9 research outputs found
The separation of the East Australian Current: A Lagrangian approach to potential vorticity and upstream control
The East Australian Current (EAC) is the western boundary current flowing along the east coast of Australia separating from the coast at approximately 34°S. After the separation two main pathways can be distinguished, the eastward flowing Tasman Front and the extension of the EAC flowing southward. The area south of the separation latitude is eddy-rich and the separation latitude of the EAC is variable. Little is known of the properties of the water masses that separate at the bifurcation of the EAC. This paper presents new insights from the Lagrangian perspective, where the water masses that veer east and those that continue south are tracked in an eddy-permitting numerical model. The transport along the two pathways is computed, and a 1:3 ratio between transport in the EAC extension and transport in the Tasman Front is found. The results show that the "fate" of the particles is to first order already determined by the particle distribution within the EAC current upstream of the separation latitude, where 85% of the particles following the EAC extension originate from below 460 m and 90% of the particles following the Tasman Front originate from the top 460 m depth at 28°S. The separation and pathways are controlled by the structure of the isopycnals in this region. Analysis of anomalies in potential vorticity show that in the region where the two water masses overlap, the fate of the water depends on the presence of anticyclonic eddies that push isopycnals down and therefore enable particles to travel further south
SARS Coronavirus nsp1 Protein Induces Template-Dependent Endonucleolytic Cleavage of mRNAs: Viral mRNAs Are Resistant to nsp1-Induced RNA Cleavage
SARS coronavirus (SCoV) nonstructural protein (nsp) 1, a potent inhibitor of host gene expression, possesses a unique mode of action: it binds to 40S ribosomes to inactivate their translation functions and induces host mRNA degradation. Our previous study demonstrated that nsp1 induces RNA modification near the 5′-end of a reporter mRNA having a short 5′ untranslated region and RNA cleavage in the encephalomyocarditis virus internal ribosome entry site (IRES) region of a dicistronic RNA template, but not in those IRES elements from hepatitis C or cricket paralysis viruses. By using primarily cell-free, in vitro translation systems, the present study revealed that the nsp1 induced endonucleolytic RNA cleavage mainly near the 5′ untranslated region of capped mRNA templates. Experiments using dicistronic mRNAs carrying different IRESes showed that nsp1 induced endonucleolytic RNA cleavage within the ribosome loading region of type I and type II picornavirus IRES elements, but not that of classical swine fever virus IRES, which is characterized as a hepatitis C virus-like IRES. The nsp1-induced RNA cleavage of template mRNAs exhibited no apparent preference for a specific nucleotide sequence at the RNA cleavage sites. Remarkably, SCoV mRNAs, which have a 5′ cap structure and 3′ poly A tail like those of typical host mRNAs, were not susceptible to nsp1-mediated RNA cleavage and importantly, the presence of the 5′-end leader sequence protected the SCoV mRNAs from nsp1-induced endonucleolytic RNA cleavage. The escape of viral mRNAs from nsp1-induced RNA cleavage may be an important strategy by which the virus circumvents the action of nsp1 leading to the efficient accumulation of viral mRNAs and viral proteins during infection
The implementation of the serial trial intervention for pain and challenging behaviour in advanced dementia patients (STA OP!): a clustered randomized controlled trial
<p>Abstract</p> <p>Background</p> <p>Pain (physical discomfort) and challenging behaviour are highly prevalent in nursing home residents with dementia: at any given time 45-80% of nursing home residents are in pain and up to 80% have challenging behaviour. In the USA Christine Kovach developed the serial trial intervention (STI) and established that this protocol leads to less discomfort and fewer behavioural symptoms in moderate to severe dementia patients. The present study will provide insight into the effects of implementation of the Dutch version of the STI-protocol (STA OP!) in comparison with a control intervention, not only on behavioural symptoms, but also on pain, depression, and quality of life. This article outlines the study protocol.</p> <p>Methods/Design</p> <p>The study is a cluster randomized controlled trial in 168 older people (aged >65 years) with mild or moderate dementia living in nursing homes. The clusters, Dutch nursing homes, are randomly assigned to either the intervention condition (training and implementation of the STA OP!-protocol) or the control condition (general training focusing on challenging behaviour and pain, but without the step-wise approach). Measurements take place at baseline, after 3 months (end of the STA OP! training period) and after 6 months.</p> <p>Primary outcome measures are symptoms of challenging behaviour (measured with the Cohen-Mansfield Agitation Inventory (CMAI) and the Neuropsychiatric Inventory-Nursing Home version (NPI-NH)), and pain (measure with the Dutch version of the Pain Assessment Checklist for Seniors (PACSLAC-D) and the Minimum Data Set of the Resident Assessment Instrument (MDS-RAI) pain scale). Secondary outcome measures include symptoms of depression (Cornell and MDS-RAI depression scale), Quality of Live (Qualidem), changes in prescriptions of analgesics and psychotropic drugs, and the use of non-pharmacological comfort interventions (e.g. snoezelen, reminiscence therapy).</p> <p>Discussion</p> <p>The transfer from the American design to the Dutch design involved several changes due to the different organisation of healthcare systems. Specific strengths and limitations of the study are discussed.</p> <p>Trial registration</p> <p>Netherlands Trial Register (NTR): <a href="http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=1967">NTR1967</a></p
Modelling the transmission of healthcare associated infections: a systematic review.
