dataesr/scientific-paragraphs-categorization huggingface dataset:
The cumulative H3K27ac profile of Fig. 2awas computed with a custom Python script available on the GitHub repository. To reduce the impact of variations in gastruloid growth speed for the H3K27ac and H3K27me3 datasets in Extended Data Figs. 8 version 1. 16 (-a 'GATCGGAAGAGCACACGTCTGAACTCCAGTCAC' -A 'GATCG GAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTAT CATT' for ChIP and -a 'CTGTCTCTTATACACATCTCCGAGCCCACGAG AC' -A 'CTGTCTCTTATACACATCTGACGCTGCCGACGA' for ChIPmenta- tion --quality-cutoff=30). Trimmed reads were mapped on the mm10 genome (or the mutant genome Ins(2×CBS-d4d8) for the correspond- ing mutant CTCF ChIP-M) using Bowtie2 version 2. 3. 4. 1 (ref. 75). Only alignments with proper pairs and mapping quality above 30 were kept using Samtools 1. 8. Peaks and coverage were obtained with macs2 version 2.
Parameter optimisation and subsequent posterior decoding is performed as detailed by  (Barroso et al. , 2019). Under strict neutrality, the inferred θ landscape is an approximation to the landscape of de novo mutations (μ). iSMC can therefore be used to infer genome-wide variation in mutation rates with single-nucleotide resolution, and statistical noise is reduced by averaging the posterior estimates of θ within larger genomic windows. In order to increase power, we further extend iSMC to accommodate multiple genomes. In this augmented model, input genomes are combined in pairs such that the underlying genealogies have a trivial topology which is reduced to their τ (Figure  1 ). Although under Kingman's Coalescent  (Kingman, 1982)  the genealogies of multiple pairs of genomes are not independent, we approximate and compute the composite log-likelihood of the entire dataset by summing over the "diploid" loglikelihoods, similarly to MSMC2  (Malaspinas et al.
036) is in line with previous estimates  (Chan et al. , 2012) , and the coalescence rates suggest a relatively recent ~4-fold bottleneck followed by a fast recovery. We used these parameters estimated from D. melanogaster to simulate 10 replicate datasets (see Methods). The aims of these simulations are (1) to benchmark iSMC's accuracy in reconstructing the mutation landscape; and (2) to understand how the genomic landscapes of ρ, θ and τ interact to influence 8 diversity levels under neutrality, thereby providing a measure of contrast for the analyses of real data (where natural selection is present). We first report strong correlations between simulated and inferred maps, ranging from 0. 975 to 0. 989  (Table 1, Figure 2 ), showing that our model is highly accurate under strict neutrality and when mutation rate varies along the genome in Markovian fashion.
We then used the genomic landscapes from these simulated datasets to investigate how evolutionary forces shape the distribution of genetic diversity along the genome, measured as π, the average heterozygosity of the sample. The structure of our hypothesized causal model of diversity (Figure  3 ) suggests the absence of "backdoor paths" creating non-causal associations between ρ, θ, τ and π  (Pearl & Mackenzie, 2018). Therefore, we represented our model as an ordinary least squares regression (OLS) that seeks to explain π as a linear combination of the centered variables ρ, θ and τ, 9 variables and serves as a ground truth assessment of how neutral evolutionary forces influence genetic diversity. In all replicates, we found that model selection using Akaike's information criterion favours a regression with an interaction term between the two variables that directly influence π π i =β 1 ⋅τ i +β 2 ⋅θ i +β 3 ⋅ρ i +β 4 ⋅θ : τ +ϵ i over the simpler model π i =β 1 ⋅τ i +β 2 ⋅θ i +β 3 ⋅ρ i +ϵ i.
Simulation study Using SCRM  (Staab et al. , 2015) , we simulated 10 haploid sequences of length 30 Mb with value over 10 replicates, but the standard deviation of these estimates are very small and confidence intervals were therefore omitted. All scripts necessary to reproduce the analyses and figures, as well as supplementary figures are available at https://github. com/gvbarroso/ismc_dm_analyses.
