dataesr/scientific-paragraphs-categorization huggingface dataset:
92 Jy at 228. 6 GHz and a spectral index of -0. 59. For the compact configuration, we used Callisto as amplitude calibrator assuming the Butler-JPL-Horizons 2012 model. For the CO(2-1) spectral window, we subtracted the continuum (rest-frame 232 GHz) in the uv plane by fitting the continuum with a constant in the line free channels. Then, we combined and cleaned the data from the two configurations using the CASA clean task. The output frequency reference frame was set to the kinematic local standard of rest (LSRK). We produced three sets of cleaned data For the first one we used natural weight to emphasize the large scale emission of NGC 5643. For the other two we used the Briggs weighting  (Briggs 1995) , with robustness parameters of b = 0. 8 and b = -0. 5 to increase sequentially the angular resolution at slightly decreased sensitivity.
For the b = -0. 5 data set we kept the original spacings of the frequency channel whereas for the b = 0. 8 set we rebinned the data by a factor of 3 in frequency to increase the signal-to-noise ratio. This resulted in an approximate resolution in velocities of 5 km s -1 and CO(2-1) full-width half-maximum (FWHM) of 0. 16×0. 11 with beam position angle (PA beam ) of -67 • for the b = -0. 5 data set, and 15 km s -1 and FWHM of 0. 26×0. 17 with PA beam = -58 • for the b = 0. 8 data set. For the continuum images, the beams FWHM are 0. 16×0. 10 at PA beam = -65 • for b = -0. 5 and and 0. 23×0. 13 at PA beam = -68 • for b = 0. 8. The pixel sizes were set to 0. 03 and 0. 04 to properly sample the beam sizes, for the b = -0. 5 and b = 0. 8 data sets, respectively. The achieved 1σ sensitivities are ∼1.
The line map shows a bright nuclear source, a nuclear two-arm spiral already seen in optical to near-IR color maps as a dusty nuclear spiral extending for several arcseconds  (Quillen et al. 1999; Martini et al. 2003; Davies et al. 2014)  as well as emission in the spiral arms on larger scales. We measure a CO(2-1) line intensity over a region 40 ×40 of 480 Jy km s -1. For comparison the single-dish measurement for a beam size of 30 is 912 ± 60 Jy km s -1  (Monje et al. 2011 ). Thus we recovered approximately 50% of the single-dish observation flux which is typical of this kind of comparisons. We downloaded the pipeline processed data cube from the ESO data archive. We estimate a seeing of ∼0. 5 from the foreground stars in the FoV. Four dithered exposures of 875 s were taken and later combined into a single cube by the pipeline.
Kinematics We used the 3D BAROLO code (Di Teodoro & Fraternali 2015) to model the CO(2-1) kinematics of the central 8 × 8 region of NGC 5643 using the ALMA b = 0. 8 data cube. 3D BAROLO was designed to fit simple disk models using 3D tilted rings to a variety of emission line data, including ALMA data cubes. The fit is done in two steps. During the first step for each ring 3D BAROLO fits the free parameters, namely, the kinematic center, the systemic velocity, the disk inclination and PA of the major axis, the scale height of the In grey scale is the V -H color map produced with HST images  (Martini et al. 2003; Davies et al. 2014)  where dark colors indicate regions with higher extinction. We show the ALMA CO(2-1) emission with two red contours to guide the eye. We also mark with the blue dashed lines the approximate boundary of the ionization bicone (semi-opening angle of 55 • ) and with the yellow dotted line the bicone axis (the cone inclination out of the plane of the sky towards Earth is ∼ 25 • , and ∼ 40 • with respect to the galaxy disk plane), as modelled by  Fischer et al.
(2013). disk, the circular velocity and velocity dispersion. The second step fixes the kinematic center and systemic velocity to the mean fitted values from the first iteration and finds the best solution for the other free parameters. 3D BAROLO also allows the user to fix any of the above parameters during the first iteration. After fitting the data cube, 3D BAROLO creates, among other products, a map of the observed velocity-integrated intensity (0th moment), a map of the observed mean velocity field (1st moment) and velocity dispersion field (2nd moment) as well as the same maps for the best fit model. We first run 3D BAROLO on the CO(2-1) data cube using the disk PA and inclination derived from the nearinfrared stellar kinematics by Davies et al. (2014) over the same FoV allowing for relatively small variations (approximately ±20 • for the disk PA and ±10 • for the disk inclination).
