dataesr/scientific-paragraphs-categorization huggingface dataset:
Audit Administration Procedures The VENDing audit tool was used to evaluate vending machines on 9 campuses of U. S. post-secondary institutions in 7 different states during spring 2016. Each campus team independently selected vending machines for evaluation, audited them, and recorded data in the VENDing Survey. All student auditors completed online training, the IRR quiz, and practiced with the VENDing audit. Each campus team evaluated at least one snack and one beverage machine in each building. In general, campus teams chose the busiest buildings on campus for this VENDing audit. Vending machines with the highest traffic flow were selected for evaluation, i. e. , machines on the main floor of a building. To insure a consistent/static view a vending machine for the audit, research assistants used digital cameras or smartphones to take photographs of the vending machine selected for evaluation.
Data Analysis Data were analyzed using SPSS (Version 24, 2016, IBM Corp. IBM SPSS Statistics for Windows, Version 24. 0. Armonk, NY, USA). Snack and beverage mean VENDing scores were not normally distributed, thus non-parametric statistics were used to describe these data. Differences between mean nutrient-density, % healthy, VENDing scores, and Support sub-scores by machine type and building type were determined with analysis of variance (ANOVA) and post hoc Tukey-B. Chi Square was used to determine differences between support questions by machine types. Pearson's correlations, T-tests and sensitivity and specificity were applied between a subset of individual vending machine scores evaluated by both the NEMS-V and the VENDing Nutrient-Density Score. Sensitivity and specificity were calculated on individual vending machines comparing VENDing Nutrient-Density Score categories to NEMS-V healthfulness categories.
Apoptosis Assay. Phosphatidylserine on the outer leaflet of apoptotic cells was detected using annexin V-fluorescein isothiocyanate (FITC) binding (Invitrogen, Eugene, OR, USA). After 24 h of irradiation, cells were dissociated in cell dissociation buffer enzyme-free PBS-based (Life Technologies/Gibco Laboratories, Grand Island, NY, USA), washed with HBSS 0. 25% EDTA, resuspended in 100 μL of binding buffer (140 mM NaCl, 5 mM CaCl 2 , 10 mM HEPES-Na, pH 7. 4), followed by staining with 2 μL annexin V-FITC for 15 min at room temperature. Then, 400 μL of binding buffer was added and 10,000 events were acquired using a flow cytometer (Becton Dickinson, FACSCalibur). Data were analyzed using CellQuest software.
Acknowledgments The authors would like to thank  Capacitação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)  and  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  for financial support. This work was also partly supported by  Financiadora de Estudos e Projetos (FINEP)  and  Programa de Apoio a Núcleos de Excelência (PRONEX/Fundação Araucária ). The authors thank  Complexo de Centrais de Apoio à Pesquisa (COMCAP/UEM)  for technical support.
It implies in this case: F tot = F 3 (8) r The statistic function F is defined as the SD as it reliably informs us about the data diversity. Then we consider the following assignation: F ← Standar d D evi ati on (9) r Regarding the noise present in our images, a 3 × 3 sampling grid is selected for an improved approximation during the PD. Finally, applying all these points, the F tot is our FMPOD. Results of focus research using the FMPOD are presented in the next section.
To evaluate the robustness of our method, we applied additional distortions to the acquisitions: noise, blur and sharpness increase. These distortions may simulate a practical use case like using another optical system, a better or defective IR camera, a SOI with sharper material reflectivity, lower brightness, etc. Below are two synthetic tables of the focus success rate using each of the four methods (1, 2, 3 and 4) with respect to the distortion applied. We make the evaluation on two different datasets: in Table  14  the acquisition is done for a classical use case, i. e. for an SOI with normal contrast; in Table  15  the SOI is of poor quality, implying an SOI with lower contrast.
