dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction DPANN superphylum (an acronym of five candidate phyla names, "Candidatus Diapherotrites", "Candidatus Parvarchaeota", "Candidatus Aenigmarchaeota", "Candidatus Nanoarchaeota", and "Candidatus Nanohaloarchaeota") is a group of archaea with nanosized cell and small genome size  (1) (2) (3). Despite different classifications amid Genome Taxonomy Database (GTDB) and NCBI taxonomy database (4), more and more lineages are classified into the DPANN superphylum, including "Candidatus Micrarchaeota"  (5) , "Candidatus Woesearchaeota"  (6) , "Candidatus Pacearchaeota"  (6) , "Candidatus Huberarchaeota"  (7) , and "Candidatus Undinarchaeota"  (8). The DPANN group formed at the early stage of life's evolution  (8) (9) (10) (11) (12). A general symbiotic lifestyle is proposed from the reduced metabolic potentials of most members  (3, 5, 6, 13).
Moreover, the well-researched "Ca. Nanohaloarchaeota" taxa belong to the family "Ca. Nanosalinaceae", including the two cocultures with Halobacteria  (16, 17). According to 95% AAI and ANI for species boundaries  (31) , six replicated genomes with low completeness or high contamination were removed (Fig. 1 , Table  S5 ). During downloading the genomes, we found that 10 assemblies were classified in the "Ca. Nanohaloarchaeota" (under taxid 1462430) of DPANN group, while 29 were in the class "Ca. Nanohaloarchaea" (under taxid 1051663) of Euryarchaeota in NCBI taxonomy database. However, it is clear that they share a very close relationship with each other. The misinterpretation was considered as the result of inadequate outgroup representation  (1).
Halobacteria-associated symbiosis Considering the genes exchange between "Ca. Nanohaloarchaeota" and Halobacteria as a result of their intimate association with each other in natural habitats  (8) , we detected the putative horizontal gene transfers (HGTs) using the HGTector tool, which is founded on sequence homology search hit distribution statistics  (32). First, we created a GTDB archaeal taxonomy-based database (described in Methods) because the default database follows NCBI taxonomy, in which 29 genomes were incorrectly classified into the class "Ca. Nanohaloarchaea" (taxid 1051663) of Euryarchaeota. Afterward, the HGT events were predicted in the 14 representative genomes of the family "Ca. Nanosalinaceae" by following the tutorials. The result shows that HGT events could be found in them (Table  S6 ), and these genomes harbor high proportions (46.
The root was located at the node between the clade of "Ca. Nanohaloarchaeota" and EX4484-52. The tree structure of the three lineages was in line with the other three phylogenetic analyses based on 122 archaeal single-copy conserved proteins of GTDB, ribosome proteins, and 16S rRNA gene (Figs. S4-S6 ). ALE was performed based on the consensus species tree and 1,629 genes' UFBOOT trees to estimate the frequencies of ancestral events and copies. Then the outputs were parsed using the scripts provided in the previous literature  (41). The output named DTLO table was obtained, and the data on the frequencies of historical events at each node or the last common ancestor (LCA) of the subordinate taxa were exhibited in the "Ca. Nanohaloarchaeota" tree with EX4484-52 as an outgroup (Fig. 5a ). The result revealed that the inferred genome sizes (copy number frequency in DTLO table) sharply increase from node 54 (the LCA of "Ca.
The family "Ca. Nanoanaerosalinaceae" broaden the archaeal diversity of "Ca. Nanohaloarchaeota". This research sheds light on the evolution of symbiosis with Halobacteria, and will provide a model for similar studies in other DPANN lineages. n. hals, (gen. halos), salt; N. L. neut. n. alkali, alkali; N. L. adj. philus -a -um, friend, loving; from Gr. adj. philos -ê -on, loving; N. L. fem. adj. halalkaliphila, salt and alkali-loving). The type material is the metagenome-assembled genome NHA20, whose GenBank accession number (WGS) is JALDAE000000000.
