Enhancing ocean environment prediction in Yellow Sea through targeted observation using ocean acoustic tomography

Ocean Acoustic Tomography (OAT) is an efficient and economical marine acoustic observation technique. Targeted observation is an appealing procedure to reduce the uncertainty of ocean environment prediction through additional observation. This study aimed to assess the validity of OAT as an observation method for targeted observation. OAT based on Niche Genetic Algorithm was employed to extract sound speed and temperature profiles from acoustic transmission time, utilizing data from the 2019 Yellow Sea experiment. The inversion results were compared with measurement data, which are found to be accurate and reliable. To further evaluate OAT as targeted observation method, the vertical bias structure of OAT was added on synchronous measurement data in the sensitive area of targeted observation to simulate OAT observation in sensitive area. This simulated data was then incorporated into a 3D-Var assimilation system to improve the short-term prediction of the target region. Comparing the predictions derived with the measurement data at the verification time, it shows that the simulated OAT observation improved the quality of target region prediction, indicating that OAT can be an effective observation method for targeted observation. An Observing System Simulation Experiment was conducted to assess the impact of OAT characteristics on prediction improvement. The results show that both adding observation nodes and extending the observation duration have positive effects, while extending the observation duration performs better

Video Superresolution via Parameter-Optimized Particle Swarm Optimization

Video superresolution (VSR) aims to reconstruct a high-resolution video sequence from a low-resolution sequence. We propose a novel particle swarm optimization algorithm named as parameter-optimized multiple swarms PSO (POMS-PSO). We assessed the optimization performance of POMS-PSO by four standard benchmark functions. To reconstruct high-resolution video, we build an imaging degradation model. In view of optimization, VSR is converted to an optimization computation problem. And we take POMS-PSO as an optimization method to solve the VSR problem, which overcomes the poor effect, low accuracy, and large calculation cost in other VSR algorithms. The proposed VSR method does not require exact movement estimation and does not need the computation of movement vectors. In terms of peak signal-to-noise ratio (PSNR), sharpness, and entropy, the proposed VSR method based POMS-PSO showed better objective performance. Besides objective standard, experimental results also proved the proposed method could reconstruct high-resolution video sequence with better subjective quality

Video Superresolution via Parameter-Optimized Particle Swarm Optimization

Video superresolution (VSR) aims to reconstruct a high-resolution video sequence from a low-resolution sequence. We propose a novel particle swarm optimization algorithm named as parameter-optimized multiple swarms PSO (POMS-PSO). We assessed the optimization performance of POMS-PSO by four standard benchmark functions. To reconstruct high-resolution video, we build an imaging degradation model. In view of optimization, VSR is converted to an optimization computation problem. And we take POMS-PSO as an optimization method to solve the VSR problem, which overcomes the poor effect, low accuracy, and large calculation cost in other VSR algorithms. The proposed VSR method does not require exact movement estimation and does not need the computation of movement vectors. In terms of peak signal-to-noise ratio (PSNR), sharpness, and entropy, the proposed VSR method based POMS-PSO showed better objective performance. Besides objective standard, experimental results also proved the proposed method could reconstruct high-resolution video sequence with better subjective quality

Targeting GSTP1-dependent ferroptosis in lung cancer radiotherapy: Existing evidence and future directions

Radiotherapy is applied in about 70% patients with tumors, yet radioresistance of tumor cells remains a challenge that limits the efficacy of radiotherapy. Ferroptosis, an iron-dependent lipid peroxidation regulated cell death, is involved in the development of a variety of tumors. Interestingly, there is evidence that ferroptosis inducers in tumor treatment can significantly improve radiotherapy sensitivity. In addition, related studies show that Glutathione S-transferase P1 (GSTP1) is closely related to the development of ferroptosis. The potential mechanism of targeting GSTP1 to inhibit tumor cells from evading ferroptosis leading to radioresistance has been proposed in this review, which implies that GSTP1 may play a key role in radiosensitization of lung cancer via ferroptosis pathway

