dataesr/scientific-paragraphs-categorization huggingface dataset:
In  [10] , authors proposed a framework to rapidly build local mobile ad-hoc social networks on top of the Android platform. They used it to build prototype application which shows its flexibility to expedite various services. In  [11] , authors presented a middleware architecture for MASNs that provides software developers a platform for developing mobile apps that enable social connections. Efforts have been made in the literature to address the issue of integrating MASNs with infrastructure networks. In  [3] , the authors present the challenges related to this integration and review the strategies used in the literature. The solutions proposed in  [15] ,  [4]  and  [12]  consist in extending a routing protocol used in MANETs to provide bi-directional connectivity between ad-hoc stations and hosts in infrastructure networks.
V SOFTWARE ARCHITECTURE AND IMPLEMENTATION In this section we present the software architecture we proposed, and an implementation of a prototype mobile application for the Android platform. The goal of the application is the sharing of information using a publish/subscribe communication model within a MASN formed by the participants of a scientific conference. The information to be disseminated is grouped into topics. Topics' names reflect the categories in which the participants of the conference can be classified: speakers (plenary speaker, session speaker, and talker), the organizing committee (secretariat, protocol, and logistics), chairs (plenary chair, session chair), guests, etc. Users have to subscribe to a given topic in order to receive related publications. We simulate device-to-device connectivity in ad-hoc mode using Google's Nearby Connections API  [19].
Communication Layer: it is responsible of functions such as discovering and updating information about neighbouring stations, and communicating with them. This layer also is responsible for detecting the connection status of the mobile station to an infrastructure network and for communicating with the RRP in that network. In this architecture, the IHM layer represents a set of UI components allowing the user to interact with the software. The storage is where topics and publications are stored. The RRP has a three layers structure; those are: 1. Controller: this is the entry point of all the requests sent to the RRP. The Controller layer access the layer to handle the request according to the data sent by the mobile station, then return back the response returned by the Service layer to the mobile station; 2.
Service: this is where the business logic of the RRP is implemented. The service layer is responsible of analysing the data sent by the mobile station in order to determine which publication it can send back and which publication it can request back to the mobile station. It communicates with the Repository layer to store and load publications and topics; 3. Repository: the logics to access publications and topics are implemented here. The Repository layer is responsible of accessing the storage to save and retrieve publications and topics.
Integrating additional metrology with Rheo-SANS, such as dielectric and light scattering methods, can widen the length scale of structures probed, measure dynamic processes on the micro to millisecond scale, and detect flow instabilities. As instrumentation upgrades continue to generate larger datasets more rapidly, new, and faster data analysis methods will be critical. The growing data size requires integration of new simulation tools for designing experiments, on-the-fly analysis of 2D scattering data sets, and tomographic reconstruction of the full 3D microstructure obtained from multiple 2D scattering projections. Standard procedures and calibrations for Rheo-SANS sample environments would also promote consistency across different beamlines, providing a more user-friendly and accessible experiment for the Rheo-SANS user community.
Advances in Science and Technology to Meet Challenges Development is underway to build a true 4D Rheo-SANS sample environment capable of measuring stress and microstructure in all three plans of flow (Fig. 1 ) with the same instrument and with time resolution down to 10ms. A key goal is to provide the Rheo-SANS user community with a commercial 4D Rheo-SANS instrument that is integrated with beamline data acquisition software (SBIR Phase II DE-SC0019595). Recently, various other Rheo-SANS environments have been developed with different advantages. Capillary Rheo-SANS (CRSANS) measures complex fluids at high shear rates up to 10 6 s -1. 14 The highshear capillary geometry, together with traditional concentric cylinder Rheo-SANS geometries, can now provide structural and rheological signatures across eight orders of magnitude in shear rate.
In addition to accessing a lower q-range with a 2D detector, VSANS will be particularly advantageous for time-resolved rheological measurements, low volume geometries, reacting systems, and stopped-flow mixing experiments. Ongoing developments in community-driven software tools such as SasView (www. sasview. org), computational power, and recent statistical methods to determine orientation distribution functions from anisotropic scattering data 16 will help the broader scattering community extract more structural information from two-dimensional scattering projections and with different applied flow fields. Advances in computing, machine learning algorithms, AI pattern recognition, and 3D data visualization will help meet challenges in extracting, analyzing, and presenting data-rich Rheo-SANS measurements.
