dataesr/scientific-paragraphs-categorization huggingface dataset:
0. 7. 15 with -M and -t 15 options  54. Pre-processing analysis for variant calling-ready bam le per sample (tumor and normal bam) was performed by various steps following the Genome Analysis Toolkit 4 (GATK4) v. 4. 1. 0. 0 best practices  55  , namely Picard AddOrReplaceReadGroups (http://broadinstitute. github. io/picard), and GATK4 Mark Duplicates, Indel Realignment, and Base Quality Score Recalibration. Alignment quality control metrics were calculated using Picard CollectHSMetrics tools and GATK4 DepthOfCoverage (for WES and WGS, respectively).
Somatic variant calling Somatic SNVs and indels of WES and WGS samples were identi ed using two callers: GATK4 (v. 4. 1. 0. 0) 56 , Mutect2 57 and Strelka v. 1. 0. 13/Manta v. 1. 6. 0 58,59 using default arguments according to previous literature  60, 61. Mutect2 was run in tumor-matched normal mode. To reduce germline artifacts, a panel of normals (PON) was built using the GATK4 CreateSomaticPanelOfNormals tool by using all matched normal samples (n=77). An additional lter was added to exclude known single-nucleotide polymorphisms as annotated in the dbSNP 146 downloaded from the Dog Genome SNP Database (http://dogsd. big. ac. cn/)  62. Strelka was run together with Manta for best somatic indels performance and for the processing of WES samples, the -exome and -callRegions parameters were added to restrict the calls to the targeted regions only.
Resulting raw somatic calls of SNVs and indels from the two callers were then ltered using caller-speci c lters and hard lters as de ned in the respective developer's guidelines. Brie y, Mutect2 lters were performed with GATK4 FilterMutectCalls tool and comprised, for instance, the removal of calls under germline risk, calls with low depth, artifacts related to sequencing platforms or repeat regions; hard lters were based on the ltering of variants present in dbSNP and PONs to avoid false positive calls (https://software. broadinstitute. org/gatk/documentation/article? id=11136). For Strelka, the default lters were used (https://github. com/Illumina/strelka/blob/v2. 9. x/docs/userGuide/README. md#somatic). A consensus calling was then applied to ltered somatic calls from the two callers (in VCF version 4.
1/4. 2 format) by overlapping the remaining variants using the bcftools software package's -isec option  63. Finally, resulting consensus VCF les were annotated with ANNOVAR 64. Mutation Annotation Format (MAF) using vcf2maf utility (https://github. com/mskcc/vcf2maf) was also created. Annotated VCFs and MAFs were used for all the downstream analyses.
Copy Number Analysis CNA analysis was performed on WES and WGS data using the NEXUS Copy Number v. 10. 0 software (Biodiscovery Inc. , El Segundo, CA). CNAs and loss of heterozygosity (LOH) were identi ed starting from pre-processed BAM les using a matched approach ("BAM ngCGH-matched") and against the CanFam3. 1 reference genome. Then, a FASST2 segmentation algorithm with a signi cance threshold of 1. 0e-12 was applied. The dbSNP was also given to the software to assess germline events. In the case of WGS, the bin size was reduced from 1000 (default for WES) to 500. Aberrations were de ned as a minimum of 3 consecutive segments and a maximum probe spacing of 1000 kbp between adjacent probes before breaking a segment. Segments were classi ed with log2 tumor/reference ratio value of >0. 5 as high gains, 0.
5 to 0. 2 as gains, -0. 25 to -1. 5 as losses, <-1. 5 as big losses, and the heterozygous imbalance threshold was set to 0. 4. Furthermore, independently from the automatically generated CNA calls, each sample was visually inspected for using Nexus Copy Number software v. 10. 0 (Biodiscovery Inc. , El Segundo, CA). Recurrent CNAs were determined within NEXUS using the GISTIC algorithm with a G-score cut-off of G > 1. 0 and a signi cance of Q < 0. 05. CNA frequency comparisons amongst groups (i. e. clinical features, presence/absence of somatic mutations) were performed with a two-tailed Fisher's exact test (p < 0. 05).
Intra-Tumor Heterogeneity analysis To verify the presence of multiple subclonal populations of cells tumor samples were inferred using sciClone R package 68 applying default parameters according to the developer's manual. Copy number segments were extracted from WES samples using circular binary segmentation (CBS) method implemented in CNVkit 69 , whereas mutations were extracted from Strelka/Mutect2 intersection VCF les. LOH regions inferred from NEXUS Copy Number were excluded.
