dataesr/scientific-paragraphs-categorization huggingface dataset:
1. 3. 3 (Mai and Mirarab, 2018)  with the false positive error rate set to 0. 05. To avoid unsupported topologies influencing subsequent analyses in ASTRAL  (Zhang et al. , 2018) , internal branches with bootstrap support below 10 % were collapsed using Newick utilities v. 1. 6  (Junier and Zdobnov, 2010) , following optimal thresholds identified by the simulations of  Zhang et al. (2018). Four species trees were inferred from all individual exon or supercontig trees with or without model testing using the coalescent-based method ASTRAL v. 5. 6. 3 (Zhang et al. , 2018). Maximum likelihood analysis of concatenated datasets can estimate species trees more accurately than coalescent-based analyses when the amount of incomplete lineage sorting is low or gene tree error is high  (Mirarab and Warnow, 2015).
We therefore concatenated all individual exon and supercontig alignments, respectively, using AMAS  (Borowiec, 2016)  and then estimated two additional species trees for each concatenated alignment using RAxML with the same settings as above, but with an extended majority-rule criterion for stopping bootstrapping after a sufficient number of replicates had been sampled  (Pattengale et al. , 2010). This threshold was reached after 50 bootstrap replicates for the concatenated exons, and after 100 replicates for the concatenated supercontigs. In addition, we tested the effect of partitioning of the concatenated datasets by selecting partitioning schemes using PartitionFinder v. 2. 2. 1  (Lanfear et al. , 2017)  with a general time reversible model of nucleotide substitution with gamma distributed rate variation among sites and the rcluster algorithm  (Lanfear et al.
Data availability Raw sequence data are deposited in the European Nucleotide Archive of the European Bioinformatics Institute (https://www. ebi. ac. uk/ena) under project number PRJEB40689. Scripts for all phylogenetic analyses are available on GitHub at https://github. com/BenKuhnhaeuser/PhyloFrame. The PhyloPalm target file, alignments, gene trees, and species trees are deposited on Zenodo at https://doi. org/10. 5281/zenodo. 4359280 (Kuhnhäuser et al. , 2020).
Phylogenetic relationships within the Calamoideae The eight species trees inferred in this study resolved identical higher-level relationships but differed at shallower levels (Fig. 3 , Supplementary Fig. 2 a ). Overarching methodologies (coalescence vs. concatenation) and datasets (exons vs. supercontigs) had the strongest effect on differences in tree topology, whereas substitution model testing in the coalescence analyses and partitioning of concatenated analyses had the smallest effects (Supplementary Fig. 2 b ). When only considering overarching methodologies and datasets, the coalescence analyses inferred highly similar relationships, whereas larger degrees of conflict existed between the other possible pairwise comparisons of species tree topologies (Supplementary Fig. 2 b ). All species trees inferred in this study consistently supported the monophyly of all currently recognized tribes, subtribes and genera of Calamoideae, and inferred identical relationships among all tribes and subtribes with maximal support (Fig.
Acknowledgements This research would not have been possible without the generous provision of silica-dried and herbarium material by the  Royal Botanic Gardens, Kew (UK) , the  New York Botanical Garden (USA) ,  Naturalis (Netherlands) , the  Sarawak Herbarium (Malaysia) , the  Forest Research Institute Malaysia , the  Conservatoire et Jardin botaniques de la Ville de Genève (Switzerland) ,  Fairchild Tropical Botanic Garden (USA) , and  Ángela Cano  ( University of Cambridge ). In addition, we thank  Imalka Kahandawala ,  Edith Kapinos  and  Rosie Woods  for providing samples from the DNA bank at the  Royal Botanic Gardens, Kew. We are grateful for support in conducting DNA extractions by  László Csiba , assistance in library preparation by  Michelle Siros , and general phylogenomic lab support by  Robyn Cowan ,  Panagiota Malakasi  and  Dion Devey.
