dataesr/scientific-paragraphs-categorization huggingface dataset:
Austro-Asiatic The possible origins of the AA family include southern China, MSEA, or India  (Sidwell 2014). It is thought to be the oldest language family in MSEA, which emerged after the Hoabinhian tradition $4-5 ka  (Sidwell 2014 ). Ancient genome studies have suggested that the present-day AA groups in MSEA are descendants of Hoabinhian hunter-gatherers and ancestral EAs from southern China admixing during the Neolithic farming expansion  (Lipson et al. 2018; McColl et al. 2018). Consistent with this scenario, we find that the indigenous AA groups Htin Mal and Khomu have 9% and 11% ancestry from the Hoabinhian hunter-gatherers and 91% and 89% ancestry from the ancestors of southern Chinese, respectively (fig. 8A  and B ). The AA-speaking Mang are closer to the Khomu compared with the Kinh and Muong, but they also share ancestry with the ST-speaking Chinese Lahu in the TreeMix analysis (supplementary fig.
0 data in population structure analyses. Similarly, incorporating ancient DNA data also greatly decreased the number of informative sites due to missing data, so we excluded the ancient samples from the phasing and IBD analyses. For quality control, we first checked individual relatedness using KING version 2. 1. 6 (Manichaikul et al. Genetic Diversity of Vietnamese Populations. doi:10. 1093/molbev/msaa099 MBE 2010) and removed one from each pair of individuals with first degree of kinship. After that, we examined the global and within population missing site numbers using the missing command in PLINK version 1. 90b5. 2 (Purcell et al. 2007 ). We removed modern individuals with more than 5% global missing data, and ancient individuals with <15,000 informative sites. Then, we excluded variant sites in modern samples with more than 5% global missing data, or 50% missing data within a population.
Population Structure Analyses We used PCA and ADMIXTURE version 1. 3. 0 (Alexander et al. 2009)  to visualize how the populations cluster. For both methods, variants were pruned beforehand for linkage disequilibrium using PLINK, excluding one variant from pairs with r 2 > 0. 4 within windows of 200 variants and a step size of 25 variants. We performed PCA by computing eigenvalues only from the less isolated modern populations and then projecting the more isolated modern populations (Mamanwa, Mlabri, Onge, and Jehai) and the ancient samples, using smartpca from EIGENSOFT with "lsqproject" and "autoshrink" options. We performed heatmap visualization of downstream PCs using the pheatmap package in R version 3. 6. 0. For running the ADMIXTURE program, we also first estimated the allele frequency of the inferred ancestral populations (i.
Allele Sharing Analyses We used admixr version 0. 7. 1  (Petr et al. 2019)  to compute f3and f4-statistics from ADMIXTOOLS version 5. 1  (Patterson et al. 2012) , with significance assessed through block jackknife resampling across the genome. Outgroup f3-statistics of the form f3(X, Y; Outgroup) were used to measure the shared drift between populations X and Y since their divergence from the outgroup. We performed heatmap visualization of f3 profiles using the pheatmap package in R. f4-statistics of the form f4(W, X; Y, Outgroup) were used to formally test whether W or X shares more ancestry with population Y. We used Mbuti as the outgroup for all analyses; to ensure there is no excess shared ancestry between any test population and the outgroup, we also repeated the outgroup f3statistics with French as the outgroup.
IBD Analyses We used refinedIBD  (Browning BL and Browning SR 2013)  to identify shared IBD blocks between each pair of individuals and homozygous-by-descent blocks within each individual. We considered both identified IBD and homozygous-bydescent blocks as IBD blocks in our analyses, which have been called pairwise shared coalescence segments in a previous study  (Al-Asadi et al. 2019). Then, we merged IBD blocks within a 0. 6-cM gap and allowed only one inconsistent genotype between the gap and block regions using the program merge-ibd-segments from BEAGLE utilities  (Browning SR and Browning BL 2007; Browning et al. 2018). These results were used to create four data sets based on the length of identified IBD blocks: 1-5 cM, 5-10 cM, over 10 cM, and at least 2 cM. The first three were used to compare the IBD sharing between populations in different time periods  (Ralph and Coop 2013; Al-Asadi et al.
