We develop a multi-model approach to explore how abundance of a forage fish (Pacific sardine Sardinops sagax) impacts the ecosystem and predators in the California Current, a region where sardine and ...anchovy Engraulis mordax have recently declined to less than 10% of contemporary peak abundances. We developed or improved applications of 3 ecosystem modeling approaches: Ecopath, Model of Intermediate Complexity for Ecosystem assessment (MICE), and Atlantis. We also used Ecopath diets to predict impacts to predators using a statistical generalization of the dynamic Ecosim model (Predator Response to the Exploitation of Prey PREP). Models that included brown pelican Pelecanus occidentalis at the species level (MICE and Ecopath/PREP) both predict moderate to high vulnerability of brown pelicans to low sardine abundance. This vulnerability arises because sardine comprises a large fraction of their diet, and because other important prey (anchovy) also exhibit large population fluctuations. Two of the ecosystem models (MICE and Atlantis) suggest that California sea lions Zalophus californianus exhibit relatively minor responses to sardine depletion, due to having broader diets and lower reliance on another fluctuating species, anchovy. On the other hand, Ecopath/PREP suggests that sardine declines will have a stronger impact on California sea lions. This discrepancy may in part reflect structural differences in the models: Atlantis and MICE explicitly represent density dependence and age-structure, which can mitigate effects of prey depletion in these models. Future work should identify fisheries management strategies that are robust to uncertainties within and among models, rather than relying on single models to assess ecosystem impacts of management and forage fish abundance.
Amyloid A nephropathy is a possible complication of chronic inflammatory disease. Proteinuria and kidney failure are the main features of the disease. Tocilizumab (TCZ), an IL6-R antibody approved ...for rheumatoid arthritis, is a promising choice for histologically demonstrated nephropathy. We describe a case of kidney amyloid associated with Sweet syndrome treated with TCZ. The patient was affected by Sweet syndrome associated with proteinuria. Kidney biopsy showed amyloid deposits. During the follow-up, cutaneous and renal findings were refractory to many immunosuppressive regimen (cyclophosphamide, leflunomide, interferon and steroid). After few years, the patient developed rapidly progressive nephropathy associated with nephrotic syndrome (proteinuria up to 6 g/die). A second kidney biopsy was performed and it showed worsening of amyloid nephropathy. Thus, TCZ was administrated (8 mg/kg once a month) and it stabilized kidney function and induced partial remission of the nephrotic syndrome in the following 2 years.
Background
Like most hospitals, our hospital experienced COVID-19 pandemic-related supply chain shortages. Our additive manufacturing lab’s capacity to offset these shortages was soon overwhelmed, ...leading to a need to improve the efficiency of our existing workflow. We undertook a work system analysis guided by the Systems Engineering Initiative for Patient Safety (SEIPS) construct which is based on human factors and quality improvement principles. Our objective was to understand the inefficiencies in project submission, review, and acceptance decisions, and make systematic improvements to optimize lab operations.
Methods
Contextual inquiry (interviews and workflow analysis) revealed suboptimal characteristics of the system, specifically, reliance on a single person to facilitate work and, at times, fractured communication with project sponsors, with root causes related to the project intake and evaluation process as identified through SEIPS tools. As interventions, the analysis led us to: 1) enhance an existing but underused project submission form, 2) design and implement an internal project scorecard to standardize evaluation of requests, and 3) distribute the responsibility of submission evaluation across lab members. We implemented these interventions in May 2021 for new projects and compare them to our baseline February 1, 2018 through – April 30, 2021 performance (1184 days).
Results
All project requests were submitted using the enhanced project submission form and all received a standardized evaluation with the project scorecard. Prior to interventions, we completed 35/79 (44%) of projects, compared to 12/20 (60%) of projects after interventions were implemented. Time to review new submissions was reduced from an average of 58 days to 4 days. A more distributed team responsibility structure permitted improved workflow with no increase in staffing, allowing the Lab Manager to devote more time to engineering rather than administrative/decision tasks.
Conclusions
By optimizing our workflows utilizing a human factors approach, we improved the work system of our additive manufacturing lab to be responsive to the urgent needs of the pandemic. The current workflow provides insights for labs aiming to meet the growing demand for point-of-care manufacturing.
Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep ...learning curve and the different methodologies employed by DL compared to traditional approaches, along with the high complexity of DL models, which often leads to the need of employing specialized hardware accelerators, further increase the effort and cost needed to employ DL models in robotics. Also, most of the existing DL methods follow a static inference paradigm, as inherited by the traditional computer vision pipelines, ignoring active perception, which can be employed to actively interact with the environment in order to increase perception accuracy. In this paper, we present the Open Deep Learning Toolkit for Robotics (OpenDR). OpenDR aims at developing an open, non-proprietary, efficient, and modular toolkit that can be easily used by robotics companies and research institutions to efficiently develop and deploy AI and cognition technologies to robotics applications, providing a solid step towards addressing the aforementioned challenges. We also detail the design choices, along with an abstract interface that was created to overcome these challenges. This interface can describe various robotic tasks, spanning beyond traditional DL cognition and inference, as known by existing frameworks, incorporating openness, homogeneity and robotics-oriented perception e.g., through active perception, as its core design principles.
The electronic health record (EHR) offers an exciting opportunity for quality improvement efforts. An understanding of the nuances of a site's EHR landscape including the best practices in clinical ...decision support design, basics of data capture, and acknowledgment of the potential unintended consequences of technology change is essential to ensuring effective usage of this powerful tool.
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