dataesr/scientific-paragraphs-categorization huggingface dataset:
The current dairy NRC (  2001 ) model predicts DMI based on the cow's metabolic BW, FCM yield, and stage of lactation. Dry matter intake prediction models for other categories of dairy cattle or beef cattle involve a variable for BW (metabolic BW or initial shrunk BW) and NE M concentration  (NRC 2000 (NRC , 2001 (NRC , 2016)). Numerous DMI prediction models have been proposed and evaluated  (Ingvartsen, 1994; Mertens, 1995). An in-depth review of these models is outside the scope of this analysis and the examples given here are to illustrate the variable approaches (e. g. , feed composition; animal factors   (Niu et al. , 2018) ; All data = all data from the database, RC = data from studies in which methane emission was measured in respiration chambers, GF = data from studies in which methane emission was measured using GreenFeed (C-Lock Inc.
Prediction of CH 4 Emissions Prediction models have been widely used to estimate variation in CH 4 emissions for a variety of purposes  (Kebreab et al. , 2006). Many countries and regions of the world have set targets for the reduction of GHG emissions including CH 4. For example, California recently passed legislation mandating a reduction in the statewide emission of CH 4 by 40% below the 2013 levels by 2030 (State of California, 2017). Assessment of baseline emission in 2013 was determined using mathematical models, particularly those recommended by the IPCC (  2006 ) and used in almost all national inventory protocols. Therefore, the accuracy of the model used is important in setting and assessing achievable targets. As existing models are based on limited databases, new and more-accurate models are required to establish the baseline for assessing any reduction in emissions or estimating global CH 4 emissions attributable to enteric fermentation.
Normally, this number would not encompass the diversity of diets and animal factors in various regions of the world. Therefore, empirical models should ideally be developed from a database containing well over 1,000 individual observations or treatment means with accompanying information about dietary and animal factors that are known to affect enteric CH 4 emissions. In some cases, the improvement could be limited to the average animal, and observed variation might not be explained if key parameters are not included. Such databases will allow the development of robust estimates of average CH 4 emissions that can be tailored to be specific to a region and allow for various types of models ranging from simple one-covariate models to much more complex models that include several dietary and animal variables.
Most of the CH 4 emission data in the literature originate from Europe, North America, Australia, and New Zealand  (Appuhamy et al. , 2016). Recently, there has been an increase in data being published from Central and South America (e. g. ,  Dini et al. , 2012; Muñoz et al. , 2015) , but there is still a dearth of data from Asia and Africa. Further research is required to produce data from indigenous and improved breeds of dairy cattle in Asia and Africa, and the data should encompass the production systems and feeds available in those regions. In our opinion, further development of enteric CH 4 prediction models would need regional data sets with as many data points as possible that have reliable DMI and CH 4 emission measurements. For the purpose of national inventories, the IPCC Tier 2 model with region-specific Y m factors would be most suitable.
In addition, the trade-off between model complexity and predictive ability should be quantified so users can decide whether the extra resources required for better prediction are justified by the increase in prediction. The trade-off has to be determined in the context of the aim for which the models are going to be used, such as for national inventory, assessment of mitigation options, and others. Therefore, it is important that future models for a broad application be developed from large data sets with collaboration of scientists worldwide, as in the GLOBAL NETWORK project, and using robust stateof-the-art statistical techniques for model development and evaluation. The data sets should encompass a wide range of diets and production systems within regions and globally. It is also possible to develop a multi-model ensemble to improve enteric CH 4 emission prediction and determine uncertainty associated with the prediction.
CONCLUSIONS There are large uncertainties in livestock CH 4 national and global inventories; sources of uncertainties in enteric CH 4 emission include animal inventories, feed DMI, ingredient and chemical composition of the diet, and CH 4 emission factors. There is also significant uncertainty associated with enteric CH 4 measurements. Widely used measurement techniques are respiration chambers, the SF 6 tracer technique, and the GreenFeed system. All 3 methods need to be correctly and appropriately used to generate reliable and accurate data and valid tests of effects of diets and other treatments on enteric CH 4 emission or animal variation in CH 4 emission rates; some uncertainty remains as direct comparisons of techniques have shown inconsistent results. We emphasize that each of these techniques can have low accuracy and precision or produce misleading results if not properly implemented.
