dataesr/scientific-paragraphs-categorization huggingface dataset:
Reliable measurements need to be developed in the future to assure complete anonymity of the source individuals given the released AGs. In particular, we will investigate whether the differential privacy framework is performant in the context of large population genomics datasets  (50, 51). Imputation results demonstrated promising outcomes especially for population specific low frequency alleles. However, imputation with both RBM and GAN AGs integrated reference panels showed slight decrease of info metric for higher frequency alleles compared to only 1000 Genomes panel (Figure  4 ). Increasing the number of AGs did not affect the results significantly. We initially speculated that this decrease might be related to the disturbance in haplotypic structure and therefore, tried to filter AGs based on chunk counts from ChromoPainter results, preserving only AGs which are below the average chunk count of real genomes.
It is expected that these interpretable representations of the data will be relevant for downstream tasks  (54)  and can be used as a starting point for various population genetics analyses. We want to highlight that AGs are created without requiring the knowledge of the underlying evolutionary history, or the pre-processing bioinformatic pipelines (SNP ascertainment, data filtering). Unlike coalescent simulations, for which one needs to control parameters, AGs in their current form are solely constructed on raw information of real genomes. Our method offers a direct way to generate artificial genomes for any original dataset. On the other hand, the genomes generated using a coalescent simulator required substantial upstream work (from previous studies) as they were based on an explicitly parameterized model that had been inferred on real data using advanced methods for demographic reconstruction.
In particular, this approach is not suitable when we want to generate AGs for highly complex datasets (eg full 1000 Genomes) for which it is arduous to infer a full evolutionary model accurately fitting the data and even more so, to mimic all the biases induced by potentially unknown bioinformatic pipelines. Last but not least, this coalescent generated data cannot be merged directly with real public genomes because there is no direct correspondence between the real SNPs and those generated, and coalescent approaches might struggle to match, among other things, real complex patterns of LD  (35). To summarize, while the classical coalescent simulator only allows unconditional sampling of a new haplotype h from a predefined distribution P(h|θ) where the demographic parameters θ have to be given, our generative models learn how to generate h from the conditional sampling distribution (CSD) P(h|h1,.
hn), where (hi) are the observed haplotypes. Computing, approximating or sampling from this CSD is known to be a difficult task  (34, 56, 57). We believe it will be possible in the future to extend our approach with conditional GAN/RBM methods to allow fine control over the composition of artificial datasets based on (i) additional labels such as population names or any environmental covariate, or (ii) evolutionary parameters. While the former is based only on real datasets, the latter requires training on genetic simulations (coalescent-based or forward) and has a different goal: it may provide an alternative simulator and/or permit inference of evolutionary models. We envision three main applications of our generative methods: (i) improve the performance of genomic tasks such as imputation, ancestry reconstruction, GWAS studies, by augmenting public genomic panels with AGs that serve as proxies for private datasets that are not accessible; (ii) enable preliminary genomic analyses and proof-of-concept before committing to long term application protocols and/or to facilitate future collaborations to access private datasets; (iii) use the encoding of real data learned by generative models as a starting input of various tasks, such as recombination, demography or selection inference or yet unknown tasks.
Data: We used 2504 individual genomes from 1000 Genomes Project (1000 Genomes Project Consortium 2015) and 1000 individuals from Estonian Biobank  (26)  to create artificial genomes (AGs). Additional 2000 Estonian genomes were used as a test dataset. Another Estonian dataset consisting of 8678 individuals which were not used in training were used for imputation via Impute2 software  (32). Analyses were applied to a highly differentiated 805 SNPs selected as a subset from (  25.
GAN model: We implemented the GAN model using python-3. 6, Keras 2. 2. 4 deep learning library with TensorFlow backend (58), pandas 0. 23. 4 (59) and numpy 1. 16. 4  (60). We implemented a fully-connected generator network consisting of an input layer with the size of the latent vector size 600, one hidden layer with size proportional to the number of SNPs as SNP_number/1. 2 rounded, another hidden layer with size proportional to the number of SNPs as SNP_number/1. 1 rounded and an output layer with the size of the number of SNPs. The latent vector is drawn from a Gaussian distribution with zero-mean and unit-variance. The discriminator is also a fully-connected network including an input layer with the size of the number of SNPs, one hidden layer with size proportional to the number of SNPs as SNP_number/2 rounded, another hidden layer with size proportional to the number of SNPs as SNP_number/3 rounded and an output layer of size 1.
