dataesr/scientific-paragraphs-categorization huggingface dataset:
Reduction of data dimensionality Due to the curse of dimensionality (there are 3. 6 times more dimensions than available samples in our raw ISFC dataset), and given the likely presence of many irrelevant dimensions (i. e. , connections not responding to the movie), reliable analytical outcomes may be difficult to obtain  (Hinneburg and Keim, 1999). Furthermore, since the goal of our study is to understand ASD, it would be desirable to focus analyses on movie-driven changes that are not related to differences in IQ across our subjects, but to ASD per se. Therefore, we deployed feature selection based on a recently developed metric that extracts, for each connection, the moments of significant ISFC transients in the data  (Bolton et al. , 2018). Briefly, this operates through a comparison between movie watching and resting-state ISFC time courses, the latter being used to construct a connection-specific null distribution.
05, the probed connection was flagged IQ-related. To account for possible remaining impacts of head motion, FD (computed on non-scrubbed frames) was included as a covariate of no interest. Fig. 2A  depicts how many connections were not linked to IQ, and hence retained for subsequent analyses, as a function of the α-level. As α increases (less conservative thresholding), there is first a larger amount of detected relationships with IQ-and hence, less retained connections for ensuing analyses, due to the fact that more of the ISFC transients become captured. For even larger α, the number of significant IQ relationships decreases due to the greater noise (false positive ISFC excursions) that starts contaminating the data. For the analyses presented in this work, we selected the case that yielded the lowest number of retained connections, so that those with possible link to IQ would be maximally discarded, while at the same time lowering the dimensionality of our data as much as possible.
ISFC state analysis In a first analysis, we wished to track the evolution of whole-brain ISFC configurations over time in our subjects. To do so, we applied a state-based approach, where a set of connectivity states are derived from the whole sliding window ISFC dataset. Details regarding the generation of the states, and the origin of the approach-borrowed from the restingstate dynamic functional connectivity field, are provided in the Supplementary Material, section 1. Below, we detail the aspects specific to the present work: the selection of an optimal number of states into which to segment the data (Section 2. 5. 1), the design of population-level metrics reflective of ISFC state expression (Section 2. 5. 2), the extraction of the most influential connections from the ISFC states (Section 2.
Metrics reflective of population-level ISFC state expression Following clustering into K ISFC states (each of size jC j ¼ 2604), our goal was to extract measures reflective of population-level state expression in TD and ASD subjects. We considered three different metrics, detailed below: (1) within-group homogeneity of ISFC state expression, (2) within-group idiosyncrasy in ISFC state expression, and (3) crossgroup difference in ISFC state expression. Moments when different members of the same subject population simultaneously visit the same ISFC state are likely driven by particularly salient movie features, and are thus relevant to the understanding of the underlying functional brain responses. We computed homogeneity within a population of subjects at time t as the maximal fraction of subjects expressing the same state: if S ses data sessions are considered, it can thus vary between 1 Sses (all subjects express a different state) and 1 (all subjects express the same state).
Second, we created 998 surrogates of ISFC state expression in the assessed population: in each case, at time t, the state expressed by a given subject was randomly sampled with replacement out of n med possibilities. Because homogeneity and idiosyncrasy are directly dependent, we extracted significant time points twodimensionally, and retained the moments when the two-dimensional (Homogeneity, Idiosyncrasy) data point would fall outside of the area spanned by null values. For population dissimilarity, at time t, each of 998 null realisations was built by randomly shuffling subject labels between the TD and ASD groups, and subsequently computing population dissimilarity from this transformed ISFC state expression data. We deemed significant the moments when dissimilarity exceeded the range of the null distribution.
Partial least squares analysis As a second main analysis, we wanted to determine whether the idiosyncratic expression of ISFC states in the ASD population could be related to subject-specific symptomatology. To do so, we used partial least squares (PLS), an approach that extracts overlapping covariance relationships between two sets of data. Below, we summarise the gist of PLS analysis; details regarding significance assessment can be found in the Supplementary Material, section 4 (McIntosh and Lobaugh, 2004;  Krishnan et al. , 2011). In our case, the first dataset consisted in ISFC state expression features. For each ASD subject, we used the time courses of expression of all ISFC states, thus inputing a space/time representation. The resulting data was summarised in the imaging features matrix F 2 R ðK Á TÞÂ24 , with K the number of ISFC states and T the number of time points when ISFC estimates were obtained.
