Recent Submissions

  • Negative Frames Matter in Egocentric Visual Query 2D Localization

    Xu, Mengmeng; Fu, Cheng-Yang; Li, Yanghao; Ghanem, Bernard; Perez-Rua, Juan-Manuel; Xiang, Tao (arXiv, 2022-08-03) [Preprint]
    The recently released Ego4D dataset and benchmark significantly scales and diversifies the first-person visual perception data. In Ego4D, the Visual Queries 2D Localization task aims to retrieve objects appeared in the past from the recording in the first-person view. This task requires a system to spatially and temporally localize the most recent appearance of a given object query, where query is registered by a single tight visual crop of the object in a different scene. Our study is based on the three-stage baseline introduced in the Episodic Memory benchmark. The baseline solves the problem by detection and tracking: detect the similar objects in all the frames, then run a tracker from the most confident detection result. In the VQ2D challenge, we identified two limitations of the current baseline. (1) The training configuration has redundant computation. Although the training set has millions of instances, most of them are repetitive and the number of unique object is only around 14.6k. The repeated gradient computation of the same object lead to an inefficient training; (2) The false positive rate is high on background frames. This is due to the distribution gap between training and evaluation. During training, the model is only able to see the clean, stable, and labeled frames, but the egocentric videos also have noisy, blurry, or unlabeled background frames. To this end, we developed a more efficient and effective solution. Concretely, we bring the training loop from ~15 days to less than 24 hours, and we achieve 0.17% spatial-temporal AP, which is 31% higher than the baseline. Our solution got the first ranking on the public leaderboard.
  • Designing Asymptotic Geodesic Hybrid Gridshells

    Schling, Eike; Wang, Hui; Hoyer, Sebastian; Pottmann, Helmut (Computer-Aided Design, Elsevier BV, 2022-07-30) [Article]
    Fabrication and assembly of freeform shells can be simplified significantly when controlling the curvature of structural elements during the design phase. This approach has produced fundamental insights to bending-active construction, using the elastic property of elements to form efficient load-bearing structures. This paper is focused on gridshells that are built from straight and flat slats. The slats are combined in two orientations, tangential and normal to the design surface, to create robust and versatile triangulated grids. For this purpose, we generate hybrid webs of asymptotic and geodesic paths on freeform surfaces. The research combines geometric computing with architectural building practice. We present a computational workflow for the design and interactive modification of hybrid asymptotic geodesic webs. At its core are discrete models that are based on concepts of differential geometry and allow to compute constrained structures within an optimization framework. The resulting webs are tested for architectural applications. We derive a strategy for the elastic erection process, in which geodesic lamellas are used as a guide and bracing of the spatial structure. Two architectural scenarios, a timber roof and a steel facade are presented. Their feasibility for construction is verified through prototypical joints and physical models. The research introduces a new class of networks and related surfaces and offers insights into the practical challenges of freeform construction from elastic slats.
  • Asymmetrical Pythagorean-hodograph spline-based C-4 continuous local corner smoothing method with jerk-continuous feedrate scheduling along linear toolpath

    Jiang, Xin; Hu, Yifei; Huo, Guanying; Su, Cheng; Wang, Bolun; Li, Hexiong; Shen, Li-Yong; Zheng, Zhiming (INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, Springer Science and Business Media LLC, 2022-07-30) [Article]
    In computer numerical control systems, linear segments generated by computer-aided manufacturing software are the most widely used toolpath format. Since the linear toolpath is discontinuous at the junction of two adjacent segments, the fluctuations on velocity, acceleration and jerk are inevitable. Local corner smoothing is widely used to address this problem. However, most existing methods use symmetrical splines to smooth the corners. When any one of the linear segments at the corner is short, the inserted spline will be micro to avoid overlap. This will increase the curvature extreme of the spline and reduce the feedrate on it. In this article, the corners are smoothed by a C4 continuous asymmetric Pythagorean-hodograph (PH) spline. The curvature extreme of the proposed spline is investigated first, and K=2.5 is determined as the threshold to constrain the asymmetry of the spline. Then, a two-step strategy is used to generate a blended toolpath composed of asymmetric PH splines and linear segments. In the first step, the PH splines at the corners are generated under the condition that the transition lengths do not exceed half of the length of the linear segments. In the second step, the splines at the corners are re-planned to reduce the curvature extremes, if the transition error does not reach the given threshold and there are extra linear trajectories on both sides of the spline trajectory. Finally, the bilinear interpolation method is applied to determine the critical points of the smoothed toolpath, and a jerk-continuous feedrate scheduling scheme is presented to interpolate the smoothed toolpath. Simulations show that, under the condition of not affecting the machining quality, the proposed method can improve the machining efficiency by 7.27 to 75.11% compared to G3 and G4 methods.
  • 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.
  • 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.
  • 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).
  • Evaluation of Diverse Convolutional Neural Networks and Training Strategies for Wheat Leaf Disease Identification with Field-Acquired Photographs

