dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics and general methods All sample sizes were determined based on preliminary studies and prior knowledge of expected variability within assays. For animal studies, age-matched (6-8 weeks) female ATHYMIC NCrnu/nu mice and C57BL/6 mice were randomly assigned to control and test groups. Blinding was used to quantify lung metastases counts. Where blinding was not used, data was analyzed using automated image analysis software when possible. All statistical tests used were deemed appropriate and met the assumptions required. Where necessary unequal variance was corrected for, or if no correction was used variation was assumed equal based on prior knowledge of the Acetic Acid fixation solution with subsequent staining using 0. 01% crystal violet staining (Sigma-Aldrich)/ 10% Methanol solution.
The mass spectrometry data were analyzed and either dimethyl labeling or label-free quantitation performed using MaxQuant version 1. 5. 7. 4  74, 75     a, KDELR3-001 and KDELR3-002 patient expression data. Empirical Bayes moderated tstatistic (unpaired two-tailed test); KDELR3-001, ENST00000216014, P = 0. 0202, t = 2. 36, df = 102. 17; KDELR3-002, ENST00000409006, P = 0. 87, t = 0. 16, df = 102. 17. Boxplots of patient expression data from TCGA-SKCM dataset  30, 42  , depicting the 25th,.
Acknowledgements This research was supported in part by the  NCI   Intramural Research Program  of the  NIH. PJM was also supported in part by the  NCI   Director's Innovation Award. MRZ was supported in part by the following grant:  NIH/NCI   K22CA163799. TG was supported in part by the  HHMI   Research Scholars Program ,  Howard Hughes Medical Institute. HTM funded in part by the  NIH   Comparative Biomedical Scientist Training Program  in partnership with  University of Maryland, College Park , and the  National Cancer Institute. We would like to thank the  Dr. Meenhard Herlyn  for providing melanoma cell lines included in this study. We are grateful to  Cari Graff-Cherry  for maintenance and care of mouse lines,  Dr. Stephen Locket  for microscopy analysis,  Dr. Joe Kalen  and  Nimit Patel  for imaging of developing mice, and  Drs.
50th (median), 75th percentile, and extreme values of the transcript expression. "Early" stage (stages I/II, 62 patients). "Late" stage (stages III/IV, 39 patients). b, Kaplan-Meier estimated survival curves according to KDELR3 expression in early-stage (GSE8401; n = 27, Stages I/II) and late-stage (GSE8401; n = 47, Stages III/IV) melanomas. Log-rank test. c, Association of KDELR3 expression and survival in metastatic melanoma (GSE19234, n = 38; GSE8401, n = 47, Stages III/IV); HR = 1. 62 (P = 0. 028) and HR = 1. 49 (P = 0. 032) for GSE19234 and GSE8401, respectively. No significant association was found in the primary tumors (GSE8401, n = 27, Stages I/II); HR = 0. 76 (P = 0. 509). Cox regression model was used to test the association.
c-d, Tail vein metastasis of KDELR1 siRNA-mediated knockdown human 1205Lu cells transduced with Ferh-luc-GFP. Parental, siControl and siKDELR3 were used as controls. ANOVA with Tukey's multiple comparisons test; siControl Vs siKDELR1, P = 0. 0013, [mean difference (95% CI); -312. 5(-521. 7; -103. 4)]; siKDELR3 Vs siKDELR1, P <0. 0001, [mean difference (95% CI); -415. 3(-616. 8; -213. 7)]. df = 49, F = 10. 8. CI, Confidence interval of differences. e, KDELR1-001 and KDELR1-002 patient expression data. Empirical Bayes moderated t-statistic (unpaired two-tailed test); KDELR1-001, ENST00000330720, P = 0. 73, t = 0. 35, df = 102. 17; KDELR1-002, ENST00000597017, P = 0. 39, t = -0. 86, df = 102. 17. Boxplots of patient expression data from TCGA-SKCM dataset 30, 42  , depicting the 25th, 50th (median), 75th percentile, and extreme values of the transcript expression.
