dataesr/scientific-paragraphs-categorization huggingface dataset:
A robust pipeline that can be used in high-throughput conditions While other 3D + t phenotyping pipelines have already been proposed, they were mostly tested on a few number of plants and time points, for early growth stages (ca. 5-10 time points and 5-10 plants). Instead, the pipeline presented here was tested on a dataset of 355 plants of various genotypes grown under different environmental conditions. Each plant was observed through a time-series of ~ 43 time points covering all growth stages, making a total of 237,600 images analysed. Most traits were validated on a subset of only 30 plants due to time-consuming annotations, but leaf stages outputs were evaluated on the full dataset of 355 plants (see Fig. 6B-C ). Other traits outputs are shown on Fig. 7  for the entire dataset of late harvesting plants, and overall exhibit coherent patterns for all ranks and growth stages.
All these results suggest that this pipeline can be used to phenotype large panels of maize plants. In this study, Phenotrack3D was tested on dense (average frequency of 24 h), high quality 3D acquisition (isolated plants to avoid occlusions), which is typical of image phenotyping in controlled conditions. This type of data is the primary target for Phenotrack3D and only minor adaptations in data preparation are expected to allow re-using our method in such conditions. Some methods using Terrestrial LiDAR Point Cloud were also successful at segmenting organs in field conditions (maize grown at   [54, 55] , hence producing data that could fit the requirement of Phenotrack3D, despite the lower quality of the segmentation of individual plants. The main issue would be the precise localisation of stem tip, since collars may not be visible in point clouds, or the availability of an alternative method to perform the separation between the mature and the growing part of the plant.
This pipeline is fully automatic and works smoothly with available automatic reconstruction-segmentation pipelines, such as Phenomenal  [16]. It could therefore be used to measure phenotypic traits on thousands of plants in HTP platforms, with sufficient accuracy to compare the development and architecture of various GxE interactions along the entire growth cycle. LCB reviewed and edited it. All the authors have approved the manuscript and have made all required statements and declarations. All authors read and approved the final manuscript.
June 2021 For the large June imaging data set, all ten imaging runs exhibit flux at the same location to the south west of Sgr A*. In five instances, this flux is detected in a single pixel with high significance (>5σ). Its position scatters by at most a pixel. In the other five instances, the flux is spread out over several connected pixels, which exhibit larger flux variations between the individual samples. Since the source has moved further away from the field center and is more strongly affected by fiber damping than it was in May, the question arises why no similar multi-modality of the posterior is observed. On June 24, we have a much better A82, page 8 of 24  (u, v)-coverage and the dirty beam pattern becomes considerably smoother without the isolated, strongly peaked side-maxima that induce the misplacement in the May images.
Images for the mosaicing data set To produce images for the mosaicing data set, we used the exact same strategy as for the Sgr A*-centered exposures. The pointing directions and the known stars in the field are summarized in Fig. 4. In Table  2 , we list all stars that we model as point sources with a Gaussian position prior and the anchor, whose positional variance we set to zero in order to break the translation invariance inherent to closure phases and visibility amplitudes (cf. Sect. 2. 3). In contrast to the Sgr A*-centered images and with exception of the S2 pointings, we now have fewer than ten exposures available for each individual image. We thus expect (and also go on to find) that the solutions become more noisy and that there is a greater variability between the ten runs which we perform for each data set.
Sensitivity estimation As a first step to estimate the sensitivity in our images, we performed a series of injection tests that are reported in Appendix F. They consider sources at two different locations and four magnitudes, between 19. 7 and 22. 7, which are inserted into the May 29 data set. The ability of G R to recover the injected source depends on its position. In the first scenario, the new star is located close to S300, and we managed a high-significance detection A82, page 9 of 24 Fig. 5. Images obtained for the mosaicing data set, where north is up and east to the left. The flux is normalized by S29 in the mid and northwest pointings and by S38 in the rightmost panel. Images are computed as sample mean over all samples of a single G R run and have been convolved with a Gaussian of 1.
Confirmation of S300 with CLEAN So far, the standard for deep imaging of the GC with GRAVITY has been set by CLEAN (GRAVITY Collaboration 2021a), and comparing both methods is important to judge the performance of our new G R algorithm. We therefore carried out an independent analysis of the 2021 data with CLEAN (using the AIPS implementation,  Greisen 2003) , which was already informed of the detection of S300 with G R. In Fig. 6 , we provide a representative image for each observing epoch and in Table  7 , we list the corresponding data sets.
