dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors acknowledge funding by the  Andrew W. Mellon  and  Samuel H. Kress  foundations. Reference paintout samples were made by  Brian Baade. We acknowledge advice and reference materials from  Marcello Picollo  and  Maurizio Aceto. Finally , we dedicate this paper to  Mr Ross Merrill , former  Chief of Conservation, National Gallery of Art, Washington ,  DC  whose vision has helped guide our studies in the application of imaging spectroscopy to conservation.
A classical approach to evaluate the conditional quantile function from a sample (X i , Y i ) on a discrete finite set is the local linear quantile regression method from Yu and Jones (denoted QYJ) in  [16]. As its name suggests, q τ (x) is approximated by a piecewise linear curve, resulting is a broken line that emphasizes local fidelity to the detriment of regularity, difficult to interpret as a reference chart. Cannon  [2]  developed in R the QRNN package (for Quantile Regression Neural Network) based on the work from Taylor  [11]  in the field of time series forecasting. J. W. Taylor used a one hidden-layer feedforward neural network (FNN) to fit a non-local quantile model. FNN where successfully used in several domains like robotics and image processing (see for example  [6, 7] ) as non linear parametric models.
denotes the distance function that is taken, in general, to be the Euclidean norm. In this work we use the Mahalanobis distance, because it takes into account the correlations inside the data set and is scale-invariant; in our case, it contributes to a better adjustment of the neurons width with the data. This distance is defined as x -c = (x -c) t Σ -1 (x -c), ( 7 ) where Σ is the covariance matrix of (x 1 ,. , x n ),andx i ∈ R d (i = 1,…, n) are the training data input. We use in this work an iterative design of the network: neurons are added one at a time until the mean sum-squared error (MSE) falls beneath an error goal or a maximum number of neurons has been reached. The choice of the number N of neurons on the hidden layer of FNN and RBFN networks is crucial. A low number means a very poor performance, or fidelity, of the network.
Instead, a large number of neurons will allow the network to fit exactly all the data (resulting in a very low MSE), including noise and biased observations: its regularity will be low. Because N is an integer defining the structure of the network, it cannot be adjusted like the synaptic weights. It exists for FNN some meta-heuristics (like genetic algorithms) to adjust N , but this is a slow process. Unlike FNN, RBFN authorizes a faster constructive approach because of the locality of the radial basis transfer function in the hidden layer. Let x i ∈ R d be the training data inputs and y i ∈ R be the training data outputs for i = 1,…, n. We now explain our RBFN learning algorithm.
Improvement of Conditional Quantile Estimation with a RBFN Network We explain below our algorithm to improve the QYJ estimator using the RBFN model. Let (X 1 , Y 1 ),. , (X n , Y n ) be a random sample from (X, Y ), and assume the homoscedastic model: Y i = m(X i ) + γǫ i , i = 1,. , n, (9) where m(. ) is an unknown function, γ unknown reel parameter and the residuals ǫ i are uncorrelated random variables of known distribution with zero mean and one variance. Therefore the conditional quantile function is defined by: q τ (x) = m(x) + γ F -1 ǫ (τ ). (10) The proposed algorithm is explained as follow : -Estimate first the conditional quantile function q τ (x) using the QYJ estimator with the Yu and Jone method to select its smoothing parameter; we get q QY J τ (x). -F r o m( 10), we have m QY J (x k ) =q QY J τ (x k ) -γ F -1 ǫ (τ ) , where γ 2 is the variance of the data outputs, and x k , k = 1,.
Simulations with a Known Underlying Model The following applications have been made using the R software, with the package "quantreg" from Koenker  [17]  for our implementation of the QYJ algorithm and the QRNN R-package implemented by Cannon  [18]. Consider the model Y i = exp(-X i ) + exp -4(X i -1) 2 + ǫ i for i = 1,. , n,where{ǫ i } are independent and identically N (0, 1) random variables, and {X i } are exponential random variables with mean 1 independent from {ǫ i }. Then the true conditional quantile function is given by q τ (x) = exp(-x) + exp -4(x -1) 2 + F -1 ǫ (τ ) , where F ǫ is the cumulative N (0, 1) distribution function. First, we compare for different sample sizes n and different quantile values τ the performance of the QRBF estimator without constraint with the QYJ estimator and the QRNN estimator with 5 neurons on the hidden layer (after several tries, 5 neurons appear to be a good compromise between the error goal and the calculation speed).