All layer outputs except for output layers have LeakyReLU activation functions with leaky_alpha parameter 0. 01 and L2 regularization parameter 0. 0001. The generator output layer activation function is tanh and discriminator output layer activation function is sigmoid. Both discriminator and combined GAN were trained thanks to the Adam optimization algorithm with binary cross entropy loss function. We set the discriminator learning rate as 0. 0008 and combined GAN learning rate as 0. 0001. For 5000 SNP data, the discriminator learning rate was set to 0. 00008 and the combined GAN learning rate was set to 0. 00001. The training to test dataset ratio was 3:1. We used batch size of 32 and trained all datasets up to 20000 epochs. We also investigated stopping the training based on AATS scores.
The score was calculated at 200 epoch intervals. For 805 SNP data, AATS converged very quickly close to optimum 0. 5 score. However, the difference between AAtruth and AAsyn scores indicates possible overfitting to multiple data points so it was difficult to define a stopping point. For 10K SNP data, convergence was observed after ~30K epochs (to around 0. 75) and reduced the number of fixed alleles in AGs but the gain was very minimal (Supplementary Figure  22 ). Additionally, GAN was prone to mode collapse especially after 20K epochs which resulted in multiple failed training attempts. Therefore, this study presents results for AGs generated at 20k epochs, since the first two PCs of AGs combined with real genomes were visually coherent for all targeted datasets (Figure  1 , Supplementary Figure  5 ).
Note that it could be possible to utilize AGs before or after the 20K epoch point. During each batch in the training, when only the discriminator is trained, we applied smoothing to the real labels  [1]  by vectoral addition of random uniform distribution via numpy. random. uniform with lower bound 0 and upper bound 0. 1. Elements of the generated outputs were rounded to 0 or 1. After the training is complete, it is possible to generate as many AGs as desired. The code is available at "https://gitlab. inria. fr/ml_genetics/public/artificial_genomes".
001 and 0. 0001 and we used batch size of 32. The negative term of the gradient of the likelihood function was approximated using the PCDk method  (62) , with k=10 and 100 of persistent chains. As a stopping criterion, we looked at when the AATS score converges to the ideal value of 0. 5 when sampling the learned distribution. When dealing with large and sparse datasets for selection tests, RBM model did not manage to provide reasonable AATS scores because the sampling is intrinsically difficult for large systems with strong correlation. In that case, we used visually coherent PCA results as a stopping criterion. Once the RBM is trained over the dataset, it is possible, in order to avoid running a very long Monte Carlo Markov Chain, to initialize the chain on the training set. However, in the case of the large dataset (Estonian), we observe that the RBM is overfitting the dataset and therefore, starting from the training dataset makes the overfitting even worse.
In order to prevent this effect as much as possible, we used another independent dataset of Estonian individuals (denoted sampling set) to start the Monte Carlo Markov Chain. With this trick, we observe that the AATS score exhibits less overfitting than when the Markov Chain was started on the training dataset. We measure the privacy scores for both training and sampling sets compared to a test set. Similar to the GAN, it is possible to generate as many AGs as wanted after training. The relevant RBM code is available at "https://gitlab. inria. fr/ml_genetics/public/artificial_genomes".
Summary statistics: We define here the statistics that are not commonly used in population genetics. The PCA were computed on all datasets combined (e. g. Figure  1 ) as well as on "pairs" of datasets (the combination of real and a single type of generated data). 2D-Wasserstein distances for these paired PCA representations were computed based on the entropic regularized optimal transport problem with square euclidean distances computed from PCs 1 and 2 and a regularization parameter set to 0. 001 (POT library, (64)). To have reference values regarding the best achievable distances or correlations between real and generated summary statistics we split randomly the 1000Genomes dataset in two and considered half of it as the real dataset and half as a "perfectly generated" dataset (called test).
