dataesr/scientific-paragraphs-categorization huggingface dataset:
We generated a Maximum Likelihood tree with no migration and then added migration events and examined the incremental change in the variance explained by the model and the residual values between individuals. The goal was to detect any potential high residual value or migration edge between wild and domestic individuals. To further explore the statistical relevance of possible admixture vectors identified by TreeMix (Supplementary Table  3 ), we calculated the three-population test f3  28  as a formal test of genetic introgression, using the qp3Pop program of the ADMIXTOOLS suite  58  for each combination of groups. For Capra, the wpCH group was divided between Australian breeds, French breeds, and Italian breeds. Results are reported in Supplementary Data 2. Demographic inference. For each genus, we carried out ancestral demographic inference analyses using the MSMC model implemented in the MSMC2 software  25.
MSMC is based on the pairwise sequentially Markovian coalescent  59  ; however, it uses haplotypes of phased genome sequence data as input. For each analysis we used two individuals from one group, thus 4 haplotypes. Each analysis was repeated for another random set of two individuals, i. e. , a replicate of the analysis per group. Input and output files were generated and analyzed with the python scripts provided with the MSMC software and found at https://github. com/stschiff/msmctools. Analyses parameters were kept as default, except the mutation rate that was set to 2. 5×10 -8 and the generation length was set to 2 years. In order to estimate the uncertainty on the time estimates, we varied these parameters (mutation rate of 2. 5×10 -8 and 1. 0×10 -8 in combination with generation length of 2 and 4 years) and provided a rough estimate of the domestication period (see Supplementary Fig.
Acknowledgements This work was supported by the  European Union  7th framework project  NEXTGEN  (Grant Agreement no. 244356 , coordinated by P. T. ), the  FACCE ERA-NET Plus  project  CLIMGEN  (grant  ANR-14-JFAC-0002-01 ), the  LabEx OSUG@2020  (Investissements d'avenir - ANR10LABX56 ), the  Wellcome Trust  [ WT108749/Z/15/Z ], and the  European Molecular Biology Laboratory  (I. S. , L. Cl. , P. F. ). We thank  Marina Naval-Sanchez  for fruitful interactions. Maps were made using data freely distributed by the  Land Processes Distributed Active Archive Center (LP DAAC) , located at  USGS/EROS ,  Sioux Falls , SD (http://lpdaac. usgs. gov).
We test our method alongside other previously-published user-friendly methods, with focus on mid-gestation embryos, 3D cultures of pluripotent stem cells and pluripotent stem cell-derived neural rosettes. These contexts are chosen because they exemplify many of the problems in 3D segmentation that we set out to address: crowded overlapping nuclear signals, non-spherical nuclei, large numbers of nuclei, and decreases in signal intensity towards the centre of large structures. Using our editing tool, we manually annotated thousands of nuclei in these contexts to generate a set of segmented images which are made publicly available and which could be useful as a standard segmentation benchmarking dataset  (Ulman et al. , 2017). We consistently obtain accurate segmentation rates of >90% in these challenging contexts.
We demonstrate the utility of this tool by measuring the expression of the transcription factor Tcf15 at single cell resolution across the anterior-posterior axis of the E8. 75 mouse embryo. We also developed a non-disruptive method to fluorescently label the NE in live cells and used this to follow the history of cell-cell interactions and to document neighbour-exchange as pluripotent cells differentiate in 3D culture. This new tool, named "NesSys (Nuclear Envelope Segmentation System), adds to the expanding toolkit of segmentation approaches, each of which has its particular strengths. NesSys is particularly well suited for segmenting large numbers of nuclei arranged in complex 3D configuration without requiring extensive amounts of time, computational power, or userinput. We provide NesSys as an open-access, well documented, easy to install and user-friendly software.
