dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION, CONTRIBUTIONS AND CONCLUSION We started this research endeavour with the observation that currently available ASR systems do not support isiXhosa. We also argued that state-of-the-art approaches to ASR development, with their big data and computational requirements  [51] , are placed out of reach of all but the largest companies, and bypass the very communities that are currently unheard  [12]. However, for the baseline isiXhosa system we developed early on in this programme of work, using more traditional approaches to ASR development, we could only utilise existing datasets. These consisted of a larger, read-speech dataset  [3]  for training and a smaller, code-switched isiXhosa-English TV soap-opera dataset  [54]  for testing. The decision to leverage the more 'in-domain', soap-opera dataset for testing resulted in poorer WER and CER metrics than splitting the larger read-speech dataset into training and testing subsets.
We tailored the SpeechBox with the community liaison, decided on the much discussed topic of COVID-19, and surfaced and addressed usability issues through a formative design workshop. Deploying the SpeechBox in two public locations, residents recorded 318 responses. In contrast, only about 10 % of those respondents completed an online survey that would have earned them a further incentive payment. Workshops revealed further difculties installing mobile apps, which often had cracked screens, not enough storage to install an additional app, and degraded battery life (see  [62] ). Compared to surveys and apps, we contend that the SpeechBox is a more efective and accessible tool to gather responses from people that otherwise face barriers to digital participation. The contents of the audio responses also refected the messiness and dynamic language of social life in Langa writ large that was missing from existing datasets.
Here audio contents consisted of mostly isiXhosa mixed with occasional English terms or phrases (e. g. , 'iCovid', 'iLockdown', or 'iMask'). To involve community members in the transcription of collected audio data, we created the TranscriptTool mobile app to support and scafold the core audio transcription task. Through a formative design workshop we again surfaced and addressed usability issues, then enlisted community members to transcribe the corpus of recordings. Interviews with participants and examination of the transcribed dataset itself revealed that participants completed transcript tasks of often noisy recordings diligently and comprehensively. This contrasts with previous speech-based research in Langa that did not support transcription through bespoke tooling and where transcripts often omitted words and only paraphrased what was said  [45].
Such competitions are a common organising force within many domains of AI-including NLP-to drive the feld forward  [39]. However, engaging directly with community and context, as we have done through our research and with the help of our toolkit, rather than indirectly through competition, also forced us to confront core assumptions of ASR orthodoxy. While community/user engagement-outside of usability labs-has become a central tenant of contemporary HCI research  [47] , this is not (yet) the case in ASR research. So the community-generated dataset, in all its messiness and variability, represents a further, conceptual part of our toolkit. For the dataset gives concrete evidence of the wicked problems ASR developers face when modelling languages that do not appear to have a well-defned standard  [6] , where language speakers have limited practice with the written norms of the language, and where those written norms are intertwined with colonial encounters and have not kept abreast with the vibrancy 13 A top-placed award at a multi-lingual and code-switched challenge for low-resource languages in India  [20].
Introduction Complex catalytic processes in biological systems are often performed by one or more discrete functional components, comprised of a small number of chemical groups in a defined conformation. Those groups are contributed by basic units, the catalytic residues, that have evolved to be arranged in a defined geometry and are brought into close proximity by the folding of the polypeptide chain. Over the years, our group has been collecting and curating information on the structure and function of enzyme catalytic sites, cataloguing over 1000 enzyme families in the Mechanism and Catalytic Site Atlas (M-CSA)  [1, 2]. In this database, we have empirically observed that several enzymes in nature adhere to the "discrete functional entities" paradigm, with a simple example shown in Fig. 1.
Basic statistics of derived templates Fig. 2a  presents some statistics for the template library. In total, we extracted 3953 partial templates (to reflect 813 active sites), providing the principal dataset for all analyses. 3-residue templates are the most abundant (1474), with the number of larger templates decreasing with size (4-residue: 1042, 5-residue: 640, 6-residue: 417, 7-residue: 234, 8-residue: 146). Templates cover all seven EC classes (1: Oxidoreductases, 2: Transferases, 3: Hydrolases, 4: Lyases, 5: Isomerases, 6: Ligases, 7: Translocases), with hydrolases being clearly more numerous, and translocases (only recently added to E. C. nomenclature) occupying only a small fraction. This distribution, reflecting the enzyme functional space covered by UniProt and PDBe. Structural space is also diverse, covering 377 unique superfamilies, as defined at the H level of the CATH domain fold classification  [15]  and 259 unique topologies (T level).