search 10 ppm; maximum of two missed cleavages; protein and peptide FDR threshold of 0. 01; min unique peptides 1; match between runs; label-free quantitation, with minimal ratio count 2. Proteins were identified using a Uniprot human database from November 2016 (20,072 entries). Statistical analysis was performed using Perseus version 1. 5. 6. 0 76. After removal of contaminant and reversed sequences, as well as proteins that were only quantified in one of the three replicate experiments, the quantitation values were base 2 logarithmized and non-assigned values were imputed from a normal distribution of the data. Statistically significant differences were assigned using a two-way t-test with a p-value cut-off of 0. 05. complexes, using an anti-gp78 or anti-DDK antibody and their respective IgG isotypes: rabbit IgG (BD Pharmingen) and mouse IgG (Santa Cruz).
A tool based on the mass action law was developed to calculate plasma compositions and thermodynamic properties for pure gases and mixtures, assuming a local thermodynamic equilibrium for pressures of up to 300 bar. The collection of the data that was necessary for tool calculation was automated by another tool that was written using Python, and the formats for the model were adapted directly from the NIST and JANAF websites. In order to calculate the plasma compositions for high pressures, virial correction was introduced. The influences of the parameters that were chosen to calculate the Lennard-Jones (12-6) potential were studied. The results at high pressure show the importance of virial correction for low temperatures and the dependence of the dataset used. Experimental data are necessary to determine a good dataset, and to obtain interaction potential.
In the literature, we can find that, for some gases, the second and sometimes the third coefficients are given as a function of the temperature  [19, [22] [23] [24] [25] [26]. These data are very dependent on the pressure and temperature of the gas, and they are not valid for a large range of temperatures and pressures. The densities of the species, and the mass density of the gases depend very strongly on the applied dataset. This mass density has a direct effect on convection modeling for its applications. In our research fields, at high pressures, we are particularly focused on three topics: the plasma behavior in high-voltage circuit breakers (HVCBs)  [27] [28] [29] , where pressures in heating volumes can reach bar values of several tens; -hydro-electro formation and hydraulic fracturing using an electric arc in water  [30] ; -the combustion of methane in thermal gas engines  [31].
For each of these topics, the pressure considered and the gases used are different, and they include, within a high-pressure range, SF 6 for the first application, H 2 O for the second, and mixtures of air and CH 4 for the third. In the case of HVCB, some compositions and thermal properties are available in the literature; nevertheless, a complete databank for describing the current cutoff from the initial pressure to the one obtained during the high-current phase is not given, and authors seem to use the same parameters to calculate the Lennard-Jones potential for the entire pressure range  [14, 32, 33]. This problem is the same for the case of hydro-electro formation and hydraulic fracturing: little data is available for H 2 O, and only few details on the parameters that are used to calculate the Lennard-Jones potential are given.
This can be estimated from the Debye-Hückel formula (Equation (3)): ∆E = (Z + 1). e 2 4. π. ε 0. λ D (eV) (3) where -Z is the electrical charge of the considered species; -e is the electrical charge of the electron; ε 0 is the vacuum permittivity; λ D is the Debye length, given by Equation (4): λ D = ε 0. k B. T e 2. n e + ∑ i =e Z 2 i. n i (4) where n e is the electronic density, and n i is the density of each species. The various data on the energy levels, and the angular momentum for the calculation of the statistical weights of the considered levels, were extracted from the NIST database  [38].
The Internal Partition Function for Molecules For molecules, the internal partition function can be obtained from Equation  (5) , by using the spectroscopic data available in the JANAF database  [37] , and especially the free Gibbs energy and the enthalpy. Q int (T) = P r k B. T h 2 2. π. m. k B. T 3 2 exp 1 N a. k B [H(0) -H(T r )] J AN AF T - G -H(T r ) T J AN AF (5) where -T r and P r are the temperature and pressure, respectively, of the reference in the JANAF database; -N a is the Avogadro constant; -H(0) -H(T r ) represents the enthalpy at absolute zero; -[G -H(T r )]/T is the Gibbs energy, which must be known for each temperature that is considered. For the electrons, the internal partition function is constant and equal to 2.