Funding This work was supported by funding of the  Natural Environment Research Council  ( NE/L002612/1 ), a  Clarendon Scholarship  by the  Clarendon Fund and Brasenose College , a  Graduate Student Research Award  by the  Society of Systematic Biologists , and an  Emily Holmes Memorial Scholarship  by the  Amar-Franses & Foster-Jenkins Trust  to BGK; funding by the  Garfield Weston Foundation  under the  Global Tree Seed Bank  project to SB; a research grant ( 00025354 ) from VILLUM FONDEN to WLE; two  Bentham-Moxon Trust  grants ( BMT1-2017  and  BMT36-2018 ) to BGK and WJB; a grant from  IKEA of Sweden AB  to WJB; and grants from the  Calleva Foundation  and the  Sackler Trust  to the Plant and Fungal Tree of Life Project (PAFTOL) at the  Royal Botanic Gardens ,  Kew. Declarations  of interest: none.
1 ). To study the seismic noise variations, we analyze the continuous recordings from the vertical component of velocity and accelerometer sensors of the RSN stations that cover the time period from 4 March 2019 to 27 September 2020. The data processing was performed following the approach described by  Lecocq et al. (2020a)  and using the publicly available SeismoRMS software package  (Lecocq et al. , 2020b). The probabilistic power spectral density (PPSD) acceleration amplitudes  (McNamara and Buland, 2004)  are computed for each day using 1800 s time windows with 50 % overlap. The PPSDs are then converted to displacement spectral powers and finally, using Parseval's identity, to the displacement root mean square (DRMS) in the frequency domain of interest. We investigate the changes in the DRMS in four frequency bands: 2-8, 4-14, 15-25 and 25-40 Hz (hereinafter referred to as Band 1, Band 2, Band 3, and Band 4, respectively).
S1 ). However, for one particular station (MLR), the noise reduction was investigated on the broadband velocity sensor. With the crisis caused by the COVID-19 pandemic, Google and Apple have made the mobility data they have used in their map products available to authorities and the general public to mitigate the effect of the emergency situation. To analyze the potential connections between seismic noise variation and mobility data across Romania, we used both data sets (Google and Apple mobility data) for two time intervals, 15 February-27 September 2020 and 13 January-27 September 2020.
In total, 54 stations show noise reduction in Band 4. From Fig. 2  we also observe that the reduction of noise is weaker for the stations in less populated and remote areas. At these stations, the drop in seismic noise varies between 10 % and 20 %, except for the station CJR where the noise decreased by 34 % in Band 4 during the lockdown. Analysis of the data over a longer time period allowed us to compare COVID-19 related noise changes with those observed during Orthodox celebrations (Easter and Christmas), as well as the summer and winter holidays. During these periods, human mobility and activities decrease considerably compared to normal weekdays. In Fig. 3 , we show the evolution of the DRMS at four stations during the March 2019-September 2020 period for different frequency bands. The stations are installed in different locations: DJISU (Fig.
Therefore, any reduction in anthropogenic seismic noise improves the detection capability of urban stations  (Lecocq et al. , 2020a) , and such an increase in detectability would translate into more data for research on seismic risk reduction in earthquake-prone cities. The reduction of seismic noise during the Romanian lockdown could also favor an increase in the earthquake detection capability. Figure  13  shows examples of unfiltered accelerograms (from two sensors sited in urban areas) of two moderate (ML = 3. 8) intermediate-depth (∼ 116 km) earthquakes from Vrancea. One of the earthquakes occurred before the lockdown, on 3 August 2017 (Thursday) at 13:13:16 local time, and the other during the lockdown on 18 April 2020 (Saturday) at 19:17:03 local time. It is worth noting that seismic signals are clearly recorded for the earthquake generated during the lockdown despite anthropogenic noise usually masking earthquake signals from a local event of this size.
In these cases, the seismic noise level was reduced less than during the religious celebrations, suggesting that the traffic during the lockdown period was not as diminished in comparison with the days off associated with Easter and Christmas 2019. Our results finally reveal that noise reduction caused by the measures taken to mitigate the COVID-19 pandemic may indicate a potential improvement in the earthquake detection capability of the accelerometers located in noisy urban environments. Therefore, we consider it as essential to continue the efforts to reduce the seismic noise in the seismological data acquired by urban stations, as these efforts would lead to the improvement of seismological databases used for seismic risk reduction in earthquake-prone cities.