An open access utility program and dataset for the benchmarking of nuclear segmentation methods. Determining the performance of a segmentation method requires a gold standard (or Ground Truth -GT) image dataset  (Amat et al. , 2014; Coelho et al. , 2009; Maška et al. , 2014; Ulman et al. , 2017). GT datasets may be computer-generated  (Rajaram et al. , 2012; Svoboda and Ulman, 2017; Wiesmann et al. , 2017) , or may consist of images which have been manually segmented by biologist specialists  (Coelho et al. , 2009). Recent initiatives have undertaken to make such datasets publicly available  (Ulman et al. , 2017). However, currently available GT datasets for nuclear segmentation only contain a 'nuclear content' signal and in most cases do not encompass the diversity in nuclear shape and sizes or the level of complexity found in mammalian systems.
For these reasons, we have generated a new image dataset with thousands of manually annotated nuclei for segmentation benchmarking. We used both a NE signal (LaminB1) and the more conventional chromatin signal (Dapi) (Fig. 3  and Table  S1 ). This dataset includes images with crowded, overlapping, heterogeneous, non-spherical nuclei in several different contexts, representing the particular segmentation challenges that we have set out to address (Fig. 3 ). Images include: • High density monolayer culture of differentiating mouse ES cells and we intend to deposit this dataset on a publicly available repository which will then be linked to from our website [https://framagit. org/pickcellslab/nessys]. In order to perform both manual annotation of nuclei in 3D and segmentation benchmarking, we developed additional programs that we provide alongside the Nessys method (Fig.
S4 ): 1) a 3D painting tool to establish GT segmentation data (we estimate that ~100 3D nuclei were manually segmented per hour). Note that this tool can also be used as a visual inspector and 3D editor to curate outputs from any automated methods. 2) An extensible segmentation comparator which can compute the benchmarking metrics described in  (Coelho et al. , 2009)  as well as 3D maps of accurate hits and segmentation errors (Fig. 3  and S4 ). This makes it possible to visually compare the frequency and distribution of each class of segmentation error for each tested segmentation output. Using these new tools and our DISCEPTS dataset, we compared the performance of Nessys to other previously published popular methods (Fig. 3  and 4  and Materials and Methods) listed below: • Mins  (Lou et al.
Quantification of performance metrics of NesSys in comparison with other methods using the DISCEPTS dataset To more throughoughy assess the performance of the tested methods on the DISCEPTS dataset, we represented precision and recall for each method and biological specimen as a radar plot (Fig. 4A ). Precision measures the proportion of the shapes in the output of a method which correspond to actual shapes in the input dataset while recall indicates the proportion of shapes in the input dataset that have been recognised by a method (see also supplementary materials section B 1 and 2). The F-measure, which combines precision and recall, is given in Table  S2  together with additional performance measures as described in  (Coelho et al, 2009)  and Table  S3  for reference. We confirm that all methods perform well for segmenting nuclei in blastocysts, where nuclei are generally near-spherical in the densest region (ICM) or well separated (TE).
Finally, we addressed the problem of processing time becoming limiting for large images. Using a desktop computer with 64GB of RAM, no method other than Nessys was able to process images larger than 2GB in our hands, whereas Nessys was able to process a time lapse image dataset of more than 120GB (not shown). This property of Nessys was enabled by the underlying SCIFIO I/O library  (Hiner et al. , 2016)  and ImgLib2 image structure  (Pietzsch et al. , 2012, 2 ) which allowed to process each time frame sequentially and which handled lager than memory images. Processing limitations depend on the computing resources available, but Nessys has the advantage of making it feasible to use a typical lab computer to segment large images. Some of the methods we tested failed to complete segmentation or resulted in prohibitively long segmentation times when images were larger than 200MB, we therefore report processing times for non-Nessys methods for image crops <200MB only (Fig.
Measurement of gene expression and morphological features in post-implantation embryos To illustrate the usefulness of Nessys segmentation and the associated 3D painter for the study of large embryos, we made use of mouse embryos that we have engineered to express venus under the control of the regulatory elements of Tcf15 (Lin, Tatar et al, in preparation), a transcription factor that marks the formation of somites  (Burgess et al. , 1996). The signal of the Tcf15 venus reporter is included in the E8. 75 image of the DISCEPTS dataset. Using Nessys segmentation on the full image, we generated a plot of the median Tcf15 intensity per nucleus versus position of nuclei along the antero-posterior (AP) axis of the embryo (Fig. 5A ). We observed that within the fraction of cells which expressed higher levels of Tcf15 than average (> 1000 AFU) several clusters of points along the AP axis were clearly apparent.
