dataesr/scientific-paragraphs-categorization huggingface dataset:
In this study we define MIZ as an area with 0-30% sea ice concentration close to the ice edge. The ship was equipped with an underway measurement system Aqualine Ferrybox, widely known as thermosalinograph, TSG. The instrument had a temperature and a conductivity (MiniPack CTG, CTD-F) sensors and a CTG UniLux fluorometer installed; thus, continuous temperature, salinity and chlorophyll-a estimations were obtained along the ship's trajectory. The inflow is situated at 6. 5 m below the surface (the inflow hole is on the ship's hull). All data were processed and filtered for random noise and bad quality data, and then compared and calibrated with CTD measurements. The cruise data can be found at https://uaf-iarc. org/nabos/.
Satellite data Satellite data provide an instant information about surface distribution of geophysical characteristics over the whole study area and their temporal evolution. All listed below products were considered from August 1, 2018 to September 25, 2018 (the last day of ARKTIKA-2018 expedition). If not indicated additionally, for consistency all products were interpolated on a regular grid within the bounding box 74-85N 90-170E, with 0. 01 degree step in latitude, and 0. 05 degree in longitude. The spatial resolution of selected grid roughly corresponds to 1 km on the studied latitudes.
Reanalysis data Reanalysis data were used to include some additional parameters not available from satellite and in situ data. Atmospheric forcing fields -sea level pressure, SLP, and air temperature, were obtained from the ERA5 reanalysis  (Bertino et al. (2008) ). The latest reanalysis of ERA5 still have relatively crude spatial grid of 0. 5 • for the SLP and 0. 25 • for air temperature.
Over most of the southern and the central part of the free-of-ice Laptev and the East-Siberian seas, the error is below 0. 5 • C and over the eastern part, it is below 1 • C for selected day. The first step of satellite and in situ dataset evaluation was a value-by-value comparison of collocated dataset (nearest neighbor) and calculation of basic statistics. For this analysis, we collocated satellite-estimated temperature with the in situ measurements in the upper 6. 5 m layer: CTD averaged every half a metre above 6. 5 m depth and TSG at 6. 5 m depth averaged every 30 minutes. The median depth of the collocated temperature measurements is 5. 25 m due to the absence of CTD data in the very first metres. As the ship was moving with an average (median) speed of 8 knots, 30 minutes averaged TSG's temperature corresponds to the median of measured temperature over approximately 7.
5 km, which is comparable with the spatial resolution of DMI SST L4 10 km. There were 1707 points in the analysis. Though, in fact, satellite sea surface temperature estimates may differ from the measurements at 6. 5 m, we expect some general consistency between the datasets. Studies carried out by  Castro et al. (2017) , devoted to the validation of MODIS SST in the MIZ, and that by  Vivier et al. (2016)  which describes in situ measurements in iced-covered area, report that the first 7-10 m layer below the surface is mostly homogeneous. Nevertheless, diurnal warming and local vertical mixing can affect the temperature distribution in the very surface layer. Local diurnal variations of temperature are supposed to be filtered out in the blended DMI product, as it only uses observations between 21:00 and 7:00 local time  (Høyer et al.
Acknowledgements. We thank  Jean-Luc Vergely  for fruitful discussions about SMOS SSS data filtering in the Arctic ocean. This study was supported by the  French CNES-TOSCA   SMOS-OCEAN  project. Anastasiia Tarasenko ,  Vladimir Ivanov  and  Nikita Kusse-Tiuz  acknowledges financial support from the  Ministry of Science and Higher Education of the Russian Federation , project  RFMEFI61617X0076. 20 https://doi. org/10. 5194/os-2019-60Preprint. Discussion started: 31 July 2019 c Author(s)  2019. CC BY 4. 0 License.
Even more complex MIG structures are also used for the coaxial cavity gyrotrons  [1]  due to the presence of the coaxial insert which is fixed at the rear part of the MIG. Theoretical models have been developed for the estimation of the beam electron trajectories in the MIG region where a strong electric field and convergent magnetic field are applied  [2]. For more accurate calculations, electron optics codes have been developed, such as EGUN  [3] , EPOSR  [4] , MICHELLE  [5] , etc. In Europe (EU) three electron optics codes have been developed for gyrotron MIG study and design. These are (i) the Ariadne code  [6]  (finite element, parallel, three and two dimensional, electrostatic, tracking and self-consistent), (ii) the ESRAY/ESPIC code  [7]  (finite difference, two dimensional, tracking and PIC), and (iii) the DAPHNE code  [8]  (finite element, two dimensional, electrostatic, tracking and self-consistent).