, 2020) , are emerging to analyze the local material structure. However, so far system-specific solutions are often required, but universal tools are starting to emerge  (Pachitariu, 2022; Kirillov, 2023). Image analysis is also important for techniques to measure forces and rheological properties of living systems (Roca-Cusachs et al. , 2017). While these techniques are currently being adopted more broadly, we believe that they should be combined more often with techniques to experimentally manipulate the 3D environment, such as microfluidics and micro-confinement. Step 3 -from data to models. To compare data to a theoretical model, problem-specific data analysis tools often need to be developed. For noisy or incomplete data, the calibration of theoretical models A c c e p t e d M a n u s c r i p t requires the use of statistical inference techniques (Frishman and Ronceray, 2020).
Acknowledgements Financial support from  ERC Advanced  Grant No. 884902  'SoftML' is acknowledged. Acknowledgements DJP acknowledges support from the  US Army Research Office  (Award No. W911NF-17-1-0328 ) and the  US Department of Energy  (Award No. DE-SC0007991 ). Acknowledgements C. M. P. gratefully acknowledges  Deutsche Forschungsgemeinschaft  for funding ( PA 771/19-1 ). C. M. P. and C. T. thank  DAAD  and  IKY  for funding within the  IKYDA program. Acknowledgements I. I. S. is grateful to students and postdocs in his research group who worked on liquid crystals and colloids. I. I. S. acknowledges the support by the  U. S. Department of Energy, Office of Basic Energy Sciences , under contract  DE-SC0019293 , and the  National Science Foundation  grant  DMR-1810513.
Acknowledgements We acknowledge discussions with numerous researchers from academia and industry. We acknowledge funding from an  ERC Consolidator Grant  (agreement  819511 -METAFOAM ), from the region  Grand Est  ( Young Investigator Grant ), and from the  Interdisciplinary Institute  " Hierarchical Functional Materials " supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ) and  SFRI  ( STRAT ' US  project,  ANR-20-SFRI-0012 ) under the framework of the  French Investments for the Future Program.
Acknowledgements This manuscript was prepared under cooperative agreements  70NANB20H133  &  370NANB17H302  from  NIST ,  U. S. Department of Commerce , as well as  DOE SBIR/STTR  RPM acknowledges support from the  Center for High Resolution Neutron Scattering (CHRNS)  which is jointly funded by the  NCNR  and  NSF  under Agreement No. DMR-2010792. Identification of commercial equipment or materials in this work does not imply recommendation or endorsement by the  National Institute of Standards and Technology. Acknowledgements This work was supported by the  National Science Foundation  under Grant No. CBET-1804186. The author thanks  Y. Han ,  J. C. Conrad , and  D. L. Blair  for providing copies of their figures and for helpful discussions, and  J. C. Crocker  for introducing him to the joys of microscopy a while ago.
Acknowledgements This work was supported by the  Hong Kong RGC  grant ( CRF-C6016-20G ). We thank  Huijun Zhang  for developing Figure 1e. Acknowledgements We thank the  French CNES  and  ANR  (grants No. ANR-14-CE32-0005 ,  FAPRES , and  ANR-20-CE06-0028 ,  MultiNet)  for financial support. LC acknowledges support from the  Institut Universitaire de France.
Acknowledgements This work was supported by  NSF CBET  award numbers:  2010118 ,  1804963 ,  1509308 , and an  NSF   DMR  award number:  1507607  Acknowledgements This material is based upon work supported by the  National Science Foundation  under Grant No. CBET-1637991. Acknowledgments This work was supported by the  NSF Center for the Chemistry of Molecularly Optimized Networks (MONET) ,  CHE-2116298. We thank  X. Mao  and  S. Wang  for providing the graphics in Figures 1 and2, respectively. Acknowledgements RG thanks  Jawaharlal Nehru Centre for Advanced Scientific Research  and the  Department of Science and Technology (DST) ,  Govt. of India  ( Swarna Jayanti Fellowship Grant  and  DST-Nanomission Grant ) for financial support. RG also thanks members of the  Experimental Soft Matter Research group at JNCASR  for many discussions over the years.
