dataesr/scientific-paragraphs-categorization huggingface dataset:
We obtain the fitting parameters of Eqs. (  2 ) and (  3 ) using unconstrained nonlinear least squares curve fitting, relying on python script and a Levenberg-Marquardt optimization as implemented in the scipy. optimize. curve_fit routine in scipy  50. The script has been made open-source and can be found online at [https:// github. com/smerkel/kinetics-py]. Standard deviation errors on the fit parameters are calculated from the diagonal coefficients of the covariance matrix. Experimental uncertainties in pressure and temperature are not directly accounted for in the Levenberg-Marquardt optimization. They are, however, accounted for indirectly by changing the location of the Pv/Pv + pPv equilibrium boundary (see below). Calculations of overpressures are sensitive to the location of the Pv/Pv+pPv equilibrium boundary, which is ill-defined for (Mg,Fe)SiO 3.
Acknowledgements We wish to thank  S. Bellayer  for assistance with the electron probe analysis and  J-P. Perrillat  and  M. Roskosz  for useful discussions. The study was financed from a grant by the  Université de Lille  and the  Région Hauts-de-France  for C. L. , with support from the  Programme National de Planétologie (PNP)  of the CNRS, the bilateral  ANR-DFG   TIMEleSS  project ( ANR-17-CE31-0025 ;  TH 1530/18-1 ;  SA 2585/3-1 ), the bilateral PROCOPE Grant n. 40555PC , and the  ARCHI-CM  project. The project  ARCHI-CM of Chevreul Institute  ( FR 2638 ) is a grant from the  Ministère de l'Enseignement Supérieur et de la Recherche ,  Région Nord-Pas de Calais  and  European Regional Development Fund (ERDF). The electron probe microanalysis (EPMA) facility in Lille (France) is supported by the  European Regional Development Fund (ERDF).
Despite the use of predictive software examining the impact of genetic variants on protein functionality (i. e. , PANTHER, MutationTaster, Human Splicing Finder, PhyloP, VarSome)  [7]  and because three-dimensional models may not be available for some proteins, it is clear that only detailed functional characterization of the protein variants can predict the deleterious impact of genetic variation. Recently, using an NGS gene panel composed of 84 protein-coding genes to search for genetic alterations potentially associated with specific clinical endocrine phenotypes, we incidentally discovered six heterozygous missense variants of the nuclear receptor subfamily 3 group C member 1 (NR3C1) gene encoding the human glucocorticoid receptor (hGR) isoform a. This ligand-dependent transcription factor belongs to the nuclear receptor superfamily  [8]  and mediates most glucocorticoid (GC) actions in a large variety of tissues.
Chambers were mounted using ProLong Gold mounting medium (Thermo Fisher Scientific). Fluorescence deconvolution microscopy was performed using Image Pro Plus AMS software (Media Cybernetics Inc. , Marlow, UK) on a Mono Q Imaging Retiga 2000R Fast 1394 camera (Q Imaging Inc. , Surrey, BC, Canada). Images were acquired with an automated upright BX61 microscope (Olympus, Rungis, France) equipped with an X60 objective (1. 4 NA). A z-series of focal planes was digitally imaged and deconvolved with the threedimensional blind iterative algorithm (Image Pro Plus AMS) to generate high-resolution images  [27].
A. Identification of the NR3C1 Genetic Variants Among the six heterozygous genetic variants identified (Fig. 1 ; Table  3 ), three were private variants (A304E, N374S, and R386Q), indicating that they have been rarely if ever described in any databases (ExAC and 1000G), whereas the other three variants (F65V, M86V, and A229T) have already been reported. M86V has been described in nine patients, whereas the allelic frequency of A229T was at 0. 0014 (165/120812) and that of F65V was at 0. 0013 (153/ 121406) in the general population, although the latter variant is more frequently associated with the African population (0. 014) (http://exac. broadinstitute. org/). All these variants except M86V were classified as having unknown significance according to Varsome, whereas the M86V variant was classified as likely pathogenic by Varsome.
