dataesr/scientific-paragraphs-categorization huggingface dataset:
When this happens, our implementation of CATCH raises an error and suggests that the user provide less stringent choices of parameter values. Design of viral probe sets presented here. Input sequences for design of probe sets. We designed four probe sets using publicly available sequences. The design of V ALL (356 viral species) incorporated available sequences up to June 2016; V WAFR (23 viral species) up to June 2015; V MM (measles and mumps viruses) up to March 2016; and V ZC (chikungunya and Zika viruses) up to February 2016. Most sequences we used as input for designing probe sets are genome neighbors (that is, complete or near-complete genomes) provided in NCBI's accession list of viral genomes  65  and were downloaded from NCBI GenBank  30. We selected a small number of other genomes using the NIAID Virus Pathogen Database and Analysis Resource (ViPR)  66.
In V ZC we included, along with genome neighbors, partial sequences of Zika virus from NCBI GenBank  30. Exploring the parameter space across taxa. To explore the parameter space in the design of V ALL and V WAFR , we varied m d (number of mismatches) and e d (cover extension) while fixing all other parameters. We precomputed probe sets over a grid with m d in {0, 1, 2, 3, 4, 5, 6} and e d in {0, 10, 20, 30, 40, 50} when finding optimal parameters. In designing V ALL , we ran the optimization procedure 1,000 times, each time with random starting conditions, and picked the parameter values from the run with the smallest loss. Supplementary Table  1  lists the selected parameter values of each dataset for each probe set, as well as other fixed parameter values. Design additions for synthesis and probe set data.
w d = 1 for all d, β 1 = 1, and β 2 = 1/100. We calculate s(d, θ d ) for each d over a grid of values of θ d before solving for {θ d }*. CATCH interpolates |s(d, θ d )| for non-computed values of θ d and rounds integral parameters in {θ d }* to integers while ensuring that |S({θ d }*)| ≤ N. The probe set pooled across datasets is then S({θ d }*).
Acknowledgements We thank  S. Ye ,  C. Myhrvold ,  S. Weingarten-Gabbay ,  C. Freije ,  S. Schaffner , and other members of the  Sabeti laboratory  for useful discussions and feedback on the manuscript;  B. Chak  for assistance with ethical approvals and compliance; and  Boca Biolistics , the  Florida Department of Health, Miami-Dade County Mosquito Control, Research Blood Components , the  Ragon Institute Cellular Immunology Database, and Brigham and Women's Hospital's Crimson Core  for support with samples. This project has been funded in whole or in part with federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services , under grant number  U19AI110818  to the  Broad Institute. This project was also funded in part by  NIH NIAID  contract  HHSN272200900049C , a Broadnext10 gift from the  Broad Institute, Henry M.
Jackson Foundation  award  W81XWH-11-2-0174 , and the  Bill & Melinda Gates Foundation. IAV samples were funded by  NIH NIAID  contract  HHSN272201400008C  to J. A. R. K. J. S. is supported by a fellowship from the  Human Frontiers in Science Program  ( LT000553/2016 ). S. I. and S. F. M. are supported by  NIH   NIAID R01AI099210. C. T. H. is supported by  NIH   NHGRI U01HG007480  and  U54HG007480  and by  World Bank  project  ACE019.
Data availability Sequences used as input for probe design are available in the repository at https:// github. com/broadinstitute/catch (see Supplementary Table  1  for links to specific versions used). Sequences of the probe designs (with 20-nt adaptors where applicable) developed here are available at https://github. com/broadinstitute/ catch/tree/cf500c6/probe-designs. Sequencing data from this study, as well as viral genomes generated as part of this work, have been deposited in NCBI databases under BioProject accession PRJNA431306 (PRJNA436552 for the 2018 Lassa virus genomes).
We computed the network of channels of the 3A4 isoform of the cytochrome P450 (CYP) on the basis of 16 crystal structures extracted from the Protein Data Bank (PDB). The calculations were performed with version 2 of the CCCPP software that we developed for this research project. We identified the minimal cost paths (MCPs) output by CCCPP as probable ways to access to the buried active site. The algorithm of calculation of the MCPs is presented in this paper, with its original method of visualization of the channels. We found that these MCPs constitute four major channels in CYP3A4. Among the many channels proposed by Cojocaru et al. in 2007, we found that only four of them open in 3A4. We provide a refined description of these channels together with associated quantitative data.
