexes (4 to 6 loci) following  Aguillon et al.
Genetic diversity and structure Based on the new cyt b sequences that we produced, we reassessed the levels of genetic variation in WCA and Gab, after removing sequences with missing data (remaining N = 565). We used DnaSP v6. 12  (Rozas et al. , 2017)  to compute haplotype number (h), haplotype diversity (Hd) and nucleotide diversity (π) (Supplementary Table  S2  online). We mapped the geographic distribution of WCA and Gab haplotypes using ArcGIS 10. 1 (Esri France). We ran Network v10. 2. 0. 0  (Polzin & Daneshmand, 2008)  to build a median-joining haplotype network with ε = 0, so to minimize the presence of alternative median networks.
Genetic structure Global genetic variance among all genotyped individuals was assessed through pairwise population matrix unbiased genetic distances, and was visualized in GenAlEx using Principal Coordinates Analysis (PCoA). S1  online) were excluded because not proximate enough to any site to be merged and reach a sufficient number of samples. We used STRUCTURE 2. 3. 4  (Pritchard et al. , 2009)  to conduct a clustering analysis, following a two-step dataset refinement procedure, where (i) all the individuals from the study area were included (WCA + Gab; N = 558), and (ii) only the WCA individuals were included (WCA; N = 181), discarding large urban markets and cyto-nuclear hybrids or admixed individuals (who with at least 20% of shared ancestry). The first scheme allowed screening the global genetic structure of WBP at the largest study scale.
tau. ac. il/) was used to generate the bar plots of population assignment. We also inferred the most likely number of geographic populations using WCA georeferenced individuals, through the Geneland package  (Guillot et al. , 2005)  in R 4. 0. 5 (R Core Team, 2021). We followed  Coulon et al. (2006)  by first allowing K to vary from 1 to 20 and launched five runs of 5. 10 5 MCMC iterations (thinning = 500; burnin = 500) under the frequency-correlated model and 1 km of uncertainty for spatial coordinates. Second, we fixed the number of estimated populations to K = 10-13 after the results of the first analysis [highest mean posterior densities for: K = 10 (0. 21), K = 11 (0. 30), K = 12 (0. 26) and K = 13 (0. 15)], and performed 20 independent runs with the same parameters. In the final analysis, the stability of individual assignment to populations was assessed among the best five runs (i.
e. with the highest posterior probabilities). We used 500 × 500 pixels to map the posterior probabilities of population membership. Pairwise differentiation (FST) among the 11 populations as defined above was computed in Arlequin 3. 5  (Excoffier & Lischer, 2010). Significant differentiation values (P ≤ 0. 05) were estimated using 10 5 MCMC iteration chains and 10,000 dememorization steps. We tested isolation-by-distance (IBD) within WCA individuals and among WCA populations through the R package pegas  (Paradis et al. , 2004)  using a Mantel test. The significance of the correlation (r) between individual-based genetic (Edward's) and geographic (Euclidean) distances was estimated through 10,000 permutations.
Demographic history We tested for the signature of demographic bottlenecks in WCA (excluding hybrid and admixed individuals) using both the Single Mutation Model (SMM) and the Two Phase Model (TPM) in BOTTLENECK 1. 2. 02  (Piry et al. , 1999). We applied the Wilcoxon sign-rank test to estimate heterozygote excess/deficit using 10,000 replications. We assessed temporal changes in the effective population size of WCA through an approximate-likelihood approach implemented in the R package VarEff  (Nikolic & Chevalet, 2014). We followed  Zanvo et al. (2022)  by fixing a generation time of 2 yrs and a microsatellites mutation rate of 5. 10 -4 per generations  (Schlotterer, 2000). We ran the analysis with three mutation models (single, geometric and two phases), using 10,000 MCMC batches (length = 1, thinned every 100 batches; JMAX = 3).
Tracing the pangolin trade The discriminative power of our 20 microsatellite markers among WBP individuals was evaluated after three different approaches: (i) counting the number of identical genotypes among samples using the Multilocus tagging option in GenAlEx (suboption Matches), (ii) computing the probability of encountering the same genotype more than once by chance using the R package poppr (method=single;  Kamvar et al. (2014) , and (iii) calculating the indices of unbiased probability of identity and probability of identity among siblings (uPI and PIsibs) using Gimlet 1. 3. 3  (Valière, 2002). Because we could not find a deep level of genetic structure within WCA, we followed the protocol based on private allele frequency rarefaction described by  Zanvo et al. (2022)  to trace the trade of WBP in our study area.
Because introgression may occur between other neighboring lineages of WBP  (Gossé et al. , in prep. ) , it will be important in the future to rely on a global reference database where all the WBP lineages will have been genotyped. and PG analyzed the data; ADD and PG led the writing; ADM, SA, EL, MT, FN, BRM, KA, SN and ALC read and revised the drafted work. All the co-authors have read and approved the final version of the manuscript.