Code availability. This work has benefited from open-source initiatives such as Obspy (https://doi. org/10. 1088/1749-4699/8/1/014003, Krischer et al. , 2015)  and QGIS-A Free and Open Source Geographic Information System (https://qgis. org/en/site/, QGIS. org, 2021). Data analysis has been done using the publicly available SeismoRMS code kindly distributed by Lecocq et  al. (2020b)  (https://doi. org/10. 5281/zenodo. 3820046). Data availability. Part of the seismic data used in this study are available from the FDSN service, provided by the Romanian Seismic Network of the National Institute for Earth Physics: https://doi. org/10. 7914/SN/RO (National Institute for Earth Physics (NIEP Romania), 1994). For the stations that are not available through FDSN, the authors have used data provided by the NIEP's Data Center, which are available upon request.
The mobility data for Bucharest were provided by Apple (https://covid19. apple. com/mobility, last access: 29 September 2021), and for all the large cities in Romania data was provided by Google (https://www. google. com/covid19/ mobility, last access: 29 September 2021). The authors are very grateful to Iren Adelina Moldovan from NIEP for providing the meteorological data recorded at Muntele Rosu. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/se-12-2351-2021-supplement. Author contributions. BG designed the study, and all the authors contributed to the manuscript. The data processing was performed by all authors with the following division of responsibilities: BG and DT processed the local accelerometric data from urban areas, NP processed the data available through FDSN, AT processed the data in schools and buildings, and RD and FB processed the local free-field seismic data.
The identification of subnetworks of interest-or active modules-by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e. g. , their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins.
subnetworks of interest, aka active modules. We here propose MOGAMUN, a multiobjective genetic algorithm that optimizes both the overall deregulation and the density to identify active modules, considering jointly multiple sources of biological interactions. We demonstrate the performance of MOGAMUN over state-of-the-art methods, and illustrate its usefulness in unveiling perturbed biological processes in Facio-Scapulo-Humeral muscular Dystrophy. This is a PLOS Computational Biology Methods paper.
Many tools exist that can take as input a list of genes, selected after defining a threshold for significance or ranked according to their p-values  [1]. Such enrichment approaches will consider only the genes/proteins annotated in databases. Another set of successful approaches try to overcome this limitation by integrating scores or measures with biological networks. Biological networks are composed of nodes representing the biological macromolecules, often genes or proteins, and edges representing physical or functional interactions between those macromolecules. The goal is to identify active modules, i. e. , subnetworks enriched in interactions and in nodes of interest. These active modules then facilitate the investigation of the perturbed cellular responses, as functional modules are the building blocks of cellular processes and pathways  [2].
The identification of active modules from networks is an NP-hard problem  [3] [4] [5]. Some active module identification algorithms are based on clustering co-expression networks  [6, 7]  or memetic algorithms  [4]. However, most approaches rely on greedy searches, simulated annealing, and genetic algorithms (see  [2]  and  [8]  for general surveys of active module identification methods). Algorithms based on greedy searches, such as PinnacleZ  [9]  and MATISSE  [10] , follow three general steps: i) selection of seed(s), ii) expansion of seed(s), and iii) significance test. In the selection of seed(s), a set of genes of interest (for instance, significantly differentially expressed genes) are picked. Then, the seed(s) are iteratively expanded (adding one node at a time), following a greedy criteria, i.
e. choosing the node in the network neighborhood of the seed(s) that maximizes a score, which improves the module fitness. The expansion stops when any of the following three conditions is met: 1) the improvement of the score of the subnetwork is below a minimum threshold, 2) the subnetwork reached a maximum size, or 3) a maximum distance from the seed(s) is reached. As a last step, the subnetworks are tested for significance, by comparing the score of each subnetwork with the score of a random subnetwork. These three steps are common to greedy searches algorithms, but every method has variations. For instance, the seeds selected by PinnacleZ are single nodes, whereas MATISSE selects connected subnetworks. The main drawback of greedy searches is that they can get trapped in local optima because at every step they only look at the local options.
