dataesr/scientific-paragraphs-categorization huggingface dataset:
PNAS 2023 Modeling and Prediction of Reassortment. To better understand and predict reassortment statistics, we applied direct coupling analysis (DCA) to our datasets. DCA is a machine learning technique that trains a probabilistic model of high-order distributions from pairwise frequencies  (58). It has been shown to predict sequenceto-activity relationships in enzymes  (59)  and is applicable to any relationships given measured pairwise frequencies. We applied a DCA model to learn parameters associated with single segments and segment pairs by maximizing the likelihood of the observed distribution of all segment combinations (Fig. 4A ). This way, DCA can generate eight-segment combination frequencies learning from the observed occurrences of lower-order combinations down to pairs. As we show below, this allows us to exploit incomplete but numerous data from dropout measurements and increases the prediction quality for genotype frequencies, including those not present in the experimental data on which the model is trained.
To extract the statistical signal from dropouts, we considered a three-state DCA model (each segment can be H1N1p, H3N2, or missing) in which the probability to observe dropout segments is explicitly taken into account. Such a model has 240 free parameters, whereas a two-state model that only considers H1N1p and H3N2 segment probabilities has 90 free parameters. These additional parameters allow the dropout biases to be absorbed without requiring preliminary normalizations. We compared a two-state DCA model trained on the nonnormalized dataset comprising eight-segment reassortants (2,592 droplets) and a three-state DCA model trained on the nonnormalized dataset comprising 2-to 8-segment reassortants (9,438 droplets). We assessed the quality of the models by bootstrapping, where 30% of the genotypes among all observed combinations were reserved for later validation (in practice, a random subset of 48 genotypes among the 159 observed ones).
The two-state model was first trained on the frequencies measured for the 70% of genotypes distinct from the 48 genotypes of the validation set, while the three-state model was trained on 70% of the 2-to 8-segment dataset after the removal of the validation set. Validation was then performed by computing the correlation between the genotype frequencies predicted by the model and those measured for the validation set (SI Appendix, Supplementary Methods). We found that the three-state DCA model trained on the dropout data is more predictive, as the correlation between the model and the validation set improved significantly, from Pearson's correlation of 0. 74 (P ≤ 10 -3 ) without dropout genomes to 0. 83 (P ≤ 10 -3 ) including dropout genomes (Fig. 4 B, Left ). This process was repeated on 30 random training and validation sets for each model, showing on average an increase from 0.
69 to 0. 79 of the correlation coefficient (Fig. 4 B, Right ). Taking into account dropout genomes, genotypes were predicted with better confidence than without dropout genomes for frequencies lower than 2. 7 × 10 -3 (Student's t test, P ≤ 10 -3 , SI Appendix, Fig. S16A ). For both two-state and three-state models, the correlation converged when varying the size of the training dataset including dropout genomes, but the correlation was less noisy and higher with the three-state model (SI Appendix, Fig. S16 B  and C ). The model did not overfit as fitting on 58% of the dataset with 2-to 8-segments (4,532 reassortant genomes) was sufficient to predict the 36% eight-segment reassortants (921 reassortant genomes) left for validation with Pearson's correlation of > 0. 80 and P ≤ 10 -5 (SI Appendix, Fig.
S16C ). A three-state model trained on the complete 2-to 8-segment dataset that only considers independent single-segment frequencies poorly reproduces reassortant genome frequencies observed in the merged dataset containing 2,592 reassortant genomes with all eight-segments (Fig. 4  C, Left), consistent with the existence of physical interactions between them, as previously reported for certain segments  (11, 13, 19, 37). In contrast, taking into account pairwise terms in the DCA model leads to a Pearson's correlation of 0. 87 with P ≤ 10 -5 between predicted and measured reassortant genome frequencies, as good as the correlation between experimental replicates (ranging from correlation of 0. 72 to 0. 94 with P ≤ 10 -5 (Fig. 4  C, Right and SI Appendix, Table  S6 ). The pairwise segment cosegregation frequencies generated by the DCA model trained on 9,438 reassortants with 2-to 8-segments closely resembled the data from eight-segment genomes (Fig.
To test this, we subsampled 2,592 genomes from the 254 possible reassortant genotypes using the probabilities given by the DCA model: 95% of the reassortant genotypes not observed in the subsamples had frequencies of <0. 07% in the model. Accordingly, 95% of the reassortant genotypes not observed in the eight-segment dataset had frequencies of <0. 0038% in the model, which is below the threshold obtained by subsampling.
