dataesr/scientific-paragraphs-categorization huggingface dataset:
Bibliometric Mapping and Clustering This study is based on the latest advancements in science mapping, to provide the most comprehensive and systematic review of bio-inspired robotics marine research. While a traditional narrative review may base its findings on 50-200 studies subjectively chosen by a researcher, this study uses the entire Elsevier Scopus database with a statistically formal approach that has slimmed down a list of 6980 articles on since 1950. The results are robust and reproducible, which infers the reliability of the offered methodology. This is a modern and quantitative way of reviewing a subject area  [60]. A general quantitative description of the bibliographic records was conducted, based on all the publications returned by the query. A word cloud of country names with different font sizes as a proxy of the number of publications (based on co-authors affiliations), was constructed.
The number of publications for a single country was computed considering all the co-authorships of each published item. Bibliometric maps were created on retrieved publications, using the VOSviewer software version 1. 6. 5. 0. The software was specifically developed for creating, visualizing and exploring science's bibliometric maps  [61]. A term-map, also called co-word map, is a two-dimensional representation of a research field. The larger the number of publications in which two terms occurring in titles, abstracts and keywords, co-occur, the stronger the terms are related  [49] [50] [51] 62]. In a term-map, strongly related terms are located close to each other, and the weaker the relationship is between terms, the bigger the distance is between them  [63]. For this purpose, VOSviewer uses a clustering technique that can be seen as a kind of weighted variant-of-modularity-based clustering  [64, 65].
Journals, Subject Categories and Countries A total of 6980 publications were retrieved from the Scopus database encompassing 70 years (i. e. , 1950-2020). The selected pool of scientific references includes 63. 1% of them are research papers, while the remaining 36. 9% are distributed in this way: 23. 7% of conference papers, 8. 9% reviews, 3. 1% book chapters, and 1. 3% is of miscellaneous origin (e. g. , letters to editors, short communications, notes). The query results showed that a total of 146 journals published at least eight or more papers in the field of biomimicking. The top ten journals, in terms of the number of papers, are reported in Table  1 , and the top three are the following: Bioresource Technology (1991; n = 167; 2. 39%) Bioinspiration and Biomimetics (2006; n = 147; 2.
Limitations of Our Bibliographic Meta-Analysis There are some limitations in the term-map analysis regarding both the production and the interpretation of results. There are mainly two kinds of limitations: those generated by the data and those imposed by the map. For the first aspect, during the creation of the bibliometric dataset, the record's availability could have been somehow limited mainly due to the arbitrary keywords' choice for the primary search. Also, regarding this aspect, synonyms and homonyms represent another kind of problem that could arise. Although this problem was solved with an accurate manual thesaurus polishing by merging the synonymous terms (see the methodological section "Bibliometric Mapping and Clustering"), more advanced approaches can be used based on the formal conceptualization of the marine science domain  [130].
For example, the Semantic Web for Earth and Environmental Terminology (SWEET) ontology  [131]  and the Natural Environment Research Council (NERC; https://vocab. nerc. ac. uk/ assessed on 28 May 2021) vocabulary could be used for the automated merging of the terms resulting from the abstract, titles and keyword analysis. Similarly, natural processing language techniques can also be used to identify the most relevant terms, on which the map is built up  [132, 133]. For the second aspect, the interpretation of a bibliometric map is not always straightforward. A term-map represents a simplified version of reality, leading to loss of information and a partial representation of the investigated field  [134]. VOSviewer generates such an inevitable loss of information due to the two-dimensional representation of the terms in a Euclidean space.
Funding: This research was developed within the framework of the  Tecnoterra Associate Research UNIT (ICM-CSIC/UPC)  and the following project activities:  ARIM (Autonomous Robotic sea-floor Infrastructure for benthopelagic Monitoring; MartTERA ERA-Net Cofund ), and  RESBIO  [ TEC2017-87861-R ;  Ministerio de Ciencia, Innovación y Universidades ; PI: J. A. ]. S. M. was funded by the projects:  ENDURUNS [ERC Research Grant  Agreement  H2020-MG-2018-2019-2020  n. 824348] and by  JERICO-S3 [Horizon 2020 ; Grant Agreement n° 871153]. H. I. G. acknowledges the  University of Nottingham  for her funding through an  Anne McLaren Fellowship. This work also acknowledges the ' Severo Ochoa Centre of Excellence ' accreditation to ICM ( CEX2019-000928-S ).