Acknowledgements We are grateful to the  Ministère de la Recherche Scientifique   et de l'Innovation  for providing the research permit ( 000094/MINRESI/B00/C00/C10/ nye). We thank the staff of the " Plateau technique -Biologie moléculaire et microbiologie" at EDB  for assistance during lab work. We thank  Franklin and Jean  for their help during field work. The local hunters and vendors gently provided us with the necessary tissue samples by free consent. We are grateful to  V. Benoît  and  L. Cervantes  ( Institut de Recherche Criminelle de la Gendarmerie Nationale, Cergy Pontoise, France ) for giving us access to the samples seized at Roissy airport (France). ADD, SA and PG received support from the  Agence Nationale de la Recherche  ( ANR-17-CE02-0001 ;  PANGO -GO). ADD and PG were also supported by the  Fundação para a Ciência e Tecnologia  ( FCT IC&DT 02/SAICT/2017 -n° 032130 ;  BUSHRISK ).
c -d, Box plots for the c, rupture tension, d, rupture strain, and e, rupture time for monolayers pre-incubated with different treatments perturbing the actomyosin cytoskeleton. showing rupture tension as a function of rupture time and f, rupture tension as a function of rupture strain in response to external stretch for WT (black dots) and K14-R125C (grey dots) monolayers. Data points are acquired for different strain rates. Coloured dots show the population average for a given strain rate (0. 1% s -1 n = 6, 0. 3% s -1 n = 9, 1% s -1 n = 10, 2% s -1 n = 11, 3% s -1 n = 11 for WT monolayers. 0. 3% s -1 n = 8, 1% s -1 n = 9, for K14,R125C monolayers). Each coloured dot represents a simulation run. 100 simulations were run for each strain rate. Each grey dot represents an experimental data point.
To knock down desmoplakin, we purchased shRNAs targeting dog desmoplakin in a lentiviral vector (V3LHS 302846 and 302847, pGIPZ vector, Horizon discovery, Perkin Elmer, UK). We generated lentiviral particles following manufacturer instructions, transduced MDCK cells, selected cells with puromycin and sorted the cells with flow cytometry to achieve a homogenous knock down. As a control, we also generated a cell line expressing non-silencing shRNA. Protein depletion was verified by immunoblotting (Fig. S8 ). The antibodies used were anti-desmoplakin (Progen) and with anti-GAPDH as a loading control (Abcam). Appropriate HRP-coupled secondary antibodies (dilution 1:10,000) were from Jackson ImmunoResearch and Cytiva. The shRNA used in this work was shRNA2 from Fig. S8. of the cell lines in this study were found in the database of commonly misidentified cell lines maintained by International Cell Line Authentication Committee and National Center for Biotechnology Information Biosample.
This deflection was transformed into a voltage that was converted into a digital signal using a data acquisition system (USB-1608G, Measurement Computing) and recorded on a computer. The motorized manipulator was controlled using a custom-written code in Labview (National Instruments). During the experiment, images of the monolayer were taken every 1 s using a 2X objective (2X PLN Olympus) and a GS3-U3-60QS6M-C, Point Grey camera.
These masks were then processed in MATLAB. A second registration is performed to ensure the masks overlap correctly with each of the images of the movie. Finally, the intensity of each pixel within the mask was extracted from the aligned movies and averaged to output the mean fluorescence in the region of interest at each time point. All processing after segmentation was performed in MATLAB (as shown in Fig. 10 ) a dashpot η, the response to the ramp in strain with rate ε is given by η ε(1 -exp(-t/τ )) with τ = η/k. The full response is again obtained by superposition: Γ(t) = η ε εt 0 p s (ε ′ ) 1 -exp - εt -ε ′ ετ dε ′ + k a εt. (8) The distribution p s is adjusted to mimic the high strain rate limit, identical to the non-linear spring model (Fig. S 9d ). The extra parameter η (or equivalently τ ) is adjusted to account for the observed timescale associated with the shear stiffening behaviour.
Acknowledgements The authors thank past and present members of the  Charras and Kabla labs  for discussions. The authors acknowledge technical support in LabVIEW development from A. Lisica. JD and GC were supported by  European Research Council  consolidator grant ( CoG-647186 ) and a  sLOLA grant  from the  British Biotechnology and Biological Sciences Research council (BBSRC ,  BB/V019015/1 ) to GC. AB was supported by the  seal of Excellence (SoE)  fellowship from  Politecnico di Milano. JF was supported by a grant from the  BBSRC  ( BB/M003280  and  BB/M002578 ) to GC and AK. AH was supported by a  BBSRC  grant ( BB/K013521 ) to GC and AK. EF was supported by  BBSRC  grant ( BB/V019015/1 ) to GC.
Shaded areas show the standard deviation. (ii) Box plots of the tension gradient at low (L ≃ 15% strain) and high strain (H ∼ ≃ 70% strain) in WT and K14-R125C monolayers. The fold change between low and high strains is indicated in between the box plots. c, Tension-Strain curves for NS control (cyan, n = 11) and DSP-shRNA (magenta, n = 12) monolayers subjected to ramps in deformation at 1% s -1. Solid lines represent the average value and shaded areas the standard deviation. e, Graphshowing rupture tension as a function of rupture time and f, rupture tension as a function of rupture strain in response to external stretch for WT (black dots) and K14-R125C (grey dots) monolayers. Data points are acquired for different strain rates. Coloured dots show the population average for a given strain rate (0.