Data collection The RNA sequence data used in this study is derived from the brains of an aged population obtained from the Aging, Dementia and Traumatic Brain Injury Study, accessed through the Allen Brain Atlas (http://aging. brain-map. org/). To obtain the data, a gene search specifying APOE was first performed. APOE was then selected and used to extract correlates for each brain region through drop down menus located on the same page. The drop-down menus allowed for the selection of brain region, APOE4-positive or -negative data, and sex. Once the selections were made, the "find correlates" button was used to retrieve the data. The datasets consist of gene expression data together with a clinical diagnosis comparison between males and females. The cohorts used in this study are as follows: APOE4-positive: females 7, males 13; and APOE4-negative: females 31, males 49.
APOE gene correlates Genes whose expression correlated with that of the APOE gene were collected for the four available brain regions: frontal white matter (FWM) (associated with cognitive function, learning, dementia, and personality changes in AD), hippocampus (HIP), parietal cortex (PCx) (associated with sensory processing, attention, motor function, executive function, and spatial reasoning), and temporal cortex (TCx) (involved in sensory processing as well as declarative and long term memory). Data for each brain region was collected separately for all groups. For information about the RNA-sequence data generation and analysis, see the Quantitative Data Generation white paper in Documentation NA-Seq.
REVISED common to each group, as well as possibly identify genes related to sex differences. Keyword search of the Database for Annotation, Visualization and Integrated Discovery (DAVID) version 6. 8  (Huang da et al. , 2009)  table output was used to identify genes associated with biological processes related to APOE, sex, and AD. Venny 2. 0, an online program that compares lists of items, was used to determine the common and unique genes between groups, sex and brain regions.
Statistical analysis Chi square analyses were used to compare the numbers of gene correlates between the APOE4-positive and APOE4-negative groups and between females and males (Microsoft Office Professional Plus, Excel 2013, Version 15). Gene expression patterns of AD risk factor genes [CNTNAP2, PSEN2, APOE, PSEN1, APP, ADAM10, and TREM2] for APOE4-postive and APOE4negative females and males were analyzed by repeated measures ANOVA with genotype and sex as between-subject factors and brain area (FWM, HIP, PCx and TCx) as a within-subject factor. As post hoc test for interactions, Least Squares Means were used (SAS Studio, Release 3. 8, Basic Edition). The relationship between CERAD score, Braak stage, and gene expression patterns of AD risk factor genes in APOE4-females and males were analyzed using Pearson product-moment correlations with the HMISC package in R, version 3.
Gene expression sex differences The differential search function of the RNA-Seq page of the Allen Brain Database was used to find genes that show enrichment between females and males, and between males and females for each group and each brain region to identify sex related differences in gene expression patterns. Genes with a 1. 5-fold difference expression and greater were evaluated. Gene Ontology characterization DAVID was used to obtain functional information based on Gene Ontology (GO) annotations for the gene correlates. Genes associated with a keyword search term related to APOE, AD, and sex were identified and noted. Pathway enrichment KEGG pathway enrichment (using DAVID) was used to further characterize the positive and negative APOE4 gene correlates for each group. Pathways were assessed manually and partitioned based on common themes.
The greatest number of pathways for males were obtained for the TCx-positive correlates and these were related to fatty acid metabolism, amino acid biosynthesis, cell attachment/cytoskeleton, neuro-related processes such as GABA axon guidance, viral infection, and immune function. The HIP positive correlates were related to fatty acid metabolism, amino acid metabolism, Lupus, and viral carcinogenesis. For the HIPnegative correlates two pathways were obtained: alcoholism and RNA transport. For the PCx-negative correlates there were four pathways: spliceosome, RNA degradation, NF-κβ signaling and pertussis. For the other brain regions in males there was one pathway for both positive and negative correlates associated with the FWM: Fatty acid/lipid, carbohydrate metabolism, degradation and glycosphingolipid biosynthesis-lacto and neolacto series (Extended data Workbook 5;  Delprato et al.