Choices of Elements and Chemical Species The chemical elements are the basic components. When an ionized gas is described, the electric charge needs to be considered and added to the list of the basic components. The chemical species are constituted of one or several of these basic components. For instance, the oxygen atom is the basic component of several chemical species, such as O, O + , O -, O 2 , and O 2 +. Of course, the element and species choices depend on the plasma of interest. The only limit is the availability of the species data in the NIST  [38]  and JANAF  [37]  databases for the internal partition functions. In order to properly differentiate the basic components and the chemical species, two parameters, M and N, are defined. M is the number of chemical (or basic) elements, and N is the number of chemical species, with M ≤ N.
Data from the NIST and JANAF Tables To simplify the searching and formatting of the data available in the NIST and JANAF tables, software was developed using Python. This enables all of the required data for the species of the chosen composition to be concatenated and formatted automatically. In the case of the data originating from the NIST  [38] , the added parameters for each species, complementing the different energy levels and angular momentums, are as follows: the name of the species, for a link between the initialization file and the data file; -the energy of formation, needed for the calculation of the total partition function; -the energy of ionization that is used in order to satisfy the limit condition of Equation (2); -the units for the energy levels, depending on the extracted data (in cm -1 or in eV); -the number of energy levels to consider, up to the ionization of the considered species; -the mass of the considered species.
For data from the JANAF database  [37] , two parameters are added: the name of the species and the mass. We have collected 296 chemical species, which can be used for calculating compositions containing carbon (C), hydrogen (H), oxygen (O), nitrogen (N), sulfur (S), fluorine (F), argon (Ar), helium (He), copper (Cu), and iron (Fe). All kinds of particles have been taken into account in this databank: atoms, molecules, positive and negative ions, and molecular ions.
When the temperature increased, we observed different types of processes. In the case of argon, the ionization of the atoms and the simultaneous apparition of electrons occurred. In the case of air, we noted the dissociation of the molecules, the recombination processes, the apparition of electrons, and the ionization of atoms, but also the electronic attachments. Nevertheless, for some species, some of the results displayed differences between the literature and our calculations. These were generally due to the sources used for the internal partition function of each species. In Figure  1 , the results from Godin  [2]  are based on the data available from Drawin & Felenbok  [39]. In this database, the internal partition functions are given versus temperature, with values of the lowering of ionization potential.
Gas Chemical Species Argon Ar, Ar + , Ar +2 , Ar +3 , e -. Air N, N + , N +2 , O, O + , O +2 , O -, C, C + , C +2 , C -, Ar, Ar + , Ar +2 , N2, N2 + , NO, NO + , O2, O2 + , C2, CO, N2O, NO2, CO2, e -. The composition obtained from our tool in Case 1 is shown with those of three other studies  [2, 11, 16]  in Figure  1. For the air plasma, our results were compared with the calculation of André  [5] , and are plotted in Figures  2  and 3  (Figure  2  shows the atomic and ionic species, and Figure  3  shows the molecular species). Comparison of ours results with Colombo et al  [11] , Godin  [2]  and Rat et al  [16]  Figure  1. Evolution of species: argon; P = 1 barlocal thermal equilibrium (LTE). Comparison of ours results with Colombo et al  [11] , Godin  [2]  and Rat et al  [16]. Although most papers from the literature used tabulated values for the partition functions, we chose to calculate them from the available data of the NIST database  [38]  for monoatomic species, and from the JANAF database  [37]  for molecules.
1866 v 1 3 c Z -6 5 c (34) ε k B = 65. 3 T c Z 18 5 c ( 35 ) where v c , Z c , and T c are critical properties. In order to choose the better dataset, it is necessary to compare the results with the experimental data. Hirschfelder  [18]  recommends the use of parameters deduced from experimental viscosity for calculations of transport properties, the experimental second virial coefficients for the state equation calculation, and the calculation of thermodynamic properties. Nevertheless, the data in this last case were scarce, and they were only available for some species. In Table  4 , a comparison of the σ and ε k B parameters is given. The values from the literature used for the comparison with Stiel  [51]  are included in the range given in Table  3. These values were chosen, as they corresponded to the experimental values deduced from the viscosity.
