dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work has been supported by  COST Action IC1201 Behavioural Types for Reliable Large-Scale Software Systems (BETTY). The authors are grateful to all members of the BETTY Working Group on Programming Languages (WG3) for interesting related discussions and to the anonymous reviewer who provided precious comments and suggestions for improving an early version of this survey. Mario Bravetti ,  Elena Giachino  and  Einar Broch Johnsen  have been supported by the  EU  project  FP7-610582  Envisage: Engineering Virtualized Services (http://www. envisage-project. eu). We warmly acknowledge the enormous influence of  Kohei Honda  ( 1959-2012 ) on the research area covered by this survey, on the process of establishing  COST Action IC1201 , and on our own careers.
simple case of an invasion scenario involving one native population and two independently introduced populations, because it is the sole scenario with two introduced populations that can be rejected when obtaining a particular clustering with a STRUCTURE analysis at K=2 (two clusters). Results were classified as "misleading" or "non-misleading". We then investigated the influence of two demographic parameters (effective size and bottleneck severity) and different numbers of loci on the type and frequency of misleading results. We showed that misleading STRUCTURE results were obtained for 10% of our simulated datasets and at a frequency of up to 37% for some combinations of parameters. Our results highlighted two different categories of misleading output. The first occurs in situations in which the native population has a low level of diversity.
g. Estoup and Guillemaud, 2010; Guillemaud et al. , 2010). Among population genetics methods, unsupervised individual Bayesian clustering methods are widely used. The popularity of these methods is due to their ability to infer genetic structure correctly in many situations and their apparent simplicity (several "clickand-play" software suites are available). STRUCTURE  (Pritchard et al. , 2000; Falush et al. , 2003; Hubisz et al. , 2009)  is the most frequently used software for clustering, with more than 27,000 citations for the three references indicated above in Google Scholar in May 2017. STRUCTURE aims to sort individuals in an unsupervised way into K clusters (K being defined by the user), assuming Hardy-Weinberg/linkage equilibrium within clusters  (Porras-Hurtado et al. , 2013). In theory, if K is set to the true number of population, samples belonging to the same population will be classified into the same cluster.
More broadly, because knowing or inferring the true number of population is not always possible, samples belonging to the same cluster are at least considered as sharing a close evolutionary history. STRUCTURE is known to perform well in most cases, but it can be misleading in some situations, particularly in the presence of isolation by distance  (Frantz et al. , 2009; Schwartz and McKelvey, 2009) , clusters of very different sizes  (Kalinowski, 2011; Puechmaille, 2016) , family groups  (Anderson and Dunham, 2008) , or high proportions of missing data  (Smith and Wang, 2014). STRUCTURE and other software suites based on similar methods are frequently used in the context of introduction routes inferences  (Estoup and Guillemaud, 2010; Lawson Handley et al. , 2011; Cristescu, 2015). In some cases, STRUCTURE is used directly to contrast models of invasion history, mainly for comparisons of scenarios involving either multiple independent introductions from a native population, or a single introduction from the native area followed by subsequent introduction(s) from this primary introduced area.
They found that all samples from invasive populations clustered together when analyzing the data with STRUCTURE at K=2 and concluded that only one introduction from the native area occurred. They then used approximate Bayesian computation to test whether the oldest invasive population in the USA was the source of all other invasive populations in distant areas. Similarly,  Cordero et al. (2017)  analyzed 378 individuals of the Manila clam Ruditapes philippinarum from 9 geographic locations, including 3 native (Asia) and 6 invaded (North America and Europe) areas. They found that STRUCTURE analyses at K=2 grouped all samples of invasive populations into the same cluster. They concluded that a single native Asian introduction of the species into North America was very likely, and that North America then became the source of the European outbreak.
In this study, we evaluated the risk of incorrect introduction route inferences based on STRUCTURE analyses, for the simple case of an invasion scenario involving one native population and two independently introduced populations. We chose to simulate this scenario because it is the sole one that can be rejected when obtaining a particular clustering with a STRUCTURE analysis at K=2 (Fig. 1 ). We simulated a large number of microsatellite datasets drawn from populations of various effective sizes and bottleneck severities. STRUCTURE analyses were performed on these simulated datasets and the resulting clustering patterns at K=2 were classified as "misleading" or "non-misleading". We then explored the effect of demographic parameters on the likelihood of misleading patterns being obtained, to identify and predict the situations in which the use of STRUCTURE in a context of introduction routes inference may be risky.
