Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics.

Biotic carbon feedbacks in a materially-closed soil-vegetation-atmosphere system

The magnitude and direction of the coupled feedbacks between the biotic and abiotic components of the terrestrial carbon cycle is a major source of uncertainty in coupled climate–carbon-cycle models1, 2, 3. Materially closed, energetically open biological systems continuously and simultaneously allow the two-way feedback loop between the biotic and abiotic components to take place4, 5, 6, 7, but so far have not been used to their full potential in ecological research, owing to the challenge of achieving sustainable model systems6, 7. We show that using materially closed soil–vegetation–atmosphere systems with pro rata carbon amounts for the main terrestrial carbon pools enables the establishment of conditions that balance plant carbon assimilation, and autotrophic and heterotrophic respiration fluxes over periods suitable to investigate short-term biotic carbon feedbacks. Using this approach, we tested an alternative way of assessing the impact of increased CO2 and temperature on biotic carbon feedbacks. The results show that without nutrient and water limitations, the short-term biotic responses could potentially buffer a temperature increase of 2.3 °C without significant positive feedbacks to atmospheric CO2. We argue that such closed-system research represents an important test-bed platform for model validation and parameterization of plant and soil biotic responses to environmental changes.

Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.

Impact of land cover uncertainties on estimates of biospheric carbon fluxes

Large-scale bottom-up estimates of terrestrial carbon fluxes, whether based on models or inventory, are highly dependent on the assumed land cover. Most current land cover and land cover change maps are based on satellite data and are likely to be so for the foreseeable future. However, these maps show large differences, both at the class level and when transformed into Plant Functional Types (PFTs), and these can lead to large differences in terrestrial CO2 fluxes estimated by Dynamic Vegetation Models. In this study the Sheffield Dynamic Global Vegetation Model is used. We compare PFT maps and the resulting fluxes arising from the use of widely available moderate (1 km) resolution satellite-derived land cover maps (the Global Land Cover 2000 and several MODIS classification schemes), with fluxes calculated using a reference high (25 m) resolution land cover map specific to Great Britain (the Land Cover Map 2000). We demonstrate that uncertainty is introduced into carbon flux calculations by (1) incorrect or uncertain assignment of land cover classes to PFTs; (2) information loss at coarser resolutions; (3) difficulty in discriminating some vegetation types from satellite data. When averaged over Great Britain, modeled CO2 fluxes derived using the different 1 km resolution maps differ from estimates made using the reference map. The ranges of these differences are 254 gC m−2 a−1 in Gross Primary Production (GPP); 133 gC m−2 a−1 in Net Primary Production (NPP); and 43 gC m−2 a−1 in Net Ecosystem Production (NEP). In GPP this accounts for differences of −15.8% to 8.8%. Results for living biomass exhibit a range of 1109 gC m−2. The types of uncertainties due to land cover confusion are likely to be representative of many parts of the world, especially heterogeneous landscapes such as those found in western Europe.

Comparison of different boundary layer surface schemes using single point micrometeorological field data

 In the last decade, a vast number of land surface schemes has been designed for use in global climate models, atmospheric weather prediction, mesoscale numerical models, ecological models, and models of global changes. Since land surface schemes are designed for different purposes they have various levels of complexity in the treatment of bare soil processes, vegetation, and soil water movement. This paper is a contribution to a little group of papers dealing with intercomparison of differently designed and oriented land surface schemes. For that purpose we have chosen three schemes for classification: i) global climate models, BATS (Dickinson et al., 1986; Dickinson et al., 1992); ii) mesoscale and ecological models, LEAF (Lee, 1992) and iii) mesoscale models, LAPS (Mihailović, 1996; Mihailović and Kallos, 1997; Mihailović et al., 1999) according to the Shao et al. (1995) classification. These schemes were compared using surface fluxes and leaf temperature outputs obtained by time integrations of data sets derived from the micrometeorological measurements above a maize field at an experimental site in De Sinderhoeve (The Netherlands) for 18 August, 8 September, and 4 October 1988. Finally, comparison of the schemes was supported applying a simple statistical analysis on the surface flux outputs.

