dataesr/scientific-paragraphs-categorization huggingface dataset:
Briefly, quantitative PCR reactions were assembled as follows: 2 μL of template DNA (3 ng/μL isolated DNA), 2 μL of mtDNA or nDNA target-specific primer pair (400 nM final concentration each), 12. 5 μL SYBR Green PCR Master Mix, and 8. 5 μL H 2 O in 1 well of the 96-well PCR plate. All qPCR experiments were performed in triplicate wells in three independent experiments. Primer sequences can be found in Supplementary Reactive oxygen species. ROS were determined by incubating cells with Mito-SOX™ Red Mitochondrial Superoxide Indicator (Invitrogen, ref. M36008; 5 µM) at 37 °C for 10 min in the dark, then washed with PBS-5% FBS and analyzed by flow cytometry with LSRFortessa or LSR II cell analyzers (BD Biosciences). MFI was quantified using FlowJo software (Tree Star, Inc. ). Western blot. Total cell lysates from all protein samples in this manuscript were incubated on ice in lysis buffer for 20 min (50 mM Tris, pH 7.
Acknowledgements We would like to thank the  Deep Sequencing, Imaging ,  FACS ,  Fish , and bioinformatics facilities of the  MPI  for resources and support and  David Stachura  for valuable protocols. We thank colleagues for reading this manuscript. F. G. K. was supported by a return scholarship of the  Forschungskommission, Faculty of Medicine, University of Freiburg , a grant of the  Wissenschaftliche Gesellschaft in Freiburg (Scientific Society in Freiburg) , and an  EXCEL-Fellowship  of the  Faculty of Medicine, University of Freiburg , funded by the  Else-Kröner-Fresenius-Stiftung. E. T. was supported by the  Max Planck Society , a  Marie Curie Career Integration Grant  ( 631432   Bloody Signals ) and the  Deutsche Forschungsgemeinschaft ,  Research Training Group GRK2344  " MeInBio -BioInMe ".
INTRODUCTION Wikidata  [10] , originally created as a central storage for information of Wikipedia, developed into a widely used open knowledge base. It has become a source of linked data at large. Not only in but also outside the Wikimedia universe, its linked data is used. All data in Wikidata is assembled by the contributions of a community of users, evolving without a centralized process as in e. g. governmental open data  [8]  or extraction from a third party (e. g. YAGO  [9] ). All of the data in Wikidata is contributed by volunteers, including its schema. We define Wikidata's schema as the properties, the structure-giving part of the triples. Wikidata's triples contain usually two items and a property connecting them e. g. <Ada Lovelace> <occupation> <computer scientist>, where <occupation> is the property.
One of the main factors for the reliability of a knowledge base (KB) is its schema. If the schema is stable, the data is easily reusable for third parties and the KB becomes attractive for reuse and further editing. Wikidata's schema is not constrained by e. g. the software, but can be adjusted as of the community's needs. In this paper, we want to investigate if the collaborative modelling of a schema is sustainable. We therefore quantify the way properties are changed. This gives us an insight not only if it works now, but also whether it is reasonable to assume that the schema is still usable in the same way in 5 years from now. We focus on labels in our investigation. URIs in Wikidata are opaque as their function differs from the one for labels  [5]. That means, each entity is addressed by a unique identifier, that does not suggest anything about the concept of the entity describes.
For example, the item Ada Lovelace is identified with the ID Q7259, the property capital with the ID P36. Labels are used to describe the content of an entity, identified with such an opaque URI. For example the property P31 has the label "instance of" in English and "nature de l'élément" in French. Labels are not only the access point for humans to the data  [2] , but usually also the way third party application will reuse the data. For example, Question Answering systems  [1, 3]  or ontology modeling  [6]  depend on natural language description of entities. Quantifying the stability of labels of the schema gives us an impression of how realistic the reusability of Wikidata's data is on a long perspective time-wise. Wikidata is inherently multilingual. This means, editors add labels in over 400 languages.