The matrix only contains 24 (instead of 29) columns, as data on individual symptomatology was not available for some subjects. The second dataset consisted in four scores from the Autism Diagnostic Interview-Revised: social component, nonverbal communication component, verbal communication component, and stereotyped behaviours component (note that larger score values highlight greater impairment), summarised in the behavioural matrix B 2 R 4Â24. Both matrices were normalised across subjects prior to PLS analysis. PLS considers the cross-covariance matrix between ISFC features and behavioural domain scores: R ¼ BF > : (2) This matrix can be equivalently expressed through singular value decomposition as: R ¼ UΣV > ; (3) where U 2 R 4Â4 contains the left singular vectors of R, arranged in successive columns.
These vectors form an orthonormal basis; i. e. , U > U ¼ I. The same property applies to the right singular vectors, arranged in the columns of V 2 ℝ ðK Á TÞÂ4. As for Σ 2 R 4Â4 , it is a diagonal matrix containing the singular values σ i ; i ¼ f1; 2; 3; 4g. We assume here that singular vectors and singular values are sorted in decreasing singular value order (that is, decreasing percentage of explained data covariance). The intuition behind this decomposition is that the full crosscovariance between both datasets is expressed as a weighted low-rank approximation: R ¼ X 4 i¼1 σ i u i v > i ; (4) where u i and v i are the i th left and right singular vectors, respectively. In the case of this work, since we consider only 4 behavioural scores (limiting dimension of the decomposition), four pairs of singular vectors are derived, and represent four overlapping modes of covariance.
Introduction NeXT-Grenoble is a Neutron and X-ray Tomograph launched in 2016 from the joint effort of Université Grenoble Alpes, UGA (and specifically Laboratoire 3SR) and the Institut Laue-Langevin, ILL, and takes advantage of its worldleading neutron flux. Design started in February 2016 and the instrument was available to users in October 2016. It is currently fully open for proposals through its dedicated website https://next-grenoble. fr. The high neutron flux available automatically makes this a unique instrument, since tomography performance (in terms of spatial resolution, temporal resolution and measurement noise) is wholly dependent by noise in data collection and therefore flux. Another key feature of the instrument is the possibility to perform x-ray absorption imaging, in order to take advantage of the high complementarity of these two techniques, sketched in Fig.
Concrete is a material of particular interest in porous media and an excellent example of the high complementary of the information which can be obtained combining x-ray and neutrons, as highlighted in Fig. 12d  Another example of the high complementary of x-ray and neutrons is the work of  [9] , where the hydro-mechanical response of a Callovo Oxfordian claystone is studied combining the high opacity of hydrogen to neutrons with the spatio-temporal resolution and density contrast of x-ray, as shown in Fig. 12e. It goes without saying that, much in the same way early 4D datasets required the development of new analysis algorithms as DVC, new algorithmic developments are equally required to quantitatively interpret combined x-ray and neutron data. For example, in  [15]  novel theoretical developments, extending classical digital volume correlation are proposed.
Acknowledgements This work is supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ) as well as by the company TOTAL. The authors also wish to thank the members of the Committees of the instrument (in alphabetical order  I. Betremieux ,  J. Desrues ,  G. Fragneto ,  G. Galliero ,  S. Hall ,  M. Johnson ,  M. Letiche ,  P. Moonen ,  C. Simon ,  N. C. Tranh ) for their help and support. We also wish to thank the help of  Nikolay Kardjilov  and  Jaime Segura , as well as the essential technical support of  K. Buckley. Finally , the authors wish to thank the anonymous reviewers for their helpful comments and recommendations, which helped improve the quality of this paper.