Tracking cell-cell interactions during differentiation of pluripotent cells To explore the utility of our tools for identifying cells within time-lapse datasets, we performed confocal live imaging of pluripotent cells differentiating into neural cells. We used mouse ES cells containing a Sox1-GFP reporter  (Ying et al. , 2003)  in order to detect Sox1+ neural progenitors as they emerge over time (Fig. 7A , Movie S3). We chose this system because during the first few days of this process, flat cells form tight 3D clusters in which it becomes difficult to distinguish individual cells, (Fig. 7A ). Notably, the neural monolayer culture condition was amongst the most challenging dataset to segment according to our benchmarking results (Fig. 4A  and B ). Although excellent tools exist for the automated live tracking of 2D cultures  (Bove et al.
, 2017; Toth et al. , 2018). For this reason we decided to label all the Sox1-GFP cells with our NE-mKate2 construct in order to analyse the collective behaviours of differentiating cells in 3D. Unlike snapshot images, where Nessys segmented cells with high accuracy without user intervention, tracking of this challenging time-laspe image required a significant amount of manual correction. However, NesSys segmentation plus manual correction could be completed comfortably within one day (approx 7h) whereas manual segmentation of the entire dataset would have taken around 70h (220 time points with an average of 30 cells per frame and at a rate of 100 cells segmented per hour). After segmenting each individual frame, we ran a basic tracker (Supplementary Methods section C) and were able to obtain accurate lineage trees (Fig.
Notably we confirmed that this observation was unlikely to be explained by a change in cell velocity (Fig. 7H right ). A division representative of this phenomenon is shown in (Fig. 7K ). While this dataset is too small to draw definitive biological conclusions, it illustrates that in principle it is possible to use Nessys and associated tools to measure the position of each cell in three dimensions and assess cellular behaviours of these cells with respect to their neighbours over time, even in challenging contexts where individal cells are tightly packed and difficult to distinguish by eye.
Discussion In this study we set out to explore the idea of using the nuclear envelope (NE) for nuclear segmentation as an alternative to DNA/chromatin labelling. This idea was proposed more than fifteen years ago and preliminary results on small 2D images were promising  (Ortiz De Solorzano et al. , 2001). Surprisingly however, this idea had not yet, to our knowledge, been revisited for complex 3D datasets. Here we present Nessys, a method tailored to automatically segment nuclei on the basis of the NE. We show that this method performs particularly well for 'difficult' datasets, where images contain a large array of nuclear shapes and sizes and where nuclei overlap. It is also a method that is fast and which scales well to large images. We discuss below the strengths and weaknesses of our method and we summarise our effort to make the method easy to adopt by the community and the usefulness of the tools and dataset we provide with this article.
Features of the NE signal explain Nessys performance To construct the DISCEPTS dataset we focused in sampling a diversity of nuclei shape, size, texture and 3D tissue organisations. The images we gathered provide a good illustration of the multiple challenges which still need to be addressed in nuclear segmentation and we believe this dataset will be useful as a GT dataset for benchmarking purposes. Importantly, this dataset illustrates that different cell types harbour different organisations of their chromatin leading to very heterogeneous appearances of the nuclear content. For example, the nuclei of neural cells contain regions of high affinity for dapi which can be seemingly randomly distributed within the nucleus, including at the periphery. This property can confound methods relying on the assumption that brighter regions reside at the centre of the shape.
2) Software usability The community is in need of open-source, well documented and easy to use softwares in order to accelerate quantitative, reproducible and collaborative research  (Cardona and Tomancak, 2012; Carpenter et al. , 2013; Meijering et al. , 2016). In order to align with this goal, and to facilitate adoptability of Nessys, the software is free and open-source (https://framagit. org/pickcellslab/nessys), the installation process only takes a couple of minutes and does not require particular knowledge in computing. Nessys has also been tested on Linux, Windows and MacOS. A video tutorial and documentation for Nesys are available online and the user interface includes interactive tools for aiding parameter adjustments (see also the issue tracker which allows comments from the community).
