The current status of mercury repair technology in the environment

In recent years, due to the pollution of heavy metals in the environment, it has brought a serious crisis to my country's ecological balance, especially the pollution of heavy metal mercury (Hg), so the repair of mercury in the environment is crucial. At present, there are many technologies for repairing mercury in the environment. The main repair techniques include physical repair technology and chemical repair technology. However, there are many problems in these two repair methods, such as high repair costs, and it is easy to cause secondary pollution. Microbial repair method is a method of repairing the environment. It can not only adsorb and fix heavy metal mercury, and does not bring pollution to the environment. Therefore, using microorganisms to remove mercury in the environment is by far the most promising environmental repair technology

The current status of mercury repair technology in the environment

In recent years, due to the pollution of heavy metals in the environment, it has brought a serious crisis to my country's ecological balance, especially the pollution of heavy metal mercury (Hg), so the repair of mercury in the environment is crucial. At present, there are many technologies for repairing mercury in the environment. The main repair techniques include physical repair technology and chemical repair technology. However, there are many problems in these two repair methods, such as high repair costs, and it is easy to cause secondary pollution. Microbial repair method is a method of repairing the environment. It can not only adsorb and fix heavy metal mercury, and does not bring pollution to the environment. Therefore, using microorganisms to remove mercury in the environment is by far the most promising environmental repair technology

Automatic Branch–Leaf Segmentation and Leaf Phenotypic Parameter Estimation of Pear Trees Based on Three-Dimensional Point Clouds

The leaf phenotypic traits of plants have a significant impact on the efficiency of canopy photosynthesis. However, traditional methods such as destructive sampling will hinder the continuous monitoring of plant growth, while manual measurements in the field are both time-consuming and laborious. Nondestructive and accurate measurements of leaf phenotypic parameters can be achieved through the use of 3D canopy models and object segmentation techniques. This paper proposed an automatic branch–leaf segmentation pipeline based on lidar point cloud and conducted the automatic measurement of leaf inclination angle, length, width, and area, using pear canopy as an example. Firstly, a three-dimensional model using a lidar point cloud was established using SCENE software. Next, 305 pear tree branches were manually divided into branch points and leaf points, and 45 branch samples were selected as test data. Leaf points were further marked as 572 leaf instances on these test data. The PointNet++ model was used, with 260 point clouds as training input to carry out semantic segmentation of branches and leaves. Using the leaf point clouds in the test dataset as input, a single leaf instance was extracted by means of a mean shift clustering algorithm. Finally, based on the single leaf point cloud, the leaf inclination angle was calculated by plane fitting, while the leaf length, width, and area were calculated by midrib fitting and triangulation. The semantic segmentation model was tested on 45 branches, with a mean Precisionsem, mean Recallsem, mean F1-score, and mean Intersection over Union (IoU) of branches and leaves of 0.93, 0.94, 0.93, and 0.88, respectively. For single leaf extraction, the Precisionins, Recallins, and mean coverage (mCoV) were 0.89, 0.92, and 0.87, respectively. Using the proposed method, the estimated leaf inclination, length, width, and area of pear leaves showed a high correlation with manual measurements, with correlation coefficients of 0.94 (root mean squared error: 4.44°), 0.94 (root mean squared error: 0.43 cm), 0.91 (root mean squared error: 0.39 cm), and 0.93 (root mean squared error: 5.21 cm2), respectively. These results demonstrate that the method can automatically and accurately measure the phenotypic parameters of pear leaves. This has great significance for monitoring pear tree growth, simulating canopy photosynthesis, and optimizing orchard management