Functional annotation analysis The analysis comparing mutational processes and transcriptome to evaluate enriched gene sets in WT/mutated samples was performed using the GSEA in a preranked mode, with a threshold of signi cance of FDR <5%  70. The curated canonical KEGG pathways (c2. cp. kegg. v7. 4), GO terms (c5. all. v7. 4), oncogenic (c6. all. v7. 4) and immunologic (c7. all. v7. 4) signatures, and Hallmarks (h. all. v7. 4) catalogs, were from Molecular Signatures Database (MsigDB) were used.
Prediction models Two predictive models were tested and implemented in the scikit-survival module (version 0. 15. 0) in Python (version 3. 8. 9): Cox's proportional hazard's model with elastic net penalty and random survival forest. Separate models were built for LSS and TTP. Performance of the models was evaluated by eightfold cross-validation with two holdout sets, repeated twenty times on random permutations of the samples. One holdout set was needed to select the optimal model by hyperparameter grid search and the other was used to evaluate the nal performance of the optimal model. Performance was evaluated by the area under the receiver operating characteristic (AUROC) for classifying dogs with survival time lower than the median survival in the dataset. Given the limited number of samples and the much larger number of available features and the observed poor performances of models tted using too many features, we decided to eschew automatic feature selection methods.
Thus, various datasets with different features were tested to nd those that were most predictive and combine them in a single predictive model. Categorical clinical features were one-hot-encoded producing 41 numeric and binary features. We tested a dataset of 77 samples with 2832 genetic mutations (binary) and clinical features and a dataset of 43 samples with clinical features and the top 100 most signi cantly differentially expressed between tumors and controls. Features whose regression coe cient were consistently different from zero among the cross-validation ttings of the best performing models were selected and put together in the nal dataset.
Benatia et al. [54] , sought to find the near-optimal solution for WSNs deployment in smart buildings through two evolutionary algorithms: GA and NSAG-II. Therefore, the proposed approach deals with multiple objectives: deployment cost, coverage, connectivity, and over-coverage. According to the authors, the choice of the adequate metaheuristic algorithm depends on the user requirements; the NSGA-II is recommended when the approach is not a-priori. Dahmane et al. [80] , dealt with the problem of deploying temperature sensor nodes in smart buildings; their approach is based on NSGA-II with two objectives to optimize coverage and cost. In their solution, the authors included BIM database information to model obstacles of the building. Indeed, their coverage model depends mainly on the distance between the target point and the sensor node and the heat flow resistance of materials constituting obstacles.
Additionally, the authors have considered singlehop routing and modeled the installation of relay nodes as a binary linear program. The problem was solved using a hybrid algorithm that combines a heuristic using randomized algorithms and Approximate Nondeterministic Tree-Search (ANTS) and an exact binary linear program that represents a large-variable neighborhood search. The hybrid algorithm directs the process of variable fixing during the feasible solutions' construction using linear relaxations for the same problem. A total of 30 realistic instances were used in the experiments, and the results confirm that the proposed algorithm outperforms the CPLEX solver in terms of speed and solution quality. The same authors have developed a hybrid solution for designing a body WSN in  [165]. They proposed an Integer Linear Programming heuristic based on deterministic and probabilistic variable fixing methods.
B. TESTED METAHEURISTICS 1) motivation of the selection of algorithms to be tested The selection of these algorithms is based on the research presented in section V and summarized in Table  5. According to this Table, the NSGA-II and the MOEA/D algorithms were used in the majority of the Pareto optimization-based techniques. As for single-objective approaches, we noticed that the most commonly used algorithms are the GA, which belongs to evolutionary algorithms, and PSO, which belongs to swarm optimization algorithms. Therefore, we chose to implement and test two multi-objective algorithms: NSGA-II and the MOEA/D, and the mono objective algorithms: GA and PSO, to which we have added ABC and FPA to enrich our comparison. All the algorithms were implemented in python, and simulation parameters are summarized in Table  7.
The specificity of our synaptic population was made possible because each of the 334 neurons taking part in the 1736 connections in our cortical wiring diagram could be identified as a L2/3 PyC based on a soma and sufficient dendrite and axon contained in the ssEM volume. The closest precedents for wiring diagrams between cortical neurons of a defined type had 29 connections between 43 L2/3 PyCs in mouse visual cortex  (Lee et al. , 2016)  , 63 connections between 22 L2 excitatory neurons in mouse medial entorhinal cortex  (Schmidt et al. , 2017)  , and 32 connections between 89 L4 neurons in mouse somatosensory cortex  (Motta et al. , 2019). Our cortical reconstruction will be made available for programmatic as well as 3D interactive access upon or before publication of this work. The code that generated the reconstruction is already freely available (Data and Code Availability).