Discussion With the aim of characterizing genomic variation, we demonstrated that the six heterozygous genetic missense GR variants (F65V, M86V, A229T, A304E, N374S, and R386Q) incidentally discovered after NGS analysis exert a normal transactivation capacity compared with that of the WT, excluding any major impacts on the GC signaling pathway as well as their association with specific diseases. The current study has two important facets. First, it offers improved insight into GC signaling related to NR3C1 variations and increases our understanding of certain critical steps of GR-mediated transcriptional activation. Second, it confidently assesses association performed from at least three independent experiments. Representation was fitted with the "log(agonist) vs response three parameters" function of GraphPad software.
Guidance on reporting secondary findings  [44]  as well as the need for functional characterization of such genetic variations remains very controversial. In this context, management of NGS data and of the discovery of multiple variants of unknown significance remains quite difficult  [7, 45] , especially after a recent study revealed that one-fourth of healthy volunteers carried a monogenic disease risk with uncertain clinical usefulness, as revealed by genome sequence analysis  [46]. It is often necessary to avoid false variants and exclude false-positive and false-negative results  [1]. The pathogenic potential of new variants, notably missense mutations, requires the careful use of shared international databases  [6] , examination of interspecies nucleotide conservation, and determination of variation frequency, as well as familial segregation of the variants  [47].
Representation was fitted with the "log(agonist) vs response three parameters" function of GraphPad software. (B) Comparison of EC50 (DXM concentration inducing half-maximum transactivation) and of maximum transcriptional activity (B max ) of the six GR variants compared with the WT. Values are mean 6 SEM, obtained from four independent experiments (nonparametric ANOVA Kruskal-Wallis tests followed by Dunn posttests). (C) The absence of dominant negative effects of the variants on the WT is shown. HEK293T cells plated in 96well plates were cotransfected with 40 ng of plasmid encoding GR WT or 20 ng of plasmid encoding WT plus 20 ng encoding GR variants (F65V, A304E, and N374S), together with the reporter luciferase plasmid pMMTV-Luc, in which luciferase expression is under the control of GR response elements, and pMIR-b-gal coding b-galactosidase.
Acknowledgments The authors thank  Isabelle Boucly  and  Chrislaine Saujot  ( CHU Bicêtre ) for their excellent technical assistance in GR mutation screening. Financial Support: This work was supported by grants from  Institut National de la Santé et de la Recherche Médicale (Inserm)  and  Paris-Sud University. L. F. was the recipient of a master degree fellowship from the  French Endocrine Society and HRA Pharma laboratories. G. V. is the recipient of a doctoral fellowship from the  Fondation pour la Recherche Médicale. The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of this study. Author Contributions: M. L. contributed to the study concept and design. L. F. and G. V. performed experiments and statistical analysis. J. B. performed the NGS experiments and discovered the GR variants.
EMC = El Mayor-Cucapah; EF = Elsinore fault; SJFZ = San Jacinto fault zone; ASG = Anza seismic gap; SAF = San Andreas fault; SS = Salton Sea; IV = Imperial Valley. (b) Enlarged study area. Triangles and inverted triangles indicate surface and borehole stations. Blue circles indicate seismicity in the SJF box in (a) in the 5 days after the EMC event that led to the spike at t 0 in the black data in Figure  2b. PFO, KNW, and PMD are stations referred to in the text. The tripod indicates the configuration of the long base strainmeter at the PFO site. Light blue dots indicate hot springs. TA = trifurcation area. that are more pronounced to the northeast of the EMC rupture, along with triggered shallow slip by the Ocotillo aftershock that extends to the Elsinore fault  (Donnellan et al. , 2018).
Cross-Correlation Functions For the seismic velocity change analysis we make two databases of noise cross-correlation functions between all sensors within 40-km distance of each other using daily seismic records from 2008 to 2015 following the procedure of  Hillers, Ben-Zion, et al. (2015) ,  Poli et al. (2012), and Zigone et al. (2015). We first remove the instrument response. Processing is then applied to 1-hr-long windows. We discard 0. 05-Hz high-pass filtered hourly seismograms that contain large amplitudes associated with earthquake signals or other transients or data acquisition problems. This transient detection is based on markers of the amplitude distribution in three 20-min-long subwindows. We then apply spectral whitening in the 0. 05-to 5-Hz frequency range to the data in each hour-long window  (Bensen et al.
