• COVID-19
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A brand new, state-of-the-art Microgrid Laboratory Setup was built at the Technical University of Denmark’s (DTU) Ballerup campus to aid with practical, hands-on teaching in the field of power system engineering. The primary focus of the Microgrid Setup is to closely emulate the behaviour of thermal power plants, e.g. emergency power plants, modern distributed combined cycle gas turbine (CCGT) and combined heat and power (CHP) plants, especially with regard to synchronous generator control. During the COVID-19 pandemic, an additional requirement came to the fore, in that this Microgrid Setup should also be fully accessible via the web. The design was broadened to include remote, hands-on and flexible experimentation [1], in order for student groups to engage in remote collaborative learning [2].

We introduce the novel concept of a Sparse Layered Graph (SLG) for s-t graph cut segmentation of image data. The concept is based on the widely used Ishikawa layered technique for multi-object segmentation, which allows explicit object interactions, such as containment and exclusion with margins. However, the spatial complexity of the Ishikawa technique limits its use for many segmentation problems. To solve this issue, we formulate a general method for adding containment and exclusion interaction constraints to layered graphs. Given some prior knowledge, we can create a SLG, which is often orders of magnitude smaller than traditional Ishikawa graphs, with identical segmentation results. This allows us to solve many problems that could previously not be solved using general graph cut algorithms. We then propose three algorithms for further reducing the spatial complexity of SLGs, by using ordered multi-column graphs. In our experiments, we show that SLGs, and in particular ordered multi-column SLGs, can produce high-quality segmentation results using extremely simple data terms. We also show the scalability of ordered multi-column SLGs, by segmenting a high-resolution volume with several hundred interacting objects.

The Covid-19 pandemic has caused untold disruption and enhanced mortality rates around the world. Understanding the mechanisms for transmission of SARS-CoV-2 is key to preventing further spread but there is confusion over the meaning of "airborne" whenever transmission is discussed. Scientific ambivalence originates from evidence published many years ago, which has generated mythological beliefs that obscure current thinking. This article gathers together and explores some of the most commonly held dogmas on airborne transmission in order to stimulate revision of the science in the light of current evidence. Six 'myths' are presented, explained, and ultimately refuted on the basis of recently published papers and expert opinion from previous work related to similar viruses. There is little doubt that SARS-CoV-2 is transmitted via a range of airborne particle sizes subject to all the usual ventilation parameters and human behaviour. Experts from specialties encompassing aerosol studies, ventilation, engineering, physics, virology and clinical medicine have joined together to present this review, in order to consolidate the evidence for airborne transmission mechanisms and offer justification for modern strategies for prevention and control of Covid-19 in healthcare and community.

AbstractRecent global trade disruptions, due to blockage of the Suez Canal and cascading effects of COVID-19, have altered the movement patterns of commercial ships and may increase worldwide invasions of marine non-indigenous species. Organisms settle on the hulls and underwater surfaces of vessels and can accumulate rapidly, especially when vessels remain stationary during lay-ups and delays. Once present, organisms can persist on vessels for long-periods (months to years), with the potential to release propagules and seed invasions as ships visit ports across the global transportation network. Shipborne propagules also may be released in increasing numbers during extended vessel residence times at port or anchor. Thus, the large scale of shipping disruptions, impacting thousands of vessels and geographic locations and still on-going for over two years, may elevate invasion rates in coastal ecosystems in the absence of policy and management efforts to prevent this outcome. Concerted international and national biosecurity actions, mobilizing existing frameworks and tools with due diligence, are urgently needed to address a critical gap and abate the associated invasion risks.