Genomic coordinates for coding sequences and their summary statistics (π N , and π S ) were taken from  (Moutinho et al. , 2019). Data analyses procedures (starting from the inferred iSMC landscapes) such as building linear models and testing Gauss-Markov assumptions are detailed in the script dm_analyses. Rmd, available in the GitHub repository: https://github. com/gvbarroso/ismc_dm_analyses /r_scripts/analyses. Intermediate tables are provided in the FigShare repository: 10. 6084/m9. figshare. 13164320.
, 2022). Colocalisation analyses of gene expression used lung tissue expression quantitative trait loci (eQTL) summary statistics from the Genotype-Tissue Expression (GTEx) data version 8. Analyses of molecular phenotypes were performed using 343 lung adenocarcinoma samples of European ancestry from The Cancer Genome Atlas (TCGA) cohort with germline profile, RNAsequencing, and epidemiological data available. Genotyping and imputation of germline variants have been described elsewhere  (Gabriel et al. , 2022). The total somatic mutation burden of TCGA samples was obtained from  Ellrott et al. , 2018 , and DNA mutational signatures were extracted and attributed, as previously described  (Gabriel et al. , 2022). RNA-sequencing data were obtained from TCGA data portal using TCGA biolinks package in R  (version 2.
Linkage disequilibrium score regression Genetic correlations across traits were calculated using linkage disequilibrium score regression (LDSC) by the LDSC package (v1. 0. 0;  Bulik-Sullivan et al. , 2015). Linkage disequilibrium (LD) scores were generated on the 1000 Genomes Project Phase 3 reference panel with the HLA region excluded as provided by the package due to long range LD patterns. The genome-wide correlations that passed Bonferroni correction (adjusted p-values<0. 05) were considered statically significant.
Principal component analysis based on RNA-sequencing data Read counts of RNA-sequencing data were normalised within (GC-content and gene length) and between (sequencing depth) lane procedures by EDASeq R package (version 2. 28. 0;  Risso et al. , 2011)  and excluding low read counts. Principal component analysis was applied using singular value decomposition method, after excluding extreme outliers. Pathway analyses were conducted using Gene Set Enrichment Analysis software  (GSEA, version 4. 2. 3; Subramanian et al. , 2005)  on gene annotations from Gene Ontology database. Pathway analyses were restricted to the top 500 genes positively and negatively correlated with each principal component that passed multiple-testing correction (Bonferroni-adjusted p-value<0. 05 for 74,465 tests), which is the maximum number of genes supported by the online software.
The PRS for LTL was composed of the same 144 variants used in the MR analysis and was computed as the sum of the individual's beta-weighted genotypes using PRSice-2 software  (Choi and O'Reilly, 2019). Associations were estimated per SD increase in the PRS, which was normalised to have a mean of zero across lung adenocarcinoma samples of European ancestry within the TCGA cohort. The associations between the eigenvalues of the gene expression principal components (outcome) and demographic, clinical, and genomic features related to genome stability (predictors derived from TCGA published papers and TCGA data portal, except for the DNA mutational signatures  [Gabriel et al. , 2022] ) were calculated using a multivariate linear regression model.
Inferring PC2 gene expression signature based on RNA-sequencing data The TCGA-lung adenocarcinoma (LUAD) tumour samples were split into training (70%, N=255) and validation (30%, N=108) datasets. Principal components analysis based on RNA-sequencing data was performed to summarise the gene expression profiles of lung adenocarcinoma tumours into five principal components in the training and validation datasets separately, as previously described (see 'Principal component analysis based on RNA-sequencing data' section in methods). Subsequently, we applied the partial least squares-based method called rigid transformation  (Hubert and Branden, 2003)  to align the first five principal components in both datasets. This method compares embeddings, low-dimensional representations in both datasets, performing a slightly rotation of principal components in order to translate and match them in both training and validation datasets.
To select the most informative genes of PC2 in the training dataset, RNA levels of the genes correlated with PC2 (N=3914 out of 14,893 genes, FDR <0. 05 for 14,893 tests) were selected as variables for the LASSO regression models. The LASSO tune parameters were chosen by resampling the training dataset (1000 bootstraps: root mean of SE=0. 12 ± 0. 0004, Lambda = 10 x 10 -10 , r 2 =0. 99 ± 0. 00007) using the tidymodels metapackage in R (v1. 0. 0; wrapper of glmnet). The 10 genes selected by the LASSO model were used to infer the gene expression signature of PC2 by adding up the scaled values of the lognormalised read counts of each gene multiplied by the respective LASSO regression coefficients. For validation purpose, the inferred PC2 signature was calculated in the validation dataset and compared with the observed principal components.