Contig assembly and genome binning Quality control of raw reads of each metagenome was performed using the read_qc module with default parameters of metaWRAP v1. 2. 2 pipeline  (43). Clean reads generated for each sample were individually assembled into contigs using the assembly module of metaWRAP with default assembler MEGAHIT v1. 1. 3 (44) , and short contigs (<1,000 bp) were removed. Three different metagenomic tools for genome binning, CONCOCT v1. 0. 0 (45), MetaBAT2 v2. 12. 1  (46) , and MaxBin2 v2. 2. 6 (47) integrated into the binning module of metaWRAP, were used to recover initial MAGs from each metagenome, respectively. In addition, to obtain more "Ca. Nanohaloarchaeota" MAGs, eighteen metagenomes of brine and surface sediment samples from our previous study  (23)  were re-analyzed by following the genome binning approach as described below.
Genomes collection, quality estimation, and functions prediction The genome sequences of "Ca. Nanohaloarchaeota" and its closely related lineages, including the phyla "Ca. Aenigmarchaeota", EX4484-52, PWEA01, and QMZS01, were collected (Table  S2 ) by October 9, 2021. Firstly, 232 genomes were downloaded from the GenBank of the National Center for Biotechnology Information (NCBI, https://www. ncbi. nlm. nih. gov/) under the following taxonomy IDs: 743724 for the phylum "Ca. Aenigmarchaeota", 1462430 for the phylum "Ca. Nanohaloarchaeota" in the DPANN group, 1051663 for the class "Nanohaloarchaea" in the phylum Euryarchaeota, 2565780 for the unclassified DPANN group. Two genomes of "Candidatus Nanohaloarchaeum antarcticus"  (16)  were obtained via the identification numbers 2643221421 and 2791354821 in the Integrated Microbial Genomes (IMG, https://img.
jgi. doe. gov/). Additionally, 148 archaeal genomes of our previous studies  (23, 28)  were also used. Besides, 2,285 archaeal representative genomes in GTDB were downloaded (Table  S3 ) for phylogeny analysis. Taxonomy was generally pre-analyzed using classify workflow in GTDB-Tk (V1. 7. 0)  (49)  based on Release 202 in Genome Taxonomy Database (https://gtdb. ecogenomic. org/). was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted May 21, 2022. ; https://doi. org/10. 1101/2022. 05. 20. 492899 doi: bioRxiv preprint Subsequently, basic statistics (including contigs number, genome size, N50, N90, and GC content) of genomes were generated using bbstats. sh script (Last modified  July 25, 2019)  in BBTools suite (sourceforge.
25 The putative horizontal gene transfer (HGT) events were computed using the HGTector pipeline version 2. 0b3  (32). Firstly, the GTDB taxonomy-based database for HGTector analysis was built by considering that different genomes of "Ca. Nanohaloarchaeota" were assigned to Euryarchaeota or DPANN group. In brief, the taxonomy file was downloaded from GTDB within Release 202 data, and then it was reformatted into NCBI taxdump style using a Python 3 script named gtdb_to_taxdump. py provided by HGTector contributors (https://github. com/qiyunlab/HGTector). Three files named names. dmp, nodes. dmp and taxid. map were produced. All proteins of 2,339 archaeal representatives in GTDB were directly downloaded from GenBank or predicted based on the genomes using Prodigal (v2. 6. 3) (52). GTDB-based and local prot.
accession2taxid file was created based on the produced taxid. map file and all proteins. The database was built from all the proteins and the GTDB-based taxonomy files using makedb command in DIAMOND v0. 9. 26. 127  (56). After that, a batch homology search was performed using a diamond method for each proteome. HGT events were predicted using the analyze command with the following settings: "self" group, "Ca. Nanohaloarchaeota" (whose taxid in the local GTDB-based taxonomy was 18); "close" group, "Ca. Nanohaloarchaeota" and EX8848-52 (taxid: 20); maximum number of hits, 12; maximum E-value cutoff, 1e-8; minimum percent identity cutoff, 30%; minimum percent query coverage cut off, 50%; bandwidth for Gaussian KDE, auto. Donor's taxonomy of HGT-derived gene was deciphered using lineage command in TaxonKit (v0.
Comparative genomic analysis for ancestral reconstruction The comparative genomic analysis was performed by referring to the published research  (41, 42, 69). Briefly, orthogroups were found from the genome set (20 "Ca. was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted May 21, 2022. ; https://doi. org/10. 1101/2022. 05. 20. 492899 doi: bioRxiv preprint Nanohaloarchaeota" and 9 EX4484-52 genomes) using OrthoFinder version 2. 4. 0 with default settings  (70). The gene counts matrix of orthogroups in the genomes was used to compare the functional profile. Non-metric Multidimensional Scaling (NMDS) analysis was performed using the metaMDS function with default options in R package vegan v2.