Computational study of the strong binding mechanism of SARS-CoV-2 spike and ACE2

The spike protein of SARS-CoV-2 (SARS-CoV-2-S) helps the virus attach to and infect human cells. With various computational methods applied in this work, the accessibility of its RBD to ACE2, its key residues for stronger binding to ACE2 than the SARS-CoV spike (SARS-CoV-S), the origin of the stronger binding, and its potential sites for drug and antibody design were explored. It was found that the SARS-CoV-2-S could bind ACE2 with an RBD-angle ranging from 52.2º to 84.8º, which demonstrated that the RBD does not need to fully open to bind ACE2. Free energy calculation by an MM/GBSA approach not only revealed much stronger binding of SARS-CoV-2-S to ACE2 (ΔG=-21.7~-29.9 kcal/mol) than SARS-CoV-S (ΔG=-10.2~-16.4 kcal/mol) at different RBD-angles but also demonstrated that the binding becomes increasingly stronger as the RBD-angle increases. In comparison with the experimental results, the free energy decomposition disclosed more key residues interacting strongly with ACE2 than with the SARS-CoV-S, among which the Q493 might be the decisive residue variation (-5.84 kcal/mol) to the strong binding. With the mutation of all 18 different residues of SARS-CoV-S on the spike-ACE2 interface to the corresponding residues of SARS-CoV-2-S, it was found that the mutated SARS-CoV-S has almost the same binding affinity as SARS-CoV-2-S to ACE2, demonstrating that the remaining mutations outside the spike-ACE2 interface have little effect on its binding affinity to ACE2. Simulation of the conformational change pathway from “down” to “up” states disclosed 5 potential ligand-binding pockets correlated to the conformational change. Taking together the key residues, accessible RBD-angle and pocket correlation, potential sites for drug and antibody design were proposed, which should be helpful for interpreting the high infectiousness of SARS-CoV-2 and for developing a cure.</p

D3Similarity: A Ligand-Based Approach for Predicting Drug Targets and for Virtual Screening of Active Compounds Against COVID-19

Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing COVID-19. Protein structure based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by, e.g., various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure based platform for COVID-19, termed as D3Docking in our recent work, we developed the ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses SARS, MERS and SARS-CoV-2, some of which have target or mechanism information but some don’t. Based on the two-dimensional and three-dimensional similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity for drug discovery and target prediction against COVID-19. D3Similarity is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php.</p

D3Targets-2019-nCoV: A Web Server to Identify Potential Targets for Antivirals Against 2019-nCoV

2019-nCoV has caused more than 560 deaths as of 6 February 2020 worldwide, mostly in China. Although there are no effective drugs approved, many clinical trials are incoming or ongoing in China which utilize traditional chinese medicine or modern medicine. Moreover, many groups are working on the cytopathic effect assay to fight against 2019-nCoV, which will result in compounds with good activity yet unknown targets. Identifying potential drug targets will be of great importance to understand the underlying mechanism of how the drug works. Here, we compiled the 3D structures of 17 2019-nCoV proteins and 3 related human proteins, which resulted in 208 binding pockets. Each submitted compound will be docked to these binding pockets by the docking software smina and the docking results will be presented in ascending order of compound-target interaction energy (kcal/mol). We hope the computational tool will shed some light on the potential drug target for the identified antivirals. D3Targets-2019-nCoV is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Docking/index.php.</p

D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19

A highly effective medicine is urgently required to cure coronavirus disease 2019 (COVID-19). For the purpose, we developed a molecular docking based webserver, namely D3Targets-2019-nCoV, with two functions, one is for predicting drug targets for drugs or active compounds observed from clinic or in vitro/in vivo studies, the other is for identifying lead compounds against potential drug targets via docking. This server has its unique features, (1) the potential target proteins and their different conformations involving in the whole process from virus infection to replication and release were included as many as possible; (2) all the potential ligand-binding sites with volume larger than 200 Å3 on a protein structure were identified for docking; (3) correlation information among some conformations or binding sites was annotated; (4) it is easy to be updated, and is accessible freely to public (https://www.d3pharma.com/D3Targets-2019-nCoV/index.php). Currently, the webserver contains 42 proteins [20 severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) encoded proteins and 22 human proteins involved in virus infection, replication and release] with 69 different conformations/structures and 557 potential ligand-binding pockets in total. With 6 examples, we demonstrated that the webserver should be useful to medicinal chemists, pharmacologists and clinicians for efficiently discovering or developing effective drugs against the SARS-CoV-2 to cure COVID-19

Uniform Distribution of Pd on GO-C Catalysts for Enhancing the Performance of Air Cathode Microbial Fuel Cell

Metal, as a high-performance electrode catalyst, is a research hotspot in the construction of a high-performance microbial fuel cell (MFC). However, metal catalyst nanoparticles and their dispersed carriers are prone to aggregation, producing catalytic electrodes with inferior qualities. In this study, Pd is uniformly dispersed on the graphene framework supported by carbon black to form nanocomposite catalysts (Pd/GO-C catalysts). The effect of the palladium loading amount in the catalyst on the catalytic performance of the air cathode was further studied. The optimized metal loading afforded a reduced resistance and improved accessibility of Pd particles for the ORR. The maximum current output of the 0.250 Pd (mg/cm2) MFC was 1645 mA/m2, which is 4.2-fold higher than that of the carbon paper cathode. Overall, our findings provide a novel protocol for the preparation of high-efficient ORR catalyst for MFCs