Sequential extracellular matrix-focused and baited-global cluster analysis of serial transcriptomic profiles identifies candidate modulators of renal tubulointerstitial fibrosis in murine adriamycin-induced nephropathy

Transcriptome analysis using microarray technology represents a powerful unbiased approach for delineating pathogenic mechanisms in disease. Here molecular mechanisms of renal tubulointerstitial fibrosis (TIF) were probed by monitoring changes in the renal transcriptome in a glomerular disease-dependent model of TIF ( adriamycin nephropathy) using Affymetrix (mu74av2) microarray coupled with sequential primary biological function-focused and secondary

Circulating and synovial antibody profiling of juvenile arthritis patients by nucleic acid programmable protein arrays.

Introduction Juvenile idiopathic arthritis (JIA) is a heterogeneous disease characterized by chronic joint inflammation of unknown cause in children. JIA is an autoimmune disease and small numbers of auto-antibodies have been reported in JIA patients. The identification of antibody markers could improve the existing clinical management of patients. Methods A pilot study was performed on the application of a high-throughput platform, nucleic acid programmable protein arrays (NAPPA), to assess the levels of antibodies present in the systemic circulation and synovial joint of a small cohort of juvenile arthritis patients. Plasma and synovial fluid from ten JIA patients was screened for antibodies against 768 proteins on NAPPA. Results Quantitative reproducibility of NAPPA was demonstrated with >0.95 intra- and inter- array correlations. A strong correlation was also observed for the levels of antibodies between plasma and synovial fluid across the study cohort (r=0.96). Differences in the levels of 18 antibodies were revealed between sample types across all patients. Patients were segregated into two clinical subtypes with distinct antibody signatures by unsupervised hierarchical cluster analysis. Conclusions NAPPA provides a high-throughput quantitatively reproducible platform to screen for disease specific autoantibodies at the proteome level on a microscope slide. The strong correlation between the circulating antibody levels and those of the inflamed joint represents a novel finding and provides confidence to use plasma for discovery of autoantibodies in JIA, thus circumventing the challenges associated with joint aspiration. We expect that autoantibody profiling of JIA patients on NAPPA could yield antibody markers that can act as criteria to stratify patients, predict outcomes and understand disease etiology at the molecular level.

Synovial membrane protein expression differs between juvenile idiopathic arthritis subtypes in early disease

Introduction: Juvenile idiopathic arthritis (JIA) is the most common rheumatological disease of childhood with a prevalence of around 1 in 1000. Without appropriate treatment it can have devastating consequences including permanent disability from joint destruction and growth deformities. Disease aetiology remains unknown. Investigation of disease pathology at the level of the synovial membrane is required if we want to begin to understand the disease at the molecular and biochemical level. The synovial membrane proteome from early disease-stage, treatment naive JIA patients was compared between polyarticular and oligoarticular subgroups.

Methods: Protein was extracted from 15 newly diagnosed, treatment naive JIA synovial membrane biopsies and separated by two dimensional fluorescent difference in-gel electrophoresis. Proteins displaying a two-fold or greater change in expression levels between the two subgroups were identified by matrix assisted laser desorption ionization-time of flight mass spectrometry with expression further verified by Western blotting and immunohistochemistry.

Results: Analysis of variance analysis (P <= 0.05) revealed 25 protein spots with a two-fold or greater difference in expression levels between polyarticular and oligoarticular patients. Hierarchical cluster analysis with Pearson ranked correlation revealed two distinctive clusters of proteins. Some of the proteins that were differentially expressed included: integrin alpha 2b (P = 0.04); fibrinogen D fragment (P =0.005); collagen type VI (P = 0.03); fibrinogen gamma chain (P = 0.05) and peroxiredoxin 2 (P = 0.02). The identified proteins are involved in a number of different processes including platelet activation and the coagulation system.

Conclusions: The data indicates distinct synovial membrane proteome profiles between JIA subgroups at an early stage in the disease process. The identified proteins also provide insight into differentially perturbed pathways which could influence pathological events at the joint level.

Implication of modified molecular structure of lipid through heat-related process to fatty acids supply in Brassica carinata seed.