Scenario description and data simulation We chose to simulate a scenario with two independent introductions because it is the only one that can be rejected from a STRUCTURE analysis when considering two introduced populations and a native one (Fig. 1 ). We thus defined a simple historical scenario in which two invasive populations (populations 2 and 3) were independently founded 50 generations ago from the same native population (population 1). Both invasive populations were subject to a demographic bottleneck lasting 20 generations (Fig. 2a ). The effective sizes of all three populations at equilibrium (N) and the effective number of founders of the two invasive populations during the bottlenecks (NF) could take different values: 10000, 1000, 100, 10 and 2 individuals, with N ≥ NF. Log10(N/NF) was considered to quantify bottleneck severity.
We used DIYABC version 2. 0. 4 software  (Cornuet et al. , 2014)  to generate 500 microsatellite multilocus genotype datasets for each of the 15 different combinations of N and NF values, through a coalescent process. For all datasets, a sample of 30 diploid individuals per population was simulated. We evaluated the effect of the number of loci on the analyses, by performing simulations with 10, 20 and 100 unlinked microsatellite markers. We used a generalized stepwise mutation model, with realistic values for all three parameters  (Jarne and Lagoda, 1996; Estoup et al. , 2002) : the mean mutation rate (set to 5x10 -4 ), the mean parameter of the geometric distribution defining the number of microsatellite repeats gained or lost during mutation events (set to 0. 22) and the mean mutation rate for single-nucleotide insertion/deletion (set to 10 -8 ).
STRUCTURE analyses and misleading clustering For each of the 22,500 simulated datasets, a Bayesian clustering analysis was performed in parallel, on a 120-nodes computer cluster, with STRUCTURE software version 2. 3. 4  (Pritchard et al. , 2000). We chose the admixture model with correlated allele frequencies. We used default values for all the other parameters. Each run consisted of a burn-in period of 10 5 Markov chain Monte Carlo (MCMC) iterations, followed by 5x10 5 MCMC iterations. This run length is considered to be long enough to obtain precise estimates of parameters  (Pritchard et al. , 2010) , but we also tried runs of double this length for some combinations of parameters with 100 loci. The results obtained were the same (data not shown). We carried out ten replicate runs for each dataset and each value of K, the number of genetic clusters, with K taking values of 1, 2, 3 and 4.
Given the simulated scenario of independent introductions of the two invasive populations, 0/100/100 was the code considered to correspond to misleading clustering (Fig. 2b ). All other clustering codes were considered non-misleading in the context of introduction routes inference. Focusing on the codes instead of the proportions of membership made it possible to pool together slightly different clustering patterns in the same category. Given the stochastic processes involved in the MCMC analysis, the ten replicated STRUCTURE runs performed on a single dataset could conceivably generate different results, a phenomenon called genuine multimodality  (Jakobsson and Rosenberg, 2007; Porras-Hurtado et al. , 2013). Clustering results for a given dataset were considered to be homogeneous if the same clustering code (as defined above) was obtained in all ten runs.
They were otherwise considered to be heterogeneous. We evaluated the global occurrence of misleading clustering in the analyses of the simulated datasets, and focused on two critical categories of misleading clusterings (Fig. 2c ): (i) "Misleading homogeneous clusterings": for one dataset, all ten runs homogeneously provide the misleading clustering pattern 0/100/100. (ii) "Misleading heterogeneous clusterings": for one dataset, the ten runs are not homogeneous (i. e. genuine multimodality is observed) and the misleading clustering pattern 0/100/100 predominates. For each dataset, we also inferred the best value of K, as follows: if the mean natural logarithm of the likelihood of the data ln(P(X|K)) with K in [1, 2, 3, 4] is maximal for K=1, then the inferred number of clusters is 1; otherwise, we determined the best value of K (either K=2 or K=3) by the ΔK method  (Evanno et al.