Handling of ssEM image defects We acquired a 250×140×90 μm 3 ssEM dataset (Extended Data Fig. 1 ) from L2/3 primary visual cortex of a P36 male mouse. When we aligned a pilot subvolume and applied state-of-the-art convolutional nets, we found many reconstruction errors, mainly due to misaligned images and damaged or incompletely imaged sections. This was disappointing given reports that convolutional nets can approach human-level performance on one benchmark ssEM image dataset  (Beier et al. , 2017; Zeng, Wu and Ji, 2017). The high error rate could be explained by the fact that image defects are difficult to escape in large volumes, though they may be rare in small (<1000 μm 3 ) benchmark datasets. Indeed, ssEM images were historically considered problematic for automated analysis  (Briggman and Bock, 2012; Lee et al.
Wiring diagram between L2/3 pyramidal cells After alignment and automatic segmentation (Methods), we manually identified 364 pyramidal cells (PyCs) with somas in the volume based on morphological characteristics (Fig. 1d , e, Methods). Remaining errors in the segmentation of these PyCs were corrected using an interactive system that enabled human experts to split and merge objects. Overall, the PyC reconstructions were corrected through ~1,300 hours of human proofreading to yield 670 mm cable length (axon: 100 mm, dendrite: 520 mm, perisomatic: 40 mm, Extended Data Fig. 2 ). We examined 12 randomly sampled axons and conservatively estimated 0. 28 merge errors per millimeter remain after proofreading (see Methods for other estimates). The dendrites of the 364 PyCs receive one quarter of the synapses (~8.
96x10 5 of 3. 24x10 6 ) that were automatically detected in the volume (Methods,  (Turner et al. , 2019)  ). However, the synapses onto PyC dendrites are almost all from "orphan" axons, defined as those axonal fragments that belong to somas of unknown location outside the volume. Using these automatically detected synapses as a starting point, we mapped all connections between the L2/3 PyCs (Methods). The end result was a wiring diagram of 1,961 synapses from 1,736 connections between 334 of the 364 L2/3 PyCs in the dataset (Fig. 2a ). Note that some connections are multisynaptic, i. e. , they are mediated by multiple synapses sharing the same presynaptic and postsynaptic cells (Fig. 2b , Extended Data Fig. 3 , see Supplementary Information 7 for a tabular overview of these statistics). For clarity, we emphasize that our usage of the term "multisynaptic" refers to multiple synapses in parallel rather than in series.
It should be noted that there is some overlap between mixture components (Fig. 3f ), so that an S synapse can be larger than an L synapse. To validate this finding, we quantified synaptic cleft size as the number of voxels labeled by the output of our automated cleft detector (Supplementary Information 4). We found a close relationship between spine volume and cleft size in our data (Extended Data Fig. 6a ), in accord with previous studies  (Harris and Stevens, 1989; Arellano et al. , 2007; Bartol et al. , 2015). When spine volume is replaced by cleft size in the preceding analysis, we obtain similar results (Extended Data Fig. 6 ). Error bars are of the model fit.
Our experiences with ssEM data are paralleled by other efforts to transform artificial intelligence research into real-world systems. For example, consider that self-driving cars may perform impressively under "normal" conditions, but fail in the so-called "edge" or "corner" cases  (Madrigal, 2018)  that currently stand in the way of fully autonomous operation. Similarly, continued progress in handling image defects would be critical for processing the trillions of gigabytes envisioned by a bold proposal for ssEM imaging of an entire mouse brain (Advisory Committee to the NIH Director BRAIN Initiative Working Group 2. , 2019).
Methods Mouse. All procedures were in accordance with the Institutional Animal Care and Use Committees at the Baylor College of Medicine and the Allen Institute for Brain Science. Mouse lin e. Functional imaging was performed in a transgenic mouse expressing fluorescent GCaMP6f. For this data set, the mouse we used was a triple-heterozygote for the following three genes: (1) Cre driver: CamKIIa-Cre (Jax: 005359< https://www. jax. org/strain/005359 >), (2) tTA driver: B6;CBA-Tg(Camk2a-tTA)1Mmay/J (Jax: 003010 < https://www. jax. org/strain/003010 >), (3) GCaMP6f Reporter: Ai93 (Allen Institute). Cranial window surgery. Anesthesia was induced with 3% isoflurane and maintained with 1. 5% to 2% isoflurane during the surgical procedure. Mice were injected with 5-10 mg/kg ketoprofen subcutaneously at the start of the surgery.