Implementation The FISH-, WPT-, COW-based methods and the FMPOD were implemented in Python language with additional features such as: r PyWavelets package for the WPT and the DWT-based methods. r For the FMPOD (4), an additional tool was developed in C++ for the Polynomial decomposition, and made compatible with Python using PyBind11. Moreover, OpenMP was used for C++ loop parallelization on the CPU side. A comparison of the different algorithms in terms of complexity and runtime may be irrelevant, since each implementation relies on very different technologies. Nevertheless, here is the runtime of each algorithm: r FMPOD: 3. 5 ms per image r FM using COW: 2. 24 ms per image r FM using WPT: 3. 7 ms per image Fig. 14. Rate of focus correctness depending on the FM and the distortion applied (colors) on images acquired in the standard use case (normal brightness and SOI contrasts).
Real-Time RT-PCR ANRIL, p16-CDKN2A, p15-CDKN2B, and p14-ARF gene mRNA expression levels were quantified using real-time RT-PCR. Quantitative values were obtained from the cycle number (C t value) at which the increase in the fluorescence signal associated with exponential growth of PCR products started to be detected by the laser detector of the ABI Prism 7900 Sequence Detection System (Perkin-Elmer Applied Biosystems), using PE Biosystems analysis software according to the manufacturer's manuals. The precise amount of total RNA added to each reaction mix (based on optical density) and its quality (i. e. , lack of extensive degradation) are both difficult to assess. We therefore also quantified transcripts of the TBP gene (Genbank accession: NM_003194) encoding the TATA box-binding protein (a component of the DNA-binding protein complex TFIID) as an endogenous RNA control, and normalized each sample on the basis of its TBP content.
We scanned the dbEST and nr databases to confirm the total gene specificity of the nucleotide sequences chosen for the primers and the absence of SNPs. To avoid amplification of contaminating gDNA, one of the two primers was placed at the junction between two exons or on two different exons. The primer pairs for each CDKN2A/B cluster genes were selected to be unique when compared to the sequences of the two other CDKN2A/B cluster genes-in particular, comparison between p14ARF/CDKN2A and p16/CDKN2A. Moreover, to avoid amplification of contaminating genomic DNA, one of the two primers was placed in a different exon. For example, the upper primer of TBP was placed at the junction between exons 5 and 6, whereas the lower primer was placed in exon 6. The nucleotide sequences of the oligonucleotide hybridization primers are shown in Supplementary Table  S2.
Database Sources RNA Sequencing data and mRNA expression levels in the TCGA series used for statistical analysis between ANRIL, p16-CDKN2A, p14-ARF, and p15-CDKN2B were downloaded from the publicly available database cBioPortal for Cancer Genomics (https://www. cbioportal. org). Graphical view of the ANRIL (CDKN2B-AS1) gene was adapted from the University of California Santa Cruz (UCSC) Genome Browser on Human Dec. 2013 (GRCh38/hg38) Assembly. RNA and protein expressions overviews of p14-ARF, p15-CDKN2B, and p16-CDKN2A in various types of cancer and protein expression of p14-ARF, p15-CDKN2B, and p16-CDKN2A in prostate cancer TMA samples were adapted from Protein Atlas website-version 18. 1 (https://www. proteinatlas. org).
Experimental The homogeneity and phases purity of the samples were characterized by X-ray diffraction (XRD) at room temperature using a BRUKER diffractometer with CuKa radiation (l ¼ 1, 5406 Å). The data acquisition was in the 2q range of 15-80 , with a step of 0. 02 and an acquisition time for each step of 1 s. The XRD data were used to obtain the lattice structure and cell  parameters of the synthesized compounds by means of Rietveld analysis using the FULLPROF program. Microstructures and grain sizes were observed by a Merlin scanning electron microscope (SEM) equipped with an Oxford Instruments silicon dri detector (SDD)-X-Max 50 used for the elemental analysis of various phases. The electrical characterization was assured by impedance spectroscopy using an Agilent 4294A impedance analyzer.
In the renement, the rened parameters were lattice parameters, scale factors, shape parameters, isotropic thermal parameters, and oxygen positions (x ¼ y ¼ z). The background was corrected by the pseudo-Voigt function. Various R factors are listed in Table  1. The values of R p were found to be large. However, we observed a low value for the goodness of t (c 2 ), which suggested that the rening of the samples was effective and that the samples obtained were of better quality. The lattice constant a exp of the Ni-Zn nanoparticles was determined from X-ray data analysis using the relation a ¼ l 2 sin q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi h 2 þ k 2 þ l 2 p :  24  The crystallite size, lattice constant a exp , lattice constant a r (obtained from Rietveld renement), density, and porosity of the annealed samples are summarized in Table  1.