In this article, SNPs originating from diesel engines were studied. The objective was to measure and analyze the mechanical properties of single SNPs at the nanoscale. In order to achieve this, a series of in situ compression tests were performed on individual SNPs using a commercially available nanoindentation device (manufactured by Hysitron, Inc. ) inside a TEM. TEM imaging provided structural information about the nanoparticles before and after compression. Based on the data provided by the nanoindentation device and the TEM images, Young's moduli and hardness values were reported. Recording electron energy loss spectra of the SNPs allowed calculation of the sp 2 /sp 3 ratio. A correlation was observed between this ratio and the behavior of the nanoparticles under consecutive compression.
The graphene layers used for modeling the outer shell were composed of 24 carbon atoms and 3 hydrogen atoms which preserve the 8:1 carbon to hydrogen atom ratio reported in previous publications  [10]. PACKMOL software  [33]  was employed to create parallel stacked graphene layers and combine them with the amorphous core into a single model. Surface oxidation was neglected in the model particle. During all simulations, the interactions between atoms were modeled using the reactive force-field ReaxFF  [34, 35]  with parameters developed for a Pt/Ni/C/H/O system  [36]. The ReaxFF potential has been used in previous simulations to model the formation of a soot particle  [37]. To equilibrate the system, thermal equilibrium simulations under the NVT ensemble (constant number of atoms, volume and temperature) were performed with the model nanoparticle in a large simulation box at 300 K for 10 ps.
At each loading stage, the nanoparticle was compressed until its height decreased to 40% of the initial value, and then unloading was performed until the net force acting on the upper wall was zero. All the MD simulations were performed using the software LAMMPS (Large Atomic/Molecular Massively Parallel Simulation)  [40]. During each compression cycle, the force-displacement diagram, sp 2 /sp 3 carbon atom ratio, and hardness were recorded. To calculate the hardness value, the maximum force acting on the upper wall at the end of loading stages was used in equation (4).
2 GPa during the first compression and 20. 9 GPa during the second compression. The same increasing trend can be observed for the calculated hardness values (see figure  10(b) ). On average, a hardness of 4. 9 GPa was calculated based on the first compression, and of 12. 3 GPa on the second compression. On analyzing both the Young's modulus and hardness averages it should be noted that the third compression dataset was only available for one particle. Average values are not calculated in this case, although all parameters follow the trend observed during the first and second compressions. The hardness of SNPs originating from diesel engines was measured by Jao et al  [7]  by recording the low-loss EELS spectra of the nanoparticles. According to the theory developed by Oleshko et al  [41] , the position of the plasmon peak in a low-loss EELS spectrum can be related to the hardness of the sample.
The zero-loss and core-loss EELS spectra of the soot nanoparticle were recorded before the compression and after each compression. Using a method described by Daniels et al  [44] , a ratio proportional to the sp 2 /sp 3 ratio (denoted as the ' sp sp µ ratio') could be calculated by fitting Gaussians in the core-loss spectrum to the peaks corresponding to the respective carbon hybridization states. In order to obtain absolute values for the sp 2 content  calculation, a graphite standard needs to be used. In the absence of this, the calculated ratio still provides useful information in the form of the relative change of sp 2 /sp 3 ratio as compared with the 'before compression' state of the particle. The EELS dataset was collected on particle D2C (see figure  11 ). Figure  12  shows the measured and calculated parameters.
Acknowledgments This work was supported by  LABEX iMUST  ( ANR-10-LABX-0064 ) of  Université de Lyon , within the  program 'Investissements d'Avenir'  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). Thanks are also due to CLYM for access to the ETEM microscope. The authors thank  Dr Jean-Luc Loubet  and  Dr Julien Fontaine  for valuable discussions.
In vivo two-photon imaging experiments were performed during a state of 'quite wakefulness', after having been habituated to head fixation the prior 2-3 days. Periodic water rewards were given to keep animals hydrated and passive. For comparisons of intensity and bleaching, we used a custom two-photon microscope emitting 960 nm light from a Coherent Chameleon ultrafast laser. All experiments were performed using a 25×, 1. 05 NA Olympus objective immersed in water. Image acquisition was performed with ScanImage (Vidrio) software and analyzed post hoc using ImageJ (NIH). Images were acquired at a variety of speeds/zooms, and powers in order to assess the impact of pulse energy and dwell time on bleaching and intensity. Images at each setting were acquired for 5 seconds. To analyze the data, images were averaged and thresholded to create a signal (above threshold) and background mask.
