Self-Assembled Sphere Covalent Organic Framework with Enhanced Herbicidal Activity by Loading Cyhalofop-butyl

Nanopesticides are considered to be a novel and efficient kind of tool for controlling pests in modern agriculture. Covalent organic frameworks (COFs), with high surface areas, ordered structures, and rich functional groups for loading pesticides, are a class of promising carrier materials that can be used to develop efficient nanopesticide delivery systems. However, until now, only a strong ionic interaction between the pesticide and COF can be utilized to achieve the combination between the pesticide and COF. On the basis of this method, charged pesticide molecules are the only choice for COF-based nanopesticides, which limits the exploitation. The way to load the uncharged pesticide molecules into COF still needs to be explored. Herein, in this research, we provided a commonly mild and high-efficacy strategy for loading an uncharged pesticide molecule into COF. The herbicide cyhalofop-butyl (CB), as a neutral model pesticide molecule, was loaded into the sphere COF (SCOF, a model COF synthesized at room temperature) without any ionic interaction via the host–guest strategy. The loading capacity of CB into SCOF (CB@SCOF) was determined at 57% (w/w). Smaller CB@SCOF particles (150–200 nm) can efficiently enter the weed leaves and stems, enhancing the accumulation of the effective concentration in weeds, thus increasing herbicidal activity, in comparison to CB emulsifiable (EC, micrometer scale). Furthermore, CB@SCOF had a solubilization effect for CB in water and can improve the photostability of CB. Thus, the CB-loaded COF nanosphere showed excellent herbicidal activities against the target weeds Echinochloa crus-galli and Leptochloa chinensis compared to commercial CB EC. In conclusion, this study also provides a mild and high-efficacy pesticide loading strategy for COFs. The constructed efficient delivery system and pesticide formulation containing herbicidal COF nanospheres exhibit great potential applications for controlling weeds in sustainable agriculture

Additional file 1 of Effects of fenclorim on rice physiology, gene transcription and pretilachlor detoxification ability

Additional file 1: Figure S1. GO classification and statistical results for all genes. The genes were summarized in biological process, cellular component and molecular function terms. A total of 28,662 genes were categorized

Additional file 7 of Effects of fenclorim on rice physiology, gene transcription and pretilachlor detoxification ability

Additional file 7: Table S5. Primer pairs used for qRT-PCR verification of gene expression in rice

<i>Eleusine indica</i> Cytochrome P450 and Glutathione S‑Transferase Are Linked to High-Level Resistance to Glufosinate

Eleusine indica has become a global nuisance weed and has evolved resistance to glufosinate. The involvement of target-site resistance (TSR) in glufosinate resistance in E. indica has been elucidated, while the role of nontarget-site resistance (NTSR) remains unclear. Here, we identified a glufosinate-resistant (R) population that is highly resistant to glufosinate, with a resistance index of 13.5-fold. Molecular analysis indicated that the resistance mechanism of this R population does not involve TSR. In addition, pretreatment with two known metabolic enzyme inhibitors, the cytochrome P450 (CYP450) inhibitor malathion and the glutathione S-transferase (GST) inhibitor 4-chloro-7-nitrobenzoxadiazole (NBD-Cl), increased the sensitivity of the R population to glufosinate. The results of subsequent RNA sequencing (RNA-seq) and quantitative real-time PCR (RT–qPCR) suggested that the constitutive overexpression of a GST gene (GSTU3) and three CYP450 genes (CYP94s and CYP71) may play an important role in glufosinate resistance. This study provides new insights into the resistance mechanism of E. indica

Additional file 5: of Target-site and non-target-site based resistance to the herbicide tribenuron-methyl in flixweed (Descurainia sophia L.)

Volcano plot of differentially expression genes (DEGs) between resistant (N11) and susceptible (SD8) populations. Red spots represent up-regulated DEGs and green spots indicate down-regulated DEGs. Those shown in blue are unigenes that did not show obvious changes. (PDF 146 kb

Additional file 4 of Effects of fenclorim on rice physiology, gene transcription and pretilachlor detoxification ability

Additional file 4: Table S3. List of DEGs between Fen treatment and CK at 4 h

Additional file 6 of Effects of fenclorim on rice physiology, gene transcription and pretilachlor detoxification ability

Additional file 6: Figure S2. GO classification and statistical results for DEGs at 4 h (A) and 24 h (B) of treatment. The genes were summarized in biological process, cellular component and molecular function terms. A total of 168 differentially expressed genes at 4 h of treatment and 68 differentially expressed genes at 24 h of treatment were annotated

Maximal Information Coefficient and Support Vector Regression Based Nonlinear Feature Selection and QSAR Modeling on Toxicity of Alcohol Compounds to Tadpoles of Rana temporaria

Efficient evaluation of biotoxicity of organics is of vital significance to resource utilization and environmental protection. In this study, toxicity of 110 alcohol compounds to tadpoles of Rana temporaria is adopted as the dependent variable and 1388 physiochemical parameters (features) calculated by PCLIENT are used for representing each compound. A feature selection pipeline with three steps is developed to refine the feature subset: 282 features that significantly correlated with biotoxicity of chemical compounds are preliminarily selected via the maximum information coefficient (MIC); 138 descriptors that have positive contribution to the model’s performance are reserved after a support vector regression (SVR) based backward elimination; 18 descriptors are finally selected via a forward selection process that integrated minimal redundancy maximal relevance (mRMR), MIC and SVR. In terms of feature subsets with different numbers of variables, quantitative structure activity relationship (QSAR) models are built using multiple linear regression (MLR), partial least square regression (PLS) and SVR, respectively. The independent prediction evaluation index, Q2, increases from -74.787, 0.824 and 0.868 to 0.892, 0.878 and 0.940, for the three regression models, respectively. Results suggest that nonlinear feature selection methods involved in MIC and SVR can effectively eliminate irrelevant descriptors. SVR outperforms classical statistical models to QSAR modeling on high-dimensional data containing nonlinear relationship between features. The methods proposed in this study have a potential application in the QSAR research field such as biotoxicity compounds.</div

Additional file 5 of Effects of fenclorim on rice physiology, gene transcription and pretilachlor detoxification ability

Additional file 5: Table S4. List of DEGs between Fen treatment and CK at 24 h

Additional file 6: of Target-site and non-target-site based resistance to the herbicide tribenuron-methyl in flixweed (Descurainia sophia L.)

Histogram of GO classification of the DEGs. The results are summarized in three main GO categories: biological process, cellular component and molecular function. The x-axis indicates the subcategories, and the y-axis indicates the numbers related to the total number of GO terms present; the DEGs numbers that are assigned the same GO terms are indicated at the top of the bars. (PDF 282 kb