Additionally, ~37% of the template dataset consists of 3-residue templates for which, due to the presence of fewer matching constraints, it is easier to find matches from unrelated proteins. In contrast, larger templates requiring more geometrical constraints, are frequently found in active sites of related proteins. Divergent and convergent templates behave differently when their geometry is compared. In both cases, there is a broad spread of RMSD values. RMSD is sensitive to fluctuations when small coordinate sets (like templates) are compared. It would be expected for RMSD to increase when the template size increases, and this is the case in convergent template pairs, which overall exhibit higher RMSD values than divergent pairs. RMSD is also more variable in 3,4 and 5-residue template pairs, with significantly lower mean values (<1Å) than in convergent pairs, indicating that these functional residues tend to retain a stable geometry during evolution.
Templates are extensively used as tools to identify functional sites in protein structures and for functional annotation  [12, [18] [19] [20]. In Fig. 2c  we benchmark the ability of our template library to identify catalytic residues and examine the effect of template size and template/query evolutionary relationship on RMSD. 6405 non-redundant chains from the PDB  [21] , for which catalytic residues are annotated in M-CSA, were scanned by our library of partial templates using the constraint based structural template matching program Jess  [22]. Searching complete PDB structures means that we find template matches within the active sites, but also distributed around the protein, since some geometrical arrangements of three residues occur quite widely in structures but are not necessarily associated with enzyme mechanism.
Functional diversity is also visible when looking at the extended module DE-H-STY-X-X which includes the two residues forming the oxyanion hole using their backbone atoms. Similarly, generic modules that bind metal ions, for instance DE-DE-DE-K and K-STY-STY, have also remained structurally unaltered while enzyme functions diverged. Those two perfectly exemplify the evolutionary constraints placed by co-factor binding on catalytic residue conformation. The former module is almost always seen to bind Mg 2+ or Mn 2+ ions; three acidic residues (Asp or Glu) in any combination are directly coordinated by the metal, while an amide group contributed by an adjacent Lys residue, acts as a stabiliser of negative charges during this protein/ligand interaction. The need to bind organic co-factors might also lead to convergence during evolution, like in the K-STY-STY module that is observed in crystal structures to interact with NAD and its analogues, making it one of the few examples in our dataset where a consistent residue geometry is coordinated by a flexible organic cofactor.
A limitation of this work is that this library is populated only by data stored in M-CSA. This automatically biases the analyses towards well studied enzymes that include detailed mechanistic information. However, as shown in Fig. 2a , the database covers a large functional landscape in terms of EC classes, therefore the amount and diversity of active site annotations in the current version provides a good start in examining the abundance of modules. There are two ways of expanding this coverage: 1) Use catalytic residue annotations from other resources like UniProt  [53]. This though, comes with a compromise in reliability, since the advantage of M-CSA is the human-hand annotation of functionally important residues, compared to the automated ways used in general purpose resources. 2) Expansion of structural coverage and identification of further convergent evolution examples by scanning experimental and/or predicted  [54] [55] [56]  protein structures with the templates forming the modules.
We are currently developing a protocol to perform exactly this, with the biggest challenge lying in distinguishing true, functional template matches from spurious ones, especially when functional information on the target proteins is limited (e. g. in uncharacterized predicted models from trEMBL  [53] ). Beyond the academic interest that led us to investigate the modularity of active sites, these modules can be used as tools in computational enzymology. A library of functionally characterised, recurring small motifs can be used to identify functional sites in experimental or predicted protein structures. For each module, one or more templates can serve as probes to identify instances of the module in a protein structure  [12, [57] [58] [59] , using template matching software like Jess  [22].