Acknowledgements I acknowledge the support from  MIUR PRIN   2017  (Project  2017Z55KCW ). Acknowledgements S. S. acknowledges support through the  JC Bose Fellowship  ( JBR/2020/000015 ) SERB,  DST (India). MM was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under  Projektnummer 398962893 ,  Projektnummer 21150405 , and  Projektnummer 390685813. Acknowledgements The  Flatiron Institute  is a division of the  Simons Foundation , which also supported this work through the collaboration " Cracking  the glass problem" via Grant No. 454945  (SAR and AJL) and  Simons Investigator  Grant No. 327939  (AJL). This work was also supported by the  U. S. Department of Energy, Office of Science ,  DE-DE-SC0020963  (AJL), and the  National Science Foundation  via  NSF-DMR-2005749  (MS and IT), and the  UPenn MRSEC   NSF-DMR-1720530  (GZ).
References Acknowledgements C. O. O. acknowledges financial support from  NSF  through  DMR-1945966  and  DMR-2223705  Acknowledgements Contributions to this article have been made possible due to research funded by the  Australian Government  through the  Australian Research Council (ARC)  Grants  DP180101919  and  LP160100239. Acknowledgements M. M. thanks the  Centre Interdisciplinaire de Nanoscience de Marseille (CINaM)  for providing office space. M. M. , P. R. and J. -F. R. received funding from the  «Investissements d'Avenir» French Government program  managed by the  French National Research Agency  ( ANR-16-CONV-0001 ) and from the  Excellence Initiative of Aix-Marseille University  -A*MIDEX. Acknowledgements The authors gratefully acknowledge financial support by the  Deutsche Forschungsgemeinschaft (DFG) , the  German Ministry for Education and Research (BMBF) , the  Crafoord Foundation  as well as various large-scale research facilities (in particular Diamond Light Source, the  ISIS Neutron and Muon Source , the  European Synchrotron Radiation Facility , the  Forschungsreaktor München II , the  Julich Center for Neutron Science , the  Institut Laue-Langevin and Oak Ridge Neutron Laboratory)  for beamtime allocation and excellent on-site support.
Acknowledgements This publication is part of the project How cytoskeletal teamwork makes cells strong (with project number  VI. C. 182. 004  of the  NWO Talent Programme  which is financed by the  Dutch Research Council (NWO)) , and the project  Light-responsive microalgal living materials  (ERC starting grant No. 101042612 ). Images are created with BioRender. com. Acknowledgements This material is based upon work supported by the  National Science Foundation  under Grant Nos. CMMI-2035122 ,  CMMI-1435920 , and  Convergence RAISE program   IOS-1848671.
SVs can impact gene expression near breakpoints SVs can influence gene expression by impacting the sequence of the open reading frame, modifying their copy numbers or changing their regulatory elements. By leveraging a recent survey that generated the transcriptome of more than 1,000 S. cerevisiae isolates  35  , we explored the relationship between gene expression and SVs. For 51 isolates, we analyzed the expression levels for 6,445 transcripts against 1,876 SVs, encompassing a similar proportion of the different SV types than the entire dataset (Extended Data Fig. 3a ). We first defined a set of 2,808 SV-gene pairs with more than half of the pairs involving insertion or deletion events. We then compared the expression of genes associated or not with a given SV (Extended Data Fig. 3b ). We found that 124 SV-gene pairs (4.
Evolutionary dynamics of complex genetic elements Telomere length variation at TEL03L. The mean telomere size varies by a factor of 4 across different isolates (Extended Data Fig. 4 ), from 166 bp in the CBS2183 wine strain (AFI) to 686 bp in the CLIB561 French dairy strain (BGN_3a). Some strains harbor homogeneous telomeres length across different chromosome extremities while others exhibit large variance. The average telomere length per strain correlates positively with variance (Extended Data Fig. 5a ). We found no substantial correlation between telomere length and ploidy, heterozygosity or ecology (Extended Data Fig. 5b-d ). We also examined telomere length variation between individual chromosome ends across the population of 100 strains. Despite a globally homogeneous distribution, TEL03L, and to a lesser extent TEL07R, are substantially longer than all other telomeres (Fig.
