dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset properties A set of properties introduced earlier was computed to characterize each dataset. These properties primarily include a set of measures calculated based on the silver standard segmentation annotations of test videos: signal-to-noise ratio, contrast ratio, internal signal heterogeneity of the cells (Het i ), heterogeneity of the signal between cells (Het b ), resolution (Res), regularity of the cell shape (Sha), cell spacing (Spa) measured as the average distance to the closest neighbor, an absolute change of the average intensity of the cells with time (Cha), and level of cell overlap in consecutive frames (Ove). The number of division events (Mit) was computed based on the gold standard tracking annotations. The remaining qualitative parameters were determined based on the visual inspection of the datasets: synchronization of division events (Syn), cells entering or leaving the field of view (Ent/Leav), and the presence of apoptotic cells (Apo) and of moving debris (Deb).
Top-performing algorithms CALT-US (*) is a segmentation-only algorithm that performs semantic segmentation with an optimized U-Net followed by post-processing probability maps to identify individual cells. It provides excellent results for 2D bright-field datasets with high signal heterogeneity (high-Het i and high-Het b ) but varying levels of Res and Spa (BF-C2DL-HSC, DIC-C2DH-HeLa and PhC-C2DL-U3T3), and for two 3D fluorescence datasets that, in general, have image quality dissimilar to 2D bright-field, as shown in Fig. 2  (Fluo-C3DL-MDA231 and Fluo-N3DH-CHO), indicating a high degree of versatility of the method, as evidenced by its top rank in the generalizability study (see 'Generalizability study' below). The algorithm uses a new regularization term for the loss that is designed to handle imbalanced classes and promote sharper segmentation, and introduces a novel four class-based semantic representation aimed to improve separation of cells, specially for those cases when small gaps exist between adjacent cells  40, 41.
KIT-GE (3) uses a two-branched 2D U-Net segmentation architecture. One branch predicts cell distance maps in which each cell pixel represents a normalized distance to the nearest pixel not belonging to the same cell. The other branch predicts neighbor distance maps in which each cell pixel represents an inverse normalized distance to the nearest pixel of the nearest surrounding cell. Tracking is based on the estimation of inter-frame displacement using cross-correlation, followed by cell matching across frames using a coupled minimum cost flow function  42, 43. The method has been designed to segment dense (low Spa), convex cells (high Sha). Accordingly, good segmentation results are obtained for the low resolution (low-Res) BF-C2DL-HSC (Fig. 2 ). However, the combination of cell and neighbor distance maps with an adapted tracker for error correction provides reasonable results also for more complex cell shapes such as those in the low-Res bright-field datasets PhC-C2DL-PSC and BF-C2DL-MuSC, regardless of their, respectively, very high mitotic activity (high Mit) and high variability (low Ove), and in two completely different 3D fluorescent datasets (Fluo-C3DH-H157 and Fluo-C3DL-MDA321).
This variety of modalities and image properties also demonstrates a high level of versatility. DKFZ-GE is a segmentation-only method based on a selfconfiguring U-Net pipeline called nnU-Net. It performs well for densely annotated datasets (Fluo-N2DH-SIM+, Fluo-N3DH-SIM+, Fluo-C3DH-A549 and Fluo-C3DH-A549-SIM+). For each dataset, nnU-Net is trained from scratch using no additional external training data. Given that nnU-Net is designed for semantic segmentation, instance segmentation was implemented through conversion into a two-class semantic segmentation problem (cell border and center), the predictions being converted back to cells by outgrowing the center predictions. The core strength of nnU-Net lies in the fact that its recipe for automatically configuring the segmentation pipeline was developed and optimized on the 10 datasets of the medical segmentation decathlon  44  , yielding more robust design choices and hyperparameters than methods tuned on individual datasets.