2015). However, the X-ray column is modelled through a narrow pencil beam towards the accretion disk. For type 2 AGN one would expect it to intersect high column clouds in the torus (or even the broad line region). On the other hand, the column measured from our ALMA data is averaged over a larger area which is defined by the beam of ∼ 13 pc × 9 pc of the observations. Another source of uncertainty in this comparison is the assumed CO-to-H 2 conversion factor. Wada et al. (2018)  computed the CO line chemistry from their radiation-driven fountain model fitted to the Circinus galaxy to derive the CO-to-H 2 conversion factor. They demonstrated that this conversion factor depends strongly on the integrated intensity of the line for a given line of sight. For the brightness temperature derived at the location of the AGN or the peak of the CO(2-1) emission of NGC 5643 the predictions for CO-to-H 2 conversion factor for the CO(2-1) line are 5-10 higher than the Galactic value with a large scatter (see their figure  5 ).
A. H. -C. acknowledges funding by the  Spanish Ministry of Economy and Competitiveness  under grant  AYA2015-63650-P. S. F. H. acknowledges support from the  EU/Horizon 2020   ERC Starting Grant DUST-IN-THE-WIND  ( ERC-2015-StG-677117 ). C. R. A. acknowledges the  Ramón y Cajal Program  of the  Spanish Ministry of Economy and Competitiveness  through project  RYC-2014-15779  and  AYA2016-76682-C3-2-P. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2016. 1. 00254. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. Based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere under ESO programme:  095.
The aim of this study is to assess the capacity of the Surface Water Ocean Topography (SWOT) satellite to resolve fine scale oceanic surface features in the western Mediterranean. Using as input the Sea Surface Height (SSH) fields from a high-resolution Ocean General Circulation Model (OGCM), the SWOT Simulator for Ocean Science generates SWOT-like outputs along a swath and the nadir following the orbit ground tracks. Given the characteristic temporal and spatial scales of fine scale features in the region, we examine temporal and spatial resolution of the SWOT outputs by comparing them with the original model data which are interpolated onto the SWOT grid. To further assess the satellite's performance, we derive the absolute geostrophic velocity and relative vorticity. We find that instrument noise and geophysical error mask the whole signal of the pseudo-SWOT derived dynamical variables.
We therefore address the impact of removal of satellite noise from the pseudo-SWOT data using a Laplacian diffusion filter, and then focus on the spatial scales that are resolved within a swath after this filtering. To investigate sensitivity to different filtering parameters, we calculate spatial spectra and root mean square errors. Our numerical experiments show that noise patterns dominate the spectral content of the pseudo-SWOT fields at wavelengths below 60 km. Application of the Laplacian diffusion filter allows recovery of the spectral signature within a swath down to the 40-60 km wavelength range. Consequently, with the help of this filter, we are able to improve the observation of fine scale oceanic features in pseudo-SWOT data, and in the estimation of associated derived variables such as velocity and vorticity.
This phase covers the first 60-90 days after launch, during which the satellite will provide daily high resolution Sea Surface Height (SSH) measurements over a limited repeated orbit for purposes of calibration/validation of the SWOT sensor/instrument. The goal of this study is to assess the capacity of SWOT to resolve the fine scales in the western Mediterranean. Our first objective is to generate pseudo-SWOT data from numerical model outputs in this region in order to understand its temporal and spatial sampling pattern in this area. We then apply a noise-reduction processing technique to pseudo-SWOT data to find out the spatial scales that SWOT may ultimately be able to resolve. Given SWOT's irregular time sampling and consequent variable spatial coverage, in this paper, we focus on the spatial scales resolved within a swath.
