dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion In this paper, we have designed an improved concolic testing scheme that is based on  [9]  but adds support for negative constraints and defines selective unification problems as constraints on Herbrand terms. Our approach overcomes some of the problems in previous approaches  [10, 11] , mainly regarding the scalability of the technique as well as a potential source of incompleteness due to the lack of negative information. We have implemented an SMT-based concolic testing tool in SWI-Prolog that uses the Z3 library for C through the foreign language interface of SWI-Prolog. Our preliminary experimental evaluation has shown promising results regarding the scalability of the method in comparison to previous approaches. As for future work, we plan to extend and improve our concolic testing tool.
According to the OECD definition, one CU was attributed for the first adult in the household, 0. 5 CU for other persons aged 14 or older, and 0. 3 CU for children under 14  [38]. In addition, data regarding weight status (weight, height), purchasing behavior for food products (type of food supply chain, retailers), and self-reported frequency of reading nutrition labeling were also collected. The participants were aged 15 or above.
Statistical Analyses To allow inferences on the general population, a quota method was used based on the following variables: gender, age, occupational category of the participant and the reference person in the household (upper category: head of businesses, managerial staff, intermediate profession; lower category: employees, laborers; inactive: students, homemaker), region, and agglomeration size. In addition, weighing was applied to all analyses to take into account socio-demographic differences between the panel and the French population (data from the 2015 national census of the National Institute of Statistics and Economic Studies (INSEE)). Pearson's chi-square tests were used to compare individual characteristics, as well as the Nutri-Score's awareness and support of the measure across the different waves.
, 2014)  consisting of food ratings in a neutral compared to a negative condition, which allowed for a high level of control over error variances. Preceding this task, negative emotions were induced with a validated procedure based on idiosyncratic autobiographic recall  (Hilbert et al. , 2011) , this also ensured that emotional changes would cause changes in appetitive responding (and not the reverse,  Adriaanse et al. , 2011b). High density EEG was recorded, covering all relevant neural regions to reveal underlying mechanisms. As an indicator for a physiological appetitive reaction, corrugator supercilii activity was measured, a muscle that reacts with relaxation to positive stimuli and with contraction to negative stimuli  (Larsen et al. , 2003). As an indicator for general physiological arousal, heart rate (HR) and heart rate variability (HRV) data during the two conditions (neutral, negative) was collected.
Electroencephalography (EEG) Autonomic, facial, and neural measures were recorded with a 64-channel-amplifier (TMSi, Twente Medical Systems International, EJ Oldenzaal, Netherlands) at a sampling rate of 512 Hz. Online average reference EEG was recorded from 63 actively shielded, equidistant, and passive electrodes (sintered Ag/AgCl electrodes) and a ground electrode attached to the left wrist. Impedances were kept below 10 k. Preprocessing in EEG lab  (Delorme and Makeig, 2004)  involved down-sampling (256 Hz), low-pass filtering (70 Hz), high-pass filtering (0. 1 Hz), and notch filtering (45-55 Hz). Noisy channels were identified through visual inspection and removed, data was re-referenced to average and eye blinks and -movements were removed in an independent component analysis (ICA) using AMICA  (Palmer, 2016).
Electrocardiogram (ECG) Generalized emotional or arousal related effects of the conditions might also be evidenced in cardiac activity. Thus, ECG was recorded using disposable solid-gel snap electrodes placed on the upper sternum and distal end of the left costal arch. Data were inspected offline and artifacts were corrected manually with ANSLAB 2. 6  (Blechert et al. , 2016). The number of heartbeats in each condition was automatically determined by detecting the number of R-spikes. Overall HR and HRV scores (natural logarithm of fast-Fourier transformation derived HRV for very low, low, and high frequency band power) for each condition were calculated. No picture level ECG responses were calculated due to the fast picture presentation mode and the resulting inability of the ECG to return to baseline.
