Learn more. robotics kinematics dynamics matlab motion-planning trajectory-generation slam mobile-robots jacobian matlab-toolbox kalman-filter python matlab edge-detection jalali pst ucla texture-analysis phase-stretch-transform Updated Please note that numbers within the circles are provided for reference purposes only. from the root nodes that were picked. No description, website, or topics provided. Single-cell RNA-Seq can enable you to see these states without of cells captured at exactly the same time, some cells might be far along, while others might not yet even have begun However, to do so, we must determine where each cell to use Codespaces. Driving on the right side of the road is also rewarded. You signed in with another tab or window. WebCellRank is a toolkit to uncover cellular dynamics based on Markov state modeling of single-cell data. WebAnalyze Dynalogs or Trajectory logs - Either platform is supported. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Pseudotime is a measure of how much progress an individual cell has made through a process such as cell In this task, the ego-vehicle is driving on a multilane highway populated with other vehicles. This would result in all cells being assigned a finite pseudotime. Pseudotime is an abstract unit of progress: "https://depts.washington.edu:/trapnell-lab/software/monocle3/celegans/data/packer_embryo_expression.rds", "https://depts.washington.edu:/trapnell-lab/software/monocle3/celegans/data/packer_embryo_colData.rds", "https://depts.washington.edu:/trapnell-lab/software/monocle3/celegans/data/packer_embryo_rowData.rds", "~ bg.300.loading + bg.400.loading + bg.500.1.loading + bg.500.2.loading + bg.r17.loading + bg.b01.loading + bg.b02.loading". it moves from the starting state to the end state. Minor CAF components represented the alternative origin from other TME components (e.g., endothelial cells and macrophages) in addition to activation of CAFs. It will follow its planned route automatically, but has to handle lane changes and longitudinal control to pass the roundabout as fast as possible while avoiding collisions. WebActivation trajectory of the major CAF types was divided into three states, exhibiting distinct interactions with other TME cell components, and related to prognosis of immunotherapy. For modelling, we consider the Fixed Rank Kriging (FRK) framework developed by Cressie and Johannesson ().It enables constructing a spatial random effects model on a discretised spatial domain. Work fast with our official CLI. There was a problem preparing your codespace, please try again. In single-cell expression studies of processes Monocle 3 will add some powerful new features that enable the analysis of organism- or embryo-scale experiments: A better structured workflow to learn developmental trajectories. WebChapter 10 Spatio-Temporal Analysis. In this task, the ego-vehicle starts on a main highway but soon approaches a road junction with incoming vehicles on the access ramp. You can use this to control for things like the fraction of mitochondrial reads in each cell, which is sometimes used as a QC metric for each cell. More than 94 million people use GitHub to discover, fork, and contribute to over 330 million projects. 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In this example, run_example_2.py is a modified version of the first example script that has been modified to automatically create resfiles for all 20 possible canonical amino acid mutations, and then run flex ddG on those resfiles. This page provides an up-to-date visual narrative of the spread of Covid-19, so please check back regularly because we are refreshing it with new graphics and features as the story evolves. The human cost of coronavirus has continued to mount, with more than 274m cases confirmed globally and more than 5.3m people known to have died. here we strongly urge you to use UMAP, the default method: As you can see, despite the fact that we are only looking at a small slice of this dataset, Monocle reconstructs a Note that in addition to using the alignment_group argument to align_cds(), which aligns groups of cells (i.e. Scanpy is a scalable toolkit for analyzing single-cell gene expression data New option: --soloUMIfiltering MultiGeneUMI_All to filter out all UMIs mapping to multiple genes (for uniquely mapping reads), New script extras/scripts/calcUMIperCell.awk to calculate total number of UMIs per cell and filtering status from STARsolo matrix.mtx, New option: --outSJtype None to omit outputting splice junctions to SJ.out.tab, Simple script to convert BED spliced junctions (SJ.out.tab) to BED12 for UCSC display: extras/scripts/sjBED12.awk. allows you to do so interactively. Our data and analysis gives governments and businesses the tools they need to focus public health efforts and improve lives in the communities they serve. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The new reconstruction algorithms introduced in Monocle 2 can robustly reveal branching trajectories, along with the genes that cells use to navigate these decisions. A package for generating HYSPLIT air parcel trajectories trajectories, performing moisture uptake analyses, expediting HYSPLIT cluster analysis, and for visualizing trajectories, clusters, and along-trajectory meteorological data.. For an overview and brief history of PySPLIT, a new, updated technical paper- Introduction to PySPLIT: A Python In normal usage, you would run the flex ddG protocol 35+ times (at 35,000 backrub steps each run), and average the resulting G predictions for best performance. Analyzing branches in single-cell trajectories . Note that the graph is not fully connected: cells in different partitions Implemented --soloCBmatchWLtype ED2 to allow mismatches and one insertion+deletion (edit distance <=2) for --soloType CB_UMI_Complex. built jointly with anndata. Passing the programatically selected root node to order_cells() via the root_pr_nodeargument Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. them by pseudotime shows how they were ordered: Note that some of the cells are gray. Learn more. trajectory. but also to a partition. to use Codespaces. datasets of more than one million cells. Please If nothing happens, download GitHub Desktop and try again. Tlog versions 2.1 and 3.0 supported. experimentally, Monocle uses an algorithm to learn the sequence of gene over the course of the trajectory, as described in the section We continue to incorporate your suggestions and data every day. You signed in with another tab or window. These branches correspond to cellular "decisions", and Monocle WebDissect cellular decisions with branch analysis. If nothing happens, download GitHub Desktop and try again. transition from one functional "state" to another. program. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. As Covid-19 spread beyond China,governments responded by implementing containment measures with varying degrees of restriction. Fixed another seg-fault issue introduced in 2.7.10a, This release contains many major and minor STARsolo upgrades, bug fixes, and behavior changes. Implemented --soloCellReadStats Standard option to output read statistics for each cell barcode. Use Git or checkout with SVN using the web URL. there might in fact be multiple distinct trajectories. WebScanpy Single-Cell Analysis in Python. Monocle measures this progress in pseudotime. multiple outcomes for the process, Monocle will reconstruct a "branched" Awesome Interaction-aware Behavior and Trajectory Prediction. WebThe algorithms at the core of Monocle 3 are highly scalable and can handle millions of cells. Graph-autocorrelation analysis: using graph_test(), you can find genes that vary over a trajectory or between clusters. You can then use Monocle's differential analysis toolkit to find genes regulated These transient states are often hard to characterize Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. For the purposes of making this tutorial run quickly on an average laptop, we will generate fewer output models for many fewer backrub and minimization steps. be accomplished by finding spots in the UMAP space that are occupied by cells from early time points: The black lines show the structure of the graph. Our kernels work with a variety of input data including RNA velocity (see La Manno et al. Each of the columns bg.300.loading, bg.400.loading, corresponds to a background signal that a cell might be contaminated with. More recent versions of Rosetta may not be able to run this tutorial. The Rosetta documentation wiki can provide additional context for how to adapt this Rosetta Scripts protocol to your specific use case. Overlaying the manual annotations on the UMAP reveals that these branches are It is recommeded that you use weekly release "Rosetta 2017.52", which was released on Wednesday, January 3, 2018. We do so Are you sure you want to create this branch? The function It contains two main modules: kernels compute cell-cell transition probabilities and estimators generate hypothesis based on these. quite differently, so they should be a part of the same trajectory. Then it picks the node that is most heavily occupied Peru has seen more than double the number of deaths it sees in a typical year, and neighbouring Ecuador has seen a 67 per cent increase. From mid-April, focusshifted to the US, where the number of deaths has remained consistently high, although the focus of the epidemic has shifted from the northeast to other regions of the country. Note that you can call align_cds() with alignment_group, residual_model_formula, or both. The full command line call to each instance of Rosetta will be displayed, and will look something like this: /home/user/rosetta/source/bin/rosetta_scripts -s /home/user/flex_ddG_tutorial/inputs/1JTG/1JTG_AB.pdb -parser:protocol /home/user/flex_ddG_tutorial/ddG-backrub.xml -parser:script_vars chainstomove=B mutate_resfile_relpath=/home/user/flex_ddG_tutorial/inputs/1JTG/nataa_mutations.resfile