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"Stephens, David"
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Coastal habitats
Introduces coastal habitats, covering such topics as landforms, waves, currents, estuaries, coral reefs, plants and animals, and the threats posed by pollution, overfishing, and global warming.
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Antiviral Activity of Novel Quinoline Derivatives against Dengue Virus Serotype 2
Dengue virus causes dengue fever, a debilitating disease with an increasing incidence in many tropical and subtropical territories. So far, there are no effective antivirals licensed to treat this virus. Here we describe the synthesis and antiviral activity evaluation of two compounds based on the quinoline scaffold, which has shown potential for the development of molecules with various biological activities. Two of the tested compounds showed dose-dependent inhibition of dengue virus serotype 2 in the low and sub micromolar range. The compounds 1 and 2 were also able to impair the accumulation of the viral envelope glycoprotein in infected cells, while showing no sign of direct virucidal activity and acting possibly through a mechanism involving the early stages of the infection. The results are congruent with previously reported data showing the potential of quinoline derivatives as a promising scaffold for the development of new antivirals against this important virus.
Journal Article
Global Control of Meningococcal Disease
The exclusively human pathogen
Neisseria meningitidis
, which was first described in 1805 after an outbreak of 33 cases in Geneva, Switzerland, has been feared worldwide for more than 215 years as a cause of epidemics, pandemics, and sporadic cases of meningitis, rapidly fatal sepsis, and less commonly, pneumonia, often in otherwise-healthy young persons.
1-3
Although disease rates have been decreasing in many regions of the world owing in part to the introduction of highly effective vaccines,
4
case fatality remains high at 10 to 20%, and 11 to 19% of persons who survive infection have long-term consequences such as neurologic disabilities, sensory . . .
Journal Article
The diary of a country parson
The diary of James Woodforde during his time over a quarter of a century spent as a country parson within the same village.
A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data
Detailed seabed substrate maps are increasingly in demand for effective planning and management of marine ecosystems and resources. It has become common to use remotely sensed multibeam echosounder data in the form of bathymetry and acoustic backscatter in conjunction with ground-truth sampling data to inform the mapping of seabed substrates. Whilst, until recently, such data sets have typically been classified by expert interpretation, it is now obvious that more objective, faster and repeatable methods of seabed classification are required. This study compares the performances of a range of supervised classification techniques for predicting substrate type from multibeam echosounder data. The study area is located in the North Sea, off the north-east coast of England. A total of 258 ground-truth samples were classified into four substrate classes. Multibeam bathymetry and backscatter data, and a range of secondary features derived from these datasets were used in this study. Six supervised classification techniques were tested: Classification Trees, Support Vector Machines, k-Nearest Neighbour, Neural Networks, Random Forest and Naive Bayes. Each classifier was trained multiple times using different input features, including i) the two primary features of bathymetry and backscatter, ii) a subset of the features chosen by a feature selection process and iii) all of the input features. The predictive performances of the models were validated using a separate test set of ground-truth samples. The statistical significance of model performances relative to a simple baseline model (Nearest Neighbour predictions on bathymetry and backscatter) were tested to assess the benefits of using more sophisticated approaches. The best performing models were tree based methods and Naive Bayes which achieved accuracies of around 0.8 and kappa coefficients of up to 0.5 on the test set. The models that used all input features didn't generally perform well, highlighting the need for some means of feature selection.
Journal Article
Epidemic meningitis, meningococcaemia, and Neisseria meningitidis
Meningococcus, an obligate human bacterial pathogen, remains a worldwide and devastating cause of epidemic meningitis and sepsis. However, advances have been made in our understanding of meningococcal biology and pathogenesis, global epidemiology, transmission and carriage, host susceptibility, pathophysiology, and clinical presentations. Approaches to diagnosis, treatment, and chemoprophylaxis are now in use on the basis of these advances. Importantly, the next generation of meningococcal conjugate vaccines for serogroups A, C, Y, W-135, and broadly effective serogroup B vaccines are on the horizon, which could eliminate the organism as a major threat to human health in industrialised countries in the next decade. The crucial challenge will be effective introduction of new meningococcal vaccines into developing countries, especially in sub-Saharan Africa, where they are urgently needed.
Journal Article
Biology and pathogenesis of the evolutionarily successful, obligate human bacterium Neisseria meningitidis
For at least two hundred years, Neisseria meningitidis (the meningococcus), the cause of epidemic meningitis and sepsis, has inflicted rapid death, disability and fear on disparate human populations. The meningococcus is also recognized as a highly successful commensal organism exclusively found in humans. The evolution of N. meningitidis as an exclusive human commensal and sometimes a fulminant and fatal pathogen represents an important case study in microbial pathogenesis. We review the general status of our knowledge of pathogenesis of meningococcal carriage, transmission and virulence behavior with particular emphasis on the relevance of research on this topic to vaccine development.
Journal Article
Macrolide Resistance in Streptococcus pneumoniae
is a common commensal and an opportunistic pathogen. Suspected pneumococcal upper respiratory infections and pneumonia are often treated with macrolide antibiotics. Macrolides are bacteriostatic antibiotics and inhibit protein synthesis by binding to the 50S ribosomal subunit. The widespread use of macrolides is associated with increased macrolide resistance in
, and the treatment of pneumococcal infections with macrolides may be associated with clinical failures. In
, macrolide resistance is due to ribosomal dimethylation by an enzyme encoded by
(B), efflux by a two-component efflux pump encoded by
(E)/
(
(D)) and, less commonly, mutations of the ribosomal target site of macrolides. A wide array of genetic elements have emerged that facilitate macrolide resistance in
; for example
(B) is found on Tn
, while the
(E)/
operon is carried on the 5.4- or 5.5-kb Mega element. The macrolide resistance determinants,
(B) and
(E)/
, are also found on large composite Tn
-like elements most notably Tn
, Tn
, and Tn
. Introductions of 7-valent and 13-valent pneumococcal conjugate vaccines (PCV-7 and PCV-13) have decreased the incidence of macrolide-resistant invasive pneumococcal disease, but serotype replacement and emergence of macrolide resistance remain an important concern.
Journal Article
Towards Quantitative Spatial Models of Seabed Sediment Composition
There is a need for fit-for-purpose maps for accurately depicting the types of seabed substrate and habitat and the properties of the seabed for the benefits of research, resource management, conservation and spatial planning. The aim of this study is to determine whether it is possible to predict substrate composition across a large area of seabed using legacy grain-size data and environmental predictors. The study area includes the North Sea up to approximately 58.44°N and the United Kingdom's parts of the English Channel and the Celtic Seas. The analysis combines outputs from hydrodynamic models as well as optical remote sensing data from satellite platforms and bathymetric variables, which are mainly derived from acoustic remote sensing. We build a statistical regression model to make quantitative predictions of sediment composition (fractions of mud, sand and gravel) using the random forest algorithm. The compositional data is analysed on the additive log-ratio scale. An independent test set indicates that approximately 66% and 71% of the variability of the two log-ratio variables are explained by the predictive models. A EUNIS substrate model, derived from the predicted sediment composition, achieved an overall accuracy of 83% and a kappa coefficient of 0.60. We demonstrate that it is feasible to spatially predict the seabed sediment composition across a large area of continental shelf in a repeatable and validated way. We also highlight the potential for further improvements to the method.
Journal Article