A Facile-Synthesized Star Polycation Constructed as a Highly Efficient Gene Vector in Pest Management

Abstract

Gene vectors have been extensively applied in various fields. However, the high economic cost of gene vectors limits their development and application, and there is an urgent demand for developing highly efficient gene vectors with low cost, especially for large-scale application. Here, we reported a simple but effective star polycation as a gene vector for pest management. Our polycation was constructed by commercial and cheap material sources, and the facile synthesis procedure was simplified to two reaction steps, decreasing the cost to a large extent. This vector showed a low cytotoxicity as well as a high gene delivery efficacy into live cells. The vector-doublestranded RNA (dsRNA) downregulated the pest key developmental gene expression to inhibit the pest growth. Our work provides an efficient gene vector with low cost for scientific researches, which may also promote the practice and development of RNA interference (RNAi)-based pest management.

Nanomaterial‐wrapped dsCYP15C1, a potential RNAi‐based strategy for pest control against Chilo suppressalis

“…These dsRNAs correspond to the same regions of the EGFP and CYP15C1 coding sequences as those in the bacterial system. dsRNAs at a final concentration of 1 μg μL −1 were mixed manually in a 10:1 volume ratio with a previously reported star polycation (SPc) nanomaterial composed of polymerized DMAEMA . The polycationic nanomaterial is biocompatible, water‐soluble, weakly cationic, and sensitive to variations in both pH and temperature …”

“…In recent years, polycationic complexes have been proposed as alternative carriers . One such complex, composed of 2‐ N ‐(dimethyl aminoethyl) methacrylate (DMAEMA) polymers, which are efficiently transfected and have low toxicities, significantly impacted the efficacy of dsRNAs targeting a V‐ATPase in black cutworm ( Agrotis ipsilon ) larvae …”

Deep‐Learning‐Enabled Fast Optical Identification and Characterization of 2D Materials

“…[ 5 ] An automatic optical identification and/or characterization system requires an algorithm that performs reliably for different materials with high accuracy, is fast enough for real‐time processing, and is easily adaptable to different optical setups and different user requirements with minimal additional human efforts. However, existing identification approaches [ 6 ] only exploited the information about the optical contrast (Figure S1, Supporting Information) of the 2D crystals. As a result, they are often limited to specific types of 2D crystals and microscope conditions, which do not meet the aforementioned requirements.…”

“…For this, we intentionally included OM images taken under different optical microscopes and different users as mentioned previously. As a comparison, existing optical identification methods are completely based on optical contrast of the 2D crystals, [ 6 ] and as a result, they are often specific to types of 2D crystals, conditions and configurations of the microscopes being used, image qualities, and so on. In addition, optical contrast based methods would fail for harder problems in which the classes to be differentiated are not separable in the color space, such as identifying the materials in unlabeled optical images (Figure S12, Supporting Information).…”

“…After further analysis on the 512 channels of the D5 encoder layer with more test images randomly chosen from our database, we conclude that the trained 2DMOINet is able to capture deep and subtle graphical features that were overlooked by previously reported optical contrast based approaches. [ 6 ] Many of the deep graphical features reflect in part the physical properties of the 2D materials. To illustrate this, we select 21 easily interpreted channels and discuss their associated graphical features and the related physical properties as summarized in Figure , Figures S14–S34, and Tables S6,S7, Supporting Information.…”