Nearest Neighbour Adversarial Accuracy (AATS) and privacy loss We used the following equations for calculating AATS and privacy loss scores  (29)  : 𝐴𝐴 𝑡𝑟𝑢𝑡ℎ = 1 𝑛 ∑ 𝟏(𝑑 𝑇𝑆 (𝑖) > 𝑑 𝑇𝑇 𝑛 𝑖=1 (𝑖)) 𝐴𝐴 𝑠𝑦𝑛 = 1 𝑛 ∑ 𝟏(𝑑 𝑆𝑇 (𝑖) > 𝑑 𝑆𝑆 𝑛 𝑖=1 (𝑖)) 𝐴𝐴 𝑇𝑆 = 1 2 (𝐴𝐴 𝑡𝑟𝑢𝑡ℎ + 𝐴𝐴 𝑠𝑦𝑛 )   =    -   where n is the number of real samples as well as of artificial samples; 1 is a function which takes the value 1 if the argument is true and 0 if the argument is false; dTS(i) is the distance between the real genome indexed by i and its nearest neighbour in the artificial genome dataset; dST(i) is the distance between the artificial genome indexed by i and its nearest neighbour in the real genome dataset; dTT(i) is the distance of the real genome indexed by i to its nearest neighbour in the real genome dataset; dSS(i) is the distance of the artificial genome indexed by i to its nearest neighbour in the artificial genome dataset.
point scores measure the correlation patterns for SNP triplets. The 3-point correlation for SNPs i, j, and k is defined as (63) :ci jk(a,b,c) = fi jk(a,b,c) -fi j(a,b) fk(c) -fi k(a,c) fj (b) -fj k(b,c) fi (a) + 2 fi (a) fj (b) fk(c) ,where the alleles (a,b,c) ∈ {0,1}3 , fi (a) is the frequency of allele a at SNP i, fi j(a,b) is the frequency of the combination of allele a at SNP i and b at SNP j, and finally fi jk(a,b,c) is the frequency of the combination (a,b,c) at SNPs (i,j,k). We computed the 3-point correlations for 8,000 randomly-picked triplets under different conditions (SNPs separated by 1, 4, 16, 64, 256, 512 or 1024 SNPs, as well as SNPs chosen at random) in each dataset.
Acknowledgements This work was supported by the  European Union  through the  European Regional Development Fund  (Project No. 2014-2020. 4. 01. 16-0024 ,MOBTT53: LP, DM, BY; Project No. 2014-2020. 4. 01. 16-0030 :LO, FM); the  Estonian Research Council  grant  PUT (PRG243 ): LP; DIM One Health 2017 (number  RPH17094JJP ): FJ;  Comunidad de Madrid  and the  Complutense University of Madrid (Spain)  through the  Atracción de Talento program  (Ref. 2019-T1/TIC-13298 ): AD;  Laboratoire de Recherche en Informatique  " Promoting Collaborations & Scientific Excellence of Young Researchers ": FJ. Thanks to  Inria TAU  team and  High Performance Computing Center of the University of Tartu  for providing computational resources. Thanks to  Isabelle Guyon  and  Adrien Pavao  for their valuable insight into AATS score,  Susana Ribeiro  for comments on the manuscript and  Lofti Slim  for discussion.
3 - 3 point scores measure the correlation patterns for SNP triplets. The 3-point correlation for SNPs i, j, and k is defined as (63) :ci jk(a,b,c) = fi jk(a,b,c) -fi j(a,b) fk(c) -fi k(a,c) fj (b) -fj k(b,c) fi (a) + 2 fi (a) fj (b) fk(c) ,where the alleles (a,b,c) ∈ {0,1}3 , fi (a) is the frequency of allele a at SNP i, fi j(a,b) is the frequency of the combination of allele a at SNP i and b at SNP j, and finally fi jk(a,b,c) is the frequency of the combination (a,b,c) at SNPs (i,j,k). We computed the 3-point correlations for 8,000 randomly-picked triplets under different conditions (SNPs separated by 1, 4, 16, 64, 256, 512 or 1024 SNPs, as well as SNPs chosen at random) in each dataset.
S2 ). In brief, MorphoGraphX  (16)  was used to extract the meristem surface, and segment [twodimensional (2D)] cells on which the curvature was mapped. In addition, three-dimensional (3D) cell segmentation of the epidermal layer followed by cell tracking was performed using multiangle image acquisition, three-dimensional reconstruction, and cell segmentation-automated lineage tracking (MARS/ALT, ref. 17). The nuclei were segmented in 3D using the MARS watershed algorithm. Finally, meristem regions and nuclear shapes were extracted and quantified using MATLAB (The MathWorks, Inc. ) scripts after projecting the nuclear signal in 2D. A subpopulation of nuclei at the boundary region exhibited a significant increase in their aspect ratio. In each boundary we could find a group of at least 10 nuclei with an aspect ratio superior to 1.