Radiographic analysis The selected scans were performed with two tools: Cranex 3X ® and Scanora 3X ® (Soredex Orion Corporation Ltd, Helsinki, Finland). The size of the field of view was 8 x 8 cm and 8 x 10 cm, and the average resolution (voxel) was 0. 15 mm. The term "field of view" refers to the volume of the CBCT scan. Subsequently, the DICOM files were reviewed in a dark room by two observers (JG and RA) with the OnDemand-3D ® software (Cybermed, Daejeon, Korea) in a 1380 x 800 pixel resolution screen. Whenever required, tools were used to increase and change contrast/brightness in order to improve structure visibility and measurement precision. Each observer measured a maximum of 10 CBCT scans per day.
Statistical analysis The statistical analysis of the data related to this study was conducted through the Statistical Package for the Social Sciences (SPSS) software for Windows, version 20. 0 (Chicago, Illinois, USA). Initially, with the objective of describing and characterising the study sample, a descriptive analysis of the data was conducted according to the nature of the studied variables. The statistical inference necessary for In order to obtain the mentioned locations, a straight line was drawn intersecting perpendicularly the tooth's long axis and crossing the vestibular CEJ (A). Afterwards, a new straight line was drawn parallel to the previous one, intersecting the tooth's long axis, 4 mm apical to the previous straight line. This new line intersected the buccal bone plate and created P1.
When the arrow is pointing left the black hole rotates in a clockwise direction, and when the arrow is pointing right the black hole rotates in a counterclockwise direction. The field of view for each model image is 80 as m (half of that used for the image libraries) with resolution equal to 1 as m /pixel (20 times finer than the nominal resolution of EHT2017, and the same employed in the library images). data are insensitive to the rescaling procedure unless the angular scaling factor or flux scaling factor is large. 113  The comparisons with the data are also insensitive to image resolution. 114  A representative set of time-averaged images from the Image Library are shown in Figures  2  and 3. From these figures it is clear that varying the parameters a * , f, and R high can change the width and asymmetry of the photon ring and introduce additional structures exterior and interior to the photon ring.
Procedure for Comparison of Models with Data As described above, each model in the Simulation Library has two dimensionless parameters: black hole spin a * and magnetic flux f. Imaging the model from each simulation adds five new parameters: R high , i, PA, M, and D, which we set to 16. 9 Mpc. After fixing these parameters we draw snapshots from the time evolution at a cadence of 10 to r c 50 g 1 -. We then compare these snapshots to the data. The simplest comparison computes the 2 c n (reduced chi square) distance between the data and a snapshot. In the course of computing 2 c n we vary the image scale M/D, flux density F ν , position angle PA, and the gain at each VLBI station in order to and 160, all with a 0. 94 * =. The region between the dashed curves is the locus of existence of (unstable) photon orbits  (Teo 2003).
The 2 c n comparison alone does not provide a sharp test of the models. Fluctuations in the underlying GRMHD model, combined with the high signal-to-noise ratio for EHT2017 data, imply that individual snapshots are highly unlikely to provide a formally acceptable fit with 1 2 c n . This is borne out in practice with the minimum 1. 79 2 c = n over the entire set of the more than 60,000 individual images in the Image Library. Nevertheless, it is possible to test if the 2 c n from the fit to the data is consistent with the underlying model, using "Average Image Scoring" with THEMIS(THEMIS-AIS), as described in detail in Appendix F of Paper VI). THEMIS-AIS measures a 2 c n distance (on the space of visibility amplitudes and closure phases) between a trial image and the data. In practice we use the average of the images from a given model as the trial image (hence THEMIS-AIS), but other choices are possible.