5-7 (https://github. com/vegandevs/vegan/). We retained those orthogroups with 4 or more genes according to the previous literature  (69). The genes of identical orthogroup were aligned using MAFFT v7. 407 with L-INS-i method of high accuracy  (71). The columns were removed using the heuristic automated1 method of trimAl version 1. 2rev59  (72) , and then the sequences containing too many gaps were abandoned with the following options: minimum overlap of a position with other positions, 0. 3; minimum percentage of the satisfied positions, 50. For the reconstruction of the species tree, 124 single-copy orthogroups were manually selected that were present in no less than 18 "Ca. Nanohaloarchaeota" genomes (≥90%) and no less than 5 EX4484-52 genomes (>50%). The aligned and trimmed sequences were concatenated using AMAS  (62).
27 ". uml_rec" files of ALE output using the Python scripts set named ALE helper  (41). Ancestral reconstruction tree was visualized using ETE Toolkit version 3. 1. 2 (73). The orthogroups that achieved a threshold of 0. 3 in the raw reconciliation frequencies were counted. This threshold is relaxed but necessary to avoid misses of many true events  (42). A medoid sequence was selected from each orthogroup as the representative for functional annotation. The medoid sequences have the highest sum of similarity scores with all other sequences based on the BLOSUM62 substitution matrix using Protein-Protein BLAST 2. 6. 0+ (66). The process was estimated based on the genes involved in genetic information processing, metabolism, and environmental stress response. Solid with different colors respectively revealed the presence of the process or gene(s) in the three lineages, NHA21, the other four members of "Ca.
Acknowledgements This study was funded by the  National Natural Science Foundation of China  (No. 91751201 ,  32000046 ), and partially supported by the  Senior User Project of RV KEXUE  (No. KEXUE2019GZ05 ) provided by the  Center for Ocean Mega-Science, Chinese Academy of Sciences. The authors declare no competing interests. was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Data availability The "Ca. Nanohaloarchaeota" genomes are available from the NCBI under the BioProject identifier PRJNA797678. DNA sequencing data have been deposited in BioProject identifiers PRJNA549802 and PRJNA679647. Metagenomic sequencing data of 14 enrichment samples are deposited in BioProject identifiers PRJNA769545. Raw data (including protein files, tree files, horizontal gene transfer analysis, comparative genomics files, etc. ) generated in this study are available through [https://doi. org/10. 6084/m9. figshare. 19549495].
Testbed Implementation To validate the feasibility of our proposal, we are currently implementing a proof-of-concept prototype based on the architecture proposed in this paper. The code and results of our simulations are available online, in a companion github repository available at https://j. mp/QuTanksTestbed. The Matlab simulations provide a dataset that we will compare with the testbed and show in a follow up. We are working to add the SDN network in the testbed. This way, real network noises are considered. The testbed works as follows. All the elements can be distributed across several nodes in a shared network using the IEC-60870-5-104 protocol. The feedback controllers (running on a Personal Computer) communicate with three Remote Terminal Units (RTUs) running on three Raspberry Pi boards to control some representative sensors (e.
To start the testbed is necessary to launch a Java program on the controller and the intermediary Java software in the Raspberry Pi boards. The components are connected using an SDN switch Dell S3048 and the Floodlight SDN controller. To test the resilience approach, we implemented a cyber-physical adversary who intercepts the traffic using a Man in the Middle and captures traffic to learn the model parameters described in Equation 2. Then, the adversary uses the model to inject malicious traffic into the network and avoid the detection mechanism, using evasion techniques as those explained in  (Barbeau et al. , 2021). The objective of the adversary is to cause a malfunction in the system by performing actions that affect the control system. The adversary is situated in a remote location but gained access to the internal network exploiting some cyber vulnerabilities and uses the network traffic to perform the attack as an insider.
