dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, due to the design of the iCOGS array (Section 2. 1), the genome of GENESIS participants has not been unbiasedly surveyed: some regions are finemapped-which might distort gene structure in GM and GI networks-while others are understudied-hindering the accuracy with which the GS network captures the genome structure. A strong LD pruning might address such problems. To produce the two consensus solutions, we faced practical challenges due to the differences in interfaces, preprocessing steps, and unexpected behaviors of the various methods. To make it easier for others to apply these methods to new datasets and aggregate their solutions, we built six nextflow pipelines  [69]  with a consistent interface and, whenever possible, parallelized computation. They are available on GitHub: hclimente/gwas-tools (Section 2.
(C) Runtime of the evaluated methods, by type of network used (PPIN or SNP). For gene-based methods, inverted triangles represent the runtime of the algorithm alone, and circles the total time, which includes the algorithm themselves and the additional 119 980 seconds (1 day and 9. 33 hours) that VEGAS2 took on average to compute the gene scores from SNP summary statistics. (D) True positive rate and false positive rate of the methods, obtained using different parameter combinations (Section 3. 7). We used as true positives BCAC-significant SNPs (for SConES and χ 2 + Bonferroni) and genes (for the remaining methods, Section 2. 6). We used the whole dataset in this panel.
Acknowledgments We wish to thank  Om Kulkarni  for helpful discussion on gene-based GWAS and PPIN databases,  Vivien Goepp  for his constructive comments of the manuscript, and the genetic epidemiology platform (the  PIGE, Plateforme d'Investigation en Genetique et Epidemiologie :  S. Eon-Marchais ,  M. Marcou ,  D. Le Gal ,  L. Toulemonde ,  J. Beauvallet ,  N. Mebirouk ,  E. Cavaciuti ), the biological resource center ( S. Mazoyer ,  F. Damiola ,  L. Barjhoux ,  C. Verny-Pierre ,  V. Sornin ). We thank all the  GENESIS collaborating cancer clinics (Clinique Sainte Catherine, Avignon :  H. Dreyfus ;  Hopital Saint Jacques, Besancon: M-A. Collonge-Rame ;  Institut Bergonie, Bordeaux:  M. Longy ,  A. Floquet ,  E. Barouk-Simonet ;  CHU, Brest: S. Audebert; Centre Francois Baclesse, Caen: P.
Funding: C. -A. A. received funding from  Agence Nationale de la Recherche  ( ANR-18-CE45-0021-01 ). H. C. -G. was supported from the  European Union 's  Horizon 2020 research and innovation program  ( Marie Skłodowska-Curie  [666003]). Financial support for GENESIS resource and genotyping was provided by the  Ligue Nationale contre le Cancer  (grants  PRE05/DSL  and  PRE07/ DSL , to D. S. -L. ; and grant  PRE11/NA , to N. A. ), the  French National Institute of Cancer  (grant  INCa  grant  b2008-029/LL-LC , to D. S. -L. ) and the comprehensive cancer center  SiRIC (Site de Recherche Inte  ´gree sur le Cancer: grant  INCa-DGOS-4654 , to N. A. )The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
A Pearson correlation of 1 means the two solutions are the same. (C) Runtime of the evaluated methods, by type of network used (PPIN or SNP). For gene-based methods, inverted triangles represent the runtime of the algorithm alone, and circles the total time, which includes the algorithm themselves and the additional 119 980 seconds (1 day and 9. 33 hours) that VEGAS2 took on average to compute the gene scores from SNP summary statistics. (D) True positive rate and false positive rate of the methods, obtained using different parameter combinations (Section 3. 7). We used as true positives BCAC-significant SNPs (for SConES and χ 2 + Bonferroni) and genes (for the remaining methods, Section 2. 6). We used the whole dataset in this panel.