This dataset σ = 5. 21279 Å and ε k B = 222. 27 K gives a better approximation of the calculated mass densities for a pressure range of between 1 and 300 bar. The use of the Stiel  [51]  formulation is a good option for pressures and temperatures that are not too close to the critical point Pc ≈ 37. 5 bar and Tc ≈ 318. 8 K (cf. Figures  4  and 6 ), when no other data are available. The curves from Figures  4 5 6 show discrepancies in the variation of the LJ potential parameters on the mass densities deduced from the composition. It should be underlined that a dataset for σ and ε k B , obtained from the experimental measurements for the given values of temperature and pressure, cannot be used without care over the whole pressure range. for the pressure range (1 bar < P < 300 bar) and temperature (300 K < T < 60 kK) are given in Table  8.
Conclusions Software using the mass action law for obtaining the plasma composition was developed. Under the assumption of LTE, a variation of species densities with temperature can be obtained for atmospheric pressures of up to 300 bar. For such high pressures, the virial corrections that are associated with classical Debye-Hückel corrections are systematically used in ours developments. After a presentation of the theory for calculating the plasma composition, the results of the software were validated with compositions reported from the literature concerning argon and air plasmas at atmospheric pressure. Additional routines were developed using Python to automatically collect the required data from the NIST and JANAF Internet databases. To consider the high pressures, potential interactions that are formally used in virial correction were studied.
Previous computational modeling suggests it is much easier to segment words from child-directed speech (CDS) than adult-directed speech (ADS). However, this conclusion is based on data collected in the laboratory, with CDS from play sessions and ADS between a parent and an experimenter, which may not be representative of ecologically collected CDS and ADS. Fully naturalistic ADS and CDS collected with a nonintrusive recording device as the child went about her day were analyzed with a diverse set of algorithms. The difference between registers was small compared to differences between algorithms; it reduced when corpora were matched, and it even reversed under some conditions. These results highlight the interest of studying learnability using naturalistic corpora and diverse algorithmic definitions.
Previous modeling research documents much higher segmentation scores for CDS than ADS corpora  (Batchelder, 1997 (Batchelder, , 2002;; Daland & Pierrehumbert, 2011; Fourtassi, Borschinger, Johnson, & Dupoux, 2013). Most of this work compared CDS recorded in the home or in the lab (in the CHILDES database; MacWhinney, 2014), against lab-based corpora of adultadult interviews including open-ended questions ranging from profession to politics (e. g. , the Buckeye corpus;  Pitt, Johnson, Hume, Kiesling, & Raymond, 2005). As a result, differences in segmentability could be due to confounded variables: Home recordings capture more informal speech than interviews do, with shorter utterances and reduced lexical diversity; moreover, since different researchers transcribed the CDS and ADS corpora, their criteria for utterance boundaries may not be the same.
Additionally, coders sometimes considered a sequence of segments as continuations of each other (6% of CDS utterances and 7% of ADS utterances). We derived several versions of the ADS and CDS subcorpora crossing two factors (see Table  1  for characteristics). First, we used the automatic utterance boundaries provided by the LENA software ("A," short for "automatic boundaries"), as well as combined together the text from segments labeled as continuations of each other by coders ("H" for "human boundaries"). Second, since performance is dependent on corpus size (see  Bernard et al. , 2018) , we had three versions of each CDS corpus: the full one, a shortened CDS corpus to match the ADS corpus in number of words, and a shortened CDS corpus to match the ADS corpus in number of utterances. After crossing these two factors, performance could be compared between, on the one hand, ADS-A/H (ADS with automatic or human utterance boundaries), and, on the other hand, one of (1) CDS-A/H-full (corresponding full CDS corpus), (2) CDS-A/H-WM (cut at the same number of word tokens found in the corresponding ADS), or (3) CDS-A/H-UM (cut at the same number of utterances).
Each algorithm then returns the corpus with spaces where word boundaries are hypothesized. Each algorithm (with default parameters, except as noted below) was run using the WordSeg package  (Bernard et al. , 2018) , which also performs the evaluation. Due to space restrictions, we cannot provide fuller descriptions here, but we refer readers to  Bernard et al. (2018) , where the algorithms and the evaluation are explained. In a nutshell, both training and evaluation are done over the whole corpus because these algorithms are unsupervised, and thus there is no risk of overfitting. In the case of incremental algorithms, performance was calculated on an output corpus that represented the algorithm's segmentation level in the last 20% of the data. We provide pseudo-confidence intervals estimated as two standard deviations over 10 runs of resampling with replacement.
