dataesr/scientific-paragraphs-categorization huggingface dataset:
To avoid the contribution of thermal fluctuations, each distance matrix was averaged over 100 conformations with an output rate of 10k steps. To demonstrate the independence of the final result on the initial conformation, we repeated the calculation of the system ten times with the maximal number of contacts. For each repeat, we created a new independent initial conformation, but we kept the same set of additional bonds. The initial conformation does not affect the final result in the simulation protocol. Visualization of epigenetic states. The visualization was performed using the pymol software v. 2. 3. 2 (https://pymol. org/2/). 1D epigenetic data were added to the structure as a bead type and represented with a corresponding color. Analysis of different epigenetic states was performed via Python scripts (https://github.
Acknowledgements This work was supported by  Russian Science Foundation (RSF)  grant # 19-14-00016  to  S. V. R. Bioinformatics  analysis of the data was supported by  RSF  grant # 19-74-00112  to  E. E. K and Russian Foundation for Support of Fundamental Science (RFBR)  grant # 18-29-13013  to S. K. N. A. A. Gal. was supported by  RFBR  grant # 19-34-90136. The research is carried out using the equipment of the shared research facilities of  HPC computing resources at Lomonosov Moscow State University  and the Makarich HPC cluster provided by the  Faculty of Bioengineering and Bioinformatics. The research of P. I. K. is supported partly by RFBR grant # 18-29-13041  and by  Skoltech Systems Biology Fellowship. The research of A. V. C. is supported by  RFBR  grant # 18-29-13041. S. V. U. and S.
Data availability Raw and processed snHi-C and bulk BG3 in situ Hi-C data are available in the GEO NCBI under accession number "GSE131811". List of publicly available GEO sources used in this study: "GSE122603" (Hi-C for Kc167 and BG3 cell lines for comparison of stable TAD boundaries), "GSE58821" (MSL; ChIP-seq), "GSE69013" (RNA-Seq). List of publicly available modENCODE data sources used in this study: total RNA of ML-DmBG3-c2 cell line assessed by RNA tiling array (modENCODE id 713) and the ChIPchip for MOF (id 3041), BEAF-32 (id 921), Chriz (id 275), CP190 (id 924), CTCF (id 3280), dmTopo-II (id 5058), GAF (id 2651), H1 (id 3299), HP1a (id 2666), HP1b (id 3016), HP1c (id 942), HP2 (id 3026), HP4 (id 4185), ISWI (id 3030), JIL-1 (id 3035), mod(mdg4) (id 324), MRG15 (id 3045), NURF301 (id 5063), Pc (id 325), RNApolymerase-II (id 950), Su(Hw) (id 951), Su(var)3-7 (id 2671), Su(var)3-9 (id 952), WDS (id 5148), H3 (id 3302), H3K27ac (id 295), H3K27me3 (id 297), H3K36me1 (id 299), H3K36me3 (id 301), H3K4me1 (id 2653), H3K4me3 (id 967), H3K9me2 (id 310), H3K9me3 (id 312), H4K16ac (id 316).
To better characterize and mitigate challenges in the field through collaboration, ancient metagenomic researchers organized a workshop for Standards, Precautions, and Advances in Ancient Metagenomics (SPAAM) in 2016  (1). This served as an inspiration to formally establish the SPAAM community in 2020 (https://spaam-workshop. github. io/). spaam-community. github. io Ancient An organism or tissue is often considered to qualify as ancient when it is more than 100 years old. This cutoff, however, is arbitrary.
SAMPLING STRATEGIES IN AN ANCIENT METAGENOMIC STUDY Small sample sizes are a consistent feature of ancient metagenomic studies, meaning that there is often little opportunity for large-scale sampling or elaborate sampling design, PIA: a taxonomic read binner used to identify the likely host source of typically sedimentary aDNA (sedaDNA) reads, with a focus on accounting for extinct taxa not present in modern databases. SNP_Evaluation: a tool allowing for evaluating and visualizing confidence in variant calling of low-coverage pathogen genomes. MEx-IPA: an interactive viewer of output from the HOPS metagenomic authentication pipeline. cuperdec: an R package for the estimation and visualization of the endogenous taxonomic content of ancient microbiomes. PyDamage: a tool for separating ancient and modern contigs from de novo assembly of ancient DNA data.