Admixture Graph Analyses We used admixture graphs to model population histories that fit the genomic data. We separated our Vietnamese data by language family and modeled the admixture graph, together with related source populations, for each family. We first modeled a global admixture graph with the related present-day source populations, ancient samples, and all the Vietnamese groups. These present-day source populations were chosen based on excess ancestry sharing in the f4 analyses. Only the ancient samples with <65% missing data were used here in order to have at least 20,000 SNPs for the model estimation. As the ancient samples are not closely related to the Vietnamese groups in the global admixture analysis, and their inclusion decreases the number of SNPs while increasing the complexity of the modeling, we decided to use only the present-day source populations for dissecting the Vietnamese admixture graph.
We first modeled an admixture graph with only the related modern source populations, which we call the backbone populations. For each language family and the backbone populations, we pruned the SNPs as we did in the population structure analyses and calculated allele frequencies with PLINK. Using the covariance of the allele frequency profiles as input, we first ran TreeMix version 1. 12 (Pickrell and Pritchard 2012) with 0-3 migration events and ten independent runs, and selected the topology with the highest likelihood for further investigation. We also checked and confirmed that the likelihood and topologies of these ten runs are mostly similar, which indicates that the model estimation has reached convergence. Next, we used AdmixtureBayes  (Nielsen 2018)  to estimate the top ten posterior admixture graphs, based on the covariance of the allele frequency profiles.
When more populations are added to the model, more steps will be needed for the Markov chain Monte Carlo to converge. We hence limited the maximum number of populations to 11, for which the model can converge and finish in a reasonable time. To do so, we selected suitable combinations of source populations for each language family, based on the topology showing the lowest standard error in the TreeMix residual plots with three migration events. We used 300,000 Markov chain Monte Carlo steps for each AdmixtureBayes run with stop criteria stopping the run if the summaries of effective sample size are all above 200. We then used the estimated graphs as input for qpGraph from ADMIXTOOLS to test the goodness of fit of the graphs. We accepted the graph as a good fit when the absolute value of the Z-score of the worst f4 statistic output by qpGraph was <3.
For the cases where we failed to find a fit, we adjusted the source populations based on the f4 outliers output by qpGraph. Then, we used the -subnodes option in AdmixtureBayes to calculate the posterior of the adjusted subsets and tested the results again in qpGraph. We iterated these procedures until we were able to fit graphs for all of the five language families as well as only the source populations. We ran qpGraph with parameters outpop: NULL, blgsize: 0. 05, forcezmode: YES, diag: 0. 0001, bigiter: 6, hires: YES, and lambdascale: 1.
Acknowledgments This research was funded by the  Ministry of Science and Technology, Vietnam  ( DTDL. CN-05/15 ) and by the  Max Planck Society. We thank the sample donors for contributing to this research. We thank  David Reich  for assistance with genotyping. We thank  Enrico Macholdt ,  Irina Pugach ,  Wibhu Kutanan , and  Ben Peter  for helpful discussion and for assistance with analyses. We thank the anonymous reviewers for valuable comments. We thank the  Multimedia Department of the Max Planck Institute for Evolutionary Anthropology  for assistance with figures. B. P. acknowledges the  LABEX ASLAN  ( ANR-10-LABX-0081 ) of  Universit e de Lyon  for its financial support within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) of the French government operated by the  National Research Agency (ANR).
The aim of this study is to revisit the parametrization of SNe Ia SED in light of the SNfactory spectrophotometric dataset  (Aldering et al. 2002) , and to seek new sources of variability by statistical analysis. Our model is trained using spectral indicators, derived around maximum light in B-band, as features to describe the model. They provide both a reduced dimensionality description of spectra, and a description which is linked to the physics of the explosion. In addition, we select indicators insensitive to reddening in order to decouple the characterization of the reddening from effects purely linked to the intrinsic part of the explosion. This new SNe Ia SED model is named the SUpernova Generator And Reconstructor (SUGAR) model. Another approach to develop a new SED model was undertaken in parallel by  Saunders et al.
