dataesr/scientific-paragraphs-categorization huggingface dataset:
Transcriptome analysis RNA samples were treated with the Ribo-Zero Kit for Bacteria to remove ribosomal RNA (rRNAs). Multiplex RNA-Seq libraries were prepared with the Illumina TruSeq Stranded Total RNA Sample Preparation and sequenced (HiSeq2000). The complete genome sequence of the Bacillus subtilis subsp. subtilis str. 168 (NCBI accession number for chromosome: NC 000964. 3) and its annotation were retrieved from the NCBI: (https://www. ncbi. nlm. nih. gov/ genome/?term=Bacillus±subtilis) and from Subtiwiki  (34). Reads processing, mapping and differential gene expression analysis were performed according to  (35). Briefly, singleend 100-nt reads were mapped to the reference genome using bwa and allowing soft-clipping in the alignment parameters (bwa mem)  (36). Analysis of the mappings used samtools for processing, sorting and indexing of the alignment files; BEDtools for computing coverage; the IGV browser for displaying the alignment files  (36) (37) (38).
Reads counts were normalized either as reads per million (RPM) or as transcripts for million (TPM). Differential gene expression analysis between strains was performed with the EdgeR package (39) using the Trimmed Mean of M-values (TMM) scaling factor as normalization method. Gene features were considered as significantly up-or down-regulated in the mutants versus WT samples if the log2 fold-change (FC) ratio was ≥1 or ≤-1 and the p-value adjusted for multiple testing False Discovery Rate (FDR) calculated using the Benjamini-Hochberg (BH) method in edgeR was equal or lower than 5% (FDR ≤ 0. 05). Multi-dimensional scaling (MDS) and MA plots (visualizing measurement differences between two samples by transforming the data onto M (log ratio) and A (mean average) scales) were generated, respectively, through the 'plotMDS.
The full-length wild type protein migrating with an apparent MW of 180 kDa was expressed most strongly including some degradation intermediates similar to what we observed in E. coli (Figure  2B ). The lowest levels of expression were observed for the truncated 688 aa and 529 aa RNase E proteins (Figure  2B ) even if their absolute levels are underestimated by a factor of ∼2fold (see Discussion and Supplementary Figure  S1 ). We also measured the transcript levels of the RNase E versions from RNA-Seq data generated from the mutant strains. Since differences in gene length generate unequal read counts for genes expressed at the same level (e. g. longer gene have more reads), we normalized read counts for gene length and sequencing depth, generating TPM (transcript per million). The rne ( MTS), rne (688) and rne (529) transcript levels were about 20, 29 and 30% of that measured for wild type rne (1061), respectively.
To explore differential gene expression between all strains, we measured the distance of gene expression values between any pair of samples. Our datasets included the analysis of 3932 RNA features including open reading frames (ORFs) and untranslated regions (UTRs, riboswitches, small RNAs) derived from the chromosome and retrieved from Subtiwiki  (34). On a multidimensional scaling (MDS) plot (Figure  5B ), the triplicates within each group (strain) clustered closely, indicating similar gene ex- pression patterns and consistency between biological replicates. A certain degree of dissimilarity was instead observed among the different groups. The rny and rny + Pxylrne(529) strains were very close on the MDS plot indicating similar expression levels (Figure  5B ). Together with the rny + Pxyl-rne( MTS) strain they were clearly separated from the WT samples.
FUNDING Centre National de la Recherche Scientifique  [ UMR 8261 ];  Université de Paris ;  Agence Nationale de la Recherche  [RNAJAY and IB-mRND]; ' Initiative d'Excellence' program  from the  French State  [' DYNAMO ,'  ANR-11-LABX-0011-01 ]. Funding for open access charge: CNRS [UMR8261].
Using landmark GF datasets, we first show how polynomial (Bayesian) regressions can be used for precise and accurate predictions of peaks and valleys in performance landscapes, irrespective of the underlying structure of the data (i. e. , individual food intakes vs fixed diet ratios). Using trigonometric relationships, we then identified the known nutritional trade-off between lifespan and reproductive rate both in terms of nutrient balance and concentration. Nutrigonometry enables a fast, reliable and reproducible quantification of nutritional tradeoffs in multidimensional performance landscapes, thereby broadening the potential for future developments in comparative research on the evolution of animal nutrition.
