Graphene Quantum Dots (GQD)-Mediated dsRNA Delivery for the Control of Fusarium Head Blight Disease in Wheat.

Graphene quantum dots (GQDs), a class of fluorescent carbon materials, have displayed significant potential in various fields such as energy devices, catalysis, sensing, bioimaging, and drug delivery. Because of their extremely small size, generally less than 100 nm, they also have tremendous potential in plant science research, especially for the delivery of nucleic acids, breaking the barrier of cell walls. In this study, we synthesized GQDs with a size range of 2-5 nm, characterized them, and surface-functionalized them with branched polyethylenimine (bPEI). We then used the surface-functionalized GQDs as carriers to deliver double-stranded RNA (dsRNA) that target two growth-and-development-related genes in Fusarium graminearum─the causative organism of the Fusarium head blight disease of wheat. The successful binding of dsRNA to GQDs-bPEIs was demonstrated through gel-shifting assays, showcasing the potential for efficient dsRNA delivery. We designed dsRNAs targeting the MGV1 and RAS1 genes of F. graminearum by using the pssRNAit pipeline, ensuring high specificity and no off-target effects. The coding sequences of the designed dsRAS1 and dsMGV1 were cloned into the L4440 vector and transformed into the Escherichia coli HT115 strain for dsRNA production. Fungal culture analysis revealed that the inclusion of dsRNAs in potato dextrose agar (PDA) media effectively slowed down the growth. Exogenous spraying experiments both in plate cultures and in intact wheat spikes demonstrated that the dsRNA:GQDs-bPEIs treatment was more effective in restricting fungal mycelium growth or the number of infected spikelets compared to naked dsRNA treatment. Our study demonstrates the promising potential of graphene quantum dots as carriers for dsRNA-based fungal disease management in wheat and other crops.

Utility-Value Score: A Case Study in System Generalization for Writing Analytics.

Collection and analysis of students' writing samples on a large scale is a part of the research agenda of the emerging writing analytics community that promises to deliver an unprecedented insight into characteristics of student writing. Yet with a large scale often comes variability of contexts in which the samples were produced-different institutions, different purposes of writing, different author demographics, to name just a few possible dimensions of variation. What are the implications of such variation for the ability of automated methods to create indices/features based on the writing samples that would be valid and meaningful? This paper presents a case study in system generalization. Building on a system developed to assess the expression of utility value (a social-psychology-based construct) in essays written by first-year biology students at one postsecondary institution, we vary data parameters and observe system performance. From the point of view of social psychology, all these variants represent the same underlying construct (i.e., utility value), and it is thus very tempting to think that an automatically produced utility-value score could provide a meaningful analytic, consistently, on a large collection of essays. However, findings from this research show that there are challenges: Some variations are easier to deal with than others, and some components of the automated system generalize better than others. The findings are then discussed both in the context of the case study and more generally.