    Jiang, Jiale; Liu, Haiyan; Zhao, Chen; He, Can; Ma, Jifeng; Cheng, Tao; Zhu, Yan; Cao, Weixing; Yao, Xia (REMOTE SENSING, MDPI AG, 2022-07-18) [Article]
    Tools for robust identification of crop diseases are crucial for timely intervention by farmers to minimize yield losses. Visual diagnosis of crop diseases is time-consuming and laborious, and has become increasingly unsuitable for the needs of modern agricultural production. Recently, deep convolutional neural networks (CNNs) have been used for crop disease diagnosis due to their rapidly improving accuracy in labeling images. However, previous CNN studies have mostly used images of single leaves photographed under controlled conditions, which limits operational field use. In addition, the wide variety of available CNNs and training options raises important questions regarding optimal methods of implementation of CNNs for disease diagnosis. Here, we present an assessment of seven typical CNNs (VGG-16, Inception-v3, ResNet-50, DenseNet-121, EfficentNet-B6, ShuffleNet-v2 and MobileNetV3) based on different training strategies for the identification of wheat main leaf diseases (powdery mildew, leaf rust and stripe rust) using field images. We developed a Field-based Wheat Diseases Images (FWDI) dataset of field-acquired images to supplement the public PlantVillage dataset of individual leaves imaged under controlled conditions. We found that a transfer-learning method employing retuning of all parameters produced the highest accuracy for all CNNs. Based on this training strategy, Inception-v3 achieved the highest identification accuracy of 92.5% on the test dataset. While lightweight CNN models (e.g., ShuffleNet-v2 and MobileNetV3) had shorter processing times (<0.007 s per image) and smaller memory requirements for the model parameters (<20 MB), their accuracy was relatively low (~87%). In addition to the role of CNN architecture in controlling overall accuracy, environmental effects (e.g., residual water stains on healthy leaves) were found to cause misclassifications in the field images. Moreover, the small size of some target symptoms and the similarity of symptoms between some different diseases further reduced the accuracy. Overall, the study provides insight into the collective effects of model architecture, training strategies and input datasets on the performance of CNNs, providing guidance for robust CNN design for timely and accurate crop disease diagnosis in a real-world environment.
  • Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms

    Salim, Adil; Condat, Laurent Pierre; Mishchenko, Konstantin; Richtarik, Peter (JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, Springer Science and Business Media LLC, 2022-07-13) [Article]
    We consider minimizing the sum of three convex functions, where the first one F is smooth, the second one is nonsmooth and proximable and the third one is the composition of a nonsmooth proximable function with a linear operator L. This template problem has many applications, for instance, in image processing and machine learning. First, we propose a new primal–dual algorithm, which we call PDDY, for this problem. It is constructed by applying Davis–Yin splitting to a monotone inclusion in a primal–dual product space, where the operators are monotone under a specific metric depending on L. We show that three existing algorithms (the two forms of the Condat–Vũ algorithm and the PD3O algorithm) have the same structure, so that PDDY is the fourth missing link in this self-consistent class of primal–dual algorithms. This representation eases the convergence analysis: it allows us to derive sublinear convergence rates in general, and linear convergence results in presence of strong convexity. Moreover, within our broad and flexible analysis framework, we propose new stochastic generalizations of the algorithms, in which a variance-reduced random estimate of the gradient of F is used, instead of the true gradient. Furthermore, we obtain, as a special case of PDDY, a linearly converging algorithm for the minimization of a strongly convex function F under a linear constraint; we discuss its important application to decentralized optimization.
  • A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging

    Zhang, Jingang; Su, Runmu; Fu, Qiang; Ren, Wenqi; Heide, Felix; Nie, Yunfeng (Scientific reports, Springer Science and Business Media LLC, 2022-07-13) [Article]
    Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically expensive and very complicated, hindering the promotion of their application in consumer electronics, such as daily food inspection and point-of-care medical screening, etc. Recently, many computational spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from widely available RGB images. These reconstruction methods can exclude the usage of burdensome spectral camera hardware while keeping a high spectral resolution and imaging performance. We present a thorough investigation of more than 25 state-of-the-art spectral reconstruction methods which are categorized as prior-based and data-driven methods. Simulations on open-source datasets show that prior-based methods are more suitable for rare data situations, while data-driven methods can unleash the full potential of deep learning in big data cases. We have identified current challenges faced by those methods (e.g., loss function, spectral accuracy, data generalization) and summarized a few trends for future work. With the rapid expansion in datasets and the advent of more advanced neural networks, learnable methods with fine feature representation abilities are very promising. This comprehensive review can serve as a fruitful reference source for peer researchers, thus paving the way for the development of computational hyperspectral imaging.
  • Differentiable holography enables single-shot auto-focused complex field imaging

    Chen, Ni; Wang, Congli; Heidrich, Wolfgang (2022-07-09) [Preprint]
    We present a differentiable holography computational technique that takes optical system setup imperfection and misalignment into account, demonstrated by successful auto-focused complex field imaging from single-shot inline holograms.
  • Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning

    Malinovsky, Grigory; Yi, Kai; Richtarik, Peter (arXiv, 2022-07-09) [Preprint]
    We study distributed optimization methods based on the {\em local training (LT)} paradigm: achieving communication efficiency by performing richer local gradient-based training on the clients before parameter averaging. Looking back at the progress of the field, we {\em identify 5 generations of LT methods}: 1) heuristic, 2) homogeneous, 3) sublinear, 4) linear, and 5) accelerated. The 5th generation, initiated by the ProxSkip method of Mishchenko, Malinovsky, Stich and Richtárik (2022) and its analysis, is characterized by the first theoretical confirmation that LT is a communication acceleration mechanism. Inspired by this recent progress, we contribute to the 5th generation of LT methods by showing that it is possible to enhance them further using {\em variance reduction}. While all previous theoretical results for LT methods ignore the cost of local work altogether, and are framed purely in terms of the number of communication rounds, we show that our methods can be substantially faster in terms of the {\em total training cost} than the state-of-the-art method ProxSkip in theory and practice in the regime when local computation is sufficiently expensive. We characterize this threshold theoretically, and confirm our theoretical predictions with empirical results.
  • Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox

    Sadiev, Abdurakhmon; Kovalev, Dmitry; Richtarik, Peter (arXiv, 2022-07-08) [Preprint]
    Inspired by a recent breakthrough of Mishchenko et al (2022), who for the first time showed that local gradient steps can lead to provable communication acceleration, we propose an alternative algorithm which obtains the same communication acceleration as their method (ProxSkip). Our approach is very different, however: it is based on the celebrated method of Chambolle and Pock (2011), with several nontrivial modifications: i) we allow for an inexact computation of the prox operator of a certain smooth strongly convex function via a suitable gradient-based method (e.g., GD, Fast GD or FSFOM), ii) we perform a careful modification of the dual update step in order to retain linear convergence. Our general results offer the new state-of-the-art rates for the class of strongly convex-concave saddle-point problems with bilinear coupling characterized by the absence of smoothness in the dual function. When applied to federated learning, we obtain a theoretically better alternative to ProxSkip: our method requires fewer local steps (O(κ1/3) or O(κ1/4), compared to O(κ1/2) of ProxSkip), and performs a deterministic number of local steps instead. Like ProxSkip, our method can be applied to optimization over a connected network, and we obtain theoretical improvements here as well
  • Lattice Gauge Theory and a Random-Medium Ising Model

