dataesr/scientific-paragraphs-categorization huggingface dataset:
Cont. Numbers in parentheses are the number of actual sampled individuals from Chinese populations. Others are the sequences of the mitochondrial Cytochrome Oxydase subunit I (COI) gene used in this study based on 322 bp fragment. In bold, the specimens are amplified in our Chinese and French laboratories; Italics are the specimens amplified at the Canadian Centre for DNA Barcoding; others are retrieved from databases. n ht Number of haplotypes, h Haplotype diversity, π Nucleotide diversity.
Acknowledgments: We also thank  Marie-Anne Auger-Rozenberg INRAE URZF Orléans , and  Thierry Noblecourt ,  Forest Health Department (DSF) in France , for helping collecting the Sirex populations abroad, especially in native regions in Europe;  Natalia Kirichenko  ( Institute of Forest Siberian Branch of Russian Academy of Sciences, Russia ),  Emmanuelle Magnoux ,  Béatrice Courtial  and  Claudine Courtin  ( URZF, INRAE, France ) for assistance in the lab of URZF;  Marie-Anne Auger-Rozenberg  and  Audrey Bras  ( URZF, INRAE, France ) for advice on data analysis. We thank all workers of the forestry departments in Heilongjiang, Liaoning, Jilin provinces and Inner Mongolia Autonomous Region for their assistance with fieldwork. This study was supported based on the  Memorandum of Understanding between The China Scholarship Council and Agreenium  (a French public establishment for scientific cooperation bringing together the main agricultural and veterinary research organizations and higher education establishments in France).
511 0. 00159 Oceania 16 1 0. 226 0. 0007 Australia Austr-DRYAD Austra, Austr AustrES, AustrEB 16 1 0. 226 0. 0007 Europe 44 4 0. 533 0. 00194 Switzerland Swit-DRYAD Switz Swit, SW, Swi 15 0 0 0 Italy Ita-XS 1 0 0 0 Poland Pol-XS 1 0 0 0 Hungary Hun, Hun-XS 2018 4,2 0 0 0 Ger-Berlin Ger-Ber 2 0 0 0 Ger-Stuttgart Ger-Stu 1 0 0 0 Spain Spain-DRYAD Spain Spain 3 0 0 0 Fr-Labenne Fr6-Lab. , Fr-Lab. XS30 2017 1,1 0 0 0 Fr-Meolans Revel Fr03-MR 1 0 0 0 Fr-Villes sur Auzon Fr01-V. S. A 1 0 0 0 Fr-Marseille Fr3-Mar. 2013 1 0 0 0 Fr-Tarascon Fr11-Tara 2017 1 0 0 0 Fr-Brain sur Allones Fr8-B. S. A. , Fr-B. S. A. XS31 2017 1,1 1 0. 667 0. 00207 Fr-Etaules Fr4-Eta. 2017 1 0 0 0 Fr-Lavercantière Fr18-Lav. 2011 1 0 0 0 Fr-Orléans Fr21-Orle, Fr87-Orle 2017. 2 2 1 0. 667 0. 00207 Fr-St Alban des Hurtiè Fr16-St.
Methods COVID-19 patients undergoing an operative procedure were included in a prospective database within the framework of the CovidSurg international multi-centric cohort study (NCT04323644, French national approval by the CHU Rennes ethical committee on March 20, 2020, #20. 25). All patients had pulmonary involvement to some extent (Table ) and all but one had symptoms. All patients granted informed consent and were treated with the usual institutional standard of care. From March 22, 2020 to May 21, 2020, a total of 7 patients with a preoperative diagnosis of COVID-19 infection underwent emergency abdominal surgery for a variety of conditions. Their demographic features and operative details of the study cohort are summarized in the Table. Peritoneal fluid was sampled intraoperatively and analyzed at the virology laboratory of the University of Strasbourg (France).
Images were recorded on a Nikon TE-2000 inverted microscope. Identical camera and microscope settings were maintained to facilitate intensity quantification of control and treated cells using a custom MATLAB script. Intensities were normalized by DAPI intensity. Generation of iPS Cells: Replication-incompetent lentiviruses containing reprogramming factors (OSKM 3:3:3:1) were packaged with a lentiviral packaging mix (7. 5 µg psPAX2 and 2. 5 µg pGDM. 2) in 293T cells and collected 48, 60, and 72 h after transfection. The supernatants were filtered through a 0. 45 µm filter, supplemented with 8 mg mL -1 of polybrene (Sigma), and then used to infect MEFs. Six hours following the third infection, medium was replaced with fresh DMEM containing 10%FBS. Eighteen hours later, medium was replaced ESC reprogramming medium (DMEM supplemented with 10%FBS, 0.
