COVID-19 Mixed Impact on Hospital Antimicrobial Stewardship Activities: A Qualitative Study in UK-Based Hospitals

Antimicrobial resistance (AMR) is a well-known global threat due to the subsequent increase in antimicrobial usage. Several antimicrobial stewardship (AMS) strategies have been implemented to curb irrational prescribing and reduce the AMR burden. However, since the beginning of the COVID-19 pandemic, it has enormously impacted the healthcare system and jeopardized public health, causing millions of deaths globally. Our semi-structured qualitative study aimed to explore the impact of COVID-19 on AMS activities in the UK hospitals. Seventeen interviews were conducted with health care professionals who were part of AMS teams (consultant medical microbiologists, infectious disease consultants, antimicrobial pharmacists). Interviews were audio-recorded and transcribed. An inductive thematic framework was adopted to analyse and create the themes. After agreement of the hierarchical framework definition, all transcripts were coded accordingly. Four main themes and 15 sub-themes were identified. These main themes were: (1) AMS activities or strategies before and during the pandemic; (2) challenges to implementing AMS activities before and during the pandemic; (3) information from public authorities on AMS during the pandemic; and (4) new AMS activities/strategies adopted during the pandemic. Staff vacancies, redeploying of AMS staff to other duties and meeting the burden related to the COVID-19 and lack of resources were the most frequently identified contributing factors to withheld AMS activities during the pandemic. However, modifications to the hybrid working environment, i.e., remote or flexible working, allowed for resumption of AMS activities including virtual ward rounds, virtual meetings and other activities. Further research needs to assess the impact of the hybrid delivery system on AMS activities

A ferromagnetic Eu-Pt surface compound grown below hexagonal boron nitride

One of the fundamental applications for monolayer-thick 2D materials is their use as protective layers of metal surfaces and in situ intercalated reactive materials in ambient conditions. Here we investigate the structural, electronic, and magnetic properties, as well as the chemical stability in air of a very reactive metal, Europium, after intercalation between a hexagonal boron nitride (hBN) layer and a Pt substrate. We demonstrate that Eu intercalation leads to a hBN-covered ferromagnetic EuPt2 surface alloy with divalent Eu2+ atoms at the interface. We expose the system to ambient conditions and find a partial conservation of the di-valent signal and hence the Eu-Pt interface. The use of a curved Pt substrate allows us to explore the changes in the Eu valence state and the ambient pressure protection at different substrate planes. The interfacial EuPt2 surface alloy formation remains the same, but the resistance of the protecting hBN layer to ambient conditions is reduced, likely due to a rougher surface and a more discontinuous hBN coating

Fungal systematics and evolution : FUSE 6

Fungal Systematics and Evolution (FUSE) is one of the journal series to address the “fusion” between morphological data and molecular phylogenetic data and to describe new fungal taxa and interesting observations. This paper is the 6th contribution in the FUSE series—presenting one new genus, twelve new species, twelve new country records, and three new combinations. The new genus is: Pseudozeugandromyces (Laboulbeniomycetes, Laboulbeniales). The new species are: Albatrellopsis flettioides from Pakistan, Aureoboletus garciae from Mexico, Entomophila canadense from Canada, E. frigidum from Sweden, E. porphyroleucum from Vietnam, Erythrophylloporus flammans from Vietnam, Marasmiellus boreoorientalis from Kamchatka Peninsula in the Russian Far East, Marasmiellus longistipes from Pakistan, Pseudozeugandromyces tachypori on Tachyporus pusillus (Coleoptera, Staphylinidae) from Belgium, Robillarda sohagensis from Egypt, Trechispora hondurensis from Honduras, and Tricholoma kenanii from Turkey. The new records are: Arthrorhynchus eucampsipodae on Eucampsipoda africanum (Diptera, Nycteribiidae) from Rwanda and South Africa, and on Nycteribia vexata (Diptera, Nycteribiidae) from Bulgaria; A. nycteribiae on Eucampsipoda africanum from South Africa, on Penicillidia conspicua (Diptera, Nycteribiidae) from Bulgaria (the first undoubtful country record), and on Penicillidia pachymela from Tanzania; Calvatia lilacina from Pakistan; Entoloma shangdongense from Pakistan; Erysiphe quercicola on Ziziphus jujuba (Rosales, Rhamnaceae) and E. urticae on Urtica dioica (Rosales, Urticaceae) from Pakistan; Fanniomyces ceratophorus on Fannia canicularis (Diptera, Faniidae) from the Netherlands; Marasmiellus biformis and M. subnuda from Pakistan; Morchella anatolica from Turkey; Ophiocordyceps ditmarii on Vespula vulgaris (Hymenoptera, Vespidae) from Austria; and Parvacoccum pini on Pinus cembra (Pinales, Pinaceae) from Austria. The new combinations are: Appendiculina gregaria, A. scaptomyzae, and Marasmiellus rodhallii. Analysis of an LSU dataset of Arthrorhynchus including isolates of A. eucampsipodae from Eucampsipoda africanum and Nycteribia spp. hosts, revealed that this taxon is a complex of multiple species segregated by host genus. Analysis of an SSU–LSU dataset of Laboulbeniomycetes sequences revealed support for the recognition of four monophyletic genera within Stigmatomyces sensu lato: Appendiculina, Fanniomyces, Gloeandromyces, and Stigmatomyces sensu stricto. Finally, phylogenetic analyses of Rhytismataceae based on ITS–LSU ribosomal DNA resulted in a close relationship of Parvacoccum pini with Coccomyces strobi.http://www.sydowia.at/index.htmpm2021Medical Virolog