Pipeline Holi: a pipeline for taxonomic profiling of ancient metagenomic reads based on competitive mapping to large databases. Often used to profile ancient environmental samples (e. g. , sedaDNA) for reads assigned to animal or plant taxa. metaBIT: the first pipeline with configurations for high-throughput ancient metagenomic screening with MetaPhlAn and a range of taxonomic profile comparison analyses. coproID: a pipeline for the prediction of the host organism of ancient fecal material, including taxonomic profiling of the endogenous content of both host and microbial DNAs. HOPS: a pipeline integrating MALT alignment with postalignment ancient DNA characteristic authentication. Includes damage profiling, fragment length, and visualization of possible contamination. nf-core/eager: a general ancient DNA genomics pipeline with taxonomic profiling for pathogen screening (and microbiome reconstruction) and authentication components, as well as steps allowing for analysis of microbial genomes.
Hence, it is very important that we do spend the appropriate amount of time and energy on validating our analyses to show that they do not suffer from biases introduced by unfit methods and on developing tailored software when necessary (examples of existing benchmarking studies, software, and pipelines can be found in Table  2 ). To test the suitability of a method, simulations of ancient metagenomic sequencing data with programs such as gargammel (54) can provide the required framework for such validations. Reviewers should thus consider requesting such validation to ensure the high quality of the analysis, while we should prioritize these types of analyses, improving quality over quantity.
ACKNOWLEDGMENTS We thank  Jaelle C. Brealey ,  Becky Cribdon ,  Katerina Guschanski ,  Ophélie Lebrasseur ,  Aleksandar D. Kostic ,  Frank Maixner ,  Mohamed S. Sarhan ,  Laura S. Weyrich ,  Marsha Wibowo , and  Sterling L. Wright  for providing reviews and summaries of common review critiques. We also thank  Peter D. Heintzman ,  Nikolay Oskolkov ,  Adam Koziol ,  Jessica Hider ,  Katerina Guschanski ,  Ophélie Lebrasseur ,  Anan Ibrahim ,  Miriam J. Bravo-Lopez ,  Eleanor J. Green ,  Siobhan Mor ,  Aida Andrades Valtueña ,  Christina Warinner , and members of the  Department of Paleobiotechnology at the Leibniz Institute for Natural Product Research and Infections Biology Hans Knöll Institute  for comments on the manuscript. Furthermore, we thank the attendees of SPAAM2 and the  SPAAM community.
We also thank  Tom Gilbert ,  Ludovic Orlando , and  Christina Warinner  for further comments on the manuscript and their support for early career researchers. We declare that we have no competing interests. I. M. V. was supported by the  Werner Siemens-Stiftung  (Paleobiotechnology, awarded to  Pierre Stallforth ,  Hans-Knöll Institute , and  Christina Warinner ,  Max Planck Institute for Evolutionary Anthropology). A. K. F. was supported by the  Danish National Research Foundation  award ( CEH-DNRF143  awarded to  M. Thomas P. Gilbert ,  University of Copenhagen, Denmark ). Å. J. V. is supported by  Carlsbergfondet Semper Ardens  grant  CF18-1109  (to  M. Thomas P. Gilbert ,  University of Copenhagen, Denmark ). A. H. is funded by the  Deutsche Forschungsgemeinschaft (DFG ;  German Research Foundation ) under Germany's  Excellence Strategy EXC 2051 , project  ID 390713860.