Differential gene expression between females and males in APOE4-positive and APOE4-negative groups Genes expressed higher in females. For all groups and for each brain region in the female and male comparison there is one gene, XIST, associated with X chromosome inactivation, that is highly expressed and female specific  (Ji et al. , 2015). For the APOE4-positive group and for each brain region there are several genes that have higher expression values for females over males. Seven of these genes are heat shock proteins, which are involved in the cellular stress response, and chaperones, which assist in protein folding and unfolding: BAG3, DNAJB1, CRYAB1, HSPA1A, HSPA1B, HSPA6, HSPB1. The two highest expressed genes aside from XIST are HSPA1A and HSPA1B. The latter two are present in female datasets but not in males.
Gene expression patterns of AD For the other risk factor genes, only significant differences between brain areas were obtained: APP [F(3,231)=3. 92, Pr=0. 0105] with highest expression in APOE4-positive males for the TCx (Extended data Figure  5 ), ADAM10 [F(3,219)=42. 01, P <0. 0001] with highest expression for APOE4-positive males in the FWM (Extended data Figure  6 ), and TREM2 [F(3,219)=33. 53, P<0. 0001] with highest expression for APOE4-positive males in the HIP (Extended data Figure  7 ).
To analyse the effect of treatment on egg-laying date, we performed two analyses on the data obtained in the field study. In the first analysis, data from the three years were pooled and we performed a linear mixed-effects model analysis followed by a Kenward-Roger approximation ('KRmodcomp' function in the R package pbkrtest  (Halekoh and Højsgaard 2014) ) for testing the significance of fixed effects in mixed-effects models. The full model included treatment, experimental year and their interaction (treatment × experimental year) as fixed effect and bird identity as a random effect. Bird identity was included in the model as some of the birds used in the previous year(s) as control were re-captured and used again in the following year(s). To perform this analysis, we only considered data from the clipped and the control groups as the data relative to the behavioural control group were available for two out of the three experimental years only.
Acknowledgements -We are grateful to  Family Van Boetzelaer van Oosterhout  for kind permission to work in their woodland. We thank  S. Schaper  for help in performing the laparotomies and the animal caretakers for taking good care of the birds in captivity. P. Gienapp gave valuable comments on the statistical analyses and two anonymous reviewers helped improving an earlier version of this manuscript. Funding -This work was supported by the  Netherlands Organization for Scientific research [NWO-VICI  grant to M. E. V. and  NWO-VENI  grant to S. P. C. ]. S. P. C. was also supported by a  Wallonie-Bruxelles International  ( WBI-World) grant. Competing interests -The authors declare no competing interests.
Appropriateness of using reanalyses for ocean transport investigations is particularly relevant to understand large to regional scale climate changes  (Zuo et al. , 2011; Vidar et al. , 2016). Transports are mostly unobserved, unavailable from objective analyses and poorly reproduced by free-model running simulations. Reanalyses have been shown to reproduce the AMOC variability seen in the RAPID data at 26 • N  (Jackson et al. , 2016 (Jackson et al. , , 2018;; Masina et al. , 2017) , although these data are not assimilated. Reanalyses extending back prior to the deployment of the RAPID array may indicate longer term variability, although the changing observational coverage from distinct periods may affect results. Heat and volume transports through the Indonesian Throughflow, which are not continuously observed and are relevant to ENSO monitoring, are another excellent example where ocean reanalyses successfully reproduce anomalous events  (Mayer et al.
, 2019). This implies that ocean reanalyses are increasingly less reliable going further back in time. So, it is crucial to carefully assess their performance, particularly in light of the problems noted in the section "Introduction. " Estimating ocean reanalysis skill and uncertainty can be performed in several ways. First, validating the reanalyses against independent (non-assimilated) datasets, typically available from special campaigns at regional scale (e. g. ,  Koenig et al. , 2017b , for the Arctic Ocean), delayed time reprocessed data (e. g. , drifterderived currents or cross-calibrated tide-gauge measurements) or by using multi-source data to evaluate complex frontal dynamics  (Artana et al. , 2018)  and adopting popular skill score metrics (bias, root mean square error, etc. ). Second, probabilistic reanalyses based on ensemble data assimilation possibly maintained through perturbed forcing and physics, provide probability distribution functions of the ocean state.
