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Recent Submissions

  • The SWI/SNF chromatin remodeling factor DPF3 regulates metastasis of ccRCC by modulating TGF-β signaling

    Cui, Huanhuan; Yi, Hongyang; Bao, Hongyu; Tan, Ying; Tian, Chi; Shi, Xinyao; Gan, Diwen; Zhang, Bin; Liang, Weizheng; Chen, Rui; Zhu, Qionghua; Fang, Liang; Gao, Xin; Huang, Hongda; Tian, Ruijun; Sperling, Silke R.; Hu, Yuhui; Chen, Wei (Nature Communications, Springer Science and Business Media LLC, 2022-08-09) [Article]
    DPF3, a component of the SWI/SNF chromatin remodeling complex, has been associated with clear cell renal cell carcinoma (ccRCC) in a genome-wide association study. However, the functional role of DPF3 in ccRCC development and progression remains unknown. In this study, we demonstrate that DPF3a, the short isoform of DPF3, promotes kidney cancer cell migration both in vitro and in vivo, consistent with the clinical observation that DPF3a is significantly upregulated in ccRCC patients with metastases. Mechanistically, DPF3a specifically interacts with SNIP1, via which it forms a complex with SMAD4 and p300 histone acetyltransferase (HAT), the major transcriptional regulators of TGF-β signaling pathway. Moreover, the binding of DPF3a releases the repressive effect of SNIP1 on p300 HAT activity, leading to the increase in local histone acetylation and the activation of cell movement related genes. Overall, our findings reveal a metastasis-promoting function of DPF3, and further establish the link between SWI/SNF components and ccRCC.
  • Various Wavefront Sensing and Control Developments on the Santa Cruz Extreme AO Laboratory (SEAL) Testbed

    Gerard, Benjamin L.; Perez-Soto, Javier; Chambouleyron, Vincent; Kooten, Maaike A. M. van; Dillon, Daren; Cetre, Sylvain; Jensen-Clem, Rebecca; Fu, Qiang; Amata, Hadi; Heidrich, Wolfgang (arXiv, 2022-08-05) [Preprint]
    Ground-based high contrast imaging (HCI) and extreme adaptive optics (AO) technologies have advanced to the point of enabling direct detections of gas-giant exoplanets orbiting beyond the snow lines around nearby young star systems. However, leftover wavefront errors using current HCI and AO technologies, realized as "speckles" in the coronagraphic science image, still limit HCI instrument sensitivities to detecting and characterizing lower-mass, closer-in, and/or older/colder exoplanetary systems. Improving the performance of AO wavefront sensors (WFSs) and control techniques is critical to improving such HCI instrument sensitivity. Here we present three different ongoing wavefront sensing and control project developments on the Santa cruz Extreme AO Laboratory (SEAL) testbed: (1) "multi-WFS single congugate AO (SCAO)" using the Fast Atmospheric Self-coherent camera (SCC) Technique (FAST) and a Shack Hartmann WFS, (2) pupil chopping for focal plane wavefront sensing, first with an external amplitude modulator and then with the DM as a phase-only modulator, and (3) a laboratory demonstration of enhanced linearity with the non-modulated bright Pyramid WFS (PWFS) compared to the regular PWFS. All three topics share a common theme of multi-WFS SCAO and/or second stage AO, presenting opportunities and applications to further investigate these techniques in the future.
  • Prevalence of anti-SARS-CoV-2 antibody in hemodialysis facilities: a cross-sectional multicenter study from Madinah

