dataesr/scientific-paragraphs-categorization huggingface dataset:
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 "click-and-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 28,000 citations for the three references indicated above in Google Scholar in September 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.
Indeed, one of the clustering patterns that can be obtained makes it possible to reject the hypothesis of independent introductions: In this study, we focused on "misleading homogeneous clusterings" and "misleading heterogeneous clusterings", in which "misleading patterns" were found in all ten runs or predominated, respectively, for a given dataset. Runs displaying the misleading pattern are boxed if all samples from the invaded areas group together in one cluster, and all samples from the native area group in the other cluster, this allows rejecting the hypothesis of independent introductions and is considered to provide fairly conclusive evidence about a single introduction from the native area (Fig. 1 ). For example,  Ascunce et al. (2011)  explored the worldwide invasion history of the fire ant Solenopsis invicta with a total of 2144 colonies sampled from 75 geographic locations, including 39 native (South America) and 36 invaded (USA, China, Australia) areas.
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 "nonmisleading". 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.
We used DIYABC version 2. 0. 4 software  (Cornuet et al. 2014)  to generate microsatellite multilocus genotype datasets through a coalescent process. We evaluated the effect of the number of loci on the analyses, by performing simulations with 10, 20, or 100 unlinked microsatellite markers. For each combination of N, NF, and number of loci, a total of 500 replicate datasets were generated. For all datasets, a sample of 30 diploid individuals per population was simulated. 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 5 × 10 -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 ). In total, we simulated 22,500 datasets (15 sets of parameters × 3 numbers of loci × 500 replicate datasets). We developed a pipeline with PERL scripts, available at https://doi. org/10. 5281/zenodo. 1002658, to automate the processing of the datasets (simulations and subsequent STRUCTURE and post-STRUCTURE analyses).
STRUCTURE analyses and misleading clustering For each of the 22,500 simulated datasets, a Bayesian clustering analysis was performed in parallel, on a 120cores computer cluster, with STRUCTURE software version 2. 3. 4  (Pritchard et al. 2000). We chose the admixture model with correlated allele frequencies and no linkage between loci. 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 5 × 10 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.
Although K = 2 constituted the focal analysis of our study, we tested multiple values of K in order to infer its most likely value as follow: for each dataset, if the mean natural logarithm of the likelihood of the data ln(P(X|K)) was maximal for K = 1, then the inferred number of clusters was 1; otherwise, we determined the best value of K (either K = 2 or K = 3) by the ΔK method  (Evanno et al. 2005). We investigated the ability of STRUCTURE to clarify introduction routes by focusing on K = 2 analyses. With K = 2, the two samples from an introduced population may or may not cluster together. With the scenario simulated here, in which the two invasive populations result from two independent introductions, the two samples of the introduced populations would not be expected to cluster together (Fig.
For example, the clustering code would be 0/0/100 for a STRUCTURE run output in which Q1 A = 0. 12, Q2 A = 0. 05, and Q3 A = 0. 96. Note that belonging to cluster A or B has no specific meaning, and the subscripts A and B can thus be permuted. For example, clustering codes 0/0/100 and 100/100/0 summarize the same pattern and are pooled together as 0/0/100. 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. In addition, we quantified the proportion of datasets for which the most frequent clustering pattern was also represented by the run with the highest log-likelihood of the data.
Link between summary statistics of genetic diversity and STRUCTURE patterns We summarized each simulated dataset, by using ARL-SUMSTAT 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 F ST 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) (L i→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.
e. , F ST 2-3 > F ST 1-2 and F ST 2-3 > F ST 1-3). We would also expect both invasive population samples to be best assigned to the native population sample (i. e. , L 2→1 > L 2→3 and L 3→1 > L 3→2 ). For each dataset, a global exact test for population genotypic differentiation  (Raymond and Rousset 1995a ) was carried out with GENEPOP software version 4. 3  (Raymond and 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 intra-population and higher inter-population genetic diversities. Increasing bottleneck severity (log 10 (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 C 1A /100/0 and C 1A /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 belonged 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 Q iA cutoff values (instead of 0. 2 and 0. 8 for Q iA ) 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.
