Chemical and aerosol characterisation of the troposphere over West Africa during the monsoon period as part of AMMA

During June, July and August 2006 five aircraft took part in a campaign over West Africa to observe the aerosol content and chemical composition of the troposphere and lower stratosphere as part of the African Monsoon Multidisciplinary Analysis (AMMA) project. These are the first such measurements in this region during the monsoon period. In addition to providing an overview of the tropospheric composition, this paper provides a description of the measurement strategy (flights performed, instrumental payloads, wing-tip to wing-tip comparisons) and points to some of the important findings discussed in more detail in other papers in this special issue. The ozone data exhibits an "S" shaped vertical profile which appears to result from significant losses in the lower troposphere due to rapid deposition to forested areas and photochemical destruction in the moist monsoon air, and convective uplift of ozone-poor air to the upper troposphere. This profile is disturbed, particularly in the south of the region, by the intrusions in the lower and middle troposphere of air from the southern hemisphere impacted by biomass burning. Comparisons with longer term data sets suggest the impact of these intrusions on West Africa in 2006 was greater than in other recent wet seasons. There is evidence for net photochemical production of ozone in these biomass burning plumes as well as in urban plumes, in particular that from Lagos, convective outflow in the upper troposphere and in boundary layer air affected by nitrogen oxide emissions from recently wetted soils. This latter effect, along with enhanced deposition to the forested areas, contributes to a latitudinal gradient of ozone in the lower troposphere. Biogenic volatile organic compounds are also important in defining the composition both for the boundary layer and upper tropospheric convective outflow. Mineral dust was found to be the most abundant and ubiquitous aerosol type in the atmosphere over Western Africa. Data collected within AMMA indicate that injection of dust to altitudes favourable for long-range transport (i.e. in the upper Sahelian planetary boundary layer) can occur behind the leading edge of mesoscale convective system (MCS) cold-pools. Research within AMMA also provides the first estimates of secondary organic aerosols across the West African Sahel and have shown that organic mass loadings vary between 0 and 2 μg m−3 with a median concentration of 1.07 μg m−3. The vertical distribution of nucleation mode particle concentrations reveals that significant and fairly strong particle formation events did occur for a considerable fraction of measurement time above 8 km (and only there). Very low concentrations were observed in general in the fresh outflow of active MCSs, likely as the result of efficient wet removal of aerosol particles due to heavy precipitation inside the convective cells of the MCSs. This wet removal initially affects all particle size ranges as clearly shown by all measurements in the vicinity of MCSs.

Highly accurate detection of ovarian cancer using CA125 but limited improvement with serum matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling

Objectives: Our objective was to test the performance of CA125 in classifying serum samples from a cohort of malignant and benign ovarian cancers and age-matched healthy controls and to assess whether combining information from matrix-assisted laser desorption/ionization (MALDI) time-of-flight profiling could improve diagnostic performance. Materials and Methods: Serum samples from women with ovarian neoplasms and healthy volunteers were subjected to CA125 assay and MALDI time-of-flight mass spectrometry (MS) profiling. Models were built from training data sets using discriminatory MALDI MS peaks in combination with CA125 values and tested their ability to classify blinded test samples. These were compared with models using CA125 threshold levels from 193 patients with ovarian cancer, 290 with benign neoplasm, and 2236 postmenopausal healthy controls. Results: Using a CA125 cutoff of 30 U/mL, an overall sensitivity of 94.8% (96.6% specificity) was obtained when comparing malignancies versus healthy postmenopausal controls, whereas a cutoff of 65 U/mL provided a sensitivity of 83.9% (99.6% specificity). High classification accuracies were obtained for early-stage cancers (93.5% sensitivity). Reasons for high accuracies include recruitment bias, restriction to postmenopausal women, and inclusion of only primary invasive epithelial ovarian cancer cases. The combination of MS profiling information with CA125 did not significantly improve the specificity/accuracy compared with classifications on the basis of CA125 alone. Conclusions: We report unexpectedly good performance of serum CA125 using threshold classification in discriminating healthy controls and women with benign masses from those with invasive ovarian cancer. This highlights the dependence of diagnostic tests on the characteristics of the study population and the crucial need for authors to provide sufficient relevant details to allow comparison. Our study also shows that MS profiling information adds little to diagnostic accuracy. This finding is in contrast with other reports and shows the limitations of serum MS profiling for biomarker discovery and as a diagnostic tool

Communicability across evolving networks

Many natural and technological applications generate time ordered sequences of networks, defined over a fixed set of nodes; for example time-stamped information about ‘who phoned who’ or ‘who came into contact with who’ arise naturally in studies of communication and the spread of disease. Concepts and algorithms for static networks do not immediately carry through to this dynamic setting. For example, suppose A and B interact in the morning, and then B and C interact in the afternoon. Information, or disease, may then pass from A to C, but not vice versa. This subtlety is lost if we simply summarize using the daily aggregate network given by the chain A-B-C. However, using a natural definition of a walk on an evolving network, we show that classic centrality measures from the static setting can be extended in a computationally convenient manner. In particular, communicability indices can be computed to summarize the ability of each node to broadcast and receive information. The computations involve basic operations in linear algebra, and the asymmetry caused by time’s arrow is captured naturally through the non-mutativity of matrix-matrix multiplication. Illustrative examples are given for both synthetic and real-world communication data sets. We also discuss the use of the new centrality measures for real-time monitoring and prediction.

