Photoacoustic‐Guided Surgery with Indocyanine Green‐Coated Superparamagnetic Iron Oxide Nanoparticle Clusters

A common cause of local tumor recurrence in brain tumor surgery results from incomplete surgical resection. Adjunctive technologies meant to facilitate gross total resection have had limited efficacy to date. Contrast agents used to delineate tumors preoperatively cannot be easily or accurately used in the real‐time operative setting. Although multimodal imaging contrast agents are developed to help the surgeon discern tumor from normal tissue in the operating room, these contrast agents are not readily translatable. This study has developed a novel contrast agent comprised solely of two Food and Drug Administration approved components, indocyanine green (ICG) and superparamagnetic iron oxide (SPIO) nanoparticles—with no additional amphiphiles or carrier materials, to enable preoperative detection by magnetic resonance (MR) imaging and intraoperative photoacoustic (PA) imaging. The encapsulation efficiency of both ICG and SPIO within the formulated clusters is ≈100%, and the total ICG payload is 20–30% of the total weight (ICG + SPIO). The ICG–SPIO clusters are stable in physiologic conditions; can be taken up within tumors by enhanced permeability and retention; and are detectable by MR. In a preclinical surgical resection model in mice, following injection of ICG–SPIO clusters, animals undergoing PA‐guided surgery demonstrate increased progression‐free survival compared to animals undergoing microscopic surgery.     

A primary influence vertex approach to identify driving factors in complex integrated agri-industrial systems – an example from sugarcane supply and processing systems

Integrated agri-industrial systems (IAISs), such as sugarcane supply and processing systems, are complex systems and hence generally difficult to understand and manage. The large number factors in IAISs coupled with the complex interrelationships among the factors make it challenging to identify the points of intervention for improving their overall performance. Several approaches, such as the network theory and the Theory of Constraints have been used to identify important factors in systems with variations in success. This paper demonstrates a primary influence vertex approach for identifying and ranking the factors that drive the performance of IAISs. The approach is based on comprehensive causal network analyses and was tested in four relatively diverse large-scale sugarcane milling operations in South Africa. Results from the analyses were found to be consistent with the literature and external knowledge of the milling areas as at the time of the study. It is concluded that the approach can proffer a sound basis from which deeper rooted problems in systems can be identified on an ongoing basis. It is, however, recommended that the approach should be systematically compared with other relevant methods that are used to analyse complex systems.     

Lightly supervised word-sense translation-error detection and resolution in an interactive conversational spoken language translation system

Lexical ambiguity can cause critical failure in conversational spoken language translation (CSLT) systems that rely on statistical machine translation (SMT) if the wrong sense is presented in the target language. Interactive CSLT systems offer the capability to detect and pre-empt such word-sense translation errors (WSTEs) by engaging the human operators in a precise clarification dialogue aimed at resolving the problem. This paper presents an end-to-end framework for accurate detection and interactive resolution of WSTEs to minimize communication errors due to ambiguous source words. We propose (a) a novel, extensible, two-level classification architecture for identifying potential WSTEs in SMT hypotheses; (b) a constrained phrase-pair clustering mechanism for identifying the translated sense of ambiguous source words in SMT hypotheses; and (c) an interactive strategy that integrates this information to request specific clarifying information from the operator. By leveraging unsupervised and lightly supervised learning techniques, our approach minimizes the need for expensive human annotation in developing each component of this framework. Each component, as well as the overall framework, was evaluated in the context of an interactive English-to-Iraqi Arabic CSLT system.     

Speaking their Mind: Populist Style and Antagonistic Messaging in the Tweets of Donald Trump,Narendra Modi,Nigel Farage,and Geert Wilders

The authors in this study examined the function and public reception of critical tweeting in online campaigns of four nationalist populist politicians during major national election campaigns. Using a mix of qualitative coding and case study inductive methods, we analyzed the tweets of Narendra Modi, Nigel Farage, Donald Trump, and Geert Wilders before the 2014 Indian general elections, the 2016 UK Brexit referendum, the 2016 US presidential election, and the 2017 Dutch general election, respectively. Our data show that Trump is a consistent outlier in terms of using critical language on Twitter when compared to Wilders, Farage, and Modi, but that all four leaders show significant investment in various forms of antagonistic messaging including personal insults, sarcasm, and labeling, and that these are rewarded online by higher retweet rates. Building on the work of Murray Edelman and his notion of a political spectacle, we examined Twitter as a performative space for critical rhetoric within the frame of nationalist politics. We found that cultural and political differences among the four settings also impact how each politician employs these tactics. Our work proposes that studies of social media spaces need to bring normative questions into traditional notions of collaboration. As we show here, political actors may benefit from in-group coalescence around antagonistic messaging, which while serving as a call to arms for online collaboration for those ideologically aligned, may on a societal level lead to greater polarization.     

Network-analysis approaches to deal with causal complexity in a supply network

Large integrated supply networks can exhibit several complex system characteristics. In such systems, researchers tend to misperceive feedback relationships and have difficulty in identifying dynamic causal behaviour, even when they have an understanding of the underlying structural relationships within a system. Supply-network researchers often make use of simulation models, but this is only appropriate if a high degree of knowledge concerning the supply network is available. Many disciplines, including a limited number of supply network researchers, have used network analyses to represent complex systems, and several advanced graph theory techniques exist to support such studies. The aim of this paper is to demonstrate the use of network-analysis approaches in order to analyse supply networks. The research was carried out in four case-study areas within a sugarcane-production-and-processing environment and demonstrates two network-analysis approaches. Semi-structured interviews with stakeholders from different sectors were carried out, and issues (or problems) in the supply network were incorporated into a single coherent network. An energy-transformation approach as well as transitivity produced valuable information. A cause-and-effect network-analysis approach could depict suitable key performance indicators as well as leverage points within the supply network. These methodologies enable researchers to achieve a high degree of understanding in a relatively short time span. The analyses of the supply network occur at a higher degree of abstraction, hence obviating any need to model and understand the intricate detail of the system before any conclusion can be reached.