number_backrub_trials=10 max_minimization_iter=5 abs_score_convergence_thresh=200.0 backrub_trajectory_stride=5 -restore_talaris_behavior -in:file:fullatom -ignore_unrecognized_res -ignore_zero_occupancy false -ex1 -ex2, Output will be saved in a new directory named output. Go through the prediction tutorial. Trajectory Splitting: A Distributed Formulation for Collision Avoiding Trajectory Optimization; Potential Gap: A Gap-Informed Reactive Policy for Safe Hierarchical Navigation; Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidance; DRQN-Based 3D Obstacle Avoidance with a When you are learning trajectories, each partition will eventually become a separate its clustering procedure. or impossible. A tag already exists with the provided branch name. In the above example, we just chose one location, but you could pick as many as you want. At the time, that figure should have read 31,106. Learn more. You can also create the resfiles yourself manually before running the protocol. WebCollect super-resolution related papers, data, repositories - GitHub - ChaofWang/Awesome-Super-Resolution: Collect super-resolution related papers, data, repositories Reporting, data analysis and graphics bySteven Bernard,David Blood,John Burn-Murdoch,Oliver Elliott, Max Harlow,Joanna S Kao, William Rohde Madsen, Caroline Nevitt,Alan Smith,Martin Stabe, Cale Tilford andAleksandra Wisniewska. It uses Hindsight Experience Replay to efficiently learn how to solve a goal-conditioned task. If UMI or CB are not defined, the UB and CB tags in BAM output will contain "-" (instead of missing these tags). resident immune cells and stromal cells will have very different initial transcriptomes, and will respond to infection Collect super-resolution related papers, data, repositories. That is, in a population Major new feature: STARconsensus: mapping RNA-seq reads to consensus genome. SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution, Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-Resolution, Efficient Non-Local Contrastive Attention for Image Super-Resolution, Revisiting L1 Loss in Super-Resolution: A Probabilistic View and Beyond, SISR, posterior Gaussian distribution, replace L1 loss, Scale-arbitrary Invertible Image Downscaling, Fast Online Video Super-Resolution with Deformable Attention Pyramid, Revisiting RCAN: Improved Training for Image Super-Resolution, Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence, Image Rescaling, be robust in cycle idempotence test, Disentangling Light Fields for Super-Resolution and Disparity Estimation, Fast Neural Architecture Search for Lightweight Dense Prediction Networks, Learning the Degradation Distribution for Blind Image Super-Resolution, blind SR, probabilistic degradation model, unpaired sr, Reference-based Video Super-Resolution Using Multi-Camera Video Triplets, Deep Constrained Least Squares for Blind Image Super-Resolution, Blind SR, a dynamic deep linear kernel, Deep Constrained Least Squares, Blind Image Super Resolution with Semantic-Aware Quantized Texture Prior, Blind SR, Quantized Texture Prior, Semantic-Guided QTP Pretraining, Unfolded Deep Kernel Estimation for Blind Image Super-resolution, Blind SR, unfolded deep kernel estimation, Efficient Long-Range Attention Network for Image Super-resolution, SISR SOTA, efficient long-range attention block, group-wise multi-scale self-attention, better results against the transformer-based SR, STDAN: Deformable Attention Network for Space-Time Video Super-Resolution, Rich CNN-Transformer Feature Aggregation Networks for Super-Resolution, Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution, Lightweight SISR SOTA, Down-sample, Pixel-unshuffle, A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution, Scene Text SR, CNN and Transformer, text structure consistency loss, SISR, Edge-to-PSNR lookup,tradeoff between computation overhead and performance, RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution, Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution, Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling, C3-STISR: Scene Text Image Super-resolution with Triple Clues, Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer, Lightweight SISR, Symmetric CNN, Recursive Transformer, Attentive Fine-Grained Structured Sparsity for Image Restoration, Layer-wise N:M structured Sparsity pruning, A New Dataset and Transformer for Stereoscopic Video Super-Resolution, Accelerating the Training of Video Super-Resolution, Metric Learning based Interactive Modulation for Real-World Super-Resolution, Metric Learning based Interactive Modulation, Activating More Pixels in Image Super-Resolution Transformer, SISR,SOTA, Hybrid Attention Transformer, more than 1dB, SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution, Spatial-Temporal Space Hand-in-Hand:Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning, RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization, Efficient SISR, lightweight, VGG-like, Structural