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Systena frontalis (F) (Coleoptera: Chrysomelidae), also known as the red-headed flea beetle, is a defoliating pest of a variety of crop systems, such as ornamentals and food crops. Leaf consumption by this beetle renders ornamental nursery plants, such as hydrangeas (Hydrangea paniculata Siebold, Hydrangeaceae), unsaleable. In Virginia, this insect has become a major pest at commercial nurseries, and their feeding potential on affected crops has not been quantified. In this study, the extent of their damage to individual leaves and host preference between leaf ages were determined. The rate of defoliation on mature and young hydrangea leaves was measured over 24 and 48 h and between different numbers of adults. A single adult caused up to 10% damage to a young leaf or 5% to a whole mature leaf in 24 h. Without choice, there was a higher percent damage to young leaves. When the size of leaves was controlled by cut-out mature leaves, the area damaged was still higher in young leaves when compared with mature leaves. Adult feeding between mature or young leaves was further investigated by choice assays on a caged plant and within a containerized system. In these choice assays, adults inflicted higher percent damage on mature leaves in both caged plant assays and containerized direct choice assays. The choice assays were more similar to field conditions than the nonchoice assays. This demonstrates that S. frontalis showed a preference for mature leaves over young leaves within hydrangeas.

Cloud-native computing principles such as virtualization and orchestration are key to transferring to the promising paradigm of edge computing. Challenges of containerization, operative models and scarce availability of established tools make a thorough review indispensable. Therefore, the authors have described the practical methods and tools found in the literature as well as in current community-led development projects, and have thoroughly exposed the future directions of the field. Container virtualization and its orchestration through Kubernetes have dominated the cloud computing domain, while major efforts have been recently recorded focused on the adaptation of these technologies to the edge. Such initiatives have addressed either the reduction of container engines and the development of specific tailored operating systems or the development of smaller K8s distributions and edge-focused adaptations (such as KubeEdge). Finally, new workload virtualization approaches, such as WebAssembly modules together with the joint orchestration of these heterogeneous workloads, seem to be the topics to pay attention to in the short to medium term.

Delivering tools for genome analysis to users is often difficult given the complex dependencies and conflicts of such tools. Container virtualization systems (such as Singularity) isolate environments, thereby helping developers package tools. However, these systems lack mutual composability, i.e., an easy way to integrate multiple tools in different containers and/or on the host. Another issue is that one may be unable to use a single container system of the same version at all the sites being used, thus discouraging the use of container systems. We developed LPMX, an open-source pure rootless composable container system that provides composability; i.e., the system allows users to easily integrate tools from different containers or even from the host. LPMX accelerates science by letting researchers compose existing containers and containerize tools/pipelines that are difficult to package/containerize using Conda or Singularity, thereby saving researchers' precious time. The technique used in LPMX allows LPMX to run purely in userspace without root privileges even during installation, thus ensuring that we can use LPMX at any Linux clusters with major distributions. The lowest overhead for launching containers with LPMX gives us courage to isolate tools as much as possible into small containers, thereby minimizing the chance of conflicts. The support for the layered file system keeps the total size of container images for a single genomic pipeline modest, as opposed to Singularity, which uses mostly a flat single-layer image. LPMX is pure rootless container engine with mutual composability, thus saving researchers' time, and accelerating science.