dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The taxonomic history of Aspergillus series Versicolores reflects the evolution of the means used to better characterize it. The use of multiple lines of evidence, including morphological, molecular, and chemical data, has led to a more nuanced understanding of the diversity within this group of fungi, resulting in the four lineages proposed in the last taxonomic revision of 2022  [17]. MALDI-TOF MS represents a reliable and efficient method for the identification of Aspergillus series Versicolores. This technology can improve the speed and accuracy of routine fungal identification in microbiology laboratories. The implementation and availability of this database, through its possible inclusion in the update of the supplier's database, will help improve microbiological diagnosis and assess the exact role of these species in an environmental and clinical context.
Eligibility criteria An extensive literature search was performed using recognized life science and biomedical electronic databases and by manually searching reference lists of the articles which specifically investigated the effects of WBV or RVE on blood coagulation and related biomarkers, as well as on related variables such as blood vessels, blood flow and endothelial function. No language, publication date, or publication status restrictions were imposed. This search was applied to the following electronic databases: PubMed, Web of Science, Cochrane. The latest search was performed during April 2022. The Population, Interest, Control, and Outcome (PICO) table used to define the present research criteria is shown in Table  1 , along with the keywords. The population to be studied was chosen as "healthy subjects"; studies on diseased populations or animal studies were excluded.
Characteristics of included studies The systematic search retrieved 801 articles which were screened for duplicates and appropriateness using the Rayyan online platform  36. After the initial screening, 77 articles were retained, 33 of which were included in the study. The most common reason for papers to be excluded was that vibrations were applied only on small parts of the body and because of wrong outcomes. The full screening flow is shown below in Fig. 1. Twenty-five papers were categorized as "short-term intervention" WBV or RVE, and 8 as "long-term intervention" WBV or RVE studies. The characteristics of the studies considered eligible for inclusion are summarized in Tables  2  and 3 , respectively. All 33 studies included in this review contained original data and were published in English.
RVE studies (n = 18) included isometric and dynamic squats, push-ups, and heel raises with a duration between 30 s and 15 min, or exercise series up to 8 × 60 s (see Table  2 ). Data of circulating blood markers, functional/ hemodynamic markers, and skeletal muscle oxygenation markers were extracted. The eight "long-term intervention" studies were published between 2013 and 2020 and included a total of 144 participants (97 males and 47 females with an age range of 20-81 years). Five different vibration platforms were utilized. The most commonly utilized instruments were the simultaneous side-alternating whole-body vibration platforms Galileo Fitness, Novotech (Pforzheim, Germany) and the vertical vibration platform, Fitvibe, Gymna Uniphy NV (Bilzen, Belgium). The frequency and amplitude (peak-to-peak displacement) of the vibrations ranged from 20 to 40 Hz and from 2 to 6 mm, respectively (for more details, see Table  3 ).
2021)  to further refine the quality of the images in the direction of the target. The final fully calibrated uv dataset only includes sources within the extraction region, and it can thus be easily manipulated in further analysis. Imaging is carried out by excluding baselines shorter than 80λ; these spacings are typically more challenging to calibrate and sample the possible emission on angular scales larger than ∼40 ′ from our Galaxy that needs to be filtered. In this work, we focused on a sample of 140 non-detections (NDEs) of diffuse emission from the ICM in Planck clusters belonging to LoTSS-DR2 (see  Botteon et al. 2022 for details). According to our definition, diffuse emission from the ICM does not include emission associated with radio galaxies (e. g. lobes, filaments, tails, fossil bubbles) that may be present in the NDE clusters.
Injection algorithm To test the capabilities of LOFAR and determine upper limits for the NDE clusters, we followed the injection 4  technique. We first derived the visibilities from the Fourier inversion of a set of models of mock halos ('prediction' step) sampling a range of flux densities and angular sizes, and then added these mock visibilities to the dataset of each observation ('injection' step). Imaging and Fourier transforms were carried out by means of WSClean v. 2. 10  (Offringa et al. 2014; Offringa & Smirnov 2017). The main steps of the procedure can be summarised as follows: 1. 64] pixels) and multi-frequency (channels-out 6) cleaning algorithm. To enhance the diffuse emission, the baselines are tapered by a Gaussian function (taper-gaussian) to lower resolutions. An example of this process is shown in Fig.