(2018) , using the same dataset and based on a "SALT2 like" framework. Saunders et al. (2018)  generalized a strategy that was originally proposed by  Guy et al. P. -F. Léget & SNfactory: SUGAR model (2007)  and which was first performed on broad band photometry from the SNfactory dataset in  Kim et al. (2013). In brief,  Saunders et al. (2018)  did a Principal Component Analysis-like study on interpolated spectral time series in order to go beyond the classical  Tripp (1998)  relation. Saunders et al. (2018)  is able to significatively improve the SED description with respect to the SALT2 model. The SNEMO model developed in  Saunders et al. (2018)  differs from the SUGAR model in that SNEMO attempts to find principal components purely based on spectral time series variability in the relative luminosities at each wavelength and as a function of time, whereas SUGAR uses spectral features to try to find a compact description of such spectral time series.
(2010) , and is restricted to objects with at least one observation in a time window of ±2. 5 days around maximum light in B-band. The 1 The data link will become active upon journal publication. size of this window is kept identical with respect to the study of  Chotard et al. (2011)  and is discussed in  Léget (2016). After flux calibration, host-galaxy subtraction and correction for Milky Way extinction, the flux of the 171 spectra is integrated in synthetic top-hat filters defined in  Pereira et al. (2013)  and for reference a SALT2 fit is applied with the model from  Betoule et al. (2014)  in order to obtain the X 1 , C and m B parameters. The spectra of the 171 SNe Ia are transformed to the rest frame with a fiducial cosmology and an arbitrary Hubble constant. It is allowing to respect blinding of future cosmological analysis using this dataset.
Derived data Any empirical SN Ia modeling must solve three problems 2  : choosing what features to model, accounting for color, and dealing with the data sampling. For the features modeling of SUGAR, we used spectral features at maximum light to describe the intrinsic part of the SED. This is described successively in Section 3. 1 and Section 3. 2. Section 3. 1 describes how the spectral features are selected and measured. Section 3. 2 explains how the spectral features are projected onto a new basis that allows us to work in an orthogonal basis for the SUGAR training. To estimate the average color curve of SNe Ia, we will generalize the method of  Chotard et al. (2011). This is described in Section 3. 3. To deal with the data sampling, we project the observed spectra onto a common time grid so that the SED model can be calculated on the same time grid.
The net effect described by q 2 is that higher velocities are associated with slightly dimmer SNe Ia. At later phases velocity effects are still observable, and at all phases after -6 days, high-velocity SNe Ia have localized differences in the 4700 -5100Å spectral region. In addition, they are bluer in V, R, and Î after maximum, with a maximal effect on the Î-band at around +12 days. A slight stretch effect is visible in Û and B, and also results in a later phase for the second maximum in Î for the brightest SNe Ia in B, as was mentioned for q 1. The shape of variations of the light curve in Î are very different for each factor, indicating that this band can help reconstruct the q 2 factor when using photometric data only. Furthermore, while the q 2 factor has a small impact on the variations of individual light curves, it has a sizable influence on colors, especially R -Î, Û -B, and after +25 days, on B -V.
∆M grey is the equivalent of a linear combination of (log X 0 , X 1 , C). Although the major differences between the models come from the inclusion of 2 additional intrinsic components in SUGAR, its color law, and the data on which they were trained, there are many other differing characteristics, which are summarized Table  1. The inclusion of two additional intrinsic components is enabled by the level of detail available in our spectrophotometric time series. We therefore expect the SUGAR model to provide a more faithful representation of the spectral details than SALT2. Though analogous in spirit, X 1 and q 1 are derived employing a quite different paradigm: F 1 comes from a PCA analysis performed in flux space, while the α i are trained in magnitude space and driven to reproduce the prominent spectral indicators.
