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Abstract Virus symbionts are important mediators of ecosystem function, yet we know little of their diversity and ecology in natural populations. The alarming decline of pollinating insects in many regions of the globe, especially the European honey bee, Apis mellifera, has been driven in part by worldwide transmission of virus pathogens. Previous work has examined the transmission of known honey bee virus pathogens to wild bee populations, but only a handful of studies have investigated the native viromes associated with wild bees, limiting epidemiological predictors associated with viral pathogenesis. Further, variation among different bee species might have important consequences in the acquisition and maintenance of bee-associated virome diversity. We utilized comparative metatranscriptomics to develop a baseline description of the RNA viromes associated with wild bee pollinators and to document viral diversity, community composition, and structure. Our sampling includes five wild-caught, native bee species that vary in social behavior as well as managed honey bees. We describe 26 putatively new RNA virus species based on RNA-dependent RNA polymerase phylogeny and show that each sampled bee species was associated with a specific virus community composition, even among sympatric populations of distinct host species. From 17 samples of a single host species, we recovered a single virus species despite over 600 km of distance between host populations and found strong evidence for isolation by distance in associated viral populations. Our work adds to the small number of studies examining viral prevalence and community composition in wild bees. 

Abstract Introduction Digital twins, a form of artificial intelligence, are virtual representations of the physical world. In the past 20 years, digital twins have been utilized to track wind turbines' operations, monitor spacecraft's status, and even create a model of the Earth for climate research. While digital twins hold much promise for the neurocritical care unit, the question remains on how to best establish the rules that govern these models. This model will expand on our group’s existing digital twin model for the treatment of sepsis. Methods The authors of this project collaborated to create a Direct Acyclic Graph (DAG) and an initial series of 20 DELPHI statements, each with six accompanying sub-statements that captured the pathophysiology surrounding the management of acute ischemic strokes in the practice of Neurocritical Care (NCC). Agreement from a panel of 18 experts in the field of NCC was collected through a 7-point Likert scale with consensus defined a-priori by ≥ 80% selection of a 6 (“agree”) or 7 (“strongly agree”). The endpoint of the study was defined as the completion of three separate rounds of DELPHI consensus. DELPHI statements that had met consensus would not be included in subsequent rounds of DELPHI consensus. The authors refined DELPHI statements that did not reach consensus with the guidance of de-identified expert comments for subsequent rounds of DELPHI. All DELPHI statements that reached consensus by the end of three rounds of DELPHI consensus would go on to be used to inform the construction of the digital twin model. Results After the completion of three rounds of DELPHI, 93 (77.5%) statements reached consensus, 11 (9.2%) statements were excluded, and 16 (13.3%) statements did not reach a consensus of the original 120 DELPHI statements. Conclusion This descriptive study demonstrates the use of the DELPHI process to generate consensus among experts and establish a set of rules for the development of a digital twin model for use in the neurologic ICU. Compared to associative models of AI, which develop rules based on finding associations in datasets, digital twin AI created by the DELPHI process are easily interpretable models based on a current understanding of underlying physiology. 

Using robots capable of collaboration with humans to complete physical tasks in unstructured spaces is a rapidly growing approach to work. Particular examples where increased levels of automation can increase productivity include robots used as nursing assistants. In this paper, we present a mobile manipulator designed to serve as an assistant to nurses in patient walking and patient sitting tasks in hospital environments. The Adaptive Robotic Nursing Assistant (ARNA) robot consists of an omnidirectional base with an instrumented handlebar, and a 7-DOF robotic arm. We describe its components and the novelties in its mechanisms and instrumentation. Experiments with human subjects that gauge the usability and ease of use of the ARNA robot in a medical environment indicate that the robot will get significant actual usage, and are used as a basis for a discussion on how the robot's features facilitate its adaptability for use in other scenarios and environment.