KIT-GE (4) extends the concept of cell instance learning  45  by simultaneously segmenting and tracking cells using an efficient, real-time deep neural network (ERFNet  46  ). This network predicts the offset of each cell pixel to its corresponding cell center in two consecutive frames. Because the ERFNet is small and has very few parameters, the model avoids overfitting on the training data. This method performs best on two low-Res, bright-field datasets (BF-C2DL-MuSC and BF-C2DL-HSC) and one 2D fluorescence dataset (Fluo-N2DH-SIM+). KTH-SE (1) uses a Gaussian bandpass filter for noise removal. Segmentation is based on a seeded h-dome-based watershed transform, which separates clustered cell masks on a thresholded version of the filtered image. The tracking part of the algorithm uses a global track linking algorithm, which greedily adds tracks one at a time using the Viterbi algorithm.
The track linking optimizes each track over all frames at once and can therefore make linking decisions based on prior and future frames. Because of that, it can handle spurious detections as well as under-and over-segmentation. This lightweight segmentation strategy has outstanding performance for most fluorescence datasets (Fluo-C2DL-MSC, Fluo-C3DH-H157, Fluo-N2DH-GOWT1, Fluo-N3DH-CE, Fluo-N3DH-CHO, Fluo-C3DH-A549-SIM+ and Fluo-N3DH-SIM+) of varying quality properties. It is also the most convenient and computationally efficient strategy for extremely large mesoscopic datasets. A closely related algorithm, KTH-SE (2) (ref. 36), top-1 performer for the embryonic datasets Fluo-N3DL-DRO and Fluo-N3DL-TRIC and top-3 for Fluo-N3DL-TRIF, takes advantage of the dynamic nature of the nuclei motion to preprocess the detected locations using a Gaussian mixture probability hypothesis density (GM-PHD) filter.
5. Note that low values of average human performance and high values of standard deviation are typically caused by the high level of difficulty of the dataset, not by poor work performance of the annotators. The variability of these human performance values can also serve as the baseline for algorithm performance comparisons. To this end, algorithms with scores in the range of the average manual scores ±1 s. d. can be considered to perform at the level of human annotators, and algorithms with scores above or below that range are said to perform better or worse, respectively, than the human annotators. Such a classification of the benchmarked submissions can be found in the Cell Segmentation Benchmark, Cell Tracking Benchmark and individual Analysis https://doi. org/10. 1038/s41592-023-01879-y sheets of Supplementary Data Tabs 5-9, Tabs 10-14 and Tabs 15-20, with the methods performing above and at the level of human performance being highlighted in green and blue, respectively.
To characterize the quality of silver standard segmentation annotations (silver truth, ST), we compared these annotations with the gold standard annotations (gold truth). Specifically, to evaluate how similar the masks in silver truth are to those in gold truth, we computed SEG ST using the standard SEG measure as if the silver truth was a participant's result. Similarly, the detection quality of the silver truth was calculated, and is denoted as DET ST. Owing to how silver truth is constructed (see 'Silver standard reference annotations' above), DET ST can decrease from 1. 0 only due to false negatives because silver truth annotations cannot have false positives. The DET ST values, therefore, implicitly reflect the coverage of the silver annotation. The SEG ST and DET ST values are summarized per dataset in Fig.
In the case of outliers, that is, values higher or lower than 1. 5-fold the interquartile range, points (outliers) exceed the whiskers. The Evolution of the Segmentation and Tracking Paradigms section includes plots with descriptive statistics for the machine learning and the non-machine learning techniques separately, by year. In these plots, median values are represented by a solid line; a dashed line plots the mean values. This analysis also consists of sums of the median and/ or mean values per dataset over the years (Extended Data Figs. 3, 5  and 7 ). For each year, the median and/or mean value of the performance on existing datasets was computed for each group (machine learning and non-machine learning) separately. These median and/or mean values were summed up and plotted in connected lines.
The significance level (alpha) was set at 0. 05 in all of the statistical analyses. Furthermore, to screen out low-performing or poorly fine-tuned CTC submissions, only those algorithms that simultaneously achieved DET and SEG scores (in the case of the CSB submissions), and SEG and TRA scores (in the case of the CTB submissions) above 50% of the reported human performance were considered. All statistical analyses were performed using R Statistical Software (v3. 4. 3; R Core Team) 47.