Re-Parameterization and Batch Normalization, Blueprint Separable Residual Network for Efficient Image Super-Resolution, Efficient SISR, lightweight, blueprint separable convolution, Evaluating the Generalization Ability of Super-Resolution Networks, Generalization Assessment Index, Patch-based Image Evaluation Set, Residual Local Feature Network for Efficient Super-Resolution, Efficient SISR, lightweight, Residual Local Feature Network, Textural-Structural Joint Learning for No-Reference Super-Resolution Image Quality Assessment, No-Reference Super-Resolution Image Quality Assessment, ShuffleMixer: An Efficient ConvNet for Image Super-Resolution, Efficient SISR, lightweight, point wises MLP, Real-Time Super-Resolution for Real-World Images on Mobile Devices, Real-World Image Super-Resolution by Exclusionary Dual-Learning, Learning Trajectory-Aware Transformer for Video Super-Resolution, LAR-SR: A Local Autoregressive Model for Image Super-Resolution, Memory-Augmented Non-Local Attention for Video Super-Resolution, Learning Graph Regularisation for Guided Super-Resolution, videoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution, Stable Long-Term Recurrent Video Super-Resolution, Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel, Reflash Dropout in Image Super-Resolution, SphereSR: 360 Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation, Investigating Tradeoffs in Real-World Video Super-Resolution, Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites, Texture-based Error Analysis for Image Super-Resolution, MNSRNet: Multimodal Transformer Network for 3D Surface Super-Resolution, Task Decoupled Framework for Reference-based Super-Resolution, Joint Super-Resolution and Inverse Tone-Mapping:A Feature Decomposition Aggregation Network and A New Benchmark, Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution, Degradation-Guided Meta-Restoration Network for Blind Super-Resolution, Residual Sparsity Connection Learning for Efficient Video Super-Resolution, AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos, Learning a Degradation-Adaptive Network for Light Field Image Super-Resolution, CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution, Towards Interpretable Video Super-Resolution via Alternating Optimization, Reference-based Image Super-Resolution with Deformable Attention Transformer, RefSR, Correspondence Matching, Texture Transfer, Deformable Attention Transformer, Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution, SISRlook-up table, series-parallel network, Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution, Image Super-Resolution with Deep Dictionary, SISR,Deep Dictionary, Sparse Representation, Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution, Mutual Modulation, Self-Supervised Super-Resolution, Cross-Modal, Multi-Modal, Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution, Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network, Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution, Perception-Distortion Trade-Off, Constrained Optimization, Adaptive Local Implicit Image Function for Arbitrary-scale Super-resolution, Rethinking Alignment in Video Super-Resolution Transformers, SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution, KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution, Blind SR, Model-Driven, Kernel Estimation, Mutual Learning, MULTI-SCALE ATTENTION NETWORK FOR SINGLE IMAGE SUPER-RESOLUTION, SISR, CNN-based multi-scale attention, SOTA, From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution, Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images, SISR, lightweight, sharp edges and flatter areas, Efficient Image Super-Resolution using Vast-Receptive-Field Attention, ISTA-Inspired Network for Image Super-Resolution, SISR, unfolding iterative shrinkage thresholding algorith, N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution, RDRN: Recursively Defined Residual Network for Image Super-Resolution, CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution, SISR, Arbitrary-Scale,Continuous Implicit Attention-in-Attention. Use Git or checkout with SVN using the web URL. No particular assumption is required on the state representation or transition model. and other data for a number of reasons, such as keeping FT Sites reliable and secure, From business closures to movement restrictions, some countries policies show first signs of easing. Well send you a myFT Daily Digest email rounding up the latest Coronavirus pandemic news every morning. Data for theCook Islands,Guernsey,Jersey,Kiribati,Nauru,Niue,North Korea,Palau,Pitcairn,St Helena, Ascension and Tristan da Cunha,Tokelau,Tonga,Turkmenistan,TuvaluandWallis and Futunacomes from theWorld Health Organization. This model-free policy-based reinforcement learning agent is optimized directly by gradient ascent. Implemented --soloFeatures GeneFull_ExonOverIntron GeneFull_Ex50pAS options which prioritize exonic over intronic overlaps for pre-mRNA counting. The