2018) ] and therefore ensures that the estimate of ߠ is calculated in the same domain of the GF fitness landscapes. However, this Vector of Positions approach relies on the peak estimates from a SVM machine learning model which is computationally expensive particularly in n dimensions, where n > 3, and sensitive to the characteristics of the input data (e. g. , if the data contains food intakes as in  (Lee et al. 2008; House et al. 2015; Jensen et al. 2015; Morimoto and Lihoreau 2019)  or a grid of fixed diet ratios as in  (Kutz et al. 2019) ], identifying local as well as global peaks that introduce noise into the analysis (shown here). Albeit useful, the Vector of Positions approach can be cumbersome to implement across different datasets, computationally expensive to obtain estimates of peaks (or valleys), and as a result can generate inaccurate estimates of the strength of nutritional trade-offs.
Thus, to date, there are no proposed solutions that address the above limitations, which creates a significant bottleneck in studies of nutrition that limits the multidimensional power of the GF framework. Here, we address the limitations of current models by proposing a simpler framework (Nutrigonometry) upon which the strength of nutritional trade-offs can be calculated in 3D fitness landscapes, irrespective of the structure of the nutritional data to analyze. Using landmark GF datasets, we first investigated the performance of different 'off-the-shelf' machine learning models in predicting the peak in the fitness landscapes, in order to find the most accurate and computationally inexpensive model. We achieved this by integrating several measurements of predictive error, variance in predicted peak area, and topological characteristics of the predicted peak region.
The angle ߙ is the angle of the nutritional rail, relative from the x-axis, that passes through the peak in the landscape for the trait i, and the hypothenuse ݄ of the triangle shows how far from the origin the peak in the landscape sits for the trait i (Fig  1a ). ߙ and ݄ can be calculated using Pythagorean theorem and the relationship between the angle and the sides of right-angle triangles (i. e. , sines and cosines), as shown in Fig  1a. Once ߙ are known, we can estimate the angle ߠ [as in  (Morimoto and Lihoreau 2019) ] which is the difference in the angle between nutritional rails that maximize two traits, i and j, and provides a measure of the strength of the nutritional trade-off that exists between traits i and j Here when applying this model for empirical datasets (see below), inferences on the strength of nutritional trade-offs were made using confidence intervals for ݄ , and ߠ , , whereby nutritional trade-offs were stronger when confidence intervals did not overlap zero and the magnitude of the difference was large.
Predicting peak (or valley) location and size As with previous approaches, the model presented here depends on accurate estimates of the coordinates for the peak in the multidimensional performance landscape. Without this, estimates of ݄ , and ߠ are inaccurate which in turn affects the ability of the model to estimate the strength of nutritional trade-offs. To overcome this, the basic algorithms underpinning the identification of peak regions in multidimensional fitness landscapes were designed as following: 1) Empirical data was split into training (75%) and test (25%) datasets; 2) A machine learning model was fitted to the training set using 10-fold cross-validation, with the fitness trait as dependent variable and the nutrient intakes (or fixed ratios) as independent variables. The model included main and interactive effects of protein and carbohydrate, as well as quadratic effects of each nutrient (for non-linear relationships); 3) The model's predictive performance was evaluated with root-mean-square-error (RMSE) with respect to the observed values of the test dataset; 4) A set of 500 random points corresponding to (protein, carbohydrate) coordinates were generated covering the nutritional space, and the model of step 2 was used to predict the value of the fitness value for each point; 5) A quantile threshold was used to crop the data to the specific region of interest.