A82, page 11 of 24 To successfully apply CLEAN to GRAVITY GC observations, a distinct procedure was developed and described in GRAVITY Collaboration (2021a). Here, we adapt it to the changed field configuration in 2021. The major steps are laid out in the following. Just as in 2019, we start by computing the coherent flux from the complex visibilities and the photometric flux observed at each telescope. To correct for flux variations (introduced e. g. , by changes in air mass or the performance of the adaptive optics system), we interpolated the photometric flux of S2 across all frames collected for a night and normalized the coherent flux in the Sgr A*-centered pointings by this value. We then cleaned on Sgr A* and S29 on an exposure-byexposure basis. In particular, S29 is the brightest source in the field, but cleaning on Sgr A* is important due to its flux variations, which would otherwise render the data mutually inconsistent.
The new source neither corresponds to any of the known S-stars  (Gillessen et al. 2017)  nor is it present in the Gaia catalog (Gaia Collaboration 2021), and we refer to it as S300. From the flux ratio with known stars in the field, particularly S29 and S38, we estimate that m K (S 300) = 19. 0-19. 3. If fiber damping is taken into account, this source is at the detection limit estimated in GRAVITY Collaboration (2021a) in the March images and becomes dimmer during the rest of the year. The detection clearly demonstrates that G R can deliver significantly deeper images. With the knowledge of the S300 position, we also searched for it in 2021 CLEAN images and, indeed, we can identify a bright residual at the correct location in March, May, June, and the mid pointings. The mid pointings are part of a larger mosaicing data set (cf.
In the analysis of the mock data set, we varied all the aforementioned hyper-parameters with the aim to optimally recover the ground truth. We find that an iteration controller that checks the relative energy change between steps works best for drawing samples and minimizing the KL, and for the latter task we picked a Newton Conjugate Gradient minimizer. None of our results show significant changes after 30 MGVI iterations. Arras et al. (2022)  noted that tempering during the early MGVI iterations could improve their results and to this end, they iterated between minimizing the closure and amplitude likelihoods separately. We do not find a similar improvement in our reconstructions and thus consider the full likelihood at all MGVI steps. The performance on the actual data can differ from the mockimaging tests, for example, if not all noise correlations are modeled or if some residual systematic effects remain unaccounted for by the response function.
D. 2. Excluding other identifications for S29 GRAVITY Conserved dynamical quantities can be used to tag stars, since the conserved quantity needs to apply at each point on the orbit for a given star. Here, we use the z-component of the angular momentum vector, h z = xv y -yv x , which can be derived from astrometric data alone. For S29 GRAVITY , we can determine that value for four occasions in 2021: during each of the four observing campaigns, we were not only able to determine the positions, but also the proper motions. This yields h z = (-1. 09±0. 03) × 10 20 m 2 /s, where the value is the mean over the four epochs and the error the corresponding standard deviation. The two 2019 points for S29 GRAVITY yield h z ≈ -1. 1 × 10 20 m 2 /s, consistent with the 2021 value. This excludes, for example, that S29 GRAVITY is identical to the source candidate from  Peißker et al.
(2021) , which we henceforth refer to as S62 Peissker. The latter star has a significantly smaller z-component for the angular momentum vector, h z ≈ +1. 4 × 10 19 m 2 /s. Indeed, a star with such a high value of h z as S29 GRAVITY would need to show an (tangential) on-sky motion of ≈ 880 km/s, for a projected radius of 0. 1 mas, at appocenter. We also inspected the two 2019 data sets obtained on S29 GRAVITY. Using the same imaging technique as in Sect. 5. 1 and GRAVITY Collaboration (2021a), we find that there is only one dominant source in the field. Furthermore, the residual level is fainter than m K = 19. The structure of these residuals can be attributed to S2, which is significantly brighter than S29 GRAVITY but located further away from the field center and thus appears significantly damped by the Gaussian acceptance profile of the GRAVITY fibers.