METHODS Wikidata is collaboratively edited. Each edit in Wikidata is recorded in the editing history of the respective entity. We extract the editing history of Wikidata for each property. For each revision we retrieve the labels and aliases of the property at this revision and the timestamp of the revision. Based on this data we can display timelines of changes in the property label 1. We analyze the property label changes in four different metrics to get a comprehensive overview on the stability of the schema in Wikidata. We define Lifetime as the time a property has the same label as the current one over the time this property existed. The metric Shared Labels measures how many properties share a label with any other property, making a lookup based on name a challenging task. Stability describes the probability, that a label of a property picked at a randomly chosen (with uniform distribution) point in time where such label exists will still be this property label or an alias now.
Also, planned experiments with hydrogen instead of helium could give insight into the role of vibronic coupling on the absorption lines. ■ EXPERIMENTAL AND COMPUTATIONAL METHODS Helium-nanodroplets, with an average size of 2 × 10 5 He atoms, were doped with C 60 in a pick-up cell and ionized via electron impact at 60 eV. The abundance distribution of He n C 60 + ions was recorded with a high-resolution reflectron time-of-flight mass spectrometer. Overlapping peaks for similar masses could be distinguished in an automated procedure using the IsotopeFit software. 32 Cluster cations were irradiated with a Ti:Sa cw laser Matisse TR from Sirah with 10 MHz bandwidth and ca. 0. 6 W power. Laser-induced depletion of the ion yield at different mass-to-charge ratios was measured with the mass spectrometer while scanning the photon wavelength.
■ ACKNOWLEDGMENTS This study was supported by the  Austrian Science Fund (FWF)  Projects  P26635 ,  W1259-N27 ,  P28979-N27 ,  M2001-NBL ; by the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 692335 ; and by the  Swedish Research Council  (Grant Number  2016-06625 ). The computational results presented have been achieved using the HPC infrastructure at the KoçUniversity and  LEO3  of the  University of Innsbruck.
Neural Machine Translation (MT) has radically changed the way systems are developed. A major difference with the previous generation (Phrase-Based MT) is the way monolingual target data, which often abounds, is used in these two paradigms. While Phrase-Based MT can seamlessly integrate very large language models trained on billions of sentences, the best option for Neural MT developers seems to be the generation of artificial parallel data through back-translation -a technique that fails to fully take advantage of existing datasets. In this paper, we conduct a systematic study of back-translation, comparing alternative uses of monolingual data, as well as multiple data generation procedures. Our findings confirm that back-translation is very effective and give new explanations as to why this is the case.
They are optimized using Adam (Kingma and Ba, 1 Version 7, see www. statmt. org/europarl/. 2 www. statmt. org/wmt08. 2014) and early stopped according to the validation performance. Training lasted for about three weeks on an Nvidia K80 GPU card. Systems generating back-translated data are trained using the same out-of-domain corpus, where we simply exchange the source and target sides. They are further documented in § 3. 1. For the sake of comparison, we also train a system that has access to a large batch of in-domain parallel data following the strategy often referred to as "fine-tuning": upon convergence of the baseline model, we resume training with a 2M sentence in-domain corpus mixed with an equal amount of randomly selected out-of-domain natural sentences, with the same architecture and training parameters, running validation every 2000 updates with a patience of 10.
Since BPE units are selected based only on the out-of-domain statistics, finetuning is performed on sentences that are slightly longer (ie. they contain more units) than for the initial training. This system defines an upperbound of the translation performance and is denoted below as natural. Our baseline and topline results are in Table  2 , where we measure translation performance using BLEU  (Papineni et al. , 2002) , BEER (Stanojević and Sima'an, 2014) (higher is better) and char-acTER  (Wang et al. , 2016)  (smaller is better). As they are trained from much smaller amounts of data than current systems, these baselines are not quite competitive to today's best system, but still represent serious baselines for these datasets. Given our setups, fine-tuning with in-domain natural data improves BLEU by almost 4 points for both translation directions on in-domain tests; it also improves, albeit by a smaller margin, the BLEU score of the out-of-domain tests.
GAN setups In our setups, we use a marked target copy, viewed as a fake source, which a generator encodes so as to fool a discriminator trained to distinguish a fake from a natural source. Our architecture contains two distinct encoders, one for the natural source and one for the pseudo-source. The latter acts as the generator (G) in the GAN framework, computing a representation of the pseudo-source that is then input to a discriminator (D), which has to sort natural from artificial encodings. D assigns a probability of a sentence being natural. During training, the cost of the discriminator is computed over two batches, one with natural (out-of-domain) sentences x and one with (indomain) pseudo-sentences x. The discriminator is 4 Our implementation is available at https://github. com/franckbrl/ nmt-pseudo-source-discriminator a bidirectional-Recurrent Neural Network (RNN) of dimension 1024.
