Towards data warehousing and mining of protein unfolding simulation data

OBJECTIVES: The prediction of protein structure and the precise understanding of protein folding and unfolding processes remains one of the greatest challenges in structural biology and bioinformatics. Computer simulations based on molecular dynamics (MD) are at the forefront of the effort to gain a deeper understanding of these complex processes. Currently, these MD simulations are usually on the order of tens of nanoseconds, generate a large amount of conformational data and are computationally expensive. More and more groups run such simulations and generate a myriad of data, which raises new challenges in managing and analyzing these data. Because the vast range of proteins researchers want to study and simulate, the computational effort needed to generate data, the large data volumes involved, and the different types of analyses scientists need to perform, it is desirable to provide a public repository allowing researchers to pool and share protein unfolding data. METHODS: To adequately organize, manage, and analyze the data generated by unfolding simulation studies, we designed a data warehouse system that is embedded in a grid environment to facilitate the seamless sharing of available computer resources and thus enable many groups to share complex molecular dynamics simulations on a more regular basis. RESULTS: To gain insight into the conformational fluctuations and stability of the monomeric forms of the amyloidogenic protein transthyretin (TTR), molecular dynamics unfolding simulations of the monomer of human TTR have been conducted. Trajectory data and meta-data of the wild-type (WT) protein and the highly amyloidogenic variant L55P-TTR represent the test case for the data warehouse. CONCLUSIONS: Web and grid services, especially pre-defined data mining services that can run on or 'near' the data repository of the data warehouse, are likely to play a pivotal role in the analysis of molecular dynamics unfolding data.

Distributed hoeffding trees for pocket data mining

Collaborative mining of distributed data streams in a mobile computing environment is referred to as Pocket Data Mining PDM. Hoeffding trees techniques have been experimentally and analytically validated for data stream classification. In this paper, we have proposed, developed and evaluated the adoption of distributed Hoeffding trees for classifying streaming data in PDM applications. We have identified a realistic scenario in which different users equipped with smart mobile devices run a local Hoeffding tree classifier on a subset of the attributes. Thus, we have investigated the mining of vertically partitioned datasets with possible overlap of attributes, which is the more likely case. Our experimental results have validated the efficiency of our proposed model achieving promising accuracy for real deployment.

Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.

Homogeneous and heterogeneous distributed classification for pocket data mining

Pocket Data Mining (PDM) describes the full process of analysing data streams in mobile ad hoc distributed environments. Advances in mobile devices like smart phones and tablet computers have made it possible for a wide range of applications to run in such an environment. In this paper, we propose the adoption of data stream classification techniques for PDM. Evident by a thorough experimental study, it has been proved that running heterogeneous/different, or homogeneous/similar data stream classification techniques over vertically partitioned data (data partitioned according to the feature space) results in comparable performance to batch and centralised learning techniques.

In situ preservation of wetland heritage: hydrological and chemical change in the burial environment of the Somerset Levels, UK.

In Situ preservation is a core strategy for the conservation and management of waterlogged remains at wetland sites. Inorganic and organic remains can, however, quickly become degraded, or lost entirely, as a result of chemical or hydrological changes. Monitoring is therefore crucial in identifying baseline data for a site, the extent of spatial and or temporal variability, and in evaluating the potential impacts of these variables on current and future In Situ preservation potential. Since August 2009, monthly monitoring has taken place at the internationally important Iron Age site of Glastonbury Lake Village in the Somerset Levels, UK. A spatial, stratigraphic, and analytical approach to the analysis of sediment horizons and monitoring of groundwater chemistry, redox potential, water table depth and soil moisture (using TDR) was used to characterize the site. Significant spatial and temporal variability has been identified, with results from water-table monitoring and some initial chemical analysis from Glastonbury presented here. It appears that during dry periods parts of this site are at risk from desiccation. Analysis of the chemical data, in addition to integrating the results from the other parameters, is ongoing, with the aim of clarifying the risk to the entire site.

