dataesr/scientific-paragraphs-categorization huggingface dataset:
Apparatus. Stimuli were presented at 57 cm distance using a desktop computer (2. 09 GHz Intel processor, Windows XP) with a cathode ray monitor (resolution: 800 × 600 pixels; refresh rate: 140 Hz, Gamma corrected luminance function). Stimuli were designed and presented via the Psychophysics Toolbox (Brainard, 1997) running in MATLAB (MathWorks).
Subjects were presented with 4 or 8 blocks of 200 trials with 85% main experimental trials and 15% catch trials (the first 6 of the 14 subjects performed only 4 blocks of the present experiment, together with 4 blocks of another experiment that was eventually canceled and whose data were not analyzed). Subjects were instructed to fixate the fixation point all the time, judge the luminance of the disks and respond using the arrow keys (left arrow to indicate that left disk is brighter, right arrow for right disk brighter) on a standard 105 key keyboard when the question mark appeared. There was no feedback after the response. EEG data acquisition and analysis. EEG was recorded at 1024 Hz using a Biosemi system (64 active electrodes). Horizontal and vertical electro-oculograms (EOG) were recorded by three additional electrodes around the subjects' eyes.
For data pre-processing, the EEG and EOG data were downsampled offline to 256 Hz, re-referenced to average reference and epoched around the stimulus onset in each trial for data analysis via the MATLAB (MathWorks) and EEGLAB toolbox  49. Individual electrode data were visually inspected, and channel data containing artifacts were interpolated by the mean of adjacent electrodes (three subjects had one electrode containing artifacts, one subject had two; the positions of the interpolated electrodes were different across subjects). As the post-stimulus spatial choice was lateralized on each trial to the left or right side, the pre-stimulus oscillatory correlates of the post-stimulus luminance judgment may not only reflect the oscillation's influence on shape perception and predictive coding, but also its influence on spatial choice (i.
e. , pre-stimulus oscillations may bias the left/right spatial choice independently of the 3D-shape/random-lines content inside of the disk). To avoid any contribution from the spatial choice, we first divided the trials for each subject into two trial datasets based on the post-stimulus spatial choice, and performed the time-frequency analysis (described below) within each dataset. We reasoned that this analysis would lead to shape perception correlates not on a given fixed set of electrodes, but rather on different electrode groups depending on the side of choice (i. e. , electrodes "contralateral" or "ipsilateral" to the spatial choice). Therefore, we arbitrarily chose to permute the electrode locations for the dataset corresponding to a right-side choice: we replaced the left-hemisphere electrodes by the symmetric ones from the right and vice versa (midline electrodes were unaffected).
The product or the sum of the two subgroup inter-trial phase coherence values can therefore summarize, in a single variable, the strength of the phase-behavior relation  18, 19, 51. Here we chose to use the product of the inter-trial phase coherence values, a measure we call Phase Opposition Product (POP): = ⋅ POP ITPC ITPC (4) a b Again, high ITPC values for the two subgroups (suggesting different phase angles for the different trial subgroups) will result in a high POP value; and two low ITPC values (suggesting random/uniformly distributed phases for the different trial subgroups) will result in a low POP value. Thus, the measure of POP will be maximal when two subgroups show strong inter-trial phase coherence, in other words, strong phase opposition between two subgroups. To accurately assess the significance of the phase-behavior relation without any assumption about the probability distribution of the POP values, we performed a nonparametric permutation test: We first computed the POP values for each point in the time-frequency plane from -650 to 150 ms, from 2 to 50 Hz for each electrode, dataset, and subject and then averaged across all datasets and all subjects.
Surrogate POP values were obtained by randomly assigning the trials to one or the other condition for each subject (keeping the number of trials in each condition constant) and recalculating the grand-average POP values. We computed the P value by simply and directly counting the number of surrogate POP values that were more extreme than the observed value. Here, we used 80,000,000 surrogates and thus assigned the P value of 1. 25 × 10 -8 to the points without any more extreme POP values in the surrogates. The P values were corrected for multiple comparisons over time points, frequencies and electrodes using the FDR method (FDR α = 0. 05, corresponding to a P value threshold of 9. 53 × 10 -6 ). To show the overall POP in the time-frequency domain, we computed a z-score by combining the observed POPs across all datasets, subjects, and electrodes and comparing the value with the mean and SD of a null-hypothesis distribution with 10,000 surrogate POP values (generated using the procedure described before, and also combined across all electrodes, subjects, and datasets).