g. , Diaph3-driven, Fascin-driven, or Ena-driven FLSs). To do this, we performed a large series of experiments in which we examined triple combinations of our proteins of interest with the actin bundles visualized in 3D by wide-field microscopy in a fourth channel at steady state (Table  2 ). It was evident that actin bundles emerge from tip complexes with widely differing compositions (Fig. 4  A shows an example field of view from one combination). One possible explanation for such heterogeneity is an amplification of initial small protein number fluctuations by cooperative networks of protein interactions. We pooled our two datasets to complete a matrix of correlations of each protein with every other protein and with morphology (Fig. 4  B, Fig. S3  A, Table  1,  and Table 2 ). We found several protein pairs that exhibit strong intratip complex correlations: Ena/VASP, TOCA-1/Diaph3, VASP/Cdc42•GTP, and N-WASP/Cdc42•GTP.
The dynamics of the concentration fluctuations X are controlled by a single parameter, the relaxation rate θ, which determines how quickly a given protein concentration returns to its baseline after a random change. Together, this provides a framework that explains the appearance of the Laplace distribution in the growth dynamics of FLSs from stochastic regulator concentration fluctuations. We used the framework to simulate growth velocity time series and, via numerical integration, FLS length time series, resembling those obtained experimentally (Fig. 5 C ). We collected length data points at the 20-min time point, both in our simulations and in experimental data. The resulting length distributions were well matched (Fig. 5 D ), with only the single parameter θ needing to be fit. The framework also reproduced the shape of the distribution of persistence time for the FLS growth/shrinkage phases (Fig.
To quantify the effects of perturbing Fascin, we measured the length distributions of filopodia during Drosophila dorsal closure under conditions of loss of Fascin using the singed mutant or gain of Fascin by overexpression of GFP-Fascin using the engrailed-GAL4 driver by comparison with the GFP knock-in Fascin. The length distribution of dorsal closure filopodia from control embryos, GFP-Fascin knock-in filopodia, and filopodia in the Fascin mutant embryos was well fitted by exponentials (Fig. 7, E  and F ). In agreement with the theory, overexpression data deviated markedly from an exponential distribution, in contrast to all cases of filopodia length measurements we have examined so far (Fig. 7, E  and F ).
Laplace-distributed filopodial growth dynamics in vivo Our previous use of semiautomated methods to quantify filopodia in Drosophila myotube migration  (Richier et al. , 2018)  gave  us access to a rich dataset of manually annotated filopodia that could be used to train a machine learning algorithm to segment filopodia in tissue over time. In this tissue, we found that filopodia lengths were exponentially distributed. Moreover, there is a developmental transition where the myotubes attach to tendon cells within the wall of the Drosophila and the myotube filopodia are naturally suppressed  (Richier et al. , 2018). The filopodial growth and shrinkage velocities at early and late stages (where the data were independently acquired) were both well approximated by a Laplace distribution (Fig. 7  G), proving our results from the FLS system in vivo (Fig.
Plasmids Xenopus tropicalis TOCA-1 (GenBank accession no. BC080954) was PCR amplified from IMAGE clone 6980375 (Source Bioscience), with oligos of sequence 59-GCATGGCCGGCCACCATGAGC TGGGGTACTG-39 and 59-GGCGCGCCTTAGATATAAGTTACTGC-39 using Phusion High-Fidelity DNA Polymerase and cloned into pET SNAP precision FA. X. tropicalis N-WASP (WASL; GenBank accession no. BC067309) was PCR amplified from IMAGE clone 5379332 (Source Bioscience), with oligos of sequence 59-GAT CGGCCGGCCAATGAGTAACAATA-39 and 59-GATCGGCGCGCC TCAATCCTCCCATT-39, and cloned into pCS2-his-SNAP-FA. ZZ-TEV-WIP  (Ho et al. , 2004 ) was a kind gift from the Kirschner Laboratory (Harvard University, Cambridge, MA). Xenopus laevis VASP (GenBank accession no. BC077932) was PCR amplified from IMAGE clone 5515353 with additional residues KCK added before the ATG, with oligos of sequence 59-GCATGGCCGGCC TAAGTGCAAGATGAGTGAGACAGCACTGG-39 and 59-GGCGCG CCGGTCAAGGAGTACCC-39, and cloned into pET SNAP precision FA.