Statistical analysis of microarray data The R3. 1. 3 software was used for statistical analyses (R Core Team, 2014). The raw microarray data were imported and normalised according to the quantile method for normalisation between arrays using the limma package  (Smyth and Speed, 2003). On the normalised data set, we analysed gene expression changes over time using the maSigPro package and the single series approach. maSigPro is a regression-based approach for identifying genes with temporal expression changes  (Conesa et al. , 2006). For the first step of this method, we defined a binomial regression model for each gene expression over the 4 time-points: Y=b 0 +b 1 time + b 2 time 2 + ε with: -Y: normalized expression value -time: the quantitative variable of the timepoints (min) -b 0 : start value -b 1 : the slope estimation (induction or repression of the gene) -linear effect -b 2 : the shape estimation (first change in the temporal profile) -quadratic effect -ε are independent N(0, σ²) error terms We adjusted this model by applying the leastsquared technique for each gene, and we only selected genes with significant changes over time (i.
med. utoronto. ca/; adjusted p. value = 0. 05 with a Bonferroni correction method) and genes were classified into functional categories, biological processes and protein cellular localisations using the GO database  (Robinson et al. , 2002). In order to obtain individual patterns for some stress (or stress-related) gene expressions (OSH3, OSH6, ISC1, SIR2), the corresponding data were extracted from the transcriptomic analysis. The complete microarray data set was deposited in the Gene Expression Omnibus (GEO) public repository (accession number GSE95152). Microarray description is under GEO accession number GPL17690.
However, transcriptomics data with overexpression of both RNR4 (which codes for a ribonucleotide-diphosphate reductase) and NRT1 (which codes for a high-affinity ribosyl nicotinamide transporter), are consistent with a response to a synthesis need and nucleotide import. These results would, therefore, be consistent with mortality related to a lack of Sir2 activity. This Sir2 activity will have to be quantified to assess this hypothesis.
Synchrotron scanning and 3D modeling All these scans were performed at the beamline ID19 (European Synchrotron Radiation Facility, France) with a current of 200 mA or 16 bunch. Scanning and 3D-modeling of the humerus of Eusthenopteron were performed as described by  Sanchez et al. , 2014. The data were reconstructed using a single distance phase retrieval approach  (Sanchez et al. , 2012)  based on a modified version of the algorithm of  Paganin et al. , 2002 , applying an unsharp mask to the radiographs after the phase retrieval to enhance the trabecular mesh. The humerus of Seymouria was imaged with a voxel size of 3. 48 mm using an optical system associated with a 47 mm-thick GGG (i. e. , a gadolinium gallium garnet crystal) scintillator and a FreLON 2k14 CCD detector  (Labiche et al. , 2007).
The height and diameters of the top and side surfaces of the epiphysis were measured with a digital caliper. For each animal, one set of tibia and femur was used for control and the other for treatment. All comparisons involved the bones from the same animal. Still, in dissection medium on ice, the bones were subjected to pressure with the Instron Electro-Puls E1000 Test Instrument. The load was applied at a speed of 0. 1 N per second and load/digital position curves recorded in the Bluehill three software. Control bones went through the same procedures, but without loading. For culturing, bones were first washed twice in dissection medium before being placed into DMEM/F12 medium supplemented with 1 mM b-glycerophosphate, 0. 2% bovine serum albumin, 50 mg/mL ascorbic acid, and 50 mg/mL gentamycin for 48 hr as described previously  (Cooper, 2009).
Quantification of positive cells (PI+, TUNEL+, YAP+, etc) For each data point of the PI+ and TUNEL+ cell number quantification, at least three sections per animal and at least three animals were analyzed. Double-blind counting was performed for all quantification analysis, including the mediolateral distribution of PI+, TUNEL+, caspase-3+, p73+, YAP+, and double p73+YAP+ cells.
06-0. 09 N/m. All measurements were conducted at 25˚C and all tissue was handled in DMEM/F12 HEPES-containing medium on ice. At least 70 individual force-distance curves were acquired for each type of cell by ramping over the surface and a total of 30 cells were measured from three different animals. These force-distance curves were processed with the NanoScope Analysis v. 1. 10 software (Bruker). Utilizing retract curves, the elastic modulus E was extracted from these force-distance curves by fitting in accordance with the Hertzian model of contact mechanics.
Acknowledgements This work was supported financially by  EMBO Long-Term Fellowships (MX)  and grants from the  Swedish Research Council  ( ASC, SS, AI, AnSi ),  Karolinska Institutet  (MX, ASC, LL, IA, including an  SFO Stem/Regen junior grant  to ASC),  Bertil Hallsten Foundation (IA) ,  EMBO Young Investigator.
Single-molecule DNA unzipping assays DNA unzipping experiments were performed on an M-trap optical tweezer from LUMICKS (Amsterdam, Netherlands). Sample chamber preparation was similar to that previously described  (Sun & Wang, 2017). Briefly, glass coverslips were cleaned and functionalized with partially biotinylated polyethylene glycol (PEG) (Laysan Bio;  Yardimci et al, 2012)  and then coated with streptavidin (Thermo). Biotin-tagged DNA was incubated to form DNA tethers. Anti-digoxigenin-coated 0. 48 lm polystyrene microspheres (Polysciences) were added into the chamber. For single-molecule DNA unzipping experiments, wild-type SaCas9 or dSaCas9 was first complexed at 30 nM with a 1:3 ratio of protein to sgRNA at room temperature for 10 min and then flowed into the chamber just prior to data acquisition.
