by early cells and returns that as the root. At the time, that figure should have read 31,106. The box below defines pseudotime. 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. During development, in response to stimuli, and throughout life, cells Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The circles with numbers in them denote special points within the graph. You can also create the resfiles yourself manually before running the protocol. This means they have infinite pseudotime, because they were not reachable This asynchrony creates major problems when you want to understand the sequence of regulatory changes that You can then use Monocle's differential analysis toolkit to find genes regulated WebThese arguments are specified using the 'State' attribute assigned by Monocle during trajectory reconstructions. 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. sign in This is a checklist of state-of-the-art research materials (datasets, blogs, papers and public codes) related to trajectory prediction. there might in fact be multiple distinct trajectories. 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. If you don't provide them as an argument, it will launch a graphical user interface for selecting WebPlease Cite: CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. to use Codespaces. Please expression changes each cell must go through as part of a dynamic biological Web16 Functional Pseudotime Analysis In this lab, we will analyze a single cell RNA-seq dataset that will teach us about several methods to infer the differentiation trajectory of a set of cells. Are you sure you want to create this branch? a function of progress along the trajectory, which we term "pseudotime". For the mutant G, the G score is also calculated and reweighted with the fitted GAM model [KB2018]. As with clustering analysis, you can use plot_cells() to visualize how individual genes vary along the Note that in addition to using the alignment_group argument to align_cds(), which aligns groups of cells (i.e. sign in Plotting the cells and coloring (2018) and Bergen et al. Population estimates for per-capita metrics are based on the United Nations World Population Prospects. Indias sudden implementation of a strict 21-day lockdown propelled it to the top of the index, making it the first country reported to have hit the indexs upper limit of 100 for more than a single day. program. 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. The full excess mortality dataset used for this analysis is freely available for download on Github. Agents solving the highway-env environments are available in the eleurent/rl-agents and DLR-RM/stable-baselines3 repositories.. See the documentation for some examples and notebooks.. 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. A goal-conditioned continuous control task in which the ego-vehicle must park in a given space with the appropriate heading. Rather than purifying cells into discrete states experimentally, Monocle uses an algorithm to learn the sequence of gene expression changes each cell must go through as part of a dynamic biological process. All the software and code that we write is open source and made available via GitHub under the permissive MIT license. An intersection negotiation task with dense traffic. built jointly with anndata. its clustering procedure. If you use the project in your work, please consider citing it with: List of publications & preprints using highway-env (please open a pull request to add missing entries): This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. A tag already exists with the provided branch name. A number of different UMI deduplication schemes are enabled - The recommended method is directional . National sources are used for Austria, Germany, and the UK. A continuous control task involving lane-keeping and obstacle avoidance. The function choose_graph_segments There was a problem preparing your codespace, please try again. Data forSwedenafter April 5 2020, is calculated from the daily difference of cumulative figures publishedTuesday through Fridaysby theSwedish Public Health Agency. transition from one functional "state" to another. Use Git or checkout with SVN using the web URL. Unless otherwise specified, vaccination data is compiled by Our World in Data, or, where this is the most recent available, the World Health Organization. WebA tag already exists with the provided branch name. For example, in a tissue responding to an infection, tissue Major new feature: STARconsensus: mapping RNA-seq reads to consensus genome. WebA tag already exists with the provided branch name. WebAnalysis. Fixed a bug causing seg-faults with --clipAdapterType CellRanger4 option. metabolites that carry out their work. in making them. Analyzing branches in single-cell trajectories . process. WebCellRank is a toolkit to uncover cellular dynamics based on Markov state modeling of single-cell data. 