Selection of studies and data extraction There are varying definitions to demarcate Mediterranean climate regions worldwide, which are typically based on climate and plant associations. We chose the widely used delineation of the Mediterranean biome from the collection of ecoregions mapped by the World Wildlife Fund (Fig. 1 ). We selected studies in this area and in marginal areas defined as 'Mediterranean' by the authors of the original papers. Soil N 2 O emission data from field-based studies investigating fertilizer-induced soil N 2 O emissions were collected from these Mediterranean regions, including the Mediterranean Sea Basin, California, Australia and Chile (Fig. 1 ). We are not aware of any field study reporting N 2 O emissions in the Mediterranean region of South Africa (Mary Scholes, Wits University, personal communication).
The criteria for inclusion of a study in the dataset were: (i) area-scaled N 2 O emissions were reported for N fertilizer treatments, (ii) the number of replicates was reported unambiguously with a minimum of three replicates per treatment, (iii) only field studies were considered and (iv) only when N 2 O emissions were reported for at least an entire growing season. The cumulative N 2 O emissions for each N fertilizer treatment were extracted from published papers and reports, together with a measure of variance, the number of replicates and the N application rate (kg N ha -1 ) during the observational period. Key characteristics (location, climate data, soil type, soil management, irrigation, type of fertilizers, etc. ) were collected when available (Supplementary material 1). When data were presented graphically, WebPlot Digitizer was used to extract data points (http:// arohatgi.
info/WebPlotDigitizer/). If cumulative N 2 O emissions or other information were not reported, the authors of the field study were contacted to supply missing information. In some cases, cumulative emissions were estimated by integrating the average daily fluxes over the measurement period  (Alluvione et al. , 2010; Castaldi et al. , 2011; Kong et al. , 2009; Ranucci et al. , 2011; Vitale et al. , 2013). Experiments assessing the effect of nitrification/ urease inhibitors were studied as a separate group (when evaluating the influence of the type of fertilization), but were not included to obtain the mean EF for Mediterranean crops(EF Med ) because they were not considered representative of current management practices. Fifty-three studies and 223 datasets were included in the meta-analyses (Table  1 , Supplementary material 1).
Since most of the field studies in our database focus on assessing the performance of specific crop management practices over both emissions and crop yields, they often do not include post-harvest season emissions. While full year emissions are desirable for determining EFs  (IPCC, 2016) , in the systems we are studying, we assume that the inclusion of growing season only emissions will have minimal influence on our calculated EFs, since emissions in the intercrop period will be a) low in summer (fallow of winter crops), when the soil is dry, due to decreased microbiological activity, and b) very low in winter (fallow of summer crops) under cold conditions without freeze-thaw cycles  (Aguilera et al. , 2013a). In the few studies where emissions were measured over an entire year, those during the fallow period were 10% or less of the total (e.
Calculation of emission factors Most studies included in the meta-analysis did not explicitly report EFs since they were designed with different aims. We calculated EFs as the difference between N 2 O emissions from a fertilized treatment (kg N 2 O N ha -1 ) and the non-fertilized (control) treatment (kg N 2 O N-N ha -1 ) divided by applied N fertilizer (kg N ha -1 ). In 39% of cases, there was no control treatment and these missing data were obtained through multiple imputation by chained equations  (Azur et al. , 2011)  with IBM SPSS Statistics 24 (for a detailed description of missing data treatment and sensitivity tests see Supplementary material 2).
We performed a sensitivity test (see Supplementary material 2) which demonstrated that including these studies had no impact on the mean EF Med (EF: 0. 496% including all studies and EF: 0. 463% excluding cases without control, see Supplementary material 2). We, therefore, conclude that the EF Med is robust, but due to the high heterogeneity of the studies included in the dataset, it was often difficult to find significant differences between different management strategies. Further field research, measuring emissions over the whole year and including control treatments, is merited to better quantify EFs for the various management options in Mediterranean systems.