Of note, targeted scRNA-seq using custom-made and commercial droplet microfluidic systems is becoming more common, for example, for targeted antibody and T cell receptor sequencing ((63); 10X Genomics Single Cell Immune Profiling system (  64 )). Targeted scRNA-seq is also being achieved through transcript enrichment prior to poly(A) capture and sequencing  (65) (66) (67). Our experimental design demonstrated high technical and biological reproducibility of the procedure (SI Appendix, Table  S6 ) and permitted the analysis of 2,592 complete (with eight segments) reassortant genomes (Dataset S2), vastly exceeding the number of genotypes analyzed by standard methods (≤72)  (35, 42, 43). With the microfluidic device being able to encapsulate 0. 5 × 10 5 to 1 × 10 5 "indicator" cells in < 10 min, there is virtually no limit to the number of reassortant genomes that can be analyzed-it is simply a tradeoff with cost, which is largely driven by sequencing.
Focusing on single cells where all eight segments were identified, our data demonstrate that reassortment of segments between IAV strains is far from random (SI Appendix, Table  S7 ). In this dataset, 159 of 254 possible reassortant genotypes were observed as a result of reassortment between the H1N1p and H3N2 strains (Dataset S1 and Fig. 3B ). Their distribution approximates a negative exponential, with very few genotypes present at high frequencies (maximum 8. 45%) and a large number at very low frequencies (minimum 0. 039%) (Fig. 3B ). Based on pairwise combination statistics, all 112 possible pairs of segments were detected, albeit at very different frequencies ranging from 0. 5 to 41% (SI Appendix, Fig. S9 ). Based on control experiments in which reassortment is impossible (SI Appendix, Fig.
S5B ), the frequency of false reassortants is below 1% (0. 93% for No-MixCtrl, 0. 14% for Lib-MixCtrl, and 0. 68% for Premix 1 + Premix2). The most frequent false reassortant genotype identified (an eight-segment genotype identified four times among the 9,546 eight-segment containing BCs of Premix2) represents 0. 042% of total BCs (cells) identified (Dataset S2 and SI Appendix, Table  S4 ), which constitutes a practical lower limit for the detection of rare genotypes. Hence, only the genotypes resulting from coinfection that were detected once or twice (41 genotypes, 0. 020 and 0. 040%, respectively, of all 4,952 eight-segment genotypes) fall below this limit. Inclusion of an additional 6,846 incomplete reassortant genomes ("dropout genomes") that, due to imperfect vRNA capture, contained missing (and nonidentified) segments ("dropout segments"), increased the power to determine cosegregation frequencies (SI Appendix, Fig.
S10  compared to SI Appendix, Fig. S13 ) but also introduced a minor bias in the data. Normalized frequencies of pairwise combinations of segments from the 9,324 reassortant genomes containing 4 to 8 segments (Fig. 3 G  and H ) closely matched those found in the 2,592 reassortant genomes of the eight-segment dataset (Fig. 3  D and E), as exemplified by the high Pearson correlation coefficient (r = 0. 95728, P < 4. 24 × 10 -60 (Fig. 3I ), justifying the use of dropout genomes. Of note, µFlu only detects viruses that are able to efficiently infect the "indicator" cells, efficiently replicate their vRNAs inside the newly infected cells, and efficiently express, at the cell-surface, the HA and M2 proteins used to sort the indicator cells by FACS. Such viruses must possess an active polymerase complex, i.
Limited or no detection in nature of reassortants that were detected in our experiments could be due to insufficient or biased sampling of natural reassortants or due to different selection pressures in MDCK cells vs. the human airway. Cosegregation of vRNAs due to optimal compatibilities between encoded viral proteins is expected to be most prominent within the polymerase complex, encoded by PB1, PB2, and PA, together with the nucleoprotein (NP), which are collectively required for vRNA replication, and between the HA and NA proteins, responsible for a regulated balance between cellular attachment and release, respectively. In our eight-segment dataset, we find that 19. 29% of all reassortant genomes (8. 8% of all reassortant genotypes) carry the homologous H3N2-(PB1-PB2-PA-NP) gene combination, while 14.