In particular, they cannot pick low scoring nodes, even if these can be key for escaping local optima and have access to several high scoring nodes. Methods based on simulated annealing, such as jActiveModules  [3] , follow a hill-climbing philosophy, but instead of always picking the best option, i. e. , the best neighbor node to be added to the subnetwork, they can also choose unfavorable options (i. e. options decreasing the global score), and thereby escape local optima. Algorithms based on simulated annealing follow two steps: i) initialization of nodes states, and ii) toggling of nodes states. In the initialization of nodes states, each node in the network gets either the active or inactive state, with a given probability. The set of active nodes constitutes the initial subnetwork, and the subnetwork's score is calculated as the aggregated score of its nodes.
Then, in the second step, the nodes states are toggled: in every iteration, the state of a random node is changed from active to inactive, or vice versa. If the toggling improves the score of the subnetwork, it is always accepted; otherwise, it is accepted with a probability calculated based on the temperature parameter, which decreases gradually in every iteration. After toggling states for a given number of iterations, the highest scoring subnetwork found in any iteration is given as result. Some algorithms, such as jActiveModules, have a third step to evaluate the significance of the final subnetwork by comparing its score to scores obtained on randomized expression data. The main drawback of simulated annealing is that the bigger the input network is, the more iterations are needed in order to explore the full search space.
As an option, one can design customized crossover and mutation operators, as in  [12, 13]. Importantly, genetic algorithms are capable of optimizing multiple (often conflicting) objectives simultaneously. If the problem is tackled as mono-objective, all the objectives are added into a single objective function by considering weights for each one of them, and the result is usually a single solution. In contrast, if the problem is defined as multi-objective, each objective is associated with an independent objective function, and the result generally leads to several solutions that provide a trade-off for the values of the different objective functions. By definition, active modules are expected to be enriched in interactions. However, to our knowledge, only few methods, such as SigMod  [14] , consider the density of interactions.
Benchmark to compare the performance of MOGAMUN with existing methods In order to compare the performance of MOGAMUN with state-of-the-art approaches, we used and extended the benchmark initially proposed by Batra et al. [29]. It is worth noticing that this benchmark works with a single interaction network as, to our knowledge, no active module identification method can consider multiplex networks. It artificially generates expression data to simulate a differentially expressed subnetwork. To this goal, the nodes of the network (i. e. , the genes) are separated into two groups: Foreground Genes (FG) and Background Genes (BG). A seed-and-select algorithm is defined to randomly select the FG as a connected subnetwork. Such an algorithm selects a random seed and proceeds to iteratively add one node at a time to the subnetwork, by picking it up from the list of neighbors, such that the subnetwork remains connected.
Benchmark artificial expression data. We simulated 2 different artificial expression datasets, one following a normal distribution and another one sampled from real RNA-seq data, as follows: 1. Sim_normal. We simulated expression data following a normal distribution. The mean values μ of the FG and BG groups of genes are μ(FG) = 5, and μ(BG) = 2, respectively, and a standard deviation SD = 1 for both groups of genes. This situation corresponds to a high signal strength, as in  [29]. To test for differential expression, we performed a series of ttests, and considered a gene as significantly differentially expressed if the p-value �0. 05. We used a set of 20 FG, and did not correct the p-value for multiple testing in order to make the test more challenging.
2. Samp_TCGA. We sampled data from real expression data, in order to have an RNA-seq distribution-like. We downloaded breast cancer RNA-seq expression dataset from The Cancer Genome Atlas breast cancer project (TCGA-BRCA) from the US National Cancer GDC portal (https://portal. gdc. cancer. gov/), as of May, 2019. This dataset is composed of 1 102 cases and 112 controls (we removed the outlier control sample "d5f0ea64. 6660. 49ac. a37e. 3cd747045147"). To test for differential expression, we used the R package edgeR version 3. 26. 8 [33]. We consider a gene to be significantly differentially expressed if its False Discovery Rate FDR �0. 05 and |log 2 (FC)| > 1, where FC (Fold Change) is the ratio of the difference in expression between cases/patients and controls. The expression data for the FG and BG were randomly sampled from the set of significantly differentially expressed and non-differentially expressed genes, respectively.