Site-directed mutagenesis with oligos 59-GGTTGTAAT AAACTCCCCACTGGTGAGAG-39 and 59-CTCTCACCAGTGGGG AGTTTATTACAACC-39 was performed to destroy endogenous cysteine (conversion to serine at amino acid 63). X. laevis Ena (GenBank accession no. BC073107) was PCR amplified from IMAGE clone 5379332, with oligos of sequence 59-GCATGGCCG GCCACCATGAGTGAACAGAGCATC-39 and 59-GGCGCGCCCTAT GCGCTGTTTG-39, and cloned into pCS2 his SNAP acceptor. X. laevis Fascin (GenBank accession no. BC097600) was PCR amplified from IMAGE clone 4970584, with oligos 59-GCATGGCCG GCCACCATGAGTTCTGGACCC-39 and 59-GGCGCGCCTTAGTAT TCCCAGAG-39, and cloned into pCS2 his GFP FA (GFP was PCR amplified flanked with ecoRI/fseI restriction sites using oligos of sequence 59-GCATGAATTCACCATGGTGAGCAAGGGC-39 and 59-GATCGGCCGGCCGGATCCCCTTG-39 and cloned into pCS2 his FA acceptor).
Human N-WASP GBD domain was PCR amplified from pCS2-mRFP-GBD (a kind gift from William Bement, Addgene plasmid 26733), with XhoI/AscI flanking restriction sites using oligos of sequence 59-GATCCTCGAGCCGGACCTAGC CCAGC-39 and 59-GGCGCGCCTCACTCCTGGCGCCTCATC-39, and cloned into pET-pmKate2. X. laevis Diaph3 was synthesized by Life Technologies based on sequence alignments made with Mayball cDNA information flanked with fseI/ascI restriction sites and cloned into pCS2 his GFP FA. X. laevis IRSp53 (GenBank accession no. BC108583) was PCR amplified from IMAGE clone 7981694 (Source Bioscience), with oligos of sequence 59-GCA TGGCCGGCCACCATGTCCCGGGACGCAG-39 and 59-GGCGCG CCTCATCGGATGATTGGCG-39 and cloned into pCS2-his-SNAP-FA.
926-32211) diluted in blocking buffer for 30 min at room temperature. Blots were imaged on a LI-COR BioSciences Odyssey CLx scanner using a solid-state diode laser at 785 nm. Intensities of the dilution series of the recombinant protein with known concentrations were measured using LI-COR image studio software and used as a standard curve for the extract samples. Blots were repeated at least twice for each protein. The TOCA-1 antibody  (Ho et al. , 2004) , VASP antibody, and Fascin antibody  (Lee et al. , 2010)  were raised in rabbits and were a kind gift from Marc Kirschner, Harvard University, Cambridge, MA. The N-WASP antibody was raised against full-length purified X. tropicalis 6His-SNAP-N-WASP/human ZZ-WIP in rabbit and affinity purified against the same protein sample. The Ena antibody was raised against full-length purified X.
, four FLS tips and four corresponding background regions) were selected. Five frames were recorded in 1-s intervals before bleaching using the 488-nm or 561-nm laser at 25% power for two iterations. Fluorescence recovery was recorded by the corresponding laser line at 5% laser power for 120 frames, with intervals of 0. 5 s for the first 40 frames and 1 s for all following frames. Changes in fluorescence intensity in the ROI after photobleaching were analyzed in ImageJ using a publicly available script developed at the Image Processing School Pilsen 2009 according to procedures previously described  (Bancaud et al. , 2010). The bleach frame was identified by the largest frame-to-frame drop in intensity. For each analysis, four ROIs-bleached FLS, nonbleached FLS, bleached background, and nonbleached background-were specified.