Transmission electron microscopy. We made several custom modifications to a JEOL-1200EXII 120kV transmission electron microscope  (Yin et al. , 2019). A column extension and scintillator magnified the nominal field of view by tenfold with negligible loss of resolution. A high-resolution, large-format camera allowed fields-of-view as large as (13 µm) 2 at 3. 58 nm resolution. Magnification reduced the electron density at the phosphor, so a high-sensitivity sCMOS camera was selected and the scintillator composition tuned to generate high quality EM images with exposure times of 90-200 ms. Sections were acquired as a grid of 3840 x 3840 px images ("tiles") with 15% overlap. Alignment in two blocks. The dataset was divided by sections into two blocks (1216 & 970 sections), with the first block containing substantially more folds.
Initial alignment and reconstruction tests proceeded on the second block of the dataset. After achieving satisfactory results, the first block was added, and the whole dataset was further aligned to produce the final 3D image. The alignment process included stitching (assembling all tiles into a single image per section), rough alignment (aligning the set of section images with one affine per section), coarse alignment (nonlinear alignment on lower resolution data), and fine alignment (nonlinear alignment on higher resolution data). Alignment, block one. The tiles of the first block were stitched into one montaged image per section and rough aligned using a set of customized and automated modules based on the "TrakEM2"  (Cardona et al. , 2012)  and "Render"  (Zheng et al. , 2018)  software packages.
The final render used a piecewise affine model defined by the mesh. Fine alignment. Using 2x downsampled images, NCC-based block matching proceeded 200 px block radius, 113 px search radius, and 100 px spacing. Matches were computed between nearest and next-nearest section pairs, then manually filtered by a skilled operator in 24 hrs. Modeling and rendering proceeded as with coarse alignment, using spring constants were 1/20th of the constant for the between-section springs. Alignment, whole dataset. Blank sections were inserted manually between sections where the cutting thickness appeared larger than normal (11). The alignment of the whole dataset was further refined using the methods of  (Saalfeld et al. , 2012)  as implemented in Alembic  (Macrina and Ih, no date). Coarse alignment. Using 64x downsampled images, NCC-based block matching proceeded 128 px block radius, 512 px search radius, and 128 px spacing.
Rendered image intensities were linearly rescaled in each section based on the 5th and 95th percentile pixel values. Image volume estimation. The imaged tissue has a trapezoidal shape in the sectioning plane. Landmark points were placed in the aligned images to measure this shape. We report cuboid dimensions for simplicity and comparison using the trapezoid midsegment length. The original trapezoid has a short base length of 216. 9μm, long base length of 286. 2μm, and height 138. 3μm. The imaged data has 2176 sections, which measures 87. 04μm with a 40nm slice thickness. Image defect handling. Cracks, folds, and contaminants were manually annotated as binary masks on 256x downsampled images, dilated by 2 px, then inverted to form a defect mask. A tissue mask was created using nonzero pixels in the 256x downsampled image, then eroded by 2 px to exclude misalignments at the edge of the image.
The image mask is the union of the tissue and defect masks, and it was upsampled and applied during the final render to set pixels not included in the mask to zero. We created a segmentation mask by excluding voxels that had been excluded by the image mask for three consecutive sections. The segmentation mask was applied after affinity prediction to set affinities not included in the mask to zero. Affinity prediction. Human experts used VAST  (Berger, Seung and Lichtman, 2018)  to manually segment multiple subvolumes from the current dataset and a similar dataset from mouse V1. Annotated voxels totaled 1. 29 billion at full image resolution. We trained a 3D convolutional network to generate three nearest neighbor  (Turaga et al. , 2010)  and 13 long-range affinity maps  (Lee et al. , 2017).