To address these research gaps, this paper provides a comparative phonetic analysis of pitch variation in English spoken in three Eastern Caribbean islands  (Dominica, Grenada, and Trinidad). It is based on read and spontaneous data from 243 speakers  (64 Dominicans, 110 Grenadians, 69 Trinidadians). In line with the study's focus on English rather than Creole, the data were gathered from students and teachers recorded in a sociolinguistic interview context in a secondary (and tertiary) education setting, a domain generally associated with the use of (standardized) English in the Caribbean. Two other varieties (standardized southern British English and standardized Indian English, with around 20 speakers each) are included for the purpose of comparison: While earlier forms of BrE served as the historical input in the development of English in the Caribbean, standardized southern BrE was the exonormative standard in the colonial period and, to date, sometimes functions as a conservative, external standard in the region, including in the domain of education  (Hänsel et al.
Additionally, the study sets out to provide an exploratory analysis of sociophonetic variation in prosody in Dominica and Grenada, two smaller islands, where, in contrast to Trinidad  (Meer & Fuchs, 2021) , sociophonetic evidence concerning prosody is not yet available. Specifically, the following research questions are investigated: RQ1 To what extent can differences between the Caribbean (Dominican, Grenadian, Trinidadian) versus non-Caribbean (British, Indian) datasets be observed in terms of pitch variation? RQ2 To what extent are there differences between the three Caribbean datasets? RQ3 Which sociolinguistic factors influence variation in pitch across the Caribbean datasets? In the following, we first provide information on the sociolinguistic situation in Dominica, Grenada, and Trinidad and summarize previous research on (socio-)prosodic variation in these territories (Section 2).
Data The present study draws on read and spontaneous English language data from Dominica, Grenada, and Trinidad as well as southern England and India. The data are generally comparable, but small differences exist between the individual datasets concerning recording contexts, size, and distribution of speakers. The collection of the Caribbean data followed the same protocol across territories and involved a total of 243 speakers: 64 from Dominica, 110 from Grenada, and 69 from Trinidad. The speakers are students and teachers; they were recorded in a sociolinguistic interview context in secondary schools (two Dominican, four Grenadian, and seven Trinidadian schools). The Grenadian data additionally includes recordings of students and teachers from two tertiary educational institutions. The interview context was relatively formal and aimed to elicit standardized speech.
In the reading task, speakers were prompted to produce declarative sentences; questions were not included. 3 The spontaneous data consists of semistructured, directed conversation about language topics, including language use in school, at home, or with friends. Teachers were recorded alone, students in pairs. The data were collected by the researchers during field trips in 2015 and 2016. 4 All recordings were made with Zoom H4n recorders equipped with external SHURE cardioid MX150B/C clip-on microphones with a 44. 1 kHz sampling rate and 16-bit quantization. Recordings mostly took place in separate rooms provided by the schools and generally have a good signal-to-noise ratio. The non-Caribbean datasets include 22 speakers from southern England and 20 from India. The British data is taken from the DyViS Project  (Nolan et al.
The spontaneous data consists of staged police interviews involving a map task and mainly prompted speakers to produce declarative utterances. The IViE data consists of a reading passage, which includes a few questions but mostly declarative sentences (88%), and conversations. The gender distribution in the Caribbean datasets is slightly imbalanced toward female speakers (Table  1 ), which is related to the fact that most secondary school teachers in the Caribbean are female  (Trading Economics, 2016). The Indian data is balanced for gender; the British set includes more male speakers. The Caribbean data covers a range of age groups ( Further sociodemographic information is available for the Caribbean speakers (Table  3 ): Approximately half of the teachers (29/59 or 11. 9% of all Caribbean speakers) -but none of the students 7 -had previously lived abroad for over a year, mostly in North America, elsewhere in the anglophone Caribbean, or England.
These speakers were deliberately retained in the corpus, considering that a high degree of transnational mobility is common among educated Caribbean people  (Orozco, 2020). The Grenadian and Trinidadian datasets differ in the number of speakers from urban and rural areas: While the Trinidadian data mostly consists of urban speakers, more rural speakers are featured in the Grenadian dataset (Table  3 ). Information concerning the urban-rural distinction is not available for Dominica. 8.
