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Modified vaccinia virus Ankara (MVA) is an attenuated vaccinia virus with restricted replication in human cells. The virus serves as an ideal vaccine vector suitable for safe use even in immune-compromised individuals. With its inherently large packaging capacity, expression cassettes encoding bulky genes can be inserted into deletion regions within the MVA genome. These deletion sites develop during the process of the attenuation of the virus by passage in Chicken Embryo Fibroblasts (pCEFs). Transgene stability in MVA is important to assure immunogenicity and efficacy. In the present study, we assessed the effect of substantial passage of recombinant MVA vectors on the stability of expression cassette encoding SIV Gag/Tat genes inserted at the Del-II site, as part of generating a vaccine to protect from HIV. Our data indicated that after 15 passages there was a significant loss or mutation of the inserted genes.

Building Information Modeling (BIM) is mainly used for the design and construction of new buildings. However, one of the main challenges facing BIM adoption for existing or old buildings constructed without consideration for their modeling is obtaining raw and accurate building data and converting it to the BIM model. Based on this challenge, this study develops a framework that uses different data capture techniques for existing buildings, then transforms this captured data into 3D BIM models through which it can improve its management processes as facilities during their service life. Specifically, 3D laser scanning techniques were used to capture indoor building data, while drones equipped with high-definition cameras (UAV) were used to capture outdoor building data such as geometric dimensions, material shapes, surfaces and various shapes. Then the captured data was converted into a high-resolution 3D model. This has been verified the accuracy of this engineering model by converting the two-dimensional engineering plans to three-dimensional through the well-known engineering design tools. Moreover, the 3D model has been integrated with the Web Based Building Management System (BMS) platform. This research framework helps in developing engineering facility management processes and modern digital transformation processes that aim at speeding up facility management during their service period using modern technologies. This study aims to develop current knowledge in the construction industry and facility management by reviewing a comprehensive understanding of how to use modern technologies to obtain various information to develop BIM models for existing and non-modeled buildings in order to exploit and integrate them with advanced facility management systems and platforms, which constitutes a major progress in this field allowing to save a lot of effort and money in the future.

Heart Rate Variability (HRV) is a well-known method to assess the human Autonomic Nervous System (ANS) functions. Currently, in the health care information system, HRV properties are very useful to provide medical and other vital signs of the patients. The HRV assessment was derived using the ECG R-R beat interval series, however, using the ECG probes with the electrode’s gel is not convenient for certain people such as who has an allergic symptom and or probes localization in front of the body part that needs certain knowledge how to put it on the correct location. Photoplethysmography (PPG) was considered to be an alternative way to assess HRV using the Peak-to-Peak interval series (PPI). Since previous studies already reported the availability to use PPG to assess HRV instead ECG, this study investigated how the HRV properties using those two methods during different postures with a fixed breathing pattern. Ten subjects participated in this study and we observed the R-R and the Peak-Peak beat interval time series from ECG and PPG respectively during sitting-supine, and standing-supine postures with 3 seconds breathing pattern. The results showed that using PPG to assess HRV still can be used with caution, especially during supine posture that had a deviation (SD) difference between ECG and PPG from the inter-subject analysis (p

Background: The rise of new SARS-CoV-2 variants worldwide requires global molecular surveillance strategies, in support of public health control. Early detection and evaluation of their risk of spreading within the population is pivotal. Methods: Between April 2020 and February 2021, the Lighthouse Laboratory at Alderley Park Network tested more than eight million nose and throat swab samples for the presence of SARS-CoV-2. The PCR assay used, targets three genomic regions of the virus N, Orf1ab and S. Whole genome next-generation sequencing was used to confirm PCR positive results. Positive sample results were mapped to postal code origin of samples to allow real-time tracking of the spread of the variant through the UK. Findings: In mid-November 2020 the assay identified an increasing number of S gene negative, N and Orf1ab positive samples. Whole genome sequencing demonstrated that the loss of S gene detection was due to the appearance of a SARS-CoV-2 lineage (B.1.1.7) designated as Variant of Concern (VOC) 202012/01. By the beginning of January 2021, the new SARS-CoV-2 VOC had risen to comprise 70% of daily positive samples tested at Alderley Park and ~98% by the end of February 2021. Interpretation: The timeline view identified the rapid spread of the variant across England during the first three weeks of December. Coupling high throughput diagnostics and molecular surveillance was pivotal to the early detection of the spread of this variant. The availability of real time tracking of an emerging variant is an important new tool to inform decision-making for risk mitigation. In a respiratory pandemic, a tool for the timely response to the emergence and spread of a novel variant is vital especially when a variant is associated with enhanced transmission, as has occurred with VOC 202012/01. Funding Statement: The Lighthouse laboratory network was generated using funding from DHSC. Declaration of Interests: None to declare. Ethics Approval Statement: The manuscript was reviewed by the Head of Approvals Support, NHS Health Research Authority (HRA-NHS, UK), who confirmed that the study, which used unlinked and anonymised data was health surveillance and did not require NHS Research ethics review.

