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Owing to their ultra-high accelerating gradients, combined with injection inside micrometer-scale accelerating wakefield buckets, plasma-based accelerators hold great potential to drive a new generation of free-electron lasers (FELs). Indeed, the first demonstration of plasma-driven FEL gain was reported recently, representing a major milestone for the field. Several groups around the world are pursuing these novel light sources, with methodology varying in the use of wakefield driver (laser-driven or beam-driven), plasma structure, phase-space manipulation, beamline design, and undulator technology, among others. This paper presents our best attempt to provide a comprehensive overview of the global community efforts towards plasma-based FEL research and development.
Coronavirus disease 2019 (COVID-19) has rapidly spread globally, forcing countries to apply lockdowns and strict social distancing measures. The aim of this study was to assess eating habits and lifestyle behaviours among residents of the Middle East and North Africa (MENA) region during the lockdown. A cross-sectional study among adult residents of the MENA region was conducted using an online questionnaire designed on Google Forms during April 2020. A total of 2970 participants from eighteen countries participated in the present study. During the pandemic, over 30 % reported weight gain, 6·2 % consumed five or more meals per d compared with 2·2 % before the pandemic (P < 0·001) and 48·8 % did not consume fruits on a daily basis. Moreover, 39·1 % did not engage in physical activity, and over 35 % spent more than 5 h/d on screens. A significant association between the frequency of training during the pandemic and the reported change in weight was found (P < 0·001). A significantly higher percentage of participants reported physical and emotional exhaustion, irritability and tension either all the time or a large part of the time during the pandemic (P < 0·001). Although a high percentage of participants reported sleeping more hours per night during the pandemic, 63 % had sleep disturbances. The study highlights that the lockdown due to the COVID-19 pandemic caused a variety of lifestyle changes, physical inactivity and psychological problems among adults in the MENA region.
The objective of this study was to describe the epidemiology of COVID-19 in Nigeria with a view of generating evidence to enhance planning and response strategies. A national surveillance dataset between 27 February and 6 June 2020 was retrospectively analysed, with confirmatory testing for COVID-19 done by real-time polymerase chain reaction (RT-PCR). The primary outcomes were cumulative incidence (CI) and case fatality (CF). A total of 40 926 persons (67% of total 60 839) had complete records of RT-PCR test across 35 states and the Federal Capital Territory, 12 289 (30.0%) of whom were confirmed COVID-19 cases. Of those confirmed cases, 3467 (28.2%) had complete records of clinical outcome (alive or dead), 342 (9.9%) of which died. The overall CI and CF were 5.6 per 100 000 population and 2.8%, respectively. The highest proportion of COVID-19 cases and deaths were recorded in persons aged 31–40 years (25.5%) and 61–70 years (26.6%), respectively; and males accounted for a higher proportion of confirmed cases (65.8%) and deaths (79.0%). Sixty-six per cent of confirmed COVID-19 cases were asymptomatic at diagnosis. In conclusion, this paper has provided an insight into the early epidemiology of COVID-19 in Nigeria, which could be useful for contextualising public health planning.
Convolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed tomography images.
Consecutive high-resolution computed tomography scans of the paranasal sinuses were retrospectively collected between January 2016 and December 2018 at a tertiary rhinology hospital in Australia. The classification layer of Inception-V3 was retrained in Python using a transfer learning method to interpret the computed tomography images. Segmentation analysis was also performed in an attempt to increase diagnostic accuracy.
The trained convolutional neural network was found to have diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0–89.0 per cent) with an area under the curve of 0.93.
A trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.
Deep learning using convolutional neural networks represents a form of artificial intelligence where computers recognise patterns and make predictions based upon provided datasets. This study aimed to determine if a convolutional neural network could be trained to differentiate the location of the anterior ethmoidal artery as either adhered to the skull base or within a bone ‘mesentery’ on sinus computed tomography scans.
Coronal sinus computed tomography scans were reviewed by two otolaryngology residents for anterior ethmoidal artery location and used as data for the Google Inception-V3 convolutional neural network base. The classification layer of Inception-V3 was retrained in Python (programming language software) using a transfer learning method to interpret the computed tomography images.
A total of 675 images from 388 patients were used to train the convolutional neural network. A further 197 unique images were used to test the algorithm; this yielded a total accuracy of 82.7 per cent (95 per cent confidence interval = 77.7–87.8), kappa statistic of 0.62 and area under the curve of 0.86.
Convolutional neural networks demonstrate promise in identifying clinically important structures in functional endoscopic sinus surgery, such as anterior ethmoidal artery location on pre-operative sinus computed tomography.
Do the various forms of literary theory - deconstruction, Marxism, new historicism, feminism, post-colonialism, and cultural/digital studies - have anything in common? If so, what are the fundamental principles of theory? What is its ideological orientation? Can it still be of use to us in understanding basic intellectual and ethical dilemmas of our time? These questions continue to perplex both students and teachers of literary theory. Habib finds the answers in theory's largely unacknowledged roots in the thought of German philosopher Hegel. Hegel's insights continue to frame the very terms of theory to this day. Habib explains Hegel's complex ideas and how they have percolated through the intellectual history of the last century. This book will interest teachers and students of literature, literary theory and the history of ideas, illuminating how our modern world came into being, and how we can better understand the salient issues of our own time.