This study was conducted to explore the effect of different autoclave heating times (30, 60 and 90 min) on fatty acids supply and molecular stability in Brassica carinata seed. Multivariate spectral analyses and correlation analyses were also carried out in our study. The results showed that autoclaving treatments significantly decreased the total fatty acids content in a linear fashion in B. carinata seed as heating time increased. Reduced concentrations were also observed in C18:3n3, C20:1, C22:1n9, monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), omega 3 (ω-3) and 9 (ω-9) fatty acids. Correspondingly, the heated seeds showed dramatic reductions in all the peak intensities within lipid-related spectral regions. Results from agglomerative hierarchical cluster analysis (AHCA) and principal component analysis (PCA) indicated that the raw oilseed had completely different structural make-up from the autoclaved seeds in both CH3 and CH2 asymmetric and symmetric stretching region (ca. 2999–2800 cm−1) and lipid ester Cdouble bond; length as m-dashO carbonyl region (ca. 1787–1706 cm−1). However, the oilseeds heated for 30, 60 and 90 min were not grouped into separate classes or ellipses in all the lipid-related regions, indicating that there still exhibited similarities in lipid biopolymer conformations among autoclaved B. carinata seeds. Moreover, strong correlations between spectral information and fatty acid compositions observed in our study could imply that lipid-related spectral parameters might have a potential to predict some fatty acids content in oilseed samples, i.e. B. carinata. However, more data from large sample size and diverse range would be necessary and helpful to draw up a final conclusion.

Pattern Recognition Approach for Road Collision Hotspots Analysis: Case Study of Northern Ireland

In order to address road safety effectively, it is essential to understand all the factors, which
attribute to the occurrence of a road collision. This is achieved through road safety
assessment measures, which are primarily based on historical crash data. Recent advances
in uncertain reasoning technology have led to the development of robust machine learning
techniques, which are suitable for investigating road traffic collision data. These techniques
include supervised learning (e.g. SVM) and unsupervised learning (e.g. Cluster Analysis).
This study extends upon previous research work, carried out in Coll et al. [3], which
proposed a non-linear aggregation framework for identifying temporal and spatial hotspots.
The results from Coll et al. [3] identified Lisburn area as the hotspot, in terms of road safety,
in Northern Ireland. This study aims to use Cluster Analysis, to investigate and highlight any
hidden patterns associated with collisions that occurred in Lisburn area, which in turn, will
provide more clarity in the causation factors so that appropriate countermeasures can be put
in place.

Compositional Data Analysis in Forensic Geoscience

Statistics are regularly used to make some form of comparison between trace evidence or deploy the exclusionary principle (Morgan and Bull, 2007) in forensic investigations. Trace evidence are routinely the results of particle size, chemical or modal analyses and as such constitute compositional data. The issue is that compositional data including percentages, parts per million etc. only carry relative information. This may be problematic where a comparison of percentages and other constraint/closed data is deemed a statistically valid and appropriate way to present trace evidence in a court of law. Notwithstanding an awareness of the existence of the constant sum problem since the seminal works of Pearson (1896) and Chayes (1960) and the introduction of the application of log-ratio techniques (Aitchison, 1986; Pawlowsky-Glahn and Egozcue, 2001; Pawlowsky-Glahn and Buccianti, 2011; Tolosana-Delgado and van den Boogaart, 2013) the problem that a constant sum destroys the potential independence of variances and covariances required for correlation regression analysis and empirical multivariate methods (principal component analysis, cluster analysis, discriminant analysis, canonical correlation) is all too often not acknowledged in the statistical treatment of trace evidence. Yet the need for a robust treatment of forensic trace evidence analyses is obvious. This research examines the issues and potential pitfalls for forensic investigators if the constant sum constraint is ignored in the analysis and presentation of forensic trace evidence. Forensic case studies involving particle size and mineral analyses as trace evidence are used to demonstrate the use of a compositional data approach using a centred log-ratio (clr) transformation and multivariate statistical analyses.

Controls on the contemporary distribution of lake thecamoebians (testate amoebae) in the Greater Toronto Area and their potential as water quality indicators.