For instance, for peaks in the nutritional landscape, the default value used throughout this study was set to 0. 95 (i. e. , the highest 5% predicted fitness values are subset, from which coordinates of protein and carbohydrate are used). 6) Steps 4-5 were repeated 100 times. We then made statistical inferences on peak area from 95% confidence intervals using the 'ci' function of the 'Rmisc' package  (Hope et al. 2013 ) whereby we resampled with replacement the selected random points obtained from steps 5 and 6 above. To test the performance of nutrigonometry in estimating nutritional trade-offs, we used the most commonly used models to test relationships between traits in behavioral ecology (e. g. , general linear model), machine learning models used in regression models in ecology and evolution [e.
g. , Random Forest,  (Rabinovich 2021) ], as well as models that have been specifically used to analyse multidimensional performance landscapes in GF studies (e. g. , SVM, GAMs)  (Ponton et al. 2015; Morimoto and Lihoreau 2019). In particular, we tested the performance of Bayesian linear regression (Bayes), general linear regression (LM), k-nearest neighbors (KNN), Gradient boost (GBoost), random forest (RF), support vector machine (SVM) with radial basis function as well as generalized additive models (GAMs) with both smooth term or tensor product term. With the exception of GAMs that were fitted using the 'mgcv' package  (Wood and Wood 2015) , all other models were fitted using the 'tidymodels' package of the tidyverse  (Wickham et al. 2019). For the Bayesian regression, we used the flexible Cauchy prior from the 'rstanarm' package for all analysis  (Goodrich et al.
2020 ). Fitness landscapes were estimated using the 'Tps' function of the 'fields' package  (Nychka et al. 2017). All plots were done using the 'ggplot2' package  (Wickham 2016). All models were fitted to a training set (75% of the data) and model performance (i. e. , RMSE) was calculated from the performance of the models in the remaining test dataset (25%).
Datasets used for model application We demonstrate the applications of the Nutrigonometry framework using two datasets, which vary in structure. The first dataset is a landmark dataset which contains Drosophila melanogaster individual adult nutrient intake as well as the consequences of nutrient intake to lifespan and reproduction  (Lee et al. 2008). This dataset was previously used to test the Vector of Position approach and therefore has important benchmark status in the field  (Morimoto and Lihoreau 2019). We also investigate the effect of data structure on Nutrigonometry estimates of peak and valley regions. In GF, data can be divided into two structures: intake data and fixed ratio data. Intake data is ideal in GF studies because it allows for exploration of realized nutritional effects, that is, nutritional effects exerted upon traits given by the amount of nutrient eaten  (Simpson and Raubenheimer 1993b).
However, collecting intake data can be cumbersome or challenging, and recent approaches have adapted GF experiments to draw landscapes of traits based on the fixed ratio of the nutrients in the diets  (Kutz et al. 2019). To date, however, we still do not know how this adaptation influence estimates of nutritional trade-offs in multidimensional performance landscapes. Here, to test whether the structure of the data is important for model predictions, we used  Lee et al. , (2008)  dataset with individual intakes ('intakes') as well as with fixed ratios ('fixed'). Using this data and for the purpose of the demonstration of Nutrigonometry, we estimated the nutritional trade-off between lifespan and reproductive rate which are known to trade-off in this species. We also used a second dataset from  (Kutz et al.
Discussion We proposed a new simple analytical framework to analyse nutritional trade-offs in multidimensional fitness landscapes. Nutrigonometry uses trigonometric relationships from right-angle triangles to identify and compare peaks (or valleys) in 3D fitness landscapes between traits. Using landmark GF datasets with different structures, we demonstrated the accuracy and performance of standard (machine learning) models in finding the peak regions in these multidimensional landscapes and subsequently quantifying the strength of nutritional trade-offs between traits. As with the Vector of Positions approach  (Morimoto and Lihoreau 2019) , the Nutrigonometry strictly considers coordinates in the real positive region of the nutrient space, whereby the true separation between key regions (peaks and valleys) are quantified within the correct domain in which the fitness landscapes exist.