This filter is a useful tool for studies comparing the capacities of pseudo-SWOT data with the present altimetric satellite constellation data. As this filter is particularly effective in removing the spatially uncorrelated KaRIn noise, it may allow the application of already developed techniques that more effectively remove other correlated errors  [38]. We find that the presence of structures of different scales and regimes governing-for example, the mesoscale (Ro. order(0. 1)) or submesoscale (Ro. order(1)) affects our results on the efficiency of the Laplacian diffusion filter. This filter is therefore sensitive to the presence of different patterns, depending on the region. To reach even smaller scales, it is important to use filtering techniques that conserve and/or retrieve the gradients and, thus, the intensity of the signal present in the observed field.
A covariance model with a large correlation length may lead to an over-smoothing of fine-scale structures, whereas shorter correlation lengths result in filling large missing areas with the background field. From a computational point of view, OI requires a matrix inversion, whose complexity evolves as the cube of the number of observation points. The image-like structure of SWOT data may then be highly computationally-demanding when considering large correlation lengths to fill in large missing areas. Multi-scale OI may be an alternative. However, we expect dynamical interpolation and other data assimilation methods  [40] [41] [42]  to be more adapted both in terms of computational complexity and in their ability to embed relevant dynamic priors to reconstruct horizontal scales down to a few tens of kilometres from SWOT data.
On the other hand, SWOT data will greatly improve the present-day OI altimetric products  [5]. In addition, SWOT gridded data could be improved in the 40-60 km wavelength range by combining it with the data of a higher temporal resolution. Dynamic interpolation is an example of a technique that has been investigated by  [40]  that could help to obtain gridded, daily SSH maps from SWOT. When they apply this method to the Gulf Stream region, they recover the SSH field down to 80 km wavelength. Data-driven schemes recently introduced by  [41, 42]  are also of interest to better reconstruct horizontal scales below 100 km. Overall, for such approaches, it is important to recover the lowest wavelengths possible as spatial resolution loss is likely when producing the gridded maps. Moreover, this spatial resolution loss might be even higher when addressing gridded maps of derived variables.
Therefore, the cut-off wavelength should be adjusted to the variables that are to be studied. Another reconstruction technique that has been investigated in the context of SWOT is a 3D multivariate reconstruction of ocean state. Ref. [43]  do this by combining information from SSH and high resolution image structure observations. Once this is achieved, study of the capacity of SWOT to detect fine scale structures could be improved by, for example, better characterizing eddies. As the dataset would be of a higher spatial and temporal resolution than the L2 product, it would then be possible to apply eddy-tracking algorithms like the py-eddy tracker  [44]  or the code developed by  [45] , which have already been implemented in this region to characterize the western Mediterranean eddy field.
A comparison could then be made with the eddies characterized in data from the WMOP model, in the presently available altimetric data and in pseudo-SWOT data. In future work, the effect of the inter-and intra-annual (or seasonal) variability in the region on the results obtained could be studied too. Although mean spectra were obtained, we focused on two dates in winter. Refs. [46, 47]  found that there is a strong winter-summer difference in the upper ocean dynamics due to the change of stratification, with the mixed layer depth being deeper in winter. For example, the reconstruction of mesoscale structures in the upper ocean from pseudo-SWOT data in the Kuroshio Extension region has been studied. They found that the simulated and reconstructed vorticity correlation coefficients varied both inter-and intra-annually  [48].
The implementation of filtering techniques that take into account the first and second order SSH derivatives has been started. With this, we hope that in future studies we will be able to recover even smaller wavelengths and to conserve the intensity of the signal after having applied the filter. New versions of the SWOT simulator will allow the simulation of pseudo-SWOT data during the fast-sampling phase. This makes it possible to start preparing for the calibration/validation phase including the comparison with high resolution in situ data collected during future intensive multi-platform experiments in the western Mediterranean Sea. On the other hand, the only source of geophysical error implemented in the SWOT simulator is still just that related to the wet troposphere. New releases of the SWOT simulator may include the effects of sea state and internal tides  [49].
Conclusions We have generated simulated pseudo-SWOT data for the western Mediterranean Sea using a SWOT simulator and outputs from an ocean numerical model. To evaluate the output SWOT data, we derived absolute geostrophic velocities and relative vorticities from the pseudo-SWOT SSH data. We find that, due to the satellite's instrumental noise and geophysical errors, the features observed in the pseudo-SWOT SSH are lost in the derived dynamical variables. Looking at the spatial spectra, we find that noise dominates the signal at wavelengths smaller than 60 km. We applied a Laplacian diffusion filtering technique to attempt to remove the noise and hence observe finer scales. We estimated the appropriate cut-off wavelength for each parametrization. To filter out the noise, we applied a series of ascending cut-off wavelengths: 15, 30, 40, 50, 60, 100 and 200 km.
