dataesr/scientific-paragraphs-categorization huggingface dataset:
Data were analyzed using the LightCycler 480 Software (Roche). Phenotypic analysis. For root and hypocotyl length analyses, seedlings were photographed and lengths were measured with ImageJ software version 1. 52 (https://imagej. nih. gov/ij/). About 10-30 seedlings were processed and three independent experiments were performed. t-test was used for statistics. Analysis and statistics of the apical hook development. Development of seedlings was recorded at 1-h intervals for 5 days at 21 °C with an infrared light source (880 nm LED; Velleman, Belgium) by a spectrum-enhanced camera (EOS035 Canon Rebel Xti; 400DH) with built-in clear wideband-multicoated filter and standard accessories (Canon) and operated by EOS utility software. Angles between hypocotyl axis and cotyledons were measured by ImageJ software version 1.
52. Colocalization analysis. Pearson's correlation coefficient (R) was used for colocalization analyses: the analysis is based on the pixel intensity correlation over space and was performed using Image J software version 1. 52. After splitting the two channels, region of interest (ROI) was identified. For our analysis, one cell was considered as one ROI; in every root ~1-5 cells (five ROIs) were measured and a minimum of ten roots were used. Colocalization plug-in using an automatic threshold was used to obtain Rcoloc value, which represent Pearson's correlation coefficient. Phylogenetic analysis. Sequences from the Arabidopsis thaliana SYAC1 protein family (AT1G15590, AT1G15600, AT1G15610, AT1G15620, AT1G15625, AT1G15630, AT1G15640, and AT1G44010) were aligned using ClustalW  92. Phylogenetic tree was generated with MEGA7 (Molecular Evolutionary Genetics Analysis Version 7.
0 for Bigger Datasets; www. megasoftware. net) using Neighborjoining methods with a complete deletion mode. Bootstrap tests were performed with 1000 replications. Poisson correction distance was adopted and rates among sites were set as uniform. Confocal imaging and image analysis. Zeiss LSM 700 confocal scanning microscope using either ×20 or ×40 (water immersion) objectives were employed to monitor expression of fluorescent reporters. GFP (YFP) and Cy3 signals were detected either at 488 nm excitation/507 nm emission or 550 nm excitation/570 nm emission wavelength, respectively. Quantification of immunodetected PIN1 and PIN2 expression in root meristems was performed by measurement of membrane signal in cortex and epidermal cells, respectively. Signal in approximately ten cells in a minimum of ten roots was measured using ImageJ software version 1.
52. For cell size measurement, roots of 5-day-old wild-type and SYAC1ox plants were stained for 5 min with FM4-64 dye (Thermo Fisher, T13320) followed by image acquisition. Cell size was measured by ImageJ software version 1. 52. Significant differences were evaluated using t-test; n = 15-20 roots, two biological replicates. Transient expression in root suspension culture protoplasts. The transient expression assays were performed on 4-day-old Arabidopsis root suspension culture by PEG mediated transformation. Protoplasts were isolated in enzyme solution (1% cellulose (Serva), 0. 2% macerozyme (Yakult), in B5-0. 34 M glucose-mannitol solution (2. 2 g MS with vitamins, 15. 25 g glucose, 15. 25 g mannitol, pH to 5. 5 with KOH) with slight shaking for 3-4 h, and afterward centrifuged at 800 × g for 5 min.
Microscopic examination was performed using an Axiovert200 epifluorescence microscope fitted with an LD Achroplan ×63/ 0. 75 Korr Ph2 (DICIII) objective and an AxioCam HR color camera, and using Axio Vision Rel 4. 6 Software (all Carl Zeiss Jena, Germany). Staining patterns were manually examined for deposition thickness, or automatically for the apical occupancy of red dye using Fiji software version 1. 52 (https://fiji. sc/). The subcellular localization of SYAC1-mCherry in tobacco pollen tubes was performed using an LSM 880 Airyscan confocal microscope (Carl Zeiss Jena, Germany) and Zen blue software as previously described  95. Pollen tube morphologies were assessed by scoring the incidence of normal, stunted or growtharrested (nongerminating) cells among fluorescent pollen tubes  96. Co-immunoprecipitation (Co-IP) assays.