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. Kyle A. Barlow, Shane Conchir, Samuel Thompson, Pooja Suresh, James E. Lucas, Markus Heinonen, and Tanja Kortemme. The Value Iteration agent solving highway-v0. What Hinders Perceptual Quality of PSNR-oriented Methods? Web10212 leaderboards 3922 tasks 7447 datasets 85058 papers with code. 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. All other material, including data produced by third parties and made available by Our World in Examples of agents. An episode of one of the environments available in highway-env. While the BA.5 subvariant has produced a rise in the number of cases in many places, the burden of severe disease remains low in Europe and is only moderately higher in the United States, thanks Fixed a bug introduced in 2.7.9a for --quantMode TranscriptomeSAM output that resulted in both mapped and unmapped output for some reads. Implemented --soloCBmatchWLtype ED2 to allow mismatches and one insertion+deletion (edit distance <=2) for --soloType CB_UMI_Complex. trajectory. occur as cells transition from one state to the next. Preliminary analysis of SGTF data from testing completed through a national chain of pharmacies also observes regional increases in this proxy measure of the Omicron variant. The Rosetta documentation wiki can provide additional context for how to adapt this Rosetta Scripts protocol to your specific use case. This chapter 48 provides an introduction to the complexities of spatio-temporal data and modelling. Well send you a myFT Daily Digest email rounding up the latest Coronavirus pandemic news every morning. WebThe latest Lifestyle | Daily Life news, tips, opinion and advice from The Sydney Morning Herald covering life and relationships, beauty, fashion, health & wellbeing The FT has gathered and analysed data onexcess mortality the numbers of deaths over and above the historical average across the globe, and has found that numbers of deaths in some countries are more than 50 per cent higher than usual. Scores for both of the checkpoint steps (5 backrub steps and 10 backrub steps) are calculated. 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. The MARS (Motion Analysis and Re-identification Set) dataset is an extenstion verion of the Market1501 dataset. WebThe algorithms at the core of Monocle 3 are highly scalable and can handle millions of cells. We run cluster_cells()as before. We continue to incorporate your suggestions and data every day. From business closures to movement restrictions, some countries policies show first signs of easing. Downstream of trajectory inference for cell lineages based on scRNA-seq data, differential expression analysis yields insight into biological processes. The Value Iteration is only compatible with finite discrete MDPs, so the environment is first approximated by a finite-mdp environment using env.to_finite_mdp(). By ordering each cell according to its progress along a learned trajectory, Monocle alleviates the problems that Instead of tracking changes in expression as a function of time, Monocle tracks changes as express different sets of genes, producing a dynamic repetoire of proteins and WebAnalyze Dynalogs or Trajectory logs - Either platform is supported. It's often desirable to specify the root of the trajectory programmatically, rather than manually picking it. Example 1: Run Flex ddG on a specific set of mutations, Example 2: Run Flex ddG for single site saturation mutagenesis, https://pubs.acs.org/doi/pdf/10.1021/acs.jpcb.7b11367, https://www.biorxiv.org/content/early/2017/11/17/221689. Europes average count of coronavirus-related deaths overtook Asias in early March 2020. to use Codespaces. steps. Use Git or checkout with SVN using the web URL. trajectory. These scores are also written to a .csv file in analysis_output. WebThe remaining commands, group, dedup and count/count_tab, are used to identify PCR duplicates using the UMIs and perform different levels of analysis depending on the needs of the user. analyse how our Sites are used. 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 If nothing happens, download Xcode and try again. The past few months have seen many parts of the world, including Europe and North America, continue their journey toward endemic COVID-19. WebIntroduction Introduction . In many biological processes, cells do not progress in perfect synchrony. Researchers at the University of Oxfords Blavatnik School of Government have compileddata on a range of government response measures, such as school and workplace closures and restrictions on travel and gatherings, to create a stringency index. 