Since the logistic (log) hazard is a non-linear function of the parameters θ = [K, β 0 , β 1 ], we estimate the model's parameters by maximising the Poisson log-likelihood ln L (θ | Y t , N ) ∝ t [Y t ln (h(θ; t)) -N h(θ; t)]. (9) In R, this can be performed using the maxLik package  [5]. As for the log-polynomial models, the slope and the curvature of the logistic model at a given time t can be computed from the first and second derivatives with respect to time ∂ ∂t h(t) = K β 1 exp β 0 + β 1 t 1 + exp β 0 + β 1 t 2 ∂d 2 ∂t 2 h(t) = K β 2 1 exp β 0 + β 1 t 1 -exp β 0 + β 1 t 1 + exp β 0 + β 1 t 3. ( 10 ) An important feature of the logistic function, which we will exploit for the model selection, is its inflection point. This can be computed by letting the second derivative be equal to zero ∂d 2 ∂t 2 h(t) = 0 ⇔ t = - β 0 β 1 (11) Finally, we compute 95% confidence intervals for the inflection point by delta method, i.
Output and graphical displays We first produce a separate plot for each province and region. In each plot, we show the reported cases over time together with a line showing the fitted values from the selected model. The plot also contains the indication of the degree of the polynomial that was selected (or the logistic model for cumulative responses), and the estimated rate of increase at the last time point, and the possible messages signaling that no modeling was performed due to the low prevalence in that province, or for lack of convergence of all models. We then plot the estimated slopes on a map of Italy as obtained from google maps using the ggmap package (  [4] ;  [7] ). For each province or region, we plot an empty circle centered at the representative coordinates, with the area of the circle proportional to the estimated prevalence at the most recent time, i.
e. Y t /N. When no appropriate model is found, we average the last two observed reported positives over the resident population. Furthermore, we use color-coded circles to indicate the value of the growth rates, estimated as described in Subsection 2. 1. We employ the perceptually uniform color palette from the viridis package to map the growth rates to each circle  [3]. Lastly, the text output contains the observed rate of cases over the population; all the details about the models that were fit to each province; the estimated maxima / minima; and a final tables containing the names of all provinces and the estimated slope at the last time point, together with the class used to color the circle in the country map.
One option, which allows for forecasting from dynamic models, is described in  [1]  and recently implemented at https://github. com/cgaetan/COVID-19. In addition, one may entertain models with spatial correlation across provinces, but the spread of the epidemic q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Background map tiles by Stamen Design, under CC BY 3. 0. Data by OpenStreetMap, under ODbL likely follows transportation routes rather than pure distance among provinces, so that it would not be trivial to implement such an extended, global model over a country. In addition, the starting times of the local epidemics in different regions appear to differ. Indeed, as shown in Figure  1 , the choice of fitting separate regression models on the log scale estimated allows one to capture local epidemics that may (and do in our case) start at different times in different locations.
Generation of atmospherically relevant molecular structures Atmospherically relevant species were generated using the simulated oxidation of precursor hydrocarbons. Three hydrocarbons --pinene, decane, and toluene -were selected to represent different chemical classes common in the atmosphere (cyclic alkene, saturated alkane, and aromatic, respectively) as well as different expected emissions sources. The gas-phase oxidation mechanism for these hydrocarbons were generated using the Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A). GECKO-A is a computer program designed to automatically generate the complete mechanism involved in the oxidation of a broad range of atmospherically important hydrocarbons. The tool generates chemical mechanisms according to a prescribed protocol, providing reaction rates based on experimental and theoretical data and SARs.
The species representing the approximately 200 most abundant molecular formulas in each the gas and particle phase were analyzed for each oxidation system. "Abundance" is considered here as the summed concentration across the modeled period. Separately considering the abundance of gas-and particle-phase compounds ensures a dataset spanning the atmospherically-relevant range of properties. Some of the same formulas may be abundant in both the gas-and particlephase components of a given oxidation system, but a given formula may be comprised of a different set of isomers or the same isomers in different proportions. A total of 1193 formulas were consequently included in this analysis, roughly evenly split between the three oxidation systems, as well as between gas-and particle-phase components.
For each formula, isomers were included in this analysis if they accounted for at least 0. 1% of the abundance of each formula. This threshold was selected to maximize statistical robustness by providing a large dataset, while minimizing the impact of species expect to be produced at negligibly small concentrations. Selection of higher thresholds (e. g. 1%, 10%) were investigated but not observed to significantly change the results of this work. In order to prevent this analyses from being too strongly impacted by the specific chemistry of the model, isomers were not weighted by their abundance in any of the analyses below; rather isomers were included with equal weight so long as they exceeded the 0. 1% threshold. A total of 38,594 species exceeded the 0. 1% threshold in at least one oxidation system and phase.