Ultimately, we may want to test algorithms that operate directly from the acoustic representation  (Ludusan et al. , 2014; Versteegh et al. , 2015). Second, we studied only North American English. We look forward to extending the current approach to ecologically valid databases in additional typologically diverse languages, although none containing both CDS and ADS is currently available, and therefore a priority in future research should be to build larger, matched, multilingual corpora. We predict segmentation scores are lower in languages where words and syllable boundaries are less well-aligned than in English  (Loukatou, Stoll, Blasi, & Cristia, 2018) , but regardless of overall performance levels, there will be no or little learnability advantages for CDS versus ADS for segmentation: North American English has been described as having more marked CDS-ADS differences than other languages (e.
FUNDING INFORMATION AC acknowledges financial support from Agence Nationale de la Recherche (ANR-14-CE30-0003 MechELex); ED from European Research Council (ERC-2011-AdG-295810 BOOTPHON), the Fondation de France, the Ecole de Neurosciences de Paris, the Region Ile de France (DIM cerveau et pensée); MS from SSHRC  (Insight Development Grant 430-2011-0459, and Insight Grant 435-2015-0628). AC and ED acknowledge the institutional support of Agence Nationale de la Recherche (ANR-17-EURE-0017).
Methods Study Population The authors declare that all supporting data are available within the article and its supplemental material. The ISACS-TC (International Survey of Acute Coronary Syndromes in Transitional Countries; ClinicalTrials. gov: NCT01218776) registry is a large, prospective, multicenter cohort study designed to record clinical characteristics, treatments, and clinical outcomes for patients with acute coronary syndrome. 5, 15 The ISACS-TC registry enrolled 5590 patients who underwent primary PCI for STEMI within 12 hours from symptom onset between January 2010 and January 2016. The designated physician collected the registry data at the time of clinical assessment. Patients were asked about the quality of their symptoms. All patients presented with chest pain or equivalent symptoms, such as dyspnea and fatigue.
Data on coronary hemodynamics were available in 2651 of the 5590 patients. In the remaining patients, the ISACS-TC registry generated reports with summarizing information on PCI procedural success without any further detail. Among the 2651 patients with data on coronary hemodynamics, those who had an unknown time of symptom onset (n=55) were excluded, giving a final study population of 2596 patients (Figure). Patients who were transferred from a PCI noncapable hospital were included in this analysis. Time of symptom onset relied on patient recall and required documentation by each hospital participating in this registry. Delay time from symptom onset to hospital presentation included door-in to door-out time, defined as the duration of time from arrival to discharge at the first referral hospital, and transfer time to a STEMI receiving hospital for primary PCI.
Statistical Analysis Patient and procedure characteristics were examined according to sex. Statistical testing was performed with the use of the Pearson's v² test for categorical variables and the 2sample t test for continuous variables. We had complete data on time from symptom onset to PCI and mortality. Some patients had missing data on other variables. We imputed the missing values of the clinical variables whose missing rate was <10% using IVEWARE software. 18 Only 1 variable, Killip class, had missing rates that exceeded 10%. We used k-nearest neighbor algorithms as imputation method  [19] [20] [21]  (Data S1). Estimates of the odds ratios (ORs) and associated 95% CIs were obtained with the use of multivariable logistic regressions. Fixed covariates included demographic information and baseline clinical characteristics (Tables  1  and 2 ).
44, 45 The American Heart Association established "Mission: Lifeline" to develop STEMI systems of care that would improve timely access to PCI. 46  However, despite the increased focus on delays in STEMI care, it is difficult to translate guideline recommendations into clinical practice. The NCDR (National Cardiovascular Data Registry) found that only 15% of patients are treated within 120 minutes from symptom onset. 47 In the current study, we examined the prehospital and hospital components of the total ischemic time: time from symptom onset to hospital presentation and door-to-balloon time. We found that the median time from symptom onset to hospital presentation ranged from 280 minutes in women to 240 minutes in men and that the median door-to-balloon times of women and men were 40 and 38 minutes, respectively.