The articles represent the result of a joint French-Austrian interdisciplinary project with the title 'Human Behaviour and Machine Simulation in the Processing of (Mor)Phonotactics' funded by the ANR (Agence Nationale de la Recherche, ANR-13-ISH2-0002) and the FWF Austrian Science Fund (Fonds zur Förderung der wissenschaftlichen Forschung, I-1394-G23). The main focus of investigation of the project was the study of the psycho-computational representation of (mor)phonotactics in French and German speakers from two angles simultaneously: human behaviour and machine simulation. Both of them cover a broad range of activities: from computational simulations (computational models appealing solely to distributional information for the linguistic data and processing the statistical regularities of representative corpora) to longitudinal studies of acquisition (in order to test whether there are systematic differences with regard to the phases at which phonotactic and morphonotactic clusters are acquired), psycholinguistic analyses (aiming at verifying the psychological plausibility of hypotheses on the phonotactics/morphonotactics distinction) and production analysis (focusing on the phonetic repair mechanisms and the systematic differences in the production of morphonotactic and phonotactic clusters in actual speech data).
morphonotactic status in the successful pronunciation of the different French consonant clusters. The authors analyse distributionally longitudinal CHILDES data from four children (aged 1;6 to 3;0) collected in spontaneous speech interactions between a parent and the child. The analysis shows a high variation of error types (such as reduction, substitution, omission, repetition, epenthesis, shifted cluster or mixed sounds) in the characterization of consonant clusters. A more detailed exploration of the individual developmental trajectories, however, demonstrates the presence of an overall developmental pattern with the number of omissions decreasing while the number of reductions increases within the age groups. Concerning the consonant cluster's position in the word, overall French children have a tendency to a left-side preference in the development of the pronunciation of clusters.
Dziubalska-Kołaczyk 2009 , 2014). It is generally assumed that consonantal languages have more dispreferred consonant clusters than vocalic languages. In order to operationalize this assumption and to determine the status of consonant clusters in German, a software package, namely the Phonotactic Calculator developed by Dziubalska-Kołaczyk, Pietrala and Aperliński (2014) based on earlier work by Grzegorz Krynicki, can be applied. The default parameter values of the calculator include the manner of articulation (MOA), the place of articulation (POA), and a hierarchy of S/O (sonorant/obstruent) distinctions. Due to the Phonotactic Calculator's settings, the maximum number of consonant sequences to be analysed is bounded by triple clusters. Therefore, the current analysis of cluster preferredness in German is demonstrated based on triple consonant clusters.
DataBase The corpus linguistic research was based on the data extracted from the Austrian Media Corpus (AMC), which was developed at the Austrian Academy of Sciences (cf. Ransmayr, Mörth & Matej 2017). It is considered to be one of the largest corpus collections of the German language. It covers all printed resources from Austrian printed media for the last two decades, including the transcripts of Austrian television and broadcast news plus the news reports of the Austria Press Agency APA. This corpus contains about 40 million texts of various genres containing about 10 billion word tokens. It is linguistically annotated with morphosyntactic information and lemmatized. Due to its functionality, a list of all word types and word tokens containing the specific clusters in a given corpus can be selected along with the frequency of occurrence and part of speech.
Clearly the numbers of types (inflectional word forms) given in the lists below refer to what is attested in the AMC; the number of potential correct forms is higher. The starting point of the research was obtaining the data from the AMC. The corpus automatically allows identification of the position of a cluster, thus different queries were specified in the research. For instance, for the word-initial position the following query was involved "str. +". It reads word-initial triple cluster /str-/ followed by one or more character. Thus, all consonant clusters along with their frequency of occurrence in the corpus were retrieved, according to their position in the word, for further analysis. The next stage included the elimination of all irrelevant words, such as proper names, misspellings or non-words.
Since the NAD calculator is not able to measure all the distances within the quadruple clusters, no preferences can be deduced, but we chose the type-token ratio (TTR) calculation in order to arrive at some generalizations about the morphonotactic vs. phonotactic distribution of these clusters: The type-token ratio is the most commonly used index of lexical diversity of a text, i. e. the number of tokens divided by the number of word types  (McEnerny & Hardie 2012) , which allows us to analyse the lexical variation of vocabulary containing a specific cluster in the corpus. It can be observed that: 1) the overall number of tokens increases along with the number of word types); 2) the growth of tokens is exponential. Thus, relying on the data from the AMC corpus, it can be concluded that for word-final quadruple clusters the number of occurrences is in direct relation to the type frequency.