Postprocessing of snapshot data We filtered out segmented FLSs with an effective diameter of <0. 5 µm because base size information as well as fluorescent intensity measurements become unreliable at or below the resolution limit. When considering FLS length distributions, we truncated the distribution to lengths between 5 µm and 20 µm to exclude structures that have not yet matured into FLSs and artifacts stemming from the lower z-resolution in our confocal stacks. Fluorescence intensities of observed tagged proteins were determined by subtracting the local background intensity from the intensity averaged over all pixels in the FLS base area. To determine protein-protein and protein-morphology correlations, we calculated Spearman correlation values for each field of view separately and averaged over the resulting ensemble.
, 2009). The GFP (variant mGFP6) coding sequence was inserted at the N-terminus of the EVH1 domain with a short linker amino acid sequence at the C-terminus of GFP, the fusion protein junction that corresponds to YKASSS-SEQS. GFP will be fused to all annotated isoforms of Ena. Live imaging and data analysis of Drosophila embryo filopodia Dechorionated embryos (washed in 50% bleach) were mounted on a glass-bottomed dish with heptane glue and submerged in water. To identify GFP-Ena homozygous embryos, balancer chromosome CyO with Dfd-YFP was used, and we did negative selections for Dfd-YFP expression in the head region of embryos. Live microscopy was performed on an inverted Leica TCS-SP5 equipped with a 63× 1. 4 NA Plan Apo oil immersion objective at room temperature. To visualize filopodial movement and respective protein intensities within filopodia in dorsal closure, embryos were imaged at the end of stage 14.
Microscopic measurements were performed by taking z-stacks of 7-11 z sections (0. 5-µm spacing). Time lapses were taken at 15-s intervals for 10 min. Ena and Scar/WAVE intensities at the tips of filopodia were quantified manually using ImageJ. Filopodial lengths and fascin intensities were quantified using our previously developed open-source pipeline Filopodyan  (Urbančič et al. , 2017). Using ImageJ, a maximum projection of the respective stacks was applied for filopodia reconstruction. A combination of automated detection with some manual editing was used to track identified filopodia over time. Tracing and tracking of filopodia in Drosophila myotube timelapse movies Time-lapse stacks of myotubes in Drosophila early and late stage-16 embryos from the dataset used in  Richier et al. (2018)  were reanalyzed using the following machine-learning pipeline: (1) Preprocessing: Stacks were z-projected to single images using average pixel intensities.
Data availability All data are available in the main text or the supplementary information. All data, code, and materials used in the analysis are available for the purposes of reproducing or extending the analysis. mNeonGreen constructs are supplied by the Allele Biotech repository. Online supplemental material Fig. S1  shows the concentration quantification of our labeled proteins in egg extracts, together with the purified protein gels. Fig. S2  contains the number and morphology of FLSs over time, showing that extracts are not overly depleted of protein after an assay and that FLSs grown from previously used extract have similar lengths and counts. Fig. S3  shows FLS numbers for the correlation matrix shown in Fig. 4  B and similar matrices for large and small FLS width cutoffs. Video 1.
Acknowledgments We thank  Bishara Marzook  and  Louise Whiteley  for protein preparation,  Vivek Bhogadi  and  Sven Huelsmann  for the creation of fly stocks, and  Nick Brown ,  Helen Mott ,  David Owen ,  Sandra Schmid , and  Marc Kirschner  for critical feedback on the manuscript. We thank the  Gurdon Institute imaging facility. This work was supported by  European Research Council  grant  281971 ,  Wellcome Trust   Research Career Development Fellowship   WT095829AIA  and  Wellcome Trust   Senior Research Fellowship   219482/Z/19/Z  to  J. L. Gallop , a  Wellcome Trust Senior Investigator Award 098357  to  B. D. Simons , and an  Austrian Science Fund  grant ( P31639 ) to  E. Hannezo. We acknowledge core funding by the  Wellcome Trust  ( 092096 ) and  Cancer Research UK  ( C6946/A14492 ).