Application to the Building of Brain Maturation Reference Curves The next results were obtained from the automatic analysis of 416 EEG recorded between years 2003 and 2004 on newborn infants, with relative informations such that the infant outcome, age (from birthday and gestational), other neurological evaluations, etc. Moreover, each EEG in this database was visually analyzed and categorized as "artefact", "normal", "doubtful" or "pathological". A specific algorithm was built during this research to detect IBIs using an approach mimicking visual analysis. The algorithm is designed to study on each channel the variation of the signal estimated variance between contiguous short timewindows. An IBI on one channel is detected if this variation is lower than a given threshold and EEG (global) IBIs are finally computed as the intersection of channel's IBIs.
HCV-derived small non-coding RNAs The positive sense HCV RNA genome is replicated in infected cells using the minus strand viral genomic RNA as a template. We reasoned that the conserved hairpinloop structure of the minus strand genomic RNA, which is not engaged in translation, could serve as possible source of viral small non-coding RNAs (vmrs). To determine candidate vmrs, we employed bioinformatics methods to search the ~9,600 bp minus strand RNA genome for structurally conserved hairpins that could act as putative viral microRNA precursors. Sequence conservation at the 'seed' position in 37 GenBank HCV genomes as well as the configuration geometry of the hairpin arm and loop helped select 13 candidates, whose presence in HCVinfected cells were validated with qRT-PCR (see Methods and Additional file 1: Figure  S1  (and Additional file 1: Table  S1 , for details).
Functional analyses using the KEGG database  [15]  revealed several pathways enriched among differentially expressed genes and in common between the two sets of comparisons (H77 and 'mimic'). In particular, genes involved in lipid biosynthesis, intracellular signaling, and control of cell growth and patterning were enriched among the up-regulated genes; whereas genes involved in the regulation of cell motility, and members of the peroxisome proliferator activated receptors (PPAR) signaling pathway involved in lipid metabolism, were over-represented among the down-regulated genes (Additional file 3).
Bioinformatics prediction of HCV small non-coding RNAs The 9,646 bp negative sense HCV type 1 genome (Gen-Bank accession: NC_004102) was divided, starting every 20 bp, into fixed size segments of length L = 70 (80, 90 and 100). RNAfold  [41]  was employed to test each short sequence to determine stable hairpin structures that can form viral small RNA precursors. Hairpins that scored in the top 5% of the score distributions (determined by comparison to simulated random samples) were analyzed for conserved secondary structure by comparing 37 HCV type 1 complete genome sequences. Lastly, putative viral microRNA sequences were determined starting from conserved sequences 7 bp or longer ('seeds') that could be extended to mature 20-25 viral small RNAs (see Additional file 1 for details). Transfection of PPH using H77 run-off transcript pCV-H77c plasmid DNA was linearized for run-off transcription of full length genomic RNA using T7 Ribomax Express (Promega).
Data analysis The Agilent Feature Extraction software (version 11. 0. 1. 1) was used to analyze acquired array images. Quantile normalization and subsequent data processing were performed with the GeneSpring GX v12. 0 software package (Agilent Technologies). Differentially expressed mRNAs were identified through Fold Change filtering (cutoff 2. 0). Pathway analyses were performed using the standard enrichment computation method. Lastly, 3′UTRs of Gen-Bank RefSeq genes for vmr11 putative target analysis with PITA were extracted from the UCSC Genome Browser tables. (Details are provided in Additional file 1).
Statistical Analysis. Values are expressed as mean, median, and range. Statistical analysis was performed using GraphPad Software. Data are expressed as mean ± standard deviation or median with interquartile range. The differences between the two groups were analyzed using the Mann-Whitney  test or Student's -test. One-way analysis of variance or Kruskal-Wallis test was performed to compare more than two groups. Multivariate survival analysis using Cox's regression model was conducted. To compare categorical variables, the chi-square or Fisher exact test was used. Kaplan-Meier method was used to estimate overall and relapse-free survival. For all tests, a  value of less than 0. 05 was considered significant.