The wavelength and phase coverage of SUGAR are restricted compared to SALT2. This is directly related to the training data: SUGAR is trained on low redshift spectrophotometric time series, while SALT2 is trained on a wide redshift range of pho-tometric data, employing a handful of spectra to help refine the SED details. As SALT2 uses low and high redshift data it offers a better coverage of the UV domain. It also has a larger phase coverage thanks to the rolling cadence strategy, while SNfactory SNe Ia have to be specifically targeted. While SALT2 could be easily trained with SNfactory data added to their sample, the reverse is not true: major adaptation would be needed to allow SUGAR to incorporate photometric data in its training. However, this does not prevent fitting photometric light curves with SUGAR in the same way as with SALT2, provided they fall within the phase and spectral coverage of SUGAR.
Spectral residuals The SUGAR model is designed to improve the spectral description of SNe Ia by adding components beyond stretch and color. While the ideal model would perfectly match the data, the level of mismatch can be quantified by the residuals, i. e. the difference between the left and right term of Equation  22. The improvement with respect to SALT2 is then studied by comparing the data to the spectral reconstruction predicted by both models. One example of these comparisons is presented Figure  18 , which shows the spectra and residuals for SN-training-4. For this supernova, SUGAR gives a better description than SALT2. The improvement is especially clear in the UV after +17 days and in the infrared at any phase, and particularly in the O i λ7773 and Ca ii IR regions. This is not surprising insofar as SALT2 is essentially trained to reproduce the bluer part of the spectrum.
Conclusion In this paper we have presented a new spectra-temporal empirical model of SNe Ia, named SUGAR. This model significantly improves the spectral description of SNe Ia compared to the current state of the art by going beyond the classical stretch and color parametrization. In Section 2, we presented the SNfactory spectrophotometric dataset that was used to train the model. In Section 3, we presented the intermediary data that were used to train the full SUGAR model. In a first step, we selected a set of 13 spectral indicators near maximum light in B-band that are composed of pseudo-equivalent widths and minima of P-Cygni profiles. Those spectral indicators were chosen to describe the intrinsic part of the SUGAR model because they are easy to define and are independent of host-galaxy dust extinction.
Acknowledgements. We thank the technical staff of the  University of Hawaii 2. 2m telescope, and  Dan Birchall  for observing assistance. We recognize the significant cultural role of  Mauna Kea  within the indigenous Hawaiian community, and we appreciate the opportunity to conduct observations from this revered site. This work was supported in part by the Director,  Office of Science, Office of High Energy Physics of the U. S. Department of Energy  under Contract No. DE-AC025CH11231. Support in France was provided by  CNRS/IN2P3 ,  CNRS/INSU , and PNC; LPNHE acknowledges support from LABEX  ILP , supported by  French  state funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02. NC is grateful to the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-0066 ) of the  University de Lyon  for its financial support within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) of the French government operated by the  National Research Agency (ANR).
Implementation We developed a Python script to derive summary statistics in the marginal model and in each group of individuals separately. As input, the script takes one file with the summary statistics from the joint model, that are genetic and interaction effect sizes, their standard errors, the correlation between the two effect sizes and the sample size per SNP corresponding to the number of genotypes available for this SNP (which may differ from the sample size of the study because of missing data). This file corresponds to the output of the METAL software to meta-analyze GxE screenings using the joint test  [9]. In addition to this file, the script also takes two arguments that are the total sample size N of the study and the number of exposed individuals N e included in the study. These two arguments are used to infer the sample sizes N v Â ðN-N e Þ.
N and N v Â N e. N in the group of unexposed and exposed individuals respectively for each SNP, where N v is the sample size for the SNP. We also implemented a filtering procedure to exclude variants with a low sample size compared to the distribution of the sample sizes: a SNP with a sample size below the 9th decile of the sample size distribution divided by 1. 5 is excluded from the analysis. As output, the script generates a single file containing the genetic effect size and its standard error in the group of unexposed individuals, in the group of exposed individuals and in the total sample. The script and a detailed documentation using an example are available at https://gitlab. pasteur. fr/statistical-genetics/j2s.