The FWI is applied to woodwind instruments, allowing an explicit characterization of the gradient of the cost function. Considerations regarding the observable are carefully laid out for the case of an elementary instrument in Section 4, resulting in a reconstruction strategy applicable to measured impedance data. The performance of this technique is then discussed, and a conclusion is given in Section 5. All the tools presented here are implemented and available in the open source software OpenWInD  [20]  under GPLv3.
Here, with i 0 the index of the degree of freedom corresponding to the pressure at the entrance of the instrument: R i ¼ S 1 ð0Þ qc if i ¼ i 0 0 otherwise ( ð19Þ with S 1 ð0Þ the cross sectional area at the entrance of the instrument. The input impedance is computed for each of the N x frequencies and for each of the N fing fingerings (N x Â N fing matrix inversions) using the python 3 toolbox OpenWInD  [20].
Moreover, unlike the adjoint-state method, the method presented here enables the Jacobian of the residual to be computed: r n rðnÞ ¼ dr dZ ðnÞR oU on j ðnÞ: ð30Þ The knowledge of this Jacobian allows the simple computation of an approximation of the Hessian: H % r n r T r n r such as used in optimization algorithms specifically suited for least-square problems (e. g. Gauss-Newton or Levenberg-Marquardt)  [35] , Chap. 10). In this study the optimizations are performed using the trust region reflective algorithm implemented in the Scipy python module, which takes advantage of this approximation.
(C) A clonogenic assay reveals survivors following a DSB at a chromosome-internal locus in the parental and 1 H Rrad50 cell lines. 96-480 cells were plated out into media with or without tetracycline and assessed after 7 days of growth. Box-plot centre lines show the medians; box limits indicate one standard deviation. The proportion of survivors was calculated by dividing the number of induced survivors by uninduced. SD from seven or more 96-well plates: 1 HR uninduced 7, 1 HR induced 8 and for 1 HRrad50 uninduced 9, 1 HRrad50 induced 8. P-value calculated using a Student t-test. White boxes, genes; RFP:PAC, red fluorescent protein: puromycin fusion gene; black boxes UTR. a C1000 Touch Thermal cycler (Bio-Rad), using the following PCR cycling conditions: 95 • C for 3 min, followed by 40 cycles of 95 • C for 10 s and 55 • C for 30 s and fluorescence intensity data collected at the end of the last step.
Data was then analysed by relative quantification using the Ct method (CFX Maestro software -Bio-Rad). ssDNA at each time was quantified by subtracting the mock digested from the HindIII digested sample to give the Ct and then using the following formula from  (65)  where f is the fraction cut by I-SceI: % resected = [100/[(1 + 2 Ct )/2]]/f. For the 1 HR cell lines primer pairs to detect ssDNA in the RFP:PAC fusion gene are RFPF. 2 (CTCG CGGTGCTGATTTCTG) and RFPR. 2 (TGAAGCGCAT GAACTCCTTG). For the VSG up cell lines, the primer pairs used are VSG2F1b (AGGCCAAGAAAGCGCTAACA) and VSG2R1b (CCACTGGCTGCTCGGATATG).
03 and 9. 01 million reads for VSG UP uninduced, 7. 60 and 7. 22M for VSG UP induced, 6. 66, 7. 07M for VSG UP rad50 uninduced, 6. 90 and 6. 98M for VSG UP rad50 induced, 4. 6M reads for each VSG UP mre11 uninduced and 4. 6, 4. 6 and 3. 9M reds for VSG UP mre11 induced. Reads were aligned to the T. brucei Lister 427 genome  (15)  with the cohort of minichromosomal VSGs added from the Lister 427 VSGnome (13) using bowtie2  (66)  with the parameters--very-sensitive and BAM files created with samtools  (67)   (68). Differential expression analysis was performed using EdgeR (69) on all genes, followed by filtering for VSG genes (1848 VSG sequences in total). An R script (https://github. com/LGloverTMB/DNArepair-mutant-VSG-seq) was used to perform differential expression analysis, and generate Volcano and genome scale plots.
