Artificial genetic circuits are becoming important tools for controlling cellular behavior and studying molecular biosystems. To genetically optimize the properties of complex circuits in a practically feasible fashion, it is necessary to identify the best genes and/or their regulatory components as mutation targets to avoid the mutation experiments being wasted on ineffective regions, but this goal is generally not achievable by current methods. The Random Sampling-High Dimensional Model Representation (RS-HDMR) algorithm is employed in this work as a global sensitivity analysis technique to estimate the sensitivities of the circuit properties with respect to the circuit model parameters, such as rate constants, without knowing the precise parameter values. The sensitivity information can then guide the selection of the optimal mutation targets and thereby reduce the laboratory effort. As a proof of principle, the in vivo effects of 16 pairwise mutations on the properties of a genetic inverter were compared against the RS-HDMR predictions, and the algorithm not only showed good consistency with laboratory results but also revealed useful information, such as different optimal mutation targets for optimizing different circuit properties, not available from previous experiments and modeling.

Firearm related assault injuries disproportionately affect young men of color related to a variety of social & ecological vulnerabilities. Delaware, and particularly the city of Wilmington, has experienced a disproportionately high number of these injuries, and this article follows the public health approach in defining the scope of the problem, establishing what is known about the pathophysiology and transmission of injury, describing the effectiveness of newer prevention programs in both public safety and public health, and highlighting important constraints and considerations for program evaluation and research.

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Since the beginning of the COVID-19 pandemic, the State of Delaware has implemented various strategies including a stay-at-home order, mask-wearing requirements in public places, and community-based testing to control the spread of the disease. Health systems across the U.S. have taken actions including symptom monitoring and screening for visitors and healthcare workers, providing personal protection equipment (PPE), and contact tracing of confirmed infected individuals to provide maximum possible protection for healthcare workers. Despite such efforts, there remains a significant risk of intra-hospital transmission of COVID-19. Healthcare workers who contact patients with COVID-19 or were exposed to the disease in the community may transmit the infection to coworkers in the inpatient setting. In addition to universal and case-based precautions to prevent exposure and disease transmission, contact tracing is essential to minimizing the impact of outbreaks among healthcare workers and the community. A rapid increase in cases can quickly diminish hospital infection control and prevention program capacity to perform high-quality contact tracing. This article will describe an approach using the application of social network analysis (SNA) and Electronic Medical Records (EMR) to enhance the current efforts in COVID-19 contact tracings.