8% of datasets (Fig. S2 ). K = 2 was most often (70%) inferred for datasets yielding "misleading homogeneous clusterings", and K = 3 was most often (91%) inferred for datasets leading to "misleading heterogeneous clusterings" (Fig. 4b ). For 85. 8% of all datasets, the code of the run with the highest log-likelihood of the data was the code that predominates over the 10 runs. However, this proportion dropped to only 40. 5% when considering specifically the datasets displaying "Misleading heterogeneous clusterings".
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 significantly lower than that for "non-misleading" datasets, with median values ranging from 0. 025 to 0. 126 depending on the number of loci considered (Fig. 6 ). On the contrary, no clear trend could be Fig. 7  Venn diagrams illustrating the distribution of misleading results for three methods: the FST-based method, the assignment likelihood-based method, and STRUCTURE. Each diagram was produced with 7,500 datasets. The Venn diagrams on the left correspond to homogeneous misleading clustering with STRUCTURE, whereas those on the right correspond to heterogeneous misleading clustering with STRUCTURE 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. 6 ). In comparisons with the "non-misleading" datasets, the proportion of alleles shared by the two invasive populations was higher for the datasets with "misleading homogeneous clusterings" regardless of the number of loci, and lower for "misleading heterogeneous clusterings", except for 100 loci for which it was higher (Fig. 6 ). Outcomes for comparisons of STRUCTURE clusterings with results from F ST -based and assignment likelihoodbased 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 F ST or assignment likelihood, when considering 10, 20, and 100 loci, respectively (Fig. 7 ). 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. 7 ). Note that, overall, STRUCTURE generates less misleading results than the other two methods, while the assignment likelihood-based method generates the worse results.
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 serial 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.
As a matter of fact, "homogeneous misleading clusterings" were observed with datasets in which the native population displayed a very low expected heterozygosity, implying that there were only one or a few highly frequent alleles at each locus  (Allendorf 1986). The interpretation of "heterogeneous misleading clusterings" is less clear-cut, but several lines of evidence suggest the involvement of convergence issues in STRUCTURE runs. Indeed, "heterogeneous misleading clusterings" at K = 2 most often occurred when the best K value was undoubtedly 3, which corresponds to the true number of population. More generally, this category of misleading clusterings was associated with a better ability to differentiate the three populations. This may explain why the proportion of "heterogeneous misleading clusterings" was higher for a larger number of loci, for which more information is available to properly differentiate populations  (Evanno et al.
In such situations, other methods (e. g. , F ST -based, assignment likelihood-based or principal component analysis) may make it possible to determine whether the STRUCTURE results are misleading or not. Also, the log-likelihood of the data estimated by the software may help to reject a meaningless clustering, but this statistic should be interpreted with great care  (Jakobsson and Rosenberg 2007). More generally, it is important to keep in mind that STRUCTURE results have to be interpreted cautiously  (Pritchard et al. 2010)  and, in the context of invasion routes inferences, it should rather be used as a tool to clarify the scenery and decrease the number of genetic units from a large number of population samples to a few main clusters before quantitative analyses, such as approximate Bayesian computation, are performed  (Lombaert et al.
For context, the United States Pharmacopeia (USP) threshold values for MA are: oxygen < 19. 5% v/v and > 23. 5% v/v, carbon monoxide > 10 ppm, carbon dioxide > 500 ppm, nitrous oxide + nitric oxide > 2. 5 ppm, sulphur dioxide > 5 ppm, and humidity <-5 °C pressure dew point  [3]. The question to be addressed in this paper is how to assess the purity of onsite produced MA and if a lack of purity poses a medical problem. That is, what are the elements of a Quality Risk Assessment for onsite produced MA that should be understood and addressed by the medical community. We take advantage of a unique sample dataset; high frequency measurements (one per minute) over a two-month period of carbon dioxide (CO 2 ) to frame the discussion.