ScRAP strain selection The full panel is composed of three distinct datasets as follows: (1) 100 newly sequenced and de novo assembled genomes, (2) 18 re-assembled genomes using previously available raw Nanopore read data  25  and (3) 24 publically available complete genome assemblies, including the S288C reference genome  [22] [23] [24] 26, 28, 29, 51, 52  (Supplementary Fig. 1 ). The rationale for the selection of the 100 strains de novo sequenced in this study was based mainly on the knowledge gained from the 1,011 genome project  31, 53. We selected one per clade and subclades, with a good sporulator phenotype. We selected some strains with a known signature of SV (for example, AIF with segmental duplications). The AIS strain that contained chromosome-scale introgression was first detected in the 1,011 work but excluded because of its complex genome structure.
The adaptor-ligated DNA was cleaned up by adding a 0. 4× volume of AMPure XP beads followed by incubation for 5 min at room temperature. When using the SQK-LSK108 kit for FLO-MIN106 MinION flow cells, twice 140-µl adapter bead binding buffer (ABB) washes were performed. When using the SQK-LSK109 kit for FLO-PRO002 flow cells, twice 250-µl L fragment buffer washes were performed. The final library was eluted in 15 µl Elution Buffer and loaded into the MinION or PromethION flow cells according to the ONT manuals. Raw fast5 files were basecalled using Guppy (version: 3. 4. 5) followed by Porechop (version: 0. 2. 4;github. com/rrwick/Porechop) removal of adapters and barcodes. The entire project generated close to 204 Gbp of Nanopore sequencing data. Sequencing statistics are detailed in Supplementary Table  16.
For fast5 file storage/sharing, single-fast5 files were stripped of basecalling data using Picopore (version: 1. 2. 0; github. com/scottgigante/picopore), ensuring all files contain only data necessary for re-basecalling. Next, single-fast5 files were converted into multi-fast5 files using the ont-fast5-api (version: 0. 3. 2; github. com/nanoporetech/ont_fast5_api) single_to_multi command, followed by the fast5_subset command to generate strain-specific fast5 files containing fast5 files for all reads within each strain-specific fastq file. This was done to reduce the complexity of reanalysis using fast5 files from strains run with both/either multiple barcodes and across several flow cells and remove fast5 files for reads of insufficient quality. All adapter/barcode-free fastq files and their associated strain-specific fast5 files are available under the accession PRJEB50706/ERP135326.
Analysis of complex regions Subtelomere dynamics. The subtelomeric regions were annotated and named in the same way as our previous study proposed  29. Manual examination and adjustment were further applied to curate subtelomeric ends with incomplete sequence information or substantial reshuffling (Supplementary Table  17 ). tDNAs and Ty elements. We defined a consistent set of 100 haploid or homozygous genome assemblies for the analyses of the dynamics of tDNAs and Ty elements by first excluding the diploid, triploid and tetraploid phased assemblies because they contained numbers of annotated tDNA and Ty copies that were proportional to their ploidy and, therefore, difficult to compare with haploid and collapsed genome assemblies (Supplementary Table  11 ). We also removed eight haploid genomes from one specific study  22  because they contained a much lower number of tDNAs than all other genomes of the dataset, probably indicative of local assembly errors.
Additionally, by analyzing raw genome assemblies, four complex aneuploidy chromosomes that were completely or partially assembled, at least containing the complex region, were identified and extracted (CBS1586/AHG +1×chr10c; CBS457/AIF +1×chr11c; CBS4255/ ASB +2×chr9c; CBS1489/ASG +1×chr3c). Aneuploidy detection in a dataset of 909 strains. The aneuploidy detection pipeline  56  using Illumina data from refs. 31,36 is available at https://github. com/SAMtoBAM/aneuploidy_detection and consists of the following steps: 1. Illumina reads were aligned with BWA-MEM (version: 0. 7. 17), and the coverage was calculated using BEDTools genomecov (version: 2. 27. 1; options: -d -ibam). 2. Coverage was binned for calculating the median coverage in 30-kb bins with a 10-kb stepsize using both BEDTools makewindows and map, during which regions covering 15 kb (half a window) from each chromosome end were removed to reduce telomere mapping/variation issues.
9. Identify any NCRs >100 kb that are present within a strain containing an aneuploidy detected above. Label as complex aneuploidy-related and use in the less conservative estimate of complex aneuploidy count. The newly generated large aneuploidy dataset overlaps, by strain and chromosome, 88% (303/343) with that of the ref. 31 dataset. This leaves only 40 aneuploidies (12%) not redetected. Of these 40, manual inspection identified that nine were clearly false positives in ref. 31, eight came from the same strain and are an issue of defining whether eight chromosomes were lost or the other eight were gained, one contains a large increase in coverage close to the centromere but not covering and three contain a slight change in coverage but are well below the set threshold for indicating a change in copy number.