) (refs. 19,20), CALT-US (*), DKFZ-GE, IGFL-FR (*) (ref. 21), KIT-GE (3), KIT-GE (4), MU-CZ (2*) (ref. 22), MU-US (3*) (ref. 23), MU-US (4*) (ref. 24) and PURD-US (*) followed all of the reusability guidelines. KTH-SE (1*), implemented in MATLAB, was also considered reusable. The Colab notebook implementations are available in the CTC GitHub repository (https://github. com/CellTrackingChall enge/2021-edition-available-colabs).
Acknowledgements The authors thank  J. Padilla Pérez , who worked for many hours on the annotation of the new datasets;  J. -Y. Tinevez , who kindly added the CTC measures into the popular TrackMate software and who, with  T. Pietzsch , developed the Mastodon software that became instrumental for us when preparing the tracking annotations of the large embryonic datasets; and the participants of the challenge not included in the list of authors of this analysis paper, listed on the challenge website (http://celltrackingchallenge. net/participants/). This work was funded by the  Ministerio de Ciencia, Innovación y Universidades ,  Agencia Estatal de Investigación  ( MCIU/AEI/10. 13039/50110011033 )and  FEDER funds UE  under Grants  RTI2018-094494-B-C22 ,  TED2021-131300B-I00 ,  PDI2021-122409OB-C22  (C.
Code availability The evaluation routines used to produce the results reported in this article are freely available at the CTC website or the CellTrackingChallenge update site as a Fiji plugin, the source codes of which can be found at https://github. com/CellTrackingChallenge/. Furthermore, this public GitHub repository contains links to the executable versions of the individual algorithms and Colab Notebooks of those 11 participants who agreed to share their tools in a reusable form. The parameters used by the participants to produce their benchmarked results are listed on the CTC website.
Analysis https://doi. org/10. 1038/s41592-023-01879-y 47. R Core Team. R: a language and environment for statistical computing https://www. R-project. org/ (2017). Analysis https://doi. org/10. 1038/s41592-023-01879-y Extended Data Fig. 6 | Tracking (TRA) performance according to linking technique and modality. a) Bright-field (BF) b) DIC c) Phase Contrast (PhC), d) Fluorescence 2D (Fluo 2D) and e) Fluorescence 3D (Fluo 3D). For comparison of the techniques Kruskal-Wallis test with Dunn's post-hoc test were performed. The absolute difference between particular medians is reported as diff.
Discussion Since our last report  9  some important advances have occurred in the field, leading to significant improvements in the CTC, as discussed here. The CTC's first and possibly most important news, which reflects the evolution in the field, is the overwhelming presence of machine learning, most notably, deep learning models for cell segmentation. This is reflected by the number of submitted algorithms that rely on machine learning for the segmentation (60 out of 89). More importantly, these models outperform traditional, non-machine learning-based methods, as four out of the five algorithms at the top of the CSB and CTB leaderboards (KIT-GE (3), KIT-GE (4), CALT-US (*) and DKFZ-GE) use deep learning-based segmentation strategies. However, a few algorithms based on traditional segmentation approaches remain at the top-1 of the leaderboards for one or more datasets, and have a performance at the level of the methods based on machine learning.
Supplementary information The online version contains supplementary material available at https://doi. org/10. 1038/s41592-023-01879-y. stable rankings across all the three ranking schemes used (see Supplementary Data (Tabs 21-33) (CSB) and (Tabs 34-42) (CTB) for more details). Datasetspecific algorithm configurations trained on gold truth (GT), silver truth (ST), or both (GT+ST) are compared to universal configurations trained on all 13 datasets (prefix all). The unseen data types are marked by (U) and naturally miss datasetspecific configuration results. Legend for datasets names: (Please see Fig. 2 ).
, 2016)  and proposed future telescopes such as eXTP and STROBE-X  (Ray et al. , 2019; Watts et al. , 2019). While there are some open-source libraries for simulating the X-ray signals from rapidly spinning neutron stars and more generally from the vicinity of general relativistic compact objects including black holes  (Nättilä & Pihajoki, 2018; Pihajoki et al. , 2018)  the scope of these projects does not include statistical modeling, which necessitates tractable parametrised models and a modular framework for constructing those models. X-PSI addresses this need, coupling code for likelihood functionality (simulation) with existing open-source software for posterior sampling (inference).