Background: India carries 27% of the global tuberculosis (TB) burden with a regional increase in multidrug-resistance (MDR) over the last decade. We aimed to characterize MDR-TB patient risk factors, delays in care and pathways to diagnosis and treatment in Pune, India. Methods: Between Jan-2018 and Sep-2019, we conducted structured and unstructured interviews with patients (>=15 years age) registered with the National TB Elimination Program (NTEP) for MDR (n=128) and non-MDR TB (n=269) treatment. We collected treatment record and GeneXpert-TB/RIF resistance probe binding data. Findings: MDR-TB was associated with continuous residence in a crowded locality [adjusted-OR (aOR) =2.02, 95%CI 1.12-3.63] and young age (aOR= 0.71, 95%CI 0.57-0.89 per 10-year increase). GeneXpert binding patterns demonstrated low diversity with 72.5% (n=233) of MDR isolates losing Probe-E binding. Median time from symptom onset to diagnosis was longer for MDR than non-MDR-TB (90 days, IQR 60-180 vs. 60 days IQR 30-90, Wilcoxon P

Cool flame due to low-temperature chemistry (LTC) has received great attention recently. However, previous studies mainly focused on cool flames in homogenous systems without transport or non-premixed cool flames in droplet combustion or counterflow configuration. There are only a few studies on premixed cool flames, and the transient initiation and propagation of premixed cool flames are still not well understood. In this study, the initiation, propagation and disappearance of one-dimensional premixed cool flames in dimethyl ether (DME)/air mixture is investigated through transient simulation considering detailed chemistry and transport. The premixed cool flame governed by LTC can be initiated by a hot spot. When the hot spot temperature is not high enough to directly trigger the high-temperature chemistry (HTC), only the LTC reactions take place initially and thereby a cool flame is first initiated. During the cool flame propagation, HTC autoignition occurs at the hot spot and it induces a hot flame propagating behind the cool flame. Therefore, double-flame structure for the coexistance of premixed cool and hot flames is observed. Since the hot flame propagates much faster than the cool flame, it eventually catches up and merges with the leading cool flame. A well-defined cool flame speed is found in this study. We inverstigate different factors affecting the cool flame speed and the appearance of hot flame. It is found that at higher equivalence ratio, higher initial temperature or higher oxygen concentration, the premixed cool flame propagates faster and the hot flame appears earlier. Three chemical mechanisms for DME oxidation are considered. Though these three mechanisms have nearly the same prediction of hot flame propagation speed, there are very large discrepancy in the prediction of cool flame propagation speed. Therefore, experimental data of premixed cool flame speed are useful for developing LTC.

Augmented reality (AR) has gained increased recognition in varying fields, in particular educational contexts. In the wake of the Covid-19 pandemic, home-based learning becomes a reality and is already in place across the globe, and learning via augmented reality technology will help learners comprehend learning content in a more creative frame of mind than ever before. Very little research has examined the adoption behavior of augmented reality in developing country perspectives. Therefore, there is a pressing necessity to understand the dynamics of augmented reality adoption for the benefit of motivating and inspiring students to adopt this highly innovative and impactful type of technology in the learning process. Against this background, the authors proposed and tested a model based on integrating Task-Technology Fit (TTF) and UTUAT2 theories. The results reveal the positive effect of task technology fit, performance expectancy, effort expectancy, social influence, facilitating condition, and hedonic motivation on behavioral intention (BI) in the adoption process of augmented reality in educational settings, where price value is found to exert little influence on behavioral intention. This model explains 49% of the variance in intentional behavior to adopt AR technology in the educational context. The conclusions of this study will add to the literature more informative knowledge leading to increased awareness of the dynamics and behaviors of AR adoption in a developing country perspective. We present and discuss the theoretical contributions and practical implications of our findings.