The experiments were conducted in a climatecontrolled room at a temperature of 23. 3 °C; however, owing to local laser trap heating the temperature increased slightly to 25 AE 1 °C  (Peterman et al, 2003). The experiments were conducted by mechanically unzipping the dsDNA at a slow velocity of 50 nm/s to probe the potential interactions at the fork. Single-molecule DNA unzipping data were taken at 5 kHz and later filtered to 50 Hz. The acquired data signals were converted into force and DNA extension as described previously  (Zhang et al, 2019). The elasticity parameters of both the dsDNA and ssDNA for data conversion were obtained from DNA force-extension measurements in previous studies  (Zhang et al, 2019). In the unzipping experiments, one separated base pair generated two nucleotides of ssDNA.
Stopped-flow experiments Stopped-flow experiments were performed using an SMF400 stopped-flow instrument (Biologic). 2-AP labeled oligos were purchased from Sangon (Table  EV1 ). dSaCas9 (1. 28 lM) was assembled with sgRNA (0. 64 lM) for 2 h at 37 °C in reaction buffer (50 mM Tris-HCl pH 8. 0, 100 mM NaCl, and 10 mM MgCl 2 ). For each experiment, 500 ll of the 160 nM DNA template was rapidly mixed with 500 ll of 1. 28 lM dSaCas9 at 37 °C in reaction buffer. The samples were excited at 310 nm, and the time-dependent fluorescence changes were monitored at 376 nm using a single-band pass filter with a 30-nm bandwidth. All experiments were repeated at least seven times for each condition, and the averaged data are shown.
5, 100 mM NaCl, 1 mM MgCl 2 , 0. 1 g/ml BSA, 2 mM ATP, and 3 mM DTT) was flowed into the chamber before data acquisition. Finally, the DNA tether was stretched until the force reached 12 pN; the force remained constant while the helicase unwound the dsDNA. The DNA length was recorded in real time. DnaB helicase unwinding assays were conducted as follows. Briefly, 25 ll of 250 nM DnaB hexamer and 680 nM DnaC (the DnaB and DnaC complex is referred to as DnaB in the text) in unwinding buffer (20 mM Tris-HCl pH 7. 5, 10 mM MgAc 2 , 100 lM EDTA, 20% glycerol, 40 lg/ml BSA, 5 mM ATP, and 5 mM DTT) was flowed into the sample chamber before data acquisition. During the experiments, hundreds of base pairs of dsDNA were first mechanically unzipped to produce ssDNA for helicase loading. The DNA length was maintained until the force dropped below a threshold, indicating DnaB unwinding of the DNA fork.
Instrumentation and data analysis A nitrate-ion-based (NO - 3 -based) chemical ionizationatmospheric pressure interface-time-of-flight (CI-APi-ToF) mass spectrometer was used for measuring HOMs. The instrument has been shown to be sensitive towards this group of compounds, detecting them as adducts with the nitrate ion. Due to a lack of suitable calibration methods, the CI-APi-ToF was not calibrated for HOMs during this study. In order to estimate rough HOM concentrations ([HOM]), we directly use the calibration coefficient C = 1. 6 × 10 10 molec. cm -3 utilized by  Ehn et al. (2014)  to convert the measured HOM ion signals according to the equation below  (Jokinen et al. , 2014) : [HOM] = C × HOM • NO - 3 NO - 3 + HNO 3 • NO - 3. This value of C is very close to values utilized in several other studies using a CI-APi-ToF  (Jokinen et al.
We use the variation in alpha-pinene and HOM concentrations during this run to calculate both chlorine atom concentrations and HOM yields. Each change in alpha-pinene concentration was due to the change in Cl atom concentration, and with knowledge of the reaction rate, the concentration of Cl atoms as a function of the number of lights turned on was determined (Fig. 3 ). The calculated  [Cl]  concentrations are in the range of (1 -5) × 10 5 molecules cm -3 , which is within atmospherically relevant concentration ranges  (Tham et al. , 2016). Raw data from the CI-APi-ToF were recorded in 10 s resolution in HDF format. We used tofTools for data analysis, and detailed protocols of the software have been introduced by Junninen et al. (2010).