BACKGROUND: Dynamic transmission models are increasingly being used to improve our understanding of the epidemiology of healthcare-associated infections (HCAI). However, there has been no recent comprehensive review of this emerging field. This paper summarises how mathematical models have informed the field of HCAI and how methods have developed over time. METHODS: MEDLINE, EMBASE, Scopus, CINAHL plus and Global Health databases were systematically searched for dynamic mathematical models of HCAI transmission and/or the dynamics of antimicrobial resistance in healthcare settings. RESULTS: In total, 96 papers met the eligibility criteria. The main research themes considered were evaluation of infection control effectiveness (64%), variability in transmission routes (7%), the impact of movement patterns between healthcare institutes (5%), the development of antimicrobial resistance (3%), and strain competitiveness or co-colonisation with different strains (3%). Methicillin-resistant Staphylococcus aureus was the most commonly modelled HCAI (34%), followed by vancomycin resistant enterococci (16%). Other common HCAIs, e.g. Clostridum difficile, were rarely investigated (3%). Very few models have been published on HCAI from low or middle-income countries.The first HCAI model has looked at antimicrobial resistance in hospital settings using compartmental deterministic approaches. Stochastic models (which include the role of chance in the transmission process) are becoming increasingly common. Model calibration (inference of unknown parameters by fitting models to data) and sensitivity analysis are comparatively uncommon, occurring in 35% and 36% of studies respectively, but their application is increasing. Only 5% of models compared their predictions to external data. CONCLUSIONS: Transmission models have been used to understand complex systems and to predict the impact of control policies. Methods have generally improved, with an increased use of stochastic models, and more advanced methods for formal model fitting and sensitivity analyses. Insights gained from these models could be broadened to a wider range of pathogens and settings. Improvements in the availability of data and statistical methods could enhance the predictive ability of models
A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation
Transmission of Equine Influenza Virus during an outbreak is characterized by frequent mixed infections and loose transmission bottlenecks
The ability of influenza A viruses (IAVs) to cross species barriers and evade host immunity is a major public health concern. Studies on the phylodynamics of IAVs across different scales - from the individual to the population - are essential for devising effective measures to predict, prevent or contain influenza emergence. Understanding how IAVs spread and evolve during outbreaks is critical for the management of epidemics. Reconstructing the transmission network during a single outbreak by sampling viral genetic data in time and space can generate insights about these processes. Here, we obtained intra-host viral sequence data from horses infected with equine influenza virus (EIV) to reconstruct the spread of EIV during a large outbreak. To this end, we analyzed within-host viral populations from sequences covering 90% of the infected yards. By combining gene sequence analyses with epidemiological data, we inferred a plausible transmission network, in turn enabling the comparison of transmission patterns during the course of the outbreak and revealing important epidemiological features that were not apparent using either approach alone. The EIV populations displayed high levels of genetic diversity, and in many cases we observed distinct viral populations containing a dominant variant and a number of related minor variants that were transmitted between infectious horses. In addition, we found evidence of frequent mixed infections and loose transmission bottlenecks in these naturally occurring populations. These frequent mixed infections likely influence the size of epidemics