AFM measurements and apparent Young's modulus calculations. The AFM data were collected and analyzed as described elsewhere with minor changes 66. To examine extracellular matrix properties the turgor pressure was suppressed by seedlings immersion in a hypertonic solution (0. 55 M mannitol) for at least 30 min before examination. 4 day-old seedlings grown in darkness were placed on petri dishes filled with 1% agarose and 10% mannitol and immobilized by low melting agarose (0. 7% with 10% mannitol). The focus was set on the anticlinal (perpendicular to the organ surface) cell walls and its extracellular matrix. To ensure proper indentations (especially in the bottom of the doom shape between two adjacent cells regions), cantilevers with long pyramidal tip (14-16 μm of pyramidal height, AppNano ACST-10), with a spring constant of 7.
8 N/m were used. The instrument used was a JPK Nano-Wizard 3. 0 and indentations were kept to <10% of cell height (typically indentations of 500 nN force). Three scan maps per sample were taken over an intermediate region of the hypocotyls, using a square area of 25 × 25 μm, with 16 × 16 measurements, resulting in 1792 force-indentation experiments per sample. The lateral deflection of the cantilever was monitored and in case of any abnormal increase the entire dataset was discarded. The apparent Young's modulus (EA) for each force-indentation experiment was calculated using the approach curve (to avoid any adhesion interference) with the JPK Data Processing software (JPK Instruments AG, Germany). To calculate the average EA for each anticlinal wall, the total length of the extracellular region was measured using masks with Gwyddion 2.
Eluted proteins were subjected to a short NuPAGE run and in-gel digested with Trypsin. Protein interactors were identified by mass spectrometry using an LTQ Orbitrap Velos mass spectrometer. Proteins with at least two matched high confident peptides were retained. Background proteins were filtered out based on frequency of occurrence of the copurified proteins in a large dataset containing 543 TAP experiments using 115 different baits  98. Cell wall analyses. Analyses were performed on 4-day-old dark grown hypocotyls using an alcohol-insoluble residue (AIR) prepared as follows. Seeds of Arabidopsis were plated and grown on square plates with Milieu Arabidopsis medium (Duchefa) supplemented with 0. 32 g L -1 CaNO 3 , 10 g L -1 Suc, 0. 8% agar and pH adjusted to 5. 75. The plates were incubated at 4 °C for 48 h to synchronize seed germination, and then vertically grown in darkness at 18 °C.
5 and 7, Bruker Optics GmbH, Ettlingen, Germany), cut to the fingerprint region of 900-1800 cm -1 and exported as. mat files for subsequent processing and analysis. The exported spectra were pre-processed by an open-source software developed at the Vibrational Spectroscopy Core Facility in Umeå (https://www. umu. se/en/research/infrastructure/visp/downloads/), written in MATLAB (version 2014a-2018b, Mathworks, USA), using asymmetric least squares baseline correction  103  ; (lambda: 100,000 and p = 0. 001), Savitzky-Golay smoothing  104  ; using a first-order polynomial, with a frame number of 5; and total area normalization. Multivariate Curve Resolution-Alternating Least Squares analysis was performed on the spectra using 5 components (based on singular value decomposition of the initial dataset).
A maximum of 50 iterations and a convergence limit 0 f 0. 1 were used, with initial estimates in the spectrum direction (determined automatically by the built-in SIMPLISMA based algorithm) and a noise level of 10% given in the script. Only nonnegativity constraints were used, both in the spectrum and concentration dimensions. The resulting profiles explained 99. 84% of the variation. For classification, k-means clustering was performed within this open-source software, using the resolved spectral profiles and MATLAB's built-in algorithm. Clubroot infection rating. All experiments were performed with the Plasmodiophora brassicae single-spore isolate e3  105  and Arabidopsis thaliana ecotype Columbia was used as the wild type. Resting spores were extracted by the homogenization of mature clubroot galls (stored at -20 °C) of Chinese cabbage, followed by filtration through gauze (25-mm pore width) and two centrifugation steps (2500 × g, 10 min).