    Housawi, Abdulrahman A.; Qazi, Shazada Junaid S.; Jan, Abdulhalem A.; Osman, Rashid A.; Alshamrani, Mashil M.; AlFaadhel, Talal A.; AlHejaili, Fayez F.; Al-Tawfiq, Jaffar A.; Wafa, Ahmed A.; Hamza, Abdulmageed E.; Hassan, Moustafa A.; Alharbi, Suliman A.; Albasheer, Hamza; Almohmmdi, Majed M.; Alsisi, Salem A.; Mankowski, Michal; van de Klundert, Joris; Alhelal, Amal M.; Sala, Fatima H.; Kheyami, Ali; Alhomayeed, Bader A. (Annals of Saudi Medicine, King Faisal Specialist Hospital and Research Centre, 2022-08-04) [Article]
    BACKGROUND: Since the occurrence of coronavirus disease in 2019 (COVID-19), the global community has witnessed its exponential spread with devastating outcomes within the general population and specifically within hemodialysis patients. OBJECTIVES: Compare the state of immunity to SARS-CoV-2 among hemodialysis patients and staff. DESIGN: Cross-sectional study with a prospective follow-up period. SETTING: Hemodialysis centers in Madinah region. PATIENTS AND METHODS: We prospectively tested for SARS-CoV-2 antibodies in dialysis patients using dialysis centers staff as controls. The participants were tested on four occasions when feasible for the presence of anti-SARS-CoV-2 antibodies. We also analyzed factors that might be associated with seropositivity. MAIN OUTCOME MEASURES: SARS-CoV-2 positivity using immunoglobulin G (IgG) levels SAMPLE SIZE: 830 participants, 677 patients and 153 dialysis centers staff as controls. RESULTS: Of the total participants, 325 (257 patients and 68 staff) were positive for SARS-CoV-2 IgG antibodies, for a prevalence of 38.0% and 44.4% among patients and staff, respectively (P=.1379). Participants with a history of COVID-19 or related symptoms were more likely to have positive IgG (P<.0001). Surprisingly, positivity was also center-dependent. In a multivariable logistic regression, a history of infection and related symptoms contributed significantly to developing immunity. CONCLUSION: The high prevalence of SARS-CoV-2 antibody among hemodialysis patients and previously asymptomatic staff suggested past asymptomatic infection. Some centers showed more immunity effects than others. LIMITATIONS: Unable to collect four samples for each participant; limited to one urban center. CONFLICT OF INTEREST: None.
  • Differentially Private ℓ1-norm Linear Regression with Heavy-tailed Data

    Wang, Di; Xu, Jinhui (IEEE, 2022-08-03) [Conference Paper]
    We study the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) with heavy-tailed data. Specifically, we focus on the ℓ1-norm linear regression in the ϵ-DP model. While most of the previous work focuses on the case where the loss function is Lipschitz, here we only need to assume the variates has bounded moments. Firstly, we study the case where the ℓ2 norm of data has bounded second order moment. We propose an algorithm which is based on the exponential mechanism and show that it is possible to achieve an upper bound of O~(dnε−−√) (with high probability). Next, we relax the assumption to bounded θ-th order moment with some θ ∈ (1,2) and show that it is possible to achieve an upper bound of O~((dnε−−√)θ−1θ). Our algorithms can also be extended to more relaxed cases where only each coordinate of the data has bounded moments, and we can get an upper bound of O~(dnε−−√) and O~(d(nε)θ−1θ) in the second and θ-th moment case respectively.
  • Set-aware Entity Synonym Discovery with Flexible Receptive Fields (Extended Abstract)

    Pei, Shichao; Yu, Lu; Zhang, Xiangliang (IEEE, 2022-08-02) [Conference Paper]
    Entity synonym discovery (ESD) from text corpus is an essential problem in many entity-leveraging applications. This paper aims to address three limitations that widely exist in the current ESD solutions: 1) the lack of effective utilization for synonym set information; 2) the feature extraction of entities from restricted receptive fields; and 3) the incapacity to capture higher-order contextual information. We propose a novel set-aware ESD model that enables a flexible receptive field for ESD by using entity synonym set information and constructing a two-level network. Extensive experimental results on public datasets show that our model consistently outperforms the state-of-the-art with significant improvement.
  • Multi-Agent Covering Option Discovery Based on Kronecker Product of Factor Graphs

    Chen, Jiayu; Chen, Jingdi; Lan, Tian; Aggarwal, Vaneet (IEEE Transactions on Artificial Intelligence, Institute of Electrical and Electronics Engineers (IEEE), 2022-08-02) [Article]
    Covering option discovery has been developed to improve the exploration of reinforcement learning in single-agent scenarios, where only sparse reward signals are available. It aims to connect the most distant states identified through the Fiedler vector of the state transition graph. However, the approach cannot be directly extended to multi-agent scenarios, since the joint state space grows exponentially with the number of agents thus prohibiting efficient option computation. Existing research adopting options in multi-agent scenarios still relies on single-agent algorithms and fails to directly discover joint options that can improve the connectivity of the joint state space. In this paper, we propose a new algorithm to directly compute multi-agent options with collaborative exploratory behaviors while still enjoying the ease of decomposition. Our key idea is to approximate the joint state space as the Kronecker product of individual agents' state spaces, based on which we can directly estimate the Fiedler vector of the joint state space using the Laplacian spectrum of individual agents' transition graphs. This decomposition enables us to efficiently construct multi-agent joint options by encouraging agents to connect the sub-goal joint states which are corresponding to the minimum or maximum of the estimated joint Fiedler vector. Evaluation on multi-agent collaborative tasks shows that our algorithm can successfully identify multi-agent options, and significantly outperforms prior works using single-agent options or no options, in terms of both faster exploration and higher cumulative rewards.
  • Unveiling the “Template-Dependent” Inhibition on the Viral Transcription of SARS-CoV-2