The semispontaneous speaking task was conducted only once. After subtracting a few mispronounced and misread items, this resulted in a total of 2,402 analysable target words (SAG + SGG + FR; word-medial + word-final). In order to conduct the acoustic analyses, the recordings were manually segmented and annotated with  STx (Noll et al. 2007 ) on a sentence, word and phoneme level. The duration and intensity values of the following segments were measured and semi-automatically extracted: target words, surrounding words, consonant clusters, individual consonants of the clusters, and phonemes surrounding the clusters. The data was statistically analysed with R (R Core Team 2015) by using mixed-effects models  (Bates et al. 2015). The variables subject and word were included in the models as random factors.
Concerning nouns, no effect occurred for female speakers. For male speakers, however, the phonotactic clusters were shorter compared to the clusters of female speakers and compared to male speakers producing morphonotactic clusters (see Figure  5 ). An articulation-rate*cluster interaction (F(4,470) = 5. 970, p < 0. 001) revealed the same effects as the analyses of the entire dataset (see above). In addition, an effect of variety/age (F(2,12) = 21. 320, p < 0. 001) revealed that speakers of SGG produced the clusters with longer durations compared to both age groups of speakers of SAG. Relative cluster duration (in % of mean syllable duration) When normalizing the cluster duration by using the mean syllable duration, the fitted mixed-effects model showed the following significant effects: a task*articulation-rate interaction (F(1,878) = 9.
Discussion of word-medial consonant clusters One advantage of investigating word-medial clusters is the possibility to compare word pairs of the same grammatical category, as well as to compare French and German consonant clusters, as some consonant clusters exist in both languages, both within morphemes and across morpheme boundaries in a word-medial position. However, there are also several disadvantages: it is not possible to control the phonological context as much as for clusters in homophones; the target words are more diverse, not only in terms of the phonemes surrounding the consonant clusters, but also in terms of the exact position of the consonant clusters within the words, and, most importantly, in terms of the position of the word stress within the German target words. In order to investigate the impact of the grammatical category of the target words on the realization of the consonant clusters, the dataset was restricted to a subset including only word pairs where both items within each pair belong to an identical grammatical category.
CONCLUSION Combining the present findings with analyses of the subsegmental parts of /t/ in word-final clusters  (Leykum & Moosmüller 2015; Leykum et al. 2015b; Leykum & Moosmüller 2018) , conducted on the same material, none of the analyses could prove that morphonotactic consonant clusters are more highlighted or less susceptible to reduction processes. Yet, the present analyses do not prove that phonotactic and morphonotactic consonant clusters are identical in their phonetic realization, since statistically insignificant results do not imply that no effect exists. However, the fact that quite a lot of analyses were conducted on a relatively large dataset, all showing no or no stable effect of the morpheme boundary on speech production, leads us to the conclusion that it is very unlikely that speakers realize morphonotactic consonant clusters in German differently because of the morpheme boundary.
CONSONANT CLUSTERS IN FIRST LANGUAGE ACQUISITION Freiberger (2014) investigated the early acquisition of morphonotactic and phonotactic consonant clusters in three typically developing monolingual toddlers (1 boy, 2 girls) from high SES backgrounds (HSES), who were acquiring Standard Austrian German. They were recorded longitudinally at their homes in Vienna, from 1 year 7 months (1;7) up to 3;0 years. She divided this period into three 5  developmental phases (phase 1: 1;7-2;0, phase 2: 2;1-2;6, phase 3: 2;7-3;0). All of these data were transcribed and coded by using an adapted German version of CHILDES (cf. MacWhinney 2000). Freiberger analysed 180 minutes per child and phase and investigated all correctly and incorrectly produced consonant clusters in the spontaneous speech of these mother-child interactions in word-initial, -medial and -final position.