Acknowledgements The authors would like to thank  Holger Stark  and  Tapan Chandra Adhyapak  for helpful discussions and their support with setting up the simulations. The research leading to these results was supported by the  Max Planck Society  and by  Deutsche Forschungsgemeinschaft (DFG)  within the priority program on microswimmers SPP 1726 (Grants No. KL 818/2-2  and  FA 835/7-2  to SK and DF). Further, SM was supported by  Deutscher Akademischer Austauschdienst, DAAD  (Grant No. 57314018 ). Part of the simulations were run on the Goegrid cluster at the  University of Göttingen , which is supported by the  DFG  (Grant  INST 186/1353-1 FUGG ) and  MWK Niedersachsen  (Grant No. 45-10-19-F-02 ).
We address the problem of unsupervised abstractive summarization of collections of user generated reviews through self-supervision and control. We propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. This setting makes training simpler than previous approaches by relying only on standard log-likelihood loss. We address the problem of hallucinations through the use of control codes, to steer the generation towards more coherent and relevant summaries. Finally, we extend the Transformer architecture to allow for multiple reviews as input. Our benchmarks on two datasets against graphbased and recent neural abstractive unsupervised models show that our proposed method generates summaries with a superior quality and relevance.
Our main contributions are therefore three-fold: • performing multi-document summarization by modelling it as a self-supervised problem where one document acts as the summary of a subset. We carefully select those two, and link the resulting formulation to a recently proposed theoretical framework (Peyrard, 2019) (Sect. 3); • using control tokens to steer the model towards consistency, increasing relevance of the generated summary (Sect. 4); • an application of multi-input transformer model  (Libovický et al. , 2018)  to summarization. This model encodes each input independently, and at decoding time applies parallel attention to each encoded input (Sect. 5). Our experimental results (Sect. 6 and 7) show that our proposed approach outperforms existing models on two datasets: Yelp reviews on venues  (Chu & Liu, 2019)  and Rotten Tomatoes movie reviews  (Wang & Ling, 2016).
At inference time, MeanSum encodes every review for a product to a vector, computes the centroid of reviews' vectors and uses this centroid to seed the decoder and generate a summary. However, averaging representations of statements that are sometimes contradictory tends to confuse the decoder, and to lead it to rely on only language modeling for generating the output summary, thus ignoring the input signal. To deal with this limitation,  Coavoux et al. (2019)  proposed to add a clustering step to identify similar reviews and to generate one sentence per such found cluster: the averaging step only targets similar reviews. Contemporaneous to this work,  Brainskas et al. (2019)  proposed to solve the problem of unsupervised summarization of reviews through an auto-encoder with latent variables.
Self-Supervision In order to create our training dataset we assume that a review s i for an entity (venue or product) can serve as a summary for a set of other similar reviews D i. This simple intuition allows us to create training points (D i , s i ) in a very similar way to what the model will experience at inference time. However, there are two issues with this approach. First, the potential set of training points is too large to explore exhaustively. Given the set of all reviews D the total number of possible input-output pairs is 2 |D|-1 × |D|. Second, the assumption that any review is fit to serve as a summary for any set of other reviews is obviously not true, and might yield a very noisy training dataset. To solve the combinatorial explosion, we limit the size of D i to k, and from a given s i , we look for a set of k good re-views D i , for which s i serves as a good summary.
In this setting Peyrard (2019) reduces then to Eq. 1 Then, we sort the data-points (d i , rel(d i )) according to the value of the relevance ( dj ∈rel(di) sim(d i , d j )). Depending on the desired size of the target dataset, we keep the top-T pairs for training. Limiting T inherently increases informativeness, since it limits the creation of training examples where input and outputs are repetitive similar reviews that might be very prominent on corpora level (e. g. "Great restaurant. "). In addition to simplicity, this method enables a fast implementation using state-of-the-art nearest neighbour search libraries  (Pedregosa et al. , 2011b). For all our experiments we defined sim to be the cosine similarity over a tf-idf bag-of-word representation  (Ramos et al. , 2003).