The X-PSI package and science use X-PSI is an open-source Python package for Bayesian modeling of time-and energy-resolved X-ray pulsations. X-PSI provides a framework for the implementation of custom models, including likelihood and prior functions, and for feeding those models to open-source statistical sampling software for use on high-performance computing systems. X-PSI supplies modules for post-processing posterior sample sets, and supplies tools for visualisation. For example, one can generate time-and energy-resolved images and animations of model X-ray pulsars by tracing radiation from the stellar surface to a distant observer, such as a space telescope, as it propagates through spacetime; a snapshot of such an animation may be found in Figure  1. Posterior summaries can be plotted of the time-and energy-domain signals that a model pulsar is inferred to generate, conditioned on observational data.
X-PSI is being used by the NICER collaboration for pulse-profile modeling of X-ray emission from rotation-powered millisecond pulsars  (Riley et al. , 2021b (Riley et al. , , 2019a)). Many more papers have used the accompanying open-source analysis pipeline and products published on Zenodo  (Riley et al. , 2021a (Riley et al. , , 2019b)). The first were  Raaijmakers et al. (2019)  and  Bilous et al. (2019) , respectively on the topics of dense matter inference and multipolar magnetic fields. The numerical likelihood routines native to X-PSI are written in Cython  (Behnel et al. , 2011) , and are dependent on the GNU Scientific Library (GSL,  Gough, 2009). High-level objectoriented model construction is performed by a user in the Python language, as is the interfacing with sampling software.
Low-level customisation is encouraged in the extensions, either directly in Cython or via calls to external C libraries. X-PSI is Unix source code compatible, and release versions are freely available on GitHub under the GNU General Public License. Extensive documentation, step-by-step tutorials, and reproduction code for existing data analyses, are available via the GitHub repository, along with a growing suite of unit tests. Future plans include migration to Python 3, further improvements to post-processing software, and the implementation of an expanded suite of atmosphere models. Software: Python/C language  (Oliphant, 2007) , GNU Scientific Library (GSL,  Gough, 2009 ), NumPy (van der Walt et al. , 2011) , Cython  (Behnel et al. , 2011) , OpenMP  (Dagum & Menon,.
A publicly available human genome serves as both a valuable resource for researchers and a potential risk to the individual who provided the genome. Many actors with selfish intentions could exploit it to extract information about the donor's health or that of their relatives. Recent efforts have employed artificial intelligence models to simulate genomic data, aiming to create synthetic datasets with scientific merit while preserving patient anonymity. However, a major challenge arises in dealing with the vast amount of data that constitutes a complete human genome and the resources required to process it. We have developed a dimension reduction method that combines artificial intelligence with our knowledge of in vivo mutation association mechanisms. This approach enables the processing of large amounts of data without significant computational resources.
Our genome segmentation follows chromosomal recombination hotspots, closely resembling mutation transmission mechanisms. Training data is sourced from the 1000 Genomes Project, which catalogues over 2500 genomes from diverse ethnic groups. Variational autoencoders, utilising neural networks, serve as an extension to the generative model. Wasserstein Generative Adversarial Networks (WGAN) are a benchmark among generation methods for various data types. After optimisation of our data simulation strategy our pipeline allows the generation of a simulated population meeting several essential criteria. It demonstrates good diversity, closely resembling that found in the reference dataset. It is plausible, as newly generated combinations of mutations do not disrupt the linkage disequilibria found in humans.
Graphical abstract Pipeline for increased scale of synthetic genome generation. Genomic data is segmented into subsections, then compressed using deep autoencoders. Finally a generative neural network learns from this compressed genomic data to create novel encoded samples, which are then decoded back into full samples. We present here the Haplotypic Human Genome Generator (H2G2), a method to generate human genomic data on an increased scale. We first segment the data based on available biological knowledge and create a compressed representation of these subsections of data using dimension reduction methods. Then, these genomic identities are processed by a generative neural network to simulate novel samples, while remaining coherent with the source dataset. We use Human genomic data from the 1000 Genomes Project (The 1000 Genomes Project Consortium 2015), realigned onto HG38  (Lowy-Gallego et al.