Genome sequencing, genomic and genetic association analysis Bacterial genomes were sequenced by MicrobesNG (Birmingham, UK, https://microbesng. uk/) on an Illumina HiSeq platform using a 250 bp paired-end protocol. Genomic DNA libraries were prepared using the Nextera XT Library Prep Kit (Illumina, San Diego, CA, USA). High-quality paired-end reads were assembled using the Galaxy/BBRIC pipeline (https://bbric-pipelines. toulouse. inra. fr/) and genome annotations were performed using EUGENE-PP  (Sallet et al. , 2014)  and RAST (http://rast. nmpdr. org/; Tables  S8-S10 ). Pairwise average nucleotide identity (ANI) values were calculated using JSPECIES software (http://jspecies. ribohost. com/jspeciesws), and dDDH values were calculated using the GGDC2. 1 webtool  (Meier-Kolthoff et al. , 2013).
Heatmaps were built using the pheatmap R package. Core and accessory genomes of Rlv bacteria were generated using the SPINE/AGENT webtools (http:// vfsmspineagent. fsm. northwestern. edu/index_age. html; Ozer et al. , 2014) using the default parameters (except minimum core genome segment size to output = 200 bp). Genomic regions specific to strains of interest were selected and their presence/absence checked by a BLAST search in their genomes. The nucleotide sequences of the nod genes were aligned using CLUSTALOMEGA webtool (https://www. ebi. ac. uk/services) and biallelic single nucleotide polymorphisms (SNP) were selected. A Mixed Linear Model implemented in the EMMAX software  (Kang et al. , 2010)  was used to detect associations between CFN values and SNPs of the nod region. Briefly, the model, which is routinely used in plant genome-wide association studies, estimates allelic effect at each SNP accounting for the genetic relationships between individuals which are described by a kinship matrix calculated using the whole SNP dataset.
Following association tests with faba bean and pea CFN phenotypes, two clusters of 53 and 76 significant SNPs, respectively, were detected (P-value threshold of 5%; Fig. 6 ). In order to account for multiple tests, we calculated a chromosomewide 5% P-value threshold for each dataset by using permutations (see Methods S1). The empirical P-value thresholds were 0. 004 and 0. 0015 for the faba bean and pea dataset, respectively. Using these thresholds, no SNP was detected as significant, although the smallest SNP P-values were 0. 01 and 0. 0068 in the faba bean and pea association analyses, respectively, thus close to the thresholds. However, 92% (49/53) and 76% (58/76) of the top significant SNPs (i. e. with P-value < 0. 05) associated with CFNs in faba bean and pea respectively, were clustered within the nodMNO genes zone.
Using a procedure of spatial permutations of each P-value dataset (see Methods S1), the probabilities to observe in the permuted datasets > 92% (faba bean) or 76% (pea) of SNPs with a P-value threshold < 0. 05 and lying within the 3300 bp nodMNO zone, were null. This indicated that the physical clustering of these significant SNPs within the nodMNO zone is unlikely to be due to random chance. To identify loci outside of the nod region potentially associated with CFN phenotypes, we compared bacteria with contrasted CFN phenotypes sharing close or contrasted nod genotypes (i. e. A vs A or A vs B). In the first approach, nod type A bacteria displaying contrasted pea/faba bean CFN phenotypes were compared. Five significant genomic sequences were identified and annotated by reference to the genome of the strain 3841 (Tables  2, S9 ).
, 2018). However this does not exclude that some genes present on the chromosome may also contribute to the symbiotic phenotype and its variation. Most of the Rlv nodule isolates (87%) and sequences identified directly in the soil (90%) belong to the two main nodD groups A1 and B1. These groups include and extend those already defined by RFLP analysis in previous studies on bacteria trapped from European soils with pea and faba bean  (Laguerre et al. , 2003; Mutch & Young, 2004)  or from Chinese soils with faba bean  (Tian et al. , 2010). Few examples of specific nodD alleles in particular geographic location were found (even when bacteria isolated outside of Europe and present in GenBank were included in the analysis), giving little support for strong phylogeographic structuring of the Rlv diversity.
Nevertheless, these loci represent valuable markers to select Rlv strains competitive for nodulation with pea and/or faba bean and open the way to the identification of genes controlling competitiveness for nodulation. Table  S7  Pea and faba bean symbiotic efficiency of selected isolates as the function of their phylogenic classes and their geographic origins. Table  S8  Genomic data of Rhizobium bacteria sequenced in this study. Table  S9  Presence/absence of candidate genes correlated with the CFN with P. sativum or V. faba. Table  S10  Presence/absence of candidate genes correlated with the CFN with P. sativum or V. faba. Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.