Two-photon fluorescence measurements were made with ScanImage software v3. 6 (www. scanimage. org)  20 using an Olympus 60×, 1. 0 NA, LUMPlanFl/IR objective and an ultra-fast pulsed laser (Chameleon Vision II; Coherent) tuned to 927 nm. Images (512×128 pixels) were acquired at 16 frames per second; line scans were obtained at 2 kHz and downsampled (box-car average) to 500 Hz for presentation in Supp. Fig. S3. Visual stimuli were generated with an Apple iMac computer using Matlab (The MathWorks) and the Psychophysics Toolbox (psychtoolbox. org). Stimuli were displayed using a modified LED DLP video projector (HP AX325AA Notebook Projection Companion; Hewlett-Packard Co. ) with a single near-UV LED (NC4U134A, λ max = 395 nm; Nichia Co. , Japan). The projected image was miniaturized and focused onto the retinal photoreceptor layer using the microscope condenser.
Images were corrected for in-plane motion using a correlation-based approach (MATLAB). ROI drawing was performed in ImageJ  23. Fluorescence time-courses were computed as the mean of all pixels within the ROI at each time point and were extracted using Miji  24. Fluorescence time courses were then synchronized with stimulus information, and visually evoked responses were computed as changes in fluorescence relative to the baseline fluorescence. Peak ΔF/F responses for field ROIs and dendritic spines ROIs were computed using Fourier analysis to calculate mean and modulation amplitudes for each stimulus presentation, which were summed together. To identify spine SF-iGluSnFR events, ΔF/F traces during individual stimulus trials were smoothed with an exponentially weighted moving average filter (MATLAB) and peaks of possible ΔF/F events were identified.
The fluorescence traces F (t) were corrected for the background by subtracting F back from the entire trace. In order to calculate a ΔF t /F 0 trace, F 0 first was calculated from the average of a 50 msec window of points prior to the stimulus then subtracted from F t and then used to scale the difference (see equation (  1 )). ΔF F 0 t = F t -F 0 F 0 (1) Analysis of amplitudes and time course were performed and trial-averaged traces 10 pt smoothing. Data were analyzed and presented using custom-written macros in Igor Pro. In order to analyze SF-iGluSnFR responses we analyzed trials in which a fluorescence transient was detected. We denoted a response a "success" if the peak of the fluorescence response was greater than 3× standard deviation (SD) of the baseline fluorescence fluctuations. To do this, we compared the fluorescence value within 20 msec window around the peak of each transient to the SD of fluorescence values calculated from a 20 msec baseline window on the trial-averaged transient.
CaMKIIα. Cre, final titer 1×10 7 gc/mL, to the left visual cortex. The craniotomy was closed with a 4 mm round cover glass that was fixed to the skull with cyanoacrylate glue. The recordings shown were performed 12 weeks after surgery. In vivo imaging: Imaging was performed with a custom-built two-photon microscope using ScanImage acquisition software (Vidrio Technologies), using 1030 nm excitation (120 fs, 80 MHz, Insight DS+, SpectraPhysics). The mouse was placed in a temperature-controlled plastic tube and its head was immobilized via headbar clamp beneath the water-immersed microscope objective, with a nose-cone delivering 1% vol/vol isoflurane in oxygen. A vertically-oriented screen (ASUS PA248Q LCD monitor, 1920×1200 pixels), was placed 17cm from the right eye of the mouse, centered at 65 degrees of azimuth and -10 degrees of elevation.
A high-extinction 500 nm shortpass filter (Wratten 47B-type) was affixed to the screen. Stimuli were drifting square-wave gratings (0. 153 cycles per cm, 1 cycle per second, 2 second duration) in 8 equally-spaced directions, spaced by periods of mean luminance, as measured using the microscope detection path. Fluorescence was collected in two channels (500-580nm and 595-685nm) in a custom-built detection system. Isolated dendrites were imaged at 3. 4 Hz. Images were aligned to compensate for bidirectional scanning and rollingshutter motion artifacts using a series of strip-wise registrations implemented in MATLAB code, available upon request. A region of interest was selected manually corresponding to a single dendritic spine. The baseline F0 was calculated as the mean fluorescence intensity over one second prior to stimulus onset.