Catalytic site library A library of enzyme catalytic site structures was generated by integrating data primarily from the Mechanism and Catalytic Site Atlas (M-CSA)  [1]  and the Protein Data Bank (PDBe)  [63]. A detailed description of the method for building a catalytic site library is provided in the methods section of our recent work on catalytic site 3D variation  [17]. In brief, curated catalytic residue annotations for 1003 enzyme families were collected from M-CSA, 734 of which included a detailed description of the catalytic mechanism in a computer readable format  [50]. These annotations were used to extract atomic coordinates of catalytic residues from experimentally resolved protein structures (biological assemblies) and a clustering algorithm was applied to reconstruct all catalytic sites.
[68]. Three functional atoms per catalytic residue are used for superposition with the exact definitions shown in Fig. S1. The next step was the calculation of an average active site structure and the selection of a representative template active site having the closest similarity to the average after superposition. After excluding those structures for which a template could not be generated due to insufficient electron density (some residues might be missing from the structure), we ended up with 813 "complete" templates. Those are formatted using a modified PDB nomenclature  [64]  and are compatible with the template matching software Jess  [22, 64].
Template-template search Templates of the same size were cross-compared to capture groups of similar templates. This template-template search was performed in an all-vs-all fashion using the software Jess  [22]. The standard functionality of Jess involves a template and a query structure; in our case the query structures are replaced by templates themselves, permitting the identification of template pairs that share structural similarity up to a defined atom displacement (functional atoms' RMSD). Template matching is fuzzy both at the residue and atom level as mentioned in the previous paragraph, allowing matching of chemically similar residues and atoms (Fig  S1 ). Here again, for residues known to function via their main chain or those which are posttranslationally modified, three backbone atoms of any residue can be matched.
Module annotation After assignment to a module and structural cluster, and subsequent residue reordering, each template was annotated with structural and functional information. Basic information includes template size, mean inter-residue distance and RMSD to the corresponding reference template. The name of a module corresponds to its fuzzy residue composition (e. g. DE-DE-NQ-X), where each residue is written in one-letter code. Alternative residues in a position are written as multiple letters, with X and Z used for unspecific residue types contributing backbone atoms and post-translationally modified residues, respectively. Further annotations were collected from the M-CSA public API, such as EC number  [69] , CATH superfamily  [15]  and functional roles  [70, 71]  for each residue position.
In order to identify ligands interacting with module residues, we utilised the CSA-3D framework of our previous work  [17] , with the exact methodology being described thoroughly therein. For each template in a module, all ligands located 6Å or closer to all catalytic residue (minimum interatomic distance between ligand and residue in 3D space) are extracted and classified in four categories: Metal ions, co-factors (organic or inorganic co-factors composed of more than one atom, such as NAD and Fe-S metal clusters respectively), substrates (small molecule or polymer) and crystallographic artefacts (such as glycerol or ions irrelevant to the catalysed reaction). Classification involves an initial step of ion, co-factor and artefact identification by looking up their PDB three-letter codes in respective curated databases  [45, 47].
Additionally, a score is calculated for each of the three ligand categories in each template: if a site within a template contains at least one ligand of a given category, the corresponding score in the template is incremented by 1. An average score is then calculated at the module level. Last, we added mechanistic information associated with the catalytic site from which each template derives; this information is coded in the form of "the rules of enzyme catalysis", a concept introduced in our recent work  [50]. Each rule represents a complete or partial step of the catalytic mechanism and is coded in a SMARTS string, representing a reaction involving the reaction centres of the catalytic step plus all atoms within two bonds of the reaction centre atoms. We compiled a database of these rules as part of M-CSA, that can be used to search an active site in 3D for possible mechanisms.
The pluripotency transcription factor POU5F1 (OCT4) was expressed similarly in BJ H2B-2FPs cells as in the original ES line  (Fig EV1G) , and also showed a similar growth kinetics (Fig  EV1H ). Together, these data indicate that the BJ H2B-2FPs ESCs stably co-expressed H2B-GFP and mCherry-H2B at low levels, with a similar distribution, and showed identical behaviour to the original ESC line. FRET efficiency images were generated by measuring the decrease in the nanosecond fluorescence lifetime of H2B-GFP due to proximal interactions with mCherry-H2B. We named the monitored chromatin organisation "nanocompaction," since the measured FRET is triggered by nanometre proximity (< 10 nm) of the fluorophore-tagged nucleosomes. Within BJ H2B-2FPs ESC colonies, the cells exhibited comparable H2B-GFP and mCherry-H2B fluorescence intensities (Fig  1B ).