1. 1 Based on sequence homology to the Rlv 3841 reference genome. 2 Positions of the homologous sequences in the Rlv 3841 reference genome. 3 Annotations of RAST databases. 4 Positions of the homologous sequences in the Rlv RSP1E11 genome. New Phytologist (2020) 226: 555-568 Ó 2019 The Authors New Phytologist Ó 2019 New Phytologist Trust www. newphytologist. com.
Two-terminal device with nanohole arrays for photoconductivity measurements: All the 2-terminal electrodes were realized on a glass substrate BK7 (25×25 mm) after standard cleaning procedures with Hellmanex™ III solution (1. 0% solution in milliQ water) in a 35 kHz sonication bath, then rinsed with water and sonicated for 1 h in (spectroscopically pure) ethanol. The glass substrates were then dried in an oven covered with Aluminium foil. 100 nm thick Ag electrodes were fabricated using a metallic cross mask containing 12 fingers (approx. 50 µm width) in an electron beam evaporator (Plassys ME 300) at optimized working pressure ( ∼ 10 -6 mbar) and deposition rates ( ∼ 2 nm s -1 ). Plasmonic hole arrays were generated by the NPVE software program and milled using a Carl Zeiss Auriga FIB system.
On top of the sputtered SiO x , organic semiconductor and PVA layer were spin coated as explained above. The electrical characterization of the 2-terminal device was carried out by means of a Cascade Microtech MPS-150 probe station equipped with micro-positioners to contact the electrode pads. Both the dark current and photoconductivity I-V characteristics are recorded by means of a Keithley 2636B source meter interfaced with Labtracer 2. 0 software. Photocurrent generation was tested under white light cw illumination and with a supercontinuum ps pulsed laser (NKT Photonics) with a nominal power of 100 mW to 200 mW, with variable wavelength (500-800 nm; 10 nm FWHM) provided by a SuperK tunable single line filter. The output of the FC/PC fiber was directed to the center of the electrode covering a spot size of 2 mm in diameter.
ACKNOWLEDGMENT We acknowledge support of the  International Center for Frontier Research in Chemistry (icFRC, Strasbourg) , the  ANR Equipex Union  ( ANR-10-EQPX-52-01 ),  CSC  ( ANR-10-LABX-0026 CSC ) within the  Investissement d'Avenir program   ANR-10-IDEX-0002-02. TWE acknowledges the support of the  ERC  (project no  788482 MOLUSC ). The authors declare no competing financial interest.
This paper is structured as follows: Sect. 2 presents the snow models employed in the study, while Sect. 3 describes the station of Torgnon and the datasets employed for the experiments. Section 4 describes in detail the set of 12 devised experiments and, for each experiment, the method employed to derive the forcing. Section 5 focuses on the evaluation of snow model outputs against observations, and finally Sects. 6 and 7 discuss the results and draw the conclusions.
Precipitation measurements are performed with an OTT Pluvio2 Weighing Rain Gauge, which employs a weight-based technique to measure both liquid and solid fractions (i. e. the total precipitation amount). This is a consolidated technique that provides higher confidence in the reliability of precipitation data than standard rain gauges  (Kochendorfer et al. , 2017a). Despite the station being equipped with a reliable pluviometer and being exposed to low wind speeds, possible issues of precipitation undercatch can be present. The uncertainty associated with precipitation measurement has been estimated and the impact of the uncertainty of the precipitation input on snow model simulations is assessed and discussed in Appendix A. As the OTT pluviometer has been operational since mid-2012, in our analysis we consider the dataset spanning the period from 1 October 2012 to 30 June 2017, covering five complete snow seasons.
Spatial interpolation of meteorological forcings from neighbouring stations The spatial interpolation of ground meteorological observations represents one of the most commonly used practices in the operational applications of hydrological models. In order to test the performances of the models in this condition, an interpolated dataset has been generated for the Torgnon monitoring site by using the MeteoIO library  (Bavay and Egger, 2014). Meteorological data from six neighbouring stations have been interpolated over a squared digital elevation model of 16 km 2 with a grid resolution of 50 m centered on the coordinates of Torgnon (Appendix B Fig. B1  and Table  B1 ). The algorithm used for the interpolation is the inverse distance weight (IDW) as the first choice for all the meteorological variables.
