Verification of specific G-quadruplex structure by using a novel cyanine dye supramolecular assembly: II. The binding characterization with specific intramolecular G-quadruplex and the recognizing mechanism

The supramolecular assembly of a novel cyanine dye, 3,3′-di(3-sulfopropyl)-4,5,4′,5′-dibenzo-9-ethyl-thiacarbocyanine triethylammonium salt (ETC) was designed to verify specific intramolecular G-quadruplexes from duplex and single-strand DNAs. Spectral results have shown that ETC presented two major distinct signatures with specific intramolecular G-quadruplexes in vitro: (i) dramatic changes in the absorption spectra (including disappearance of absorption peak around 660 nm and appearance of independent new peak around 584 nm); (ii) ∼70 times enhancement of fluorescence signal at 600 nm. Furthermore, based on 1H-nuclear magnetic resonance and circular dichroism results, the preferring binding of ETC to specific intramolecular G-quadruplexes probably result from end-stacking, and the loop structure nearby also plays an important role

Real-Time Stereo Visual Odometry Based on an Improved KLT Method

Real-time stereo visual odometry (SVO) localization is a challenging problem, especially for a mobile platform without parallel computing capability. A possible solution is to reduce the computational complexity of SVO using a Kanade–Lucas–Tomasi (KLT) feature tracker. However, the standard KLT is susceptible to scale distortion and affine transformation. Therefore, this work presents a novel SVO algorithm yielding robust and real-time localization based on an improved KLT method. First, in order to improve real-time performance, feature inheritance is applied to avoid time-consuming feature detection and matching processes as much as possible. Furthermore, a joint adaptive function with respect to the average disparity, translation velocity, and yaw angle is proposed to determine a suitable window size for the adaptive KLT tracker. Then, combining the standard KLT method with an epipolar constraint, a simplified KLT matcher is introduced to substitute feature-based stereo matching. Additionally, an effective veer chain matching scheme is employed to reduce the drift error. Comparative experiments on the KITTI odometry benchmark show that the proposed method achieves significant improvement in terms of time performance than the state-of-the-art single-thread approaches and strikes a better trade-off between efficiency and accuracy than the parallel SVO or multi-threaded SLAM

Real-Time Stereo Visual Odometry Based on an Improved KLT Method

Real-time stereo visual odometry (SVO) localization is a challenging problem, especially for a mobile platform without parallel computing capability. A possible solution is to reduce the computational complexity of SVO using a Kanade–Lucas–Tomasi (KLT) feature tracker. However, the standard KLT is susceptible to scale distortion and affine transformation. Therefore, this work presents a novel SVO algorithm yielding robust and real-time localization based on an improved KLT method. First, in order to improve real-time performance, feature inheritance is applied to avoid time-consuming feature detection and matching processes as much as possible. Furthermore, a joint adaptive function with respect to the average disparity, translation velocity, and yaw angle is proposed to determine a suitable window size for the adaptive KLT tracker. Then, combining the standard KLT method with an epipolar constraint, a simplified KLT matcher is introduced to substitute feature-based stereo matching. Additionally, an effective veer chain matching scheme is employed to reduce the drift error. Comparative experiments on the KITTI odometry benchmark show that the proposed method achieves significant improvement in terms of time performance than the state-of-the-art single-thread approaches and strikes a better trade-off between efficiency and accuracy than the parallel SVO or multi-threaded SLAM

Color Edge Detection Based on Data Fusion Technology in Presence of Gaussian Noise

AbstractA color edge detection method was developed to correctly reproduce distinct, continuous edges based on the multichannel data fusion technique and the gradient direction information contained in the image. Since the proposed techniques for edge detection are very sensitive to noise, subprefiltering or prefiltering algorithms are generally adopted and very critical. A new nonlinear prefilter is used to reduce noise in the R, G, and, B components of the image. The method preserves edges, corners and fine image details, smoothes Gaussian noise and does not require any a priori knowledge

Biosynthesis of Fungal Natural Products Involving Two Separate Pathway Crosstalk

Fungal natural products (NPs) usually possess complicated structures, exhibit satisfactory bioactivities, and are an outstanding source of drug leads, such as the cholesterol-lowering drug lovastatin and the immunosuppressive drug mycophenolic acid. The fungal NPs biosynthetic genes are always arranged within one single biosynthetic gene cluster (BGC). However, a rare but fascinating phenomenon that a crosstalk between two separate BGCs is indispensable to some fungal dimeric NPs biosynthesis has attracted increasing attention. The hybridization of two separate BGCs not only increases the structural complexity and chemical diversity of fungal NPs, but also expands the scope of bioactivities. More importantly, the underlying mechanism for this hybridization process is poorly understood and needs further exploration, especially the determination of BGCs for each building block construction and the identification of enzyme(s) catalyzing the two biosynthetic precursors coupling processes such as Diels–Alder cycloaddition and Michael addition. In this review, we summarized the fungal NPs produced by functional crosstalk of two discrete BGCs, and highlighted their biosynthetic processes, which might shed new light on genome mining for fungal NPs with unprecedented frameworks, and provide valuable insights into the investigation of mysterious biosynthetic mechanisms of fungal dimeric NPs which are constructed by collaboration of two separate BGCs

Microbial chassis engineering drives heterologous production of complex secondary metabolites

The cryptic secondary metabolite biosynthetic gene clusters (BGCs) far outnumber currently known secondary metabolites. Heterologous production of secondary metabolite BGCs in suitable chassis facilitates yield improvement and discovery of new-to-nature compounds. The two juxtaposed conventional model microorganisms, Escherichia coli, Saccharomyces cerevisiae, have been harnessed as microbial chassis to produce a bounty of secondary metabolites with the help of certain host engineering. In last decade, engineering non-model microbes to efficiently biosynthesize secondary metabolites has received increasing attention due to their peculiar advantages in metabolic networks and/or biosynthesis. The state-of-the-art synthetic biology tools lead the way in operating genetic manipulation in non-model microorganisms for phenotypic optimization or yields improvement of desired secondary metabolites. In this review, we firstly discuss the pros and cons of several model and non-model microbial chassis, as well as the importance of developing broader non-model microorganisms as alternative programmable heterologous hosts to satisfy the desperate needs of biosynthesis study and industrial production. Then we highlight the lately advances in the synthetic biology tools and engineering strategies for optimization of non-model microbial chassis, in particular, the successful applications for efficient heterologous production of multifarious complex secondary metabolites, e.g., polyketides, nonribosomal peptides, as well as ribosomally synthesized and post-translationally modified peptides. Lastly, emphasis is on the perspectives of chassis cells development to access the ideal cell factory in the artificial intelligence-driven genome era. © 2022 Elsevier Inc.National Natural Science Foundation of China; Natural Science Foundation of Shandong Province; National Key Research and Development Program of China: This work was supported by the National Key R&D Program of China (Grant nos. 2021YFC2100500 , 2019YFA0905700 ), National Natural Science Foundation of China (Grant nos. 32070060 , 32161133013 ), Shandong Provincial Natural Science Foundation (Grant no. ZR2019JQ11 , ZR2019ZD18 )