Link between summary statistics of genetic diversity and STRUCTURE patterns We summarized each simulated dataset, by using ARLSUMSTAT version 3. 5 software  (Excoffier and Lischer, 2010)  to compute the mean number of alleles and the mean expected heterozygosity in each population sample, and the pairwise FST values between each pair of populations. We also used in-house PERL scripts to compute (i) the mean individual assignment likelihood  (Rannala and Mountain, 1997 ) (Li→j) of each invading population (samples 2 and 3) to each possible source population (i. e. either the native population or the other invasive population), and (ii) the number of alleles shared by the invasive population samples. For the comparison of datasets leading to "misleading homogeneous clusterings", "misleading heterogeneous clusterings" and "non-misleading clusterings", we specifically explored a few genetic diversity summary statistics: (i) expected heterozygosity of the native population sample, (ii) mean expected heterozygosity of both invasive population samples and (iii) the ratio of alleles shared by the two invasive population samples to the total number of alleles in the two samples.
We would also expect both invasive population samples to be best assigned to the native population sample (i. e. L2→1 > L2→3 and L3→1 > L3→2). For each dataset, a global exact test for population genotypic differentiation (Raymond & Rousset, 1995a) was carried out with GENEPOP software version 4. 3  (Raymond & Rousset, 1995b). If a dataset displayed no population differentiation, we made the prudent and standard decision of not trying to infer any evolutionary relationship between the population samples. Consequently, such datasets were considered to generate non-misleading results for all methods.
Effect of demographic parameter values on simulated datasets The 500 simulated datasets for each parameter set are summarized with some common statistics in Table  S1. Decreasing effective population sizes (N) generate lower intrapopulation and higher inter-population genetic diversities. Increasing bottleneck severity (log10(N/NF)) generates lower intra-population genetic diversities for both invasive samples, and overall higher inter-population genetic diversity. The main impact of a larger number of loci is a decrease in the variance of all summary statistics. Overall, the chosen parameter values (for N and NF) yield a large number of different combinations of genetic diversity for evaluation of the ability of STRUCTURE software to explore introduction routes in different situations.
Overall STRUCTURE results The best value of K inferred was most frequently three (Fig. 3  and Fig. S1 ). The proportion of datasets for which the best number of clusters was K=3 increased strongly with increasing numbers of loci (41. 9%, 50. 6% and 74. 9% for 10, 20 and 100 loci, respectively). More than 80% of the simulated datasets for which K=3 was inferred by the ΔK method had heterogeneous clustering codes (i. e. genuine multimodality) at K=2 (Fig. 3 ). By contrast, when the number of inferred clusters was one or two, multimodality at K=2 was found in less than 10% of all datasets.
Occurrence of misleading STRUCTURE patterns Three categories of clustering codes at K=2 accounted for more than 95% of all runs (see Table  S2  for details): (i) clusterings in which all populations were fully admixed and undistinguishable with STRUCTURE (i. e. the 50/50/50 code), (ii) clusterings in which the two invasive samples belonged to different clusters (i. e. the C1A/100/0 and C1A/0/100 codes) and (iii) the misleading clusterings defined earlier (see Methods), in which the two invasive samples belonged to the same cluster, whereas the native sample belong to the other cluster (i. e. the 0/100/100 code, Fig. 2b ). Overall, the proportion of datasets with at least one misleading clustering pattern over the ten STRUCTURE runs ("misleading homogeneous clusterings", "misleading heterogeneous clusterings" and non-misleading clusterings with at least one run yielding a misleading pattern) was 15.
31%, 22. 07% and 47. 01% for 10, 20 and 100 simulated loci, respectively (Fig. 4a  and Fig. S2 ), and very similar proportions were obtained with more (0. 1 and 0. 9) and less (0. 3 and 0. 7) stringent QiA cutoff values (instead of 0. 2 and 0. 8 for QiA) for the encoding of pattern results (Table  S3 ). The frequency of "misleading homogeneous clusterings" was similar for different numbers of loci, and was rather low overall (between 4. 24% and 5. 59% of the datasets, Fig. 4a ). "Misleading heterogeneous clusterings" were also infrequent, but their frequency increased with the number of loci: 2. 71%, 3. 96% and 8. 41% for 10, 20 and 100 loci, respectively (Fig. 4a ). Overall, 7. 45%, 9. 55% and 12. 65% of datasets for 10, 20 and 100 loci, respectively, yielded misleading results. For some combinations of parameters, this proportion reached 36.