Acknowledgements We would like to acknowledge the TCGA Research Network (https://www. cancer. gov/tcga) and the contribution of specimen donors and research groups involved in this resource. We also would like to acknowledge the ILCCO consortium, the participants of the UK biobank and GTEx project and the supporting bodies (https://commonfund. nih. gov/GTEx), specimen donors, and research groups. This work was supported by the  Institut National du Cancer (INCa)  ( GeniLuc 2017-1-TABAC-03-CIRC-1 -[TABAC 17-022 ]),  NIH/NCI ,  INTEGRAL NIH   5U19CA203654-03 ,  Cancer Research UK  (grant number  C18281/A29019 ), the  France Génomique National infrastructure , funded as part of the  « Investissements d'Avenir » program  managed by the  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ).
Data availability Lung cancer GWAS summary statistics obtained from ILCCO can be accessed by the database of Genotypes and Phenotypes (dbGAP) under accession phs000876. v1. p1. The GWAS summary statistics for tobacco-smoking behaviors (GSCAN: https://conservancy. umn. edu/handle/11299/ 201564), LTL (https://figshare. com/s/caa99dc0f76d62990195), and GTEx version 8 (downloaded via GTEx google cloud resource) are publicly available. Germline data of TCGA cohorts were accessed by dbGAP under accession number phs000178. v11. p8 and project application #2731. RNA-sequencing data from TCGA cohorts were retrieved from GDC open-access data portal (https://portal. gdc. cancer. gov/) using TCGAbiolinks package in R. TCGA-related data are publicly available as described in the data section. The code for LDSC analysis is available at: https:// github.
com/bulik/ldsc/wiki/Heritability-andGenetic-Correlation. The codes used in this study for two-sample MR, colocalisation, multi-trait colocalisation, and principal component analyses are available at https://github. com/ricardocortezcardoso/Telomere_Length_Code, (Penha, 2023a, copy archived at swh:1:rev:f365df300919c46bb99a96b4040d90576fc878e2). Plots were created using R packages 'meta' (v5. 5, forest plots), 'corrplot' (v0. 92, correlation matrix), and 'ggplot2'  (v3. 3. 6). The R package to generate stackplots for visualisation of the multi-trait colocalisation results is available at https://github. com/jrs95/gassocplot, (Penha, 2023b, copy archived at swh:1:rev:ae6a59dff2e43d39eead3d483af1d50f151c3d5b).
Although the holy grail to store and manipulate data in Edge infrastructures is yet to be found, state-of-the-art approaches demonstrated the relevance of replication strategies that bring content closer to consumers: The latter enjoy better response time while the volume of data passing through the network decreases overall. Unfortunately, locating the closest replica of a specific content requires indexing every live replica along with its location. Relying on remote services enters in contradiction with the properties of Edge infrastructures as locating replicas may effectively take more time than actually downloading content. At the opposite, maintaining such an index at every node would prove overly costly in terms of memory and traffic, especially since nodes can create and destroy replicas at any time.
When a client wants to access specific content, it requests a remote node to provide at least one node identity to retrieve this content from. After retrieving the content, the client can create another replica to improve the performance of future accesses, but it must recontact the indexing service to notify of the creation of this new replica. Such approaches are in contradiction with the objectives of Edge infrastructures that aim at reducing the impact of latency as well as the volume of data passing through the network. First, accessing a remote node to request content location(s) raises hot spots and availability issues. But most importantly, it results in additional delays  [3, 12]  that occur even before the actual download started. Second, the client gets a list of content locations at the discretion of content indexing services.