The models also implement a decoder to restore the compressed data into its full state, as faithful to the original samples as possible over the course of the training process. Their strengths in dealing with any type of data, as well as handling nonlinear datasets with variable multimodal distributions, are particularly relevant for this project. The generation method we chose was the Wasserstein GANs  (Arjovsky, Chintala, and Bottou 2017). Their robust training process, by avoiding mode collapse and being less sensitive to hyperparameters when compared to other similar models, is particularly relevant for a project that seeks to increase the scale of data to be simulated. Additionally, we validated the realism of our simulated population by applying Chromopainter to reference and synthetic genomes  (Lawson et al.
Dimension reduction by autoencoders Segments generated from the previous section were then compressed using autoencoders (see methods section for full details). We first investigated the frequency of each mutation before and after compression via the autoencoders. We discovered that mutations with a low frequency in the reference dataset tend to be missing from the decoded dataset (figure  2a , upper left plot, highlighted by red box). This can be due to the tradeoff between higher compression rates and encoding rare variations. The autoencoder is not learning the rare occurrences in which the mutation should be present as a reasonable tradeoff for a gain in compression The poor representation of rare mutations could also be due to neurons in the model "dying", which can happen when using the ReLU activation function  (Ramachandran, Zoph, and Le 2017).
We therefore investigated multiple alternative activation functions, as well as a Variational AutoEncoder (VAE)  (Kingma and Welling 2013). Results in figure  2a  show that different activation functions do indeed affect the number of disappearing mutations. Interestingly, the TanH activation function yields the fewest of these disappearing mutations, but also systematically tends to overrepresent the frequency of mutations in the dataset (figure  2a , bottom row, highlighted in red). Overall, using a VAE with either sigmoid or TanH activation function show the lowest number of disappearing mutations as well as a low frequency drift (supp. figure  2a ). Sigmoid activation function was chosen, so as to avoid the visible bias of highly overrepresented mutations that occur when using TanH, both in VAE and classical AE.
To this end we used a tool called Chromopainter which reconstitutes the genomic ancestry of a query population based on the mutation profile of a donor population. Chromopainter was applied to both 78 reference genomes (three per sub-population listed in (Lowy-Gallego et al. 2019)) and 100 simulated genomes. From the raw results obtained, we established metaprofiles of ancestry switches, based on all reference or simulated genomes analysed (figure  3b ). These metaprofiles show strong correlation between the reference and simulated datasets. This means that our simulated population could realistically be obtained through population admixture in vivo. One other characteristic to analyse for this new population is privacy loss -that is, how close are these simulated genomes to the ones found in the reference dataset? To this end we use the Hamming distance to measure the number of substitutions necessary to transform one sequence into another of same length  (Hamming 1950).
It was used here as a part of the edit score, which is the ratio between the Hamming distance from a simulated genome to its nearest neighbour in the reference dataset and the total length of the sequence. This score therefore ranges from 0 (identical sequences) to 1 (no common elements in sequence). Applying it to the simulated section of chromosome 1 (figure  3c ) shows that none of our simulated genomes are exact copies of any reference samples. The minimum score is 0. 01, which is equivalent to just over 150 mutations difference.
Discussion In this study we propose a novel approach for generating human genomic data at a large scale and to a high degree of detail. This pipeline combines a novel dimension reduction approach, compression and a GAN structure. These allow a high level of parallelisation and a reduction of computational resources required to generate genomes. Specifically, we show that our models can generate realistic and novel mutation data at a larger scale and at an increased level of detail than previous AI based methods  (Yelmen et al. 2021). One of the main applications we envision for this sample generation pipeline is to create synthetic cohorts for specific sets of patients. A WGAN model that has been trained to process general genomes could be specialised on a smaller dataset, obtained for example by a healthcare establishment sequencing all its patients bearing a disease, or exhibiting similar traits.