Thecamoebians were examined from 71 surface sediment samples collected from 21 lakes and ponds in the Greater Toronto Area to (1) elucidate the controls on faunal distribution in modern lake environments; and (2) to consider the utility of thecamoebians in quantitative studies of water quality change. This area was chosen because it includes a high density of kettle and other lakes which are threatened by urban development and where water quality has deteriorated locally as a result of contaminant inputs, particularly nutrients. Fifty-eight samples yielded statistically significant thecamoebian populations. The most diverse faunas (highest Shannon Diversity Index values) were recorded in lakes beyond the limits of urban development, although the faunas of all lakes showed signs of sub-optimal conditions. The assemblages were divided into five clusters using Q-mode cluster analysis, supported by Detrended Correspondence Analysis. Canonical Correspondence Analysis (CCA) was used to examine species-environment relationships and to explain the observed clusterings. Twenty-four measured environmental variables were considered, including water property attributes (e.g., pH, conductivity, dissolved oxygen), substrate characteristics, sediment-based phosphorus (Olsen P) and 11 environmentally available metals. The thecamoebian assemblages showed a strong association with phosphorus, reflecting the eutrophic status of many of the lakes, and locally to elevated conductivity measurements, which appear to reflect road salt inputs associated with winter de-icing operations. Substrate characteristics, total organic carbon and metal contaminants (particularly Cu and Mg) also influenced the faunas of some samples. A series of partial CCAs show that of the measured variables, sedimentary phosphorus has the largest influence on assemblage distribution, explaining 6.98% (P < 0.002) of the total variance. A transfer function was developed for sedimentary phosphorus (Olsen P) using 58 samples from 15 of the studied lakes. The best performing model was based on weighted averaging with inverse deshrinking (WA Inv, r jack 2= 0.33, RMSEP = 102.65 ppm). This model was applied to a small modern thecamoebian dataset from a eutrophic lake in northern Ontario to predict phosphorus and performed satisfactorily. This preliminary study confirms that thecamoebians have considerable potential as quantitative water quality indicators in urbanising regions, particularly in areas influenced by nutrient inputs and road salts.

Do changes in illness perceptions predict changes in psychological distress among oesophageal cancer survivors?

This study aimed to examine whether changes in the illness perceptions of oesophageal cancer survivors explain changes in their levels of psychological distress relative to demographic and biomedical variables and coping strategies. Oesophageal cancer survivors completed the Illness Perception Questionnaire — Revised, the Cancer Coping Questionnaire and the Hospital Anxiety and Depression Scale at two points in time, 12 months apart. Cluster analysis was used to identify groups of respondents who reported a similar profile of change in their illness perception scores over time. Findings suggested that enhancing control cognitions and encouraging a positive focus coping strategy may be important in improving psychological health.

Illness perception clusters at diagnosis predict psychological distress among women with breast cancer at 6 months post-diagnosis.

OBJECTIVE:

This study aimed to examine the extent to which illness perceptions and coping strategies among women diagnosed with breast cancer explain psychological distress at diagnosis and at 6?months post diagnosis relative to demographic and illness-related variables.

METHODS:

Women were recruited to the study shortly after diagnosis. A total of 90 women completed study materials (Illness Perception Questionnaire-Revised, the Cancer Coping Questionnaire and the Hospital Anxiety and Depression Scale) at time 1. The same questionnaires were sent approximately 6?months later to those who had consented at time 1, and completed questionnaires were returned by 72 women.

RESULTS:

Cluster analysis was used to identify groups of respondents who reported a similar profile of illness perception scores. Regression analysis demonstrated that one of these clusters was more likely to experience psychological distress than the other both at diagnosis and at 6?months post diagnosis. Illness perception cluster membership and positive focus type coping were the most important and consistent predictors of lower psychological distress at diagnosis and at 6?months post diagnosis.

CONCLUSIONS:

Illness perceptions remained relatively stable over the study period, and therefore we are unable to clarify whether changes in illness cognitions are associated with a corresponding change in psychological symptoms. Future research should evaluate the impact on psychological distress of interventions specifically designed to modify illness cognitions among women with breast cancer.

High-resolution reconstruction of atmospheric deposition of trace metals and metalloids since AD 1400 recorded by ombrotrophic peat cores in Hautes-Fagnes, Belgium

The objective of our study was to determine the trace metal accumulation rates in the Misten bog, Hautes-Fagnes, Belgium, and assess these in relation to established histories of atmospheric emissions from anthropogenic sources. To address these aims we analyzed trace metals and metalloids (Pb, Cu, Ni, As, Sb, Cr, Co, V, Cd and Zn), as well as Pb isotopes, using XRF, Q-ICP-MS and MC-ICP-MS, respectively in two 40-cm peat sections, spanning the last 600 yr. The temporal increase of metal fluxes from the inception of the Industrial Revolution to the present varies by a factor of 5–50, with peak values found between AD 1930 and 1990. A cluster analysis combined with Pb isotopic composition allows the identification of the main sources of Pb and by inference of the other metals, which indicates that coal consumption and metallurgical activities were the predominant sources of pollution during the last 600 years.

Predicting family caregiver psychosocial functioning in palliative care

Health professionals are expected to support family caregivers of patients requiring palliative care. However, there is a dearth of empirical evidence to help clinicians identify caregivers who might be at risk of poor psychosocial functioning.