We compute the 2 c n distance between the trial image and synthetic data produced from each snapshot. The model can then be tested by asking whether the data's 2 c n is likely to have been drawn from the model's distribution of 2 c n. In particular, we can assign a probability p that the data is drawn from a specific model's distribution. In this Letter we focus on comparisons with a single data set, the 2017 April 6 high-band data (Paper III). The eight EHT2017 data sets, spanning four days with two bands on each day, are highly correlated. Assessing what correlation is expected in the models is a complicated task that we defer to later publications. The 2017 April 6 data set has the largest number of scans, 284 detections in 25 scans (see Paper III) and is therefore expected to be the most constraining.
Model Constraints: EHT2017 Alone The resolved ring-like structure obtained from the EHT2017 data provides an estimate of M/D (discussed in detail in Paper VI) and the jet PA from the immediate environment of the central black hole. As a first test of the models we can ask whether or not these are consistent with what is known from other mass measurements and from the orientation of the large-scale jet. Figure  7  shows the distributions of best-fit values of M/D for a subset of the models for which spectra and jet power estimates are available (see below). The three lines show the M/D distribution for all snapshots (dotted lines), the best-fit 10% of snapshots (dashed lines), and the best-fit 1% of snapshots (solid lines) within each model. Evidently, as better fits are required, the distribution narrows and peaks close to M D 3.
One group of models, however, is rejected by THEMIS-AIS: MAD models with a 0. 94 * = -. On average this group has p = 0. 01, and all models within this group have p 0. 04 . Snapshots from MAD models with a 0. 94 * =exhibit the highest morphological variability in our ensemble in the sense that the emission breaks up into transient bright clumps. These models are rejected by THEMIS-AIS because none of the snapshots are as similar to the average image as the data. In other words, it is unlikely that EHT2017 would have captured an a 0. 94 * = -MAD model in a configuration as unperturbed as the data seem to be. The remainder of the model categories contain at least some models that are consistent with the data according to the average image scoring test. That is, most models are variable and the associated snapshots lie far from the average image.
Discussion We have interpreted the EHT2017 data using a limited library of models with attendant limitations. Many of the limitations stem from the GRMHD model, which treats the plasma as an ideal fluid governed by equations that encode conservation laws for particle number, momentum, and energy. The eDF, in particular, is described by a number density and temperature, rather than a full distribution function, and the electron temperature T e is assumed to be a function of the local ion temperature and plasma p b. Furthermore, all models assume a Kerr black hole spacetime, but there are alternatives. Here we consider some of the model limitations and possible extensions, including to models beyond general relativity.
Radiative Effects Post-processed GRMHD simulations that are consistent with EHT data and the flux density of 1. 3 mm emission in M87 can yield unphysically large radiative efficiencies (see Section 6). This implies that the radiative cooling timescale is comparable to or less than the advection timescale. As a consequence, including radiative cooling in simulations may be necessary to recover self-consistent models (see  Mościbrodzka et al. 2011; Dibi et al. 2012). In our models we use a single parameter, R high , to adjust T e and account for all effects that might influence the electron energy density. How good is this approximation? The importance of radiative cooling can be assessed using newly developed, state-of-the-art general relativistic radiation GRMHD ("radiation GRMHD") codes. Saḑowski et al.
Conclusion In this Letter we have made a first attempt at understanding the physical implications of a single, high-quality EHT data set for M87. We have compared the data to a library of mock images produced from GRMHD simulations by GRRT calculations. The library covers a parameter space that is substantially larger than earlier model surveys. The results of this comparison are consistent with the hypothesis that the compact 1. 3 mm emission in M87 arises within a few r g of a Kerr black hole, and that the ring-like structure of the image is generated by strong gravitational lensing and Doppler beaming. The models predict that the asymmetry of the image depends on the sense of black hole spin. If this interpretation is accurate, then the spin vector of the black hole in M87 points away from Earth (the black hole spins clockwise on the sky).