Background Therapeutic cancer vaccines represent an intriguing approach to cancer immunotherapy and they have been widely explored for the last decade. As opposed to standard modalities, such as surgery and chemotherapy, an effective vaccine-based immune response may provide protection against metastatic disease. Peptide based vaccines can elicit a highly targeted immune response and include a simple, fast and cost-effective production due to recent developments in solid phase peptide synthesis. Recent development within the field of COVID-19 vaccines has highlighted the use of lipid nanoparticles as an effective drug delivery system for vaccination. Incorporation of peptide antigens into engineered micro- and nanoparticles enables induction of a potent T cell response, partly attributed to prolonged and improved antigen presentation by dendritic cells after particle internalization. Peptide-based vaccines are often based on delivery of high-affinity T cell model epitopes. However, the therapeutic relevance of vaccination with low-affinity epitopes is gaining increasing support following the observation that high-affinity epitopes can promote T cell exhaustion resulting from excessive T cell receptor stimulation. Here, we characterize and evaluate a novel lipid nanoparticle (LNP) vaccine platform that is suited for delivery of both high- and low-affinity epitopes in the setting of therapeutic cancer vaccination.Methods LNPs were formulated to carry high- or low-affinity peptide epitopes from Ovalbumin (OVA) in conjunction with the TLR7 agonist 1V270. The peptides were anchored to the surface of the LNPs via a reducible DSPE-PEG2000 linker system. The therapeutic vaccine platform was evaluated in vivo both as a monotherapy and in combination with adoptive transfer of OT-I T cells in the syngeneic B16-OVA murine melanoma model.Results The LNP vaccine promotes efficient antigen-release and ensures high, continuous antigen-presentation by antigen-presenting cells. While the LNPs can be administered via multiple routes, intratumoral vaccination favors enhanced particle uptake in dendritic cells in the tumor. Formulated with either high- or low-affinity epitopes, intratumorally delivered vaccine particles promote superior tumor-infiltration of adoptively transferred T cells, which translates into potent anti-tumor efficacy in vivo. Finally, we show that vaccination with both CD8+ and CD4+ epitopes can delay tumor growth and prolong survival in an antigen-dependent manner.Conclusions This study presents a versatile and multi-purpose LNP vaccine platform that ensures effective delivery of high- and low-affinity epitopes. Intratumoral administration promotes vaccine particle uptake by intratumoral dendritic cells, which is followed by T cell infiltration and anti-tumor efficacy in vivo.

AbstractEffective vaccines protect individuals by not only reducing the susceptibility to infection, but also reducing the infectiousness of breakthrough infections in vaccinated cases. To disentangle the vaccine effectiveness against susceptibility to infection (VES) and vaccine effectiveness against infectiousness (VEI), we took advantage of Danish national data comprising 24,693 households with a primary case of SARS-CoV-2 infection (Delta Variant of Concern, 2021) including 53,584 household contacts. In this setting, we estimated VES as 61% (95%-CI: 59-63), when the primary case was unvaccinated, and VEI as 31% (95%-CI: 26-36), when the household contact was unvaccinated. Furthermore, unvaccinated secondary cases with an infection exhibited a three-fold higher viral load compared to fully vaccinated secondary cases with a breakthrough infection. Our results demonstrate that vaccinations reduce susceptibility to infection as well as infectiousness, which should be considered by policy makers when seeking to understand the public health impact of vaccination against transmission of SARS-CoV-2.

The ongoing COVID-19 pandemic caused by infection with SARS-CoV-2 has created an urgent need for animal models to enable study of basic infection and disease mechanisms and for development of vaccines, therapeutics, and diagnostics. Most research on animal models for COVID-19 has been directed toward rodents, transgenic rodents, and non-human primates. The primary focus has been on the angiotensin-converting enzyme 2 (ACE2), which is a host cell receptor for SARS-CoV-2. Among investigated species, irrespective of ACE2 spike protein binding, only mild (or no) disease has occurred following infection with SARS-CoV-2, suggesting that ACE2 may be necessary for infection but is not sufficient to determine the outcome of infection. The common trait of all species investigated as COVID models is their healthy status prior to virus challenge. In contrast, the vast majority of severe COVID-19 cases occur in people with chronic comorbidities such as diabetes, obesity, and/or cardiovascular disease. Healthy pigs express ACE2 protein that binds the viral spike protein but they are not susceptible to infection with SARS-CoV-2. However, certain pig breeds, such as the Ossabaw pig, can reproducibly be made obese and show most aspects of the metabolic syndrome, thus resembling the more than 80% of the critically ill COVID-19 patients admitted to hospitals. We urge considering infection with porcine respiratory coronavirus of metabolic syndrome pigs, such as the obese Ossabaw pig, as a highly relevant animal model of severe COVID-19.