The global distribution of phytoplankton size spectrum and size classes from their light-absorption spectra derived from satellite data

The absorption spectra of phytoplankton in the visible domain hold implicit information on the phytoplankton community structure. Here we use this information to retrieve quantitative information on phytoplankton size structure by developing a novel method to compute the exponent of an assumed power-law for their particle-size spectrum. This quantity, in combination with total chlorophyll-a concentration, can be used to estimate the fractional concentration of chlorophyll in any arbitrarily-defined size class of phytoplankton. We further define and derive expressions for two distinct measures of cell size of mixed populations, namely, the average spherical diameter of a bio-optically equivalent homogeneous population of cells of equal size, and the average equivalent spherical diameter of a population of cells that follow a power-law particle-size distribution. The method relies on measurements of two quantities of a phytoplankton sample: the concentration of chlorophyll-a, which is an operational index of phytoplankton biomass, and the total absorption coefficient of phytoplankton in the red peak of visible spectrum at 676 nm. A sensitivity analysis confirms that the relative errors in the estimates of the exponent of particle size spectra are reasonably low. The exponents of phytoplankton size spectra, estimated for a large set of in situ data from a variety of oceanic environments (~ 2400 samples), are within a reasonable range; and the estimated fractions of chlorophyll in pico-, nano- and micro-phytoplankton are generally consistent with those obtained by an independent, indirect method based on diagnostic pigments determined using high-performance liquid chromatography. The estimates of cell size for in situ samples dominated by different phytoplankton types (diatoms, prymnesiophytes, Prochlorococcus, other cyanobacteria and green algae) yield nominal sizes consistent with the taxonomic classification. To estimate the same quantities from satellite-derived ocean-colour data, we combine our method with algorithms for obtaining inherent optical properties from remote sensing. The spatial distribution of the size-spectrum exponent and the chlorophyll fractions of pico-, nano- and micro-phytoplankton estimated from satellite remote sensing are in agreement with the current understanding of the biogeography of phytoplankton functional types in the global oceans. This study contributes to our understanding of the distribution and time evolution of phytoplankton size structure in the global oceans.

Temperament, family environment and anxiety in preschool children

This research examines the relationship between behavioural inhibition (BI), family environment (overinvolved and negative parenting, parental anxiety and parent-child attachment) and anxiety in a sample of 202 preschool children. Participants were aged between 3 years 2 months and 4 years 5 months, 101 were male. A thorough methodology was used that incorporated data from multiple observations of behaviour, diagnostic interviews and questionnaire measures. The results showed that children categorised as behaviourally inhibited were significantly more likely to meet criteria for a range of anxiety diagnoses. Furthermore, a wide range of family environment factors, including maternal anxiety, parenting and attachment were significantly associated with BI, with inhibited children more likely to experience adverse family environment factors. No interactions between temperament and family environment were found for child anxiety. However, a significant relationship between current maternal anxiety and child anxiety was found consistently even after controlling for BI. Additionally, there was some evidence of a relationship between maternal negativity and child anxiety, after controlling for BI. The results may suggest that temperament and family environment operate as additive, rather than interactive risk factors for child anxiety. This is discussed in the context of theoretical models of child anxiety and directions for future research.

Storing and manipulating environmental big data with JASMIN

JASMIN is a super-data-cluster designed to provide a high-performance high-volume data analysis environment for the UK environmental science community. Thus far JASMIN has been used primarily by the atmospheric science and earth observation communities, both to support their direct scientific workflow, and the curation of data products in the STFC Centre for Environmental Data Archival (CEDA). Initial JASMIN configuration and first experiences are reported here. Useful improvements in scientific workflow are presented. It is clear from the explosive growth in stored data and use that there was a pent up demand for a suitable big-data analysis environment. This demand is not yet satisfied, in part because JASMIN does not yet have enough compute, the storage is fully allocated, and not all software needs are met. Plans to address these constraints are introduced.

The Joint UK Land Environment Simulator (JULES), model description – part 1: energy and water fluxes

This manuscript describes the energy and water components of a new community land surface model called the Joint UK Land Environment Simulator (JULES). This is developed from the Met Office Surface Exchange Scheme (MOSES). It can be used as a stand alone land surface model driven by observed forcing data, or coupled to an atmospheric global circulation model. The JULES model has been coupled to the Met Office Unified Model (UM) and as such provides a unique opportunity for the research community to contribute their research to improve both world-leading operational weather forecasting and climate change prediction systems. In addition JULES, and its forerunner MOSES, have been the basis for a number of very high-profile papers concerning the land-surface and climate over the last decade. JULES has a modular structure aligned to physical processes, providing the basis for a flexible modelling platform.