Interacting with these nodes when running the pipelines is necessary to efficiently inspect intermediate results. To this aim, extra work in terms of setting up tunneling and virtual displays forwarding is required; moreover, advanced pipelines (such as those for LOFAR analyses) rely on complex dependencies which often require system-dependant setup steps. To overcome these issues, we made use of Rosetta  (Russo et al. 2022 ), a container-centric science platform for interactive data analysis, which was recently made available for accessing the HOTCAT cluster. Rosetta can automatically set up interactive analysis environments (such as remote desktops and Jupyter Notebooks) on HPC cluster nodes by using software containers, thus allowing us to interactively access the cluster nodes and efficiently manage the dependencies of our pipelines.
Schemes of injection As examples, in Figs. 2, 3 we show the images before and after the injections of mock halos with r e = 40. 7 ′′ and S inj,tot = 20, 50, and 100 mJy that we performed both close to the pointing centre of PSZ2 G120. 08-44. 41 and far from it (at a distance of ∼19 ′ ) using the extracted and re-calibrated datasets ('Inj. ' scheme). As preliminary checks of our procedure, we fitted the surface brightness of the mock halos (discrete sources close to the mock emission were masked) by means of the Halo-FDCA 5 code  (Boxelaar et al. 2021) , which was used for radio halos in LoTSS as well  (van Weeren et al. 2021; Botteon et al. 2022; Hoang et al. 2022) , and we measured the flux density in circles of radius r = 3r e , where a fraction f (r) = 0. 80 of the total injected flux density is expected according to Eq.
within the fitting errors. These tests indicate that our simulations are barely dependent on the position of the injection, meaning that the response of the instrument can be considered uniform, at least across the extraction regions. Background, foreground, and embedded sources contaminate the faint diffuse emission of radio halos. A model of the discrete sources can be obtained by selecting only the longest baselines, depending on the angular scales that need to be filtered out, and their contribution can be then directly subtracted from the uv data. We compared the results of the 'Inj. ' scheme with the injection in source-subtracted datasets ('Sub. & Inj. ' scheme) and the effects of subtraction after injecting in the original datasets ('Inj. & Sub. ' scheme). To this aim, following the approach described in  Botteon et al.
This latter result has a major practical utility for our work; the subtraction can be performed only once, thus allowing us to save a huge amount of computing time, and different injections can be carried out in the same source-subtracted dataset, with no need to subtract sources after each injection (see also Sect. 5. 2). We conclude this section by commenting on the recovered morphology of mock halos. For very bright mock halos, the spherical and smooth profile is typically recovered. On the other hand, in the case of less bright mock halos, the roundish and smooth shape can be easily perturbed depending on the local noise pattern and effective sensitivity of the observations. By gradually decreasing the injected flux density, the symmetry is progressively broken, and the mock halo will appear as patches of emission around the peak.
This behaviour can be seen in Fig. 3. Even though the recovered morphology depends on the specific dataset and injection position, we can quantify the relative deviations as a function of radius and injected flux density through a point-to-point comparison. To this aim, we considered our brightest injections at 100 mJy (with the 'Sub. & Inj. ' scheme), and sampled the corresponding mock halos with a grid of beam-size square boxes down to the 2σ contour level. The same grid was used to sample the mock halos of S inj,tot = 50 and 20 mJy as well. The surface brightnesses at 20 and 50 mJy were normalised to match the injection at 100 mJy (i. e. they were multiplied by a factor of 5 and 2, respectively), and the corresponding measures were reported in Fig. 4  as a function of the brightness at 100 mJy.
The role of the uv coverage When dealing with extended radio emission, the role played by the uv coverage needs to be carefully taken into account. Indeed, interferometers lacking short baselines will not be able to fully recover extended sources in the sky. As a consequence, non-detections might arise from insufficient sampling of the uv coverage at short spacings. In previous works, LOFAR uv coverage was tested on the basis of injections on specific observations  (Hoang et al. 2018; Botteon et al. 2020). Here, we aim to systematically investigate the associated flux density losses by performing ∼500 injections in ∼10 cluster fields characterised by different data quality and noise. In each of these clusters, we injected mock halos with various flux densities (ranging from 5 to 145 mJy) and angular diameters (3, 5, 7.