Wilcoxon rank-sum test) was used. For comparisons of three or more datasets, Kruskal-Wallis and post hoc Dunn's multiple comparisons tests were used. A finite differencing model was used to simulate diffusion of glutamate in water (diffusivity 700 μm 2 /s), with an isotropic spatial resolution of 200 nm, from an initial distribution determined from a 3D intensity pattern than approximates the uncaging light pattern used in our experiments (5 μm spot, 0. 8 NA). A time step of 1. 43 μs was used, which ensures stability of the simulation with these parameters. The data were fit with custom MATLAB code (glutamateDiffusion. m), available upon request. Statistical testsData are presented as mean with standard error (std. err. ) or standard deviation (std. dev. ), as noted in each case. All values of n are provided; no data were excluded.
TiB 2 ceramic material The static tests were performed with commercially available titanium diboride (Alluter Advanced Manufacture Ltd. ) (≥98 wt. %). The supplied solid block was cut by water beam. Diamond cutting tools were used to facilitate the TiB 2 samples (dimensions 45 × 6 × 6 mm) for corrosion tests. After cutting, the samples were cleaned in ethanol (Emsure, Slovakia, 99. 5%) and placed into the glove box with an inert argon atmosphere (Messer, Slovakia, 99,999%, air moisture below 10 ppm, oxygen content below 1 ppm). Before the corrosion tests, itself, the microstructure of TiB 2 material was characterized and the density and porosity were determined. The data are listed in Table  1. Both, density and porosity were determined by the Archimedean method in water at ambient temperature.
Three samples taken from different parts of the original material were used for this initial characterisation. Samples were weighted in dry, soaked and immersed state. Density and porosity (open, closed and overall) were, then, calculated. Obtained TiB 2 has quite high overall porosity (ca 6. 7%). Inhomogeneity was observed, the open and closed porosity varied from place to place, as listed in Table  1. The density of the TiB 2 material ranges between 4. 19 and 4. 25 g × cm -3 (it is in a good agreement with the data provided by supplier). The microstructure of TiB 2 material predominantly consists of spherical titanium diboride grains, the intergranular phase and pores. The average grain size is within the 25-50 μm range. The grains with the larger size (up to 100 μm) were, however, also observed.
XRD powder diffraction X-ray powder diffraction patterns were collected with Empyrean Xray diffractometer (PANalytical, Netherlands) with Cu K α radiation in Bragg-Brentano reflection geometry, equipped with a PIXcel 3D detector. The K β radiation was subtracted using Ni-filter. The records were taken in the 2Θ range of 10-70°with a step size 0. 02°of 2Θ at room temperature and K α2 software subtraction. Phase analysis was performed with X'Pert HighScore Plus PANanalytical software with PDF2 2014 database.
MAS NMR spectroscopy Solid-state NMR experiments were performed using Bruker Avance III spectrometer operating at 20. 0T using a specially designed Doty probe optimized for 11 B signal observation and equipped with a "boronfree" stator ensures the acquisition of spectra completely representative of the sample and free from probe signal. The spectra were acquired at an observation frequency of 272. 8 MHz for 11 B and at a MAS frequency 18. 4 kHz after a short excitation pulse to ensure homogeneous irradiation of boron species. The recycling delay was fixed at 5 s and number scans were 256. The 11 B chemical shifts were referenced relative to a BF 3 OEt 2 solution. In order to determine the chemical shift and line width, the MAS NMR spectrum is fitted using the Dmfit software  [18].
The powder XRD analysis of the solidified corrosion medium (after 200 h of corrosion exposition time) was also carried out Fig. 4. The major identified crystalline phases are KAlF 4 and K 3 AlF 6. Both species are product of the reaction between the constituents of the melt  [1, 19]. The XRD patterns of the solidified melts also show one unknown peak at 2Θ = 13. 22°, which was not possible to assign (based on PDF2 database) to any known species. Due to the high intensity of that peak, we expect, that it does not come from TiB 2. We assume that this peak is rather related to the present of special type of alumina (potassium doped alumina) in the solidified melt. A similar behaviour of alumina (in this case doped by sodium) was also observed in the system NaF-AlF 3 -Al 2 O 3  [22] [23] [24].