Each formula may include a variable number of isomers, so compounds are not equally distributed between oxidation systems: 5% a-pinene gas-phase components, 6% decane gas, 10% toluene gas, 20% a-pinene particle, 16% decane particle, and 43% toluene particle. From this distribution it is apparent that in general, the model predicts particle-phase formulas to contain three to four times as many isomers as gasphase formulas, and toluene oxidation produces twice as many isomers per formulas as the other two systems studied. Due to these differences, the six datasets are discussed separately where relevant throughout this work. Furthermore, species that have both a gas-and particle-phase component exceeding the 0. 1% threshold (N=3241 species) are included in both systems when gas-and particle-phase compounds are analyzed or discussed separately.
SIMPOL SIMPOL is a structure-activity relationship in which the subcooled liquid vapor pressure contributions of individual chemical functional groups are summed to generate a subcooled pure liquid vapor pressure  (Pankow and Asher, 2008). No second-order interaction terms are included to account for neighboring functional groups. Two implementations of SIMPOL are publicly available: the GECKO-A online interface (http://geckoa. lisa. u-pec. fr/), and the Python package APRL Substructure Search Program, developed and made publicly available by Dr. S. Takahama  (Ruggeri and Takahama, 2016). As of the time of publication, the GECKO-A online interface does not accept standard SMILES strings, requiring instead a modified notation that uses explicit hydrogens and a few other differences, making its widespread use somewhat more difficult.
EVAPORATION EVAPORATION is a structure-activity relationship for the estimation of subcooled liquid vapor pressure that includes vapor pressure contributions of individual chemical functional groups, as well as terms to account for interactions between neighboring groups  (Compernolle et al. , 2011). Currently, this method lacks terms to describe several less abundant but nevertheless atmospherically-relevant functional groups, including -NO2 and -C(=O)ONO2. For the purpose of this analysis, these groups were replaced by -ONO2 and -C(=O)OONO2 respectively, which are predicted to have similar impacts on vapor pressure based on SIMPOL and EPI. EVAPORATION currently also lacks a treatment of aromaticity, but this limitation has little impact on this dataset. Though toluene is aromatic, oxidation quickly breaks its aromaticity and fewer than 200 oxidation products contained aromatic carbon; aromatic carbons were replaced with aliphatic carbons for these compounds, which is expected to introduce bias of approximately half an order of magnitude for this small subset of compounds.
Two implementations of the EVAPORATION method are publicly-available as online resources. A direct online interface is available through the Royal Belgian Institute for Space Aeronomy (hereafter referred to as "IASB", found at https://tropo. aeronomie. be), the institution at which the SAR was developed. A separate implementation is available as part of the UManSysProp package for the estimation of a wide range of physicochemical and system parameters, developed and published by researchers at the University of Manchester  (Topping et al. , 2016). UManSysProp is available both as a standalone Python package, and an online interface at http://umansysprop. seaes. manchester. ac. uk. Both the IASB and UManSysProp implementations of EVAPORATION were compared as part of this work in order to ensure that inclusion of this estimation method in this work is as faithful as possible to the published SAR.
Not all differences in methods could be obviously explained by extrapolation beyond prescribed functional groups, but these differences nevertheless highlight the difficulties encountered in implementing a given SAR for highly diverse and complex molecular structure. This work relies on the IASB implementation for estimation of vapor pressures by the EVAPORATION method based on its agreement with manual implementation and the fact that this implementation is provided by the institution at which the SAR was developed. We note that the open-source nature of the UManSysProp package allows a user to understand and/or modify its source code, so future updates may impact these comparisons, but no attempt was made in this work to reconcile the two methods.