We used a set of 20 FG. In Table  3  we describe our two datasets. Columns "Cases" and "Controls" show the number of patients/cases and controls, respectively, "Genes" shows the total number of genes in the simulated dataset, corresponding to the total number of nodes in the networks, and "Significant DE" is the number of significantly differentially expressed genes. 2. 4. 3 State-of-the-art algorithms selected for comparison. We compared MOGAMUN with three selected methods, representative of the main approaches seeking for active modules: jActiveModules  [3] , PinnacleZ  [9] , and COSINE  [11]. jActiveModules. Ideker et al. [3]  proposed jActiveModules. They use a simulated annealing algorithm to find subnetworks with the highest scores, calculated from the differential expression of the subnetwork nodes.
jActiveModules is available as a Cytoscape plugin. COSINE. Ma et al. [11]  proposed COSINE, a method based on a standard mono-objective genetic algorithm. The goal of COSINE is to find the subnetwork with the highest change in expression among conditions, represented as node weights. COSINE further allows considering the level of co-expression between pairs of genes, represented as edge weights. To compute the edge weights, we calculated the co-expression of every pair of nodes connected in the benchmark networks. COSINE further allows giving more importance to either the weights of the nodes or the edges, with a parameter lambda. For the tests performed here, we used the same parameters as reported in  [11] , where COSINE is compared with other methods (number of iterations = 5000; zero to one ratio = 30), and we set lambda to 0.
PinnacleZ then performs three tests of significance on each of the identified active subnetworks, in order to guarantee that their individual mutual information is higher than the mutual information of a random subnetwork. We used the same parameters reported in  [9]  (distance from the seed = 2 nodes, minimal mutual information score improvement threshold = 0. 05), and we set the maximum size per subnetwork = 50 (the same size that we allowed for MOGAMUN). PinnacleZ was originally available as a Java program and a Cytoscape plugin, but this latter one is no longer supported.
Application to Facio-Scapulo-Humeral muscular Dystrophy type 1 (FSHD1) 2. 5. 1 RNA-seq expression data. We used five Facio-Scapulo-Humeral muscular Dystrophy type 1 (FSHD1) RNA-sequencing expression datasets publicly available  [34] [35] [36] , extracted from the Gene Expression Omnibus  [37]. We performed the differential expression analyses using the R package edgeR version 3. 26. 8 [33]. As recommended in the user guide of edgeR, we performed glmQLF tests for the two datasets with samples from different batches  [35, 36] , and Fisher Exact tests for the three datasets with samples from a single batch  [34]. We considered a gene as a significantly Differentially Expressed Gene (significantly DEG) if the False Discovery Rate FDR �0. 05 and the |log 2 (FC)| > 1, where the FC (Fold-Change) is the ratio of the difference in expression between cases and controls.
These cells were on one hand derived from a mosaic patient and described in Krom et al. [39]  (54-12; 54-45; 54-2 for FSHD1 cells with 3 D4Z4 units and 54-6; 54-A10 as controls with 13 D4Z4 units). On the other hand, the 12Ubic and 16Ubic cells obtained from two FSHD1 patients and the 12Abic and 16ABic cells from matching controls are described in Homma et al. [40]. We identified 192 significantly DEGs comparing all the FSHD1 to control samples. The last dataset was obtained from Banerji et al. 2019  [36]  and corresponds to myotubes collected at the end of myoblasts to myotubes differentiation. These myotubes are derived from the myoblasts described in Banerji et al. 2017  [35]. In Banerji et al. 2019, a time course expression during differentiation was analyzed. We considered here only the last time point (T8) and selected triplicated samples for 5 FHSD1 patients and 4 controls (Supplementary Table  S3  in S1 File).