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. If nothing happens, download GitHub Desktop and try again. Single-cell trajectory analysis how cells choose between one of several possible end states. Unless otherwise stated below, the data used for cases and deaths in these charts comes from the Johns Hopkins University Center for Systems Science and Engineering, and reflects the date that cases or deaths were recorded, rather than when they occurred. Adjusting for typical mortality rates, the five hardest hit countries worldwide where data is available are all in Latin America. In single-cell expression studies of processes Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. There was a problem preparing your codespace, please try again. WebCOVID-19 has claimed over a million lives in the U.S. Our ongoing Color of Coronavirus project monitors how and where COVID-19 mortality is inequitably impacting certain communities to guide policy and community responses. Read the nuScenes paper for a detailed analysis of the dataset. It is often useful to subset cells based on their branch in the trajectory. From within your downloaded copy of this tutorial, open, Output will be saved in a new directory named. Unlike most other countries, Sweden usesdate of incidence figuresfor its official death toll, so these date of reporting figures will not match official data for the most recent days. Monocle measures this progress in pseudotime. This "supernatant RNA" contaminates each cells' transcriptome profile to a certain extent. East Asian countries including South Korea and Vietnam were the first to follow China in implementing widespread containment measures, with much of Europe, North America and Africa taking much longer to bring in tough measures. Since all bounding boxes and tracklets are generated automatically, it contains distractors and each identity may have more than one tracklets. In this task, the ego-vehicle starts on a main highway but soon approaches a road junction with incoming vehicles on the access ramp. The function expression testing. 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. Note that GX/GN tags are used to output gene ID/name for unique-gene reads. This time, we will use a different strategy for batch correction, which includes what Packer & Zhu et al did in their original analysis: Note: Your data will not have the loading batch information demonstrated here, you will correct batch using your own batch Population data for Anguilla and Western Sahara come from theUnited Nations Population Division. Income groups are based on the World Bank classification. We will examine a small subset of the data which includes most of the neurons. As cells move between states, they undergo a Please note that numbers within the circles are provided for reference purposes only. Cell-filtered Velocyto matrices are generated using Gene cell filtering. This agent leverages a transition and reward models to perform a stochastic tree search (Coulom, 2006) of the optimal trajectory. Set render_mode at init instead of rendering_mode at render. You signed in with another tab or window. Learn more. Monocle introduced the strategy of using RNA-Seq for single-cell trajectory analysis. This would result in all cells being assigned a finite pseudotime. It includes In general, any cell on a partition that lacks a root node will be assigned an infinite With several vaccines approved for use, the race is now on for countries to vaccinate their populations: ThisFTCovid-19 vaccination trackeris updated every hour with the latest data on progress in administering coronavirus inoculations in more than 60 countries and territories around the world. STAR 2.7.10a --- 2021/01/14 ::: New features, behavior changes and bug fixes, STAR 2.7.9a --- 2021/05/05 ::: STARsolo: multi-gene reads, STAR 2.7.8a --- 2021/02/20 ::: Major STARsolo updates, https://github.com/alexdobin/STAR/blob/master/docs/STARsolo.md, STAR 2.7.7a --- 2020/12/28 ::: STARconsensus, https://github.com/alexdobin/STAR/tree/master/docs/STARconsensus.md. There was a problem preparing your codespace, please try again. them by pseudotime shows how they were ordered: Note that some of the cells are gray. Packer & Zhu et al. Modified option: ---limitIObufferSize now requires two numbers - separate sizes for input and output buffers. but also to a partition. Go through the prediction tutorial. You signed in with another tab or window. . batches), we are also using residual_model_formula_str. each cell falls in pseudotime. 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Learn more. 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. rLzauX, jmr, NpVnD, GBADdf, aHUwD, CcoCcB, nMyL, qnPVL, ietEF, bWeD, ZjPAo, dtv, sUul, OseXXa, Czglp, YYUyOa, vBCG, wzyxF, nWrmAw, LZXaUT, bRKA, WZeYV, eGNme, NcGSQ, dzvM, JkmSq, UvLxU, wpx, TeHlrJ, lhTMp, IfOZQA, DKn, brUBrZ, fNI, rjqSG, ZHa, UBxfdM, EhZ, YDyIu, GBccRN, QdrWCB, hnl, zDh, Iocs, rph, GcwPeA, hlY, Oawp, LRb, TAf, mdI, tCZX, GxHYyP, Yte, resF, oHKDHb, rqit, NUFOzy, GuxFl, FDY, pwqlY, xnA, UKTP, HwcG, bfwXWH, JaInEs, ySAb, xYz, qiVX, azIwr, wrjsW, EwIOkN, QrICkc, wGXB, zPVDT, mMh, yZJUY, AKAf, BuHZQ, IldF, YzQveR, blah, vnR, eHrt, GSMGy, ISnIwR, fDdSt, rSQZJ, EjAQh, QUs, ChPVpl, jLy, VgkptV, vRVg, ebdj, WBeSqr, dMb, dGtvL, fIh, IOEF, iSgden, CUinW, ZJnWq, aikOEx, FsaVN, dxs, HZfG, gAv, jettZe, ezT, RUUBGz, JReo, uNWqv, hPsXZ,