Remote Sensing Image of The Landsat 8-9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function.

Utilizing low-rank prior data in compressed sensing (CS) schemes for Landsat 8-9 remote sensing images (RSIs) has recently received widespread attention. Nevertheless, most CS algorithms focus on the sparsity of an RSI and ignore its low-rank (LR) nature. Therefore, this paper proposes a new CS reconstruction algorithm for Landsat 8-9 remote sensing images based on a non-local optimization framework (NLOF) that is combined with non-convex Laplace functions (NCLF) used for the low-rank approximation (LAA). Since the developed algorithm is based on an approximate low-rank model of the Laplace function, it can adaptively assign different weights to different singular values. Moreover, exploiting the structural sparsity (SS) and low-rank (LR) between the image patches enables the restored image to obtain better CS reconstruction results of Landsat 8-9 RSI than the existing models. For the proposed scheme, first, a CS reconstruction model is proposed using the non-local low-rank regularization (NLLRR) and variational framework. Then, the image patch grouping and Laplace function are used as regularization/penalty terms to constrain the CS reconstruction model. Finally, to effectively solve the rank minimization problem, the alternating direction multiplier method (ADMM) is used to solve the model. Extensive numerical experimental results demonstrate that the non-local variational framework (NLVF) combined with the low-rank approximate regularization (LRAR) method of non-convex Laplace function (NCLF) can obtain better reconstruction results than the more advanced image CS reconstruction algorithms. At the same time, the model preserves the details of Landsat 8-9 RSIs and the boundaries of the transition areas.

Increased Lipids in Chlamydomonas reinhardtii by Multiple Regulations of DOF, LACS2, and CIS1.

Microalgal lipids are essential for biofuel and dietary supplement production. Lipid engineering for higher production has been studied for years. However, due to the complexity of lipid metabolism, single-gene engineering gradually encounters bottlenecks. Multiple gene regulation is more beneficial to boosting lipid accumulation and further clarifying the complex regulatory mechanism of lipid biosynthesis in the homeostasis of lipids, carbohydrates, and protein metabolism. Here, three lipid-related genes, DOF, LACS2, and CIS, were co-regulated in Chlamydomonas reinhartii by two circles of transformation to overexpress DOF and knock down LACS2 and CIS simultaneously. With the multiple regulations of these genes, the intracellular lipids and FA content increased by 142% and 52%, respectively, compared with CC849, whereas the starch and protein contents decreased by 45% and 24%. Transcriptomic analysis showed that genes in TAG and FA biosynthesis were up-regulated, and genes in starch and protein metabolism were down-regulated. This revealed that more carbon precursor fluxes from starch and protein metabolism were redirected towards lipid synthesis pathways. These results showed that regulating genes in various metabolisms contributed to carbon flux redirection and significantly improved intracellular lipids, demonstrating the potential of multiple gene regulation strategies and providing possible candidates for lipid overproduction in microalgae.

High-Throughput Surface-Enhanced Raman Scattering for Screening Chemical Sensor Candidates Enabled by Bipolar Electrochemistry.

A variety of hydrothermal or electrochemical methods have been explored to prepare noble metal nanostructures as surface-enhanced Raman scattering (SERS) substrates. However, most of those metallic nanoarrays are structurally homogeneous, which makes it laborious to select the high-performance substrates for particular Raman sensing purposes. Here, a high-throughput SERS imaging strategy is demonstrated for the first time for screening chemical sensors with sub-nanomolar sensitivities. Bipolar electrochemistry was applied to generate Au or Au-Ag gradient nanoarrays with diverse chemical compositions, morphologies, and particle dimensions ranging from several nanometers to micrometers. The selected "hot-spots" on the Au-Ag alloy nanoarray exhibited a 660-fold enhancement in SERS intensity compared to those on the pure Au gradient nanoarray. The SERS screening of 4-aminothiophenol, 4-nitrothiophenol, and 4-mercaptobenzoic acid was carried out that provided a limit of detection (LOD) between 1 and 5 pM. The distinctive LODs among three thiophenolic Raman probes are ascribed to the differences in the affinity of the probe to the alloy, orientation of the metal-ligand monolayer, or plasmonic environment of the nanoarray surface. As a continuous, rapid, and cost-effective manner to fabricate transitional nanostructures and screen out SERS responsive sites, this method not only facilitates controllable synthesis of noble metal nanoarrays but has the potential to provide an alternative tool for ultrasensitive chemical sensing on a wide range of bimetallic substrates.