These channels were computed with the software CAVER  [65] , and gave rise to a channel nomenclature which is still in use  [64]. However, there are dozens of other cavities and channels calculation softwares (see  [57]  for a review). They give a variety of results due to the diversity of output data structures. Thus, it is rather difficult to compare these results. For example, the CAVER based nomenclature established in  [64]   [66] , one of the successors of CAVER. In fact, only three channels were attributed to 3A4 in the 2006 study. Such discrepancies are not unusual. They appeared also between the P450 cam (CYP101) channels calculated with CAVER and MOLEonline and the ones computed in  [67] , and the situation remains unclear despite the help of several molecular dynamics simulations  [35, 37].
The Standard Approach: Terminology The channels in proteins were calculated with the CCCPP software (binaries and documentation available at http://petitjeanmichel. free. fr/itoweb. petitjean. freeware. html). The first part of the method implemented in CCCPP is described in  [57]. For clarity, we summarize it as follows. The smallest convex domain enclosing the heavy atoms of the protein is a polyhedron partitioned in non overlapping tetrahedral cells with atoms at their vertices (Delaunay triangulation). Two adjacent cells are separated by a triangle with atoms at its vertices, acting as a door between two tetrahedral rooms, which let or not the ligand pass through to travel from one cell to its neighbor. Having flagged all triangular doors with their status, open or closed, it is easy to exhibit the protein shape and its concavities: the protein shape is modelized by the set of tetrahedral cells interconnected by triangles, which can not be passed by the ligand, although the other cells are part of the concavities.
The Improved Approach: Minimal Cost Paths The new part of CCCPP that we developed in the framework of the present study is presented below. The full CCCPP software is publicly available on a repository located at http://petitjeanmichel. free. fr/itoweb. petitjean. freeware. html. It appeared that the channels of the CYPs have large parts at the protein surface and that the main channel to the active site is a funnel which permits several potential pathways for the ligand. To find preferential trajectories for the ligand, we defined a minimal cost path, denoted MCP, as follows. To each edge of the facial graph is associated the cost CV/CV max , where CV is the critical value of the current ligand, and CV max is the maximal critical value which would allow a hypothetical ligand to pass through the triangle associated to this edge.
Although the overall fold of the CYPs is well conserved, the length and secondary structure of the B-C and F-G blocks vary considerably inside the P450s family. The flexibility of these 2 blocks is the main source of conformational change for a given isoform  [32]. The facial graph (see Section 2. 1) of this main channel is connected, i. e. , there is only one channel there: see Figure  2a, b. Knowing the volumes of the tetrahedra issued from the triangulation, we computed that about 88% of the void appears to be surface pockets and only 12% of the void is located at the distal face of the heme (Figure  2a, b ). Some ligands are in surface binding pockets, such as the progesterone  [71]  (PDB code 1W0F; see also Figure  4  in  [57] ). It could be considered that surface pockets are concavities which are not part of the protein domain, but there is no consensus in the literature about the definition of the protein domain and of the pockets.
Detection of Narrow Channels 2e and S The narrow channel 2e appears at CV = 5. 75 Å in 1TQN and at 5. 5 Å in 2V0M. It appears with a total volume in the apo form of 44,500 Å 3 (bounding surface: 44,100 Å 2 ) and a volume in the holo form of 51,000 Å 3 (bounding surface: 48,200 Å 2 ). It is a rather common channel observed in twelve different crystallized P450 isoforms  [64]. This channel 2e was observed in the 16 crystallized 3A4 structures (available in the pdb data bank) considered this study (see Table  1  in Section 3. 3). It has been observed that the channel 2e opens for CV in the range of CV between 5. 5 Å and 6 Å, without any relationship with the fact that the protein is bound or not. Channel 2e appears for all conformations of CYP3A4 (C, O1, O2). It threads through the B-C loop (Figure  6 ), and its opening could depend on the length and of the flexibility of this loop.