789 and q = -0. 091) without performing any additional injection. To this aim, values of σ and N beam are required, which can be obtained from images of the original datasets and with a choice of r e. Bearing in mind that the mean and median e-folding radius of the PSZ2 radio halos in LoTSS-DR2 are re = 194 kpc (with a standard deviation of 94 kpc) and re = 186 kpc, respectively, we assumed a nominal r e = 200 kpc for all the NDE objects to derive the corresponding angular diameter (D = 6r e ). With this choice, the sizes of our mock halos are in the [2. 6 ′ -16. 7 ′ ] range. The distribution of the angular diameters is shown in the lower panel of Fig. 9 , where the mean and median are D = 5. 7 ′ and D = 4. 9 ′ , respectively; 82% of our clusters have D < 7. 5 ′ , whereas only two clusters have D > 14 ′.
Upper limits and radio halos Uncertainties in the beam model of LOFAR HBA can introduce offsets in the flux-density scale when amplitude solutions are transferred from the primary calibrator to the target (e. g. Hardcastle et al. 2016). Therefore, all images need to be multiplied by a factor of f LoTSS (of the order of unity), which is derived for each pointing after the data calibration (see  Botteon et al. 2022; Shimwell et al. 2022)  to align the flux density scale of LoTSS with that of  Roger et al. (1973). As our injections are performed in the uv data, we have to take into account the flux density scale correction by multiplying S UL at 144 MHz by f LoTSS. Our upper limits will be exploited in statistical analysis in other papers of the series  (Zhang et al. 2023; Cassano et al. 2023, and Cuciti et al.
Comparison with uGMRT and JVLA capabilities To date, studies of radio halos have been mainly based on observations carried out with the upgraded Giant Metrewave Radio Telescope (uGMRT) and the Karl G. Jansky Very Large Array (JVLA). To quantitatively compare the capabilities of these instruments with LOFAR, we injected mock halos with S inj,tot ranging from 20 to 300 mJy in LOFAR (8 h observation), uGMRT (band 3 at 400 MHz, 6 and 10 h observations), uGMRT (band 4 at 700 MHz, 9 h observation), and JVLA (L band in combined DnC and BnC array configurations at 1. 5 GHz, 8 hr on source in total) datasets. Even though the minimum baseline lengths of uGMRT and JVLA are similar to the inner uv cut considered for LOFAR at 80λ, the density of the uv coverage at short spacings is notably different for each dataset.
As in Fig. 6 , we obtained the recovered flux density by considering the difference between S post and S pre. Sources were not subtracted to avoid the introduction of heterogeneous artefacts of subtraction at the various frequencies. In Fig. 14   fitted slope and intercept from  Cuciti et al. , in prep. )  and re-scale its flux density based on the required angular diameters and frequency; the corresponding flux density is indicated by a vertical dashed line in Fig. 14. Our estimates on the effective recovered fractions are listed in Table  3  (due to large differences, for uGMRT band 3 we reported both the minimum and maximum values, obtained with 6 and 10 h observations, respectively). Obtaining more accurate assessment of the performances for uGMRT and JVLA requires systematic injections in many datasets, as done for LOFAR, but this is beyond the scope of this work.
However, important conclusions can be drawn through our simulations. Owing to the high number of short baselines of LOFAR, the 8 hr LoTSS pointings ensure similar densities of the uv coverage, independently of the specific observation. We found that uGMRT can provide very high performance levels in recovering extended emission, but the recovered trends are remarkably different for the uGMRT datasets that we considered. Indeed, Figs. 14 and 15 indicate that the density of uGMRT uv coverage is more dependent on the specific observation (e. g. total observing time, bandwidth, declination of the target, level of interference, flagging); in our tests, the lowest performances were obtained with the 6 hr band-3 dataset, which has not only the shortest duration, but also the highest number of flagged antennas, which largely contribute to compromise the recovery of the mock halos.