Definition 2 (Scoped broadcast). When Node x scoped broadcasts b x (m) a message m, every correct node y within a scope receives r y (m) and delivers it d y (m). The scope depends on the state σ of each node, the metadata µ piggybacked by each message, and a predicate ϕ verified from node to node: (b x (m) ∧ r y (m)) =⇒ ∃π xy : ∀z ∈ π xy , ϕ(µ z , σ z ). This definition encompasses more specific definitions of related work  [22, 28, 38]. It underlines the transitive relevance of messages. It also highlights that the functioning of epidemic propagation is well-aligned with the objectives of scoped broadcast. As consequence, we assume implementations relying on the forwarding of messages from neighbor-to-neighbor. Definition 3 (Forwarding). When x forwards f x (m) a message m, it sends it (s xy (m)) to all its neighbors y accumulating and aggregating (⊕) metadata that depends on its local knowledge σ x : f x (m) =⇒ ∀⟨x, y⟩ ∈ E : s xy (m ⊕ σ x ).
Having the entire knowledge of all the replica locations along with distance information carried into messages, each node easily determine where the closest copy of specific content resides, without contacting any remote service. In a routing context, distance-vector routing protocols RR n°9418 such as BGP or OSPF  [32]  similarly broadcast information to all other nodes to infer a topology map of the network and derive routing tables. Conflict-free replicated datatype (CRDT)  [34]  for set data structures could also implement such a location service. Nodes would eventually have a set of all replicas, assuming eventual delivery of messages. Such solutions imply that every node receives every message, which contradicts the principles of Edge infrastructures that aim at reducing the volume of data passing through the network.
Multitask deep learning enables simultaneous tree counting and crown segmentation We established a multitask deep learning-based network for jointly solving the individual tree counting (Task 1 in Fig. 1 ) and crown segmentation tasks (Task 2 in Fig. 1 ) from 2D imagery for both forest and nonforest trees (66). As input data, we used RGB and nearinfrared (NIR) aerial images at 20-cm resolution from summer 2018 and a canopy height map projected from airborne LiDAR data at 40-cm resolution. Note that the input data can be aerial images with different spectral band compositions and the inclusion of a height map from LiDAR is optional. Performance comparison between models trained with different sources of input data can be found in Table  S1  and Fig. S1. As target output references, 21,787 individual tree crowns from different forest and nonforest landscape types were manually delineated by visually inspecting the aerial images without any semiautomatic assistance (Fig.
S9b ). We evaluated the tree counting performance using 1,645 Finnish NFI field plots (12. 62-m radius) collected in 2019  (70)  (see plot locations in Fig. 6A ). For each field plot, the tree count predictions from aerial images captured in 2019 were compared against the number of tree trunks from the inventory data (see examples in Figs. 6C  and S10 ). The relative bias was -26. 2% for all plots. The bias was low (-8. 0%) for less dense (tree count < 500/ha) plots (43. 5% of all plots), yet higher (-40. 2%) for highly dense (tree count > 500/ha) plots (56. 5% of all plots). Notably, the NFI data included all trees taller than 1. 3 m, and trees branched below breast height were counted separately for each branch  (70). We evaluated the performance of individual tree height prediction on randomly sampled images (3,000 ha containing 925,597 predicted tree crowns) from three regions in Finland (21,600 ha), where images and LiDAR height data were collected during the same period (2019).
Discussion We established a deep learning-based framework for individual overstory tree mapping and height prediction in forest and nonforest areas from high-resolution aerial images and applied it to two European countries with dissimilar data sets and landscape types. Our approach enabled the derivation of the height information, normally only available from high-cost LiDAR data, from less expensive aerial imagery  (62, 63, 71). While aerial images cannot be considered "low cost," the availability of submeter resolution images from nanosatellites, such as SkySat (  72 ), provides a comparable quality for a reasonable price  (73, 74). We propose such an individual tree localization and characterization approach as the means to produce a comprehensive tree database with a focus on nonforest trees, which are essential yet often not systematically investigated by conventional forest inventories  (9).
The quantification of these nonforest tree resources is important, as we found that following existing forest maps and definitions, about 91 million trees in Denmark were located outside forests and thus were not part of the NFI statistics  (69). Taking advantages of the high-resolution aerial data, our method unambiguously determined trees as separate objects. Such information could supplement the existing coarser resolution (10-30 m) tree cover products derived from coarser spectral data  (11, 66). Particularly, the comparison against the Copernicus map suggested over 50% underestimation of tree cover in nonforest regions. Moreover, carbon stocks could be reported for individual trees outside forests, for example in urban areas. Using local or global allometric equations  (53) , crown diameter and height of the detected trees could be directly converted to carbon stocks  (75).
