dataesr/scientific-paragraphs-categorization huggingface dataset:
This makes it possible to also visualize spatial patterns at subcellular resolution, but leads to a very large dataset and hence is computationally heavy  (Partel & Wählby, 2021). While the grid-based approach can be run in several hours, neighborhood embedding takes several days. Finally, we performed a segmentation method of the nuclei and assigned mRNA molecules to each nucleus. While this is the most intuitive method, several challenges occur in the fly head. To start, different densities of nuclei require imaging with different parameters to visualize both sparse and dense nuclei regions. In addition, the dense nuclei regions at the edge of the OL and CB are very difficult to segment with normal imaging techniques (Fig. 5b ). To compare different label transfer methods we applied lasso regression, Tangram and SPaGE.
5d, e ). However, Tangram also labels some non-peptidergic segmented nuclei as insulin-producing unless thresholds for mapping scores are manually adjusted to reflect the different proportions of cell types that are present in the spatial transcriptomics data (Fig. S8e ). In a more posterior central brain sample, nuclei segmentation and grid methods are able to identify the Kenyon cells of the mushroom body. These are also found in the neighborhood embedding, but the signal is noisier (Fig. 5e ). Neighborhood embedding likely performs poorly on cell types that are determined by high expression of only 1 gene (e. g. neuropeptides), since the neighborhood is not affected by this. Finally, we investigated how the three approaches perform in linking unknown scRNA-seq clusters to spatial locations.
Discussion Here we present the first SRT dataset of the adult fly brain and body using highly multiplexed FISH. We used a 50-gene panel in the body, which made it possible to identify all major cell types in the adult male fly. Several measures of co-expression were used, which showed high concordance with published single-cell RNA-seq data  (Li et al. , 2022). Furthermore, expression of key marker genes could be used to identify the location of rare cell populations in the adult body. For example, expression of Hml showed the location of resident hemocytes, while esg was used to mark stem cells in the reproductive and intestinal tract. Therefore, SRT can be used to locate different cell populations in the fly body. Most of the published classical mRNA in situ hybridizations  (Tomancak et al.
b. SCANPY body All body samples (5) were analyzed together in SCANPY (1. 9. 3)  (Wolf et al. , 2018 ). An increment of 1000 was added to both spatial x-and y-coordinates to arrange all samples together. Only squares with more than 3 counts were kept, leading to 132,642 squares with information for 50 genes. The data was subsequently normalized with 10,000 as size factor and log transformed. This matrix was then used as input for PCA, after which 40 components were retained by evaluating variance ratio plots. Harmony was then used to correct for batch effects between samples. 30 PCs were used to calculate neighbors, Leiden clustering (resolutions 0. 2, 0. 5, 1 and 2) and UMAP embeddings.
c. SCANPY head All head samples (13) were analyzed together in SCANPY (1. 9. 3)  (Wolf et al. , 2018 ). An increment of 1000 was added to both spatial x-and y-coordinates to arrange all samples together. Only squares with more than 3 counts were kept, leading to 83,064 squares with information for 99 genes (ninaE was discarded from most samples due to optical crowding). The data was subsequently normalized with 10,000 as size factor and log transformed. This matrix was then used as input for PCA, after which 30 components were retained by evaluating variance ratio plots. Harmony was then used to correct for batch effects between samples. 25 PCs were used to calculate neighbors, Leiden clustering (resolutions 0. 2, 0. 5, 1 and 2) and UMAP embeddings.
Nuclei segmentation Nuclei in DAPI images were segmented with Cellprofiler (version 4. 0. 7)  (Stirling et al. , 2021). Features of the DAPI images were enhanced (EnhanceOrSuppressFeatures, Enhance, Speckles) using a feature size of 100 pixels. Initial nuclei were detected using a size of 10-100 pixels, global Otsu thresholding with two classes, intensity for distinguishing clumped objects, propagate for drawing dividing lines. Initial nuclei were extended by 25 pixels. We then collected all transcripts inside a segmented cell to compute counts of each gene per segmented cell.