This study took place within the framework of the MOPERA research programme ( Modélisation Probabiliste pour l'Évaluation du Risque Avalanche -Probabilistic modelling of avalanche risk assessment) funded by the ANR 2009, programme « Risques Naturels : Compréhension et Maîtrise » (Natural risks: comprehension and mastery). We would like to thank the GIS service of the Hautes-Alpes' Conseil Général (general council) for the availability of the "events" database and the reviewers of this article for their suggestions. 1 Road accessibility is particularly strategic for the upkeep of economic activities but also for rescue interventions. In the French Alps, accessibility is reduced, particularly at the bottom of the valley, and it may be further degraded by various natural hazards. In this regard, avalanches are a considerable threat because, other than the victims and direct damage they may cause, they often incur roads being cut off over more or less prolonged periods at a time of year when networks are already considerably altered by seasonal closures.
, 2011). By contrast, other scientists have recently applied these theories to natural risks  (Chang, 2003; Gleyze, 2005; Nabaa, 2011) , without formalising a territorial conception of risk confronting the notion of direct to indirect risk areas, via accessibility. Recent work undertaken in Mayotte for the tsunami risk  (Leone et al. , 2013)  or in the Alps for debris flow  (Leone et al. , 2011) , tend to formalise this territorial approach of risk based on deterministic models (for a given scenario of road closures) and not according to a closure risk analysis as suggested here for avalanches. In order to partially resolve such shortcomings, and retrospectively assess the physical, functional and human vulnerability of road networks to avalanche risk in the French Alps, the first stage of our study consisted in geo-referencing and harmonising in a GIS, the information given by the main databases available on avalanche consequences in the three French Alpine departments (Alpes-de-Haute-Provence, Hautes-Alpes, Alpes-Maritimes).
Some data comes from public organisations (INSEE, IGN, RTM/ONF), other data is provided by local authorities. They cover several themes and were subjected to a time-consuming compilation, integration, harmonisation and validation work, in the field particularly. Two local scale topographic and demographic referentials were used: • The IGN route500 vectorial database (2010) which represents the main roads with a precision of 100 m. It differentiates 5 classes of roads for speed calculations (highways, main roads, regional roads, local roads and access roads). It also informs us of the main roads' winter closures. • INSEE (2012)'s gridded population database provides the number of permanent residents per km 2 and was established from a specific use of fiscal files. Additional data on the periods of cols' usual closures or certain secondary roads, were provided by various sources (Internet, regional councils, Topo IGN DB, testimonies).
10 Most of our work consisted in setting up a new database of the impacts of avalanches on the road network of the three departments between 1937 and 2011. This base merges the information from three main sources which we had to georeference manually, case by case, after checking and removing duplicates. A total of 1,042 usable impacts were censured and located. 60% of the data came from the EPA base (Enquête Permanente sur les Avalanches-permanent survey on avalanches) managed by the Irstea. The EPA is a chronicle of events of over near 4,000 corridors, sometimes since the early 20 th Century, describing avalanches' physical characteristics and incurred damage or disturbances. The duration and quality of series has already proved its worth in other studies, notably the analysis of risk for urbanism including avalanche protection designs  (Eckert et al.
Conclusion and perspectives 31 This research, based on the avalanche risk's spatial and quantitative analysis, informs the vulnerability of a territory on a regional scale on the basis of its road network's exposure. Direct risk was studied retrospectively through the analysis of road impacts over more than seventy years. Indirect risk was assessed prospectively via indexes of network closures and population isolation. This enabled us to valorise exceptional historical data that was yet underexploited in risk geography and use the potentiality of road graphs provided by the IGN for accessibility calculations. It is a rather innovative approach in the avalanche field where nearly all road vulnerability assessment works are limited to the direct consequences of impacts on the network. 32 The new databases produced are georeferenced and adapted to a geographical interpretation of damage on roads.
We encourage the pursuing of their development and fulfilment, notably by integrating more numerous and elaborate socio-economic data that will gradually refine the obtained indicators. For example, the proposed damage typologies should in the future estimate more precisely the direct and indirect costs; taking their inventory on a regional scale is always a real challenge. Starting from these databases, a possible statistical approach to assess direct annual costs attributable to avalanches on the road network would be to multiply the average costs of each intensity of damage by their annual frequency. This implies to better take inventories of average costs, which is our study's weakness. Moreover, the macroeconomic analysis of indirect costs accountable to the territorial isolation could occur on the basis of scenarios on a local (valleys) or regional (mountains) scale, by accumulating activity losses, additional costs linked to possibly longer trajectories, rescue costs or dealing with isolated people (supplies, treatment) etc.