BLAST analysis was performed locally using a database containing all VSG genes, including 2 kb of sequence upstream and downstream of the start and stop codons, respectively (except where sequences in the contigs 5 or 3 to the CDS were shorter than 2 kb exclud-ing). The resulting database of 2,153VSGs was queried using the VSG2 sequence including 2 kb of sequence upstream of the CDS and all sequence between the stop codon and end of contig (1658 nt). The BLASTn algorithm was used query the database using default parameters except allowing 1 hit per subject sequence. Alignments were plotted using a custom R script (https://github. com/LGloverTMB/ DNA-repair-mutant-VSG-seq). Lengths of average alignments were calculated for cohorts of VSGs up-regulated in each VSG up , VSG up rad50 or VSG up mre11.
8%, respectively (Figure  5B ). However, in contrast to 1 HR cells, when we assessed induced cell survival as a proportion of the uninduced, there was a significant (P = 0. 0053) increase in survival of the VSG up rad50 nulls (Figure  5B  and Table  1 ). These data indicate that RAD50 suppresses DSB repair at a VSG-ES, the opposite of its role at a chromosome-internal DSB. We then assessed the DDR in the VSG up cell lines. As in the 1 HR cells, following a DSB, the number of cells that accumulate in G 2 /M increased to ∼25% in the VSG up WT cell line  (27) , and this cell cycle checkpoint was lost in the VSG up rad50 cells (Figure  5C ). A similar pattern was seen with ␥ H2A, with the number of foci increasing from 9% to 35% after I-SceI induction in the VSG up WT cell line  (27) , but in the VSG up rad50 nulls, only increasing from 5% to 12% following a DSB (Figure  5D ).
This suggests that I-SceI cutting efficiency is comparable across all three cell lines. We then looked at surface expression of VSG2 by immunofluorescence. All uninduced subclones tested were VSG2 positive (data not show) as expected, as no DNA break had been induced, whereas in the induced panels, all VSG up (20 tested) and VSG up mre11 (20 tested) sublcones were VSG2 negative (Figure  7C ). Of the 24 puromycin sensitive VSG up rad50 subclones, 23 were VSG2 negative (Figure  7C ); a single puromycin resistant clone was VSG2 positive, suggesting I-SceI did not cut (Supplementary Figure  S4 , subclone 14). These data suggest that loss of neither RAD50 nor MRE11 affected the cell's ability to undergo VSG switching. We then used PCR to look at DNA rearrangements in the BES using primers specific to VSG2 and ESAG1.
05). In the VSG up rad50 cell line, a greater number of VSGs were detected: here 187 VSG transcripts were significantly enriched after I-SceI induction (Figure  8A ). Surpris-ingly, given the resection defect, 230 VSG transcripts were detected in the mre11 nulls, suggesting a similar alteration in VSG switching to that seen in the rad50 null cells (Figure  8A  and B ). To understand if the increased VSG transcripts in the rad50 and mre11 nulls arose from the same change in VSG switching, we looked at the genomic position of the VSG cohorts. In the VSG up cells, we found that approximately equal numbers of enriched VSGs mapped to the VSG-ES (Figure  8B , and inset showing the silent BES VSGs) and minichromosomes relative to the megabase VSG arrays, despite the much greater number of genes in the latter component of the archive (Figure  8B , Supplementary Figure  S6A ).
More broadly, the increased survival and corresponding VSG switching data may also suggest an unconventional signalling pathway following a DSB at the active VSG-ES that allows the cells to bypass the conventional MRN/ATM pathway. One way that might occur is that the DSBs in the VSG-ES do not arise directly, but are generated by routes such as clashes between replication and transcription  (31) , or the formation of RNA-DNA hybrids (97-99), which might not elicit recruitment of MRN or ATM  (100, 101). Such a scenario may explain why previous work did not detect a change in VSG switching after loss of MRE11  (52). Nonetheless, our data reveals that VSG up rad50 and mre11 null cells are able to access more of the genomic VSG archive for repair, and the repair reaction in the absence of RAD50 or MRE11 occurs as a result of recombination using shorter stretches of homology.
ACKNOWLEDGEMENTS A-K. M. , M. P, A. D. -H. and L. G. performed the experiments, S. H. performed the VSG-Seq data analysis, R. M. and L. G. discussed the results and wrote the manuscript. A. -K. M. , S. H. , R. M. and L. G. edited the manuscript. We would also like to that  David Horn  for discussions and support.