Links between summary statistics for genetic diversity and STRUCTURE patterns The diversity of the native population, as assessed by its expected heterozygosity in the datasets with "misleading homogeneous clusterings", was lower than that for "nonmisleading" datasets, whatever the number of loci considered (Fig. 5 ). On the contrary, no clear trend could be observed for datasets with "misleading heterogeneous clusterings". For these datasets, the mean expected heterozygosity was relatively high with 10 loci, intermediate with 20 loci and low with 100 loci, but, in each case, extreme low and high values were observed. The diversity of invasive populations, which was affected by both the diversity of the native population and bottleneck severity, was low for both kinds of misleading clusterings (Fig.
Outcomes for comparisons of STRUCTURE clusterings with results from FST-based and assignment likelihood-based methods were very mixed, depending on the type of "misleading clusterings" considered. 86. 5%, 93. 6% and 99. 1% of datasets with "misleading homogeneous clusterings" in STRUCTURE provided misleading results with at least one of the methods based on FST or assignment likelihood, when considering 10, 20 and 100 loci, respectively (Fig. S4 ). By contrast, datasets with "misleading heterogeneous clusterings" in STRUCTURE analysis were rarely (for 10 and 20 loci), or at least not as strongly (for 100 loci), associated with misleading results with the other methods: this was the case for 15. 2%, 24. 9% and 67. 5% of these datasets for 10, 20 and 100 loci, respectively (Fig. S4 ). Note that, overall, STRUCTURE generates less misleading results than the other two methods.
Discussion We used simulated microsatellite datasets for a particular invasion scenario to determine whether the method implemented in the widely used STRUCTURE software  (Pritchard et al. , 2000)  could mislead users trying to infer introduction routes. We focused on a scenario with two independent introductions from a native population because this scenario can be rejected when obtaining some particular clustering results, which is not true for successive introductions scenarios when the chronology of introductions is not known. We found that, for a true scenario of two independent invasions from a single source, STRUCTURE runs could give misleading clustering patterns (i. e. the two invasive populations clustered together at K=2). In about 10% of all simulated datasets, the results led to incorrect interpretation, with all ("homogeneous misleading clusterings") or most ("heterogeneous misleading clusterings") of the runs for a given dataset yielding the misleading pattern.
Sample collection A novel silica-based ''sebum kit'' (Fig. 1 ) was specifically developed for this study, based on the use of an inert sterile glass rod, specifically treated to collect lipids (Synelvia SAS proprietary method). Hair around the designated scalp sample area was cleanly parted, the rod rubbed on the sample area for 45 s, then suspended within a non-contact inert glass receptacle. Samples were stored at 4 °C until all samples were collected, then frozen at -20 °C until analysis. This sampling technique facilitates analysis of scalp surface lipids (free fatty acids, squalene, glycerides, wax and cholesterol esters, and ceramides) and lipid oxidative stress pathway markers such as SQOOH. Advantages of this non-invasive technique include ease of use, no scalp shaving, absence of solvents which can often impair analysis, and greater surface collection area resulting in larger sample volumes.
In-house testing showed an absence of phthalate interference when compared to common plastic strip or tape scalp sampling methods (data not shown)  [4]. The second sampling technique utilized a sterile cotton swab (ref. MW943, MWE Medical Wire) for the study of MDA, catalase, vitamin E and total proteins. After hair parting, swabs were rolled onto a 3 cm 2 scalp area for 45 s, and then swab heads cut from the handle were placed in a stabilizing storage solution comprising a mixture of antioxidant/chelating agents (vitamin C, EDTA; Synelvia SAS proprietary method). Samples were stored at -20 °C until analysis.