    Luo, Xueying; Wang, Xiaowei; Yao, Yuan; Gao, Xin; Zhang, Lu (The Journal of Physical Chemistry Letters, American Chemical Society (ACS), 2022-07-30) [Article]
    Remdesivir is one nucleotide analogue prodrug capable to terminate RNA synthesis in SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) by two distinct mechanisms. Although the “delayed chain termination” mechanism has been extensively investigated, the “template-dependent” inhibitory mechanism remains elusive. In this study, we have demonstrated that remdesivir embedded in the template strand seldom directly disrupted the complementary NTP incorporation at the active site. Instead, the translocation of remdesivir from the +2 to the +1 site was hindered due to the steric clash with V557. Moreover, we have elucidated the molecular mechanism characterizing the drug resistance upon V557L mutation. Overall, our studies have provided valuable insight into the “template-dependent” inhibitory mechanism exerted by remdesivir on SARS-CoV-2 RdRp and paved venues for an alternative antiviral strategy for the COVID-19 pandemic. As the “template-dependent” inhibition occurs across diverse viral RdRps, our findings may also shed light on a common acting mechanism of inhibitors.
  • Trends & Opportunities in Visualization for Physiology: A Multiscale Overview

    Garrison, Laura A.; Kolesar, Ivan; Viola, Ivan; Hauser, Helwig; Bruckner, Stefan (Computer Graphics Forum, Wiley, 2022-07-29) [Article]
    Combining elements of biology, chemistry, physics, and medicine, the science of human physiology is complex and multifaceted. In this report, we offer a broad and multiscale perspective on key developments and challenges in visualization for physiology. Our literature search process combined standard methods with a state-of-the-art visual analysis search tool to identify surveys and representative individual approaches for physiology. Our resulting taxonomy sorts literature on two levels. The first level categorizes literature according to organizational complexity and ranges from molecule to organ. A second level identifies any of three high-level visualization tasks within a given work: exploration, analysis, and communication. The findings of this report may be used by visualization researchers to understand the overarching trends, challenges, and opportunities in visualization for physiology and to provide a foundation for discussion and future research directions in this area.
  • RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates

    Condat, Laurent Pierre; Richtarik, Peter (arXiv, 2022-07-26) [Preprint]
    Proximal splitting algorithms are well suited to solving large-scale nonsmooth optimization problems, in particular those arising in machine learning. We propose a new primal-dual algorithm, in which the dual update is randomized; equivalently, the proximity operator of one of the function in the problem is replaced by a stochastic oracle. For instance, some randomly chosen dual variables, instead of all, are updated at each iteration. Or, the proximity operator of a function is called with some small probability only. A nonsmooth variance-reduction technique is implemented so that the algorithm finds an exact minimizer of the general problem involving smooth and nonsmooth functions, possibly composed with linear operators. We derive linear convergence results in presence of strong convexity; these results are new even in the deterministic case, when our algorithms reverts to the recently proposed Primal-Dual Davis-Yin algorithm. Some randomized algorithms of the literature are also recovered as particular cases (e.g., Point-SAGA). But our randomization technique is general and encompasses many unbiased mechanisms beyond sampling and probabilistic updates, including compression. Since the convergence speed depends on the slowest among the primal and dual contraction mechanisms, the iteration complexity might remain the same when randomness is used. On the other hand, the computation complexity can be significantly reduced. Overall, randomness helps getting faster algorithms. This has long been known for stochastic-gradient-type algorithms, and our work shows that this fully applies in the more general primal-dual setting as well.
  • Comparison of different power flow techniques for power grid vulnerability assessment against symmetrical faults using bus impedance matrix