    Skopenkov, Mikhail (Mathematical Physics Analysis and Geometry, Springer Science and Business Media B.V., 2022-07-06) [Article]
    We study linearization of lattice gauge theory. Linearized theory approximates lattice gauge theory in the same manner as the loop O(n)-model approximates the spin O(n)-model. Under mild assumptions, we show that the expectation of an observable in linearized Abelian gauge theory coincides with the expectation in the Ising model with random edge-weights. We find a similar relation between Yang-Mills theory and 4-state Potts model. For the latter, we introduce a new observable.
  • Egocentric Video-Language Pretraining @ Ego4D Challenge 2022

    Lin, Kevin Qinghong; Wang, Alex Jinpeng; Soldan, Mattia; Wray, Michael; Yan, Rui; Xu, Eric Zhongcong; Gao, Difei; Tu, Rongcheng; Zhao, Wenzhe; Kong, Weijie; Cai, Chengfei; Wang, Hongfa; Damen, Dima; Ghanem, Bernard; Liu, Wei; Shou, Mike Zheng (arXiv, 2022-07-04) [Preprint]
    In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for four Ego4D challenge tasks, including Natural Language Query (NLQ), Moment Query (MQ), Object State Change Classification (OSCC), and PNR Localization (PNR). Especially, we exploit the recently released Ego4D dataset \cite{grauman2021ego4d} to pioneer Egocentric VLP from pretraining dataset, pretraining objective, and development set. Based on the above three designs, we develop a pretrained video-language model that is able to transfer its egocentric video-text representation or video-only representation to several video downstream tasks. Our Egocentric VLP achieves 10.46R@1&IoU @0.3 on NLQ, 10.33 mAP on MQ, 74% Acc on OSCC, 0.67 sec error on PNR.
  • Characterization of a thermostable Cas13 enzyme for one-pot detection of SARS-CoV-2

    Mahas, Ahmed; Maršić, Tin; Masson, Mauricio Lopez Portillo; Wang, Qiaochu; Aman, Rashid; Zheng, Cheng; Ali, Zahir; Alsanea, Madain; Al-Qahtani, Ahmed; Ghanem, Bernard; Alhamlan, Fatimah Saeed; Mahfouz, Magdy M. (Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, 2022-06-28) [Article]
    Type VI CRISPR-Cas systems have been repurposed for various applications such as gene knockdown, viral interference, and diagnostics. However, the identification and characterization of thermophilic orthologs will expand and unlock the potential of diverse biotechnological applications. Herein, we identified and characterized a thermostable ortholog of the Cas13a family from the thermophilic organism Thermoclostridium caenicola (TccCas13a). We show that TccCas13a has a close phylogenetic relation to the HheCas13a ortholog from the thermophilic bacterium Herbinix hemicellulosilytica and shares several properties such as thermostability and inability to process its own pre-CRISPR RNA. We demonstrate that TccCas13a possesses robust cis and trans activities at a broad temperature range of 37 to 70 °C, compared with HheCas13a, which has a more limited range and lower activity. We harnessed TccCas13a thermostability to develop a sensitive, robust, rapid, and one-pot assay, named OPTIMA-dx, for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection. OPTIMA-dx exhibits no cross-reactivity with other viruses and a limit of detection of 10 copies/μL when using a synthetic SARS-CoV-2 genome. We used OPTIMA-dx for SARS-CoV-2 detection in clinical samples, and our assay showed 95% sensitivity and 100% specificity compared with qRT-PCR. Furthermore, we demonstrated that OPTIMA-dx is suitable for multiplexed detection and is compatible with the quick extraction protocol. OPTIMA-dx exhibits critical features that enable its use at point of care (POC). Therefore, we developed a mobile phone application to facilitate OPTIMA-dx data collection and sharing of patient sample results. This work demonstrates the power of CRISPR-Cas13 thermostable enzymes in enabling key applications in one-pot POC diagnostics and potentially in transcriptome engineering, editing, and therapies.

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