Discussion and conclusion In this paper a dataset of CO 2 concentration measurements in onsite produced MA indicates periodic breaches in the USP threshold. To our knowledge, this is the first example of testing of onsite production of MA to appear in the peer reviewed literature. The reason for the breaches in CO 2 threshold are attributed by us to the dryer, which captures CO 2 concomitant to humidity. When it is almost saturated with humidity, before the swing to purge mode, previously captured CO 2 is released into the airstream at higher concentrations than in the original supply. Thus, this is considered a systematic impurity source. We believe this example of CO 2 concentration presented herein showing periodic systematic impurity breaches is representative of potential extrinsic impurity problems, though during the measurement period at this site there were only the nine other threshold breaches of NOx (maximum 2.
Blue whales are sighted every year around the Azores islands, which apparently provide an important seasonal foraging area. In this paper we aim to characterize habitat preferences and analyze the temporal distribution of blue whales around São Miguel Island. To do so, we applied Generalized Additive Models to an opportunistic cetacean occurrence dataset and remotely sensed environmental data on bathymetry, sea surface temperature, chlorophyll concentration and altimetry. We provide a brief description of the oceanography of the area, emphasizing its high spatio-temporal variability. In order to capture this dynamism, we used environmental data with two different spatial resolutions (low and high) and three different temporal resolutions (daily, weekly and monthly), thus accounting for both long-term oceanographic events such as the spring bloom, and shorter-term features such as eddies or fronts.
Hence, knowing the strengths and weaknesses of each dataset beforehand is a key point in order to maximise its use while avoiding false conclusions or misconceptions. Previous research on baleen whales in the Azores examined the timing of their arrival in relation to the spring bloom and their foraging behaviour around the archipelago before they continue to travel further north  [21, 24]. Their distribution has been studied in relation to sea depth  [9]  and several oceanographic variables with monthly resolution  [31, 32]. So far, other temporal scales have not been adequately investigated, although some recent studies suggest that cetaceans, as highly mobile species, and particularly in very dynamic environments, may be affected by both short-and long-term ocean variability  [33] [34] [35].
We use sightings collected between 2008 and 2014 from whale watching vessels and a set of bathymetric and oceanographic remotely sensed variables. We apply Generalized Additive Models (GAMs) to investigate relationships between whale presence and environmental conditions and thus to make inferences about their habitat preferences. GAMs are a non-parametric tool used to quantify linear or non-linear relationships between an independent variable and one or more explanatory variables  [44, 45]. GAMs have been widely employed to model cetacean distribution worldwide e. g. [46] [47] [48] [49] [50] [51]. There have been few studies of blue whale habitat use in the Azores and these have used presence-only modelling techniques, such as Maximum Entropy (MaxEnt)  [31, 32]. In this study we modelled blue whale habitat use in the Azores at different temporal (monthly, weekly and daily) and spatial resolutions (low and high) in order to provide a deeper understanding of blue whale habitat ecology, getting the most from the different resolutions of the environmental data available.
Sightings Cetacean sightings were recorded during the commercial trips of a whale watching company with base port in Ponta Delgada (São Miguel, Azores). Unlike many other opportunistic data sources, data were gathered throughout the year, in so far as the weather, the sea state and the number of tourists were good enough to conduct the trips. For each trip, experienced observers with binoculars (Steiner 20x80 mm), stationed at strategic points on land, typically started searching for cetaceans around one hour prior to launch and would continue observing and relaying this information to the boat, until cetaceans are sighted from the boat. Thus, cetaceans were located first from land and then boats were piloted directly towards the location of the animals, observing current legislation about approaching free-living cetaceans (Decreto Legislativo Regional n˚10/2003/A and DLR 13/2004/A).