Conclusions Our analysis, carried out with CCCPP program on several crystal structures of CYP3A4, enabled identifying relevant ingress/egress channels, with their lining heavy atoms and residues. Our calculations support the hypothesis of channels 2a and 2f as major channels of substrate/product egress in CYP3A4, plus two secondary ones, 2e and S. We propose potential pathways of the ligands, inside these channels, toward the distal face of the heme together with information on the movements of the residues associated to the opening/closing of the channels. Our analysis suggests that block 1 anchored in the membrane opens at ligand entrance and channel 2a is the only ingress/egress channel. Then, in the case of one large ligand or two ligands, block 2 opens. Smaller channels 2e and S could be involved in the egress of the metabolites, either by enlarging or by use for circulation of water or dioxygen.
from all CYP crystal structures available in the Protein Data Bank (PDB) in March 2006 (total: 143 PDB files, 192 chains, 26 CYPs) indicates 14 channels, while CYP3A4 alone (PDB code 1TQN, 1 chain, included in the 2006 study) gave rise in 2012 to 21 channels with MOLEonline 2. 0.
Summary: Molecular profiles of neurons influence information processing, but bridging the gap between genes, circuits, and behavior has been very difficult. Furthermore, the behavioral state of an animal continuously changes across development and as a result of sensory experience. How behavioral state influences molecular cell state is poorly understood. Here we present a complete atlas of the Drosophila larval central nervous system composed of over 200,000 single cells across four developmental stages. We develop polyseq, a python package, to perform cell-type analyses. We use singlemolecule RNA-FISH to validate our scRNAseq findings. To investigate how internal state affects cell state, we optogentically altered internal state with high-throughput behavior protocols designed to mimic wasp sting and over activation of the memory system.
We did this across 4 different life stages, providing a developmental profile of gene expression. Given that the Drosophila larva has a nervous system of approximately 10,000-15,000 neurons  (Hartenstein and Campos-Ortega, 1984; Hartenstein et al. , 1987; Truman et al. , 1993; Scott et al. , 2001) , our atlas of 202,107 cells has up to 20X coverage of the entire nervous system and is the largest sequencing effort in Drosophila to date. All previously identified cell types were recognizable in our atlas, including motor neurons, Kenyon cells of the mushroom body, insulin-producing cells, brain dopaminergic and serotonergic cells, and all glial subtypes. While scRNAseq provides nearly complete information about the transcriptional program being used by a cell at the time of collection, a drawback to the technique is a loss of spatial information.
Polyseq software performs cell type discovery A complete transcriptomic atlas of 202,107 single cells from the larval central nervous system was built (Figure  1 ; Table  S1 ). Nervous systems were captured at four time points in development (1 hour, 24 hours, 48 hours, and 96 hours after larval hatching) and for three nervous system dissections (full CNS, brain only, and ventral nerve cord only). These developmental timepoints and anatomical regions were analyzed separately and in combination. Four non-neuronal and non-glial tissues, including ring gland cells, hemocytes, imaginal disc cells, and salivary gland cells, were also captured and analyzed as outgroups. We developed polyseq (github. com/jwittenbach/polyseq), an open source Python package, to perform cell type analyses. polyseq performs functions of many popular R packages such as Seurat or Monocle  (Trapnell et al.
, 2014; Satija et al. , 2015; Qui et al. , 2017; Cao et al. , 2019)  with significantly improved runtime and the extensibility and modularity of Python. polyseq starts with a gene by cell matrix, which is then filtered for high quality cells, normalized, regressed, reduced, and clustered. Visualization can then be performed with tSNE or umap for the full dataset  (Maaten and Hinton, 2008; McInnis et al. , 2018). The software also includes inbuilt functionality for violin plots and heatmaps. The data remain in a form that is easy to integrate with the vast community of Python packages for further visualization and analysis (full details and analysis examples on github: github. com/jwittenbach/polyseq). We first used polyseq to discover cell type clusters and confirmed that our findings were in agreement with current state of the art analysis methods (Figure  2 ).
In two separate early third instar samples, we found the same cell types when analyzing the data in Seurat, Monocle, and polyseq (Figure  2A ). In these samples, the cells separated into seven groups of developing neurons (which included subtypes of adult developing neurons, neuroblasts, and ganglion mother cells), four groups of glia, immune cells, and three groups of larval functional neurons (including distinct motor neuron and Kenyon cell groupings). To correct for batch effects, both the align function used in the monocle R package  (Haghverdi et al. , 2018)  and our own linear regression method in polyseq were tested (Figure  2B, C ). Both methods removed the visible batch effects in the umap plots (i. e. , clusters that were made entirely of a single sample due to signal from separate batches collapsed into a single co-mingled population).