The software and associated documentation will continue to be improved in response to user feedback. A longer term goal is to remove the need for parameter adjustments altogether. Notably, we envision the possibility to use a few annotated volumes with the Nessys editor as a training set for automated parameter adjustments. An important practical aspect of Nessys is that it is designed to be fast and to avoid problems due to processing times-per-cell becoming limiting. We have shown that Nessys can produce accurate segmentation results in a reasonable time frame even for large datasets on a standard lab workstation (Fig. 4 ) 3) Code portability, extensibility and future work We developed Nessys using good software design practice. For instance, the user interface uses a modular wizard pattern so that individual subtasks may be improved without modifying others.
This will facilitate future improvements of the method. Such improvements may include replacing the last morphological filtering step with a modern shape refining method  (Machado et al. , 2018)  or using more sophisticated classifiers  (Domingos, 2012)  during the shape ranking process to further improve accuracy. Future work will also focus on enhancing visualisation of segmentation quality in order to speed up the segmentation editing process, for example by including a colour map of the segmentation confidence score as described in  (Nketia et al. , 2015). Nessys has initially been developed as a module for our own framework but is designed to be portable to other general purpose frameworks such as ImageJ  (Schindelin et al. , 2015)  or Icy  (de Chaumont et al. , 2012). This will help bridge our method with other powerful image analysis components.
Conclusion In conclusion, we hope that the tools presented here will contribute to accelerating quantitative research. We provide a robust 3D nuclear segmentation method together with a 3D editor. The software is easy to install as a stand-alone application and is designed to be portable to general purpose frameworks such as ImageJ. We also provide a dataset and a utility program for segmentation benchmarking in the hope that they will be useful to the community. These tools are free and open-source and designed to work on a standard lab computer with datasets generated from conventional microscopes.
Software Development and availability NESSys and associated programs are written in java. Our programs only depend on open-source libraries. These include ImgLib2  (Pietzsch et al. , 2012)  for the Image data structure, SCIFIO  (Hiner et al. , 2016)  and BioFormats  (Linkert et al. , 2010)  for Image Input/Output. We developed our code in Eclipse March 0. 3 (https://www. eclipse. org/). Dependencies are managed using Maven, and version control with Git. Source code is available on GitLab at framasoft https://framagit. org/pickcellslab/nessys. Implementation details, source code and user documentation are subject to changes as we continue to improve the software. Source code and updated documentation may be found on our GitLab repository hosted by framasoft ( https://framagit. org/pickcellslab/nessys).
Acini / 3D cell culture Wild-type E14tg2 alpha ES cells, Tcf15-Het and Tcf15-KO ES cells (Lin, Tatar et al. , in preparation) were used for this dataset (The name of the cell lines used for a given image is included in the name of the image). Cells were maintained in 2i-Lif condition and then plated onto Matrigel coated dishes into N2B27/1%KSR medium supplemented with 12ng of bFgf and 20ng of Activin A as described in  (Hayashi et al. , 2011)  and containing 600µg/ml of growth factor reduced Matrigel to induce the formation of 3D structures. The cells were fixed for immunostaining on day 2 of EpiLC induction.
Segmentation methods: versions, parameter adjustments and benchmarking: 64 bits versions of Ilastik (version 1. 1. 9), Farsight linux version 0. 4. 4 and Nessys v0. 1. 0 were tested on a desktop computer (i7 6700K 8 cores cpu, 64GB of RAM and a 7200rpm HDD) running Linux Opensuse Leap 42. 3. MINS version 1. 3 was run on the same computer but on a Windows 8 OS with Matlab (version R2016a 9. 0. 0. 341360). In order to obtain the best segmentation output possible for each method tested, we followed recommendations available for each tool (http://katlab-tools. org/, http://farsighttoolkit. ee. uh. edu/wiki/Nuclear_Segmentation, https://www. ilastik. org/documentation/index. html). For Nessys, Mins and Farsight, we screened 5 segmentation attempts with distinct sets of parameters and retained the output resulting in the best F-measure when compared to the GT (Table  S2 ).