FUNDING A-K. M. was supported by the  Erasmus + program  of the  European Union ; work in the L. G. laboratory has received financial support from the  Institut Pasteur  ( G5 Junior group ) and the  National Research Agency [ANR -VSGREG ]; aspects of the work in this grant was support by the  Wellcome Trust  [089172, 206815]; further work in R. M. 's lab was supported by the  BBSRC  [ BB/K006495/1 ,  BB/N016165/1 ];  Wellcome Centre for Integrative Parasitology  is supported by core funds from the  Wellcome Trust  [104111]; S. H. is a  Marie Curie fellow , this project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  [794979]. Funding for open access charge:  Institut Pasteur   G5  funding.
DATA AVAILABILITY All scripts are hosted on GitHub: https://github. com/ LGloverTMB/DNA-repair-mutant-VSG-seq. All data has been deposited onto the ENA under study accession number: PRJEB37290 and unique study name: ena-STUDY-INSTITUT PASTEUR-15-03-2020-20:11:26:661-115. Sanger sequencing of 1 HRrad50 MMEJ PCR products on Zenodo: 10. 5281/zenodo. 4109616.
A3 Backazimuth analysis We also determine the dominant icequake backazimuth by using the previously defined templates. We use an approach based on a singular value decomposition (SVD) of a complex covariance matrix  (Greenhalgh et al. , 2018). Figure  A4  shows a polar histogram (results of backazimuth calculations are grouped into regular bins of 30 • ). 15 clusters are pointing either towards the back of the glacier (90-180 • ), where a crevasse is visible and where glacier is not frozen to the bed or the glacier front (considering the 180 • backazimuth ambiguity). The backazimuth is calculated from the north (0 • ). The complex covariance matrix is formed over 0. 05 s window of data to extract polarized seismic arrivals. The minimum time-window length for the covariance matrix calculations should be at least half of the dominant period to ensure stable backazimuth estimates  (Greenhalgh et al.
Acknowledgements. This work was funded by the  Swiss National Science Foundation (SNSF)  project  Glacial Hazard Monitoring with Seismology  (GlaHMSeis, grant  PP00P2 157551 ). We thank  John Clinton ,  Roman Racine , and  Stefan Wiemer ; the  Swiss Seismological Service and its electronic laboratory (ELAB)  for technical support and data archiving. We thank  Geopraevent AG  for allowing us to use their.
Code and data availability. Obspy Python routines (www. obspy. org) were used to download waveforms and pre-process seismic data. REDPY can be downloaded from: https://github. com/ahotovec/REDPy, and FMF from https://github. com/beridel/fast_matched_filter. The data from the EIG network is collected under the network code 4D (https://doi. org/10. 12686/sed/networks/4D) and all seismic data available on request in the archives in the Swiss Seismological Service, http://www. seismo. ethz. ch/en/research-and-teaching/products-software/fdsnweb-services/ and the European Integrated Data Archive (EIDA), http://www. orfeus-eu. org/data/eida/. Interferometric radar data supporting this research have restricted availability at https://data. geopraevent. ch/index. php. To gain access, please contact Lorenz Meier.
FUNDING This study was supported by the  French National Research Agency  ( Projet-ANR-19-CE35-0004 ), the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement no. 731060  ( Infravec2 , research infrastructures for the control of vector-borne diseases; http://infravec2. eu/),and the  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  (Grant No. ANR-10-LABX-62-IBEID ).
Discussion Here we used whole-brain models to address a fundamental question in neuroscience of the origin of the fluctuations and oscillations found in global brain dynamics. We used strength-dependent perturbations of the whole-brain model fitting the empirical data to give a causal mechanistic description of human brain function, showing that this is generated by a delicate balance between fluctuation and oscillation on the edge of criticality. These results were obtained using the large-scale Human Connectome neuroimaging dataset of 1003 participants, which were subsequently used for massive computational whole-brain modelling studies. Crucially, the underlying causal mechanistic principles of empirically reported modulation of specific resting state network can be explained by stimulation in the fluctuating regime.
Structural connectivity using dMRI The structural connectivity was obtained from the Human Connectome Project (HCP) database, which contains diffusion spectrum and T2 weighted imaging data from 32 participants. The acquisition parameters are described in detail on the HCP website. Briefly, the neuroimaging data were processed using a generalised q-sampling imaging algorithm developed in DSI studio (http://dsistudio. labsolver. org). A white-matter mask was estimated by segmenting the T2-weighted images and images were co-registered to the b0 of the diffusion data by using SPM12. In each participant, 200,000 fibres were sampled within the white-matter mask. Fibres were transformed into MNI space using Lead-DBS  (Horn & Blankenburg, 2016). We used the standardised methods in Lead-DBS to produce the structural connectomes for both Schaefer 1000 parcellation  (Schaefer et al.
