Facile Fabrication of Hierarchical Flower-Like BSA/Layered Double Hydroxide Hybrids

<p>This work presents the synthesis of hierarchical flower-like bovine serum albumin/layered double hydroxides (BSA/LDH) hybrids based on the assembly of biological proteins and LDH nanosheets. The BSA/alumina sols are first obtained by a sol-gel process, followed by self-assembling into spherical aggregates. These preformed hybrid sols are then used as biohybrid precursors and aluminum sources to fabricate the hierarchical architectures based on <i>in</i> situ growth of LDH nanoplatelets around biomolecules. The facile method is expected to be used for fabricating other hierarchical bioinorganic hybrids for potential application in the areas of biocatalysts and drug delivery.</p

Media 6: Visualization of brain circuits using two-photon fluorescence micro-optical sectioning tomography

Originally published in Optics Express on 22 April 2013 (oe-21-8-9839

Media 1: Visualization of brain circuits using two-photon fluorescence micro-optical sectioning tomography

Originally published in Optics Express on 22 April 2013 (oe-21-8-9839

Media 2: Visualization of brain circuits using two-photon fluorescence micro-optical sectioning tomography

Originally published in Optics Express on 22 April 2013 (oe-21-8-9839

Media 5: Visualization of brain circuits using two-photon fluorescence micro-optical sectioning tomography

Originally published in Optics Express on 22 April 2013 (oe-21-8-9839

Fine-grained wetland classification for national wetland reserves using multi-source remote sensing data and Pixel Information Expert Engine (PIE-Engine)

Timely and accurate wetland information is necessary for wetland resource management. Recent advances in machine learning and remote sensing have facilitated cost-effective monitoring of wetlands. However, reliable methods for fine-grained and rapid wetland mapping are still lacking. To address the issue, a wetland sample set with 20 categories for China was collected based on a sampling strategy that combines automatic sample generation and visual interpretation. Simultaneously, a novel multi-stage method for fine-grained wetland classification was proposed, which integrates pixel-based and object-based strategies using ensemble learning algorithms and multi-source remote sensing data. First, a pixel-based ensemble learning algorithm was implemented to classify five rough wetland categories and six non-wetland categories. Second, an object-based ensemble learning approach was designed to separate the water cover in the pixel-based classification results into eight detailed categories. Third, the merged pixel-based and object-based classification results were refined with knowledge-based post-processing procedures to identify 14 fine-grained wetland categories. Results using the Pixel Information Expert Engine (PIE-Engine) cloud platform proved the effectiveness of the proposed wetland classification method. The overall accuracy, kappa, and weighted F1 reached 87.39%, 82.80%, and 86.02%, respectively. The adopted ensemble learning algorithm yielded better performance than classifiers such as CatBoost, random forest, and XGBoost. The incorporation of spectral, texture, shape, topographic, and geographic features from multi-source data contributed to differentiating wetland categories. According to the relative contribution, spectral indexes (NDVI and NDWI), texture features (sum average and contrast), and topographic features (slope and elevation) were identified as important leading predictors for the first-stage pixel-based classification. Shape features (shape index and compactness) and auxiliary features (geographic location) were crucial predictors for the second-stage object-based classification. Compared with other products, our 10-m wetland mapping results for national wetland reserves were rich in detail and fine in categories. Overall, the constructed sample set and developed classification method show promise in laying a foundation for large-scale wetland mapping. The derived wetland maps can provide support for wetland protection and restoration.</p

Discovery and Mechanism Study of SARS-CoV‑2 3C-like Protease Inhibitors with a New Reactive Group

3CLpro is an attractive target for the treatment of COVID-19. Using the scaffold hopping strategy, we identified a potent inhibitor of 3CLpro (3a) that contains a thiocyanate moiety as a novel warhead that can form a covalent bond with Cys145 of the protein. Tandem mass spectrometry (MS/MS) and X-ray crystallography confirmed the mechanism of covalent formation between 3a and the protein in its catalytic pocket. Moreover, several analogues of compound 3a were designed and synthesized. Among them, compound 3h shows the best inhibition of 3CLpro with an IC50 of 0.322 μM and a kinact/Ki value of 1669.34 M–1 s–1, and it exhibits good target selectivity for 3CLpro against host proteases. Compound 3c inhibits SARS-CoV-2 in Vero E6 cells (EC50 = 2.499 μM) with low cytotoxicity (CC50 > 200 μM). These studies provide ideas and insights to explore and develop new 3CLpro inhibitors in the future

Discovery and Mechanism Study of SARS-CoV‑2 3C-like Protease Inhibitors with a New Reactive Group

3CLpro is an attractive target for the treatment of COVID-19. Using the scaffold hopping strategy, we identified a potent inhibitor of 3CLpro (3a) that contains a thiocyanate moiety as a novel warhead that can form a covalent bond with Cys145 of the protein. Tandem mass spectrometry (MS/MS) and X-ray crystallography confirmed the mechanism of covalent formation between 3a and the protein in its catalytic pocket. Moreover, several analogues of compound 3a were designed and synthesized. Among them, compound 3h shows the best inhibition of 3CLpro with an IC50 of 0.322 μM and a kinact/Ki value of 1669.34 M–1 s–1, and it exhibits good target selectivity for 3CLpro against host proteases. Compound 3c inhibits SARS-CoV-2 in Vero E6 cells (EC50 = 2.499 μM) with low cytotoxicity (CC50 > 200 μM). These studies provide ideas and insights to explore and develop new 3CLpro inhibitors in the future

Discovery and Mechanism Study of SARS-CoV‑2 3C-like Protease Inhibitors with a New Reactive Group

3CLpro is an attractive target for the treatment of COVID-19. Using the scaffold hopping strategy, we identified a potent inhibitor of 3CLpro (3a) that contains a thiocyanate moiety as a novel warhead that can form a covalent bond with Cys145 of the protein. Tandem mass spectrometry (MS/MS) and X-ray crystallography confirmed the mechanism of covalent formation between 3a and the protein in its catalytic pocket. Moreover, several analogues of compound 3a were designed and synthesized. Among them, compound 3h shows the best inhibition of 3CLpro with an IC50 of 0.322 μM and a kinact/Ki value of 1669.34 M–1 s–1, and it exhibits good target selectivity for 3CLpro against host proteases. Compound 3c inhibits SARS-CoV-2 in Vero E6 cells (EC50 = 2.499 μM) with low cytotoxicity (CC50 > 200 μM). These studies provide ideas and insights to explore and develop new 3CLpro inhibitors in the future