Arcellacea (Testate Amoebae) as Bio-indicators of Road Salt Contamination in Lakes

Winter deicing operations occur extensively in mid- to high-latitude metropolitan regions around the world and result in a significant reduction in road accidents. Deicing salts can, however, pose a major threat to water quality and aquatic organisms. In this paper we examine the utility of Arcellacea (testate amoebae) for monitoring lakes that have become contaminated by winter deicing salts, particularly sodium chloride. We analysed 50 sediment samples and salt-related water-property variables (chloride concentrations; conductivity) from 15 lakes in the Greater Toronto Area and adjacent areas of southern Ontario, Canada. The sampled lakes included lakes in proximity to major highways and suburban roads, and control lakes in forested settings away from road influences. Samples from the most contaminated lakes, with chloride concentrations in excess of 400 mg/l and conductivities of >800 μS/cm, were dominated by species typically found in brackish and/or inhospitable lake environments and by lower faunal diversities (lowest Shannon Diversity Index values) than samples with lower readings. Q-R-mode cluster analysis and Detrended Correspondence Analysis (DCA) resulted in the recognition of four assemblage groupings. These reflect varying levels of salt contamination in the study lakes, along with other local influences, including nutrient loading. The response to nutrients can, however, be isolated if the planktic eutrophic indicator species Cucurbitella tricuspis is removed from the counts. The findings show that the group have considerable potential for biomonitoring in salt-contaminated lakes, and through application to lake sediment cores, may provide significant insights into long-term benthic community health, which is integral for remedial efforts.

Assessing the distribution of social-ecological resilience and risk: Ireland as a case study of the uneven impact of famine

Explanations for the causes of famine and food insecurity often reside at a high level of aggregation or abstraction. Popular models within famine studies have often emphasised the role of prime movers such as population stress, or the political-economic structure of access channels, as key determinants of food security. Explanation typically resides at the macro level, obscuring the presence of substantial within-country differences in the manner in which such stressors operate. This study offers an alternative approach to analyse the uneven nature of food security, drawing on the Great Irish famine of 1845–1852. Ireland is often viewed as a classical case of Malthusian stress, whereby population outstripped food supply under a pre-famine demographic regime of expanded fertility. Many have also pointed to Ireland's integration with capitalist markets through its colonial relationship with the British state, and country-wide system of landlordism, as key determinants of local agricultural activity. Such models are misguided, ignoring both substantial complexities in regional demography, and the continuity of non-capitalistic, communal modes of land management long into the nineteenth century. Drawing on resilience ecology and complexity theory, this paper subjects a set of aggregate data on pre-famine Ireland to an optimisation clustering procedure, in order to discern the potential presence of distinctive social–ecological regimes. Based on measures of demography, social structure, geography, and land tenure, this typology reveals substantial internal variation in regional social–ecological structure, and vastly differing levels of distress during the peak famine months. This exercise calls into question the validity of accounts which emphasise uniformity of structure, by revealing a variety of regional regimes, which profoundly mediated local conditions of food security. Future research should therefore consider the potential presence of internal variations in resilience and risk exposure, rather than seeking to characterise cases based on singular macro-dynamics and stressors alone.

Comparison of methods used to calculate typical threshold values for potentially toxic elements in soil

The environmental quality of land can be assessed by calculating relevant threshold values, which differentiate between concentrations of elements resulting from geogenic and diffuse anthropogenic sources and concentrations generated by point sources of elements. A simple process allowing the calculation of these typical threshold values (TTVs) was applied across a region of highly complex geology (Northern Ireland) to six elements of interest; arsenic, chromium, copper, lead, nickel and vanadium. Three methods for identifying domains (areas where a readily identifiable factor can be shown to control the concentration of an element) were used: k-means cluster analysis, boxplots and empirical cumulative distribution functions (ECDF). The ECDF method was most efficient at determining areas of both elevated and reduced concentrations and was used to identify domains in this investigation. Two statistical methods for calculating normal background concentrations (NBCs) and upper limits of geochemical baseline variation (ULBLs), currently used in conjunction with legislative regimes in the UK and Finland respectively, were applied within each domain. The NBC methodology was constructed to run within a specific legislative framework, and its use on this soil geochemical data set was influenced by the presence of skewed distributions and outliers. In contrast, the ULBL methodology was found to calculate more appropriate TTVs that were generally more conservative than the NBCs. TTVs indicate what a "typical" concentration of an element would be within a defined geographical area and should be considered alongside the risk that each of the elements pose in these areas to determine potential risk to receptors.