Statistical analysis For both cohorts, biochemical measurements were described per zone using the median and the interquartile range (Q1, Q3). For the first cohort, biochemical measurements of ND volunteers were compared with those from both dandruff zone (DZ) and non-dandruff zone (NDZ) of dandruff-affected volunteers using the exact Wilcoxon two-sample test. For both cohorts, paired NDZ and DZ from dandruff-affected volunteers were compared using the Wilcoxon sign rank test. The two-sided significance threshold was set at 5 %. Statistical analyses were performed using SAS software 9. 3 (SAS Institute Inc. , Cary, NC, USA).
Biochemical exploration between dandruff and nondandruff subjects The quantified compounds are listed in Online Resource 2 (Table  1 ). As sample analysis was carried out under identical standardized conditions, the quantitative sebum composition of each volunteer was analyzed by examining neutral lipid levels via an analytical data processing lipidomics approach, where each variable was considered as independent. Neutral lipid chromatographic profiles of both scalp zones from a given dandruff subject are illustrated by Fig. 2a. Upon chromatographic analysis, DZ of dandruff subjects showed lower levels of waxes and glycerides (as    3 ) as compared to ND subjects. Squalene was not significantly different between NDZ of dandruff subjects and ND subjects, but was significantly decreased in DZ versus NDZ of the dandruff subjects (p \ 0.
Acknowledgments We greatly thank  Dr. Bruno Bernard  for his critical reading of the manuscript and helpful comments. Funding All funding was provided by  L'OREAL Research and Innovation. SYNELVIA SAS  received funding from  L'OREAL SA  for this study. R. J. , P. V. , C. E. R. , F. P. , L. S. , P. B. and L. B. are L'OREAL SA employees. A. M. and N. A. work for SYNELVIA SAS.
Inspired from the Icasso algorithm, a new ICA method, MetICA was developed to handle the instability of ICA on complex datasets. Like the original Icasso algorithm, MetICA evaluated the algorithmic and statistical reliability of ICA runs. In addition, MetICA suggests two ways to select the optimal number of model components and gives an order of interpretation for the components obtained. Conclusions: Correlating the components obtained with prior biological knowledge allows understanding how nontargeted metabolomics data reflect biological nature and technical phenomena. We could also extract mass signals related to this information. This novel approach provides meaningful components due to their independent nature. Furthermore, it provides an innovative concept on which to base model selection: that of optimizing the number of reliable components instead of trying to fit the data.
The spectra obtained from the different samples generated from each of these platforms are usually aligned in an intensity matrix whose rows correspond to samples and columns of overlapping chemical signals. This matrix allows the simultaneous study of mass spectra. Previous studies have used various statistical learning methods on such data matrices to reveal differences between classes of samples and to isolate chemical signals specific to a certain class or trend  [9, 13, 14]. In the context of non-targeted metabolomics, the reliability of these multivariate methods might suffer from the curse of the dimensionality problem  [15]. This problem arises when datasets contain too many sparse variables (over 2000, most contain more than 10 % missing values) and very few samples (less than 100).
Making a statistical model conform closely to such datasets with a limited number of training samples could result in loss of predictive power (i. e. , overfitting). From another angle, since non-targeted techniques capture inegligible chemical noise and experimental bias, it may be difficult for a mathematical model to properly isolate the structure of interest  [16]. Therefore applying statistical learning requires intensive method selection and validation work  [8, [17] [18] [19]. Indeed, it is recommended to apply various learning algorithms in the same study to improve the reliability of the information extracted  [13, 20, 21]. One common way of doing this is to use unsupervised learning (e. g. , clustering, component analysis) prior to supervised methods (e. g. , discriminant analysis, random forest, support vector machine), since basic data structure is revealed through simple dimension reduction, unbiased by the target information.
To apply ICA we assume that the data observed X (n rows, p columns) are linear combinations of unknown fundamental factors or sources S, independent of each other (Fig. 1 ). Matrix A describes the linear combination. The sources are estimated by searching statistical components that are as independent as possible. Compared to PCA, ICA as a linear method could provide three potential benefits for non-targeted metabolomics: More meaningful components would be extracted by optimizing independence condition instead of variance maximization in PCA  [31]. Fig. 1  Matrix decomposition in non-targeted metabolomics. a X is an aligned data matrix from mass spectra of all the samples studied. The goal of ICA is to decompose X to a matrix S which contains independent sources and matrix A describes the linear mixture of theses source.