As additional validation of cell type discovery, we used Garnett, a newly developed machine learning software package in R, to build a classifier based on cell type markers  (Pliner et al. , 2019). We found consistent results with our own annotation of known and newly discovered markers for larval functional neurons, neural stem cells, motor neurons, kenyon cells, and glia (Figure  2D, E ). Using more specific markers for known cells which are small in overall number (such as insulin-producing cells, dopaminergic cells, octopaminergic cells, etc. ) led to overfitting of the data. These known gene markers could be used to extract cells of interest without unsupervised methods.
We extracted and combined NPCs cells from three stages of development (1 hour, 24 hours, and 48 hours) and performed an analysis in Monocle  (Trapnell et al. , 2014; Qiu et al. , 2017; Cao et al. , 2019)  (Figure  3 ). Garnett was used to predict cell types  (Pliner et al. , 2019). Known cell ages were used to anchor a psuedotime analysis, which aligned the data from early to late NPCs. Gene expression in these populations revealed known markers (such as insensible (insb) in Ganglion Mother Cells) and unexpected markers, including long non-coding RNA (CR31386 in early NBs). IGF-II mRNA-binding protein (Imp) and Syncrip (Syp) form important gradients that mark NB age  (Liu et al. , 2015)   3D ,E; Table  S2 ). Early NPCs were characterized by the expression of genes important for genome organization, chromatin remodeling, and gene splicing.
Precisely timed patterns of temporal transcription factors guide this development. We are able to investigate these patterns over space and time by collecting the brain and VNC separately and collecting multiple stages of larval development (Figure  3 ). The mushroom body continues to grow and develop during larval life. We were able to identify mushroom body neuroblasts in our dataset, which were found in brain NB clusters and characterized by high expression of the late neuroblast marker Syp, genes for cell cycling, including pendulin (Pen) and cyclin E (CycE), and by the long noncoding RNA pncr002:3R (Figure  4 ). Five glial subtypes were recognizable in our atlas, including midline/cortex, astrocytelike, chiasm, peripheral/surface, and longitudinal body glia (Figure  4 )  (Freeman, 2015).
By subsetting the data to look only at 96 cells in the putative IPCs cluster, the neurotransmitters and receptors expressed by these could be analyzed (Figure  5C ). Previous reports show IPCs are regulated by canonical neurotransmitters. This includes modulation by serotonin through the 5-HT1A receptor and octopamine through the Octbeta1 receptor  (Luo et al. , 2012)  and by the neuropeptide allatostatin A  (Hentze et al. , 2015). We confirmed this known expression of 5-HT1A and Octbeta1 receptor. In our atlas, we also see the strong expression of additional (previously unknown for these cells) receptors for dopamine (Dop2R), glutamate (GluClalpha), and Allatostatin C Receptor 2(AstC-R2) in IPCs (Figure  5C ). To validate the specificity of our scRNAseq approach for identifying AstC-R2 in ICP cells, we probed AstC-R2 mRNA in a HaloTag reporter line for the ICPs.
After barcoding and sequencing, a cell by gene matrix is generated. This cell by gene matrix was then analyzed with polyseq, a custom python package. In total, 202,107 neurons and glia were sequenced. The anatomical location of newly defined molecular cell types were validated and identified using RNA-FISH with confocal imaging. To push the technique forward, RNA-FISH combined with Bessel beam selective plane illumination microscope (BB-SIM) was used to obtain the absolute quantification and subcellular location of transcripts in these new cell types. Optogenetic manipulations were performed to alter the internal state of the animal, either with two hours of fictive wasp sting or two hours of overactivation of 10% of brain neurons, and scRNAseq was used once more to search for a change in molecular cell state between conditions.
QUANTIFICATION AND STATISTICAL ANALYSIS scRNA-seq analysis Bioinformatic analysis was performed using Cell Ranger software (Version 1. 3. 1, 10x Genomics, Pleasanton, CA, USA), the Seurat R package  (Satija et al. , 2015)  and custom software in R and Python, including the polyseq Python package developed here. Software to train classifiers using neural networks was built with TensorFlow. The polyseq package as well as jupyter notebooks containing code used for analysis in the study are available on GitHub (https://github. com/jwittenbach/polyseq). Briefly, Cell Ranger was used to perform demultiplexing, alignment, filtering, and counting of barcodes and UMIs, with the output being a cell-by-genes matrix of counts. To further ensure that only high-quality cells were retained, any cell that registered counts in a unique number of genes below a baseline threshold was removed.