The H3N2-PB1 and -NA relationship that we and others  (13, 36, 37)  observed is also made apparent using this tool. Surprisingly, a strong relationship between PB2 and NA, which are not expected to cooperate in the viral replication cycle, was detected and supported by colocalization of the PB2 and NA vRNAs, inferring putative vRNA-vRNA interaction  (94). Accordingly, we also observed significant cosegregation of the H3N2-PB2 and -NA segments in an otherwise H1N1p-background. The highest degree of parallel evolution at the protein level, coupled with the lowest 10 of 12 https://doi. org/10. 1073/pnas. 2211098120 pnas. org parallel evolution at the vRNA level, was found between the H3N2-NA and -HA proteins  (94). In our eight-segment dataset, the H3N2-(HA+NA) combination represents 71. 9% of all reassortant genomes in an H3N2 background whilst the H1N1p-(HA+NA) represents 34.
As a direct extension of this work and any future reassortment analysis, characterization and comparison of the replication capacity of frequently vs. rarely observed reassortants will be of interest. To date, the database of genomic sequences compiles over 300,000 influenza virus strains. In recent years, efforts have been made to develop automated methods to identify reassortant strains  (95) (96) (97) (98) (99) , allowing the documentation of previously unrecognized reassortment events. Experimental data generated with the µFlu system combined with predictive methods such as DCA and with data mining using new algorithms (95-99) should enable a better understanding of the rules underlying IAV reassortment and prediction of the outcome of genetic reassortment. Finally, our work demonstrates that droplet microfluidics techniques originally developed for single-cell RNA-seq can be adapted to become a powerful tool to study viral evolution, with many applications beyond the field of influenza viruses Methods Cells and Viruses.
e. , cells containing evidence for expression of one or more segments from both viral strains) and low-quality cells were excluded from downstream analyses. For details, see SI Appendix, Supplementary Methods. Scripts can be found at http://github. com/ONeillMB1/uFlu. Dropout Normalization. The data on pairwise segment cosegregation frequencies from single-cell genomes, termed "dropout genomes," that contained one or more missing (and nonidentified) segments, termed "dropout segments," were normalized using the pairwise frequencies of the eight-segment reassortant genomes of individual experimental replicates (R1. 1, R1. 2, R1. 3, R2. 1, R2. 2, and R2. 3) For details, see SI Appendix, Supplementary Methods. Data Modeling. DCA was used to model the probability of genotypes P(s 1 ,s 2 ,. ,s 8 ) where s i is the segment identity in {H1N1, H3N2} at position i in {PB1.
Vol. 120 No. 6 e2211098120 https://doi. org/10. 1073/pnas. 2211098120 5 of 12 incomplete capture during RT. Primers were optimized to maximize the efficient and uniform amplification of all segments using all 16 H1N1p and H3N2 in vitro transcribed vRNAs. Next-generation sequencing results obtained with the final set of primers used in this study are shown in SI Appendix, Fig. S12A. Cellular expression levels of all 16 vRNAs, in the combined 8-segment dataset The 35 most frequent pair-wise The 35 frequent inter-strain pair-wise The 35 frequent pair-wise The 35 most frequent inter-strain pair-wise E D H G B y = 8. 91e -0. 122x R 2 = 0. 96 r = 0. 8996 p < 2. 27e-41 y = 0. 621x + 1. 794 of 8-segment and all of 8-segment and normalized dataset I r = 0. 95728 p < 4. 24e-61 y = 0. 862 x + 3. 445.
PNAS 2023 12 *Fig. 4. 2023124 Fig. 4. Modeling and prediction of reassortment. (A) The probabilistic DCA model trained using the frequencies of H1N1p/H3N2 at single or pairs of segments. The probability of observing H1N1p, H3N2, or a dropout at segment i depends on what is observed at the other seven segments j. The interactions with the other segments are modeled by the J ij terms. (B) Model generalization using dropouts. Forty-eight random genotypes among the 159 of the eight-segment genotypes observed were reserved as the validation set. The Left panel shows the comparison of two models: a two-state model trained on the 70% remaining genotypes of the eight-segment dataset (111 genotypes, gray dots) and a three-state model trained on 70% of the 2-to 8-segment dataset (1,208 among the 1,726 2-to 8-segment genotypes remaining after the removal of the validation set, orange dots).
The predicted (log) genotype frequencies are plotted against the measured (Observed) (log)frequencies of the remaining 30% reassortant genotypes [48] of the eight-segment dataset used as validation set. Pearson's correlation, r, is indicated. The box plots in the Right panel show the distribution of Pearson's correlation for the model trained on the eight-segment dataset (no-DO) and trained on the 2-to 8-segment dataset (DO), each obtained for 30 randomly drawn training and validation sets. The median values are indicated as a line at the center of the boxes. All correlations obtained have P ≤ 10 -3. The average gain (green dot) of predicted power resulting from using the 2-to 8-segment dataset is significant by t test (two-sample and one-tailed) on the two samples (****P < 10 -4 ). (C) Prediction quality of the three-state pairwise model.