LM Setup We have followed  Gulcehre et al. (2017)  and reimplemented 5  their deep-fusion technique. It requires to first independently learn a RNN-LM on the in-domain target data with a cross-entropy objective; then to train the optimal combination of the translation and the language models by adding the hidden state of the RNN-LM as an additional input to the softmax layer of the decoder. Our RNN-LMs are trained using dl4mt 6  with the target side of the parallel data and the Europarl corpus (about 6M sentences for both French and German), using a one-layer GRU with the same dimension as the MT decoder (1024).
Natural selection plays a variety of roles in hybridization, speciation, and admixture. Most research has focused on two extreme cases: crosses between closely related inbred lines, where hybrids are fitter than their parents, or crosses between effectively isolated species, where hybrids suffer severe breakdown. But many natural populations must fall into intermediate regimes, with multiple types of gene interaction, and these are more difficult to study. Here, we develop a simple fitness landscape model, and show that it naturally interpolates between previous modeling approaches, which were designed for the extreme cases, and invoke either mildly deleterious recessives, or discrete hybrid incompatibilities. Our model yields several new predictions, which we test with genomic data from Mytilus mussels, and published data from plants (Zea, Populus, and Senecio) and animals (Mus, Teleogryllus, and Drosophila).
We develop a simple Brownian bridge approximation, and show that it can naturally interpolate between previous modeling approaches  (Wright 1922 (Wright , 1977;; Orr 1995; Gavrilets 2004) , which are appropriate for the two extreme types of hybridization. We then show how the model can account for surprising empirical patterns that have been observed in both regimes  (Wright 1977; Moehring 2011; Moran et al. 2017). Finally, we show that the model yields novel predictions for selection on heterozygosity, and test these predictions with a wide range of new and existing datasets (Table  1 ).
Despite this predictive success,  Wright (1977)  noted a pattern that single-locus theory could not explain. In Wright's words: "the most consistent deviation from expectation [. ] is the low yield of F2 in comparison with the first backcrosses"  (Wright 1977, p. 39). Because E( p 12 ) = 1 2 for both crosses, this cannot be explained without epistasis, or coadaptation between the alleles in the parental lines. In fact, the pattern is predicted by Fisher's model, when there is a small amount of coadaptation, such that 1 < f P < 2. This yields a fitness surface with a small amount of curvature, which is intermediate between those shown in Figure  2  A and 2 B. Figure  3  B plots the four relevant datasets collated by Wright, and compares the results to predictions from equations (  6 ) and (11), with f P = 1.
We therefore repeated our analysis after excluding these "F2-like" backcrosses. Results, shown in the fourth column of Table  2 , showed that the reduced BC dataset showed no tendency for elevated heterozygosity. However, the bias toward higher heterozygosities remained in the F2, even when we subsampled to equalize the sample sizes. Despite the problems of interpretation due to introgression and shared variants, the results support the prediction of equations (  13 )-(  15 ): that directional selection on heterozygosity should act in the F2, but weakly or not at all in the backcross.
The interaction between h X and p 12,A may also help to explain a broader set of patterns observed by  Moehring (2011)  in multiple datasets of Drosophila backcrosses  (Macdonald and Goldstein 1999; Noor et al. 2001; Moehring et al. 2006a Moehring et al. , 2006b  and see Table  1 ). Moehring found that male fertility correlated strongly and negatively with h X , while correlations with p 12,A were weak and inconsistent. This pattern was not consistent with predictions of a simple model of X-autosome incompatibilities  (Moehring 2011 ), but it is consistent with Fisher's geometric model (see Fig. 1  C, Fig. 2  B, and Appendix 4).
