Carpenterkirkegaard4842

Responsible implementation of engineered nanomaterials (ENMs) into commercial applications is an important societal issue, driving demand for new approaches for rapid and comprehensive evaluation of their bioactivity and safety. An essential part of any research focused on identifying potential hazards of ENMs is the appropriate selection of biological endpoints to evaluate. Herein, we use a tiered strategy employing both targeted biological assays and untargeted quantitative proteomics to elucidate the biological responses of human THP-1 derived macrophages across a library of metal/metal oxide ENMs, raised as priority ENMs for investigation by NIEHS's Nanomaterial Health Implications Research (NHIR) program. Our results show that quantitative cellular proteome profiles readily distinguish ENM types based on their cytotoxic potential according to induction of biological processes and pathways involved in the cellular antioxidant response, TCA cycle, oxidative stress, endoplasmic reticulum stress, and immune diverse set of cellular pathways and biological processes impacted by ENM exposure in an important immune cell type, laying the foundation for multivariate, pathway-level structure activity assessments of ENMs in the future.Despite the increasing prevalence of engineered nanomaterials (ENMs) in consumer products, their toxicity profiles remain to be elucidated. ENM physicochemical characteristics (PCC) are known to influence ENM behavior, however the mechanisms of these effects have not been quantified. Further confounding the question of how the PCC influence behavior is the inclusion of structural and molecular descriptors in modeling schema that minimize the effects of PCC on the toxicological endpoints. In this work, we analyze ENM physico-chemical measurements that have not previously been studied within a developmental toxicity framework using an embryonic zebrafish model. In testing a panel of diverse ENMs to build a consensus model, we found nonlinear relationships between any singular PCC and bioactivity. By using a machine learning (ML) method to characterize the information content of combinatorial PCC sets, we found that concentration, surface area, shape, and polydispersity can accurately capture the developmental toxicity profile of ENMs with consideration to whole-organism effects.The characterization of cellulose-based nanomaterial (CNM) suspensions in environmental and biological media is impaired because of their high carbon content and anisotropic shape, thus making it difficult to derive structure activity relationships (SAR) in toxicological studies. Here, a standardized method for the dispersion preparation and characterization of cellulose nanofibrils (CNF) and nanocrystals (CNC) in biological and environmental media was developed. Specifically, electron microscopy was utilized and allowed to specify optimum practices for efficiently suspending CNF and CNC in water and cell culture medium. Furthermore, a technique for measuring the in vitro particle kinetics of CNF and CNC suspended in cell culture medium utilizing fluorescently tagged materials was developed to assess the delivery rate of such CNM at the bottom of the well. Interestingly, CNF were shown to settle and create a loosely packed layer at the bottom of cell culture wells within a few hours. On the contrary, CNC settled gradually at a significantly slower rate, highlighting the discordance between administered and delivered mass dose. This work is both novel and urgent in the field of environmental health and safety as it introduces well-defined techniques for the dispersion and characterization of emerging, cellulose-based engineered nanomaterials. It also provides useful insights to the in vitro behavior of suspended anisotropic nanomaterials in general, which should enable dosimetry and comparison of toxicological data across laboratories as well as promote the safe and sustainable use of nanotechnology.Participatory systems thinking methods are often used in community-based participatory research to engage and respond to complexity. Participation in systems thinking activities creates opportunities for participants to gain useful insights about complexity. It is desirable to design activities that extend the benefits of this participation into communities, as these insights are predictive of success in community-based prevention. This study tests an online, computer-mediated participatory system modelling platform (STICKE) and associated methods for collating and analysing its outputs. STICKE was trialled among a group of community members to test a computer-mediated system modelling exercise. The causal diagrams resulting from the exercise were then merged, and network analysis and DEMATEL methods applied to inform the generation of a smaller summary model to communicate insights from the participant group as a whole. Participants successfully completed the online modelling activity, and created causal diagrams consistent with expectations. The DEMATEL analysis was identified as the participant-preferred method for converging individuals causal diagrams into a coherent and useful summary. STICKE is an accessible tool that enabled participants to create causal diagrams online. Methods trialled in this study provide a protocol for combining and summarising individual causal diagrams that was perceived to be useful by the participant group. STICKE supports communities to consider and respond to complex problems at a local level, which is cornerstone of sustainable effective prevention. Understanding how communities perceive their own health challenges will be important to better support and inform locally owned prevention efforts. © The Author(s) 2020.Likelihood-free inference for simulator-based models is an emerging methodological branch of statistics which has attracted considerable attention in applications across diverse fields such as population genetics, astronomy and economics. selleck compound Recently, the power of statistical classifiers has been harnessed in likelihood-free inference to obtain either point estimates or even posterior distributions of model parameters. Here we introduce PYLFIRE, an open-source Python implementation of the inference method LFIRE (likelihood-free inference by ratio estimation) that uses penalised logistic regression. PYLFIRE is made available as part of the general ELFI inference software http//elfi.ai to benefit both the user and developer communities for likelihood-free inference. Copyright © 2019 Kokko J et al.