D. 3. Looking for the counterpart of S62 Peissker There are two sets of GRAVITY pointings which contain the position predicted by the S62 Peissker orbit in  Peißker et al. (2020). First, we would have expected it to show up in the 2019 GRAV-ITY pointings to S29 GRAVITY. Second, we pointed to the northwest of Sgr A* this year as part of the mosaicing data set (see Table  2  and Fig. 4 ). In the former, we detect S29 at the expected position and with the expected velocity, as discussed in the previous sub-section. In the images from the latter data set (see Fig. 4 ), we can identify S42, S2 and Sgr A*. However, we do not find any further sources. Given the predicted brightness of S62 Peissker , it would have to be outside the FOV of either pointing. In 2022, the star is predicted even closer to Sgr A* and should then appear in the central pointing, that is, when positioning the GRAVITY fibers directly onto Sgr A*.
Acknowledgements. We are very grateful to our funding agencies (MPG,  ERC ,  CNRS  [PNCG, PNGRAM],  DFG, BMBF, Paris Observatory [CS, PhyFOG], Observatoire des Sciences de l'Univers de Grenoble , and the  Fundação para a Ciência e Tecnologia) , to ESO and the  ESO/Paranal  staff, and to the many scientific and technical staff members in our institutions, who helped to make GRAVITY a reality. This publication is based on observations collected at the  European Southern Observatory  under  ESO  programme  105. 20B2. 002. A. A. , P. G. and V. C. were supported by  Fundação para a Ciência e a Tecnologia , with grants reference  SFRH/BSAB/142940/2018 ,  UIDB/00099/2020  and  PTDC/FIS-AST/7002/2020. P. A. acknowledges the financial support by the  German Federal Ministry of Education and Research (BMBF)  under grant  05A17PB1  ( Verbundprojekt D-MeerKAT ).
, 2012; Mod & Luoto, 2016 ; K E Y W O R D S Arctic tundra, field survey methods, model validation, shrubs, spatial scale, species abundance model, species distribution model  Wisz et al. , 2013). While several frameworks for the inclusion of biotic interactions in spatial models have been proposed (e. g. ,  Kissling et al. , 2012; Staniczenko et al. , 2017; Wisz et al. , 2013) , more work is needed to implement these in the Arctic (see e. g. ,  Mod & Luoto, 2016). Properly assessing model performance, and specifically the need to use spatially independent validation data, is a decades-long issue  (Fielding & Bell, 1997). Randomly partitioning a dataset into training and validation sets has long been criticised  (Chatfield, 1995)  and often yields overly optimistic model performance  (Roberts et al.
, 2017; Schratz et al. , 2019 ). Yet, this 'split-sample' validation approach remains widely used in SDM studies (reviewed in  Araújo et al. , 2005; Chardon et al. , 2020; Santini et al. , 2021). Given the plethora of work examining how validation approaches affect model predictive ability (e. g. ,  Araújo et al. , 2005; Morrison et al. , 1987; Raxworthy et al. , 2003; Roberts et al. , 2017) , it is surprising that models are rarely validated with independent datasets collected within the training spatial region  (Lee-Yaw et al. , 2021; but see Angert et al. , 2018; Elith et al. , 2006) , such as those from different survey designs. This is especially relevant in the Arctic, where many of the existing data have been collected with varying survey methods  (Walker et al. , 2016). In this study, we use high resolution SDMs and SAMs to examine the distribution and abundance of two deciduous shrub species, Betula nana L.
Does including biotic predictors improve SAM model performance? To answer these questions, we first conducted separate field surveys in two adjacent fjords in Southwest Greenland, during which we compiled detailed datasets on shrub presence-absence, abundance, species composition, and canopy height. We started with a stratified systematic survey in one fjord and then used the resulting data to construct a preliminary SAM for a stratified random survey in the second fjord. Second, we used the data from each fjord to train high resolution (~90 m) SDMs and SAMs to assess the influence of local scale environmental predictors on B. nana and S. glauca distribution versus abundance within the fjord. We chose predictors based on their known influence on Arctic shrub distributions  (Bjorkman et al.