Structure-function analysis of CYP719As involved in methylenedioxy bridge-formation in the biosynthesis of benzylisoquinoline alkaloids and its de novo production

Benzylisoquinoline alkaloids (BIAs) are a type of secondary metabolite with clinical application value. (S)-stylopine is a special BIA which contains methylenedioxy bridge structures. CYP719As could catalyze the methylenedioxy bridge-formation on the A or D rings of protoberberine alkaloids, while displaying significant substrate regiospecificity. To explore the substrate preference of CYP719As, we cloned and identified five CyCYP719A candidates from Corydalis yanhusuo. Two CyCYP719As (CyCYP719A39 and CyCYP719A42) with high catalytic efficiency for the methylenedioxy bridge-formation on the D or A rings were characterized, respectively. The residues (Leu 294 for CyCYP719A42 and Asp 289 for CyCYP719A39) were identified as the key to controlling the regioselectivity of CYP719As affecting the methylenedioxy bridge-formation on the A or D rings by homology modeling and mutation analysis. Furthermore, for de novo production of BIAs, CyCYP719A39, CyCYP719A42, and their mutants were introduced into the (S)-scoulerine-producing yeast to produce 32\ua0mg/L (S)-stylopine. These results lay a foundation for understanding the structure-function relationship of CYP719A-mediated methylenedioxy bridge-formation and provide yeast strains for the BIAs production by\ua0synthetic biology

Expansion within the CYP71D subfamily drives the heterocyclization of tanshinones synthesis in Salvia miltiorrhiza

Tanshinones are the bioactive nor-diterpenoid constituents of the Chinese medicinal herb Danshen (Salvia miltiorrhiza). These groups of chemicals have the characteristic furan D-ring, which differentiates them from the phenolic abietane-type diterpenoids frequently found in the Lamiaceae family. However, how the 14,16-epoxy is formed has not been elucidated. Here, we report an improved genome assembly of Danshen using a highly homozygous genotype. We identify a cytochrome P450 (CYP71D) tandem gene array through gene expansion analysis. We show that CYP71D373 and CYP71D375 catalyze hydroxylation at carbon-16 (C16) and 14,16-ether (hetero)cyclization to form the D-ring, whereas CYP71D411 catalyzes upstream hydroxylation at C20. In addition, we discover a large biosynthetic gene cluster associated with tanshinone production. Collinearity analysis indicates a more specific origin of tanshinones in Salvia genus. It illustrates the evolutionary origin of abietane-type diterpenoids and those with a furan D-ring in Lamiaceae

Fast Shape Recognition via the Restraint Reduction of Bone Point Segment

In computer science, and especially in computer vision, the contour of an object is used to describe its features; thus, the shape descriptor plays an indispensable role in target detection and recognition. Further, Fourier is an important mathematical description method, and the Fourier transform of a shape contour has symmetry. This paper will demonstrate the symmetry of shape contour in the frequency domain. In recent years, increasing numbers of shape descriptors have come to the fore, but many descriptors ignore the details of shape. It is found that the most fundamental reason affecting the performance of shape descriptors is structural restraints, especially feature structure restraint. Therefore, in this paper, the restraint of feature structure that intrinsically deteriorates recognition performance is shown, and a fast shape recognition method via the Bone Point Segment (BPS) restraint reduction is proposed. An approach using the inner distance to find bone shapes and segment the shape contour by these bones is proposed. Then, Fourier transform is performed on each segment to form the shape feature. Finally, the restraints of the shape feature are reduced in order to build a more effective shape feature. What is commendable is that its discriminability and robustness is strong, the process is simple, and matching speed is fast. More importantly, the experiment results show that the shape descriptor has higher recognition accuracy and the matching speed runs up to more than 1000 times faster than the existing description methods like CBW and TCD. More importantly, it is worth noting that the recognition accuracy approaches 100% in the self-build dataset