Energy exchange in a dense urban environment Part II: impact of spatial heterogeneity of the surface

The centre of cities, characterised by spatial and temporal complexity, are challenging environments for micrometeorological research. This paper considers the impact of sensor location and heterogeneity of the urban surface on flux observations in the dense city centre of London, UK. Data gathered at two sites in close vicinity, but with different measurement heights, were analysed to investigate the influence of source area characteristics on long-term radiation and turbulent heat fluxes. Combining consideration of diffuse radiation and effects of specular reflections, the non-Lambertian urban surface is found to impact the measurements of surface albedo. Comparisons of observations from the two sites reveal that turbulent heat fluxes are similar under some flow conditions. However, they mostly observe processes at different scales due to their differing measurement heights, highlighting the critical impact of siting sensors in urban areas. A detailed source area analysis is presented to investigate the surface controls influencing the energy exchanges at the different scales

Landscape evolution and ice-sheet behaviour in a semi-arid polar environment: James Ross Island, NE Antarctic Peninsula

This study of landscape evolution presents both new modern and palaeo process-landform data, and analyses the behaviour of the Antarctic Peninsula Ice Sheet through the Last Glacial Maximum (LGM), the Holocene and to the present day. Six sediment-landform assemblages are described and interpreted for Ulu Peninsula, James Ross Island, NE Antarctic Peninsula: (1) the Glacier Ice and Snow Assemblage; (2) the Glacigenic Assemblage, which relates to LGM sediments and comprises both erratic-poor and erratic-rich drift, deposited by cold-based and wet-based ice and ice streams respectively; (3) the Boulder Train Assemblage, deposited during a Mid-Holocene glacier readvance; (4) the Ice-cored Moraine Assemblage, found in front of small cirque glaciers; (5) the Paraglacial Assemblage including scree, pebble-boulder lags, and littoral and fluvial processes; and (6) the Periglacial Assemblage including rock glaciers, protalus ramparts, blockfields, solifluction lobes and extensive patterned ground. The interplay between glacial, paraglacial and periglacial processes in this semi-arid polar environment is important in understanding polygenetic landforms. Crucially, cold-based ice was capable of sediment and landform genesis and modification. This landsystem model can aid the interpretation of past environments, but also provides new data to aid the reconstruction of the last ice sheet to overrun James Ross Island.

Data stream mining in ubiquitous environments: state-of-the-art and current directions

In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.

Pocket data mining - big data on small devices

Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

Effect of inclusion of lactulose and Lactobacillus plantarum on the intestinal environment and performance of piglets at weaning

The prebiotic lactulose, a probiotic strain of Lactobacillus plantarum (L. plantarum) and a synbiotic combination of these two agents were evaluated as growth promoters in 25–39-day old commercial weaning pigs. Ninety-six weaning pigs were allocated into 32 pens, taking initial weight into account, and distributed into four groups as follows: a control diet (CTR), the same diet supplemented daily with L. plantarum (109 CFU/mL sprayed on top; 20 mL/pig) (LPN); 10 g/kg lactulose (LAC) or a combination of both treatments (SYN). At day 14, eight piglets from each group were euthanized and proximal colon digesta was sampled for luminal pH, short-chain fatty acids (SCFA) and lactic acid concentrations. Deoxyribonucleic acid was extracted from colonic digesta and the microbial community was profiled by terminal restriction fragment length polymorphism analysis (T-RFLP) and qPCR. Blood urea nitrogen (BUN) and acute-phase proteins (Pig-MAP) were measured. Lactulose treatment (LAC) improved feed intake (P<0.05), average daily gain (P<0.01), feed:gain ratio (P<0.05) and reduced BUN (P<0.01). Both, LAC and LPN treatment, decreased the Enterobacteriaceae:Lactobacillus spp. ratio in the colonic luminal contents (P<0.05). Moreover LPN treatment promoted a decrease in the percentage of branched fatty acids (P<0.01) suggesting a reduction in proteolytic microbial activity. Microbial profiling of colonic luminal contents by T-RFLP revealed changes in some microbial species. Terminal restriction fragments (TRFs) compatible with Bifidobacterium thermoacidophilum were more frequently detected in experimental diets compared to CTR (P<0.05). Pigs receiving SYN diet demonstrated the combined positive effects of individual LAC and LPN treatment although we were not able to show a specific increase in the probiotic strain with the inclusion of lactulose. Collectively, these data suggest the combination of lactulose and L. plantarum acts as a complementary synbiotic, but not as a synergistic combination.