Regional heterogeneity data Here, we use different sources of regional heterogeneity to compare with the node-level hierarchy establish by the perturbation response. We consider the ratio T1w:T2w, which is sensitive to myelin content  (Burt et al. , 2018)  and the first principal component (PC1) of transcriptional activity for 1,926 brain-specific genes. To this end, we use data from the Allen Institute Human Brain Atlas (AHBA), which comprises microarray data quantifying the transcriptional activity of >20,000 genes in >4,000 different tissue samples distributed throughout the brain, taken from six post-mortem samples. The AHBA data were processed following the pipeline developed in Arnatkevicuite et al. (Arnatkeviciute et al. , 2019). To adapt the gene expression information into node-level heterogeneity information to Desikan-Killiany parcellation, we used the same approach explained in previous work  (Deco, Kringelbach, et al.
Identification of a Rare Homozygous SUN5 Variant in Seven ASS Subjects We recruited nine unrelated infertile men originating from North Africa with an ASS characterized by the presence of a high proportion of headless tails and isolated heads (Figure  1A ). Sperm parameters of the nine studied subjects are summarized in Table  1. Table  1. Detailed semen parameters of subjects carrying the SUN5 homozygous variant (c. 211+1_211+2dupGT) and statistical comparison of the mean of the semen parameters between these subjects and those with unknown cause. Among the nine recruited patients, seven were subjected to WES analysis and Sanger sequencing for validation. Because the ASS phenotype is rare and deleterious variants are expected to be negatively selected, we excluded all variants with a frequency greater than 1% in the gnomAD database.
The purple box represents a transmembrane domain (106-122 aa). The pink box represents a coiled-coil domain (155-183 aa), and the green box represents the Sad1/UNC-like C-terminal domain (205-364 aa) according to the Ensembl database (protein domains for ENSP00000348496. 3). The red box represents the predicted protein in silico. Among the nine recruited patients, seven were subjected to WES analysis and Sanger sequencing for validation. Because the ASS phenotype is rare and deleterious variants are expected to be negatively selected, we excluded all variants with a frequency greater than 1% in the gnomAD database. We then considered all the truncating variants as potentially deleterious, all variants scored as deleterious or probably damaging by sorting intolerant from tolerant (SIFT) and/or polymorphism phenotyping (PolyPhen), respectively, and all exonic or intronic near exonic variants were predicted to have a significant impact on splicing (predicted to create or obliterate a donor or acceptor splice site and scored as probably affecting splicing or potentially altering splicing) by HSF (Human Splicing Finder).
Finally, we retained a rare homozygous truncating variant of SUN5 (ENST00000356173. 8; c. 211+1_211+2dupGT) in five subjects (P0166, P0168, P0177, P0386, and P0504) which is predicted to shift the reading frame, leading to the production of a truncated and nonfunctional protein (Figure  1B ). This variant was absent in all subjects of our control cohort (n = 485). In addition, it has only been reported in the gnomAD database in the heterozygous state and with an allelic frequency (AF) of 0. 0166%. In the two remaining patients, we did not identify any bi-allelic deleterious variant in potential candidate genes, which were defined as follows: (1) other genes involved in acephalic spermatozoa syndrome, particularly in PMFBP1 and TSGA10  [17] [18] [19] [20] ; (2) genes previously described in a context of male infertility; or (3) genes with a predominant testicular expression or restricted expression in the testes.
Subject Selection and Recruitment Nine unrelated infertile men were included in the present study, all originating from North Africa and displaying non-syndromic infertility due to acephalic spermatozoa syndrome (ASS). Subjects were recruited from three reproductive biology centers: seven individuals sought consultation for primary infertility at the Clinique des Jasmins in Tunis (Tunisia), one individual was recruited at the Jean Verdier Hospital in Paris (France), and one individual was recruited at the Rouen University Hospital (France). Informed consent was obtained from all individuals participating in the study according to local protocols and the principles of the Declaration of Helsinki. The study was approved by local ethics committees, and samples were then stored in the Centre de Resources Biologiques Germethèque (certification under ISO-9001 and NF-S 96-900) according to a standardized procedure, or were part of the Fertithèque collection declared to the French Ministry of Health (DC-2015-2580) and the French Data Protection Authority (DR-2016-392).