Tangram Tangram (version 1. 0. 2)  (Biancalani et al. , 2021)  was used to project cell type labels from single cell data. Here we used the cell mapping mode to map single cell data to each spatial slice separately. The mapping was computed on a NVIDIA A100-SXM4-80GB GPU. Prior to computing the mapping, segmented cells with less than three expressed genes and genes that were expressed in less than three cells were removed. Single cells and segmented cells were log-normalized in SCANPY  (Wolf et al. , 2018)  with a target-sum of 10 4. All genes that are shared between the spatial data and single cell data after filtering were used for the mapping. Cell type labels for fly head samples were assigned separately for Glia, optic lobe (OL), central brain (CB) and unknown clusters (UNK) by considering only the subset cell types that we grouped into the category.
Neighborhood embedding and SpaGE Segmentation-free analysis of fly head and body SRT datasets have been carried out using spage2vec  (Partel & Wählby, 2021). Briefly, spatial graphs of all mRNAs are first constructed for each different sample. Then, a graph convolution neural network trained in a self-superwise fashion projects each mRNA into an embedding space based on its spatial neighborhood composition. Such that mRNAs that share similar neighbors are mapped close together in the embedding space. Downstream clustering or visualization of mRNA embeddings unveil spatial gene expression patterns described by specific combinations of genes at subcellular resolution. Pseudo-cell counts have been generated for each mRNA by aggregating counts of k neighboring mRNAs (i. e. k=100) in the embedding space.
Funding This work was in part funded by  ERC AdG Genome2Cells  (S. A. ;  101054387 ); a  FWO PhD Fellowship  to (J. J. ;  1199518N ); a  FWO Postdoctoral fellowship  (N. H. ;  1273822N ); GP was funded by  Opening The Future. This work was supported by the  Centre National de la Recherche Scientifique  (CNRS, F. S. ),  Aix-Marseille University  (P. M. ), the  European Research Council  under the European Union's  Horizon 2020 Programme  ( ERC-2019-SyG 856118 F. S. ),  FP/2007-2013 )/ ERC  Grant  310939  (F. S. ) and by funding from  France 2030 , the  French Government  program managed by the  French National Research Agency  ( ANR-16-CONV-0001 ) and from  Excellence Initiative of Aix-Marseille University -A * MIDEX  ( Turing Centre for Living Systems ).
APP E NDIX B : PROCEDURE TO EXTRACT THE NETWORK CHARACTERISTICS OF THE GREATER PARIS CSS Geographic Information Systems offer many functionalities to extract the hydrographic network from Digital Elevation Models (DEM) using the D8 algorithm  (O'Callaghan & Mark, 1984; Tarboton & Ames, 2004; Tarboton et al. , 1991). However, the network in urban areas cannot be extracted from the DEM, and the flow does not follow the greatest slope at the ground surface. For this, we have developed specific procedures using functions available both in commercial and open-source GIS systems. To compute the above morphometric descriptors and the GIUHs of a natural or urban catchment, a script based on ArcGIS 10. 8 functions and coded in Python is developed. The first step consists in defining the catchment and identifying its hydrographic network.
In the case of natural catchments, these two tasks are carried out using the ArcHydro library functions and a DEM. In the case of the Greater Paris combined sewer system, the database provides the catchments that correspond to each subcatchment and its network. Through these files (network and catchment) the main raster is extracted: it is the raster of directions as defined by the D8 principle. This raster is obtained through the "Euclidean back direction" function, implemented in ArcGIS, which allows sending each cell to the nearest junction of the network by assigning it a direction value in degrees. Then these directions are modified into D8 form (each cell can be drained to a single upstream cell according to 8 drainage directions) so that this file is compatible with the ArcHydro library that is then used to extract the morphological and hydrological properties.
Regarding the Horton-Strahler laws, the classification is done manually, and the numbers, lengths, and drained areas of each stream order are calculated in the attribute table of the network. Then Equations (  1 ), (  2 ) and (  3 ) are computed in Python to draw the graphs and extract the Horton Strahler parameters (R B , R L , and R A ). To classify the internal and external nodes, and to calculate the geomorphological relationships (number of external points and total length of the network for a given threshold area), a loop of iterations is created, which takes as a hypothesis a value of threshold area and extracts the cells of the network that are able to drain at least this value through the "Stream Definition" from ArcHydro tools and then calculates the total length of the resulting network and the number of external points by using the "Data management" library of ArcGIS.