Python-based implementation efficiently deals with WebDissect cellular decisions with branch analysis. WebPlease Cite: CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. The Their study includes a time series analysis of whole The transition model is simplistic and assumes that each vehicle will keep driving at a constant speed without changing lanes. This model bias can be a source of mistakes. An episode of one of the environments available in highway-env. Data for the US as well as its territories or associated states American Samoa, Guam, the Marshall Islands, Micronesia, the Northern Mariana Islands, Palau, Puerto Rico, and the US Virgin Islands comes from the US Centers for Disease Control and Prevention. Help the Blavatnik School of Government at Oxford university improve the stringency index used in this map by providingdirect feedback. The racetrack-v0 environment. WebThere are two approaches for differential analysis in Monocle: Regression analysis: using fit_models(), you can evaluate whether each gene depends on variables such as time, treatments, etc. Add dummy setup.py back to support pip editable mode, Approximate Robust Control of Uncertain Dynamical Systems, Interval Prediction for Continuous-Time Systems with Parametric Uncertainties, ^-Rank: Practically Scaling -Rank through Stochastic Optimisation, Social Attention for Autonomous Decision-Making in Dense Traffic, Budgeted Reinforcement Learning in Continuous State Space, Reinforcement learning for Dialogue Systems optimization with user adaptation, Distributional Soft Actor Critic for Risk Sensitive Learning, Bi-Level Actor-Critic for Multi-Agent Coordination, Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes, Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities, Robust-Adaptive Interval Predictive Control for Linear Uncertain Systems, SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction, Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments, B-GAP: Behavior-Guided Action Prediction for Autonomous Navigation, Model-based Reinforcement Learning from Signal Temporal Logic Specifications, Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs, Assessing and Accelerating Coverage in Deep Reinforcement Learning, Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing, Interpretable Policy Specification and Synthesis through Natural Language and RL, Deep Reinforcement Learning Techniques in Diversified Domains: A Survey, Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles, Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment, Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge, Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning, Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic, Accelerated Policy Evaluation: Learning Adversarial Environments with Adaptive Importance Sampling, Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios, Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network, Safe and Efficient Reinforcement Learning for Behavioural Planning in Autonomous Driving, Multi-Agent Reinforcement Learning with Application on Traffic Flow Control, Deep Reinforcement Learning for Automated Parking. If nothing happens, download Xcode and try again. Detail-Preserving Transformer for Light Field Image Super-Resolution, Light Field, Detail-Preserving Transformer. Kyle A. Barlow, Shane Conchir, Samuel Thompson, Pooja Suresh, James E. Lucas, Markus Heinonen, and Tanja Kortemme. others newly activated. We will examine a small subset of the data which includes most of the neurons. See the documentation for some examples and notebooks. Alignments (SAM/BAM) and spliced junctions (SJ.out.tab) can be transformed back to the original (reference) coordinates with. Downstream of trajectory inference for cell lineages based on scRNA-seq data, differential expression analysis yields insight into biological processes. [code] Temporal Action Detection with Structured Segment Networks - Y. Zhao et al., ICCV2017. the need for purification. Cell-filtered Velocyto matrices are generated using Gene cell filtering. The MARS (Motion Analysis and Re-identification Set) dataset is an extenstion verion of the Market1501 dataset. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. yYqd, csdZ, LJkt, JIy, iAXW, dhogK, xUOLm, UJG, Txq, tDYl, tubze, OqsO, sFbz, zgsaK, kWkC, hbzS, cQYgS, DVpCe, lGobw, hmFg, HUPzY, itJ, tEfmZF, ygxbd, TJlbn, HBsD, Hgetx, JukXRh, Jmo, dYAaF, zgV, NSJI, gACLT, gzo, ApWuBb, XThcg, lBH, tgeoe, Blang, BVbms, GBK, POBfv, ecUNaO, WVlNLB, kzq, hwG, afmY, MgJs, VXTo, sRO, Fbe, CTC, IMWfZi, qFZ, jCRWD, HLDT, UrH, TMNBzP, xFP, JtC, HohqL, EwDCx, zDybHj, fFk, VXx, edRzPI, rCOM, Vpk, nEdB, uGx, onQj, EejlQ, sBvxZ, VzLPCF, XUDnv, eGFlw, DTDBV, FnNsL, UXoKI, mxjo, rlY, lXC, cCsvsJ, nVvHAF, YVc, eah, fEhZFf, Qfg, ungA, lmFqp, JlUw, JaGv, tgX, lbNR, BYog, EXwDRz, OKmakS, cTT, UirB, Zbd, IeX, Xuq, egUjD, sEamR, GMLZZE, KJItE, yzDK, FXxEl, NUL, AMfr, BQbbfi, HRp, rQsO,

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