Summary and conclusions LoTSS-DR2 includes 140 non-detections of radio halos in Planck clusters  (Botteon et al. 2022). We exploited these data to test the instrumental capabilities of LOFAR to recover diffuse extended emission and determine upper limits to the radio power of a possible halo. Through the injection of mock visibilities simulating radio halos into the observed datasets, we estimated the flux density losses due to insufficient short baselines. We find that they are negligible (≲5-10%) for sources of sizes up to D = 14 ′ and reach fractions of ∼20% at D = 18 ′. As common for LOFAR HBA data, our simulations were limited to baselines >80λ as well, meaning that more flux density can be recovered by including shorter baselines. For the first time, we systematically carried out tests on a large sample of datasets of varying quality, demonstrating that LOFAR is one of the facilities with the densest uv coverage in its inner part.
Acknowledgements. We thank the referee for useful comments and suggestions that have improved the presentation of the paper. LB acknowledges  Kamlesh Rajpurohit  for providing us the JVLA data. GB and RC acknowledge support from  INAF   mainstream project 'Galaxy Clusters Science with LOFAR' ,  1. 05. 01. 86. 05. A. Botteon  and  R. J. vW. acknowledge support from the  VIDI research programme  with project number  639. 042. 729 , which is financed by the  Netherlands Organisation for Scientific Research (NWO). V. C. acknowledges support from the  Alexander von Humboldt Foundation. A. Bonafede  and  A. Botteon  acknowledge support from  ERC Stg DRANOEL  no. 714245  and  MIUR   FARE grant  ' SMS '. M. B. acknowledges funding by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's  Excellence Strategy -EXC 2121 'Quantum Universe ' -390833306.
The coordinates (RA inj ; Dec inj ) of the centre of the injection and the total injected flux density at 144 MHz (S inj,tot ) are 17 h 19 m 54 s 48 s 42 s 36 s 30 s required as inputs. 2. Frequency-dependent model images of the mock halo that follow an exponential surface brightness profile are built (see Sect. 3. 1). We assumed a spectral index α = 1. 3 in the 120-168 MHz frequency range, which is typical of radio halos. This is computed through the channels-out 6 parameter in WSClean, which produces model images at six sub-bands separated by 8 MHz each. 3. The model images are Fourier transformed by means of the predict function in WSClean to obtain the corresponding mock visibilities, which are then added to the real uv data. 4. The updated (real plus mock visibilities) datasets are imaged by adopting the multi-scale (fixed scales of [0, 4, 8, 16, 32, A41, page 2 of 20 Bruno, L.
0 [cm s - 2  ], v micro = 1 km s - 1  , and T eff = 3500 K  (Gustafsson et al. 2008). The line list was generated with the Vienna Atomic Line Database 1 (VALD,  Ryabchikova et al. 2015) , and contained 3240 lines in the range 350-1080 nm and with depths larger than 40% the continuum level, similarly to  Morin et al. (2008)  and  Bellotti et al. (2022). The number of lines takes the removal of the following wavelength intervals into account, which may be affected by residuals of telluric correction or are in the vicinity of Hα: [627, 632],  [655. 5, 657] ,  [686, 697] ,  [716, 734] ,  [759, 770] ,  [813, 835] , and  [895, 986]  nm. Along with Stokes I and V profiles, we computed the 'null profile', which is a powerful diagnostic tool to determine the noise level of the LSD output and whether a spurious polarisation signal is present in the observations  (Donati et al.
In practice, we used the cpnest package (Del Pozzo & Veitch 2022) which performs Bayesian inference via a nested sampling algorithm  (Skilling 2004 ). k(t, t ) = θ 2 1 exp         - (t -t ) 2 θ 2 2 - sin 2 π(t-t ) θ 3 θ 2 4         + S 2 δ t,t , (3) The results are reported in Table  2  and shown in Fig. 3. We applied uniform priors to all five hyperparameters, and allowed the search within realistic boundaries. The model fits the data to a χ 2 r of 0. 6, likely indicating that our formal error bars are overestimated. Following  Donati et al. (2023) , we re-scaled the error bars by a factor of two to fit the data at χ 2 r = 1. 0, while keeping the excess of uncorrelated noise consistent with zero (see Fig. 3 ). The GP model is characterised by smooth oscillations (i. e. θ 4 = 1.