3). Within each grid, we chose (g) three randomly placed plots to (h) sample using a 1 m × 1 m quadrat with delineations of 1%. In both surveys, we collected data on (i) Betula In K fjord, we conducted a random stratified plant survey in Summer (August) 2020. We used a preliminary SAM trained with summed pin counts of B. nana in NK fjord (see Figure  S1. 3) to stratify the ~27 km × 17 km fjord landscape into predicted low, medium and high abundances classes. We randomly selected ~90 m × 90 m spatio-climatic grids to survey in each class for a total of 200 grids, ensuring that they were accessible by foot or boat (for details see Appendix S2). Within each grid, we sampled three 1 m 2 quadrats arranged in a randomly rotated equilateral triangle centred on the mid-point of the cell. We used a gridded sampling quadrat with 1% delineations (Figure  1h ) to record shrub species presences, absences and composition, estimated percent cover and measured maximum shrub species vegetative height.
| Predictor variables We computed high resolution temperature, precipitation and solar radiation at local scales for the study area by statistically downscaling a climate time series  from the monthly CHELSA V1. 2 data. CHELSA (Climatologies at High resolution for the Earth's Land Surface Areas) is a global temperature and precipitation climatology dataset downscaled to 30 arc sec (~400 m at the latitude of our study)  (Karger et al. , 2017 (Karger et al. , , 2018)). We downscaled these data to our target grid size of ~90 m with geographic weighted regression and using the MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model (DEM) v. 1  (Howat, Negrete, & Smith, 2014 , 2015). We then calculated 30-year averages of the climate param-.
| Separate survey data as a useful validation approach In our study, split-sample validation yielded overly optimistic SDM and SAM performance compared to the separate survey validation approach, in line with other work showing that independent validation data yield more realistic, but weaker, results (e. g. ,  Dobrowski et al. , 2011; Eskildsen et al. , 2013; Roberts et al. , 2017; Schratz et al. , 2019). This highlights the importance of using separate survey data for spatially independent validation  (Hijmans, 2012 ). Yet, few studies use independent and distinct datasets collected across the range of predictions for model validation  (Araújo et al. , 2005; Elith et al. , 2006 Elith et al. , , 2020;; Lee-Yaw et al. , 2021; Santini et al. , 2021) , even though this would be ideal  (Araújo & Guisan, 2006).
BRTs extrapolate at a constant value from the last observed point in environmental training space, and GLMs continue their fitted function  (Elith & Graham, 2009). However, when we validated our models with the separate survey approach in the subset of the adjacent fjord's environmental space that overlaps with the training space, the separate survey validation still yielded substantially poorer results than split-sample validation (see Table  S1. 5). As model validation with this subset data decreased statistical power, we are cautious in our conclusions here. While others have used different datasets to train and validate models (e. g. ,  Angert et al. , 2018; Araújo et al. , 2005; Elith et al. , 2006; Eskildsen et al. , 2013) , to our knowledge we are the first to compare validation approaches within the overlapping   1 ) and the corresponding SAM with the indicated added biotic predictor.
We calculated the correlation coefficient using predicted and observed values in five repetitions of ⅔-⅓ split-sample validation of models trained in either Nuup Kangerlua (NK) fjord or Kangerluarsunnguaq (K) fjord. Each biotic predictor represents indices at the shrub species community level (i. e. excluding non-shrub species), which we added to each SAM one at a time. Note that we did not collect all biotic data in NK fjord. a Grid = richness calculated from grid-level scans for species presences; plot = richness calculated from species presences within sampling plot; competition = sum of abundances of all shrub species excluding the target species; canopy height = community weighted mean (by abundance) of maximum vegetative shrub height excluding the target species. training environmental predictor space and urge more research on this approach.
ACK N OWLED G M ENTS We pay our respects to the Kalaallit members past and present, who are the original and current inhabitants of the lands on which we con- Mobility fellowship (project no. P400PB_194331). JJA was funded by the Independent Research Fund, Denmark (grant no. 7027-00133B to SN). We thank Isabelle Boulangeat, Amy Angert and Oliver Baines for helpful discussions, as well as Susan Jarvis for constructive editorial feedback.
Mass Spectrometry Quantification and Statistical Analysis Preview software (Byos TM, Protein Metrics, Cupertino, CA, USA) was employed to estimate the quality of the tryptic digestion in the samples and to predict the post-translational modifications present. The result was used for the "database search/identification" part. The sequence of the peptide was determined based on the fragmentation pattern. A search of an updated UniProt Homo sapiens database was performed in Peaks XPro software (Peaks XPro softwar Bioinformatic Solutions Inc. , Waterloo, ON, Canada). The variable modifications allowed were as follows: K-acetylation, methionine oxidation, Deamidation (NQ), and Nerm-PyroGlu (Q). In addition, C-Propionoamide was set as fix modification. "Trypsin" was selected as Specific. Mass accuracy was set to 20 ppm and 0.