Data are displayed as percentage of plants in the individual disease classes since this gives a more detailed view on the differences. Data presented are means of two independent experiments for the 10 6 and 10 5 spore concentrations and one for the 10 4 spore concentration. The qualitative disease assessment data were analyzed using the Kruskal-Wallis test and by subsequently comparing the mean rank differences as described in ref. 50. Fresh weight of shoots of wild type, syac1-3 and syac1-5 and lines overexpressing SYAC1ox were measured 28 days after P. brassicae inoculation with control (=buffer) treatment (mock), 10 4 and 10 5 spore concentration. For the experiment 30-35 plants per line and treatment were analyzed, one point in the box plot graphs represents an average fresh weight from two to three plants.
Acknowledgements We thank  Daria Siekhaus ,  Jiri Friml  and  Alexander Johnson  for critical reading of the manuscript,  Peter Pimpl ,  Christian Luschnig  and  Liwen Jiang  for sharing published material,  Lesia Rodriguez Solovey  for technical assistance. This work was supported by the  Austrian Science Fund  ( FWF01_I1774S ) to A. H. , K. Ö. , and E. B. , the  German Research Foundation  (DFG;  He3424/6-1  to I. H. ), by the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement n°[291734] (to N. C. ), by the  EU  in the framework of the  Marie-Curie FP7 COFUND People Programme  through the award of an  AgreenSkills+  fellowship No. 609398  (to J. S. ) and by the  Scientific Service Units of IST-Austria  through resources provided by the  Bioimaging Facility , the  Life Science Facility.
Data availability The authors declare that the data supporting the findings of this study are available within the paper, and its Supplementary Information files. A reporting summary for this Article is available as a Supplementary Information files. The datasets generated and analyzed during the current study are available from the corresponding author upon request. The source data underlying Figs. 1a, b ; 2a-f; 3b; 4c; 5a, c, d, e; 6a; and 7d, e, as well as Supplementary Figs. 1a, b; g; 3b, d; 4c, d, e, f; 5d, f, i ; 6a; and 7d, are provided as a Source Data file. Transcriptome profile data associated with this study has been deposited in the NCBI Sequence Read Archive under accession number GSE146778. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD018159 [10.
To answer this question, we use rsfMRI data from 94 male subjects from the Autism Brain Imaging Data Exchange (ABIDE) database  (Di Martino et al. , 2017; Di Martino et al. , 2014). The subjects are divided into two groups (N=47, age-matched pairs): typical controls (TC) and Autism Spectrum Disorder patients (ASD). fMR images are smoothed using Gaussian kernels with FWHM from 0 mm to 32 mm. We assess group differences using Network Based Statistics (NBS)  (Zalesky et al. , 2010)  and compare the results obtained for all spatial smoothing levels. In addition, to address the generalizability of the results, we repeat the analysis for fMRI data from 44 subjects from the UCLA Consortium for Neuropsychiatric Phenomics LA5c Study  (Gorgolewski et al. , 2017). These subjects are also divided into two groups (N=22): bipolar disorder patients and typical controls.
Autism Brain Imaging Data Exchange (ABIDE). To investigate the effects of spatial smoothing on group-level differences in functional brain networks, we employ unpreprocessed data from the Autism Brain Imaging Data Exchange (ABIDE) (Di  Martino et al. , 2017; Di Martino et al. , 2014). This dataset contains anonymized MRI and rsfMRI images and phenotypic information from 2156 individuals, collected at 19 different institutions. The subjects are classified as typical healthy controls (TC) or diagnosed with the Autism Spectrum Disorder (ASD). ABIDE provides several advantages. First, the use of open data makes the replication of the study easier. Second, using data from different institutions helps to take possible multisite effects into account. Finally, since ABIDE has been used in many analyses to identify differences between ASD and TC populations, it supplies a baseline for comparison  (Maximo et al.
, 2014). The sites and the voxel size of their images is listed in Supplementary Table  1. Subjects were selected based on three initial criteria. (i) In terms of gender, we chose only male subjects due to the prevalence of ASD in males  (Faras et al. , 2010) ; (ii) Regarding age, we excluded subjects below 18 years, since brain networks evolve with age, in particular in childhood and adolescence  (Vasa et al. , 2016) ; and finally, (iii) in terms of repetition time (TR), we chose data acquired with TR=2 s to reduce the analysis complexity. These initial limitations yielded 231 potential subjects, whose images were preprocessed using BRAMILA v2. 0 (see below)  (Glerean, 2017). As an Image Quality Control measure, we visually inspected the images before preprocessing. We discarded 50 subjects whose images had noticeable artifacts, like high motion, missing brain regions, or regions with unexplained changes in intensity.
