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Fungal pathogens pose escalating challenges to global food security, as resistance has emerged against nearly all major fungicides used in agriculture. RNA-based antifungals offer a sustainable and environmentally friendly alternative for disease control, but their deployment is hindered by RNA instability under environmental conditions, especially in soil. In this study, we engineered two plant-beneficial soil bacteria-Bacillus subtilis (Gram-positive) and Pseudomonas putida (Gram-negative)-to produce double-stranded RNAs (dsRNAs) targeting fungal genes in the foliar and postharvest pathogen Botrytis cinerea and the soilborne pathogen Verticillium dahliae. We found that both bacterial species secrete RNA through extracellular vesicles (EVs) and that these RNAs are transported into fungal cells, demonstrating cross-kingdom RNA trafficking from bacteria to fungi. Application of dsRNA-containing bacterial EVs to plant leaves suppressed B. cinerea infection. In addition, direct treatment with dsRNA-producing bacteria protected both Arabidopsis thaliana and tomato plants from infections by B. cinerea and V. dahliae. Our findings establish beneficial bacteria as a scalable platform for continuous production and delivery of antifungal RNAs, enabling a cost-effective strategy for sustainable crop protection. 

Abstract This paper serves as a comprehensive system description of version 2.0 of the Marabou framework for formal analysis of neural networks. We discuss the tool’s architectural design and highlight the major features and components introduced since its initial release. 

Deep neural networks (DNNs) are increasingly being employed in safety-critical systems, and there is an urgent need to guarantee their correctness. Consequently, the verification community has devised multiple techniques and tools for verifying DNNs. When DNN verifiers discover an input that triggers an error, that is easy to confirm; but when they report that no error exists, there is no way to ensure that the verification tool itself is not flawed. As multiple errors have already been observed in DNN verification tools, this calls the applicability of DNN verification into question. In this work, we present a novel mechanism for enhancing Simplex-based DNN verifiers with proof production capabilities: the generation of an easy-to-check witness of unsatisfiability, which attests to the absence of errors. Our proof production is based on an efficient adaptation of the well-known Farkas' lemma, combined with mechanisms for handling piecewise-linear functions and numerical precision errors. As a proof of concept, we implemented our technique on top of the Marabou DNN verifier. Our evaluation on a safety-critical system for airborne collision avoidance shows that proof production succeeds in almost all cases and requires only minimal overhead. 

We presented a novel radiomics approach using multimodality MRI to predict the expression of an oncogene (O6-Methylguanine-DNA methyltransferase, MGMT) and overall survival (OS) of glioblastoma (GBM) patients. Specifically, we employed an EffNetV2-T, which was down scaled and modified from EfficientNetV2, as the feature extractor. Besides, we used evidential layers based to control the distribution of prediction outputs. The evidential layers help to classify the high-dimensional radiomics features to predict the methylation status of MGMT and OS. Tests showed that our model achieved an accuracy of 0.844, making it possible to use as a clinic-enabling technique in the diagnosing and management of GBM. Comparison results indicated that our method performed better than existing work. 

In this work we present a framework of designing iterative techniques for image deblurring in inverse problem. The new framework is based on two observations about existing methods. We used Landweber method as the basis to develop and present the new framework but note that the framework is applicable to other iterative techniques. First, we observed that the iterative steps of Landweber method consist of a constant term, which is a low-pass filtered version of the already blurry observation. We proposed a modification to use the observed image directly. Second, we observed that Landweber method uses an estimate of the true image as the starting point. This estimate, however, does not get updated over iterations. We proposed a modification that updates this estimate as the iterative process progresses. We integrated the two modifications into one framework of iteratively deblurring images. Finally, we tested the new method and compared its performance with several existing techniques, including Landweber method, Van Cittert method, GMRES (generalized minimal residual method), and LSQR (least square), to demonstrate its superior performance in image deblurring.