For example, the potentially isolated stakes studied here correspond to the resident population censured by the INSEE on a gridded base. But we can imagine other assessments taking in account temporal population variations (day / night or school holidays / not holidays), or other socio-economic indicators such as the number of days / skiers or the daily turnover of ski resorts. It is also possible to change the scale by refining the road network considered (IGN Topo DB for example) merging it with a finer territorial grid. The associated isolation risk assessment will be even more precise on the geographical aspect. Conversely, data can be aggregated to smaller scales to express indexes and losses of accessibility on the valley, mountain or variable administrative entity scales (communal, administrative districts, departments, etc.
These data suggest that LIF2 affects a variety of defense-related pathways. The 38 TFs that were both deregulated in the lif2-1 rosette transcriptome (Table  S2 ) and belong to the AP2/EREBP, WRKY, NAC, MYB, or bHLH family, as well as the 6 negative regulators (Fig. 8B ) represent a sort of molecular signature of the lif2 mutant phenotype. We thus wondered how these 44 genes were deregulated in wild-type genetic backgrounds in response to biotic stress. To address this question, we selected 62 transcriptome profiles from plants treated with pathogens compared with those that were not, in the Genevestigator database  [51]  and extracted the expression of these 44 genes. We then performed a hierarchical clustering analysis using of the MultiExperiment Viewer application  [52] , and identified three main gene clusters, which are characterized by specific co-expression profiles of MYC and MYB (Fig.
After serial dilutions, homogenates were plated on King's B medium and incubated at 28uC for 2 days before colonies were counted. The surface area of each leaf was measured using ImageJ software in order to calculate cfu/cm 2. Day 0 titers ranged from log 10 = 3. 6-3. 9. Leaves were photographed 5 days post-infiltration (dpi). All experiments were repeated at least twice with similar results.
Fungal pathogen inoculation procedure Four-week-old plants were inoculated with discs carrying S. sclerotiorum (S55 strain) mycelia, as previously described  [29]. Plant inoculations were performed in a Helios 1200 Phytotron under a light period of 9 h at 22uC and 80% relative humidity during the first 3 days and 66% thereafter. At least 20 plants of each genotype were inoculated. The disease score of each line was evaluated seven days post-inoculation. Two independent experiments were performed with similar results. Leaves of seven-week-old plants were inoculated with mycelial plugs of the wild-type strain B. cinerea B0510 and Bd90 (diameter of 3 mm), as previously described  [89]. Infected leaves were placed in a Petri dish under high humidity and incubated at 21uC. Lesion surfaces were measured daily for three days using ImageJ software.
70 kV (negative mode); extractor, 3 V; and source block and desolvation gas temperatures, 120uC and 350uC, respectively. Nitrogen was used for the nebulization and desolvation (77 L h 21 and 365 L h 21 , respectively), and argon was used as the collision gas at 2. 83 10 23 mbar. For a 5-mL injection volume of sample prepared and reconstituted in 140 ml of 50/50 acetonitrile/H2O (v/v), the limit of detection (LOD) and limit of quantification (LOQ) were extrapolated for each hormone from a calibration curve and sample using the Quantify module of MassLynx (version 4. 1 software). The parameters used for MRM quantification and the LOD and LOQ are listed in Tables  S4  and S5 , respectively. The amount of JA was expressed as a ratio of peak areas (209. 62/214. 62) per fresh weight, due to impurities contained in the D5-JA standard.
The extracts were then evaporated to dryness using a Thermo Savant Speedvac overnight, at ambient temperature. A water/acetonitrile solution (95/5) was added to the dried extracts, which were filtered and diluted to the third with water/acetonitrile (95/5), prior to negative electrospray LC-MS analysis on an Alliance 2695 system coupled to Quattro LC (Waters). Glucosinolates were identified by retention time, mass, isotopic pattern, and fragment ions  [90, 91]. The concentrations of the metabolites of interest were quantified using the sinigrin response, as previously described  [92]. Three independent extraction analyses were carried out per biological experiment and two to three biological replicates were performed. Biochemical information about the different classes of GLSs and their biosynthesis pathways can be found in various databases (e.