Influence of soil characteristics on EF Soil characteristics show very limited impact of EFs. This finding seems to contradict previous studies where soil organic C concentration and pH had a clear impact on denitrification and therefore N 2 O emissions  (Li et al. , 2005; Šimek and Cooper, 2002). However, these relationships might be difficult to find in our dataset, where most soils had a neutral or slightly alkaline pH and similar (in general low) concentrations of organic C, with other variables having a stronger effect on N 2 O emissions (N application rate, soil water content, type of fertilizer applied, etc. ). In addition, although denitrification is generally identified as the major process generating N 2 O in most cropping systems, this does not necessarily stand for studies under Mediterranean conditions, where the importance of nitrifier-nitrification and nitrifier-denitrification have been documented  (Sánchez-García et al.
(2012) , and it ranged from 20 to 30% and 40-60% in  Kallenbach et al. (2010)  and  Kennedy et al. (2013) , respectively. Therefore, our results suggest that drip irrigation represents an effective N 2 O mitigation practice in Mediterranean irrigated systems. These benefits, however, should be evaluated together with other effects on the GHG balance and further socioenvironmental consequences. For example, increased infrastructure material requirements and energy needs for pressurizing the irrigation water might offset drip irrigation N 2 O-related emission savings in certain situations, while reduced water use (and related energy consumption) might be the main component responsible for emission reduction in other situations  (Sanz-Cobena et al. , 2017). The lower EFs found under furrow irrigation compared to sprinkler irrigation might be related to a slightly lower average N application in the furrow systems included in our dataset and to a different soil wetting pattern, favoring complete denitrification to N 2 after irrigation events in furrows  (Sánchez-Martín et al.
A recent meta-analysis found the IPCC Tier II model underestimated N 2 O emissions from cattle manure in the United States by an order of magnitude  (Owen and Silver, 2015). Davidson (2009)   (Owen et al. , 2015). As expected, nitrification/urease inhibitors effectively reduced EFs from Mediterranean systems  (Mosier et al. , 1996). In a recent review,  Gilsanz et al. (2016)  developed EFs of 0. 42% 2. 2 and 0. 70% 3. 3 for DCD and DMPP, respectively, two commonly used nitrification inhibitors. The lower EF found in our study (0. 14% 0. 32) agrees with the low baseline EFs found in the studies included in our dataset. Thus, inhibitors seem to be a good strategy to mitigate direct N 2 O emissions under Mediterranean conditions, although the potential is lowered by the relatively small baseline emissions in Mediterranean systems.
In agreement with previous studies  (Kim et al. , 2013; Shcherbak et al. , 2014) , increasing fertilizer application rates led to increased EFs. We found that applying N fertilizers over 400 kg N ha -1 resulted in EFs that did not significantly differ from the 1% IPCC Tier I default value. The lack of statistical significance between N doses is probably related to the fact that in our dataset most studies only considered one N application rate, with a limited number of cases with very low or high N fertilization rates.
Acknowledgements The authors are grateful to  M. Scholes ,  D. Plaza-Bonilla ,  S. Menendez ,  P. Merino ,  S. C. Maris ,  H. Heller ,  D. Savvas ,  C. K. Kontopoulou , who were contacted and kindly supplied any missing information necessary for the meta-analysis. Special thanks to  J. P. C. Eekhout  for preparing Fig. 1 andF. Estellés for providing the basic data for the calculation of the fertilization in Spain. Also thanks to two anonymous reviewers for their helpful comments. M. L. Cayuela was supported by a ' Ramon y Cajal ' research contract from the  Spanish Ministry of Economy and Competitiveness. Thanks to  Fundación Séneca, Agencia Regional de Ciencia y Tecnología de la Región de Murcia  for support (grant number  19281/PI/14 ). Australian studies included in the meta-analysis were funded by the  Australian Government , the  Grains Research and Development Corporation , and the  Department of Agriculture and Food WA.