7. 17) aligned Illumina reads. The resulting multisample VCF was then filtered for variants with a Quality-by-Depth, StrandOddsRatio, FisherStrand, Mapping Quality, MappingQualityRankSum and/or ReadPosRankSum of more than two s. d. from the average. Finally, variants were removed from regions labeled as repetitive by RepeatMasker and/or in ref. 57 to generate the final VCF. We used the vcf2phylip  58  python script (https://github. com/ edgardomortiz/vcf2phylip; versions: 2. 8; options: -I $input_vcf https://doi. org/10. 1038/s41588-023-01459-y -resolve-IUPAC -o S288C -fasta -output-prefix) to convert the VCF file into the fasta format. The corresponding SGDref entry in fasta format was extracted based on the reference allele column of the input vcf file. MAFFT (version: 7. 471; options: --auto --thread $threads --preservecase --addfragments) was used to align these two fasta files by using the extracted SGDref entry as the reference sequence for alignment.
The resulting alignment was further filtered by ClipKIT (version: GitHub commit cccc8bf; options: -m gappy). The filtered alignment was fed into IQtree for tree building (version: 1. 6. 12; options: -s $prefix. fasta -m GTR+ASC -bb 1000 -alrt 1000 -nt $threads -pre $prefix. iqtree -safe). A thousand rounds of UB and aLRT were used to assess the branch supports. SV-based nuclear tree. The input SV VCF file (homo_and_hetero_ noDoublonsInCoordinates. vcf. gz) was generated using the nonredundant SV dataset. Based on the presence/absence information of these identified SVs in each assembly entry, a phylip-formatted 0/1 matrix was generated accordingly and used for tree building. IQtree (version: 1. 6. 12; options: -s $prefix. phylip -st MORPH -m MK+ASC -bb 1000 -alrt 1000 -nt $threads -pre $prefix.
iqtree -safe) was used to generate the phylogenetic tree. A thousand rounds of UB and aLRT were used to assess the branch supports. Phylogenetic tree processing and visualization. For the phylogenetic trees generated above, tree-based operations such as rerooting, branch trimming and tip label extraction were performed by the nw_reroot, nw_prune, nw_labels tools from the Newick Utilities package (version: 1. 6. 0). Tree visualization was performed by the R package ggtree (version: 3. 2. 1). Cophylo comparison was conducted by the R package phytools (version: 1. 0-3). The distance between trees was evaluated in terms of the quantity of information that the trees' splits hold in common with the clustering information distance implemented in the R package TreeDist (version: 2. 4. 1).
5 times the interquartile range. c. Number of validated SV per strain split by ploidy (n = 51, n = 76, n = 6 and n = 7 for haploid, diploid, triploid and tetraploid strains, respectively. The horizontal lines in the boxplots correspond to the median, the lower and upper hinges correspond to the first and third quartiles and the whiskers extend up to 1. 5 times the inter quartile range. Extended Data Fig. 4  | Distribution of telomere length at all chromosome ends in each strain of the ScRAP split by dataset. The vertical lines in the boxplots correspond to the median, the lower and upper hinges correspond to the first and third quartiles and the whiskers extend up to 1. 5 times the interquartile range. For each of the 142 strains, the number of telomeres used to derive the boxplot is indicated in Supplementary Table  1.
The inset plot shows the distribution of the average telomere length per strain for each dataset. Median values are indicated by the gray crossbars in each violin plot (for de novo assemblies n = 100 strains, for public assemblies n = 24 strains and for re-assemblies n = 18). Two-sided Wilcoxon mean comparison p-values are indicated. between number of solo-LTRs per insertion site and the number of strains sharing an insertion site. The Pearson correlation coefficient and its associated twotailed t-test p-value were calculated using the stat_cor method in R. f. Map of the de novo insertions of complete Ty elements across the 100 homozygous explored strains. The map shows the 61 insertion sites in which only complete elements are found and never soloLTRs, which strongly suggests that these sites correspond to recent insertions.