To eliminate the mechanical contributions of the surrounding cytoskeleton, cells were trypsin-detached, resuspended in MEF medium supplemented with Cytochalasin-D (1 µg mL -1 ) and incubated for 30 min inside a humidified cell culture incubator at 37 °C, 5% CO 2. Suspended cells were then placed on a glass-bottom plate and mounted onto an inverted fluorescent microscope (Nikon Eclipse, Ti-U). The pipette tip was brought into contact with the cell nucleus and basal suction pressure was applied to stably engage it. Using the H2B-Orange fluorescent channel, nucleus aspiration dynamics was recorded (Andor Zyla sCMOS controlled via Nis Elements 4. 50. 00 command software) in response to stably applied ≈1 kPa suction inside the pipette relative to basal level. The mechanical properties of the nuclei were evaluated based on the relationship between aspiration length L p (t) and applied suction ∆P using the half-space model.
About 22 million single-end 60-bp reads were sequenced per sample on Illumina HiSeq 2500 V4 instrument. Transcript reads were validated using FastQC and aligned using BOWTIE against hg19 indices. Gene abundance profiles were evaluated based on assembled transcripts using Cufflinks. Gene expression profiles, depleted of ribosomal RNA, were normalized to mean gene count across all cell lines. Technical variability was removed by fitting the log squared correlation of variations (CV2) relative to the log mean expression of all genes across each cell line was fitted to a squared polynomial function and outlier genes were removed. Proteomic Analysis (Mass Spectrometry)-Nucleus Isolation: MEF's were harvested using Trypsin (Biological Industries, 03-050-1A) and centrifuged at 300 g for 5 min to remove excess medium.
Data were acquired in data dependent acquisition (DDA) mode, using a Top10 method. MS1 resolution was set to 70 000 (at 400 m/z), mass range of 300-1650 m/z, AGC of 3e6 and maximum injection time was set to 60 ms. MS2 resolution was set to 17 500, quadrupole isolation 1. 7 m/z, AGC of 1e6, dynamic exclusion of 60 s and maximum injection time of 60 ms. Proteomic Analysis (Mass Spectrometry)-Data Processing and Analysis: Raw data were processed in Maxquant version 1. 6. 0. 16. Data were searched against the SwissProt murine database (March 2017 version) appended with common laboratory contaminant proteins. Fixed modification was set to carbamidomethylation of cysteine and variable modifications were set to protein N-term acetylation, oxidation of methionine and deamidation of asparagine and glutamine.
Acknowledgements The authors greatly appreciate support from the  Israel Science Foundation  (Grant Number  1246/14 ), the  European Research Council  ( ERC-StG 678977 ), and the  Niedersächsisches Vorab  (Grant No. ZN 3183 ). The authors thank  S. Safran  and  D. Deviri  ( Weizmann Institute of Science ),  D. Harries  and  A. Eden  ( Hebrew University of Jerusalem ), and  Y. Shokef  and  H. Diamant  ( Tel Aviv University ) for fruitful discussions. The authors are grateful for the lab of  Prof. Y. Buganim  ( Hebrew University ) for blastocyst injections and for the mES and iPS cells.
In the North Atlantic their chlorophyll concentration can range from <0. 01 mg m -3 in the subtropical gyre to greater than 3 mg m -3 in productive coastal regions during the spring bloom  (Brewin et al. 2017). The purpose of this paper is to exploit the 21-year climate-quality CMEMS diatom dataset to study the interannual variability of diatom concentration and dominance, hence providing a remote-sensing counterpart to these model calculations.
This dataset is distributed within CMEMS as part of the North Atlantic chlorophyll reprocessed observation product  (product ref. 2. 1. 1). From this daily dataset, we computed regional weekly, monthly and annual mean composites of diatom chlorophyll concentration and its fractional contribution to total chlorophyll-a concentration  (Brewin et al. 2017 ). The regional annual anomaly in diatom concentration for 2018 was computed by subtracting a reference annual climatology  from the 2018 annual mean concentration. A time series of the monthly area-averaged diatom concentration was derived; and seasonal cycles and trends over the 1997-2018 time period were calculated for both the absolute and fractional concentrations. For the diatom domination analysis, and starting again from the daily dataset (product ref.
