dataesr/scientific-paragraphs-categorization huggingface dataset:
Microarray data statistical analysis Microarray data were analyzed using R (R Core Team, 2018) and Bioconductor packages  (Huber et al. , 2015)  as described in GEO accession GSE196257. Raw data (median signal intensity) were filtered, log2 transformed, and normalized using the quantile method  (Bolstad et al. , 2003). A model was fitted using the limma lmFit function  (Ritchie et al. , 2015). Pairwise comparisons between biological conditions were applied using specific contrasts. A correction for multiple testing was applied using the Benjamini-Hochberg procedure  (Klipper-Aurbach et al. , 1995)  to control the false discovery rate (FDR). Probes with FDR ≤ 0. 05 were considered to be differentially expressed between conditions. Hierarchical clustering was applied to the samples and the differentially expressed probes using the Pearson correlation coefficient as distance and Ward's criterion for agglomeration.
Gene clustering according to the cardiac cell populations Processed single-cell RNA-seq data from  Tucker et al. , 2020  were retrieved from the Broad Institute's Single Cell Portal (study ID SCP498). Highly variable genes were selected on the scaled logNormalize dataset to perform cell clustering using Seurat 4. 0. 1 (using the default parameters in the FindVariableFeatures function in the Seurat R package). The uniform manifold approximation and projection (UMAP) were performed based on the first 30 principal components. Cell identities were made available by  (Tucker et al. , 2020)  and used to color the cells on the UMAP plot.
Acknowledgements We thank  Claire Naylies  and  Yannick Lippi  ( Toxalim, Universite de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France ) for their contribution to microarray fingerprints acquisition and microarray data analysis carried out at  GeT Genopole Toulouse Midi-Pyrenees facility  (https://doi. org/10. 15454/1. 5572370921303193E12 ). We are also grateful to  TRI Genotoul network  facilities, specifically  ANEXPLO platform (Toulouse)  for help with echocardiography, the  'Centre de Microscopie Electronique Appliquee a la biologie-CMEAB' (Faculte de Medecine Rangueil-Toulouse ) and the  Cellular Imaging Facility-I2MC (Toulouse). This work was in part supported by the  'Fondation Bettencourt Schueller'  (to CG), the  'Fondation de France ' grant no. 75807  (to CG), the  'Fondation pour la Recherche Medicale'  grant  DEQ20170336733  (to CG) and grant  FDM201906008682  (to BT) and the  'Societe Francophone du Diabete'  (to BT).
In the present study, we analyze two test case eruptive sequences at Etna, the first one including six successive paroxysms from the New Southeast crater (NSE) that occurred in 38 days between October 26 and 2 December 2013, the second sequence including four paroxysms from the Voragine crater (VOR) that occurred in less than 3 days between 3 and 5 December 2015. The methods and data used in this study are described in Section 2, which also describes the details of the two eruptive sequences, the volcanic emission estimates determined using the V2B radar data, and the model setup. In Section 3, we discuss the modelling results, in terms of the meteorological and ash transportation characteristics in the numerical model domain. The conclusions and aims for future works are presented in Section 4.
During the 2011-2015 period, the V2B radar successfully recorded about 50 paroxysms of vigorous lava fountains feeding tephra plumes up to over 13 km high. The level 1 data are available from an open access database by the Observatoire de Physique du Globe de Clermont-Ferrand (http://voldorad. opgc. fr/bddtr. php, accessed on 16 June 2023;  [33] ), as well as more elaborated (level 2) products upon request, such as the MER, the onset and end of the fountaining phase, and the eruptive crater.
• Potential health hazards may stem from the stagnation of PM10 ash at ground level for several days, commonly above 500 µg m -3 , and sometimes punctually exceeding the aviation threshold. Our methodology has the potential to issue timely alerts in an operational setup, including at Catania airport. Improving the accuracy of ash dispersal models is necessary both for nowcasting and forecasting purposes and to compensate for ash detection gaps in monitoring systems. Future WRF-Chem model developments will include refining the injection heights and the initial columnar ash distribution. The semi-empirical law from Mastin et al. [35] 's dataset used in our study averages all the observed ash plume heights from mainly large silicic explosive eruptions that have occurred in a variety of wind conditions, tending to overestimate the height from a given MER (Freret-Lorgeril et al.