Based on these results, the three graphs corresponding to Equations (4), (  5 ) and (  6 ) are generated and the equivalent Strahler parameters are determined (Equation  10 ). In order to verify the power law, the raster of the accumulated drained surfaces must be calculated. The "Flow accumulation" function of ArcHydro is used to generate this raster using as input the raster of the drainage direction and the shapefile of the drainage network. In the attribute table of this raster, we find the number of cells that drain a specific area. This table is transformed into a list in python, and then the graph corresponding to the probability that a catchment cell drains at least a specific surface is generated and the slope of the curve (ϴ) corresponding to the power law is calculated. For the width function, the flow distance function is used, which assigns a value for the distance to the outlet for each cell.
ACKNOWLEDGEMENTS The authors thank the  French Research Institute for Sustainable Development (IRD)  for financial support. The reconstructed network has been financially supported by the research program  PIREN-Seine 7  and allowed by  SIAAP  and the management teams of the sewer nework of  Ville de Paris, Hauts-de-Seine, Seine-Saint-Denis  and  Val-de-Marne. The authors thank them for providing data and expertise. The authors gratefully acknowledge the reviewers for their valuable comments. Their insightful observations and constructive criticism greatly enhanced the quality and depth of the research. The authors thank them for the time and effort they dedicated to providing such detailed and thoughtful comments.
outlets. In this project, only the CSS is studied. The study area (Figure 1, area in red) covers only the area drained by CSS: a total area of 310 km 2. A reconstructed version of the Greater Paris sanitation network was provided by Chancibault et al. (2020) based on the real network data provided by SIAAP, the City of Paris and the three departments of the study area (Figure 2). Unfortunately, urban data banks locating the pipes, when they are not used for the daily management of the sanitation system, present many gaps and topological discontinuities. The catchment map corresponding to the reconstructed network is based on a 250 m-resolution regular grid. Each junction of the network drains the cell that intersects it, while for the cells that do not intersect a junction, a geometric method implemented in ArcGis (Average Nearest Neighbour) is used that allows the cells to be F I G U R E 2 Greater Paris combined sewer system catchments: C1 to C8 are source subcatchments (a), C9 to C11 are intermediate subcatchments (b), and C12 represents the whole catchment (c).
D. Stable Allocation Patterns In mature applications and language implementations, chances are that memory corruption bugs will appear in seemingly random situations when rare conditions arise. We argue that this happens because common situations happen often and have been previously tested and debugged, and applications present stable allocation patterns that do not stress continuously memory manager corner cases. In other words, memory corruptions appear in extraneous cases, difficult to reproduce and debug. To understand application allocation patterns, we instrumented all object allocations in 111 example programs. Our 111 programs included synthetic benchmarks, and the entire test suite executions of Pharo's standard library, its IDE, and high-level libraries such as its bytecode compiler and its Roassal3 visualization engine  1.
B. Experimental Platform We implemented our approach on top of the Pharo Virtual Machine  [9] , an industrial-level Virtual Machine supported by an industrial consortium  2. The VM implements a sequential stop-the-world generational scavenger garbage collector that uses a copying collector for young objects and a mark-compact collector for older objects  [30]. We implemented our prototype to run on Pharo VM's simulator  [22]  and using the pre-existing Pharo VM testing infrastructure  [25]. Our prototype implementation is hosted in https://github. com/Alamvic/heapFuzzer and is MIT licensed. We run all our benchmarks on the following configuration: • Pharo Version: Pharo 11 alpha build #219  3 • Heap Fuzzer commit 4  c18a831 • Pharo VM commit 5  3172ed61 Unless stated otherwise, each benchmark is run 30 times (so 30 scenarios) with a limit of 1024 events per scenario.
B. Automated Virtual Machine Testing Besides work on validating GCs, a lot of work focuses on testing VMs in general, and more specifically JIT compilers. The teams of Maxine and Pharo reported recently QEMUbased unit testing infrastructures for cross-ISA testing and debugging  [8] ,  [20] ,  [25]. They reported that these infrastructures helped them in porting their VMs to ARMv7 and ARMv8 64bits respectively. Although their approaches are based on unit testing, they rely on manually written tests, while our approach performs automatic test generation. Lately, several works have explored the path of program fuzzing and differential testing. Recent work explores the generation of test cases for JIT compilers using available interpreters  [24]. Other solutions make use of differential testing between different Virtual Machines for a single language.