Accordingly, each pressure and temperature point was measured for at least 10 hours to ensure a sufficient signal-to-noise ratio. We probed the samples at pressure points of 20 bar and 3 kbar and at temperatures of 280 and 310 K. Moreover, we recorded a temperature point close to the lipid phase transition, that is to say at 297 K (24 °C) for N-LDL, at 292 K (19 °C) for TG-LDL and 300 K (27 °C) for Ox-LDL. For correction and normalization purposes, the empty cell, the buffer (10 mM NaPi, 0. 1% EDTA in D 2 O) and a vanadium rod (a completely incoherent scatterer) were probed under similar conditions. Absorption corrections based on the formula of Paalman-Pings  [24]  were applied and the data reduction was carried out using the LAMP software available at the ILL  [25]. All samples were normalised in the same way to give comparable absolute intensities.
Conclusions Here we report on dynamic data of lipoproteins as a function of temperature and pressure using elastic incoherent neutron scattering. We chose LDL with a normolipidemic composition and two modified forms of LDL to mimic pathological conditions. Triglyceride-rich LDL characteristic for hyperlipidemia and oxidatively modified proatherogenic LDL were investigated. As LDL particles show a lipid phase transition, we measured the samples below, at and above the phase transition temperature at two extreme pressure points of 20 and 3000 bar. In accordance with our recent findings  [8] , normolipidemic LDL seems therefore to be the optimized form of LDL, coping well with harsh external conditions and staying flexible. On the contrary, for both modified forms of LDL the MSD is strongly reduced under pressure and the expected increase of MSD with increasing temperature is also damped under pressure.
Acknowledgments We are grateful to ILL for the allocation of beam-time. We would like to thank  Hanna Lindermuth  for excellent technical assistance. This work has been supported by the  Austrian Science Fund  ( FWF Project No. I 1109-N28  to RP) and by the  Agence Nationale de la Recherche  ( ANR ; project number  ANR-12-ISV5-0002-01 LDLPRESS  to JP).
The authors provide two sets of architectures: one optimized for speed, and one for accuracy. We chose the one for accuracy. To give a sense on how critical the quality of the sparse disparity map is for the performance of our algorithm, we previously used the semi-global MGM [?] algorithm for producing our initial maps. The final disparity maps' average error on the Middlebury dataset  [20]  was more than 50% higher compared to the current algorithm.
Results We benchmarked our TDSR method using the well-known Middlebury dataset  [20]. The dataset contains several stereo-images pairs with their associated disparity map ground truth. An executable is also provided allowing to quantitatively compare disparity maps to their ground truth according to various criteria. We chose the following two commonly used criteria in the Middlebury comparative D R A F T Table: "Bad 2. 0", which computes the percentage of disparity values farther than 2. 0 from their associated ground truth, and "Avg. error", that computes the average absolute difference between the disparity values and their associated ground truth. These criteria have been separately computed on all values and only on values where no occlusion is occurring. They were computed on full resolution (F).
Parameter Produced disparity maps can be qualitatively compared on the "Vintage" pair of the Middlebury dataset in Figure  3. They can be quantitatively compared on the whole training set in Table  2. Compared to other stereo methods in the Middlebury benchmark, on the training set, using the "Avg. error" criterion on all pixels, our solution currently ranks 1st over 59. The results are therefore comparable with state of the art methods. Interpolation methods are very sensitive to the noise in the sparse disparity map and they don't work well on occlusions. Depending on the chosen parameters, the Fast Global Smoothing method can either produce a less noisy disparity D R A F T.
Transcriptome analyses Pelleted fMV suspended in lysis buffer with 1% β-mercaptoethanol were submitted to strong agitation with ceramic beads using a tissue lyser (Qiagen, Courtaboeuf, France) for cell disruption. RNA extraction was performed using the RNeasy Micro Kit (Qiagen) and labelled with either Cy3 or Cy5 fluorochromes then two by two cohybridised on whole mouse genome oligo 4x44K microarrays according to manufacturer's indications and detailed in S1 File. Validation of the quantitative fluorochrome ratio for inter-age comparisons was performed by reverse fluorochrome labelling controls (dye swap assay), and validation of intergene expression levels was done on 14 genes showing low or high mRNA expression by multiplex PCR and hybridization data showing high correlation (r 2 = 0. 898)  [31].