A. Detailed description of the segmentation method An overview of the segmentation procedure is depicted in Fig. 2. This section provides a more detailed description of some of the key steps included in the segmentation method. Please also note that source code and the latest documentation (including advice on how to adjust parameters) can be found on the Nessys online repository: https://framagit. org/pickcellslab/nessys 1. Tree-structured ridge following procedure (Fig. S1 ) The purpose of this step is to identify outlines of nuclei within a given 2D image plane. Inputs received from the previous steps include: • The original NE signal for a specific plane • The corresponding ridge-enhanced plane (steerable filters output) • An image location to use for initialisation The procedure starts from a unique location in the 2D plane (maximum identified in the previous step) and follows the ridges in the vicinity.
Morphometric analysis We performed the computation of morphometric features in a software built in our lab called PickCells  (Blin et al. , in preparation, source  code is available at https://framagit. org/pickcellslab) and exported features to R (R Core Team, 2013) in order to build the plots shown in Fig. 4C  and Fig. S5. Features were computed as follows: ▪ Inter-centroid distance: The inter-centroid distance corresponds to the euclidian distance between the centroid of the GT shape and the best matching tested shape.
C. Additional analysis of Nessys outputs 1. Tcf15 expression analysis The Tcf15 expression analysis shown in Fig. 5  was performed in PickCells. The Nessys segmentation module was used to perform the segmentation of the full E8. 75 image of the DISCEPTS dataset. For Fig. 5C  and D, embryonic regions were manually annotated using the Nessys 3D painter module. A rule was created in PickCells to assign nuclei to a given embryonic region if their shape overlapped with the annotated region by at least 95%. Fig  5A  and B  3D representations were created using the 3D scene module in PickCells.
3D cultures of differentiating mouse ES cells embedded in matrigel • E3. 5 mouse blastocysts • Gastrulating mouse embryo at E7. 5, focusing on a region of the distal epiblast • Post-gastrulation mouse embryo at E8. 75, focusing on three regions of the embryo which capture both epithelial and mesenchymal structures as well as variations in signal intensity due to light scattering or antibody penetration issues. We called this dataset DISCEPTS (Differentiating Stem Cells & Embryos are a Pain To Segment).
Acknowledgements We would like to thank  Dr Chia-Yi Lin  for the Tcf15 embryos and  Dr Anna Kreshuk  for advice in using Ilastik and feedback on segmentation outputs. We are also grateful to all our colleagues who have taken the time to test Nessys and have given precious feedback. This manuscript has been generated with free and open-source software (Zotero, Inkscape, LibreOffice, Linux).
• 3D cultures of differentiating mouse ES cells embedded in matrigel • E3. 5 mouse blastocysts • Gastrulating mouse embryo at E7. 5, focusing on a region of the distal epiblast • Post-gastrulation mouse embryo at E8. 75, focusing on three regions of the embryo which capture both epithelial and mesenchymal structures as well as variations in signal intensity due to light scattering or antibody penetration issues. We called this dataset DISCEPTS (Differentiating Stem Cells & Embryos are a Pain To Segment).
| Participants We analyzed brain images from the TrackOn-HD study. Participants were recruited into the TrackOn-HD study at four study sites (London, Paris, Leiden, Vancouver) as previously described  (Kloppel et al. , 2015; Orth et al. , 2016). For the present analyses, we used data of those participants who had complete Diffusion Tensor Imaging (DTI) data. Sixty-one individuals (F 5 44%; mean age 6 SD: 43. 1 6 9. 1) carried the HTT gene with a CAG trinucleotide repeat expansion mutation of 39 but did not have a clinical diagnosis of HD. Thirteen participants (F 5 62%, mean age 6 SD: 43. 1 6 3. 3) had early motor manifest HD (UHDRS diagnostic confidence level of 3 or 4), and 79 were controls (F 5 61%, mean age 6 SD: 49. 1 6 9. 7). A global cognitive composite score was derived from nine cognitive tasks that were completed in testing sessions separate to the MRI procedures: Stroop Word Reading test, Symbol Digit Modality Test, Paced Tapping, Circle Tracing (two conditions), Map Search test, Cancelation task, the Spot the Change visual working memory task, Mental Rotation task  (Jones et al.