[20] ; Figure  7 ). Other models relating to the MER and plume height advantageously consider the vertical wind profile (e. g. , Degruyter and Bonadonna  [34] ). Wind profiles may come from meteorological data online coupled with WRF-Chem and cross-checked with atmospheric balloon measurements achieved several times a day in the region around Etna. Additional datasets for wind profiles at high resolution (30 km) could be considered, such as ERA5 data available from the Climate Data Store in NetCDF format  [45]. Plume heights controlled by wind conditions specific to the eruption could, thus, be systematically computed from the radarderived MER and compared with new compiled observational datasets relating to the MER and plume height (IVESPA)  [14] , even including discrimination of the height of the plume regions (top height, spreading level) and the SO 2 height  [46].
Afterward, the LS source catalog is constructed using the LEGACYPIPE 50  pipeline, which uses the TRACTOR 51  software  (Lang et al. 2016 ) for pixel-level forward modeling of astronomical sources. LEGACYPIPE initially detects sources and defines contiguous sets of pixels associated with each detection. TRACTOR then fits these pixels with surface brightness models (e. g. , point source, de Vaucouleurs galaxy profile) on individual optical images (g, r, and z bands), taking into account their different point-spread function (PSF) and sensitivity. Based on a penalized χ 2 , LEGACYPIPE then determines which surface brightness model best describes the light profile and whether to keep the source in the catalog. The resulting LS source catalog includes source positions, fluxes, r-band fiber flux, and measures of the quality of the source fits.
From the noiseless galaxy spectra, we construct realistic DESI-like spectra using the DESI specsim package 58   (Kirkby et al. 2021) , which simulates the source profile, atmosphere, and DESI instrument characteristics. The atmosphere model accounts for the variance added to the source spectrum (i. e. , our noiseless galaxy spectra) from the sky emission spectrum and the attenuation of the spectrum by its passage through the atmosphere. In our case, we use the dark sky spectrum derived from UVES  (Hanuschik 2003)  and the nominal air mass for the attenuation. The total photon count entering the fiber is then modeled using the spectrum and specified exposure time. Afterward, the DESI instrument model simulates the resolution effects and throughput for each of the three spectrograph cameras.
4. We achieve a >95% redshift success rate for both BGS Bright and Faint over the range of survey speeds expected to be available to BGS (Figure  15 ). This demonstrates that the impact of observing conditions is well understood and that the spectroscopic pipeline is robust to systematic error in challenging conditions. Figure  19. Comoving number density distribution, ¯( ) n z , of BGS galaxies from the EDR (black; SV1 and the One-Percent Survey). We plot ¯( ) n z for BGS Bright (blue) and Faint (orange). We show external data sets of interest, SDSS (dashed), BOSS (dotted), and GAMA DR4 (dashed-dotted), and DESI LRGs (red) in the overlapping range. BGS will provide the highest-density cosmological galaxy sample to date at low redshifts. It will provide maximum leverage against higher-redshift measurements and CMB constraints for dark energy constraints.
Legacy Surveys also uses data products from the Near-Earth Object Wide-field  Infrared Survey Explorer  (NEOWISE), a project of the  Jet Propulsion Laboratory/California Institute of Technology , funded by the  National Aeronautics and Space Administration. Legacy Surveys was supported by the  Director, Office of Science, Office of High Energy Physics of the U. S. Department of Energy ; the  National Energy Research Scientific Computing Center , a  DOE Office of Science User Facility ; the  U. S. National Science Foundation, Division of Astronomical Sciences ; the  National Astronomical Observatories of China ; the  Chinese Academy of Sciences ; and the  Chinese National Natural Science Foundation. LBNL is managed by the  Regents of the University of California  under contract to the  U. S.
(2021). We thus asked whether the circuit persistence time scale and activity distribution consistently varied between animals and trials for a given temperature. We plotted, for all datasets, the mean persistence time as a function of the mean activity of both populations (see Figure  2E  and Methods). We found a quasi-linear dependence between both quantities (R = 0. 91), showing the strong correlation between these two characteristics of the circuit internal dynamics. 2. 2 A data-driven energy-based model reproduces the statistical and dynamical properties of ARTR neural activity 2. 2. 1 Inference of an Ising model from low-order statistics of neural activity of the ARTR. We used the recordings of the ARTR spontaneous activity to infer of an energy-based network model. Our approach, going from raw fluorescence data to the model, is summarized in Figure  3A.
