Zinc Transporters YbtX and ZnuABC Are Required for the Virulence of \u3cem\u3eYersinia pestis\u3c/em\u3e in Bubonic and Pneumonic Plague in Mice

A number of bacterial pathogens require the ZnuABC Zinc (Zn2+) transporter and/or a second Zn2+ transport system to overcome Zn2+ sequestration by mammalian hosts. Previously we have shown that in addition to ZnuABC, Yersinia pestis possesses a second Zn2+ transporter that involves components of the yersiniabactin (Ybt), siderophore-dependent iron transport system. Synthesis of the Ybt siderophore and YbtX, a member of the major facilitator superfamily, are both critical components of the second Zn2+ transport system. Here we demonstrate that a ybtX znu double mutant is essentially avirulent in mouse models of bubonic and pneumonic plague while a ybtX mutant retains high virulence in both plague models. While sequestration of host Zn is a key nutritional immunity factor, excess Zn appears to have a significant antimicrobial role in controlling intracellular bacterial survival. Here, we demonstrate that ZntA, a Zn2+ exporter, plays a role in resistance to Zn toxicity in vitro, but that a zntA zur double mutant retains high virulence in both pneumonic and bubonic plague models and survival in macrophages. We also confirm that Ybt does not directly bind Zn2+in vitro under the conditions tested. However, we detect a significant increase in Zn2+-binding ability of filtered supernatants from a Ybt+ strain compared to those from a strain unable to produce the siderophore, supporting our previously published data that Ybt biosynthetic genes are involved in the production of a secreted Zn-binding molecule (zincophore). Our data suggest that Ybt or a modified Ybt participate in or promote Zn-binding activity in culture supernatants and is involved in Zn acquisition in Y. pestis

Social media and sensemaking patterns in new product development: demystifying the customer sentiment

Artificial intelligence by principle is developed to assist but also support decision making processes. In our study, we explore how information retrieved from social media can assist decision-making processes for new product development (NPD). We focus on consumers’ emotions that are expressed through social media and analyse the variations of their sentiments in all the stages of NPD. We collect data from Twitter that reveal consumers’ appreciation of aspects of the design of a newly launched model of an innovative automotive company. We adopt the sensemaking approach coupled with the use of fuzzy logic for text mining. This combinatory methodological approach enables us to retrieve consensus from the data and to explore the variations of sentiments of the customers about the product and define the polarity of these emotions for each of the NPD stages. The analysis identifies sensemaking patterns in Twitter data and explains the NPD process and the associated steps where the social interactions from customers can have an iterative role. We conclude the paper by outlining an agenda for future research in the NPD process and the role of the customer opinion through sensemaking mechanisms

A Very High-Order Accurate Staggered Finite Volume Scheme for the Stationary Incompressible Navier–Stokes and Euler Equations on Unstructured Meshes

International audienceWe propose a sixth-order staggered finite volume scheme based on polynomial reconstructions to achieve high accurate numerical solutions for the incompressible Navier-Stokes and Euler equations. The scheme is equipped with a fixed-point algorithm with solution relaxation to speed-up the convergence and reduce the computation time. Numerical tests are provided to assess the effectiveness of the method to achieve up to sixth-order con-2 Ricardo Costa et al. vergence rates. Simulations for the benchmark lid-driven cavity problem are also provided to highlight the benefit of the proposed high-order scheme

A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data

Factors influencing user acceptance of public sector big open data

In recent years Government departments and public/private organizations are becoming increasingly transparent with their data to establish the whole new paradigm of big open data. Increasing research interest arises from the claimed usability of big open data in improving public sector reforms, facilitating innovation, improving supplier and distribution networks and creating resilient supply chains that help improve the efficiency of public services. Despite the advantages of big open data for supply chain and operations management, there is severe shortage of empirical analyses in this field, especially with regards to its acceptance. To address this gap, in this paper we use an extended Technology Acceptance Model (TAM) to empirically examine the factors affecting users’ behavioural intentions towards public sector big open data. We outline the importance of our model for operations and supply chain managers, the limitations of the study, and future research directions

Impact of internet of things (IoT) in disaster management: a task-technology fit perspective

YesDisaster management aims to mitigate the potential damage from the disasters, ensure immediate and suitable assistance to the victims, and attain effective and rapid recovery. These objectives require a planned and effective rescue operation post such disasters. Different types of information about the impact of the disaster are, hence, required for planning an effective and immediate relief operation. The IoT technology available today is quite mature and has the potential to be very useful in disaster situations. This paper analyzes the requirements for planning rescue operation for such natural disasters and proposes an IoT based solution to cater the identified requirements. The proposed solution is further validated using the task-technology fit (TTF) approach for analyzing the significance of the adoption of IoT technology for disaster management. Results from the exploratory study established the core dimensions of the task requirements and the TTF constructs. Results from the confirmatory factor analysis using PLS path modelling, further, suggest that both task requirements and IoT technology have significant impact on the IoT TTF in the disaster management scenario. This paper makes significant contributions in the development of appropriate constructs for modeling TTF for IoT Technology in the context of disaster management