Method Photosynthesis occurs in the euphotic layer, generally characterised by a homogeneous production profile in case of mixing conditions and by a subsurface maximum in case of stratification. Estimates of the marine ecosystem primary production are usually integrated vertically over the productive layer. In the present study the vertically integrated primary production at global and regional scales is assessed through combining satellitederived datasets of chlorophyll-a and model reanalysis over the past 20 years  (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018). Satellite results are integrated over the productive layer (i. e. 1. 5 times the euphotic depth), while the Mediterranean Sea model output is integrated over 0-200 m depth, which safely covers the whole productive layer.
2009 ). Thus, large-scale, persistent barrier layer monitoring may offer a more robust signature for the intensification of the water cycle than the change in surface salinity. Yet, competing mechanisms at work (such as erosion of the barrier layer by strong wind events, changes in sheared currents), might render the interpretation not straightforward. Due to the insufficient concurrent temperature and salinity profiles observations, a comprehensive description of the decadal variability and trend of the barrier layers in the last three decades is still lacking. In this study, we utilise the CMEMS ARMOR3D  (Guinehut et al. 2012, product ref: 2. 3. 1) monthly dataset over the 1993-2018 period to compute inter-annual fluctuations and trends of barrier layers in the tropical Pacific. This multi-observation product takes advantage of the resolution of the satellites (altimetry and sea surface temperature) to complement the in situ observations (TAO moorings, XBT lines and ARGO profiles).
Interannual variability We use decompositions in Empirical Orthogonal Functions to explore the Pacific variability. The seasonal cycle was removed before the decompositions. The empirical function 1 of isothermal depth layer (not shown), mixed layer depth (not shown), and BLT (Figure  2. 3. 3(a)) over 1993-2018 are similar to those found over 1979-2015 with a different dataset (Wang and Xu 2018, Fig. 2 ) and over 1951-2010 in the western Pacific  (Wang and Liu 2016) , both in terms of spatial pattern and timing. The patterns reflect the El Nino Southern Oscillation pattern in sea surface salinity  (Singh et al. 2011) , and barrier layer  (Wang and Liu 2016) , also similar to the Pacific Decadal Oscillation K-shape pattern in sea surface salinity and barrier layer  (Cravatte et al. 2009, Wang and Xu, 2018) , although the amplitude of the signal is weaker.
The analysis of current datasets reveals that cold intermediate layer thickness fluctuations are (quasi) synphase in all subregions as periods of rise and fall alter every 4-5 years (Figure  2. 5. 2(A)). In autumn, perhaps due to the larger data gaps, an 8-year variation cycle can be detected (Figure  2. 5. 2(C)). This is corroborated by the correlation between data series, which is higher than 0. 87 for both seasons and across all subregions. In general, the correlation between basin periphery and interior is slightly weaker in comparison with the correlation between the peripheral subregions themselves, as the lowest one is observed between 4 and 5 (0. 89). This outcome indicates that the variability in the cyclonic gyres is not similar to the one in area 4, which partially confirms the importance of slope convection, occurring on the northwestern shelf, for evolution of the cold intermediate waters  (Stanev et al.
Kara et al. (2005)  considered the penetrative solar radiation as the major component of the heat balance in the Black Sea in an investigation of the seasonal mixed layer depth variability. Their model simulations revealed that the dynamics of the mixed layer are quite sensitive to solar radiation attenuation. Miladinova et al. (2017)  investigated longterm trends and variations of thermohaline properties and showed that temperature is seasonally variable at the surface, decreasing with depth down to the coldest intermediate layer. There have been several works that have estimated the recent temperature trends in the Black Sea using databases and model results. Ginzburg et al. (2004)  used satellite measurements to reveal a positive trend of 0. 09°C year -1 in sea surface temperature over the years  1982 -2000 , whereas Degtyarev (2000) )  noted a positive temperature trend of 0.
Method The operational wave forecast and reanalysis delivered by Iberia-Biscay-Ireland Monitoring and Forecasting Center, hereinafter named as CMEMS reference products 2. 7. 2 and 2. 7. 1, respectively, provide two different datasets with hourly instantaneous fields of Significant Wave Height. These fields have a 0. 1°horizontal resolution. The CMEMS product 2. 7. 2 delivers the best estimates of the system spanning the time period from 2015 up to the present, whereas the CMEMS product 2. 7. 1 is a product that currently ranges 1992-2016. More information on the system description and validation can be found in its corresponding Product User Manuals available in the CMEMS web page, see the products table. The Strong Wave Incidence index is proposed to quantify the variability of strong wave conditions in the Iberia-Biscay-Ireland regional seas.