Monte The distribution of the KL divergence computed between experimental datasets and corresponding Ising models is shown in green in 4C. These values are found to be systematically smaller than those obtained between any combination of datasets and Ising models trained on different datasets (red distribution). This result establishes that the Ising model can accurately reproduce the ARTR activity distribution associated to each specimen and temperature. We also computed from each synthetic signal a mean persistence time, using the same procedure as for the experimental data. We found that the persistence times extracted from the data and from the MC simulations of the inferred models are strongly correlated (Figure  4D , R = 0. 84). In particular, the persistence time of the MC dynamics capture the temperature dependence and inter-individual variability of the neural persistence time.
To analyze these datasets we first follow the approach described in Figure  3 , and inferred, for each fish, the sets of biases h i and interactions J ij using the spontaneous activity recording only (Suppl. Fig. S3C-D ). In a second step, we exploited recordings of the visually-driven activity to infer additional biases δh i to the neurons, while keeping the interactions J ij fixed (Figure  6C ); in practice we defined two sets of additional biases, δh ← i and δh → i , corresponding, respectively, to leftward and rightward illuminations. The underlying intuition is that biases encode inputs due to the stimulation, while the interactions between neurons can be considered as fixed on the experimental time scale. The inferred values of the additional biases, averaged over the entire sub-population (right or left), are shown in Figure  6D  for the ipsilateral, e.
Interpretation of the functional connectivity The inferred functional couplings J ij 's are not expected to directly reflect the corresponding structural (synaptic) connectivity. However, their spatial distribution appears to be in line with the known ARTR organization  (Dunn et al. , 2016; Kinkhabwala et al. , 2011)  characterized by large positive (excitatory) interactions within the left and right population, and by the presence of negative (inhibitory) contralateral interactions. Although the contralateral couplings are found to be, on average, almost null, compared to the ipsilateral excitatory counterparts, they drive a subtle interplay between the left and right regions of the ARTR. As shown in Suppl. Table  S2  the inferred parameters largely vary across datasets. This variability is partially due to the difficulty to separately infer the interactions J ij and the biases h i , a phenomenon not specific to graphical model but also found with other neural e.
Acknowledgements Turn angles distributions could be fitted as the sum of two distributions (Gaussian and Gamma), whose intersection was used to define an angular threshold to categorize events into forward (F), left turn (L) or right turn (R, Figure  1A ). This threshold was found to be close to 10 degrees for all tested temperatures. Then we ternarized δθ values, based on F, L or R classification (Figure  1B ) and computed the power spectrum of the binary signals defined from symbols L and R only, with the periodogram MATLAB function and averaged by temperature (Figure  1C ). The outcome was fitted to the Lorentzian expression corresponding to a memory-less equiprobable two-state process  (Odde & Buettner, 1998) : S (f ) ∝ 2k f lip 4k 2 f lip + (2πf ) 2 , ( 5 ) where k f lip is the rate of transition from one state to another.
A type T thermocouple (Omega) is placed near the fish head (< 5mm) to record the fish surrounding temperature. The signal from a thermocouple amplifier (Adafruit) is used in a PID loop implemented on an Arduino board, which mitigate the Peltier power to achieve the predefined temperature target, stable at ±0. 5 • C. The temperature regulation softwares and electronics design are available on Gitlab under a GNU GPLv3 licence (https://gitlab. com/GuillaumeLeGoc/arduino-temperature-control). The ARTR neurons were selected using a method described elsewhere  (Wolf et al. , 2017). First, a group of neurons was manually selected on a given slice based on a morphological criterion such that the ARTR structure (ipsilateral correlations and contralateral anticorrelation) is revealed. Then, neurons showing Pearson's correlation (anti-correlation) higher than 0.
Real data and Ising models comparison To quantify the quality of the log-probability landscapes reproduction by the Ising models (Figure  4C ), we used the Kullback-Leibler divergence between (1) a dataset i and the synthetic signals generated with the model trained on that dataset i (green) and (2) the dataset i with synthetic signals generated with every other models (red). With c i the count in the two-dimensional bin i (10×10 bins used) and α a pseudocount (set to 1), the probability in bin i is defined as P i = ci+α j (cj +α). The Kullback-Leibler divergence between a data/model pair is then defined as D KL = i P data,i log 10 P data,i P model,i (8) 7. 7 Mean-field theory for the ARTR activity 7. 7. 1 Derivation of the free energy We consider an Ising model with N L and N R neurons in, respectively, the left and right regions.