ESTIMATING THE FITNESS SURFACE Across a diverse collection of hybrids, equation (  6 ) predicts that the hybrid index will be under disruptive selection, and heterozygosity under directional selection. This prediction can be tested with datasets containing estimates of fitness, h and p 12 for many hybrid individuals. Exactly such an analysis was presented by  Christe et al. (2016) , for families of wild hybrids from the forest trees, Populus alba and P. tremula  (Lindtke et al. 2012 (Lindtke et al. , 2014;; Christe et al. 2016 ). These authors scored survival over four years in a common-garden environment, and fit a generalized linear model to their binary data (binary logistic regression, with "family" as a random effect), and predictors including linear and quadratic terms in p 12 and h.
To obtain confidence intervals on these parameters, we fit equation (  17 ) to the raw data of  Christe et al. (2016). We also searched for other data sets, from which we could estimate the hybrid fitness surface. After applying some quality controls (see Methods and Table  S2 ), we identified one other dataset of wild hybrids, from the mouse subspecies Mus musculus musculus/domesticus, where male testes size was the proxy for fertility  (Turner and Harr 2014). We also found four datasets of controlled crosses: F2 from the same mouse subspecies  (White et al. 2011) , and the ragworts Senecio aethnensis and S. chrysanthemifolius  (Chapman et al. 2016) ; and the Drosophila backcrosses discussed above  (Macdonald and Goldstein 1999; Moehring et al. 2006a Moehring et al. , 2006b)). Unlike the data from wild hybrids, these singlecross datasets leave large regions of the fitness surface unsampled (see Fig.
1 ); nevertheless, they each contain enough variation in h and p 12 for a meaningful estimation. Details of   S5  and S6. all six datasets are shown in Table  1 , and they are plotted in Figures  S9-S11. Figure  5  shows a summary of the estimated parameters, and full results are reported in Tables  S5  and S6 , and Figures  S9-S11. Taken together, the results show good support for the predictions of equation (  6 ). For all six datasets there was evidence of significant positive selection on heterozygosity ( β0 > 0 was preferred in all cases). Furthermore, for all six datasets, we inferred diversifying selection acting on the hybrid index. Estimates of β 2 , shown in the upper panel of Figure  5 , show that this selection was near-symmetrical in all cases, such that β2 ≈ 1. The poorest fit to the predictions was found for the Drosophila backcrosses, where estimates of β 1 were significantly greater than the predicted upper bound of β 1 = 4 (Fig.
5  lower panel). But these datasets were least suited to our purpose, because estimates of h and p 12 depend strongly on our rough estimate of the relative contributions of the X and autosomes (see Methods), and because they lack F2-like genotypes, from the center of the fitness surface (Fig. 1  A; Fig. S11 ). By contrast, results for the Mus musculus F2  (White et al. 2011) , are remarkably close to the predictions of equation (  9 ) (Fig. 5 ; Fig. S10 ). Two other features of the results deserve comment. First, for the two F2 datasets, it was not possible to provide meaningful confidence intervals for β 1 and β 2. This is because, for these two datasets, the terms in h and h 2 did not make a significant contribution to model fit, and so the preferred model contained only selection on p 12 (see Table  S6 ).
This is consistent with our earlier prediction of equation (  13 ), and stems from the low variation in 4h(1h) among F2 hybrids (see Appendix 3 and Figs. S9  and S10 ). Second, for two of the datasets, Populus and Senecio, the estimates of β 1 are substantially lower than 4 (Fig. 5 ; Fig. S9 ). This is suggestive of parental maladaptation, creating true heterosis in the hybrids (see eq. 6). Consistent with this inference, there is independent evidence of parental load and F1 hybrid vigor in both species pairs (Populus:  Caseys et al. 2015; Christe et al. 2017; Senecio: Abbott and Brennan 2014).
Discussion In this article, we have used Fisher's geometric model to develop predictions for the relative fitness of any class of hybrid. The modeling approach is simple, with few free parameters, and it generates a wide range of testable predictions. We have tested some of these predictions with new and published datasets (Table  1 ), and the major predictions of the model are well supported. We emphasize that our approach is designed for coarsegrained patterns in the data, and typical outcomes of hybridization, without considering the particular set of substitutions that differentiate the parental lines, or the particular combination of alleles in an individual hybrid. The limitations of such an approach are seen in the low r 2 values associated with our model fitting (Table  S5 ). Nevertheless, our approach should enable novel and complementary uses of genomic datasets, which do not depend on identifying individual loci with anomalous effects.