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

Mitochondrial and chloroplast DNA-based phylogeny of Pelargonium (Geraniaceae)

Overall phylogenetic relationships within the genus Pelargonium (Geraniaceae) were inferred based on DNA sequences from mitochondrial(mt)-encoded nad1 b/c exons and from chloroplast(cp)-encoded trnL (UAA) 5' exon-trnF (GAA) exon regions using two species of Geranium and Sarcocaulon vanderetiae as outgroups. The group II intron between nad1 exons b and c was found to be absent from the Pelargonium, Geranium, and Sarcocaulon sequences presented here as well as from Erodium, which is the first recorded loss of this intron in angiosperms. Separate phylogenetic analyses of the mtDNA and cpDNA data sets produced largely congruent topologies, indicating linkage between mitochondrial and chloroplast genome inheritance. Simultaneous analysis of the combined data sets yielded a well-resolved topology with high clade support exhibiting a basic split into small and large chromosome species, the first group containing two lineages and the latter three. One large chromosome lineage (x = 11) comprises species from sections Myrrhidium and Chorisma and is sister to a lineage comprising P. mutans (x = 11) and species from section Jenkinsonia (x = 9). Sister to these two lineages is a lineage comprising species from sections Ciconium (x = 9) and Subsucculentia (x = 10). Cladistic evaluation of this pattern suggests that x = 11 is the ancestral basic chromosome number for the genus.

A geography of the UK commercial property market

This paper revisits some ideas that were first raised seriously in the mid-90s; that it should be possible to establish linkages (in spatial terms) between local economic factors and sector performance in commercial real estate markets. There have been a number of developments in the quality and quantity of relevant data over the intervening period that make it appropriate to return to have another look at some of these ideas in a more ‘modern’ technological context. Using data from several sources this exploratory paper seeks therefore to look at some of the spatial patterns that can be derived from the data. It examines the extent to which it is possible to make linkages and visualise the geographical structure of those markets and their change over time. Naturally there remain strong limitations on the extent to which it is possible to achieve ‘good’ results in this kind of analysis, and one major intention of the paper is to encourage a debate about how data sets can be developed and improved to allow these methods to be taken further.

Expert judgement in the processes of commercial property market forecasting

In this paper, we investigate the role of judgement in the formation of forecasts in commercial property markets. The investigation is based on interview surveys with the majority of UK forecast producers, who are using a range of inputs and data sets to form models to predict an array of variables for a range of locations. The findings suggest that forecasts need to be acceptable to their users (and purchasers) and consequently forecasters generally have incentives to avoid presenting contentious or conspicuous forecasts. Where extreme forecasts are generated by a model, forecasters often engage in ‘self‐censorship’ or are ‘censored’ following in‐house consultation. It is concluded that the forecasting process is significantly more complex than merely carrying out econometric modelling, forecasts are mediated and contested within organisations and that impacts can vary considerably across different organizational contexts.

A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems

This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier's structure and the parameters of RBF kernels are determined using a PSO algorithm based on the criterion of minimising the leave-one-out misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

Expert judgement in the processes of commercial property market forecasting

In this paper we investigate the role of judgement in the formation of forecasts in commercial real estate markets. Based on interview surveys with the majority of forecast producers, we find that real estate forecasters are using a range of inputs and data sets to form models to predict an array of variables for a range of locations. The findings suggest that forecasts need to be acceptable to their users (and purchasers) and consequently forecasters generally have incentives to avoid presenting contentious or conspicuous forecasts. Where extreme forecasts are generated by a model, forecasters often engage in ‘self-censorship’ or are ‘censored’ following in-house consultation. It is concluded that the forecasting process is more complex than merely carrying out econometric modelling and that the impact of the influences within this process vary considerably across different organizational contexts.

Evaluation of satellite-based and model re-analysis rainfall estimates for Uganda

The dependence of much of Africa on rain fed agriculture leads to a high vulnerability to fluctuations in rainfall amount. Hence, accurate monitoring of near-real time rainfall is particularly useful, for example in forewarning possible crop shortfalls in drought-prone areas. Unfortunately, ground based observations are often inadequate. Rainfall estimates from satellite-based algorithms and numerical model outputs can fill this data gap, however rigorous assessment of such estimates is required. In this case, three satellite based products (NOAA-RFE 2.0, GPCP-1DD and TAMSAT) and two numerical model outputs (ERA-40 and ERA-Interim) have been evaluated for Uganda in East Africa using a network of 27 rain gauges. The study focuses on the years 2001 to 2005 and considers the main rainy season (February to June). All data sets were converted to the same temporal and spatial scales. Kriging was used for the spatial interpolation of the gauge data. All three satellite products showed similar characteristics and had a high level of skill that exceeded both model outputs. ERA-Interim had a tendency to overestimate whilst ERA-40 consistently underestimated the Ugandan rainfall.

Hyperresolution global land surface modeling: meeting a grand challenge for monitoring Earth's terrestrial water

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface‐subsurface interactions due to fine‐scale topography and vegetation; improved representation of land‐atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.