, 2007). For the subsequent time domain amplitude clipping we find that sign-bit clipping yields higher signal-to-noise ratios (SNR) of the resulting correlation functions compared to clipping at some multiple of the standard deviation of the noise amplitude distribution in each processing window. Sign-bit or 1-bit clipping is followed by another 0. 05-to 5-Hz bandpass filter before the seismograms are tapered, cross-correlated, and stacked to daily correlation functions. The first database is made to study seasonal variations between  2008 and 2015 (Figures 4-6). For this we use vertical component data from days 1 to 300 in each year. The daily correlations are stacked over ±2 days and downsampled by a factor of 2. The second database facilitates the high-resolution analysis of the responses to the EMC and Collins Valley events in 2010.
This database consists of daily nine-component (9C) correlations between t 0 -50 days and t 0 + 150 days, where t 0 is day 94, 4 April 2010 (Figure  7 ). This window is centered on the 70-to 90-day-long transient following the EMC event, and its extent is limited by the computational cost of the employed dv∕v time series estimation described below. The velocity change 10. 1029/2018JB017143 signals in this window are imaged in Figure  8 , the depth resolution is discussed in Figure  9 , and the signals are compared to records from the long base laser strainmeter at the PFO site (Figures  10a-10c ).
Relative Velocity Change Estimates To assess the robustness of the obtained dv∕v results and the sensitivity to wavefield variations, we apply three different approaches for estimating relative velocity change time series from the 2010 9C high-resolution database using surface station data in the 0. 2-to 2-Hz frequency range. The associated results are shown columnwise in Figure  3. The first two approaches estimate dv∕v changes from variations between a daily correlation function and a reference stack compiled from the correlations in the analysis period. We use the time domain stretching method  (Grêt et al. , 2005; Lobkis & Weaver, 2003; Sens-Schönfelder & Wegler, 2006)  and the frequency domain doublet or Moving Window Cross Spectral (MWCS) technique  (Clarke et al. , 2011; Poupinet et al.
We compare the dv∕v time series to the strain data and strain rate estimates  (Rivet et al. , 2011). For clean estimates we limit the analysis to 55-and 40-day-long time windows that end after the occurrence of the peak strain amplitudes following the two earthquakes. The 55-day range after the EMC event stops just before the emergent onset in the  yy and  data (Figure  10a ). This excursion seen on one strainmeter at t = 56 days has been traced to an instrumental problem. We then fit a fifth-order polynomial to these daily sampled strain data segments. These are the colored line segments in Figure  10a. Strain rate is obtained by differentiating these functions. The choice of the order 5 is not important for our conclusions. Figures  10b  and 10c  show the scaled absolute values of the obtained strain and strain rate functions.
The ellipse at "SS" indicates the Salton Sea Geothermal Field. Shallow slip has been triggered on faults in the hatched areas to the left of the "IV" and below the "SAF" labels. Deep slow slip has been inferred along the yellow indicated SJFZ fault segment that extends to the orange star. EMC = El Mayor-Cucapah; EF = Elsinore fault; SJFZ = San Jacinto fault zone; ASG = Anza seismic gap; SAF = San Andreas fault; SS = Salton Sea; IV = Imperial Valley. (b) Enlarged study area. Triangles and inverted triangles indicate surface and borehole stations. Blue circles indicate seismicity in the SJF box in (a) in the 5 days after the EMC event that led to the spike at t 0 in the black data in Figure 2b. PFO, KNW, and PMD are stations referred to in the text. The tripod indicates the configuration of the long base strainmeter at the PFO site.
Results were collected, tabulated in Excel (Microsoft, Redmond, WA, USA) and bar graphs were generated. Pearson's Chi-squared test was used to compare between geographical subregions. Data were analysed with R version 3. 5. 2 (www. r-project. org). In addition, a literature search was conducted for articles about the care pathway and unmet needs of patients with PF/IPF. PubMed and Embase were searched for articles published between January 2010 and March 2018, using the Medical Subject Headings (MeSH) terms "idiopathic pulmonary fibrosis", "pulmonary fibrosis", "interstitial lung disease" or "diffuse parenchymal lung disease" in combination with "care pathway", "unmet needs" and/or "barriers". The search was limited to adults and articles published in English. The reference lists of articles were manually screened for additional publications.