UCLA Consortium for Neuropsychiatric Phenomics LA5c Study. To verify that our results generalize to other datasets, we repeated all analysis using unpreprocessed data from a second, independent dataset. This dataset, UCLA, contains fMRIs of 272 subjects divided in 4 populations: healthy controls (130 subjects), patients diagnosed with ADHD (43 subjects), bipolar disorder (49 subjects), and schizophrenia (50 subjetcs)  (Gorgolewski et al. , 2017). We chose to compare patients diagnosed with bipolar disorder and healthy controls, as the bipolar population had the largest number of scans that comply with our restrictions in image quality control and framewise displacement. Out of the 272 initial subjects, 8 were discarded for missing T1-weighted or rsfMRI images, 93 were discarded because they were not bipolar or controls, and 80 were not selected for not complying with the framewise displacement requirement.
Image preprocessing. ABIDE Structural MRI data were preprocessed with the FSL software (www. fmrib. ox. ac. uk, version 5. 0. 9) (Jenkinson et al. , 2012; Smith et al. , 2004; Woolrich et al. , 2009). The T1-weighted images were segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), whilst also correcting for radiofrequency field inhomogeneities using the FMRIB Automated Segmentation Tool (FAST)  (Zhang et al. , 2001). Then, non-brain tissue was deleted from the image with the FSL brain extraction tool (BET)  (Smith, 2002). UCLA Structural MRI data were preprocessed using the fmriprep T1w preprocessing workflow  (Esteban et al. , 2017) , which apply the following steps: brain extraction, brain tissue segmentation, and registration to the MNI space. fMRI data were preprocessed using the FSL software and the BRAMILA pipeline.
This commonly used method smooths the data independently of its intrinsic smoothness, which can vary between measurement sites and subjects. Because of this, we also separately employed AFNI 3dBlurToFWHM  (Cox, 1996)  to take into account the influence of the intrinsic smoothness of the data. This function smooths an image until it reaches a specified smoothness. At each iteration, the smoothness of the data is estimated globally. Voxels where the local smoothness is higher than the goal will not be further smoothed. As the global smoothness approximates to the target value, the smoothing rate is decreased until the target smoothness is reached. This method is a form of adaptive smoothing in which each image in a dataset is smoothed with a different kernel, so that the smoothness variation within the dataset is decreased.
Network differences visualization. We use circos  (Krzywinski et al. , 2009)  to visualize the network differences between groups. To make the data representation easier, we sort the ROIs in 13 systems. These systems are defined for a 264 ROI atlas in  Power et al. (2011). To assign the system labels, we compute the distance between the ROI centroids of the two atlases and select the label of the closest ROI. For the Brainnetome atlas, if two or more labels match one region, we choose the label that is closest to the same region in the other hemisphere. We base our plots in the implementation of  Van Horn et al. (2012). The relevant code to replicate the plots is available in the Zenodo repository (see section Data and software availability).
Data and software availability The data that support the main findings of this study are openly available at http://fcon_1000. projects. nitrc. org/indi/abide/. For details about the full dataset and its procedures, see Di  Martino et al. (2017) ; Di  Martino et al. (2014). The data that support the verification of the findings are openly available at https://openneuro. org/datasets/ds000030/versions/00016, version 00016 and details about the full dataset can be found in  Gorgolewski et al. (2017). The preprocessing pipeline used is available at https://version. aalto. fi/gitlab/BML/brami la/tree/d8d9457ad 2c2b44b18710faf242c19a7a749ff4f, commit hash 4f1e6388d6b2e5024ef2 380d29e6526bb878242a. The code used to analyze the data and generate the plots in the study is available at https://doi.
A3 A ). Moreover, the large number of significant links makes it difficult to detect possible patterns from visualizations (see Supplementary Fig. A2 ). For the set of graph measures calculated on binary, thresholded networks, we find group-level differences for betweenness centrality, clustering coefficient, global efficiency, and local efficiency. However, most of those differences are found at high levels of smoothing (FWHM> 12 mm) which are rarely used in practice. An exception is the local efficiency of the left dorsal dysgranular insula (node 173), for which we find differences at 8 mm. We did not find significant differences for the degree or the mean clustering coefficient at any smoothing level. The significant results are available in supplementary tables 8, 9, 10, and 11. The complete results (significant and not significant) are available in Zenodo (see section Data and Software availability).