Despite the uncertainties immanent in the labels, the relatively high performance when evaluating against the manual delineations was encouraging, as it implied that the model could transfer the knowledge it learned from the labels to unseen cases. Secondly, the nature of the aerial image data limited the detection of trees merely to the topmost layer for dense or closed canopies. While larger trees that towered above the surrounding understory could be mapped with a rather low bias, small understory trees (down to a height of 1. 3 m), which made up a considerable part of the total tree count, could not be detected. The NFI field data, which include records of small trees, could be used to correct for this bias by adding extrapolated numbers for the understory trees. Lastly, we designed the model to solve a semantic segmentation rather than an instance segmentation problem, as the latter usually involves a more complicated twostep scheme, i.
In our experiments, the input data consisted of aerial images (RGB + NIR bands) with a spatial resolution of 20 cm and canopy height maps at 40-cm resolution derived from LiDAR data. The coarser resolution height maps were fed into the network after the first down-sampling layer. The model was trained by minimizing a combined loss l seg count from the two branches. The segmentation loss l seg was based on the Tversky index (89), a generalized version of the F1-score  (90) , which penalizes false positives and false negatives differently. To account for the segmentation failures in separating densely connected or overlaid tree crowns, we highlighted the between-crown gaps. Specifically, crown gap maps were generated based on the crown delineations by morphological operations, with the gap pixels being assigned a higher weight than other pixels.
Individual tree height prediction Individual tree heights were obtained by combining the canopy heights with the individual tree crown segmentation results through the following steps. First, the predicted individual tree crowns were polished by removing tiny segments with an area of <2 pixels (0. 08 m 2 on site). Second, to mitigate the uncertainties induced by slight mismatches between the aerial images and LiDAR data (84) (Fig. S17 ), each predicted tree crown was expanded by a distance (d = α ���� s/π √ ) proportional to its area s. We set the expanding factor α to 0. 2 in the experiments. Finally, the 95th percentile height within each refined tree crown was determined as the tree height. The individual tree-level height prediction performance was evaluated by comparing the referential and the predicted tree height derived from the LiDAR height references and the canopy height predictions.
For Denmark We evaluated our tree count predictions with the Danish NFI field plot data collected in the same year as the aerial images (2018). Each field plot consisted of three concentric circles with radius of 3. 5, 10, and 15 m. All trees taller than 1. 3 m were measured exhaustively in the inner circle (3. 5-m radius), trees with dbh larger than 10 cm were measured exhaustively in the middle circle (10-m radius), and trees with dbh larger than 40 cm were measured exhaustively in the entire circle (15-m radius) (see Fig. 5B ). Exact locations of the measured trees were unavailable, and the tree counts had been summarized in a way that exhaustive count of trees larger than 10 cm in dbh could not be obtained for a certain area. Note that the Danish NFI scheme was delicately designed for efficient field-based studies other than for evaluations such as used for our study, and therefore, a certain discrepancy cannot be excluded.
For Finland We evaluated our tree count predictions with the Finish NFI data collected in the same year as the aerial images (2019). We used the field plots for grown-up trees (12. 62-m radius circles), thus excluding the nursery experiment study areas. In the Finnish NFI design, trees are defined as perennial tree-stemmed plants measured taller than 1. 3 m in height with a distinct trunk and are exhaustively counted for each plot  (70).
By inspecting the highresolution aerial images, we manually annotated the part of tree crowns visible from an aerial image perspective, therefore excluding shadows or between-crown gaps. Notably, tree crowns annotated by inspecting the aerial image data and tree crowns observed in the field would not match perfectly due to viewing angles, branches, observation time, etc. Oppositely, the Copernicus maps used the common forest/tree cover definition, which estimated the percentage of tree cover at a coarser resolution (≥10 m) (Fig. S8c ). We evaluated the bias caused by the visibility differences by plotting crown areas obtained from our manual delineations against those from the Copernicus maps. The Copernicus-based crown area was 63% higher than the delineation-based crown area, indicating a systematic bias (Fig.