Acknowledgements: The European Idiopathic Pulmonary Fibrosis and Related Disorders Federation (EU-IPFF) conducted the two surveys in collaboration with the  ILD Core Network  of the European Reference Network on Rare Lung Diseases (ERN-LUNG). The authors would like to thank all the respondents of the surveys for their valuable input and EU-IPFF members for having supported the dissemination of the questionnaire through their channels. Support statement: This study was financially supported by  Boehringer Ingelheim International GmbH and F. Hoffmann-La Roche Ltd , who had no influence over the content or reporting of the results. Funding information for this article has been deposited with the Crossref Funder Registry.
One of the biggest stakes in nanoelectronics today is to meet the needs of Artificial Intelligence by designing hardware neural networks which, by fusing computation and memory, process and learn from data with limited energy. For this purpose, memristive devices are excellent candidates to emulate synapses. A challenge, however, is to map existing learning algorithms onto a chip: for a physical implementation, a learning rule should ideally be tolerant to the typical intrinsic imperfections of such memristive devices, and local. Restricted Boltzmann Machines (RBM), for their local learning rule and inherent tolerance to stochasticity, comply with both of these constraints and constitute a highly attractive algorithm towards achieving memristor-based Deep Learning. On simulation grounds, this work gives insights into designing simple memristive devices programming protocols to train on chip Boltzmann Machines.
Among other RBM-based neural networks, we advocate using a Discriminative RBM, with two hardware-oriented adaptations. We propose a pulse width selection scheme based on the sign of two successive weight updates, and show that it removes the constraint to precisely tune the initial programming pulse width as a hyperparameter. We also propose to evaluate the weight update requested by the algorithm across several samples and stochastic realizations. We show that this strategy brings a partial immunity against the most severe memristive device imperfections such as the non-linearity and the stochasticity of the conductance updates, as well as device-to-device variability. Fast progress in machine learning and big data processing make conventional electronics hardware unable to cope with it in the long run, and calls for breakthrough in artificial intelligence hardware design 1.
In the best case (near-linear) the stack of RBMs is not useful, in the worst case (non-linear), learning is dramatically jeopardized when passing corrupted features into downstream RBMs. On the other hand, this inefficiency also stems from not fine-tuning the stack of RBMs with backpropagation, as per our choice to solely focus on local learning rules. For advanced applications, Discriminative RBMs could nonetheless be used within deep neural networks to learn complex tasks, if the transfer learning approach is used. This approach consists in importing upstreams weights previously trained on software for feature extraction on a particular kind of data, and training in situ only the last layers on similar but more specific data  6. This strategy has been proven in various contexts, and allows training neural networks on new tasks with relatively modest amounts of data, if the neural network has been previously trained on a different but yet similar task with important amounts of data  51.
We assumed that the memristive devices are associated with an access device (transistor  6  or resistive switching selector device  52, 53  ), and therefore neglected sneak paths current effects. Sneak paths currents, if present, would significantly decrease the accuracy in programming the synaptic weights. Our goal is to focus on the effects of the weight update physics and the learning rules it enables on the different neural network architectures introduced above, so as to motivate further realistic investigations. When training any architecture throughout the paper, we used 40,000 samples for training and 10,000 for test from the MNIST data base. The different neural network topologies (bias included) were set as follows: 785-301-10 for the RBM + softmax stack, 794(784 + 10) + 300 for the Discriminative RBM and 785 + 501 + 511(501 + 10) + 2001 for the Deep Belief Net.
     .      - - ( ) , 0 1 G G G G If not stated otherwise, simulations were performed over 30 epochs, over 5 trials, with error bars indicating median, first quartile, third quartile with a mini-batch size of 100. Finally, the multiplicative coefficient η + appearing in Alg. (2) describing RProp was set empirically set to 1. 01 and we fixed η -= 1/η +. We selected η + by simply drawing the cumulative distribution function of the final pulse widths and ensured that it was spread enough over the whole range [0, Δt max ] -with η = 1. 05 conversely, 40% of the device were shut off after 30 epochs of learning (Δt = 0). All simulation scripts can be found on: https://github. com/ernoult/mem-RBM. git.