An eXtreme Gradient Boosting Method for Identifying the Factors Contributing to Crash/Near-Crash Events: A Naturalistic Driving Study

Despite the research efforts for reducing traffic accidents, the number of global annual vehicle accidents is still on the rise. This continues to motivate researchers to examine the factors contributing to Crash and Near-Crash events (CNC). Recently, many studies attempted to identify the associated crash factors using Naturalistic Driving Study (NDS-SHRP2) data. Despite the many classifiers developed in the literature, the high dimensionality and multicollinearity within the NDS-SHRP2 data limit the accuracy and reliability of the developed models. This study develops an eXtreme Gradient Boosting (XGB) classifier, robust to multicollinearity, using the NDS-SHRP2 dataset for identifying the factors contributing to CNC events. The performance of the XGB classifier is evaluated against three other advanced machine-learning algorithms. Results indicate that the XGB model outperformed the other models with a detection accuracy of 85% and identified the “Driver Behaviour” and “Intersection Influence” as the most contributing factors to CNC detection.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Analysis of Queue Estimation Process at Signalized Intersections Under Low Connected Vehicle Penetration Rates

This study investigates the factors affecting estimation accuracy of queue length at signalized intersections under low penetration of connected vehicles. A shockwave-based algorithm is proposed to estimate the maximum queue length and residual queue on a cycle-by-cycle basis. Simulation data collected from three consecutive signalized intersections were used to extract trajectories of CVs under five different market penetration rates and two different traffic conditions (under-saturated and moderate). The results confirm that the queue length estimation process is probabilistic and affected by the stochastic changes in traffic conditions. This probabilistic nature is defined by a queue formation coverage index (QI) that proved to significantly affect the queue length estimation accuracy. Overall, the results show that the queue estimates accuracy is acceptable when a QI value of at least 50% is achieved. In such limited data environments, the QI showed the potential to help as an assessment tool to evaluate the obtained queue estimates.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Automated new license plate recognition in Egypt

License plate recognition (LPR) was an effective form of Automatic Vehicle Identification (AVI) systems. In this paper, a new and simple technique was presented for Egyptian vehicle’s LPR system. The proposed technique consists of three major parts: Extraction of plate region, recognition of plate characters, and database communication. A video stream was one of the most important advantages of this system. The real-time was capability, and that it did not require any additional sensor input such as infrared sensors. This approach provided a good direction and performance for Automated New License Plate Recognition in Egypt

A distraction index for quantification of driver eye glance behavior: A study using SHRP2 NEST database

Distracted driving behavior and driving inattention are two leading causes of roadway crashes. The state-of-the-art safety research made several attempts to understand and quantify distracted driving and driver inattention. While each attempt had its limitation, there was a consensus on the relevance of eye glance behavior as a promising parameter in understanding distracted driving. In this study, a renewal cycle approach is implemented to provide deeper insights into how drivers allocate their attention while driving. This approach is then applied to the Naturalistic Engagement in Secondary Tasks (NEST) dataset to analyze drivers’ eye glance patterns and determine the relationship between their visual behavior and engagement in different types of secondary tasks (activities performed while driving). The analysis revealed that distracted driving behavior could be well characterized by two new measures: the number of renewal cycles per event (NRC) and a distraction level index (DI). Consequently, mixed-effects modeling is implemented to test the effectiveness of the two measures to differentiate crash/near-crash events from non-crash events. The analysis showed that the two measures increase significantly for crash/near-crash events compared to non-crash driving events with p-values less than 0.0001. The findings of this paper are promising to the quantification of the risk associated with distraction related visual behavior. The finding can also help build reliable algorithms for in-vehicle driving assistance systems to alert drivers before crash/near-crash events