Acknowledgements. This work is supported by the  European Research Council  project and  Academy of Finland , via the  Centre of Excellence in Atmospheric Science. We acknowledge the tofTools team for providing the software. Financial support. This work is supported by a  European Research Council  (grant no. 638703-COALA ) project and  Academy of Finland , via the  Center of Excellence in Atmospheric Sciences  and project numbers  317380  and  320094. Review statement. This paper was edited by  Timothy Bertram  and reviewed by two anonymous referees.
Materials and Methods The study was performed according to the Declaration of Helsinki and approved by the Maastricht University Medical Centre's Medical Research Ethics Committee (METC). The PREVIEW study was registered on 29 January 2013 at ClinicalTrials. gov (NCT01777893). Written informed consent for participation was collected from all participants prior to the study. The substudy was performed from February 2017 until February 2018 in Maastricht, The Netherlands.
Statistical Analyses Sample size was powered based on the primary outcome energy expenditure  [27]. SPSS version 25 was used for statistical analyses (SPSS for Macintosh; SPSS Inc. , Chicago, IL, USA). Data are presented as means ± standard deviation (SD). Data were log-transformed if not normally distributed as tested by the Shapiro-Wilk test. The trapezoidal method was used to calculate the area under the curve (AUC) for endocannabinoids and related substances  [28]  for the whole day. Differences at baseline between groups were determined by ANOVA and the treatment effect was calculated by an ANCOVA where the baseline variable was used as a covariate. Partial correlations with body fat % as covariate were used to assess associations between cardiovascular parameters and endocannabinoids.
Funding: This research was funded by the  EU   7th Framework Programme , grant number  312057  ( FP7-KBBE-2012. 2. 2-03 ) and by the  Dutch Organization for Scientific Research (NWO ASPASIA  (grant no. 015. 010. 034 ). D. C. and I. M. are supported by  INSERM. The analytical chemistry platform at Neurocentre Magendie is supported through  INSERM and Labex BRAIN   ANR-10-LABX-43.
Statistical Analysis. e data were compared statistically by analysis of variance (ANOVA) with Duncan's multiple range test at the 0. 05 probability level in Statistix 8. 0. Arithmetic means were calculated for each of the three replicates separately. e obtained data were analysed statistically (correlation) using the SPSS Statistics for Mac OS X, version 20 (SPSS Inc. , Chicago, IL, USA). Principal component analysis (PCA) was performed to evaluate the relationships among the different cultivation systems, soil properties, and the 2AP content. Soil parameters including pH, EC, SOM, HA, CEC, TN, P, K, Ca, Mg, and S and the microbial population, as well as the sand, silt, and clay percentages, were introduced as variables in the PCA using R 1. 2. 1335  [24].
Funding This work, which was conducted in the framework of the  ProMedFoods  project " Promotion of local Mediterranean  fermented foods through a better knowledge and management of microbial resources", was funded through the ARIMNet2 2016 Call by the following funding agencies:  ANR (France) ,  MERS (Algeria) ,  ELGO-DEMETER (Greece) ,  MIPAF (Italy) ,  INIA (Spain)  and  MHESR (Tunisia). ARIMNet2  ( ERA-NET ) has received funding from the  European Union "s  Seventh Framework Programme  for research, technological development and demonstration under grant agreement no. 618127.
48 to -11. 10 Tg C yr -1 and a mean trend of -17. 74 Tg C yr -1. Land use and land cover change (LULCC) was primarily responsible for the simulated downward trend (-50. 71 to -24. 28  Tg C yr -1 ), while increasing atmospheric CO 2 concentration lead to increased vegetation biomass (+9. 27 to + 13. 37 Tg C yr -1 ). Climate change had limited impacts on the long-term trend (-3. 75 to + 5. 06 Tg C yr -1 ). This study highlights the importance of LULCC for historical reconstruction and future projection of vegetation biomass over China. It also suggests that the incorrect change in China's forest area for 1980-2000 in the LULCC dataset used as model input data of many existing and ongoing model intercomparison projects (MIPs) has likely led to inaccurate estimations of historical vegetation biomass changes in China.
In this study, we used simulations by six stateof-the-art, fire-enabled DGVMs which were run following the same protocol and the same forcing datasets. We evaluated these simulations using sitespecific biomass data from 3757 inventory plots in China. Biases in each model simulation were statistically corrected using machine learning (Gaussian process regression, GPR). The bias-corrected simulations provided the first multi-model estimates of vegetation biomass for the 20th century. We then used three sensitivity experiments in which potential drivers of 20th century vegetation changes, specifically, climate, CO 2 concentration and land-use, were constant, respectively, throughout the historical period to analyze the causes of the simulated changes in vegetation biomass.