VII. CONCLUSION In this article, we proposed to fuzz heaps by generating large sequences of randomly expert-guided heap events. Heap events directly manipulate the memory manager and circumvent the programming language semantics to get access to more precise fuzzing e. g. , the fuzzer can decide where or how to allocate an object. Such events exercise the GC algorithm to produce VM crashes and find bugs. Once a bug is found, we use a test case reduction algorithm to find the smaller subset of events that reproduces the issue. We implemented our approach on top of the VM simulator of the Pharo VM and showed that our prototype can find realworld bugs quickly. We further described six bugs our solution found in Pharo's garbage collection: three severe ephemeron implementation bugs that produced memory corruptions, two bugs in the compaction algorithm that prevented the compaction of some objects at the end of the heap, and one bug in the simulation environment used during the VM development.
Longitude the average size of the fragments of some random samples was determined by digital electrophoresis on a Tape Station 4200 (Agilent). Finally, the genomic libraries were diluted to a concentration of 10 nM and pooled in groups of 96 samples. The pooled samples were sequenced using a pairedend strategy with 150 sequencing cycles in Illumina HiSeq X equipment (Macrogen, Inc. Korea). The demultiplexing of sequences for each genotype was implemented using the bcl2fastq Illumina software  (Illumina 2022). Raw data sequences were deposited in the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI) database under the Bioproject number PRJNA878519.
SNPs identification for the CGB, SR, RM, and O samples To discover SNPs among all avocado samples, we made the SNP calling using the pipeline reported by  Osorio-Guarín et al. (2020). In summary, for all sequence files of each analyzed sample (downloaded and generated in this study), we used the FastQC software  (Andrews 2010)  to verify the quality of the Fastq files. The adapter's primers and sequences with a quality of less than Q30, and a length of less than 50 bp were filtered using Trim-Galore software  (Krueger 2012). The Burrows-Wheeler Aligner (BWA) software  (Li and Durbin 2009)  was used to align the sequences against the P. americana var. Hass cultivar reference genome version 2 (~ 913 Mb)  (Rendón-Anaya et al. 2019). We used Picard (BROAD Institute 2022b) and the Genome Analysis ToolKit-GATK (BROAD Institute 2022a) software to remove PCR duplicates, correct mapping quality assignment, and realign and recalibrate the reads.
The SNP calling was done using the Unifiedgenotyper option of the GATK algorithm. Finally, we used the VCFtools software  (Danecek et al. 2011)  for maintaining the SNP markers with a Minimum Allele Frequency (MAF) of 5%, a minimum of 2X of sequencing depth (SD) per position, keeping biallelic SNPs and excluding InDels (insertion-deletion). Samples and SNPs with percentages higher than 20% (genotype level) and 5% (marker level) of missing data were excluded. Because the nucleotide sequences were gathered from different tissues (leaves and roots) as well as from published genomic resources  (Rendón-Anaya et al. 2019; Talavera et al. 2019) , we differentially implemented a final filter for polymorphic SNPs discovery to get three goal-oriented datasets with contrasting SNP density (Table  1 ).
Summary statistics, such as Mean Depth of Sequencing (MDS), nucleotide diversity (Pi), transition/transversion ratio (Ts/Tv), MAF, and Polymorphism Information Content (PIC), were determined in dataset 1 using VCFtools software  (Danecek et al. 2011)  and R-SNPready package  (Granato et al. 2018)  in R software (R project 2022). We relied on three datasets and approaches, as described below, to answer the research questions proposed in this study about the classification of the Colombian avocado germplasm given the classical races (ME, GU, and WI), as well as the presence and evolutionary origin of potential new genetic groups.
Unsupervised non-parametric genetic clustering Population stratification was characterized using dataset 1, which included 4931 SNPs identified in 199 samples (Table  1 ), using two complementary non-parametric approaches from the unsupervised machine learning paradigm: partitioning and hierarchical clustering. We had to perform a preliminary step to reduce the dimensionality of the SNP dataset through principal component analysis (PCA)  (Alhusain and Hafez 2018; Foote et al. 2019 ) using the R-glPca function in the R-adegenet package  (Jombart and Ahmed 2011). To find the optimum number of principal components needed as input in the clustering analysis, we carried out the Tracy-Widom test  (Tracy and Widom 1994; Patterson et al. 2006 ) to obtain the statistical significance of each component employing the Eigenstrat function performed in R-AssocTest  (Wang et al.