Generalizability of the results To demonstrate the generality of our results, we repeated the analysis after changing several aspects of the pipeline: i) the NBS suprathreshold value, ii) the parcellation scheme and number of ROIs: two Craddock parcellations  (Craddock et al. (2012) , see Methods section) instead of Brainnetome, iii) the network density used to study weighted and binary thresholded networks, and iv) the spatial smoothing method: adaptive smoothing  (Cox (1996) , see Methods section) instead of the non-adaptive Gaussian smoothing. Besides, we demonstrated that the results generalize for a second, independent dataset, the UCLA dataset (see Methods section). Our main results are robust against changes in these parameters: the level of spatial smoothness affects group-level differences in brain network structure in nontrivial and unexpected ways.
Discussion In this work, we investigated the effects of spatial smoothing on group-level differences in functional brain networks. Resting-state fMRI data from clinical populations (ASD and bipolar disorder) and matched controls (TC) were spatially smoothed with Gaussian filters with increasing smoothing kernel width. Functional networks for each subject were estimated using Pearson's correlation coefficients and group differences were computed for the full correlation matrix using the Network-based Statistic Toolbox (NBS). We also reproduced the findings for weighted, thresholded functional brain networks. Our results establish three findings: (i) the choice of spatial smoothing kernel affects group-level differences in resting-state functional brain networks, (ii) some links are significantly different between the two groups at all kernel sizes, while most links are different only at specific smoothing kernels, (iii) the effects are not explained by the spatial properties of the parcellation used and are in general non-systematic and difficult to predict.
Here, we use ABIDE data from different scan centers to make our results more reproducible. However, the highly imbalanced number of subjects from the sites could pose a problem, despite regressing possible site-effect confounds in our analysis of the ABIDE data. To further address the multisite effects, we have replicated our study in another dataset whose images were taken at the same site and in the same scanner (UCLA). Remarkably, the results obtained from this second dataset are close to those obtained from the ABIDE data, indicating that the observed effects do not relate to site effects and are not exclusive for one dataset. We have seen the unpredictability of the effects of spatial smoothing, which is an optional but commonly used fMRI preprocessing step. But what are the effects of the spatial smoothness introduced by other preprocessing steps like interpolation? And what is its role in data sets like ABIDE, whose images come from different scan sites and may have different amounts of intrinsic smoothness? In the case of ABIDE, this may pose problems, as the images come from different sites.
Conclusions To smooth or not to smooth? The decision might still depend on the goals of the researcher. While it has been recommended to avoid smoothing in multivoxel pattern analysis  (Kamitani and Sawahata, 2010; Mahmoudi et al. , 2012) , other authors have suggested to adopt a kernel width of three times the voxel size for GLM analysis  (Lindquist, 2008) , or twice the voxel size for taskbased inter-subject correlation analysis  (Pajula and Tohka, 2018). Here, we have seen that although spatial smoothing the data affects the result similarly for different parcellations and datasets, there is no unique level that will fit every case. Functional connectivity studies often rely on brain parcellations to reduce the spatial dimensionality of the data, which in practice apply a level of smoothing proportional to the size of the ROIs.
1%. Cell growth was monitored by flow cytometry and microscopy after 2 weeks. For serial dilution either 500 or 50 μL of water sample was added to 15 mL of L1. Then, 24 wells of a Greiner Bio-One™ 96 Deep Well plate (Dominique Dustscher, Brumath, France) were filled with 0. 5 mL of each dilution. Wells were later screened by optical microscopy and with a Guava® (Merck, Darmstadt, Germany) flow cytometer. Unialgal wells were transferred to ventilated T-25 CytoOne® flasks (Starlab, Orsay, France) with 15 mL of L1 media. All cultures were incubated at 4°C with a 12:12 light-dark cycle and transferred to new medium once a month. Light intensity was approximately 100 μmole photons. m -2. s -. The isolation method, culture medium and environmental conditions for each strain are reported in Supplementary Data S1.