Confocal Laser Scanning Microscopy and Image Analysis. Dissected shoot apical meristems from greenhouse plants or naked meristems from in vitro-grown NPA-treated seedlings were imaged with a water-dipping lens (×25, numerical aperture = 0. 8) using an upright SP8 confocal microscope to generate a stack of optical sections with an interval of 0. 15 μm between slices. The membranes were stained with FM4-64 [also called N-(3-triethylammoniumpropyl)-4-(6-(4-(diethylamino) phenyl) hexatrienyl) pyridinium dibromide, ThermoFisher]. Images are presented using the Fire lookup table (ImageJ). Image analysis for chromocenter and CG-methylated regions detection was performed using the 3D object counter option in Fiji software (https://fiji. sc/wiki/index. php/Fiji). The nuclear average signal density was calculated following along the line of the protocol, described in ref.
Computational Methods. Segmentation. Surface extraction, 2D cell segmentation, and Gaussian curvature quantification were performed for the membrane channel (FM4-64) of the confocal stack images, using the LSM package following a published protocol [http://forge. cbp. ens-lyon. fr/redmine/projects/lsm3d (49) ] in Mor-phoGraphX software [https://www. mpipz. mpg. de/MorphoGraphX (16) ]. For 3D segmentation of the epidermal layer of cells and corresponding nuclei, an in-house script using the MARS/ALT algorithms was developed  (50). The confocal images were processed by cropping and applying Gaussian smoothing and alternating sequential filter. The resulting images were segmented using a watershed algorithm with automatic seeding  (50, 17). The segmentation was done for both the membrane channel (FM4-64) and the nuclear marker channel (p35S::SUN1-YFP) of each image.
For each segmented object (cell or nucleus), the z-slice with the largest area was extracted for further analysis. The aspect ratio was calculated as the length of the main axis divided by the length of the minor axis of the ellipse representing the covariance matrix of the pixel positions for the region. All properties were extracted using the MATLAB function regionprops. Software/protocol. Scripts required to do the segmentation and quantitative analysis are provided via the Sainsbury Laboratory GitLab repository (https:// gitlab. com/slcu/teamhj/publications/fal_et_al_2021), where also a more detailed protocol for executing the steps of the pipeline is provided. All scripts have been developed in Python (https://python. org) and MATLAB (Math-Works Inc. ), and the protocol is dependent on the MorphoGraphX [https:// www.
software. Data Availability. Original confocal data are available via the University of Cambridge Data Repository (https://doi. org/10. 17863/CAM. 64310). Scripts for the analysis pipeline are available via the Sainsbury Laboratory GitLab repository (https://gitlab. com/slcu/teamHJ/publications/fal_etal_2020). Tissue folding at the organ-meristem boundary results in nuclear compression and chromatin compaction https://doi. org/10. 1073/pnas. 2017859118 PLANT BIOLOGY.
ACKNOWLEDGMENTS. We thank  Célia Baroux ,  Fred Berger ,  Katja Graumann , and  Daniel Grimanelli  for providing material used in this article. We are grateful to  Grégoire Malandain  for the use of Block-matching software. We thank  Platim  for help with imaging. We also thank  Adrienne Roeder  for insightful suggestions on this manuscript. This work was supported by the  European Research Council  (Grant  ERC-2013-CoG-615739  "MechanoDevo"),  Human Frontier Science Program  Grant  RGP0009/2018 , the  Schlumberger Foundation for Education and Research, CNRS (mécanobiologie "NEstress") , and the  Gatsby Charitable Foundation  (Grant  GAT3395-PR4B ).
software. Data Availability. Original confocal data are available via the University of Cambridge Data Repository (https://doi. org/10. 17863/CAM. 64310). Scripts for the analysis pipeline are available via the Sainsbury Laboratory GitLab repository (https://gitlab. com/slcu/teamHJ/publications/fal_etal_2020). Tissue folding at the organ-meristem boundary results in nuclear compression and chromatin compaction https://doi. org/10. 1073/pnas. 2017859118 PLANT BIOLOGY Fal et al. PNAS | 7 of 8.