The top K principal components (PCs) were retained as features for downstream analyses. For the lager cell atlas dataset, K was chosen to retain a desired percentage of the total variance. For the smaller cell state datasets, K was chosen automatically via a shuffle test -on each shuffle, gene expression levels for each gene were randomly permuted across all cells and the percent variance explained by the top PC was recorded; the 95 th percentile of this value across all shuffles was then used as a threshold to determine the cutoff point for keeping PCs with respect to percent variance explained by a particular PC. Based on these top PCs, cells were clustered using the Louvain-Jaccard graph-based clustering approach. Briefly, the k-nearest neighbor graph between cells was is computed. Edge weights are then determined using the Jaccard index, which measures the fraction of shared neighbors between any two nodes.
Finally, the Louvain community detection algorithm is applied to this graph to partition the nodes into clusters; this algorithm seeks to optimize weight of connections with each cluster relative to those between clusters. In order to visualize the results of the analysis, the PC features were also used to perform a nonlinear embedding into two dimensions. This was performed via either the t-SNE or the UMAP algorithm. Once cluster identities were determined, the original gene expression level data was to determine important genes for defining each cluster. For each cluster, gene expression levels were used as features, and a binary indicator of whether or not a cell came from the cluster in question was used as a target. This data was then used to fit a linear classifier (viz, a support vector classifier) to separate in-cluster cells from the rest of the population.
Imaging analysis To quantify the number of vGlut and AstC mRNAs in cells contain both vGlut and AstC, we first manually segmented cells from BB-SIM z-stacks that have both vGlut and AstC FISH signals using the Fiji plugin TrakEM2  (Schindelin et al. , 2012; Meissner et al. , 2019). After identifying the individual fluorescent spots in segmented cells used a previously described Matlab algorithm  (Lionnet et al. , 2011) , we calculate the number of mRNAs per cell. Reconstructed images were generated using Matlab code that draws spots centered on each of the detected spots positions  (Lionnet et al. , 2011)   We dissected the brain from the nerve cord and sequenced the RNA from each population separately. This provided ground truth labels which we could then feed into (B) a neural network to train a classifier to predict spatial origin from the brain of the VNC.
Acknowledgments: We thank  J. Grimm  for sharing reagents;  J. Etheredge  for fly stocks;  S. Harrison , M. Mercer, and the  Janelia Fly Core  for assistance with fly husbandry;  A. Lemire  and K. Aswath of  Janelia Quantitative Genomics  for assistance with sequencing. Funding: Supported by  Janelia HHMI  (M. Z. ),  Gates Cambridge Trust  (B. T. C. ),  HHMI Medical Fellows Program (B. T. C. ) ,  NSF  ( 1146575 ,  1557923 ,  1548121  and  1645219 ; L. L. M. ).
which lay dormant until adult life. At 48 hours ALH the functional larval and developing adult populations are still identifiable. 858 REAGENT or RESOURCE SOURCE IDENTIFIER Critical Commercial Assays RNA-FISH probes Biosearch N/A Technologies RNase-free 1x PBS Fisher Scientific BP2438-4 Acetic Acid, Glacial (Certified ACS), Fisher Chemical Fisher Scientific A38S-500 Sodium borohydride, 99%, VenPure™ SF powder Acros Organics AC448481000 SSC (20X) Fisher AM9763 Hi-Di formamide Applied Biosystems 4311320 Denhardt's solution (50X) Alfa Aesar AAJ63135AD tRNA from baker's yeast Roche 10109495001 UltraPure™ Salmon Sperm DNA Solution Fisher Scientific 15632011 SDS, 10% Corning 46-040-CI Deionizedformamide Ambion AM9342 Chromium Single Cell 30 Library & Gel Bead Kit v2 10x Genomics PN-120237 Chromium Single Cell A Chip Kit 10x Genomics PN-120236 Chromium i7 Multiplex Kit 10x Genomics PN-120262 Deposited Data Raw and analyzed scRNAseq data This paper GEO: GSE135810 Experimental Models: Organisms/Strains D.