Reanalysis data In many remote mountain areas, in situ observations to force snow models are unavailable. In this study we explore the use of reanalysis datasets extracted at the Torgnon grid point. GLDAS (Global Land Data Assimilation System) is a global dataset exploiting satellite and ground-based observational data combined with advanced modelling and data assimilation techniques in order to generate optimal fields of surface variables  (Rodell et al. , 2004). In particular, the GLDAS-2. 1 archive used in this study contains 36 land surface fields from January 2000 and is updated regularly at 0. 25 • (long-lat) spatial and 3 h temporal resolutions  (Rui and Beaudoing, 2018). ERA-Interim  (Dee et al. , 2011)  is a global reanalysis including a variety of 3-hourly surface parameters describing atmospheric and land-surface conditions, and 6-hourly upper-air parameters covering the troposphere and stratosphere.
ERA-Interim has a spatial resolution of 0. 75 • , at the latitude of Torgnon corresponding to about 59 km in the zonal and 83 km in the meridional direction. This coarse grid, which is comparable to those of state-of-the-art global climate models, implies a smooth representation of the topography and coarse information on climate variables. ERA5  (Hersbach and Dee, 2016)  is the latest ECMWF global reanalysis product, providing data at higher resolution than ERA-Interim, both in space (30 km) and in time (1 h). ERA5 uses one of the most recent versions of the https://doi. org/10. 5194/hess-24-4061-2020 Hydrol. Earth Syst. Sci. , 24, 4061-4090, 2020 Earth system model and data assimilation methods applied at ECMWF and modern parameterizations of Earth processes compared to older versions used in ERA-Interim.
Experimental design We devised a set of 12 experiments at the Torgnon site employing snow models in stand-alone mode, i. e. in which the meteorological forcing is prescribed. The list of experiments is summarized in Table  2. The first experiment is a control run (CTL) in which the models are forced by optimal input data provided by the Torgnon station at 30 min temporal resolution. This run allows testing of the accuracy of the models in describing the temporal evolution of the snow-related variables in optimal conditions, namely when high-quality, high-frequency point measurements are available. Experiments RAD-ERAI and SWIN-CLS assess the sensitivity of the models to the radiation input. As most stations, the Torgnon site is equipped with an unheated radiation sensor, which is likely to provide unreliable measurements when obstructed by snow during snowfall events.
Therefore, in experiment RAD-ERAI we take into account the shading of the radiation sensor in case of snowfall by replacing radiometer measurements with ERA-Interim reanalysis data. In the third experiment, SWIN-CLS, we employ external SWIN data resulting from the clear-sky radiation  (Yang et al. , 2001 (Yang et al. , , 2006) )  attenuated through the cloud masks from the Meteosat Second Generation (MSG) satellite in the following way. For each of the 34 radiometers in the Aosta Valley, an averaged attenuation factor F is computed as F = 1 N N i=1 R i st SWIN i , (1) where N is the number of cloud-covered stations determined from the MSG cloud mask, R i st is the measured radiation at the ith station and SWIN i is the corresponding modelled radiation in clear-sky conditions. The incident solar radiation in cloudy conditions at location j is given by R j = SWIN j F.
(2) Experiments TIME-3h, TIME-6h and TIME-12h investigate the sensitivity of the models to the temporal resolution of the meteorological forcing, since the temporal resolution of many available datasets is coarser than that employed in the CTL run. We have employed the Torgnon data every 3, 6 and 12 h since 1 October 2012, time 00:00 UTC+1, and linearly interpolated them at the 30 min time step for all variables except for total precipitation. Precipitation is accumulated over 3, 6 or 12 h time periods and the totals are equally distributed among the corresponding 30 min subperiods. Incoming shortwave radiation is linearly interpolated at the 30 min time step for all experiments, i. e. TIME-3h, TIME-6h and TIME-12h. However, when we apply linear interpolation to derive the forcing for the TIME-12h experiment, we obtain poor SWIN estimates, with a large difference between the estimated and CTL average SWIN fluxes (+97 W m -2 ).