The fault restores the observations attached to the faulted geological feature to their location using the fault kinematics from the fault operator. The faulted geological feature can then be interpolated using the restored locations. To evaluate the scalar field of the faulted geological feature the fault transformation must be added to the implicit surface representation. This is done by applying the fault restoration kinematics to the data points prior to evaluating the scalar field values. φ f aulted (x, y, z) = φ base f dis p (x, y, z) (3) Using this work flow it is possible to stack multiple fault operators and apply, backwards in time, the appropriate displacements to restore the location to its position prior to faulting. This approach can be applied to any structural elements in the model, including lineations, fold vergence, tectonic foliations and fold frames  (Grose et al.
Acknowledgements. The authors would like to thank  Dr. Gabriel Godefroy  for fruitful discussions and reading an early draft of this manuscript. This research has been supported by  LP170100985 :  Loop -Enabling Stochastic 3D Geological Modelling  is a OneGeology initiative funded by the  Australian Research Council  and supported by  Monash University, University of Western Australia, Geoscience Australia , the  Geological Surveys of Western Australia, Northern Territory ,  South Australia  and New South Wales as well as the Research for.
Code and data availability. All of the 3D modelling examples presented in this paper have been generated using the open source 3D modelling package LoopStructural. LoopStructural can be downloaded from github https://www. github. com/Loop3d/LoopStructural or installed using pip install LoopStructural. The examples presented in this paper are included on an Jupyter notebooks including datasets are provided at 10. 5281/zenodo. 4698304 and can be run using the provided docker file or google colab. Author contributions. All authors contributed to the conceptual design of the fault implementation. LG implemented the methods in Loop-Structural. LG prepared the manuscript with help from all authors editing and improving.
Oxford Nanopore Technologies' (ONT) long read sequencers offer access to longer DNA fragments than previous sequencer generations, at the cost of a higher error rate. While many papers have studied read correction methods, few have addressed the detailed characterization of observed errors, a task complicated by frequent changes in chemistry and software in ONT technology. The MinION sequencer is now more stable and this paper proposes an up-to-date view of its error landscape, using the most mature flowcell and basecaller. We studied Nanopore sequencing error biases on both bacterial and human DNA reads. We found that, although Nanopore sequencing is expected not to suffer from GC bias, it is a crucial parameter with respect to errors. In particular, low-GC reads have fewer errors than high-GC reads (about 6% and 8% respectively).
Introduction Nanopore sequencing is based on measuring changes in the electrical signal generated from DNA or RNA molecules passing through nano-scaled pores. This third-generation technology is developed and marketed by Oxford Nanopore Technologies (ONT), that uses a small portable sequencing device called MinION  [1]. It offers many interesting features, including long read sequencing (the mean read length often exceeds 10 kb, and maximal read length now reaches up to 880 kb  [2] ), a real-time analysis and a low initial investment. However, although this technology has improved over recent years, it still exhibits a relatively high error rate on raw sequences compared to standard Next-Generation Sequencing (NGS) devices such as Illumina. In the Phase 1 early access program from Nanopore, a study from the MinION Analysis and Reference Consortium  [3]  showed that the 2D pass reads had a total error of 10.
5%, including about 3% for mismatch and insertion and slightly more for deletion. The software in charge of the translation from signal to nucleic sequences, the basecaller, has proven to be crucial over the years for the accuracy of the resulting raw read sequences  [4]. With the current technology and the most mature basecaller, and with great variations depending on organisms and read quality, the current mean global error rate on raw reads seems to be around 6% for quality scores at least equal to 10 (the basecaller filters reads whose quality scores are below a certain threshold). Many papers have studied ways to reduce the error rate of long read sequencing (either ONT or non-CCS (Circular Consensus Sequencing) Pacbio) by computing consensus sequences over subsets of reads. In fact, there is even a tool to evaluate error correction methods  [5].
Since the DNA translocation speed is not constant, this results in difficulties determining the exact length of homopolymers. In contrast to error-correction methods, relatively few papers have proposed to delimit the landscape of errors made by the MinION and other sequencers of the Nanopore series. This knowledge is important but not publicly available, as Nanopore does not publish its benchmarks and software content. The task is made difficult by the rapid changeover of equipment, chemistry and programs  [11]. Legget et al. have proposed an open-source software, NanoOK, to compare sets of references versus reads and produce an alignment-based analysis of errors and quality  [12]. They look for indel and substitution errors, and analyze k-mers that are either error-free or over-/under-represented before error positions.