Structure-based estimation of Henry's law constant Two structure-based methods were considered in this work for the estimation of Henry's Law Constants (HLCs). One method used here is "HWINb", the bond contribution method implemented by the HENRYWIN module of the EPI Suite (US Environment Protection Agency, 2019). This method is similar to a group contribution method, but instead of using groups, individual bonds are considered with correction factors for different chemical classes  (Hine and Mookerjee, 1975; Meylan and Howard, 1991). The other method used here is "GROMHE" (GROup contribution Method for Henry's law Estimate), a group contribution method that also includes a group contribution term for the effect of hydration of carbonyls  (Raventos-Duran et al. , 2010). GROMHE is the HLC estimation method used by GECKO-A, which is the implementation used in this work.
Previous work has suggested GROMHE to be more accurate than HWINb, but this conclusion was based on a relatively small amount of experimental data (<500 compounds) with relatively low HLCs  (Raventos-Duran et al. , 2010). We consequently do not assume the accuracy of one method over another and instead assume the variability between methods is due to uncertainty in structure-based estimation of HLC. The GECKO-A implementation of GROMHE sets a threshold of 10 18 M/atm for HLC, capping all higher estimations at this value; consequently species estimated by either GROMHE or HWINb as having HLC at or above this threshold are excluded from all analyses in this work. https://doi. org/10. 5194/acp-2020-1038 Preprint. Discussion started: 22 October 2020 c Author(s) 2020. CC BY 4. 0 License.
Structure-based estimation of kOH Two structure-based methods were considered in this work for the estimation of kOH. Perhaps the most common method is that developed by Kwok and Atkinson, a group contribution method that includes additive terms for hydrogen abstraction from or radical addition to individual atoms or bonds  (Kwok and Atkinson, 1995). An additional second-order term accounts for substituent effects on each atom. The implementation of this method used here is the AOPWIN module of the EPI Suite  (Meylan and Howard, 1993 ; US Environment Protection Agency, 2019). The other method used here is the group contribution method of Jenkin et al. , which functions similarly to Kwok and Atkinson approach but with updated and extended coefficients  (Jenkin et al. , 2018a (Jenkin et al. , , 2018b)).
Jenkin et al. is the kOH estimation method used by GECKO-A, which is the implementation used in this work, and which has an available interface through the GECKO-A online interface. 2. 5 Formula-based estimation of vapor pressure 2. 5. 1 Daumit et al. Daumit and co-workers use a basic set of assumptions about the structures of atmospheric components to apply the SIMPOL estimation method in the absence of molecular structure  (Daumit et al. , 2013). Essentially, all oxygen atoms in a molecule are apportioned between hydroxyl and carbonyl groups based on the degree of unsaturation calculated from the H/C and O/C ratios. To accurately calculate degrees of unsaturation, an assumption must be made about the number of rings present in the molecule. We assume there to be no rings, as this is consistent with the majority of compounds in this dataset, but the need to make this assumption represents a general source of uncertainty in the Daumit et al.
, 2016)  expected to be significantly biased. We test this hypothesis here in order to more accurately apply this method to the dataset. Comparison of vapor pressures estimated by Li et al. to vapor pressures estimated for the same compound using structurebased methods (Figure  S4 ) demonstrates significant biases that increase with the number of nitrogen atoms, which in this dataset are almost wholly contained in nitrate, nitro, and peroxynitrate groups. To address this bias we propose two similar possible approaches based on the observation that a nitrate group (NO3) has a similar impact on vapor pressure as a hydroxyl group (OH), and thus each nitrogen has the effect of canceling the effect of two oxygen atoms. Either the nitrogen coefficient for CHON formulas can be forced to equal twice the negative of the oxygen atom (bN = -2*bO), or the formula used to estimate vapor pressure can be amended to convert all potential nitrate groups into hydroxyl groups as described in the implementation of Daumit et al.
Both approaches are shown in Figure  S4  to similarly remove the nitrogen-dependent bias, and are generally equivalent in this dataset. In mixed environments in which oxygenated amines and organonitrates may co-exist, formulaically converting nitrate groups to hydroxyl groups may be preferred in order to more accurately treat nitrogen in excess of potential nitrate groups (i. e. in cases where the number of nitrogens is greater than the number of sets of 3 oxygens). However, given the nitrate-dominate nature of this dataset, for simplicity we use a Modified Li et al. method in which bN = -2*bO.