Model simulations We used vegetation simulations performed as part of the Fire Model Intercomparison Project  (FireMIP, Hantson et al 2016 , Rabin et al 2017)  from six stateof-the-art fire-enabled DGVMs: CLM4. 5 with an updated fire module for CLM5 (CLM4. 5), JULES-INFERNO (JULES), JSBACH-SPITFIRE (JSBACH), LPJ-GUESS-SPITFIRE (LPJ-GS), LPJ-GUESS-SIMFIRE-BLAZE (LPJ-GSB), and ORCHIDEE-SPITFIRE (ORCHIDEE). The distribution of natural vegetation was simulated in JULES, LPJ-GS, and LPJ-GSB, but prescribed in the others. Only CLM4. 5 and LPJ-GSB simulated an interactive nitrogen cycle (table  1 ). All the DGVMs are not stochastic except for LPJ-GUESS. LPJ-GUESS includes stochastic representation of individual establishment, mortality, and disturbance impacts. This does not affect the interpretability of the results because the simulations used here feature sufficient (50 for LPJ-GS and 25 for LPJ-GSB) replications so that the effect of the stochastic variability is small and the effect of drivers is dominant.
Field observations The field observations of vegetation biomass used here were collated and derived from Chinese forest survey inventories of  Luo (1996)  and  Pan et al (2004)  and from literature review (supplement B. xlsx and supplement B reference. docx). The vegetation biomass is derived as the sum of leaf, stem and branches, and root biomass or the sum of above-ground and below-ground biomass. The dataset from Luo (1996) and Pan et al (2004) focused on forest plots, and provided information on location, forest type, and leaf, branch, stem, and root biomass for trees for all plots. It included 1266 forest plots, sampling tropical and monsoon forests, subtropical evergreen broadleaf and coniferous forests, temperate deciduous broadleaf forests, boreal evergreen/deciduous coniferous forests where the measurements were made on forests aged from 3 to 350 years during 1956-1993.
Most of the organ biomass (leaf, branch and stem, root) was obtained by the biomass allometric growth model method. Missing values of tree roots were calculated from the ratio of above-ground to below-ground biomass of the same species in nearby stands  (Luo 1996). Furthermore, we compiled an additional dataset of above-/below-ground vegetation biomass for 1112 forest plots and 1379 grassland plots measured during 2000-2013 from published literature. We selected plots with observations of both above-and belowground biomass and a clear description of the functional types or species. Observations in the literature were mainly obtained by the clear-cutting method, average standard tree method, and/or biomass allometric growth method. A coefficient of 0. 45 was used to convert vegetation biomass density to C density (kg C m -2 ) (e.
Three methods for bias correction were tested: first order linear regression: B i,j,obs = a i,j,0 + a i,j,1 × B i,j,uncorrected (1) cubic regression: B i,j,obs = a i,j,0 + a i,j,1 × B i,j,uncorrected + a i,j,2 × B 2 i,j,uncorrected + a i,j,3 × B 3 i,j,uncorrected (2) and machine learning (Gaussian process regression, GPR). Here, a i,j,n (n = 0,. , 3) was regression coefficient for the ith functional type of the jth model, B i,j,obs and B i,j,uncorrected were the spatial arrays of field observations and the default (uncorrected) model outputs, respectively. For the machine learning GPR, we used the GPR package of Matlab version 2018b. We carried out batch training based on the GPR model to optimize the parameters of kernel function (ardsquared exponential function). Hyperparameters were then determined by automatic hyperparameter optimization based on minimizing five-fold crossvalidation loss.
) Tg C yr -1. This is consistent with the results from Wang et al (2007) based on the InTEC model, but disagrees with the simulated increase of 0. 02 Pg C yr -1 by Mao et al (2009) based on a version of SDGVM. The discrepancy is primarily because Mao et al (2009) did not account for the impact of LULCC, which we have shown is the main driver of the decrease in vegetation biomass over the century. LULCC datasets showed that China's forest area had declined overall during the 20th century (Houghton and Hackler 2003, Hurtt et al 2011, Lawrence et al 2016). Fang et al (2018) estimated that vegetation carbon density for forests is > 5 times of other functional types in China based on field observation campaigns. Together, they supported our conclusions that vegetation biomass had decreased over the 20th century and LULCC was the main driver for this decrease.