Among the six annotated DAM genes, four were differentially expressed in the dataset. PavDAM1, PavDAM3 and PavDAM6 were highly expressed during paradormancy and at the beginning of endodormancy (cluster 4, Fig. 5 ) whereas the expression peak for PavDAM4 was observed at the end of endodormancy (cluster 6, Fig. 5 ). In addition, we found that genes coding for 1,3-β-glucanases from the Glycosyl hydrolase family 17 (PavGH17), as well as a PLASMO-DESMATA CALLOSE-BINDING PROTEIN 3 (PavPDCB3) gene were repressed during dormancy (clusters 1 and 10, Fig. 5 ).
0  [37]. We decided to focus on results for TFs that are themselves DEGs between dormant and non-dormant bud stages. Results show that different pathways are activated throughout bud development. Among the genes expressed during the organogenesis and paradormancy phases (clusters 1, 2, 3 and 4), we Fig. 4  Enrichments in gene ontology terms for biological processes and average expression patterns in the different clusters in the sweet cherry cultivar 'Garnet'. a Using the topGO package  [36] , we performed an enrichment analysis on GO terms for biological processes based on a classic Fisher algorithm. Enriched GO terms with the lowest p-value were selected for representation. Dot size represents the number of genes belonging to the clusters associated with the GO term. b Average z-score values for each cluster.
The clustering of samples along the two main axes of the PCA using these seven markers is very similar, if not almost identical, to the PCA results obtained using expression for all DEGs (Fig. 6 ). This indicates that the transcriptomic data can be reduced to only seven genes and still provides accurate information about the flower bud stages. To test if these seven markers can be used to define the flower bud stage, we used a multinomial logistic regression modelling approach to predict the flower bud stage in our dataset based on the expression levels for these seven genes in the three cultivars 'Garnet', 'Regina' and 'Cristobalina' (Fig. 9 ). For this, we trained and tested the model, on randomly picked sets, to predict the five bud stage categories, and obtained a very high model accuracy (100%; Additional file 1: Figure  S9 ).
a Relative expressions were obtained by RT-qPCR and normalized by the expression of two reference constitutively expressed genes PavRPII and PavEF1. Data were obtained for two bud dormancy cycles: 2015/2016 (orange lines and symbols) and 2017/2018 (blue lines and symbols). b Evaluation of the dormancy status 'Fertard' flower buds during the two seasons using the percentage of open flower buds (BBCH stage 53). c Predicted vs experimentally estimated bud stages. SRP: STRESS RESPONSIVE PROTEIN; TCX2: TESMIN/TSO1-like CXC 2; CSLG3: Cellulose Synthase like G3; GH127: Glycosyl Hydrolase 127; PP2C: Phosphatase 2C; UDP-GalT1: UDP-Galactose transporter 1; MEE9: maternal effect embryo arrest 9 distinct transcriptional states (Figs. 2, 3 ) and we highlighted the different pathways activated during the main cherry tree flower bud dormancy stages (Fig.
These results are supported by previous reports where genes involved in ABA signaling are differentially expressed during dormancy in various tree species (for e. g. , see  [19, 20, 22, 24, 68] ). We find ABArelated pathways to be central in our transcriptomic analysis of sweet cherry bud dormancy, with the enrichment of GO terms related to ABA found in the genes highly expressed during endodormancy. These genes, including ABA-degradation gene PavCYP707A2, ABA-response factor PavABF2, and the Protein phosphatase 2C (PavPP2C) marker gene, are then inhibited after dormancy release in the three cultivars. Accordingly, we identified a key role for ABA-associated genes PavABI5 and PavABF2 in the regulation of dormancy progression in our dataset. These two transcription factors are mainly expressed around the time of dormancy release, like their target, and their homologs in Arabidopsis are involved in key ABA processes, especially during seed dormancy  [69].
Mapping and differential expression analysis The raw reads obtained from the sequencing were analysed using several publicly available software and inhouse scripts. The quality of reads was assessed using FastQC (www. bioinformatics. babraham. ac. uk/projects/ fastqc/) and possible adaptor contaminations were removed using Trimmomatic  [96]. Trimmed reads were mapped to the peach (Prunus persica (L) Batsch) reference genome v. 2  [97]  (genome sequence and information can be found at the following address: https:// phytozome. jgi. doe. gov/pz/portal. html#!info?alias=Org_ Ppersica) using Tophat  [38]. Possible optical duplicates were removed using Picard tools (https://github. com/ broadinstitute/picard). The total number of mapped reads of each samples are given in (Additional file 2: Table  S6  ).