Hospital outbreak of carbapenem-resistant Enterobacterales associated with a bla OXA-48 plasmid carried mostly by Escherichia coli ST399

A hospital outbreak of carbapenem-resistant Enterobacterales was detected by routine surveillance. Whole genome sequencing and subsequent analysis revealed a conserved promiscuous blaOXA-48 carrying plasmid as the defining factor within this outbreak. Four different species of Enterobacterales were involved in the outbreak. Escherichia coli ST399 accounted for 35 of all the 55 isolates. Comparative genomics analysis using publicly available E. coli ST399 genomes showed that the outbreak E. coli ST399 isolates formed a unique clade. We developed a mathematical model of pOXA-48-like plasmid transmission between host lineages and used it to estimate its conjugation rate, giving a lower bound of 0.23 conjugation events per lineage per year. Our analysis suggests that co-evolution between the pOXA-48-like plasmid and E. coli ST399 could have played a role in the outbreak. This is the first study to report carbapenem-resistant E. coli ST399 carrying blaOXA-48 as the main cause of a plasmid-borne outbreak within a hospital setting. Our findings suggest complementary roles for both plasmid conjugation and clonal expansion in the emergence of this outbreak

Improved ring potential of QED at finite temperature and in the presence of weak and strong magnetic field

Using the general structure of the vacuum polarization tensor $\Pi_{\mu\nu}(k_{0},\mathbf{k})$ in the infrared (IR) limit, $k_{0}\to 0$, the ring contribution to QED effective potential at finite temperature and non-zero magnetic field is determined beyond the static limit, $(k_{0}\to 0,\mathbf{k}\to \mathbf{0})$. The resulting ring potential is then studied in weak and strong magnetic field limit. In the limit of weak magnetic field, at high temperature and for $\alpha\to 0$, the improved ring potential consists of a term proportional to $T^{4}\alpha^{5/2}$, in addition to the expected $T^{4}\alpha^{3/2}$ term arising from the static limit. Here, $\alpha$ is the fine structure constant. In the limit of strong magnetic field, where QED dynamics is dominated by the lowest Landau level (LLL), the ring potential includes a novel term consisting of dilogarithmic function $(eB){Li}_{2}(-\frac{2\alpha}{\pi}\frac{eB}{m^{2}})$. Using the ring improved (one-loop) effective potential including the one-loop effective potential and ring potential in the IR limit, the dynamical chiral symmetry breaking of QED is studied at finite temperature and in the presence of strong magnetic field. The gap equation, the dynamical mass and the critical temperature of QED in the regime of LLL dominance are determined in the improved IR as well as in the static limit. For a given value of magnetic field, the improved ring potential is shown to be more efficient in decreasing the critical temperature arising from one-loop effective potential.Comment: V1: 39 pages, 2 figures, 2 tables, LaTeX format; V2: 53 pages, 3 figures, 1 table, Sect. IV revised, results are unchanged, 3 appendices and references added, version accepted for publication in Phys. Rev.

FILTWAM and Voice Emotion Recognition

This paper introduces the voice emotion recognition part of our framework for improving learning through webcams and microphones (FILTWAM). This framework enables multimodal emotion recognition of learners during game-based learning. The main goal of this study is to validate the use of microphone data for a real-time and adequate interpretation of vocal expressions into emotional states were the software is calibrated with end users. FILTWAM already incorporates a valid face emotion recognition module and is extended with a voice emotion recognition module. This extension aims to provide relevant and timely feedback based upon learner's vocal intonations. The feedback is expected to enhance learner’s awareness of his or her own behavior. Six test persons received the same computer-based tasks in which they were requested to mimic specific vocal expressions. Each test person mimicked 82 emotions, which led to a dataset of 492 emotions. All sessions were recorded on video. An overall accuracy of our software based on the requested emotions and the recognized emotions is a pretty good 74.6% for the emotions happy and neutral emotions; but will be improved for the lower values of an extended set of emotions. In contrast with existing software our solution allows to continuously and unobtrusively monitor learners’ intonations and convert these intonations into emotional states. This paves the way for enhancing the quality and efficacy of game-based learning by including the learner's emotional states, and links these to pedagogical scaffolding.The Netherlands Laboratory for Lifelong Learning (NELLL) of the Open University of the Netherlands

Improved Multimodal Emotion Recognition for Better Game-Based Learning

What is this research about? What is the target group? Why this research? How to do this research? What have been done so far? Future direction.The Netherlands Laboratory for Lifelong Learning (NELLL) of the Open University of the Netherlands