Real data application We assessed the accuracy of our estimations using real data from the Gene-Lifestyle Interaction Working Group of the CHARGE consortium  [11]. This Working Group recently published genome-wide SNP-by-alcohol interaction screenings  [13]  using joint tests and focusing on three lipids level: triglycerides (TG), high-density lipoproteins (HDL), and low-density lipoproteins (LDL). Genome-wide screenings for genetic marginal effects were also performed in unexposed and exposed individuals separately and in the whole sample. Here, we used summary statistics from the genome wide SNP by exposure interaction screenings in individuals from European ancestry and derived marginal summary statistics in unexposed and exposed individuals separately, and in the whole sample. We then compared the inferred summary statistics with the empirical summary statistics derived using individual-level data (Figs.
Conclusion In this work, we derived accurate estimations of the marginal genetic effects in unexposed and exposed individuals separately and in the whole sample in the context of genomewide GxE interaction screenings using the joint test. This method can not only lead to a more intuitive understanding of GxE interactions but also be used to perform additional studies that can guide further functional analyses. We implemented j2s, a Python3 script to easily apply this method, available at https://gitlab. pasteur. fr/statistical-genetics/j2s.
Availability and requirements Project name: j2s Project home page: https://gitlab. pasteur. fr/statistical-genetics/j2s Operating systems: Linux Programming language: Python3 Other requirements: None License: MIT Any restrictions to use by non-academics: None Availability of data and materials The data that support the findings of this study are part of the The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and are available from the authors upon reasonable request.
From that figure, Lake Champlain, bordered by the Adirondack and Green Mountains, can be clearly seen. The soil type was estimated using the 30″ resolution database of  Shangguan et al. (2014) , while the land cover was estimated from different sources over the Quebec province and the 300-m resolution Globcover2009  (Bontemps et al. , 2011)  in the United States. The soil type (Figure  6b ) is mainly loam over the United States and clay over Canada. The land cover (Figure  6c ) is mainly deciduous forest in the United States and cropland in Canada. Finally, due to the high resolution, most RHHUs are smaller than 25 km 2 where the elevation is high in the United States and close to Lake Champlain; meanwhile in Canada, the elevation is low, and RHHUs are often larger than 25 km 2 (Figure  6d ).
For instance, initial calibrations of HYDROTEL over the Richelieu basin led to poor results over the large Otter Creek basin (1643 km 2 ) when global parameters were used. Further investigations revealed that this basin contains many wetlands that were not taken into account. A custom wetland module was activated in HYDROTEL to account for their presence over the basin. The wetland module is described in details in  Fossey et al. (2015). The default wetland parameter values were used over the whole Richelieu River basin except for the Otter Creek basin. For the latter, a manual adjustment of the isolated wetlands was done by adjusting the normal and maximum values of the water depth to 0. 5 and 1 m, respectively. Moreover, the parameter controlling the surface occupied by the water in the wetland was set to 1.
Acknowledgments The authors are grateful to one anonymous reviewer and  Professor Alain Rousseau  for their valuable comments that helped improving the manuscript. The authors would like to thank  Dr. Jean Morin  from  Environment and Climate Change Canada (ECCC)  and scientists of the  International Joint Commission (IJC)  for their important scientific advices about the paper. The authors also acknowledge the access to the NBS of Lake Champlain developed by  ECCC. This work was supported by the  IJC Lake Champlain  and  Richelieu River  project and by the  Direction de l'Expertise Hydrique du Ministère de l'Environnement et de la Lutte  contre.
In silico models to predict which tumors will respond to a given drug are necessary for Precision Oncology. However, predictive models are only available for a handful of cases (each case being a given drug acting on tumors of a specific cancer type). A way to generate predictive models for the remaining cases is with suitable machine learning algorithms that are yet to be applied to existing in vitro pharmacogenomics datasets. Here, we apply XGBoost integrated with a stringent feature selection approach, which is an algorithm that is advantageous for these high-dimensional problems. Thus, we identified and validated 118 predictive models for 62 drugs across five cancer types by exploiting four molecular profiles (sequence mutations, copy-number alterations, gene expression, and DNA methylation).