, 2017)  , and the Adam optimizer  (Kingma and Ba, 2014). The learning rate started from 10 -3 , and was manually annealed three times (505k training updates), before adding 67. 2μm 3 of extra training data for another 670k updates. The extra training data focused on false positive examples from the network's predictions at 505k training updates, mostly around blood vessels. The trained network achieved 93. 0% precision and 90. 9% recall in detecting clefts of the test set. This network was applied to the entire dataset using the same distributed inference setup as affinity map inference. Connected components of the thresholded network output that were at least 50 voxels at 7. 16×7. 16×40nm 3 resolution were retained as predicted synaptic clefts. Synaptic partner assignment. Presynaptic and postsynaptic partners were annotated for 387 clefts, which were split into 196, 100, and 91 examples for training, validation, and test sets.
Those axons were allowed to contain errors elsewhere and we did not proofread any axons to obtain these pairs. Dendritic spine heads. We extracted a 7. 33 x 7. 33 x 4 μm 3 cutout around the centroid of each synapse. The postsynaptic segment within that cutout was skeletonized using kimimaro ( https://github. com/seung-lab/kimimaro ), yielding a set of paths traveling from a root node to each leaf. The root node was defined as the node furthest from the synapse coordinate. Skeleton nodes participating in fewer than three paths were labeled as "spine" while others were labeled as "shaft. " The shaft labels were dilated along the skeleton until either (1) the distance to the segment boundary of the next node was more than 50 nm less than that of the closest (shaft) branch point, or (2) dilation went 200 nodes beyond the branch point.
The algorithm was initialized using the k-means algorithm with k=2. For cleft size, the normal distributions were truncated at a lower bound of log 10 (50) voxels, the cutoff used in cleft detection. The truncation was implemented by modifications to the source code of Pomegranate. (A similar truncation was used when fitting a single normal distribution to cleft sizes. ) The joint distribution at dual connections was fit by hidden Markov Models (HMMs) with two latent states and emission probabilities given by normal or truncated normal distributions. Hidden Markov models are trained on ordered pairs, yet each dataset used for training was reflected to contain both orders of each synapse pair. Parametric test for bimodality. For binary mixtures of normal distributions, the parameter regimes for bimodal and unimodal behaviors are known  (Robertson and Fryer, 1969).
For visualization in Fig. 4g ,h we applied a kernel density estimation (bw=0. 15 in log10-space). Skeletonization. We developed a skeletonization algorithm similar to  (Sato et al. , 2000)  that operates on meshes. For each connected component of the mesh graph, we identify a root and find the shortest path to the farthest node. This procedure is repeated after invalidating all mesh nodes within the proximity of the visited nodes until no nodes are left to visit. We make our implementation available through our package MeshParty ( https://github. com/sdorkenw/MeshParty ). Estimation of path lengths. We skeletonized all PyCs and labeled their first branch points close to the soma according to the compartment type of the downstream branches (axon, dendrite, ambiguous). If no branch point existed in close proximity a point at similar distance was placed.
Alembic: Stitching and alignment. CloudVolume: Reading and writing volumetric data, meshes, and skeletons to and from the cloud Chunkflow: Running convolutional nets on large datasets DeepEM: Training convolutional nets to detect neuronal boundaries. DynamicAnnotationFramework: Proofreading and connectome updates (visit https://github. com/seung-lab/AnnotationPipelineOverview for repository list) Igneous: Coordinating downsampling, meshing, and data management. MeshParty: Interaction with meshes and mesh-based skeletonization ( https://github. com/sdorkenw/MeshParty ) MMAAPP: Watershed, size-dependent single linkage clustering, and mean affinity agglomeration. PyTorchUtils: Training convolutional nets for synapse detection and partner assignment ( https://github. com/nicholasturner1/PyTorchUtils ).
Acknowledgements Supported by the  Intelligence Advanced Research Projects Activity (IARPA)  via  Department of Interior/ Interior Business Center (DoI/IBC ) contract numbers  D16PC00003 ,  D16PC00004 , and  D16PC0005. The  U. S. Government  is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. HSS also acknowledges support from  NIH/NINDS   U19 NS104648 ,  ARO W911NF-12-1-0594 ,  NIH/NEI   R01 EY027036 ,  NIH/NIMH   U01 MH114824 ,  NIH/NINDS   R01NS104926 ,  NIH/NIMH   RF1MH117815 , and the  Mathers Foundation , as well as assistance from  Google, Amazon , and  Intel. We thank  S. Koolman ,  M. Moore ,  S. Morejohn ,  B. Silverman ,  K. Willie , and  R. Willie  for their image analyses,  Garrett McGrath  for computer system administration, and  May Husseini  and  Larry  and  Janet Jackel  for project administration.