    Mirsaeidi, Sohrab; Rahman, Mahmuda; He, Jinghan; Lu, Geye; Said, Dalila Mat; Konstantinou, Charalambos; Li, Meng; Muttaqi, Kashem M.; Dong, Xinzhou (Electric Power Systems Research, Elsevier BV, 2022-07-26) [Article]
    Due to the widespread proliferation of distributed generation resources and the current market situation, ensuring the security and reliability of power grids against fault events has become a more challenging task. The aim of this paper is to compare different power flow techniques for power grid vulnerability assessment against symmetrical fault incidents using bus impedance matrix. In this study, first, the relationship of the post-fault voltage phasor at each bus with the pre-fault voltage phasors at that bus and the faulted bus, impedance matrix elements, and fault impedance is investigated through power system analysis under pre-fault and post-fault circumstances. Subsequently, the accuracy of different iterative and non-iterative power flow algorithms, i.e. Newton Raphson (NR), Fast Decoupled (FD), and Direct Current (DC) methods, for the power grid vulnerability assessment is compared. To achieve this, the fault analysis at each bus is performed commencing with a very large fault impedance and ending with the fault impedance at which one of the buses reaches the low voltage violation limit. Finally, to appraise the proposed strategy, several simulations have been undertaken on IEEE 14 bus system using MATLAB software. The simulation results indicate that the power grid vulnerability against symmetrical faults is highly influenced by the type of applied power flow technique.
  • An industrial perspective on web scraping characteristics and open issues

    Chiapponi, Elisa; Dacier, Marc; Thonnard, Olivier; Fangar, Mohamed; Mattsson, Mattias; Rigal, Vincent (IEEE, 2022-07-25) [Conference Paper]
    An ongoing battle has been running for more than a decade between e-commerce websites owners and web scrapers. Whenever one party finds a new technique to prevail, the other one comes up with a solution to defeat it. Based on our industrial experience, we know this problem is far from being solved. New solutions are needed to address automated threats. In this work, we will describe the actors taking part in the battle, the weapons at their disposal, and their allies on either side. We will present a real-world setup to explain how e-commerce websites operators try to defend themselves and the open problems they seek solutions for.
  • CLIP2StyleGAN: Unsupervised Extraction of StyleGAN Edit Directions

    Abdal, Rameen; Zhu, Peihao; Femiani, John; Mitra, Niloy; Wonka, Peter (ACM, 2022-07-24) [Conference Paper]
    The success of StyleGAN has enabled unprecedented semantic editing capabilities, on both synthesized and real images. However, such editing operations are either trained with semantic supervision or annotated manually by users. In another development, the CLIP architecture has been trained with internet-scale loose image and text pairings, and has been shown to be useful in several zero-shot learning settings. In this work, we investigate how to effectively link the pretrained latent spaces of StyleGAN and CLIP, which in turn allows us to automatically extract semantically-labeled edit directions from StyleGAN, finding and naming meaningful edit operations, in a fully unsupervised setup, without additional human guidance. Technically, we propose two novel building blocks; one for discovering interesting CLIP directions and one for semantically labeling arbitrary directions in CLIP latent space. The setup does not assume any pre-determined labels and hence we do not require any additional supervised text/attributes to build the editing framework. We evaluate the effectiveness of the proposed method and demonstrate that extraction of disentangled labeled StyleGAN edit directions is indeed possible, revealing interesting and non-trivial edit directions.
  • Ecoclimates: climate-response modeling of vegetation

    Pałubicki, Wojtek; Makowski, Miłosz; Gajda, Weronika; Hadrich, Torsten; Michels, Dominik L.; Pirk, Sören (ACM Transactions on Graphics, Association for Computing Machinery (ACM), 2022-07-22) [Article]
    One of the greatest challenges to mankind is understanding the underlying principles of climate change. Over the last years, the role of forests in climate change has received increased attention. This is due to the observation that not only the atmosphere has a principal impact on vegetation growth but also that vegetation is contributing to local variations of weather resulting in diverse microclimates. The interconnection of plant ecosystems and weather is described and studied as ecoclimates. In this work we take steps towards simulating ecoclimates by modeling the feedback loops between vegetation, soil, and atmosphere. In contrast to existing methods that only describe the climate at a global scale, our model aims at simulating local variations of climate. Specifically, we model tree growth interactively in response to gradients of water, temperature and light. As a result, we are able to capture a range of ecoclimate phenomena that have not been modeled before, including geomorphic controls, forest edge effects, the Foehn effect and spatial vegetation patterning. To validate the plausibility of our method we conduct a comparative analysis to studies from ecology and climatology. Consequently, our method advances the state-of-the-art of generating highly realistic outdoor landscapes of vegetation.
  • Seeing through obstructions with diffractive cloaking