Ammonia (NH3) is one of the most important gases emitted from agricultural practices. It affects air quality and the overall climate, and in turn influenced by long term climate trends as well as by short term fluctuations in local and regional meteorology. Previous studies have established the capability of the Infrared Atmospheric Sounding Interferometer (IASI) series of instruments aboard the Metop satellites to measure ammonia from space since 2007. In this study, we explore the interactions between atmospheric ammonia, land and meteorological variability, and long-term climate trends in Europe. We investigate the emission potential (𝛤 𝑠𝑜𝑖𝑙 ) of ammonia from the soil, which describes the soilatmosphere ammonia exchange. 𝛤 𝑠𝑜𝑖𝑙 is generally calculated in-field or in laboratory experiments; here, and for the first time, we investigate a method which assesses it remotely using satellite data, reanalysis data products, and model simulations.
We specifically study the relationship between satellite-derived ammonia concentration at the start of the growing season, soil emission potentials and their spatial variability over Europe during March of 2011. Section 2 provides the methods/datasets used. Simulation results are described in Sect. 3, and regional emission potentials are shown and discussed in Sect. 4. Using a climate model, future projections of ammonia columns are investigated under different climate scenarios in Sect. 5. Discussion and conclusions are listed in Sect. 6.
, 2009). The IASI Fourier-transform spectrometer monitors the atmosphere in the spectral range between 645 and 2760 cm -1 (thermal infrared), and is nadir-looking. IASI has a swath width that measures 2200 km, with a pixel size of ~12 km. Ammonia was first detected with IASI using the ʋ2 vibrational band of ammonia (~950 cm -1 )  (Clerbaux et al. , 2009; Coheur et al. , 2009). The ammonia total columns used in this study are the product of an Artificial Neural Network and re-analyzed temperature data from the European Centre for Medium-Range Weather Forecasts (ECMWF) product ERA5 ANNI-NH3-v3R-ERA5  (Van Damme et al. , 2021). Several studies used ammonia data from IASI to study hotspots of ammonia of different source types including both natural and anthropogenic sources  (Clarisse, Van Damme, Clerbaux, et al.
, 2019) , showing consistency when studying seasonal and inter-annual variability  (Viatte et al. , 2020). In addition to ammonia, we look at skin temperature (T skin or land surface temperature LST) data from the ECMWF reanalysis (ERA5) at a grid resolution of 0. 25 × 0. 25 °  (Hersbach et al. , 2020). ERA5 Temperatures are interpolated temporally and spatially to the IASI morning overpass (~9:30 A. M. local time), since we only consider daytime ammonia. ERA5 temperature data are also used in the retrieval process of the ammonia data we used in this study  NH3-v3R-ERA5 (Van Damme et al. , 2021). T skin is defined as the temperature of the uppermost surface layer when radiative equilibrium is reached. It also represents the theoretical temperature required in order to reach the surface energy balance  (ECMWF, 2016).
Output includes the monthly mean for selected diagnostics. Anthropogenic emissions are taken primarily from the global Community Emissions Data System (CEDS) inventory  (Hoesly et al. , 2018). Biogenic non-agricultural ammonia, as well as ocean ammonia sources, are taken from the Global Emission Inventories Activities database (GEIA,  (Bouwman et al. , 1997) ). Open fire emissions are generated using the GFED 4. 1s inventory  (Randerson et al. , 2015). We used the Harmonized Emissions Component module (HEMCO) to obtain the ammonia emissions over Europe  (Keller et al. , 2014).
Ammonia columns from GEOS-Chem are underestimated by up to 2 × 10 16 molecules/cm² in some source regions/over hotspots, especially in England, North Eastern France, the North European Plain (Netherlands, Belgium), and Spain (around Barcelona). Similar results were found in the study of  Whitburn et al. (2016) , in which they show that GEOS-Chem underestimates ammonia columns by up to 1 × 10 16 molecules/cm² in Europe on a yearly average in 2009, notably in the North European Plain. It is important to note that, in our study, we compare only one month of data (March, 2011) that marks the start of the growing season in the majority of the countries of interest (FAO, 2022; USDA, 2022). The differences are mainly because of the time coincidence, and the fact that only cloud-free data are used to retrieve ammonia; IASI observes ammonia during the satellite overpass (~9:30 AM local time), whereas the GEOS-Chem simulation is averaged over the whole month including all hours of the day.
In Western and Northern Europe, the MRD is mostly less than -50 %, for instance, in the North European Plain (-49 %). If we look at the average MRD in regions of focus, we see that the Po Valley in Italy has the highest MRD value (+110 %), whereas the best represented region is New Aquitaine in the southwest of France (-20 %). The rest of the regions have mean MRDs that fluctuate between -64 % and -42 %. A summary of the results of this study, including the MRD over some source regions is listed in Table  1. Although the bias and MRD can be considered high, the spatial distribution is consistent between IASI and GEOS-Chem. Therefore, according to the steady state approximation, the meteorological and soil parameters affecting one dataset (e. g. IASI NH3) are applicable to the other (e. g. model simulation).