The COVID-19 pandemic has drastically impacted people's travel behaviour and introduced uncertainty in the demand for public transport. To investigate user preferences for travel by London Underground during the pandemic, we conducted a stated choice experiment among its pre-pandemic users (N = 961). We analysed the collected data using multinomial and latent class logit models. Our discrete choice analysis provides two sets of results. First, we derive the crowding multiplier estimate of travel time valuation (i.e., the ratio of the value of travel time in uncrowded and crowded situations) for London underground users. The results indicate that travel time valuation of Underground users increases by 73% when it operates at technical capacity. Second, we estimate the sensitivity of the preference for the London Underground relative to the epidemic situation (confirmed new COVID-19 cases) and interventions (vaccination rates and mandatory face masks). The sensitivity analysis suggests that making face masks mandatory is a main driver for recovering the demand for the London underground. The latent class model reveals substantial preference heterogeneity. For instance, while the average effect of mandatory face masks is positive, the preferences of 30% of pre-pandemic users for travel by the Underground are negatively affected. The positive effect of mandatory face masks on the likelihood of taking the Underground is less pronounced among males with age below 40 years, and a monthly income below 10,000 GBP. The estimated preference sensitivities and crowding multipliers are relevant for supply–demand management in transit systems and the calibration of advanced epidemiological models.

Porcine epidemic diarrhoea virus, strain CV777, was initially characterized in 1978 as the causative agent of a disease first identified in the UK in 1971. This coronavirus has been widely distributed among laboratories and has been passaged both within pigs and in cell culture. To determine the variability between different stocks of the PEDV strain CV777, sequencing of the full-length genome (ca. 28kb) has been performed in 6 different laboratories, using different protocols. Not surprisingly, each of the different full genome sequences were distinct from each other and from the reference sequence (Accession number AF353511) but they are >99% identical. Unique and shared differences between sequences were identified. The coding region for the surface-exposed spike protein showed the highest proportion of variability including both point mutations and small deletions. The predicted expression of the ORF3 gene product was more dramatically affected in three different variants of this virus through either loss of the initiation codon or gain of a premature termination codon. The genome of one isolate had a substantially rearranged 5´-terminal sequence. This rearrangement was validated through the analysis of sub-genomic mRNAs from infected cells. It is clearly important to know the features of the specific sample of CV777 being used for experimental studies.

Driven by an increasing number of connected medical devices, Internet of Medical Things (IoMT), as an application of Internet of Things (IoT) in healthcare, is developed to help collect, analyze, and transmit medical data. During the outbreak of a pandemic like COVID-19, IoMT can be useful to monitor the status of patients and detect main symptoms remotely, by using various smart sensors. However, due to the lack of emotional care in the current IoMT, it is still a challenge to reach an efficient medical process. Especially under COVID-19, there is a need to monitor emotional status among particular people like the elderly. In this work, we propose an emotion-aware healthcare monitoring system in IoMT, based on brainwaves. With the fast development of electroencephalography (EEG) sensors in current headsets and some devices, brainwave-based emotion detection becomes feasible. The IoMT devices are used to capture the brainwaves of a patient in a scenario of smart home. Also, our system involves the analysis of touch behavior as the second layer to enhance the brainwave-based emotion recognition. In the user study with 60 participants, the results indicate the viability and effectiveness of our approach in detecting emotions like comfortable and uncomfortable, which can complement existing emotion-aware healthcare applications and mechanisms.