For the model trained on the complete 2-to 8-segment (i. e. , including dropout genomes) dataset (no training/validation split), the interaction terms J ij (Right) improved the Pearson's correlation, r, for predicted (log)frequencies vs. observed frequencies of the validation set compared to the model without terms J ij (Left). (D) Prediction of pairwise segment frequencies by the three-state DCA model trained on the complete 2-to 8-segment dataset. Left: correlation between pairwise segment frequencies predicted by the DCA model (for eight-segment genomes) and observed frequencies in the eight-segment dataset (r = 0. 969, P <10 -5 ). Right: chord plot showing the pairwise segment frequencies predicted by the DCA model. (E) Histogram of observed genotype frequencies from the eight-segment dataset vs.
predicted frequencies of reassortant genotypes (N = 254) in the merged 2-to 8-segment dataset. The genotypes are shown as colored lines (red: H1N1p-derived segment, blue: H3N2-derived segment) and are ordered according to the number of H1N1p-derived segments. Observed and predicted frequencies are represented with black bars on the left and gray bars on the right, respectively. Red lines indicate the 25 to 75% confidence intervals obtained from the bootstrap experiment.
5 mM TCEP. Binding reactions were loaded onto a nondenaturing 10% polyacrylamide gel for electrophoresis as described previously  (34). After electrophoresis (14 V/cm at 4 • C), dried gels were exposed for autoradiography using a Typhoon Molecular Imager (Amersham) and quantified using ImageQuant software. Triplicate EMSA titration experiments were performed to extract apparent dissociation constants K Dapp which is close to the enzyme concentration needed for half-maximal binding under the experimental conditions chosen  (34). We used the Origins software (version 9. 0. 0, OriginLab) to fit the curves of the relative amounts of DNA/protein complex (C) as a function of the protein concentration (P) with a non-linear regression logistics function according to Hill equation: C = C max P n K n Dapp + P n , (1) where C max is the maximal amount of DNA/protein complex, K Dapp is the apparent dissociation constant and n is the Hill coefficient.
Analysis of the fluorescence correlation spectroscopy data Raw time traces were autocorrelated using the Fluctuation Analyzer 4G software after detrending for slow fluctuations using a detrending frequency of 0. 125 Hz  (36). When characterizing the exploration dynamics of WT OGG1 within the nucleus, the autocorrelation curves were fitted with the following diffusion model  (26) : G (τ ) = 1 N 1 + τ τ D α -1 1 + 1 κ 2 τ τ D α -1/2 , ( 5 ) where N is the number of fluorescent protein within the confocal volume, τ D is the characteristic residence time within the confocal volume, α is the anomalous parameter and κ is the structural parameter, which was fixed to 6. α was either fixed to 1 to fit the curves with a simple diffusion model or left free for the fitting with the anomalous diffusion model.
Statistics Statistics, figures and quantification post-treatments were performed using python home-made routines. Statistics were performed using the statannot (https://pypi. org/ project/statannot/) module from python. For the recruitment, FRAP and FCS curves, median ± SD are shown. Unless stated otherwise, all experiments were performed in at least three independent replicates and when data from a single experiment is shown they correspond to a representative replicate. For the boxplots, the bold line indicates the median value, the box limits correspond to the 25th and 75th percentiles and the whiskers extend 1. 5 times the interquartile range. p values were calculated with an independent t-test when comparing the Pearson's coefficients, with the Wilcoxon test when comparing the acquisitions pre and post DNA damage within the same nuclei, and with the Mann-Whitney test when comparing different conditions within a given experiment.
ACKNOWLEDGEMENTS We thank the  Microscopy-Rennes Imaging Center (BIOSIT, Université Rennes 1) , member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ), for providing access to the imaging setups, as well as  S. Dutertre  and  X. Pinson  for technical assistance on the microscopes. We would like to thank  J. Ellenberg  for sharing the plasmids coding for the GFP dimer and GFP pentamer,  Gianluca Tell  for sharing the plasmids coding for APE1 and the mutant  E96A  fused to GFP, and  F. Zhang  for the one coding for the GFP-tagged Cas9. Author contributions: O. D. A, V. G. , C. S. A. M. D. G and J. P. R. completed the experiments within the manuscript. R. S. , C. C. J. D. , X. V. and D. B. generated cell lines and DNA constructs.