Bioinformatics, biostatistics, and data management Raw mass spectrometry files from LTQ-Orbitrap were analysed using Raw Meat software (version 2. 1; Vast Scientific) to assess run quality. All raw files were analysed to describe protein profiles using Proteome Discoverer (v 1. 3. 0. 339, Thermo Fisher Scientific) based on spectral counting (setting peptide confidence high). Inter-age quantitative comparisons were performed on fMV using Progenesis LC-MS (v 4. 0. 4441. 29989, Nonlinear Dynamics). Quantification was based on extracted ionic currents with inter-age ANOVA statistical parameters set at p < 0. 05, q < 0. 05, and power > 0. 8 at peptide and protein levels. The two software packages used Mascot (v 2. 2, Matrix Sciences) with a cut-off identity score set at 25 for fMV, tryptic peptides, one missed cleavage allowed, and taking into account cysteine carbamidomethylation and methionine oxidation as variable modifications, according to the IPI database (version 20070119), fragment mass tolerance set at 10 ppm, and peptide tolerance set at 0.
5 Da. Protein content was analysed from three independent fMV pools for P5 and P10 and two pools for adults. The criterion for protein recording, at a definite stage of development, was its occurrence after signal quality application in at least two of the three independent biological samples of fMV, using Proteome Discoverer. Subsequent relative quantitation of protein per sample, age, and fraction was performed using Progenesis. For any two-age comparison, all raw MS/ MS files obtained were uploaded into the software. Reconstituted three-dimensional LC-MS/ MS maps were aligned semi-automatically. A minimum of 84. 1% and 70. 9% overlap were observed for S01 and S2 analyses, respectively. Normalisation of isotopic signatures was achieved automatically on the entire map. A first ANOVA comparison was performed on peptide with the following cut-off parameters: p and q values < 0.
05 and power > 0. 8. Statistically filtered data allowed protein identification by Mascot (see above for identification parameters). Mascot decoy-basis searches provided false positive levels at a maximum of 5. 48%. A second ANOVA analysis was done at protein levels with the same cut-off requirements. Conflicting peptides, showing sequences present in several related proteins, were excluded. Protein quantification was based on normalised cumulative abundances of the remaining peptides associated with a given protein. As protein extractions were performed on pooled enriched microvessels, normalization of abundances was done on total protein amount in each run. Data collected from Proteome Discoverer and Progenesis LC-MS allowed the compilation of protein lists at each stage. In the transcriptomic study, hybridization microarrays were analysed using GeneSpring software (Agilent Technologies, Santa Clara, CA, USA).
Transcriptome analysis in microvessels An ontology study of the regulation of fMV transcripts (|FC| ! 2) using DAVID revealed 56 significantly enriched pathways (p < 0. 05) in at least one two-age comparison. Contrary to protein-based ontogeny, transcription-based pathway analysis did not converge towards energy metabolism pathways or protein metabolism pathways. This mismatch could reflect the fact that transcription analysis was based on time-dependent changes regardless of expression levels. Enriched pathways were distributed in seven groups (in decreasing order of significance): cell adhesion, development and morphogenesis, cancer, signalling, vascular functions and pathology, immunity, and metabolism (S3A Table ). The major regulations related to extracellular components and cell adhesion have been discussed previously  [31].
Protein detection was based on the highest abundance at one stage, whereas gene transcription was evaluated as dynamic interstage comparisons, regardless of expression level. Although our proteomic study detected many pathways related to energy metabolism and protein biosynthesis and degradation, transcript analysis did not converge towards these functions. This apparent discrepancy likely reflects differences in statistical methodology. In the proteomic study, the top 20 LC-MS/MS strategy revealed the most abundant genes and isolated enriched pathways within a subset of proteins actually identified, whereas transcript analyses referred to the full genome. The low level of convergence may also reflect biological differences in, for example, proteins and mRNA turnover. Nevertheless, transcriptomics evolution data strengthened the proteomic data at the individual gene level for a large number of genes related to energy metabolism (Hmgcs2, Aldoa, Aldh2), antioxidant activity (Prdx2, Gpx1, Cat), and transport (Slc2a1 (Glut1), Slc1a2 (Eaat1) or caveolins).