The data provided by the CMEMS wave forecast and reanalysis systems in Iberia-Biscay-Ireland region has been used for this study (products referenced as 2. 7. 1 and 2. 7. 2). Since the validation of those systems is published in the QUID documents (both referenced in the Products Table ), we consider the Significant Wave Height provided by this dataset reliable. The robustness of the proposed indicator has been tested by (1) comparing its results with the North Atlantic Oscillation index, (2) comparing the anomalous events detected in the last decades with events described in literature, and (3) examining the atmospheric patterns associated to the occurrence of intense wave events. Part of the study has analysed the relationships between the Strong Wave Incidence index and the large-scale atmospheric patterns.
We exclude then the periods where R < 0. 8, which represent < 5% of the computed periods for both study areas and datasets. c, d, g, h )) periods, and as we will demonstrate quantitatively later on, difference in the values of the residence times in both study areas for different months suggests a seasonal behaviour, with much higher residence times in spring/summer than in autumn/winter. In the example given in Figure  2. 8. 3, the spatial distribution of residence times for 6-8 January 2014 (a,b) shows a meridional gradient, with higher values over the south of the domain, while a zonal gradient is observed for 8-9 July 2014 (c, d) with larger retention times (over one week) at the eastern half of the domain. The residence times in 5-8 January 2013 (e,f) for the Ibiza Channel region are very low and under 2-3 days for almost all the area with higher values distributed along filaments, while in the example for August 21 (h), larger times (> 12 days) can be observed almost for the whole domain.
Shorter escape times (20-30%) are observed when using the observed currents in both areas. The escape times are much higher in the shelf of the South-East Bay of Biscay (of 18. 5/14. 4 days from model/observations) than in the Ibiza Channel (3. 82/ 2. 68 days from model/observations), which was expected because of the different extension and configuration of the study areas. In both areas, we can observe a seasonal modulation of the escape times. In the South-East Bay of Biscay shelf, the lower escape times are observed in autumn and are around 14. 7 (12. 3) days for the CMEMS-IBI-MFC (HF radar) datasets, while escape times are between 30 and 40% higher at the end of winter (for CMEMS-IBI-MFC dataset) or spring (for the HF radar dataset). In this area in autumn both the wind and slope current regime favour the rapid evacuation of the particles through the northern frontier of the computation domain.
High-retention conditions are observed for certain periods like winter/spring of  1994, 1996, 2005, 2007, 2009, 2014, 2015 and 2016, or summer 2000. For the Ibiza Channel, the escape times from the CMEMS-IBI-MFC dataset suggests higher retention in autumn and winter (>4 days; 40% higher than in summer), while the periods of higher retention from the HF radar data are spring and summer (3-3. 3 days; 38% higher than in autumn). In this area again wind induced currents and the northward branch of the Atlantic inflow circulating along the eastern side of the channel are expected to be the main mechanisms of particle evacuation through (mainly) the northern boundary of the computation domain  (Font et al. 1988; Garcia-Lafuente et al. 1995; André et al. 2005; Heslop et al. 2012; Sayol et al. 2013).
The very different spatial resolution of the datasets in this area can explain the discrepancies in terms of escape rates observed at shorter and seasonal timescales, partially because part of the observed mesoscale and sub mesoscale processes are not properly resolved by the CMEMS-IBI-MFC, of much coarser resolution. Besides, the HF radar could better capture wind-induced currents. High-retention conditions are observed for certain periods like autumn/winter  of 1992, 1993, 1995, 1997, 2004, 2007 and spring of 2001, 2003, 2012.
Method The daily, global, spatially complete sea surface temperature maps on a regular grid ('L4' data) from the ESA CCI and C3S datasets were aggregated to create monthly and annual means. These were further averaged over the period 1993-2014 to generate a climatology. From these, anomalies (the difference between the monthly and annual averages and the climatology) were calculated. The monthly anomalies were aggregated over the full globe and in regions using grid cell area weighted averaging to generate time series. These were further analysed to calculate a linear trend estimate using the procedure described in  Mulet et al. (2016). In summary, the time series were each decomposed into a residual seasonal component, a trend component and errors using the X-11 seasonal adjustment method (e.