The theory of high temperature, collisionless plasmas must be better understood if this core physical uncertainty of sub-Eddington black hole accretion is to be eliminated. At present our understanding is inadequate, and alternative eDF models occupy a large, difficult-to-explore parameter space with the potential to surprise. Despite these uncertainties, many of the models produce images with similar morphology that is consistent with EHT2017 data. This suggests that the image shape is controlled mainly by gravitational lensing and the spacetime geometry, rather than details of the plasma physics. Although the EHT2017 images are consistent with the vast majority of our models, parts of the parameter space can be rejected on physical grounds or by comparison with contemporaneous data at other wavelengths.
We reject some models because, even though all models are variable, some models are too variable to be consistent with the data. We can also reject models based on a radiative efficiency cut (the models are not self-consistent and would cool quickly if radiative effects were included), an X-ray luminosity cut using contemporaneous Chandra and NuSTAR data, and on a jetpower cut. The requirement that the jet power exceed a conservative lower limit of 10 erg s 42 1 -turns out to eliminate many models, including all models with a 0 * =. We have examined the astrophysical implications of only a subset of EHT2017 data; much remains to be done, and there are significant opportunities for further constraining the models. EHT2017 data includes tracks from four separate days of observing; each day is r c 2.
8 g 1 -(see Paper IV). This timescale is short compared to the decorrelation timescale of simulated images, which is r c 50 g 1 ~-, and smaller than the light-crossing time of the source plasma. Analysis techniques that use short-timescale variations in the data will need to be developed and are likely to recover new, more stringent constraints on the model from the EHT2017 data set. EHT2017 took polarized data as well. Our simulations already predict full polarization maps, albeit for our simple eDF model. Comparison of model polarization maps of the source with EHT2017 data are likely to sharply limit the space of allowed models  (Mościbrodzka et al. 2017). Finally, in this Letter the only multiwavelength companion data that we consider are X-ray observations. Simultaneous data are available at many other wavelengths, from the radio to the gamma-rays, and is likely to further limit the range of acceptable models and guide the implementation of predictive electron physics models.
2014 Kino et al. , 2015)) , the core shift  (Hada et al. 2011) , and the symmetric limb-brightening structure  (Takahashi et al. 2018; Kim et al. 2018). The jet width profile is potentially very interesting because it depends on the magnetic flux f: the jet internal magnetic pressure 2 f µ. We therefore expect (and see in our numerical simulations; see Figure  4 ) that MAD jets are wider at the base than SANE jets. Future theoretical work will help connect the ring-like structure seen in EHT2017 to the largescale jet (M. Nakamura et al. 2019, in preparation). A second epoch of observations ( r c 50 2 g 1  weeks after EHT2017, when the models suggest that source structure will decorrelate) will increase the power of the average image analysis to reject models. The EHT2017 data were able to reject one entire category of models with confidence: high magnetic flux (MAD), retrograde, high-spin models.
Other categories of models, such as the low magnetic flux, high-spin models, are assigned comparatively low probabilities by the average image scoring scheme. Data taken later, more than a decorrelation time after EHT2017 (model decorrelation times are of order two weeks), will provide an independent realization of the source. The probabilities attached to individual models by average image scoring will then multiply. For example, a model with probability 0. 05 that is assigned probability 0. 05 in comparison to a second epoch of observation would then have probability 0. 05 2. 5 10 2 3.
The authors of this Letter thank the following organizations and programs: the  Academy of Finland  (projects  274477 ,  284495 ,  312496 ); the  Advanced European Network of E-infrastructures for Astronomy with the SKA (AENEAS)  project, supported by the  European Commission   Framework Programme Horizon 2020 Research and Innovation action  under grant agreement  731016 ; the  Alexander von Humboldt Stiftung ; the  Black Hole Initiative at Harvard University , through a grant ( 60477 ) from the  John Templeton Foundation ; the  China Scholarship Council ;  Comisión Nacional de Investigación Científica y Tecnológica (CONICYT, Chile , via PIA ACT172033, Fondecyt  1171506 ,  BASAL AFB-170002 , ALMA-conicyt 31140007);  Consejo Nacional de Ciencia y Tecnológica (CONACYT, Mexico , projects  104497 ,  275201 ,  279006 ,  281692 ); the  Delaney Family via the Delaney Family John A.