The total volatile matter content (standard NF V 03 hexane extracted matter content NF V 03-908) were the determined For the study of the operating conditions effects on biodiesel production yield and biodiesel quality, the experiments were conducted using the values previously optimized for methanol to seed ratio and amount of KOH (6:1 and 0. 075 mol/L methanol, respectively). The different operating conditions were examined by varying the amount of n-hexane to methanol and seed ratio (1:5:1, 2:4:1, 3:3:1), the stirring speed (200-600 rpm), the temperature (40-50 °C) and the reaction time (4-6 h). Sample collection and analysis were performed according to procedure developed in the previous study. The randomized factorial experimental design with 2 replications and ANOVA (F were applied to study the effects of amount of n-hexane to methanol and seed ratio, stirring speed, temperature and reaction time on biodiesel production yield and biodiesel quality using SAS software.
The total volatile matter (standard NF V 03-903) and the nhexane extracted matter content (standard e the determined. For the study of the operating conditions effects on biodiesel production yield and biodiesel quality, the experiments using the values previously optimized for methanol to seed ratio and amount of KOH (6:1 and 0. 075 mol/L in. The different operating conditions were examined by hexane to methanol and seed ratio (1:5:1, 2:4:1, 3:3:1), the 600 rpm), the °C) and the reaction time ple collection and analysis were performed according to procedure developed in the previous study. The randomized factorial experimental design with 2 replications and ANOVA (F-test at α = 0. 05) were applied to study the effects of amount thanol and seed ratio, stirring speed, temperature and reaction time on biodiesel production yield and biodiesel quality using SAS software.
2. 6. Rheological Tests (Hydrolysis at High Temperature Analysis) In order to assess the significance of hydrolysis in the molten state, time sweep tests at constant frequency were performed  [34, 35]. The rheological tests were carried out by a Haake Mars II (Thermo scientific, Waltham, MA, USA) rotational rheometer in dynamic mode in a parallel plate configuration under nitrogen atmosphere. The diameter of the plates used was 20 mm with a gap of 0. 45 mm. Rheological tests were performed at 200 • C, with a stress set to 500 Pa and a frequency of 1 rad/s, maintaining all values constant for 3 h. The materials, in order to evaluate the effect of the presence of water, were not dried before the tests. The rheological data will be reported in the plots versus the whole time at test temperature, including the time needed to set the gap.
Hydrolysis at High Temperature (Rheological Analysis) In Figure  7 , the results of the rheological analysis are reported. The rheological tests were carried out in order to assess the significance of hydrolysis in the molten state. Time sweep test were performed on 4032D and on 4032D + 3% (LDH-organic acids). From the data reported above, it can be observed that all materials presented a decrease of viscosity during time, due to hydrolysis and thermal degradation. Intercalating LDH with acids, allowed for the significant reduction of the decrease of viscosity that takes place when pure LDH is used as filler. This was a clear advantage for processing: it could be stated that the use of LDH with intercalated acids does not induce a dramatic degradation in the molten state, which can make the materials impossible to be processed.
Acknowledgements This work was supported by  DIT   Fiosraigh Scholarship. The authors express their cordial gratitude to  Focas Research Institute at DIT  for providing access to their experimental facilities. The authors would like to acknowledge the contribution of  Dr. Luke O'Neill  and  Dr. Brian Goery  who collected the SEM data for TEOS and TEOS/LTL nanocomposite. The authors are also thankful to the IEO team members for their co-operation during the execution of this research.
Contributions: A. L. V. and C. C. wrote the paper; F. D. , C. C. , F. C. , L. P. -R. and F. E. gathered and compiled the data, A. L. V. developed the model. All authors contributed equally to the scientific discussion. Funding: The authors thank the European communities (FEDER), the "Région Nouvelle Aquitaine" and ANR (INCH project) for financial support. Acknowledgments: Authors thank S. Baranton and C. Coutanceau (IC2MP Poitiers, France) for scientific discussions.