COLLATION OF PUBLISHED DATA We searched the literature for published datasets combining measures of individual hybrid fitness, with genomic data that could be used to estimate p 12 and h. We rejected any datasets where the measure of fecundity or fertility took an extreme low value for one of the pure species, suggesting that it is not a good proxy for fitness (e. g. ,  Orgogozo et al. 2006) , data where the fitness proxy correlated strongly with a measure of genetic abnormality such as aneuploidy  (Xu and He 2011) , or data where the states of many markers could not be unambiguously assigned, for example, due to shared variation. Before estimating the fitness surface, we also excluded any dataset where there was a highly significant rank correlation between the proportion of missing data in an individual, and either their heterozygosity, or fitness.
For this reason, we did not proceed with reanalyses of the excellent datasets of  Li et al. (2011) , or  Routtu et al. (2014)  (see Table  S2  for full details). For our reanalysis of the Teleogryllus data of  Moran et al. (2017) , we did not consider data from the second backcross, to avoid complications due to selection on heterozygosity during the first backcross, and because of an anomalous hatching rate in the T. oceanicus controls. For our reanalysis of the Mus musculus F2  (White et al. 2011) , we used a conservative subset of these data; we excluded any individual where any X-linked marker was scored as heterozygous (indicative of sequencing errors in heterogametic males;  White et al. 2011) , and controlled for variation in the uniparentally inherited markers, by retaining only individuals carrying M.
m. domesticus mitochondria, and the M. m. musculus Y. However, results were little changed when we used all 304 individuals with fertility data (Table  S6 ). Results were also unaffected when we used alternative proxies for fitness (Table  S6 ). For the wild Mus musculus hybrids data  (Turner and Harr 2014) , fixed markers and their orientation between species were determined using previously published data of  Staubach et al. (2012)  and  Yang et al. (2009). See details in the Dryad data package.
GLM METHODS The linear model results shown in Table  3 , Figure  5 , Tables  S5  and S6 , and Figures  S9-S11 , were all fit in R v. 3. 3. 2 (R Core Team 2016). For datasets with quantitative fitness measures  (White et al. 2011; Turner and Harr 2014 ; Fig. S10 ) we used the standard general linear models, with Gaussian errors, and chose data transformations to reduce heteroscedasticity. For binary fitness data  (Noor et al. 2001; Christe et al. 2016; Chapman et al. 2016 ; Table  3 ; Fig. S9 ), we used binomial regression with a logit link implemented in the glm function; and with ordinal fitness data  (Macdonald and Goldstein 1999; Moehring et al. 2006b ; Fig. S11 ) we used proportional odds logistic regression  (Agresti 2003) , implemented in the polr function. In these cases, the P-values shown in Table  S6  were calculated by comparing the t-value to the upper tail of normal distribution, as in a Wald test.
The value of f mal depends on the relative positions in phenotypic space of the two parental types. It is useful to consider a few simple cases, which are summarized in Table  S1 , and which we also use in the main text. First, let us consider the case where maladaptation accrues independently in the two parental lines. In this case, shown in Table  S1b , we expect no covariance between their phenotypic deviations, and so f mal is just a weighted sum of the relative breakdown scores for the two parental lines f mal = (1 -h) 2 f P1 + h 2 f P2 , z P1,i z P2,i = 0 (A9) where f P1 = i λ i z 2 P1 / i λ i dv i. The assumption of independent maladaptation should apply to many real datasets, and so equation (  A9 ) is used in the main text, as the basis of equations (  6 )-(  9 ) (see below for details).
All datasets scored fertility in each hybrid fly as a binary, or ordinal trait (see Table  1 ), and so Moehring (2011) asked whether fertility problems varied systematically with p 12,A and h X. She found, in all six crosses, that backcross fertility problems correlated strongly and positively with h X , but correlations with p 12,A were weak and inconsistent. This is shown in Figure  S7 , and Table  3 of   x ≡ g X h X (A61) Figure  S8a  depicts the fitness surface of equation (A60) as a function of x and a. Each dataset of hybrid males could occupy a rectangular region of this surface, as determined by the its value of g X , and this is how the data are plotted in Figure  S7. The regions also correspond to a region of Figure  1  C, comprising the parallelogram delimited by the dotted horizontal line and the two arrows.