Carlo sampling of Ising model reproduces temperature-dependent neural persistence. Once inferred, the Ising model can be used to generate synthetic activity configurations s of the network. In practice we use a Metropolis Monte Carlo (MC) algorithm to sample the probability distribution P (s) in Eq. 1. Figure 4A shows the activity maps obtained through MC sampling of the Ising models trained on the three same datasets as in Figure 2A. The time evolution of the mean activities of the left and right sub-populations are shown in Figure 4B. For these synthetic signals, we use MC rounds, i. e. the number of MC steps divided by the total number of neurons (Methods) as a proxy for time. Remarkably, although the Ising model is trained to reproduce the low-order statistics of the neuronal activity within a time bin only, and is therefore blind to the circuit dynamics, the generated signals still capture the main characteristics of the ARTR dynamics, i.
are the average activities in the L,R regions. A power spectrum density is estimated for each signal with the Thomson's multitaper method through the pmtm MATLAB function (time-halfbandwidth product set to 4). The power spectrum densities were then fitted with a Lorentzian spectrum, see Eq. 5. ARTR left and right persistence times (Figure2E) are defined as the time m L and m R signals spend consecutively above an arbitrary threshold set at 0. 1. Left and right signals are treated altogether. Changing the threshold does induce a global offset but does not change the observed effect of temperature, the relation with m L and m R mean signals, nor the relation with the persistence times of the synthetic signals. The latter are computed according to the same procedure.
Methods Participants. Our recruitment procedure comprised two stages. First, potential participants were invited to a pre-screening session, during which we assessed their hypnotic susceptibility using the Harvard Group Scale of Hypnotic Susceptibility, Form A (HGSHS:A;  Shor & Orne, 1962). Subsequently, we selected 75 individuals from this initial pool to undergo EEG recordings. For the present study, we focused on neural activity data from 40 individuals (27 females, mean age of 21. 98 y. o. (st. dev. = 4. 41)) based on their HGSHS:A scores. Specifically, we included individuals who scored above 8, indicating high hypnotic susceptibility (HHSIs), and individuals who scored below 4, indicating low hypnotic susceptibility (LHSIs). This work aligns with previous studies focusing on high and low hypnotic susceptible individuals to maximize the detection of the effect.
We recorded EEG signals using 64 Ag/AgCl active electrodes at a sampling rate of 1000 Hz (ActiCap System; Brain Products GmbH; Gilching, Germany). We positioned additional bipolar electrodes at the outer canthi, as well as the superior and inferior orbits of the left eye. We kept impedances of all electrodes below 10 kΩ, while all electrophysiological signals were amplified (ActiChamp System; Brain Products GmbH; Gilching, Germany). Electrode impedances were kept below 10 kÙ. Data was collected online at 1000Hz and down sampled to 250Hz offline for data analysis. Data acquisition was done via Brain Recorder (Brain Products Inc. , GmbH) while preprocessing and data analysis were completed through Brain Vision Analyzer, Brainstorm  (Tadel et al. , 2011), and custom MATLAB scripts (version R2022A; MathWorks Inc, MA).
Bad electrodes were topographically interpolated using spherical splines (< 3% electrodes). EEG recordings were re-referenced to the average of all EEG electrodes offline. All EEG data were inspected for muscle-related, eye movement or technical artifacts, which were removed before continuing the analysis. Spectral features. Spectral features were computed individually for each channel using the specparam algorithm for spectral parametrization as implemented in Brainstorm (Figure  1A ;  Donoghue et al. , 2020). The specparam algorithm decomposes power spectra into periodic and aperiodic (1/f-like) components by fitting the power spectral density in log-log space. The periodic component is characterized by the center frequency, bandwidth, and relative amplitude of the oscillatory peak, while the aperiodic component is defined by the offset and slope of the power spectrum density.
e. , one-tail) surrogate distribution. To facilitate interpretation, we further transformed the weights of the SVM classifiers based on the covariance of the training dataset  (Haufe et al. , 2014). These transformed weights were then averaged across all training iterations. We also extracted PSD and graph theoretical values for the channels where we observed the top 5% SVM weights to better understand how hypnotic susceptibility and the induction procedure impacted rs-EEG. We evaluated spectral features and clustering coefficients for pre-induction and Δinduction separately. Note that we also evaluated the post-induction rs-EEG recording alone (available in supplementary material). In addition to EEG, we applied our multivariate classification approach to vertical and horizontal electro-oculogram signals  (VEOG and HEOG)  to determine whether we could discriminate between HHSIs and LHSIs across both pre-induction and Δinduction separately.
