Suchergebnisse

Although the empirical association of heroin use and income‐generating crime is well established in past research, the magnitude of the association after control of other factors such as legal income is not known. The relationship between the use of cocaine and income‐generating crime has not received adequate attention. Moreover, the explanatory basis for the expensive drug use/income‐generating crime association is not well understood. This article tests the robustness of the heroin use/income‐generating crime relationship and examines the same question for cocaine use. Hypotheses derived from two explanatory perspectives (the compulsion/demand and life‐style models) are tested. Data were collected from more than 3,500 individuals who were interviewed at the time they entered publicly funded drug abuse treatment programs in 1979. Regression analyses show that daily use of heroin and weekly and daily use of cocaine are strongly associated with illegal income. Theoretical interpretation of the findings suggest: (1) the addiction/compulsion explanatory model is an insufficient explanation, (2) the life‐style concept is useful for understanding the expensive drug use/income‐generating crime relationship, and (3) the concept of addiction needs refinement and elaboration.

Synthetic biology aims to develop engineering-driven approaches to the programming of cellular functions that could yield transformative technologies. Synthetic gene circuits that combine DNA, protein, and RNA components have demonstrated a range of functions such as bistability, oscillation, feedback, and logic capabilities. However, it remains challenging to scale up these circuits owing to the limited number of designable, orthogonal, high-performance parts, the empirical and often tedious composition rules, and the requirements for substantial resources for encoding and operation. Here, we report a strategy for constructing RNA-only nanodevices to evaluate complex logic in living cells. Our 'ribocomputing' systems are composed of de-novo-designed parts and operate through predictable and designable base-pairing rules, allowing the effective in silico design of computing devices with prescribed configurations and functions in complex cellular environments. These devices operate at the post-transcriptional level and use an extended RNA transcript to co-localize all circuit sensing, computation, signal transduction, and output elements in the same self-assembled molecular complex, which reduces diffusion-mediated signal losses, lowers metabolic cost, and improves circuit reliability. We demonstrate that ribocomputing devices in Escherichia coli can evaluate two-input logic with a dynamic range up to 900-fold and scale them to four-input AND, six-input OR, and a complex 12-input expression (A1 AND A2 AND NOT A1*) OR (B1 AND B2 AND NOT B2*) OR (C1 AND C2) OR (D1 AND D2) OR (E1 AND E2). Successful operation of ribocomputing devices based on programmable RNA interactions suggests that systems employing the same design principles could be implemented in other host organisms or in extracellular settings. ; National Institutes of Health (U.S.) (Grant 1DP2OD007292) ; National Institutes of Health (U.S.) (Grant 1R01EB018659) ; United States. Office of Naval Research (Award N000141110914) ; United States. Office of Naval Research (Grant N000141010827) ; United States. Office of Naval Research (Grant N000141310593) ; United States. Office of Naval Research (Grant N000141410610) ; United States. Office of Naval Research (Grant N000141612410) ; National Science Foundation (U.S.) (Grant CCF1054898) ; National Science Foundation (U.S.) (Grant CCF1317291) ; National Science Foundation (U.S.) (Grant CCF1162459) ; United States. Defense Advanced Research Projects Agency (Grant HR001112C0061) ; Defense Threat Reduction Agency (DTRA) (Grant HDTRA1-15-1-0040)

The extensive data from the Blairet al.(1)epidemiology study of occupational acrylonitrile exposure among 25,460 workers in eight plants in the United States provide an excellent opportunity to update quantitative risk assessments for this widely used commodity chemical. We employ the semiparametric Cox relative risk (RR) regression model with a cumulative exposure metric to model cause‐specific mortality from lung cancer and all other causes. The separately estimated cause‐specific cumulative hazards are then combined to provide an overall estimate of age‐specific mortality risk. Age‐specific estimates of the additional risk of lung cancer mortality associated with several plausible occupational exposure scenarios are obtained. For age 70, these estimates are all markedly lower than those generated with the cancer potency estimate provided in the USEPA acrylonitrile risk assessment.(2)This result is consistent with the failure of recent occupational studies to confirm elevated lung cancer mortality among acrylonitrile‐exposed workers as was originally reported by O'Berg,(3)and it calls attention to the importance of using high‐quality epidemiology data in the risk assessment process.

A call for risk assessment approaches that better characterize and quantify uncertainty has been made by the scientific and regulatory community. This paper responds to that call by demonstrating a distributional approachthat draws upon human data to derive potency estimates and to identify and quantify important sources of uncertainty. The approach is rooted in the science of decision analysis and employs an influence diagram, a decision tree, probabilistic weights, and a distribution of point estimates of carcinogenic potency. Its results estimate the likelihood of different carcinogenicrisks (potencies) for a chemical under a specific scenario. For this exercise, human data on formaldehyde were employed to demonstrate the approach. Sensitivity analyses were performed to determine the relative impact of specific levels and alternatives on the potency distribution. The resulting potency estimates are compared with the results of an exercise using animal data on formaldehyde. The paper demonstrates that distributional risk assessment is readily adapted to situations in which epidemiologic data serve as the basis for potency estimates. Strengths and weaknesses of the distributional approach are discussed. Areas for further application and research are recommended.