05 Da for MS and MS/MS mode, respectively. Data were filtered according to an FDR of 5%. Protein redundancy was eliminated based on the presence of an identical set or subset of peptides. To quantify the relative levels of protein abundance between different groups, samples were analyzed using the label-free quantification feature in the PEAKS XPro software. Feature detection was performed separately on each sample by the expectation-maximization-based algorithm. The features of the same peptide from all replicates of each sample were aligned through the retention time alignment algorithms. Mass error tolerance was set at 30 ppm, ion mobility tolerance (1/k0) at 0. 07 and retention time tolerance at 7 min. Normalization factors of the samples were obtained by the total ion current (TIC) of each sample.
The level of protein abundance was calculated as the summed area of the top three unique peptides. A significance ≥15 using ANOVA (p < 0. 05) as statistical method were used to determine those enriched proteins from the three compared conditions. (One or two accessions were significantly modulated from the three compared conditions). A heatmap was generated by the Spearman clustering method, using the ComplexHeatmap package in R. Enrichments in molecular process, cellular process and pathways (KEGG, Wikipathway and Reactome) were performed using ClueGo App from Cytoscape software (ver3. 9. 1, San Diego, CA, USA). ClueGO functional analysis was performed using GO BiologicalProcess EBI UniProt GOA-ACAP-ARAP  (25.
Quantification of Cytokine Secretion by HUVECs We simultaneously measured the secretion of IL-6, IL-8, IP-10, GM-CSF, MCP-1, VEGF and TNFα in supernatants of bystander HUVECs collected after 24 h of culture with conditioned media. We used a custom multiplex ELISA bead-based assay (LEGENDplex, Biolegend, OZYME, St Cyr L'Ecole, France), according to manufacturer's instructions. Samples were run on a FAC-SCalibur flow cytometer (Becton-Dickinson Life Sciences-Biosciences, Le Pont-de-Claix, France), and results were analyzed with LEGENDplex v8. 0 software (LEGENDplex, Biolegend, OZYME, St Cyr L'Ecole, France). Cytokine concentrations in supernatants were normalized on the number of live HUVECs in each condition. Results are presented as fold change compared to control conditions (HUVECs cultured in non-irradiated conditioned media).
Acknowledgment We would like to thank the CIRIL platform ( Cimap Caen, France ) for C-ions dosimetry and the  GANIL facility (Caen, France)  for providing us the C-ion beam. The C-ion experiments were performed according to projects  P1146-H  and  P1243-H (F. Chevalier ) of the iPAC committee proposal for High Energy beamline on interdisciplinary research at GANIL.
Funding This work was supported in large part by a grant from  EDF  ( Electricité de France) , by funding from the  Life Sciences group  of the four-way national agreement CEA-EDF-IRSN-FRAMATOME ( 2019-2022 ) and by funding of the Master 2 internships of A. G. and V. P. Further support was provided by " Agence Nationale de la Recherche ",  Equipex Rec-Hadron  ( ANR-10-EQPX-1401 ). This work was also supported by a grant from the  Region of Normandy , the  RIN Tremplin CHOXTRACC  ( 2021-2022 ) "Recherche de combinaisons thérapeutiques hadronthérapie -thérapies ciblant la radiorésistance biologique liée à l'hypoxie et au stress oxydant tumoral", co-funded by the  Normandy County Council  and the  French State , in the framework of the interregional development contract, " Vallée de la Seine "  2015-2020 (HAdronBIOlogie en NORmandie: Programme de recherche en Radiobiologie pour l'hadronthérapie au centre ARCHADE ).
To impro v e the understanding of the relation between granulation and stellar parameters, we provide in this paper a better characterization of the stellar brightness fluctuations by using for the first time very long time series (i. e. more than 1000 conv ectiv e turno v er times), from 3D stellar atmosphere models distributed across the Hertzsprung-Russell (HR) diagram. Particularly, for the first time, we show that both the standard deviation and autocorrelation timescale in terms of a simple power law ∼ν α max , and explore their dependencies with different metallicities. We found very good agreement when compared to observational Kepler data from K14 , and a selected sample of stars with accurate stellar parameters. This paper is structured as follows. In Section 2. 1 , we described the STAGGER code and how the 3D stellar atmosphere models are generated.