Early–Middle Holocene vegetation history, climate change and human activities at Lago Riane (Ligurian Apennines, NW Italy)

The radiocarbon-dated palaeoecological study of Lago Riane (Ligurian Apennines, NW Italy) presented here forms part of a wider investigation into the relationships between Holocene vegetation succession, climate change and human activities in the northern Apennines. The record of vegetation history from Lago Riane indicates that, since the end of the last glaciation, climate change and prehistoric human activities, combined with several local factors, have strongly influenced the pattern and timing of natural vegetation succession. The pollen record indicates an important change in vegetation cover at Lago Riane at ~8500–8200 cal. years b.p., coincident with a well-known period of rapid climate change. At ~6100 cal. years b.p., Fagus woodland colonised Lago Riane during a period of climate change and expansion of Late Neolithic human activities in the upland zone of Liguria. A marked decline in Abies woodland, and the expansion of Fagus woodland, at ~4700 cal. years b.p., coincided with further archaeological evidence for pastoralism in the mountains of Liguria during the Copper Age. At ~3900–3600 cal. years b.p. (Early to Middle Bronze Age transition), a temporary expansion of woodland at Lago Riane has been provisionally attributed to a decline in human pressure on the environment during a period of short-term climate change

Obesity and body fat classification in the metabolic syndrome: impact on cardiometabolic risk metabotype

Obesity is a key factor in the development of the metabolic syndrome (MetS), which is associated with increased cardiometabolic risk. We investigated whether obesity classification by body mass index (BMI) and body fat percentage (BF%) influences cardiometabolic profile and dietary responsiveness in 486 MetS subjects (LIPGENE dietary intervention study). Anthropometric measures, markers of inflammation and glucose metabolism, lipid profiles, adhesion molecules and haemostatic factors were determined at baseline and after 12 weeks of 4 dietary interventions (high saturated fat (SFA), high monounsaturated fat (MUFA) and 2 low fat high complex carbohydrate (LFHCC) diets, 1 supplemented with long chain n-3 polyunsaturated fatty acids (LC n-3 PUFAs)). 39% and 87% of subjects classified as normal and overweight by BMI were obese according to their BF%. Individuals classified as obese by BMI (± 30 kg/m2) and BF% (± 25% (men) and ± 35% (women)) (OO, n = 284) had larger waist and hip measurements, higher BMI and were heavier (P < 0.001) than those classified as non-obese by BMI but obese by BF% (NOO, n = 92). OO individuals displayed a more pro-inflammatory (higher C reactive protein (CRP) and leptin), pro-thrombotic (higher plasminogen activator inhibitor-1 (PAI-1)), pro-atherogenic (higher leptin/adiponectin ratio) and more insulin resistant (higher HOMA-IR) metabolic profile relative to the NOO group (P < 0.001). Interestingly, tumour necrosis factor alpha (TNF-α) concentrations were lower post-intervention in NOO individuals compared to OO subjects (P < 0.001). In conclusion, assessing BF% and BMI as part of a metabotype may help identify individuals at greater cardiometabolic risk than BMI alone.

The monitoring of ecological quality and the classification of standing waters in temperate regions: a review and proposal based on a worked scheme for British waters

This paper reviews the ways that quality can be assessed in standing waters, a subject that has hitherto attracted little attention but which is now a legal requirement in Europe. It describes a scheme for the assessment and monitoring of water and ecological quality in standing waters greater than about I ha in area in England & Wales although it is generally relevant to North-west Europe. Thirteen hydrological, chemical and biological variables are used to characterise the standing water body in any current sampling. These are lake volume, maximum depth, onductivity, Secchi disc transparency, pH, total alkalinity, calcium ion concentration, total N concentration,winter total oxidised inorganic nitrogen (effectively nitrate) concentration, total P concentration, potential maximum chlorophyll a concentration, a score based on the nature of the submerged and emergent plant community, and the presence or absence of a fish community. Inter alia these variables are key indicators of the state of eutrophication, acidification, salinisation and infilling of a water body.