, 2015b; Park et al. , 2018) , revealing consistency problems otherwise neglected. In the context of high-resolution ocean reanalyses that approach eddying scales, additional challenging topics, derived by the increase of ocean model resolution, include among others (i) the coupling with tides and waves; (ii) dealing with mesoscale eddies and sharp gradients in properties; (iii) two-way nested analysis schemes (e. g. ,  Barth et al. , 2007) ; (iv) discontinuities at choke points or in key coastal areas; (v) downscaling of atmospheric forcing, and (vi) accuracy of bathymetry. All these aspects call for a close collaboration between the ocean modeling and the reanalysis communities. Furthermore the increasing demand for temporal and spatial resolution by end-users, for instance for ocean downscaling applications, translate into a big data challenge for high-resolution reanalyses, making the dissemination of fullresolution datasets prohibitively expensive with the current data infrastructure paradigms.
On top of all these observational requirements, independent research-quality validation datasets (e. g. , research quality tide-gauge data,  Caldwell et al. , 2015)  must be sustained and made available in standard format (e. g. , ADCP data from vessel-mounted or fixed location instruments to validate currents from reanalyses) to ensure reliable estimation of reanalysis uncertainty. The same applies to the development of independent remote sensing capabilities, both from existing and new satellites, such as the use of Gravity Recovery and Climate Experiment (GRACE) data to verify steric sea level rise in ocean reanalyses  (Storto et al. , 2017) , or the use of IceSat sea ice freeboard measurements to validate sea ice thickness in reanalyses  (Chevallier et al. , 2017; Uotila et al. , 2018).
Emerging multi-scale data assimilation schemes (e. g. ,  Miyoshi and Kondo, 2013)  may further reconcile the mesoscale and large-scale circulation variability, whose simultaneous correction is generally lacking in the current state-of-the-art reanalyses. The increase in resolution also poses the challenge of data infrastructures capable of handling these large datasets; cloud computing services, new format standards, extraction services, etc. , should all be investigated to facilitate access to high-resolution reanalysis datasets. Flow-dependent error characterization for both rapidly evolving processes (marine weather) and low frequency processes (climate modes, changes in observational sampling, etc. ) will likely become an important component of both long-and shortterm ocean reanalyses, inheriting the positive experience from atmospheric reanalyses  (Poli et al.
Candidate MiRNA Selection To identify miRNAs with a potential functional role in TAA, we used a network-based approach (Figure  1D ). First, the literature search and National Center for Biotechnology Information (NCBI) Gene database allowed the identification of 690 genes presumably associated with TAA (Table  S4 ). Second, we interrogated the targetScan, microRNA Target Prediction Database (miRDB) and DIANA microT-CDS databases to predict interactions between the 232 miRNAs identified as differentially expressed between the control and TAA in aortic media using microarrays. 5510 interactions were predicted by at least two databases. Third, we interrogated the IntAct database and we identified 1072 protein-protein interactions between the 690 TAA-associated proteins. A network combining both the 5510 interactions between the 232 differentially expressed miRNAs and the 690 TAA-associated genes, and the 1072 interactions between the 690 TAA-associated proteins, is displayed in Figure  S2.
A network illustrating these interactions is shown in Figure  1E. (C) Heat-map displaying modulated miRNAs between TAA and control samples. A log2-transformed fold change > 0. 5 or < -0. 5, a q-value <5% and a relative expression level > 5 were used as cut-offs. Red color corresponds to miRNAs up-regulated in TAA patients and green color corresponds to miRNAs down-regulated in TAA patients compared to controls. (D,E) MiRNA-gene and protein-protein interaction network. (D) Procedure followed to create the interaction network. 690 TAA-associated genes were identified using the National Center for Biotechnology Information (NCBI) Gene database and literature search. 232 miRNAs were found to be differentially expressed between aneurysmal and non-aneurysmal aortic tissue using microarrays. Interactions between TAA-associated genes and differentially expressed miRNAs were predicted using TargetScan, microRNA Target Prediction Database (miRDB) and DIANA microT-CDS databases.