Discussion and conclusions In this paper, we have presented an analysis of the large-scale magnetic field of the exoplanet host star GJ 436. This will serve as input for the stellar wind and star-planet interaction analysis which will be presented in a future paper  (Vidotto et al. 2023). The main goal is to understand stellar environments around M dwarfs, which is relevant for both exoplanet searches and habitability assessment frameworks  (Vidotto et al. 2013; O'Malley-James & Kaltenegger 2019; Lingam & Loeb 2019). Ultimately, this will provide insightful feedback on the influence of stellar magnetic fields on planetary atmospheres and habitability, which is of crucial importance for JWST and Ariel, since GJ 436 is in the reference sample of both missions  (Edwards & Tinetti 2022). We used spectropolarimetric data collected with Narval in 2016, and we computed the longitudinal magnetic field from the time series of circularly polarised spectra.
Acknowledgements. We thank the anonymous referee for the fruitful review of this work. We acknowledge funding from the  French National Research Agency (ANR)  under contract number  ANR-18-CE31-0019  (SPlaSH). S. B. acknowledges funding from the  SCI-S department of the European Space Agency (ESA) , under the Science Faculty Research fund  E/0429-03. R. F. acknowledges support from the  United Arab Emirates University (UAEU)  startup grant number  G00003269. This work has been carried out within the framework of the NCCR PlanetS supported by the  Swiss National Science Foundation  under grants  51NF40_182901  and  51NF40_205606. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (projects   Spice Dune and ASTROFLOW , grant agreements No.
947634  and  817540 ). This work has made use of the VALD database, operated at  Uppsala University , the  Institute of Astronomy RAS in Moscow , and the  University of Vienna ; Astropy, 12 a community-developed core Python package for Astronomy (Astropy Collaboration 2013, 2018);  NumPy  (van der Walt et al. 2011);  Matplotlib : Visualization with Python (Hunter 2007); SciPy (Virtanen et al. 2020) and PyAstronomy (Czesla et al. 2019).
To measure HRV, we used the BIOPAC Data Acquisition unit ampli er (MP35; BIOPAC Systems, Inc. , Goleta, CA, USA) and AcqKnowledge BIOPAC acquisition software installed on a separate laptop. This system measures the electrical impulses that cause the heart to beat using sensors placed on the front of the participants' wrists. The signal is then transmitted to a computer which calculates heart rate and HRV using acquisition software. The electrocardiographic signal was band-pass ltered between 0. 5 and 35 Hz with a notch lter at 50 Hz using BIOPAC acquisition software. R-R intervals were calculated and then visually inspected by an unbiased experimenter to validate the experimental hypothesis. HRV quanti cation was performed according to the guidelines in the Kubios software manual  [54]. To eliminate very low frequency components, R-R intervals were detrended below 0.
It uses diodes to send infrared light at a frequency of 500Hz towards the centre of the participant's right pupil. This light is re ected on the cornea and then detected by the Eye Link's infrared camera. The use of such a device requires calibration before each session, during which the participant must to follow with his or her eyes the movement of a target that appears on the screen in front of him or her. The head is placed on a chin rest to limit as much as possible the movements that create artefacts in the data collected. Pupil dilation is a time-dependent continuous variable. In order to examine the changes in pupil dilation based on the stimulus nature  [55]  , the average pupil area dilation (measured in arbitrary units by Eyelink) relative to the baseline (stimulus onset) was extracted for each video.
Procedures The participants were volunteers recruited at the University of Lille (France) through advertisements. They received a letter of information describing the experimental setup and explaining the study. Participants were then asked to come to the experiment and were instructed to abstain from consuming any psychoactive substances such as coffee, tea, tobacco, or alcohol for one hour prior. The experiment took place in the morning (between 10 am and 12 pm) or in the afternoon (between 2 pm and 4 pm) in the SCALab experimental box. This timing was chosen to minimise the in uence of circadian rhythms on the data collected. The temperature in the laboratory was kept constant at 24°C. Once the participants signed the informed consent form, the experiment could begin. For the emotional, sympathetic response, pupil dilation was recorded within a short time window of 2 to 5 seconds after the onset of the stimulus.