2020)  as in  Foote et al. (2019). Once dimensionality was reduced in 21 principal components (66. 28% accumulated variance), we determined the optimal number of genetic pools or K groups for each unsupervised non-parametric clustering algorithm. Then, we conducted in the R software two complementary methods. First, we ran the NbClust  (Charrad et al. 2014 ) algorithm, an internal measure's function that integrates 30 indexes to determine the optimal K value based on ward. D2 method and the Euclidean distance, evaluating three (K = 3) to eight (K = 8) putative genetic pools. Second, we ran the optCluster  (Sekula et al. 2017) , an improvement of the traditional clValid algorithm  (Brock et al. 2008 ) that optimizes both the K score, and the clustering approach based on internal stability and biological measures.
Maximum likelihood phylogenetic reconstruction The phylogenetic analysis was conducted using dataset 2, which included 3899 SNPs for 202 avocado samples (Table  1 ) and an outgroup (O), the close relative species P. schiedeana. Although this species could potentially hybridize with avocados, it behaved as an outgroup for our analyses  (Ashworth and Clegg 2003). In a preliminary test (data not shown), in which we also used Phoebe bournei as an outgroup, P. schiedeana was more related to this species than avocados. Therefore, we decided to use only P. schiedeana as an outgroup because we could recover more SNPs. The alignment of nucleotide sequences across all target SNPs was visualized and verified in Geneious software  (Kearse et al. 2012 ). The phylogenetic tree was reconstructed using the Maximum Likelihood (ML) method in the PhyML software  (Guindon et al.
Genetic structure and contemporary directional migration rates among avocado genetic clusters To quantify the genetic differentiation among clusters detected in the unsupervised non-parametric genetic cluster (K = 4) and phylogenetic (K = 5) analyses, we conducted inferences of molecular variance (AMOVA) using dataset 1. We calculated the global Phi scores (like F ST , both provide population differentiation summary statistics) using 1000 permutations in the R-poppr package  (Kamvar et al. 2014). Pairwise F ST values were calculated in the R-dartR package  (Gruber et al. 2018)  to compare genetic clusters, while the genetic diversity was measured through observed (Ho) and expected (He) heterozygosity and inbreeding coefficient statistics (F IS ) calculated in the R-SNPready software  (Granato et al.
2018). Genotypes from the CGB with geo-referenced information (Fig. 1 ) were used to implement Mantel correlation tests with 1000 permutations between genetic differentiation and geographic distance within and between pairs of clusters. The R-geosphere package  (Hijmans et al. 2021 ) was utilized to gather geographic distances. The R-ape package via the mantel. test function was used for the explicit test. Two diagrammatic versions were drawn in the same R software depicting genetic distance vs. geographic distance (km), as well as decaying correlograms against distance classes, the latter using the mantel. correlog functionality also from the R-ape package. Finally, we conducted a Bayesian Markov Chain Monte Carlo (MCMC) analysis on dataset 1 using the BayesAss software v. 3. 05  (Wilson and Rannala 2003)  to estimate contemporary and directional gene flow migration rates (m) among the four K = 4 (CO, ME, GU, and WI) and five K = 5 (ME, GU, WI, Colombian Andean, and Colombian Caribbean) genetic groups detected in the unsupervised nonparametric genetic clustering and phylogenetic analyses, respectively.
We used the BA3SNP option  (Mussmann et al. 2019). We set the parameters for migration rates (m: 0. 15 in both analyses), allele frequencies (a: 0. 3 in both analyses), and inbreeding coefficients (F IS : 0. 01 in K = 4 and 0. 005 in K = 5) to achieve an optimal acceptance rate between 20% and 60%, as recommended in the manual. We ran three independent analyses for each K using different seed values (100, 200, and 300) evaluated in 10 M iterations and 1 M burn-in, sampling every 1000 steps. We checked the convergence of the results and calculated the mean, standard deviation, and 95% Confidence Intervals (CI) of gene flow over the three independent runs, using the Tracer v. 1. 7. 2 software  (Rambaut et al. 2018). We considered gene flow migration rates with CI over 0 as significant. We drew the pairwise F ST and m results using the R-qgraph package  (Epskamp et al.