The fall armyworm, J.E. Smith (Lepidoptera: Noctuidae), is a significant agricultural pest, and its management is increasingly impeded by resistance to conventional insecticides, underscoring the need for alternative control strategies. This research examined the effects of the synthetic peptide GORE3 on digestive protease homeostasis and larval performance using an integrated approach that combined molecular dynamics simulations, enzymatic assays, physiological analyses, and histopathology. Comparative analyses employed benzamidine and soybean Kunitz trypsin inhibitor (SKTI) as reference inhibitors. Molecular dynamics simulations demonstrated that the trypsin-GORE3 complex is highly stable, with lower Root Mean Square Deviation (RMSD) values and sustained intermolecular interactions compared with benzamidine, while maintaining a favorable binding free-energy profile. In vivo analyses demonstrated that GORE3 induces a dynamic regulation of midgut digestive enzymes, initially suppressing activity, followed by a significant compensatory enhancement in trypsin-like and overall protease activities. Despite this enzymatic compensation, caterpillars exposed to GORE3 has low survival rates, reduced larval weight, prolonged developmental periods, and impaired food conversion efficiency. Nutritional indices indicated a breakdown between digestion and biomass conversion, implying metabolic costs linked to compensatory responses. Histopathological investigations identified midgut damage, including epithelial disruption, epithelial cell disorganization, and structural deterioration with all trypsin inhibitors tested. Oxidative stress responses were detected but played a secondary role relative to digestive dysregulation. Overall, GORE3 disrupts digestive homeostasis by triggering a compensatory yet ineffective proteolytic response, thereby reducing caterpillar fitness. These findings underscore the potential of synthetic peptides targeting digestive proteases as potential bioinsecticides and establish a mechanistic framework linking molecular interactions to organismal effects.
Artificial Intelligence (AI) models, encompassing both traditional machine learning (ML) and more advanced approaches such as deep learning and large language models (LLMs), play a central role in modern applications. AI model lifecycle management involves the end-to-end process of managing these models, from data collection and preparation to model building, evaluation, deployment, and continuous monitoring. This process is inherently complex, as it requires the coordination of diverse services that manage AI artifacts such as datasets, dataflows, and models, all orchestrated to operate seamlessly. In this context, it is essential to isolate applications from the complexity of interacting with heterogeneous services, datasets, and AI platforms. In this paper, we introduce Gypscie, a cross-platform AI artifact management system. By providing a unified view of all AI artifacts, the Gypscie platform simplifies the development and deployment of AI applications. This unified view is realized through a knowledge graph that captures application semantics and a rule-based query language that supports reasoning over data and models. Model lifecycle activities are represented as high-level dataflows that can be scheduled across multiple platforms, such as servers, cloud platforms, or supercomputers. Finally, Gypscie records provenance information about the artifacts it produces, thereby enabling explainability. Our qualitative comparison with representative AI systems shows that Gypscie supports a broader range of functionalities across the AI artifact lifecycle. Our experimental evaluation demonstrates that Gypscie can successfully optimize and schedule dataflows on AI platforms from an abstract specification.
The Multiscale Hybrid-Mixed (MHM) method is a multiscale finite element method based on a hybrid weak formulation, originally proposed for problems linked to flow in porous media. Its starting point is a variational formulation that guarantees that the flux variable is H(div; Q)-conforming. This property is lost when the local problems, in their elliptic form, are discretized using primal finite element methods. In this work, we close that gap by proposing and analyzing a new flux reconstruction for the MHM method, computed elementwise on submeshes, that belongs to H(div; Q). The reconstruction converges optimally in the L-2(Q)-norm. Furthermore, the divergence of the reconstructed flow is the piecewise continuous polynomial projection of the source term onto submeshes and thus it also converges optimally in the L-2(Q)-norm. As a by-product of the reconstruction technique, a fully computable a posteriori error estimator is presented and analyzed. These theoretical results are validated experimentally via numerical computations.
Peripheral artery disease currently affects over 202 million people worldwide. The ankle-brachial index is widely used to assess a reduction in blood flow to the foot, but it cannot characterise tissue perfusion. MRI-based per fusion measurements can provide this information, yet they are time-consuming and can be painful if induction of ischemia is warranted for the scan. As an alternative, we model foot perfusion during a cuff-induced ischemia test to characterise how arterial occlusions affect perfusion in foot regions. Simulations, not patient-specific at this stage, are conducted on a 1D arterial network model which includes 154 foot and calf arterial segments, pro viding a realistic description of the topology of the foot vasculature. A baseline model characterizes angiosome perfusion under healthy conditions, which is then modified to reflect 42 pathological scenarios by introducing occlusions and different levels of collateral impairment. This approach enables a novel in-silico comparison of angiosome-targeted and best-vessel strategies in a realistic whole-limb geometry, uncovering mechanistic phe nomena relevant for revascularization planning. Results show a marked influence of collateral impairment on angiosome perfusion under the condition of a single-artery occlusion, highlighting the role of blood redistribu tion. If two feeding arteries are occluded, perfusion markedly decreases at all collateral impairment levels due to the severe reduction in incoming blood flow. These results provide a bridge between the angiosome-targeted and "best-vessel" strategies for revascularization, showing that both can be correct depending on collateral sufficiency.