FUNDING S. H. and B. C. received joint financial support from the  Ligue contre le Cancer du Grand-Ouest  [committees 22, 29, 35, 37 and 45]; S. H. received support from the  Institut Universitaire de France ; B. C. was also funded by the  Région Centre-Val de Loire  [ 2013-00082978 ,  2017-00117252 ];  Cancérop ôle Grand-Ouest  [project  CONCERTO ,  2018-001240994 ]; R. S. and J. P. R. are supported by the  Fondation ARC pour la recherche sur le cancer  [ PDF20181208405 ,  PJA20181207762 ]; A. C. lab received funding from the  Commissariat à l'Energie Atomique (CEA) Radiobiology program  and by the  Agence Nationale de la Recherche  [ PRCI-2018  TG-TOX]; O. D. A. is supported by a joint fellowship funded by the  Région Bretagne , the  CEA  and the  Ligue Nationale Contre le Cancer. Funding for open access charge:  Institut Universitaire de France  and the  Commissariat à l'Energie Atomique.
Introduction Peculiar (i. e. , gravitational) velocities of galaxies are a robust probe for the search of dark matter on the large scales in the Universe. Their radial component can be computed in a basic way directly from galaxy distances. This method is immensely prone to a variety of Malmquist biases. In order to map the local dark matter distribution and to measure various cosmological parameters, modern cosmologists would rather use a full reconstruction in 3D of peculiar velocities. Such reconstructions are based on the Wiener filter algorithm, as well as forward modeling of the dataset and likelihood. Unfortunately, very few public releases of 3D peculiar velocity reconstructions are available to date, the largest one being the reconstruction from the redshift survey 2MASS by  Lavaux & Hudson (2011).
Data The fourth release of the CosmicFlows catalog  (Tully et al. 2022)  provides about 56 000 measurements of galaxy distances and about 1000 supernovae distance moduli measurements. Such a composite catalog of distances delivers the raw material to compute radial peculiar velocities. Since the first Cos-micFlows catalog, our peculiar velocity computational tools have evolved from direct analysis of Malmquist-biased radial peculiar velocities (CF1 1600 galaxies), to the Wiener filter linear 3D-reconstructed dataset (CF2: 8000 galaxies), which allowed for some modern cosmography of our local Universe to be built  (Courtois et al. 2013) , to a forward modeling iterative procedure for the third, much larger dataset (CF3: 18 000 galaxies). CF3 reached greater distances in the southern terrestrial hemisphere and was used to discover distant features such as the Cold Spot Repeller  (Courtois et al.
2017 ) and the Vela Supercluster  (Courtois et al. 2019). The current CF4 dataset is three times larger in terms of the number of galaxies than CF3 and it is doubling its reach in the northern hemisphere. Estimator to compute the growth rate of structures using 1008 supernovae (in green), 36 000 galaxy groups (in blue), and 56 000 individual galaxy distances (in red). Computation was carried out as in  Dupuy et al. (2019). The distance r is the pairwise separation bin in h -1 100 Mpc between radial peculiar velocities in the datasets. Error bars represent the observational errors of the distance moduli (no cosmic variance is considered in this plot). The number density of data per bin of pairwise separation is of crucial importance in order to assess the result. The current supernovae sample is too sparse for separations smaller than 20-30 h -1 100 Mpc.
Growth rate measurement from radial peculiar velocities In this Letter, we use a velocity statistical indicator, noted ψ 1 , introduced by  Gorski et al. (1989). This indicator only depends on radial peculiar velocities, and ψ 1 is defined as follows: ψ 1 (r) = u A • u B (r A • rB ) 2 = u A u B cos θ AB cos 2 θ AB. (1)  Dupuy et al. (2019) , using the direct radial peculiar velocities of the CF3 dataset, found f σ 8 = 0. 43(±0. 03) obs (±0. 11) cos. var. out to z = 0. 05. Figure  1  shows the computation of the estimator Ψ 1 using the radial peculiar velocities of the 1008 supernovae in CF4 (in green), and of the 36 000 galaxy groups in CF4 (in blue) and the full 56 000 individual galaxies (in red). It appears clearly that the small number of supernovae per bin of pairwise separation is too small for pairwise separations smaller than 20-30 h -1 100 Mpc.