    Shi, Zheng; Bahat, Yuval; Baek, Seung-Hwan; Fu, Qiang; Amata, Hadi; Li, Xiao; Chakravarthula, Praneeth; Heidrich, Wolfgang; Heide, Felix (ACM Transactions on Graphics, Association for Computing Machinery (ACM), 2022-07-22) [Article]
    Unwanted camera obstruction can severely degrade captured images, including both scene occluders near the camera and partial occlusions of the camera cover glass. Such occlusions can cause catastrophic failures for various scene understanding tasks such as semantic segmentation, object detection, and depth estimation. Existing camera arrays capture multiple redundant views of a scene to see around thin occlusions. Such multi-camera systems effectively form a large synthetic aperture, which can suppress nearby occluders with a large defocus blur, but significantly increase the overall form factor of the imaging setup. In this work, we propose a monocular single-shot imaging approach that optically cloaks obstructions by emulating a large array. Instead of relying on different camera views, we learn a diffractive optical element (DOE) that performs depth-dependent optical encoding, scattering nearby occlusions while allowing paraxial wavefronts to be focused. We computationally reconstruct unobstructed images from these superposed measurements with a neural network that is trained jointly with the optical layer of the proposed imaging system. We assess the proposed method in simulation and with an experimental prototype, validating that the proposed computational camera is capable of recovering occluded scene information in the presence of severe camera obstruction.
  • NeAT: neural adaptive tomography

    Rückert, Darius; Wang, Yuanhao; Li, Rui; Idoughi, Ramzi; Heidrich, Wolfgang (ACM Transactions on Graphics, Association for Computing Machinery (ACM), 2022-07-22) [Article]
    In this paper, we present Neural Adaptive Tomography (NeAT), the first adaptive, hierarchical neural rendering pipeline for tomography. Through a combination of neural features with an adaptive explicit representation, we achieve reconstruction times far superior to existing neural inverse rendering methods. The adaptive explicit representation improves efficiency by facilitating empty space culling and concentrating samples in complex regions, while the neural features act as a neural regularizer for the 3D reconstruction. The NeAT framework is designed specifically for the tomographic setting, which consists only of semi-transparent volumetric scenes instead of opaque objects. In this setting, NeAT outperforms the quality of existing optimization-based tomography solvers while being substantially faster.
  • MetastaSite: Predicting metastasis to different sites using deep learning with gene expression data

    Albaradei, Somayah; Albaradei, Abdurhman; Alsaedi, Asim; Uludag, Mahmut; Thafar, Maha A.; Gojobori, Takashi; Essack, Magbubah; Gao, Xin (Frontiers in Molecular Biosciences, Frontiers Media SA, 2022-07-22) [Article]
    Deep learning has massive potential in predicting phenotype from different omics profiles. However, deep neural networks are viewed as black boxes, providing predictions without explanation. Therefore, the requirements for these models to become interpretable are increasing, especially in the medical field. Here we propose a computational framework that takes the gene expression profile of any primary cancer sample and predicts whether patients’ samples are primary (localized) or metastasized to the brain, bone, lung, or liver based on deep learning architecture. Specifically, we first constructed an AutoEncoder framework to learn the non-linear relationship between genes, and then DeepLIFT was applied to calculate genes’ importance scores. Next, to mine the top essential genes that can distinguish the primary and metastasized tumors, we iteratively added ten top-ranked genes based upon their importance score to train a DNN model. Then we trained a final multi-class DNN that uses the output from the previous part as an input and predicts whether samples are primary or metastasized to the brain, bone, lung, or liver. The prediction performances ranged from AUC of 0.93–0.82. We further designed the model’s workflow to provide a second functionality beyond metastasis site prediction, i.e., to identify the biological functions that the DL model uses to perform the prediction. To our knowledge, this is the first multi-class DNN model developed for the generic prediction of metastasis to various sites.
  • A fast unsmoothed aggregation algebraic multigrid framework for the large-scale simulation of incompressible flow

    Shao, Han; Huang, Libo; Michels, Dominik L. (ACM Transactions on Graphics, Association for Computing Machinery (ACM), 2022-07-22) [Article]
    Multigrid methods are quite efficient for solving the pressure Poisson equation in simulations of incompressible flow. However, for viscous liquids, geometric multigrid turned out to be less efficient for solving the variational viscosity equation. In this contribution, we present an Unsmoothed Aggregation Algebraic MultiGrid (UAAMG) method with a multi-color Gauss-Seidel smoother, which consistently solves the variational viscosity equation in a few iterations for various material parameters. Moreover, we augment the OpenVDB data structure with Intel SIMD intrinsic functions to perform sparse matrix-vector multiplications efficiently on all multigrid levels. Our framework is 2.0 to 14.6 times faster compared to the state-of-the-art adaptive octree solver in commercial software for the large-scale simulation of both non-viscous and viscous flow.
  • ChromoEnhancer: An Artificial-Intelligence-Based Tool to Enhance Neoplastic Karyograms as an Aid for Effective Analysis