85). This approach has been extensively used to quantify changes in heterochromatin organisation upon drug treatments affecting histone modifications or genetic modifications  (Casas-Delucchi et al, 2012; Gr ezy et al, 2016; Erdel et al, 2020; Martin et al, 2021; Neguembor et al, 2021). We calculated the CV as (σ/μ), where σ represents the standard deviation of the H2B-GFP intensity values and μ the mean value of H2B-GFP intensity of individual nuclei. Importantly, as previously reported, our CV measurements show an increased chromatin density upon ATP depletion and, inversely, decompaction in TSA-treated ESCs  (Appendix Fig S1E; Casas-Delucchi et al, 2012). The combined data confirm that compaction levels measured by FRET depend on internucleosomal proximity and that nanocompaction is regulated by HDAC activity and the metabolic state of the cell.
Histone H2B mobility measurements by FRAP experiments FRAP experiments were conducted using a Zeiss LSM 880 confocal microscope equipped with a 63× oil immersion objective. All chromocenter FRAP experiments were conducted with a pixel dwell of 3. 29 μs, an image size of 124 × 124 pixels and a pixel size of 0. 14 μ m. The 488 nm argon laser (100% laser power) and the laser pulse duration were adjusted to photo-bleach 60% of GFP fluorescence intensity. Time-lapse sequences of single optical sections for imaging fluorescence were collected every second for 180 s. The displacement of the nucleus during the time of acquisition was corrected using a custom-made macro in ImageJ. The fluorescence intensities in the bleached and nonbleached nucleus regions, and background before and after laser photobleaching, were extracted using ImageJ software.
Fluorescence lifetime measurements were acquired over 70s, and fluorescence lifetimes were calculated for each individual pixel in the field of view (256 × 256 pixels). The pixel size is x: 260 nm; y: 260 nm. FLIM analyses were performed using SPCImage software  (Becker & Hickl). FRET causes a decrease in the fluorescence lifetime of the donor molecules (GFP). In all the FRET measurements performed in this study (drug treatments, siRNAs, fixation procedures, cellular differentiation, etc), first, the mean fluorescence lifetime τD of the donor (H2B-GFP) expressed in BJ H2B-GFP in the absence of the acceptor (corresponding to the non-FRET conditions) was calculated by applying a mono-exponential decay model to fit the fluorescence lifetime decays. On the contrary, we applied a biexponential fluorescence decay model to fit the experimental decay curves in the FRET conditions where τDA is the mean fluorescence lifetime of the donor (H2B-GFP) in the presence of the acceptor (mCherry-H2B) expressed in BJ H2B-2FPs.
By fixing the noninteracting proteins' lifetime τD using data from control experiments (BJ H2B-GFP ), the value of τDA was estimated. Then, the conversion of fluorescence lifetime into FRET efficiency for each pixel in the images was achieved according to the formula: FRET efficiency = 1 -(τ DA /τ D ) and the obtained spatial FRET efficiencies were depicted using pseudo-colours at each pixel in a selected region of interest (ROI) using SPCImage software  (Ll eres et al, 2007). FRET distributions were extracted from SPCImage and then normalised and graphically represented using Excel and R programming, respectively. Importantly, we considered the volume of one voxel, that is 0. 25 × 0. 25 × 0. 759 μm = 0. 047 μm 3. Then, we approximated the mean volume of ESC nuclei V nucleus = 733 μm 3 , which corresponds to 15,596 voxels/nucleus.