Only the interactions predicted by at least two databases were considered. Functional protein-protein interactions identified in the IntAct database were also included in the network. (E) MiR-574-5p-gene interaction network. The network shows the association between-miR-574-5p, its predicted gene targets, and the top five biological processes enriched by these targets. Solid line: Interaction between miRNA and the target gene. Dash line: Association between gene targets and biological processes.
This expression pattern was also observed in Fbn1 C1041G/+ mice, a monogenic model that mimics the Marfan syndrome whereas patients had various and highly heterogenous etiologies, suggesting a high implication of miR-574-5p in TAA pathogenesis. According to the NCBI Gene database, the miR-574-5p sequence is localized in the FAM114A1 gene and is highly conserved between human and mice and may suggest conservation of function. However, circulating levels of miR-574-5p were correlated with the aortic diameter of Fbn1 C1041G/+ mice, although they were not associated with the severity of the disease in patients. This discrepancy could be explained by the limited statistical power in human samples or by the heterogeneity of etiologies in patients while the Fbn1 C1041G/+ mouse model represents only one syndromic etiology.
Materials and Methods Microarray data have been made publicly available at the Gene Expression Omnibus under the accession number GSE110527 and can be accessed at https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi. Other data that support the findings of this study are available from the corresponding author upon reasonable request. Following a case-control design, we used two different cohorts of patients, a "tissue cohort" and a "serum cohort". All supporting data are available in the Supplementary Materials. 2008). Aneurysmal ascending aortic specimens were collected during aortic surgery which allowed a visual assessment of the presence of TAA in the ascending aorta, from 2006 onwards. Normal ascending aortas were obtained from deceased organ transplant donors with the authorization of the French Biomedicine Agency (PFS09-007) and in accordance with the Declaration of Helsinki, recovered from 2006 onwards.
Number and Size Distribution of EVs To measure the concentration and size distribution of isolated EVs we used a NanoSight LM10 system (Malvern Panalytical, Malvern, UK). In brief, we diluted EV samples 1:20 with filtered (0. 22 µm) 1× PBS to achieve the instrument's optimal particles per frame range. At the end of each measurement, the pump was washed several times with filtered 1× PBS to avoid cross-sample contamination. The camera settings were adjusted per the manufacturer's instructions, samples were recorded in three repeats of 20 s, and data were analyzed using the NanoSight NTA 2. 3 software (Malvern Panalytical, Westborough, USA).
Bioinformatics Analyses To identify genes known to be associated with the TAA pathogenesis, we used the following keywords in the NCBI gene database: (Thoracic AND aortic AND aneurysm) OR (Destructive AND "extracellular matrix" AND remodeling AND aorta) OR (Elastin AND fragmentation AND aorta AND media) OR (("Vascular smooth muscle cells" OR "Vascular smooth muscle cell") AND (proliferation OR secretion OR migration OR apoptosis)) OR ("Extracellular matrix" AND (homeostasis OR structure)) OR (Inflammation AND "extracellular matrix" AND aorta AND media) OR (Elastogenesis AND aorta AND media) OR (Aorta AND vasculogenesis). The following 12 genes known to be involved in monogenic TAA or dissection were also selected: FBN1; EFEMP2; COL3A1; TGFBR1; TGFBR2; SLC2A10; NOTCH1; JAG1; SMAD3; ACTA2; MYH11; FLNA  [15].
Predicted interactions between differentially expressed miRNAs and TAA-associated genes were obtained using TargetScan [46], miRDB  [47]  and DIANA microT-CDS  [48]. Protein-protein interactions between TAA-associated genes were obtained from the IntAct database  [49]. The interaction network of miRNAs and genes was generated using Polar Mapper  [50]. Enriched biological processes and pathways were analyzed with DAVID, the Database for Annotation, Visualization and Integrated Discovery  [51].
Sequential FISH and Immunofluorescence Microscopy miRCURY-FISH Probe recognizing murine miR-574-5p (YD00616643-BED) labeled with 5'-FAM and 3'-FAM (Qiagen) was hybridized to tissue samples at 65 • C for 4 h, followed by incubation with the F-actin conjugated antibody (ActinGreen™ 488 ReadyProbes, ThermoFisher Scientific), and with CD63, CD9 or Fetuin antibodies. Two-dimensional and white light images were analyzed and quantified using the ImageJ software (version 1. 8. 0_112, National Institutes of Health, Bethesda, USA).