Acknowledgements The authors thank  Prof. Thomas Hermans  for kindly providing the microfluidic cell and  Dr. Fanny Richard  for recording the XPS spectra. The authors acknowledge funding from the  European Commission  through the  ERC  projects  SUPRA2DMAT  ( GA-833707 ) and  FUTURE-PRINT  ( GA-694101 ), the  Graphene Flagship Core 3 project  ( GA-881603 ), the  Agence Nationale de la Recherche  through the  Interdisciplinary Thematic Institute SysChem via the IdEx Unistra  ( ANR-10-IDEX-0002 ) within the  program Investissement d'Avenir , the  Foundation Jean-Marie Lehn (Strasbourg)  and the  Institut Universitaire de France (IUF). The work carried out in Poland was financially supported by the  National Science Center  (grant no. 2019/35/B/ST5/01568 ). J. N. C. thanks  Science Foundation Ireland (SFI)  for support.
Procedure and data acquisition Total protein extraction, trypsin digestion of proteins, phosphopeptides enrichment and LC-MS/MS (Supplemental figure  1 ) is fully described in  Arico et al. , 2021. Three biological replicates per condition were analyzed. The identification of phosphopeptides was performed using the Max-Quant/Andromeda engine, setting as fixed modifications: Carbamidomethylation (C), and variable modifications: Oxidation (M) + Phosphorylation (STY). The database employed was UniProt CP_Arabidopsis Thaliana. 1% FDR filter was applied. The quantification was done using signal intensity values of peptides. A peptide was considered quantifiable if it had at least 2 signal intensity values among the three biological replicates. Missing signal intensity values were imputed with a noise value corresponding to the 1-percentile of all intensity values by sample.
EF-1α (Elongation Factor 1α, At5g60390) was used as normalization control (Table  S1 ). The conditions for PCR were optimized with respect to primer concentrations, primer annealing temperatures, and duration of steps. Cycling conditions were 95°C for 15 min followed by 38 cycles of 15 s at 94°C, 12 s at 60°C, and 12 s at 72°C. PCR for each gene fragment was performed alongside standard dilution curves of cDNA pool. All gene fragments were amplified in duplicate from the same RNA preparation, and the mean value was considered for each replicate. Data are means of 2 independent experiments.
Measurements were performed on cortical microtubules located towards the most external periclinal cell wall of hypocotyls' epidermal cells. 3 to 6 cells were measured per seedling, selecting ROIs with the size of the cell. At least 100 cells 8 were measured per genotype in each experiment. Experiments were repeated at least 8 times. Average angles with anisotropy values higher than 0. 05 were organized in three categories: transverse (0°-30°), oblique (30°-60°) and longitudinal (60°-90°) (Fig. S8 ). Those with anisotropy values minor to 0. 05 were considered to be random. For each genotype, absolute frequency was calculated per category and illustrated with a bar plot using Graphpad Software. Significant differences among frequencies were analyzed using a one-way or two-way analysis of variance and Tuckey test.
Hypocotyl length measurements Seeds were sown on 0. 5X MS on square petri dishes (12cm x 12cm). After stratification and germination induction, they were kept vertically in darkness during the number of days specified in each experiment. Twelve to fifteen seedlings of a maximum of 8 genotypes were sown in each plate at a time. The plates were imaged and the hypocotyl length was measured using the segmented line tool of ImageJ/Fiji. An average of 10 seedlings' measurements per genotype was considered as a biological replicate. Between 3 and 6 biological replicates per genotype were analyzed in each experiment. Data was analyzed by one way ANOVA followed by a posteriori Tuckey test for multiple comparisons. Homoscedasticity was evaluated by Levene's test and normality by modified Shapiro-Wilks test.