All speech data was saved in wav format and automatically annotated on the phoneme level with the help of forced alignment (either FAVE or MAUS;  Rosenfelder et al. , 2014; Schiel, 2015). 9 In datasets for territories where phoneme boundaries had not already been corrected in the context of previous research, the locations of utterance boundaries were checked manually. Moreover, for this data, very short pauses of less than 150 ms (which may be erroneously introduced by the forced aligners, for example, in the closure phase of voiceless plosives) were automatically removed. Spot checks confirmed that these cases were indeed not actual utterance boundaries. Subsequently, a dedicated Praat script (available at https://osf. io/qkea5/) determined F0 at 20 ms time steps in Hertz (Hz); this is a modified version of the method used in  Meer and Fuchs (2021) , supplemented by additional measures adapted from  Barbosa et al.
Statistical analysis The statistical analysis followed three approaches. First, cross-territorial differences in the pitch parameters between the five datasets were examined via a series of linear models, 13 one for each pitch parameter in each speaking style (see  Meer & Fuchs, 2021, pp. 13-15  for a detailed description of reasons for this modeling strategy). Overall crossterritorial differences were tested for by including the main effect of TERRITORY. An effect for GENDER was added to control for (i) confounding physiological sex effects  (Keating & Kuo, 2012 ) and (ii) sociolinguistic effects of gender on pitch parameters  (Biemans, 1998). 14 We added an interaction term, TERRITORY*GENDER, since previous research has shown that different types and degrees of gender effects for pitch level, range, dynamism, and other parameters may be found across different languages and varieties  (Cheng, 2020; Pépiot, 2014).
Lastly, ARTICULATION_RATE was added as a covariate in the modeling of tone rate. This was done to avoid potential confounding effects, considering that, all else being equal, a higher articulation rate will lead to a higher tone rate. Articulation rate was operationalized as the number of phones relative to articulation time (excluding pauses;  Gut & Fuchs, 2017). Table  4  presents an overview of all independent variables. Second, we analyzed cross-territorial differences in the covariation of the parameters expressing variation in pitch (that is, excluding pitch level). Thus, we explored to what degree speakers across the datasets from the five territories may simultaneously have wide pitch ranges, very dynamic pitch, high rates of change, high variability in this rate, and a high tone rate.
To that end, we first conducted a principal components analysis (PCA, using varimax rotation) to see how the different acoustic parameters covary and pattern overall. We then computed speaker-specific factor scores for the components extracted (using the regression method for the estimation of scores to maximize their validity; DiStefano, Zhu, & Mîndrilã, 2009) and examined cross-territorial patterns of covariation in the factor space. 4 ). The effects of TERRITORY, GENDER, and TERRI-TORY*GENDER (as well as ARTICULATION_RATE in the case of tone rate) were included again to control for them during the analysis. Educational INSTITUTION was modeled as a random intercept 15 to take into account that speakers were (unequally) sampled from different schools and tertiary institutions. The statistical analysis was conducted in SPSS 27.
All linear models relied on maximum likelihood estimation, using the Satterthwaite method for degrees of freedom correction  (Twisk, 2006, p. 29). The mixed linear models of socioprosodic variation in the Caribbean datasets were built in a (theoretically informed) manual backward elimination process, starting with a full model including the fixed and random effects specified above  (Twisk, 2006, p. 82). Model fit was compared using the Akaike (AIC) and Bayesian information criteria (BIC). In the following sections, apart from observed measures of central tendencies, we report the means and confidence intervals estimated by the linear models for each fixed effect to control for confounding factors. Significant differences between these estimated means were investigated via Bonferroni-adjusted pairwise comparisons.
Spontaneous speech In spontaneous speech, an overall tendency exists such that, across all variability parameters with significant differences, India ranks highest, generally followed by both southern England and Trinidad, with Dominica and Grenada in the lowest position (Figure  1 ). Concerning pitch range, India has the widest range (10. 9 st), followed by southern England (9. 7 st), Trinidad (9. 6 st), Dominica (8. 8 st), and Grenada (8. 8 st); only the differences between India-Dominica (p =. 160) and India-Grenada (p =. 089) approach significance. Regarding rate of change, pairwise comparisons suggest that the datasets cluster in two groups: (i) India (2. 7 Hz), Trinidad (2. 2 Hz), southern England (2. 1 Hz), on the one hand, and (ii) Dominica (1. 6 Hz) and Grenada (1. 5 Hz) on the other hand.