Structure-based estimation of vapor pressure:  "SIMPOL" -calculated from SIMPOL as implemented by GECKO-A  "EVAPORATION" -calculated from EVAPORATION as implemented by the Royal Belgian Institute for Space Aeronomy ("IASB")  "Nannoolal" -calculated based on  Nannoolal et al. (2008)  using boiling points estimated by  Nannoolal et al. (2004) , as implemented by GECKO-A  "Myrdal and Yalkowsky" -calculated based on Myrdal and Yalkowsky (1997) using boiling points estimated by  Nannoolal et al. (2004) , as implemented by the UManSysProp Python package  "EPI" -calculated by the EPI Suite, an implementation of the Modified Grain Method using boiling points estimated by  Stein and Brown (1994).
The overall average of <Δp>formula is between 0. 8 and 1. 0 log units across all five estimation methods, indicating that the central tendency is for two isomers to differ by approximately one log unit in vapor pressure. The distribution of <Δp>formula depends on the oxidation system studied, as is more similar for decane oxidation products (<Δp>formula ≈ 0. 5 log units), and less similar for toluene oxidation products (<Δp>formula~1. 5 log unit), with a-pinene oxidation products in between (<Δp>formula≈ 1 log unit). Phase of the compound, which serves in large part as a proxy for volatility (i. e. particle-phase components have lower vapor pressures), also has some impact, with somewhat higher <Δp>formula for formulas abundant in the particle phase. Note that the components are distinguished as gas-and particle-phase based on their abundance in either phase -a minor fraction of species is represented in both datasets.
In turn, Nannoolal estimates somewhat lower vapor pressures than SIMPOL and EVAPORATION for low-volatility compounds, but to a lesser extent. Similar trends between SIMPOL, Nannoolal, EVAPORATION, and Myrdal and Yalkowsky have been previously shown for the oxidation products of -pinene  (Compernolle et al. , 2011; Valorso et al. , 2011). There is no sufficiently large database of known vapor pressures to know which of these methods is most accurate in these regions. We instead assume that the best available estimate for the vapor pressure of a compound is the average of the SIMPOL, Nannoolal, and EVAPORATION estimates. This assumption is based on the similarity of the SIMPOL,  Nannoolal, and EVAPORATION methods in this and in previous work, previous work  demonstrating agreement between Nannoolal and experimental data  (Barley and McFiggans, 2010) , and the more recent development of these  SARs (2008 SARs ( , 2008 SARs ( , and 2011, respectively), respectively).
The EPI and Myrdal and Yalkowsky methods are treated as outliers based on their overestimation of low vapor pressures relative to experimental data (shown by  Barley and McFiggan, 2010)  and to other methods (Figure  S8 ). By averaging the vapor pressures estimated for each species with Nannoolal, SIMPOL and EVAPORATION methods, we mitigate any biases present in any one method. The average of these three methods provides an average structure-based estimate for a given species, denoted here as ̅. To understand precision in structure-based estimation, we quantify the differences between methods in the predicted property of a given species. For each species, the vapor pressure is estimated using the three selected methods above. We denote Δmp as the absolute difference in vapor pressure of a given species between any 2 methods q and r ( Δmp = |log(mp,q) -log(mp,r)| ) and <Δmp> as the average value for the 3 possible combinations.
Discussion started: 22 October 2020 c Author(s) 2020. CC BY 4. 0 License. have lower <Δmp> than those from toluene). Increasing <Δmp> at low volatility is not unexpected, as most estimation methods require extrapolation from relatively high vapor pressure data. Furthermore, volatility in this dataset acts in part as a proxy for functionality. All species studied are formed through the oxidation of three relatively volatile precursors, so decreases in volatility are generally caused by the addition of oxygenated functional groups. The decrease in vapor pressure caused by each functional group is of course uncertain, so methods diverge as number of functional groups increase and volatility decreases  (Valorso et al. , 2011). As in our discussion of vapor pressure differences between isomers, it is difficult to provide a single number to characterize our skill in estimating vapor pressure from a molecular structure.