We find that in this study region, using this technique, we cannot resolve the expected 15 km wavelength. On the other hand, we are able to recover the signal within a swath down to a 40-60 km wavelength range. This is still an improvement in comparison to wavelengths resolved by present-day 1D altimeters, especially at eastern basins. Robust swath-filtering is an important first step towards meeting our goals for reconstruction techniques that will enable us to combine SWOT and altimetric data in order to produce gridded SSH maps of significantly higher resolution than contemporary products. New versions of the SWOT simulator code include improved representation of instrumental and geophysical errors, and also give us the option to obtain pseudo-data for the SWOT fast-sampling phase. New pseudo-SWOT data will allow us to better refine the results of this study and to examine a wider range of scenarios.
Acknowledgments: The research leading these results has received funding from the  Sea Level Thematic Assembly Center (SL-TAC) of the Copernicus Marine and Environment Monitoring Service (CMEMS)  and from the  Centre National d'Études Spatiales (CNES)  through  Ocean Surface Topography Science Team (OST/ST)  project  MANATEE. L. Gómez-Navarro  acknowledges  CNES  and FP-7 PhD funding. This work was supported by a  Short Term Scientific Mission (STSM)  grant from  COST Action ES140. E. Mason  was supported by the  Copernicus Marine Environment Monitoring Service (CMEMS)   MedSUB  project. E. Cosme  and  J. Le Sommer  are supported by the  CNES  through the  OST/ST  and the  SWOT Science Team. The WMOP simulation used in this study was produced in the framework of the  MEDCLIC  project funded by  "La Caixa" Foundation.
Comparison with micromagnetic simulations We have carried out micromagnetic simulations in order to confirm the predictions obtained using our analytical model and to estimate the impact of our approximations. The simulations have been performed using the Mumax3 open source software from Ghent University  [55]. The skyrmion stability under an applied external magnetic field has been computed after discretising the system into orthorhombic cells (finite difference approach). Periodic boundary conditions has been used to limit size effects and the damping coefficient is set to 0. 5 to speed up convergence (no magnetization dynamics). The system is a 2048 x 2048 nm 2 square box with a mesh size of 0. 5 x 0. 5 x 0. 5 nm 3 , in which a nanometer sized Néel-type skyrmion is first relaxed. Once the skyrmion is initialized, a 5 mT perpendicular magnetic field is applied and reduced in steps of 0.
Molecular characterization, amplification of rRNA ITS gene fragments and sequencing For molecular identification of the isolated microorganisms, cells were grown in liquid SDA medium for 48 h at 30 ˚C and 130 rpm. 2 mL of the solution were sampled and the DNA was extracted using alkaline lysis  [33]. The rRNA ITS gene fragments which are the DNA-barcode established for fungi  [34]  were amplified using the primer pair ITS1 and ITS4  [35]. PCR was performed as described in  White et al. , 1990. Before sequencing the amplified products, the DNA was purified using the innuPREP-PCR pure Kit (Analytik Jena, Jena, Germany), and eluted in double-distilled water according the instructions of the manufacturer. The purified PCR products were sequenced by GATC (GATC Biotech AG, Konstanz, Germany). The retrieved rRNA ITS sequences were compared with sequences available in the non-redundant nucleotide database of the National Center or Biotechnology database (http://www.
Uranium removal capacity of KS5 In order to determine the influence of KS5 on the immobilization capacity of uranium at natural conditions, uranium removal studies were performed at pH 5. 0. Several previous studies demonstrate that the optimum pH value for uranium immobilization by fungal biomass is between 4. 0 and 6. 0  [61] [62] [63]. Due to the relation of biosorption to the number of negative surface groups, which is depending on the dissociation of these functional groups, this phenomenon could be explained. The majority of the functional groups at microbial membranes become dissociated only at neutral pH values, thus only a low amount of metal ions retained at microbial sorbents at pH values below 4. 0  [62]. Furthermore, at pH 5. 0 uranium was predicted (data not shown) to be soluble at the prevalent conditions within the tap water.