Acknowledgments We thank  Bruno Letarnec  and HerveFerry for plant care in the IJPB greenhouses, Gregory Mouille,  Alessandra Maia-Grondard , and the  IJPB Green Chemistry Platform  for the quantification of metabolic compounds,  Julia Courtial  for technical assistance with pathogen infection, and  Marie-Christine Soulie  ´for helpful comments. We are grateful to  Fabienne Granier  for bioinformatics advice and to  Lionel Navarro  and  Martine Boccara  for seeds of the sid2-2 mutant and genotyping advice. We thank  Nicole Houba-He  ´rin for material preparation, her constant support, and helpful discussion.
PSTs can accumulate in the DG  [7, 21]  and are able to induce modulation of the expression of key genes (α-amylase and triacylglycerol lipase B) involved in the digestive process  [16]. No other effects were observed suggesting complex mechanisms occurring in this tissue. In the present study we investigate specific relationships between PSTs accumulation and metabolic process that occur on the digestive gland of C. gigas exposed to toxic strain of A. catenella in comparison with non-toxic A. tamarense. To do this, the temporal expressions of 5 putative related genes known to be involved in detoxification processes and eight key genes known to be involved in apoptosis were determined. These genes were previously identified from the 210,895 ESTs from C. gigas reported in the National Center for Biotechnology Information database of May 2013 or described by  Medhioub et al.
After sodium acetate precipitation, the quantity and quality of total RNA was determined using a NanoDrop spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) and agarose gel electrophoresis, respectively. Following heat denaturing (70 °C for 5 min), reverse transcription was performed using 1 μg of DG RNA prepared with 50 ng/μL oligo-(dT) 12 mer-18 mer in a 20-μL reaction volume containing 1 mM dNTPs, 1 unit/μL of RNAseOUT and 200 units/μL Moloney Murine Leukemia Virus Reverse Transcriptase (M-MLV RT) in reverse transcriptase buffer, according to the manufacturer's instructions (Invitrogen Life Technologies SAS, Saint Aubin, France). The primer pairs used to quantify the expression level of apoptotic-related genes were designed according to the sequence available in GenBank. The expression of the ribosomal protein, F40, was used as the housekeeping gene control.
All sequences of primers used for the amplification of apoptotic-related genes were published by Medhioub  [20]. Sequences of primers used for the amplification of putative detoxification related genes are shown in Table  1. Real-time PCR amplifications were performed in the Light Cycler 480 (Roche Diagnostics GmbH, Mannheim, Germany). In short, the following components were mixed to the indicated end-concentration: 5 mM MgCl2, 0. 5 µM of each primer, 2. 5 μL of reaction mix (Light Cycler ® 480 SYBR ® Green I Master mix, Roche Diagnostics GmbH, Mannheim, Germany) in a final volume of 5 μL. Reverse transcribed RNA (1 μL) diluted 1/10 was added as the PCR template to the Light-Cycler master mix, and the following run protocol was used: initial denaturing at 95 °C for 5 min; 95 °C for 10 s; 10 s at 58 °C; 72 °C for 10 s with a single fluorescence measurement; a melting curve program (65-97 °C) with a heating rate of 0.
11 °C/s; a continuous fluorescence measurement; and a cooling step to 40 °C. Each PCR was performed in triplicate. To determine the qPCR efficiency of each primer pair used, standard curves were generated using six serial dilutions (1:1, 1:3, 1:7, 1:15, 1:31, 1:63) of a unique cDNA sample constituted from a pool of all cDNAs obtained from each condition; qPCR efficiencies of the tested genes varied between 1. 85 and 1. 99. Moreover, the real-time PCR product analysis on agarose gel and by melting curve revealed a unique lane and a unique peak, respectively, indicating the formation of a single PCR product with no artefacts (data not shown). For further expression level analysis, the Crossing Points (CP) were determined for each transcript using the Light Cycler software (Roche Diagnostics GmbH, Mannheim, Germany).