Comparative image analysis The image analysis pipeline FRENETIC was structured into three parts: segmentation of whole nuclei or sub-nuclear regions using fluorescence intensity signal, analysis of the FRET efficiency using SPCimage software and then pixel-based merging of the segmented matrix (intensity) and FRET efficiency matrix. Briefly, whole ESC nuclei were segmented based on H2B-GFP two-photons' fluorescence or tagged protein intensity. Nucleosome-rich foci were segmented with a manual threshold. Finally, for each associated pixel within the nucleus, the correlation coefficient between intensity and FRET values was analysed. The image analysis software and associated graphical user interface developed for this study are available from the corresponding authors upon request.
Acknowledgements We thank  Patricia Cavelier  for ESC culture and karyotyping. We thank  Dr. Alejandro Vaquero-Garcia  ( Josep Carreras Leukaemia Research Institute, Barcelona ) for the generous gift of pcDNA4T0 HP1α plasmid. We thank  Dr. Eric Julien  ( IRCM, Montpellier ) for the generous gift of HP1β and H4K20me2 antibodies, and  M. Boyer  and  C. Duperray  for the FACS technical assistance. We acknowledge the  "Montpellier Ressources Imagerie" facility  ( BioCampus Montpellier ,  Centre National de la Recherche Scientifique (CNRS), INSERM, University of Montpellier ), a member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 , "Investments for the future"). We are grateful to all members of the Feil.
Comparative Genomic Analysis The genomes of cultivated strains T. elfii DSM9442 and T. elfii subsp. lettingae DSM14385 were retrieved from the NCBI FTP site (ftp://ftp. ncbi. nlm. nih. gov/) under the accession numbers AP014507 and CP000812, respectively. The gene/protein predictions and genome annotation were performed by using the Prokka package  [25]. The clustering of orthologous proteins for core/pan-genomic analysis was carried out by using the Roary pan genome pipeline v 3. 6. 1  [26]  and applying a 50% minimum of sequence identity and coverage, as recommended by Tettelin and colleagues  [27]. The Roary pipeline generated a core gene alignment and gene presence/absence matrix that were used by Roary scripts to retrieve strain-specific genes (not belonging to any orthologous cluster) of T.
elfii DSM9442 and T. elfii subsp. lettingae DSM14385. Each strain-specific protein was carefully checked for functional annotation by using the Conserved Domains Database at NCBI (ftp://ftp. ncbi. nlm. nih. gov/pub/mmdb/cdd (accessed on 6 January 2020)), which includes the COG (Clusters of Orthologous Groups of proteins) functional database (rpsblast of the predicted proteins against COG profiles included in the COG annotation selected for E-value below 10 -5 ). The collinearity of genomes was evaluated via MAUVE tool alignment  [28].
Sequencing libraries were validated by using a DNA Chip on the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) and were quantified by using a Qubit 2. 0 Fluorometer (Invitrogen, Carlsbad, CA, USA), as well as via quantitative PCR (Applied Biosystems, Carlsbad, CA, USA). The sequencing libraries were multiplexed and clustered on six lanes of a flowcell. After clustering, the flowcell was loaded on the Illumina HiSeq 2500 instrument according to the manufacturer's instructions. The multiplexed libraries were sequenced by using paired-end 150 cycle chemistry for the HiSeq 2500 (Illumina). Image analysis and base calling were conducted via the HiSeq Control Software (HCS) v2. 2. 68 on the HiSeq 2500 instrument. Raw sequence data generated from Illumina HiSeq 2500 was converted into fastq files and de-multiplexed by using Illumina CASAVA 1.
5. 3a" ("alignMatesGapMax" = 10,000, and disabling splicing alignment by setting "alignSJoverhangMin" = 999 and "alignIntronMax" = 1)  [31]. Post-alignment statistics were computed with the "RSeQC 2. 6. 4" [32] , "preseq 2. 0"  [33] , and "R 3. 4. 4" package "dupRadar"  [34]. A summary of read counts for annotation categories was computed with "featureCounts"  [35]  from "subread 1. 6. 0". Finally, a FPKM score was computed with "StringTie 1. 3. 3b" [36]  to allow the computation of MDS plots and correlation maps between replicates. All results were compiled into an HTML report by using "MultiQC 1. 6"  [37]. During the analysis, the selected replicates were submitted to a custom R script for differential expression analysis by using the "edgeR" package  [38]. Two sets of samples, including all RNA-seq replicates for P.