Acknowledgments This work was supported by the  Bundesministerium fu ¨r Bildung und Forschung (BMBF)  grand n˚02NUK030F (TransAqua). Further support took place by the ERDF-co-financed Grants  CGL2012-36505  and  315 CGL2014-59616R  ( Ministerio de Ciencia e Innovacion, Spain :  80%  funded by  FEDER. The authors are grateful to the  Wismut GmbH  for allowing access to the sampling site,  S. Ro  ¨der for assistance in water sampling,  S. Gurlit  and  S. Schubert  for ICP-MS measurements,  S. Weiß  for his assistance during preparation of EXAFS samples,  N. Huittinen  for her support by the interpretation of the EXAFS data, and  T. Schneider  for his grammatical correction of this manuscript. We thank  J. Lazuen-Alcon  ( Flow Cytometry Service, Centro de Instrumentacion Cientı ´fica University of Granada, Spain ) for his help with flow cytometry measurements.
Results: The literature search resulted in 28 articles (25 articles/4975 patients for the incidence comparison and three articles/166 patients for the treatment comparison). In the incidence comparison, heterogeneity was present in different subgroups. Administration of dexmedetomidine was associated with significantly lower overall incidence of delirium when compared to placebo (RR 0. 52; 95% CI 0. 39-0. 70; I 2 = 37%), standard sedatives (RR 0. 63; 95% CI 0. 46-0. 86; I 2 = 69%), as well as to opioids (RR 0. 61; 95% CI 0. 44-0. 83; I 2 = 0%). Use of dexmedetomidine significantly increased the risks of bradycardia and hypotension. Limited data were available on circulatory insufficiency and mortality. In the treatment comparison, the comparison drugs in the three RCTs were placebo, midazolam, and haloperidol.
Data extraction Study-relevant information was extracted by two independent investigators (JF and AH) for each included RCT. Any conflict of opinion was resolved by consensus with a third reviewer (MS). Interventional (dexmedetomidine) and control drugs, publication date, study location and date of study conduct, as well as patient characteristics, total number of patients, and conducted procedure were considered relevant for data extraction. Control drugs were divided into three groups: placebo, standard sedatives (including propofol, midazolam, and lorazepam), and opioids (including morphine and remifentanil). Delirium assessment tools (e. g. , confusion assessment method for the ICU = CAM-ICU or the intensive care delirium screening checklist = ICDSC) were assessed. Furthermore, we recorded if delirium was assessed as the primary, a secondary, or no specific endpoint.
Although our inclusion criteria focused only on the incidence of delirium, we also conducted further analyses of other patient-relevant factors, such as adverse events (i. e. , mortality, bradycardia, tachycardia, hypotension, hypertension, and circulatory insufficiency) and clinical outcome data (i. e. , ICU length of stay, and time to extubation or duration of MV). These analyses have only an explorative character and raise no claim to completeness. According to whether dexmedetomidine and control drugs were applied for treatment or prevention of ICU delirium, included trials were assigned to incidence comparison or delirium treatment (treatment comparison) and analyzed according to their affiliation. For assessment of adverse events, we also considered studies reporting adverse events during anesthesia and subsequent intensive care sedation to increase the reliability of the study since only a few studies reported adverse events during intensive care sedation with dexmedetomidine.
Risk of bias assessment Risk of bias for each included RCT was assessed using the Cochrane Collaboration's tool  [25]. Two investigators (JF and BS) reviewed each RCT for risk of bias considering the following five key domains: (1) random sequence generation (selection bias); (2) allocation concealment (selection bias); (3) blinding of participants and personnel (performance bias); (4) blinding of outcome assessment (detection bias); and (5) incomplete outcome data (attrition bias). Each domain was judged as "low risk, " "high risk, " or "unclear risk" of bias. Any disagreement in opinion was resolved by consensus. We estimated the overall risk of bias for a RCT as low if the risk of bias was low in all key domains, as unclear if the risk of bias was unclear in at least one key domain, and as high if the risk of bias was high in at least one key domain.