Results 2. 9. 3. 1. Changes in sea surface temperature over the period 1993-2018 Figure  2. 9. 1(a) shows the time series of global average monthly sea surface temperature anomalies (thin black line) and the trend component generated using the X-11 seasonal adjustment method (thick black line). The estimate of the linear trend in global average sea surface temperature is 0. 014 ± 0. 001°C / year (95% confidence range). Therefore, there has been a statistically significant warming trend in sea surface temperature over the period 1993-2018 according to these data and this statistical model. Huang et al. (2019)  reported similar trends calculated from three different datasets, although for a shorter period of 2000-2018. The trend in one dataset was 0. 013°C / year and 0. 016°C / year in the other two with an uncertainty of between ±0.
006 and ±0. 009°C / year. They also provided trends for the period 1950-2018 from two datasets. These were 0. 008 and 0. 010°C / year. Hausfather et al. (2017)  examined four datasets and found trends of 0. 007-0. 012°C / year for 1997 through to 2015, although they found that 'instrumentally homogeneous' in situ data and an earlier, experimental version of the ESA CCI data were most consistent with the dataset with the largest trend. The residual differences between the trend component and the linear trend line are shown in Figure  2. 9. 1(b) (black line). The residuals contain structure, indicating that a linear trend is an incomplete statistical  The strong El Niño -Southern Oscillation variability within the time series might impact on the linear trend estimate, despite the use of a robust method to calculate this.
This was nominally the fourth warmest in the dataset, ranking behind the previous three years. This relative cooling is associated with a transition between El Niño and weak La Niña conditions. Sea surface temperature anomalies in the North Atlantic showed a tripole pattern consistent with the positive  The remainder has been taken up by the ocean (155 ± 30 PgC) and terrestrial ecosystems (180 ± 80 PgC)  (Ciais et al. 2013). The uptake of CO 2 by the ocean, the ocean carbon (C) sink, is evaluated on a yearly basis since 2007 by the Global Carbon Project (GCP, https://www. globalcarbonproject. org/). Ocean C sink estimates derived from atmospheric inversions, forced biogeochemical ocean general circulation models and statistical interpolation converge on the amplitude of the global integrated ocean C sink at yearly and decadal time scales.
g. Copernicus datasets) are not yet corrected for this drift. A reprocessing of the TOPEX-A dataset is currently in progress by JPL/CNES space agencies and the recommendation of the Ocean Surface Topography Science Team is that the altimeter sea level products distributed to the users should not be corrected for the TOPEX-A drift before the release of this reprocessed dataset (expected in 2020). However, in case the drift issue will not be solved thanks to this reprocessing effort, the community will have to find an agreement regarding the correction to be used. As presented above, three different approaches are available, based on an update of the altimeter range correction, comparison with in situ data or with independent contributions to the sea level. The anomaly of the onboard calibration correction is not expected to have any spatial signature on the regional sea level trends since it affects all measurements equally.
A further potential application of models is to identify the relevant importance of different natural and anthropogenic drivers on eutrophication indicators. This was explored for bottom oxygen concentrations by  Große et al. (2016) , who investigated the relative influence of spatial and temporal variations in seasonal stratification and primary production on c(O 2 ). However, some care must be taken in this context. The use of chlorophyll data assimilation in the reanalysis makes the interpretation of governing relationships and their interdependencies in the model more complex. Furthermore, it is evident from comparisons with in situ data that the model dataset contains some significant biases compared to observed values (see QUID). Specifically, there is an overall negative median bias in oxygen concentrations of ∼0.
5 mg L -1 when compared with on shelf in situ observations from the ICES databases, which likely result from a warm bias in temperature; while nitrate concentrations are generally positively biased, especially in the Southern North Sea, which has direct relevance to studies of eutrophication, given the history of this area. If used to support eutrophication assessments, it is important that these errors are taken into consideration. Section 3. 3: The value of carbon sink ecosystem services in the Mediterranean Sea Authors: Canu, D. , A. Ghermandi, P. Lazzari, G. Bolzon, P. A. L. D. Nunes, S. Zunino, C. Solidoro Statement of main outcome: Marine ecosystems services worldwide are often ignored or underestimated, due to their limited or indirect visibility and it is critically important to assess them to improve marine management and policy.