    Bokhari, Yahya; Alhareeri, Areej; Aljouie, Abdulrhman; Alkhaldi, Aziza; Rashid, Mamoon; Alawad, Mohammed; Alhassnan, Raghad; Samargandy, Saad; Panahi, Aliakbar; Heidrich, Wolfgang; Arodz, Tomasz (Cells, MDPI AG, 2022-07-20) [Article]
    Cytogenetics laboratory tests are among the most important procedures for the diagnosis of genetic diseases, especially in the area of hematological malignancies. Manual chromosomal karyotyping methods are time consuming and labor intensive and, hence, expensive. Therefore, to alleviate the process of analysis, several attempts have been made to enhance karyograms. The current chromosomal image enhancement is based on classical image processing. This approach has its limitations, one of which is that it has a mandatory application to all chromosomes, where customized application to each chromosome is ideal. Moreover, each chromosome needs a different level of enhancement, depending on whether a given area is from the chromosome itself or it is just an artifact from staining. The analysis of poor-quality karyograms, which is a difficulty faced often in preparations from cancer samples, is time consuming and might result in missing the abnormality or difficulty in reporting the exact breakpoint within the chromosome. We developed ChromoEnhancer, a novel artificial-intelligence-based method to enhance neoplastic karyogram images. The method is based on Generative Adversarial Networks (GANs) with a data-centric approach. GANs are known for the conversion of one image domain to another. We used GANs to convert poor-quality karyograms into good-quality images. Our method of karyogram enhancement led to robust routine cytogenetic analysis and, therefore, to accurate detection of cryptic chromosomal abnormalities. To evaluate ChromoEnahancer, we randomly assigned a subset of the enhanced images and their corresponding original (unenhanced) images to two independent cytogeneticists to measure the karyogram quality and the elapsed time to complete the analysis, using four rating criteria, each scaled from 1 to 5. Furthermore, we compared the enhanced images with our method to the original ones, using quantitative measures (PSNR and SSIM metrics).
  • Alternative role of motif B in template dependent polymerase inhibition

    Luo, Xueying; Xu, Tiantian; Gao, Xin; Zhang, Lu (Chinese Journal of Chemical Physics, AIP Publishing, 2022-07-19) [Article]
    Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) relies on the central molecular machine RNA-dependent RNA polymerase (RdRp) for the viral replication and transcription. Remdesivir at the template strand has been shown to effectively inhibit the RNA synthesis in SARS-CoV-2 RdRp by deactivating not only the complementary UTP incorporation but also the next nucleotide addition. How-ever, the underlying molecular mechanism of the second inhibitory point remains unclear. In this work, we have performed molecular dynamics simulations and demonstrated that such inhibition has not directly acted on the nucleotide addition at the active site. Instead, the translocation of Remdesivir from +1 to −1 site is hindered thermodynamically as the post-translocation state is less stable than the pre-translocation state due to the motif B residue G683. Moreover, another conserved residue S682 on motif B further hinders the dynamic translocation of Remdesivir due to the steric clash with the 1′-cyano substitution. Overall, our study has unveiled an alternative role of motif B in mediating the translocation when Remdesivir is present in the template strand and complemented our understanding about the inhibitory mechanisms exerted by Remdesivir on the RNA synthesis in SARS-CoV-2 RdRp.
  • BeeCast: A Device-to-Device Collaborative Video Streaming System

    Alghamdi, Asaad; Balah, Younes; AlBejadi, Mohammad; Felemban, Muhamad (IEEE, 2022-07-19) [Conference Paper]
    In this paper, we propose BeeCast, a collaborative video streaming system that facilitates collaborative video streaming for a group of mobile users with limited Internet connectivity. The novelty of the proposed system is the ability to watch the video on a shared screen or to watch the video on multiple screens. The latter option entails proposing a method to exchange the downloaded video segments among the users using device-to-device communication. The proposed system is composed of two components: BeeBuzzer, and BeePlanner. The BeeBuzzer component manages and coordinates the segment exchange among devices, while BeePlanner component enhances the overall Quality of Experience (QoE) through effective segment assignments decisions for each user. Simulation results show that using BeeCast in an unstable network produces a more consistent QoE than individual streaming while eliminating 80% of redundant network traffic.

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