Vegetation and environmental dynamics in the southern Black Sea region since 18 kyr BP derived from the marine core 22-GC3

Sediments from the Black Sea, a region historically dominated by forests and steppe landscapes, are a valuable source of detailed information on the changes in regional terrestrial and aquatic environments at decadal to millennial scales. Here we present multi-proxy environmental records (pollen, dinoflagellate cysts, Ca, Ti and oxygen isotope data) from the uppermost 305 cm of the core 22-GC3 (42°13.53′N, 36°29.55′E) collected from a water depth of 838 m in the southern part of the Black Sea in 2007. The records span the last ~ 18 kyr (all ages are given in cal kyr BP). The pollen data reveal the dominance of the Artemisia-steppe in the region, suggesting rather dry/cold environments ~ 18–14.5 kyr BP. Warming/humidity increase during melt-water pulses (~ 16.1–14.5 kyr BP), indicated by δ18O records from the 22-GC3 core sediment and from the Sofular Cave stalagmite, is expressed in more negative δ13C values from the Sofular Cave, usually interpreted as the spreading of C3 plants. The records representing the interstadial complex (~ 14.5–12.9 kyr BP) show an increase in temperature and moisture, indicated by forest development, increased primary productivity and reduced surface run-off, whereas the switch from primary terrigenous to primary authigenic Ca origin occurs ~ 500 yr later. The Younger Dryas cooling is clearly demonstrated by more negative δ13C values from the Sofular Cave and a reduction of pines. The early Holocene (11.7–8.5 kyr BP) interval reveals relatively dry conditions compared to the mostly moist and warm middle Holocene (8.5–5 kyr BP), which is characterized by the establishment of the species-rich warm mixed and temperate deciduous forests in the low elevation belt, temperate deciduous beech-hornbeam forests in the middle and cool conifer forest in upper mountain belt. The border between the early and middle Holocene in the vegetation records coincides with the opening of the Mediterranean corridor at ~ 8.3 kyr BP, as indicated by a marked change in the dinocyst assemblages and in the sediment lithology. Changes in the pollen assemblages indicate a reduction in forest cover after ~ 5 kyr BP, which was likely caused by increased anthropogenic pressure on the regional vegetation.

PMCRI: a parallel modular classification rule induction framework

In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction of Decision Trees (TDIDT) algorithm is a very widely used technology to predict the classification of newly recorded data. However alternative technologies have been derived that often produce better rules but do not scale well on large datasets. Such an alternative to TDIDT is the PrismTCS algorithm. PrismTCS performs particularly well on noisy data but does not scale well on large datasets. In this paper we introduce Prism and investigate its scaling behaviour. We describe how we improved the scalability of the serial version of Prism and investigate its limitations. We then describe our work to overcome these limitations by developing a framework to parallelise algorithms of the Prism family and similar algorithms. We also present the scale up results of a first prototype implementation.

Parallel induction of modular classification rules

The Distributed Rule Induction (DRI) project at the University of Portsmouth is concerned with distributed data mining algorithms for automatically generating rules of all kinds. In this paper we present a system architecture and its implementation for inducing modular classification rules in parallel in a local area network using a distributed blackboard system. We present initial results of a prototype implementation based on the Prism algorithm.

P-Prism: a computationally efficient approach to scaling up classification rule induction

Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algorithms have been developed such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the increasing size of databases, many existing rule learning algorithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have been introduced. As TDIDT is the most popular classifier, even though there are strongly competitive alternative algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism.

Towards a computationally efficient approach to modular classification rule induction

Induction of classification rules is one of the most important technologies in data mining. Most of the work in this field has concentrated on the Top Down Induction of Decision Trees (TDIDT) approach. However, alternative approaches have been developed such as the Prism algorithm for inducing modular rules. Prism often produces qualitatively better rules than TDIDT but suffers from higher computational requirements. We investigate approaches that have been developed to minimize the computational requirements of TDIDT, in order to find analogous approaches that could reduce the computational requirements of Prism.